April 2024 arXiv papers — page 181

Inder Kaur, Artie Prendergast-Smith

For a Calabi-Yau variety X, Oguiso gave a useful criterion for primitivity of a self-map of X in terms of the associated linear map on the Neron--Severi space of X. In this short note, we prove a variant of Oguiso's criterion and use it to verify primitivity of a certain birational automorphism of a Calabi--Yau threefold, to which Oguiso's original criterion

Hyunjong Ok, Taeho Kil, Sukmin Seo, Jaeho Lee

Recent advances in named entity recognition (NER) have pushed the boundary of the task to incorporate visual signals, leading to many variants, including multi-modal NER (MNER) or grounded MNER (GMNER). A key challenge to these tasks is that the model should be able to generalize to the entities unseen during the training, and should be able to handle the tr

Orestis Papageorgiou, Lasse Börtzler, Egor Ermolaev, Jyoti Kumari

Permissioned blockchains have been proposed for a variety of use cases that require decentralization yet address enterprise requirements that permissionless blockchains to date cannot satisfy -- particularly in terms of performance. However, popular permissioned blockchains still exhibit a relatively low maximum throughput in comparison to established centra

Antoine Soulas

We build a discrete model that simulates the ubiquitous competition between the free internal evolution of a two-level system and the decoherence induced by the interaction with its surrounding environment. It is aimed at being as universal as possible, so that no specific Hamiltonian is assumed. This leads to an analytic criterion, depending on the level of

Huaiqiang Wang, Yiliang Fan, Haijun Zhang

Isolated flat bands with significantly quenched kinetic energy of electrons could give rise to exotic strongly correlated states from electron-electron interactions. More intriguingly, the interplay between topology and flat bands can further lead to richer physical phenomena, which have attracted much interest. Here, taking advantage of the recently propose

Maksim Dzabraev, Alexander Kunitsyn, Andrei Ivaniuta

In this work, we present an unsupervised method for enhancing an image captioning model (in our case, BLIP2) using reinforcement learning and vision-language models like CLIP and BLIP2-ITM as reward models. The RL-tuned model is able to generate longer and more comprehensive descriptions. Our model reaches impressive 0.90 R@1 CLIP Recall score on MS-COCO Car

Quentin Bourgeais, Eric Sanlaville, Rodolphe Charrier, Ludovic Seifert

In this study, a temporal graph model is designed to model the behavior of collective sports teams based on the networks of player interactions. The main motivation for the model is to integrate the temporal dimension into the analysis of players' passing networks in order to gain deeper insights into the dynamics of system behavior, particularly how a syste

Luca Colagrande, Luca Benini

Heterogeneous multi-core architectures combine a few "host" cores, optimized for single-thread performance, with many small energy-efficient "accelerator" cores for data-parallel processing, on a single chip. Offloading a computation to the many-core acceleration fabric introduces a communication and synchronization cost which reduces the speedup attainable

Ying Zhou, Ben He, Le Sun

With the development of large language models (LLMs), detecting whether text is generated by a machine becomes increasingly challenging in the face of malicious use cases like the spread of false information, protection of intellectual property, and prevention of academic plagiarism. While well-trained text detectors have demonstrated promising performance o

Michele Coti Zelati, Helge Dietert, David Gérard-Varet

This paper is devoted to the nonlinear analysis of a kinetic model introduced by Saintillan and Shelley to describe suspensions of active rodlike particles in viscous flows. We investigate the stability of the constant state $\Psi(t,x,p) = \frac{1}{4\pi}$ corresponding to a distribution of particles that is homogeneous in space (variable $x \in \mathbb{T}^3$

Renaud Leplaideur, Jairo K Mengue

We prove that when the Aubry set for a Lipschitz continuous potential is a subshift of finite type, then the pressure function converges exponentially fast to its asymptote as the temperature goes to 0. The speed of convergence turns out to be the unique eigenvalue for the matrix whose entries are the costs between the different irreducible pieces of the Aub

Rui-Jie Ji, Xiang-Cun Meng, Zheng-Wei Liu

The origin of intermediate helium (He)-rich hot subdwarfs are still unclear. Previous studies have suggested that some surviving Type Ia supernovae (SNe Ia) companions from the white dwarf~+~main-sequence (WD+MS) channel may contribute to the intermediate He-rich hot subdwarfs. However, previous studies ignored the impact of atomic diffusion on the post-expl

