May 2023 arXiv papers — page 5

A. Palmese, R. Kaur, A. Hajela, R. Margutti

We present a new constraint on the Hubble constant $H_0$ using the latest measurements of the electromagnetic counterpart to the gravitational wave (GW) event GW170817. We use the latest optical, X-ray and radio observations of the afterglow up to $\sim 3.5$ years after the GW detection, and properly take into account the impact of the host galaxy peculiar v

Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonić, Roberto Molinaro

Recently, operator learning, or learning mappings between infinite-dimensional function spaces, has garnered significant attention, notably in relation to learning partial differential equations from data. Conceptually clear when outlined on paper, neural operators necessitate discretization in the transition to computer implementations. This step can compro

Xinze Li, Zhenghao Liu, Chenyan Xiong, Shi Yu

This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes

Alex Foote, Neel Nanda, Esben Kran, Ioannis Konstas

Advances in Large Language Models (LLMs) have led to remarkable capabilities, yet their inner mechanisms remain largely unknown. To understand these models, we need to unravel the functions of individual neurons and their contribution to the network. This paper introduces a novel automated approach designed to scale interpretability techniques across a vast

Marco Seiz, Henrik Hierl, Britta Nestler

The resulting microstructure after the sintering process determines many materials properties of interest. In order to understand the microstructural evolution, simulations are often employed. One such simulation method is the phase-field method, which has garnered much interest in recent decades. However, the method lacks a complete model for sintering, as

Jussi Nurminen, Andrey Zhdanov, Wan Jin Yeo, Joonas Iivanainen

In magnetoencephalography, linear minimum norm inverse methods are commonly employed when a solution with minimal a priori assumptions is desirable. These methods typically produce spatially extended inverse solutions, even when the generating source is focal. Various reasons have been proposed for this effect, including intrisic properties of the minimum no

Christian Bargetz, Franz Luggin

We consider the method of alternating (metric) projections for pairs of linear subspaces of finite dimensional Banach spaces. We investigate the size of the set of points for which this method converges to the metric projection onto the intersection of these subspaces. In addition we give a characterisation of the pairs of subspaces for which the alternating

Yogesh Verma, Markus Heinonen, Vikas Garg

Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens holds the key to accelerating vaccine discovery. However, this co-design of the amino acid sequence and the 3D structure subsumes and accentuates some central challenges from mult

Noah E. Wolfe, Salvatore Vitale, Colm Talbot

The detection of a sub-solar mass black hole could yield dramatic new insights into the nature of dark matter and early-Universe physics, as such objects lack a traditional astrophysical formation mechanism. Gravitational waves allow for the direct measurement of compact object masses during binary mergers, and we expect the gravitational-wave signal from a

Chen Yang, Kailing Wang, Yuehao Wang, Xiaokang Yang

Reconstructing deformable tissues from endoscopic stereo videos in robotic surgery is crucial for various clinical applications. However, existing methods relying only on implicit representations are computationally expensive and require dozens of hours, which limits further practical applications. To address this challenge, we introduce LerPlane, a novel me

Aaron Mueller, Tal Linzen

Accurate syntactic representations are essential for robust generalization in natural language. Recent work has found that pre-training can teach language models to rely on hierarchical syntactic features - as opposed to incorrect linear features - when performing tasks after fine-tuning. We test what aspects of pre-training are important for endowing encode

Raphael Trumpp, Martin Büchner, Abhinav Valada, Marco Caccamo

Autonomous driving involves complex decision-making in highly interactive environments, requiring thoughtful negotiation with other traffic participants. While reinforcement learning provides a way to learn such interaction behavior, efficient learning critically depends on scalable state representations. Contrary to imitation learning methods, high-dimensio

Kaixuan Chen, Shunyu Liu, Tongtian Zhu, Tongya Zheng

Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data. However, existing GNNs typically ignore crucial structural characteristics in node-induced subgraphs, which thus limits their expressiveness for various downstream tasks. In this paper, we strive to strengthen the representative capab

