Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study of Conv-Tasnet

Conv-TasNet is a recently proposed waveform-based deep neural network that achieves state-of-the-art performance in speech source separation. Its architecture consists of a learnable encoder/decoder and a separator that operates on top of this learned space. Various improvements have been proposed to Conv-TasNet. However, they mostly focus on the separator, leaving its encoder/decoder as a (shallow) linear operator. In this paper, we conduct an empirical study of Conv-TasNet and propose an enhancement to the encoder/decoder that is based on a (deep) non-linear variant of it. In addition, we experiment with the larger and more diverse LibriTTS dataset and investigate the generalization capabilities of the studied models when trained on a much larger dataset. We propose cross-dataset evaluation that includes assessing separations from the WSJ0-2mix, LibriTTS and VCTK databases. Our results show that enhancements to the encoder/decoder can improve average SI-SNR performance by more than 1 dB. Furthermore, we offer insights into the generalization capabilities of Conv-TasNet and the potential value of improvements to the encoder/decoder.
Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario.
Multicolor Optical Monitoring of the Blazar S5 0716+714 from 2017 to 2019

We continuously monitored the blazar S5 0716+714 in the optical g, r, and i bands from 2017 November 10 to 2019 June 6. The total number of observations is 201 nights including 26,973 data points. This is a very large quasi-simultaneous multicolor sample for the blazar. The average time spans and time resolutions are 3.4 hr and 2.9 minutes per night, respectively. During the period of observations, the target source in the r band brightens from 14. ͫ16 to 12. ͫ29 together with five prominent subflares, and then becomes fainter to 14. ͫ76, and again brightens to 12. ͫ94 with seven prominent subflares. For the long-term variations, we find a strong flatter-when-brighter (FWB) trend at a low-flux state and then a weak FWB trend at a higher-flux state. A weak FWB trend at a low-flux state and then a strong FWB trend at a higher-flux state are also reported. Most subflares show strong FWB trends, except for two flares with a weak FWB trend. The particle acceleration and cooling mechanisms together with the superposition of the different FWB slopes from the subflares likely explain the optical color behaviors. A scenario of bent jet is discussed.
An AGB Star with a Thick Circumstellar Shell

The Asymptotic Giant Branch is the terminal phase of red giant evolution with timescales of millions of years and a total mass lost from the star that is a significant fraction of the initial mass. Investigation of one of these stars, WISEA J173046.10–344455.5, a kpc in the direction of the center of the Galaxy, reveals a cool oxygen rich star with a dust shell of blackbody temperature 1305 K.
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG). We generalize existing results of Poincare recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting the reward (by adding a regularization term) of the game can give strong convergence guarantees in monotone games. We continue by showing how this reward adaptation technique can be leveraged to build algorithms that converge exactly to the Nash equilibrium. Finally, we show how these insights can be directly used to build state-of-the-art model-free algorithms for zero-sum two-player Imperfect Information Games (IIG).
A Scalable Framework for Sparse Clustering Without Shrinkage

Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of features are relevant in distinguishing clusters. This has motivated the development of sparse clustering techniques that typically rely on k-means within outer algorithms of high computational complexity. Current techniques also require careful tuning of shrinkage parameters, further limiting their scalability. In this paper, we propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms. We show that our algorithm enjoys consistency and convergence guarantees. Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings. We showcase these contributions via simulated experiments and benchmark datasets, as well as a case study on mouse protein expression.
The Fluidity of Concept Representations in Human Brain Signals

Cognitive theories of human language processing often distinguish between concrete and abstract concepts. In this work, we analyze the discriminability of concrete and abstract concepts in fMRI data using a range of analysis methods. We find that the distinction can be decoded from the signal with an accuracy significantly above chance, but it is not found to be a relevant structuring factor in clustering and relational analyses. From our detailed comparison, we obtain the impression that human concept representations are more fluid than dichotomous categories can capture. We argue that fluid concept representations lead to more realistic models of human language processing because they better capture the ambiguity and underspecification present in natural language use.
A Novel Framework for Selection of GANs for an Application

