January 2022 arXiv papers — page 34

Jee-weon Jung, Hemlata Tak, Hye-jin Shim, Hee-Soo Heo

ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or converted) inputs. However, there is little consideration for how ASV systems themselves should be adapted when they are expected to encounter spoofing attacks, nor when they operate in

Na Xu, Jing Chen, Yu-Peng Zhang, Yu-Xiao Liu

Einstein-Gauss-Bonnet gravity in high dimensional spacetime is intriguing. Here, the properties of thick branes generated by a bulk scalar field in the five-dimensional Einstein-Gauss-Bonnet gravity were studied. With the help of the superpotential method, we obtain a series of multi-kink brane solutions. We also analyze the linear stability of the brane sys

M. López-Sánchez, A. Villena-Rodríguez, G. Gómez, F. J. Martín-Vega

In this paper, we propose a novel deep reinforcement learning framework to maximize user fairness in terms of delay. To this end, we devise a new version of the modified largest weighted delay first (M-LWDF) algorithm, which is called $\beta$-M-LWDF, aiming to fulfill an appropriate balance between user fairness and average delay. This balance is defined as

David Monniaux, Sylvain Boulmé

CompCert is the first realistic formally verified compiler: it provides a machine-checked mathematical proof that the code it generates matches the source code. Yet, there could be loopholes in this approach. We comprehensively analyze aspects of CompCert where errors could lead to incorrect code being generated. Possible issues range from the modeling of th

Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian

Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often l

O. Vallhagen, I. Pusztai, M. Hoppe, S. L. Newton

An effective disruption mitigation system in a tokamak reactor should limit the exposure of the wall to localized heat losses and to the impact of high current runaway electron beams, and avoid excessive forces on the structure. We evaluate with respect to these aspects a two-stage deuterium-neon shattered pellet injection in an ITER-like plasma, using simul

Keshari Nandan, Barun Ghosh, Amit Agarwal, Somnath Bhowmick

We report the performance of field-effect transistors (FETs), comprised of mono-layer of recently synthesized layered two-dimensional MoSi2N_4 as channel material, using the first principles quantum transport simulations. The devices' performance is assessed as per the International Roadmap for Devices and Systems (IRDS) 2020 roadmap for the year 2034 and co

Duilio De Santis, Claudio Guarcello, Bernardo Spagnolo, Angelo Carollo

The generation of travelling sine-Gordon breathers is achieved through the nonlinear supratransmission effect in a magnetically driven long Josephson junction, in the presence of losses, a current bias, and a thermal noise source. We demonstrate how to exclusively induce breather modes by means of controlled magnetic pulses. A nonmonotonic behavior of the br

Jin Huang, Jantien Stoter, Ravi Peters, Liangliang Nan

We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical wa

Guy Andre Boy

This chapter focuses on the evolution of Human-Centered Design (HCD) in aerospace systems over the last forty years. Human Factors and Ergonomics first shifted from the study of physical and medical issues to cognitive issues circa the 1980s. The advent of computers brought with it the development of human-computer interaction (HCI), which then expanded into

Luwei Xiao, Xingjiao Wu, Wen Wu, Jing Yang

Nowadays, with the explosive growth of multimodal reviews on social media platforms, multimodal sentiment analysis has recently gained popularity because of its high relevance to these social media posts. Although most previous studies design various fusion frameworks for learning an interactive representation of multiple modalities, they fail to incorporate

Amber Srivastava, S. M. Salapaka

In this paper we address the class of Sequential Decision Making (SDM) problems that are characterized by time-varying parameters. These parameter dynamics are either pre-specified or manipulable. At any given time instant the decision policy -- that governs the sequential decisions -- along with all the parameter values determines the cumulative cost incurr

Yilong Wang, Zhenyu Li, Daofu Gong, Haoyu Lu

To address the security risk caused by fixed offset mapping and the limited recoverability of random mapping used in image watermarking, we propose an image self-embedding fragile watermarking algorithm based on deneighborhood mapping. First, the image is divided into several 2*2 blocks, and authentication watermark and recovery watermark are generated based

