The U.S. Supreme Court declined to take up a case that held the potential to deal a further blow to public-employee unions after last year's "Janus" decision.

DOJ-CLASI agreement covers most disputes with community owners Attorney General Kathy Jennings announced Monday that manufactured home owners in disputes with their community owners will be eligible for free legal representation by the non-profit Delaware Community Legal Aid Society, Inc. (CLASI), under a contract it entered with the Delaware Department of Justice. “Constituents in all three counties have […]

[s.l.] : Universitaire Pers Leuven, 2023.

New York; Oxford : Berghahn Books, [2021]

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There is a need for women-centric perspectives which would pave the way for greater participation of women in the judiciary

Wiesbaden : Vieweg, 2007

Boston : Birkhäuser, [2005]

Providence, Rhode Island : American Mathematical Society, [2021]

Lewis Library - ML410.L7 K54 2019

The Court of Appeal has ruled that doctors and dentists employed by NHS bodies in England are entitled to legal representation at disciplinary hearings. The appeal court’s ruling suggests that employees of other public sector organisations sho...

The Supreme Court has overturned decisions of the Court of Appeal and High Court that a music assistant at a primary school should have been allowed legal representation at an internal disciplinary hearing. This decision curtails the scope for empl...

A review from Alberta's Occupational Health and Safety has found that Cargill did not attempt to engage worker representation as it investigated the circumstances that led to the largest COVID-19 outbreak linked to a single facility in Canada.

The section of the Bilbao Crystallographic Server (http://www.cryst.ehu.es) dedicated to subperiodic groups includes a new tool called LSITESYM for the study of materials with layer and multilayer symmetry. This new program, based on the site-symmetry approach, establishes the symmetry relations between localized and extended crystal states using representations of layer groups. The efficiency and utility of the program LSITESYM is demonstrated by illustrative examples, which include the analysis of phonon symmetry in Aurivillius compounds and in van der Waals layered crystals MoS2 and WS2.

A new report from the National Academies of Sciences, Engineering, and Medicine urges systemic action to change the culture in STEMM (science, technology, engineering, mathematics, and medicine) to address the underrepresentation of women in these fields.

A map of science could assist research planning strategies by helping to identify emerging topics. The map — which is based on links to almost 20 million scientific articles that have been published over the past 16 years — clusters and links scientific disciplines by citation-based relationships and serves as a highly detailed and scalable infographic. The authors hope it will be used by research planners to help distinguish — and potentially forecast — the research areas in science which have longevity, and also those which are innovative.

Personal Injury Lawyers Needed

Brady, Brannon and Rich Talent Agency is proud to have Steve Tyler join our commercial acting roster," says Stuart K. Robinson, Chief Executive Officer/Partner Brady, Brannon & Rich/Bx2 Entertainment.

The Securities and Exchange Commission today filed settled charges against registered broker-dealer Bloomberg Tradebook LLC for making material misrepresentations and omitting material facts about how the firm handled certain customer trade orders. The…

We introduce an infinite-dimensional $p$-adic affine group and construct its irreducible unitary representation. Our approach follows the one used by Vershik, Gelfand and Graev for the diffeomorphism group, but with modifications made necessary by the fact that the group does not act on the phase space. However it is possible to define its action on some classes of functions.

The Gabriel-Roiter measure is used to give an alternative proof of the finiteness of the representation dimension for Artin algebras, a result established by Iyama in 2002. The concept of Gabriel-Roiter measure can be extended to abelian length categories and every such category has multiple Gabriel-Roiter measures. Using this notion, we prove the following broader statement: given any object $X$ and any Gabriel-Roiter measure $mu$ in an abelian length category $mathcal{A}$, there exists an object $X'$ which depends on $X$ and $mu$, such that $Gamma = operatorname{End}_{mathcal{A}}(X oplus X')$ has finite global dimension. Analogously to Iyama's original results, our construction yields quasihereditary rings and fits into the theory of rejective chains.

Bernstein, Frenkel, and Khovanov have constructed a categorification of tensor products of the standard representation of $mathfrak{sl}_2$, where they use singular blocks of category $mathcal{O}$ for $mathfrak{sl}_n$ and translation functors. Here we construct a positive characteristic analogue using blocks of representations of $mathfrak{sl}_n$ over a field $ extbf{k}$ of characteristic $p$ with zero Frobenius character, and singular Harish-Chandra character. We show that the aforementioned categorification admits a Koszul graded lift, which is equivalent to a geometric categorification constructed by Cautis, Kamnitzer, and Licata using coherent sheaves on cotangent bundles to Grassmanians. In particular, the latter admits an abelian refinement. With respect to this abelian refinement, the stratified Mukai flop induces a perverse equivalence on the derived categories for complementary Grassmanians. This is part of a larger project to give a combinatorial approach to Lusztig's conjectures for representations of Lie algebras in positive characteristic.

We prove that there are no irreducible representations of $B_n$ of dimension $n+1$ for $ngeq 10.$

Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. However, in cross-lingual scenarios, e.g. machine translation, the PEs of source and target sentences are modeled independently. Due to word order divergences in different languages, modeling the cross-lingual positional relationships might help SANs tackle this problem. In this paper, we augment SANs with emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence. Specifically, we utilize bracketing transduction grammar (BTG)-based reordering information to encourage SANs to learn bilingual diagonal alignments. Experimental results on WMT'14 English$Rightarrow$German, WAT'17 Japanese$Rightarrow$English, and WMT'17 Chinese$Leftrightarrow$English translation tasks demonstrate that our approach significantly and consistently improves translation quality over strong baselines. Extensive analyses confirm that the performance gains come from the cross-lingual information.

