February 2024 arXiv papers — page 13

Giulia Preti, Adriano Fazzone, Giovanni Petri, Gianmarco De Francisci Morales

Despite the widespread adoption of higher-order mathematical structures such as hypergraphs, methodological tools for their analysis lag behind those for traditional graphs. This work addresses a critical gap in this context by proposing two micro-canonical random null models for directed hypergraphs: the Directed Hypergraph Configuration Model (DHCM) and th

Andreas Abels, Mariia Anapolska, Christina Büsing

Interval-constrained online bipartite matching problem frequently occurs in medical appointment scheduling: Unit-time jobs representing patients arrive online and are assigned to a time slot within their given feasible time interval. We consider a variant of this problem where reassignments are allowed and extend it by a notion of time that is decoupled from

M. A. Fontelos, R. Lecaros, J. López-Ríos, A. Pérez

In this paper, we study the approximate controllability of a system governed by an evolution problem known as the sloshing problem. This problem involves a spatial, nonlocal differential operator inherent in the dynamics of a two-dimensional, incompressible, non-viscous fluid within a confined domain. Our work establishes unique continuation results that ena

Zhiwei Yang, Kexue Fu, Minghong Duan, Linhao Qu

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However, attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels, the challenging co-occurrence problem is widely present and leads to false activation of objects in WSSS.

Khalil Zakeri, Albrecht von Faber, Sergiy Mankovsky, Hubert Ebert

Chirality is an inherent characteristics of some objects in nature. In magnetism chiral magnetic textures can be formed in systems with broken inversion symmetry and due to an antisymmetric magnetic interaction, known as Dzyaloshinskii--Moriya interaction (DMI). Here, aiming on a fundamental understanding of this chiral interaction on the atomic scale, we de

Lukas Brand, Yan Wang, Maurizio Magarini, Robert Schober

The recently emerged molecular communication (MC) paradigm intends to leverage communication engineering tools for the design of synthetic chemical communication systems. These systems are envisioned to operate at nanoscale and in biological environments, such as the human body, and catalyze the emergence of revolutionary applications in the context of early

Ashleigh Simonis, Yulin Pan

It is well known that wave collapses can emerge from the focusing one-dimensional (1-D) Majda-McLaughlin-Tabak (MMT) model as a result of modulational instability. However, how these wave collapses affect the spectral properties and statistics of the wave field has not been adequately studied. We undertake this task by simulating the forced-dissipated 1-D MM

Yiluo Wei, Gareth Tyson

In the last two years, Artificial Intelligence Generated Content (AIGC) has received significant attention, leading to an anecdotal rise in the amount of AIGC being shared via social media platforms. The impact of AIGC and its implications are of key importance to social platforms, e.g., regarding the implementation of policies, community formation, and algo

Simran Arora, P. H. R. S. Moraes, P. K. Sahoo

We construct the energy conditions for the recently proposed $f(R,L,T)$ gravity theory, for which $f$ is a generic function of the Ricci scalar $R$, matter lagrangian density $L$ and trace of the energy-momentum tensor $T$. We analyse two different forms for the $f(R,L,T)$ function within the framework of the Friedmann-Lem\^aitre-Robertson-Walker universe. W

CMS Collaboration

A search is presented for baryon number violating interactions in top quark production and decay. The analysis uses data from proton-proton collisions at a center-of-mass energy of 13 TeV, collected with the CMS detector at the LHC with an integrated luminosity of 138 fb$^{-1}$. Candidate events are selected by requiring two oppositely-charged leptons (elect

Kateryna Korshynska, Maximilian Löschner, Mariia Marinichenko, Krzysztof Mękała

We study the discrimination power of future multi-TeV muon colliders for a large set of models with extended gauge symmetries and additional neutral gauge bosons ("$Z'$-models"). Our study is carried out using a $\chi^2$-analysis of leptonic observables of s-channel scattering in effective $Z'$-models. We make use of angular and chiral asymmetries induced in

Divyakant Tahlyan, Hani Mahmassani, Amanda Stathopoulos, Maher Said

We present an employer-side perspective on remote work through the pandemic using data from top executives of 129 employers in North America. Our analysis suggests that at least some of the pandemic-accelerated changes to the work location landscape will likely stick; with some form of hybrid work being the norm. However, the patterns will vary by department

Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen

We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large Language Models (LLMs) without the need for model fine-tuning. Leveraging meta-task prompting, MetaEOL guides LLMs to produce embeddings through a series of carefully designed promp

Dirk Frettlöh

A vertex colouring of some graph is called perfect if each vertex of colour $i$ has the same number $a_{ij}$ of neighbours of colour $j$. Here we determine all perfect colourings of the edge graphs of the hypercube in dimensions 4 and 5 by two and three colours, respectively. For comparison we list all perfect colourings of the edge graphs of the simplex in

Elliot Eichen, Arvind Aradhya, Ljiljana Simić

A simple 'RF-flashlight' (or ground to satellite) interference testbed is proposed to experimentally verify real-time geofencing (RTG) for protecting passive Earth Exploration Satellite Services (EESS) radiometer measurements from 5G or 6G mm-wave transmissions, and ground to satellite propagation models used in the interference modeling of this spectrum coe

Wenjun Jiang, Peiyan Li, Tianlong Fan, Ting Li

Robustness is pivotal for comprehending, designing, optimizing, and rehabilitating networks, with simulation attacks being the prevailing evaluation method. Simulation attacks are often time-consuming or even impractical, however, a more crucial yet persistently overlooked drawback is that any attack strategy merely provides a potential paradigm of disintegr

N. Aghanim, T. Tuominen, V. Bonjean, C. Gouin

Multiple-cluster systems, superclusters, contain large numbers of galaxies assembled in clusters inter-connected by multi-scale filamentary networks. As such, superclusters are a smaller version of the cosmic web and can be considered as miniature universes. Superclusters also contain gas, hot in the clusters and warmer in the filaments. Thus, they are ideal

Yi Zhuang, Louis E. Strigari, Lei Jin, Samiran Sinha

We study the prospects for measuring the time variation of solar and atmospheric neutrino fluxes at future large-scale Xenon and Argon dark matter detectors. For solar neutrinos, a yearly time variation arises from the eccentricity of the Earth's orbit, and, for charged current interactions, from a smaller energy-dependent day-night variation to due flavor r

Stefano Boccelli, Fabien Giroux, James G. McDonald

This work explores the different shapes that can be realized by the one-particle velocity distribution functions (VDFs) associated with the fourth-order maximum-entropy moment method. These distributions take the form of an exponential of a polynomial of the particle velocity, with terms up to the fourth-order. The 14- and 21-moment approximations are invest

Fabian Dvorak, Urs Fischbacher

Despite strong evidence for peer effects, little is known about how individuals balance intrinsic preferences and social learning in different choice environments. Using a combination of experiments and discrete choice modeling, we show that intrinsic preferences and social learning jointly influence participants' decisions, but their relative importance var

Jiahao Huang, Liutao Yang, Fanwen Wang, Yang Nan

The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks. This study introduces MambaMIR, a Mamba-based model for medical image reconstruction, as well as its Generative Adversarial Network-based variant, MambaMIR-GAN. Our proposed MambaMIR inherits several advantages, such as linear comp

George Karabatsos

This invited feature article introduces and provides an extensive simulation study of a new Approximate Bayesian Computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, which unifies and extends previous ABC method. This framework, copulaAB

Umberto Michieli, Mete Ozay

Continual Learning (CL) aims to learn a sequence of problems (i.e., tasks and domains) by transferring knowledge acquired on previous problems, whilst avoiding forgetting of past ones. Different from previous approaches which focused on CL for one NLP task or domain in a specific use-case, in this paper, we address a more general CL setting to learn from a s

Elliot Eichen

The impact of 5G networks transmitting between 24.25- 27.5 GHz on Earth Exploration Satellite Services (EESS) microwave sounders used to measure atmospheric water vapor and temperature was widely discussed and modeled in preparation for setting emission recommendations by International Telecommunications Union (ITU) at the 2019 World Radio Congress (WRC-19).

