Miracle-dz/UniFAR-FTUs huggingface dataset:
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding

Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar-constrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterancel and meaning representation. During synchronously decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.
Taylor Expansion of Discount Factors

In practical reinforcement learning (RL), the discount factor used for estimating value functions often differs from that used for defining the evaluation objective. In this work, we study the effect that this discrepancy of discount factors has during learning, and discover a family of objectives that interpolate value functions of two distinct discount factors. Our analysis suggests new ways for estimating value functions and performing policy optimization updates, which demonstrate empirical performance gains. This framework also leads to new insights on commonly-used deep RL heuristic modifications to policy optimization algorithms.
Quantum logic inspired techniques for spacetime-symmetry tests with (anti-)protons

Cosmological observations as well as theoretical approaches to physics beyond the standard model provide strong motivations for experimental tests of fundamental symmetries, such as CPT invariance. In this context, the availability of cold baryonic antimatter at CERN has opened an avenue for ultrahigh-precision comparisons of protons and antiprotons in Penning traps. This work discusses an experimental method inspired by quantum logic techniques that will improve particle localization and readout speed in such experiments. The method allows for sympathetic cooling of the (anti-)proton to its quantum-mechanical ground state as well as the readout of its spin alignment, replacing the commonly used continuous Stern–Gerlach effect. Both of these features are achieved through coupling to a laser-cooled ‘logic’ ion co-trapped in a double-well potential. This technique will boost the measurement sampling rate and will thus provide results with lower statistical uncertainty, contributing to stringent searches for time dependent variations in the data. Such measurements ultimately yield extremely high sensitivities to CPT violating coefficients acting on baryons in the standard-model extension, will allow the exploration of previously unmeasured types of symmetry violations, and will enable antimatter-based axion-like dark matter searches with improved mass resolution.
Union and intersection contracts are hard, actually

Union and intersection types are a staple of gradually typed languages such as TypeScript. While it's long been recognized that union and intersection types are difficult to verify statically, it may appear at first that the dynamic part of gradual typing is actually pretty simple. It turns out however, that in presence of higher-order contracts union and intersection are deceptively difficult. The literature on higher-order contracts with union and intersection, while keenly aware of the fact, doesn't really explain why. We point and illustrate the problems and trade-offs inherent to union and intersection contracts, via example and a survey of the literature.
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction

The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. Recent work has challenged this belief, showing that, on standard benchmarks, complex encoder-decoder architectures perform similarly to nearest-neighbor baselines or simple linear decoder models that exploit large amounts of per-category data. However, building large collections of 3D shapes for supervised training is a laborious process; a more realistic and less constraining task is inferring 3D shapes for categories with few available training examples, calling for a model that can successfully generalize to novel object classes. In this work we experimentally demonstrate that naive baselines fail in this few-shot learning setting, in which the network must learn informative shape priors for inference of new categories. We propose three ways to learn a class-specific global shape prior, directly from data. Using these techniques, we are able to capture multi-scale information about the 3D shape, and account for intra-class variability by virtue of an implicit compositional structure. Experiments on the popular ShapeNet dataset show that our method outperforms a zero-shot baseline by over 40%, and the current state-of-the-art by over 10%, in terms of relative performance, in the few-shot setting.
Noncommutative Christoffel-Darboux kernels

We introduce from an analytic perspective Christoffel-Darboux kernels associated to bounded, tracial noncommutative distributions. We show that properly normalized traces, respectively norms, of evaluations of such kernels on finite dimensional matrices yield classical plurisubharmonic functions as the degree tends to infinity, and show that they are comparable to certain noncommutative versions of the Siciak extremal function. We prove estimates for Siciak functions associated to free products of distributions, and use the classical theory of plurisubharmonic functions in order to propose a notion of support for noncommutative distributions. We conclude with some conjectures and numerical experiments.
Generative Adversarial Networks in Finance: An Overview

Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field.
A new goodness of fit test for uniform distribution with censored observations

