March 2024 arXiv papers — page 117

Juan Carlos Idrobo, Ján Rusz, Gopal Datt, Daegeun Jo

Conversion of charge to orbital angular momentum through the orbital Hall effect (OHE) holds transformative potential for the development of orbital-based electronics, however, it is challenging to directly observe the electrically generated orbital accumulation. Here, we detect the OHE by directly quantifying the orbital accumulation along the edges of a ti

C. Amsler, H. Breuker, M. Bumbar, S. Chesnevskaya

The Antiproton Decelerator (AD) at CERN provides antiproton bunches with a kinetic energy of 5.3 MeV. The Extra-Low ENergy Antiproton ring at CERN, commissioned at the AD in 2018, now supplies a bunch of electron-cooled antiprotons at a fixed energy of 100 keV. The MUSASHI antiproton trap was upgraded by replacing the radio-frequency quadrupole decelerator w

Antonio Briola, Silvia Bartolucci, Tomaso Aste

We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release `LOBFrame', an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art

Frédéric Déglise, Jean Fasel

In this article, we produce Grothendieck-Riemann-Roch formulas for cohomology theories that are not oriented in the classical sense. We then specialize to the case of cohomology theories that admit a so-called symplectic orientation and show how to compute the relevant Todd classes in that situation. At the end of the article, we illustrate our methods on th

Johannes Knörr, Martin Bichler, Teodora Dobos

The European electricity market is based on large pricing zones with a uniform day-ahead price. The energy transition leads to changes in supply and demand and increasing redispatch costs. In an attempt to ensure efficient market clearing and congestion management, the EU Commission has mandated the Bidding Zone Review (BZR) to reevaluate the configuration o

Ivan Di Liberti, Gabriele Lobbia

Inspired by the theory of classifying topoi for geometric theories, we define rounded sketches and logoi and provide the notion of classifying logos for a rounded sketch. Rounded sketches can be used to axiomatise all the known fragments of infinitary first order logic in $\mathbf{L}_{\infty,\infty}$, in a spectrum ranging from weaker than finitary algebraic

Keshav Ramani, Daniel Borrajo

Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographical scope it refers to. The task of matching addresses happens everyday and is present in various fields like mail redirection, entity resolution, etc. Our work defines, and formalizes a framework to generate matchi

Fugui Ding, Guixiang Hong, Xumin Wang

In this paper, we explore Fourier analysis for noncommutative $L_p$ space-valued functions on $G$, where $G$ is a totally disconnected non-abelian compact group. By additionally assuming that the value of these functions remains invariant within each conjugacy class, we establish a noncommutative maximal inequality for Fej\'er means utilizing the associated

Fadillah Maani, Anees Ur Rehman Hashmi, Mariam Aljuboory, Numan Saeed

Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount importance in diagnosing, monitoring, and treating highly fatal brain tumors. The BraTS challenge serves as a platform for researchers to tackle this issue by participating in open

Dietrich Häfner, Christiane Klein

In a recent paper by G\'erard, H\"afner, and Wrochna, the Unruh state for massless fermions on a Kerr spacetime was constructed and the authors showed its Hadmard property in the case of very slowly rotating black holes $\vert a\vert\ll M$. In this note, we extend this result to the full non extreme case $\vert a\vert<M$.

Salwa Alamir, Armineh Nourbakhsh, Cecilia Tilli, Sameena Shah

Research into COVID-19 has been rapidly evolving since the onset of the pandemic. This occasionally results in contradictory recommendations by credible sources of scientific opinion, public health authorities, and medical professionals. In this study, we examine whether this has resulted in a lack of trust in scientific opinion, by examining the belief patt

Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza

Active learning (AL) techniques reduce labeling costs for training neural machine translation (NMT) models by selecting smaller representative subsets from unlabeled data for annotation. Diversity sampling techniques select heterogeneous instances, while uncertainty sampling methods select instances with the highest model uncertainty. Both approaches have li

