August 2022 arXiv papers — page 65

Qinying Wang, Shouling Ji, Yuan Tian, Xuhong Zhang

Facilitated by messaging protocols (MP), many home devices are connected to the Internet, bringing convenience and accessibility to customers. However, most deployed MPs on IoT platforms are fragmented and are not implemented carefully to support secure communication. To the best of our knowledge, there is no systematic solution to perform automatic security

Nuo Chen, Chenyu You

Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i.e., passage and q

Xia Zeng, Arkaitz Zubiaga

To mitigate the impact of the scarcity of labelled data on fact-checking systems, we focus on few-shot claim verification. Despite recent work on few-shot classification by proposing advanced language models, there is a dearth of research in data annotation prioritisation that improves the selection of the few shots to be labelled for optimal model performan

Saverio Monaco, Oriel Kiss, Antonio Mandarino, Sofia Vallecorsa

Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the ph

Markus Risse

Cosmic rays and air showers at ultra-high energy are unique tools to test the validity of Lorentz invariance. A brief overview is given on such tests focusing on isotropic, non-birefringent Lorentz violation (LV) in the photon sector. Based on the apparent absence of vacuum Cherenkov radiation and photon decay, the LV parameter $\kappa$ is bound to $-0.6 \cd

Alessio Calvelli

This paper analyzes the pricing of collateralized derivatives, i.e. contracts where counterparties are not only subject to financial derivatives cash flows but also to collateral cash flows arising from a collateral agreement. We do this along the lines of the brilliant approach of the first part of Moreni and Pallavicini (2017): in particular we extend thei

Mariya Shmalko, Alsharif Abuadbba, Raj Gaire, Tingmin Wu

Email phishing has become more prevalent and grows more sophisticated over time. To combat this rise, many machine learning (ML) algorithms for detecting phishing emails have been developed. However, due to the limited email data sets on which these algorithms train, they are not adept at recognising varied attacks and, thus, suffer from concept drift; attac

Xuyang Chen, Jingliang Duan, Yingbin Liang, Lin Zhao

The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study its instability, existing works mostly consider the uncommon double-loop variant or basic models with finite state and action space. We investigate the more practical single-sampl

Muhammad Azeem Akbar, Sajjad Mehmood, Arif Ali Khan

Context: Software industry is continuously exploring better ways to develop applications. A new phenomenon to achieve this is Cloud based Global Software Development (CGSD), which refers to the adoption of cloud computing services by organizations to support global software development projects. The CGSD approach affects the strategic and operational aspects

Muhammad Firdaus, Kyung-Hyune Rhee

The number of internet-connected devices has been exponentially growing with the massive volume of heterogeneous data generated from various devices, resulting in a highly intertwined cyber-physical system. Currently, the Edge Intelligence System (EIS) concept that leverages the merits of edge computing and Artificial Intelligence (AI) is utilized to provide

Daolang Huang, Louis Filstroff, Petrus Mikkola, Runkai Zheng

Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating expert knowledge into BO, with the goal of further accelerating the optimization, which has received very little attention so far. We design a multi-task learning architecture for t

Quanshi Zhang, Xu Cheng, Yilan Chen, Zhefan Rao

Compared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. This paper provides a new perspective to explain the success of knowledge distillation, i.e., quantifying knowledge points encoded in intermediate layers of a DNN for classification, based on the information theory. To this end, we cons

Anna Jenčová, Sylvia Pulmannová

Order unit spaces with comparability and spectrality properties as introduced by Foulis are studied. We define continuous functional calculus for order unit spaces with the comparability property and Borel functional calculus for spectral order unit spaces. Applying the conditions of Alfsen and Schultz, we characterize order unit spaces with comparability pr

Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier

The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability. This research paradigm disproportionately ignores the larger group of non-technical end users, who have a much higher demand for AI explanations in diverse explanation goals, such as making safer and

Chang Xu, Jinwang Wang, Wen Yang, Huai Yu

Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on tiny object detection tasks. In this paper, we point out that either box prior in the anchor-based detector or point prior in the anchor-free detector is sub-optimal for tiny object

Manosh T. Manoharan, N. Shaji, Titus K. Mathew

This article investigates the relationship between the holographic principle and the laws of thermodynamics in explaining the late-time acceleration of the universe. First, we explore the possibilities of generating the standard holographic dark energy (SHDE) from the laws of horizon thermodynamics. Except for entropies that follow an exponent stretched area

