Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths and weaknesses. Our tutorial will survey the most important commonsense knowledge resources, and introduce a new commonsense knowledge graph (CSKG) to integrate several existing resources. The tutorial will also introduce several tools to work with CSKG including query mechanisms, knowledge graph embeddings, and existing methods that answer commonsense questions with knowledge graphs.
Our tutorial will provide a comprehensive overview of both top-down and bottom-up commonsense knowledge graphs. We will introduce each graph in turn, and contrast their design principles. Next, we will discuss the benefits and challenges for their integration and consolidation in a single CSKG, followed by our approach to perform this consolidation. Finally, we will discuss how to compute embeddings and ground text to such CSKGs, as well as how such CSKGs are used to reason on downstream question answering tasks. The tutorial will include demos based on publicly available resources and Jupyter Notebooks.
Agenda:
All times are given in Pacific Standard Time (PST).
|
Time |
Length |
Content |
Slides |
|
08:00 PST |
1h 50min |
Part I - Review of CSKGs |
|
|
15 min |
Introduction to commonsense knowledge (slides) - Pedro |
||
|
25 min |
Review of top-down commonsense knowledge graphs (slides) - Mayank |
||
|
70 min |
Review of bottom-up commonsense knowledge graphs (slides+demo) - Mayank, Filip, Pedro |
||
|
10 min |
Break |
||
|
10:00 PST |
45 min |
Part II - Integration and analysis |
|
|
35 min |
Consolidating commonsense graphs (slides) - Filip |
||
|
10 min |
Consolidating commonsense graphs (demo) - Pedro |
||
|
10 min |
Break |
||
|
10:55 PST |
1h 05min |
Part III - Downstream use of CSKGs |
|
|
35 min |
Answering questions with CSKGs (slides) - Filip |
||
|
15 min |
CSKG embeddings and grounding (demo) - Filip |
||
|
15 min |
Wrap-up (slides) - Mayank |
Learning Outcomes:
Presentation Style style will be informal and very hands-on. We will avoid representation specifics and terminology used by the individual graphs to the best extent possible (i.e. without losing rigor, or over-simplifying). Our slides will focus on visual intuition, use actual examples, present lessons learned from our latest implementations of algorithms and commonsense reasoning frameworks, and will be accompanied by demos and hands-on activities that participants will be able to do without requiring extensive platform-dependent setup. We will permit questions and interactions throughout the tutorial. All three of us will be mostly present throughout the tutorial but will be individually presenting our sections.
Capturing, representing, and leveraging commonsense knowledge has been a paramount for AI since its early days, cf. (McCarthy, 1960). In the light of the modern large (commonsense) knowledge graphs and various neural advancements, the recently introduced DARPA Machine Common Sense program represents a new effort to understand commonsense knowledge through question-answering evaluation benchmarks. Intuitively, graphs of (commonsense) knowledge are essential in such tasks in order to inject background knowledge that humans possess and apply, but machines cannot access or distill directly in communication.
Our team has been working on several aspects of commonsense knowledge found in knowledge graphs. Firstly, we have been integrating a number of knowledge graphs in a single commonsense knowledge graph, including: ConceptNet, WordNet, Visual Genome, FrameNet, ATOMIC, WebChild, Wikidata, and Cyc. Secondly, we have been building software to perform intrinsic operations on the consolidated CSKG, such as generating embeddings and performing knowledge graph completion. Thirdly, we have a framework that allows us to integrate (parts of) our CSKG into a reasoning system that answers questions phrased in natural language. These questions come from several commonsense evaluation datasets, focused on social, physical, visual, and situational reasoning.
It is important for the semantic web community to keep pace with this new ‘wave’ of commonsense knowledge representation and reasoning for downstream applications. Aspects that we seek to address during through our presentation and activities are: what is a commonsense knowledge graph? Which kind of knowledge is captured by existing CSKGs? What are their major strengths and weaknesses? How can they be integrated into a graph that is more than ‘a sum of its parts’? How can this graph be refined/enriched with more semantics or missing information? How can it be applied on downstream applications in natural language understanding? Our tutorial will be very practical, and will answer these questions in a focused, foundational manner that all Semantic Web researchers will be able to easily follow.
Prior knowledge expected from participants (beyond fairly basic Python 3 skills, and familiarity with Semantic Web concepts like RDF) will be minimal. Some knowledge of machine learning, including basic concepts like training, testing and validating, feature engineering etc. will be helpful but are not absolute prerequisites, as we will not go into advanced machine learning math or optimization. Additionally, where possible, we will introduce basic machine learning concepts so that everyone has an opportunity to follow along. Participants are not expected to have any knowledge of answering natural language commonsense questions.
We will be using our own computers for presenting demos and PowerPoint slides and only require equipment to facilitate such projection for an extended period of time (e.g., projector, table, power outlet). We (and also the participants) will require an internet/wifi connection to access the tutorial material. There are no audio elements to our presentation. All demos and hands-on activities will be doable on a reasonable laptop by interested participants. We will also bring extra USB storage devices with copies of code, programs and slides in case some participants did not download the material prior to the tutorial.
Tools and data:
Bibliography:


MY WEB DEV WANNABE BLOG
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Welcome to a day in the life of a Web Dev Wannabe
My names Indigo and I'm a Dev Academy student


