Miracle-dz/UniFAR-FTUs huggingface dataset:
SIGTYP 2020 Shared Task: Prediction of Typological Features

Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world’s languages. They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing. A major drawback hampering broader adoption of typological KBs is that they are sparsely populated, in the sense that most languages only have annotations for some features, and skewed, in that few features have wide coverage. As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs, which is also the focus of this shared task. Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations. However, our error analysis reveals that even the strongest submitted systems struggle with predicting feature values for languages where few features are known.
On the Guaranteed Almost Equivalence Between Imitation Learning From Observation and Demonstration

Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) because of the nonnecessity of expert actions when reconstructing the expert policy from the expert data. However, previous studies imply that the performance of LfO is inferior to LfD by a tremendous gap, which makes it challenging to employ LfO in practice. By contrast, this article proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness. In the deterministic robot environment, from the perspective of the control theory, we show that the inverse dynamics disagreement between LfO and LfD approaches zero, meaning that LfO is almost equivalent to LfD. To further relax the deterministic constraint and better adapt to the practical environment, we consider bounded randomness in the robot environment and prove that the optimizing targets for both LfD and LfO remain almost the same in the more generalized setting. Extensive experiments for multiple robot tasks are conducted to demonstrate that LfO achieves comparable performance to LfD empirically. In fact, the most common robot systems in reality are the robot environment with bounded randomness (i.e., the environment this article considered). Hence, our findings greatly extend the potential of LfO and suggest that we can safely apply LfO in practice without sacrificing the performance compared to LfD.
AAMDRL: Augmented Asset Management With Deep Reinforcement Learning

Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how Deep Reinforcement Learning (DRL) can tackle this challenge. Our contributions are threefold: (i) the use of contextual information also referred to as augmented state in DRL, (ii) the impact of a one period lag between observations and actions that is more realistic for an asset management environment , (iii) the implementation of a new repetitive train test method called walk forward analysis, similar in spirit to cross validation for time series. Although our experiment is on trading bots, it can easily be translated to other bot environments that operate in sequential environment with regime changes and noisy data. Our experiment for an augmented asset manager interested in finding the best portfolio for hedging strategies shows that AAMDRL achieves superior returns and lower risk.
FPRaker: A Processing Element For Accelerating Neural Network Training

We present FPRaker, a processing element for composing training accelerators. FPRaker processes several floating-point multiply-accumulation operations concurrently and accumulates their result into a higher precision accumulator. FPRaker boosts performance and energy efficiency during training by taking advantage of the values that naturally appear during training. It processes the significand of the operands of each multiply-accumulate as a series of signed powers of two. The conversion to this form is done on-the-fly. This exposes ineffectual work that can be skipped: values when encoded have few terms and some of them can be discarded as they would fall outside the range of the accumulator given the limited precision of floating-point. FPRaker also takes advantage of spatial correlation in values across channels and uses delta-encoding off-chip to reduce memory footprint and bandwidth. We demonstrate that FPRaker can be used to compose an accelerator for training and that it can improve performance and energy efficiency compared to using optimized bit-parallel floating-point units under iso-compute area constraints. We also demonstrate that FPRaker delivers additional benefits when training incorporates pruning and quantization. Finally, we show that FPRaker naturally amplifies performance with training methods that use a different precision per layer.
HARQ in Full-Duplex Relay-Assisted Transmissions for URLLC

The Release 16 completion unlocks the road to an exciting phase pertain to the sixth generation (6G) era. Meanwhile, to sustain far-reaching applications with unprecedented challenges in terms of latency and reliability, much interest is already getting intensified toward physical layer specifications of 6G. In support of this vision, this work exhibits the forward-looking perception of full-duplex (FD) cooperative relaying in support of upcoming generations and adopts as a mean concern the critical contribution of hybrid automatic repeat request (HARQ) mechanism to ultra-reliable and low-latency communication (URLLC). Indeed, the HARQ roundtrip time (RTT) is known to include basic physical delays that may cause the HARQ abandonment for the 1 ms latency use case of URLLC. Taking up these challenges, this article proposes a hybrid FD amplify-and-forward (AF)-selective decode-and-forward (SDF) relay-based system for URLLC. Over this build system, two HARQ procedures within which the HARQ RTT is shortened, are suggested to face latency and reliability issues, namely, the proposed and the enhanced HARQ procedures. We develop then an analytical framework of this relay based HARQ system within its different procedures. Finally, using Monte-Carlo simulations, we confirm the theoretical results and compare the proposed relay-assisted HARQ procedures to the source-to-destination (S2D) HARQ-based system where no relay assists the communication between the source and the destination.
Electrolytes for Li-ion all-solid-state batteries: a first-principles comparative study of Li10GeP2O12 and Li10GeP2S12 in the LISICON and LGPS phases.

