Overview
Water scarcity already affects every continent. Around 1.2 billion people, or almost one-fifth of the world's population, live in areas of physical scarcity, and 500 million people are approaching this situation. Another 1.6 billion people, or almost one quarter of the world's population, face economic water shortage (where countries lack the necessary infrastructure to take water from rivers and aquifers).
Water scarcity is among the main problems to be faced by many societies and the World in the XXIst century. Water use has been growing at more than twice the rate of population increase in the last century, and, although there is no global water scarcity as such, an increasing number of regions are chronically short of water.
Water scarcity is both a natural and a human-made phenomenon. There is enough freshwater on the planet for seven billion people but it is distributed unevenly and too much of it is wasted, polluted and unsustainably managed.
What is the Problem?
In the early 2000's there had been many debates on water scarcity and the proportionaly inverse relationship between water availability and population size.
Though at a global level there is more than sufficient water to meet all of humanity's needs water scarcity is still a problem.
Causes
-
Just like wealth, water, is not distributed equally among countries as well as within them.
For example, While there are several countries in the Middle east that water-stressed Brazil and Canada have more water than they can ever use. Yet areas of northeast Brazil are drought-prone. - Access to water as a productive resource has infrustructural requirements. These infrustructural requirements are also unequally distributes among and within countries.
Implications
Currently, about 700 million persons in 43 countries live below the water-stress threshold of 1700 cubic metres per person. It is estimated that by 2025 that number would reach 3 billion, as water-stressed in China, India and Sub-Saharan Africa.
Water stress is also reflected in ecological stress. River systems that no longer reach the sea,
shrinking lakes and sinking groundwater tables are among the most noticeable symptoms of water overuse.
Another cause for concern is the depletion of groundwater in South Asia. Ground water tables are falling by more than a metre a year in parts of India. This then puts future agricultural production at risk.
Video
What can be done?
- Desalinization Technology
- Water Conservation
- Water Sharing Treaties
Desalinization technology
This system is described as filtering salty water through membranes and removing the salt through electrodialysis and reverse osmosis.
This procedure has worked for about 130 nations in North Africa and the Middle East. With this system, these nations are currently producing six billion gallons of usable water a day.
Despite these improvements, only 2.3% of wastewater that is generated by municipalities is currently being recycled and reused. If water were saved by utilizing reclaimed water for irrigation in Tuscon, Arizona alone, 31,000 families could be supported from this amount
Globally implementing simple recycling and filtration systems would be a relatively easy task that would reap outstanding benefits. Making these global advancements would be a monetarily and environmentally friendly step in the right direction towards the reduction of global water scarcity
Water Conservation
The task of conserving water can also be conquered on a smaller scale, beginning with small improvements in individual homes. One solution is to develop and mandate more efficient household water heaters. In old homes with poor insulation, the time needed to heat up the water from a faucet or showerhead can often be extensive. With heated pipes and better insulation throughout the system, this wasted water would no longer be an issue as hot water would immediately pour from the faucet. In addition to conservation of water, heating costs would also be lower
More tips
- Check faucets, pipes and toilets for leaks
- Take shorter showers
- Turn off the water after you wet your toothbrush
- Use your dishwasher and clothes washer for only full loads
- Water your lawn only when it needs it
- Don't run the hose while washing your car
Water Sharing Treaties
Another solution to this problem, which tackles a different aspect of water scarcity, is to compose new multinational treaties to specify water sharing. There are currently a significant amount of treaties in effect regarding water, yet most of them don’t properly distribute. These ineffective treaties also cause apprehension between nations. Disputes over shared water resources have also led to violence and even international tension. Currently, watersheds of 261 major rivers are shared by two or more countries.
These new treaties would work towards eliminating unequal distribution so that areas with plentiful water resources would not be able to hoard them. Instead, the treaty would distribute the water according to the aridity and need of the region to assure appropriate allocation. A new multinational treaty would be exceedingly beneficial in resolving water disputes as well as reducing tension and animosity between nations.
Hydrological interdependence is not an abstract concept. Two in every five people in the world live in international water basins shared by more than one country (table 1).
Table 1. Thirty-nine countries receive most of their water from outside their borders
| Region | Countries receiving between 50% and 75% of their water from external sources | Countries receiving more than 75% of their water from external sources |
|---|---|---|
| Arab States | Iraq, Somalia, Sudan, Syrian Arab Republic | Bahrain, Egypt, Kuwait |
| East Asia and the Pacific | Cambodia, Viet Nam | |
| Latin America and the Caribbean | Argentina, Bolivia, Paraguay, Uruguay | |
| South Asia | Bangladesh, Pakistan | |
| Sub-Saharan Africa | Benin, Chad, Congo, Eritrea, Gambia, Mozambique, Namibia | Botswana, Mauritania, Niger |
| Central and Eastern Europe and CIS | Azerbijan, Croatia, Latvia, Slovakia, Ukraine, Uzbekistan | Hungary, Moldova, Romania, Serbia and Montenegroa,Turkmenistan |
| High-income OECD | Luxembourg | Netherlands |
| Other | Israel |
Let's hear from you.
Water some of the ways we can prevent or stop the scarcity of water. Leave your name and country along with a comment for others to see.
Jane Bush of St Kitts
Don't water the gutter
Position your sprinklers so water lands on the lawn or garden, not on paved areas. Also, avoid watering on windy days.
Kal El of Krypton
I think this site is SUPER awesome


