February 2024 arXiv papers — page 33

Mattia Utzeri, Marco Sasso, Vikram S. Deshpande, S. Kumar

Additive manufacturing (AM) enables the development of high-performance architected cellular materials, emphasizing the growing importance of establishing programmable and predictable energy absorption capabilities. This study evaluates the impact of a precisely tuned fused filament fabrication (FFF) AM process on the energy absorption and failure characteri

Andrii Khrabustovskyi

Let $\Omega\subset\mathbb{R}^n$ be a domain, $\Gamma$ be a hyperplane intersecting it. Let $\varepsilon>0$, and $\Omega_\varepsilon=\Omega\setminus\overline{\Sigma_\varepsilon}$, where $\Sigma_\varepsilon$ ("sieve") is an $\varepsilon$-neighbourhood of $\Gamma$ punctured by many narrow passages. When $\varepsilon\to0$, the number of passages tends to infinit

Kurusch Ebrahimi-Fard, Timothé Ringeard

We investigate the algebraic structure underlying Voiculescu's S-transform in the setting of operator-valued free probability. We show that its twisted factorisation property gives rise to post-groups, crossed morphisms, as well as pre- and post-Lie algebras.

Tao Zhu, Hoang Ky Nguyen, Mustapha Azreg-Aïnou, Mubasher Jamil

A novel class of Buchdahl-inspired metrics with closed-form expressions was recently obtained based on Buchdahl's seminal work on searching for static, spherically symmetric metrics in ${\cal R}^{2}$ gravity in vacuo. Buchdahl-inspired spacetimes provide an interesting framework for testing predictions of ${\cal R}^{2}$ gravity models against observations. T

Yu Zhang, Guangyao Tian, Long Wen, Xiangtong Yao

This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static and moving obstacles. This framework addresses two significant challenges associated with traditional dynamic control barrier functions (D-CBFs): their online construction and the

Dominic Laniewski, Eric Lanfer, Bernd Meijerink, Roland van Rijswijk-Deij

Low Orbit Satellite (LEO) networks such as Starlink promise Internet access everywhere around the world. In this paper, we present WetLinks - a large and publicly available trace-based dataset of Starlink measurements. The measurements were concurrently collected from two European vantage points over a span of six months. Consisting of approximately 140,000

Morriel Kasher, Michael Tinston, Predrag Spasojevic

Dithering is a technique that can improve human perception of low-resolution data by reducing quantization artifacts. In this work we formalize and analytically justify two metrics for quantization artifact prominence, using them to design a novel dithering method for distortion-controlled data compression. We present theoretical entropy calculations for thi

Shanglin Yang, Yohann Benedic, Dinh-Thuy Phan-Huy, Jean-Marie Gorce

In this paper, we present a new ultra-low power method of indoor localization of smartphones (SM) based on zero-energy-devices (ZEDs) beacons instead of active wireless beacons. Each ZED is equipped with a unique identification number coded into a bit-sequence, and its precise position on the map is recorded. An SM inside the building is assumed to have acce

Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu

Recent advances in Protein Language Models (PLMs) have transformed protein engineering, yet unlike their counterparts in Natural Language Processing (NLP), current PLMs exhibit a fundamental limitation: they excel in either Protein Language Understanding (PLU) or Protein Language Generation (PLG), but rarely both. This fragmentation hinders progress in prote

Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang

The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs' responses in an aligned, customizable and explainable manner. In this paper, we propose ShieldLM, an LLM-based safety detector, which aligns with common safety standards, supports

Hiroki Masuda, Lorenzo Mercuri, Yuma Uehara

The aim of this paper is to discuss an estimation and a simulation method in the \textsf{R} package YUIMA for a linear regression model driven by a Student-$t$ L\'evy process with constant scale and arbitrary degrees of freedom. This process finds applications in several fields, for example finance, physic, biology, etc. The model presents two main issues. T

