KLEE Options
1. Search Heuristics
2. Query Logging
Search Heuristics
Main search heuristics
KLEE provides four main search heuristics:
- Depth-First Search (DFS): Traverses states in depth-first order.
- Random State Search:Randomly selects a state to explore.
- Random Path Selection: Described in our KLEE OSDI'08 paper.
- Non Uniform Random Search (NURS): Selects a state randomly according to a given distribution. The distribution can be based on the minimum distance to an uncovered instruction (MD2U), the query cost, etc.
$ klee --search=dfs demo.oruns demo.o using DFS, while
$ klee --search=random-path demo.oruns it using the random path selection strategy. The full list of options is shown in KLEE's help message:
$ klee --help -search - Specify the search heuristic (default=random-path interleaved with nurs:covnew) =dfs - use Depth First Search (DFS) =random-state - randomly select a state to explore =random-path - use Random Path Selection (see OSDI'08 paper) =nurs:covnew - use Non Uniform Random Search (NURS) with Coverage-New heuristic =nurs:md2u - use NURS with Min-Dist-to-Uncovered heuristic =nurs:depth - use NURS with 2^depth heuristic =nurs:icnt - use NURS with Instr-Count heuristic =nurs:cpicnt - use NURS with CallPath-Instr-Count heuristic =nurs:qc - use NURS with Query-Cost heuristic
Interleaving search heuristics
Search heuristics in KLEE can be interleaved in a round-robin fashion. To interleave several search heuristics to be interleaved, use the --search multiple times. For example:
$ klee --search=random-state --search=nurs:md2u demo.ointerleaves the Random State and the NURS:MD2U heuristics in a round robin fashion.
Default search heuristics
The default heuristics used by KLEE are random-path interleaved with nurs:covnew.
Query Logging
To log the queries issued by KLEE during symbolic execution, you can use the following options:-
--use-query-log=TYPE:FORMAT, where:
- TYPE is either all to log all the queries KLEE made during execution before any optimisation (e.g. caching, constraint independence) is performed, or solver to log only the queries passed to KLEE's underlying solver. Note that it is possible that some of the unoptimized queries are never executed or are modified before being executed by KLEE's underlying solver.
- FORMAT is the format in which queries are logged and can be either pc for the KQuery format, or smt2 for the SMT-LIBv2 format.
-
--min-query-time-to-log=TIME (in ms) is used to log only queries that exceed a certain time limit. TIME can be:
- 0 (default): to log all queries
- <0: a negative value specifies that only queries that timed out should be logged. The timeout value is specified via the --max-stp-time option.
- >0: only queries that took more that TIME milliseconds should be logged.


Add an Employee
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UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>Understanding your invoice evidence file
Overview
The UKCloud evidence file is a CSV file provided with your invoice that contains data on your hourly metered charges per month and corroborates how the totals invoiced are derived.
Evidence file columns containing information about charges
The evidence file includes information to help you understand how your usage of the UKCloud platform determines how the totals in your invoice are calculated. For a quick look at the different charges that contribute to the overall cost of each service, and the corresponding evidence file columns, see the Invoice and billing FAQs.
All evidence file columns
For information about how to use the evidence file to answer common questions about your invoice, see the Invoice and billing FAQs.
| Column | Name | Usage | Applies to |
|---|---|---|---|
| A | EventDate | Date charges apply to, in UTC, so daylight savings are not applied | OpenStack, VMware |
| B | ProjectID | OpenStack Project ID | OpenStack |
| C | vAPP | Name of the vApp | VMware |
| D | vDC | Name of the virtual data centre | VMware |
| E | ResourceName | Client-friendly name of the resource | OpenStack, VMware |
| F | Resource Id | Identifier of the resource | OpenStack, VMware |
| G | OSID | Operating system | OpenStack, VMware |
| H | Service | The UKCloud service to which the charge applies: OpenShift Application OpenShift Control Plane OpenShift Infrastructure OpenShift Master OpenStack Virtual Machine OpenStack Block Storage Tier 1 OpenStack Block Storage Tier 1 Encrypted OpenStack Block Storage Tier 2 OpenStack Block Storage Tier 2 Encrypted OpenStack Image OpenStack Instance Backup Storage CDSZ Walled Garden Disaster Recovery as a Service High Performance Compute Secure Remote Access VMware Dedicated VM VMware Independent Disk VMware Media VMware Template VMware VM |
OpenStack, VMware |
| I | Metadata | Customer VM metadata | VMware |
| J | StartTime | Charging period start time on the EventDate, in UTC | OpenStack, VMware |
| K | EndTime | Charging period end time on the EventDate, in UTC | OpenStack, VMware |
| L | PowerState | On or Off | VMware |
| M | UsageMinsWithinPeriod | 60 x UsageHoursWithinPeriod (may not match difference between the start time and end time due to rounding) | OpenStack, VMware |
| N | UsageHoursWithinPeriod | Typically rounded up usage hours matching start time to end time period | OpenStack, VMware |
| O | ComputeMachineType | For example m1.medium (OpenStack), Medium High Memory (VMware) | OpenStack, VMware |
| P | PowerType | ESSENTIAL, POWER or PRIORITY | VMware |
| Q | Security Domain | ASSURED or ELEVATED | OpenStack, VMware |
| R | Compute vCPU | Number of CPUs | OpenStack, VMware |
| S | ComputeMemory | Memory in GiB | OpenStack, VMware |
| T | ComputePricePerHour | Service catalogue price for the machine type, security domain, vCPU, memory and power level | OpenStack, VMware |
| U | ComputeTotalPrice | ComputePricePerHour * UsageHoursWithinPeriod | OpenStack, VMware |
| V | GPUType | compute or visual | VMware |
| W | GPUCount | Any positive integer (1 to 16) | VMware |
| X | GPUPricePerHour | As per current pricing guide | VMware |
| Y | GPUTotalPrice | GPUPricePerHour * GPUCount * UsageHoursWithinPeriod | VMware |
| Z | Tier1StorageUsed | Tier 1 storage allocated | OpenStack, VMware |
| AA | Tier1SnapshotStorageUsed | Tier 1 storage allocated for vCloud snapshots | VMware |
| AB | Tier1StorageIncluded | Tier 1 storage amount that is not charged | VMware |
| AC | Tier1StorageChargeable | Tier1StorageUsed + Tier1SnapshotUsed - Tier1StorageIncluded | OpenStack, VMware |
| AD | Tier1StoragePricePerHour | Service catalogue price for Tier 1 storage and security domain converted into hourly charge per GiB | OpenStack, VMware |
| AE | Tier1StoragePrice | Tier1StorageChargeable * Tier1StoragePricePerHour * UsageHoursWithinPeriod | OpenStack, VMware |
| AF | Tier2StorageUsed | Tier 2 storage allocated | OpenStack, VMware |
| AG | Tier2SnapshotStorageUsed | Tier 2 storage allocated for vCloud snapshots | VMware |
| AH | Tier2StorageIncluded | Tier 2 storage amount that is not charged (60GiB of Tier 2 storage is included if powered on (10GiB for Micro VMS)) | VMware |
| AI | Tier2StorageChargeable | Tier2StorageUsed + Tier2SnapshotUsed - Tier2StorageIncluded | OpenStack, VMware |
| AJ | Tier2StoragePricePerHour | Service catalogue price for Tier 2 storage and security domain converted into hourly charge per GiB | OpenStack, VMware |
| AK | Tier2StoragePrice | Tier2StorageChargeable * Tier2StoragePricePerHour * UsageHoursWithinPeriod | OpenStack, VMware |
| AL | Geo-resilientStorageUsed | Storage used in GiB | VMware |
| AM | Geo-resilientSnapshotStorageUsed | Geo-resilient storage allocated for vCloud snapshots | VMware |
| AN | Geo-resilientStorageIncluded | Geo-resilient storage amount that is not charged (60GiB included if powered on (10GiB for Micro VMS)) | VMware |
| AO | Geo-resilientStorageChargeable | Geo-resilientSnapshotStorageUsed + Geo-resilientSnapshotStorageUsed - Geo-resilientStorageIncluded | VMware |
| AP | Geo-resilientPricePerHour | Service catalogue price for geo-resilient storage and security domain converted into hourly charge per GiB | VMware |
| AQ | Geo-resilientStoragePrice | Geo-resilientStorageChargeable * Geo-resilientStoragePricePerHour * UsageHoursWithinPeriod | VMware |
| AR | Protection Type | Type of protection applied: None Backup Storage 14 Day Snapshot Protection 28 Day Snapshot Protection Synchronous Protection 2 Day Journaling Protection 7 Day Journaling Protection 14 Day Journaling Protection 28 Day Journaling Protection Any combination of the above (apart from None) separated by & |
OpenStack, VMware |
| AS | ComputeProtectionPerHour | Price per hour dependent on values in Protection Type (taking into account free 14 day snapshot with synchronous) | VMware |
| AT | ComputeProtectionTotalPrice | ComputeProtectionPerHour * UsageHoursWithinPeriod | VMware |
| AU | Tier1ProtectionPricePerHour | Monthly charge from service catalogue (per GB per month) converted into hourly charge per GiB (0 if no protection; blank if not relevant) | VMware |
| AV | Tier2ProtectionPricePerHour | Monthly charge from service catalogue (per GB per month) converted into hourly charge per GiB (0 if no protection; blank if not relevant) | VMware |
| AW | Geo-resilientProtectionPerHour | Monthly charge from service catalogue (per GB per month) converted into hourly charge per GiB (0 if no protection or not relevant). This is either just geo-resilient storage protection or geo-resilient plus 28 day snapshot | VMware |
| AX | Tier1ProtectionTotalPrice | The cost of protection for Tier 1 storage. This could be journaling or snapshot or both (Tier1StorageChargeable - Tier1SnapshotUsed) * Tier1ProtectionPricePerHour * UsageHours |
VMware |
| AY | Tier2ProtectionTotalPrice | The cost of protection for Tier 2 storage. This could be journaling or snapshot or both (Tier2StorageChargeable - Tier2SnapshotUsed) * Tier2ProtectionPricePerHour * UsageHours |
VMware |
| AZ | Geo-resilientProtectionTotalPrice | The cost of protection for geo-resilient storage. As 14 day snapshot is included with synchronous protection, this is only for when 28 day snapshot protection is taken with geo-resilient storage. (Geo-resilientStorageChargeable - Geo-resilientSnapshotUsed) * Geo-resilientProtectionPricePerHour * UsageHours |
VMware |
| BA | ProtectionTotalPrice | Total cost for protection in period (0 if no protection) (ComputeProtectionTotalPrice + Tier1ProtectionTotalPrice + Tier2ProtectionTotalPrice + Geo-resilientProtectionTotalPrice) |
VMware |
| BB | LicensePricePerHour | Licence charges for the VM | OpenStack, VMware |
| BC | LicenseTotalPrice | LicensePricePerHour * UsageHoursWithinPeriod | OpenStack, VMware |
| BD | TotalPrice | ComputeTotalPrice + GPUTotalPrice + Tier1StoragePrice + Tier2StoragePrice + Geo-resilientStoragePrice + ProtectionTotalPrice + LicenseTotalPrice | OpenStack, VMware |
Key changes to the Evidence File (November 2017 onwards)
August 2021
Support for OpenShift infrastructure VMs on OpenStack has been added to the evidence files.
Column H : has 4 new service types
- OpenShift Application
- OpenShift Control Plane
- OpenShift Infrastructure
- OpenShift Master
Columns BB and BC
- OpenShift infrastructure on OpenStack carries a license charge.
March 2020
Support for new OpenStack services has been added to the evidence files.
- Column H : has 3 new service types
- OpenStack Block Storage Tier 1 Encrypted
- OpenStack Block Storage Tier 2 Encrypted
- OpenStack Instance Backup Storage
- Column AR : OpenStack Instance Backup Storage has a Protection Type of "Backup Storage".
December 2018
From December 2018, you'll see the following changes to your evidence file. You'll receive the Evidence File for December at the start of January 2019, detailing your consumption during December.
The key changes to the evidence file are as follows:
Service type categories
- In column H the OpenStack Instance service type has been renamed to OpenStack Virtual Machine
November 2017
New column:
- Column H - UKCloud for VMware Independent Disks and Media charges
October 2017
Added new columns:
Metadata (column I) - if an UKCloud for VMware VM is tagged with metadata, the data will appear here. Please note -- the VM metadata will appear on the subsequent day in the Evidence File. For example, if it is tagged on the 3rd, the metadata will appear on the 4th, unless a VM power cycle occurs on the 3rd (This is currently available for UKCloud for VMware VMs only).
Cloud GPU (columns V, W, X and Y) - will include details and a breakdown of Cloud GPU compute charges.
vCloud Snapshot storage (columns AA, AG and AM) - charges will appear in this column if a VM is manually snapshotted in VMware Cloud Director. Charges for the storage will not be applied for October, though values may appear in these columns. Billing will commence from November 1 2017.
Breakdown of protection costs (columns AT, AX, AY and AZ) - new columns added to better understand the breakdown of protecting each storage option.
September 2017
More columns --- Providing better understanding of the breakdown of individual charges for a VM between compute, storage, protection and licence.
Common format --- The new Evidence File provides a consistent format for our UKCloud for VMware and UKCloud for OpenStack services.
Categorisation of service type --- The first UKCloud for VMware evidence file using the new format will cover the following service types:
Enterprise Compute VM
Enterprise Compute Dedicated VM
Compute Intensive Infrastructure
Secure Remote Access
CDSZ Walled Garden
Time standardised on UTC --- Each day is treated as 24 hours, with no daylight savings changes applied to the reported times, resulting in no variation to charges when daylight savings are in effect.
Inclusion of power state changes for ECC --- All relevant information is included in a single file, removing the need for a separate Power Events CSV file.
Future enhancements
These initial changes to the Evidence File will be followed by further enhancements over the coming months to add more products and services. Future enhancements include:
- Metadata for UKCloud for OpenStack instances
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