Overview
Wikidata is an open knowledge base hosted by the Wikimedia Foundation that can be read and edited by both humans and machines. Wikidata acts as the central source of common, open structured data used by Wikipedia, Wiktionary, Wikisource, and others. It is used in a variety of academic and industrial applications.
In recent years, we have seen an increase in the number of scientific publications around Wikidata. While there are a number of venues for the Wikidata community to exchange, none of those publish original research. We want to bridge the gap between these communities and the research events and give the research-focused part of the Wikidata community a venue to meet and exchange information and knowledge.
The Wikidata Workshop 2022 focuses on the challenges and opportunities of working on a collaborative open-domain knowledge graph such as Wikidata, which is edited by an international and multilingual community. We encourage submissions that observe the influence such a knowledge graph has on the web of data, as well as those working on improving this knowledge graph itself. This workshop brings together everyone working around Wikidata in both the scientific field and industry to discuss trends and topics around this collaborative knowledge graph.
Call for Papers
This workshop will have two tracks: Novel Work, and Previously Published Work.
Papers in the Novel Work track will be published as part of the workshop proceedings. The Previously Published Work track is for papers already published in other conferences, giving the community the chance to access and discuss relevant work that has been presented elsewhere as part of the workshop.
Novel Work Track
The papers will be peer-reviewed by at least three researchers. Selected papers will be published on CEUR (we only publish to CEUR if the authors agree to have their papers published).
For the Novel Work track, we will accept papers up to 12 pages (excluding references, contribution of the paper should justify the length of the paper). We invite the following types of papers:
Previously Published Work Track
Published papers will be reviewed by the organising committee in terms of topical fit and prominence of the publication venue. They will not be published as part of the proceedings.
For the Previously Published Work track, we will accept papers with no page limit, prioritizing instead the importance and relevance of the publication. We invite the following types of papers:
Submission
Papers have to be submitted through EasyChair.
We ask authors to declare the track they intend on submitting to. To do so, please add, at the beginning of the "title" field on the EasyChair submission, either the string "[Novel]", for the Novel Work track, or the string "[Published]", for the Previously Published track.
Submission Link: https://easychair.org/conferences/?conf=wikidataworkshop2022
Important Dates
Deadline extended!
Papers due: Friday, 5 August 2022
Notification of accepted papers: Friday, 30 September 2022
Camera ready papers due: Monday, 10 October 2022
Workshop date: Monday, 24 October 2022
Submission Guidelines
Submissions must be as PDF, for the [Novel] track formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). For details on the LNCS style, see Springer’s Author Instructions. For the [Published] track, no reformatting of the original PDFs is needed.
Journal extensions
Extended versions of journal papers are invited for submission to a special issue of the Semantic Web Journal.
Schedule Detail
The workshop time is: 2 - 6 pm (CEST), 1 - 5 pm (UK), 5 - 9 am (California, US)
All times below in CEST.
-
14:00 - 14:10
Welcome
Welcome from the organisers, agenda, rules of engagement -
14:10 - 14:55
Keynote 1: Lydia Pintscher
-
14:55 - 15:15
Lightning Talks 1
-
15:15 - 15:35
Poster Session 1
-
15:35 - 15:45
Break
-
15:45 - 16:30
Keynote 2: Tiago Lubiana
-
16:30 - 16:50
Lightning Talks 2
-
16:50 - 17:10
Poster Session 2
-
17:10 - 17:30
Lightning Talks 3
-
17:30 - 17:50
Poster Session 3
-
17:50 - 18:00
Closing
Concluding remarks, closing
Sessions / Papers
Session 1: Wikidata Ontology and Schema
Talks: 14:55 - 15:15 (CEST); Posters: 15:15 - 15:35 (CEST)
- Armin Haller, Axel Polleres, Daniil Dobriy, Nicolas Ferranti and Sergio J. Rodrı́guez Méndez: An Analysis of Links in Wikidata (Room 1)
- Nicolas Ferranti, Axel Polleres, Jairo Francisco De Souza and Shqiponja Ahmetaj: Formalizing Property Constraints in Wikidata (Room 2)
- Valentina Anita Carriero, Paul Groth and Valentina Presutti: Towards improving Wikidata reuse with emerging patterns (Room 3)
- Jose Emilio Labra Gayo: WShEx: A language to describe and validate Wikibase entities (Room 4)
- Leila Feddoul, Frank Löffler and Sirko Schindler: Analysis of Consistency between Wikidata and Wikipedia categories (Room 5)
- Sofia Baroncini, Margherita Martorana, Mario Scrocca, Zuzanna Smiech and Axel Polleres: Analysing the Evolution of Community-Driven (Sub-)Schemas within Wikidata (Room 6)
- Daniil Dobriy and Axel Polleres: Analysing and promoting ontology interoperability in Wikibase (Room 7)
- Wolfgang Fahl, Tim Holzheim, Andrea Westerinen, Christoph Lange and Stefan Decker: Property cardinality analysis to extract truly tabular query results from Wikidata (Room 8)
Session 2: Wikidata Querying and Quality
Talks: 16:30 - 16:50 (CEST); Posters: 16:50 - 17:10 (CEST)
- Bohui Zhang, Filip Ilievski and Pedro Szekely: Enriching Wikidata with Linked Open Data (Room 1)
- Wolfgang Fahl, Tim Holzheim, Andrea Westerinen, Christoph Lange and Decker Stefan: Getting and hosting your own copy of Wikidata (Room 2)
- Antoine Willerval, Dennis Diefenbach and Pierre Maret: Easily setting up a local Wikidata SPARQL endpoint using the qEndpoint (Room 3)
- Nicholas Klein, Filip Ilievski, Hayden Freedman and Pedro Szekely: Identifying Surprising Facts in Wikidata and Kartik Shenoy, Filip Ilievski, Daniel Garijo, Daniel Schwabe and Pedro Szekely: A Study of the Quality of Wikidata (Room 4)
- Seyed Amir Hosseini Beghaeiraveri: Towards Automated Technologies in the Referencing Quality of Wikidata (Room 5)
- Hans Chalupsky and Pedro Szekely: Hybrid Structured and Similarity Queries over Wikidata plus Embeddings with Kypher-V (Room 6)
Session 3: Personalisation and usability
Talks: 17:10 - 17:30 (CEST); Posters: 17:30 - 17:50 (CEST)
- Nicholas Klein, Filip Ilievski and Pedro Szekely: Generating Explainable Abstractions for Wikidata Entities (Room 1)
- Fariz Darari: COVIWD: COVID-19 Wikidata Dashboard (Room 2)
- Seyed Amir Hosseini Beghaeiraveri, Alasdair Gray and Fiona McNeill: Experiences of Using WDumper to Create Topical Subsets from Wikidata (Room 3)
- Philipp Scharpf, Moritz Schubotz, Andreas Spitz, André Greiner-Petter and Bela Gipp: Collaborative and AI-aided Exam Question Generation using Wikidata in Education (Room 4)
- Sola Shirai, Aamod Khatiwada, Oktie Hassanzadeh and Debarun Bhattacharjya: Rule-Based Link Prediction over Event-Related Causal Knowledge in Wikidata (Room 5)
- Lozana Rossenova, Paul Duchesne and Ina Blümel: Wikidata and Wikibase as complementary research data management services for cultural heritage data (Room 6)
- Lucas Jarnac and Pierre Monnin: Wikidata to Bootstrap an Enterprise Knowledge Graph: How to Stay on Topic? (Room 7)
Accepted Papers
Sofia Baroncini, Margherita Martorana, Mario Scrocca, Zuzanna Smiech and Axel Polleres
Analysing the Evolution of Community-Driven (Sub-)Schemas within Wikidata
Seyed Amir Hosseini Beghaeiraveri
Towards Automated Technologies in the Referencing Quality of Wikidata
Seyed Amir Hosseini Beghaeiraveri, Alasdair Gray and Fiona McNeill
Experiences of Using WDumper to Create Topical Subsets from Wikidata
Valentina Anita Carriero, Paul Groth and Valentina Presutti
Towards improving Wikidata reuse with emerging patterns
Hans Chalupsky and Pedro Szekely
Hybrid Structured and Similarity Queries over Wikidata plus Embeddings with Kypher-V
Fariz Darari
COVIWD: COVID-19 Wikidata Dashboard
Daniil Dobriy and Axel Polleres
Analysing and promoting ontology interoperability in Wikibase
Wolfgang Fahl, Tim Holzheim, Andrea Westerinen, Christoph Lange and Stefan Decker
Property cardinality analysis to extract truly tabular query results from Wikidata
Wolfgang Fahl, Tim Holzheim, Andrea Westerinen, Christoph Lange and Decker Stefan
Getting and hosting your own copy of Wikidata
Leila Feddoul, Frank Löffler and Sirko Schindler
Analysis of Consistency between Wikidata and Wikipedia categories
Nicolas Ferranti, Axel Polleres, Jairo Francisco De Souza and Shqiponja Ahmetaj
Formalizing Property Constraints in Wikidata
Jose Emilio Labra Gayo
WShEx: A language to describe and validate Wikibase entities
Armin Haller, Axel Polleres, Daniil Dobriy, Nicolas Ferranti and Sergio J. Rodrı́guez Méndez
An Analysis of Links in Wikidata
Lucas Jarnac and Pierre Monnin: Wikidata to Bootstrap an Enterprise Knowledge Graph
How to Stay on Topic?
Nicholas Klein, Filip Ilievski, Hayden Freedman and Pedro Szekely
Identifying Surprising Facts in Wikidata
Nicholas Klein, Filip Ilievski and Pedro Szekely
Generating Explainable Abstractions for Wikidata Entities
Lozana Rossenova, Paul Duchesne and Ina Blümel
Wikidata and Wikibase as complementary research data management services for cultural heritage data
Philipp Scharpf, Moritz Schubotz, Andreas Spitz, André Greiner-Petter and Bela Gipp
Collaborative and AI-aided Exam Question Generation using Wikidata in Education
Kartik Shenoy, Filip Ilievski, Daniel Garijo, Daniel Schwabe and Pedro Szekely
A Study of the Quality of Wikidata
Sola Shirai, Aamod Khatiwada, Oktie Hassanzadeh and Debarun Bhattacharjya
Rule-Based Link Prediction over Event-Related Causal Knowledge in Wikidata
Antoine Willerval, Dennis Diefenbach and Pierre Maret
Easily setting up a local Wikidata SPARQL endpoint using the qEndpoint
Bohui Zhang, Filip Ilievski and Pedro Szekely
Enriching Wikidata with Linked Open Data
Our Speakers
Organization
Organizing Committee
Lucie-Aimée Kaffee, University of Southampton<EMAIL_ADDRESS>Lucie-Aimée Kaffee is a postdoctoral research fellow at the University of Copenhagen. She acquired her PhD from the University of Southampton and was previously a research intern at Bloomberg, London, a research fellow at TIB Hannover and software developer in the Wikidata team, Wikimedia Germany. Her research focus is multilingual linked data in collaborative knowledge graphs and natural language processing. Lucie was part of the OC of the Wikidata Workshop co-located with ISWC'20, proceedings chair of ISWC'20 and ISWC'21, OC of AMAR: First International Workshop on Approaches for Making Data Interoperable at SEMANTiCS'19 and participated in the PC of a variety of conferences and workshops.
Simon Razniewski, Max Planck Institute for Informatics<EMAIL_ADDRESS>Simon Razniewski is a senior researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, where he heads the Knowledge Base Construction and Quality research area. His research focuses on methods for knowledge base construction, as well as quality assessment, with applications in Wikidata and beyond. He has held senior roles in program committees of major conferences such as IJCAI'21 (area chair), or ISWC'20 and CIKM'20 (senior PC member). He has held visiting positions at places such as AT&T Labs-Research, the University of Queensland, and UCSD, and his research has been recognized with multiple awards and research grants.
Gabriel Amaral, King's College London<EMAIL_ADDRESS>Gabriel Amaral is a computer scientist, graduated summa cum laude from the Federal University of Ceará, and a PhD candidate at King's College London. He is part of the Marie Curie European training network Cleopatra, delivering technologies to build and use large-scale, multilingual knowledge graphs. His research tackles the quality of references and the verification of claims found in Wikidata.
Kholoud Saad Alghamdi, King's College London<EMAIL_ADDRESS>Kholoud Saad Alghamdi is a PhD candidate at King's College London. She obtained her master's degree in Computer Science from the University of Southampton. Her PhD project develops an items recommender system for Wikidata editors. Before that, she was lecturer at King Abdulaziz University and worked previously as a data analyst in the industry.
Program Committee
Seyed Amir Hosseini Beghaeiraveri, Heriot-Watt University
Niel Chah, University of Toronto
Houcemeddine Turki, Faculty of Medicine of Sfax
David Abián, King's College London
John Samuel, CPE Lyon, LIRIS - UMR 5205
Luis Galárraga, Inria
Filip Ilievski, Information Sciences Institute, USC
Lydia Pintscher, Wikimedia Deutschland
Elisavet Koutsiana, King's College London
Pierre-Henri Paris, CNAM
Alessandro Piscopo, BBC
Mahir Morshed, University of Illinois at Urbana-Champaign
Dennis Diefenbach, The QA Company
Alasdair Gray, Heriot-Watt University
Daniel Garijo, Universidad Politécnica de Madrid
Andrew D. Gordon, Microsoft Research and University of Edinburgh
Thomas Pellissier Tanon, Télécom ParisTech
Cristina Sarasua, University of Zurich
Pavlos Vougiouklis, University of Southampton


Has anyone done any load testing of numerous TXs to one base?
I want to move to putting clamps on all my distribution board feeds, and before I go to the expense, want to be sure it works.
I think I saw that someone had modded a TX to take nine clamps - I'm not bothered about doing that yet, but wondered if the base will cope with, say, 6 TXs.
I'm running 2 fine at the moment, but am possibly seeing some loss when I add my remaining TX.
Re: EmonBase capacity with multiple TXs
No we havent really done any testing with lots of emonTx's to one base. We would be intersted to hear how you get on. As long as they all don't transmitt at exactly the same time and have different node ID's they should work ok. We have a new emonBase example which makes adding extra Tx's easy. It automatically forwards data from new nodes as it detects them to emoncms in a new CSV format. The naming of the feeds and nodes is done on emoncms: https://github.com/openenergymonitor/NanodeRF/tree/master/NanodeRF_multinode
Re: EmonBase capacity with multiple TXs
As far as the address range is concerned, you can have a lot. But long before that limit is reached, I think you will be into problems with collisions of the data packets. Is that your worry? I recently read somewhere - I think it was on Jeenode - a suggestion to poll the TX's from the base as a means of getting round that problem. I only noticed it in passing, I didn't follow it up; but if you can find it again, it might be worth investigation. The snag with that is, the TX has to be at full power listening on the r.f. all the time (in the simplest implementation) so battery life is abysmal, but OK if mains powered.
This http://jeelabs.org/2011/06/27/time-controlled-transmissions/ might be what I read - it looks a bit familiar. Search for "Jeenode RFM12 collisions" - there are a few more mentions.
Re: EmonBase capacity with multiple TXs
More here http://jeelabs.org/2009/12/26/carrier-detection/


