October 2024 arXiv papers — page 35

Justin Deschenaux, Caglar Gulcehre

Autoregressive (AR) Large Language Models (LLMs) have demonstrated significant success across numerous tasks. However, the AR modeling paradigm presents certain limitations; for instance, contemporary autoregressive LLMs are trained to generate one token at a time, which can result in noticeable latency. Recent advances have indicated that search and repeate

Eric R. Beyerle, Pratyush Tiwary

Contemporary work implies generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. In this Letter, we utilize a score-based modeling procedure called Thermodynamic Maps to describe the isotropic-nematic phase transition in a melt of $N=343$ calamitic Gay-Berne ellipsoids. When trained

James Sharpnack, Kevin Hao, Phoebe Mulcaire, Klinton Bicknell

In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Calibration - learning item parameters in a test - is done using AutoIRT, a new method that uses automated machine learning (AutoML) in combination with item response theory (IRT), or

Gernot Akemann, Noah Aygün, Mario Kieburg, Patricia Päßler

Recently, a conjecture about the local bulk statistics of complex eigenvalues has been made based on numerics. It claims that there are only three universality classes, which have all been observed in open chaotic quantum systems. Motivated by these new insights, we compute and compare the expectation values of $k$ pairs of complex conjugate characteristic p

Joseph Breen

We investigate the relationship between regular and decomposable Lagrangian cobordisms in $4$-dimensional symplectizations. First, we show that regular sliceness implies once-stably decomposable sliceness, and offer a stabilization-free strategy. On the other hand, we show that satelliting preserves regularity of concordance, suggesting that regularity and d

Wojciech Czaja, Brandon Kolstoe, David Koralov

The main result of our paper offers an alternative, simpler, proof of Mallat's result on the translation invariance of the limiting behavior of sequences of Wavelet Scattering Transforms, which (unlike Mallat's proof) does not rely on the admissibility condition or on the density of a logarithmic Sobolev space in $L^2$. Furthermore, this result is generalize

Jannis Weil, Jonas Ringsdorf, Julian Barthel, Yi-Ping Phoebe Chen

Multimedia streaming accounts for the majority of traffic in today's internet. Mechanisms like adaptive bitrate streaming control the bitrate of a stream based on the estimated bandwidth, ideally resulting in smooth playback and a good Quality of Experience (QoE). However, selecting the optimal bitrate is challenging under volatile network conditions. This m

Gabriele Borg, Charlie Abela

This research is focused on traffic congestion in the small island of Malta which is the most densely populated country in the EU with about 1,672 inhabitants per square kilometre (4,331 inhabitants/sq mi). Furthermore, Malta has a rapid vehicle growth. Based on our research, the number of vehicles increased by around 11,000 in a little more than 6 months, w

Ankit Anand, Anshul Mishra, Phongpichit Channuie

In this study, we investigate the Weak Gravity Conjecture (WGC) and Weak Cosmic Censorship Conjecture (WCCC) for a quantum-corrected Reissner-Nordstr\"om Anti-de Sitter (RN-AdS) black hole embedded in Kiselev spacetime. By making small perturbations to the action and using WGC, we investigate the stability of black holes and predict the existence of lighter

Xinyu Lu, Xueru Wen, Yaojie Lu, Bowen Yu

As post-training processes utilize increasingly large datasets and base models continue to grow in size, the computational demands and implementation challenges of existing algorithms are escalating significantly. In this paper, we propose modeling the changes at the logits level during post-training using a separate neural network (i.e., the value network).

Ruixiao Sun, Vivek A. Sujan, Gurneesh Jatana

The decarbonization of road freight transport is crucial for reducing greenhouse gas emissions (GHG) and achieving climate neutrality goals. This study develops a comprehensive Total Cost of Ownership (TCO) model to evaluate the economic viability and strategic pathways for decarbonizing road freight transport. The model integrates vehicles with infrastructu

Weidong Wu, Yong Zhang, Lili Hao, Yang Chen

Physics-Informed Neural Operators provide efficient, high-fidelity simulations for systems governed by partial differential equations (PDEs). However, most existing studies focus only on multi-scale, multi-physics systems within a single spatial region, neglecting the case with multiple interconnected sub-regions, such as gas and thermal systems. To address