110.57 Distribution Files
Downloads
- Unix
-
The only file you need to download manually is
config.tgz.
Unpack, configure by editing config/targets, and install
by running config/install.sh. The installer
automatically downloads any additional files it might need.
For more information, see INSTALL.
- MacOS X
-
You can use the "generic Unix" procedure above, or you can install
a pre-built system using the installer package in
smlnj-110.57.dmg.
For more information, see MACOSXINSTALL.
- Microsoft Windows
-
Download
smlnj.zip
and follow the instructions in WININSTALL.
Documentation
Binary Files
Source files
Mac OS X Disk Image
MS Windows .zip archive
RPM Packages
| SML/NJ Home Page |
Send your comments to .
Copyright © 2005, SML/NJ Fellowship.


TRANSFORMS2D description of functions operating on transforms By 'transform' we mean an affine transform. A planar affine transform can be represented by a 3x3 matrix. Example % create a translation by the vector [10 20]: T = createTranslation([10 20]) T = 1 0 10 0 1 20 0 0 1 See also: createTranslation, createRotation, createScaling createHomothecy, createLineReflection, fitAffineTransform2d transformPoint, transformVector, transformLine, transformEdge ------ Author: David Legland e-mail<EMAIL_ADDRESS>Created: 2008-10-13, using Matlab <IP_ADDRESS>7 (R2007a) Copyright 2008 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas. Licensed under the terms of the LGPL, see the file "license.txt"