François De Saint Moulin, Guillaume Thiran, Christophe Craeye, Luc Vandendorpe

Radar targets are traditionally modelled as point target reflectors, even in the near-field region. Yet, for radar systems operating at high carrier frequencies and small distances, traditional radar propagation models do not accurately model the scatterer responses. In this paper, a novel electromagnetic-based model is thus developed for the multistatic rad

Hong Liu, Haosen Yang, Paul J. van Diest, Josien P. W. Pluim

The Segment Anything Model (SAM) marks a significant advancement in segmentation models, offering robust zero-shot abilities and dynamic prompting. However, existing medical SAMs are not suitable for the multi-scale nature of whole-slide images (WSIs), restricting their effectiveness. To resolve this drawback, we present WSI-SAM, enhancing SAM with precise o

Maximilian Neidhardt, Robin Mieling, Sarah Latus, Martin Fischer

Robot-assisted surgery has advantages compared to conventional laparoscopic procedures, e.g., precise movement of the surgical instruments, improved dexterity, and high-resolution visualization of the surgical field. However, mechanical tissue properties may provide additional information, e.g., on the location of lesions or vessels. While elastographic imag

Yuefei Liu, Ivan P. Miranda, Lee Johnson, Anders Bergman

The Landau-Lifshitz-Gilbert (LLG) and Landau-Lifshitz (LL) equations play an essential role for describing the dynamics of magnetization in solids. While a quantum analog of the LL dynamics has been proposed in [Phys. Rev. Lett. 110, 147201 (2013)], the corresponding quantum version of LLG remains unknown. Here, we propose such a quantum LLG equation that in

Fatemeh Tahmasbi, Aakarsha Chug, Barry Bradlyn, Jeremy Blackburn

The increasing frequency of mass shootings in the United States has, unfortunately, become a norm. While the issue of gun control in the US involves complex legal concerns, there are also societal issues at play. One such social issue is so-called "gun culture," i.e., a general set of beliefs and actions related to gun ownership. However relatively little is

Shuting Yang, Bing Qi, Mingzi Sun, Wenjing Dai

Upconversion persistent luminescence (UCPL) phosphors that can be directly charged by near-infrared (NIR) light have gained considerable attention due to their promising applications ranging from photonics to biomedicine. However, current lanthanide-based UCPL phosphors show small absorption cross-sections and low upconversion charging efficiency. The develo

Masahito Hayashi

The recent paper (IEEE Trans. IT 69, 1680) introduced an analytical method for calculating the channel capacity without the need for iteration. This method has certain limitations that restrict its applicability. Furthermore, the paper does not provide an explanation as to why the channel capacity can be solved analytically in this particular case. In order

Davide Zucco

The primary objective of this paper is to establish the Ahlfors regularity of minimizers of set functions that satisfy a suitable maxitive condition on disjoint unions of sets. Our analysis focuses on minimizers within continua of the plane with finite one-dimensional Hausdorff measure. Through quantitative estimates, we prove that the length of a minimizer

Tomo Higashihara, Ryotaro Asama, Ryoya Nakamura, Mori Watanabe

Rare-earth tritellurides are van-der-Waals antiferromagnets which have been attracting attention as materials not only with high mobility, but also with various states such as superconductivity under high pressure, incommensurate charge-density-wave (CDW) phase, and multiple antiferromagnetic phases. In this work, we performed longitudinal resistivity and Ha

Imanol Echeverria, Maialen Murua, Roberto Santana

Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions. However, DRL approaches often fail to fully harness the strengths of existing techniques such as exact methods or constraint programming (CP), which can excel at fin

Chad Giusti, Giuliamaria Menara

In this paper we explore the connection between the ranks of the magnitude homology groups of a graph and the structure of its subgraphs. To this end, we introduce variants of magnitude homology called eulerian magnitude homology and discriminant magnitude homology. Leveraging the combinatorics of the differential in magnitude homology, we illustrate a close

Václav Klika, Eamonn A. Gaffney, Philip K. Maini

This study investigates transient wave dynamics in Turing pattern formation, focusing on waves emerging from localised disturbances. While the traditional focus of diffusion-driven instability has primarily centred on stationary solutions, considerable attention has also been directed towards understanding spatio-temporal behaviours, particularly the propaga

