March 2024 arXiv papers — page 180

Yangchao Zhou, Moonkwang Jeong, Meng Zhang, Xuexin Duan

On-demand controlled drug delivery is essential for the treatment of a wide range of chronic diseases. As the drug is released at the time when required, its efficacy is boosted and the side effects are minimized. However, so far, drug delivery devices often rely on the passive diffusion process for a sustained release, which is slow and uncontrollable. Here

Francesco Rosini, Simone Pacetti, Olga Shekhovtsova, Egle Tomasi-Gustafsson

We present an analysis of the recent near threshold BESIII data for the nucleon time-like effective form factors. The damped oscillation emerging from the subtraction of the dipole formula is treated in non-perturbative-QCD, making use of the light cone distribution amplitudes expansion. Non-perturbative effects are accounted for by considering Q2-dependent

Yali Jia, Zihan Mo, Qi Liu, Zhaohua Tian

The electromagnetic vacuum construction based on micro-nano photonic structures is able to engineer the photon-exciton interaction at the single quantum level. Here, through engineering the electromagnetic vacuum background formed by edge states, we demonstrate a strong photon-exciton coupling in topological photonic crystal containing a dielectric nanoanten

Hao Wu, Haomin Wen, Guibin Zhang, Yutong Xia

The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting obstacle to their industrial level deployment. Concretely, earth science systems rely heavily on the extensive deployment of sensors, however, the data collection from sensors is c

Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed

Closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation's accuracy. Recently, scientific machine learning approaches have been proposed as a way to tackle the closure problem, combining traditional (physics-based) modeling with data-driven (mac

Tomás González, Cristóbal Guzmán, Courtney Paquette

We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-points in the $\ell_1$ setting. We propose $(\varepsilon, \delta)$-DP algorithms based on stochastic mirror descent that attain nearly dimension-independent convergence rates for the expected duality gap, a type of guarantee that was known before only for bilinear objectiv

Wuraola Oyewusi

To effectively navigate the AI revolution, AI literacy is crucial. However, content predominantly exists in dominant languages, creating a gap for low-resource languages like Yoruba (41 million native speakers). This case study explores bridging this gap by creating and distributing AI videos in Yoruba.The project developed 26 videos covering foundational, i

Luis Thomas, Roberto Saglia, Luca Pasquini, Anna Brucalassi

Planets orbiting members of open or globular clusters offer a great opportunity to study exoplanet populations systematically as stars within clusters provide a mostly homogeneous sample at least in chemical composition and stellar age. However, even though there have been coordinated efforts to search for exoplanets in stellar clusters, only a small number

Xijia Tao, Shuai Zhong, Lei Li, Qi Liu

There has been an increasing interest in the alignment of large language models (LLMs) with human values. However, the safety issues of their integration with a vision module, or vision language models (VLMs), remain relatively underexplored. In this paper, we propose a novel jailbreaking attack against VLMs, aiming to bypass their safety barrier when a user

Abeer Banerjee, Naval K. Mehta, Shyam S. Prasad, Himanshu

In this paper, we address the intricate challenge of gaze vector prediction, a pivotal task with applications ranging from human-computer interaction to driver monitoring systems. Our innovative approach is designed for the demanding setting of extremely low-light conditions, leveraging a novel temporal event encoding scheme, and a dedicated neural network a

Maciej Grzeszczuk, Kinga Skorupska, Paweł Grabarczyk, Władysław Fuchs

At first glance, the ruins of the Roman Theatre in the Italian town of Volterra have little in common with cassette tapes containing Atari games. One is certainly considered an important historical landmark, while the consensus on the importance of the other is partial at best. Still, both are remnants of times vastly different from the present and are at ri

Antonio Riotto, Joe Silk

We discuss some of the the open questions and the roadmap in the physics of primordial black holes. Black holes are the only dark matter candidate that is known to actually exit. Their conjectured primordial role is admittedly based on hypothesis rather than fact, most straightforwardly as a simple extension to the standard models of inflation, or even, in h

Kinga Skorupska, Rafał Stryjek, Izabela Wierzbowska, Piotr Bebas

Technology is increasingly used in Nature Reserves and National Parks around the world to support conservation efforts. Endangered species, such as the Eurasian Lynx (Lynx lynx), are monitored by a network of automatic photo traps. Yet, this method produces vast amounts of data, which needs to be prepared, analyzed and interpreted. Therefore, researchers wor