SENECA TOWNSHIP
CARPENTER, JOSEPH M. CARPENTER
MITCHELL ATKINSON
a farmer and millwright of Seneca township, was born on Captina creek, now
Belmont county, Nov 19 1804. His
parents, Isaac & Margaret HOLMES ATKINSON (both deceased),
the latter of whom died Sep 20 1857, settled in Monroe county in 1802. He was elected six times to the
legislature. They came from Greene
county, PA, and settled on Captina creek, where they remained about five years,
when they came to this county, into Seneca township. They settled on Will s creek, about 1 miles above the present
town of Calais, where they remained about three years and then entered the farm
on which Mr. ATKINSON now resides.
This was about the year 1812.
There were only about six families in this township at that time, and
not a church in the township, and but one log hut, which was used as a
school-house. The roads were only
blazed paths through the woods. Isaac ATKINSON
was one of the leading spirits of that day.
He taught some of the first schools in the county and in this township;
was one of the first associate judges of the county, which position he held for
many years; was a man of superior education, and was respected by all for his
worth and integrity. He lived to his 84th
year, and died in 1853. Mitchell ATKINSON
was married near Fairview, Guernsey county, Dec 24 1838 to Nancy GREENELTCH,
who was born in Colerain township, Belmont county, Mar 14 1810. Her parents were James GREENELTCH,
who died in Sep 1864, and Phebe MOORE, who died in Sep 1866. They were among the early settlers of
Belmont county, before the county was organized, and lived to a good old
age. The children of Mr. & Mrs. ATKINSON
are: Margaret YOCKEY, born 27 May 1842, lives at Lewisville; Isaac B.,
Jul 31 1844, at home; Lydia L. KRONING, Mar 31 1846, resides at Calais;
Phoebe L. CALVERT, May 21 1848, died Jun 12 1872; Lucius W., Dec 7 1850,
lives in Seneca township; Hamer F., Oct 3 1852, died Jul 5 1860; Milton M., Nov
27 1855, at home. Mr. ATKINSON
held the office of trustee in this township for two years, and the office of
treasurer about 10 years. His
son-in-law, Casper YOCKEY, served three years in the late war, as a
member of the 92nd OVI. His
uncle, Mitchell ATKINSON, served in the war of 1812, and was one of the
first surveyors in this county, and also held the office of justice of the
peace about 14 years; and another uncle, James, served in the wars with the
Indians in early days. Address, Calais,
Monroe county, OH.
JOHN CLAUS resides in Seneca
township, Monroe county, and is engaged in farming and the dairy business. He is a son of Nicholas CLAUS &
Catharine REMENSCNEIDER (deceased), and was born in this county Jul 9
1850. On the 24th of Apr
1877, he was married by Rev. F. REELER to Kate BAUMAN, daughter
of Henry BAUMAN (deceased) and his wife. They have three children: Albert, born Feb 26 1878; Willie J.,
Nov 9 1879; Elizabeth C., Feb 8 1881.
Postoffice address: Calais, Monroe county, OH.
GEORGE W. COULTAS
son of Robert COULTAS, born Jan 11 1823, and Thamar DEMENT, born
Mar 22 1825, was born in Monroe county, Nov 29 1847. He married at Miltonsburg, in this county, Dec 24 1869, Lydia J.,
daughter of James & Nancy STEELE JOHNSON, and she was born in
this county, Nov 25 1850. Children:
Mary A., born Oct 8 1870; Edward D., Jul 16 1872; Shirley T., Dec 20 1880. Mr. COULTAS is a contractor and
builder, and lives in Calais, Seneca township, Monroe county, OH.
WILLIAM DANFORD settled in this county in 1837. He was born Mar 11 1815 near Armstrong s
Mills, in Belmont county. His parents
were Samuel DANFORD, who died Apr 8 1845, and Nancy METHENEY,
also deceased, Jul 16 1871. His
great-grandfather came from Wales and settled on Delaware Bay, NJ. He reared a family of three sons and two
daughters. The sons were named Samuel,
William and Peter; Samuel and William died without male issue. Peter raised to maturity three sons and four
daughters, being the issue by three wives.
Born of the first wife, Samuel, (father of the subject of this sketch),
William, Rebecca & Sarah; by the second, Ambrose; by the third, Margaret
& Nancy. Mr. DANFORD s
great-grandfather s name on his father s mother s side was Christopher MORRIS. He came from England about 1700, and was a
near kinsman of the great Robert MORRIS, of Philadelphia, PA. The MORRIS family were very numerous
and scattered over the United States at an early day. His great-grandfather s name on his mother s side, was John METHENEY. He was a native of England, and from him and
his brothers descended the race of METHENEYs in the United States. His great-grandfather s name, on his
mother s mother s side, was Michael FLOYD, and from him has sprung the
race of FLOYDs. Mr. DANFORD s
father, Samuel, was born in the state of NJ, Dec 29 1774. He came to what is now Belmont county, in
1798. He there married Nancy METHENEY,
daughter of Benjamin & Rachel FLOYD METHENEY, Aug 20
1800. He resided near the mouth of the
bend fork of Captina creek until the year 1807; he then settled in what was
afterward the northwestern portion of Monroe county, (now Noble county). During the Indian troubles in 1813, he was
compelled to flee for protection into the settlement in Belmont county. He returned to his home in Monroe county in
1817, took up a settler s patent for his land, went into the woods and cleared
a farm, and reared a family of nine daughters and six sons, of whom, William,
the subject of this sketch, was fourth son.
Mr. DANFORD s father, Samuel, was one of the hardy pioneers who
pushed forward into the wilderness and helped to lay the foundations to make
our beautiful country what it is today.
He lived to the age of 70 years and died respected by all who knew
him. Mrs. DANFORD s ancestors,
the DELANCEYs were among the pioneers of southeastern OH, coming in at a
very early day, settling in Monroe county about 1807. Her grandfather s name was David WATSON, and her
grandmother s name Rebecca BRYAN.
They came from PA, and settled here about 1807. William DANFORD was married in this
township, Mar 10 1836 to Margaret, daughter of Jon & Rebecca WATSON DELANCEY,
both deceased. Mrs. DANFORD was
born in this township, Mar 14 1818. The
following is a record of their numerous progeny: Joseph N., born Jan 27 1837,
residence, Summerfield; Stephen D., Feb 6 1838, lives at home; Rebecca, Feb 14
1840, deceased Feb 22 1851; Nancy J. STEWART, Apr 12 1842, resides at
Fredonia, Wilson county, KS; John M., Mar 30 1844, lives in Noble county; Mary
L. WEINSTEIN, Feb 28 1846, resides in Noble county; William M., Mar 26
1848, lives in Franklin township; Margaret E. CAVANAUGH, Oct 25 1849,
also lives in Franklin township; Chloe T. HUTCHESON, Jul 19 1851, has
her home in Jackson county WV; Samuel C., May 19 1853, lives in Franklin
township; James H., Jul 15 1856, is at home; Charles M., Jun 7 1859, deceased
Mar 31 1866. The son, John M., served
about one year in the late war, being honorably discharged at its termination. He was a member of the 185th
OVI. Mr. DANFORD has served
about 12 years as trustee in this township.
He has been a minister of the gospel in the Methodist Episcopal Church
for 38 years. He resides in Seneca
township, and follows farming.
Postoffice address, Summerfield, Noble county, OH.
ADAM J. HANNAHS a resident of Seneca township, is engaged at
farming and teaching. He was born in
this township, May 20 1852. His father,
James HANNAHS, settled in this county in 1844, and his mother, Anna MEHL,
in 1834. He was married at the
residence of his wife s parents in this township on the 2nd of Apr
1874, to Phebe R. ROCKWELL, who was born in Wayne township, Noble
county, May 11 1857, the daughter of Asa M. and Mary J. ROCKWELL, who
came into Monroe county in 1870.
Postoffice address, Calais, Monroe county, OH.
BENJAMIN HAGA is a native of Morgan
county, OH, and was born Sep 24 1823, son of John & Susanna DAY HAGA,
both deceased. He came into Monroe
county in 1843, and married here Dec 1 1847 by Elijah HATHAWAY,
esquire. His wife was Ruth DANFORD,
daughter of Samuel and Nancy MATHENEY, both deceased, and she was born
in this county Dec 7 1828. Children:
Mary D., born Aug 29 1848; Samuel M., Oct 21 1853; Orlin M. (grandchild), Feb
23 1874. Mr. HAGA s father was a
native of Huntington county, PA, and came to Morgan county when it was wild,
with only a cabin here and there through the woods, no churches nor
school-houses, but plenty of wild animals, such as bears, wolves, etc. Mrs. HAGA s ancestors, the DANFORDs,
were natives of Wales. Her
great-grandfather came to this country at a very early day and settled on
Delaware Bay, NJ. He reared a family of
three sons and two daughter, Peter, the second son, being grandfather of Mrs. HAGA. Her father, Samuel DANFORD, was the
oldest son of Peter s three sons and four daughters. He was born in NJ, Dec 29 1774, and came to what is now Belmont
county in 1798, where he met and married Nancy, daughter of Rachel FLOYD
MATHENEY, Aug 20 1800. He
resided in Belmont county until 1807, when he settled in what was afterward the
northwestern part of Monroe county. In
the year 1813, during the trouble with the Indians, he was compelled to flee
with his family to the settlement in Belmont county for protection. He returned in 1817, cleared up a farm in
the woods and possessed a liberal amount of the world s goods. He reared a family of 15 children and died
respected by all who knew him. Mr. HAGA s
business is farming and stock-raising.
Address, Calais, Monroe county, OH.
JAMES H. HAMILTON
a resident of Seneca township, is by profession a school-teacher, and his
taught since 1869. He is now teaching
at Calais, and has a large school whom all commend their teacher. He was elected to the office of justice of
the peace at the last election. He is a
son of Joseph & Susannah HAMILTON, both natives of this county. The former died shortly after the battle of
Pittsburg Landing from disease contracted while in the army. James grandfather was one of the renowned
antecedents of the county. Mr. HAMILTON
& Mary C. WISE were married at Lewisville, OH, Oct 24 1877. Their child is Is Berth I (Bertha?), born
Sep 21 1878. Mrs. HAMILTON s
parents are Washington K. & E. J. WISE. Mr. HAMILTON & wife were both born in Summit
township. Address: Calais, Monroe
county, OH.
WILLIAM KENT is a son of John KENT,
a native of this county, and Elizabeth DANFORD, who was born in Monroe
county (now Noble). He was born in
Monroe county (now Noble), Jan 27 1842; was married in this township by Rev.
Wm. D. LOWRY, Nov 27 1863, to Mary J. BLAKE, daughter of James BLAKE
(deceased) & Roseann ARCHER.
She was born Oct 8 1842.
Children: Elizabeth A., born Sep 27 1864, died Jan 20 1881; John B., Dec
27 1867; Estella C., Nov 5 1872. Mr. KENT
is a farmer and stock-dealer of Seneca township. Postoffice, Summerfield, Noble county, OH.
CHR. L. KRONING
is engaged in the mercantile business in Calais, Monroe county. He is a son of Charles Philip KRONING
and Emma EGGERS, and was born in the independent city of Bremen,
Germany, Dec 18 1846, and settled in this county in 1868. His wife, Lydia Louisa ATKINSON,
daughter of Mitchell T. & Nancy GREENELTCH ATKINSON, who came
here in 1802, was born in Seneca township in Mar 1846. They were married here May 29 1871, and have
four children, namely: Charles, born Dec 22 1872, died Sep 10 1873; Johannes
August, Feb 4 1875; Frederick Philip, Apr 5 1877; Carleton Gustavus, Mar 26
1881. Mrs. KRONING s father is
one of the few early settlers that are still living at this date, March
1882. (See the personal history Mitchell
ATKINSON). Mr. KRONING
has been clerk of Seneca township since 1875.
Address, Calais.
THOMAS A. McCOY
Was born in Alleghany county, PA, Sep 8 1838, and settled in Monroe county in
1870. His parents, John McCOY,
who died Oct 20 1878, and Mary SCOTT McCOY, never lived in this
county. Mr. McCOY was married in
Washington county, Apr 10 1870, by John T. HILL, esquire. His wife was Nancy CARPENTER, born in
Monroe county, Jan 24 1850. She is the
daughter of Joseph CARPENTER, who was born in this county, and died Nov
29 1874, and Martha McMURRAY, who died Apr 13 1875. Their child, Ruth, was born Feb 2 1880. Mr. McCOY was a member of Company B.,
148th OVI. He enlisted at
Marietta, May 2 1864, served until the following September, and was discharged
at the expiration of his time, Sep 19 1864.
He was employed in the State service two years prior to his enlistment
in 1864, and participated in the capture of Morgan on his famous raid through
Ohio. Mr. McCOY s parents were
among the first settlers of Washington county, OH. Mrs. McCOY s grandfather, Joseph CARPENTER, and his
brother, Robert, came to this county when Ohio was yet a territory, when the
county was full of wild animals and still wilder Indians. At that time there were not probably over three
families in the township. Joseph CARPENTER,
Sr., was a soldier of the war of 1812, and Joseph CARPENTER, Jr., father
of Mrs. McCOY, was his third son.
Mr. McCOY is trustee of Seneca township, and is engaged in
wagon-building and coopering at Calais village, Monroe county, OH.
JOHN B. REED was born in Belmont county
in 1818, and was also married in the same county. His parents were Edward & Mary LEMLEY REED. His wife, Catharine, was born in the state
of PA, and was a daughter of John & Elizabeth ANTLE. Their children are: George S., Mary, Edward,
Clarisse, William, Jane, John, Charlie, Martha & Russel. Mr. REED resides in Seneca township,
and is a farmer and stock-raiser. His
postoffice address is Swasey, Monroe county.
JOSEPH M. CARPENTER
a son of Robert & Mary MURY CARPENTER, was born in Guernsey
county, in 1828, and settled in Monroe county in 1857. He was married in Noble county, Dec 4 1856
to Matilda Ann ARICK, daughter of John & Sidney PETTY ARICK,
of Noble county. Mrs. CARPENTER
was born in Guernsey county in 1833.
Children: Eliza Jane, born Sep 25 1857; Francis E., Oct 26 1859, died
Aug 6 1862; John A., May 12 1862; James E., May 10 1864; Naomi I., Jul 27 1866;
Mary S., Aug 20 1872; Martha A., Sep 14 1876; an infant which died the day of
its birth was born Dec 26 1869. Their
adopted child, Robert JOHNSON, was born Nov 13 1869. Henry ARICK, a brother of Mrs. CARPENTER,
died in the army. Mr. CARPENTER
is engaged in farming and stock-raising in Seneca township. Address, Summerfield, Monroe county, OH.
DAVID T. SEARS born in Anarundle county,
MD, Mar 26 1829, is a son of John & Willie A. HOWARD SEARS,
who came into this county in 1843. His
wife, born in Guernsey county, Mar 29 1839, was Elizabeth BARLOW,
daughter of Zachariah BARLOW & Mary WELLS. They were married in Monroe county, Jan 25
1870, and their two children are: Sarah C., born Nov 2 1870, and Mary L, Mar 27
1872. Mr. SEARS enlisted at
Miltonsburg, OH, Aug 11 1862 in Co. C, 116th OVI, and was a sergeant
of his company. They went directly to
the front, and were in the battles of Moorfield, Piedmont, Lynchburg, and many
others. He was slightly wounded at the
battle of Opequan, VA, Sep 19 1864. He
was honorably discharged at Richmond, VA, Jun 14 1865. Mr. SEARS father was a native of
MD. About the year 1835 he moved to
Belmont county, where he remained about seven years, and then came to Monroe
county, and settled in Seneca township.
He reared a family of five children, and died in his seventieth year,
respected by all who knew him. Mrs. SEARS
grandfather, Zachariah BARLOW, came from MD and settled in Guernsey
county, when the whole of southeastern OH was a wilderness. They were among the very earliest settlers
in this section. Settled near Fairview,
and reared a family of nine children, of whom Mrs. SEARS father is the
third. Mr. SEARS is the eldest
of his father s family. He is by
occupation a carpenter; resides in Seneca township. Postoffice, Calais, Monroe county, OH.
ALEXANDER D. STEPHEN a resident of Seneca township, in which he was
both born and married, being born on the farm where he now resides, Dec 24
1824, son of Elijah STEPHEN and Marjora DEARTH. He was married, Apr 3 1854, to Mary MILLER,
who was born at St. Clairsville, Belmont county, Apr 6 1825. Her parents were Adam MILLER
(deceased) and Catharine SNIDER who settled in this county in 1835. Their children were: James M., Julitta,
deceased; Julia A., deceased; Johnnie, deceased; Randolph; Alexander, deceased;
and Samuel, the latter adopted. Samuel
served three years in the late war; enlisted, Aug 22 1862, in the 116th
OVI, at Calais; was in all the battles of the regiment; was wounded at
Piedmont, and mustered out with his regiment at the close of the war. Mrs. STEPHEN s first husband was
Samuel CARPENTER, deceased. Mr. STEPHEN
is engaged at farming and raising stock.
His address is Calais, Monroe county, OH.
JAMES W. WARNER
a resident of Seneca township, settled in this county in 1858. His father, John L. WARNER, was born
Nov 27 1815; his mother, Charity WELLS, May 17 1819. John L. WARNER was a native of the
state of NJ, coming when 22 years old, to Fayette county, PA, where he met
& married Charity WELLS, daughter of James & Mary SCARBRY
WELLS, Feb 2 1837. He lived
there until 1850, when he settled in the southeastern part of Guernsey county,
which is now in Noble county. In 1871
he came to Monroe county, and to Seneca township. In 1876 he went to Tyler county WV, where he now resides. James W. WARNER was born in Fayette
county, PA, Dec 16 1838. Was married in
Noble county, Dec 24 1857, to Mary F. CARPENTER, by Rev. Philip JARVIS.
Mrs. WARNER was daughter of Richard CARPENTER, who died Dec 29
1863, and Hannah McMURRAY, and she was born in Guernsey county, Oct 28
1835. Children: Joanna HOWILER,
born Jan 12 1859; Letitia J., Apr 8 1860; John B. M., Apr 28 1862; Bellzora A.,
May 1 1864; Alfred S., Nov 5 1865; Virginia M., May 6 1867; Sarah F., Mar 13
1869; Mordicia H., Sep 5 1871; James F., Jun 29 1874; Alva C., Jul 26 1876;
Mary A., Mar 19 1878, died Mar 23 1878; Shirley E., Sep 16 1880. The first named resides in Seneca township,
the others at home. Mr. WARNER
held the office of school director, in sub-district No. 3, for nine years in
succession. He was elected justice of
the peace in 1874, and has held the office continuously since. Mrs. WARNER s brother, Wilson S. CARPENTER,
served in the 23rd IA Volunteer Infantry during the entire war. Her mother s father, William McMURRAY,
was a soldier in the Revolutionary war, and lived to be almost 100 years
old. Mr. WARNER is a farmer and
stock-dealer. Address, Calais, Monroe
county, OH.
JAMES WELLS son of William and Nancy STEPHENS
WELLS, was born in Greene county, PA, Mar 7 1818, and came to this
county in 1820. He was married near
Miltonsburg, in this county, by Esquire WILLARD, Sep 20 1840, to Julitta
STEPHENS, born in this county, Apr 18 1822, daughter of Elijah and
Majora DEARTH STEPHENS, both deceased. Mrs. WELLS parents were natives of PA, and settled in
Seneca township when the country was a wilderness, and there were only a few
cabins in the township. They had to
house the sheep every night to keep them from the volves. Elijah STEPHENS was a soldier in the
war of 1812. Mr. WELLS, when a
boy, attended some of the first schools taught in the county. They had greased paper for windows, and the
seats were made of saplings split in two, and legs put in from the round side,
with no backs to them. The
writing-desks were made by boring holes in the wall, with no pegs in and laying
planks on them. Elijah STEPHENS
and wife were among the very first settlers in this part of the county. They raised 11 children to maturity, who
have scattered to different parts of the United States. Mr.  Mr. & Mrs. WELLS
children are: William S., born May 13 1841, deceased; Louisa CARPENTER,
Jun 19 1843; Priscilla, Mar 23 1845, deceased; John, Feb 20 1846; Nancy S., Apr
7 1848; Julitta J. GILMORE, Mar 25 1850; Elizabeth J. CARPENTER,
Feb 2 1852; James A., Apr 25 1855; Bird STEPHEN, Dec 25 1856; Elijah S.,
Feb 2 1859; Lana E. BROWNFIELD, Jul 12 1861. Elijah & Nancy live at home; Elijah married Emeline MERCER. All the others who are living, reside in
this township. The eldest son, William,
lost his life in the war. He enlisted,
in the fall of 1862, in the 20th OVI, served until the following
March, when he took the measles, and died at Savannah, TN. Mr. WELLS is a farmer. Address, Calais, Monroe county, OH.
Transcribed by Dr. Shirley A.
Harmon, e-mail: saharmon34@msn.com


