October 2022 arXiv papers — page 125

Gustave Cortal, Alain Finkel, Patrick Paroubek, Lina Ye

Emotion analysis in texts suffers from two major limitations: annotated gold-standard corpora are mostly small and homogeneous, and emotion identification is often simplified as a sentence-level classification problem. To address these issues, we introduce a new annotation scheme for exploring emotions and their causes, along with a new French dataset compos

Michael Wenzel, Roland Mitric

The theoretical prediction of the rates of nonradiative processes in molecules is fundamental to assess their emissive properties. In this context, global harmonic models have been widely used to simulate vibronic spectra as well as internal conversion rates and to predict photoluminescence quantum yields. However, these simplified models suffer from the lim

Ahmed Abd Elrahman, Ahmed I. Taloba, Mohammed F. Farghally, Taysir Hassan A Soliman

The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage o

Manuel Muñoz Sánchez, Jos Elfring, Emilia Silvas, René van de Molengraft

To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most appropriate for every situation. Currently, assessment of prediction models is often performed over a set of trajectories without distinction of the type of movement they capture, r

Hao-Nan Xie, Shi-Jie Wei, Fan Yang, Zheng-An Wang

Imaginary time evolution is a powerful tool applied in quantum physics, while existing classical algorithms for simulating imaginary time evolution suffer high computational complexity as the quantum systems become larger and more complex. In this work, we propose a probabilistic algorithm for implementing imaginary time evolution based on non-unitary quantu

León Carvajales, Xian Dai, Beatrice Pozzetti, Anna Wienhard

We provide a good dynamical framework allowing to generalize Thurston's asymmetric metric and the associated Finsler norm from Teichm\"uller space to large classes of Anosov representations. In many cases, including the space of Hitchin representations, this gives a (possibly asymmetric) Finsler distance. In some cases we explicitly compute the associated Fi

Gaëlle Laperrière, Valentin Pelloin, Mickaël Rouvier, Themos Stafylakis

In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU). We employ the recently-introduced SAMU-XLSR model, which is designed to generate a single embedding that captures the semantics at the utterance level, semantically aligned across different languages. This model combines the aco

Lin Yucui, Xue Jiangwei

Let $F$ be a number field, and $D$ be a quaternion $F$-algebra. We show that the class number of any residually unramified $O_F$-order (e.g. an Eichler order) in $D$ is divisible by the class number of $F$.

Luca Bellino

The incredible thermo-mechanical properties of biological materials arise from the microscopic scale due to a complex hierarchical mechanism, regulated by microinstabilities at the molecular level. The description of such complex structures is allowed by both the know-how introduced by the advent of SMFS experiments and the possibility of correctly mimicking

Elena Zampieri, Luca Franco Pavarino

This paper focuses on the spectral properties of the mass and stiffness matrices for acoustic wave problems discretized with Isogeometric analysis (IGA) collocation methods in space and Newmark methods in time. Extensive numerical results are reported for the eigenvalues and condition numbers of the acoustic mass and stiffness matrices in the reference squar

Pavel Buividovich

We analyze in detail a sharp transition between the low-energy, low-dimensional eigenstates and the high-energy chaotic bulk of the spectrum for a simple supersymmetric quantum-mechanical model with Hamiltonian $\hat{H}_S = \left(\hat{p}_1^2 + \hat{p}_2^2 + \hat{x}_1^2 \, \hat{x}_2^2 \right) \otimes I + \hat{x}_1 \otimes \sigma_1 + \hat{x}_2 \otimes \sigma_3

Taolin Zhang, Junwei Dong, Jianing Wang, Chengyu Wang

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community

Matteo Novaga, Emanuele Paolini, Eugene Stepanov, Vincenzo Maria Tortorelli

An infinite cluster $\mathbf E$ in $\mathbb R^d$ is a sequence of disjoint measurable sets $E_k\subset \mathbb R^d$, $k\in \mathbb N$, called regions of the cluster. Given the volumes $a_k\ge 0$ of the regions $E_k$, a natural question is the existence of a cluster $\mathbf E$ which has finite and minimal perimeter $P(\mathbf E)$ among all clusters with regi

