Miracle-dz/UniFAR-FTUs huggingface dataset:
A Note on the Linear Convergence of Policy Gradient Methods

We revisit the finite time analysis of policy gradient methods in the simplest setting: finite state and action problems with a policy class consisting of all stochastic policies and with exact gradient evaluations. Some recent works have viewed these problems as instances of smooth nonlinear optimization problems, suggesting suggest small stepsizes and showing sublinear convergence rates. This note instead takes a policy iteration perspective and highlights that many versions of policy gradient succeed with extremely large stepsizes and attain a linear rate of convergence.
InstanceFlow: Visualizing the Evolution of Classifier Confusion at the Instance Level

Classification is one of the most important supervised machine learning tasks. During the training of a classification model, the training instances are fed to the model multiple times (during multiple epochs) in order to iteratively improve classification performance. The increasing complexity of models has led to a growing demand to make them interpretable through visualization. Existing approaches mostly focus on the visual analysis of the final model performance after training and are often limited to aggregate performance measures. In this paper, we introduce InstanceFlow, a novel dual-view visualization tool that allows users to analyze the learning behavior of classifiers over time at the instance-level. A Sankey diagram visualizes the flow of instances throughout epochs, with on-demand detailed glyphs and traces for individual instances. A tabular view allows users to locate interesting instances by ranking and filtering. Thus, InstanceFlow bridges the gap between class-level and instance-level performance evaluation while enabling users to perform a full temporal analysis of the training process.
Video-ception Network: Towards Multi-Scale Efficient Asymmetric Spatial-Temporal Interactions

Previous video modeling methods leverage the cubic 3D convolution filters or its decomposed variants to exploit the motion cues for precise action recognition, which tend to be performed on the video features along the temporal and spatial axes symmetrically. This brings the hypothesis implicitly that the actions are recognized from the cubic voxel level and neglects the essential spatial-temporal shape diversity across different actions. In this paper, we propose a novel video representing method that fuses the features spatially and temporally in an asymmetric way to model action atomics spanning multi-scale spatial-temporal scales. To permit the feature fusion procedure efficiently and effectively, we also design the optimized feature interaction layer, which covers most feature fusion techniques as special case of it, e.g., channel shuffling and channel concatenating. We instantiate our method as a \textit{plug-and-play} block, termed Multi-Scale Efficient Asymmetric Spatial-Temporal Block. Our method can easily adapt the traditional 2D CNNs to the video understanding tasks such as action recognition. We verify our method on several most recent large-scale video datasets requiring strong temporal reasoning or appearance discriminating, e.g., Something-to-Something v1, Kinetics and Diving48, demonstrate the new state-of-the-art results without bells and whistles.
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement

While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches. This need for resource-efficient machine learning is primarily driven by the demand for embedded systems and their usage in ubiquitous computing and IoT applications. In this article, we provide a resource-efficient approach for multi-channel speech enhancement based on Deep Neural Networks (DNNs). In particular, we use reduced-precision DNNs for estimating a speech mask from noisy, multi-channel microphone observations. This speech mask is used to obtain either the Minimum Variance Distortionless Response (MVDR) or Generalized Eigenvalue (GEV) beamformer. In the extreme case of binary weights and reduced precision activations, a significant reduction of execution time and memory footprint is possible while still obtaining an audio quality almost on par to single-precision DNNs and a slightly larger Word Error Rate (WER) for single speaker scenarios using the WSJ0 speech corpus.
Graph Neural Networks with Haar Transform-Based Convolution and Pooling: A Complete Guide

Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In order to handle graphs with different features and sizes, we propose a novel graph neural network, which we call HaarNet, to predict graph labels with interrelated convolution and pooling strategies. Similar to some existing routines, the model assembles unified graph-level representations from samples by first adopting graph convolutional layers to extract mutual information followed by graph pooling layers to downsample graph resolution. By a sequence of clusterings, we embed the intrinsic topological information of each graph into the GNN. Through the fast Haar transformation, we made our contribution to forming a smooth workflow that learns multi-scale graph representation with redundancy removed. As a result, our proposed framework obtains notable accuracy gains without sacrificing performance stability. Extensive experiments validate the superiority on graph classification and regression tasks, where our proposed HaarNet outperforms various existing GNN models, especially on big data sets.
An Improved Approximation Algorithm for the Matching Augmentation Problem