More Less is Sass than less is more, the current iteration is based on the Sass version of Twitter's Bootstrap framework and after all this time, it finally has a map, driven by my latest Foursquare checkin.
View SiteOn the basis that less is more, this iteration dispensed with PHP and various web feeds and instead used a purely CSS/HTML approach with a nod to responsive design.
View SiteTruly a product of the programmable web, this iteration took RSS feeds from Delicious, Feedburner, SlideShare and Flickr using YQL to aggregate them and laid everything out using YUI. Did I mention that it was slow? I was working for Yahoo! at the time, hence the heavy use of their services.
View Site


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import config.ApplicationConfig
@import domain.models.developers.DeveloperSession
@import include._
@import model.Crumb
@import org.joda.time.format.DateTimeFormat
@import controllers.TermsOfUseForm
@import model.ApplicationViewModel
@import domain.models.applications.TermsOfUseStatus.AGREEMENT_REQUIRED
@import domain.models.applications.TermsOfUseStatus.AGREED
@this(devMain: DevMain, leftHandNav: LeftHandNav)
@(applicationView: ApplicationViewModel, form: Form[TermsOfUseForm])(
implicit request: play.api.mvc.Request[Any], loggedIn: DeveloperSession, messages: Messages, appConfig: ApplicationConfig, navSection: String = "details")
@app = @{applicationView.application}
@title = { @if(app.termsOfUseStatus == AGREEMENT_REQUIRED) { Agree to our terms of use } else { Terms of use } }
@titleError = { @if(form.errors.nonEmpty) {Error: } else {}}
@devMain(
title = titleError + title.toString,
userFullName = loggedIn.loggedInName,
breadcrumbs = Seq(
Crumb("Manage details"),
Crumb.application(app),
Crumb.viewAllApplications,
Crumb.home
),
leftNav = Some(leftHandNav(Option(applicationView), Option(navSection))),
developerSession = Some(loggedIn)
) {
@errorSummary(form)
@title
@if(app.termsOfUseStatus == AGREED) {


Teachable Machine
This is a recreation of Google's
Teachable Machines
in
ml5.js
.


So Decide To Create This Part , Because I Always Want To Create A Website Where Can I Share My Knows , Show Up My Projects And Many Things
And I'm Going To Show Up All Things I've Been Learning And How To Do It
I'm So Glad To Know You're Here
Show Some Love
Animation
I don't know if you see above the transition
Transition :
HTML
CSS
JavaScript
Date
HTML
CSS
JavaScript


Kactus is free-to-use for public repositories and open source projects on GitHub. Upgrade to work together across unlimited private repositories.
No, GitHub recently made private repository free so you do not need a pro GitHub account for the premium Kactus plan. You can find more information here.
We are independent developers and we'd like to support ourselves and the project long term. Commercial support, additional features, bug fixes and feature priority are provided with the Premium/Enterprise license, as well as ensuring the project stays alive.
Most version control systems for design don't let you work on the same file in parallel, or they force you to manually merge changes. Kactus reveals the underlying representation of your design and leverages a battle-tested system (git) to bring you true version control for your designs.


Webcharts Table - This is a simple listing of elemental data. Created using the improved table functionality in webcharts v1.9.0


Below are the strands as links to their respective GitHub repositories. For Khan Academy strands, all projects, challenges, videos and documents were completed. A lot of effort was put into each individual project.
Over the last few weeks of class, Dan Shiffman's Mitosis Simulator was a challenge I'd accepted to understand and present to the class with my group members. It was a fun activity as I got to see Daniel Shiffman's workflow and I definitely have a goal to try and hit - being more comfortable with my workflow to the point where something as small as the mitosis simulator could take me 5 minutes to code and understand from scratch, rather than many weeks of work.
By far the biggest achievement in this class was actually making a fun, and almost fully functional game. Using RPGMaker MV, we made an RPG about interestingly unique characters finding their paths to the light, with a goal to save the world from the darkness that veils its beauty.
Things like embedded microcontrollers, twitter bot, and shared canvas were all bonding experiences for our group, while also learning lots about what's going on in the circuitry of a bot, applying laws of physics, and learning more about the command prompts of our computers, and how powerful they really are. When it comes to other things I want to do in the future, related to this course, is better my workflow. I used Git here and there, text editors like Atom and Processing 3 all the time, and GitHub every single day, but I want to become so used to all these tools that I can be like Dan Shiffman. One way on doing so is to continue coding his challenges, and staying updated weith his uploads, to keep up with what he posts and wants to share with the coding community on a daily basis.