Pranav Jeevan, Amit sethi

Vision transformers (ViTs) have found only limited practical use in processing images, in spite of their state-of-the-art accuracy on certain benchmarks. The reason for their limited use include their need for larger training datasets and more computational resources compared to convolutional neural networks (CNNs), owing to the quadratic complexity of their

Sankar Das Sarma, Fengcheng Wu

Recent measurements in several different laboratories report the observation of an approximately linear-in-temperature resistivity with a large twist-angle-dependent slope (or temperature coefficient) in moir\'e twisted bilayer graphene (tBLG) down to a few K and sometimes to much lower temperatures. In this note, we theoretically discuss this `strange metal

Rocsildes Canoy, Victor Bucarey, Yves Molenbruch, Maxime Mulamba

We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the historical data: one is through a probability estimation method that lea

Yunpeng Ma, Andreas Kassler, Bestoun S. Ahmed, Pavel Krakhmalev

Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment. An essential reason for material waste is a low degree of automation, especially in industries that currently have a low degree of digitalization, such as steel forging. Tho

Udi Boker, Karoliina Lehtinen, Salomon Sickert

While the complexity of translating future linear temporal logic (LTL) into automata on infinite words is well-understood, the size increase involved in turning automata back to LTL is not. In particular, there is no known elementary bound on the complexity of translating deterministic $\omega$-regular automata to LTL. Our first contribution consists of tigh

Mohan Sridharan, Tiago Mota

Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to explore the internal representations and reasoning mechanis

M. A. Masharin, V. A. Shahnazaryan, F. A. Benimetskiy, D. N. Krizhanovskii

Exciton-polaritons offer a versatile platform for realization of all-optical integrated logic gates due to the strong effective optical nonlinearity resulting from the exciton-exciton interactions. In most of the current excitonic materials there exists a direct connection between the exciton robustness to thermal fluctuations and the strength of exciton-exc

Sanchari Pal

We revisit the holographic calculation of the decay modes of the BTZ blackhole perturbed by a scalar probe. We carry out a finite temperature expansion of the torus two point function of large central charge $(c)$ CFTs in order to obtain the corrections to blackhole quasinormal modes. We take the contribution of the lightest primary above the vacuum, with di

Zihua Chai, Ying Liu, Mengqi Wang, Yuhang Guo

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the

Mael Jullien, Marco Valentino, Andre Freitas

With the methodological support of probing (or diagnostic classification), recent studies have demonstrated that Transformers encode syntactic and semantic information to some extent. Following this line of research, this paper aims at taking semantic probing to an abstraction extreme with the goal of answering the following research question: can contempora

E. Can Artun, Ibrahim Kecoglu, Alpar Turkoglu, A. Nihat Berker

A complexity classification scheme is developed from the fractal spectra of spin-glass chaos and demonstrated with multigeographic multicultural music and brain electroencephalogram signals. Systematic patterns are found to emerge. Chaos under scale change is the essence of spin-glass ordering and can be obtained, continuously tailor-made, from the exact ren

Adith Sai Aramthottil, Utso Bhattacharya, Daniel González-Cuadra, Maciej Lewenstein

The weak ergodicity breaking induced by quantum many-body scars (QMBS) represents an intriguing concept that has received great attention in recent years due to its relation to unusual non-equilibrium behaviour. Here we reveal that this phenomenon can occur in a previously unexplored regime of a lattice gauge theory, where QMBS emerge due to the presence of

Ziyang Lu, Yiwei Zhang

Motivated by applications in DNA-based storage and communication systems, we study deletion and insertion errors simultaneously in a burst. In particular, we study a type of error named $t$-deletion-$s$-insertion-burst ($(t,s)$-burst for short) which is a generalization of the $(2,1)$-burst error proposed by Schoeny {\it et. al}. Such an error deletes $t$ co

