May 2022 arXiv papers — page 125

Kealan Dunnett, Shantanu Pal, Zahra Jadidi

The emergence of the Internet of Things (IoT) technology has caused a powerful transition in the cyber threat landscape. As a result, organisations have had to find new ways to better manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing

Mingbo Hong, Yuhang Lu, Nianjin Ye, Chunyu Lin

Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane-induced parallax, which will make the predicted homography co

Qing Li, Wengang Zhou, Zhenbo Lu, Houqiang Li

Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks. However, they still suffer two nontrivial obstacles, i.e., low sample efficiency and overestimation bias. To this end, we propose Simultaneous Double Q-learning with Conservative Advantage Learning (SDQ-CAL). Our SDQ-CAL boosts the Double Q-l

Siyang Jiang, Wei Ding, Hsi-Wen Chen, Ming-Syan Chen

Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set. While these works assume that the support set and the query set lie in the same embedding space, a distribution shift usually occurs between the support set and the query set, i.e., the Support-Query Shift, in

Myeongjun Jang, Frank Mtumbuka, Thomas Lukasiewicz

The logical negation property (LNP), which implies generating different predictions for semantically opposite inputs, is an important property that a trustworthy language model must satisfy. However, much recent evidence shows that large-size pre-trained language models (PLMs) do not satisfy this property. In this paper, we perform experiments using probing

Jakub Benda, Zdeněk Mašín

We formulate a computationally efficient time-independent method based on the multi-electron molecular R-matrix formalism. This method is used to calculate transition matrix elements for the multi-photon ionization of atoms and molecules under the influence of a perturbative field. The method relies on the partitioning of space which allows us to calculate t

Haoming Ma, Xiaojun Yuan, Zhi Ding, Dian Fan

To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). In OA-FMTL, the local updates of edge devices are sparsified, compressed, and then sent over the

Kanishka P. Wijewardena, Steven A. Grosz, Kai Cao, Anil K. Jain

Much of the success of fingerprint recognition is attributed to minutiae-based fingerprint representation. It was believed that minutiae templates could not be inverted to obtain a high fidelity fingerprint image, but this assumption has been shown to be false. The success of deep learning has resulted in alternative fingerprint representations (embeddings),

Zikai Song, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang

Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale

Orith Toledo-Ronen, Matan Orbach, Yoav Katz, Noam Slonim

Targeted Sentiment Analysis (TSA) is a central task for generating insights from consumer reviews. Such content is extremely diverse, with sites like Amazon or Yelp containing reviews on products and businesses from many different domains. A real-world TSA system should gracefully handle that diversity. This can be achieved by a multi-domain model -- one tha

Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He

With the representation learning capability of the deep learning models, deep embedded multi-view clustering (MVC) achieves impressive performance in many scenarios and has become increasingly popular in recent years. Although great progress has been made in this field, most existing methods merely focus on learning the latent representations and ignore that

Tingxiu Chen, Jianqin Yin, Jin Tang

In recent years, audio-visual event localization has attracted much attention. It's purpose is to detect the segment containing audio-visual events and recognize the event category from untrimmed videos. Existing methods use audio-guided visual attention to lead the model pay attention to the spatial area of the ongoing event, devoting to the correlation

Anton Plaksin

The paper deals with path-dependent Hamilton-Jacobi equations with a coinvariant derivative which arise in investigations of optimal control problems and differential games for neutral-type systems in Hale's form. A viscosity (generalized) solution of a Cauchy problem for such equations is considered. The existence, uniqueness, and consistency of the vis

Meng Ding, Xiao Fu, Xi-Le Zhao

The block-term tensor decomposition model with multilinear rank-$(L_r,L_r,1)$ terms (or, the "LL1 tensor decomposition" in short) offers a valuable alternative for hyperspectral unmixing (HU) under the linear mixture model. Particularly, the LL1 decomposition ensures the endmember/abundance identifiability in scenarios where such guarantees are not s

Michael Savva, Iacovos Ioannou, Vasos Vassiliou

The investigation in this paper targets the design and the evaluation of jamming intrusion detection based on Fuzzy Logic in wireless mesh IoT Networks in a distributed manner. Our approach uses information collected at local nodes and from the sink as input to the fuzzy logic controller. In order to find the best set of inputs, distributed or centralized, w