Sen Wang, Jiangning Zhang, Xin Tan, Zhifeng Xie

The body movements accompanying speech aid speakers in expressing their ideas. Co-speech motion generation is one of the important approaches for synthesizing realistic avatars. Due to the intricate correspondence between speech and motion, generating realistic and diverse motion is a challenging task. In this paper, we propose MMoFusion, a Multi-modal co-sp

V. N. Potapov, S. V. Avgustinovich

It is proved that 1) the indicator function of some onefold or multifold independent set in a regular graph is a perfect coloring if and only if the set attain the Delsarte--Hoffman bound; 2) each transversal in a uniform regular hypergraph is an independent set attaining the Delsarte--Hoffman bound in the vertex adjacency multigraph of this hypergraph; 3) c

Borja Pérez-Díaz, Enrique Pérez-Montero, Juan A. Fernández-Ontiveros, José M. Vílchez

The infrared (IR) range is extremely useful in the context of chemical abundance studies of the gas-phase interstellar medium (ISM) due to the large variety of ionic species traced in this regime, the negligible effects from dust attenuation or temperature stratification, and the amount of data that has been and will be released in the coming years. Taking a

Chengguang Gan, Xuzheng He, Qinghao Zhang, Tatsunori Mori

The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the performance of both classification levels can be mutually enhanced. However, this mechanism has not been adequately demonstrated or explained in prior research. To address this gap,

Yang Zhang, Hanlei Jin, Dan Meng, Jun Wang

Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP) algorithms, aims to create concise and accurate summaries, thereby significantly reducing the human effort required in processing large volumes of text. ATS has drawn considerable interest in both academic and industrial circles. Many studies have been conducted in the past to su

J. M. Mazon, J. Toledo

In this paper we study $\infty$-Laplacian type diffusion equations in weighted graphs obtained as limit as $p\to \infty$ to two types of $p$-Laplacian evolution equations in such graphs. We propose these diffusion equations, that are governed by the subdifferential of a convex energy functionals associated to the indicator function of the set $$K^G_{\infty}:

Arefa, Mohammed Abbas Ansari, Chandni Saxena, Tanvir Ahmad

This paper presents our system development for SemEval-2024 Task 3: "The Competition of Multimodal Emotion Cause Analysis in Conversations". Effectively capturing emotions in human conversations requires integrating multiple modalities such as text, audio, and video. However, the complexities of these diverse modalities pose challenges for developing an effi

Zhekai Du, Xinyao Li, Fengling Li, Ke Lu

Conventional Unsupervised Domain Adaptation (UDA) strives to minimize distribution discrepancy between domains, which neglects to harness rich semantics from data and struggles to handle complex domain shifts. A promising technique is to leverage the knowledge of large-scale pre-trained vision-language models for more guided adaptation. Despite some endeavor

Xiangtao Zhang, Eleftherios Kofidis, Ce Zhu, Le Zhang

Coupled tensor decomposition (CTD) can extract joint features from multimodal data in various applications. It can be employed for federated learning networks with data confidentiality. Federated CTD achieves data privacy protection by sharing common features and keeping individual features. However, traditional CTD schemes based on canonical polyadic decomp

Chuan-Hung Chen, Cheng-Wei Chiang

A scotogenic model can radiatively generate the observed neutrino mass, provide a dark matter candidate, and lead to rare lepton flavor-violating processes. We aim to extend the model to establish a potential connection to the quark flavor-related processes within the framework of scotogenesis, enhancing the unexpectedly large branching ratio (BR) of $B^+\to

Sizhong Zhou, Hongxia Liu, Qiuxiang Bian

Let $\alpha\in[0,1)$, and let $G$ be a connected graph of order $n$ with $n\geq f(\alpha)$, where $f(\alpha)=14$ for $\alpha\in[0,\frac{1}{2}]$, $f(\alpha)=17$ for $\alpha\in(\frac{1}{2},\frac{2}{3}]$, $f(\alpha)=20$ for $\alpha\in(\frac{2}{3},\frac{3}{4}]$ and $f(\alpha)=\frac{5}{1-\alpha}+1$ for $\alpha\in(\frac{3}{4},1)$. A path factor is a spanning subgr