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Risotto
Yields: 1
Preparation time: 20
Ingredients
-
80g rice Carnaroli (or Arborio if you can’t find it)7g butter (or margarine/olive oil if you want the 🌿vegan🌿 variant)grated parmesan50ml of wine (variety depends on the flavor you pick, for mushrooms we’ll go with white Sauvignon or Chardonnay)0.5 shallots or white onionvegetable stock dice (I’ll upload a recipe to make them yourself) or vegetable stockextra-virgin olive oilFlavor: see description below, for this we will go with 50g of mushrooms
Directions
-
Put on a kettle with some boiling water. If you are using stock put it in a pot to warm up instead.Finely dice the onion/shallotsPut shallots in a pan with tall sides or a pot (I prefer to use non-stick ones).Stir-fry the onions on medium heat with some olive oil until transparent (don’t brown them)Add the rice (no water yet) and toast it for a while until you hear some faint popping (~2 minutes)Add the wine, which should evaporate more or less instantlyWait for the wine to be evaporated/absorbedAdd a bit of boiling water/stock, just enough to cover the riceAdd the stock dice (if you are not using actual stock)Add the flavor (in this case mushrooms)Set a timer for 17 minutes (this depends on the rice you are using)Keep stirring for the entire duration (at least once every couple of minutes in a pot, once every 5 if you use a large skillet). Every time water is absorbed and it doesn’t cover the rice anymore add some boiling water/stockA couple of minutes before the timer expires stop adding waterWhen the timer expires remove from the heat and add the butter/margarine and the grated parmesanStir vigorously until butter is melted and cheese is incorporatedCover with a lid or a towel and let rest for a minute or two before serving
Doses are per person, I suggest making it for at least two people.
Flavors
Flavors can be composed, like sausage and mushrooms or sausage and saffron, let your fantasy guide you.
- Saffron (use red wine with tannins like salice, primitivo or any non-sweet red wine)
- Sausage (in this case add the sausage 5 minutes before the end)
- Squid ink
- Pumpkin
- Zucchini and shrimps
- Cheeses
- Radicchio
Category: First course
Origin: Italian


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{% import 'shared/_forms.html' as forms %} {% extends 'layouts/base.html' %} {% block main_content %}


from abc import abstractmethod
import torch
import torch.nn as nn
[docs]class Conceptizer(nn.Module):
def __init__(self):
"""
A general Conceptizer meta-class. Children of the Conceptizer class
should implement encode() and decode() functions.
"""
super(Conceptizer, self).__init__()
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
[docs] def forward(self, x):
"""
Forward pass of the general conceptizer.
Computes concepts present in the input.
Parameters
----------
x : torch.Tensor
Input data tensor of shape (BATCH, *). Only restriction on the shape is that
the first dimension should correspond to the batch size.
Returns
-------
encoded : torch.Tensor
Encoded concepts (batch_size, concept_number, concept_dimension)
decoded : torch.Tensor
Reconstructed input (batch_size, *)
"""
encoded = self.encode(x)
decoded = self.decode(encoded)
return encoded, decoded.view_as(x)
[docs] @abstractmethod
def encode(self, x):
"""
Abstract encode function to be overridden.
Parameters
----------
x : torch.Tensor
Input data tensor of shape (BATCH, *). Only restriction on the shape is that
the first dimension should correspond to the batch size.
"""
pass
[docs] @abstractmethod
def decode(self, encoded):
"""
Abstract decode function to be overridden.
Parameters
----------
encoded : torch.Tensor
Latent representation of the data
"""
pass
[docs]class IdentityConceptizer(Conceptizer):
def __init__(self, **kwargs):
"""
Basic Identity Conceptizer that returns the unchanged input features.
"""
super().__init__()
[docs] def encode(self, x):
"""Encoder of Identity Conceptizer.
Leaves the input features unchanged but reshapes them to three dimensions
and returns them as concepts (use of raw features -> no concept computation)
Parameters
----------
x : torch.Tensor
Input data tensor of shape (BATCH, INPUT_FEATURES).
Returns
-------
concepts : torch.Tensor
Unchanged input features but with extra dimension (BATCH, INPUT_FEATURES, 1).
"""
return x.unsqueeze(-1)
[docs] def decode(self, z):
"""Decoder of Identity Conceptizer.
Simulates reconstruction of the original input x by undoing the reshaping of the encoder.
The 'reconstruction' is identical to the input of the conceptizer.
Parameters
----------
z : torch.Tensor
Output of encoder (input x reshaped to three dimensions), size: (BATCH, INPUT_FEATURES, 1).
Returns
-------
reconst : torch.Tensor
Unchanged input features (identical to x)
"""
return z.squeeze(-1)
[docs]class VaeConceptizer(nn.Module):
"""Variational Auto Encoder to generate basis concepts
Concepts should be independently sensitive to single generative factors,
which will lead to better interpretability and fulfill the "diversity"
desiderata for basis concepts in a Self-Explaining Neural Network.
VAE can be used to learn disentangled representations of the basis concepts
by emphasizing the discovery of latent factors which are disentangled.
"""
def __init__(self, image_size, num_concepts, **kwargs):
"""Initialize Variational Auto Encoder
Parameters
----------
image_size : int
size of the width or height of an image, assumes square image
num_concepts : int
number of basis concepts to learn in the latent distribution space
"""
super().__init__()
self.in_dim = image_size * image_size
self.z_dim = num_concepts
self.encoder = VaeEncoder(self.in_dim, self.z_dim)
self.decoder = VaeDecoder(self.in_dim, self.z_dim)
[docs] def forward(self, x):
"""Forward pass through the encoding, sampling and decoding step
Parameters
----------
x : torch.tensor
input of shape [batch_size x ... ], which will be flattened
Returns
-------
concept_mean : torch.tensor
mean of the latent distribution induced by the posterior input x
x_reconstruct : torch.tensor
reconstruction of the input in the same shape
"""
concept_mean, concept_logvar = self.encoder(x)
concept_sample = self.sample(concept_mean, concept_logvar)
x_reconstruct = self.decoder(concept_sample)
return (concept_mean.unsqueeze(-1),
concept_logvar.unsqueeze(-1),
x_reconstruct.view_as(x))
[docs] def sample(self, mean, logvar):
"""Samples from the latent distribution using reparameterization trick
Reparameterization trick: z = mu + sigma * epsilon
where epsilon is drawn from a standard normal distribution
Parameters
----------
mean : torch.tensor
mean of the latent distribution of shape [batch_size x z_dim]
log_var : torch.tensor
diagonal log variance of the latent distribution of shape [batch_size x z_dim]
Returns
-------
z : torch.tensor
sample latent tensor of shape [batch_size x z_dim]
"""
if self.training:
std = torch.exp(0.5 * logvar)
epsilon = torch.randn_like(std)
z = mean + std * epsilon
else:
z = mean
return z
[docs]class VaeEncoder(nn.Module):
"""Encoder of a VAE"""
def __init__(self, in_dim, z_dim):
"""Instantiate a multilayer perceptron
Parameters
----------
in_dim: int
dimension of the input data
z_dim: int
latent dimension of the encoder output
"""
super().__init__()
self.in_dim = in_dim
self.z_dim = z_dim
self.FC = nn.Sequential(
nn.Flatten(),
nn.Linear(in_dim, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 100),
nn.ReLU()
)
self.mean_layer = nn.Linear(100, z_dim)
self.logvar_layer = nn.Linear(100, z_dim)
[docs] def forward(self, x):
"""Forward pass of the encoder
"""
x = self.FC(x)
mean = self.mean_layer(x)
logvar = self.logvar_layer(x)
return mean, logvar
[docs]class VaeDecoder(nn.Module):
"""Decoder of a VAE"""
def __init__(self, in_dim, z_dim):
"""Instantiate a multilayer perceptron
Parameters
----------
in_dim: int
dimension of the input data
z_dim: int
latent dimension of the encoder output
"""
super().__init__()
self.in_dim = in_dim
self.z_dim = z_dim
self.FC = nn.Sequential(
nn.Linear(z_dim, 100),
nn.ReLU(),
nn.Linear(100, 256),
nn.ReLU(),
nn.Linear(256, 512),
nn.ReLU(),
nn.Linear(512, in_dim)
)
[docs] def forward(self, x):
"""Forward pass of a decoder"""
x_reconstruct = torch.sigmoid(self.FC(x))
return x_reconstruct
[docs]class ConvConceptizer(Conceptizer):
def __init__(self, image_size, num_concepts, concept_dim, image_channels=1, encoder_channels=(10,),
decoder_channels=(16, 8), kernel_size_conv=5, kernel_size_upsample=(5, 5, 2),
stride_conv=1, stride_pool=2, stride_upsample=(2, 1, 2),
padding_conv=0, padding_upsample=(0, 0, 1), **kwargs):
"""
CNN Autoencoder used to learn the concepts, present in an input image
Parameters
----------
image_size : int
the width of the input image
num_concepts : int
the number of concepts
concept_dim : int
the dimension of each concept to be learned
image_channels : int
the number of channels of the input images
encoder_channels : tuple[int]
a list with the number of channels for the hidden convolutional layers
decoder_channels : tuple[int]
a list with the number of channels for the hidden upsampling layers
kernel_size_conv : int, tuple[int]
the size of the kernels to be used for convolution
kernel_size_upsample : int, tuple[int]
the size of the kernels to be used for upsampling
stride_conv : int, tuple[int]
the stride of the convolutional layers
stride_pool : int, tuple[int]
the stride of the pooling layers
stride_upsample : int, tuple[int]
the stride of the upsampling layers
padding_conv : int, tuple[int]
the padding to be used by the convolutional layers
padding_upsample : int, tuple[int]
the padding to be used by the upsampling layers
"""
super(ConvConceptizer, self).__init__()
self.num_concepts = num_concepts
self.filter = filter
self.dout = image_size
# Encoder params
encoder_channels = (image_channels,) + encoder_channels
kernel_size_conv = handle_integer_input(kernel_size_conv, len(encoder_channels))
stride_conv = handle_integer_input(stride_conv, len(encoder_channels))
stride_pool = handle_integer_input(stride_pool, len(encoder_channels))
padding_conv = handle_integer_input(padding_conv, len(encoder_channels))
encoder_channels += (num_concepts,)
# Decoder params
decoder_channels = (num_concepts,) + decoder_channels
kernel_size_upsample = handle_integer_input(kernel_size_upsample, len(decoder_channels))
stride_upsample = handle_integer_input(stride_upsample, len(decoder_channels))
padding_upsample = handle_integer_input(padding_upsample, len(decoder_channels))
decoder_channels += (image_channels,)
# Encoder implementation
self.encoder = nn.ModuleList()
for i in range(len(encoder_channels) - 1):
self.encoder.append(self.conv_block(in_channels=encoder_channels[i],
out_channels=encoder_channels[i + 1],
kernel_size=kernel_size_conv[i],
stride_conv=stride_conv[i],
stride_pool=stride_pool[i],
padding=padding_conv[i]))
self.dout = (self.dout - kernel_size_conv[i] + 2 * padding_conv[i] + stride_conv[i] * stride_pool[i]) // (
stride_conv[i] * stride_pool[i])
if self.filter and concept_dim == 1:
self.encoder.append(ScalarMapping((self.num_concepts, self.dout, self.dout)))
else:
self.encoder.append(Flatten())
self.encoder.append(nn.Linear(self.dout ** 2, concept_dim))
# Decoder implementation
self.unlinear = nn.Linear(concept_dim, self.dout ** 2)
self.decoder = nn.ModuleList()
decoder = []
for i in range(len(decoder_channels) - 1):
decoder.append(self.upsample_block(in_channels=decoder_channels[i],
out_channels=decoder_channels[i + 1],
kernel_size=kernel_size_upsample[i],
stride_deconv=stride_upsample[i],
padding=padding_upsample[i]))
decoder.append(nn.ReLU(inplace=True))
decoder.pop()
decoder.append(nn.Tanh())
self.decoder = nn.ModuleList(decoder)
[docs] def encode(self, x):
"""
The encoder part of the autoencoder which takes an Image as an input
and learns its hidden representations (concepts)
Parameters
----------
x : Image (batch_size, channels, width, height)
Returns
-------
encoded : torch.Tensor (batch_size, concept_number, concept_dimension)
the concepts representing an image
"""
encoded = x
for module in self.encoder:
encoded = module(encoded)
return encoded
[docs] def decode(self, z):
"""
The decoder part of the autoencoder which takes a hidden representation as an input
and tries to reconstruct the original image
Parameters
----------
z : torch.Tensor (batch_size, channels, width, height)
the concepts in an image
Returns
-------
reconst : torch.Tensor (batch_size, channels, width, height)
the reconstructed image
"""
reconst = self.unlinear(z)
reconst = reconst.view(-1, self.num_concepts, self.dout, self.dout)
for module in self.decoder:
reconst = module(reconst)
return reconst
[docs] def conv_block(self, in_channels, out_channels, kernel_size, stride_conv, stride_pool, padding):
"""
A helper function that constructs a convolution block with pooling and activation
Parameters
----------
in_channels : int
the number of input channels
out_channels : int
the number of output channels
kernel_size : int
the size of the convolutional kernel
stride_conv : int
the stride of the deconvolution
stride_pool : int
the stride of the pooling layer
padding : int
the size of padding
Returns
-------
sequence : nn.Sequence
a sequence of convolutional, pooling and activation modules
"""
return nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride_conv,
padding=padding),
# nn.BatchNorm2d(out_channels),
nn.MaxPool2d(kernel_size=stride_pool,
padding=padding),
nn.ReLU(inplace=True)
)
[docs] def upsample_block(self, in_channels, out_channels, kernel_size, stride_deconv, padding):
"""
A helper function that constructs an upsampling block with activations
Parameters
----------
in_channels : int
the number of input channels
out_channels : int
the number of output channels
kernel_size : int
the size of the convolutional kernel
stride_deconv : int
the stride of the deconvolution
padding : int
the size of padding
Returns
-------
sequence : nn.Sequence
a sequence of deconvolutional and activation modules
"""
return nn.Sequential(
nn.ConvTranspose2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride_deconv,
padding=padding),
)
[docs]class Flatten(nn.Module):
[docs] def forward(self, x):
"""
Flattens the inputs to only 3 dimensions, preserving the sizes of the 1st and 2nd.
Parameters
----------
x : torch.Tensor
Input data tensor of shape (dim1, dim2, *).
Returns
-------
flattened : torch.Tensor
Flattened input (dim1, dim2, dim3)
"""
return x.view(x.size(0), x.size(1), -1)
[docs]class ScalarMapping(nn.Module):
def __init__(self, conv_block_size):
"""
Module that maps each filter of a convolutional block to a scalar value
Parameters
----------
conv_block_size : tuple (int iterable)
Specifies the size of the input convolutional block: (NUM_CHANNELS, FILTER_HEIGHT, FILTER_WIDTH)
"""
super().__init__()
self.num_filters, self.filter_height, self.filter_width = conv_block_size
self.layers = nn.ModuleList()
for _ in range(self.num_filters):
self.layers.append(nn.Linear(self.filter_height * self.filter_width, 1))
[docs] def forward(self, x):
"""
Reduces a 3D convolutional block to a 1D vector by mapping each 2D filter to a scalar value.
Parameters
----------
x : torch.Tensor
Input data tensor of shape (BATCH, CHANNELS, HEIGHT, WIDTH).
Returns
-------
mapped : torch.Tensor
Reduced input (BATCH, CHANNELS, 1)
"""
x = x.view(-1, self.num_filters, self.filter_height * self.filter_width)
mappings = []
for f, layer in enumerate(self.layers):
mappings.append(layer(x[:, [f], :]))
return torch.cat(mappings, dim=1)