Freja Thoresen, Aidan Cowley, Romeo Haak, Jonas Lewe

Human exploration of the moon is expected to resume in the next decade, following the last such activities in the Apollo programme time. One of the major objectives of returning to the Moon is to continue retrieving geological samples, with a focus on collecting high-quality specimens to maximize scientific return. Tools that assist astronauts in making info

Avery Meiksin, Ewald Puchwein

We assess the impact of QSOs on the high redshift (z > 4) Intergalactic Medium using Monte Carlo realisations of QSO populations and the HeIII regions they generate, applied to the Sherwood-Relics simulations, allowing for uncertainties in the QSO luminosity function, its evolution, and QSO spectra and ages. While QSO luminosity functions based on optical-in

Niklas Miska, Daniel Balzani

Reliability-based design optimization (RBDO) approaches aim to identify the best design of an engineering problem, whilst the probability of failure (PoF) remains below an acceptable value. Thus, the incorporation of the sharpest bounds on the PoF under given constraints on the uncertain input quantities strongly strenghtens the significance of RBDO results,

Killian Long, Asaf Pe'er

Synchrotron maser emission is a leading candidate to explain the coherent emission from Fast Radio Bursts (FRBs). This mechanism requires a population inversion in order to operate. We show that nonresonant interactions between Alfv\'{e}n waves and a relativistic plasma result in the formation of population inversions across a wide range of magnetizations, $

Heesang Lee, Songhee Kim, Bokgyeong Kang, Jaewoo Park

Bayesian inference for doubly intractable distributions is challenging because they include intractable terms, which are functions of parameters of interest. Although several alternatives have been developed for such models, they are computationally intensive due to repeated auxiliary variable simulations. We propose a novel Monte Carlo Stein variational gra

Büşra Demirkol, Oğuz Kucur

This paper focuses on evaluating the outage performance of a dual-hop single-phase non-orthogonal multiple-access (NOMA) system. The base station employs the Alamouti space-time block coding technique (Alamouti-STBC), enabling simultaneous communication with two mobile users, and the far user employs a maximal ratio combining (MRC) scheme. In this setup, the

Mari-Liis Aru, Karina Mauco, Carlo F. Manara, Thomas J. Haworth

The evolution of protoplanetary disks in regions with massive OB stars is influenced by externally driven winds that deplete the outer parts of disks. These winds have previously been studied via forbidden oxygen emission lines, which also arise in isolated disks in low-mass star forming-regions (SFRs) with weak external UV fields in photoevaporative or magn

Yuanhao Cui, Xiaowen Cao, Guangxu Zhu, Jiali Nie

Future sixth-generation (6G) networks are envisioned to support intelligent applications across various vertical scenarios, which have stringent requirements on high-precision sensing as well as ultra-low-latency data processing and decision making. Towards this end, a new paradigm of edge perception networks emerges, which integrates wireless sensing, commu

Minghao Li, Ling Yang

Through exploring the embedded transnormal systems of codimension 1, we show the existence of a transnormal function on a connected complete Riemannian manifold requires the underlying manifold to have a vector bundle structure or a linear double disk bundle decomposition. Conversely, any smooth manifold with either of these structures can be endowed with a

Somayeh Farahani, Marjaneh Hejazi, Mehnaz Tabassum, Antonio Di Ieva

Objectives We aimed to evaluate the diagnostic performance of deep learning (DL)-based radiomics models for the noninvasive prediction of isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status in glioma patients using MRI sequences, and to identify methodological factors influencing accuracy and generalizability. Materials and methods Followin

Nalinikanta Pradhan, Rina Kanamoto, M. Bhattacharya, Pankaj Kumar Mishra

The Andreev-Bashkin (AB) effect, corresponding to the dissipationless dragging of one superfluid by another, was predicted almost fifty years ago but has so far eluded experimental detection. In this work, we theoretically introduce an entirely new detection paradigm to this quest, and show that it enables the observation of the hitherto undetected AB effect

Helen Schneider, Sebastian Nowak, Aditya Parikh, Yannik C. Layer

Image-based diagnostic decision support systems (DDSS) utilizing deep learning have the potential to optimize clinical workflows. However, developing DDSS requires extensive datasets with expert annotations and is therefore costly. Leveraging report contents from radiological data bases with Natural Language Processing to annotate the corresponding image dat

Akhilesh Kakolu Ramarao, Kevin Tang, Dinah Baer-Henney

Over the past decade, various studies have addressed how speakers solve the so-called `The Paradigm Cell Filling Problem' (PCFP) \citep{ackerman2009parts} across different languages. The PCFP addresses a fundamental question in morphological processing: how do speakers accurately generate inflected forms of words when presented with incomplete paradigms? Thi

