February 2024 arXiv papers — page 104

Weiheng Chai, Brian Testa, Huantao Ren, Asif Salekin

Deep neural networks are extensively applied to real-world tasks, such as face recognition and medical image classification, where privacy and data protection are critical. Image data, if not protected, can be exploited to infer personal or contextual information. Existing privacy preservation methods, like encryption, generate perturbed images that are unre

Jie Mei, Mingyuan Jiu, Hichem Sahbi, Xiaoheng Jiang

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for fine-tuned models to ef

Bayan Karimi, Gorm Ole Steffensen, Andrew P. Higginbotham, Charles M. Marcus

A Josephson junction (JJ) has been under intensive study ever since 1960's. Yet even in the present era of building quantum information processing devices based on many JJs, open questions regarding a single junction remain unsolved, such as quantum phase transitions, coupling of the JJ to an environment and improving coherence of a superconducting qubit. He

Zhong-Qiu Wang

We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than

N. E. Rieger

Misner spacetime is obtained by quotienting a timelike wedge of two-dimensional Minkowski spacetime by a discrete boost. The familiar Hausdorff extensions and the Hawking--Ellis non-Hausdorff extension are classical, but the passage from covering constructions of the punctured Minkowski plane to genuine extensions of Misner spacetime is subtler than is often

Sergi Julià-Farré, Javier Argüello-Luengo, Loïc Henriet, Alexandre Dauphin

We study Thouless pumps, i.e., adiabatic topological transport, in an interacting spin chain described by the dimerized XXZ Hamiltonian. In the noninteracting case, quantized Thouless pumps can only occur when a topological singularity is encircled adiabatically. In contrast, here we show that, in the presence of interactions, such topological transport can

Zhen-Min Zeng, Zong-Kuan Guo

We investigate the first-order phase transition catalyzed by primordial black holes~(PBHs) in the early Universe. We find that super-horizon curvature perturbations generated in this scenario lead to the production of gravitational waves when the scalar modes re-enter the horizon. If PBHs with masses about $10^{-13}M_{\odot}$ constitute all dark matter, the

V. H. Jorge-Pérez, J. A. Lima

Let $M$ be a finitely generated module over a local ring $(R,\mathfrak{m})$. By $\mathcal{S}_j(M)$, we denote the $j$th symmetric power of $M$ ($j$th graded component of the symmetric algebra $\mathcal{S}_R(M)$). The purpose of this paper is to investigate the minimal free resolutions $\mathcal{S}_j(M)$ as $R$-module for each $j\geq 2$ and determine the Bett

Francisco José Aguayo-Canela, Héctor Alaiz-Moretón, María Teresa García-Ordás, José Alberto Benítez-Andrades

The spread of the Internet of Things (IoT) is demanding new, powerful architectures for handling the huge amounts of data produced by the IoT devices. In many scenarios, many existing isolated solutions applied to IoT devices use a set of rules to detect, report and mitigate malware activities or threats. This paper describes a development environment that a

Th. K. Mavrogordatos

We discuss the conditional measurement of field amplitudes by a nonclassical photon sequence in the Jaynes-Cummings (JC) model under multiphoton operation. We do so by employing a correlator of immediate experimental relevance to reveal a distinct nonclassical evolution in the spirit of [G. T. Foster et al., Phys. Rev. Lett. 85 3149 (2000)]. The correlator r

Alexander Miloshevsky, Lucas M. Cohen, Karthik V. Myilswamy, Muneer Alshowkan

We showcase a fully on-chip CMOS-fabricated silicon photonic integrated circuit employing a bidirectionally pumped microring and polarization splitter-rotators tailored for the generation of ultrabroadband ($>$9 THz), high-fidelity (90-98%) polarization-entangled photons. Spanning the optical C+L-band and producing over 116 frequency-bin pairs on a 38.4 GHz-