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For now this will be a place to put some examples for various data science, machine learning, code, and concepts. For more info look at my new and old git accounts.
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Cultural Blog: Values
April 12, 2015
Last week, after going over the Stereotype Threat in my head, I realized just how influenced I was by the thoughts of people around me. It really stuck with me, and had me examine how I think about myself. I came to realize that much of what I thought about myself was actually put there by other’s people’s beliefs.
Was this how I always was, or did it start when I came to America?
I started to ask the question: Had my values - what I believed to be true about myself, vanished and been replaced by false judgements of others. To retrieve a bit of myself and combat the effects of Stereotype Threat, I went back to my basic values in an attempt to build a foundation to battle against the thoughts of others.
For me, my most essential value was self-confidence and a belief that I was strong enough to accomplish whatever I set my mind to. I have always been a very confident person, but since moving to America more than 10 years ago, I may have let other people’s perception of me erode some of that.  They get frustrated if you struggle to find a word or express yourself perfectly. I have found this to be true in both work and social settings.
For example, if I am in a store asking the clerk about a product, they appear to treat me as less important than an native speaker. I may have let their perceptions about intelligence influence how I thought about my own ability.
However, if I stop to think about it, I not only speak my native language, but I have learned English fluently. Can they say the same? What would happen if they went to another country and were forced not only to learn the language, but to survive financially? I am proud that I have done that here, so my core value of confidence can help to battle Stereotype Threat in that regard.
I would urge all other students who are not native English speakers to take heart and have confidence that they are in a different country yet have still managed to learn the language and flourish. This affirmation will help you battle prejudice and other phobias that may be thrown your way.


TriviaParty - Privacy Policy
1. Information we collect
- When you register an account on TriviaParty your username and email are stored as supplied by you.
- When you join a game, we store your client data in order to dispatch the correct data to the correct clients.
- Upon winning or losing a game, we store the amount of won and lost game for a user.
- When you use and access TriviaParty, we store the IP address from which the request was sent.
2. Requesting your data or removal of your data
- Upon request and verification of identity (Via username and email address) we will provide you with the personal data associated with your account (Username, User ID, Email Address) within 14 days of reading your request.
- Upon request and verification of identity (Via username and email address) we will delete your account and personal data.
- We cannot remove access log data.
3. Reasons for collecting and processing data
- We store your Discord username, user ID and Discriminator for communication aswell as identification.
- We store your email address so we can send information about important updates to the service aswell as to provide functionality such as resetting a forgotten password.
4. How we seek consent
- When an account token is created, users are required to read through this document and agree to it before they can create an account.
- When we update terms, you will be notified via the email associated with your account.
5. In the event of a data breach
- We will publish a notification within 5 days of the data breach being discovered
- We will publish a full transparency document outlining what occurred within 3 months of the data breach being discovered
6. Data Protection Impact Assessments (DPIA) and Data Protection Officer (DPO)
- We do not regularly or systematically monitor data subjects on a large scale or process particularly sensitive data so we do not need to carry out a DPIA or have a designated DPO (articles 35-39)
- "Mackan" will take responsibility for data protection compliance and can be contacted at<EMAIL_ADDRESS>8. Data storage locations
- We store all data on servers in Austria.


Microbiome Diversity Inspector
A tool for visual analysis of microbiome data.
Choose a file to analyze its entropy.
Analyzing {{myCtrl.fileName}} ...
Entropy = {{myCtrl.entropy}}
The number of A's are - {{myCtrl.countOfA}}
The number of T's are - {{myCtrl.countOfT}}
The number of G's are - {{myCtrl.countOfG}}
The number of C's are - {{myCtrl.countOfC}}


What will you gain if you learn C?
 Sure, your application works fine and all. But, if you can’t say why while (*s++ = *p++); copies a string, you’re programming on a superstition. ( Joel Spolsky’s words, not mine ).
You will understand how a computer works.
C is the lingua franca of programming.
Opportunity to work on open source projects that impact millions of people.
You will write better programs.
You will find it much easier to learn other programming languages.


You do not currently have any crops registered.
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Current Meetings
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{{meeting.meetingdate | date:"yyyy/MM/dd HH:mm"}} |
{{meeting.district}} |
{{meeting.meetingid}} |
{{meeting.note}} |
{{meeting.status}} |
|
| Command |
sms key |
Example |
| Tutorial |
T |
T |
| Register District |
rd:DISTRICT_NAME |
rd:SF |
| List Districts |
ld |
ld |
| Register PhoneNumber |
rn/rp:DISTRICT_NAME |
rp:SF |
| Plant |
p |
p:SF:corn:100 |
| Harvest |
h |
h:SF:corn:10 |
| Sell |
s |
s:SF:corn:10 |
| Schedule Meeting |
sm:DATEXDUATION:NOTE |
sm:SF:June 5, 2015, 17-30X45:meeting message |
| Cancel Meeting |
dm |
dm:SF |
| List RSVPs |
rsvl:MEETING_ID |
rsvl:JSX (Case Sensitive) |
| RSVP |
RSVP:MEETING_ID:RESPONSE |
RSVP:JSX:Yes (Case Sensitive) |
| Broadcast Everyone |
bca:message |
bca:Hi Everyone |
| Broadcast Group |
bc:message |
bc:SF:Hi SF |


Equivalent to clicking the Solver command on the Tools menu, clicking the Options button in the Solver Parameters dialog box, and clicking the Save Model button in the Solver Options dialog box. Saves the Solver problem specifications on the worksheet.
If this function is not available, you must install the Solver add-in.
Syntax
SOLVER.SAVE (save_area)
Save_area is a reference on the active sheet to a range of cells or to the upper-left corner of a range of cells into which you want to paste the current problem specification.
If you specify only one cell for save_area, the area is extended downwards for as many cells as are required to hold the problem specifications (3 plus the number of constraints).
If you specify more than one cell and if the area is too small, the last constraints (in alphabetic order by cell reference) or options will be omitted and the function will return a nonzero value.
Save_area must be on the active worksheet, but it need not be the current selection.
Return to index