Generative Adversarial Network (GAN) is a current focal point of research. The body of knowledge is fragmented, leading to a trial-error method while selecting an appropriate GAN for a given scenario. We provide a comprehensive summary of the evolution of GANs starting from its inception addressing issues like mode collapse, vanishing gradient, unstable training and non-convergence. We also provide a comparison of various GANs from the application point of view, its behaviour and implementation details. We propose a novel framework to identify candidate GANs for a specific use case based on architecture, loss, regularization and divergence. We also discuss application of the framework using an example, and we demonstrate a significant reduction in search space. This efficient way to determine potential GANs lowers unit economics of AI development for organizations.
Linear-time Erasure List-decoding of Expander Codes

We give a linear-time erasure list-decoding algorithm for expander codes. More precisely, let r > 0 be any integer. Given an inner code ${\mathcal{C}_0}$ of length d, and a d-regular bipartite expander graph G with n vertices on each side, we give an algorithm to list-decode the expander code $\mathcal{C} = \mathcal{C}\left( {G,{\mathcal{C}_0}} \right)$ of length nd from approximately δδrnd erasures in time n•poly(d2r/δ), where δ and δr are the relative distance and the r’th•generalized relative distance of ${\mathcal{C}_0}$, respectively. To the best of our knowledge, this is the first linear-time algorithm that can list-decode expander codes from erasures beyond their (designed) distance of approximately δ2nd. To obtain our results, we show that an approach similar to that of (Hemenway and Wootters, Information and Computation, 2018) can be used to obtain such an erasure-list-decoding algorithm with an exponentially worse dependence of the running time on r and δ; then we show how to improve the dependence of the running time on these parameters.
Heat, Work and Entropy Production in Open Quantum Systems: A Microscopic Approach Based on Observational Entropy

We provide an axiomatic and microscopic approach to the second law in open quantum systems based on the recently introduced notion of observational entropy. By starting with the latter as a definition for the nonequilibrium thermodynamic entropy of the `universe' (the system and the bath), various conceptual shortcomings of previous attempts are overcome. The second law as quantified by an always positive entropy production is expressed as a change of a state function (namely, observational entropy) and it obeys an integral fluctuation theorem. Furthermore, we can treat a larger class of initial states beyond the standard paradigm of canonical Gibbs ensembles. Our approach also provides an alternative perspective on the conventional definition of mechanical work in quantum systems caused by an external time-dependent field. Moreover, the entropy production in our formalism can be unambiguously divided into a quantum and a classical component. In the conventional scenario of a weakly coupled ideal thermal bath our approach is in quantitative agreement with previous ones, thus also reassuring their thermodynamic consistency.
A General Pairwise Comparison Model for Extremely Sparse Networks

Abstract Statistical estimation using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this article, we propose a general framework to model the mutual interactions in a network, which enjoys ample flexibility in terms of model parameterization. Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity. This condition is sharp in terms of the leading order asymptotics describing the sparsity. Our analysis uses a novel chaining technique and illustrates an important connection between graph topology and model consistency. Our results guarantee that the maximum likelihood estimator is justified for estimation in large-scale pairwise comparison networks where data are asymptotically deficient. Simulation studies are provided in support of our theoretical findings. Supplementary materials for this article are available online.
Mono Lay out: Amodal scene layout from a single image

In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving platform, we aim to predict the bird’s eye view layout of the road and other traffic participants. The estimated layout should reason beyond what is visible in the image, and compensate for the loss of 3D information due to projection. We dub this problem amodal scene layout estimation, which involves hallucinating scene layout for even parts of the world that are occluded in the image. To this end, we present MonoLayout, a deep neural network for realtime amodal scene layout estimation from a single image. We represent scene layout as a multi-channel semantic occupancy grid, and leverage adversarial feature learning to hallucinate " plausible completions for occluded image parts. We extend several state-of-the-art approaches for road-layout estimation and vehicle occupancy estimation in bird’s eye view to the amodal setup and thoroughly evaluate against them. By leveraging temporal sensor fusion to generate training labels, we significantly outperform current art over a number of datasets.
Stretching the limits of dynamic and quasi-static flow testing on limestone powders