In this work we address Li-ion diffusion in thio-LISICON materials and in their oxide counterparts, exploring both the orthorhombic and tetragonal phases of Li10GeP2S12(LGPS) and Li10GeP2O12(LGPO) through extended Car-Parrinello molecular dynamics in the canonical and isobaric-isothermal ensemble. The (quasi-)orthorhombic and tetragonal phases are studied both for the oxide and for the sulfide, with the aim of comparing their conductivity with the same approach; out of these four case studies, tetragonal LGPO has not been reported and, while dynamically stable, it sits (0.04 Ha/formula unit) above orthorhombic LGPO. We calculate activation energies for diffusion of 0.18 eV and 0.23 eV for tetragonal and orthorhombic LGPS, and of 0.22 eV and 0.34eV for tetragonal and orthorhombic LGPO. In line with experiments, we find orthorhombic LGPO orders of magnitude less conductive, at room temperature, than the two sulfide systems. However, this is not the case for tetragonal LGPO, which, although less stable than its orthorhombic allotrope, shows at room temperature a conductivity comparable to orthorhombic and tetragonal LGPS, and, if synthesized, could make a very attractive Li-ion conductor.
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers

Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. In this work, we find that transformer attentions can be used to rank sentences for unsupervised extractive summarization. Specifically, we first pre-train a hierarchical transformer model using unlabeled documents only. Then we propose a method to rank sentences using sentence-level self-attentions and pre-training objectives. Experiments on CNN/DailyMail and New York Times datasets show our model achieves state-of-the-art performance on unsupervised summarization. We also find in experiments that our model is less dependent on sentence positions. When using a linear combination of our model and a recent unsupervised model explicitly modeling sentence positions, we obtain even better results.
Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles

We propose a novel method for planning shortest length piecewise-linear motions through complex environments punctured with static, moving, or even morphing obstacles. Using a moment optimization approach, we formulate a hierarchy of semidefinite programs that yield increasingly refined lower bounds converging monotonically to the optimal path length. Our global moment optimization approach natively handles continuous time constraints without any need for time discretization. For computational tractability, we derive an iterative motion planner which compares favorably with sampling-based and nonlinear optimization baselines.
Combinations of quantum observables and instruments

This article points out that observables and instruments can be combined in many ways that have natural and physical interpretations. We shall mainly concentrate on the mathematical properties of these combinations. After an introduction in section 1, section 2 reviews the basic definitions and observables are considered in section 3. We study parts of observables, post-processing, generalized convex combinations, sequential products and tensor products. These combinations are extended to instruments in section 4. We consider properties of observables measured by combinations of instruments. We introduce four special types of instruments, namely Kraus, Lüders, trivial and semitrivial instruments. We study when these types are closed under various combinations. In this work, we only consider finite-dimensional quantum systems. A few of the results presented here have appeared in the author’s previous articles Gudder (2020 arXiv:2005.08117 [quant-ph], arXiv:2005.13642 [quant-ph], arXiv:2009.07371 [quant-ph]).
Cascade of Phase Transitions for Multi-Scale Clustering

We present a framework exploiting the cascade of phase transitions occurring during a simulated annealing of the expectation-maximization algorithm to cluster datasets with multiscale structures. Using the weighted local covariance, we can extract, a posteriori and without any prior knowledge, information on the number of clusters at different scales together with their size. We also study the linear stability of the iterative scheme to derive the threshold at which the first transition occurs and show how to approximate the next ones. Finally, we combine simulated annealing together with recent developments of regularized Gaussian mixture models to learn a principal graph from spatially structured datasets that can also exhibit many scales.
Learnable Graph-Regularization for Matrix Decomposition

Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields, including computer vision, text mining, bioinformatics, and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations. In order to make the learned representations inherit the structures in the original data, graph-regularization terms are often added to the loss function. However, the prior graph construction often fails to reflect the true network connectivity and the intrinsic relationships. In addition, many graph-regularized methods fail to take the dual spaces into account. Probabilistic models are often used to model the distribution of the representations, but most of previous methods often assume that the hidden variables are independent and identically distributed for simplicity. To this end, we propose a learnable graph-regularization model for matrix decomposition (LGMD), which builds a bridge between graph-regularized methods and probabilistic matrix decomposition models for the first time. LGMD incorporates two graphical structures (i.e., two precision matrices) learned in an iterative manner via sparse precision matrix estimation and is more robust to noise and missing entries. Extensive numerical results and comparison with competing methods demonstrate its effectiveness.
Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method

This paper studies the safe reinforcement learning (RL) problem without assumptions about prior knowledge of the system dynamics and the constraint function. We employ an uncertainty-aware neural network ensemble model to learn the dynamics, and we infer the unknown constraint function through indicator constraint violation signals. We use model predictive control (MPC) as the basic control framework and propose the robust cross-entropy method (RCE) to optimize the control sequence considering the model uncertainty and constraints. We evaluate our methods in the Safety Gym environment. The results show that our approach achieves better constraint satisfaction than baseline safe RL methods while maintaining good task performance. Additionally, we are able to achieve several orders of magnitude better sample efficiency when compared to constrained model-free RL approaches. The code is available at this https URL.
Autonomous Person-Specific Following Robot

Following a specific user is a desired or even required capability for service robots in many human-robot collaborative applications. However, most existing person-following robots follow people without knowledge of who it is following. In this paper, we proposed an identity-specific person tracker, capable of tracking and identifying nearby people, to enable person-specific following. Our proposed method uses a Sequential Nearest Neighbour with Thresholding Selection algorithm we devised to fuse together an anonymous person tracker and a face recogniser. Experiment results comparing our proposed method with alternative approaches showed that our method achieves better performance in tracking and identifying people, as well as improved robot performance in following a target individual.
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness

Adversarial data augmentation has shown promise for training robust deep neural networks against unforeseen data shifts or corruptions. However, it is difficult to define heuristics to generate effective fictitious target distributions containing "hard" adversarial perturbations that are largely different from the source distribution. In this paper, we propose a novel and effective regularization term for adversarial data augmentation. We theoretically derive it from the information bottleneck principle, which results in a maximum-entropy formulation. Intuitively, this regularization term encourages perturbing the underlying source distribution to enlarge predictive uncertainty of the current model, so that the generated "hard" adversarial perturbations can improve the model robustness during training. Experimental results on three standard benchmarks demonstrate that our method consistently outperforms the existing state of the art by a statistically significant margin.
Early-stage COVID-19 diagnosis in presence of limited posteroanterior chest X-ray images via novel Pinball-OCSVM

It is evident that the infection with this severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) starts with the upper respiratory tract and as the virus grows, the infection can progress to lungs and develop pneumonia. According to the statistics, approximately 14\% of the infected people with COVID-19 have severe cough and shortness of breath due to pneumonia, because as the viral infection increases, it damages the alveoli (small air sacs) and surrounding tissues. The conventional way of COVID-19 diagnosis is reverse transcription polymerase chain reaction (RT-PCR), which is less sensitive during early stages specially, if the patient is asymptomatic that may further lead to more severe pneumonia. To overcome this problem an early diagnosis method is proposed in this paper via one-class classification approach using a novel pinball loss function based one-class support vector machine (PB-OCSVM) considering posteroanterior chest X-ray images. Recently, several automated COVID-19 diagnosis models have been proposed based on various deep learning architectures to identify pulmonary infections using publicly available chest X-ray (CXR) where the presence of less number of COVID-19 positive samples compared to other classes (normal, pneumonia and Tuberculosis) raises the challenge for unbiased learning in deep learning models that has been solved using class balancing techniques which however should be avoided in any medical diagnosis process. Inspired by this phenomenon, this article proposes a novel PB-OCSVM model to work in presence of limited COVID-19 positive CXR samples with objectives to maximize the learning efficiency while minimize the false-positive and false-negative predictions. The proposed model outperformed over recently published deep learning approaches where accuracy, precision, specificity and sensitivity are used as performance measure parameters.
Multi-Shard Private Transactions for Permissioned Blockchains