Keras data preprocessing utils.
image module: Set of tools for real-time data augmentation on image data.
image
sequence module: Utilities for preprocessing sequence data.
sequence
text module: Utilities for text input preprocessing.
text


I was born in Kisii County in January 25th 2002. I went to Rockfields Junior School in Umoja,Nairobi for Kindergaten School all the way till I did my K.C.P.E. I was then called to Pangani Girls School where I did my K.C.S.E in 2019. Currently,I am studying Business Information Technology at the KCA University,just finished my first year.I decided to join Moringa first of all,to keep me busy during my long holiday and secondly,to gain knowledge in coding.
My hobbies include:
I can sing and do first aid.
I also enjoy watching YouTube videos and series. My favourite YouTubers are East Meets West, Abel Mutua ,Footasylum and sidemen among others.
First Webpage The first webpage I created as i started learning this first week.
My Pet This is a simple website about my dog pet called Gerald


- searchBooks(String) - Method in class com.team41.boromi.BookViewModel
-
Backend call to search for books with keywords
- searchForBooks() - Method in class com.team41.boromi.book.SearchFragment
-
Searches for keywords
- SearchFragment - Class in com.team41.boromi.book
-
SearchFragment is used to manage the search tab.
- SearchFragment() - Constructor for class com.team41.boromi.book.SearchFragment
-
- setAuthor(String) - Method in class com.team41.boromi.models.Book
-
- setBookId(String) - Method in class com.team41.boromi.models.Book
-
- setBookWithRequests(Map<Book, List<BookRequest>>) - Method in class com.team41.boromi.adapters.GenericListAdapter
-
Sets the books that have requests.
- setBorrower(String) - Method in class com.team41.boromi.models.Book
-
- setBorrowerName(String) - Method in class com.team41.boromi.models.Book
-
- setDesc(String) - Method in class com.team41.boromi.models.Book
-
- setEmail(String) - Method in class com.team41.boromi.models.User
-
- setExchangeBook(Book) - Method in class com.team41.boromi.BookViewModel
-
- setExchangeBookRequest(BookRequest) - Method in class com.team41.boromi.BookViewModel
-
- setExchangeLocation(LatLng) - Method in class com.team41.boromi.BookViewModel
-
Set the location to meet
- setExchangeStage(CommonConstants.ExchangeStage) - Method in class com.team41.boromi.models.Book
-
- setImg64(String) - Method in class com.team41.boromi.models.Book
-
- setISBN(String) - Method in class com.team41.boromi.models.Book
-
- setLocation(LatLng) - Method in class com.team41.boromi.models.BookRequest
-
- setLocationLat(Double) - Method in class com.team41.boromi.models.Book
-
- setLocationLon(Double) - Method in class com.team41.boromi.models.Book
-
- setMode(int) - Method in class com.team41.boromi.book.MapFragment
-
Sets viewing mode.
- setOwnerName(String) - Method in class com.team41.boromi.models.Book
-
- setRequestDate(Date) - Method in class com.team41.boromi.models.BookRequest
-
- setRequestorName(String) - Method in class com.team41.boromi.models.BookRequest
-
- setStatus(CommonConstants.BookStatus) - Method in class com.team41.boromi.models.Book
-
- SettingsFragment - Class in com.team41.boromi.book
-
SettingsFragment manages the user information tab
- SettingsFragment() - Constructor for class com.team41.boromi.book.SettingsFragment
-
- setTitle(String) - Method in class com.team41.boromi.models.Book
-
- setupBundle(int, ArrayList<Book>, String, String, String) - Method in class com.team41.boromi.BookActivity
-
Sets up bundle for GenericListFragment
- setUsername(String) - Method in class com.team41.boromi.models.User
-
- setUsersRequested(ArrayList<BookRequest>) - Method in class com.team41.boromi.adapters.SubListAdapter
-
Sets the users that requested the book
- setWorkflow(CommonConstants.BookWorkflowStage) - Method in class com.team41.boromi.models.Book
-
- signOut() - Method in class com.team41.boromi.controllers.AuthenticationController
-
Signs out of the app
- SignupFragment - Class in com.team41.boromi.auth
-
SignupFragment manages the signup page and creates an account for the user and redirects the user
to the book activity when their account is created.
- SignupFragment() - Constructor for class com.team41.boromi.auth.SignupFragment
-
- status - Variable in class com.team41.boromi.controllers.BookController
-
- status - Variable in class com.team41.boromi.dbs.BookDB
-
- SubListAdapter - Class in com.team41.boromi.adapters
-
This class is recyclerview adapter that is used on the owner requests tab to show the user
request for each book
- SubListAdapter(ArrayList<BookRequest>, Book, BookActivity, GenericListAdapter) - Constructor for class com.team41.boromi.adapters.SubListAdapter
-
- SubListAdapter.ViewHolder - Class in com.team41.boromi.adapters
-
ViewHolder class to hold the model


| Event Type | Date | Description | Course materials | Reference |
|---|---|---|---|---|
| Lecture 1 | Feb 7 | Machine learning in the computational life sciences |
Read Goodfellow Part I [Slides] |
DL in Bioinformatics DL for computational biology The Roots of Bioinformatics ML in Genomic Medicine Awesome DeepBio Visual Information Theory |
| Lecture cancelled | Feb 9 | Cancelled | ||
| Recitation 1 | Feb 9,10 | Neural Networks, Tensor Flow, Notebooks | [Notebook] | |
| Lecture 2 | Feb 14 | Neural Networks and Tensor Flow | Read Goodfellow Chapter 6 [Slides] Feed Forward Backprop |
Damien Nick Locascio TF site tutorials |
| Lecture 3 | Feb 16 | Neural Networks (CNN, RNN) | Read Goodfellow Chapter 9 and 10 [Slides] |
|
| Recitation 2 | Feb 16,17 | Implementing CNNs | ||
| No class | Feb 21 | President’s Day | ||
| Lecture 4 | Feb 23 | RNA-seq reveals the expressed genome | [Slides] |
A survey of best practices for RNA-seq data analysis Comparative assessment of methods |
| PS1 due | Feb 23 | PS1 due | ||
| Recitation 3 | Feb 23,24 | RNNs/LSTMs | [Notes] | |
| Student presentation | Feb 28 | Modeling gene regulation and expression |
DECRES [slides] LINCS [slides] Splicing codes [slides] |
|
| Lecture 5 | Mar 02 | PCA, Dimensionality reduction (t-SNE), Autoencoders | [Slides] | |
| Recitation 4 | Mar 02,03 | PCA, Autoencoders | ||
| Lecture 6 | Mar 07 | Characterizing Protein-DNA interactions using ChIP Seq and Motif Discovery | [Notes] | |
| Student Presentation | Mar 09 | Deep Learning for Protein-DNA Interactions |
DeepBind [slides] DeepMotif [slides] Architecture Analysis [slides] |
|
| Recitation 5 | Mar 09,10 | EM for motif discovery | [Notes] | |
| Lecture cancelled | Mar 14 | Lecture cancelled | ||
| PS2 due | Mar 15 | PS2 due | ||
| Lecture 7 | Mar 16 | Characterizing the regulatory genome |
[Slides] [Notes on BIC Score] |
|
| Recitation 6 | Mar 16,17 | Neural Network Tips (Dropout, Uncertainty Estimation, etc) | ||
| Guest lecture | Mar 21 | Medical Record Understanding (David Sontag) | ||
| Student Presentation | Mar 23 | Predicting the regulatory genome with deep learning |
PEDLA Basset SPEID |
|
| Recitation 7 | Mar 23,24 | Discussion of project ideas | ||
| No class | Mar 28 | Spring break | ||
| No class | Mar 30 | Spring break | ||
| Lecture 8 | Apr 4 | eQTL prediction and variant prioritization | [Slides] | |
| PS3 due | Apr 5 | PS3 due | ||
| Student Presentation | Apr 6 | Deep Learning for eQTL and variant prediction |
DeepSEA LINSIGHT DanQ |
|
| Recitation 8 | Apr 7,8 | Interpreting Deep Learning Models | ||
| Exam | Apr 11 | Exam | ||
| Project | Apr 11 | Project proposal due | ||
| Project | Apr 13- May 11 | Meetings with mentors | ||
| No class | Apr 18 | Patriots day | ||
| Project | May 11 | Project due | ||
| Project | May 16,18 | Project presentations |