Alfio Borzì, Gennaro Infante, Giovanni Mascali

A new optimization framework to design steady equilibrium solutions of the Vlasov-Poisson system by means of external electric fields is presented. This optimization framework requires the minimization of an ensemble functional with Tikhonov regularization of the control field under the differential constraint of a nonlinear elliptic equation that models equ

Gideon Stein, Maha Shadaydeh, Joachim Denzler

Causal discovery from time series data encompasses many existing solutions, including those based on deep learning techniques. However, these methods typically do not endorse one of the most prevalent paradigms in deep learning: End-to-end learning. To address this gap, we explore what we call Causal Pretraining. A methodology that aims to learn a direct map

Ryan Mckinven, Mohit Bhardwaj, Tarraneh Eftekhari, Charles D. Kilpatrick

Fast radio bursts (FRBs) last for milliseconds and arrive at Earth from cosmological distances. While their origin(s) and emission mechanism(s) are presently unknown, their signals bear similarities with the much less luminous radio emission generated by pulsars within our Galaxy and several lines of evidence point toward neutron star origins. For pulsars, t

Lukas S. Huber, Fred W. Mast, Felix A. Wichmann

Recent research has seen many behavioral comparisons between humans and deep neural networks (DNNs) in the domain of image classification. Often, comparison studies focus on the end-result of the learning process by measuring and comparing the similarities in the representations of object categories once they have been formed. However, the process of how the

Francisco José Aguayo-Canela, Héctor Alaiz-Moretón, María Teresa García-Ordás, José Alberto Benítez-Andrades

The increasing number of connected devices and the complexity of Internet of Things (IoT) ecosystems are demanding new architectures for managing and securing these networked environments. Intrusion Detection Systems (IDS) are security solutions that help to detect and mitigate the threats that IoT systems face, but there is a need for new IDS strategies and

Rostislav Arkhipov, Anton Pakhomov, Olga Diachkova, Mikhail Arkhipov

The coherent interaction of extremely short light pulses with a resonant medium can result in formation of population difference gratings. Such gratings have been created by pulses that are pi/2 or smaller. This paper demonstrates that a microcavity with Bragg-like mirrors can be formed by colliding two single-cycle attosecond self-induced transparency pulse

V. L. Martinez-Consentino, J. E. Amaro

The semi-inclusive cross section of two-nucleon emission induced by neutrinos and antineutrinos is computed employing the relativistic mean field model of nuclear matter and the dynamics of meson exchange currents. Within this model we explore a factorization approximation based on the product of an integrated two-hole spectral function and a two-nucleon cro

José Alberto Benítez-Andrades, María Teresa García-Ordás, María Álvarez-González, Raquel Leirós-Rodríguez

Background: Postpartum urinary incontinence (PUI) is a common issue among postnatal women. Previous studies identified potential related variables, but lacked analysis on certain intrinsic and extrinsic patient variables during pregnancy. Objective: The study aims to evaluate the most influential variables in PUI using machine learning, focusing on intrinsic

Anno Touwen, Joost W. F. van Hofslot, Thijs Qualm, Richard Borchers

An electrostatic hexapole lens is used to manipulate the transverse properties of a beam of barium fluoride molecules from a cryogenic buffer gas source. The spatial distribution of the beam is measured by recording state-selective laser-induced fluorescence on an emccd camera, providing insight into the intensity and transverse position spread of the molecu

Vahid Majdinasab, Amin Nikanjam, Foutse Khomh

Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not contain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is

Fabian Zhou, Jasmine Brewer, Aleksas Mazeliauskas

We study the energy deposition and thermalisation of high-momentum on-shell partons (minijets) travelling through a non-equilibrium Quark-Gluon Plasma using QCD kinetic theory. For thermal backgrounds, we show that the parton energy first flows to the soft sector by collinear cascade and then isotropises via elastic scatterings. In contrast, the momentum dep