S. M. Iftiquar, S. N. Riaz, S. Mahapatra

Hydrogenated amorphous silicon alloy films are generally deposited by radio frequency plasma enhanced chemical vapor deposition (RF PECVD) technique on various types of substrates. Generally it is assumed that film quality remains unchanged when deposited on textured or non-textured substrates. Here we analyzed the difference in growth of thin film silicon l

Huijie Zhang, Anja Lippert, Ronny Leonhardt, Tobias Tolle

Preventing fluid penetration poses a challenging reliability concern in the context of power electronics, which is usually caused by unforeseen microfractures along the sealing joints. A better and more reliable product design heavily depends on the understanding of the dynamic wetting processes happening inside these complex microfractures, i.e. microchanne

Kaarel August Kurik

We prove that for any $\ell_\infty$-sum $Z = \bigoplus_{i \in [n]} X_i$ of finitely many strictly convex Banach spaces $(X_i)_{i \in [n]}$, an extremeness preserving 1-Lipschitz bijection $f\colon B_Z \to B_Z$ is an isometry, by constraining the componentwise behavior of the inverse $g=f^{-1}$ with a theorem admitting a graph-theoretic interpretation. We als

Ye He, Xingchen Li, Zijun Xu, Ming Qi

A high precision beam monitor system based on silicon carbide PIN sensor is designed for China Spallation Neutron Source 1.6 GeV proton beam to monitor the proton beam fluence.The concept design of the beam monitor system is finished together with front-end electronics with silicon carbide PIN sensors, readout system and mechanical system.Several tests are p

Tim Hempel, Sarah A. M. Loos

Data-based inference of directed interactions in complex dynamical systems is a problem common to many disciplines of science. In this work, we study networks of spatially separate dynamical entities, which could represent physical systems that interact with each other by reciprocal or nonreciprocal, instantaneous or time-delayed interactions. We present a s

Driss Bennis, Ayoub Bouziri

Let $R$ be a commutative ring, and let $S$ be a multiplicative subset of $R$. In this paper, we investigate the notion of $S$-cotorsion modules. An $R$-module $C$ is called $S$-cotorsion if $\text{Ext}^{1}_{R}(F,C) = 0$ for every $S$-flat $R$-module $F$. Among other results, we establish that the pair $(S\mathcal{F}, S\mathcal{C})$, where $S\mathcal{F}$ deno

Jure Kokalj

The bad metallic phase with resistivity above the Mott-Ioffe-Regel limit, which appears also in cuprate superconductors, was recently understood by cold atom and computer simulations of the Hubbard model via charge susceptibility and charge diffusion constant. However, since reliable simulations can be typically done only at temperatures above the experiment

Muhammed Toprak, Matthias Rieckmann, Florian Kummer

In this work, a cell agglomeration strategy for the cut cells arising in the extended discontinuous Galerkin (XDG) method is presented. Cut cells are a fundamental aspect of unfitted mesh approaches where complex geometries or interfaces separating sub-domains are embedded into Cartesian background grids to facilitate the mesh generation process. In such met

Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi, Santosh Sanjeev

Large-scale generative models have demonstrated impressive capabilities in producing visually compelling images, with increasing applications in medical imaging. However, they continue to grapple with hallucination challenges and the generation of anatomically inaccurate outputs. These limitations are mainly due to the reliance on textual inputs and lack of

Ken Brown, Paula A. A. B. Carvalho, Jerzy Matczuk

This paper is a continuation of a project to determine which skew polynomial algebras $S = R[\theta; \alpha]$ satisfy property $(\diamond)$, namely that the injective hull of every simple $S$-module is locally artinian, where $k$ is a field, $R$ is a commutative noetherian $k$-algebra, and $\alpha$ is a $k$-algebra automorphism of $R$. Earlier work (which we

