Miracle-dz/UniFAR-FTUs huggingface dataset:
Stable rank of $\mathrm{C}(X)\rtimes\Gamma$

It is shown that, for an arbitrary free and minimal $\mathbb Z^n$-action on a compact Hausdorff space $X$, the crossed product C*-algebra $\mathrm{C}(X)\rtimes\mathbb Z^n$ always has stable rank one, i.e., invertible elements are dense. This generalizes a result of Alboiu and Lutley on $\mathbb Z$-actions. In fact, for any free and minimal topological dynamical system $(X, \Gamma)$, where $\Gamma$ is a countable discrete amenable group, if it has the uniform Rokhlin property and Cuntz comparison of open sets, then the crossed product C*-algebra $\mathrm{C}(X)\rtimes\Gamma$ has stable rank one. Moreover, in this case, the C*-algebra $\mathrm{C}(X)\rtimes\Gamma$ absorbs the Jiang-Su algebra tensorially if, and only if, it has strict comparison of positive elements.
Essential graded algebra over polynomial rings with real exponents

The geometric and algebraic theory of monomial ideals and multigraded modules is initiated over real-exponent polynomial rings and, more generally, monoid algebras for real polyhedral cones. The main results include the generalization of Nakayama's lemma; complete theories of minimal and dense primary, secondary, and irreducible decomposition, including associated and attached faces; socles and tops; minimality and density for downset hulls, upset covers, and fringe presentations; Matlis duality; and geometric analysis of staircases. Modules that are semialgebraic or piecewise-linear (PL) have the relevant property preserved by functorial constructions as well as by minimal primary and secondary decompositions. And when the modules in question are subquotients of the group itself, such as monomial ideals and quotients modulo them, minimal primary and secondary decompositions are canonical, as are irreducible decompositions up to the new real-exponent notion of density.
Insider Ownership and Dividend Payout Policy: The Role of Business Cycle

We investigate how the relationship between managerial stock incentives and the dividend payout policy is impacted by the business cycle by using the data of S&P 1500 companies during 2000-2018. We find a strong negative relationship between managerial stock options and annual dividend payouts of companies for the full sample. Although the direction of the relationship is also negative for the recession period, the coefficient is found to be insignificant. We also find that the mentioned relationship may vary during the recession depending on the size of the company. The impact of stock options on the dividend payout is negative for medium-sized companies and the coefficient is both economically and statistically significant. The direction of impact changes for large-cap companies indicating to deterioration of the CEO voting power in those companies and less agency problem. We also determine that the percentage of shares held by the CEO has a positive impact on annual dividends distributed for large-cap companies, whereas this relationship changes in times of recession.
SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!

Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes- with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes,overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.
A family of finite element Stokes complexes in three dimensions

We construct finite element Stokes complexes on tetrahedral meshes in three-dimensional space. In the lowest order case, the finite elements in the complex have 4, 18, 16, and 1 degrees of freedom, respectively. As a consequence, we obtain gradcurl-conforming finite elements and inf-sup stable Stokes pairs on tetrahedra which fit into complexes. We show that the new elements lead to convergent algorithms for solving a gradcurl model problem as well as solving the Stokes system with precise divergence-free condition. We demonstrate the validity of the algorithms by numerical experiments.
An Early-warning System for Electromagnetic Follow-up of Gravitational-wave Events

Binary neutron stars (BNSs) will spend ≃10–15 minutes in the band of Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo detectors at design sensitivity. Matched-filtering of gravitational-wave (GW) data could in principle accumulate enough signal-to-noise ratio (S/N) to identify a forthcoming event tens of seconds before the companions collide and merge. Here we report on the design and testing of an early-warning GW detection pipeline. Early-warning alerts can be produced for sources that are at low enough redshift so that a large enough S/N accumulates ∼10–60 s before merger. We find that about 7% (49%) of the total detectable BNS mergers will be detected 60 s (10 s) before the merger. About 2% of the total detectable BNS mergers will be detected before merger and localized to within 100 deg2 (90% credible interval). Coordinated observing by several wide-field telescopes could capture the event seconds before or after the merger. LIGO–Virgo detectors at design sensitivity could facilitate observing at least one event at the onset of merger.
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition

Speech synthesis (text to speech, TTS) and recognition (automatic speech recognition, ASR) are important speech tasks, and require a large amount of text and speech pairs for model training. However, there are more than 6,000 languages in the world and most languages are lack of speech training data, which poses significant challenges when building TTS and ASR systems for extremely low-resource languages. In this paper, we develop LRSpeech, a TTS and ASR system under the extremely low-resource setting, which can support rare languages with low data cost. LRSpeech consists of three key techniques: 1) pre-training on rich-resource languages and fine-tuning on low-resource languages; 2) dual transformation between TTS and ASR to iteratively boost the accuracy of each other; 3) knowledge distillation to customize the TTS model on a high-quality target-speaker voice and improve the ASR model on multiple voices. We conduct experiments on an experimental language (English) and a truly low-resource language (Lithuanian) to verify the effectiveness of LRSpeech. Experimental results show that LRSpeech 1) achieves high quality for TTS in terms of both intelligibility (more than $98%$ intelligibility rate) and naturalness (above 3.5 mean opinion score (MOS)) of the synthesized speech, which satisfy the requirements for industrial deployment, 2) achieves promising recognition accuracy for ASR, and 3) last but not least, uses extremely low-resource training data. We also conduct comprehensive analyses on LRSpeech with different amounts of data resources, and provide valuable insights and guidances for industrial deployment. We are currently deploying LRSpeech into a commercialized cloud speech service to support TTS on more rare languages.
Modeling the past and future activity of the Halleyid meteor showers

Context.We present a new numerical model of theη-Aquariid and Orionid meteor showers.Aims. The model investigates the origin, variability, and age of theη-Aquariid and Orionid apparitions from 1985 to the present day in order to forecast their activity over the next several decades.Methods. Through the numerical integration of millions of simulated meteoroids and a custom-made particle weighting scheme, we model the characteristics of everyη-Aquariid and Orionid apparition between 1985 and 2050. The modeled showers are calibrated using 35 yr of meteor observations, including the shower activity profiles and interannual variability.Results. Our model reproduces the general characteristics of the present-dayη-Aquariids and part of the Orionid activity. Simulations suggest that the age of theη-Aquariids somewhat exceeds 5000 yr, while a greater fraction of the Orionids is composed of older material. The 1:6 mean motion resonance with Jupiter plays a major role in generating some (but not all) Halleyid stream outbursts. We find consistent evidence for a periodicity of 11.8 yr in both the observations and modeled maximum meteor rates for the Orionids. Weaker evidence of a 10.7 yr period in the peak activity for theη-Aquariids needs to be investigated with future meteor observations. The extension of our model to future years predicts no significant Orionid outbursts through 2050 and four significantη-Aquariid outbursts, in 2023, 2024, 2045, and 2046.
The perceptions of social and information privacy risks of Inflammatory Bowel Disease patients using social media platforms for health-related support

With hundreds of thousands of individuals using social media to discuss health concerns, sensitive personal data is self-disclosed on these platforms every day. Previous research indicates an understanding of social privacy concerns by patients with chronic illnesses, but there is a lack of understanding in the perception of information privacy concerns. Qualitative interviews were conducted with 38 patients with inflammatory bowel disease (IBD) using social media platforms to engage with online communities. Using thematic analysis, we identified that patients typically demonstrate the privacy and risk dual calculus for perceived social privacy concerns. Patients demonstrate mixed knowledge of what data is collected and how it is used by social media platforms and often described their platform use as a trade-off between the unknown information privacy risks and the therapeutic affordances of engaging with the online community (the privacy calculus). Our findings illustrate the different levels of understanding between social and information privacy and the impacts on how individuals take agency over their personal data. We conclude with the suggestion for future research to further understand the relationship between knowledge, information privacy concerns and mitigating actions in the online health community context.
Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning

Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands. These networks are appealing because they replace laborious hand engineered building blocks but their black-box nature makes them difficult to delve in case of failure. Recent works have shown the importance of using an explicit intermediate representation that has the benefits of increasing both the interpretability and the accuracy of networks’ decisions. Nonetheless, these camera-based networks reason in camera view where scale is not homogeneous and hence not directly suitable for motion forecasting. In this paper, we introduce a novel monocular camera-only holistic end-to-end trajectory planning network with a Bird-Eye-View (BEV) intermediate representation that comes in the form of binary Occupancy Grid Maps (OGMs). To ease the prediction of OGMs in BEV from camera images, we introduce a novel scheme where the OGMs are first predicted as semantic masks in camera view and then warped in BEV using the homography between the two planes. The key element allowing this transformation to be applied to 3D objects such as vehicles, consists in predicting solely their footprint in camera-view, hence respecting the flat world hypothesis implied by the homography.
TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices

Advances in deep learning have led to state-of-the-art performance across a multitude of speech recognition tasks. Nevertheless, the widespread deployment of deep neural networks for on-device speech recognition remains a challenge, particularly in edge scenarios where the memory and computing resources are highly constrained (e.g., low-power embedded devices) or where the memory and computing budget dedicated to speech recognition is low (e.g., mobile devices performing numerous tasks besides speech recognition). In this study, we introduce the concept of attention condensers for building low-footprint, highly-efficient deep neural networks for on-device speech recognition on the edge. An attention condenser is a self-attention mechanism that learns and produces a condensed embedding characterizing joint local and cross-channel activation relationships, and performs selective attention accordingly. To illustrate its efficacy, we introduce TinySpeech, low-precision deep neural networks comprising largely of attention condensers tailored for on-device speech recognition using a machine-driven design exploration strategy, with one tailored specifically with microcontroller operation constraints. Experimental results on the Google Speech Commands benchmark dataset for limited-vocabulary speech recognition showed that TinySpeech networks achieved significantly lower architectural complexity (as much as $507\times$ fewer parameters), lower computational complexity (as much as $48\times$ fewer multiply-add operations), and lower storage requirements (as much as $2028\times$ lower weight memory requirements) when compared to previous work. These results not only demonstrate the efficacy of attention condensers for building highly efficient networks for on-device speech recognition, but also illuminate its potential for accelerating deep learning on the edge and empowering TinyML applications.
Corporate governance and firms financial performance in the United Kingdom

The objective of this study is to examine empirically the impact of good corporate governance on financial performance of United Kingdom non-financial listed firms. Agency theory and stewardship theory serve as the bases of a conceptual model. Five corporate governance mechanisms are examined on two financial performance indicators, return on assets (ROA) and Tobin's Q, employing cross-sectional regression methodology. The conclusion drawn from empirical test so performed on 252 firms listed on London Stock Exchange for the year 2014 indicates a positive or a negative relationship, but also sometimes no effect, of corporate governance mechanisms impact on financial performance. The implications are discussed. Thereby, so distinguishing effects due to causes, we present a proof that, when the right corporate governance mechanisms are chosen, the finances of a firm can be improved. The results of this research should have some implication on academia and policy makers thoughts.
A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets

In the current network-based computing world, where the number of interconnected devices grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasing at the same rate. To guarantee the correct functioning and performance of novel environments such as Smart Cities, Industry 4.0, or crowdsensing, it is crucial to identify the capabilities of their devices (e.g., sensors, actuators) and detect potential misbehavior that may arise due to cyberattacks, system faults, or misconfigurations. With this goal in mind, a promising research field emerged focusing on creating and managing fingerprints that model the behavior of both the device actions and its components. The article at hand studies the recent growth of the device behavior fingerprinting field in terms of application scenarios, behavioral sources, and processing and evaluation techniques. First, it performs a comprehensive review of the device types, behavioral data, and processing and evaluation techniques used by the most recent and representative research works dealing with two major scenarios: device identification and device misbehavior detection. After that, each work is deeply analyzed and compared, emphasizing its characteristics, advantages, and limitations. This article also provides researchers with a review of the most relevant characteristics of existing datasets as most of the novel processing techniques are based on Machine Learning and Deep Learning. Finally, it studies the evolution of these two scenarios in recent years, providing lessons learned, current trends, and future research challenges to guide new solutions in the area.
Bio-efficacy of Some New Herbicides against Weedin Transplanted Rice (Oryza sativa L.)

Pyrazosulfuron-ethyl at 25 g ha−1 applied 10 days after transplanting (DAT), tank mixed (3 DAT) or sequential application of butachlor 938 g ha−1 (3 DAT) and Almix at 4 g ha−1 (25 DAT) were identified as promising alternatives to the commonly used herbicide butachlor at 1:5 kg ha−1.
Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation

Binary Convolutional Neural Networks (CNNs) can significantly reduce the number of arithmetic operations and the size of memory storage, which makes the deployment of CNNs on mobile or embedded systems more promising. However, the accuracy degradation of single and multiple binary CNNs is unacceptable for modern architectures and large scale datasets like ImageNet. In this paper, we proposed a Piecewise Approximation (PA) scheme for multiple binary CNNs which lessens accuracy loss by approximating full precision weights and activations efficiently and maintains parallelism of bitwise operations to guarantee efficiency. Unlike previous approaches, the proposed PA scheme segments piece-wisely the full precision weights and activations, and approximates each piece with a scaling coefficient. Our implementation on ResNet with different depths on ImageNet can reduce both Top-1 and Top-5 classification accuracy gap compared with full precision to approximately 1.0%. Benefited from the binarization of the downsampling layer, our proposed PA-ResNet50 requires less memory usage and two times Flops than single binary CNNs with 4 weights and 5 activations bases. The PA scheme can also generalize to other architectures like DenseNet and MobileNet with similar approximation power as ResNet which is promising for other tasks using binary convolutions. The code and pretrained models will be publicly available.
Covariate Balancing by Uniform Transformer

In observational studies, it is important to balance covariates in different treatment groups in order to estimate treatment effects. One of the most commonly used methods for such purpose is the weighting method. The performance quality of this method usually depends on either the correct model specification for the propensity score or strong regularity conditions for the underlying model, which might not hold in practice. In this paper, we introduce a new robust and computationally efficient framework of weighting methods for covariate balancing, which allows us to conduct model-free inferences for the sake of robustness and integrate an extra `unlabeled' data set if available. Unlike existing methods, the new framework reduces the weights construction problem to a classical density estimation problem by applying a data-driven transformation to the observed covariates. We characterize the theoretical properties of the new estimators of average treatment effect under a nonparametric setting and show that they are able to work robustly under low regularity conditions. The new framework is also applied to several numerical examples using both simulated and real datasets to demonstrate its practical merits.
The clustering of gamma-ray bursts in the Hercules–Corona Borealis Great Wall: the largest structure in the Universe?

The Hercules-Corona Borealis Great Wall is a statistically significant clustering of gamma-ray bursts around redshift 2. Motivated by recent theoretical results indicating that a maximal Universal structure size may indeed coincide with its estimated size (2-3Gpc), we reexamine the question of this Great Wall's existence from both observational and theoretical perspectives. Our statistical analyses confirm the clustering's presence in the most reliable data set currently available, and we present a video showing what this data set looks like in~3D. Cosmological explanations (i.e. having to do with the distribution of gravitating matter) and astrophysical explanations (i.e. having to do with the rate of star formation over cosmic time and space) regarding the origin of such a structure are presented and briefly discussed and the role of observational bias is also discussed at length. This, together with the scientific importance of using gamma-ray bursts as unique cosmological probes, emphasises the need for future missions such as the THESEUS satellite which will provide us with unprecedentedly homogeneous data of gamma-ray bursts with measured redshifts. We conclude from all this that the Hercules-Corona Borealis Great Wall may indeed be the largest structure in the Universe - but to be able to decide conclusively whether it actually exists, we need THESEUS.
Does BERT Solve Commonsense Task via Commonsense Knowledge?

The success of pre-trained contextualized language models such as BERT motivates a line of work that investigates linguistic knowledge inside such models in order to explain the huge improvement in downstream tasks. While previous work shows syntactic, semantic and word sense knowledge in BERT, little work has been done on investigating how BERT solves CommonsenseQA tasks. In particular, it is an interesting research question whether BERT relies on shallow syntactic patterns or deeper commonsense knowledge for disambiguation. We propose two attention-based methods to analyze commonsense knowledge inside BERT, and the contribution of such knowledge for the model prediction. We find that attention heads successfully capture the structured commonsense knowledge encoded in ConceptNet, which helps BERT solve commonsense tasks directly. Fine-tuning further makes BERT learn to use the commonsense knowledge on higher layers.
Circular law for random block band matrices with genuinely sublinear bandwidth

We prove the circular law for a class of non-Hermitian random block band matrices with genuinely sublinear bandwidth. Namely, we show there exists $\tau \in (0,1)$ so that if the bandwidth of the matrix $X$ is at least $n^{1-\tau}$ and the nonzero entries are iid random variables with mean zero and slightly more than four finite moments, then the limiting empirical eigenvalue distribution of $X$, when properly normalized, converges in probability to the uniform distribution on the unit disk in the complex plane. The key technical result is a least singular value bound for shifted random band block matrices with genuinely sublinear bandwidth, which improves on a result of Cook in the band matrix setting \cite{cook2018lower}.