Aurélien Bénel, Joris Falip, Philippe Lacour

The article aims at identifying what, from a structural point of view, AI based automatic translators cannot fully capture. It focuses on the machine's mistakes, in order to try to explain its causes. The biblical story of Ca\"in and Abel has been chosen because of its rich interpretive and critical tradition, but also because of its semantic difficulty. The

Om Prakash, Shikha Patel, Habibul Islam

Let $\mathbb{F}_q$ be a finite field of $q=p^m$ elements where $p$ is a prime and $m$ is a positive integer. This paper considers $(\gamma,\Delta)$-cyclic codes over a class of finite non-chain commutative rings $\mathscr{R}_{q,s}=\mathbb{F}_q[v_1,v_2,\dots,v_s]/\langle v_i-v_i^2,v_iv_j=v_jv_i=0\rangle$ where $\gamma$ is an automorphism of $\mathscr{R}_{q,s}

Trapti Shrivastava, Harshal Chaudhari, Vrijendra Singh

Machine learning (ML) has advanced quickly, particularly throughout the area of health care. The diagnosis of neurodevelopment problems using ML is a very important area of healthcare. Autism spectrum disorder (ASD) is one of the developmental disorders that is growing the fastest globally. The clinical screening tests used to identify autistic symptoms are

Francesca Lucchetti, Arjun Guha

Large Language Models (LLMs) are widely used by software engineers for programming tasks. However, research shows that LLMs often lack a deep understanding of program semantics. Even minor changes to syntax, such as renaming variables, can significantly degrade performance across various tasks. In this work, we examine the task of type prediction: given a pa

Joaquin Cavieres, Michael Karkulik

Using a deterministic framework allows us to estimate a function with the purpose of interpolating data in spatial statistics. Radial basis functions are commonly used for scattered data interpolation in a d-dimensional space, however, interpolation problems have to deal with dense matrices. For the case of smoothing thin plate splines, we propose an efficie

Jan H. Hoekstra, Chris Verhoek, Roland Tóth, Maarten Schoukens

Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. In particular, recent encoder-based methods for artificial neural networks state-space (ANN-SS) models have achieved state-of-the-art performance on various benchmarks, while offering consistency and computational efficiency. Inclusion

Ali Mousavi Mohammadi, Maxim Vochten, Erwin Aertbeliën, Joris De Schutter

In previous work on learning and controlling contact-rich tasks, the procedure for choosing a proper reference frame to express learned signals for the motion and the interaction wrench is often implicit, requires expert insight, or starts from proposed frame candidates. This article presents an automatic method to derive the optimal reference frame, referre

Théodore Serbource, Mireille Courtial, Marie-Noëlle de Noirfontaine, Sandrine Tusseau-Nenez

This study compares thirteen natural and industrial samples of supplementary or emerging supplementary cementitious materials (SCMs): slag, fly ashes, pozzolan, obsidian, silica fume, and recycled glass. These materials are used or are under consideration for decarbonization in cement plants. XRF, XRD and Raman microspectroscopy were used in order to achieve

A. Zotov

We consider 1+1 field generalization of the elliptic Calogero-Moser model. It is shown that the Lax connection satisfies the classical non-ultralocal $r$-matrix structure of Maillet type. Next, we consider 1+1 field analogue of the spin Calogero-Moser model and its multipole (or multispin) extension. Finally, we discuss the field analogue of the classical IR

Nan Yin, Mengzhu Wan, Li Shen, Hitesh Laxmichand Patel

Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ordinary differential equations (ODE).

Christian Günther, Benedikt Hofmeister, Clemens Hübler, Clemens Jonscher

We propose a multi-objective global pattern search algorithm for the task of locating and quantifying damage in flexible mechanical structures. This is achieved by identifying eigenfrequencies and eigenmodes from measurements and matching them against the results of a finite element simulation model, which leads to a nonsmooth nonlinear bi-objective paramete

Fabian Mockert, Christian M. Grams, Sebastian Lerch, Marisol Osman

Reliable forecasts of quasi-stationary, recurrent, and persistent large-scale atmospheric circulation patterns (weather regimes) are crucial for various socio-economic sectors. Despite steady progress, probabilistic weather regime predictions still exhibit biases in the exact timing and amplitude of weather regimes. This study thus aims at advancing probabil

