Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stochastic Proximal Point Algorithm for Saddle-Point Problems

We consider saddle point problems which objective functions are the average of $n$ strongly convex-concave individual components. Recently, researchers exploit variance reduction methods to solve such problems and achieve linear-convergence guarantees. However, these methods have a slow convergence when the condition number of the problem is very large. In this paper, we propose a stochastic proximal point algorithm, which accelerates the variance reduction method SAGA for saddle point problems. Compared with the catalyst framework, our algorithm reduces a logarithmic term of condition number for the iteration complexity. We adopt our algorithm to policy evaluation and the empirical results show that our method is much more efficient than state-of-the-art methods.
Community Detection for Hypergraph Networks via Regularized Tensor Power Iteration

To date, social network analysis has been largely focused on pairwise interactions. The study of higher-order interactions, via a hypergraph network, brings in new insights. We study community detection in a hypergraph network. A popular approach is to project the hypergraph to a graph and then apply community detection methods for graph networks, but we show that this approach may cause unwanted information loss. We propose a new method for community detection that operates directly on the hypergraph. At the heart of our method is a regularized higher-order orthogonal iteration (reg-HOOI) algorithm that computes an approximate low-rank decomposition of the network adjacency tensor. Compared with existing tensor decomposition methods such as HOSVD and vanilla HOOI, reg-HOOI yields better performance, especially when the hypergraph is sparse. Given the output of tensor decomposition, we then generalize the community detection method SCORE (Jin, 2015) from graph networks to hypergraph networks. We call our new method Tensor-SCORE. In theory, we introduce a degree-corrected block model for hypergraphs (hDCBM), and show that Tensor-SCORE yields consistent community detection for a wide range of network sparsity and degree heterogeneity. As a byproduct, we derive the rates of convergence on estimating the principal subspace by reg-HOOI, with different initializations, including the two new initialization methods we propose, a diagonal-removed HOSVD and a randomized graph projection. We apply our method to several real hypergraph networks which yields encouraging results. It suggests that exploring higher-order interactions provides additional information not seen in graph representations.
Rolling in the Deep – Hybrid Locomotion for Wheeled-Legged Robots Using Online Trajectory Optimization

Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring rapid, and long-distance mobility skills on challenging terrain. In this letter, we present an online trajectory optimization framework for wheeled quadrupedal robots capable of executing hybrid walking-driving locomotion strategies. By breaking down the optimization problem into a wheel and base trajectory planning, locomotion planning for high dimensional wheeled-legged robots becomes more tractable, can be solved in real-time on-board in a model predictive control fashion, and becomes robust against unpredicted disturbances. The reference motions are tracked by a hierarchical whole-body controller that sends torque commands to the robot. Our approach is verified on a quadrupedal robot with non-steerable wheels attached to its legs. The robot performs hybrid locomotion with a great variety of gait sequences on rough terrain. Besides, we validated the robotic platform at the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, where the robot rapidly mapped, navigated and explored dynamic underground environments.
Diamond Mirror for High Power Lasers

High power lasers are used in a vast number of applications including laser manufacturing (laser cutting and welding), semiconductor industry (advanced photolithography) and medicine (surgical tools). These applications require optical components, mirrors in particular, that can withstand large optical powers in order to transport the optical beams from the laser to the target. Ordinarily, mirrors are implemented as multilayer coatings consisting of thin film dielectric layers of different refractive index and thickness, typically tens to around a hundred layers thick. In high power applications, imperfections and defects in these multi-layers lead to sites where laser energy can be absorbed resulting in significant thermal stress and permanent damage of the component. To address this challenge, we developed monolithic dielectric mirrors that do not suffer from thermally induced damage. Our mirrors are realized by etching nanostructures into a diamond surface, a material that exhibits many favorable optical properties and extremely high thermal conductivity. Our mirrors exhibit reflectivity greater than 98% and operate without sustaining laser induced damage under the irradiation of a 10kW 1070nm laser beam.
The influence of the scalar unparticle on the W-pair production at ILC in the Randall–Sundrum model

An attempt is made to present the contribution of the scalar unparticle on some scattering processes in the Randall - Sundum (RS) model. We have evaluated the contribution of the scalar unparticle on the W - pair production cross-sections at International Linear Colliders (ILC). The results indicate that at the low values of the scaling dimension and the bounds on scale $\Lambda_{U}$ are around few TeV, the cross-sections are much enhanced, which is quite comparable with the W-production in the standard model and hence it is worthwhile to explore in future colliders.
Aero-resonant migration