Using fixed point characterization, we develop a new goodness of fit test for uniform distribution. We also discuss how the right censored observations can be incorporated in the proposed test procedure. We study the asymptotic properties of the proposed test statistics. A Monte Carlo simulation is carried out to evaluate the finite sample performance of the tests. We illustrate the test procedures using real data sets.
Evolution Through the Post-starburst Phase: Using Post-starburst Galaxies as Laboratories for Understanding the Processes that Drive Galaxy Evolution

Post-starburst (or “E+A”) galaxies trace the fastest and most dramatic processes in galaxy evolution. Recent work studying the evolution of galaxies through this phase has revealed insights on how galaxies undergo structural and stellar population changes as well as the role of various feedback mechanisms. In this review, I summarize recent work on identifying post-starburst galaxies; tracing the role of this phase through cosmic time; measuring stellar populations, on-going star formation, morphologies, kinematics, interstellar medium properties, and active galactic nucleus activity; mechanisms to cause the recent starburst and its end; and the future evolution to quiescence (or not). The review concludes with a list of open questions and exciting possibilities for future facilities.
Towards End-to-End Synthetic Speech Detection

The constant Q transform (CQT) has been shown to be one of the most effective speech signal pre-transforms to facilitate synthetic speech detection, followed by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature extraction and a back-end binary classifier, or a deep neural network (DNN) directly for further feature extraction and classification. Despite the rich literature on such a pipeline, we show in this paper that the pre-transform and hand-crafted features could simply be replaced by end-to-end DNNs. Specifically, we experimentally verify that by only using standard components, a light-weight neural network could outperform the state-of-the-art methods for the ASVspoof2019 challenge. The proposed model is termed Time-domain Synthetic Speech Detection Net (TSSDNet), having ResNet- or Inception-style structures. We further demonstrate that the proposed models also have attractive generalization capability. Trained on ASVspoof2019, they could achieve promising detection performance when tested on disjoint ASVspoof2015, significantly better than the existing cross-dataset results. This paper reveals the great potential of end-to-end DNNs for synthetic speech detection, without hand-crafted features.
Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems

Inverse problems consist in recovering a signal given noisy observations. One classical resolution approach is to leverage sparsity and integrate prior knowledge of the signal to the reconstruction algorithm to get a plausible solution. Still, this prior might not be sufﬁciently adapted to the data. In this work, we study Dictionary and Prior learning from degraded measurements as a bi-level problem, and we take advantage of unrolled algorithms to solve approximate formulations of Synthesis and Analysis. We provide an empirical and theoretical analysis of automatic differentiation for Dictionary Learning to understand better the pros and cons of unrolling in this context. We ﬁnd that unrolled algorithms speed up the recovery process for a small number of iterations by improving the gradient estimation. Then we compare Analysis and Synthesis by evaluating the performance of unrolled algorithms for inverse problems, without access to any ground truth data for several classes of dictionaries and priors. While Analysis can achieve good results, Synthesis is more robust and performs better. Finally, we illustrate our method on pattern and structure learning tasks from degraded measurements.
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning

A commonly cited inefficiency of neural network training using back-propagation is the update locking problem: each layer must wait for the signal to propagate through the full network before updating. Several alternatives that can alleviate this issue have been proposed. In this context, we consider a simple alternative based on minimal feedback, which we call Decoupled Greedy Learning (DGL). It is based on a classic greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification. We consider an optimization of this objective that permits us to decouple the layer training, allowing for layers or modules in networks to be trained with a potentially linear parallelization. With the use of a replay buffer we show that this approach can be extended to asynchronous settings, where modules can operate and continue to update with possibly large communication delays. To address bandwidth and memory issues we propose an approach based on online vector quantization. This allows to drastically reduce the communication bandwidth between modules and required memory for replay buffers. We show theoretically and empirically that this approach converges and compare it to the sequential solvers. We demonstrate the effectiveness of DGL against alternative approaches on the CIFAR-10 dataset and on the large-scale ImageNet dataset.
Represent Your Own Policies: Reinforcement Learning with Policy-extended Value Function Approximator