George C. Alexandropoulos, Kyriakos Stylianopoulos, Chongwen Huang, Chau Yuen

The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental

Zhi Li, Rizhao Cai, Haoliang Li, Kwok-Yan Lam

Face presentation attack detection (PAD) has been extensively studied by research communities to enhance the security of face recognition systems. Although existing methods have achieved good performance on testing data with similar distribution as the training data, their performance degrades severely in application scenarios with data of unseen distributio

Jose Mari E. Ortega, Rolito G. Eballe

Harmonic centrality calculates the importance of a node in a network by adding the inverse of the geodesic distances of this node to all the other nodes. Harmonic centralization, on the other hand, is the graph-level centrality score based on the node-level harmonic centrality. In this paper, we present some results on both the harmonic centrality and harmon

Jai Kannan, Scott Barnett, Luís Cruz, Anj Simmons

Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure maintainability, developers use code analysis tools to aid them in identifying defects and maintaining standards. With the inclusion o

Cheng Lock Lim

Next-generation wireless networks like 5G promise faster speed, shorter latency, and the ability to connect more devices. Such benefits are set to make drastic changes to the future society, empowering smart cities, enabling autonomous cars, enhancing business processes, changing consumer behaviors, etc. In the financial industry, banks evaluate the deployme

Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai

Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence. In contrast, we model these properties from the whole dataset and use the dataset-level informa

Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai

Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As a result, RE still suffers from unintended entity bias, i.e., the spurious correlation between entity mentions (names) and relations. Entity bias can mislead the RE models to extr

Jiayu Yang, Jose M. Alvarez, Miaomiao Liu

Recent cost volume pyramid based deep neural networks have unlocked the potential of efficiently leveraging high-resolution images for depth inference from multi-view stereo. In general, those approaches assume that the depth of each pixel follows a unimodal distribution. Boundary pixels usually follow a multi-modal distribution as they represent different d

Hangfan Li, Xiaoxiong Zhong, Xinghan Wang, Yun Ji

With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud computing extension version. Users within MEC system could deal with data processing at edge terminals, which can reduce time for communication or data transmission. Multi-armed bandits (MAB) algorithms are powerful tools helpi

Somdatta Goswami, David S. Li, Bruno V. Rego, Marcos Latorre

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Several genetic mutations and predisposing factors that contribute to TAA have been studied in mouse models to characterize specific changes in aortic microstruct

Yuki Takezawa, Kenta Niwa, Makoto Yamada

In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., the Edge-Consensus Learning (ECL)) has been shown to be robust to heterogeneous data and has attracted significant attention in recent years. However, in the ECL, a node needs to exchange dual variables with its neighbors. These exchanges incur significant communicat

Zhihong Fu, Zehua Fu, Qingjie Liu, Wenrui Cai

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted

J. Petráček, V. Kuzmiak

We study a photonic analog of a modified Fano-Anderson model -- a waveguide array with two additional waveguides and by using the coupled mode theory we calculate its spectral and scattering properties. We classify eigenomodes according to vertical symmetry of the structure given by self-coupling coefficients of the additional waveguides and establish the co

Eileen Wang, Caren Han, Josiah Poon

Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have poor correlation with human evaluation scores and d

Wei Chen, Shuzhe Li, Chao Huang, Yanwei Yu

Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods ignore the utilization of historical data or rich contextual features in check-in data, resulting in poor performance for TUL task. In this paper, we propose a novel Mutual distilla

Jianing Wang

Recently, the explosion of online education platforms makes a success in encouraging us to easily access online education resources. However, most of them ignore the integration of massive unstructured information, which inevitably brings the problem of \textit{information overload} and \textit{knowledge trek}. In this paper, we proposed a mathematical knowl

Lijun Yuan, Mingyang Zhang, Ya Yan Lu

For lossless periodic structures with a proper symmetry, the transmission and reflection spectra often have peaks and dips that are truly $100\%$ and $0\%$, respectively. The full peaks and zero dips typically appear near resonant frequencies, and they are robust with respect to structural perturbations that preserve the required symmetry. However, current t