Developer Documentation - Pluricentric WordNet¶
Back end¶
Views.py¶
The main different between pluricentric and my wordnet type of installations is the fact that you can search in any of the implemented languages and it will display results if such are found. It will identify the language and then display the results.
Language identifying is done with language_identifier, being as simple as going through index files and seeing if the word shows up on them. Due to being a single word search, other sofisticated techniques of language identification don’t work as well. If there’s a collision, as in more than one languages are detected, then a message will be sent to the client for him/her to decide the language he/she wants to explore.
The advanced search for a translation can be done either through a pair file if there’s one present, where the translation is then between synsets. If one isn’t present, a search will be done through tab files. If there’s more than one sense detected, a pairing mismatch error will happen as it is uncertain which sense of the lemma the source language synset is in the target language.
Front End¶
language.js¶
There’s additions to language.js as more menus come up, such as the language collision pop-up.
index.js¶
Similar to the views.py, the only addition done is the collision treatment. If javascript receives a collision message, it will render a pop-up for the user to decide which language he/she wants to explore, that gets sent back to the server. Now that the server knows the language, the processes are the same as a my wordnet type installation.


Ministry's Framework for Restorative Justice
Prior to accepting any diversion referral:
-
All victims approached to participate in a community accountability program are apprised of support services available in
their communities (e.g. VictimLinkBC, a multi-lingual information line for victims available across BC 24 hours a day, 7 days a
week at 1-800-563-0808 or www.victimlinkbc.ca)
-
All participants fully understand the process.
-
The requirements and limits of confidentiality are fully explained to all participants.
-
All parties choose to participate in the restorative justice process voluntarily and are not under any threat or coercion to do
so by any party.
-
A letter of informed consent is obtained from all parties participating in the process.
Prior to accepting any diversion referral:
-
The nature of the alleged offence has been explained and the offender voluntarily accepts responsibility for the incident.
-
Alleged offenders (and parents or legal guardians) have been fully apprised of their rights to legal counsel and their right to
have the matter heard in the courts
-
The alleged offender is fully aware of their responsibility to comply with any agreement reached through the process.
-
All agreements are entered into voluntarily and with the consent of all parties.
The a CAP operates in such a manner that:
-
All persons, offenders and victims, are treated with respect and dignity at all times.
-
Involvement in the process is safe and does not endanger any participants.
-
All program volunteers have criminal records checks.
-
All volunteers are adequately trained to participate in the program.
-
A complaints process is established and accessible to all participants.
-
Appropriate record keeping is undertaken and case file management is maintained.
-
Confidentially of participants is maintained.


Class GenericTargetContainer
A special type of ITargetContainerOwner which stores targets (and potentially other containers) which are specifically keyed either to a particular open generic type or a closed generic built from it.
Inherited Members
Namespace:Rezolver
Assembly:Rezolver.dll
Syntax
public class GenericTargetContainer : TargetDictionaryContainer, ITargetContainerOwner, ITargetContainer
Remarks
You don't typically use this container directly - it is implicitly added to an ITargetContainer when generic types are registered. Indeed the TargetContainer and DecoratingTargetContainer booth create instances of this; and the Rezolver.EnumerableTargetContainer (understandably) inherits from it.
Constructors
| Improve this Doc View SourceGenericTargetContainer(Type)
Initializes a new instance of the GenericTargetContainer class.
Declaration
public GenericTargetContainer(Type genericType)
Parameters
| Type | Name | Description |
|---|---|---|
| Type | genericType | Required. The generic type definition that all targets and subcontainers will be grouped under. |
Properties
| Improve this Doc View SourceGenericType
Gets the open generic type definition which is common to all targets and containers within this container.
Declaration
public Type GenericType { get; }
Property Value
| Type | Description |
|---|---|
| Type |
Methods
| Improve this Doc View SourceFetch(Type)
Gets the target which can be used to build an instance of type.
Declaration
public override ITarget Fetch(Type type)
Parameters
| Type | Name | Description |
|---|---|---|
| Type | type | Required. The type for which a target is to be obtained. Because of the restrictions placed on the DeclaredType of the targets that can actually be registered into this container, the function will only ever return anything if type is a closed generic type whose definition equals GenericType. |
Returns
| Type | Description |
|---|---|
| ITarget |
Overrides
Remarks
Targets which have been registered specifically against the exact closed generic type represented by type take precedence over any targets which have been registered against the open generic type GenericType.
FetchAll(Type)
Implementation of FetchAll(Type)
Declaration
public override IEnumerable<ITarget> FetchAll(Type type)
Parameters
| Type | Name | Description |
|---|---|---|
| Type | type | The type whose targets are to be retrieved. |
Returns
| Type | Description |
|---|---|
| IEnumerable<ITarget> |
Overrides
| Improve this Doc View SourceRegister(ITarget, Type)
Overrides the Register(ITarget, Type) method to support registering both targets against the open generic type GenericType and against specific closed versions of that open generic type.
Declaration
public override void Register(ITarget target, Type serviceType = null)
Parameters
| Type | Name | Description |
|---|---|---|
| ITarget | target | The target to be registered |
| Type | serviceType | Service type against which the registration will be made. If null, then it will be assumed to be equal to GenericType. Only types equal to GenericType or generic types whose generic type definition is equal to GenericType are supported. |
Overrides
Remarks
Notes to overriders: When serviceType is a closed generic type, this function creates an ITargetContainer for that serviceType by calling the protected method CreateContainer(Type, ITarget) if one doesn't exist (it calls FetchContainer(Type) to check for existence), and then chains to its Register(ITarget, Type) method.
Exceptions
| Type | Condition |
|---|---|
| ArgumentException | If serviceType is not equal to GenericType or is not a closed generic type whose generic type definition is GenericType. |


Finite Geometry Notes
|
Home
|
Site Map
|
Author
|
The 2x2x2 Cube
Diamonds and Whirls
Permutable JavaScript Version
Theme and Variations
Block Designs
The Eightfold Cube
Finite Geometry and Physical Space
Non-Euclidean Blocks
A Simple Reflection Group of Order 168
Knight Moves: Geometry of the Eightfold Cube
Symmetry, Automorphisms, and Visual Group Theory


Apple to capture top spot in 5G smartphones in 2020, says Strategy Analytics
by Rodney Chan, DIGITIMES, TaipeiApple is expected to enter the 5G race in the third quarter of 2020, leapfrogging to the top in 5G smartphones market, according to Strategy Analytics.
"It may seem counterintuitive that Apple, which currently has no 5G phones in its portfolio will be able to pass current 5G market leaders Samsung and Huawei," said Ken Hyers, director at Strategy Analytics. "But with three new 5G models coming next year, Apple merely needs to match its current upgrade rates for newly introduced iPhone models to take the lead next year."
"Currently Samsung is the undisputed market leader in 5G smartphones," said Ville-Petteri Ukonaho, associate director at Strategy Analytics. "But with the two largest 5G markets in 2020, China and the USA, dominated by Huawei and Apple respectively, these two vendors are set to lead in 5G next year."
But the research firm expects Samsung to regain the 5G crown in the longer term, enabled by its dominance of the overall smartphone market and a broader portfolio of 5G devices across more price-bands.
Despite the US trade ban, Huawei will remain dominant in China, it added.


Internationalisation of PyQt5 Applications¶
PyQt5 and Qt include a comprehensive set of tools for translating applications into local languages. For a full description, see the Qt Linguist Manual in the Qt documentation.
The process of internationalising an application comprises the following steps.
- The programmer uses pylupdate5 to create or update a
.ts
translation file for each language that the application is to be translated into. A.ts
file is an XML file that contains the strings to be translated and the corresponding translations that have already been made. pylupdate5 can be run any number of times during development to update the.ts
files with the latest strings for translation. - The translator uses Qt Linguist to update the
.ts
files with translations of the strings. - The release manager then uses Qt’s lrelease utility to convert the
.ts
files to.qm
files which are compact binary equivalents used by the application. If an application cannot find an appropriate.qm
file, or a particular string hasn’t been translated, then the strings used in the original source code are used instead. - The release manage may optionally use pyrcc5 to embed the
.qm
files, along with other application resources such as icons, in a Python module. This may make packaging and distribution of the application easier.
pylupdate5¶
pylupdate5 is PyQt5’s equivalent to Qt’s lupdate utility
and is used in exactly the same way. A Qt .pro
project file is read that
specifies the Python source files and Qt Designer interface files from which
the text that needs to be translated is extracted. The .pro
file also
specifies the .ts
translation files that pylupdate5 updates (or
creates if necessary) and are subsequently used by Qt Linguist.
Differences Between PyQt5 and Qt¶
Qt implements internationalisation support through the
QTranslator
class, and the
translate()
and
tr()
methods. Usually
tr()
is used to obtain the correct translation of a
message. The translation process uses a message context to allow the same
message to be translated differently. In Qt tr()
is actually generated by moc
and uses the hardcoded class name as the
context. On the other hand, translate()
allows the context to be specified explicitly.
Unfortunately, because of the way Qt implements
tr()
it is not possible for PyQt5 to exactly
reproduce its behaviour. The PyQt5 implementation of
tr()
uses the class name of the instance as the
context. The key difference, and the source of potential problems, is that the
context is determined dynamically in PyQt5, but is hardcoded in Qt. In other
words, the context of a translation may change depending on an instance’s class
hierarchy. For example:
class A(QObject):
def hello(self):
return self.tr("Hello")
class B(A):
pass
a = A()
a.hello()
b = B()
b.hello()
In the above the message is translated by a.hello()
using a context of
A
, and by b.hello()
using a context of B
. In the equivalent C++
version the context would be A
in both cases.
The PyQt5 behaviour is unsatisfactory and may be changed in the future. It is
recommended that translate()
be used in
preference to tr()
. This is guaranteed to work
with current and future versions of PyQt5 and makes it much easier to share
message files between Python and C++ code. Below is the alternative
implementation of A
that uses
translate()
:
class A(QObject):
def hello(self):
return QCoreApplication.translate('A', "Hello")


An exploration of accessories inspired by pop cultures, oriental shapes + Melbourne City.
Each pair of earrings are
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Name
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Arduino is a simple cost effective way to dabble with coding and circuitry.
The way we learned was with a manual and a bunch of time to mess around. Overall, it was very simple if you understand how to follow directions
After doing some processes, I decided to change the code of some.
I changed the speed at which a light blinked from 1 second to .1 seconds. Simple, but it fulfilled the requirement.