Jia Guo, Liying Cheng, Wenxuan Zhang, Stanley Kok

Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, each subtask alone is insufficient for a thorough understanding of the argumentative structure and reasoning process. To learn a complete view of an argument essay and capture the i

Tommaso Bendinelli, Luca Biggio, Daniel Nyfeler, Abhigyan Ghosh

The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars. Traditionally, human experts have determined the origin and detected treatments on gemstones through visual inspections and a range of analytical methods. However, the interp

Nicolas Deutschmann, Mattia Rigotti, Maria Rodriguez Martinez

Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions, leading to non-homogeneous coverage. We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution, inspired by the Jackknife+ method, which enables the use of

K. Shih, I. Petrushina, V. N. Litvinenko, I. Pinayev

In the aspect of longitudinal beam bunching, the bunching strength can be controlled by the RF cavity phase and voltage. However, these machine parameters are different from those that interact with the beam itself. In order to gain control of the beam-cavity interaction, cavity calibration must be performed. Furthermore, it relies on fitting the beam energy

Romain Hascoët, Nicolas Jacques

This study aims to investigate whether fatigue damage induced by bottom wave slamming can be a failure mode important to consider when sizing a marine structural element. The body exposed to wave impacts is assumed to have a shape and structural arrangement such that the duration of wave-impact loads is short relative to the structure's vibratory response ti

Maor Ben-Shahar, Max Guillen

11D pure spinors have been shown to successfully describe 11D supergravity in a manifestly super-Poincar\'e covariant manner. The feasibility of its actual usage for scattering amplitude computations requires an efficient manipulation of the superfields defining linearized 11D supergravity. In this paper, we directly address this problem by finding the super

Mingjie Chen, Muhammad Imran, Gábor Ivanyos, Péter Kutas

The Isogeny to Endomorphism Ring Problem (IsERP) asks to compute the endomorphism ring of the codomain of an isogeny between supersingular curves in characteristic $p$ given only a representation for this isogeny, i.e. some data and an algorithm to evaluate this isogeny on any torsion point. This problem plays a central role in isogeny-based cryptography; it

Petros Toupas, Christos-Savvas Bouganis, Dimitrios Tzovaras

Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high computational and memory requirements, especially when integrated into systems with limited resources. This study propo

David Anderson

We give explicit presentations of the integral equivariant cohomology of the affine Grassmannians and flag varieties in type A, arising from their natural embeddings in the corresponding infinite (Sato) Grassmannian and flag variety. These presentations are compared with results obtained by Lam and Shimozono, for rational equivariant cohomology of the affine

Zhongwei Wan, Che Liu, Mi Zhang, Jie Fu

The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from various language communities. Nevertheless, the main challenge stems from the complexity of integrating diverse syntax and semantics, language-specific medical terminology, and culture-s

Alexander Brenning, Sebastian Henn

With much of our lives taking place online, researchers are increasingly turning to information from the World Wide Web to gain insights into geographic patterns and processes. Web scraping as an online data acquisition technique allows us to gather intelligence especially on social and economic actions for which the Web serves as a platform. Specific opport

Fabian Akkerman, Julius Luy, Wouter van Heeswijk, Maximilian Schiffer

Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning. Existing solution approaches can handle unstructured LDAS with up to a few million actions. However, many real-world applications in logistics, production, and transportation systems have combinatorial action spaces, whose size grows well beyond millions of actions, eve

Shivang Rawat, Stefano Martiniani

Stochasticity plays a central role in nearly every biological process, and the noise power spectral density (PSD) is a critical tool for understanding variability and information processing in living systems. In steady-state, many such processes can be described by stochastic linear time-invariant (LTI) systems driven by Gaussian white noise, whose PSD is a

Zhuokai Zhao, Takumi Matsuzawa, William Irvine, Michael Maire

Proper evaluations are crucial for better understanding, troubleshooting, interpreting model behaviors and further improving model performance. While using scalar-based error metrics provides a fast way to overview model performance, they are often too abstract to display certain weak spots and lack information regarding important model properties, such as r