The process of planet conglomeration, which primarily unfolds in a geometrically thin disc of gas and dust, is often accompanied by dynamical excitation of the forming planets and planetesimals. The ensuing orbital crossing can lead to large-scale collisional fragmentation, populating the system with icy and rocky debris. In a gaseous nebula, such leftover solid matter tends to spiral down towards the host star due to aerodynamic drag. Along the way, the inward drifting debris can encounter planets and gravitationally couple to them via mean-motion resonances, sapping them of their orbital energy and causing them to migrate. Here, we develop a simple theory for this migration mechanism, which we call ‘aero-resonant migration’ (ARM), in which small planetesimals (10 m ≲ s ≲ 10 km) undergo orbital decay due to aerodynamic drag and resonantly shepherd planets ahead of them. Using a combination of analytical calculations and numerical experiments, we show that ARM is a robust migration mechanism, able to significantly transport planets on time-scales ≲1 Myr, and present simple formulae for the ARM rate.
Evolving neutron star+helium star systems to intermediate-mass binary pulsars

Intermediate-mass binary pulsars (IMBPs) are composed of neutron stars (NSs) and CO/ONe white dwarfs (WDs). It is generally suggested that IMBPs evolve from intermediate-mass X-ray binaries (IMXBs). However, this scenario is difficult to explain the formation of IMBPs with orbital periods (Porb) less than 3 d. It has recently been proposed that a system consisting of an NS and a helium (He) star can form IMBPs with Porb less than 3 d (known as the NS+He star scenario), but previous works can only cover a few observed sources with short orbital periods. We aim to investigate the NS+He star scenario by adopting different descriptions of the Eddington accretion rate ($\skew4\dot{M}_{\rm Edd}$) for NSs and different NS masses (MNS) varying from $1.10$ to $1.80\, \rm M_{\odot }$. Our results can cover most of the observed IMBPs with short orbital periods and almost half of the observed IMBPs with long orbital periods. We found that $\skew4\dot{M}_{\rm Edd}$ ∝ MNS−1/3 could match the observations better than a specific value for all NSs. We also found that the final spin periods of NSs slightly decrease with the initial MNS. The observed parameters of PSR J0621+1002, which is one of the well-observed IMBPs whose pulsar mass has been precisely measured, can be reproduced by this work.
Steady state degree and mixed volume of chemical reaction networks

The steady-state degree of a chemical reaction network is the number of complex steady-states, which is a measure of the algebraic complexity of solving the steady-state system. In general, the steady-state degree may be difficult to compute. Here, we give an upper bound to the steady-state degree of a reaction network by utilizing the underlying polyhedral geometry associated with the corresponding polynomial system. We focus on three case studies of infinite families of networks, each generated by joining smaller networks to create larger ones. For each family, we state results on the steady-state degree or give an upper bound in terms of the mixed volume of the corresponding polynomial system.
Audio-Visual Speech Separation and Dereverberation With a Two-Stage Multimodal Network

Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments. In this study, we address joint speech separation and dereverberation, which aims to separate target speech from background noise, interfering speech and room reverberation. In order to tackle this fundamentally difficult problem, we propose a novel multimodal network that exploits both audio and visual signals. The proposed network architecture adopts a two-stage strategy, where a separation module is employed to attenuate background noise and interfering speech in the first stage and a dereverberation module to suppress room reverberation in the second stage. The two modules are first trained separately, and then integrated for joint training, which is based on a new multi-objective loss function. Our experimental results show that the proposed multimodal network yields consistently better objective intelligibility and perceptual quality than several one-stage and two-stage baselines. We find that our network achieves a 21.10% improvement in ESTOI and a 0.79 improvement in PESQ over the unprocessed mixtures. Moreover, our network architecture does not require the knowledge of the number of speakers.
Asymptotic Stability of Harmonic Maps on the Hyperbolic Plane under the Schrödinger Maps Evolution

We consider the Cauchy problem for the Schrödinger maps evolution when the domain is the hyperbolic plane. An interesting feature of this problem compared to the more widely studied case on the Euclidean plane is the existence of a rich new family of finite energy harmonic maps. These are stationary solutions, and thus play an important role in the dynamics of Schrödinger maps. The main result of this article is the asymptotic stability of (some of) such harmonic maps under the Schrödinger maps evolution. More precisely, we prove the nonlinear asymptotic stability of a finite energy equivariant harmonic map Q under the Schrödinger maps evolution with respect to non‐equivariant perturbations, provided Q obeys a suitable linearized stability condition. This condition is known to hold for all equivariant harmonic maps with values in the hyperbolic plane and for a subset of those maps taking values in the sphere. One of the main technical ingredients in the paper is a global‐in‐time local smoothing and Strichartz estimate for the operator obtained by linearization around a harmonic map, proved in the companion paper [36]. © 2021 Wiley Periodicals LLC.
Alternative Visual Units for an Optimized Phoneme-Based Lipreading System