Traditionally, blockchain systems involve sharing transaction information across all blockchain network participants. Clearly, this introduces barriers to the adoption of the technology by the enterprise world, where preserving the privacy of the business data is a necessity. Previous efforts to bring privacy and blockchains together either still leak partial information, are restricted in their functionality or use costly mechanisms like zk-SNARKs. In this paper, we propose the Multi-Shard Private Transaction (MSPT) protocol, a novel privacy-preserving protocol for permissioned blockchains, which relies only on simple cryptographic primitives and targeted dissemination of information to achieve atomicity and high performances.
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. For this study, we collect a dataset of human interactions capturing body part movements and gaze in their daily lives. Our experiments show that our self-supervised representation that encodes interaction and attention cues outperforms a visual-only state-of-the-art method MoCo (He et al., 2020), on a variety of target tasks: scene classification (semantic), action recognition (temporal), depth estimation (geometric), dynamics prediction (physics) and walkable surface estimation (affordance).
Emergent and Unspecified Behaviors in Streaming Decision Trees

Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams. These very fast decision trees are used in many real applications where data is created in real-time due to their efficiency. In this work, we extricate explanations for why these streaming decision tree algorithms for stationary and nonstationary streams (HoeffdingTree and HoeffdingAdaptiveTree) work as well as they do. In doing so, we identify thirteen unique unspecified design decisions in both the theoretical constructs and their implementations with substantial and consequential effects on predictive accuracy---design decisions that, without necessarily changing the essence of the algorithms, drive algorithm performance. We begin a larger conversation about explainability not just of the model but also of the processes responsible for an algorithm's success.
Lowbush Blueberry, Highbush Blueberry and Cranberry Extracts Protect Cucumber (Cucumis sativus L.) Cotyledons from Damage Induced by UV-B Irradiation

We prepared water extracts from four dry berries, lowbush blueberry (LBB) (Vaccinium angustifolium L.), highbush blueberry (HBB) (Vaccinium corymbosum L.), cranberry (CB) (Vaccinium macrocarpon Ait.) and grape (Vitis vinifera L.). The four berry extracts were efficient absorbers of ultraviolet (UV) light, primarily UV-B. The treatment of cucumber (Cucumis sativus L.) cotyledons with LBB, HBB and CB extracts attenuated the damage induced by continuous UV-B irradiation (0.58 W m-2) for 11 days and did not cause any side effects. Anthocyanin and polyphenol contents in the four berry extracts were analyzed using high performance liquid chromatography (HPLC) and spectrophotometry. Anthocyanins, which are major UV-absorbing compounds in higher plants, were not the primary polyphenolic compounds in the four berry extracts. A total of 51 specific polyphenols were identified. The LBB extract contained primarily chlorogenic acid, caffeic acid and protocatechuic acid; the HBB extract contained primarily chlorogenic acid, caffeic acid and syringic acid; and the CB extract contained primarily protocatechuic acid, myricetin and p-coumaric acid. These components might contribute to the protection of cucumber plants against UV-B-induced damage. Discipline: Plant protection Additional key words: antioxidants, caffeic acid, chlorogenic acid, polyphenolic compounds *Corresponding author: e-mail yamasaki@fukuoka-edu.ac. jp Received 12 April 2016; accepted 4 August 2016. Introduction The depletion of the ozone layer in the stratosphere by chlorofluorocarbons (CFCs) increases the amount of solar ultraviolet-B (UV-B; 280-320 nm) irradiation passing through to the earth’s surface (Rozema et al. 1997). In higher plants, UV-B irradiation is known to damage DNA and membranes, reduce photosynthetic activity, inhibit hypocotyl elongation, stunt growth, reduce leaf area, and induce leaf bronzing and necrosis (Teramura 1983, Ziska et al. 1992, Teramura & Sullivan 1994). UV-B irradiation is thus harmful to higher plants. In agriculture, the increase in solar UV-B reduces farm productivity. Protection against UV-B irradiation poses an important problem for higher plants including farm crops. Mutants resistant to UV-B irradiation have been isolated in higher plants. The Arabidopsis ultraviolet
Multi-Adversarial Learning for Cross-Lingual Word Embeddings

Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings - maps of matching words across languages - without supervision. Despite these successes, GANs’ performance for the difficult case of distant languages is still not satisfactory. These limitations have been explained by GANs’ incorrect assumption that source and target embedding spaces are related by a single linear mapping and are approximately isomorphic. We assume instead that, especially across distant languages, the mapping is only piece-wise linear, and propose a multi-adversarial learning method. This novel method induces the seed cross-lingual dictionary through multiple mappings, each induced to fit the mapping for one subspace. Our experiments on unsupervised bilingual lexicon induction and cross-lingual document classification show that this method improves performance over previous single-mapping methods, especially for distant languages.