Deng Li, Aming Wu, Yaowei Wang, Yahong Han

Single-domain generalization aims to learn a model from single source domain data to achieve generalized performance on other unseen target domains. Existing works primarily focus on improving the generalization ability of static networks. However, static networks are unable to dynamically adapt to the diverse variations in different image scenes, leading to

Chenghong Zhu, Chengkai Zhu, Zhiping Liu, Xin Wang

The unique features of entanglement and non-locality in quantum systems, where there are pairs of bipartite states perfectly distinguishable by general entangled measurements yet indistinguishable by local operations and classical communication, hold significant importance in quantum entanglement theory, distributed quantum information processing, and quantu

Xingyun Chen, Yan Huang, Zhenzhen Xie, Junjie Pang

In response to the challenges posed by non-independent and identically distributed (non-IID) data and the escalating threat of privacy attacks in Federated Learning (FL), we introduce HyperFedNet (HFN), a novel architecture that incorporates hypernetworks to revolutionize parameter aggregation and transmission in FL. Traditional FL approaches, characterized

Jingwei Li, Ruixuan Wang, Haipeng Zheng, Zhensheng Jia

A soliton microcomb can play a crucial role in narrow-grid optical communications by replacing many independently operated lasers in wavelength-division multiplexing systems. In this work, we designed and demonstrated power-efficient soliton microcombs with 100-GHz free spectral range in an integrated 4H-SiC platform for the first time. The combination of en

Md Hafizur Rahman, Prabuddha Chakraborty

Building efficient neural network architectures can be a time-consuming task requiring extensive expert knowledge. This task becomes particularly challenging for edge devices because one has to consider parameters such as power consumption during inferencing, model size, inferencing speed, and CO2 emissions. In this article, we introduce a novel framework de

Pavan L. Veluvali, Jan Heiland, Peter Benner

Numerical algorithms and computational tools are instrumental in navigating and addressing complex simulation and data processing tasks. The exponential growth of metadata and parameter-driven simulations has led to an increasing demand for automated workflows that can replicate computational experiments across platforms. In general, a computational workflow

Petr Hájek, Tommaso Russo

We survey several results concerning norming Markushevich bases (M-bases, for short), focusing in particular on two recent examples of a weakly compactly generated Banach space with no norming M-basis and of an Asplund space with norming M-basis that is not weakly compactly generated. We highlight the context for these problems and state several open problem

Oulin Yu, Sujatha Vijayakrishnan, R. Allgayer, T. Szkopek

Bismuth, the heaviest of all group V elements with strong spin-orbit coupling, is famously known to exhibit many interesting transport properties, and effects such as Shubnikov-de Haas and de Haas-van Alphen were first revealed in its bulk form. However, the transport properties have not yet been fully explored experimentally in thin bismuth nor in its 2D li

Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante

Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments. In leveraging the power of foundation models, it is crucial for AI research to pivot away from excessive reductionism and toward an emphasis on systems that function as cohesive wholes. Specifically, we emphasize develo

Pietro Richelli, Kareljan Schoutens, Alberto Zorzato

We study brick wall quantum circuits enjoying a global fermionic symmetry. The constituent 2-qubit gate, and its fermionic symmetry, derive from a 2-particle scattering matrix in integrable, supersymmetric quantum field theory in 1+1 dimensions. Our 2-qubit gate, as a function of three free parameters, is of so-called free fermionic or matchgate form, allowi

Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su

Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs). Yet, besides NL, LLMs have seen various non-NL formats during pre-training, such as code and logical expression. NL's status as the optimal format fo

Fabien Lacasa, Camille Bonvin, Charles Dalang, Ruth Durrer

To date, the most precise measurement of the observer's peculiar velocity comes from the dipole in the Cosmic Microwave Background (CMB). This velocity also generates a dipole in the source number counts, whose amplitude is governed not only by the observer velocity, but also by specific properties of the sources, that are difficult to determine precisely. Q

Julien Heyd, Joel Merker

We determine all affinely homogeneous models for surfaces $S^2 \subset \mathbb{R}^4$, including the simply transitive models. We employ an improved power series method of equivalence, which captures invariants at the origin, creates branches, and infinitesimalizes calculations. We find several inequivalent terminal branches yielding each to some nonempty mod