Jinlin Wang, Suyuchen Wang, Ziwen Xia, Sirui Hong

Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts, especially during generation. This paper identifies a novel "lost-in-the-middle" phenomenon, where LLMs progressively lose track of critical information throughout the generation p

Thomas E. Olausson, Maarten L. Terpstra, Niek R. F. Huttinga, Casper Beijst

We present a novel approach for the reconstruction of time-resolved free-running first-pass myocardial perfusion MRI, named CMR-MOTUS. This method builds upon the MR-MOTUS framework and addresses the challenges of a contrast varying reference image. By integrating a low-rank plus sparse (L+S) decomposition, CMR-MOTUS efficiently captures both motion fields a

Anna Cadoret, Alena Pirutka

Assuming natural variational realization conjectures, we give uniform bounds for the obstruction to the integral Tate conjecture in 1-dimensional families of algebraic varieties over an infinite finitely generated field.

Praveer K. Gollapudi, M. Kemal Döner, André Großardt

The prediction of non-local phenomena is a key attribute of quantum mechanics that distinguishes it from classical theories. It was recently suggested that state swapping is one such effect that a fundamentally classical gravitational field cannot give rise to. The reasoning was that the phenomenology of classical fields is restricted to Local Operations and

Erik Weber, Jérôme Rutinowski, Niklas Jost, Markus Pauly

This work investigates the political biases and personality traits of ChatGPT, specifically comparing GPT-3.5 to GPT-4. In addition, the ability of the models to emulate political viewpoints (e.g., liberal or conservative positions) is analyzed. The Political Compass Test and the Big Five Personality Test were employed 100 times for each scenario, providing

Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal

The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor n

Cynthia M. Cook, John J. Howard, Laura R. Rabbitt, Isabelle M. Shuggi

Human image datasets used to develop and evaluate technology should represent the diversity of human phenotypes, including skin tone. Datasets that include skin tone information frequently rely on manual skin tone ratings based on the Fitzpatrick Skin Type (FST) or the Monk Skin Tone (MST) scales in lieu of the actual measured skin tone of the image dataset

Roua Rouatbi, Juan-Esteban Suarez Cardona, Alba Villaronga-Luque, Jesse V. Veenvliet

We introduce the Push-Forward Signed Distance Morphometric (PF-SDM) for shape quantification in biomedical imaging. The PF-SDM compactly encodes geometric and topological properties of closed shapes, including their skeleton and symmetries. This provides robust and interpretable features for shape comparison and machine learning. The PF-SDM is mathematically

Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser

In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstanding results and are widely used in critical domains like autonomous driving and video surveillance. However, their adoption in high-risk applications, where errors may cause severe

Alejandro Cuetos and, Bruno Martínez-Haya, José Manuel Romero-Enrique

Discotic colloids give rise to a paradigmatic family of liquid crystals with sound applications in Materials Science. In this paper, Monte Carlo simulations are employed to characterize the low-temperature liquid crystal phase diagram and the vapour-liquid coexistence of discotic colloids interacting via a Kihara potential. Discoidal particles with thickness

Rui-Dong Zhu

In this article, we provide a short review (written in Chinese) on the Bethe/Gauge correspondence. We first explain the basic idea in an explicit example of the correspondence between XXX spin chains and 2d $\mathcal{N}=(2,2)$ gauge theories. The connection between 4d and 2d will then be explored by comparing the instanton and vortex partition functions. We

J. P. van den Berg, P. L. Els, N. E. Engelbrecht

The pitch-angle diffusion coefficient quantifies the effect of pitch-angle scattering on charged particles propagating through turbulent magnetic fields and is a key ingredient in understanding the diffusion of these particles along the background magnetic field. Despite its significance, only a limited number of studies have calculated the pitch-angle diffu

Zhilin Zhang, Jie Wang, Zhanghao Qin, Ruiqi Zhu

Medical Visual Question Answering (MedVQA) has attracted growing interest at the intersection of medical image understanding and natural language processing for clinical applications. By interpreting medical images and providing precise answers to relevant clinical inquiries, MedVQA has the potential to support diagnostic decision-making and reduce workload