Seyoung Jeon, Sukyoung Yi, Yohan Dubois, Aeree Chung

Cluster galaxies exhibit substantially lower star formation rates than field galaxies today, but it is conceivable that clusters were sites of more active star formation in the early universe. Herein, we present an interpretation of the star formation history (SFH) of group/cluster galaxies based on the large-scale cosmological hydrodynamic simulation, Horiz

Adara M. Blaga, Bang-Yen Chen

We provide conditions for a Riemannian manifold with a nontrivial closed affine conformal Killing vector field to be isometric to a Euclidean sphere or to the Euclidean space. Also, we formulate some triviality results for almost Ricci solitons with affine conformal Killing potential vector field.

Arghajit Jana

Black hole X-ray binaries (BHXBs) show rich phenomenology in the spectral and timing properties. We collected the spectral data of 20 BHXBs from the literature across different spectral states. The spectral properties are studied in the forms of the inner disc temperature ($T_{\rm in}$), photon index ($\Gamma$), hot electron temperature ($kT_{\rm e}$), X-ray

Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran

Transformer models can use two fundamentally different kinds of information: information stored in weights during training, and information provided ``in-context'' at inference time. In this work, we show that transformers exhibit different inductive biases in how they represent and generalize from the information in these two sources. In particular, we char

Mateusz Żarski, Bartosz Wójcik, Jarosław A. Miszczak, Bartłomiej Blachowski

The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interco

Fabian Wagner

A good hundred years after the necessity for a quantum theory of gravity was acknowledged by Albert Einstein, the search for it continues to be an ongoing endeavour. Nevertheless, the field still evolves rapidly as manifested by the recent rise of quantum gravity phenomenology supported by an enormous surge in experimental precision. In particular, the minim

Yuqian Fu, Yu Xie, Yanwei Fu, Jingjing Chen

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core challenge of CD-FSL lies in the domain gap between the source and novel target datasets. Though many attempts have been made for CD-FSL without any target data during model training,

William de Vazelhes, Hualin Zhang, Huimin Wu, Xiao-Tong Yuan

$\ell_0$ constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Hard-thresholding gradient descent is a dominant technique to solve this problem. However, first-order gradients of the objective function may be either unavailable or expensive to ca

Yunhao Du, Zihang Liu, Fei Su

Multiple Object Tracking (MOT) has rapidly progressed in recent years. Existing works tend to design a single tracking algorithm to perform both detection and association. Though ensemble learning has been exploited in many tasks, i.e, classification and object detection, it hasn't been studied in the MOT task, which is mainly caused by its complexity and ev

Alberto Marchisio, Vojtech Mrazek, Andrea Massa, Beatrice Bussolino

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to address multiple design objectives, we propose RoHNAS, a novel NAS framework that jointly optimizes for

Manuel Fernández López, Ben J. Powell, Jaime Merino

We explore superconductivity in strongly interacting electrons on a decorated honeycomb lattice (DHL). An easy-plane ferromagnetic interaction arises from spin-orbit coupling in the Mott insulating phase, which favors a triplet resonance valence bond spin liquid state. Hole doping leads to partial occupation of a flat band and to triplet superconductivity. T

Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras

Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically-relevant candidate drug molecules. In this work, we propose DiffLinker, an E(3)-equivariant 3D-conditional diffusion model for molecular lin

Cunquan Li, Yukai An

Two-dimensional (2D) ferromagnetic semiconductors show great interest due to their potential applications for the nanoscale electronic devices. In this work, the Janus 2$H$-Gd$XY$ ($X$, $Y$=Cl, Br, I, $X$$\neq$$Y$) monolayers with rare-earth element Gd (4$f^{7}$+5$d^{1}$) are predicted by the first-principles calculations. Small exfoliation energy of less th

Nikolai Wyderka, Giovanni Chesi, Hermann Kampermann, Chiara Macchiavello

Recent progress in quantum optics has led to setups that are able to prepare high-dimensional quantum states for quantum information processing tasks. As such, it is of importance to benchmark the states generated by these setups in terms of their quantum mechanical properties, such as their Schmidt numbers, i.e., the number of entangled degrees of freedom.