Giovanni Cabass, Oliver H. E. Philcox, Mikhail M. Ivanov, Kazuyuki Akitsu

Massive particles leave imprints on primordial non-Gaussianity via couplings to the inflaton, even despite their exponential dilution during inflation: practically, the Universe acts as a Cosmological Collider. We present the first dedicated search for spin-zero particles using BOSS redshift-space galaxy power spectrum and bispectrum multipoles, as well as P

Luc Deleaval, Christoph Kriegler

This article is the continuation of the work [DK] where we had proved maximal estimates $$\left\|\sup_{t > 0} |m(tA)f| \right\|_{L^p(\Omega,Y)} \leq C \|f\|_{L^p(\Omega,Y)}$$ for sectorial operators $A$ acting on $L^p(\Omega,Y)$ ($Y$ being a UMD lattice) and admitting a H\"ormander functional calculus(a strengthening of the holomorphic $H^\infty$ calculus to

Cheng Gong, Haoshuai Zheng, Mengting Hu, Zheng Lin

Quantization is a promising method that reduces memory usage and computational intensity of Deep Neural Networks (DNNs), but it often leads to significant output error that hinder model deployment. In this paper, we propose Bias Compensation (BC) to minimize the output error, thus realizing ultra-low-precision quantization without model fine-tuning. Instead

Artur Xarles, Sergio Escalera, Thomas B. Moeslund, Albert Clapés

In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To

Jonathan Rohleder

We review a recent new approach to the study of critical points of Laplacian eigenfunctions. Its core novelty is a non-standard variational principle for the eigenvalues of the Laplacians with Neumann and Dirichlet boundary conditions on bounded, simply connected planar domains. This principle can be used to provide simple proofs of some previously known res

Tatiana Gaintseva, Martin Benning, Gregory Slabaugh

In this paper we propose a novel modification of Contrastive Language-Image Pre-Training (CLIP) guidance for the task of unsupervised backlit image enhancement. Our work builds on the state-of-the-art CLIP-LIT approach, which learns a prompt pair by constraining the text-image similarity between a prompt (negative/positive sample) and a corresponding image (

Julian Sitarek, Mario Pecimotika, Natalia Żywucka, Dorota Sobczyńska

Atmospheric Cherenkov telescopes rely on the Earth's atmosphere as part of the detector. The presence of clouds affects observations and can introduce biases if not corrected for. Correction methods typically require an atmospheric profile, that can be measured with external atmospheric monitoring devices. We present a novel method for measuring the atmosphe

Pietro Bonazzi, Mengqi Wang, Diego Martin Arroyo, Fabian Manhardt

Synthesizing realistic and diverse indoor 3D scene layouts in a controllable fashion opens up applications in simulated navigation and virtual reality. As concise and robust representations of a scene, scene graphs have proven to be well-suited as the semantic control on the generated layout. We present a variant of the conditional variational autoencoder (c

Michael Assad, Christopher Meiklejohn, Heather Miller, Stephan Krusche

In microservice applications, ensuring resilience during database or service disruptions constitutes a significant challenge. While several tools address resilience testing for service failures, there is a notable gap in tools specifically designed for resilience testing of database failures. To bridge this gap, we have developed an extension for fault injec

Saleem Alzoubi, Mahdi H. Miraz

Enhancing robot navigation efficiency is a crucial objective in modern robotics. Robots relying on external navigation systems are often susceptible to electromagnetic interference (EMI) and encounter environmental disturbances, resulting in orientation errors within their surroundings. Therefore, the study employed an internal navigation system to enhance r

Raphael Schoof, Giuseppe Fabian Castelli, Willy Dörfler

Amorphous silicon is a highly promising anode material for next-generation lithium-ion batteries. Large volume changes of the silicon particle have a critical effect on the surrounding solid-electrolyte interphase (SEI) due to repeated fracture and healing during cycling. Based on a thermodynamically consistent chemo-elasto-plastic continuum model we investi

Yanyan Dong, Vincent Y. F. Tan

We study the problem of adversarial combinatorial bandit with a switching cost $\lambda$ for a switch of each selected arm in each round, considering both the bandit feedback and semi-bandit feedback settings. In the oblivious adversarial case with $K$ base arms and time horizon $T$, we derive lower bounds for the minimax regret and design algorithms to appr