Lipreading is understanding speech from observed lip movements. An observed series of lip motions is an ordered sequence of visual lip gestures. These gestures are commonly known, but as yet are not formally defined, as `visemes’. In this article, we describe a structured approach which allows us to create speaker-dependent visemes with a fixed number of visemes within each set. We create sets of visemes for sizes two to 45. Each set of visemes is based upon clustering phonemes, thus each set has a unique phoneme-to-viseme mapping. We first present an experiment using these maps and the Resource Management Audio-Visual (RMAV) dataset which shows the effect of changing the viseme map size in speaker-dependent machine lipreading and demonstrate that word recognition with phoneme classifiers is possible. Furthermore, we show that there are intermediate units between visemes and phonemes which are better still. Second, we present a novel two-pass training scheme for phoneme classifiers. This approach uses our new intermediary visual units from our first experiment in the first pass as classifiers; before using the phoneme-to-viseme maps, we retrain these into phoneme classifiers. This method significantly improves on previous lipreading results with RMAV speakers.
Quantum prescriptions are more ontologically distinct than they are operationally distinguishable

Based on an intuitive generalization of the Leibniz principle of `the identity of indiscernibles', we introduce a novel ontological notion of classicality, called bounded ontological distinctness. Formulated as a principle, bounded ontological distinctness equates the distinguishability of a set of operational physical entities to the distinctness of their ontological counterparts. Employing three instances of two-dimensional quantum preparations, we demonstrate the violation of bounded ontological distinctness or excess ontological distinctness of quantum preparations, without invoking any additional assumptions. Moreover, our methodology enables the inference of tight lower bounds on the extent of excess ontological distinctness of quantum preparations. Similarly, we demonstrate excess ontological distinctness of quantum transformations, using three two-dimensional unitary transformations. However, to demonstrate excess ontological distinctness of quantum measurements, an additional assumption such as outcome determinism or bounded ontological distinctness of preparations is required. Moreover, we show that quantum violations of other well-known ontological principles implicate quantum excess ontological distinctness. Finally, to showcase the operational vitality of excess ontological distinctness, we introduce two distinct classes of communication tasks powered by excess ontological distinctness.
Bridging the domain gap in cross-lingual document classification

The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridge the language barrier using language universal representations. However, even if the language problem was resolved, models trained in one language would not transfer to another language perfectly due to the natural domain drift across languages and cultures. We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language. We combine state-of-the-art cross-lingual methods with recently proposed methods for weakly supervised learning such as unsupervised pre-training and unsupervised data augmentation to simultaneously close both the language gap and the domain gap in XLU. We show that addressing the domain gap is crucial. We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification.
Neural physical engines for inferring the halo mass distribution function

An ambitious goal in cosmology is to forward model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ∼Mpc scales and the distribution of dark matter haloes (used as a proxy for galaxies) in the same region. Here, we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model. The method is based on the Bayesian analysis of simple, physically motivated, neural network-like architectures, which we denote as neural physical engines, and neural density estimation. As a result, we are able to sample the initial phases of the dark matter density field while inferring the parameters describing the halo mass distribution function, providing a fully Bayesian interpretation of both the initial dark matter density distribution and the neural bias model. We successfully run an upgraded borg (Bayesian Origin Reconstruction from Galaxies) inference using our new likelihood and neural bias model with halo catalogues derived from full N-body simulations. In preliminary results, we notice there could potentially be orders of magnitude improvement in modelling compared to classical biasing techniques.
VTC J095517.5 + 690813: A radio transient from the accretion-induced collapse of a white dwarf?

I investigate the possibility that a recently reported radio transient in M81, VTC J095517.5 + 690813, was caused by the accretion-induced collapse of a white dwarf. It became bright in the radio, but no associated optical transient was discovered. An accretion-induced collapse is predicted to be radio-bright but optically faint, satisfying the observed properties. I compare the predicted radio emission from an accretion-induced collapse with that of VTC J095517.5 + 690813 and show that this object can be reasonably explained by an accretion-induced collapse. Although it is difficult to firmly conclude that VTC J095517.5 + 690813 is an accretion-induced collapse, my results show that radio-bright transients without an optical counterpart could be related to stellar deaths.
Hashtags Are (Not) Judgemental: The Untold Story of Lok Sabha Elections 2019