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.

Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: learning to differentiate between two augmented versions of the same image and a large batch of unrelated images. Networks learn to ignore the augmentation noise and extract semantically meaningful representations. Prior work uses artificial data augmentation techniques such as cropping, and color jitter which can only affect the image in superficial ways and are not aligned with how objects actually change e.g. occlusion, deformation, viewpoint change. In this paper, we argue that videos offer this natural augmentation for free. Videos can provide entirely new views of objects, show deformation, and even connect semantically similar but visually distinct concepts. We propose Video Noise Contrastive Estimation, a method for using unlabeled video to learn strong, transferable single image representations. We demonstrate improvements over recent unsupervised single image techniques, as well as over fully supervised ImageNet pretraining, across a variety of temporal and non-temporal tasks. Code and the Random Related Video Views dataset are available at https://www.github.com/danielgordon10/vince

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The saliency and separability of the hidden representations when trained on MNIST dataset is studied using an external classifier, and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.

A time warp contour calculator for use in an audio signal decoder receives an encoded warp ratio information, derives a sequence of warp ratio values from the encoded warp ratio information, and obtains warp contour node values starting from a time warp contour start value. Ratios between the time warp contour node values and the time warp contour starting value are determined by the warp ratio values. The time warp contour calculator computes a time warp contour node value of a given time warp contour node, on the basis of a product-formation having a ratio between the time warp contour node values of the intermediate time warp contour node and the time warp contour starting value and a ratio between the time warp contour node values of the given time warp contour node and of the intermediate time warp contour node as factors.

Various embodiments present a combined visual entity that represents overlapping visual entities. The combined visual entity can include a primary visualization that represents one of the overlapping visual entities and annotations that represent others of the overlapping visual entities. For example, a map view can include multiple geographical entities that overlap. A primary visualization can be rendered that represents one of the multiple geographical entities. The primary visualization can be visually annotated (e.g., with symbols, letters, or other visual indicators) to indicate others of the multiple geographical entities. In some embodiments, a zoom operation can cause visual entities to be added and/or removed from the combined visual entity.

A tridimensional modeling apparatus, system and kit is for representing an exploration network. The apparatus, system and kit include a transparent hollow cube with six plane surfaces for representing an enclosed volume, a plurality of perforations on at least two of the six plane surfaces and indicia around each opening for marking polar coordinates and orientation. The apparatus, system and kit further include a plurality of transparent rods for representing exploration channels. The plurality of perforations on the cube are arranged for receiving rods for tridimensional modeling of the exploration network and each rod is inserted into an opening with an angle and a depth, thereby resulting in a visual representation of the exploration network within the represented volume.

In general, this disclosure describes techniques for changing a sampling frequency of a digital signal. In particular, the techniques provide a more accurate way to determining a relative timing between a desired output sample and a corresponding input sample using a non-approximated integer representation of the relative timing. The relative timing between the desired output sample and corresponding input sample may be represented using a first component that identifies a latest input sample of the digital signal used to generate intermediate samples, a second component that identifies an intermediate sample, and a third component that identifies a timing difference between the desired output sample and the intermediate sample. Each of the components may be recursively updated using non-approximated integer values.

Various embodiments present a combined visual entity that represents overlapping visual entities. The combined visual entity can include a primary visualization that represents one of the overlapping visual entities and annotations that represent others of the overlapping visual entities. For example, a map view can include multiple geographical entities that overlap. A primary visualization can be rendered that represents one of the multiple geographical entities. The primary visualization can be visually annotated (e.g., with symbols, letters, or other visual indicators) to indicate others of the multiple geographical entities. In some embodiments, a zoom operation can cause visual entities to be added and/or removed from the combined visual entity.

The general field of the invention is that of methods of three-dimensional graphical representation of a landing runway on a viewing screen of an onboard viewing system for aircraft, the said graphical representation being displayed in a synthetic view of an exterior landscape. The graphical representation according to the invention comprises a line disposed along the axis of the runway, the said line being a straight stripe inclined by an angle of between one and a few degrees with respect to the horizontal, the said line beginning at the runway threshold.

A circuit and a method for converting an analog signal to a digital value representation is disclosed. In one aspect, the circuit includes an incremental sigma-delta analog-to-digital converter (ADC). The circuit further includes a first input line for providing a primary analog signal representing a sensor measurement to the incremental sigma-delta ADC. The circuit further includes a second input line for providing a secondary analog signal to the incremental sigma-delta ADC. The incremental sigma-delta ADC receives the primary and secondary analog signals during a first period (TADC1) and a second period (TADC2), respectively. The circuit further includes a filter configured to weight the digital values in a sequence of digital values output by the incremental sigma-delta ADC, and to output a single digital value representing the sensor measurement.

3 March 2020

To the extent that perceptions of a crisis in liberal democracy in Europe can be confirmed, this paper investigates the nature of the problem and its causes, and asks what part, if any, digital technology plays in it.

V. V. Vlasov and N. A. Rautian
Trans. Moscow Math. Soc. 80 (2020), 169-188.
Abstract, references and article information

K. A. Mirzoev and T. A. Safonova
Trans. Moscow Math. Soc. 80 (2020), 133-151.
Abstract, references and article information

Hans Wenzl
Trans. Amer. Math. Soc. 373 (2020), 3295-3322.
Abstract, references and article information

M. V. Pratsiovytyi and S. P. Ratushniak
Theor. Probability and Math. Statist. 99 (2020), 211-228.
Abstract, references and article information