Daily Comics - Tue Dec 8 09:13:59 2020
Sinfest - Donate with PayPal button - Reckoning 45
Click to squash Sinfest - Donate with PayPal button - Reckoning 45
Skip this comic.
xkcd - Covid Precaution Level
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Savage Chickens
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Gasoline Alley
Click to squash Gasoline Alley
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Coronavirus Spread
Total Cases
Per Million
Active Cases
Total Deaths
No guarantee or liability for the information, in particular for its completeness, correctness and actuality.


s6
Software
skarnet.org
The s6-update-symlinks program
s6-update-symlinks links the content of several similar directory trees
under a single tree. Its main use is to replace search paths like $PATH
or $MANPATH, by creating a unique access tree based on the source trees.
Name conflicts are solved by giving precedence to the last named directory.
Subdirectories are created exactly as needed ; what can be shared is
shared.
s6-update-symlinks is useful when one wants to combine
a logical package system, like Dan Bernstein's
/package and
/command, with
physical filesystem constraints, like /, /usr and
/usr/local on separate filesystems.
Interface
s6-update-symlinks d src1 src2 ...
- d is the destination directory. It will be created if it doesn't
exist.
- src1, src2, ... are the directory containing the files
to be linked.
- d and srcn must be
absolute paths, else s6-update-symlinks refuses to run. Using
relative paths doesn't make sense here, anyway.
- If src2 is empty, then d becomes a link to src1.
- If src1 is empty or entirely overridden by src2, then
d becomes a link to src2.
- If src1/file exists but not src2/file, then d
becomes a real directory and d/file a link to src1/file.
Then if src2/file2 exists, d/file2 links to it.
- And so on with other src directories, and subdirs.
- If s6-update-symlinks manages to performs all the requested
tasks, it exits 0. If it encounters a hard error, it exits 111. If it is
unable to resolve a conflict between given sources, it exits 100.
Examples
-
s6-update-symlinks /command /bin /sbin /usr/bin /usr/sbin /usr/local/bin /usr/local/sbin
makes all files under /bin, ..., /usr/local/sbin
available under /command. The programs linked are the same as the ones
that would be accessed with PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin".
-
s6-update-symlinks /package /initrd/package /slash/package /usr/package /usr/local/package
builds a /package hierarchy with what it finds in the listed
directories. This allows oddities like, for instance, having the daemontools
sources in /usr/package/admin/daemontools/src, and the daemontools
binaries in /initrd/package/admin/daemontools/bin, but accessing
both through /package/admin/daemontools/.


|
Sets the destination operand to 0 or 1 depending on the settings of the status flags (CF, SF, OF, ZF, and PF) in the EFLAGS register. The destination operand points to a byte register or a byte in memory. The condition code suffix (cc) indicates the condition being tested for.
The terms "above" and "below" are associated with the CF flag and refer to the relationship between two unsigned integer values. The terms "greater" and "less" are associated with the SF and OF flags and refer to the relationship between two signed integer values.
Many of the SETcc instruction opcodes have alternate mnemonics. For example, SETG (set byte if greater) and SETNLE (set if not less or equal) have the same opcode and test for the same condition: ZF equals 0 and SF equals OF. These alternate mnemonics are provided to make code more intelligible. Appendix B, EFLAGS Condition Codes, in the IA-32 Intel Architecture Software Developer's Manual, Volume 1, shows the alternate mnemonics for various test conditions.
Some languages represent a logical one as an integer with all bits set. This representation can be obtained by choosing the logically opposite condition for the SETcc instruction, then decrementing the result. For example, to test for overflow, use the SETNO instruction, then decrement the result.
|


What is DROOM?
Droom is the future of job search and career placement. Leveraging technology and job placement experience seamlessly into one integrated platform where both job seekers and providers can maximize their time and increase the efficiency all in one swipe.
About Us
We are a start up of young individuals who decided to take the job finding task into our own hands and simplify the task of upgrading / starting a new career. A team of Lambda students brought together by a desire to change the very landscape we were once apart of.
Droom allows job providers to create a profile and post job oppertunities. Once the profile is created, you can begin looking to find your preferred canididate. Upon a match, you will be able to communicate with the seeker about the job opportunity.
Droom allows job seekers to customize and create a profile detailing their work history and skillset. Once you create your profile, you can begin your search with looking for jobs with similar intrest. Upon a match, both parties can begin communicating.
Droom offers many key features both seekers and providers may take advantage of. The easy to use layout makes it effortless to begin the search for that perfect job, or even that perfect canididate with just a swipe.


Supervisor: Prof. Francesc Moreno-Nogeur and Prof. Mariella Dimiccoli.
Working on developing novel approaches for 3D human pose estimation.
Created Multiview synchronized dataset.
Extracted the static and dynamic cues from videos using encoder-decoder architecture.
Proposed Self-supervised approach to improve pose estimations.
(October 2019 - Present)
Developed an architecture for autonomous Remote Rebuild of checkout counters in all Tesco stores across UK and Ireland.
Build a quick and improved optimized vehicle routing system to find most efficient route between stores and warehouses for Store delivery.
(July 2018 - October 2019)
Supervisor: Dr. Sanjay Singh.
Worked on improving signature verification for low power devices.
Feature engineered relevant features and fine-tuned hyperparameters for offline signature verification on low power devices on GPDS-160 dataset.
Implemented and trained a quantum neural networks along with Fuzzy c-means to find an improvement of 5% in the accuracy.
(January 2018 - May 2018)
Developed a recommender system to notify user about non-merged changes.
Successfully migrate a large scale Database from Wiki to Confluence.
(July 2017 - December 2017)