Yansong Peng, Hebei Li, Yueyi Zhang, Xiaoyan Sun

While recent Transformer-based approaches have shown impressive performances on event-based object detection tasks, their high computational costs still diminish the low power consumption advantage of event cameras. Image-based works attempt to reduce these costs by introducing sparse Transformers. However, they display inadequate sparsity and adaptability w

Christian Bey, Evgeniy Petrov, Ruslan Salimov

Let $X$ be a metric space. Recently in~[1] it was considered a new type of mappings $T\colon X\to X$ which can be characterized as mappings contracting perimeters of triangles. These mappings are defined by the condition based on the mapping of three points of the space instead of two, as it is adopted in many fixed-point theorems. In the present paper we co

Ali Mostafazadeh

We consider some basic problems associated with quantum mechanics of systems having a time-dependent Hilbert space. We provide a consistent treatment of these systems and address the possibility of describing them in terms of a time-independent Hilbert space. We show that in general the Hamiltonian operator does not represent an observable of the system even

Alessio Lapponi, Jorma Louko, Stefano Mancini

A communication protocol with non-zero quantum capacity is found when the two communicating parts are particle detector models in (3+1)-dimensional spacetime. In particular, as detectors, we consider two harmonic oscillators interacting with a scalar field, whose evolution is generalized for whatever background spacetime and whatever spacetime smearing of th

Zhijie Chen, Chang-Shou Lin

The Darboux-Treibich-Verdier (DTV) potential $\sum_{k=0}^{3}n_{k}(n_{k}+1)\wp(z+\tfrac{ \omega_{k}}{2};\tau)$ is well-known as doubly-periodic solutions of the stationary KdV hierarchy (Treibich-Verdier, Duke Math. J. {\bf 68} (1992), 217-236). In this paper, we study the generalized Lam\'{e} equation with the DTV potential \begin{equation*} y^{\prime \prime

Shahzeb Naeem, Ramzi Al-Sharawi, Muhammad Riyyan Khan, Usman Tariq

With the ever-growing power of generative artificial intelligence, deepfake and artificially generated (synthetic) media have continued to spread online, which creates various ethical and moral concerns regarding their usage. To tackle this, we thus present a novel exploration of the trends and patterns observed in real, deepfake and synthetic facial images.

Ziming Wang, Changwu Huang, Ke Tang, Xin Yao

Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focus on another dimension of fairness, i.e., procedural fairness. In this paper, we first define the procedural fairness of ML models by drawing from the established understanding of

Oliver Gould, Anna Kormu, David J. Weir

Nucleation rate computations are of broad importance in particle physics and cosmology. Perturbative calculations are often used to compute the nucleation rate $\Gamma$, but these are incomplete. We perform nonperturbative lattice simulations of nucleation in a scalar field theory with a tree-level barrier, computing a final result extrapolated to the thermo

Yuanming Shi, Li Zeng, Jingyang Zhu, Yong Zhou

The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these

Ji Hoon Yoon, Sang Hyuk Hong, Taek Jeong, Sin Hyuk Yim

We demonstrate an all-optical single beam pulsed atomic magnetometer assisted by laser mode-hopping in a distributed Bragg reflector (DBR) laser. We implement a temporal sequence of the laser current, with sinusoidal current modulation including the laser mode-hop current for synchronous optical pumping, and a following constant current for paramagnetic Fara

I. R. Gabdrakhmanov, N. A Gramotkov, A. V. Kotikov, O. V. Teryaev

We found good agreement between the experimental data obtained for the polarized Bjorken sum rule and the predictions of analytic QCD, as well as a strong difference between these data and the results obtained in the framework of perturbative QCD. To satisfy the limit of photoproduction and take into account Gerasimov-Drell-Hearn and Burkhardt-Cottingham sum

E. Javier Olucha, Bogoljub Terzin, Amritam Das, Roland Tóth

This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonl

Daria Sushnikova, Matteo Ravasi, David Keyes

We address the estimation of seismic wavefields by means of Multidimensional Deconvolution (MDD) for various redatuming applications. While offering more accuracy than conventional correlation-based redatuming methods, MDD faces challenges due to the ill-posed nature of the underlying inverse problem and the requirement to handle large, dense, complex-valued