Matthew Baas, Herman Kamper

We propose AudioStyleGAN (ASGAN), a new generative adversarial network (GAN) for unconditional speech synthesis. As in the StyleGAN family of image synthesis models, ASGAN maps sampled noise to a disentangled latent vector which is then mapped to a sequence of audio features so that signal aliasing is suppressed at every layer. To successfully train ASGAN, w

R. Bala, V. S. Prasannaa, M. Abe, B. P. Das

In this article, we have employed relativistic many-body theory to theoretically assess the suitability of TlF+ molecular ion in its ground state for electron electric dipole moment searches. To that end, we have computed values of the effective electric field as well as the molecular permanent electric dipole moment using both configuration interaction and

Katharina T. Huber, Liam J. Maher

Polyploidization is an evolutionary process by which a species acquires multiple copies of its complete set of chromosomes. The reticulate nature of the signal left behind by it means that phylogenetic networks offer themselves as a framework to reconstruct the evolutionary past of species affected by it. The main strategy for doing this is to first construc

Tran Van Sang, Mhd Irvan, Rie Shigetomi Yamaguchi, Toshiyuki Nakata

Natural Gradient Descent (NGD) is a second-order neural network training that preconditions the gradient descent with the inverse of the Fisher Information Matrix (FIM). Although NGD provides an efficient preconditioner, it is not practicable due to the expensive computation required when inverting the FIM. This paper proposes a new NGD variant algorithm nam

Fabian Czappa, Alexander Geiß, Felix Wolf

Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes-Hut algorithm. They demonstrate good scalability and prove a runtime complexi

Arkadij Bojko, Woonam Lim, Miguel Moreira

In enumerative geometry, Virasoro constraints were first conjectured in Gromov-Witten theory with many new recent developments in the sheaf theoretic context. In this paper, we rephrase the sheaf-theoretic Virasoro constraints in terms of primary states coming from a natural conformal vector in Joyce's vertex algebra. This shows that Virasoro constraints are

Fan Yu, Shiliang Zhang, Pengcheng Guo, Yuhao Liang

Recently cross-channel attention, which better leverages multi-channel signals from microphone array, has shown promising results in the multi-party meeting scenario. Cross-channel attention focuses on either learning global correlations between sequences of different channels or exploiting fine-grained channel-wise information effectively at each time step.

E. P. de Santana, A. K. Wigger, Z. Wang, K. Wang

Graphene is an attractive material for communications in the THz range due to its ability to support surface plasmon polaritons. This enables a graphene antenna to be smaller in size than its metallic counterpart. In addition, the possibility to control the graphene conductivity during operation by an applied bias leads to the tunability of the resonant freq

M. E. Cates, C. Nardini

Classical nucleation theory (CNT), linking rare nucleation events to the free energy landscape of a growing nucleus, is central to understanding phase-change kinetics in passive fluids. Nucleation in non-equilibrium systems is much harder to describe because there is no free energy, but instead a dynamics-dependent quasi-potential that typically must be foun

Julius Wagenbach, Matthia Sabatelli

We study whether the learning rate $\alpha$, the discount factor $\gamma$ and the reward signal $r$ have an influence on the overestimation bias of the Q-Learning algorithm. Our preliminary results in environments which are stochastic and that require the use of neural networks as function approximators, show that all three parameters influence overestimatio