Vilém Heinz, Antonín Novák, Marek Vlk, Zdeněk Hanzálek

This paper examines scheduling problem denoted as $P|seq, ser|C_{max}$ in Graham's notation; in other words, scheduling of tasks on parallel identical machines ($P$) with sequence-dependent setups ($seq$) each performed by one of the available servers ($ser$). The goal is to minimize the makespan ($C_{max}$). We propose a Constraint Programming (CP) model fo

Philippe Carette, Marie-Anne Guerry

We consider the problem of finding the transition rates of a continuous-time homogeneous Markov chain under the empirical condition that the state changes at most once during a time interval of unit length. It is proven that this conditional embedding approach results in a unique intensity matrix for a transition matrix with non-zero diagonal entries. Hence,

André Guerra, Xavier Lamy, Konstantinos Zemas

In this work we prove a sharp quantitative form of Liouville's theorem, which asserts that, for all $n\geq 3$, the weakly conformal maps of $\mathbb S^{n-1}$ with degree $\pm 1$ are M\"obius transformations. In the case $n=3$ this estimate was first obtained by Bernand-Mantel, Muratov and Simon (Arch. Ration. Mech. Anal. 239(1):219-299, 2021), with different

Maliki Moustapha, Pietro Parisi, Stefano Marelli, Bruno Sudret

System reliability analysis aims at computing the probability of failure of an engineering system given a set of uncertain inputs and limit state functions. Active-learning solution schemes have been shown to be a viable tool but as of yet they are not as efficient as in the context of component reliability analysis. This is due to some peculiarities of syst

Asad Lodhia, Jan-Christian Hütter, Caroline Uhler, Piotr Zwiernik

We study a notion of positivity of Gaussian directed acyclic graphical models corresponding to a non-negativity constraint on the coefficients of the associated structural equation model. We prove that this constraint is equivalent to the distribution being conditionally increasing in sequence (CIS), a well-known subclass of positively associated random vari

Georgios Chouliaras, P. Syntelis, V. Archontis

We have performed 3-D numerical simulations to investigate the effect of partial ionization on the process of magnetic flux emergence. In our study, we have modified the single-fluid MHD equations to include the presence of neutrals and have performed two basic experiments: one that assumes a fully ionized plasma (FI case) and one that assumes a partially io

Alexander Shen

Constructivists (and intuitionists in general) asked what kind of mental construction is needed to convince ourselves (and others) that some mathematical statement is true. This question has a much more practical (and even cynical) counterpart: a student of a mathematics class wants to know what will the teacher accept as a correct solution of a homework pro

Bálint Koczor, John Morton, Simon Benjamin

Quantum computing requires a universal set of gate operations; regarding gates as rotations, any rotation angle must be possible. However a real device may only be capable of $B$ bits of resolution, i.e. it might support only $2^B$ possible variants of a given physical gate. Naive discretization of an algorithm's gates to the nearest available options causes

Antonín Češík, Sebastian Schwarzacher

We study step-wise time approximations of non-linear hyperbolic initial value problems. The technique used here is a generalization of the minimizing movements method, using two time-scales: one for velocity, the other (potentially much larger) for acceleration. The main applications are from elastodynamics namely so-called generalized solids, undergoing lar

Subhankar Roy, Riccardo Volpi, Gabriela Csurka, Diane Larlus

Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensive pixel-level annotations to the training algorithm for all new objects, limiting the adoption of such methods in practical applications. Approaches that solely require image-leve

Kate B. Ellis, Ruth H. Keogh, Geraldine M. Clarke, Stephen O'Neill

We use a recently proposed staggered difference-in-differences approach to investigate effects of adoption of an online consultation system in English general practice on antibiotic prescribing patterns. The target estimand is the average effect for each group of practices (defined by year of adoption) in each year, which we aggregate across all adopting pra