So Okano, Teruaki Suyama

The detection of gravitational-wave events revealed that there are numerous populations of black hole (BH) binaries that can merge within the age of the Universe. Although several formation channels of such binaries are known, considerable theoretical uncertainties associated with each channel defeat the robust prediction of how much each channel contributes

Patrick Ruediger, Felix Claus, Viktor Leonhardt, Hans Hagen

We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry,

Bastian Hilder, Upanshu Sharma

Coarse-graining techniques play a central role in reducing the complexity of stochastic models, and are typically characterised by a mapping which projects the full state of the system onto a smaller set of variables which captures the essential features of the system. Starting with a continuous-time Markov chain, in this work we propose and analyse an effec

Haowei Wang, Songhao Wang, Qun Meng, Szu Hui Ng

In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with Bayesian parallel optimization. It co

Tomáš Fürst, Anna Minarovičová, Františka Sandroni, Jakub Dostál

We present a toy model of opinion spreading in a society which combines a self-reinforcing mechanism with diffusion. The relative strength of these two mechanisms - called the affectability of the system - is a free parameter of the model. The model is run on a scale-free network and its asymptotic behaviour is investigated. A surprising emergent effect is o

Jian Feng, Haoyuan Pan, Tse-Tin Chan, Jiaxin Liang

This paper investigates the information freshness of two-hop networks. Age of information (AoI) is used as the metric to characterize the information freshness, defined as the time elapsed since the latest received status update was generated. In error-prone wireless networks, prior studies indicated that Automatic Repeat-reQuest (ARQ) does not help improve

Mohamed Ali Souibgui, Sanket Biswas, Sana Khamekhem Jemni, Yousri Kessentini

Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images

Vern I. Paulsen, Dinesh Singh

The classical inequality of Bohr concerning Taylor coeficients of bounded holomorphic functions on the unit disk, has proved to be of significance in answering in the negative the conjecture that if the non-unital von Neumann inequality held for a Banach algebra then it was necessarily an operator algebra. Here we provide a rather short and easy proof of the

Adeel Malik, Berksan Serbetci, Petros Elia

This work elevates coded caching networks from their purely information-theoretic framework to a stochastic setting, by exploring the effect of random user activity and by exploiting correlations in the activity patterns of different users. In particular, the work studies the $K$-user cache-aided broadcast channel with a limited number of cache states, and e

Lorenzo Porcaro, Emilia Gómez, Carlos Castillo

Music listening in today's digital spaces is highly characterized by the availability of huge music catalogues, accessible by people all over the world. In this scenario, recommender systems are designed to guide listeners in finding tracks and artists that best fit their requests, having therefore the power to influence the diversity of the music they liste

Prabhat Kumar

This paper provides a simple, compact and efficient 90-line pedagogical MATLAB code for topology optimization using hexagonal elements (honeycomb tessellation). Hexagonal elements provide nonsingular connectivity between two juxtaposed elements and, thus, subdue checkerboard patterns and point connections inherently from the optimized designs. A novel approa

Ruochen Tai, Liyong Lin, Rong Su

In this work, we investigate how to make use of model reduction techniques to identify the vulnerability of a closed-loop system, consisting of a plant and a supervisor, that might invite attacks. Here, the system vulnerability refers to the existence of key observation sequences that could be exploited by a specific smart sensor attack to cause damage infli

Ali Emami Kopaei, Xuedong Tian, Krzysztof Giergiel, Krzysztof Sacha

It is common knowledge that atoms can form molecules if they attract each other. Here, we show that it is possible to create molecules where bound states of the atoms are not the result of attractive interactions but have the topological origin. That is, the bound states of the atoms correspond to the topologically protected edge states of a topological mode

Xiaoyu Wang, Mikael Johansson

A theoretical, and potentially also practical, problem with stochastic gradient descent is that trajectories may escape to infinity. In this note, we investigate uniform boundedness properties of iterates and function values along the trajectories of the stochastic gradient descent algorithm and its important momentum variant. Under smoothness and $R$-dissip