Weiyi Pan

Electronic correlation can strongly influence the electronic properties of two-dimensional (2D) materials with open d- or f-orbitals. Herein, by taking single-layer (SL) H-FeBr$_2$ as a representative of the SL H-FeX$_2$ (X=Cl, Br, I) family, we investigated the electronic correlation effects in the magnetic anisotropy and electronic topology of such a syste

Harsha Vardhan Simhadri, George Williams, Martin Aumüller, Matthijs Douze

Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million points~\citep{Benchmark}. However, deploying recent advances in embedding based techniques for search, recommendation and ranking at scale require ANNS indices at billion, trillion or

Mingxing Li, Li Hu, Zhiwei Xiong, Bang Zhang

Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the hardware cannot withstand the ever-increasing memory requirements as the video length increases. 2) Storing lots of information inevitably introduces lots of noise, which is not co

Chi-Luen Feng, Po-chun Hsu, Hung-yi Lee

Self-Supervised Learning (SSL) has made great strides recently. SSL speech models achieve decent performance on a wide range of downstream tasks, suggesting that they extract different aspects of information from speech. However, how SSL models store various information in hidden representations without interfering is still poorly understood. Taking the rece

Naoki Matsumoto, Yuuki Takai

The cover time of a finite connected graph is the expected number of steps needed for a simple random walk on the graph to visit all vertices of the graph. It is known that the cover time of any finite connected $n$-vertex graph is at least $(1 + o(1)) n \log n$ and at most $(1 + o(1)) \frac{4}{27} n^3$. By Jonasson and Schramm, the cover time of any bounded

Yifan Wang, Guangmo Tong

Modeling the spread of social contagions is central to various applications in social computing. In this paper, we study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can be seamlessly simulated by artificial neural networks with finite VC dimensions, which enables analytic

Chaoqi Yang, Cheng Qian, Jimeng Sun

Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online tensor completion (where incomplete slices arrive gradually). However, in many real-world settings, tensors may have more complex evolving patterns: (i) one or more modes can grow;

Shuhao Yan, Francesca Parise, Eilyan Bitar

We study sample average approximations (SAA) of chance constrained programs. SAA methods typically approximate the actual distribution in the chance constraint using an empirical distribution constructed from random samples assumed to be independent and identically distributed according to the actual distribution. In this paper, we consider a nonstationary v

Mattia Paccamiccio, Leonardo Mostarda

The importance of information security dramatically increased and will further grow due to the shape and nature of the modern computing industry. Software is published at a continuously increasing pace. The Internet of Things and security protocols are two examples of domains that pose a great security challenge, due to how diverse the needs for those softwa

Fadi Al-Turjman, Diletta Cacciagrano, Leonardo Mostarda, Mattia Paccamiccio

Optical Wireless Communication (OWC) is regarded as an auspicious communication approach that can outperform the existing wireless technology. It utilizes LED lights, whose subtle variation in radiant intensity generate a binary data stream. This is perceived by a photodiode, that converts it to electric signals for further interpretation. This article aims

Spencer Van Koevering, Yiwei Lyu, Wenhao Luo, John Dolan

With the increasing need for safe control in the domain of autonomous driving, model-based safety-critical control approaches are widely used, especially Control Barrier Function (CBF)-based approaches. Among them, Exponential CBF (eCBF) is particularly popular due to its realistic applicability to high-relative-degree systems. However, for most of the optim

Sachin Shivakumar, Amritam Das, Siep Weiland, Matthew Peet

It has been shown that the existence of a Partial Integral Equation (PIE) representation of a Partial Differential Equation (PDE) simplifies many numerical aspects of analysis, simulation, and optimal control. However, the PIE representation has not previously been extended to many of the complex, higher-order PDEs such as may be encountered in speculative o

Kathy Blaise, Chinedu Ejiogu, Andrii Iurov, Liubov Zhemchuzhna

We have developed a complete semiclassical Wentzel-Kramers-Brillouin (WKB) theory for $\alpha-\mathcal{T}_3$ model which describes a wide class of existing pseudospin-1 Dirac cone materials. By expanding the sought wave functions in a series over the powers of Planck constant $\hbar$, we have obtained the leading order expansion term which is the key quantit