Benjamin Jung, Antonio Figueroa, Malte Göttsche

Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In th

Maximilian Böther, Abraham Sebastian, Pranjal Awasthi, Ana Klimovic

Modern datasets span billions of samples, making training on all available data infeasible. Selecting a high quality subset helps in reducing training costs and enhancing model quality. Submodularity, a discrete analogue of convexity, is commonly used for solving such subset selection problems. However, existing algorithms for optimizing submodular functions

Animesh Chatterjee, Albert De Roeck

We propose to study atmospheric neutrino interactions with a unique event topology to distinguish neutrinos and anti-neutrinos using a liquid argon time projection chamber in an experiment such as DUNE. The detection of CC1P and CC0P events will allow to access neutrino oscillation physics complementary to accelerator based beam neutrinos. Our analysis shows

David Reymond, Heman Khouilla, Sandrine Wolff, Manuel Durand-Barthez

Patents and scientific papers provide an essential source for measuring science and technology output, to be used as a basis for the most varied scientometric analyzes. Authors' and inventors' names are the key identifiers to carry out these analyses, which however, run up against the issue of disambiguation. By extension identifying inventors who are also a

Aris Daniilidis, Mounir Haddou, Tri Minh Le, Olivier Ley

In this work, we show that several problems naturally represented as Nonlinear Absolute Value Equations (NAVE) can be reformulated as Nonlinear Complementarity Problems (NCP) and efficiently solved using smoothing regularization techniques under mild assumptions. As far as we know, this is the first numerical approach that directly deals with NAVE. We also i

Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang

Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. It remains a challenging problem to explain the underlying mechanisms by which LLMs process multilingual texts. In this paper, we delve into the composition of Transformer architectures in LLMs to pinpoin

Gilda Ferreira, Paulo Firmino

Realizability notions in mathematical logic have a long history, which can be traced back to the work of Stephen Kleene in the 1940s, aimed at exploring the foundations of intuitionistic logic. Kleene's initial realizability laid the ground for more sophisticated notions such as Kreisel's modified realizability and various modern approaches. In this context,

Jean-Marie Malherbe

The spectroheliograph was invented independently by Henri Deslandres (France) and George Hale (USA) in 1892, following the spectroscopic method suggested by Jules Janssen in 1869. This instrument is dedicated to the production of monochromatic images of the Sun in order to reveal the structures of the photosphere and the chromosphere at various altitudes. Sp

José Manuel de Frutos, Pablo M. Olmos, Manuel A. Vázquez, Joaquín Míguez

Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated ones using adversarial discriminators, leading to unstable training and mode-dropping issues. As reported by Zahee et al. (2017), even in the one-dimensional (1D) case, training a

Anna Bernasconi, Alessandro Berti, Gianna M. Del Corso, Riccardo Guidotti

Quantum computing sets the foundation for new ways of designing algorithms, thanks to the peculiar properties inherited by quantum mechanics. The exploration of this new paradigm faces new challenges concerning which field quantum speedup can be achieved. Towards finding solutions, looking for the design of quantum subroutines that are more efficient than th

Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Robert Schober

Modern wireless communication systems are expected to provide improved latency and reliability. To meet these expectations, a short packet length is needed, which makes the first-order Shannon rate an inaccurate performance metric for such communication systems. A more accurate approximation of the achievable rates of finite-block-length (FBL) coding regimes

Stefan Meinecke, Malte Selig, Felix Köster, Andreas Knorr

Multi-physics simulations play a crucial role in understanding complex systems. However, their computational demands are often prohibitive due to high dimensionality and complex interactions, such that actual calculations often rely on approximations. To address this, we introduce a data-driven approach to approximate interactions among degrees of freedom of

Victor Pachy, Vincent Andrieu, Pauline Bernard, Lucas Brivadis

KKL (Kazantzis-Kravaris/Luenberger) observers are based on the idea of immersing a given nonlinear system into a target system that is a linear stable filter of the measured output. In the present paper, we extend this theory by allowing this target system to be a nonlinear contracting filter of the output. We prove, under a differential observability condit