Marco Cherubini, Lorenzo Monacelli, Bingjia Yang, Roberto Car

We investigate the structural and thermodynamic properties of high-pressure ice by incorporating quantum anharmonicity at a non-perturbative level. Quantum fluctuations reduce the critical pressure of the phase transition between phase VIII (with asymmetric H-bonds) and phase X (with symmetric H-bonds) by 65 GPa from its classical value of 116 GPa at 0K. Mor

Yogesh C. Joshi, Deepak, Sagar Malhotra

We study the metallicity distribution and evolution in the Galactic disk based on the largest sample of open star clusters in the Galaxy. From the catalogue of 1879 open clusters in the range of Galactocentric distance (R_GC) from 4 to 20 kpc, we investigate the variation of metallicity in the Galactic disk as functions of R_GC, vertical distance (Z), and ag

Donglin Di, Jiahui Yang, Chaofan Luo, Zhou Xue

Text-to-3D generation represents an exciting field that has seen rapid advancements, facilitating the transformation of textual descriptions into detailed 3D models. However, current progress often neglects the intricate high-order correlation of geometry and texture within 3D objects, leading to challenges such as over-smoothness, over-saturation and the Ja

Suvendu Ghosh, Chuanchang Zeng, A. Taraphder, Jian-Xin Zhu

Photoinduced effects are now reckoned to be important tools to reveal a rich gamut of entrancing physics in topological materials, which are normally inaccessible to conventional probes. Here we investigate one of these intriguing effects, namely, optical pseudospin injection (OPI) beyond ordinary Weyl semimetals (WSMs), specifically in multi-WSMs (mWSMs) an

Andrzej Herdegen

The infrared problems of quantum electrodynamics, in contrast to ultraviolet difficulties which are of technical nature, are related to fundamental, conceptual physical questions, such as: what is a charged particle, is the particle interpretation of the electromagnetic field complete, does a vacuum state exist, or what is the quantum status of long range de

Zihan Chu

Adverse weather conditions including haze, snow and rain lead to decline in image qualities, which often causes a decline in performance for deep-learning based detection networks. Most existing approaches attempts to rectify hazy images before performing object detection, which increases the complexity of the network and may result in the loss in latent inf

Alexander Stevens, Chun Ouyang, Johannes De Smedt, Catarina Moreira

In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics. Nevertheless, the inherent opacity of these algorithms poses a significant challenge for human decision-makers, hindering their ability to understand the reasoning behind the predictions. This growing concern has sp

Ramón González Rodríguez

In this paper we introduce the notion of exact factorization of a quasigroupoid and the notion of matched pair of quasigroupoids with common base. We prove that if $({\sf A}, {\sf H})$ is a matched pair of quasigroupoids it is posible to construct a new quasigroupoid ${\sf A}\bowtie {\sf H}$ called the double cross product of ${\sf A}$ and ${\sf H}$. Also, w

Zetong Yang, Zhiding Yu, Chris Choy, Renhao Wang

Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception, the annotation of 3d bounding relies heavily on LiDAR for accurate depth information. As such, the distance of annotation is often limited due to the sparsity of LiDAR points on distant objects, which hampers the capability of existing detector

Anu Sreedevi, Bibhuti Kumar Jha, Bidya Binay Karak, Dipankar Banerjee

One of the intriguing mechanisms of the Sun is the formation of the bipolar magnetic regions (BMRs) in the solar convection zone which are observed as regions of concentrated magnetic fields of opposite polarity on photosphere. These BMRs are tilted with respect to the equatorial line, which statistically increases with latitude. The thin flux tube model, em

Ivo Pascal de Jong, Lüke Luna van den Wittenboer, Matias Valdenegro-Toro, Andreea Ioana Sburlea

Public Motor Imagery-based brain-computer interface (BCI) datasets are being used to develop increasingly good classifiers. However, they usually follow discrete paradigms where participants perform Motor Imagery at regularly timed intervals. It is often unclear what changes may happen in the EEG patterns when users attempt to perform a control task with suc

Prithviraj Manivannan, Ivo Pascal de Jong, Matias Valdenegro-Toro, Andreea Ioana Sburlea