Ivo Buttinoni, Lorenzo Caprini, Laura Alvarez, Fabian Jan Schwarzendahl

We study the motion of active patchy colloids in an optical trap using experiments, theory and numerical simulations. To achieve isotropic and harmonic confinement, we prototype microparticles with a nearly uniform refractive index and verify that, in the absence of activity, the confined motion is identical to that of optically homogeneous Brownian particle

Siddharth Mehrotra, Saurav Das, Sourabh Zanwar

One-handed use of smartphones is a common scenario in daily life. However, use of smartphones with thumb gives limited reachability to the complete screen. This problem is more severe when targets are located at corners of the device or far from the thumb's reachable area. Adjusting screen size mitigates this issue by making screen UI to be at the reach of t

Wen Chen, Shujie Cheng, Ji Lin, Reza Asgari

The correspondence between the real-complex transition in energy and delocalization-localization transition is well-established in a class of Aubry-Andr'e-Harper model with exponential non-Hermitian on-site potentials. In this paper, we study a generalized Aubry-Andr'e model with off-diagonal modulation and non-Hermitian on-site potential. We find that, when

Tomoaki Kobayashi, Oleg Kiselyov

Software-Defined Radio (SDR) is widely used not only as a practical application but also as a fitting benchmark of high-performance signal processing. We report using the SDR benchmark -- specifically, FM Radio reception -- to evaluate the recently developed single-thread stream processing library strymonas, contrasting it with the synchronous dataflow syste

Kerstin Awiszus, Thomas Knispel, Irina Penner, Gregor Svindland

The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts. We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and

Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D. H. Nguyen

According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, to

Xishuo Wei, Ge Dong, Shuying Sun, William Tang

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement f

Philip Engel, Raju Krishnamoorthy, Daniel Litt

Let $A$ be a simple abelian surface over an algebraically closed field $k$. Let $S\subset A(k)$ be the set of torsion points $x$ of $A$ such that there exists a genus $2$ curve $C$ and a map $f: C\to A$ such that $x$ is in the image of $f$, and $f$ sends a Weierstrass point of $C$ to the origin of $A$. The purpose of this note is to show that if $k$ has char

Gusi Te, Xiu Li, Xiao Li, Jinglu Wang

We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views. Our method is based on a dynamic Neural Radiance Field (NeRF) rigged by a mesh-based parametric 3D human model serving as a geometry proxy. Previous methods usually rely on multi-view videos o

Nicola Anselmi, Luca Tosi, Paolo Rocca, Giovanni Toso

The design of irregular planar phased arrays (PAs) characterized by a highly-modular architecture is addressed. By exploiting the property of self-replicating tile shapes, also known as rep-tiles, the arising array layouts consist of tiles having different sizes, but equal shape, all being generated by assembling a finite number of smaller and congruent copi

Chinthaka Dinesh, Gene Cheung, Saghar Bagheri, Ivan V. Bajic

A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, we show that for datasets with strong in

Vincenzo Basco

In the present paper, it is provided a representation result for the weak solutions of a class of evolutionary Hamilton-Jacobi-Bellman equations on infinite horizon, with Hamiltonians measurable in time and fiber convex. Such Hamiltonians are associated with a - faithful - representation, namely involving two functions measurable in time and locally Lipschit

Swagata Bhunia, Ritam Sarkar, Dhiman Nag, Dipankar Jana

The nanowire-supported quantum dot (NWQD) of GaN is an unconventional nanostructure, which is extremely promising for realization of UV photonics in general, and room-temperature single photon generation, in particular. While GaN-NWQDs have several promising attributes, the crucial challenge in exploiting their full potential, is to reduce the lateral dimens

Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu

Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the user-item interactions (collaborative domain) and social relations (social domain). However, such an approach may fail to model the users heterogeneous behavior patterns in two domain

Thibault D. Décoppet

We develop the Morita theory of fusion 2-categories. In order to do so, we begin by proving that the relative tensor product of modules over a separable algebra in a fusion 2-category exists. We use this result to construct the Morita 3-category of separable algebras in a fusion 2-category. Then, we go on to explain how module 2-categories form a 3-category.