Powders are a special class of granular matter due to the important role of cohesive forces. The flow behavior of powders depends on the flow states and stress and is therefore difficult to measure/quantify with only one experiment. In this study, the most commonly used characterization tests that cover a wide range of states are compared: (static, free surface) angle of repose, the (quasi-static, confined) ring shear steady state angle of internal friction, and the (dynamic, free surface) rotating drum flow angle are considered for free flowing, moderately and strongly cohesive limestone powders. The free flowing powder gives good agreement among all different situations (devices), while the moderately and strongly cohesive powders behave more interestingly. Starting from the flow angle in the rotating drum and going slower, one can extrapolate to the limit of zero rotation rate, but then observes that the angle of repose measured from the heap is considerably larger, possibly due to its special history. When we stretch the ring shear test to its lowest confining stress limit, the steady state angle of internal friction of the cohesive powder coincides with the flow angle (at free surface) in the zero rotation rate limit.
Consequences of neutrino self-interactions for weak decoupling and big bang nucleosynthesis

We calculate and discuss the implications of neutrino self-interactions for the physics of weak decoupling and big bang nucleosynthesis (BBN) in the early universe. In such neutrino-sector extensions of the standard model, neutrinos may not free-stream, yet can stay thermally coupled to one another. Nevertheless, the neutrinos exchange energy and entropy with the photon, electron-positron, and baryon component of the early universe only through the ordinary weak interaction. We examine the effects of neutrino self-interaction for the primordial helium and deuterium abundances and Neff, a measure of relativistic energy density at photon decoupling. These quantities are determined in, or may be influenced by, the physics in the weak decoupling epoch. Self-interacting neutrinos have been invoked to address a number of anomalies, including as a possible means of ameliorating tension in the Hubble parameter. Our calculations show that surprisingly subtle changes in BBN accompany some of these neutrino self-interaction schemes. Such minute signals require high-precision measurements, making deuterium the best abundance for BBN constraints in the models explored here.
Solar-cycle irradiance variations over the last four billion years

Context. The variability of the spectral solar irradiance (SSI) over the course of the 11-year solar cycle is one of the manifestations of solar magnetic activity. There is strong evidence that the SSI variability has an effect on the Earth’s atmosphere. The faster rotation of the Sun in the past lead to a more vigorous action of solar dynamo and thus potentially to larger amplitude of the SSI variability on the timescale of the solar activity cycle. This could lead to a stronger response of the Earth’s atmosphere as well as other solar system planets’ atmospheres to the solar activity cycle. Aims. We calculate the amplitude of the SSI and total solar irradiance (TSI) variability over the course of the solar activity cycle as a function of solar age. Methods. We employed the relationship between the stellar magnetic activity and the age based on observations of solar twins. Using this relation, we reconstructed solar magnetic activity and the corresponding solar disk area coverages by magnetic features (i.e., spots and faculae) over the last four billion years. These disk coverages were then used to calculate the amplitude of the solar-cycle SSI variability as a function of wavelength and solar age. Results. Our calculations show that the young Sun was significantly more variable than the present Sun. The amplitude of the solar-cycle TSI variability of the 600 Myr old Sun was about ten times larger than that of the present Sun. Furthermore, the variability of the young Sun was spot-dominated (the Sun being brighter at the activity minimum than in the maximum), that is, the Sun was overall brighter at activity minima than at maxima. The amplitude of the TSI variability decreased with solar age until it reached a minimum value at 2.8 Gyr. After this point, the TSI variability is faculae-dominated (the Sun is brighter at the activity maximum) and its amplitude increases with age.
A Bayes factor approach with informative prior for rare genetic variant analysis from next generation sequencing data