Zhicheng Zong, Shichen Deng, Yangjun Qin, Xiao Wan

The thermal transport across inorganic/organic interfaces attracts interest for both academic and industry due to its widely applications in flexible electronics etc. Here, the interfacial thermal conductance of inorganic/organic interfaces consisting of silicon and polyvinylidene fluoride is systematically investigated by molecular dynamics simulations. Int

Newton Loebens

In recent years, several properties and recurrence criteria of discrete-time open quantum walks (OQWs) have been presented. Recently, Pellegrini introduced continuous-time open quantum walks (CTOQWs) as continuous-time natural limits of discrete-time OQWs. In this work, we study semifinite CTOQWs and some of their basic properties concerning statistics, such

Mehrazin Alizadeh, Hina Tabassum

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework t

Siarhei Zhuk, Alexander Wieczorek, Amit Sharma, Jyotish Patidar

The discovery of new functional materials is one of the key challenges in materials science. Combinatorial high-throughput approaches using reactive sputtering are commonly employed to screen unexplored phase spaces. During reactive combinatorial deposition the process conditions are rarely optimized, which can lead to poor crystallinity of the thin films. I

Brecht. I. C Donvil, Rochus Lechler, Joachim Ankerhold, Paolo Muratore-Ginanneschi

Quantum Error Mitigation (EM) is a collection of strategies to reduce errors on noisy intermediate scale quantum (NISQ) devices on which proper quantum error correction is not feasible. One of such strategies aimed at mitigating noise effects of a known environment is to realise the inverse map of the noise using a set of completely positive maps weighted by

Xudan Chai, Teng Ma, Qihao Guo, Zhangqi Yin

In quantum information transformation and quantum computation, the most critical issues are security and accuracy. These features, therefore, stimulate research on quantum state characterization. A characterization tool, Quantum state tomography, reconstructs the density matrix of an unknown quantum state. Theoretically, reconstructing an unknown state using

Mingguo He, Zhewei Wei, Shikun Feng, Zhengjie Huang

Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking theoretical guarantees. Furthermore, these methods cannot learn arbitrar

Laya Das, Sai Munikoti, Nrushad Joshi, Mahantesh Halappanavar

We study the problem of learning features through self-supervision that are generalisable to multiple graphs. State-of-the-art graph self-supervision restricts training to only one graph, resulting in graph-specific models that are incompatible with different but related graphs. We hypothesize that training with more than one graph that belong to the same fa

Lateef O. Jolaoso, Patrick Mehlitz, Alain B. Zemkoho

In this paper, we revisit the classical problem of solving over-determined systems of nonsmooth equations numerically. We suggest a nonsmooth Levenberg--Marquardt method for its solution which, in contrast to the existing literature, does not require local Lipschitzness of the data functions. This is possible when using Newton-differentiability instead of se

Lyndon R. Duong, Colin Bredenberg, David J. Heeger, Eero P. Simoncelli

Sensory systems across all modalities and species exhibit adaptation to continuously changing input statistics. Individual neurons have been shown to modulate their response gains so as to maximize information transmission in different stimulus contexts. Experimental measurements have revealed additional, nuanced sensory adaptation effects including changes

Yangfan Hu, Qian Zheng, Xudong Jiang, Gang Pan

Spiking neural networks (SNNs) have shown advantages in computation and energy efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven representations. SNNs also replace weight multiplications in ANNs with additions, which are more energy-efficient and less computationally intensive. However, it remains a challenge to train

Hasan Iqbal, Umar Khalid, Jing Hua, Chen Chen

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnorm

Arthur Bik, Jan Draisma, Rob Eggermont, Andrew Snowden

There are many notions of rank in multilinear algebra: tensor rank, partition rank, slice rank, and strength (or Schmidt rank) are a few examples. Typically the rank $\le r$ locus is not Zariski closed, and understanding the closure (the locus with "border rank" $\le r$) is an important problem. We make two contributions in this direction: we prove a de-bord