Philipp Mondorf, Barbara Plank

Large language models (LLMs) have recently shown impressive performance on tasks involving reasoning, leading to a lively debate on whether these models possess reasoning capabilities similar to humans. However, despite these successes, the depth of LLMs' reasoning abilities remains uncertain. This uncertainty partly stems from the predominant focus on task

Pietro Tassan, Darius Urbonas, Bartos Chmielak, Jens Bolten

All-optical logic has the potential to overcome the operation speed barrier that has persisted in electronic circuits for two decades. However, the development of scalable architectures has been prevented so far by the lack of materials with sufficiently strong nonlinear interactions needed to realize compact and efficient ultrafast all-optical switches with

Carlos Plou, Ana C. Murillo, Ruben Martinez-Cantin

Efficiently tackling multiple tasks within complex environment, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions, such as reinforcement learning (RL). Model-based RL, by building a dynamic model of the robot, enables data reuse and transfer learning between tasks with the same ro

Bartosz Bieganowski, Robert Slepaczuk

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focu

P. Hadjisolomou, T. M. Jeong, P. Valenta, A. J. Macleod

The interaction of an ultra-intense laser with matter is an efficient source of high-energy particles, with efforts directed towards narrowing the divergence and simultaneously increasing the brightness. In this paper we report on emission of highly collimated, ultrabright, attosecond $\gamma$-photons and generation of dense electron-positron pairs via a tun

Phillip Semler, Martin Weiser

Generating simulated training data needed for constructing sufficiently accurate surrogate models to be used for efficient optimization or parameter identification can incur a huge computational effort in the offline phase. We consider a fully adaptive greedy approach to the computational design of experiments problem using gradient-enhanced Gaussian process

Kyuyoung Kim, Jongheon Jeong, Minyong An, Mohammad Ghavamzadeh

Fine-tuning text-to-image models with reward functions trained on human feedback data has proven effective for aligning model behavior with human intent. However, excessive optimization with such reward models, which serve as mere proxy objectives, can compromise the performance of fine-tuned models, a phenomenon known as reward overoptimization. To investig

Xu He, Qiaochu Huang, Zhensong Zhang, Zhiwei Lin

Co-speech gestures, if presented in the lively form of videos, can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons, resulting in the omission of appearance information, we focus on the direct generation of audio-driven co-speech gesture videos in this work. There are two main chall

Mohamed Amine Hamdi, Giovanni Pollo, Matteo Risso, Germain Haugou

RISC-V cores have gained a lot of popularity over the last few years. However, being quite a recent and novel technology, there is still a gap in the availability of comprehensive simulation frameworks for RISC-V that cover both the functional and extra-functional aspects. This gap hinders progress in the field, as fast yet accurate system-level simulation i

Mattia Opper, N. Siddharth

This paper presents two simple improvements to the Self-Structuring AutoEncoder (Self-StrAE). Firstly, we show that including reconstruction to the vocabulary as an auxiliary objective improves representation quality. Secondly, we demonstrate that increasing the number of independent channels leads to significant improvements in embedding quality, while simu

Anna Bobrikova, Alessandro Di Marco, Fabio La Monaca, Juri Poutanen

Weakly magnetized neutron stars (WMNS) are complicated sources with challenging phenomenology. For decades, they have been studied via spectrometry and timing. It has been established that the spectrum of WMNSs consists of several components traditionally associated with the accretion disk, the boundary or spreading layer, and the wind and their interactions

Tom Yaacov, Achiya Elyasaf, Gera Weiss

One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules that specify what the system may, must, and must not do. By that, each module can enforce a single system req

Sarah Lindau, Linnea Nilsson

An outtake from the findnings of a master thesis studying gender bias in course evaluations through the lense of machine learning and nlp. We use different methods to examine and explore the data and find differences in what students write about courses depending on gender of the examiner. Data from English and Swedish courses are evaluated and compared, in

Risto Luukkonen, Jonathan Burdge, Elaine Zosa, Aarne Talman

The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages. While including text in more than one language is an obvious way to acquire more pretraining data, multilinguality is often seen as a curse, and most model training efforts con