We present a $\frac53$-approximation algorithm for the matching augmentation problem (MAP): given a multi-graph with edges of cost either zero or one such that the edges of cost zero form a matching, find a 2-edge connected spanning subgraph (2-ECSS) of minimum cost. A $\frac74$-approximation algorithm for the same problem was presented recently, see Cheriyan, et al., "The matching augmentation problem: a $\frac{7}{4}$-approximation algorithm," {\em Math. Program. }, 182(1):315--354, 2020; arXiv:1810.07816. Our improvement is based on new algorithmic techniques, and some of these may lead to advances on related problems.
Model-based simultaneous inference for multiple subgroups and multiple endpoints

Various methodological options exist on evaluating differences in both subgroups and the overall population. Most desirable is the simultaneous study of multiple endpoints in several populations. We investigate a newer method using multiple marginal models (mmm) which allows flexible handling of multiple endpoints, including continuous, binary or time-to-event data. This paper explores the performance of mmm in contrast to the standard Bonferroni approach via simulation. Mainly these methods are compared on the basis of their familywise error rate and power under different scenarios, varying in sample size and standard deviation. Additionally, it is shown that the method can deal with overlapping subgroup definitions and different combinations of endpoints may be assumed. The reanalysis of a clinical example shows a practical application.
Fire, buried germinable seed banks and grass species establishment in an urban eucalypt forest reserve

The buried germinable seed banks of two natural contrasting savanna open-forest sites were investigated before and after a wildfire. It was found that while the number of buried germinable grass and sedge seeds remained the same, the number of buried germinable dicotyledon seeds in both sites increased after the fire. It was also found that there were similar species of grasses present in the buried germinable seed banks before and after the fire. When germinating grass seedlings in the burnt and unburnt sites were compared, similar species of grasses were found in the germinating seedlings at both sites; however, more grass seedlings germinated in the burnt site than in the unburnt site.
Battle of Opinions Over Evolving Social Networks

Social networking environments provide major platforms for the discussion and formation of opinions in diverse areas, including, but not limited to, political discourse, market trends, news, and social movements. Often, these opinions are of a competing nature, e.g., radical vs. peaceful ideologies, correct information vs. misinformation, and one technology vs. another. We study the battles of such competing opinions over evolving social networks. The novelty of our model is that it captures the exposure and adoption dynamics of opinions that account for the preferential and random nature of exposure as well as the persuasion power and persistence of different opinions. We provide a complete characterization of the mean opinion dynamics over time as a function of the initial adoption, as well as the particular exposure, adoption, and persistence dynamics. Our analysis, supported by case studies, reveals the key metrics that govern the spread of opinions and establishes the means to engineer the desired impact of an opinion in the presence of other competing opinions.
Phase separation and scaling in correlation structures of financial markets

Financial markets, being spectacular examples of complex systems, display rich correlation structures among price returns of different assets. The correlation structures change drastically, akin to critical phenomena in physics, as do the influential stocks (leaders) and sectors (communities), during market events like crashes. It is crucial to detect their signatures for timely intervention or prevention. Here we use eigenvalue decomposition and eigen-entropy, computed from eigenvector centralities of different stocks in the cross-correlation matrix, to extract information about the disorder in the market. We construct a ‘phase space’, where different market events (bubbles, crashes, etc) undergo phase separation and display order–disorder movements. An entropy functional exhibits scaling behavior. We propose a generic indicator that facilitates the continuous monitoring of the internal structure of the market—important for managing risk and stress-testing the financial system. Our methodology would help in understanding and foreseeing tipping points or fluctuation patterns in complex systems.
Nonlinear Turbulent Dynamo during Gravitational Collapse

Via amplification by turbulent dynamo, magnetic fields can be potentially important for the formation of the first stars. To examine the dynamo behavior during the gravitational collapse of primordial gas, we extend the theory of the nonlinear turbulent dynamo to include the effect of gravitational compression. The relative importance between dynamo and compression varies during contraction, with the transition from dynamo- to compression-dominated amplification of magnetic fields with the increase of density. In the nonlinear stage of magnetic field amplification with the scale-by-scale energy equipartition between turbulence and magnetic fields, reconnection diffusion of magnetic fields in ideal magnetohydrodynamic turbulence becomes important. It causes the violation of the flux-freezing condition and accounts for (a) the small growth rate of the nonlinear dynamo, (b) the weak dependence of magnetic energy on density during contraction, (c) the saturated magnetic energy, and (d) the large correlation length of magnetic fields. The resulting magnetic field structure and the scaling of magnetic field strength with density are radically different from the expectations of flux freezing.
Teachers' perception of Jupyter and R Shiny as digital tools for open education and science