Kui Ren, Nathan Soedjak, Kewei Wang, Hongyu Zhai

In this short note, we consider an inverse problem to a mean-field games system where we are interested in reconstructing the state-independent running cost function from observed value-function data. We provide an elementary proof of a uniqueness result for the inverse problem using the standard multilinearization technique. One of the main features of our

Mark J. Crumpton, Yan V. Fyodorov, Tim R. Würfel

We study the mean diagonal overlap of left and right eigenvectors associated with complex eigenvalues in $N\times N$ non-Hermitian random Gaussian matrices. In well known works by Chalker and Mehlig the expectation of this (self-)overlap was computed for the complex Ginibre ensemble as $N\to \infty$. In the present work, we consider the same quantity in the

Matt Dallas, Sara Pollock, Leo G. Rebholz

The purpose of this paper is to develop a practical strategy to accelerate Newton's method in the vicinity of singular points. We present an adaptive safeguarding scheme with a tunable parameter, which we call adaptive gamma-safeguarding, that one can use in tandem with Anderson acceleration to improve the performance of Newton's method when solving problems

Adolfo Anta, Diego Cifelli

As inverter-based generation is being massively deployed in the grid, these type of units have to take over the current roles of conventional generation, including the capability of restoring the grid. In this context, the resonances of the grid during the first steps of a black start can be concerning, given that the grid is lightly loaded. Especially relev

István Gaál

Let $0,1\ne m\in Z$ and $\alpha=\sqrt[8]{m}$. According to the results of I. Ga\'al and L. El Fadil, $\alpha$ generates a power integral basis in $K=Q(\alpha)$, if and only if $m$ is square-free and $m\not\equiv 1\;(\bmod\; 4)$. In the present paper we consider totally complex pure octic fields, that is the case $m<0$, with $m$ satisfiying the above property

Bastien Jean Quemener

In this paper the Gaussian integral is proven using contour integration on $\frac{1}{e^{x^2}+1}$ and linking it using a limit to said Gaussian integral. The limit is alsorelated to the Riemann Zeta function using a few manipulations. This new and original proof comes as an addition to the already many pre-existing proofs of the Gaussian integral.

Cyril Bachelard, Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas

The present article explores the application of randomized control techniques in empirical asset pricing and performance evaluation. It introduces geometric random walks, a class of Markov chain Monte Carlo methods, to construct flexible control groups in the form of random portfolios adhering to investor constraints. The sampling-based methods enable an exp

Chin-Ping Hu, Takuto Narita, Teruaki Enoto, George Younes

Magnetars are neutron stars with extremely high magnetic fields that exhibit various X-ray phenomena such as sporadic sub-second bursts, long-term persistent flux enhancements, and variable rates of rotation period change. In 2020, a fast radio burst (FRB), akin to cosmological millisecond-duration radio bursts, was detected from the Galactic magnetar SGR 19

José Alberto Benítez-Andrades, Natalia Arias, María Teresa García-Ordás, Marta Martínez-Martínez

This study shows the feasibility of an eHealth solution for tackling eating habits and physical activity in the adolescent population. The participants were children from 11 to 15 years old. An intervention was carried out on 139 students in the intervention group and 91 students in the control group, in two schools during 14 weeks. The intervention group ha

Michael Lanier, Ying Xu, Nathan Jacobs, Chongjie Zhang

Deep reinforcement learning has demonstrated remarkable achievements across diverse domains such as video games, robotic control, autonomous driving, and drug discovery. Common methodologies in partially-observable domains largely lean on end-to-end learning from high-dimensional observations, such as images, without explicitly reasoning about true state. We

S. Upadhyaya, K. Mazurek, T. Kozik, D. Gruyer

The emission of the pre-equilibrium particles during nuclear collisions at moderate beam energies is still an open question. This influences the properties of the compound nucleus but also changes the interpretation of the quasi-fission process. A systematic analysis of the data obtained by the FAZIA collaboration during a recent experiment with a neutron ri

Pilar Marqués-Sánchez, Cristina Liébana-Presa, José Alberto Benítez-Andrades, Raquel Gundín-Gallego

During nursing studies, it is crucial to develop emotional skills for both academic success and quality patient care. Utilizing technologies like thermography can be instrumental in nursing education to assess and enhance these skills. The study aims to evaluate the effectiveness of thermography in monitoring and improving the emotional skills of nursing stu

Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Hong Ming Tan

Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shaple

Thomas Ransford, Dashdondog Tsedenbayar

We study the real and imaginary parts of the powers of the Volterra operator on $L^2[0,1]$, specifically their eigenvalues, their norms and their numerical ranges.