Yuanhang Yang, Shiyi Qi, Chuanyi Liu, Qifan Wang

Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI). These models generally perform cross-attention over input pairs, leading to prohibitive computational costs. Recent studies propose dual-encoder and late interaction architectures for faster computation. However

Michael Klasen, Karol Kovařík, Luca Paolo Wiggering

We present the full one-loop SUSY-QCD corrections to stop-antistop annihilation into gluons and light quarks within the Minimal Supersymmetric Standard Model including Sommerfeld enhancement effects from the exchange of multiple gluons between the incoming particles. These corrections are important as stop (co)annihilation becomes the dominant contribution t

Tao Zhu, Wen Zhao, Anzhong Wang

The spatial covariant gravities provide a natural way to including odd-order spatial derivative terms into the gravitational action, which breaks the parity symmetry at gravitational sector. A lot of parity-violating scalar-tensor theories can be mapped to the spatial covariant framework by imposing the unitary gauge. This provides us with a general framewor

Tianling Liu, Ran Su, Changming Sun, Xiuting Li

Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with th

Clément Lefebvre, Niklas Stoehr

For monitoring crises, political events are extracted from the news. The large amount of unstructured full-text event descriptions makes a case-by-case analysis unmanageable, particularly for low-resource humanitarian aid organizations. This creates a demand to classify events into event types, a task referred to as event coding. Typically, domain experts cr

Pierre Berger, Bernhard Reinke

We establish a linearization criterion for skew products of contractions in any dimension. We prove their smooth or holomorphic parameter dependence. In the smooth setting, we use the language of tame Fr\'echet spaces. We apply our result to the linearization of totally projectively expanding Cantor sets.

Yang Yu

We show that the Liouville heat kernel decays fast at large distances. In particular, the Liouville semigroup $T_t$ is $C_0$-Feller, where $C_0$ is the space of real-valued continuous functions on $\mathbb C$ vanishing at infinity.

Xiaohui Liu, Meng Liu, Lin Zhang, Linjuan Zhang

The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect generated human-like speech. With our submitted system, this paper provides an overall assessment of track 1 (Low-quality Fake Audio Detection) and track 2 (Partially Fake Audio Detection). In this paper, spectro-temporal artifacts were detected using raw temporal signals, spectral fe

Charitha Madapatha, Behrooz Makki, Hao Guo, Tommy Svensson

Integrated access and backhaul (IAB) is one of the promising techniques for 5G networks and beyond (6G), in which the same node/hardware is used to provide both backhaul and cellular services in a multi-hop fashion. Due to the sensitivity of the backhaul links with high rate/reliability demands, proper network planning is needed to make the IAB network perfo

Francesca Gasparini, Elisa Cazzaniga, Aurora Saibene

This work focuses on inner speech recognition starting from EEG signals. Inner speech recognition is defined as the internalized process in which the person thinks in pure meanings, generally associated with an auditory imagery of own inner "voice". The decoding of the EEG into text should be understood as the classification of a limited number of words (com

Thibault Cordier, Tanguy Urvoy, Fabrice Lefèvre, Lina M. Rojas-Barahona

Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multidomain dialogues. However, learning

Sam Sanders

Computability theory is a discipline in the intersection of computer science and mathematical logic where the fundamental question is: given two mathematical objects X and Y, does X compute Y in principle? In case X and Y are real numbers, Turing's famous 'machine' model provides the standard interpretation of 'computation' for this question. To formalise co

Vasilis Naserentin, Anders Logg

The concept of creating digital twins, connected digital models of physical systems, is gaining increasing attention for modeling and simulation of whole cities. The basis for building a digital twin of a city is the generation of a 3D city model, often represented as a mesh. Creating and updating such models is a tedious process that requires manual work an

Jan Korbel, David H. Wolpert

Stochastic thermodynamics is formulated under the assumption of perfect knowledge of all thermodynamic parameters. However, in any real-world experiment, there is non-zero uncertainty about the precise value of temperatures, chemical potentials, energy spectrum, etc. Here we investigate how this uncertainty modifies the theorems of stochastic thermodynamics.