Xijie Xiang, Lin Zhu, Jianing Li, Yonghong Tian

The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are oft

Vincenzo Basco

In this paper we show a uniqueness result for weak epigraphical solutions of Hamilton-Jacobi-Bellman (HJB) equations on infinite horizon for a class of lower semicontinuous functions vanishing at infinity. Weak epigraphical solutions of HJB equations, with time-measurable data and fiber-convex, turn out to be viscosity solutions - in the classical sense - wh

Christopher J. Dean, Eric Finster, Ioannis Markakis, David Reutter

We give a new description of computads for weak globular $\omega$-categories by giving an explicit inductive definition of the free words. This yields a new understanding of computads, and allows a new definition of $\omega$-category that avoids the technology of globular operads. Our framework permits direct proofs of important results via structural induct

Yuval Emek, Yuval Gil, Shay Kutten

Introduced by Korman, Kutten, and Peleg (PODC 2005), a proof labeling scheme (PLS) is a distributed verification system dedicated to evaluating if a given configured graph satisfies a certain property. It involves a centralized prover, whose role is to provide proof that a given configured graph is a yes-instance by means of assigning labels to the nodes, an

Varun Hiremath, Jose E. Roman

Closed combustion devices like gas turbines and rockets are prone to thermoacoustic instabilities. Design engineers in the industry need tools to accurately identify and remove instabilities early in the design cycle. Many different approaches have been developed by the researchers over the years. In this work we focus on the Helmholtz wave equation based so

Takashi Hara, Kenichi Namikawa

Admitting the existence of conjectural motives attached to cohomological irreducible cuspidal automorphic representations of $\mathrm{GL}_n$, we write down Raghuram and Shahidi's Whittaker periods in terms of Yoshida's fundamental periods when the base field is a totally real number field or a CM field.

Xiuzhan Guo, Arthur Berrill, Ajinkya Kulkarni, Kostya Belezko

Ontology operations, e.g., aligning and merging, were studied and implemented extensively in different settings, such as, categorical operations, relation algebras, typed graph grammars, with different concerns. However, aligning and merging operations in the settings share some generic properties, e.g., idempotence, commutativity, associativity, and represe

Nan Zhang, Muye Nanshan, Jiguo Cao

Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likeli

Samuel T. Wauthier, Bram Vanhecke, Tim Verbelen, Bart Dhoedt

Active inference provides a general framework for behavior and learning in autonomous agents. It states that an agent will attempt to minimize its variational free energy, defined in terms of beliefs over observations, internal states and policies. Traditionally, every aspect of a discrete active inference model must be specified by hand, i.e. by manually de

Yuan-Pei Yang, Siyao Xu, Bing Zhang

Recently, some fast radio burst (FRB) repeaters were reported to exhibit complex, diverse variations of Faraday rotation measures (RMs), which implies that they are surrounded by an inhomogeneous, dynamically evolving, magnetized environment. We systematically investigate some possible astrophysical processes that may cause RM variations of an FRB repeater.

Jonghyun Bae, Woohyeon Baek, Tae Jun Ham, Jae W. Lee

The training process of deep neural networks (DNNs) is usually pipelined with stages for data preparation on CPUs followed by gradient computation on accelerators like GPUs. In an ideal pipeline, the end-to-end training throughput is eventually limited by the throughput of the accelerator, not by that of data preparation. In the past, the DNN training pipeli

Sourav Deb, Isha Kikani, Manish K Gupta

In the last 60 years coding theory has been studied a lot over finite fields $\mathbb{F}_q$ or commutative rings $\mathcal{R}$ with unity. Although in $1993$, a study on the classification of the rings (not necessarily commutative or ring with unity) of order $p^2$ had been presented, the construction of codes over non-commutative rings or non-commutative no

Johannes Blum, Sabine Storandt

Hub Labeling (HL) is one of the state-of-the-art preprocessing-based techniques for route planning in road networks. It is a special incarnation of distance labeling, and it is well-studied in both theory and practice. The core concept of HL is to associate a label with each vertex, which consists of a subset of all vertices and respective shortest path info

Daniel Gaina, Guillermo Badia, Tomasz Kowalski

In this paper we prove a Robinson consistency theorem for a class of many-sorted hybrid logics as a consequence of an Omitting Types Theorem. An important corollary of this result is an interpolation theorem.