Takaaki Nomura, Hiroshi Okada

We propose a supersymmetric radiative seesaw model with modular $A_4$ symmetry. Thanks to contributions of supersymmetric partners to one-loop diagrams generating neutrino masses, we successfully fit neutrino data and obtain predictions in case of normal hierarchy in a minimal framework that would not be realized in a non-supersymmetric model. We show a seve

Luis Lebron, Yvette Graham, Kevin McGuinness, Konstantinos Kouramas

Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important. Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated capti

Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, Xiong-lin Luo

Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we propose a Gaussian Mixture Discriminant Analysis (GMDA) with noisy label for each class. We introduce flipping probability and class probability and use EM algorithms to solve the

F. Chioma Onyeagusi, Jens Teiser, Niclas Schneider, Gerhard Wurm

We present a method for measurements of electric dipole moments on (sub)-mm size (basalt) particles levitated in an acoustic trap and centered within a plate capacitor. If an electric field is applied the particles oscillate with specific frequencies due to their permanent dipole moments. We observe dipole moments on the order of $D_P = 10^{-15} ... 10^{-14}

Chiara Franceschini, Patrícia Gonçalves, Beatriz Salvador

We analyze the generalized symmetric exclusion process, which allows at most $\alpha$ particles per site, and we put it in contact with stochastic reservoirs whose strength is regulated by a parameter $\theta\in\mathbb R$. We prove that the hydrodynamic behavior is given by the heat equation and depending on the value of $\theta$, the equation is supplemente

François Ollivier, Brahim Sadik

A generalization of L{\"u}roth's theorem expresses that every transcendence degree 1 subfield of the rational function field is a simple extension. In this note we show that a classical proof of this theorem also holds to prove this generalization.

Chao Zhang, Bo Li, Zhiyun Lu, Tara N. Sainath

The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR). To estimate the output distributions over subword units, RNN-T uses a fully connected layer as the joint network to fuse the acoustic representations extracted using the acoustic encoder with the text repres

Rosa Arboretti, Riccardo Ceccato, Luca Pegoraro, Luigi Salmaso

An increasing number of publications present the joint application of Design of Experiments (DOE) and machine learning (ML) as a methodology to collect and analyze data on a specific industrial phenomenon. However, the literature shows that the choice of the design for data collection and model for data analysis is often not driven by statistical or algorith

Samuel Passaglia, Wayne Hu

Ultralight axions are theoretically interesting and phenomenologically rich dark sector candidates, but they are difficult to track across cosmological timescales because of their fast oscillations. We resolve this problem by developing a novel method to evolve them efficiently and accurately. We first construct an exact effective fluid which at late times m

Jesper Byggmästar, Giorgos Nikoulis, Aslak Fellman, Fredric Granberg

We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and three-body terms, and a Gaussian approximation potential with the SOAP descriptor. All potentials are trained to the same div

Ya-nan Han, Jian-wei Liu, Bing-biao Xiao, Xin-Tan Wang

Recent years have witnessed enormous progress of online learning. However, a major challenge on the road to artificial agents is concept drift, that is, the data probability distribution would change where the data instance arrives sequentially in a stream fashion, which would lead to catastrophic forgetting and degrade the performance of the model. In this

Partha Lahiri, Nicola Salvati

In this paper we propose a flexible nested error regression small area model with high dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area specific mo

Pawan Kumar Mishra, Shradha Mishra

We study a collection of self-propelled polar particles on a two-dimensional substrate with birth and death. We introduce a minimal lattice model for the system using active Ising spins, where each particle can have two possible orientations. The activity is modeled as a biased movement of the particle along its direction of orientation. The particles also a

Balthazar Charles

In this extended abstract, we show how a bijection between parking functions and regions of the Shi arrangement from [Athanasiadis, Linusson '99] (in type $A_n$) and [Armstrong, Reiner, Rhoades '15] (in type $B_n, C_n, D_n$) allows for the computation of the minimal elements of the Shi regions. This gives a combinatorial interpretation of these minimal eleme