Geon Yeong Park, Chanyong Jung, Sangmin Lee, Jong Chul Ye

Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. While most existing debiasing methods require full supervision on either spurious attributes or target labels, training a debiased model from a limited amount of both annotations is still an open question. To address this issue, we investigate an interest

Geon Yeong Park, Sangmin Lee, Sang Wan Lee, Jong Chul Ye

Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: ``Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?" While empirical evidence has been accumulated about the exis

Alessandro Conti, Paolo Rota, Yiming Wang, Elisa Ricci

Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have demonstrated excellent performances on many datasets, they often suffer from severe performance degradation when trained and tested on different datasets due to domain shift. In addit

Tim Schopf, Simon Klimek, Florian Matthes

Keyphrase extraction is the process of automatically selecting a small set of most relevant phrases from a given text. Supervised keyphrase extraction approaches need large amounts of labeled training data and perform poorly outside the domain of the training data. In this paper, we present PatternRank, which leverages pretrained language models and part-of-

JooYoung Park, DoangJoo Synn, XinYu Piao, Jong-Kook Kim

Deep learning has recently become one of the most compute/data-intensive methods and is widely used in many research areas and businesses. One of the critical challenges of deep learning is that it has many parameters that can be adjusted, and the optimal value may need to be determined for faster operation and high accuracy. The focus of this paper is the a

Xiangbin Liu, Junping Du, Meiyu Liang, Ang Li

Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature Fusion based Adversarial Cross-modal Ret

Yuanyuan Jiang, Jianqin Yin, Yonghao Dang

Audio-visual event (AVE) localization has attracted much attention in recent years. Most existing methods are often limited to independently encoding and classifying each video segment separated from the full video (which can be regarded as the segment-level representations of events). However, they ignore the semantic consistency of the event within the sam

Chengting Yu, Zheming Gu, Da Li, Gaoang Wang

Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it suitable for processing the event stream

Vigneshwaran Shankaran, Bhaskaran V

In spite of years of research, the mechanisms that underlie the development of schizophrenia, as well as its relapse, symptomatology, and treatment, continue to be a mystery. The absence of appropriate analytic tools to deal with the variable and complicated nature of schizophrenia may be one of the factors that contribute to the development of this disorder

Alice Guionnet, Édouard Maurel-Segala

In this article we investigate the behavior of multi-matrix unitary invariant models under a potential $V_\beta=\beta U+W$ when the inverse temperature $\beta$ becomes very large. We first prove, under mild hypothesis on the functionals $U,W$ that as soon at these potentials are "confining" at infinity, the sequence of spectral distribution of the matrices a

Yang Liu, Ruihu Li, Qiang Fu, Hao Song

Let $t \in \{2,8,10,12,14,16,18\}$ and $n=31s+t\geq 14$, $d_{a}(n,5)$ and $d_{l}(n,5)$ be distances of binary $[n,5]$ optimal linear codes and optimal linear complementary dual (LCD) codes, respectively. We show that an $[n,5,d_{a}(n,5)]$ optimal linear code is not an LCD code, there is an $[n,5,d_{l}(n,5)]=[n,5,d_{a}(n,5)-1]$ optimal LCD code if $t\neq 16$,

Xiaohui Bei, Zihao Li, Junjie Luo

We study the problem of allocating multiple types of resources to agents with Leontief preferences. The classic Dominant Resource Fairness (DRF) mechanism satisfies several desired fairness and incentive properties, but is known to have poor performance in terms of social welfare approximation ratio. In this work, we propose a new approximation ratio measure

Lin Ma, Jiangtao Gong, Hao Xu, Hao Chen

Assembly sequence planning (ASP) is the essential process for modern manufacturing, proven to be NP-complete thus its effective and efficient solution has been a challenge for researchers in the field. In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database. The ASP