Qianxiao Li, Ting Lin, Zuowei Shen

We study the approximation of functions which are invariant with respect to certain permutations of the input indices using flow maps of dynamical systems. Such invariant functions includes the much studied translation-invariant ones involving image tasks, but also encompasses many permutation-invariant functions that finds emerging applications in science a

Marco Pasini, Jan Schlüter

Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use. In this work, we introduce Musika, a music genera

Hamza Malik, Jehanzeb Burki, Muhammad Zeeshan Mumtaz

Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveform

Takaaki Nomura, Hiroshi Okada

We propose a simple model to obtain sizable muon anomalous magnetic dipole moment (muon $g-2$) introducing several $SU(2)_L$ multiplet fields without any additional symmetries. The neutrino mass matrix is simply induced via type-II seesaw scenario in terms of $SU(2)_L$ triplet Higgs with $U(1)_Y$ hypercharge 1. In addition, we introduce an $SU(2)_L$ quartet

Antonio De Rosa, Aida Khajavirad

We consider the nonconvex set $\mathcal S_n = \{(x,X,z): X = x x^T, \; x (1-z) =0,\; x \geq 0,\; z \in \{0,1\}^n\}$, which is closely related to the feasible region of several difficult nonconvex optimization problems such as the best subset selection and constrained portfolio optimization. Utilizing ideas from convex analysis and disjunctive programming, we

Siyuan Ma, Lin Zhang

In this work, we compute the precise late-time asymptotics for the scalar field in the interior of a non-static subextreme Kerr black hole, based on recent progress on deriving its precise asymptotics in the Kerr exterior region. This provides a new proof of the generic $H^1_{\text{loc}}$-inextendibility of the Kerr Cauchy horizon against scalar perturbation

Peter Davies

The Lov\'asz Local Lemma is a classic result in probability theory that is often used to prove the existence of combinatorial objects via the probabilistic method. In its simplest form, it states that if we have $n$ `bad events', each of which occurs with probability at most $p$ and is independent of all but $d$ other events, then under certain criteria on $

Joshua Celeste

We consider a generalisation of the Seiberg-Witten invariant to the families Seiberg-Witten invariants of a smooth family of 4-manifolds with fibres diffeomorphic to a 4-manifold $X$. Of particular interest is the special case when the family has a smoothly varying K\"ahler structure. We obtain a general computation of the invariants when $b_1=0$ in terms of

Hans De Raedt, Fengping Jin, Kristel Michielsen

We present a comprehensive simulation study of the Newtonian and quantum model of a Stern-Gerlach experiment with cold neutrons.By solving Newton's equation of motion and the time-dependent Pauli equation, for a wide range of uniform magnetic field strengths, we scrutinize the role of the latter for drawing the conclusion that the magnetic moment of the neut

Vaisakh K. M., Thomas Xavier, Sreedevi E. P

We develop a new goodness fit test for Rayleigh distribution for complete as well as right censored data. We use U-Statistic theory to derive the test statistic. First we develop a test for complete data and then discuss, how right censored observations can be incorporated in the testing procedure. The asymptotic properties of the test statistics in both unc

Manaar Alam, Shubhajit Datta, Debdeep Mukhopadhyay, Arijit Mondal

The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to their ever-improving performance to solve challenging tasks. Despite overwhelming promises, the deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can

Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on randomly expose items, i.e., the missing-at-random data. A few works have asked certain users to rate or select randomly

Belle Collaboration, L. K. Li, W. Shan, K. Kinoshita

We report a study of $\Lambda_c^+\to\Lambda h^+$ and $\Lambda_c^+\to\Sigma^{0} h^+$ ($h\!=\!K,\,\pi$) decays based on a data sample of 980~${\rm fb}^{-1}$ collected with the Belle detector at the KEKB energy-asymmetric $e^+e^-$ collider. The first results of direct $C\!P$ asymmetry in two-body singly Cabibbo-suppressed (SCS) decays of charmed baryons are mea

D. A. Green

The Galactic source G2.4$+$1.4 is an optical and radio nebula containing an extreme Wolf--Rayet star. At one time this source was regarded as a supernova remnant, because of its apparent non-thermal radio spectrum, although this was based on limited observations. Subsequent observations instead supported a flat, optically thin thermal radio spectrum for G2.4

Xin-Bing Kong, Yong-Xin Liu, Long Yu, Peng Zhao

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate $(\min\{p_1p_2,p_2T,p_1T\})^{-1/2}$ in the average Frobenius norm, where $p_1$, $p_

Tim Berezin

The work provides a brief intuitive overview theory of graph on surfaces. We considers graphs with an additional structure, wich we call discs with ribbons, also known as one-vertex ribbon graphs. And solves the problem (Skopenkov's) about criteria for toroidal embedding of one-vertex ribbon graph.