A. Topchieva, T. Molyarova, V. Akimkin, L. Maksimova

The formation of solid macroscopic grains (pebbles) in protoplanetary discs is the first step toward planet formation. We aim to study the distribution of pebbles and the chemical composition of their ice mantles in a young protoplanetary disc. We use the two-dimensional hydrodynamical code FEOSAD in the thin-disc approximation, which is designed to model th

Fuping Fang, Wenhao Lv, Dahai Dai

Synthetic aperture radar is a high-resolution two-dimensional imaging radar, however, during the imaging process, SAR is susceptible to intentional and unintentional interference, with radio frequency interference (RFI) being the most common type, leading to a severe degradation in image quality. Although inpainting networks have achieved excellent results,

Zhitao He, Pengfei Cao, Zhuoran Jin, Yubo Chen

Event Causality Identification (ECI) refers to the detection of causal relations between events in texts. However, most existing studies focus on sentence-level ECI with high-resource languages, leaving more challenging document-level ECI (DECI) with low-resource languages under-explored. In this paper, we propose a Heterogeneous Graph Interaction Model with

Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang

This work addresses the task of long-term person re-identification. Typically, person re-identification assumes that people do not change their clothes, which limits its applications to short-term scenarios. To overcome this limitation, we investigate long-term person re-identification, which considers both clothes-changing and clothes-consistent scenarios.

Konstadinos H. Kiritsis

This paper is devoted to the design of full order proportional-integral observer for the state estimation of discrete-time linear time-invariant systems. In particular, explicit necessary and sufficient conditions are established for the existence of proportional-integral observer for the state estimation of discrete-time linear time-invariant systems and a

Csaba Fábri, Attila G. Császár, Gábor J. Halász, Lorenz S. Cederbaum

The use of cavities to impact molecular structure and dynamics has become popular. As cavities, in particular plasmonic nanocavities, are lossy and the lifetime of their modes can be very short, their lossy nature must be incorporated into the calculations. The Lindblad master equation is commonly considered as an appropriate tool to describe this lossy natu

Yakir Yehuda, Itzik Malkiel, Oren Barkan, Jonathan Weill

Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due to various reasons. One critical factor hindering their widespread adoption is the occurrence of hallucinations, where LLMs invent answers that sound realistic, yet drift away from

Nobuyuki Kanematsu

This guide is intended for authors who wish to publish an original paper in Radiological Physics and Technology. They should follow the submission guidelines of the journal as well as standard guidelines and conventions for scientific writing. The journal recommends a standard structure for the main text and requires an additional section for declarations. E

Daniele Mari, Simone Milani

Learned image compression codecs have recently achieved impressive compression performances surpassing the most efficient image coding architectures. However, most approaches are trained to minimize rate and distortion which often leads to unsatisfactory visual results at low bitrates since perceptual metrics are not taken into account. In this paper, we sho

Fei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng-Lin Liu

Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications. However, modern deep neural networks are often overconfident for their incorrect predictions, i.e., misclassified samples from known classes, and out-of-distribution (OOD) samples from unknown classes. In recent years, many confidence calibration

Yoav Ravid, Samo Penič, Nir Gov, Veronika Kralj-Iglič

In this study, we implement the deviatoric curvature model to examine dynamically triangulated surfaces with anisotropic membrane inclusions. The Monte-Carlo numerical scheme is devised to not only minimize the total bending energy of the membrane but also the in-plane nematic order of the inclusions by considering the mismatch between the curvature of the m

Zhengyang Tang, Xingxing Zhang, Benyou Wang, Furu Wei

Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data using frontier LLMs (e.g., {\tt GPT-3.5}). Inspired by the cognitive mechanism in human

Ruili Zhang, Jian Liu, Tong Liu, Wenxiang Li

The concept of guiding center is potent in astrophysics, space plasmas, fusion researches, and arc plasmas to solve the multi-scale dynamics of magnetized plasmas. In this letter, we rigorously prove that the guiding center dynamics can generally be described as a constrained canonical Hamiltonian system with two constraints in six dimensional phase space, a