Felix Christian Clemen, Emily Heath, Mikhail Lavrov

An ordered graph is a graph with a linear ordering on its vertices. The online Ramsey game for ordered graphs $G$ and $H$ is played on an infinite sequence of vertices; on each turn, Builder draws an edge between two vertices, and Painter colors it red or blue. Builder tries to create a red $G$ or a blue $H$ as quickly as possible, while Painter wants the op

Yuxin Song, Min Yang, Wenhao Wu, Dongliang He

Self-supervised video transformer pre-training has recently benefited from the mask-and-predict pipeline. They have demonstrated outstanding effectiveness on downstream video tasks and superior data efficiency on small datasets. However, temporal relation is not fully exploited by these methods. In this work, we explicitly investigate motion cues in videos a

Andreas Pfadler, Tom Szollmann, Peter Jung, Slawomir Stanczak

In this paper, we propose a novel channel estimation scheme for pulse-shaped multicarrier systems using smoothness regularization for ultra-reliable low-latency communication (URLLC). It can be applied to any multicarrier system with or without linear precoding to estimate challenging doubly-dispersive channels. A recently proposed modulation scheme using or

Hongyang Li, Jiehong Lin, Kui Jia

Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses. However, surrogate objectives of correspondence learning in 3D space are a step away from the true ones of object pose estimation, making the learning suboptimal for the end task. In this paper, we address this shortcoming by introd

O. Hryhorchak, V. Pastukhov

We discuss an equilibrium mean-field properties of mixtures consisting of bosons and spin-polarized fermionic atoms with a point-like interaction in an arbitrary dimension $2<d<4$. Particularly, we discuss except the standard weak-coupling limit of the system with slightly depleted Bose condensate and almost ideal Fermi gas, the (meta)stable phase with dimer

Lei Li, Yankai Lin, Xuancheng Ren, Guangxiang Zhao

Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. This paper explores a novel PLM reuse paradigm, Knowledge Integration (KI). Without human annotations available, KI aims to merge the knowledge from different teacher-PLMs, each of wh

Wei Jiang, Hans D. Schotten

In the context of cell-free massive multi-input multi-output (CFmMIMO), zero-forcing precoding (ZFP) is superior in terms of spectral efficiency. However, it suffers from channel aging owing to fronthaul and processing delays. In this paper, we propose a robust scheme coined delay-tolerant zero-forcing precoding (DT-ZFP), which exploits deep learning-aided c

Ursula Laa, Alex Aumann, Dianne Cook, German Valencia

This paper describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is useful for exploring the sensitivity of structure to particular variables. The user can also interactively shift the cent

Stuart Bartlett, David Louapre

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning has been widely studied in cognitive science and artificial intelligence, but are most commonly implemented in highly complex or carefully engineered systems such as animal brains, artificial neural networks, DNA compu

You Guo, Jun Wang, Trevor Cohn

Deep neural networks are vulnerable to adversarial attacks, such as backdoor attacks in which a malicious adversary compromises a model during training such that specific behaviour can be triggered at test time by attaching a specific word or phrase to an input. This paper considers the problem of diagnosing whether a model has been compromised and if so, id

Suman Sourav, Partha P. Biswas, Binbin Chen, Daisuke Mashima

In modern smart grids, the proliferation of communication-enabled distributed energy resource (DER) systems has increased the surface of possible cyber-physical attacks. Attacks originating from the distributed edge devices of DER system, such as photovoltaic (PV) system, is often difficult to detect. An attacker may change the control configurations or vari

Aurora Saibene, Silvia Corchs, Mirko Caglioni, Francesca Gasparini

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. I

Laurent Théry

This notes explains how a standard algorithm that constructs the discrete Fourier transform has been formalised and proved correct in the Coq proof assistant using the SSReflect extension.