These are notes from the talk
I attended the 2017 NIPS Conference held at Long Beach as a Microsoft employee (thanks Microsoft India for sending me!). And it was great attending the full conference: tutorials, and the main conference with invited & spotlight talks, workshops. The first day was dedicated to tutorials and this post is about the three tutorials I attended: Deep Learning Practice and Trends, Deep Probabilistic Modelling with Gaussian Processes and Geometric Deep learning on graphs and manifolds. But why write a blog post about it when you can just go back and watch the videos. Well, first reading is much faster than watching videos (300 bits/s vs 20 bits/s).
I found the first tutorial by Oriol Vinyals and Scott Reed very well prepared. All the scattered knowledge that I had from following deep learning research for over an year became much more organised. As the title says, the talk was divided into two parts: Practice, which gave a nice high-level view of the Deep Learning toolbox and Trends, which explained 5 trends in deep learning research.
Part 1: The deep learning toolbox
Different neural net architectures need to have the right inductive biases. For CNNs, the key inductive biases are 1. Locality Invariance 2. Translation invariance which is realized in a Conv layer which is a locally connected layer with weight sharing.
Revolution of depth, but training is a deep net is not easy, depth can’t be parallelized (unlike convolutions) and there are too many parameters for optimization. So how to build very deep CNNs? The tricks are:
- SGD + Momentum
- BatcnNorm after each weight layer (led to higher accuracy plus faster training in Inception v2 paper)
- Weight Initialization (Glorot works well for VGG)
- Model: Stack 3x3 convolutions
- Model: ResNET: Add residual connections that skip 2-3 weight layers, these enabled training deeper nets. Idea is that the gradient skips a few weight layers and does not vanish, so a better gradient flow is maintained. Another related archirecture is highway network and DenseNet (skip connections between everything and everything) and UNET architecture.
Part 2 of Practice is Recurrence and Attention
2 key ingredients that have brough DL into NLP are 1. Neural embeddings (key insight: vectorize contexts) and RNN language models, whoch outperformed other approaches to LMs.
RNNs have persistent memory i.e. a state variable for arbitrarily large contexts. They are SOTA in terms of the log probabilities they achieve on the test set.
Seq2Seq are an extension to RNNs, however they have the limitation that increasing sentence length causes a bottleneck which leads to a sharp drop in BLEU score. The idea in attention (Bahdanau paper, 2015) is that the decoder can query the encoder at every time step. It is really simple to implement and the softmax is differentiable. There are cool extensions to this model as well.
Now the cool part of the talk was the Trends part:
- Autoregressive models
I idea is quite simple: P(x;theta) = ?., and each factor can be parametrized by theta, which can be shared. So, as long as the ordering and grouping is conistent (i.e. it doesn’t violate cusality). Each factor can be parametrized by a DNN (just like in DQNs, which learned to play Atari games). So, the key question is 1. how do you order and group the variables and how do you parametrize them?
Part 1: Modeling Raw Waveforms using Causal Convolutions
Key thing is that each output depends only on the input from prior timesteps. And if you want to get more context for every prediction the tool is dilation. So, you have causal dilated convolutions. And of course, you can stack them, duh! But more tricks ae needed: If you have many many possible values the cross entropy loss has a large meory consumption. So they modeled the loss using a discretized mixture of logistic losses ( a mixture of sigmoids).
Another question is? How do you speed up the sampling? To sample from these models you have to go from left to right which is an O(N) operation. But you can first distill a student net from a teacher net i.e. pretrain a wavenet teacher in the usual way and then train the student net (like GANs).
Part 2: Modeling Text
Self attention: the weights are adaptive and it has a more flexible architecture than convolutions.
Part 3: Modeling Images
You can do it pixel by pixel or group by group.
So, to summarize AR models, we had 2 kinds of models:
- Fully Sequential Models: You can factorize per sample or per pixel, his gives you fast scoring but O(N) sampling. You can make conditional independence assumptions, which enable O(N) scoring and faster sampling.
- Distilled Models: Examples are Wavenet, Parallel NMT. They have O(N) scoring and O(1) sampling.
Part 2 of the Trend: Domain Alignment
Scoot Reed said this was the most promising thing in unsupervised learning. And it absolutely is. And it is the method behind two of my favorite papers: Language Style Transfer and Unsupervised Word Translation. The idea behind this trend is also very simple and the problem is immensely practical. For example, recently I purchased Amazon Echo, powered by Alexa and since my room is not very large, I do not get really great sound out of it. My guess is that since the model behind Alexa was trained in a different acoustic environment?.? Read it. Another
So lets see the problem formation:
- Input: Input is a set of images with some shared structure but there exists no pairing between the images in one domain and the other (i.e. there is no direct alignment). For text, you could have a text corpora in different languages but you don’t have the matching sentences.
- Architectures: They aren’t fancy but are cleverly hooked up.
- Losses: are wired with a loss so that an alignment emerges between the two domains. So, the losses could be in the latent space (e.g. you want the latent representations to be indistinguishable) or in the raw observation space (e.g. pixel space). Adversarial objectives and max likelihood both work well.
- Example 1: Visual Domain Aligment b/w SVHN and MNIST
So you could do the alignment in a weakly supervised way and then do cross-domain retrieval.
Approach 2: Aversarial Learning
Here, you are aligning the domains by construction. So, you share the encoder over several domains, so that the features learnt are invariant to a domain. The gradient revrsal layer (https://discuss.pytorch.org/t/solved-reverse-gradients-in-backward-pass/3589) is also employed to do the trick. The paper is Domain-Adversarial training of Neural Nets.
And of course to make tthese works their are several other trciks that are in the individual papers. for example, the lnguage style trsnfer first uses? but then it uses a? to regularize further, which is the final model.
Examples:
- Unsupervised cross domain Image generation
- Cycle Consitstent Loss
- Unsupervised Image to Image translation
- DISCOGAN
- GraspGAN
- These were for images, No models for text:
-
- Unsupervised NMT
- Unsupervised NMT using Monolingual Corpora only
Trend 3: Meta Learning
it is about a loss that models another loss. The idea is to go beyond a train and test paradigm. So, meta-learning adds a twist to the standard paradigm. The task between the training set and the test set changes, so the model has to learn to learn.
- There are three approaches to meta-learning:
- Model Based
- Metric Based
- Optmization Based
Trend 4: Deep Learning Over Graphs
graph is hard to represnt as a tensor. There are two main architectures: message passing (Borealis AI, who had the cool Yes, We GAN T-shirts had a paper on message passing), and CNNs, RNNS, attention. But again the question to ask is: What is the right inductive bias for graphs. We want our model to be order invariant. So, X = permutation(X).
-
So, we have Message passing NNs, however with graphs batching will be quite tricky.
Trend 5: Program Induction with NNs
So you have 3 ways:
- You can think of NN as a program: embed a program as weights of a NN.
- NN that generates source code. (Learning to execute paper by Zaremba et al. Follows under the paradigm of “Apply Seq2Seq to everthing”, Deep Coder, RobustFill: shows that Neural program induction works).
- Probabilistic Programming with NNs.
To conclude the trends part: Deep AR models and CNNs are in production in consumer apps.
Inductive biases are very useful: CNNs (spatial), RNNs (time) and Graphs (Perm. Invariance).
Simple tricks like resnets & skip-connections.
Adversarial Nets + Unsupervised domain alignment.
Meta-learning: more and more lifecycle of a model will be the part of the end to end training.
Program synthesis and deep geometrci learning will become very important.


This example makes use of the Signal class to handle user interaction and trigger AJAX requests.
Signal
It uses the RANDOM.ORG API to generate a number of random strings every time the "more" or "less" button is clicked.
More Less


THE MAN WHO KNEW TOO LITTLE
A Film Review by James Berardinelli
RATING: *** OUT OF ****
United States, 1997 U.S. Release Date: 11/14/97 (wide) Running Length: 1:33 MPAA Classification: PG (Sexual innuendo, cartoon violence, mild profanity) Theatrical Aspect Ratio: 1.85:1
Cast: Bill Murray, Joanne Whalley, Alfred Molina, Peter Gallagher, Richard Wilson, Geraldine James, John Standing, Anna Chancellor Director: Jon Amiel Producers: Arnon Milchan, Michael Nathanson, Mark Tarlov Screenplay: Robert Farrar and Howard Franklin based on the novel "Watch That Man" by Robert Farrar Cinematography: Robert Stevens Music: Chris Young U.S. Distributor: Warner Brothers
With a title like THE MAN WHO KNEW TOO LITTLE, one might reasonably assume that actor Bill Murray's latest endeavor is a parody of Hitchcock's twice-made film, THE MAN WHO KNEW TOO MUCH. In fact, although the movie takes a few stabs at mocking Hitchcockian plot twists, it's mainly a satire of the James Bond pictures. Unlike many similar productions, this one has a smart, knowing script that understands just how to play up all the tricks and contrivances of the secret agent genre to their best comic effect. It's a breezy, fun film that offers more than a few solid laughs.
Murray plays Wallace Richie, an American from Des Moines who decides to celebrate his birthday by purchasing a plane ticket and flying to see his brother, James (Peter Gallagher), and sister-in-law, Barbara (Anna Chancellor), in London. And, while James is delighted to see Wallace, the visit comes at an inopportune time – he is on the verge of closing a big deal with some German businessmen and he needs an quiet, conservative dinner party without a wild card like Wallace around to disturb things. So, for a birthday present, James gives Wallace an evening's participation in the "Theater of Life," an experimental form of entertainment that allows normal people to act out their fantasies. Only, instead of becoming involved in this "Theater of Life," Wallace accidentally intercepts a phone call intended for a real hit man, and, as a result, he becomes embroiled in a web of espionage, attempted assassinations, and a would-be terrorist attack at the signing of a peace treaty between Russia and England.
It's vitally impossible for the premise of a mistaken identity to sustain an entire motion picture, but THE MAN WHO KNEW TOO LITTLE employs enough variations that, until the very end, proceedings remain surprisingly lively. Of course, it could be argued that the situation in this film is so outrageous that it's as much a "fish out of water" scenario as a "mistaken identity" one. Wallace is, after all an "innocent" American thrown into the midst of a European game of murder, mayhem, and blackmail. There's a little of CROCODILE DUNDEE's charm at work here. It helps that Murray, whose films vary from the good (GROUNDHOG DAY) to the horrible (SCROOGED), is in top form. Throughout the entire film, Wallace is completely oblivious to his danger, and, as a result, Murray gets a chance to develop his own brand of comic-action hero -- a bizarre mixture of Dirty Harry, James Bond, and Austin Powers. The running joke is that everybody mistakes Wallace for one of the best in the spy business because of the ease with which he dispatches his enemies, even though every one of his victories is accidental.
Every secret agent movie, no matter how silly, demands three accessories: a beautiful woman, a nasty villain, and a threatening henchman. THE MAN WHO KNEW TOO LITTLE possesses all of these. Joanne Whalley (the British actress best-known for her role in SCANDAL) is Lori, the femme fatale who becomes involved with Wallace after she mis- identifies him as a hit man intending to kill her. Richard Wilson is absolutely delightful as the very proper Sir Roger, a top-level diplomat who yearns for the return of the Cold War, and who is absolutely terrified that the skilled and dangerous Wallace may confound his plans. Alfred Molina, last seen in a brilliant cameo in BOOGIE NIGHTS, is Boris the Butcher, the killer assigned to eliminate Wallace, but who becomes increasingly impressed by his quarry's amazing skills.
For roughly three-quarter of the film's 93-minute running time, director Jon Amiel (COPYCAT) keeps things rolling along, effectively blending satire with almost every imaginable variation on the mistaken identity plot device. However, since this is essentially a one-joke movie, it runs out of steam before the end credits arrive. The last twenty minutes of THE MAN WHO KNEW TOO LITTLE are a little too repetitious, and even a brief appearance by respected British actress Geraldine James (THE JEWEL IN THE CROWN) in an over-the-top role as a torturer can't restore the film's earlier inventiveness.
Despite the floundering ending, THE MAN WHO KNEW TOO LITTLE still leaves a favorable impression, and the closing scenes are not completely without humor. Murray recovers nicely from his elephantine flop, LARGER THAN LIFE, to essay a character who is both likable and funny. Most of the humor here works – including a mugging where things don't go as planned, a boatload of double-entendres, a unique car chase through a construction zone, and a hilarious bit with some nasal spray. Although THE MAN WHO KNEW TOO LITTLE is less audacious than AUSTIN POWERS and lacks all of the '70s references, it's frequently as enjoyable, if not moreso, and that makes it a worthy choice for a frothy, funny evening.
Copyright 1997 James Berardinelli
- James Berardinelli e-mail<EMAIL_ADDRESS>Now with more than 1300 reviews... The ReelViews web site: http://movie-reviews.colossus.net/
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- Alfred Hitchcock
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P2-E002¶
You are encouraged to familiarise yourself with a few topics prior to the exercise. The background section will provide you with some basic information and pointers. The exercise itself is designed to give you some practice in working with these concepts to give you a more intuitive grasp of them. The teacher will be able to supervise your during the exercise and explain the underlying theory if uncertainties remained during your preparation for the exercise.
Background¶
The following are topics you should try to familiarise yourself with prior to the exercise. Note that the serve as pointers and checks for your studies rather than as full instructional materials. It is expected that you internalised the theory of previous exercises.
Topic 1: Familiarise yourself with probability density functions (PDFs), cumulatve distribution functions (CDFs) and the quantile (or percent point function). The figure shows the same data displayed as a PDF (\(P (X=x)\) ) and CDF (\(P (X=\bar{x})\) ). After your studies of the topics, you should understand what is displayed and labelled here.
Topic 2: The next topics you should tacke are basic set notation, classic statistical hypothesis testing and one and two tailed testing. See if you can understand the following example after your studies:
“The DLR is investigating differences in albedo as clouds gain in thickness. Stratus clouds have an albedo of ca. 0.5, the observed albedo increase as they thicken up is 0.05, as picked up by 100 measurements. The sample standard deviation (s) is 0.4. Does thickness truly affect albedo?”
This scenario gives us:
Null hypothesis: Thickness has no effect; the mean is still 0.5 even after thickness increase. (\(H_0: \mu=0.5\) )
Alternative hypothesis: Thickness has an effect; the mean is no longer 0.5 after thickness increase. (\(H_A: \mu \ne 0.5\) )
If \(H_0: \mu=0.5\) is true, we can calculate the probability of getting a signal as extreme or even more extreme than the observed (0.05).
Finally:
The probability (p) is too high for us to reject the null hypothesis \(H_0: \mu=0.5\) . With those specific sets of measurements and knowledge, we therefore cannot confidently say that thickness has an effect on albedo.
Exercise¶
Information¶
Learning goals |
|
|---|---|
Skills |
|
What to Submit
Submit your script(s) and 1-2 sentence answers to the questions for the tasks of this exercise. You may include the answers as comments in your script(s). Make sure to also comment each of your calculations (using the terminology you were introduced to). The script should be named [your surname]_e102.[ext], where [ext] is the file extension. In case of Python, this would be .py, for Matlab it is .m, etc.
Hypothesis Testing: Temperatures near Antofagasta¶
You are provided with an ERA-Interim re-analysis time series extracted for Antofagasta (Chile):
Regional climate models were forced with boundary conditions reflecting a low-emission-scenario state of technological and demographic developments. They simulated a climate for the 2070-2100. The model produced local January temperatures that have a mean of 23.6°C, and June temperatures with a mean of 17.2°C. You want to know if we see an increase in January and June temperatures near Antofagasta under the low-emission scenario. Using the dataset you are provided with, calculate the predicted temperature changes and determine if the signals are real or still within reasonable range of the observed temperature variability. Formulate testable hypothesess and apply your knowledge of statistical hypothesis testing, “p-values” and significance levels to answer this question.
HINT
You need models (PDF) to describe your null hypotheses. Think about how you would construct such a model for the problem you want to solve.
Warning
Late submissions won’t be accepted!