Shanshan Feng, Haoming Lyu, Caishun Chen, Yew-Soon Ong

Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment. Existing studies rely on building recommendation models from large-scale users' check-in data, which is task-specific and needs extensive computational resources. Recently, the pretrained large language models (LLMs) have achieved sig

Fajri Koto, Rahmad Mahendra, Nurul Aisyah, Timothy Baldwin

Although commonsense reasoning is greatly shaped by cultural and geographical factors, previous studies have predominantly centered on cultures grounded in the English language, potentially resulting in an Anglocentric bias. In this paper, we introduce IndoCulture, aimed at understanding the influence of geographical factors on language model reasoning abili

Sihan Bai

Noisy label learning aims to train deep neural networks using a large amount of samples with noisy labels, whose main challenge comes from how to deal with the inaccurate supervision caused by wrong labels. Existing works either take the label correction or sample selection paradigm to involve more samples with accurate labels into the training process. In t

Vanessa Schöne, Florian Paysan, Eric Breitbarth

Fatigue crack growth is usually a three-dimensional problem, but it is often simplified to two dimensions to reduce complexity. However, this study investigates the relationships between microscopic effects such as crack kinking, shear lips, and plasticity that are present in reality. Therefore, crack propagation tests were carried out on 2-mm-thick MT-160 s

N. Nardetto, V. Hocdé, P. Kervella, A. Gallenne

An inner companion has recently been discovered orbiting the prototype of classical Cepheids, delta Cep, whose orbital parameters are still not fully constrained. We collected new precise radial velocity measurements of delta Cep in 2019 using the HARPS-N spectrograph mounted at the Telescopio Nazionale Galileo. Using these radial velocity measurements, we a

Ahrar N. Hamad, Walter Zibusiso Ncube, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi

The increasing demand for wireless networks of higher capacity requires key-enabling technologies. Optical wireless communication (OWC) arises as a complementary technology to radio frequency (RF) systems that can support high aggregate data rates. However, OWC systems face some challenges including beam-blockage. Intelligent reflecting surfaces (IRSs) can o

Stavros Orfanoudakis, Cesar Diaz-Londono, Yunus E. Yılmaz, Peter Palensky

As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym enviro

Raquel B Robinson, Anya Osborne, Chen Ji, James Collin Fey

Most currently accepted approaches to evaluating Research through Design (RtD) presume that design prototypes are finalized and ready for robust testing in laboratory or in-the-wild settings. However, it is also valuable to assess designs at intermediate phases with mid-fidelity prototypes, not just to inform an ongoing design process, but also to glean know

Michal Mrnka, Thomas Whittaker, David B. Phillips, Euan Hendry

The miniaturisation of optical systems is an ongoing challenge across the electromagnetic spectrum. While the thickness of optical elements themselves can be reduced using advances in metamaterials, it is the voids between these elements -- which are necessary parts of an optical system -- that occupy most of the volume. Recently, a novel optical element coi

Yuezhou Hu, Kang Zhao, Weiyu Huang, Jianfei Chen

Training large transformers is slow, but recent innovations on GPU architecture give us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication twice as fast as its dense equivalent. In the light of this property, we comprehensively investigate the feasibility of accelerating feed-forward networks (FFNs) of transformers i

Rocco D'Agostino, Kimet Jusufi, Salvatore Capozziello

We address the galaxy rotation curves through the Yukawa gravitational potential emerging as a correction of the Newtonian potential in extended theories of gravity. On the one hand, we consider the contribution of the galactic bulge, galactic disk, and the dark matter halo of the Navarro-Frenk-White profile, in the framework of the standard $\Lambda$CDM mod

Malik Muhammad Qirtas, Evi Zafeirid, Dirk Pesch, Eleanor Bantry White

Background: Loneliness among students is increasing across the world, with potential consequences for mental health and academic success. To address this growing problem, accurate methods of detection are needed to identify loneliness and to differentiate social and emotional loneliness so that intervention can be personalized to individual need. Passive sen

Taro Kimura, Norton Lee

We study the integrability and the Bethe/Gauge correspondence of the Generalized Calogero-Moser system proposed by Berntson, Langmann and Lenells which we call the elliptic quadruple Calogero-Moser system (eqCM). We write down the Dunkl operators which give commuting Hamiltonians of the quantum integrable system. We identify the gauge theory in correspondenc