Samuel J. K. Chin, Matthias Winkenbach, Akash Srivastava

In this paper, we present the Foundation Model for the Montreal Capacitated Vehicle Routing Problem (FM-MCVRP), a novel Deep Learning (DL) model that approximates high-quality solutions to a variant of the Capacitated Vehicle Routing Problem (CVRP) that characterizes many real-world applications. The so-called Montreal Capacitated Vehicle Routing Problem (MC

Nadine Nabben, Giacomo Sala, Ulrich Nowak, Matthias Krüger

Taking advantage of the anomalous Hall effect, we electrically probe low-frequency magnetization fluctuations at room temperature in a thin ferromagnetic Pt/Co/AlO$_x$ layer stack with perpendicular magnetic anisotropy. We observe a strong enhancement of the Hall voltage fluctuations within the hysteretic region of the magnetization loop. Analyzing both the

Songyot Kitthamkesorn, Anthony Chen

Stochastic User Equilibrium (SUE) models depict the perception differences in traffic assignment problems. According to the assumption of an unbounded perceived travel time distribution, the conventional SUE problems result in a positive choice probability for all available routes, regardless of their unappealing travel time. This study provides an eUnit-SUE

Anshul Mittal, Shikhar Mohan, Deepak Saini, Siddarth Asokan

Deep extreme classification (XC) aims to train an encoder architecture and an accompanying classifier architecture to tag a data point with the most relevant subset of labels from a very large universe of labels. XC applications in ranking, recommendation and tagging routinely encounter tail labels for which the amount of training data is exceedingly small.

Tomoya Shiota, Kenji Ishihara, Wataru Mizukami

Accurate prediction of diverse chemical properties is crucial for advancing molecular design and materials discovery. Here we present a versatile approach that uses the intermediate information of a universal neural network potential as a general-purpose descriptor for chemical property prediction. Our method is based on the insight that by training a sophis

A. Amouretti, C. Crépisson, S. Azadi, D. Cabaret

We present in-situ x-ray diffraction and velocity measurements of Fe$_2$O$_3$ under laser shock compression at pressures between 38-116 GPa. None of the phases reported by static compression studies were observed. Instead, we observed an isostructural phase transition from $\alpha$-Fe$_2$O$_3$ to a new $\alpha^\prime$-Fe$_2$O$_3$ phase at a pressure of 50-62

Arijit Panda, Debashis Gangopadhyay, Goutam Manna

In this work, we develop the Raychaudhuri equation in $f(\bar{R},\bar{T})$ gravity in the setting of a non-canonical theory, namely K-essence theory. We solve the modified Raychaudhuri equation for the additive form of $f(\bar{R},\bar{T})$, which is $f_{1}(\bar{R})+f_{2}(\bar{T})$. For this solution, we employ two different scale factors to give two types of

Daniel Timko, Muhammad Lutfor Rahman

While smishing (SMS Phishing) attacks have risen to become one of the most common types of social engineering attacks, there is a lack of relevant smishing datasets. One of the biggest challenges in the domain of smishing prevention is the availability of fresh smishing datasets. Additionally, as time persists, smishing campaigns are shut down and the crucia

Irene Papaefstathiou, Johannes Knolle, Mari Carmen Bañuls

Tensor network methods have demonstrated their suitability for the study of equilibrium properties of lattice gauge theories, even close to the continuum limit. We use them in an out-of-equilibrium scenario, much less explored so far, by simulating the real-time collisions of composite mesons in the lattice Schwinger model. Constructing wave-packets of vecto

Yusheng Liao, Yanfeng Wang, Yu Wang

Autoregressive (AR) and Non-autoregressive (NAR) models are two types of generative models for Neural Machine Translation (NMT). AR models predict tokens in a word-by-word manner and can effectively capture the distribution of real translations. NAR models predict tokens by extracting bidirectional contextual information which can improve the inference speed

David F. Gleich

The Eckhart-Young theorem states that the best low-rank approximation of a matrix can be constructed from the leading singular values and vectors of the matrix. Here, we illustrate that the practical implications of this result crucially depend on the organization of the matrix data. In particular, we will show examples where a rank 2 approximation of the ma