Yuan Lin, Antai Xie, Xiao Liu

Most of the current studies on autonomous vehicle decision-making and control tasks based on reinforcement learning are conducted in simulated environments. The training and testing of these studies are carried out under rule-based microscopic traffic flow, with little consideration of migrating them to real or near-real environments to test their performanc

Rahul Roy, Masato Takei, Hideki Tanemura

Gut and Stadm\"{u}ller (2021, 2022) initiated the study of the elephant random walk with limited memory. Aguech and El Machkouri (2024) published a paper in which they discuss an extension of results by Gut and Stadtm\"{u}ller (2022) for an "increasing memory" version of the elephant random walk without stops. Here we present a formal definition of the proce

Ni An, Stavros Garoufalidis, Shana Yunsheng Li

Matrix-valued holomorphic quantum modular forms are intricate objects that arise in successive refinements of the Volume Conjecture of knots and involve three holomorphic, asymptotic and arithmetic objects. It is expected that the algebraic properties of these objects can be deduced from the algebraic properties of descendant state integrals, and we illustra

Jinhong He, Minglong Xue, Aoxiang Ning, Chengyun Song

Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods lack effective bridging capabilities for unknown degradation. To address these limitations, we propose a novel zero-reference lighting estimation diffusion model for low-light image

Eloy de Jong

See thesis for complete abstract. Primordial black holes (PBHs) can form in the early universe, and there are several mass windows in which their abundance today may be large enough to comprise a significant part of the dark matter density. Additionally, numerical relativity (NR) allows one to investigate the formation processes of PBHs in the fully nonlinea

Han Lu, Xiaosong Jia, Yichen Xie, Wenlong Liao

End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm. One main bottleneck lies in its voracious appetite for high-quality labeled data e.g. 3D bounding boxes and semantic segmentation, which are notoriously expensive to manually annotate. The difficulty is further pronounced due to the prominent fact that th

Parnashree Ghosh, Dibyendu Mondal

Let $k$ be a field. In this paper, we consider Double Danielewski type algebras over an affine factorial $k$-domain $R$. We observe that this family produces a non-cancellative family of algebras over $R$. Further, when $k$ is a field of characteristic zero, we give a characterization for an affine algebra to be isomorphic to an algebra of Double Danielewski

Philipp J. Rösch, Norbert Oswald, Michaela Geierhos, Jindřich Libovický

Current multimodal models leveraging contrastive learning often face limitations in developing fine-grained conceptual understanding. This is due to random negative samples during pretraining, causing almost exclusively very dissimilar concepts to be compared in the loss function. Consequently, the models struggle with fine-grained semantic differences. To a

Pei Jin, Guobao Zhang, Yuexin Zhang, Mariano Méndez

We present a detailed analysis of the observations with the Hard X-ray Modulation Telescope of the black hole X-ray transient 4U~1543-47 during its outburst in 2021. We find a clear state transition during the outburst decay of the source. Using previous measurements of the black-hole mass and distance to the source, the source luminosity during this transit

Amit Attia, Tomer Koren

We describe a general reduction technique for analyzing learning algorithms that are subject to light-tailed (but not necessarily bounded) randomness, a scenario that is often the focus of theoretical analysis. We show that the analysis of such an algorithm can be reduced, in a black-box manner and with only a small loss in logarithmic factors, to an analysi

Ingemar Bengtsson, Markus Grassl, Gary McConnell

The existence problem for maximal sets of equiangular lines (or SICs) in complex Hilbert space of dimension $d$ remains largely open. In a previous publication (arXiv:2112.05552) we gave a conjectural algorithm for how to construct a SIC if $d = n^2+3 = p$, a prime number. Perhaps the most surprising number-theoretical aspect of that algorithm is the appeara

Fu Chen, Qinglin Zhao, Li Feng, Chuangtao Chen

Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language processing tasks. Our model leverages quantum computing principles to enhance the effectiveness of self-atten

Younghan Lee, Sohee Jun, Yungi Cho, Woorim Han

With growing popularity, deep learning (DL) models are becoming larger-scale, and only the companies with vast training datasets and immense computing power can manage their business serving such large models. Most of those DL models are proprietary to the companies who thus strive to keep their private models safe from the model extraction attack (MEA), who