Weike Yu

In this paper, we investigate the problem of prescribing Chern scalar curvatures on compact Hermitian manifolds with negative Gauduchon degree. By studying the convergence of the associated geometric flow, we obtain some existence results when the candidate curvature function is nonzero and nonpositive. Furthermore, we also consider the case that the candida

Pai Liu, Wenyang Gao, Wenjie Dong, Lin Ai

Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective absent in prior surveys. It examines the evolution of tas

Abhijitt Dhavlle

Consumer and defense systems demanded design and manufacturing of electronics with increased performance, compared to their predecessors. As such systems became ubiquitous in a plethora of domains, their application surface increased, thus making them a target for adversaries. Hence, with improved performance the aspect of security demanded even more attenti

Anna Belardinelli, Anirudh Reddy Kondapally, Dirk Ruiken, Daniel Tanneberg

Shared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the tar

Stefan Kutschera

Social media plays an important role for a vast majority in one's internet life. Likewise, sharing, publishing and posting content through social media became nearly effortless. This unleashes new threats as unintentionally shared information may be used against oneself or beloved ones. With open source intelligence data and methods, we show how unindented p

Andreas Schimpe, Domagoj Majstorovic, Frank Diermeyer

In this paper, a steering action-aware Adaptive Cruise Control (ACC) approach for teleoperated road vehicles is proposed. In order to keep the vehicle in a safe state, the ACC approach can override the human operator's velocity control commands. The safe state is defined as a state from which the vehicle can be stopped safely, no matter which steering action

Deyin Liu, Lin Yuanbo Wu, Bo Li, Zongyuan Ge

In this paper, we present an end-to-end approach to generate high-resolution person images conditioned on texts only. State-of-the-art text-to-image generation models are mainly designed for center-object generation, e.g., flowers and birds. Unlike center-placed objects with similar shapes and orientation, person image generation is a more challenging task,

Roopsa Sen, Sidharth Sinha, Parv Maheshwari, Animesh Jha

The following paper is a reproducibility report for "Social NCE: Contrastive Learning of Socially-aware Motion Representations" {\cite{liu2020snce}} published in ICCV 2021 as part of the ML Reproducibility Challenge 2021. The original code was made available by the author \footnote{\href{https://github.com/vita-epfl/social-nce}{https://github.com/vita-epfl/s

Andrea Cangiani, Zhaonan Dong, Emmanuil H. Georgoulis

We present a new residual-type energy-norm a posteriori error analysis for interior penalty discontinuous Galerkin (dG) methods for linear elliptic problems. The new error bounds are also applicable to dG methods on meshes consisting of elements with very general polygonal/polyhedral shapes. The case of simplicial and/or box-type elements is included in the

Carolyn Atkins

X-ray mirror fabrication for astronomy is challenging; this is due to the Wolter I optical geometry and the tight tolerances on roughness and form error to enable accurate and efficient X-ray reflection. The performance of an X-ray mirror, and ultimately that of the telescope, is linked to the processes and technologies used to create it. The goal of this ch

Bipin Saha, Bipanko Kumar Mondal, Shaikh Khaled Mostaque, Mainul Hossain

Ternary chalcostibite copper antimony selenide (CuSbSe2) is a promising absorber material for next generation thin film solar cells due to the non-toxic nature, earth-abundance, low-cost fabrication technique, optimum bandgap and high optical absorption coefficient of CuSbSe2. Conventional single heterojunction CuSbSe2 solar cells suffer from high recombinat

Eduardo Martín-Martínez

Informal collection of lecture notes introducing quantum mechanics in phase space and basic Gaussian quantum mechanics.

Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen

Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized online algorithms for the monotone continuous DR-submodular maximization problem, both of which reduce the number of per-function gradient evaluations and per-round communication c

P. S. V. R. A. Kishor, Prince Gollapalli, Debolina Misra, Prajeet Oza

The preference for the occupation of solute atoms like B, C, N, and O at various sites in iron is generally explained by the size of the solute and the volume available for the solute atoms to occupy. Such an explanation based on the size of solute atoms and available space at the occupation site assumes that distortion alone dictates the stability of solute

Akira Shinkyu, Naoya Sueishi

In this study, we investigate the bias and variance properties of the debiased Lasso in linear regression when the tuning parameter of the node-wise Lasso is selected to be smaller than in previous studies. We consider the case where the number of covariates $p$ is bounded by a constant multiple of the sample size $n$. First, we show that the bias of the deb

Yun Luo, Fang Guo, Zihan Liu, Yue Zhang

Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data. Previous studies are mostly cross-entropy-based methods for the task, which suffer from instability and poor generalization. In this paper, we explore contrastive learning on the c

Hung-Jui Wang, Yu-Yu Wu, Shang-Tse Chen

Malicious attackers can generate targeted adversarial examples by imposing tiny noises, forcing neural networks to produce specific incorrect outputs. With cross-model transferability, network models remain vulnerable even in black-box settings. Recent studies have shown the effectiveness of ensemble-based methods in generating transferable adversarial examp

Philippe G. LeFloch, Yue Ma

We study the initial value problem for the Einstein-Klein-Gordon system and establish the global nonlinear stability of massive matter in the near-Minkowski regime when the initial geometry is a perturbation of an asymptotically flat, spacelike hypersurface in Minkowski spacetime and the metric enjoys the harmonic decay 1/r (in term of a suitable distance fu

Debmita Bandyopadhyay, Subhadip Mukherjee, James Ball, Grégoire Vincent

We propose a novel graph-regularized neural network (GRNN) algorithm for tree species classification. The proposed algorithm encompasses superpixel-based segmentation for graph construction, a pixel-wise neural network classifier, and the label propagation technique to generate an accurate and realistic (emulating tree crowns) classification map on a sparsel

Fatma Raissi, Mandimby Nirina Ranaivo Rakotondravelona, Marwane El-Bekri, Djibrilla Amadou Kountche

For autonomous vehicles to be fully aware of its environment, it needs to collect data consistently from other vehicles and Road Side Units (RSU) in the surroundings. This heavy exchange increases latency and cybersecurity threats. This paper introduces Multi-Access Edge Computing (MEC), a 5G specification, as a promising solution to this significant issue.

Ilaria Bargigia, Elizabeth Gutiérrez-Meza, David A. Valverde-Chávez, Sarah R. Marques

We have previously demonstrated that in the context of two-dimensional (2D) coherent electronic spectroscopy measured by phase modulation and phase-sensitive detection, an \emph{incoherent} nonlinear response, due to pairs of photoexcitations produced via linear excitation pathways, contribute to the measured signal as unexpected background [Gr\'egoire et al

Pongpanut Osathitporn, Guntitat Sawadwuthikul, Punnawish Thuwajit, Kawisara Ueafuea

Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-t

Oleksandra Klymenko, Oleksandr Kosenkov, Stephen Meisenbacher, Parisa Elahidoost

Modern privacy regulations, such as the General Data Protection Regulation (GDPR), address privacy in software systems in a technologically agnostic way by mentioning general "technical measures" for data privacy compliance rather than dictating how these should be implemented. An understanding of the concept of technical measures and how exactly these can b

Siddharth Barman, Pooja Kulkarni

Cake cutting is a classic model for studying fair division of a heterogeneous, divisible resource among agents with individual preferences. Addressing cake division under a typical requirement that each agent must receive a connected piece of the cake, we develop approximation algorithms for finding envy-free (fair) cake divisions. In particular, this work i

Prateek Gupta, Sibasish Panda, Akhilesh Kumar Sahu, Deepak Kumar

The flow phenomena around a rotating elliptic cylinder in a channel is studied numerically. The value of the confinement parameter \beta is varied as \frac{1}{k}, where k = 2, 4, 6, and 8 respectively, to demonstrate the vortex-shedding patterns around the cylinder in the downstream wake. The non-dimensional rotation rate \alpha takes up 0.5, 1, and 2 as its

Mingxue Quan, Zhenhua Lin

For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer system once processed due to limited computer memory and storage. We tackle the challenge by proposing a novel one-pass estimator based on penalized orthogonal basis expansions and de

Nan Ming, Yi Feng, Rui Fan

The state-of-the-art (SoTA) surface normal estimators (SNEs) generally translate depth images into surface normal maps in an end-to-end fashion. Although such SNEs have greatly minimized the trade-off between efficiency and accuracy, their performance on spatial discontinuities, e.g., edges and ridges, is still unsatisfactory. To address this issue, this pap