User
Policy Privacy.
COASAPP LLC believe that securing user’s data is
the key to a strong trust, that’s why we
are committed to keep the privacy of the
personal information that you provide to us. This policy gives details about
our information practices and how we utilize your information to provide our
services to you. By using COASAPP (“the app and website”) you acknowledge that
the User terms and conditions in this Privacy Policy will apply to you, as well
as to any information that you give us. You are advised to take the time to
read through this Policy Privacy to get familiar with our practices. We
sometimes underline and italicize terms in this Policy privacy; if they’re not
described here; they’re detailed in our User Terms of Condition. COASAPP
LLC is the legal name for COASAPP however, we will use COASAPP in this this
Policy Privacy when referring to the COASAPP communication, online marketplace,
hospitality, instant on the gomoving,
hauling or delivering cargo (package) service that we provide through
IOS and Android platform.Information you give us: when
you are registering an account. We collect your phone number, your name, date
of birth to create a COASAPP account. Information that you post on your COASAPP
user profile (this might include profile pictures, and other information.
Content that you share with other COASAPP users on the service, including
message, photos, and videos; as well as list of members in your chat groups.
Information you give us during any support and feedback communications via
email, postal mail or chat. Your information usage by us:
to enable you to access and use our service, including to manage user
information and accounts; to contact you to answer requests for customer
support or technical support; to customize your experience, to conduct research
and development to test and introduce new features and products, and to improve
and monitor the effectiveness of the platform’s service; to enforce the
User Terms and Condition; for any other
purposes that we specifically communicate to you when we collect that
information with your consent, or for
which we have a legitimate interest, such as direct marketing, individual or
market research, or anti-fraud protection; and to market our products and
services and to provide you with materials about offers, products, and services
that may be of interest, including new content or services, including tailoring
content, and offers, and to notify you of offers, products and services that
may be of interest to you, for other purposes disclosed at the time that you
provide your information, or otherwise with your permission. Other
things you need to know: We
may remove at our sole discretion, any information that you provide when you
register for or use our service such as usernames, profile pictures, names,
etc. If we believe it is inappropriate or violates any of the policies posted
on our Policies page at https://www.coasap.com policies including but not limited to, our User Terms and Condition. Automatically
information collected: COASAPP automatically collects certain
information that your mobile device sends when you use our mobile application,
like an IP address, user settings, phone model, SIM serial, connection type
(e.g., cellular or WiFi), mobile carrier, push
notification token, and the operating system of your device. If you use
android, we automatically receive the email address associated with your
Android account when you created an account. COASAPP may use personal or
non-identifiable information transmitted to COASAPP to enhance the application,
to develop new tools, for quality improvement and as otherwise described in
this Policy Privacy or in other notices COASAPP offers. If COASAPP app gets
your permission, we collect contact information from your mobile address book
to connect you to your friends that use COASAPP. Third parties that provide
functionality on our Services may collect the same information about your
mobile device that we automatically collect, as described above.If you
choose to use shopping center, renting and bargain to driver Services we
process your payments through a third-party.
Local Information andhow we use it: When you use COASAPP
(app), we may collect and store information about your location by converting
your IP address into a rough geo-location or by accessing your mobile device’s
GPS coordinates or coarse location if you enable location services on your
device to improve and personalize our services to you.Advertisement:
We do NOT and will never display ad banners in our free messaging service. Your Chat History in regular messenger:
We do NOT collect, save and store delivered messages in your COASAPP
chat history. However, any messages, photos, file, and other documents you
shared that’s not delivered will remain in our system for 3 days, including
your photos, voice messages, videos, sticker and missed calls then will be
deleted. If you are a hoster, Seller and
Driver: first we mask your real number to anyone that decides to message you
about accommodations, delivery purpose and items in shopping center. When these
people sends you a message we do not show your real phone number to them for
your extra privacy purpose; messages you receive through this condition is kept
in our platform for four months then it will be deleted. You can permanently
delete your regular COASAPP chat history at any time to allow you to access
your chat history, photos, videos and missed calls for your own purposes. To collect aggregated data from your chat
history we use the data to build language models which help us improve the
Service. We will only collect this data on an aggregated basis, and in a manner
which does not reveal any personally information about any user. International
Transfering: COASAPP is a U.S. Company and stores data in the
United States through our third party hosting service. This data is subject to
U.S. laws. Thus, your information may be transferred to and maintained on
computers located outside of your state, province, country, or other
governmental jurisdiction where the privacy laws may not be as protective as
those in your jurisdiction. If you are located outside the United States and
choose to provide information to us, COASAPP transfers that information to the
United States and processes it there. By using the Service, you understand and
consent that your information may be transferred to the third party hosting
provider with whom we share it as described in this Policy Privacy. If you are
from the European Union or other regions with laws governing data collection
and use, please note that you are agreeing to the transfer of your information
to United States and to processing of your data globally. By providing your
personal information, you consent to any transfer and processing in accordance
with this Policy. Do we share anything? In addition to using your information for the
purposes described above, we may share your information with parties other than
COASAPP (the “Third Parties”). In most cases we may request your permission
before we share that information, but we reserve the right to share your
information with Third Parties without prior notification or without soliciting
additional permission: We discuss below situations where we may share your
information without requesting your further permission: (ia). We may share certain information about you
including information which could be used to personally identify you with the
companies we engage to facilitate our Service, perform Service-related
functions, and/or provide the service on our behalf, such as Internet Service
Providers and hosting service providers. It’s their obligation to maintain your
information in confidence, and they are prohibited from using your information
in any way other than performing services for or on behalf of COASAPP or to
comply with applicable legal requirements. (ib) We may disclose your information (including
information which could be used to personally identify you when we have a good
faith belief that disclosure is necessary: (a) to respond to court orders, or
other compulsory legal process; (b) to protect our property rights and
investigate and defend against legal claims (c) to enforce our Policy Privacy,
the User Terms and Conditions or our other policies or agreements; (d) when we
believe disclosure is necessary to protect others from death or serious matter
; (d) to respond to your requests; and last but not least (e) as otherwise
required by any authorized law. If you
prefer that we do not share your personal information with third-parties you
may opt-out of such sharing at no cost by following the instructions at the
bottom. Transfer your information in the
event that COASAPP is involved in a merger, acquisition, divestiture,
consolidation, reorganization, asset sale, or bankruptcy. Can youopt out of receivingCommunications? You have the
right to opt out of certain uses and disclosures of your personal information.
MobileDevices:
COASAPP can time to time send you notifications in the mobile applications.
It’s your choice to opt-out from receiving these types of communications by
changing the settings on your mobile device. We may also collect location-based
information if you use our mobile applications. You may opt-out of this
collection by changing the settings on your mobile device. Your Account’s Security:
User’s account and registration information are protected so that only you can
access your account. You are advised to never share your pin/password with
other and it is your responsibility to remember it in case you’ve logged out
our platform. While we are committed to implementing reasonable and appropriate
safeguards to protect your information against foreseeable risks, no computer
system or information can ever be fully protected against any possible risk,
and we cannot guarantee that unauthorized third parties will not be able to
circumvent those safeguards, or access and/or use your personal information for
improper purposes. By using the service or providing personal information to
us, you agree that we may communicate with you electronically regarding
security, privacy, and administrative issues relating to your use of the service.
If we learn of a security system’s breach, we may attempt to inform you
electronically by posting a notice in the service or on the COASAPP website. No Tracking Users: We as
(“COASAPP”) promise NOT to track you when you are browsing the internet outside
of the COASAPP services. Your access right: You can edit, update, or deactivate
your account whenever you want. Once and if you delete your chat history even
the undelivered messages will not stay in our system by selecting the Delete
chat history option in the settings of the COASAPP app. You may delete your
entire account, by selecting the “Delete COASAPP account option in settings.
When and if you delete your entire account, we do NOTkeep information about your mobile device. You
may edit your account information through the app. Although COASAPP makes
efforts to provide users with access to their personal information, there may
be circumstances in which COASAPP is unable to provide access, including but
not limited to: (a) where the information contains legal privilege, would
compromise others’ privacy or other legitimate rights, where the burden or
expense of providing access would be inappropriate to the risks of other user’s
privacy in the case in question. If COASAPP believe that access should be
restricted in any particular time, we will provide you with an explanation of
why that action has been made and a contact point for any further questions. To
protect your privacy, COASAPP will take commercially reasonable steps to verify
your identity before granting access to or making any changes to your personal
information. Data KEEP: COASAPP retains the personal information we
receive as described in this Privacy Policy for as long as you use our Services
or as necessary to fulfill the purposes for which it was collected, provide our
services, resolve disputes, establish legal defenses, conduct audits, pursue
legitimate business purposes, enforce our agreements, and comply with authority
applicable laws. Children Privacy: COASAPP is not for children under 13
years of age, and COASAPP do not intentionally collect Personally Information
from children under 13 years of age. If an adult learn that a child has
provided us with personal information without direct supervision, you must
delete your child’s entire account, by selecting the delete COASAPP account. If
we are aware that we have collected any personal information from children
under 13 we will rapidly take steps to delete such information and terminate
the child’s account. Our Policy Privacy Modification:
We have the sole right to edit and/or change our Policy Privacy from time to
time. When we modify we will inform you by posting the revised version on our
website, and will indicate at the top the date of the latest revision. You
should check this Policy Privacy often. Any update will be effective when we
post the updated Policy Privacy. You agree and understand that your continued
access to the platform’s service after any posted update to the Policy Privacy
means your acceptance of such updates. Always reach out: if you have any
questions about this Policy Privacy, you can contact us via business email<EMAIL_ADDRESS>find answers to most of your questions on FAQ.
WHAT
HAPPENS WHEN THERE IS BAD WEATHER THATCAUSES DELIVERY DELAYS? First, follow your local weather condition
notification (heavy rain, heavy storm, driving hazard snow) driver must inform
the client and COASAPP in the platform. Any emergency pertaining to your
vehicle you must inform the client and us right after it happens. If you are
found to give wrong information about things that cause a delay for your
personal benefit your account will be deactivated. If a delivery is delayed due
to manufacturer’s problem then the driver has to notify the client to agree on
a new delivery time; if the delay exceed one hour the client is responsible to
pay the minimum average state hourly delivery service requirement. CLIENTS
DELIVERY CANCELATION: Client
can cancel any delivery at any time before the driver go to the pickup location
with no penalty for that. For an advanced booked delivery the clients has 24
hours to cancel without penalties (you get your money back). If the cancelation
is not made within these 24 hours the client will pay percentage charge of the
agreed amount. If the client cancel after the driver has started driving to the
pickup location the client will pay percentage ofthe price agreed. The client will
not be charged platform fee for every canceled booked delivery. Other
factors that can lead to cancel a booked delivery: client can cancel a booking do to an expected
death in the family. The driver must get back online to wait for another
delivery. Serious illness of
a client or any member of the client’s family. The driver must get back online
to wait for another delivery. Government
related obligations: Court
appearances, travel restrictions, military duty leave, and jury duty.Security matters:Political or
civil situations in the area that the driver is travelling to for a delivery or
that the client is located in.Illness
or epidemic disease: that
abruptly affects a region or a group of people.
Examples: yellow fever, ebola cholera and zika, and if the client is in critical health condition at
the hospital etc… DRIVER DELIVERY CANCELATION: Driver can cancel a booking
do to an expected death in the family. The client must get back online to
bargain with a different driver. Serious
illness of a driver or any member of the driver’s family. The
client must get back online to bargain with a different driver. Government related obligations: Court appearances, travel
restrictions, military duty leave, and jury duty. Any emergency pertaining to
your vehicle you must inform the client right after it happens.Security
matters: Political or civil
situations in the area that the driver is travelling to for a delivery or that
the client is located in.Natural
disasters: any natural disaster situation that prevent the
client or driver to pick and receive the delivery. Examples: flooding, severe
storms, tornados, wildfires, severe winter storms, earthquakes, tsunamis.If
a carbreakdown when driver is on the way to delivery: The driver will be paid by the client per mileage from the pickup location to
the actual damaged vehicle location; driver must provide proof of vehicle’s
malfunction. If the client and the driver agree to wait until the vehicle is
fixed then there will not be any mileage charge to the client. But if the
vehicle needs a serious repair and both
the driver and client agreed to not wait until the repair is done then the
client have to look for a different driver in the platform located near the
item/load to complete the delivery. Driver
Penalties/Rules: We have rules and penalties for a reason. These rules are to protect our
clients and keep them coming back. Happy clients mean more business for
you. Rules: Leave data on throughout the duration of a
delivery (“deal”) as the customer needs to be able to track the order.Log
on and off based on your availability.All packages or itempickup or drop offs:
first the driver must inspect the package/load to make sure there is no damage
and it is the exact number of pieces mentioned by the client. If no damage found
and if they are exact pieces then driver can load the package in the vehicle
then confirm item picked up. When driver arrive to the last designated
destination of the package he or she must confirm item dropped off in the
system. What if the driver finds a damage orincomplete number of itemsthan
what the client mentioned? The driver has to notify the client
before loading the package into the vehicle; the client will decide whether the
driver can deliver it or not. What if the clientchoose not to deliver
the package if found damaged or incomplete? The client will have to
pay the driver 1 USD per mile from the driver’s start point to the place the
item is located. Does theclient have to confirm completeddelivery?
Depending on both the driver and client’s agreement of where the item should be
delivered. If the client agreed to sign off for the item in their agreement in
COASAPP chatting then the client has to confirm received delivery in the
platform, but if the client agree for the item to be left at the door or other
place base on their agreement in the platform then the driver will indicate
exactly where he/she left the package per client’s request while they were
bargaining. What if an item is lost and it confirmed by driver that the package was delivered? All
bargaining must happen in the COASAPP platform, if package is lost and the
agreement to pick up and deliver the package did not happen in COASAPP then we
(“COASAPP”) are NOT liable; if the package is lost after driver confirm
delivery and all agreements happened in COASAPP platform we will conduct an
investigation then we will pay in full value of the package. Communication to
clients is preferred in English but if both client and the driver speak a
different language they can interact within the app to agree on the
delivery.Keep all conversation on a profession manner: be polite and respectful
to your clients.COASAPP Branding is mandatory throughout an entire delivery
service please be clean! Driver maybe required having strapes, dolly, earplugs,
safety glasses, hard hat and/or steal toe boot depending on the load, package
or item that need to be delivered and manufacturing requirements.If the load,
package or item is heavy than what a person can lift then both driver and
client will decide on how the load can either be loaded or unloaded. Message
the client immediately if you are lost; if the client does not respond within
30 minutes please call COASAPP immediately. Driver must confirm to the client a
pick up if the vehicle can carry, handle, fit the load’s capacity ( if by
mistake a driver confirm a delivery request and his/her vehicle cannot handle
or carry the load, package item then the driver will not receive payment. If
the client agreed on a delivery with the driver knowing that the driver’s
vehicle cannot handle the load, package or item then the client will be charged
a percentage of the agreed amount.Inform the client and COASAPP immediately if
you are stopped by the police.Inform COASAPP immediately if you lose/damage the
package.If anything happens inform the client and COASAPP if necessary. Other
Driver Penalties: we
understand that this might sound harsh, but we have a reputation to maintain.
When one driver does something wrong it affect the COASAPP image. So, we take
necessary action to ensure it does not happen again. You (driver) will be
suspended immediately by a notice or call in case of any wrong doing caused by
you during a delivery: If the driver arrives an hour late for an immediate pick
up with no valid reason then the client has the right to cancel without paying
the driver. The client has the choice to look for another driver. If a driver
arrives an hour late for a long term reservation pick up for no valid reason
then the client has the right or can cancel the reservation without being
penalized and client can arrange another driver. If the driver loses or damages
the package due to his/her negligence after COASAPP’s investigation then the
driver is responsible to pay 29% of the value of the items damaged or lost and
COASAPP will cover 71% of the item or package. If the driver lose or damage
package by accident after COASAPP’s investigation then our liability will cover
the entire lost or damaged package or items. ACCEPTED VEHICLES: Any vehicles can be accepted as long as the
inspection agent has approved that vehicle’s inspection. If a vehicle has a
mileage limitation then this driver can only make short distance deliveries. What
is not permitted to be delivered bydriverinCOASAPP platform: (1)
any illegal substance is not permitted to be delivered by the driver in COASAPP
platform. Please check the United States’ prohibited or illegal substances. (2)
Any client or driver that founds to be entitled to do such a thing will be
subjected to the law enforcements and their account will be immediately
cancelled. (3) no fire arm will be delivered, (4) any
hazardous material or substance including flammable liquid, explosive
substances is also not permitted to be delivered in our platform. Driver is not allowed to carry a private
passenger that is not involved in the delivery service. Texting, Making and Receiving calls while
driving: In case of any call
the driver is advised to us the following: blue tooth, headsets. Other than
that the driver has to pull over to respond or to make a phone call and/or send
a text.BEINGUNDER THE INFLUENCE: The driver is restricted to
make a delivery service under the influence of alcohol or any other illegal
drugs. Do not conduct a delivery if you are on medication that can impair your
driving ability. SHOPPING
CENTER: COASAPP Payment system:
Buyer: a buyer may use his/her credit
and debit card to pay for any items purchased on COASAPP platform. An
“Electronic Payment Method”) through COASAPP has integrated with Stripe, a
payment processing service, to allow sellers to accept Electronic Payment Methods from buyers using
Stripe’s payment processing service. To process payments we partnered with a
third party; here are card payment accepted: Visa,
MasterCard, American Express, Discover and Diners Club payments from customers
worldwide; ACH Credit Transfer enables US customers to pay by sending funds
directly to a US bank account. Any payment outside of COASAPP platform
not processed by stripe (third party) will be on the buyer and seller’s own
risk. Sellers: By
using COASAPP Payment system to accept Electronic Payment Methods which is
enabled by a third party payment service.
Seller Fees: You agree to pay the service fees for any
completed sell. As a seller you
understand and agree to the stripe payment terms of transaction service fees. .
ACCEPTABLE OF SERVICE USEVIOLATIONS.
YOUR RIGHT AND/OR ABILITY TO RECEIVE SALES HANDLED TROUGH COASAPP
PAYMENT SYSTEM MAY BE REJECTED, DISABLED OR LIMITED IF THE PURCHASE OR SALE
VIOLATES OF THESE TERMS, INCLUDING FOR SALES THAT VIOLATE THE PROHIBITED ITEMS
REGULATIONS. Final Transactions: Buyer: the item purchase is considered as a final transaction unless: (1) the
item is not as described in the listing.If this happen the buyer can return
back the item to the seller and get back his/her refund. If a buyer decides to use our immediate bargain
with driver delivery service then he/she will pay COASAPP percentage service
fee. If the buyer decides to use any other delivery system that may take more
days then the buyer has 5 days to confirm received items. If you as buyer did
not confirm a completed delivery within 5 days you will be charged a fee per
day. Seller: make sure the item listed is in good condition as described
to the buyers in the platform. If the
item listed is not as described the buyer will return it back unless they
choose not to. If an item is returned by the buyer the seller should not claim
payment from the buyer and you (seller) is subjected to account
discontinued. Taxes for Sellers It is your responsibility to
determine what, if any, taxes apply to each transaction you complete through
the COASAPP payment service, including,
for example, sales, use, value added, and similar taxes. It is also your
responsibility to withhold, collect, report and remit the correct taxes to the
appropriate tax authorities. COASAPP is not responsible for withholding,
collecting, reporting, or remitting any sales, use, value added, or similar tax
arising from any transaction you complete through the COASAPP payment system. Shipping Services:We have two shipping system to make your
delivery go as planned. You may use our short immediate delivery service from
the bargain with drivers or you may also use our long distance shipping service
from the bargain with driver section. You can also choose to use post office
options.We are not responsible for any post delivery service: losses, damages, or delays related to
shipping via post office you agree not to blame COASAPP in that
situation. You agree not to ship any item like: weapons, ammunition,
explosives, living or infectious biological matter, human remains, pornography,
alcohol, prescription drugs, illegal drugs, currency, dangerous goods,
hazardous goods, or other goods that may not be shipped or mailed by law.  Anyone who sends, or causes to be sent, a
prohibited, illegal, or improperly packaged or labeled material can be subject
to legal penalties such as civil penalties, fines and/or imprisonment.Seller: COASAPP allow sellers to
list their items for sale in all 50 states. Both Sellers and buyer will make an
arrangement shipping or delivery of the item in the COASAPP platform. Buyers: From time to time,
Buyers will be able to view items listed nationwide by sellers. For these items,
the buyer is responsible to pay the cost of delivery or shipping of item from
the seller. Buyers can cancel an offer to purchase an item for shipping if the
seller has not confirmed the offer.