Deepesh Singh, Gopikrishnan Muraleedharan, Boxiang Fu, Chen-Mou Cheng

Since its advent in 2011, boson sampling has been a preferred candidate for demonstrating quantum advantage because of its simplicity and near-term requirements compared to other quantum algorithms. We propose to use a variant, called coarse-grained boson-sampling (CGBS), as a quantum Proof-of-Work (PoW) scheme for blockchain consensus. The users perform bos

Vijay Keswani, L. Elisa Celis, Krishnaram Kenthapadi, Matthew Lease

Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings in which no oracle exists to assess the quality of human decisions and task assignments made. Instead, we find ourselves in a "closed" decision-making loop in which the same fallib

Alessandro Bazzi, Miguel Sepulcre, Quentin Delooz, Andreas Festag

Vehicles and road infrastructure are starting to be equipped with vehicle-to-everything (V2X) communication solutions to increase road safety and provide new services to drivers and passengers. In Europe, the deployment is based on a set of Release 1 standards developed by ETSI to support basic use cases for cooperative intelligent transport systems (C-ITS).

Wei Shang, Dongwei Ren, Chaoyu Feng, Xiaotao Wang

Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To correct RS distortions, existing methods adopt a fully supervised learning manner, where high framerate global shutter (GS) images should be collected as ground-truth supervision. I

Ryan Carey, Tom Everitt

How can humans stay in control of advanced artificial intelligence systems? One proposal is corrigibility, which requires the agent to follow the instructions of a human overseer, without inappropriately influencing them. In this paper, we formally define a variant of corrigibility called shutdown instructability, and show that it implies appropriate shutdow

Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations a

Murat Ozer, Ismail Onat, Halil Akbas, Nelly Elsayed

Drug overdose is a pressing public health concern in the United States, resulting in a significant number of fatalities each year. In this study, we employ the Journey to Crime (JTC) framework borrowed from the field of environmental criminology to examine the association between drug sales locations and overdose death locations. In this research, our object

Uğur Çoğalan, Mojtaba Bemana, Hans-Peter Seidel, Karol Myszkowski

Full-reference image quality metrics (FR-IQMs) aim to measure the visual differences between a pair of reference and distorted images, with the goal of accurately predicting human judgments. However, existing FR-IQMs, including traditional ones like PSNR and SSIM and even perceptual ones such as HDR-VDP, LPIPS, and DISTS, still fall short in capturing the co

Xingran Chen, Ge Zhang, Jie Fu

Due to Multilingual Neural Machine Translation's (MNMT) capability of zero-shot translation, many works have been carried out to fully exploit the potential of MNMT in zero-shot translation. It is often hypothesized that positional information may hinder the MNMT from outputting a robust encoded representation for decoding. However, previous approaches treat

Zuolin Liu, Hongbin Fang, Jian Xu, Kon-Well Wang

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requir

Fan Yang, Shanxiang Lyu, Hao Cheng, Jinming Wen

This paper discusses the problem of extracting spread spectrum hidden data from the perspective of lattice decoding. Since the conventional blind extraction scheme multi-carrier iterative generalize least-squares (M-IGLS) and non-blind extraction scheme minimum mean square error (MMSE) suffer from performance degradation when the carriers lack sufficient ort

Anatolii Iu. Egorov, Victor T. Kim

The calculations based on the next-to-leading logarithm (NLL) approximation for the Balitsky-Fadin-Kuraev-Lipatov (BKFL) evolution are presented for the Mueller-Navelet (MN) dijet production cross section, as well as for their ratios at different collision energies. The MN dijet denotes the jet pair consists of jets, which were selected with $p_{\perp} > p_{

Jürgen Dölz, Fernando Henríquez

We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Consequently, the boundary integral operators themselves also depend on the parametric variables, thus leading to a parameter-to-operator map. The main result of this article is to establish the analytic or holomorphic depende