Hafiz Tiomoko Ali, Umberto Michieli, Ji Joong Moon, Daehyun Kim

The recently discovered Neural collapse (NC) phenomenon states that the last-layer weights of Deep Neural Networks (DNN), converge to the so-called Equiangular Tight Frame (ETF) simplex, at the terminal phase of their training. This ETF geometry is equivalent to vanishing within-class variability of the last layer activations. Inspired by NC properties, we e

Declan Campbell, Jonathan D. Cohen

The human cognitive system exhibits remarkable flexibility and generalization capabilities, partly due to its ability to form low-dimensional, compositional representations of the environment. In contrast, standard neural network architectures often struggle with abstract reasoning tasks, overfitting, and requiring extensive data for training. This paper inv

Nigel Lee En Hew, Shun-Li Shang, Zi-Kui Liu

According to x-ray diffraction (XRD) measurements, PbTiO$_3$ undergoes a phase transition from a tetragonal ferroelectric (FE) phase to a cubic paraelectric phase at 763 K. However, x-ray absorption fine-structure (XAFS) measurements indicate that PbTiO$_3$ is locally tetragonal even after the phase transition. The difference in these results is because XAFS

Shabnam Tafreshi, Shubham Vatsal, Mona Diab

It is important to be able to analyze the emotional state of people around the globe. There are 7100+ active languages spoken around the world and building emotion classification for each language is labor intensive. Particularly for low-resource and endangered languages, building emotion classification can be quite challenging. We present a cross-lingual em

Geoffroy Leconte, Dominique Orban

We develop an interior-point method for nonsmooth regularized bound-constrained optimization problems. Our method consists of iteratively solving a sequence of unconstrained nonsmooth barrier subproblems. We use a variant of the proximal quasi-Newton trust-region algorithm TR of arXiv:2103.15993v3 to solve the barrier subproblems, with additional assumptions

Madhumita Saha, Manas Kulkarni, Abhishek Dhar

We consider an analytically tractable model that exhibits the main features of the Page curve characterizing the evolution of entanglement entropy during evaporation of a black hole. Our model is a gas of non-interacting fermions on a lattice that is released from a box into the vacuum. More precisely, our Hamiltonian is a tight-binding model with a defect a

Maryam Sadat Amiri Naeini, Pierre Berini

Wafer-level testing is an important step for process and quality control of electronic chips in integrated circuit (IC) manufacturing which occurs before packaging. The process of wafer probing in its conventional contacting schemes, becomes more complicated as ICs move to smaller technology nodes and more compact designs, greatly increasing testing costs. N

Zeqing Zhang, Linhan Yang, Cong Sun, Weiwei Shang

Cable-driven parallel robots (CDPRs) have gained significant attention due to their promising advantages. When deploying CDPRs in practice, the kinematic modeling is a key question. Unlike serial robots, CDPRs have a simple inverse kinematics problem but a complex forward kinematics (FK) issue. So, the development of accurate and efficient FK solvers has bee

Shubham Vatsal, Ayush Singh, Shabnam Tafreshi

Health insurance companies have a defined process called prior authorization (PA) which is a health plan cost-control process that requires doctors and other healthcare professionals to get clearance in advance from a health plan before performing a particular procedure on a patient in order to be eligible for payment coverage. For health insurance companies

Marius Pfeifer, Jessica Agarwal, Raphael Marschall, Björn Grieger

Methods. We algorithmically tracked thousands of individual particles through four OSIRIS/NAC image sequences of 67P's near-nucleus coma. We then traced concentrated particle groups back to the nucleus surface, and estimated their potential source regions, size distributions, and projected dynamical parameters. Finally, we compared the observed activity to d

Mattia Pirani

Flasque resolutions play an important role in understanding birational properties of algebraic tori. For instance, Colliot-Th\'{e}l\`{e}ne and Sansuc have used them to compute $R$-equivalence classes of algebraic tori. We extend this notion to a larger class of algebraic varieties, including homogeneous spaces. This leads to a lower bound on the number of $R