David Favero, Jesse Huang

We define a basic class of algebras which we call homotopy path algebras. We find that such algebras always admit a cellular resolution and detail the intimate relationship between these algebras, stratifications of topological spaces, and entrance/exit paths. As examples, we prove versions of homological mirror symmetry due to Bondal-Ruan for toric varietie

Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski

We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We focus on obtaining tight, often matching, lower and upper bounds for the sequential minimax regret that are defined as the excess loss it incurs over a class of experts. After provi

Shounak Das, Jason Gross

In this article, we present a method for increasing adaptivity of an existing robust estimation algorithm by learning two parameters to better fit the residual distribution. The analyzed method uses these two parameters to calculate weights for Iterative Re-weighted Least Squares. This adaptive nature of the weights can be helpful in situations where the noi

Akash Kumar, Anand Louis, Rameesh Paul

The problem of finding the largest induced balanced bipartite subgraph in a given graph is NP-hard. This problem is closely related to the problem of finding the smallest Odd Cycle Transversal. In this work, we consider the following model of instances: starting with a set of vertices $V$, a set $S \subseteq V$ of $k$ vertices is chosen and an arbitrary $d$-

Alma L. Albujer, Jorge Alcázar, Magdalena Caballero

In this manuscript we study natural symmetries of Kaehler manifolds: constant holomorphic sectional curvature Kaheler manifolds, semisymmetric Kaehler manifolds and holomorphically pseudosymmetric Kaehler manifolds. We get characterization results, as well as a geometric interpretation of the complex Tachibana tensor.

Nick Heppert, Toki Migimatsu, Brent Yi, Claire Chen

Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets. Articulated objects often come with unexpected articulation mechanisms that are inconsistent with categorical priors: for example, a drawer might rotate about a hinge joint instead of sliding open. We propose

Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu

The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks. To address this issue, parameter-efficient transfer learning methods have been proposed to tune only a few parameters during fine-tuning while freezing the rest. This paper looks at existing methods along this line

Jin-Guo Liu, Xun Gao, Madelyn Cain, Mikhail D. Lukin

We introduce a unified framework to compute the solution space properties of a broad class of combinatorial optimization problems. These properties include finding one of the optimum solutions, counting the number of solutions of a given size, and enumeration and sampling of solutions of a given size. Using the independent set problem as an example, we show

Mehdi Maboudi, MohammadReza Homaei, Soohwan Song, Shirin Malihi

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The se

Zhiwei Peng, John F. Brady

Particle-tracking microrheology of dilute active (self-propelled) colloidal suspensions is studied by considering the external force required to maintain the steady motion of an immersed constant-velocity colloidal probe. If the probe speed is zero, the suspension microstructure is isotropic but exhibits a boundary accumulation of active bath particles at co

Sergey P. Vyatchanin, Albert I. Nazmiev, Andrey B. Matsko

Standard Quantum Limit (SQL) of a classical mechanical force detection results from quantum back action perturbing evolution of a mechanical system. In this paper we show that usage of a multidimensional optical transducer may enable a broadband quantum back action evading measurement. We study a corresponding technique for measurement of a resonant signal f

Yuan Liao, Vasilis Friderikos

To overcome the limited on-board battery issue of nominal airborne base stations (ABSs), we are exploring the use of robotic airborne base station (RABS) with energy neutral grasping end-effectors that are able to autonomously perch at tall urban landforms. Specifically, this paper studies a heterogeneous network (HetNet) assisted by a movable RABS as a smal

Konan Hara, Yuki Ito, Paul Koh

We develop an empirical framework for analyzing dynamic games when the underlying information structure is unknown to the analyst. We introduce \textit{Markov correlated equilibrium}, a dynamic analog of Bayes correlated equilibrium, and show that its predictions coincide with the Markov perfect equilibrium predictions attainable when players observe richer

Tan Liu, Yan Wang, Wen Zhao

In modified gravity theories, such as the Brans-Dicke theory, the background evolution of the Universe and the perturbation around it are different from that in general relativity. Therefore, the gravitational waveforms used to study standard sirens in these theories should be modified. The modifications of the waveforms can be classified into two categories

William H. Clark, Alan J. Michaels

The data used during training in any given application space is directly tied to the performance of the system once deployed. While there are many other factors that go into producing high performance models within machine learning, there is no doubt that the data used to train a system provides the foundation from which to build. One of the underlying rule

Yuantong Li, Chi-hua Wang, Guang Cheng, Will Wei Sun

Two-sided online matching platforms are employed in various markets. However, agents' preferences in the current market are usually implicit and unknown, thus needing to be learned from data. With the growing availability of dynamic side information involved in the decision process, modern online matching methodology demands the capability to track shifting

Darlison Nyirenda, Beaullah Mugwangwavari

Recently, Andrews and Merca have given a new combinatorial interpretation of the total number of even parts in all partitions of n into distinct parts. We generalise this result and consider many more variations of their work. We also highlight some connections with the work of Fu and Tang.