import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import copy
import csv
from sklearn.utils import shuffle
from pandas.io.parsers import read_csv
%matplotlib inline
# Create a directory for the images of the writeup
info_dir_out = 'info_output/'
if not os.path.exists(info_dir_out):
os.makedirs(info_dir_out)
def plot_histogram(data, title):
# histogram of label frequency
fig = plt.figure()
unique_values, counts = np.unique(data, return_counts=True)
plt.hist(data, bins=100)
plt.title(title)
plt.axis([min(data), max(data), 0, max(counts)])
plt.show()
print("max value is", max(counts))
print("min value is", min(counts))
#Save the histogram
fig.savefig('info_output/' + 'Hist_'+title+'.jpg')
###Data exploration visualization
# Source Folder for the dataset of Udacity
samples_data = "udacity_data/"
# Read the initial dataset
dataset_samples = read_csv(samples_data+"driving_log.csv", header=0, usecols=[0,1,2,3]).values;
print("The initial dataset has", len(dataset_samples), "samples")
plot_histogram(dataset_samples[:,3], 'original_dataset')
Here, it can be observed a very high peak for the data with 0 steering angle (4361 images) in comparison with the rest. In this section, the amount of 0 steering angle data is reduced to a value comparable with the rest of the classes.
filtered_data = dataset_samples[abs(dataset_samples[:,3])>0.01]
plot_histogram(filtered_data[:,3], 'original_dataset_without_0')
unique_values, counts = np.unique(filtered_data[:,3], return_counts=True)
max_value = max(counts)
peak_labels = dataset_samples[abs(dataset_samples[:,3])<0.01]
peak_labels = shuffle(peak_labels)
peak_labels = peak_labels[0:max_value, :]
dataset_balanced = np.concatenate((filtered_data, peak_labels))
print("The balanced dataset (peaks) has", len(dataset_balanced), "samples")
plot_histogram(dataset_balanced[:,3], 'balanced_data_peaks')
For this project, it was decided to use images of the 3 cameras (center, left and right), using a correction factor for the steering angles of 0.2 (0.2 for the left, -0.2 for the right)
unbalanced_data = []
for line in dataset_balanced:
new_line = copy.deepcopy(line)
new_line[1] = line[3]
unbalanced_data.append(new_line)
new_line = copy.deepcopy(line)
new_line[0] = new_line[1]
new_line[1] = line[3] + 0.2
unbalanced_data.append(new_line)
new_line = copy.deepcopy(line)
new_line[0] = new_line[2]
new_line[1] = line[3] - 0.2
unbalanced_data.append(new_line)
unbalanced_data = np.array(unbalanced_data)
unbalanced_data = unbalanced_data[:, 0:2]
print("The (unbalanced) full dataset has", len(unbalanced_data), "samples")
plot_histogram(unbalanced_data[:,1], 'full_unbalanced_dataset')
Through the tests carried out for network training (write up template), good results were obtained by reducing part of the data to balance the dataset. This balancing of the data can be observed in the following code cells.
unique_values, counts = np.unique(unbalanced_data[:,1], return_counts=True)
# average value calculation
mean_data = int(np.mean(counts))
balanced_dataset = []
def extract_data(data, mean_data, divisor_factor = 2):
if len(data) > mean_data:
data = shuffle(data)
new_size = int(len(data)/divisor_factor)
data = data[0:new_size]
for line in data:
balanced_dataset.append(line)
def balance_data(dataset, step = 0.01):
# select data for bin, with limits of bin + step
# and balance the data for bin, if the size of the bin > average value
print("Classes with more data than the calculated average value will be"+
" reduced to a factor (half for default) of the data.")
current_bin = 0.0
while current_bin<1.0:
data_vector = [data for data in dataset if (current_bin < abs(data[1]))
and (abs(data[1])<=current_bin+step)]
extract_data(data_vector, mean_data)
current_bin = current_bin+step
balance_data(unbalanced_data)
balanced_dataset = np.array(balanced_dataset)
print("The final balanced dataset has", len(balanced_dataset), "samples")
plot_histogram((balanced_dataset[:,1]), 'Final_balanced_data');
writer = csv.writer(open("my_dataset.csv", 'w'))
i=0
writer.writerow(zip("features","labels"))
for row in balanced_dataset:
writer.writerow(row)
i = i +1
print(i," samples saved")
During the training phase, the image dataset is augmented, mainly for angles with an absolute value greater than 0.5 (where the classes lack data). Bellow it can be observed some of the samples of the training dataset (original, flipped and augmented (change brightness) )
def image_brighten(image, base_change = 0.25):
# change the brightness of the image, using a (random)
# multiplication factor
hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
brightness = np.random.uniform(base_change, 1.0)
hsv_image[:, :, 2] = hsv_image[:, :, 2] * brightness
return cv2.cvtColor(hsv_image, cv2.COLOR_HSV2RGB)
def show_image_dataset(angles_vector):
f, axes = plt.subplots(5, 3, figsize=(20,10))
for it in range(len(angles_vector)):
# read one image for the actual bin
actual_bin = angles_vector[it]
mask = [(balanced_dataset[:,1]>(actual_bin)) & (balanced_dataset[:,1]<(actual_bin+0.1))]
image_path = balanced_dataset[mask][0,0]
label_title = balanced_dataset[mask][0,1]
image = cv2.imread(samples_data+image_path.strip())
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
#plot the initial and augmented images
axes[it, 0].imshow(image)
axes[it, 0].axis('off')
axes[it, 0].set_title(str(label_title))
flipped_image = cv2.flip(image, 1)
axes[it, 1].imshow(flipped_image)
axes[it, 1].axis('off')
axes[it, 1].set_title(str(label_title)+"_flipped")
augmented_image = image_brighten(image)
axes[it, 2].imshow(augmented_image)
axes[it, 2].axis('off')
axes[it, 2].set_title(str(label_title)+"_brightness")
#Save the image
plt.savefig('info_output/' + 'dataset_exploration.jpg')
angles_vector = [-0.5, -0.2, 0.0, 0.2, 0.5]
show_image_dataset(angles_vector)
In addition, the CNN performs a process of cropping of the image, to receive only the important areas of the image. Below is an example of the result of this process, and what the CNN "sees".
image_path = balanced_dataset[100,0]
label_title = balanced_dataset[100,1]
image = cv2.imread(samples_data+image_path.strip())
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
#This process is performed in one of the CNN layers.
#Here it is performed outside the network for visualization reasons.
im_shape = image.shape
image = image[50:im_shape[0]-20, :]
fig = plt.figure()
plt.imshow(image)
plt.show()
#Save the image
fig.savefig('info_output/' + 'cropped_image.jpg')