Uncertainty Quantification aims to determine when the prediction from a Machine Learning model is likely to be wrong. Computer Vision research has explored methods for determining epistemic uncertainty (also known as model uncertainty), which should correspond with generalisation error. These methods theoretically allow to predict misclassifications due to i

Chengshu Li, Ruohan Zhang, Josiah Wong, Cem Gokmen

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in 50 scenes (houses, gardens, restaurants, offices, etc.) wit

Angelo Ziletti, Leonardo D'Ambrosi

Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. Querying these databases to answer epidemiological questions is challenging due to the intricacy of medical terminology and the need for complex SQL queries. Here, we introduce an end-to-end methodology that combi

Xiang Dong, Yu-Chang Sun, Chu-Cheng Pan, Ao-Yan Cheng

This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging the JAX automatic differentiation library and the jinja2 template engine. The resulting code is constructed using the high-performance API of JAX, and includes support for the New

Inge S. Helland

A general theory based upon 7 postulates is introduced. The basical notions are theoretical variables that are associated with an observer or with a group of communicating observers. These variables may be accessible or inaccessible. From these postulates, the ordinary formalism of quantum theory is derived. The mathematical derivations are not given in this

Wenyong Han, Tao Zhu Member, Liming Chen, Huansheng Ning

The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the Channel Dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA)

Xiang Peng, Zhijin Qin, Xiaoming Tao, Jianhua Lu

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in image, particularly those arising from adversarial perturbations.

Ralf Aurich, Frank Steiner

The question of the global topology of the Universe (cosmic topology) is still open. In the $\Lambda$CDM concordance model it is assumed that the space of the Universe possesses the trivial topology of $\mathbb{R}^3$ and thus that the Universe has an infinite volume. As an alternative, we study in this paper one of the simplest non-trivial topologies given b

Xuan Fan, Ya Xiao, Yongjian Gu

Universal contextuality is the leading notion of non-classicality even for single systems, showing its advantage as a more general quantum correlation than Bell non-locality, as well as preparation contextuality. However, a loophole-free experimental demonstration of universal contextuality at least requires that both operational inequivalence and compatibil

Roy Weiss, Daniel Ayzenshteyn, Guy Amit, Yisroel Mirsky

AI assistants are becoming an integral part of society, used for asking advice or help in personal and confidential issues. In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel. We found that many vendors, including OpenAI and Microsoft, have this side-channe

Matteo Esposito, Valentina Falaschi, Davide Falessi

Context: Static Application Security Testing Tools (SASTTs) identify software vulnerabilities to support the security and reliability of software applications. Interestingly, several studies have suggested that alternative solutions may be more effective than SASTTs due to their tendency to generate false alarms, commonly referred to as low Precision. Aim: W

Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin

Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadcasting real information to combat rumors are either with high cost or disturbing users. In this paper, we intro

Hsien-Lee Tseng, Anastasija Nikiforova

Today, data has an unprecedented value as it forms the basis for data-driven decision-making, including serving as an input for AI models, where the latter is highly dependent on the availability of the data. However, availability of data in an open data format creates a little added value, where the value of these data, i.e., their relevance to the real nee

Andreas Besginow, Jan David Hüwel, Thomas Pawellek, Christian Beecks

Model selection aims to find the best model in terms of accuracy, interpretability or simplicity, preferably all at once. In this work, we focus on evaluating model performance of Gaussian process models, i.e. finding a metric that provides the best trade-off between all those criteria. While previous work considers metrics like the likelihood, AIC or dynami

Yiping Liu, Bo Yan, Bo Zhao, Hongyi Su

$k$-core is a subgraph where every node has at least $k$ neighbors within the subgraph. The $k$-core subgraphs has been employed in large platforms like Network Repository to comprehend the underlying structures and dynamics of the network. Existing studies have primarily focused on finding $k$-core groups without considering their size, despite the relevanc

Paul Manns, Marvin Severitt

We analyze integer linear programs which we obtain after discretizing two-dimensional subproblems arising from a trust-region algorithm for mixed integer optimal control problems with total variation regularization. We discuss NP-hardness of the discretized problems and the connection to graph-based problems. We show that the underlying polyhedron exhibits s