Jessica Zhu, Michel Cukier, Joseph Richardson

Objective: Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to minimize distrust, this study provides a framework for establishing AI trust and transparency with the general population. Methods: We propose a Model Facts template that is easily extendable and decomposes accuracy

Jie Lin, Benjamin MacLellan, Sobhan Ghanbari, Julie Belleville

GraphiQ is a versatile open-source framework for designing photonic graph state generation schemes, with a particular emphasis on photon-emitter hybrid circuits. Built in Python, GraphiQ consists of a suite of design tools, including multiple simulation backends and optimization methods. The library supports scheme optimization in the presence of circuit imp

Kamiba I. Kabuya, Olasupo O. Ajayi, Anotine B. Bagula

The "Smart City" (SC) concept has been around for decades with deployment scenarios revealed in major cities of developed countries. However, while SC has enhanced the living conditions of city dwellers in the developed world, the concept is still either missing or poorly deployed in the developing world. This paper presents a review of the SC concept from t

José Alberto Benítez-Andrades, Alejandro Rodríguez-González, Carmen Benavides, Leticia Sánchez-Valdeón

Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract

Zhichen Dong, Zhanhui Zhou, Chao Yang, Jing Shao

Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns and spurred recent research on LLM conversation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM co

Yining Huang, Keke Tang, Meilian Chen

Emerging Large Language Models (LLMs) like GPT-4 have revolutionized Natural Language Processing (NLP), showing potential in traditional tasks such as Named Entity Recognition (NER). Our study explores a three-phase training strategy that harnesses GPT-4's capabilities to enhance the BERT model's performance on NER. Initially, GPT-4 annotates a subset of the

Agus Hartoyo, Jan Argasiński, Aleksandra Trenk, Kinga Przybylska

Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessia

Maria Stefaniak

This work explores femtoscopic correlations in proton-proton and proton-cluster systems at the HADES experiment, GSI. Through high-precision correlation functions, it reveals the impact of strong interactions between protons and light nuclei (deuteron, triton, Helium-3). For proton-proton interactions, measurements in two centralities and five $k_T$ bins wer

Gennaro Tucci, Ramin Golestanian, Suropriya Saha

A multicomponent mixture of Janus colloids with distinct catalytic coats and phoretic mobilities is a promising theoretical system to explore the collective behavior arising from nonreciprocal interactions. An active colloid produces (or consumes) chemicals, self-propels, drifts along chemical gradients, and rotates its intrinsic polarity to align with a gra

C. Saffe, P. Miquelarena, J. Alacoria, E. Martioli

Aims. We explore different scenarios to explain the chemical difference found in the remarkable giant-giant binary system HD 138202 + CD-30 12303. For the first time, we suggest how to distinguish these scenarios by taking advantage of the extensive convective envelopes of giant stars. Methods. We carried out a high-precision determination of stellar paramet

José Alberto Benítez-Andrades, Isaías García-Rodríguez, Carmen Benavides, Héctor Alaiz-Moretón

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use o

Alessandro Benfenati, Paola Causin, Martina Quinteri

Medical imaging is nowadays a pillar in diagnostics and therapeutic follow-up. Current research tries to integrate established - but ionizing - tomographic techniques with technologies offering reduced radiation exposure. Diffuse Optical Tomography (DOT) uses non-ionizing light in the Near-Infrared (NIR) window to reconstruct optical coefficients in living b