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CSS Tutorial
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In our Css tutorials you will learn how to use Css to control the style and layout of multiple web pages all at once.
Start learning CSS!In our Css tutorials you will learn how to use Css to control the style and layout of multiple web pages all at once.
Start learning CSS!Learn by 70 examples! With our editors, you can edit CSS, and click on a test button to view the result.
Try-it-Yourself!At W3Schools you will find a complate Css2 reference, listing properties, units of measurments, controls, and more.
CSS2 Referencetest your CSS skills at W3Schools!
Star Css Quiz


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Add new Button or Text element from options above
Author: Anton Radev
License: MIT
jQuery Simple Page Builder is jQuery written tool for creating dynamically elements in web environment.
This demo can create buttons in the main manu and text elements in a container area in the page. Both buttons and text elements support color changing via color picker.


Further Reading
The Art of Computer Programming [Knu98a, 1193] is, as usual, an outstanding reference. Numerical Recipes in C [PTVF92] has implementations of several engines and distributions. e-Handbook of Statistical Methods, section <IP_ADDRESS>" [NIS] has information on many statistical distributions. See www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm.
|


{% include hwk-page-header-template.html %}
{% include hwk-name-header-template.html %}
{{page.num}}: {{ page.desc }}
| ready? | assigned | due | points |
|---|---|---|---|
| {{page.ready}} | {{page.assigned | date: "%a %m/%d %I:%M%p" }} | {{page.due | date: "%a %m/%d %I:%M%p" }} |
You may collaborate on this homework with AT MOST one person, an optional "homework buddy".
Bring this with you to your first lecture on the due date indicated.


URIs for ignored schemas and DTDs are listed in this section. (If an XML file references a URI listed in this section, IntelliJ IDEA ignores that URI and doesn't mark it as an error in the editor.) See also, Referencing XML schemas and DTDs.


Before Change
def add_guesses(self, guesser, question, fold, guesses):
// Remove the old guesses
query = "DELETE FROM guesses WHERE question=? AND guesser=?;"
c = self._conn.cursor()
c.execute(query, (question, guesser,))
// Add in the new guesses
query = "INSERT INTO guesses" + \
After Change
def add_guesses(self, guesser, question, fold, guesses):
// Remove the old guesses
query = "DELETE FROM guesses WHERE question=? AND guesser=?;"
c = self._cursor()
c.execute(query, (question, guesser,))
// Add in the new guesses
query = "INSERT INTO guesses" + \


Romania's Iohannis wins presidential ballot, will face runoff
Exit poll shows Klaus Iohannis, former leader of National Liberal Party, garnered 38.7 percent of votes.
Romania's incumbent President Klaus Iohannis won the first round of a presidential election on Sunday, and is likely to face opposition Social Democrat leader and former prime minister Viorica Dancila in a November 24 runoff, exit polls showed.
An exit poll conducted by IRES pollster showed Iohannis garnered 38.7 percent of votes followed by Dancila with 22 percent. Save Romania Union head Dan Barna got 16.1 percent.
Another poll by Curs-Avangarde ranked Iohannis first with 39 percent and Dancila with 22.5 percent.
"The official results will come tomorrow but exit poll data show that millions of Romanians from the country and from abroad voted for our project, for a normal Romania and for us to continue the important process of changing Romania for the better," Iohannis told supporters.
Data by the electoral bureau, which is expected to unveil partial official results from Monday morning, showed a turnout of 48 percent with a record high diaspora voter presence of 660,000 - which was not taken into account by the exit polls.
If no contender wins more than 50 percent, there will be a second round of voting.
More:
Voters in Romania headed to the polls earlier on Sunday to pick their next president after a lacklustre election campaign overshadowed by a political crisis which saw a minority government installed just a few days ago.
Around 18.2 million voters were eligible to vote for one of 14 candidates vying for the five-year presidency.
Dancila's Social Democrat government was forced to step down after losing a no-confidence vote in Parliament, amid corruption scandals and allegations it wanted close control of the judiciary.
Iohannis is a former leader of the National Liberal Party, which on Monday formed a minority government led by Prime Minister Ludovic Orban.
Polarisation
Political analyst Andrei Taranu said polarisation over the political crisis has benefitted the two top candidates.
"This situation kept Klaus Iohannis and Viorica Dancila in the spotlight, while other candidates had no chance to rise on the political stage," he told news agency Associated Press.
While lacking an executive role, Romania's president has significant decision-making powers, including on matters of national security and foreign policy, and can reject party nominees for the prime minister and government nominees for judicial appointments.
The president can also return legislation approved by Parliament but has to ratify it if legislators back it a second time.
Polling stations across the country opened at 05:00 GMT and will close at 19:00 GMT.
Voting opened at many locations abroad on Friday and by midday on Saturday more than 200,000 diaspora Romanians had already voted, a record for the first round of a presidential election.


--- categories: python, docutils date: 2003/11/10 18:18:08 guid: /?p=15 permalink: http://bob.pythonmac.org/archives/2003/11/10/making-docutils-do-python-syntax-highlighting-for-html-output/ tags: '' title: Making docutils do python syntax highlighting (for HTML output) ---
http://undefined.org/python/docpytils-0.0.tgz is a simple docutils directive that does simple python syntax highlighting in ReST.. basically, after importing docpytils, you can do something like this:
.. pycode:: python code here..
Like raw, you can also use :file: or :url: as arguments.
Output will look like this:
>>> from __future__ import * File "<stdin">, line 1 SyntaxError: future statement does not support import * >>>


If you want to give them a whurl you can head over to
the Github repo
where you can fork and clone the repository! The configuration to host
your solutions once you have completed them is in the repository, so
you should be able to easly share your awesomeness with
now
. Good Luck!
You can checkout some potential solutions
here or
here.
You are given a DOM element like

.
Your job is to add 10 images to that list. For the
src
attribute you can use
https://picsum.photos/200
. Please make sure that you are
writing sematically correct HTML lists, meaning that you must only
have

children of a

.
You are given a DOM <button>
element like
<button id="open-dialog">Open Dialog</button>
and a pre-fabricated modal dialog which resembles

.
Your job is to show the dialog once the button has been clicked.
You are given a DOM element like

.
Your job is have that element follow your mouse inside the result
container. A hint, the result container for this exercise has an
id
of follow-me-parent
.
You are given a list of DOM elements inside a

.
Your job is to give the first child a
blue background, the last child a
palevioletred background
and the 9th child a
green background. Then make all
of the even numbers lime.
You are given a DOM element

and an
<input type="text" />
. Your job is output what ever
is typed in the input into the
p tag.
You are given two DOM elements like
<input type="text" />
. Your job is compare the two
values and make sure that the first ones value is not less than the
second one. If the second number is larger show the error message that
has been given in

and
then add some css to turn the label of the second value
red.
You are given an object called data
, it contains a list
of pictures. Your job is to display that list of pictures with their
author and file format. You can place these elements inside the
provided


Home Categories Misc. About Me
git command (expand)
References:
Create repositories
git init
git clone [url]
Configuration tools
git config --list
git config --global user.name "[name]"
git config --global user.email "[email address]"
git config --global color.ui auto
The .gitignore file
step:1 create a text file name .gitignore
step:2 include the files you want not to git
Git Branches
git branch [branch-name] (Creates a new branch)
git checkout [branch-name] (Switches to the specified branch
and updates the working directory)
git merge [branch] (Combines the specified branch’s history into the
current branch. This is usually done in pull
requests, but is an important Git operation.
git branch -d [branch-name] (Deletes the specified branch)
Synchronize changes (Synchronize local repo with the remote repo)
git fetch (Downloads all history from the remote tracking branches)
git merge (Combines remote tracking branch into current local branch)
git push (Uploads all local branch commits to GitHub)
git pull (Updates your current local working branch with all new commits from the corresponding remote branch on GitHub. git pull is a combination of git fetch and git merge)
Make changes (Browse and inspect the evolution of project files)
git log (Lists version history for the current branch)
git log --follow [file] (Lists version history for a file, including
renames)
git diff [first-branch]...[second-branch] (Shows content differences between
two branches)
git show [commit] (Outputs metadata and content changes of the
specified commit)
git add [file] (Snapshots the file in preparation for versioning)
git commit -m "[descriptive message]" (Records file snapshots permanently in version history)
Redo commits (Erase mistakes and craft replacement history)
git reset [commit] (Undoes all commits after [commit], preserving changes locally)
git reset --hard [commit] (Discards all history and changes back to the specified commit)


Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources.
| Version: | 1.0-1 |
| Depends: | R (≥ 3.4.0) |
| Published: | 2018-11-14 |
| Author: | Zongzheng Chai [aut, cre] |
| Maintainer: | Zongzheng Chai <chaizz at 126.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | forestHES results |
| Reference manual: | forestHES.pdf |
| Package source: | forestHES_1.0-1.tar.gz |
| Windows binaries: | r-devel: forestHES_1.0-1.zip, r-release: forestHES_1.0-1.zip, r-oldrel: forestHES_1.0-1.zip |
| OS X binaries: | r-release: forestHES_1.0-1.tgz, r-oldrel: forestHES_1.0-1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=forestHES to link to this page.