Jiajun Deng, Sha Zhang, Feras Dayoub, Wanli Ouyang

In this work, we present PoIFusion, a conceptually simple yet effective multi-modal 3D object detection framework to fuse the information of RGB images and LiDAR point clouds at the points of interest (PoIs). Different from the most accurate methods to date that transform multi-sensor data into a unified view or leverage the global attention mechanism to fac

Sheng Long, Weike Deng, Jiliang Jing

We present a study on the energy radiation rate and waveforms of the gravitational wave generated by coalescing spinless binary systems up to the third post-Minkowskian approximation in the effective one-body theory. To derive an analytical expansion of the null tetrad components of the gravitational perturbed Weyl tensor $\varPsi_{4}$ in the effective space

Thomas Henning, Inga Kamp, Matthias Samland, Aditya M. Arabhavi

The study of protoplanetary disks has become increasingly important with the Kepler satellite finding that exoplanets are ubiquitous around stars in our galaxy and the discovery of enormous diversity in planetary system architectures and planet properties. High-resolution near-IR and ALMA images show strong evidence for ongoing planet formation in young disk

Xiangrui Cai, Yang Wang, Sihan Xu, Hao Li

Enterprises and organizations are faced with potential threats from insider employees that may lead to serious consequences. Previous studies on insider threat detection (ITD) mainly focus on detecting abnormal users or abnormal time periods (e.g., a week or a day). However, a user may have hundreds of thousands of activities in the log, and even within a da

Edgar Pacheco

Older adults' growing use of the internet and related technologies, further accelerated by the COVID-19 pandemic, has prompted not only a critical examination of their behaviors and attitudes about online threats but also a greater understanding of the roles of specific characteristics within this population group. Based on survey data and using descriptive

Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, Alexander Panchenko

In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical semantic tasks. As the outcome of our experiments, we present TaxoLLaMA, the everything-in-one model, lightweight due to 4-bit quantization and LoRA. It achieves 11 SotA results, 4 top-

Naoki Hayashi, Yoshihide Sawada

Concept Bottleneck Model (CBM) is a methods for explaining neural networks. In CBM, concepts which correspond to reasons of outputs are inserted in the last intermediate layer as observed values. It is expected that we can interpret the relationship between the output and concept similar to linear regression. However, this interpretation requires observing a

An Luo, Yong-Guang Zheng, Wei-Yong Zhang, Ming-Gen He

We report on an experimental simulation of the spin-1 Heisenberg model with composite bosons in a one-dimensional chain based on the two-component Bose-Hubbard model. Exploiting our site-and spin-resolved quantum gas microscope, we observed faster superexchange dynamics of the spin-1 system compared to its spin-1/2 counterpart, which is attributed to the enh

Anton Galajinsky

Integrability of N=1 supersymmetric Ruijsenaars-Schneider three-body models based upon the potentials W(x)=2/x, W(x)=2/sin(x), and W(x)=2/sinh(x) is proven. The problem of constructing an algebraically resolvable set of Grassmann-odd constants of motion is reduced to finding a triplet of vectors such that all their scalar products can be expressed in terms o

Aranyak Chakravarty, Debjit Kundu, Mahesh V. Panchagnula, Alladi Mohan

The need to understand how infection spreads to the deep lung was acutely realized during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic. The challenge of modeling virus laden aerosol transport and deposition in the airways, coupled with mucus clearance, and infection kinetics, became evident. This perspective provides a consolidat

Naoto Fujisawa

In this paper, the congruence equations for caliber and m-caliber in various discriminants are proven. Additionally, We also obtained the lengths of the periods of several continued fractions as corollaries.