Valeriy V. Dvoeglazov

We considered Weinberg-like equations in the article [1] in order to construct the Feynman-Dyson propagator for the spin-1 particles. This construction is based on the concept of the Weinberg field as a system of four field functions differing by parity and by dual transformations. We also analyzed the recent controversy in the definitions of the Feynman-Dys

Jason J. Bramburger, Matt Holzer, Jackson Williams

The goal of this work is to identify steady-state solutions to dynamical systems defined on large, random families of networks. We do so by passing to a continuum limit where the adjacency matrix is replaced by a non-local operator with kernel called a graphon. This graphon equation is often more amenable to analysis and provides a single equation to study i

Pilar Marqués-Sánchez, José Alberto Benítez-Andrades, María Dolores Calvo Sánchez, Natalia Arias

Objectives: This study analyzed adolescent physical activity, its link to overweight, and the social network structure in group sports participants, focusing on centrality measures. Setting: Conducted in 11 classrooms across 5 schools in Ponferrada, Spain. Participants: Included 235 adolescents (49.4% female), categorized as normal weight or overweight. Meth

Julien Froustey, Sherwood Richers, Evan Grohs, Samuel D. Flynn

Neutrino evolution, of great importance in environments such as neutron star mergers (NSMs) because of their impact on explosive nucleosynthesis, is still poorly understood due to the high complexity and variety of possible flavor conversion mechanisms. In this study, we focus on so-called "fast flavor oscillations", which can occur on timescales of nanoseco

Frédéric Clette, Shantanu Jain, Tatiana Podladchikova

The McNish and Lincoln (ML) method, introduced in 1949, was one of the first attempts to produce mid-term forecasts of solar activity, up to 12 months ahead. However, it has been poorly described and evaluated in the past literature, in particular its actual operational implementation by NOAA. Here, we reconstruct the exact formulation of the method, as it w

Naomi A. Arnold, Peijie Zhong, Cheick Tidiane Ba, Ben Steer

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offe

Andres Molares-Ulloa, Daniel Rivero, Jesus Gil Ruiz, Enrique Fernandez-Blanco

Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high concentrations of marine biotoxins in their tissues. To avoid the risk to human consumption, harvesting is prohibited when toxicity is detected.

Huachen Fang, Jinjian Wu, Qibin Hou, Weisheng Dong

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides

Michael L Brodie

The objective of this research is to provide a framework with which the data science community can understand, define, and develop data science as a field of inquiry. The framework is based on the classical reference framework (axiology, ontology, epistemology, methodology) used for 200 years to define knowledge discovery paradigms and disciplines in the hum

Stanisław Woźniak, Bartłomiej Koptyra, Arkadiusz Janz, Przemysław Kazienko

Large language models (LLMs) have significantly advanced Natural Language Processing (NLP) tasks in recent years. However, their universal nature poses limitations in scenarios requiring personalized responses, such as recommendation systems and chatbots. This paper investigates methods to personalize LLMs, comparing fine-tuning and zero-shot reasoning appro

Clayton Sanford, Daniel Hsu, Matus Telgarsky

We show that a constant number of self-attention layers can efficiently simulate, and be simulated by, a constant number of communication rounds of Massively Parallel Computation. As a consequence, we show that logarithmic depth is sufficient for transformers to solve basic computational tasks that cannot be efficiently solved by several other neural sequenc

Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou

Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e. "hallucinations", even when they hold relevant knowledge. To address these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we explore Self-Alignment for Factuality, wh

Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos, Daniel Rivero

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer of cultivated mussels, the opening and closing of the production areas is controlled by a monitoring program. In addition to the closures resulting fr

Nina Pardal, Santiago Cifuentes, Edwin Pin, Maria Vanina Martinez

Preferences are a pivotal component in practical reasoning, especially in tasks that involve decision-making over different options or courses of action that could be pursued. In this work, we focus on repairing and querying inconsistent knowledge bases in the form of graph databases, which involves finding a way to solve conflicts in the knowledge base and

Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham

Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by ass

Zhengcheng Wang, Fei Teng, Yanzhen Zhou, Qinglai Guo

Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, the increasing uncertainty and variabili

Corentin Royer, Bjoern Menze, Anjany Sekuboyina

We introduce MultiMedEval, an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM). MultiMedEval comprehensively assesses the models' performance on a broad array of six multi-modal tasks, conducted over 23 datasets, and spanning over 11 medical domains. The chosen tasks and performance metrics are based on

Alaa Hamdan, Mohammed Berkani

To complete the study of Fredholm type operators of [10] and [11], we define in this paper the classes of left and right semi-B-Fredholm operators (Definition 3.1). Then, we prove that an operator $T \in L(X), X$ being a Banach space, is a left( resp. right) semi-B-Fredholm operator if and only if $T$ is the direct sum of left(resp. right) semi-Fredholm oper

Zilin Ma, Yiyang Mei, Yinru Long, Zhaoyuan Su

LGBTQ+ individuals are increasingly turning to chatbots powered by large language models (LLMs) to meet their mental health needs. However, little research has explored whether these chatbots can adequately and safely provide tailored support for this demographic. We interviewed 18 LGBTQ+ and 13 non-LGBTQ+ participants about their experiences with LLM-based

Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin Vechev

Modern language models (LMs) have gained widespread acceptance in everyday and professional contexts, particularly in programming. An essential procedure enabling this adoption is instruction tuning, which substantially enhances LMs' practical utility by training them to follow user instructions and human preferences. However, existing instruction tuning sch

Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady

To harness the power of large language models in safety-critical domains, we need to ensure the explainability of their predictions. However, despite the significant attention to model interpretability, there remains an unexplored domain in explaining sequence-to-sequence tasks using methods tailored for textual data. This paper introduces SyntaxShap, a loca

Sudha, A. R. Usha Devi, B. N. Karthik, H. S. Karthik

The Bloch sphere provides an elegant way of visualizing a qubit. Analogous representation of the simplest composite state of two-qubits has attracted significant attention. Here we present a detailed mathematical analysis of the real-matrix parametrization and associated geometric picturization of arbitrary two-qubit states - up to their local SL2C equivalen

Guanxiong Sun, Yang Hua, Guosheng Hu, Neil Robertson

Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i.e., predicting one result per video. However, challenges arise when adapting existing deep video models to dense video tasks, i.e., predicting one result per frame. Specifically, these models are expensive for deployment, less effective

Prahar Mitra

Scattering amplitudes in $d+2$ dimensions can be recast as correlators of conformal primary operators in a putative holographic CFT$_d$ by working in a basis of boost eigenstates instead of momentum eigenstates. It has been shown previously that conformal primary operators with $\Delta \in \frac{d}{2} + i {\mathbb R}$ form a basis for massless one-particle r

Taishi Kurahashi, Yoshiaki Minami

We study model theoretic characterizations of various collection schemes over $\mathbf{PA}^-$ from the viewpoint of Gaifman's splitting theorem. Among other things, we prove that for any $n \geq 0$ and $M \models \mathbf{PA}^-$, the following are equivalent: 1. $M$ satisfies the collection scheme for $\Sigma_{n+1}$ formulas. 2. For any $K, N \models \mathbf{

Qingqiong Cai, Shinya Fujita, Henry Liu, Boram Park

An edge-coloured path is monochromatic if all of its edges have the same colour. For a $k$-connected graph $G$, the monochromatic $k$-connection number of $G$, denoted by $mc_k(G)$, is the maximum number of colours in an edge-colouring of $G$ such that, any two vertices are connected by $k$ internally vertex-disjoint monochromatic paths. In this paper, we sh