The discovery of rare genetic variants through next generation sequencing is a very challenging issue in the field of human genetics. We propose a novel region‐based statistical approach based on a Bayes Factor (BF) to assess evidence of association between a set of rare variants (RVs) located on the same genomic region and a disease outcome in the context of case‐control design. Marginal likelihoods are computed under the null and alternative hypotheses assuming a binomial distribution for the RV count in the region and a beta or mixture of Dirac and beta prior distribution for the probability of RV. We derive the theoretical null distribution of the BF under our prior setting and show that a Bayesian control of the false Discovery Rate can be obtained for genome‐wide inference. Informative priors are introduced using prior evidence of association from a Kolmogorov‐Smirnov test statistic. We use our simulation program, sim1000G, to generate RV data similar to the 1000 genomes sequencing project. Our simulation studies showed that the new BF statistic outperforms standard methods (SKAT, SKAT‐O, Burden test) in case‐control studies with moderate sample sizes and is equivalent to them under large sample size scenarios. Our real data application to a lung cancer case‐control study found enrichment for RVs in known and novel cancer genes. It also suggests that using the BF with informative prior improves the overall gene discovery compared to the BF with noninformative prior.
Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers’ geographic locations, is not well understood. In this paper, we leverage a spatiotemporal model, which allows one to characterize the age of information (AoI) from a joint queueing-geometry perspective, for the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we also derive accurate and tractable expressions for the peak AoI. Numerical results reveal that: i) the packet arrival rate directly affects the service process due to queueing interactions, ii) the proposed scheme can adapt to traffic variations and largely reduce the peak AoI, and iii) the proposed scheme scales well as the network grows in size. This is done by adaptively adjusting the radio access probability at each transmitter to the change of the ambient environment.
On Some Geometrical Aspects of Space-Time Description and Relativity

In order to ask for future concepts of relativity, one has to build upon the original concepts instead of the nowadays common formalism only, and as such recall and reconsider some of its roots in geometry. So in order to discuss 3-space and dynamics, we recall briefly Minkowski's approach in 1910 implementing the nowadays commonly used 4-vector calculus and related tensorial representations as well as Klein's 1910 paper on the geometry of the Lorentz group. To include microscopic representations, we discuss few aspects of Wigner's and Weinberg's 'boost' approach to describe 'any spin' with respect to its reductive Lie algebra and coset theory, and we relate the physical identification to objects in $P^{5}$ based on the case $(1,0)\oplus(0,1)$ of the electromagnetic field. So instead of following this -- in some aspects -- special and misleading 'old' representation theory, based on 4-vector calculus and tensors, we provide and use an alternative representation based on line geometry which -- besides comprising known representation theory -- is capable of both describing (classical) projective geometry of 3-space as well as it yields spin matrices and the classical Lie transfer. In addition, this geometry is capable of providing a more general route to known Lie symmetries, especially of the su(2)$\oplus$i~su(2) Lie algebra of special relativity, as well as it comprises gauge theories and affine geometry. Thus it serves as foundation for a future understanding of more general representation theory of relativity based, however, on roots known from classical projective geometry and $P^{5}$. As an application, we discuss Lorentz transformations in point space in terms of line and Complex geometry, where we can identify them as.
Learning to Walk in the Real World with Minimal Human Effort

Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for developing such control policies autonomously. In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. The key difficulties for on-robot learning systems are automatic data collection and safety. We overcome these two challenges by developing a multi-task learning procedure, an automatic reset controller, and a safety-constrained RL framework. We tested our system on the task of learning to walk on three different terrains: flat ground, a soft mattress, and a doormat with crevices. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention.
Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms

A coreset is a subset of the training set, using which a machine learning algorithm obtains performances similar to what it would deliver if trained over the whole original data. Coreset discovery is an active and open line of research as it allows improving training speed for the algorithms and may help human understanding the results. Building on previous works, a novel approach is presented: candidate corsets are iteratively optimized, adding and removing samples. As there is an obvious trade-off between limiting training size and quality of the results, a multi-objective evolutionary algorithm is used to minimize simultaneously the number of points in the set and the classification error. Experimental results on non-trivial benchmarks show that the proposed approach is able to deliver results that allow a classifier to obtain lower error and better ability of generalizing on unseen data than state-of-the-art coreset discovery techniques.