Wangguandong Zheng, Haifeng Xia, Rui Chen, Ming Shao

Recently, image-to-3D approaches have achieved significant results with a natural image as input. However, it is not always possible to access these enriched color input samples in practical applications, where only sketches are available. Existing sketch-to-3D researches suffer from limitations in broad applications due to the challenges of lacking color in

JooYoung Jang, Youngseo Cha, Jisu Kim, SooHyung Lee

Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompass

Bernd Mulansky, Andreas Potschka

We derive a mixed integer nonlinear programming formulation for the problem of finding a convex polygon with a given number of vertices that is small (diameter at most one) and has maximum perimeter. The formulation is based on a geometric construction using zonogons. The resulting zonogons can be characterized by an integer code and we study the number of c

Tanmoy Chakraborty, Parthasarathi Majumdar

Inspired by the pioneering 1968 work of L Parker, demonstrating matter quanta production in a dynamical spacetime background, we consider production of scalar quanta in a gravitational wave background. Choosing the spacetime to be a flat spacetime perturbed linearly by a linear gravitational wave, we show that scalar particles may indeed be produced in a per

Kathryn Haymaker, Michael Tait, Craig Timmons

In this paper, we study the maximum number of edges in an $N$-vertex $r$-uniform hypergraph with girth $g$ where $g \in \{5,6 \}$. Writing $\textrm{ex}_r ( N, \mathcal{C}_{<g} )$ for this maximum, it is shown that $\textrm{ex}_r ( N , \mathcal{C}_{ < 5} ) = \Omega_r ( N^{3/2 - o(1)} )$ for $r \in \{4,5,6 \}$. We address an unproved claim from [31] asserting

Panagiotis Tolias, Fotios Kalkavouras, Tobias Dornheim

A Fourier-Matsubara series expansion is derived for imaginary-time correlation functions that constitutes the imaginary-time generalization of the infinite Matsubara series for equal-time correlation functions. The expansion is consistent with all known exact properties of imaginary-time correlation functions and opens up new avenues for the utilization of q

M. Lawrence Pattersons, Freddy P. Zen, Hadyan L. Prihadi, Muhammad F. A. R. Sakti

Due to rotation, the mass correction of neutron stars arises and causes the rotational mass to be larger than the static mass. In this work, we extend the formulation of the rotational mass of anisotropic neutron stars within Rastall gravity. We apply numerical simulation on the formulation we obtained. We refer to mass of J0740+6620, GW170817, and GW190814

Christian Geller, Benedikt Haas, Amarin Kloeker, Jona Hermens

Future mobility systems and their components are increasingly defined by their software. The complexity of these cooperative intelligent transport systems (C-ITS) and the everchanging requirements posed at the software require continual software updates. The dynamic nature of the system and the practically innumerable scenarios in which different software co

Antonio Lei

Let $p$ be an odd prime number. Let $f$ be a normalized Hecke eigen-cuspform that is non-ordinary at $p$. Let $K$ be an imaginary quadratic field in which $p$ splits. We study the Artin formalism for the two-variable signed $p$-adic $L$-functions attached to $f$ over $K$. In particular, we give evidence of a prediction made by Castella--Ciperiani--Skinner--S

Alexey Dubinsky, Antonina Zinhailo

We study the decay of Dirac and massive scalar fields at asymptotically late times in the background of the charged asymptotically de Sitter dilatonic black holes. It is shown that the asymptotic decay is exponential and oscillatory for large and intermediate mass of the field, while for zero and small mass it is pure exponential without oscillations. This r

Mark Russinovich, Ahmed Salem, Ronen Eldan

Large Language Models (LLMs) have risen significantly in popularity and are increasingly being adopted across multiple applications. These LLMs are heavily aligned to resist engaging in illegal or unethical topics as a means to avoid contributing to responsible AI harms. However, a recent line of attacks, known as jailbreaks, seek to overcome this alignment.