Claudio De Persis, Monica Rotulo, Pietro Tesi

We introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancellation. These conditions take the compact form of data-dependent semi-definite programs. The method returns controllers that can be certified to stabilize the system even when data are perturb

Anna N. Morozovska, Hanna V. Shevliakova, Yaroslava Yu. Lopatina, Mykola Yelisieiev

Local current-voltage characteristics for low-dimensional transition metal dichalcogenides (LD-TMD), as well as the reconstruction of their local density of states (LDOS) from scanning tunneling microscopy (STM) experiments is of fundamental interest and can be useful for advanced applications. Most of existing models are either hardly applicable for the LD-

Polina Guseva, Anastasia Drozdova, Natalia Denisenko, Daria Sapozhnikova

A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we present a semantic code classification task and discuss methods for solving this problem on the Natural Language to Mac

Raffael Hagger

We give a characterization of compact and Fredholm operators on polyanalytic Fock spaces in terms of limit operators. As an application we obtain a generalization of the Bauer-Isralowitz theorem using a matrix valued Berezin type transform. We then apply this theorem to Toeplitz and Hankel operators to obtain necessary and sufficient conditions for compactne

Joseph Muller

In this paper, we study the cohomology of the unitary unramified PEL Rapoport-Zink space of signature $(1,n-1)$ at maximal level. Our method revolves around the spectral sequence associated to the open cover by the analytical tubes of the closed Bruhat-Tits strata in the special fiber, which were constructed by Vollaard and Wedhorn. The cohomology of these s

Jörg Liesen, Mohamed M. S. Nasser, Olivier Sète

We apply the Charge Simulation Method (CSM) in order to compute the logarithmic capacity of compact sets consisting of (infinitely) many "small" components. This application allows to use just a single charge point for each component. The resulting method therefore is significantly more efficient than methods based on discretizations of the boundaries (for e

Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier, Stéphane Marchand-Maillet

We present novel active learning strategies dedicated to providing a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached labels. Moreover, proposed strategies are designed to handle an imbalanced context in which random selection is highly inefficient. Specifically, our active learning iterations add

Subhajit Sarkar

In the search for life in the Universe, exoplanets represent numerous natural experiments in planet formation, evolution, and the emergence of life. This raises the fascinating prospect of evaluating cosmic life on a statistical basis. One key statistic is the occurrence rate of life-bearing worlds, $f_{\rm L}$, the 'frequency of life' term in the famous Dra

Marco Benini, Jonathan P. Pridham, Alexander Schenkel

We study the quantization of the canonical unshifted Poisson structure on the derived cotangent stack $T^\ast[X/G]$ of a quotient stack, where $X$ is a smooth affine scheme with an action of a (reductive) smooth affine group scheme $G$. This is achieved through an {\'e}tale resolution of $T^\ast[X/G]$ by stacky CDGAs that allows for an explicit description o

Frank de Meijer, Renata Sotirov

In this paper we study the well-known Chv\'atal-Gomory (CG) procedure for the class of integer semidefinite programs (ISDPs). We prove several results regarding the hierarchy of relaxations obtained by iterating this procedure. We also study different formulations of the elementary closure of spectrahedra. A polyhedral description of the elementary closure f

Yongsheng Wang, Xiaoxuan Luo, Bo Li, Zaoyu Chen

Asymmetric microcavities supporting Whispering-gallery modes (WGMs) are of great significance for on-chip optical information processing. We define asymmetric microcavities on topologically curved surfaces, where the geodesic light trajectories completely reconstruct the cavity mode features. The quality factors of the lossy chaotic and island modes in flat

Antonio Norelli, Giorgio Mariani, Luca Moschella, Andrea Santilli

We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g. explanations written in hieroglyphic -- by autonomously learning to interpret them. In EL, the burden of interpreting symbols is not left to humans or rigid human-coded compilers, as done in Program Synthesis. Rather, EL calls for a