{{now.date}}
SYFR /{{letter_id}}
{{donation.donor.address}}
By ShanYou
Dear {{donation.donor.name}}
On behalf of Shan You Counselling Centre and the programme beneficiaries, I want to extend our heartfelt appreciation for your contribution towards the Food Rations Distribution Service.
Each month, our Food Rations Distribution Service provides food items to a total of 300 low income families and needy elderly. They are referrals from the Community Development Councils, Family Service Centres, hospitals and other voluntary welfare organizations.
It is through the support of corporate organizations and individual donors that we are able to continue this service for the beneficiaries.
We are thankful to receive these items on {{donation.date}}:
| Item | Quantity |
|---|---|
| {{donate.stock.name}} {{donate.stock.unit_measure}} | {{donate.quantity}} |
Should you have any enquiry, please contact me at 6745 9216/ 9111 6377 (Mobile) or visit our website at www.shanyou.org.sg for monthly updates on our Food Rations Distribution Service.
Thank you once again for making our mission a reality and we look forward to your continuing support.


{% from 'macros.html' import include_src %} {% extends "base.html" %} {% block title %}fastpi{% endblock %} {% block content %} {% filter markdown %} # fastpi π = 0.0 ± 1.0 This little demo uses two random numbers \\(r_1\\) and \\(r_2\\) and then does a comparison $$r_1^2 + r_2^2 < 1.0$$ to figure out whether the generated point is inside the first quadrant of the unit circle. __Make sure you have a _redis-server_ running.__ {% endfilter %}


You have 5 lifes at the beggining.
Try to avoid clicking red circles as possible.
After reaching checkpoint you gain one life. Below level 5 at the start of the round you first have to click at least 10 green circles in a row to reach checkpoint. Next checkpoint is then only 9 green circles away. This goes down to 1 green circle.
Above level 4 is the first checkpoint calculated from formula: 8 + (currentLevel/2)
Above level 4 you do not have to click the circles, but drive mouse over those.
If you hit green circle, you get score points calculated from this formula: 15 * currentLevel If you did not hit it, then you have lost one life.
If you hit the red one, your score is decreased by the appropriate value calculated from this formula: 15 * currentLevel * RedOnesClicked
If you hit the red one and you have less score points than to be substracted, you lose one life and all left score points.
Game ends when you have no lifes.
For the best game experience turn on fullscreen by pressing F11 (Google Chrome & Mozilla Firefox).
Stay with your cursor in the center of the screen - this is the closest way to all directions.
You might think circles are too fast to click them, but if you stay focused, you can hit them all!
Take a while to relax after each 'death' - about 2 minutes is recommended to stay sharp!
Above level 4 you stay with your mouse rather above than in the bottom - you never know if the next one is red circle!
Do not freak out after hitting red circle for the first time in a round. This is okay, until you hit the red one for more than 3 times. You can loose really a lot of score by hitting more red circles in one round. See the formula above.
This is not done yet!To pause the game just move your mouse out of the window. To resume, enter the window with your mouse again.


Source code for pvl_snlinverter
import numpy as np
import pandas as pd
import pdb
import pvl_tools
def pvl_snlinverter(Inverter,Vmp,Pmp):
[docs] '''
Converts DC power and voltage to AC power using Sandia's Grid-Connected PV Inverter model
Determine the AC power output of an inverter given the DC voltage, DC
power, and appropriate Sandia Grid-Connected Photovoltaic Inverter
Model parameters. The output, ACPower, is clipped at the maximum power
output, and gives a negative power during low-input power conditions,
but does NOT account for maximum power point tracking voltage windows
nor maximum current or voltage limits on the inverter.
Parameters
----------
Inverter : DataFrame
A DataFrame defining the inverter to be used, giving the
inverter performance parameters according to the Sandia
Grid-Connected Photovoltaic Inverter Model (SAND 2007-5036) [1]. A set of
inverter performance parameters are provided with PV_LIB, or may be
generated from a System Advisor Model (SAM) [2] library using pvl_retreivesam.
Required DataFrame components are:
============= ==============================================================================================================================================================================================
Field DataFrame
============= ==============================================================================================================================================================================================
Inverter.Pac0 AC-power output from inverter based on input power and voltage, (W)
Inverter.Pdc0 DC-power input to inverter, typically assumed to be equal to the PV array maximum power, (W)
Inverter.Vdc0 DC-voltage level at which the AC-power rating is achieved at the reference operating condition, (V)
Inverter.Ps0 DC-power required to start the inversion process, or self-consumption by inverter, strongly influences inverter efficiency at low power levels, (W)
Inverter.C0 Parameter defining the curvature (parabolic) of the relationship between ac-power and dc-power at the reference operating condition, default value of zero gives a linear relationship, (1/W)
Inverter.C1 Empirical coefficient allowing Pdco to vary linearly with dc-voltage input, default value is zero, (1/V)
Inverter.C2 empirical coefficient allowing Pso to vary linearly with dc-voltage input, default value is zero, (1/V)
Inverter.C3 empirical coefficient allowing Co to vary linearly with dc-voltage input, default value is zero, (1/V)
Inverter.Pnt ac-power consumed by inverter at night (night tare) to maintain circuitry required to sense PV array voltage, (W)
============= ==============================================================================================================================================================================================
Vdc : float or DataFrame
DC voltages, in volts, which are provided as input to the inverter. Vdc must be >= 0.
Pdc : float or DataFrame
A scalar or DataFrame of DC powers, in watts, which are provided
as input to the inverter. Pdc must be >= 0.
Returns
-------
ACPower : float or DataFrame
Mdeled AC power output given the input
DC voltage, Vdc, and input DC power, Pdc. When ACPower would be
greater than Pac0, it is set to Pac0 to represent inverter
"clipping". When ACPower would be less than Ps0 (startup power
required), then ACPower is set to -1*abs(Pnt) to represent nightly
power losses. ACPower is not adjusted for maximum power point
tracking (MPPT) voltage windows or maximum current limits of the
inverter.
References
----------
[1] (SAND2007-5036, "Performance Model for Grid-Connected Photovoltaic
Inverters by D. King, S. Gonzalez, G. Galbraith, W. Boyson)
[2] System Advisor Model web page. https://sam.nrel.gov.
See also
--------
pvl_sapm
pvl_samlibrary
pvl_singlediode
'''
Vars=locals()
Expect={'Inverter':(''),
'Vmp':'',
'Pmp':''}
var=pvl_tools.Parse(Vars,Expect)
Paco=var.Inverter['Paco']
Pdco=var.Inverter['Pdco']
Vdco=var.Inverter['Vdco']
Pso=var.Inverter['Pso']
C0=var.Inverter['C0']
C1=var.Inverter['C1']
C2=var.Inverter['C2']
C3=var.Inverter['C3']
Pnt=var.Inverter['Pnt']
A=Pdco*((1 + C1*((var.Vmp - Vdco))))
B=Pso*((1 + C2*((var.Vmp - Vdco))))
C=C0*((1 + C3*((var.Vmp - Vdco))))
ACPower=((Paco / (A - B)) - C*((A - B)))*((var.Pmp - B)) + C*((var.Pmp - B) ** 2)
ACPower[ACPower > Paco]=Paco
ACPower[ACPower < Pso]=- 1.0 * abs(Pnt)
return ACPower


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# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/ # Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License.
"""Wrapper class for an http response body.
This provides a few additional conveniences that do not exist in the urllib3 model:
* Set the timeout on the socket (i.e read() timeouts) * Auto validation of content length, if the amount of bytes we read does not match the content length, an exception is raised.
""" self._raw_stream = raw_stream self._content_length = content_length self._amount_read = 0
"""Set the timeout seconds on the socket.""" # The problem we're trying to solve is to prevent .read() calls from # hanging. This can happen in rare cases. What we'd like to ideally # do is set a timeout on the .read() call so that callers can retry # the request. # Unfortunately, this isn't currently possible in requests. # See: https://github.com/kennethreitz/requests/issues/1803 # So what we're going to do is reach into the guts of the stream and # grab the socket object, which we can set the timeout on. We're # putting in a check here so in case this interface goes away, we'll # know. try: # To further complicate things, the way to grab the # underlying socket object from an HTTPResponse is different # in py2 and py3. So this code has been pushed to botocore.compat. set_socket_timeout(self._raw_stream, timeout) except AttributeError: logger.error("Cannot access the socket object of " "a streaming response. It's possible " "the interface has changed.", exc_info=True) raise
"""Read at most amt bytes from the stream.
If the amt argument is omitted, read all data. """ chunk = self._raw_stream.read(amt) self._amount_read += len(chunk) if not chunk or amt is None: # If the server sends empty contents or # we ask to read all of the contents, then we know # we need to verify the content length. self._verify_content_length() return chunk
# See: https://github.com/kennethreitz/requests/issues/1855 # Basically, our http library doesn't do this for us, so we have # to do this ourself. if self._content_length is not None and \ self._amount_read != int(self._content_length): raise IncompleteReadError( actual_bytes=self._amount_read, expected_bytes=int(self._content_length))
"""Close the underlying http response stream.""" self._raw_stream.close()
protocol = operation_model.metadata['protocol'] response_dict = { 'headers': http_response.headers, 'status_code': http_response.status_code, } # TODO: Unfortunately, we have to have error logic here. # If it looks like an error, in the streaming response case we # need to actually grab the contents. if response_dict['status_code'] >= 300: response_dict['body'] = http_response.content elif operation_model.has_streaming_output: response_dict['body'] = StreamingBody( http_response.raw, response_dict['headers'].get('content-length')) else: response_dict['body'] = http_response.content
parser = parsers.create_parser(protocol) return http_response, parser.parse(response_dict, operation_model.output_shape) |