Mona Furukawa, Daniel R. McGowan, Bartłomiej W. Papież

The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over the past decade and one common cause of treatment failure is recurrence. In this paper, we built Cox proportional hazard (CoxPH) models that predict the recurrence free survival (RFS) of oropharyngeal HNC patients. Our models utilise both clinical information and multimodal radiomic

Natalia Żywucka, Julian Sitarek, Dorota Sobczyńska, Mario Pecimotika

We present the results of a preliminary study of a correction method applied to the Imaging Atmospheric Cherenkov Telescope images affected by clouds. The studied data are Monte Carlo simulations made with CORSIKA, imitating the very high energy events registered by the Large-Sized Telescopes, a type of telescope within the future Cherenkov Telescope Array.

Dinesh Krishnamoorthy

This paper introduces a model-free real-time optimization (RTO) framework based on unconstrained Bayesian optimization with embedded constraint control. The main contribution lies in demonstrating how this approach simplifies the black-box optimization problem while ensuring "always-feasible" setpoints, addressing a critical challenge in real-time optimizati

Max Firmbach, Ivo Steinbrecher, Alexander Popp, Matthias Mayr

This paper presents a scalable physics-based block preconditioner for mixed-dimensional models in beam-solid interaction and their application in engineering. In particular, it studies the linear systems arising from a regularized mortar-type approach for embedding geometrically exact beams into solid continua. Due to the lack of block diagonal dominance of

Antonio Beltrán, Changguo Shao

Let $p$ be a prime number and suppose that every maximal subgroup of a finite group is either $p$-nilpotent or has prime index. Such group need not be $p$-solvable, and we study its structure by proving that only one nonabelian simple group of order divisible by $p$, which belongs to the family ${\rm PSL}_n(q)$, can be involved in it. For $p=2$, we specify m

Anthony Wilkie, Igor Gaidai, James Ostrowski, Rebekah Herrman

The quantum approximate optimization algorithm (QAOA) is a promising quantum algorithm that can be used to approximately solve combinatorial optimization problems. The usual QAOA ansatz consists of an alternating application of the cost and mixer Hamiltonians. In this work, we study how using Hamiltonians other than the usual cost Hamiltonian, dubbed custom

Bin Li, Ye Shi, Qian Yu, Jingya Wang

Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typically decomposed the UCIR problem into two distinct tasks: intra-domain representation learning and cross-domain feature alignment. However, these segregated strategies overlook t

Manas Kumar Mohapatra, Dhiren Panda, Rukmani Mohanta

At present, there are several measurements of $B$ decays that exhibit discrepancies with the predictions of the Standard Model, and suggest the presence of new physics in $b\to s$ and $b \to c(u)$ quark level transitions. Motivated by the prospects of the ongoing high-luminosity $B$ factories, we study the exclusive $B \to a_1 (1260) \ell^- \bar{\nu}_\ell$ p

Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix

Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although this suggests overfitting, performance was competitive on test data. We aimed to understand the behaviour of random forests by (1) visualizing data space in three real world case s

Xiujie Song, Mengyue Wu, Kenny Q. Zhu, Chunhao Zhang

Large Vision-Language Models (LVLMs), despite their recent success, are hardly comprehensively tested for their cognitive abilities. Inspired by the prevalent use of the Cookie Theft task in human cognitive tests, we propose a novel evaluation benchmark to evaluate high-level cognitive abilities of LVLMs using images with rich semantics. The benchmark consis

Yuri Hayashi Isayama

In multimode interferometers, the interaction between several modes brings a high level of complexity to the interpretation of its light patterns. With recent advances, it is possible to selectively excite only a couple of modes inside the device. This paper presents an analytical method for determining the phase difference between two propagating modes in t

Chang-Bin Jeon, Gordon Wichern, François G. Germain, Jonathan Le Roux

In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These random mixes have mismatched characteristics compared to real music, e.g., the different stems do not have consistent beat or tonality, resulting in a cacophony. In this work, we inve

Anton Arnold, Jannis Körner

This paper introduces an efficient high-order numerical method for solving the 1D stationary Schr\"odinger equation in the highly oscillatory regime. Building upon the ideas from [Arnold, Ben Abdallah, Negulescu, SIAM J. Numer. Anal., 2011], we first analytically transform the given equation into a smoother (i.e. less oscillatory) equation. By developing suf