Some time passes, and the government is now aware of you fighting crime without them, making you a fugitive of the law. Perhaps a member of your team shouldn’t have been trusted. It will be difficult to become the ultimate hero with the government on a constant lookout for you.


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Code for Boston addresses local social and civic challenges through creative uses of technology. Despite the name, we are not solely focused on coding! We foster relationships between government, nonprofit, academic, for-profit companies, residents, civic technologists, analysts, designers, and many more. All are welcome!
Hack nights are an opportunity to volunteer on software projects that benefit the Boston community. New members can join us online Tuesday nights at 7 p.m. We also meet in person about once a month at the CIC. Check Meetup.com for details and to RSVP.
Civic Tech Coffee is a monthly networking event for professionals and students interested in Civic Tech. It happens virtually on the last Thursday of the month at 8:30 a.m.


Component: tos.sensorboards.mts300.MicReadP
configuration MicReadP
Provides
interface
Read
<uint16_t>[uint8_t]
Uses
interface
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ActualRead
[uint8_t]
Wiring


THotSpotTargetEvent = procedure(Sender: TObject;
const Target, URL: string) of Object;
The THotSpotTargetEvent type is the type for the TFrameViewer and TFrameBrowser OnHotspotTargetCovered event. This event occurs when the mouse is positioned over a hypertext link. URL is the string referenced by the hot spot and Target is the (optional) associated target window or frame. For TFrameBrowser, URL is expanded into a full URL.
See also:


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Only 18 TV shows received a perfect score on Rotten Tomatoes this year — here they are
by Kim Renfro- Rotten Tomatoes published a list of 104 TV shows beloved by critics, earning them a perfect 100% rating.
- Only 18 shows on the list premiered in 2019, so let’s see what the best series of the year were.
- NBC’s “The Good Place,” HBO’s “Los Espookys” and “A Black Lady Sketch Show,” plus Amazon Prime’s “Fleabag” are among the top series.
- The list also includes Netflix’s “Tuca and Bertie” and “One Day at a Time,” which were both cancelled this year.
- Keep reading to see the full list.
- Visit Insider’s homepage for more stories.
Rotten Tomatoes keeps a running list of the top television shows of all time based on critics’ scores. Eighteen of the “Certified Fresh” series with 100% ratings premiered in 2019. From NBC sitcoms like “The Good Place” and “Brooklyn Nine-Nine” to the final season of “Jane the Virgin,” we’ve rounded up all the best shows of the year according to critics.
Keep scrolling to see the 18 shows that received a perfect score on Rotten Tomatoes this year.
“Better Things” (season three)
Synopsis: “A single mum struggles to raise three girls while trying to maintain her career as an actor.”
Number of reviews: 31
Critics’ Consensus: Pamela Adlon fully asserts her authorial voice over “Better Things” in a triumphant third season that examines the exhaustion of motherhood with exhilarating artistry.
“Big Mouth” (season three)
Synopsis: “Growth spurts, or the lack of it, add to the usual tensions faced daily by the middle schoolers of ‘Big Mouth.'”
Number of reviews: 24
Critics’ Consensus: Like the characters at its centre, “Big Mouth’s” third season continues to grow, taking on complicated new issues with the same gross-but-utterly-empathetic eye that made it so loveable in the first place.
Where to watch it:
Netflix
“A Black Lady Sketch Show” (season one)
Synopsis: “‘A Black Lady Sketch Show’ is a narrative series set in a limitless magical reality full of dynamic, hilarious characters and celebrity guests. The show presents sketches performed by a core cast of black women, including Robin Thede, Ashley Nicole Black, Gabrielle Dennis and Quinta Brunson.”
Number of reviews: 20
Critics’ Consensus: Singular, subversive, and simply hilarious, “A Black Lady Sketch Show” finds universal humour in specific spaces to craft quick-witted sketches that perfectly showcase Robin Thede and her talented cast.
Where to watch it:
HBO Now
“BoJack Horseman” (season six)
Synopsis: “Will Arnett voices BoJack, the failed legendary 90’s sitcom star from the favourite family sitcom Horsin? Around, who has been trying to find his way through a muddle of self-loathing, whisky and failed relationships. Now, in the presence of his human sidekick Todd (Aaron Paul) and his feline agent and ex-paramour Princess Carolyn (Amy Sedaris), BoJack is primed for his comeback…”
Number of reviews: 28
Critics’ Consensus: Bittersweet and brilliant to the very end, “BoJack Horseman’s” final season manages to keep surprising viewers with its empathy and depth, solidifying its place as one of TV’s greatest offerings.
Where to watch it:
Netflix
“Broad City” (season five)
Synopsis: “The cult hit web series Broad City comes compliments of the collective brains of Abbi Jacobson + Ilana Glazer (both UCB alum). Called “sneak attack feminism” by the Wall Street Journal, the series follows two friends in New York City navigating their way though the minutiae that is life.”
Number of reviews: 25
Critics’ Consensus: Glazer and Jacobson give the people exactly what they want in Broad City’s final season – relatable content, questionable intimacy, and ingenious escapades through the glorious squalor of IRL NYC.
Where to watch it: Hulu,Comedy Central
“Brooklyn Nine-Nine” (season six)
Synopsis: “A sitcom following the lives of a group of detectives in a New York precinct, including one slacker who is forced to shape up when he gets a new boss.”
Number of reviews: 26
Critics’ Consensus: “Following a period of uncertainty and a shift to NBC, ‘Brooklyn Nine-Nine’ reemerges with its cast and tone wholly intact.”
“David Makes Man” (season one)
Synopsis: “Centering on a 14-year-old prodigy from the projects who is haunted by the death of his friend and relied on by his hardworking mother to find a way out of poverty. He must choose between the streets that raised him or the higher education that may offer him a way out.”
Number of reviews: 20
Critics’ Consensus: Powerful, beautiful, and like nothing else on TV, “David Makes Man” blends dreamy aesthetics with an empathetically crafted story to create a truly unique viewing experience.
Where to watch it:
OWN
“A Discovery of Witches” (season one)
Synopsis: “A contemporary love story that begins against the backdrop of Oxford academic life, in a world where small numbers of witches, vampires and daemons live and work, unseen amongst humans, hiding in plain sight. Teresa Palmer is the brilliant historian Diana Bishop, a reluctant witch denying her heritage. The discovery of a bewitched manuscript in Oxford’s Bodleian Library throws her into the heart of a dangerous mystery – and into the path of the enigmatic vampire and geneticist, Matthew Clairmont (Matthew Goode), who hides a dark family secret.”
Number of reviews: 27
Critics’ Consensus: “A Discovery of Witches” smartly grounds its flights of fancy with a lived-in authenticity and harnesses the chemistry between its two star-crossed leads to make for a promising foray into the occult.
Where to watch it:
AMC
“Documentary Now!” (season three)
Synopsis: “Featuring mockumentaries about faux people and events.”
Number of reviews: 21
Critics’ Consensus: Incisively critical of the genre and equally delighted by its subjects, “Documentary Now!” nails mockumentary under the deft direction of Rhys Thomas and Alex Buono.
Where to watch it:IFC, Netflix
“Fleabag” (season two)
Synopsis: “A sexually-liberated, irritable woman navigates modern life in London in this comedy. Based on Phoebe Waller-Bridge’s play of the same name.”
Number of reviews: 82
Critics’ Consensus:“Fleabag” jumps back into the fray with a bracing second season that upholds its predecessor’s frenzied wit and delicate heart, replete with Phoebe Waller-Bridge’s indefatigable charisma.
Where to watch it:
Amazon Prime Video
“The Good Place” (season four)
Synopsis: “Follows Eleanor Shellstrop, an ordinary woman who enters the afterlife, and thanks to some kind of error, is sent to the Good Place instead of the Bad Place (which is definitely where she belongs). While hiding in plain sight from Good Place Architect Michael, she’s determined to shed her old way of living and earn her spot.”
Number of reviews: 21
Critics’ Consensus: A wild philosophical ride to the very end, “The Good Place” brings it home with a forking good final season.
Where to watch it:
NBC
“Jane the Virgin” (season five)
Synopsis: “A comedy-drama following a chaste young woman who is accidentally impregnated via artificial insemination as she struggles to inform her devoutly religious family and make the right choices concerning the child. Based on the telenovela ‘Juana la virgen.'”
Number of reviews: 21
Critics’ Consensus: Gina Rodriguez delivers a stellar performance in “Jane the Virgin’s” final season, grounding the series in humanity amidst all its quirky telenovela humour.
Where to watch it:
Netflix
“Los Espookys” (season one)
Synopsis: “From Julio Torres and Ana Fabrega, alongside Fred Armisen and Lorne Michaels of ‘Saturday Night Live,’ the primarily Spanish-language show follows a group of friends who turn their love for horror into a peculiar business, providing horror to those who need it, in a dreamy Latin American country where the strange and eerie are just part of daily life.”
Number of reviews: 25
Critics’ Consensus: Igualmente hilarante y horripilante, “Los Espookys” is an espooky good time.
Where to watch it:
HBO Now
“One Day at a Time” (season three)
Synopsis: “A remake of Norman Lear’s classic 1975 sitcom revolves around a Cuban-American family headed by a recently separated military mum who’s navigating a new single life while raising two children with the ‘help’ of her own mother and a building manager named Schneider.”
Number of reviews: 29
Critics’ Consensus: As timely and tender as ever, “One Day at a Time’s” third season manages to up to comedy ante without losing the intimate family moments that help it hit so close to home.
Where to watch it:
Netflix
“Sherman’s Showcase” (season one)
Synopsis: “A sketch variety show like none before, ‘Sherman’s Showcase’ travels through time via music and clips drawn from the forty-year library of a legendary (fictional) musical variety show.”
Number of reviews: 20
Critics’ Consensus: Bold, brisk, and beautiful, “Sherman’s Showcase” is a delightful and stylish sketch show that moves to its own groove and invites everyone to laugh along.
Where to watch it:
IFC
“South Side” (season one)
Synopsis: “Two friends just graduated community college and are now ready to take over the world! But until they do they’re stuck at Rent-T-Own, a retail-rental crossroads where the South Side’s vast ensemble of characters come together.”
Number of reviews: 21
Critics’ Consensus: Absurdly funny and fearless, “South Side” successfully captures the spirit of a community and confronts tough social constructs with smart writing, a spectacular cast, and just the right amount of silliness.
Where to watch it:
Comedy Central
“Tuca and Bertie” (season one)
Synopsis: “Free-spirited toucan Tuca and self-doubting song thrush Bertie are best friends — and birds – who guide each other through life’s ups and downs.”
Number of reviews: 31
Critics’ Consensus: “Tuca & Bertie” skips right past “Bojack Horseman’s” shadow with its chipper sensibility and madcap sight gags, bringing plenty of laughs as it addresses adulting anxiety and exalts the joys of friendship.
Where to watch it:
Netflix
“Undone” (season one)
Synopsis: “From acclaimed creators Kate Purdy and Raphael Bob-Waksberg (BoJack Horseman) comes ‘Undone,’ a groundbreaking and genre bending animated series about a young woman’s complex journey to unlock her past and solve the mystery of her father’s death.”
Number of reviews: 42
Critics’ Consensus: A kaleidoscopic existential crisis, “Undone” bends the rules of space, time, and rotoscoping to weave a beautifully surreal tapestry that is at once fantastical and utterly relatable.
Where to watch it:
Amazon Prime
You can find the complete list of all 104 TV shows with a perfect score on Rotten Tomatoes here.