Gernot Akemann, Noah Aygün, Tim R. Würfel

We generalise well-known integrals of Ingham-Siegel and Fisher-Hartwig type over the unitary group $U(N)$ with respect to Haar measure, for finite $N$ and including fixed external matrices. When depending only on the eigenvalues of the unitary matrix, they can be related to a Toeplitz determinant with jump singularities. After introducing fixed deterministic

Malte Heuer, Madeleine Jotz

This paper proposes a geometrisation of $\mathbb N$-manifolds of degree $n$ as $n$-fold vector bundles equipped with a (signed) $S_n$-symmetry. More precisely, it proves an equivalence between the categories of $[n]$-manifolds and the category of symmetric $n$-fold vector bundles, by finding that symmetric $n$-fold vector bundle cocycles and $[n]$-manifold c

Sjoerd de Vries

Newton's identities provide a way to express elementary symmetric polynomials in terms of power polynomials over fields of characteristic zero. In this article, we study the failure of this relation in positive characteristic and what can be recovered. In particular, we show how one can write the elementary symmetric polynomials as rational functions in the

Benedetta Catricalà, Miriam Ledda, Marco Manca, Fabio Paternò

One issue in aging is how to stimulate the cognitive skills of older adults. One way to address it is the use of serious games delivered through humanoid robots, to provide engaging ways to perform exercises to train memory, attention, processing, and planning activities. We present an approach in which a humanoid robot, by using various modalities, propose

Nicholas Smith

In these proceedings, we present several new measurements of Standard Model (SM) processes, in the Higgs sector and beyond, that push the precision frontier forward at CMS. Results are presented in the context of a framework parameterizing deviations in Higgs boson couplings, as well as in the context of SM Effective Field Theory, where new analyses targetin

Zhihong Huang, Longyue Wang, Siyou Liu, Derek F. Wong

Pretrained language models (PLMs) have produced substantial improvements in discourse-aware neural machine translation (NMT), for example, improved coherence in spoken language translation. However, the underlying reasons for their strong performance have not been well explained. To bridge this gap, we introduce a probing task to interpret the ability of PLM

Juan Sánchez-Baena, Luis A. Peña Ardila, Grigory Astrakharchik, Ferran Mazzanti

We study the quasiparticle properties of a dipolar impurity immersed in a two-dimensional dipolar bath. We use the ab-initio Diffusion Monte Carlo technique to determine the polaron energy, effective mass and quasiparticle residue. We find that both the polaron energy and quasiparticle residue follow a universal behaviour with respect to the polarization ang

Zhengyuan Liu, Yong Keong Yap, Hai Leong Chieu, Nancy F. Chen

Stance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights into social media. The ubiquitous indirect referral of targets makes this task challenging, as it requires computational solutions to model semantic features and infer the corresponding im

Yi Sun, Xin Xu, Jian Li, Xiaochang Hu

Multi-output deep neural networks(MONs) contain multiple task branches, and these tasks usually share partial network filters that lead to the entanglement of different task inference routes. Due to the inconsistent optimization objectives, the task gradients used for training MONs will interfere with each other on the shared routes, which will decrease the

Giuliano Giacalone

It is an experimental fact that multi-particle correlations in the final states of high-energy nucleus-nucleus collisions are sensitive to collective correlations of nucleons in the wave functions of the colliding nuclei. Here, I show that this connection is more direct than it intuitively seems. With an energy deposition scheme inspired by high-energy quant

Laurentiu Maxim, Jose Israel Rodriguez, Botong Wang

We survey recent applications of topology and singularity theory in the study of the algebraic complexity of concrete optimization problems in applied algebraic geometry and algebraic statistics.