Andreas Kasis, Kanwal Khan, Marios M. Polycarpou, Stelios Timotheou

The increasing participation of local generation and controllable demand units within the power network motivates the use of distributed schemes for their control. Simultaneously, it raises two issues; achieving an optimal power allocation among these units, and securing the privacy of the generation/demand profiles. This study considers the problem of desig

E. V. Petrova, E. S. Tiunov, M. C. Bañuls, A. K. Fedorov

The lattice Schwinger model (SM), the discrete version of QED in 1+1 dimensions, is a well-studied test bench for lattice gauge theories. Here we study the fractal properties of the SM. We reveal the self-similarity of the ground state, which allows one to develop a recurrent procedure for finding the ground-state wave functions and predicting ground-state e

Guixiang Hong, Congbian Ma, Yu Wang

In this paper, we continue the study of John-Nirenberg theorems for BMO/Lipschitz spaces in the noncommutative martingale setting. As conjectured from the classical case, a desired noncommutative ``stopping time" argument was discovered to obtain the distribution function inequality form of John-Nirenberg theorem. This not only provides another approach with

Tharaj Thaj, Emanuele Viterbo

This paper presents unitary-precoded single-carrier (USC) modulation as a family of waveforms based on multiplexing the information symbols on time domain unitary basis functions. The common property of these basis functions is that they span the entire time and frequency plane. The recently proposed orthogonal time frequency space (OTFS) and orthogonal time

Jaime Pons Garrido

The evolution of skyline and ranking queries has created new archetypes like flexible skylines, which have proven to be an efficient method to select relevant data from large datasets using multi objective optimization. This paper aims to study the possible applications of Poisson distribution mass function as a monotonic scoring function in flexible skyline

Giacomo Prando, Daniele Torsello, Samuele Sanna, Michael J. Graf

We report on the investigation of the magnetic superconductor EuFe$_{2}$(As$_{0.7}$P$_{0.3}$)$_{2}$ based on muon-spin spectroscopy and ac magnetic susceptibility ($\chi$) measurements. The dependence of the internal field at the muon site on temperature is indicative of a ferromagnetic ordering of Eu$^{2+}$ magnetic moments and only the conventional magnon

Federico Fatone, Stefania Fresca, Andrea Manzoni

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs - built, e.g., exclusively through proper orthogonal decomposition (POD) - when applied to nonlinear time-dependent parametrized PDEs. In particular, POD-DL-ROMs can achieve extreme efficiency in the training stage and fas

Celeste Johnson, Mbavhalelo Mulokwe, João P. Rodrigues

In the context of the $AdS_{4}/CFT_{3}$ correspondence between higher spin fields and vector theories, we use the constructive bilocal fields based approach to this correspondence, to demonstrate, at the $IR$ critical point of the interacting vector theory and directly in the bulk, the removal of the $\Delta=1$ ($s=0$) state from the higher spins field spect

Parosh Aziz Abdulla, Mohamed Faouzi Atig, Raj Aryan Agarwal, Adwait Godbole

We present $\textit{Probabilistic Total Store Ordering (PTSO)}$ -- a probabilistic extension of the classical TSO semantics. For a given (finite-state) program, the operational semantics of PTSO induces an infinite-state Markov chain. We resolve the inherent non-determinism due to process schedulings and memory updates according to given probability distribu

Chunren Tang, Dingyu Xue, Dongyue Chen

Clustering-based approach has proved effective in dealing with unsupervised domain adaptive person re-identification (ReID) tasks. However, existing works along this approach still suffer from noisy pseudo labels and the unreliable generalization ability during the whole training process. To solve these problems, this paper proposes a new approach to learn t

Spyridon Pougkakiotis, Jacek Gondzio, Dionysios S. Kalogerias

In this paper we present an active-set method for the solution of $\ell_1$-regularized convex quadratic optimization problems. It is derived by combining a proximal method of multipliers (PMM) strategy with a standard semismooth Newton method (SSN). The resulting linear systems are solved using a Krylov-subspace method, accelerated by certain general-purpose