During the last ten years advances in open-source digital technology, used especially by data science, led to very accessible ways how to obtain, store, process, analyze or share data in almost every human activity. Data science tools bring not only transparency, accessibility, and reproducibility in open science, but also give benefits in open education as learning tools for improving effectiveness of instruction. Together with our pedagogical introduction and review of Jupyter as an interactive multimedia learning tool, we present our three-years long research in the framework of a complex mixed-methods approach which examines physics teachers' perception of Jupyter technology in three groups: Ph.D. candidates in physics education research (PER) ($N = 9$), pre-service physics teachers ($N = 33$) and in-service physics teachers ($N = 40$). Despite the fact that open-source Jupyter notebooks are natural and easy as email or web, the results suggest that in-service teachers are not prepared for Jupyter technology and open analysis, but positively accept open education data presented via another open-source data science tool, R Shiny interactive web application, as an important form of immediate feedback and learning about the quality of their instruction. Simultaneously our instruction results in the frame of the Flipped Learning also indicate that young beginning PER researchers and pre-service physics teachers can master key digital skills to work with Jupyter technology appreciating its big impact on their learning, data and statistical literacy or professional development. All results support the ongoing worldwide effort to implement Jupyter in traditional education as a promising free open-source interactive learning tool to foster learning process, especially for the upcoming young generation.
Fauna associated with the horny sponge Anomoianthella lamella Pulitzer‐Finali & Pronzato, 1999 (Ianthellidae, Demospongiae) from Papua‐New Guinea

Abstract The relationships between the tropical sponge Anomoianthella lamella Pulitzer‐Finali & Pronzato, 1999 (from Papua‐New Guinea) and its endobionts, the barnacle Acasta sp. and the polychaete Haplosyllis spongicola (Grube, 1855), were analysed using optical and scanning electron microscope techniques. The specimens of Acasta sp. live in the mesohyl, close to the inhalant surface of the sponge, where they reach a mean density of 12.7‐10 cm‐2. Their apical region is delimited by a thin layer of sponge collagen, whereas sponge cells (archeocytes and lophocytes) gather along the rest of the body of the endobiont. While the barnacles adhere to the spongin skeleton network, the polychaetes H. spongicola are esclusive to the exhalant canals of the sponge aquiferous system, where they reach a mean density of 57 individuals‐cm"2. No evidence of sponge reaction to H. spongicola was observed. Potential benefits for the barnacle are protection, against predation and mechanical disturbance, and trophic advantage.
Approximate Covering with Lower and Upper Bounds via LP Rounding

In this paper, we study the lower- and upper-bounded covering (LUC) problem, where we are given a set $P$ of $n$ points, a collection $\mathcal{B}$ of balls, and parameters $L$ and $U$. The goal is to find a minimum-sized subset $\mathcal{B}'\subseteq \mathcal{B}$ and an assignment of the points in $P$ to $\mathcal{B}'$, such that each point $p\in P$ is assigned to a ball that contains $p$ and for each ball $B_i\in \mathcal{B}'$, at least $L$ and at most $U$ points are assigned to $B_i$. We obtain an LP rounding based constant approximation for LUC by violating the lower and upper bound constraints by small constant factors and expanding the balls by again a small constant factor. Similar results were known before for covering problems with only the upper bound constraint. We also show that with only the lower bound constraint, the above result can be obtained without any lower bound violation. Covering problems have close connections with facility location problems. We note that the known constant-approximation for the corresponding lower- and upper-bounded facility location problem, violates the lower and upper bound constraints by a constant factor.
Generating Trading Signals by ML algorithms or time series ones?