MONTHS_BETWEEN
calculates the number of months between two dates. When the two dates have the same day component or are both the last day of the month, then the return value is a whole number. Otherwise, the return value includes a fraction that considers the difference in the days based on a 31-day month.
NUMBER
MONTHS_BETWEEN(date1, date2)
date1
and date2
are datetime expressions. If date1
is later than date2
, then the result is positive. If date1
is earlier than date2
, then the result is negative.
MONTHS_BETWEEN('15-APR-06', '01-JAN-06')
returns the value 3.4516129
.


| symbols {graphics} | R Documentation |
This function draws symbols on a plot. One of six symbols; circles, squares, rectangles, stars, thermometers, and boxplots, can be plotted at a specified set of x and y coordinates. Specific aspects of the symbols, such as relative size, can be customized by additional parameters.
symbols(x, y = NULL, circles, squares, rectangles, stars, thermometers, boxplots, inches = TRUE, add = FALSE, fg = par("col"), bg = NA, xlab = NULL, ylab = NULL, main = NULL, xlim = NULL, ylim = NULL, ...)
x, y |
the x and y co-ordinates for the centres of the symbols.
They can be specified in any way which is accepted by
|
circles |
a vector giving the radii of the circles. |
squares |
a vector giving the length of the sides of the squares. |
rectangles |
a matrix with two columns. The first column gives widths and the second the heights of rectangles. |
stars |
a matrix with three or more columns giving the lengths
of the rays from the center of the stars. |
thermometers |
a matrix with three or four columns. The first
two columns give the width and height of the thermometer symbols. If
there are three columns, the third is taken as a proportion: the
thermometers are filled (using colour |
boxplots |
a matrix with five columns. The first two columns give the width and height of the boxes, the next two columns give the lengths of the lower and upper whiskers and the fifth the proportion (with a warning if not in [0,1]) of the way up the box that the median line is drawn. |
inches |
|
add |
if |
fg |
colour(s) the symbols are to be drawn in. |
bg |
if specified, the symbols are filled with colour(s), the
vector |
xlab |
the x label of the plot if |
ylab |
the y label of the plot. Unused if |
main |
a main title for the plot. Unused if |
xlim |
numeric vector of length 2 giving the x limits for the
plot. Unused if |
ylim |
numeric vector of length 2 giving the y limits for the
plot. Unused if |
... |
graphics parameters can also be passed to this function,
as can the plot aspect ratio |
Observations which have missing coordinates or missing size
parameters are not plotted. The exception to this is stars.
In that case, the length of any ray which is NA
is reset
to zero.
Argument inches
controls the sizes of the symbols. If
TRUE
(the default), the symbols are scaled so that the
largest dimension of any symbol is one inch. If a positive number is
given the symbols are scaled to make largest dimension this size in
inches (so TRUE
and 1
are equivalent). If inches
is FALSE
, the units are taken to be those of the appropriate
axes. (For circles, squares and stars the units of the x axis are
used. For boxplots, the lengths of the whiskers are regarded as
dimensions alongside width and height when scaling by inches
,
and are otherwise interpreted in the units of the y axis.)
Circles of radius zero are plotted at radius one pixel (which is
device-dependent). Circles of a very small non-zero radius may or may
not be visible, and may be smaller than circles of radius zero. On
windows
devices circles are plotted at radius at least one
pixel as some Windows versions omit smaller circles.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
W. S. Cleveland (1985) The Elements of Graphing Data. Monterey, California: Wadsworth.
Murrell, P. (2005) R Graphics. Chapman & Hall/CRC Press.
stars
for drawing stars with a bit more
flexibility.
If you are thinking about doing ‘bubble plots’ by
symbols(*, circles=*)
, you should really consider using
sunflowerplot
instead.
require(stats); require(grDevices) x <- 1:10 y <- sort(10*runif(10)) z <- runif(10) z3 <- cbind(z, 2*runif(10), runif(10)) symbols(x, y, thermometers = cbind(.5, 1, z), inches = .5, fg = 1:10) symbols(x, y, thermometers = z3, inches = FALSE) text(x, y, apply(format(round(z3, digits = 2)), 1, paste, collapse = ","), adj = c(-.2,0), cex = .75, col = "purple", xpd = NA) ## Note that example(trees) shows more sensible plots! N <- nrow(trees) with(trees, { ## Girth is diameter in inches symbols(Height, Volume, circles = Girth/24, inches = FALSE, main = "Trees' Girth") # xlab and ylab automatically ## Colours too: op <- palette(rainbow(N, end = 0.9)) symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N, fg = "gray30", main = "symbols(*, circles = Girth/16, bg = 1:N)") palette(op) })