Tomoei Takahashi, George Chikenji, Kei Tokita

In this study, we propose an analytic statistical mechanics approach to solve a fundamental problem in biological physics called protein design. Protein design is an inverse problem of protein structure prediction, and its solution is the amino acid sequence that best stabilizes a given conformation. Despite recent rapid progress in protein design using deep

V. S. Rusetsky, V. A. Golyashov, S. V. Eremeev, D. A. Kustov

New spin-dependent photoemission properties of alkali antimonide semiconductor cathodes are predicted based on the detected optical spin orientation effect and DFT band structure calculations. Using these results, the Na$_2$KSb/Cs$_3$Sb heterostructure is designed as a spin-polarized electron source in combination with the Al$_{0.11}$Ga$_{0.89}$As target as

Abraham Sanders, Tomek Strzalkowski, Mei Si, Albert Chang

Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse. While these agents excel at generating high-quality responses that are relevant t

Ajinkya K Mulay, Sean Lane, Erin Hennes

While running any experiment, we often have to consider the statistical power to ensure an effective study. Statistical power or power ensures that we can observe an effect with high probability if such a true effect exists. However, several studies lack the appropriate planning for determining the optimal sample size to ensure adequate power. Thus, careful

Davood Dar, Lionel Lacombe, Neepa T. Maitra

The time-dependent exchange-correlation potential has an unusual task in directing fictitious non-interacting electrons to move with exactly the same probability density as true interacting electrons. This has intriguing implications for its structure, especially in the non-perturbative regime, leading to step and peak features that cannot be captured by boo

Alexander Volberg

What can be said about the domain $\Om$ in $\bR^n$ for which its Green's function $G(z)$ satisfies $G(z)\asymp \dist (z, \pd\Om)^δ$? What can we say about $\Om$ if the Boundary Harnack Principle holds in the form $u/v=\text{real analytic}$ on the part $E$ of its boundary? Here $u, v$ are positive harmonic functions on $\Om$ vanishing on $E$. Is this part

Pieter Hofstra, Martti Karvonen

We develop a general theory of (extended) inner autoequivalences of objects of any 2-category, generalizing the theory of isotropy groups to the 2-categorical setting. We show how dense subcategories let one compute isotropy in the presence of binary coproducts, unifying various known one-dimensional results and providing tractable computational tools in the

K. Wakamatsu, Y. Ueno, K. Miyagawa, H. Taniguchi

A quantum spin liquid (QSL) would be an exotic stage for superconductivity. A promising candidate for a doped QSL is the organic triangular-lattice system, kappa-(BEDT-TTF)4Hg2:89Br8, which hosts a non-Fermi liquid and magnetism of a QSL nature and shows superconductivity upon cooling. Here, we report that its superfluid density is anomalously reduced, indic

Shumpei Kotaka, Shunsuke Adachi, Ryo Fujinaka, Shunsuke Honda

We search for the dark photon dark matter (DPDM) using a cryogenic millimeter-wave receiver. DPDM has a kinetic coupling with electromagnetic fields with a coupling constant of $\chi$, and is converted into ordinary photons at the surface of a metal plate. We search for signal of this conversion in the frequency range $18\text{--}26.5\,\mathrm{GHz}$, which c

Siming Zheng, Yujia Xue, Waleed Tahir, Zhengjue Wang

We consider the image and video compression on resource limited platforms. An ultra low-cost image encoder, named Block Modulating Video Compression (BMVC) with an encoding complexity ${\cal O}(1)$ is proposed to be implemented on mobile platforms with low consumption of power and computation resources. We also develop two types of BMVC decoders, implemented