Nithin Babu, Christos Masouros

This paper proposes block-level precoder (BLP) designs for a multi-input single-output (MISO) system that performs joint sensing and communication across multiple cells and users. The Cramer-Rao-Bound for estimating a target's azimuth angle is determined for coordinated beamforming (CBF) and coordinated multi-point (CoMP) scenarios while considering inter-ce

Ming-Yuan Gao, Yin-Hai Li, Zhao-Qi-Zhi Han, Qiang Zhou

Entanglement enables many promising applications in quantum technology. Devising new generation methods and harnessing entanglement are prerequisites for practical applications. Here we realize a distinct polarization-entangled source by simultaneously achieving type-0 and type-I backward quasi-phase matching (BQPM) through spontaneous parametric down-conver

Jakob Vandergrift, Matthew J. Zahr

High-order implicit shock tracking (fitting) is a class of high-order numerical methods that use numerical optimization to simultaneously compute a high-order approximation to a conservation law solution and align elements of the computational mesh with non-smooth features. This alignment ensures that non-smooth features are perfectly represented by inter-el

Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel, Pietro Zanuttigh

In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three groups of approaches have been proposed to handle noisy data: i) enhancer and denoiser modules to improve the quality of the noisy data, ii) data augmentation approaches, and iii)

Hossein Siadati, Sima Jafarikhah, Elif Sahin, Terrence Brent Hernandez

The Software Supply Chain (SSC) has captured considerable attention from attackers seeking to infiltrate systems and undermine organizations. There is evidence indicating that adversaries utilize Social Engineering (SocE) techniques specifically aimed at software developers. That is, they interact with developers at critical steps in the Software Development

Hanyao Wang, Yibing Zhan, Liu Liu, Liang Ding

Pretrained cross-modal models, for instance, the most representative CLIP, have recently led to a boom in using pre-trained models for cross-modal zero-shot tasks, considering the generalization properties. However, we analytically discover that CLIP suffers from the text-to-image retrieval hallucination, adversely limiting its capabilities under zero-shot l

Mouhamadou Hassane Saley, Abderrahim El Mouhafid, Ahmed Jellal

By studying the impact of a perpendicular magnetic field $B$ on AB-bilayer graphene (AB-BLG) under dual gating, we yield several key findings for the ballistic transport of gate $U_\infty$. Firstly, we discover that the presence of $B$ leads to a decrease in transmission. At a high value of $B$, we notice the occurrence of anti-Klein tunneling over a signifi

Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler

The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models show tremendous promise in synthesizing high-resolution images and text, as well as structured data such as videos and

Yuki Sato, Ruho Kondo, Ikko Hamamura, Tamiya Onodera

Solving partial differential equations for extremely large-scale systems within a feasible computation time serves in accelerating engineering developments. Quantum computing algorithms, particularly the Hamiltonian simulations, present a potential and promising approach to achieve this purpose. Actually, there are several oracle-based Hamiltonian simulation

Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid

Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-text prompting strategies. To solve this, we introduce a decomposed prompting approach for sequence labeling tasks. Diverging from the single text-to-text prompt, our prompt method

Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi

Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show t

Sam Chow, Péter P. Varjú, Han Yu

We establish a new upper bound for the number of rationals up to a given height in a missing-digit set, making progress towards a conjecture of Broderick, Fishman, and Reich. This enables us to make novel progress towards another conjecture of those authors about the corresponding intrinsic diophantine approximation problem. Moreover, we make further progres

Wenqian Lai, Ruonan Guo, Kejian J. Wu

In this paper, we address the problem of relative localization of two mobile agents. Specifically, we consider the Dual-IMU system, where each agent is equipped with one IMU, and employs relative pose observations between them. Previous works, however, typically assumed known ego motion and ignored biases of the IMUs. Instead, we study the most general case

Mingfei Cheng, Yuan Zhou, Xiaofei Xie, Junjie Wang

Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, particularly the ADS's ability to make optimal decisions and produce optimal paths for autonomous vehicles (AVs), is also vital to ensure the intelligence and reduce risks of AVs. C