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The GS equation is given by Eq. (66), i.e.,
|
| (72) |
If a solution to the GS equation is obtained, the solution can be scaled to obtain a family of solutions. Given an equilibrium with Ψ(R,Z), P(Ψ), g(Ψ), then it is ready to prove that Ψ2 = sΨ, P2 = s2P(Ψ), and g2 = ±sg(Ψ) is also a solution to the GS equation, where s is a constant. In this case, both the poloidal and toroidal magnetic ﬁelds are increased by a factor of s, and thus the safety factor remains unchanged. Also note that the pressure is increased by s2 factor and thus the value of β (the ratio of the therm pressure to magnetic pressure) remains unchanged. Note that g2 = ±sg(Ψ), which indicates that the direction of the toroidal magnetic ﬁeld can be reversed without breaking the force balance. Also note that Ψ2 = sΨ and s can be negative, which indicates that the direction of the toroidal current can also be reversed without breaking the force balance.
The second kind of scaling is to set Ψ2 = Ψ, P2 = P(Ψ), and g22 = g2(Ψ) + c. It is ready to prove that the scaled expression is still a solution to the GS equation because g2g2′ = gg′. This scaling keep the pressure and the poloidal ﬁeld unchanged and thus the poloidal beta βp remains unchanged. This scaling scales the toroidal ﬁeld and thus can be used to generate a family of equilibria with diﬀerent proﬁles of safety factor.
Another scaling, which is trivial, is to set Ψ2 = Ψ, P2 = P(Ψ) + c, and g2 = g(Ψ). This scaling can be used to test the eﬀects of the pressure (not the pressure gradient) on various physical processes.
When a numerical equilibrium is obtained, one can use these scaling methods together to generate new equilibria that satisfy particular global conditions. Note that the shape of magnetic surfaces of the scaled equilibrium remains the same as the original one.
The above scaling is made under the constraint that the the GS equation (72), is satisﬁed. In practice, we often scale Ψ by a factor while ﬁxing g and P. This does not satisfy the GS equation, but allows more ﬂexibility in changing the the safety factor proﬁle. Scaling Ψ by a factor corresponds to scaling the plasma current and the poloidal magnetic ﬁeld.


RNLI volunteer who died after car slipped into sea remembered as 'superb young man'
by Stephen McNeiceAn investigation is underway following the death of a young RNLI volunteer on Arranmore Island off County Donegal.
26-year-old Lee Early got trapped underwater when his car slipped off Poolawaddy Pier into the sea at around 5.10am yesterday morning.
A passenger managed to get out of the car and swim ashore to raise the alarm.
Speaking on Newstalk Breakfast, the RNLI's Area Lifesaving Manager Owen Medland says Lee's death is a huge loss to the community.
He said: "Lee Early was a superb young man.
"He would be a loss to any community, but none more so than in a small island community where he really was part of the fabric of this community."
Mr Medland added: "The lifeboat crew, who I have to commend, responded to the incident and made every effort on Lee's behalf.
"They're really in shock... but I think most importantly, and showing that spirit, they've maintained the lifeboat service on the island throughout."
Local Donegal TD Pat the Cope Gallagher said the whole community is in shock.
He observed: "When the news broke, a dark cloud descended over the island at the loss of this highly-regarded and popular young man from an island family."


Breno Sanvicente Vieira
Clinical Psychologist
M.S. in Psychology
PhD Candidate
PPGP - PUCRS
See More