Konrad Wojtasik, Vadim Shishkin, Kacper Wołowiec, Arkadiusz Janz

The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR) in zero-shot settings, garnering considerable attention within the research community. However, BEIR and analogous datasets are predominantly restricted to the English language. Our objective is to establish extensive large-scale resources for IR in the Polish language, there

B. -Y. Ni, Y. A. Semenov

An impulsively starting motion of two cylindrical bodies floating on a free liquid surface is considered. The shape of the cross-section of each body and the distance between them is arbitrary. The integral hodograph method is advanced to derive the complex velocity potential defined in a rectangle parameter region in terms of the elliptic quasi-doubly perio

Anthony Bardou, Patrick Thiran, Thomas Begin

Bayesian Optimization (BO) is typically used to optimize an unknown function $f$ that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorithms are efficient at optimizing low-dimensional functions, scaling them to high-dimensional spaces rema

Gonçalo Mateus, Cláudia Soares, João Leitão, António Rodrigues

The use of machine learning for time series prediction has become increasingly popular across various industries thanks to the availability of time series data and advancements in machine learning algorithms. However, traditional methods for time series forecasting rely on pre-optimized models that are ill-equipped to handle unpredictable patterns in data. I

Jan-Hendrik Bastek, Dennis M. Kochmann

The accelerated inverse design of complex material properties - such as identifying a material with a given stress-strain response over a nonlinear deformation path - holds great potential for addressing challenges from soft robotics to biomedical implants and impact mitigation. While machine learning models have provided such inverse mappings, they are typi

Bei Li, Rui Wang, Junliang Guo, Kaitao Song

Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing prompting methods are normally restricted to providing correct information, in this paper, we encourage the model to deliberate by proposing a novel Deliberate then Generate (DTG)

Dwight Nwaigwe, Lucrezia Carboni, Martial Mermillod, Sophie Achard

Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These \textit{adversarial} attacks have been the focus of extensive research. Likewise, there has been an abundance of research in ways to detect and defend against them. We introduce a novel approach of detection and interpretation

Mohammad Rezaalipour, Carlo A. Furia

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java -- the most common choices for fault localization research -- c

Timo Sprekeler

We study the homogenization of the equation $-A(\frac{\cdot}{\varepsilon}):D^2 u_{\varepsilon} = f$ posed in a bounded convex domain $\Omega\subset \mathbb{R}^n$ subject to a Dirichlet boundary condition and the numerical approximation of the corresponding homogenized problem, where the measurable, uniformly elliptic, periodic and symmetric diffusion matrix

Oleg Malafeyev, Kun Zhang

Unmanned aerial vehicles (UAVs) have become increasingly prevalent in various domains, ranging from military operations to civilian applications. However, the proliferation of UAVs has also given rise to concerns regarding their potential misuse and security threats. As a result, the search and pursuit of UAVs have become crucial tasks for law enforcement ag

Kok-Seng Wong, Manh Nguyen-Duc, Khiem Le-Huy, Long Ho-Tuan

Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications. However, due to increasing privacy concerns and regulations, these data tend to reside on devices (clients) instead of being centralized for performing traditional ML model training. Federated Le

Jia Xu, Yong Yao

Inequalities among symmetric polynomial functions are fundamental questions in mathematics and have various applications in science and engineering. This paper investigates a beautiful and inspiring conjecture, proposed by Cuttler, Greene and Skandera in 2011, on inequalities among the complete homogeneous symmetric polynomial function $H_{n,\lambda}$: It st

Wenting Ye, Chen Li, Yang Xie, Wen Zhang

Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge graph and knowledge graph embedding (KGE) models have made rapid advancements and demonstrated impressive perform

Christopher D. Mink, Michael Fleischhauer

Interfaces of light and matter serve as a platform for exciting many-body physics and photonic quantum technologies. Due to the recent experimental realization of atomic arrays at sub-wavelength spacings, collective interaction effects such as superradiance have regained substantial interest. Their analytical and numerical treatment is however quite challeng

Dan Burghelea

This paper reviews the description of "bar codes" for a continuous real-valued map and explains how to recover the Morse complex of a Morse function from them. In this presentation the bar codes appear as the support of two vector-space valued maps, one defined on the Euclidean plane and the other on the "above diagonal" half plane.