We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation. Such an extension enables PeVFA to preserve values of multiple policies at the same time and brings an appealing characteristic, i.e., value generalization among policies . We formally analyze the value generalization under Generalized Policy Iteration (GPI). From theoretical and empirical lens, we show that generalized value estimates offered by PeVFA may have lower initial approximation error to true values of successive policies, which is expected to improve consecutive value approximation during GPI. Based on above clues, we introduce a new form of GPI with PeVFA which leverages the value generalization along policy improvement path. Moreover, we propose a representation learning framework for RL policy, providing several approaches to learn effective policy embeddings from policy network parameters or state-action pairs. In our experiments, we evaluate the efﬁcacy of value generalization offered by PeVFA and policy representation learning in several OpenAI Gym continuous control tasks. For a representative instance of algorithm implementation, Proximal Policy Optimization (PPO) re-implemented under the paradigm of GPI with PeVFA achieves about 40% performance improvement on its vanilla counterpart in most environments.
Inference of causal effects when control variables are unknown

Conventional methods in causal effect inferencetypically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but thecontrol variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that themethod yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.
Regularized Softmax Deep Multi-Agent Q-Learning

Tackling overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting. In this work, we empirically demonstrate that QMIX, a popular $Q$-learning algorithm for cooperative multi-agent reinforcement learning (MARL), suffers from a more severe overestimation in practice than previously acknowledged, and is not mitigated by existing approaches. We rectify this with a novel regularization-based update scheme that penalizes large joint action-values that deviate from a baseline and demonstrate its effectiveness in stabilizing learning. Furthermore, we propose to employ a softmax operator, which we efficiently approximate in a novel way in the multi-agent setting, to further reduce the potential overestimation bias. Our approach, Regularized Softmax (RES) Deep Multi-Agent $Q$-Learning, is general and can be applied to any $Q$-learning based MARL algorithm. We demonstrate that, when applied to QMIX, RES avoids severe overestimation and significantly improves performance, yielding state-of-the-art results in a variety of cooperative multi-agent tasks, including the challenging StarCraft II micromanagement benchmarks.
Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis

This paper primarily focuses on computing the Euclidean projection of a vector onto the $\ell_{p}$ ball in which $p\in(0,1)$. Such a problem emerges as the core building block in statistical machine learning and signal processing tasks because of its ability to promote sparsity. However, efficient numerical algorithms for finding the projections are still not available, particularly in large-scale optimization. To meet this challenge, we first derive the first-order necessary optimality conditions of this problem using Frechet normal cone. Based on this characterization, we develop a novel numerical approach for computing the stationary point through solving a sequence of projections onto the reweighted $\ell_{1}$-balls. This method is practically simple to implement and computationally efficient. Moreover, the proposed algorithm is shown to converge uniquely under mild conditions and has a worst-case $O(1/\sqrt{k})$ convergence rate. Numerical experiments demonstrate the efficiency of our proposed algorithm.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we provide a systematic evaluation and comparison of three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks, and we provide insights regarding the effectiveness of different learning approaches. We open-source EPyMARL, which extends the PyMARL codebase to include additional algorithms and allow for flexible configuration of algorithm implementation details such as parameter sharing. Finally, we open-source two environments for multi-agent research which focus on coordination under sparse rewards.
Object recognition in clutter image using putative feature point matching algorithm

The goal of this project is to automatically detect and recognize some objects in an image by using a multi-agents architecture. A knowledge database is thus necessary and should contain an invariant description of known objects for the desired application. Point matching is an important aim in the research work. A digital image may undergo any arbitrary translational, rotational changes because of which the object shape may change. In this paper we present a novel approach for shape and feature matching using putative point matching.
A Novel Botnet Detection System to Identify Resilient P2P-Botnet

botnets even if botnets generated traffic is overlapped with legitimate P2P applications(e.g. Skype) running on same compromised host and to also aims to achieve high scalability. Our system, regardless of how bots perform malicious activities in response to botmaster’s commands, It identifies P2P bots from monitored network with the help of C&C communication patterns that characterize P2P botnets. Basically, it derives statistical fingerprints from all P2P applications and leverage them to differentiate between hosts that are part of legitimate P2P networks (e.g. file sharing
Electrophoretic Deposition of Thin Film TiO 2 on Ti 6 AL 4 V Alloy Surface for Biomedical Applications Muslim

