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Government review to order zero-hours contracts overhaul - BBC News
[campaigners are not happy with the watered down draft] The new "right to request" fixed hours could be used by some of the 900,000 people on zero-hours contracts, a number that has risen from 143,000 in 2008.
The contracts have been attacked for allowing some firms to keep people in insecure work, depress wages and deny people their full employee rights. [...] "Two years ago our staff started to tell us they needed some form of contracted hours because they wanted to get mobile phone contracts, car loans and - as they got older - mortgages to buy houses. [...] companies are using the "self-employment" status of people who work for them to avoid tax liabilities such as national insurance payments.
zero  hour  contract  gig  economy  Zeitarbeit  Leiharbeit  Werkvertrag  exploitation  tax  evasion  avoidance  efficiency  UK  profit  maximisation  workers  rights  union  No  Representation  wage  growth  income  inequality  insecurity  trickle-down  working  poor  Precariat  underemployed  welfare  state  credit  social  mobility 
yesterday by asterisk2a
Why don't we elect MPs by proportional representation? - BBC News
FPTP is no more fit for purpose in a diverse country like UK! two-party system is out-dated /// "Millions of voters feel they are unrepresented in parliament - whether it is because they live in one of hundreds of 'safe seats' across the country, or because they feel forced to 'hold their nose' and opt for a 'lesser evil' rather than the party they truly support." // rule by majority is not democratic! especially in environment where corporate lobby w vested interest can water down anything at will! see sugar tax, climate change, air pollution etc. // and not to forget the right win media lobby landscape - owning 70% of newspapers.
GE2017  FPTP  electoral  reform  UK  No  Representation  lobby  vested  interest  revolving  door  Capitalism  media  landscape  Generationengerechtigkeit  sustainability 
7 days ago by asterisk2a
Jane Austen And The Persistent Failure Of The White Imagination
At the heart of these adaptations — and their inability to capture the ways that Austen’s writings could easily reflect the lived reality of a diverse spectrum of modern Austen fans — lies a failure of the white imagination. When institutions from primary school onward amplify white-centered stories and histories as the only “great” art, it becomes easier to imagine zombies in an Austen landscape before people of color can be inserted therein. When non-white voices and stories are erased — or, worse, in their rare depictions, consistently presented as less than, negative, or one-dimensional — white people are rendered incapable of imagining people of color as fully human, complex, and equal to themselves, living lives just as rich as (if not richer than) the white experience.
{meta}  &jane.austen  racism  social.justice  representation 
8 days ago by talibusorabat
Internationaler Währungsfonds: IWF fordert höhere Vermögensabgaben in Deutschland - SPIEGEL ONLINE
Überraschende Wahlkampfhilfe für SPD-Kanzlerkandidat Schulz: Der IWF will von Deutschland laut einem Zeitungsbericht höhere Steuern auf Eigentum fordern. Untere Einkommen würden dagegen zu stark belastet. // System is Rigged in UK - https://www.youtube.com/watch?v=jujJ-QoBiFY
IMF  GE2017  Super  Rich  1%  Plutocracy  Oligarchy  lobby  revolving  door  Germany  UK  USA  inequality  social  mobility  income  gini  Coefficient  tax  evasion  avoidance  Capital  Picketty  Kapital  no  representation  Brexit  GFC  bank  bailout  recovery  trickle-down 
14 days ago by asterisk2a
[1609.07061] Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing the parameter gradients. During the forward pass, QNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations. As a result, power consumption is expected to be drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to their 32-bit counterparts. For example, our quantized version of AlexNet with 1-bit weights and 2-bit activations achieves 51% top-1 accuracy. Moreover, we quantize the parameter gradients to 6-bits as well which enables gradients computation using only bit-wise operation. Quantized recurrent neural networks were tested over the Penn Treebank dataset, and achieved comparable accuracy as their 32-bit counterparts using only 4-bits. Last but not least, we programmed a binary matrix multiplication GPU kernel with which it is possible to run our MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The QNN code is available online.
representation  neural-networks  deep-learning  rather-interesting  to-write-about  consider:simple-examples 
15 days ago by Vaguery
[1702.08359] Dynamic Word Embeddings via Skip-Gram Filtering
We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec [Mikolov, 2013]. These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms---skip-gram smoothing and skip-gram filtering---that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.
natural-language-processing  machine-learning  representation  to-understand  skip-grams  consider:looking-to-see  to-write-about 
15 days ago by Vaguery
[1703.01141] Dynamic State Warping
The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping and returns a distance value. DTW shows superior ability in many applications, e.g. video, image, etc. However, in DTW, two points are paired essentially based on point-to-point Euclidean distance (ED) without considering the autocorrelation of sequences. Thus, points with different semantic meanings, e.g. peaks and valleys, may be matched providing their coordinate values are similar. As a result, DTW is sensitive to noise and poorly interpretable. This paper proposes an efficient and flexible sequence alignment algorithm, dynamic state warping (DSW). DSW converts each time point into a latent state, which endows point-wise autocorrelation information. Alignment is performed by using the state sequences. Thus DSW is able to yield alignment that is semantically more interpretable than that of DTW. Using one nearest neighbor classifier, DSW shows significant improvement on classification accuracy in comparison to ED (70/85 wins) and DTW (74/85 wins). We also empirically demonstrate that DSW is more robust and scales better to long sequences than ED and DTW.
time-series  time-warping  representation  to-understand  algorithms  machine-learning  feature-construction 
15 days ago by Vaguery
[1610.09787] Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make inferences about the model given data, and criticize the model's fit to the data. Edward supports a broad class of probabilistic models, efficient algorithms for inference, and many techniques for model criticism. The library builds on top of TensorFlow to support distributed training and hardware such as GPUs. Edward enables the development of complex probabilistic models and their algorithms at a massive scale.
machine-learning  framework  rather-interesting  representation  probabilistic-languages 
15 days ago by Vaguery
[1703.08052] Dynamic Bernoulli Embeddings for Language Evolution
Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of words change over time. We use dynamic embeddings to analyze three large collections of historical texts: the U.S. Senate speeches from 1858 to 2009, the history of computer science ACM abstracts from 1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We find dynamic embeddings provide better fits than classical embeddings and capture interesting patterns about how language changes.
natural-language-processing  representation  rather-interesting  to-write-about  nudge-targets  consider:representation 
15 days ago by Vaguery

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