Yiyan Huang, Cheuk Hang Leung, Siyi Wang, Yijun Li

The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure rem

Chukri Soueidi, Ylies Falcone

Monitoring concurrent programs typically rely on collecting traces to abstract program executions. However, existing approaches targeting general behavioral properties are either not tailored for online monitoring, are no longer maintained, or implement naive instrumentation that often leads to unsound verdicts. We first define the notion of when a trace is

Xiaoguang Diao, Yubo Song, Subham Sahoo, Yuan Li

Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids. On one hand, coordination among sources is facilitated by distributed, privacy-minded processing at multiple locations, whereas on the other hand, it also creates exogenous data arrival paths fo

Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet

In the complex landscape of hematologic samples such as peripheral blood or bone marrow, cell classification, delineating diverse populations into a hierarchical structure, presents profound challenges. This study presents LeukoGraph, a recently developed framework designed explicitly for this purpose employing graph attention networks (GATs) to navigate hie

Camilla Giusti, Mario Cadelano, Francesco R. Ferraro, Barbara Lanzoni

We present a high angular resolution multi-wavelength study of the massive globular cluster NGC 1835 in the Large Magellanic Cloud. Thanks to a combination of optical and near ultraviolet images acquired with the WFC3 on board the HST, we performed a detailed inspection of the stellar population in this stellar system adopting a ``UV-guided search'' to optim

P. Acharya, M. Fritts, D. -M. Mei, G. -J. Wang

This study explores the dynamics of charge transport within a cryogenic P-type Ge particle detector, fabricated from a crystal cultivated at the University of South Dakota (USD). By subjecting the detector to cryogenic temperatures and an Am-241 source, we observe evolving charge dynamics and the emergence of cluster dipole states, leading to the impact ioni

Nithin Babu, Christos Masouros, Constantinos B. Papadias, Yonina C. Eldar

This paper proposes a framework for designing robust precoders for a multi-input single-output (MISO) system that performs integrated sensing and communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound (CRB) to measure the sensing performance and derive its expressions for two multi-cell scenarios, namely coordinated beamforming (CBF) a

Rohit Dwivedula, Sriram Sridhar, Sambhav Satija, Muthian Sivathanu

Reviews and ratings by users form a central component in several widely used products today (e.g., product reviews, ratings of online content, etc.), but today's platforms for managing such reviews are ad-hoc and vulnerable to various forms of tampering and hijack by fake reviews either by bots or motivated paid workers. We define a new metric called 'hijack

Tianze Yang, Tianyi Yang, Fuyuan Lyu, Shaoshan Liu

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inhe

Yiming Li, Zhao Zhang

Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the "Conversational Multi-Doc QA" challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large

Dima Grigoriev

For tropical $n$-variable polynomials $f, g$ a criterion of containment for tropical hypersurfaces $Trop(f)\subset Trop(g)$ is provided in terms of their Newton polyhedra $N(f), N(g)\subset \mathbb{R}^{n+1}$. Namely, $Trop(f)\subset Trop(g)$ iff for every vertex $v$ of $N(g)$ there exist a homothety $t\cdot N(f), t>0$ and a parallel shift $s:\mathbb{R}^{n+1}

Xuzhe Zhang, Elsa D. Angelini, Eric A. Hoffman, Karol E. Watson

Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans. Existing studies have explored several directions to tackle this challenge, including density correction, noise filtering, regression, hidden Marko

Chien-An Wang, Valentin John, Hanifa Tidjani, Cécile X. Yu

Qubits that can be efficiently controlled are essential for the development of scalable quantum hardware. While resonant control is used to execute high-fidelity quantum gates, the scalability is challenged by the integration of high-frequency oscillating signals, qubit crosstalk and heating. Here, we show that by engineering the hopping of spins between qua

Robert Tjarko Lange, Yingtao Tian, Yujin Tang

Large Transformer models are capable of implementing a plethora of so-called in-context learning algorithms. These include gradient descent, classification, sequence completion, transformation, and improvement. In this work, we investigate whether large language models (LLMs), which never explicitly encountered the task of black-box optimization, are in prin