Florian Auer, Johann Frei, Dominik Müller, Frank Kramer

We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become an essential asset for remote courses and a reasonable standard for structured code submission concerning increasing numbers of students in computer sciences. Utilizing automated co

Wanqing Zhu, Bing Guan, Shanshan Wang, Minghui Zhang

The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit formation of either coil sensitivity profiles or a cross-coil correlation operator, and as a result reconstruction corresponds to solving a challenging bilinear optimization probl

Jakob Björnberg, Hjalmar Rosengren, Kieran Ryan

We present a detailed analysis of certain quantum spin systems with inhomogeneous (non-random) mean-field interactions. Examples include, but are not limited to, the interchange- and spin singlet projection interactions on complete bipartite graphs. Using two instances of the representation theoretic framework of Schur--Weyl duality, we can explicitly comput

Abhishek Chakrabortty, Guorong Dai, Raymond J. Carroll

We consider quantile estimation in a semi-supervised setting, characterized by two available data sets: (i) a small or moderate sized labeled data set containing observations for a response and a set of possibly high dimensional covariates, and (ii) a much larger unlabeled data set where only the covariates are observed. We propose a family of semi-supervise

Wenyong Huang, Zhenhe Zhang, Yu Ting Yeung, Xin Jiang

We introduce a new approach for speech pre-training named SPIRAL which works by learning denoising representation of perturbed data in a teacher-student framework. Specifically, given a speech utterance, we first feed the utterance to a teacher network to obtain corresponding representation. Then the same utterance is perturbed and fed to a student network.

Ibrahim Almuslimani

A novel second order family of explicit stabilized Runge-Kutta-Chebyshev methods for advection-diffusion-reaction equations is introduced. The new methods outperform existing schemes for relatively high Peclet number due to their favorable stability properties and explicitly available coefficients. The construction of the new schemes is based on stabilizatio

Matteo Gallone, Alessandro Michelangeli

This monograph contains revised and enlarged materials from previous lecture notes of undergraduate and graduate courses and seminars delivered by both authors over the last years on a subject that is central both in abstract operator theory and in applications to quantum mechanics: to decide whether a given densely defined and symmetric operator on Hilbert

Camillo De Lellis, Jonas Hirsch, Andrea Marchese, Luca Spolaor

Consider an area minimizing current modulo $p$ of dimension $m$ in a smooth Riemannian manifold of dimension $m+1$. We prove that its interior singular set is, up to a relatively closed set of dimension at most $m-2$, a $C^{1,\alpha}$ submanifold of dimension $m-1$ at which, locally, $N\leq p$ regular sheets of the current join transversally, each sheet coun

Zhan-Wei Yao, Hong-Hui Chen, Si-Bin Lu, Run-Bing Li

We realize a Mach-Zehnder-type dual-atom-interferometer gyroscope with an interrogation arm of 40 cm length and the interference area up to 1.2 cm$^2$. The precise angular alignment of the large-scale separated Raman lasers is demonstrated by seeking the phase intersection of Ramsey-Bord$\acute{e}$ interferometers after the gravity effect is compensated and

Yun-Long Liu, Zhao-Qing Feng, Xiang-Dong Zhang

A new polymer black hole solution in loop quantum gravity was proposed recently. The difference between the polymer black hole and Schwarzschild black hole is captured by a quantum parameter $A$. In order to get the constraints on parameter $A$, we consider the observational constraints imposed on $A$ by using the Solar System experiments and calculate the d

Giacomo Ortali, Ioannis G. Tollis

A weak dominance drawing $\Gamma$ of a DAG $G=(V,E)$, is a $d$-dimensional drawing such that there is a directed path from a vertex $u$ to a vertex $v$ in $G$ if $D(u) <D(v)$ for every dimension $D$ of $\Gamma$. We have a \emph{falsely implied path (fip)} when $D(u) < D(v)$ for every dimension $D$ of~$\Gamma$, but there is no path from $u$ to $v$. Minimizing

Thomas Häner, Mathias Soeken

We determine the exact AND-gate cost of checking if $a\leq x < b$, where $a$ and $b$ are constant integers. Perhaps surprisingly, we find that the cost of interval checking never exceeds that of a single comparison and, in some cases, it is even lower.