Ziang Liu, Longfei Yin, Wonjae Shin, Bruno Clerckx

Low earth orbit (LEO) satellite systems with sensing functionality are envisioned to facilitate global-coverage service and emerging applications in 6G. Currently, two fundamental challenges, namely, inter-beam interference among users and power limitation at the LEO satellites, limit the full potential of the joint design of sensing and communication. To ef

Giuseppe Orlando, Luca Bonaventura

We analyze schemes based on a general Implicit-Explicit (IMEX) time discretization for the compressible Euler equations of gas dynamics, showing that they are asymptotic-preserving (AP) in the low Mach number limit. The analysis is carried out for a general equation of state (EOS). We consider both a single asymptotic length scale and two length scales. We t

Yang Zhong, Hongyu Yu, Jihui Yang, Xingyu Guo

While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn-Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite

Chaitanya Prasad Sishtla, Jens Pomoell, Norbert Magyar, Emilia Kilpua

Alfv\'enic fluctuations, as modelled by the non-linear interactions of Alfv\'en waves of various scales, are seen to dominate solar wind turbulence. However, there is also a non-negligible component of non-Alfv\'enic fluctuations. The Els\"asser formalism, which is central to the study of Alfv\'enic turbulence due to its ability to differentiate between para

Vladimír Kunc

Neural networks are the state-of-the-art approach for many tasks and the activation function is one of the main building blocks that allow such performance. Recently, a novel transformative adaptive activation function (TAAF) allowing for any vertical and horizontal translation and scaling was proposed. This work sets the TAAF into the context of other activ

Hodjat Mariji, Stanislav Maslovski

We develop a self-consistent theoretical formalism to model the dynamics of heat transfer in dissipative, dispersive, anisotropic nanoscale media, such as metamaterials. We employ our envelope dyadic Green's function method to solve Maxwell's macroscopic equations for the propagation of fluctuating electromagnetic fields in these media. We assume that the ph

Ivan Chajda, Helmut Länger

Tolerance relations were investigated by several authors in various algebraic structures, see e.g. the monograph by I. Chajda. Recently G. Cz\'edli studied so-called 2-uniform tolerances on lattices, i.e. tolerances that are compatible with the lattice operations and whose blocks are of cardinality 2. He showed that two such tolerances on a lattice containin

Ángel L. Corps, Jorge Dukelsky, Armando Relaño

We present a theory characterizing the phases emerging as a consequence of continuous symmetry-breaking in quantum and classical systems. In symmetry-breaking phases, dynamics is restricted due to the existence of a set of conserved charges derived from the order parameter of the phase transition. Their expectation values are determined by the privileged dir

Rajeev Goel, Utkarsh Nath, Yancheng Wang, Alvin C. Silva

Deep neural networks, including Convolutional Neural Networks (CNNs) and Visual Transformers (ViT), have achieved stunning success in medical image domain. We study thorax disease classification in this paper. Effective extraction of features for the disease areas is crucial for disease classification on radiographic images. While various neural architecture

Tao Yu, Congzheng Song, Jianyu Wang, Mona Chitnis

Asynchronous protocols have been shown to improve the scalability of federated learning (FL) with a massive number of clients. Meanwhile, momentum-based methods can achieve the best model quality in synchronous FL. However, naively applying momentum in asynchronous FL algorithms leads to slower convergence and degraded model performance. It is still unclear

Haimin Hu, Gabriele Dragotto, Zixu Zhang, Kaiqu Liang

We consider the multi-agent spatial navigation problem of computing the socially optimal order of play, i.e., the sequence in which the agents commit to their decisions, and its associated equilibrium in an N-player Stackelberg trajectory game. We model this problem as a mixed-integer optimization problem over the space of all possible Stackelberg games asso

Christian Marinoni, Riccardo Fosco Gramaccioni, Changan Chen, Aurelio Uncini

The primary goal of the L3DAS23 Signal Processing Grand Challenge at ICASSP 2023 is to promote and support collaborative research on machine learning for 3D audio signal processing, with a specific emphasis on 3D speech enhancement and 3D Sound Event Localization and Detection in Extended Reality applications. As part of our latest competition, we provide a