Manuela Aguiar, Ana Dias, Ian Stewart

We classify connected 2-node excitatory-inhibitory networks under various conditions. We assume that, as well as for connections, there are two distinct node-types, excitatory and inhibitory. In our classification we consider four different types of excitatory-inhibitory networks: restricted, partially restricted, unrestricted and completely unrestricted. Fo

Ming Li, Feng-Yan Yang, Juanjuan Lu, Guang-Can Guo

The stabilization of optical frequency comb conventionally relies on active electronic feedback loops and stable frequency references. Here, we propose a new approach for autonomous frequency locking (AFL) to generate a zero-offset frequency comb based on cooperative nonlinear optical processes in a microcavity. In a simplified few-mode system, AFL enables t

Keke Huang, Ruize Gao, Bogdan Cautis, Xiaokui Xiao

The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are accessible, cascade-based network inference and influence estimation are two essential problems to explore. Alas, existing methods exhibit limited capability to infer and process networ

Lingzhi Luo, Rui Ma, Richard V. Penty, Qixiang Cheng

Optical circuit switching is connection-oriented, being deterministic through the reservation of a complete wavelength channel or spatial path for a certain period. However, this comes at a trade-off against link dynamics, and overall capacity can thus be constrained by the time slot reservations, especially for switches with microsecond- to millisecond-scal

Thomas Weymuth, Jan P. Unsleber, Paul L. Tuertscher, Miguel Steiner

The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them e.g., as given by an intrinsic reaction coordinate) have become routine in chemistry, new developments have

Robert B. Mann

One of the major developments in classical black hole thermodynamics is the inclusion of vacuum energy in the form of thermodynamic pressure. Known as Black Hole Chemistry, this subdiscipline has led to the realization that anti de Sitter black holes exhibit a broad variety of phase transitions that are essentially the same as those observed in chemical syst

Venkatesh Vadde, Bhaskaran Muralidharan, Abhishek Sharma

We present spintronic devices based hardware implementation of UNet for segmentation tasks. Our approach involves designing hardware for convolution, deconvolution, rectified activation function (ReLU), and max pooling layers of the UNet architecture. We designed the convolution and deconvolution layers of the network using the synaptic behavior of the domai

Javier Jiménez López, V. J. García-Garrido

In this paper we apply the method of Lagrangian descriptors as an indicator to study the chaotic and regular behavior of trajectories in the phase space of the classical double pendulum system. In order to successfully quantify the degree of chaos with this tool, we first derive Hamilton's equations of motion for the problem in non-dimensional form, showing

Ha Pham, Florian Faucher, Hélène Barucq

This work is concerned with implementing the hybridizable discontinuous Galerkin (HDG) method to solve the linear anisotropic elastic equation in the frequency domain. First-order formulation with the compliance tensor and Voigt notation are employed to provide a compact description of the discretized problem and flexibility with highly heterogeneous media.

Jianting Pan, Ming Yan

This paper presents an algorithm tailored for the efficient recovery of sparse probability measures incorporating $\ell_0$-sparse regularization within the probability simplex constraint. Employing the Bregman proximal gradient method, our algorithm achieves sparsity by explicitly solving underlying subproblems. We rigorously establish the convergence proper

Erik Berglund, Mikael Johansson

Update formulas for the Hessian approximations in quasi-Newton methods such as BFGS can be derived as analytical solutions to certain nearest-matrix problems. In this article, we propose a similar idea for deriving new limited memory versions of quasi-Newton methods. Most limited memory quasi-Newton methods make use of Hessian approximations that can be writ

Divyarani C. Geetha, Anders Tranberg

We consider the instabilities of field perturbations around a homogeneous background color-electric and/or -magnetic field in SU(2) pure gauge theory. We investigate a number of distinct cases of background magnetic and electric fields, and compute the dispersion relations in the linearised theory, identifying stable and unstable momentum modes. In the case

Alona Mokhov, Nira Dyn, Elza Farkhi

A new notion of metric differentiability of set-valued functions at a point is introduced in terms of right and left limits of special set-valued metric divided differences of first order. A local metric linear approximant of a metrically differentiable set-valued function at a point is defined and studied. This local approximant may be regarded as a special

Paolo Del Santo

A re-examination of a well-known iconographic source, the "Allegory of Sight" by Jan Brueghel the Elder and Peter Paul Rubens, reveals that one of the two telescopes depicted in the painting has a highly unusual,and until now unknown to the historians of the telescope, particularity.