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Unhook The Stars (1996) A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 7.5 Alternative Scale: *** out of ****
United States, 1996 U.S. Release Date: beginning 11/1/96 (limited) Running Length: 1:44 MPAA Classification: R (Profanity, mature themes) Theatrical Aspect Ratio: 1.66:1
Cast: Gena Rowlands, Marisa Tomei, Gerard Depardieu, Moira Kelly, Jake Lloyd, David Thornton, David Sherrill Director: Nick Cassavetes Producer: Rene Cleitman Screenplay: Nick Cassavetes and Helen Caldwell Cinematography: Phedon Papamichael Music: Steven Hufsteter U.S. Distributor: Miramax Films
Nick Cassavetes, the son of acclaimed international director John Cassavetes, makes his directorial debut with UNHOOK THE STARS, a pleasantly unsentimental character study of one woman who has difficulty letting go. Although Cassavetes may want independence from his father's name and memory, this film won't earn it, for, although the style bears only a passing resemblance to the late film maker's, Nick not only uses several members of John's crew (notably Phedon Papamichael), but has cast his mother, Gena Rowlands, in the lead role. In addition, the script has a heavy autobiographical tinge -- Cassavetes is using cinema to work out his ambiguous feelings towards his famous father.
 Her oldest, a son (David Sherrill), has already left home. Now, her daughter, Annie (Moira Kelly), is on her way out. Their parting is less-than-amicable. Annie finds her mother to be a controlling and interfering influence, and wants to get away as soon as possible. Mildred watches her go sadly, wondering what happened to all the aspirations she harbored for her only female offspring. But Mildred isn't destined to be alone for long. Her new neighbor across the street, Monica Warren (Marisa Tomei), has thrown her husband (David Thornton) out following a particularly nasty quarrel. Now, she's alone with her young son, J.J. (Jake Lloyd), and doesn't have the money to pay a sitter when she goes to work. Mildred offers to help, gratis, and soon becomes J.J.'s surrogate mother.
Looking back on UNHOOK THE STARS, the first thing that strikes me is the script's openness and honesty. It doesn't try to trick and manipulate us with melodrama. Every action has a natural consequence -- nothing ever feels forced or scripted. When Monica's husband returns to town, wanting to see his son, there's no shouting match or violence. When Annie eventually comes home, there's no big, tearful reunion. And, when Mildred begins to realize that her time with J.J. is coming to a close, we're spared an emotional scene. UNHOOK THE STARS is consistently low-key, and, in the context of an intelligent script and strong acting, this represents a welcome change-of-pace from most American films.
Gena Rowlands is excellent, but that shouldn't come as a surprise. She becomes Mildred, subtly and effectively bringing the woman's every emotion to the surface. This is the kind of role that could easily be overacted, but Rowlands strikes the perfect balance between restraint and abandon. From the first scene, where she's delivering newspapers, to the last, where she turns her back on the past and strides purposely towards the future, Rowlands makes us care about Mildred.
Marisa Tomei, who has always had screen presence, gives the best performance of a relatively short career. Tomei makes Monica a swirling, contradictory font of pure emotion -- she's always nervous, agitated, elated, furious, or a combination of the above. Monica may not be the best-developed character, but Tomei invests her with such passion and energy that it's impossible not to see her as a lot more than the person on the pages of Cassavetes' screenplay.
It doesn't demand much of a stretch for Gerard Depardieu to play the likable French Canadian truck driver who becomes infatuated with Mildred. Depardieu is a pleasant addition to the cast (and got involved for the chance to work with Rowlands), but isn't a revelation. However, Moira Kelly is. The young actress, the best thing about the recent ENTERTAINING ANGELS, gives a powerhouse performance in limited screen exposure. Annie's pain and rage are palpable, going far beyond the norms of youthful rebellion, and Kelly realistically and effectively portrays them.
UNHOOK THE STARS is an engaging motion picture, mixing various flavors of comedy and drama into a satisfying whole. Admittedly, there are times when Cassavetes skims lightly over subject matter that is deserving of a fuller exploration, but the quality of the finished product allows us to overlook most of these incidents. UNHOOK THE STARS is a movie of many special, small moments, that, combined together, make for a shining cinematic experience.
- James Berardinelli e-mail<EMAIL_ADDRESS>ReelViews web site: http://www.cybernex.net/~berardin
"We go away from our parents in youth and then we gradually come back to them; and in that moment, we have grown up." -- Ingmar Bergman
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


Rembau Hospital to begin operations in Jan next year, says Dzulkefly
SEREMBAN, Nov 10 — The Rembau Hospital is expected to be fully operational by January next year, said Health Minister Datuk Seri Dzulkefly Ahmad.
However, he said, the emergency and maternity units may be opened earlier than that, at the latest by next month.
Dzulkefly said the process of bringing in, testing and certifying the medical equipment and non-medical items was being carried out.
“We realise that the people of Rembau have long waited for the hospital so that they have access to health facilities near their homes,” he told reporters after launching the Paroi State Assembly Healthy Lifestyle. — Bernama


Source code for sympy.utilities.pytest
"""py.test hacks to support XFAIL/XPASS"""
from __future__ import print_function, division
import sys
import functools
import os
from sympy.core.compatibility import get_function_name
try:
import py
from py.test import skip
USE_PYTEST = getattr(sys, '_running_pytest', False)
except ImportError:
USE_PYTEST = False
ON_TRAVIS = os.getenv('TRAVIS_BUILD_NUMBER', None)
if not USE_PYTEST:
[docs] def raises(expectedException, code=None):
"""
Tests that ``code`` raises the exception ``expectedException``.
``code`` may be a callable, such as a lambda expression or function
name.
If ``code`` is not given or None, ``raises`` will return a context
manager for use in ``with`` statements; the code to execute then
comes from the scope of the ``with``.
``raises()`` does nothing if the callable raises the expected exception,
otherwise it raises an AssertionError.
Examples
========
>>> from sympy.utilities.pytest import raises
>>> raises(ZeroDivisionError, lambda: 1/0)
>>> raises(ZeroDivisionError, lambda: 1/2)
Traceback (most recent call last):
...
AssertionError: DID NOT RAISE
>>> with raises(ZeroDivisionError):
... n = 1/0
>>> with raises(ZeroDivisionError):
... n = 1/2
Traceback (most recent call last):
...
AssertionError: DID NOT RAISE
Note that you cannot test multiple statements via
``with raises``:
>>> with raises(ZeroDivisionError):
... n = 1/0 # will execute and raise, aborting the ``with``
... n = 9999/0 # never executed
This is just what ``with`` is supposed to do: abort the
contained statement sequence at the first exception and let
the context manager deal with the exception.
To test multiple statements, you'll need a separate ``with``
for each:
>>> with raises(ZeroDivisionError):
... n = 1/0 # will execute and raise
>>> with raises(ZeroDivisionError):
... n = 9999/0 # will also execute and raise
"""
if code is None:
return RaisesContext(expectedException)
elif callable(code):
try:
code()
except expectedException:
return
raise AssertionError("DID NOT RAISE")
elif isinstance(code, str):
raise TypeError(
'\'raises(xxx, "code")\' has been phased out; '
'change \'raises(xxx, "expression")\' '
'to \'raises(xxx, lambda: expression)\', '
'\'raises(xxx, "statement")\' '
'to \'with raises(xxx): statement\'')
else:
raise TypeError(
'raises() expects a callable for the 2nd argument.')
class RaisesContext(object):
def __init__(self, expectedException):
self.expectedException = expectedException
def __enter__(self):
return None
def __exit__(self, exc_type, exc_value, traceback):
if exc_type is None:
raise AssertionError("DID NOT RAISE")
return issubclass(exc_type, self.expectedException)
class XFail(Exception):
pass
class XPass(Exception):
pass
class Skipped(Exception):
pass
def XFAIL(func):
def wrapper():
try:
func()
except Exception as e:
message = str(e)
if message != "Timeout":
raise XFail(get_function_name(func))
else:
raise Skipped("Timeout")
raise XPass(get_function_name(func))
wrapper = functools.update_wrapper(wrapper, func)
return wrapper
def skip(str):
raise Skipped(str)
def SKIP(reason):
"""Similar to :func:`skip`, but this is a decorator. """
def wrapper(func):
def func_wrapper():
raise Skipped(reason)
func_wrapper = functools.update_wrapper(func_wrapper, func)
return func_wrapper
return wrapper
def slow(func):
func._slow = True
def func_wrapper():
func()
func_wrapper = functools.update_wrapper(func_wrapper, func)
return func_wrapper
else:
XFAIL = py.test.mark.xfail
slow = py.test.mark.slow
[docs] def SKIP(reason):
def skipping(func):
@functools.wraps(func)
def inner(*args, **kwargs):
skip(reason)
return inner
return skipping