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Using a Gotek USB Floppy Emulator in a Roland XP-50
Further to my last post on recommissioning my Roland XP-50 workstation, I replaced the floppy drive with a Gotek floppy emulator. I can now use USB memory sticks to store files and transfer them between my PC. For example MIDI .mid files can be put on a virtual floppy on a USB via my PC and then accessed by the XP-50.
The Gotek USB Floppy Emulators are well proven replacements for 3.5 inch floppy drives. They use ubiquitous USB memory sticks to hold a large number of virtual floppies using image files. Replacing the stock firmware with FastFloppy provides a reliable solution that works in almost all possible applications, including old Roland workstations like my XP-50.
Additionally, the WinImage program is useful for working with the images on a PC, including inserting and removing files.
It took me a while to figure out the correct and current info so I provide a summary here. It should also apply to other musical instruments with floppy drives.
Acquiring a Gotek
Gotek only supply in bulk but there many, many suppliers who will sell individual units to end users. Confusingly, there are many models available from Gotek and several variations have appeared over time. Many suppliers badge them and do not specify which version they provide. In addition, it is possible to upgrade the stock Gotek hardware with a better display and a rotary control. Some suppliers provide these updates pre installed.
Fortunately, this complexity is well covered in the FastFloppy Gotek model documentation.
I played safe and purchased a device specifically listed as SFR1M44-U100K GoTek 1.44MB USB SSD Floppy Drive Emulator for YAMAHA KORG ROLAND Electronic Keyboard with 7 segment display and two buttons. However, I didn’t know about the significant variation over time that FastFloppy details. Fortunately though, the model I received was a recent SFRKC30AT3 using the Artery AT32F415 chip (not the very latest). Gotek changed chip manufactures to Artery due to escalating costs.
Setting up the Gotek
The first thing to do is install the FastFloppy firmware on the Gotek. The Gotek drive is then installed replacing the floppy drive.
Installing FastFloppy is straightforward. Connect the Gotek to a PC with a USB cable, install the Artery driver and flashing software on the PC and use it to flash FastFloppy on the Gotek. Full details are provided in the FastFloppy Firmware Programming docs.
Swapping the drives is also straightforward, as you’d expect. In my case just 4 screws and after finding conflicting advice over jumper settings only a single S0 jumper was needed. The floppy drive was configured for S0 (first drive) and Ready signal on pin 24, but FastFloppy just required the S0 jumper.
Turning on the XP-50 after installation, the Gotek display showed “F-F” indicating for FastFloppy was installed and running.
Testing and Using the Gotek
After some confusion caused by various aged articles, I was pleased to discover the latest FastFloppy firmware makes it “easy peasy” to use the drive. Both in the XP-50 and when transferring files between the XP-50 and a PC. I performed an initial “smoke test” as follows:
- Format the USB stick as FAT using DiskManager (or Windows Explorer)
- Grab the FastFloppy empty 1.44MB image file from the image library and put two copies on the stick
- Plug the stick into the Gotek and turn the XP-50 on
- After startup, the Gotext buttons allow switching between slot “000” and “001” indicating the two images are seen
- Use XP-50 Disk utilities to check the images can be seen format one - takes over a minute but then the Gotek is emulating a slow floppy
- Plug the stick into a PC running WinImage
- Grab a MIDI .mid file, test it on MuseScore and add it to the formatted image file with WinImage (I used Bach’s Air on a G String from MidiWorld)
- Pop the stick back in the XP-50, select the image, load the file, select General Midi mode and play it
- Enjoy the cheesy GM sounds
That’s it. I can now read and write files to the floppy images on both devices. And no nasty, unreliable and now expensive floppy discs required.
Unknowns
It’s not clear if FastFloppy has any restrictions on the type or size of USB memory stick used. I used a 32GB SansDisk job without problems.
Access on the XP-50 is fairly slow but I guess FastFloppy is emulating timings to keep the host hardware happy. There are some FastFloppy notes on how to mitigate slow Roland access which I’ll keep in mind in case I feel I needed it.
Unless I upgrade the Gotek hardware I will need to keep track of the slot numbers for each image file. By default, FastFloppy assigns slot numbers automatically but does not explain how. Perhaps based on FAT directory order? It is also possible to use a config file to change to using image filenames that include the slot number.
The green LED is on all the time. I’m not sure if this is expected or it should flash during access. Not an issue at all.
The docs say WinImage is shareware (cost $30 after 30 days) but as it’s no longer developed that might not be required. A possible alternative seems to be freeware ImgBurn but I have not tried it.


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Computes necessary configuration options from the environment. This can be used in Rails, Merb or standalone from the command line.
| APP_ROOT | = | defined?(RAILS_ROOT) ? RAILS_ROOT : defined?(Merb) ? Merb.root : File.expand_path(".") |
Extract project, package, controller names from the environment. This will respect config/restfulx.yml if it exists, you can override all of the defaults there. The defaults are:
- *base-package* same as project name downcased
- *controller-name* ‘ApplicationController‘
Here‘s a sample restfulx.yml file:
RestfulX code generation configuration options
By default flex models, commands, controllers and components are genearated into app/flex/<your rails project name> folder. If you‘d like to customize the target folder (to say append a "com" package before your rails project name) uncomment the line below base-package must follow the usual flex package notation (a string separated by ".")
base-package: com.pomodo
Main RestfulX controller is typically named AppicationController. This controller is created in <base-package>.controllers folder. You can customize the name by uncommenting the following line and changing the controller name.
controller-name: ApplicationController
[ show source ]
# File lib/restfulx/configuration.rb, line 45 45: def extract_names(project = nil) 46: if project 47: project_name = project.camelcase.gsub(/\s/, '') 48: project_name_downcase = project_name.downcase 49: else 50: project_name = APP_ROOT.split("/").last.camelcase.gsub(/\s/, '') 51: project_name_downcase = project_name.downcase 52: end 53: 54: # give a chance to override the settings via restfulx.yml 55: begin 56: config = YAML.load(File.open("#{APP_ROOT}/config/restfulx.yml")) 57: base_package = config['base-package'] || project_name_downcase 58: base_folder = base_package.gsub('.', '/').gsub(/\s/, '') 59: project_name = config['project-name'].camelcase.gsub(/\s/, '') || project_name 60: controller_name = config['controller-name'] || "ApplicationController" 61: flex_root = config['flex-root'] || "app/flex" 62: distributed = config['distributed'] || false 63: rescue 64: base_folder = base_package = project_name_downcase 65: controller_name = "ApplicationController" 66: flex_root = "app/flex" 67: distributed = false 68: end 69: [project_name, project_name_downcase, controller_name, base_package, base_folder, flex_root, distributed] 70: end
List files ending in *.as (ActionScript) in a given folder
[ show source ]
# File lib/restfulx/configuration.rb, line 73 73: def list_as_files(dir_name) 74: Dir.entries(dir_name).grep(/\.as$/).map { |name| name.sub(/\.as$/, "") }.join(", ") 75: end
List files ending in *.mxml in a given folder
[ show source ]
# File lib/restfulx/configuration.rb, line 78 78: def list_mxml_files(dir_name) 79: Dir.entries(dir_name).grep(/\.mxml$/).map { |name| name.sub(/\.mxml$/, "") } 80: end


---
title: "Speech Generator"
date: 2019-04-25
excerpt: "Generating formal speeches using word-level language model "
collection: projects
---
This is an implementation of word-level language modeling. The texts are trained for 64 epoches from ~30 formal speeches (200kb). There are 6645 unique words in the dictionary.
Built with tensorflow
Source Code


The version can be any symbol or any integer.
The symbol :newest refers to the largest version number that already exists in the file system when reading, overwriting, appending, superseding, or directory listing an existing file. The symbol :newest refers to the smallest version number greater than any existing version number when creating a new file.
The symbols nil, :unspecific, and :wild have special meanings and restrictions; see Section <IP_ADDRESS> (Special Pathname Component Values) and Section <IP_ADDRESS> (Restrictions on Constructing Pathnames).
Other symbols and integers have implementation-defined meaning.
<IP_ADDRESS>.7 Notes about the Pathname Version Component
<IP_ADDRESS> Restrictions on Constructing Pathnames


Regain control of your educational experience!
We love our community members and always welcome new ideas to improve education!
Put an image, video, animation, or anything else in the screen!
ELMS:LN is built on Drupal and other open source technologies!
Something about ELMS:LN being cool and open source!


--- layout: default title: William David Hunt ---
You have landed on the personal blog for William David Hunt.
I am an English software developer who is interested in creating elegant and useful code that solves real problems. I keep meaning to use this site to blog about software development topics that interest me but I never seem to find the time.
No relation to this other William Hunt, although I did buy one of their ties for £15 in TKMaxx once. It's a really good tie, although I never wear it anymore.


The world is a book, and those who do not travel read only a page.” – said Saint Augustine. Travelling can teach you more than any university course. You learn about the culture of the country you visit. If you talk to locals, you will likely learn about their thinking, habits, traditions and history as well. If you travel, you will not only learn about foreign cultures, but about your own as well.


Reese Witherspoon kept entire Legally Blonde wardrobe and 77 pairs of Jimmy Choos
The actress who played Elle Woods admitted she kept her whole Legally Blonde wardrobe and a staggering 77 pairs of Jimmy Choos
by Brogan-Leigh HurstIconic Reese Witherspoon has confessed that Legally Blonde "changed her life forever" as she went on to keep the whole wardrobe, including 77 pairs of Jimmy Choo shoes.
It's one of her most iconic roles to date, admitting that she had the luxury of keeping the wardrobe as it was written in her contract.
Speaking on The Graham Norton Show on Friday, the actress shared: "I was in England making the Importance of Being Earnest when Legally Blonde came out in America. When I went home my life had changed forever."
Reese, 43, went on to unveil that she did in fact keep the 77 pairs of Jimmy Choos as well as the clothes, and said: "I had it written in my contract! I didn't touch them until the 15th anniversary and then had a lot of fun trying them on with my daughter."
The hit comedy film saw Reese portray an air-headed but actually very smart woman named Elle Woods, as she makes her way through Harvard School Of Law.
Witherspoon then went on to appear in a sequel in 2003, and a third film is in the works for 2020.
The superstar sat beside her This Morning co-star Jennifer Aniston to discuss their new Apple TV+ series.
Reese talked about her character Bradley Jackson, claiming: "My character has a lot of rage. I have meltdowns, which were really fun to do because as a rule I just don't do that in my personal life.
"I had meltdowns a lot, but mine were very close to the surface. I have three kids!"
And meanwhile Jennifer discussed her decision to get Instagram where she "broke" the Internet with a picture of herself and co-stars.
Ruling out a reunion, she said: "We just love to get together whenever we can. The girls are very good at keeping up with time together but, when it happens that we are all together, it's just so much fun."


Greenhouse gases rise to highest levels ever with 'no sign of slowing down'
Experts warn time is running out to tackle climate change as greenhouse gases in the atmosphere have reached another record high.
The threat to the world’s food security, wildlife and water supplies continues to grow despite Governments making pledges to combat climate change.
Concentrations of carbon dioxide reached 407.8 parts per million (PPM) last year, up from 405.5 in 2017, according to a report from the United Nation’s World Meteorological Organisation.
It is well above the level of around 280 ppm seen before the industrial revolution, when large scale burning of fossil fuels for energy and transport began.
The latest rise is close to the increase seen between 2016 and 2017 and just above the annual average over the last decade.
Concentrations of other climate-warming greenhouse gases methane and nitrous oxide also surged by higher amounts in 2018 than in the past decade.
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WMO secretary-general Petteri Taalas said: ‘There is no sign of a slowdown, let alone a decline, in greenhouse gases concentration in the atmosphere despite all the commitments under the Paris Agreement on Climate Change.
‘We need to translate the commitments into action and increase the level of ambition for the sake of the future welfare of the mankind.’
He said the last time Earth experienced comparable concentrations of carbon dioxide in the atmosphere was three to five million years ago.
Back then temperatures were 2C to 3C warmer and sea levels were 10-20 metres higher than today.
Human activity is rapidly accelerating the process of climate change which is leading to melting glaciers, more extreme droughts and heatwaves.
The WMP warns that even with the pledges made by countries under the international Paris deal, global emissions are not expected to peak by 2030.
This falls well short of the 2020 turning point when scientists say it is necessary to curb dangerous climate change.
Preliminary findings for 2018 suggest greenhouse gas emissions continued to rise last year.
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