G. Tsialiamanis, D. Wagg, N. Dervilis, K. Worden

A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many parameters, many of which are often uncertain. Many models have been developed for the aforementioned tasks but they have been either deterministic or stochastic with the ability to t

Eliot Hodges

In 2022, Defant and Kravitz introduced extended promotion (denoted $\partial$), a map that acts on the set of labelings of a poset. Extended promotion is a generalization of Sch\"{u}tzenberger's promotion operator, a well-studied map that permutes the set of linear extensions of a poset. It is known that if $L$ is a labeling of an $n$-element poset $P$, then

Bahjat Kawar, Roy Ganz, Michael Elad

Denoising diffusion probabilistic models (DDPMs) are a recent family of generative models that achieve state-of-the-art results. In order to obtain class-conditional generation, it was suggested to guide the diffusion process by gradients from a time-dependent classifier. While the idea is theoretically sound, deep learning-based classifiers are infamously s

Yury Turkulets, Nitzan Shauloff, Or Haim Chaulker, Yoram Shapira

Yellow luminescence (YL) is probably the longest and most studied defect-related luminescence band in GaN, yet its electronic structure or chemical identity remain unclear. Most of the theoretical work so far has attributed the feature to bulk defects, whereas spectroscopic studies have suggested a surface origin. Here, we apply deep level spectroscopy using

Wenqiang Ruan, Mingxin Xu, Wenjing Fang, Li Wang

Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While MPL provides rigorous security guarantees for the computation process, the models trained by MPL are still vulnerable to attacks that solely depend on access to the models. Differ

Yi-Fan Zhang, Jindong Wang, Jian Liang, Zhang Zhang

Recent domain generalization (DG) approaches typically use the hypothesis learned on source domains for inference on the unseen target domain. However, such a hypothesis can be arbitrarily far from the optimal one for the target domain, induced by a gap termed ``adaptivity gap''. Without exploiting the domain information from the unseen test samples, adaptiv

LHCb Collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

Four decay modes of the $B_c^+$ meson into a $J/\psi$ meson and multiple charged kaons or pions are studied using proton-proton collision data, collected with the~LHCb detector at centre-of-mass energies of 7, 8, and 13~TeV and corresponding to an integrated luminosity of $9$~fb$^{-1}$. The decay $B_c^+\to J/\psi K^+ K^- \pi^+ \pi^+ \pi^-$ is observed for th

Bin Ji, Hao Xu, Jie Yu, Shasha Li

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In

Muzammil Mushtaq, Prajwal Hassan Puttasiddappa

We study the behavior of dust temperature and its infrared emission of FirstLight1 simulated galaxies at the redshift of 6 and 8, by using POLARIS2 as a Monte Carlo photon transport simulator. To calculate the dust temperature ($T_{dust}$) of the Interstellar medium (ISM) of galaxies, POLARIS requires three essential parameters as an input - (1) The physical

JinMyong An, PyongJo Ryu, JinMyong Kim

We consider the Cauchy problem for the inhomogeneous biharmonic nonlinear Schr\"{o}dinger (IBNLS) equation \[iu_{t} +\Delta^{2} u=\lambda |x|^{-b}|u|^{\sigma}u,\;u(0)=u_{0} \in H^{s} (\mathbb R^{d}),\] where $\lambda\in \mathbb R$, $d\in \mathbb N$, $0\le s<\min\left\{2+\frac{d}{2},d\right\}$, $0<b<\min \left\{4,\; d-s,\; 2+\frac{d}{2}-s \right\}$ and $0<\si

Davide Trotta, Manlio Valenti, Valeria de Paiva

This paper presents categorical formulations of Turing, Medvedev, Muchnik, and Weihrauch reducibilities in Computability Theory, utilizing Lawvere doctrines. While the first notions lend themselves to a smooth categorical presentation, essentially dualizing the traditional idea of realizability doctrines, Weihrauch reducibility and its extensions to represen

Nicholas I-Hsien Kuo, Federico Garcia, Anders Sönnerborg, Maurizio Zazzi

Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to mitigate this problem is by generating realistic synthetic datasets using generative adversarial networks (GANs). However, GANs are known to suffer from mode collapse thus creatin