Hashtags in online social media have become a way for users to build communities around topics, promote opinions, and categorize messages. In the political context, hashtags on Twitter are used by users to campaign for their parties, spread news, or to get followers and get a general idea by following a discussion built around a hashtag. In the past, researchers have studied certain types and specific properties of hashtags by utilizing a lot of data collected around hashtags. In this pa-per, we perform a large-scale empirical analysis of elections using only the hashtags shared on Twitter during the 2019 Lok Sabha elections in India. We study the trends and events unfolded on the ground, the latent topics to uncover representative hashtags and semantic similarity to discover sentiments during elections. We collect over 24 million hashtags to perform extensive experiments to find the trending hashtags, and cross-reference them with the tweets in our data set to list down notable events. We also use semantic similarity based techniques to find related hashtags and latent topics among the hashtags.
Nowhere dense graph classes and algorithmic applications. A tutorial at Highlights of Logic, Games and Automata 2019

The notion of nowhere dense graph classes was introduced by Ne\v{s}et\v{r}il and Ossona de Mendez and provides a robust concept of uniform sparseness of graph classes. Nowhere dense classes generalize many familiar classes of sparse graphs such as classes that exclude a fixed graph as a minor or topological minor. They admit several seemingly unrelated natural characterizations that lead to strong algorithmic applications. In particular, the model-checking problem for first-order logic is fixed-parameter tractable over these classes. These notes, prepared for a tutorial at Highlights of Logic, Games and Automata 2019, are a brief introduction to the theory of nowhere denseness, driven by algorithmic applications.
P-BOOST: Parallel Boosting of Optimal Narrow-Band Direction of Arrival Estimators

Optimal Maximum Likelihood (ML), narrow-band direction finding cannot be easily initialized in coherent and low signal to noise ratio environments. Sparse under-determined solvers are considered as viable solutions to this problem, since they drastically reduce the dimensionality of the search space by exploiting the array model sparseness. However, because of quantized locations, conventional sparse solvers present some ambiguity problems. In this work, we propose a novel boosting scheme for ML-type estimators, referred to as Parallel BOOSTer (P-BOOST), where a set of generalized MUSIC solutions provides pre-estimates of the directions and the number of coherent paths for arbitrary sensor array geometry and noise covariance. P-BOOST delivers improved and reliable coarse parameter estimates to a further ML or sparse optimization stage even in coherent and/or high noise scenarios. Moreover, its dataflow is highly parallel, which is essential in foreseen remote sensing and telecommunication applications and fully justifies its acronym.
Geometric PID-type attitude tracking control on SO(3)

This article develops and proposes a geometric nonlinear proportional-integral-derivative (PID) type tracking control scheme on the Lie group of rigid body rotations, SO(3). Like PD-type attitude tracking control schemes that have been proposed in the past, this PID-type control scheme exhibits almost global asymptotic stability in tracking a desired attitude profile. The stability of this PID-type tracking control scheme is shown using a Lyapunov analysis. A numerical simulation study demonstrates the stability of this tracking control scheme, as well as its robustness to a disturbance torque. In addition, a numerical comparison study shows the effectiveness of the proposed integrator term.
Meta-Learning for Few-Shot Time Series Classification

Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered practical scenario where access to labeled training data is difficult, and where DNNs would be prone to overfitting. We leverage recent advancements in gradient-based meta-learning, and propose an approach to train a residual neural network with convolutional layers as a meta-learning agent for few-shot TSC. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, etc.) such that it can solve a target task from another domain using only a small number of training samples from the target task. Most existing meta-learning approaches are limited in practice as they assume a fixed number of target classes across tasks. We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks. To the best of our knowledge, we are the first to use meta-learning based pre-training for TSC. Our approach sets a new benchmark for few-shot TSC, outperforming several strong baselines on few-shot tasks sampled from 41 datasets in UCR TSC Archive. We observe that pre-training under the meta-learning paradigm allows the network to quickly adapt to new unseen tasks with small number of labeled instances.
On the energy of digraphs

Let $D$ be a simple digraph with eigenvalues $z_1,z_2,. ,z_n$. The energy of $D$ is defined as $E(D)= \sum_{i=1}^n |Re(z_i)|$, is the real part of the eigenvalue $z_i$. In this paper a lower bound will be obtained for the spectral radius of $D$, wich improves some the lower bounds that appear in the literature \cite{G-R}, \cite{T-C}. This result allows us to obtain an upper bound for the energy of $ D $. Finally, digraphs are characterized in which this upper bound improves the bounds given in \cite{G-R} and \cite{T-C}.