Rihan Hai, Shih-Han Hung, Sebastian Feld

Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been addressed using quantum computing techniques. This tutorial aims to establish the theoretical foundation essential for enhancing methodologies

Mitchell Ryan

We apply the loop module construction of arXiv:1504.05114 in the context of Lie colour algebras. We construct a bijection between the equivalence classes of all finite-dimensional graded irreducible Lie colour algebra representations from the irreducible representations for Lie superalgebras. This bijection is obtained by applying the loop module constructio

Bin Lyu, Yining Zhang, Pengcheng Chen, Ziwei Liu

Wireless powered and backscattering mobile edge computing (WPB-MEC) network is a novel network paradigm to supply energy supplies and computing resource to wireless sensors (WSs). However, its performance is seriously affected by severe attenuations and inappropriate assumptions of infinite computing capability at the hybrid access point (HAP). To address th

Shuyuan Hu, Yu He, Gergana D. Borisova, Maximilian Hartmann

The electronic structure of atomic quantum systems and their dynamical interaction with light is reflected in transition dipole matrix elements coupling the system's energy eigenstates. In this work, we measure phase shifts of the time-dependent ultrafast absorption to determine the relative signs of. the transition-dipole matrix elements. The measurement re

Rituparna Mandal, Soma Sanyal

We investigate the quantum modified cosmological dynamical equations in a Friedmann-Robertson-Walker universe filled with a barotropic fluid and a general non-canonical scalar field characterized by a Lagrangian similar to k-essence model but with a potential term. Quantum corrections are incorporated by considering the running of gravitational and potential

Gastón De Boni Rovella, Meryem Benammar, Tarik Benaddi, Hugo Meric

In this work, we introduce a framework that enables the use of Syndrome-Based Neural Decoders (SBND) for high-order Bit-Interleaved Coded Modulations (BICM). To this end, we extend the previous results on SBND, for which the validity is limited to Binary Phase-Shift Keying (BPSK), by means of a theoretical channel modeling of the bit Log-Likelihood Ratio (bi

Victor Wu

We introduce $C^*$-algebras associated to directed graphs of groups. In particular, we associate a combinatorial $C^*$-algebra to each row-finite directed graph of groups with no sources, and show that this $C^*$-algebra is Morita equivalent to the crossed product coming from the corresponding group action on the boundary of a directed tree. Finally, we show

Søren Toxvaerd

The stability of galaxies is either explained by the existence of dark matter or caused by a modification of Newtonian acceleration (MOND). Here we show that the modification of the Newtonian dynamics can equally well be obtained by a modification of Newton's law of universal gravitational attraction (MOGA), by which an inverse square attraction from a dista

Fernando Henríquez, Jan S. Hesthaven

We introduce a method for the fast numerical approximation of linear, second-order parabolic partial differential equations (PDEs for short) with time-independent coefficients based on model order reduction techniques and the Laplace transform. We start by applying this transform to the evolution problem, thus yielding a time-independent boundary value probl

Younghan Lee, Yungi Cho, Woorim Han, Ho Bae

Federated Learning (FL) thrives in training a global model with numerous clients by only sharing the parameters of their local models trained with their private training datasets. Therefore, without revealing the private dataset, the clients can obtain a deep learning (DL) model with high performance. However, recent research proposed poisoning attacks that

Matthew Gadd, Daniele De Martini, Oliver Bartlett, Paul Murcutt

There is a growing academic interest as well as commercial exploitation of millimetre-wave scanning radar for autonomous vehicle localisation and scene understanding. Although several datasets to support this research area have been released, they are primarily focused on urban or semi-urban environments. Nevertheless, rugged offroad deployments are importan

Jutta Toscano

The addition of individual quanta of rotational excitation to a molecule has been shown to markedly change its reactivity by significantly modifying the intermolecular interactions. So far, it has only been possible to observe these rotational effects in a very limited number of systems due to lack of rotational selectivity in chemical reaction experiments.