Yuhan Liu, Jun Gao, Jiachen Du, Lanjun Zhou

A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either focus on improving the understanding of users' emotion or on proposing better responding strategies, and very few works consider both at the same time. Our work attempts to fi

Pratik Chattopadhyay

In this thesis, we study the all same helicity loop amplitudes in self-dual Yang-Mills and self-dual gravity. These amplitudes have long been conjectured to be interpreted as an anomaly and are recently linked to the UV divergence of two-loop quantum gravity. In the first part of the thesis, we study the loop amplitudes in self-dual Yang-Mills. We show that

Shimiao Li, Amritanshu Pandey, Larry Pileggi

When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interpretability. To address these challenges for power grids, many recent works have explored the inclusion of grid physics (i.e., domain expertise) into their method design, primarily

Paul A. Ogbiyele

In this paper, we consider energy decay estimates for the following nonlinear evolution problem $$\begin{split} [P(u_t(t))]_t + A u(t) + B(t , x , u_t(t)) =0,\quad t\in J=(0,\infty), \end{split}$$ under suitable assumptions on the self-adjoint operators $P$ and $B$ and the divergence operator $A$. The work extends the earlier work of Marcati, Nakao, Levin an

Quentin Renau, Johann Dreo, Alain Peres, Yann Semet

The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the qualit

J-Ph. Bernard, A. Bernard, H. Roussel, I. Choubani

The Polarized Instrument for Long-wavelength Observation of the Tenuous interstellar medium (PILOT) is a balloon-borne experiment that aims to measure the polarized emission of thermal dust at a wavelength of 240 um (1.2 THz). The PILOT experiment flew from Timmins, Ontario, Canada in 2015 and 2019 and from Alice Springs, Australia in April 2017. The in-flig

Liyuan Cao, Albert S. Berahas, Katya Scheinberg

In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value and gradient and Hessian estimates are computed with noise. These estimates are produced by generic stochastic oracles, which are not assumed to be unbiased or consistent. We introduce these oracles and show that they ar

Kion Fallah, Christopher J. Rozell

Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure. However, inference of these codes typically relies on an optimization procedure with poor computational scaling in high-dimensional problems. For example, sparse inference in the representations learned in the high-dimensional int

Yangshanshan Liu, Shiqing Zhang

In this paper we generalize some results in \cite{Yu2021} concerning stacked central configurations. We can deal with the general homogeneous potential $U_{\alpha}$ (containing the vortex case) in $\mathbb{R}^3$. We give the admissible set of $\alpha$ for a convex central configuration (with respect to the Newtonian potential i.e. $\alpha=3$). We discuss som

Ao Wang, Wenxing Xu, Hanshi Sun, Ninghao Pu

Monitoring electrocardiogram signals is of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not fr

Marco Maggesi, Cosimo Perini Brogi

We introduce our implementation in HOL Light of the metatheory for G\"odel-L\"ob provability logic (GL), covering soundness and completeness w.r.t. possible world semantics and featuring a prototype of a theorem prover for GL itself. The strategy we develop here to formalise the modal completeness proof overcomes the technical difficulty due to the non-compa

S. Sundar

Let $G$ be a locally compact abelian group with Pontraygin dual $\widehat{G}$. Suppose $P$ is a closed subsemigroup of $G$ containing the identity element $0$. We assume that $P$ has dense interior and $P$ generates $G$. Let $U:=\{U_χ\}_{χ\in \widehat{G}}$ be a strongly continuous group of unitaries and let $V:=\{V_{a}\}_{a \in P}$ be a strongly continuous s

Shining Liang, Linjun Shou, Jian Pei, Ming Gong

Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data. The recent multilingual code-switching approach achieves better alignments of model representations across languages by constructing a mixed-language context in zero-sho

Alessio Savini

Let $G$ be $\text{SO}^\circ(n,1)$ for $n \geq 3$ and consider a lattice $\Gamma < G$. Given a standard Borel probability $\Gamma$-space $(\Omega,\mu)$, consider a measurable cocycle $\sigma:\Gamma \times \Omega \rightarrow \mathbf{H}(\kappa)$, where $\mathbf{H}$ is a connected algebraic $\kappa$-group over a local field $\kappa$. Under the assumption of comp