Onel López, Ritesh Kumar Singh, Dinh-Thuy Phan-Huy, Efstathios Katranaras

Low-cost, resource-constrained, maintenance-free, and energy-harvesting (EH) Internet of Things (IoT) devices, referred to as zero-energy devices (ZEDs), are rapidly attracting attention from industry and academia due to their myriad of applications. To date, such devices remain primarily unsupported by modern IoT connectivity solutions due to their intrinsi

Jaehyuk Choi

This study proposes a fast exact simulation scheme for the Ornstein-Uhlenbeck driven stochastic volatility model. With the Karhunen-Lo\`eve expansions, the stochastic volatility path (Ornstein-Uhlenbeck process) is expressed as a sine series, and the time integrals of volatility and variance are analytically derived as infinite series of independent normal r

Pengfei Zhou, Weiqing Min, Jiajun Song, Yang Zhang

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task

Guanxiong Sun, Yang Hua, Guosheng Hu, Neil Robertson

Recently, one-stage detectors have achieved competitive accuracy and faster speed compared with traditional two-stage detectors on image data. However, in the field of video object detection (VOD), most existing VOD methods are still based on two-stage detectors. Moreover, directly adapting existing VOD methods to one-stage detectors introduces unaffordable

Siyuan Li, Zicheng Liu, Juanxi Tian, Ge Wang

Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DNN) optimization. Despite achieving better flatness, existing WA methods might fall into worse final performances or require extra test-time computations. This work unveils the full

Shubham Gupta, Srikanta Bedathur

Temporal graph neural networks Tgnn have exhibited state-of-art performance in future-link prediction tasks. Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss. During training, in the context of a positive example, the loss is computed over uninformative negatives, which introduces redundancy and sub-optimal performance

Angela A. Albanese, José Bonet, Werner J. Ricker

Recent results concerning the linear dynamics and mean ergodicity of compact operators in Banach spaces, together with additional new results, are employed to investigate various spectral properties of generalized Ces\`aro operators acting in large classes of classical BK-sequence spaces. Of particular interest is to determine the eigenvalues and the corresp

Yannis Kalantidis, Mert Bülent Sarıyıldız, Rafael S. Rezende, Philippe Weinzaepfel

State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions, due to e.g. weather or time of day, with dramatic consequences on the visual localization accuracy. In this paper, we improve this retrieval step and tailor it to the final locali

Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf

To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate thes

Zhiyuan Zheng

In this paper, the concept of weakly uniform perfectness is considered. As an analogue of the theory of uniform perfectness, we obtain the relationships between weakly uniform perfectness and Bergman kernel, Poincar\'e metric and Hausdorff content. In particular, for a bounded domain $\Omega \subset \mathbb{C}$, we show that the uniform perfectness of $\part

Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz

Crash simulations play an essential role in improving vehicle safety, design optimization, and injury risk estimation. Unfortunately, numerical solutions of such problems using state-of-the-art high-fidelity models require significant computational effort. Conventional data-driven surrogate modeling approaches create low-dimensional embeddings for evolving t

Michael Shaham, Risha Ranjan, Engin Kirda, Taskin Padir

Autonomous vehicle platoons present near- and long-term opportunities to enhance operational efficiencies and save lives. The past 30 years have seen rapid development in the autonomous driving space, enabling new technologies that will alleviate the strain placed on human drivers and reduce vehicle emissions. This paper introduces a testbed for evaluating a

Gonzalo Navarro, Cristian Urbina

We explore an extension to straight-line programs (SLPs) that outperforms, for some text families, the measure $\delta$ based on substring complexity, a lower bound for most measures and compressors exploiting repetitiveness (which are crucial in areas like Bioinformatics). The extension, called iterated SLPs (ISLPs), allows rules of the form $A \rightarrow

Alican Mertan, Nick Cheney

Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robot