Nilson C. Bernardes, Blas M. Caraballo, Udayan B. Darji, Vinícius V. Fávaro

We introduce and study the notions of (generalized) hyperbolicity, topological stability and (uniform) topological expansivity for operators on locally convex spaces. We prove that every generalized hyperbolic operator on a locally convex space has the finite shadowing property. Contrary to what happens in the Banach space setting, hyperbolic operators on Fr

Alberto Espuny Díaz, António Girão, Bertille Granet, Gal Kronenberg

A longstanding conjecture of Seymour states that in every oriented graph there is a vertex whose second outneighbourhood is at least as large as its outneighbourhood. In this short note we show that, for any fixed $p\in[0,1/2)$, a.a.s. every orientation of $G(n,p)$ satisfies Seymour's conjecture (as well as a related conjecture of Sullivan). This improves on

Belle II Collaboration, I. Adachi, K. Adamczyk, L. Aggarwal

We report on a search for a resonance $X$ decaying to a pair of muons in $e^{+}e^{-}\rightarrow \mu^+ \mu^- X$ events in the 0.212-9.000 GeV/$c^{2}$ mass range, using 178 fb$^{-1}$ of data collected by the BelleII experiment at the SuperKEKB collider at a center of mass energy of 10.58 GeV. The analysis probes two different models of $X$ beyond the standard

Benjamin R. Baer, Robert L. Strawderman

A theory of martingales for censoring is developed. The Doob-Meyer martingale is shown to be inadequate in general, and a repaired martingale is proposed with a non-predictable centering term. Associated martingale transforms, variation processes, and covariation processes are developed based on a measure of half-predictability that generalizes predictabilit

Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu

Recently, there has been a growing trend of utilizing Large Language Model (LLM) to evaluate the quality of other LLMs. Many studies have fine-tuned judge models based on open-source LLMs for evaluation. While the fine-tuned judge models are claimed to achieve comparable evaluation capability with GPT-4, in this work, we conduct an empirical study of LLM-as-

Riccardo Rizzo, Valentina Onesto, Stefania Forciniti, Anil Chandra

The detection of extracellular pH at single cell resolution is challenging and requires advanced sensibility. Sensing pH at a high spatial and temporal resolution might provide crucial information in understanding the role of pH and its fluctuations in a wide range of physio-pathological cellular processes, including cancer. Here, a method to embed silica-ba

Nikolay Pogodaev, Francesco Rossi

We discuss stabilization around trajectories of the continuity equation with nonlocal vector fields, where the control is localized, i.e., it acts on a fixed subset of the configuration space. We first show that the correct definition of stabilization is the following: given an initial error of order $\varepsilon$, measured in Wasserstein distance, one can i

Daniel Zelazo, Shin-ichi Tanigawa, Bernd Schulze

This work considers the distance constrained formation control problem with an additional constraint requiring that the formation exhibits a specified spatial symmetry. We employ recent results from the theory of symmetry-forced rigidity to construct an appropriate potential function that leads to a gradient dynamical system driving the agents to the desired

Haoning Wang, Liping Zhang

Recently, tensor time-series forecasting has gained increasing attention, whose core requirement is how to perform dimensionality reduction. In this paper, we establish a least square optimization model by combining tensor singular value decomposition (t-SVD) with autoregression (AR) to forecast third-order tensor time-series, which has great benefit in comp

Jonas Kusch

Due to its reduced memory and computational demands, dynamical low-rank approximation (DLRA) has sparked significant interest in multiple research communities. A central challenge in DLRA is the development of time integrators that are robust to the curvature of the manifold of low-rank matrices. Recently, a parallel robust time integrator that permits dynam

Mrinmay Sen, A. K. Qin, Gayathri C, Raghu Kishore N

This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the Hessian matrix for finding Newton's gradient update in large-scale stochastic optimization of machine learning models. It can be viewed as a variant of natural gradient desc

Alexander Spiridonov, Fabio Buehler, Moriz Berclaz, Valerio Schelbert

We present SpaceHopper, a three-legged, small-scale robot designed for future mobile exploration of asteroids and moons. The robot weighs 5.2kg and has a body size of 245mm while using space-qualifiable components. Furthermore, SpaceHopper's design and controls make it well-adapted for investigating dynamic locomotion modes with extended flight-phases. Inste

Serena Dipierro, Lyle Noakes, Enrico Valdinoci

As is well-known, numerical experiments show that Napoleon's Theorem for planar triangles does not extend to a similar statement for triangles on the unit sphere $S^2$. Spherical triangles for which an extension of Napoleon's Theorem holds are called ``Napoleonic'', and until now the only known examples have been equilateral. In this paper we determine all N