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nips nips2005 nips2005-27 nips2005-27-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tong Zhang, Rie Kubota Ando
Abstract: We consider a framework for semi-supervised learning using spectral decomposition based un-supervised kernel design. This approach subsumes a class of previously proposed semi-supervised learning methods on data graphs. We examine various theoretical properties of such methods. In particular, we derive a generalization performance bound, and obtain the optimal kernel design by minimizing the bound. Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can often improve the predictive performance. Experiments are used to illustrate the main consequences of our analysis.
[1] Mikhail Belkin and Partha Niyogi. Semi-supervised learning on Riemannian manifolds. Machine Learning, Special Issue on Clustering:209–239, 2004.
[2] Olivier Chapelle, Jason Weston, and Bernhard Sch:olkopf. Cluster kernels for semisupervised learning. In NIPS, 2003.
[3] Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, pages 849–856, 2001.
[4] M. Szummer and T. Jaakkola. Partially labeled classiﬁcation with Markov random walks. In NIPS 2001, 2002.
[5] D. Zhou, O. Bousquet, T.N. Lal, J. Weston, and B. Schlkopf. Learning with local and global consistency. In NIPS 2003, pages 321–328, 2004.
[6] Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-supervised learning using Gaussian ﬁelds and harmonic functions. In ICML 2003, 2003.


Originally from the Bay Area, I am sophomore at the University of Illinois at Urbana-Champaign studying Computer Science and Statistics. In my free time, I enjoy playing basketball and soccer.
> Completed freshman year at U of I, and will be working on independent projects in the Bay Area this summer.
> In the Spring Semester, I joined UIUC's Innovation Immersion Program, a consulting group on campus, as a consultant on the Financial Team.
> Trained in the Spring Semester to be a UIUC Orientation Leader this August.


Ocean [aj75], ocean, in Crystal Sea
Last updated Turn #65
Ocean [aj75], ocean, in Crystal Sea
------------------------------------------------------------------------
18: Orion I [hd3f], ship, 2 hulls, 1 rowing port, 6 sails, 14% damaged,
18: arrived from Ocean [aj76], owner:
18: Phantasm [n4b], Imperial Empire, with 12 sailors, one ox
19: Orion I [hd3f], ship, 2 hulls, 1 rowing port, 6 sails, 14% damaged,
19: departed for Ocean [aj74].
Routes leaving Ocean:
North, plain, to Endless Plains [ai75], Imperia, 2 days
East, to Ocean [aj76], 6 days
South, to Ocean [ak75], 6 days
West, to Ocean [aj74], 6 days


Sizes
- Mix Tape (12")...$10
- Underground Following (14")...$14
- Blowin' Up (18")...$20
- Superstar (22")...$26
- Young n Hungry (slice)...$3
Toppings
Cheese and red sauce- no charge
Dress it up with standard toppings ($1 per topping):
pepperoni, mushrooms, bell peppers, onions, sausage, ham, pineapple, jalapeños, pepperoncinis, tomatoes, black olives, garlic, anchovies
Make it bling with deluxe toppings ($2 per topping):
caviar, grasshoppers, marcona almonds, hershey's kisses, pop rocks, seaweed, truffle oil, artichoke hearts, feta cheese, fresh mozzarella, arugula, mama lils, sundried tomatoes, basil, kalamata olives, ricotta cheese
What's Popular (18" only):
- Max Martin - swedish fish, blow pops, marshmallow #1's...$26
- Lil' Wayne - gold flake, diamonds, lemon fruit loops...$100
- The Red Rocker - Cabo Wabo tequila drenched, topping of your choice...$51.50


Order table
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First Name
Last Name
Item Ordered
Quantity
Amount
1
Ada
lovelane
8TB Hardrive
2
$200.0 0
2
Bill
Gate
Apple Laptops
1
$1500.00
3
Steve
Jobs
Surface pro2
1
$1000.00
4
Pixel 2
2
$1200.00
5
Daryl
One Pl
1
$500.00
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