Received 27/12/2019; Accepted 15/3/2020 DOI: 10.21608/ejchem.2020. 21569.2284 ©2020 National Information and Documentation Center (NIDOC) THE electrophoretic deposition technique (EPD) is an effective method to deposit bioactive and biomedical thin films. The EPD technique was used to deposit titanium dioxide (TiO2) thin films on Ti-6Al-4V substrates under 30 V for 0.5, 1 and 2 min. The films produced in 1 and 2 minutes show the anatase phases TiO2. The bio corrosion measurement for uncoated and coated films in simulated body fluid (SBF) appears that the samples coated with TiO2 have more noble behaviors compared with uncoated samples. The corrosion rate was 2.970 × 10-4 mm/y for the sample deposited within the potential range of -273.875 mV.
Arteriovenous Access Type and Risk of Mortality, Hospitalization, and Sepsis Among Elderly Hemodialysis Patients: A Target Trial Emulation Approach.

RATIONALE & OBJECTIVE Evidence is mixed regarding the optimal choice of the first permanent vascular access for elderly patients on hemodialysis (HD). Lacking data from randomized controlled trials (RCT), we used a target trial emulation approach to compare arteriovenous fistula (AVF) versus arteriovenous graft (AVG) placement among elderly patients on HD. STUDY DESIGN Retrospective cohort study. SETTING & PARTICIPANTS Elderly patients included in the US Renal Data System (USRDS) who initiated HD with a catheter and had an AVF or AVG placed within six months of starting hemodialysis. EXPOSURE(S) Placement of an AVF versus AVG as the incident arteriovenous access. OUTCOME(S) All-cause mortality, all-cause and cause-specific hospitalization, and sepsis. ANALYTICAL APPROACH Target trial emulation approach, high-dimensional propensity score and inverse probability of treatment weighting (IPTW), and instrumental variable (IV) analysis using the proclivity of the operating physician to place fistulas as the instrumental variable. RESULTS A total of 19,867 patients were included, with 80.1% receiving an AVF and 19.9% an AVG. In unweighted analysis, AVF placement was associated with significantly lower risk of mortality and hospitalization, especially within 6 months after vascular access placement. In IPTW analysis, AVF placement was associated with lower incidence of mortality and hospitalization within 6 months after placement (HR [95% confidence interval] 0.82 [0.75, 0.91] for mortality; HR 0.82 [0.78, 0.87] for all-cause hospitalization) but not between 6 months and 3 years after access placement. No association between AVF placement and mortality, sepsis, nor all-cause, cardiovascular disease (CVD)-related, or infection-related hospitalization was found in IV analyses. However, AVF placement was associated with lower risk of access-related hospitalization not due to infection. LIMITATIONS Potential for unmeasured confounding, analyses limited to elderly patients, and absence of data on actual access use during follow-up. CONCLUSIONS Using observational data to emulate a target RCT, type of initial arteriovenous access placement was not associated with the risk of mortality, sepsis, or all-cause, CVD-, or infection-related hospitalization among elderly patients who initiated hemodialysis with a catheter.
Evaluation and Characterization of Membranes for H2SO4/Water and I2/HI/H2O Water Separation and Hydrogen Permeation for the S-I Cycle

In this report are the findings into three membrane separation studies for potential application to the Sulfur-Iodine (S-I) thermochemical cycle. The first is the removal of water from hydriodic acid/iodine mixtures. In the S-I cycle, iodine is added to the product of the Bunsen reaction to facilitate the separation of sulfuric acid (H2SO4) from hydriodic acid (HI). The amount of iodine can be as high as 83% of the overall mass load of the Bunsen product stream, which potentially introduces a large burden on the cycle’s efficiency. Removal of water from the HI and iodine mixture would substantially reduce the amount of required additional iodine. In this work, performance data for Nafion® and sulfonated poly (ether ether ketone) (SPEEK) membranes is shown.