Sema Caliskan, Jon Grumer, Anish M. Amarsi

The lack of reliable atomic data can be a severe limitation in astrophysical modelling, in particular of events such as kilonovae that require information on all neutron-capture elements across a wide range of ionization stages. Notably, the presence of non-orthonormalities between electron orbitals representing configurations that are close in energy can in

Wei Sun, Zhiyang Chen, Linze Li, Chen Shen

Low-noise microwave oscillators are cornerstones for wireless communication, radar and clocks. Optical frequency combs have enabled photonic microwaves with unrivalled noise performance and bandwidth. Emerging interest is to generate microwaves using chip-based frequency combs, namely microcombs. Here, we demonstrate the first, fully integrated, microcomb-ba

Weijie Li, Litong Gong, Yiran Zhu, Fanda Fan

Image-to-video (I2V) generation tasks always suffer from keeping high fidelity in the open domains. Traditional image animation techniques primarily focus on specific domains such as faces or human poses, making them difficult to generalize to open domains. Several recent I2V frameworks based on diffusion models can generate dynamic content for open domain i

Shahrzad Sadat Mirdamad, Doost Ali Mojdeh

An injective coloring of a given graph G = (V, E) is a vertex coloring of G such that any two vertices with common neighbor receive distinct colors. An e-injective coloring of a graph G is a vertex coloring of G such that any two vertices with common edge neighbor receive distinct colors; in the other words, if u and v are the end of a path P4 in a graph G,

Zixuan Li, Lizi Liao, Yunshan Ma, Tat-Seng Chua

Session data has been widely used for understanding user's behavior in e-commerce. Researchers are trying to leverage session data for different tasks, such as purchase intention prediction, remaining length prediction, recommendation, etc., as it provides context clues about the user's dynamic interests. However, online shopping session data is semi-structu

Giuliana Grasso, Francesco Colella, Stefania Forciniti, Valentina Onesto

The tumor microenvironment (TME) features distinct hallmarks, including acidosis, hypoxia, reactive oxygen species (ROS) generation, and altered ion fluxes, which are crucial targets for early cancer biomarker detection, tumor diagnosis, and therapeutic strategies. A variety of imaging and sensing techniques have been developed and employed in both research

Ignacio Gómez-Casares, Brais González-Rodríguez, Julio González-Díaz, Pablo Rodríguez-Fernández

Domain reduction techniques are at the core of any global optimization solver for NLP or MINLP problems. In this paper, we delve into several of these techniques and assess the impact they may have in the performance of an RLT-based algorithm for polynomial optimization problems. These techniques include i) the use of (nonlinear) conic relaxations for optima

Olivier Maher, Roy Bernini, Nele Harnack, Bernd Gotsmann

With remarkable electrical and optical switching properties induced at low power and near room temperature (68C), vanadium dioxide (VO2) has sparked rising interest in unconventional computing among the phase-change materials research community. The scalability and the potential to compute beyond the von Neumann model make VO2 especially appealing for implem

C. Coelho, M. Jing, M. Fernanda P. Costa, L. L. Ferrás

Hydropower plants play a pivotal role in advancing clean and sustainable energy production, contributing significantly to the global transition towards renewable energy sources. However, hydropower plants are currently perceived both positively as sources of renewable energy and negatively as disruptors of ecosystems. In this work, we highlight the overlooke

Buda Bajić, Johannes A. J. Huber, Benedikt Neyses, Linus Olofsson

In the wood industry, logs are commonly quality screened by discrete X-ray scans on a moving conveyor belt from a few source positions. Typically, the measurements are obtained in a single two-dimensional (2D) plane (a "slice") by a sequential scanning geometry. The data from each slice alone does not carry sufficient information for a three-dimensional tomo

Pablo Tuero, César González-Ruano, Yuan Lu, Coriolan Tiusan

Over the past decade, it has been proposed theoretically and confirmed experimentally that long-range spin triplet (LRT) superconductivity can be generated in ferromagnet-superconductor hybrids either by the presence of spin textures (ST-LRT) or thanks to spin-orbit coupling (SOC-LRT). Nevertheless, there has been no theoretical or experimental investigation