Yuansen Zhang, Xiao Wang, Zhiheng Xi, Han Xia

Large Language Models (LLMs) have showcased remarkable capabilities in following human instructions. However, recent studies have raised concerns about the robustness of LLMs when prompted with instructions combining textual adversarial samples. In this paper, drawing inspiration from recent works that LLMs are sensitive to the design of the instructions, we

Dong Qin, George Amariucai, Daji Qiao, Yong Guan

In recent years, machine learning models, especially deep neural networks, have been widely used for classification tasks in the security domain. However, these models have been shown to be vulnerable to adversarial manipulation: small changes learned by an adversarial attack model, when applied to the input, can cause significant changes in the output. Most

Ivan Magrin-Chagnolleau, Jean François Bonastre, Frédéric Bimbot

Second-order statistical methods show very good results for automatic speaker identification in controlled recording conditions. These approaches are generally used on the entire speech material available. In this paper, we study the influence of the content of the test speech material on the performances of such methods, i.e. under a more analytical approac

Alet Roux, Álvaro Guinea Juliá

This paper introduces a short rate model in continuous time that adds one or more memory (delay) components to the Merton model (Merton 1970, 1973) or the Vasi\v{c}ek model (Vasi\v{c}ek 1977) for the short rate. The distribution of the short rate in this model is normal, with the mean depending on past values of the short rate, and a limiting distribution ex

Tanzeela Kanwal, Azhar Hussain, İbrahim Avcı, Sina Etemad

We employ Mittag-Leffler type kernels to solve a system of fractional differential equations using fractal-fractional (FF) operators with two fractal and fractional orders. Using the notion of FF-derivatives with nonsingular and nonlocal fading memory, a model of three polluted lakes with one source of pollution is investigated. The properties of a non-decre

He Zhang, Wei Zhong, Xiaohui Yu, Binbin Yue

Since there is both strong electron-phonon coupling during a ferroelectric/FE transition and superconducting/SC transition, it has been an important topic to explore superconductivity from the FE instability. Sn$_2$P$_2$Se$_6$ arouses broad attention due to its unique FE properties. Here, we reported the electronic phase transitions and superconductivity in

Nenad Teofanov, Filip Tomić, Stefan Tutić

It is known that a smooth function of exponential decay at infinity can not be an orthonormal wavelet. Dziuba\'nski and Hern\'andez constructed smooth orthonormal wavelets of Gevrey type subexponential decay. We weaken the Gevrey type decay and construct orthonormal wavelets of subexponential decay related to the so-called extended Gevrey classes. The virtue

S. I. Dimitrov

In this paper, we establish a Bombieri-Vinogradov type result for prime numbers of the form $p=x^2+y^2+1$. The proof is based on the enveloping sieve.

Yuqi Li, Qingqing Long, Yihang Zhou, Ran Zhang

Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios. While considerable success has been achieved, there still exist urgent limitations. Existing works ignore the locality relationships of representations and attributes, which have effective transferability between seeable classes

Simão Correia, Raphaël Côte

We consider the modified Korteweg-de Vries equation. Given a self-similar solution, and a subcritical perturbation of any size, we prove that there exists a unique solution to the equation which behaves at blow-up time as the self-similar solution plus the perturbation. To this end, we develop the first robust analysis in spaces of functions with bounded Fou

Ismaël Castillo

These are lecture notes of the 51st Saint-Flour summer school, July 2023, on the topic of Bayesian nonparametric statistics

Wei He, Kai Han, Yehui Tang, Chengcheng Wang

Large language models (LLMs) face a daunting challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture. While state space model (SSM) is a new type of foundational network architecture offering lower computational complexity, their performance has yet to fully rival that of Transformers. This paper int

Markus Pobitzer, Filip Janicki, Mattia Rigotti, Cristiano Malossi

Instance segmentation datasets play a crucial role in training accurate and robust computer vision models. However, obtaining accurate mask annotations to produce high-quality segmentation datasets is a costly and labor-intensive process. In this work, we show how this issue can be mitigated by starting with small annotated instance segmentation datasets and