Giacomo Sesti, Daniele Varsano, Elisa Molinari, Massimo Rontani

The screening of Coulomb interaction controls many-body physics in carbon nanotubes, as it tunes the range and strength of the force that acts on charge carriers and binds electron-hole pairs into excitons. In doped tubes, the effective Coulomb interaction drives the competition between Luttinger liquid and Wigner crystal, whereas in undoped narrow-gap tubes

Neha Baranwal, Sharatkumar Chilaka

This report proposes state-of-the-art research in the field of Computer Assisted Language Learning (CALL). Mispronunciation detection is one of the core components of Computer Assisted Pronunciation Training (CAPT) systems which is a subset of CALL. Studies on automated pronunciation error detection began in the 1990s, but the development of fullfledged CAPT

Lintao Ye, Ming Chi, Zhi-Wei Liu, Vijay Gupta

We study the simultaneous actuator selection and controller design problem for linear quadratic regulation with Gaussian noise over a finite horizon of length $T$ and unknown system model. We consider both episodic and non-episodic settings of the problem and propose online algorithms that specify both the sets of actuators to be utilized under a cardinality

Shurui Li, Puneet Gupta

Applications of neural networks on edge systems have proliferated in recent years but the ever-increasing model size makes neural networks not able to deploy on resource-constrained microcontrollers efficiently. We propose bit-serial weight pools, an end-to-end framework that includes network compression and acceleration of arbitrary sub-byte precision. The

Ryan Alvarado, Fan Wang, Dachun Yang, Wen Yuan

In this article, the authors establish the pointwise characterization of Besov and Triebel-Lizorkin spaces on spaces of homogeneous type via clarifying the relationship among Haj\l asz-Sobolev spaces, Haj\l asz-Besov and Haj\l asz-Triebel-Lizorkin spaces, grand Besov and Triebel-Lizorkin spaces, and Besov and Triebel-Lizorkin spaces. A major novelty of this

Mengxue Bai, Jian Zhang, Shihui Zhu

This paper is concerned with the generalized Davey-Stewarston system in two dimensional space. Existence and stability of small solitons are proved by solving two correlative constrained variational problems and spectrum analysis. In addition, multi-solitons with different speeds are constructed by bootstrap argument.

Fatemeh Davoodi, Masoud Taleb, Florian K. Diekmann, Toon Coenen

Transition-metal dichalcogenides with their exciton-dominated optical behavior emerge as promising materials for realizing strong light-matter interactions in the visible range and at ambient conditions. When these materials are combined with metals, the energy confining ability of plasmon polaritons in metals below the diffraction limit, allows for further

Nikolaos Diamantis, Larry Rolen

Bruinier, Funke, and Imamoglu have proved a formula for what can philosophically be called the "central $L$-value" of the modular $j$-invariant. Previously, this had been heuristically suggested by Zagier. Here, we interpret this "$L$-value" as the value of an actual $L$-series, and extend it to all integral arguments and to a large class of harmonic Maass f

Dylan Savoia, Alessio Ragno, Roberto Capobianco

It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to dete

Dominik Kurzydłowski

Benzoic acid (BA) is a model system for studying proton transfer (PT) reactions. The properties of solid BA subject to high pressure (exceeding 1 kbar = 0.1 GPa) are of particular interest due to the possibility of compression-tuning of the PT barrier. Here we present a simulations aimed a evaluating the value of this barrier in solid BA in the 1 atm - 15 GP

Alessio Ragno, Dylan Savoia, Roberto Capobianco

Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial cas