Yingcui Zhao

In 2016, Hou and Wang introduced the concept of multiple mappings based on iterated function system, which is an important branch of fractal theory. In this paper, we introduce the definitions of sensitivity, accessibility, and Kato's chaos of multiple mappings from a set-valued perspective. We show that multiple mappings and its continuous self-maps do not

Fabricio N. Molinari, Maria A. Mancuso, Emanuel Bilbao, Theo Rodriguez Campos

In this work an electron radiation detector microdevices were fabricated and characterized. The devices consisted of a conductive electrospun mat made of polycaprolactone loaded with multiwalled carbon nanotubes and fullerene C60 deposited onto gold interdigitated microelectrodes. They were capable of permanently increase their conductivity upon exposure to

Ariel Neufeld, Tuan Anh Nguyen

Numerical experiments indicate that deep learning algorithms overcome the curse of dimensionality when approximating solutions of semilinear PDEs. For certain linear PDEs and semilinear PDEs with gradient-independent nonlinearities this has also been proved mathematically, i.e., it has been shown that the number of parameters of the approximating DNN increas

Hyung-Il Kim, Kimin Yun, Jun-Seok Yun, Yuseok Bae

Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer vision community. Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks, achieved th

Ayoub Gouasmi, Scott Murman

Higher-fidelity entry simulations can be enabled by integrating finer thermo-chemistry models into compressible flow physics. One such class of models are State-to-State (StS) kinetics, which explicitly track species populations among quantum energy levels. StS models can represent thermo-chemical non-equilibrium effects that are hardly captured by standard

Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin

Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geograph

Ziran Zhu, Tongda Xu, Ling Li, Yan Wang

Generative adversial network (GAN) is a type of generative model that maps a high-dimensional noise to samples in target distribution. However, the dimension of noise required in GAN is not well understood. Previous approaches view GAN as a mapping from a continuous distribution to another continous distribution. In this paper, we propose to view GAN as a di

Yanfei Song, Bangzheng Pu, Peng Wang, Hongxu Jiang

Segment Anything Model (SAM) has garnered significant attention in segmentation tasks due to their zero-shot generalization ability. However, a broader application of SAMs to real-world practice has been restricted by their low inference speed and high computational memory demands, which mainly stem from the attention mechanism. Existing work concentrated on

Hongchen Luo, Kai Zhu, Wei Zhai, Yang Cao

Ego-to-exo video generation refers to generating the corresponding exocentric video according to the egocentric video, providing valuable applications in AR/VR and embodied AI. Benefiting from advancements in diffusion model techniques, notable progress has been achieved in video generation. However, existing methods build upon the spatiotemporal consistency

Paul Gavrikov, Jovita Lukasik, Steffen Jung, Robert Geirhos

Unlike traditional vision-only models, vision language models (VLMs) offer an intuitive way to access visual content through language prompting by combining a large language model (LLM) with a vision encoder. However, both the LLM and the vision encoder come with their own set of biases, cue preferences, and shortcuts, which have been rigorously studied in u

Yizhe Xiong, Hui Chen, Tianxiang Hao, Zijia Lin

Recently, the scale of transformers has grown rapidly, which introduces considerable challenges in terms of training overhead and inference efficiency in the scope of task adaptation. Existing works, namely Parameter-Efficient Fine-Tuning (PEFT) and model compression, have separately investigated the challenges. However, PEFT cannot guarantee the inference e

Jonathan Kress, Konrad Schöbel, Andreas Vollmer

We reconsider non-degenerate second order superintegrable systems in dimension two as geometric structures on conformal surfaces. This extends a formalism developed by the authors, initially introduced for (pseudo-)Riemannian manifolds of dimension three and higher. The governing equations of non-degenerate second order superintegrability in dimension two ar

Chen Liu, Shibo He, Haoyu Liu, Jiming Chen

Trajectory prediction is an essential component in autonomous driving, particularly for collision avoidance systems. Considering the inherent uncertainty of the task, numerous studies have utilized generative models to produce multiple plausible future trajectories for each agent. However, most of them suffer from restricted representation ability or unstabl

Yu Tang Chang, Shih Fang Chen

Deep learning (DL) models encompass millions or even billions of parameters and learn complex patterns from big data. However, not all data are initially stored in a suitable formation to effectively train a DL model, e.g., gas chromatography-mass spectrometry (GC-MS) spectra and DNA sequence. These datasets commonly contain many zero values, and the sparse