Lev Kharlashkin, Melany Macias, Leo Huovinen, Mika Hämäläinen

We present our work on predicting United Nations sustainable development goals (SDG) for university courses. We use an LLM named PaLM 2 to generate training data given a noisy human-authored course description input as input. We use this data to train several different smaller language models to predict SDGs for university courses. This work contributes to b

Nadja Gruber, Johannes Schwab, Elke Gizewski, Markus Haltmeier

Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view CT, only a few projection views are acquired, causing standard reconstructions to suffer from severe artifacts and noise. To address these issues, we propose a self-supervised image

Xiaolong Wang, Xueyi Huang, Huiqiu Lin

The spectral extremal problem of planar graphs has aroused a lot of interest over the past three decades. In 1991, Boots and Royle [Geogr. Anal. 23(3) (1991) 276--282] (and Cao and Vince [Linear Algebra Appl. 187 (1993) 251--257] independently) conjectured that $K_2 + P_{n-2}$ is the unique graph attaining the maximum spectral radius among all planar graphs

Yuya Arima

We perform a multifractal analysis of the growth rate of the number of cusp windings for the geodesic flow on hyperbolic surfaces with $m \geq 1$ cusps. Our main theorem establishes a conditional variational principle for the Hausdorff dimension spectrum of the multi-cusp winding process. Moreover, we show that the dimension spectrum defined on $\mathbb{R}_{

ALICE Collaboration

The $p_{\rm T}$-differential production cross sections of non-prompt ${\rm D^0}$, ${\rm D^+}$, and ${\rm D_s^+}$ mesons originating from beauty-hadron decays are measured in proton$-$proton collisions at a centre-of-mass energy $\sqrt{s}=13$ TeV. The measurements are performed at midrapidity, $|y| < 0.5$, with the data sample collected by ALICE from 2016 to

Jinhu Ren, Fuzhong Nian, Xiaochen Yang

Modern social media networks have become an important platform for information competition among countries, regions, companies and other parties. This paper utilizes the research method of spread dynamics to investigate the influence of the control role of announcements in social networks on the spreading process. This paper distinguishes two spreading phase

Anastasios Papazafeiropoulos, Jiancheng An, Pandelis Kourtessis, Tharmalingam Ratnarajah

Stacked intelligent metasurfaces (SIM) is a revolutionary technology, which can outperform its single-layer counterparts by performing advanced signal processing relying on wave propagation. In this work, we exploit SIM to enable transmit precoding and receiver combining in holographic multiple-input multiple-output (HMIMO) communications, and we study the a

Pierre-François Loos, Antoine Marie, Abdallah Ammar

The cumulant expansion of the Green's function is a computationally efficient beyond-$GW$ approach renowned for its significant enhancement of satellite features in materials. In contrast to the ubiquitous $GW$ approximation of many-body perturbation theory, \textit{ab initio} cumulant expansions performed on top of $GW$ ($GW$+C) have demonstrated the capabi

Zhengyu Zhu, Mengfei Gong, Gangcan Sun, Peijia Liu

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing and communication (ISAC) dual-secure communication system is studied in this paper. The sensed target and legitimate users (LUs) are situated on the opposite sides of the STAR-RIS, and the energy splitting and time switching protocols are applied

Sabera Talukder, Yisong Yue, Georgia Gkioxari

This work studies the problem of time series analysis with generalist (or foundation) models, which are models trained across many data domains. Drawing inspiration from the widespread success of large language models, we consider the simple strategy of discretely tokenizing time series data drawn from a myriad of datasets via self-supervision, then using th

Mengxi Xiao, Qianqian Xie, Ziyan Kuang, Zhicheng Liu

Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in cognitive reframing mainly converted negative emotions to positive ones, but these approaches have limited effic

Yosuke Minowa, Yuki Yasui, Tomo Nakagawa, Sosuke Inui

Helices and spirals, prevalent across various systems, play a crucial role in characterizing symmetry, describing dynamics, and imparting unique functionalities, attributed to their inherent simplicity and chiral nature. A helical excitation on a quantized vortex, an example of a one-dimensional topological defect, emerges as a Nambu-Goldstone mode following