Théo Moins, Julyan Arbel, Stéphane Girard, Anne Dutfoy

Using Bayesian methods for extreme value analysis offers an alternative to frequentist ones, with several advantages such as easily dealing with parametric uncertainty or studying irregular models. However, computations can be challenging and the efficiency of algorithms can be altered by poor parametrization choices. The focus is on the Poisson process char

José Roberto Canivete Cuissa, Oskar Steiner

Context. A universally accepted definition of what a vortex is has not yet been reached. Therefore, we lack an unambiguous and rigorous method for the identification of vortices in fluid flows. Such a method would be necessary to conduct robust statistical studies on vortices in highly dynamical and turbulent systems, such as the solar atmosphere. Aims. We a

Juliette Achddou, Olivier Cappe, Aurélien Garivier

Motivated by programmatic advertising optimization, we consider the task of sequentially allocating budget across a set of resources. At every time step, a feasible allocation is chosen and only a corresponding random return is observed. The goal is to maximize the cumulative expected sum of returns. This is a realistic model for budget allocation across sub

Andrea Pollastro, Giusiana Testa, Antonio Bilotta, Roberto Prevete

In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually

Xinpeng Wang, Han Jiang, Zhihua Wei, Shanlin Zhou

Story generation has emerged as an interesting yet challenging NLP task in recent years. Some existing studies aim at generating fluent and coherent stories from keywords and outlines; while others attempt to control the global features of the story, such as emotion, style and topic. However, these works focus on coarse-grained control on the story, neglecti

Dmitrii Chaikovskii, Ye Zhang

This paper concerns the use of asymptotic expansions for the efficient solving of forward and inverse problems involving a nonlinear singularly perturbed time-dependent reaction--diffusion--advection equation. By using an asymptotic expansion with the local coordinates in the transition-layer region, we prove the existence and uniqueness of a smooth solution

B. Fetić, W. Becker, D. B. Milošević

No, we cannot! The concept of Wigner time delay was introduced in scattering theory to quantify the delay or advance of an incoming particle in its interaction with the scattering potential. It was assumed that this concept can be transferred to ionization considering it as a half scattering process. In the present work we show, by analyzing the correspondin

Haixiang Zhang, Yingjun Deng, Alan J. X. Guo, Qing-Hu Hou

Recently, graph (network) data is an emerging research area in artificial intelligence, machine learning and statistics. In this work, we are interested in whether node's labels (people's responses) are affected by their neighbor's features (friends' characteristics). We propose a novel latent logistic regression model to describe the network dependence with

Guillaume Bau, Antoine Miné, Vincent Botbol, Mehdi Bouaziz

Static analysis of smart-contracts is becoming more widespread on blockchain platforms. Analyzers rely on techniques like symbolic execution or model checking, but few of them can provide strong soundness properties and guarantee the analysis termination at the same time. As smart-contracts often manipulate economic assets, proving numerical properties beyon

Simon Jacobsson, Thomas Bäckdahl

Employing the covariant language of two-spinors, we find what conditions a curved Lorentzian spacetime must satisfy for existence of a second order symmetry operator for the massive Dirac equation. The conditions are formulated as existence of a set of Killing spinors satisfying a set of covariant linear differential equations. Using these Killing spinors, w

Youcai Zhang, Xiaohu Yang, Hong Guo

Based on galaxies in the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) and dark matter haloes in the dark matter only, cosmological and constrained ELUCID simulation, we investigate the relation between the observed radii of central galaxies with stellar mass $\gtrsim 10^{8} h^{-2}{\rm M}_\odot$ and the virial radii of their host dark matter haloes with

Kohei Fujikura, Soichiro Hashiba, Jun'ichi Yokoyama

We construct a model explaining dark matter, baryon asymmetry and reheating in quintessential inflation model. Three generations of right-handed neutrinos having hierarchical masses, and the light scalar field leading to self-interaction of active neutrinos are introduced. The lightest sterile neutrino is a dark matter candidate produced by a Dodelson-Widrow