This research investigates efficiency on-line learning Algorithms to generate trading signals.I employed technical indicators based on high frequency stock prices and generated trading signals through ensemble of Random Forests. Similarly, Kalman Filter was used for signaling trading positions. Comparing Time Series methods with Machine Learning methods, results spurious of Kalman Filter to Random Forests in case of on-line learning predictions of stock prices
Study of strange non-strange hadron ratios in pp and p-Pb collisions at LHC energies

It has been observed that the yields of strange and multi-strange hadrons relative to pion increase significantly with the event charged-particle multiplicity. We notice from experimental data that yield ratios between non-strange hadrons, like p/$\pi$ or hadrons of same strange content, like $\Lambda$/K$_s^0$, show similar enhancement. We have studied this behavior within the ambit of a parton model (EPOS3) and A Multi-Phase Transport (AMPT) model in pp and p-Pb collisions at LHC energies. We investigate model predictions of yields and yield ratios of different identified hadron productions as a function of charged-particle multiplicity and compare them with published ALICE results. The string melting versions of AMPT and EPOS are found to establish enhancements in the particle yield ratios.
Autonomous Tracking and State Estimation With Generalized Group Lasso

We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The aim is to improve the tracking and estimation accuracy with respect to the classical Bayesian filters and smoothers. We formulate the estimation problem as a dynamic generalized group Lasso problem and develop a class of smoothing-and-splitting methods to solve it. The Levenberg–Marquardt iterated extended Kalman smoother-based multiblock alternating direction method of multipliers (LM-IEKS-mADMMs) algorithms are based on the alternating direction method of multipliers (ADMMs) framework. This leads to minimization subproblems with an inherent structure to which three new augmented recursive smoothers are applied. Our methods can deal with large-scale problems without preprocessing for dimensionality reduction. Moreover, the methods allow one to solve nonsmooth nonconvex optimization problems. We then prove that under mild conditions, the proposed methods converge to a stationary point of the optimization problem. By simulated and real-data experiments, including multisensor range measurement problems, marine vessel tracking, autonomous vehicle tracking, and audio signal restoration, we show the practical effectiveness of the proposed methods.
Pushing the Physical Limits of IoT Devices with Programmable Metasurfaces

Small, low-cost IoT devices are typically equipped with only a single, low-quality antenna, significantly limiting communication range and link quality. In particular, these antennas are typically linearly polarized and therefore susceptible to polarization mismatch, which can easily cause 10-15 dBm of link loss on communication to and from such devices. In this work, we highlight this under-appreciated issue and propose the augmentation of IoT deployment environments with programmable, RF-sensitive surfaces made of metamaterials. Our smart meta-surface mitigates polarization mismatch by rotating the polarization of signals that pass through or reflect off the surface. We integrate our metasurface into an IoT network as LAMA, a Low-power Lattice of Actuated Metasurface Antennas, designed for the pervasively used 2.4 GHz ISM band. We optimize LAMA's metasurface design for both low transmission loss and low cost, to facilitate deployment at scale. We then build an end-to-end system that actuates the metasurface structure to optimize for link performance in real time. Our experimental prototype-based evaluation demonstrates gains in link power of up to 15 dBm, and wireless capacity improvements of 100 and 180 Kbit/s/Hz in through-surface and surface-reflective scenarios, respectively, attributable to the polarization rotation properties of LAMA'S metasurface.
Teleportation Systems Toward a Quantum Internet

Quantum teleportation is essential for many quantum information technologies including long-distance quantum networks. Using fiber-coupled devices, including state-of-the-art low-noise superconducting nanowire single photon detectors and off-the-shelf optics, we achieve quantum teleportation of time-bin qubits at the telecommunication wavelength of 1536.5 nm. We measure teleportation fidelities of >=90% that are consistent with an analytical model of our system, which includes realistic imperfections. To demonstrate the compatibility of our setup with deployed quantum networks, we teleport qubits over 22 km of single-mode fiber while transmitting qubits over an additional 22 km of fiber. Our systems, which are compatible with emerging solid-state quantum devices, provide a realistic foundation for a high-fidelity quantum internet with practical devices.
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics

3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to practical considerations such as computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system. Our system first obtains 3D detections from a LiDAR point cloud. Then, a straightforward combination of a 3D Kalman filter and the Hungarian algorithm is used for state estimation and data association. Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in the 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods. Therefore, we propose a new 3D MOT evaluation tool along with three new metrics to comprehensively evaluate 3D MOT methods. We show that, although our system employs a combination of classical MOT modules, we achieve state-of-the-art 3D MOT performance on two 3D MOT benchmarks (KITTI and nuScenes). Surprisingly, although our system does not use any 2D data as inputs, we achieve competitive performance on the KITTI 2D MOT leaderboard. Our proposed system runs at a rate of 207.4 FPS on the KITTI dataset, achieving the fastest speed among all modern MOT systems. To encourage standardized 3D MOT evaluation, our code is publicly available at http://www.xinshuoweng. com/projects/AB3DMOT.