Jesús Rubio

Combining quantum and Bayesian principles leads to optimality in metrology, but the optimisation equations involved are often hard to solve. This work mitigates this problem with a novel class of measurement strategies for quantities isomorphic to location parameters, which are shown to admit a closed-form optimisation. The resulting framework admits any par

Stefan Hohenegger, Francesco Sannino

Epidemiological models describe the spread of an infectious disease within a population. They capture microscopic details on how the disease is passed on among individuals in various different ways, while making predictions about the state of the entirety of the population. However, the type and structure of the specific model considered typically depend on

Daniel Andrade

Variational inference with normalizing flows (NFs) is an increasingly popular alternative to MCMC methods. In particular, NFs based on coupling layers (Real NVPs) are frequently used due to their good empirical performance. In theory, increasing the depth of normalizing flows should lead to more accurate posterior approximations. However, in practice, traini

Hanxin Zhu, Tianyu He, Zhibo Chen

Neural Radiance Field (NeRF) has shown impressive results in novel view synthesis, particularly in Virtual Reality (VR) and Augmented Reality (AR), thanks to its ability to represent scenes continuously. However, when just a few input view images are available, NeRF tends to overfit the given views and thus make the estimated depths of pixels share almost th

Erina Seh-Young Moon, Devansh Saxena, Tegan Maharaj, Shion Guha

Caseworkers in the child welfare (CW) sector use predictive decision-making algorithms built on risk assessment (RA) data to guide and support CW decisions. Researchers have highlighted that RAs can contain biased signals which flatten CW case complexities and that the algorithms may benefit from incorporating contextually rich case narratives, i.e. - caseno

Nigel Markey, Ilyass El-Mansouri, Gaetan Rensonnet, Casper van Langen

This manuscript has now been published: - Link to article on journal website: https://journals.sagepub.com/doi/10.1177/17407745251320806 - Pubmed link: https://pubmed.ncbi.nlm.nih.gov/40013826/

Ming-Yan Sun, Peng Xu, Jun-Jie Zhang, Tai-Jiao Du

We present JefiAtten, a novel neural network model employing the attention mechanism to solve Maxwell's equations efficiently. JefiAtten uses self-attention and cross-attention modules to understand the interplay between charge density, current density, and electromagnetic fields. Our results indicate that JefiAtten can generalize well to a range of scenario

Anastasios Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas, Dimitra Kaklamani

Although reconfigurable intelligent surface (RIS) is a promising technology for shaping the propagation environment, it consists of a single-layer structure within inherent limitations regarding the number of beam steering patterns. Based on the recently revolutionary technology, denoted as stacked intelligent metasurface (SIM), we propose its implementation

P. O. Kazinski, P. S. Korolev

The propagation of electromagnetic waves in helical media with spatial dispersion is investigated. The general form of the permittivity tensor with spatial dispersion obeying the helical symmetry is derived. Its particular form describing the medium made of conducting spiral wires with pitch $2\pi/|q|$ is studied in detail. The solution of the corresponding

Paul C Bressloff

The emerging field of stochastic thermodynamics extends classical ideas of entropy, heat and work to non-equilibrium systems. One notable finding is that the second law of thermodynamics typically only holds after taking appropriate averages with respect to an ensemble of stochastic trajectories. The resulting average rate of entropy production then quantifi

Xinjian Zhao, Chaolong Ying, Tianshu Yu

Graph Neural Networks (GNNs) learn from graph-structured data by passing local messages between neighboring nodes along edges on certain topological layouts. Typically, these topological layouts in modern GNNs are deterministically computed (e.g., attention-based GNNs) or locally sampled (e.g., GraphSage) under heuristic assumptions. In this paper, we for th