Jeongrak Son, Marek Gluza, Ryuji Takagi, Nelly H. Y. Ng

We introduce a quantum extension of dynamic programming, a fundamental computational method that efficiently solves recursive problems using memory. Our innovation lies in showing how to coherently generate recursion step unitaries by using memorized intermediate quantum states. Quantum dynamic programming achieves an exponential reduction in circuit depth f

Camila Correa, Matthieu Schaller, Joop Schaye, Sylvia Ploeckinger

Self-interacting dark matter (SIDM) has the potential to significantly influence galaxy formation in comparison to the cold, collisionless dark matter paradigm (CDM), resulting in observable effects. This study aims to elucidate this influence and to demonstrate that the stellar mass Tully-Fisher relation imposes robust constraints on the parameter space of

Carsten Hartmann, Philipp C. Böttcher, David Gross, Dirk Witthaut

Synchronization is essential for the operation of AC power systems: All generators in the power grid must rotate with fixed relative phases to enable a steady flow of electric power. Understanding the conditions for and the limitations of synchronization is of utmost practical importance. In this article, we propose a novel approach to compute and analyze th

Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelik, Vojtěch Forejt

We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive exploration of the state space, instead focussing on particularly relevant areas of the system, guided by heuristics. Our work

M. Mohammadi, M. Babai, M. H. F. Wilkinson

Due to advancements in digital cameras, it is easy to gather multiple images (or videos) from an object under different conditions. Therefore, image-set classification has attracted more attention, and different solutions were proposed to model them. A popular way to model image sets is subspaces, which form a manifold called the Grassmann manifold. In this

Adam A. Malik, Cecilia Krona, Soumi Kundu, Philip Gerlee

Patient-derived cells (PDC) mouse xenografts are increasingly important tools in glioblastoma (GBM) research, essential to investigate case-specific growth patterns and treatment responses. Despite the central role of xenograft models in the field, few good simulation models are available to probe the dynamics of tumor growth and to support therapy design. W

Junyi Xie, She Yang

We solve the dynamical Mordell-Lang conjecture for bounded-degree dynamical systems in positive characteristic. The answer in this case disproves the original version of the pDML conjecture.

Leila Tavakoli, Johanne R. Trippas, Hamed Zamani, Falk Scholer

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification

Namhoon Cho, Seokwon Lee, Hyo-Sang Shin

This study presents a synchronisation-oriented perspective towards adaptive control which views model-referenced adaptation as synchronisation between actual and virtual dynamic systems. In the context of adaptation, model reference adaptive control methods make the state response of the actual plant follow a reference model. In the context of synchronisatio

Dimitris Spathis, Aaqib Saeed, Ali Etemad, Sana Tonekaboni

This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.

Gentian Zavalani, Michael Hecht

We present a high-order surface quadrature (HOSQ) for accurately approximating regular surface integrals on closed surfaces. The initial step of our approach rests on exploiting square-squeezing--a homeomorphic bilinear square-simplex transformation, re-parametrizing any surface triangulation to a quadrilateral mesh. For each resulting quadrilateral domain w

Arnau Romero, Carmen Delgado, Lanfranco Zanzi, Raúl Suárez

Mission-critical operations, particularly in the context of Search-and-Rescue (SAR) and emergency response situations, demand optimal performance and efficiency from every component involved to maximize the success probability of such operations. In these settings, cellular-enabled collaborative robotic systems have emerged as invaluable assets, assisting fi

Byeongjun Park, Hyojun Go, Jin-Young Kim, Sangmin Woo

Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of multi-task learning, where each task corresponds to a denoising task at a specific noise level. While these efforts have focused on parameter isolation and task routing, they fall shor

Ittetsu Uchiyama, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii

We propose a new gradient method for holography, where a phase-only hologram is parameterized by not only the phase but also amplitude. The key idea of our approach is the formulation of a phase-only hologram using an auxiliary amplitude. We optimize the parameters using the so-called Wirtinger flow algorithm in the Cartesian domain, which is a gradient meth