Xiaojuan Li

In this paper, we study the relationship between maximum principle (MP) and dynamic programming principle (DPP) for stochastic recursive optimal control problem driven by $G$-Brownian motion. Under the smooth assumption for the value function, we obtain the connection between MP and DPP under a reference probability $P_{t,x}^{\ast}$. Within the framework of

Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Andrew Stavroulakis

Recurrent Neural Networks (RNNs) frequently exhibit complicated dynamics, and their sensitivity to the initialization process often renders them notoriously hard to train. Recent works have shed light on such phenomena analyzing when exploding or vanishing gradients may occur, either of which is detrimental for training dynamics. In this paper, we point to a

Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li

Despite the remarkable success of pre-trained language models (PLMs), they still face two challenges: First, large-scale PLMs are inefficient in terms of memory footprint and computation. Second, on the downstream tasks, PLMs tend to rely on the dataset bias and struggle to generalize to out-of-distribution (OOD) data. In response to the efficiency problem,

Xiangguang Chen, Ye Zhu, Yu Li, Bingtao Fu

Automatic human matting is highly desired for many real applications. We investigate recent human matting methods and show that common bad cases happen when semantic human segmentation fails. This indicates that semantic understanding is crucial for robust human matting. From this, we develop a fast yet accurate human matting framework, named Semantic Guided

Zhibin Shao, Shaojian Li, Yanzhao Liu, Zi Li

Recently, log-periodic quantum oscillations have been detected in topological materials zirconium pentatelluride (ZrTe5) and hafnium pentatelluride (HfTe5), displaying intriguing discrete scale invariance (DSI) characteristic. In condensed materials, the DSI is considered to be related to the quasi-bound states formed by massless Dirac fermions with strong C

Xanh Ho, Saku Sugawara, Akiko Aizawa

Several multi-hop reading comprehension datasets have been proposed to resolve the issue of reasoning shortcuts by which questions can be answered without performing multi-hop reasoning. However, the ability of multi-hop models to perform step-by-step reasoning when finding an answer to a comparison question remains unclear. It is also unclear how questions

Yu. V. Sotnikova, T. V. Mufakharov, A. G. Mikhailov, V. A. Stolyarov

We study the radio variability of galaxies with and without sources of hydroxyl (OH) megamaser radiation based on the continuum radio measurements conducted in 2019-2022 with the radio telescope RATAN-600 at frequencies of 2.3, 4.7, 8.2, and 11.2 GHz. Presumably, radio continuum emission significantly affects the megamaser radiation brightness, therefore, su

François-Xavier Dupé, Rohit Yadav, Guillaume Auzias, S. Takerkart

Multigraph matching is a recent variant of the graph matching problem. In this framework, the optimization procedure considers several graphs and enforces the consistency of the matches along the graphs. This constraint can be formalized as a cycle consistency across the pairwise permutation matrices, which implies the definition of a universe of vertex~\cit

Landry Djomegne, Cyrille Kenne, René Dorville, Pascal Zongo

The main purpose of this paper is to apply the notion of hierarchical control to a coupled degenerate non linear parabolic equations. We use the Stackelberg-Nash strategy with one leader and two followers. The followers solve a Nash equilibrium corresponding to a bi-objective optimal control problem and the leader a null controllability problem. Since the co

Philippe Wenger, Damien Chablat

Cuspidal robots can move from one inverse or direct kinematic solution to another without ever passing through a singularity. These robots have remained unknown because almost all industrial robots do not have this feature. However, in fact, industrial robots are the exceptions. Some robots appeared recently in the industrial market can be shown to be cuspid

Rong-Xin Miao, Yu-Qian Zeng

Due to the Weyl anomaly, an axial vector field produces novel anomalous chiral currents in spacetime with boundaries. Remarkably, the chiral current is not invariant under the gauge transformation of axial vector fields. As a result, more potential terms appear, and the chiral current becomes larger than the electric current near the boundary. This paper inv