Felix Dammann, Giorgio Ferrari

This paper proposes and analyzes a stationary equilibrium model for a competitive industry which endogenously determines the carbon price necessary to achieve a given emission target. In the model, firms are identified by their level of technology and make production, entry, and abatement decisions. Polluting firms are subject to a carbon price and abatement

Xing Huang

In this paper, quantitative propagation of chaos in $L^\eta$($\eta\in(0,1]$)-Wasserstein distance for mean field interacting particle system is derived, where the diffusion coefficient is allowed to be interacting and the initial distribution of interacting particle system converges to that of the limit equation in $L^1$-Wasserstein distance. The non-degener

Mehedi Hasan Raju, Lee Friedman, Dillon J Lohr, Oleg V Komogortsev

What qualities make a feature useful for biometric performance? In prior research, pre-dating the advent of deep learning (DL) approaches to biometric analysis, a strong relationship between temporal persistence, as indexed by the intraclass correlation coefficient (ICC), and biometric performance (Equal Error Rate, EER) was noted. More generally, the claim

Zhixiang Wang, Xudong Li, Yizhai Zhang, Panfeng Huang

Event cameras are bio-inspired vision sensors that asynchronously measure per-pixel brightness changes.The high-temporal resolution and asynchronicity of event cameras offer great potential for estimating robot motion states. Recent works have adopted the continuous-time estimation methods to exploit the inherent nature of event cameras. However, existing me

Biqing Qi, Junqi Gao, Yiang Luo, Jianxing Liu

The rise of generative neural networks has triggered an increased demand for intellectual property (IP) protection in generated content. Deep watermarking techniques, recognized for their flexibility in IP protection, have garnered significant attention. However, the surge in adversarial transferable attacks poses unprecedented challenges to the security of

Shuo Qin

Under suitable moment assumptions, we show that a genuinely d-dimensional step-reinforced random walk undergoes a phase transition between recurrence and transience in dimensions $d=1,2$, and that it is transient for all reinforcement parameters in dimensions $d\geq 3$, which solves a conjecture of Bertoin.

G. Barenboim, A. Calatayud-Cadenillas, A. M. Gago, C. A. Ternes

We investigate the potential impact of neutrino quantum decoherence on the precision measurements of standard neutrino oscillation parameters in the DUNE and T2HK experiments. We show that the measurement of $\delta_\text{CP}$, $\sin^2\theta_{13}$ and $\sin^2\theta_{23}$ is stronger effected in DUNE than in T2HK. On the other hand, DUNE would have a better s

Tassadaq Hussain, Kia Dashtipour, Yu Tsao, Amir Hussain

In real-world environments, background noise significantly degrades the intelligibility and clarity of human speech. Audio-visual speech enhancement (AVSE) attempts to restore speech quality, but existing methods often fall short, particularly in dynamic noise conditions. This study investigates the inclusion of emotion as a novel contextual cue within AVSE,

Jean-Guillaume Dumas, Alexis Galan, Bruno Grenet, Aude Maignan

We present new two-party protocols for the Unbalanced Private Set Union (UPSU) problem. Here, the Sender holds a set of data points, and the Receiver holds another (possibly much larger) set, and they would like for the Receiver to learn the union of the two sets and nothing else. Furthermore, the Sender's computational cost, along with the communication com

Pau de Jorge, Riccardo Volpi, Puneet K. Dokania, Philip H. S. Torr

When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. To ensure a safe deployment of such systems, it is crucial to accurately evaluate and improve their anomaly segmentation capabilities. However, acquiring and labelling semantic segmentation data is expensive and

Chenyu Wang, Zhou Yang, Yunbo Lyu, Ze Shi Li

Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness has been the main focus, but industry AI models must also satisfy many other important quality attributes. To understand how these attributes are perceived, the challenges they creat

Baptiste Filoche, Stefan Hohenegger, Francesco Sannino

We reformulate models in epidemiology and population dynamics in terms of probability distributions. This allows us to construct the Fisher information, which we interpret as the metric of a one-dimensional differentiable manifold. For systems that can be effectively described by a single degree of freedom, we show that their time evolution is fully captured

Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs perform in specific domains (e.g, the intellectual property (IP) domain). In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property

Weilin Cong, Jian Kang, Hanghang Tong, Mehrdad Mahdavi

Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves over time. Although TGL has recently seen notable progress in algorithmic solutions, its theoretical foundations remain largely unexplored. This paper aims at bridging this gap by in

Manuel Friedrich, Manuel Seitz, Ulisse Stefanelli

We study the quasistatic evolution of a linear peridynamic Kelvin-Voigt viscoelastic material. More specifically, we consider the gradient flow of a nonlocal elastic energy with respect to a nonlocal viscous dissipation. Following an evolutionary $\Gamma$-convergence approach, we prove that the solutions of the nonlocal problem converge to the solution of th

B. I. Ermolaev

Proton is a composite particle, so its spin S_P is made from the spins of the partons which the proton consists of. The discrepancy between S_P = 1/2 and the experimentally detected sum of the parton spins was named Proton Spin Puzzle. Solution to this problem includes formulae for the parton helicities valid in the whole range of x. There are approaches in

Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Arash Fayyazi, Sasan Razmkhah

A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in neuron structure allows us to design SNN accelerators with more synaptic connections, enhancing the overall network capabilities. The proposed neuron design is based on supercondu

Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum

Combining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains or rely on a suboptimal and computationally intensive two-stage process of representation learning and clustering. We propose an end-to-end deep learning-based multi-view clusteri

Domenic Rosati, Jan Wehner, Kai Williams, Łukasz Bartoszcze

Large Language Models (LLMs) are often trained with safety guards intended to prevent harmful text generation. However, such safety training can be removed by fine-tuning the LLM on harmful datasets. While this emerging threat (harmful fine-tuning attacks) has been characterized by previous work, there is little understanding of how we should proceed in cons

Ilyes Aberaouze, Mohamed Boucetta

A Lorentzian Lie group is a Lie group endowed with a left invariant Lorentzian metric. We study left-invariant Codazzi tensors on Lorentzian Lie groups. We obtain new results on left-invariant Lorentzian metrics with harmonic curvature and non-parallel Ricci operator. In contrast to the Riemannian case, the Ricci operator of a let-invariant Lorentzian metric

Ahmet Gunduz, Kamer Ali Yuksel, Kareem Darwish, Golara Javadi

Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the qual

Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu

Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, careful evaluations by human reveal that the translations produced by LLMs still contain multiple errors. Importantly, feeding back such error information into the LLMs can lead to self-refinement and result in improved translation performance. Motivated by th

Diata Traore, Julien Toulouse, Emmanuel Giner

We present the first application to real molecular systems of the recently proposed linear-response theory for the density-based basis-set correction method [J. Chem. Phys. 158, 234107 (2023)]. We apply this approach to accelerate the basis-set convergence of excitation energies in the equation-of-motion coupled-cluster singles doubles (EOM-CCSD) method. We

Jules Berry, Olivier Ley, Francisco J Silva

This work introduces a new general approach for the numerical analysis of stable equilibria to second order mean field games systems in cases where the uniqueness of solutions may fail. For the sake of simplicity, we focus on a simple stationary case. We propose an abstract framework to study these solutions by reformulating the mean field game system as an

Charles Bertucci, Jean-Michel Lasry, Pierre Louis Lions

This paper is the second of a series devoted to the study of the dynamics of the spectrum of large random matrices. We study general extensions of the partial differential equation arising to characterize the limit spectral measure of the Dyson Brownian motion. We provide a regularizing result for those generalizations. We also show that several results of p

Selim Mankaï, Sébastien Marchand, Ngoc Ha Le

The demand for voluntary insurance against low-probability, high-impact risks is lower than expected. To assess the magnitude of the demand, we conduct a meta-analysis of contingent valuation studies using a dataset of experimentally elicited and survey-based estimates. We find that the average stated willingness to pay (WTP) for insurance is 87% of expected