Vishwanath R. Singireddy, Manjanna Basappa

In this paper we consider the problem of locating $k$ obnoxious facilities (congruent disks of maximum radius) amidst $n$ demand points (existing repulsive facility sites) ordered from left to right in the plane so that none of the existing facility sites are affected (no demand point falls in the interior of the disks). We study this problem in two restrict

Zhengbo Zhang, Chunluan Zhou, Zhigang Tu

Knowledge distillation is widely adopted in semantic segmentation to reduce the computation cost.The previous knowledge distillation methods for semantic segmentation focus on pixel-wise feature alignment and intra-class feature variation distillation, neglecting to transfer the knowledge of the inter-class distance in the feature space, which is important f

Jafar Sadeghi, Mehdi Shokri, Saeed Noori Gashti, Mohammad Reza Alipour

We study the thermodynamics of the Taub-NUT AdS black holes by Visser holographic method using AdS radius as a constant parameter under the restricted phase space approach. Instead of the variables $P$ and $V$, we deal with the central charge and also chemical potential as a unique couple of conjugate thermodynamic variables. We study some interesting proper

Michael Bian, Rina Foygel Barber

The field of distribution-free predictive inference provides tools for provably valid prediction without any assumptions on the distribution of the data, which can be paired with any regression algorithm to provide accurate and reliable predictive intervals. The guarantees provided by these methods are typically marginal, meaning that predictive accuracy hol

H. T. Wang, J. S. Zhang, C. X. Zhang, Z. X. Zhao

Seismic velocity picking algorithms that are both accurate and efficient can greatly speed up seismic data processing, with the primary approach being the use of velocity spectra. Despite the development of some supervised deep learning-based approaches to automatically pick the velocity, they often come with costly manual labeling expenses or lack interpret

H. T. Wang, J. S. Zhang, Z. X. Zhao, C. X. Zhang

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of large amount of seismic data. Numerous automatic velocity picking methods have

Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig, Bettina Könighofer

Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments make testing the behavior of deep RL agents difficult. We present a search-based testing framework that enables a wide range of novel analysis capabilities for evaluating the safety

Abdullah Guvendi, Semra Gurtas Dogan

We consider a charged scalar field under the effect of an external uniform magnetic field in the near-horizon geometry of the Banados-Teitelboim-Zanelli black hole with torsion and obtain quasi-stationary states of the system under consideration through obtaining analytical solution of the corresponding Klein-Gordon equation. We obtain the solution function

Leon Witt, Mathis Heyer, Kentaroh Toyoda, Wojciech Samek

The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond smal

Alexandre Goy

Distributive laws are a standard way of combining two monads, providing a compositional approach for reasoning about computational effects in semantics. Situations where no such law exists can sometimes be handled by weakening the notion of distributive law, still recovering a composite monad. A celebrated result from Eugenia Cheng shows that combining $n$ m

Antonio Desiderio, Giulio Cimini, Gaetano Salina

The patterns of human mobility play a key role in the spreading of infectious diseases and thus represent a key ingredient of epidemic modeling and forecasting. Unfortunately, as the Covid-19 pandemic has dramatically highlighted, for the vast majority of countries there is no availability of granular mobility data. This hinders the possibility of developing

Manish Kumar, Subrata Majumdar

This paper deals with the null controllability of a coupled parabolic system, which is Kuramoto-Sivashinsky-Korteweg-de Vries equation coupled with heat equation through first order derivative. More precisely, we prove the null controllability of the system with a single localized bilinear interior control acting on either of the components of the coupled sy

Minos Axenides, Emmanuel Floratos, Stam Nicolis

According to the holographic picture of 't Hooft and Susskind, the black hole entropy, $S_{\rm BH}$, is carried by the chaotic microscopic degrees of freedom, that live in the near horizon geometry and have a Hilbert space of states of finite dimension, $d=\exp(S_{\rm BH})$. In previous work we have proposed that the near horizon geometry, when the microscop

Jean-Nicolas Jérémie, Laurent U Perrinet

Humans are able to categorize images very efficiently, in particular to detect the presence of an animal very quickly. Recently, deep learning algorithms based on convolutional neural networks (CNNs) have achieved higher than human accuracy for a wide range of visual categorization tasks. However, the tasks on which these artificial networks are typically tr