Christos Revelas, Otilia Boldea, Bas J. M. Werker

We study the effectiveness of subagging, or subsample aggregating, on regression trees, a popular non-parametric method in machine learning. First, we give sufficient conditions for pointwise consistency of trees. We formalize that (i) the bias depends on the diameter of cells, hence trees with few splits tend to be biased, and (ii) the variance depends on t

Aleš Návrat, Lenka Zalabová

This paper explicitly constructs the complete set of optimal sub-Riemannian geodesics starting from a point for certain Carnot groups of step two. These are groups of dimension 2n+1 equipped with a left-invariant distribution of dimension n+1 such that at each point, there is a unique direction defining a nontrivial Lie bracket. A suitable explicit expressio

Kyungbok Lee, Myunghee Cho Paik

We introduce a novel doubly-robust (DR) off-policy evaluation (OPE) estimator for Markov decision processes, DRUnknown, designed for situations where both the logging policy and the value function are unknown. The proposed estimator initially estimates the logging policy and then estimates the value function model by minimizing the asymptotic variance of the

Zheng Gong, Hyun Joe Jeong, Sylvia Herbert

Control Lyapunov functions (CLFs) play a vital role in modern control applications, but finding them remains a problem. Recently, the control Lyapunov-value function (CLVF) and robust CLVF have been proposed as solutions for nonlinear time-invariant systems with bounded control and disturbance. However, the CLVF suffers from the ''curse of dimensionality,''

Yuhang Zhou, Zhongyun Hua

Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques often focus on one-shot setting to maintain robustness against attack. However, new attacks can emerge in sequences in real-world deployment scenarios. As a result, it is crucial for a defense model to constantly adapt to new attacks, but the adaptati

Tran Hung Cuong, Yongdo Lim, Nguyen Nang Thieu, Nguyen Dong Yen

Two inertial DC algorithms for indefinite quadratic programs under linear constraints (IQPs) are considered in this paper. Using a qualification condition related to the normal cones of unbounded pseudo-faces of the polyhedral convex constraint set, the recession cones of the corresponding faces, and the quadratic form describing the objective function, we p

Luca Crupi, Luca Butera, Alberto Ferrante, Daniele Palossi

Smart farming and precision agriculture represent game-changer technologies for efficient and sustainable agribusiness. Miniaturized palm-sized drones can act as flexible smart sensors inspecting crops, looking for early signs of potential pest outbreaking. However, achieving such an ambitious goal requires hardware-software codesign to develop accurate deep

Ivan Arraut

It has been claimed in \cite{1}, that the idea proposed in \cite{2} has certain mistakes based on arguments of energy conditions and others. Additionally, some of the key arguments of the paper are criticized. Here we demonstrate that the results obtained in \cite{2} are correct and that there is no violation of any energy condition. The statements claimed i

A. Passeri

A new measurement of the branching fraction for the decay $K_S \rightarrow \pi e \nu$ is presented, based on a sample of 300 million $K_S$ mesons recorded by the KLOE experiment at the DA$\Phi$NE $e^+e^-$ collider. A two-step signal selection strategy is used, exploiting first kinematic variables and then time-of-flight measurements. Data control samples of

Vaidehee Thatte

We prove the explicit characterization of the so-called "best f" for degree $p$ Artin-Schreier and degree $p$ Kummer extensions of Henselian valuation rings in residue characteristic $p$. This characterization is mentioned briefly in [Th16, Th18]. Existence of best $f$ is closely related to the defect of such extensions and this characterization plays a cruc

Sachin Bhalekar, Deepa Gupta

This manuscript deals with the stability and bifurcation analysis of the equation $D^{2\alpha}x(t)+c D^{\alpha}x(t)=a x(t)+b x(t-\tau)$, where $0<\alpha<1$ and $\tau>0$. We sketch the boundaries of various stability regions in the parameter plane under different conditions on $\alpha$ and $b$. First, we provide the stability analysis of this equation with $\

Sven Beuchler, Ayhan Demircan, Bernhard Endtmayer, Uwe Morgner

In this work, we develop adaptive schemes using goal-oriented error control for a highly nonlinear flow temperature model with temperature dependent density. The dual-weighted residual method for computing error indicators to steer mesh refinement and solver control is employed. The error indicators are used to employ adaptive algorithms, which are substanti

Ivo Verhoeven, Pushkar Mishra, Rahel Beloch, Helen Yannakoudakis

Community models for malicious content detection, which take into account the context from a social graph alongside the content itself, have shown remarkable performance on benchmark datasets. Yet, misinformation and hate speech continue to propagate on social media networks. This mismatch can be partially attributed to the limitations of current evaluation