Man Wu, Xin Zheng, Qin Zhang, Xiao Shen

Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes

Min Zhao, Meirong Tang, Zhaoyi Xu

The Weak Cosmic Censorship Conjecture is a hypothesis regarding the properties of event horizons and singularities during the formation of black holes, stating that singularities are always encompassed by the event horizons(TEH) of black holes, thus preventing naked singularities from affecting the causal structure of spacetime. In this paper, we explore the

Friedemann Laue, Vahid Jamali, Robert Schober

Efficient beam training is the key challenge in the codebook-based configuration of reconfigurable intelligent surfaces (RISs) because the beam training overhead can have a strong impact on the achievable system performance. In this paper, we study the performance tradeoff between overhead and achievable signal-to-noise ratio (SNR) in RIS beam training while

Wen-Yang Lu, Eduardo Pavez, Antonio Ortega, Xin Zhao

Current video coding standards, including H.264/AVC, HEVC, and VVC, employ discrete cosine transform (DCT), discrete sine transform (DST), and secondary to Karhunen-Loeve transforms (KLTs) decorrelate the intra-prediction residuals. However, the efficiency of these transforms in decorrelation can be limited when the signal has a non-smooth and non-periodic s

Haodong Ouyang

The training paradigm of DETRs is heavily contingent upon pre-training their backbone on the ImageNet dataset. However, the limited supervisory signals provided by the image classification task and one-to-one matching strategy result in an inadequately pre-trained neck for DETRs. Additionally, the instability of matching in the early stages of training engen

Marie Roth

In 2020, Brunat-Dudas-Taylor showed that the decomposition matrix of unipotent ${\ell}$-blocks of a nite reductive group in good characteristic has unitriangular shape, under some conditions on the prime ${\ell}$, in particular ${\ell}$ being good. We extend this result to ${\ell}$ bad by adapting their proof to include the ${\ell}$-special classes dened by

Gaurav Raut, Apoorv Singh

Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI diffusion and legacy models, focusing on thei

Hendrik Möller, Robert Graf, Joachim Schmitt, Benjamin Keinert

Purpose. To present SPINEPS, an open-source deep learning approach for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole body T2w MRI. Methods. During this HIPPA-compliant, retrospective study, we utilized the public SPIDER dataset (218 subjects, 63% f

Weize Liu, Yinlong Xu, Hongxia Xu, Jintai Chen

Recently, large language models (LLMs) have achieved tremendous breakthroughs in the field of NLP, but still lack understanding of their internal neuron activities when processing different languages. We designed a method to convert dense LLMs into fine-grained MoE architectures, and then visually studied the multilingual activation patterns of LLMs through

Zetian Song, Wenhong Duan, Yuhuai Zhang, Shiqi Wang

Representing the Neural Radiance Field (NeRF) with the explicit voxel grid (EVG) is a promising direction for improving NeRFs. However, the EVG representation is not efficient for storage and transmission because of the terrific memory cost. Current methods for compressing EVG mainly inherit the methods designed for neural network compression, such as prunin

Ranendu Adhikary

Self-testing enables the characterization of quantum systems with minimal assumptions on their internal working as such it represents the strongest form of certification for quantum systems. In the existing self-testing literature, self-testing states that are not maximally entangled, but exhibit genuine multipartite nonlocality, have remained an open proble

ATLAS Collaboration

This Letter presents the first study of the energy-dependence of diboson polarization fractions in $WZ \rightarrow \ell\nu \ell'\ell'~(\ell, \ell'=e, \mu)$ production. The data set used corresponds to an integrated luminosity of 140 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector. Two fiducial regions

Tzuf Paz-Argaman, Sayali Kulkarni, John Palowitch, Jason Baldridge

When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. However, NLP navigation studies often overlook the impact of such acquired knowledge on textual descriptions. Current navigation studies concentrate on egocentric local descriptions

Zhihang Yuan, Yuzhang Shang, Yang Zhou, Zhen Dong

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is vibrant, there hasn't been a concise framework that analyzes the various methods of LLM Inference to provide a clear understanding of this domain. Our survey stands out from traditi