I. Ya. Aref'eva, I. V. Volovich

Black holes violate the third law of thermodynamics, and this gives rise to difficulties with the microscopic description of the entropy of black holes. Recently, it has been shown that the microscopic description of the Schwarzschild black hole thermodynamics in $D = 4$ spacetime dimensions is provided by the analytical continuation of the entropy of Bose g

D. Csontosová, J. Chaloupka, H. Shinaoka, A. Hariki

The density functional plus dynamical mean-field theory is used to study the spin excitation spectra of SrRu$_2$O$_6$. A good quantitative agreement with experimental spin excitation spectra is found. Depending on the size of the Hund's coupling $J_H$ the systems chooses either Mott insulator or covalent insulator state when magnetic ordering is not allowed.

Dianzhen Cui, Xi-Lin Wang, X. X. Yi, Li-Ping Yang

Accurately controlling the quantum coherence of photons is pivotal for their applications in quantum sensing and quantum imaging. Here, we propose the utilization of quantum entanglement and local phase manipulation techniques to control the higher-order quantum coherence of photons. By engineering the spatially varying phases in the transverse plane, we can

Matteo Ferraresso, Mohammad Shojaeifard, Albert Kong, Mattia Bacca

Cytoskeletal gels are engineered prototypes that mimic the contractile behavior of a cell in-vitro. They are composed of an active polymer matrix and a liquid solvent. Their contraction kinetics is governed by two dynamic phenomena: mechanotransduction (molecular motor activation), and solvent diffusion. In this paper, we solve the transient problem for the

Laura Blázquez Martínez, Philipp Wiedemann, Changlong Zhu, Andreas Geilen

We experimentally demonstrate optoacoustic cooling via stimulated Brillouin-Mandelstam scattering in a 50 cm-long tapered photonic crystal fiber. For a 7.38 GHz acoustic mode, a cooling rate of 219 K from room temperature has been achieved. As anti-Stokes and Stokes Brillouin processes naturally break the symmetry of phonon cooling and heating, resolved side

Mathias Krämer, Bar Favelukis, Ayman A. El-Zoka, Maxim Sokol

MXenes are a family of 2D transition metal carbides and nitrides with remarkable properties and great potential for energy storage and catalysis applications. However, their oxidation behavior is not yet fully understood, and there are still open questions regarding the spatial distribution and precise quantification of surface terminations, intercalated ion

Rita Ramos, Bruno Martins, Desmond Elliott

Multilingual image captioning has recently been tackled by training with large-scale machine translated data, which is an expensive, noisy, and time-consuming process. Without requiring any multilingual caption data, we propose LMCap, an image-blind few-shot multilingual captioning model that works by prompting a language model with retrieved captions. Speci

Csilla Bujtás andMichael A. Henning, Sandi Klavžar

For some $\alpha$ with $0 < \alpha \le 1$, a subset $X$ of vertices in a graph $G$ of order~$n$ is an $\alpha$-partial dominating set of $G$ if the set $X$ dominates at least $\alpha \times n$ vertices in $G$. The $\alpha$-partial domination number ${\rm pd}_{\alpha}(G)$ of $G$ is the minimum cardinality of an $\alpha$-partial dominating set of $G$. In this

Peng-Bo Ding, Tian-Xiang Ma, Yong-Qiang Wang

In this paper, we reconsider the mixed system of BSs with wormholes at their center which performed by complex scalar field and phantom field and study a whole new condition about the potential. Both the symmetric and asymmetric solutions in the two asymptotically flat regions are obtained by using numerical method and we mainly explore the change of the res

Marina Munkhoeva, Ivan Oseledets

Self-supervised methods received tremendous attention thanks to their seemingly heuristic approach to learning representations that respect the semantics of the data without any apparent supervision in the form of labels. A growing body of literature is already being published in an attempt to build a coherent and theoretically grounded understanding of the