Matteo Cioni, Mattia Perrone, Massimo Delle Piane, Giovanni Maria Pavan

Metals owe their unique mechanical properties to how defects emerge and propagate within their crystal structure under stress. However, the mechanisms leading from the early emerging (local) defects to the amplification of dislocations (collective plastic events) are not easy to track. Here, using tensile-stress atomistic simulations of a Copper lattice as a

Lasse Rempe

Consider the entire function $f(z)=\cosh(z)$. We show that the escaping set of this function - that is, the set of points whose orbits tend to infinity under iteration - has a structure known as a "spider's web". This disproves a conjecture of Sixsmith from 2020. In fact, we show that the "fast escaping set", i.e. the set of points whose orbits tend to infin

Thor Højhus Avenstrup, Boldizsár Elek, István László Mádi, András Bence Schin

Deep learning-based single-channel speaker separation has improved significantly in recent years largely due to the introduction of the transformer-based attention mechanism. However, these improvements come at the expense of intense computational demands, precluding their use in many practical applications. As a computationally efficient alternative with si

Jorge Baeza-Ballesteros

This doctoral thesis focuses on the application of lattice techniques to study two separate topics: hadron interactions within quantum chromodynamics (QCD), and the emission of particles and gravitational waves (GWs) from cosmic string loops. The first part of the dissertation deals with the study of two- an three-particle interactions using lattice QCD simu

Alexey I. Fedyushkin, Andrei A. Gnevushev, Artem S. Zakharov

This paper presents the results of numerical modeling of laminar flows of a viscous incompressible fluid in a flat diffuser with and without vibration effects. Two methods of flow symmetrization in a flat diffuser are considered: by means of periodic vibration action at the inlet to the diffuser and on its walls. Modeling has shown that it is possible to sym

A. Korepanov, J. Leppänen

We study nonstationary dynamical systems formed by sequential concatenation of nonuniformly expanding maps with a uniformly expanding first return map. Assuming a polynomially decaying upper bound on the tails of first return times that is nonuniform with respect to location in the sequence, we derive a corresponding sharp polynomial rate of memory loss. As

Mahir Bilen Can

Poset metrics in the context of stabilizer codes are investigated. MDS stabilizer poset codes are defined. Various characterizations of these quantum codes are found. Methods for producing examples are proposed.

Yan Wang, Yongqiang Li, Minghao Chen, Yu Yao

In this paper, the channel estimation (CE) of intelligent reflecting surface-aided near-field (NF) multi-user communication is investigated. Initially, the least square (LS) estimator and minimum mean square error (MMSE) estimator for the estimated channel are designed, their mean square errors (MSEs) are derived, and the Cramer-Rao lower bound (CRLB) is der

Yasir Abdul Qadir, Andrei V. Berdyugin, Vilppu Piirola, Takeshi Sakanoi

This study continues our investigation of early-type binaries using high-precision broad-band polarimetry, focusing on HD 165052, a massive O+O-type binary in the young cluster NGC 6530. Our aim was to monitor linear polarization variations and independently determine the orbital period through polarization data analysis. By examining the phase-locked Stokes

Franck Djeumou, Michael Thompson, Makoto Suminaka, John Subosits

The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where needed while bypassing expensive trajectory optimization. To

Sven Ochs, Melih Yazgan, Rupert Polley, Albert Schotschneider

As cities strive to address urban mobility challenges, combining autonomous transportation technologies with intelligent infrastructure presents an opportunity to transform how people move within urban environments. Autonomous shuttles are particularly suited for adaptive and responsive public transport for the first and last mile, connecting with smart infr

Simone Calogero

A kinetic model for the dynamics of collisionless spin neutral particles in a spacetime with torsion is proposed. The fundamental matter field is the kinetic density $f(x,u,s)$ of particles with four-velocity $u$ and four-spin $s$. The stress-energy tensor and the spin current of the particles distribution are defined as suitable integral moments of $f$ in t

Shahin Barati, Hamid Rahimpoor, Saeed H. Abedinpour

We investigate a gapped two-dimensional nodal-line semimetal subjected to a perpendicular magnetic field. We identify an unusual pattern for Landau levels, where the energy of Landau levels first decreases and then starts increasing versus the level index. These descending/ascending Landau levels form a stretched check-mark shape. Tuning the magnitude of the

Zijie Ye, Jia-Wei Liu, Jia Jia, Shikun Sun

Capturing and maintaining geometric interactions among different body parts is crucial for successful motion retargeting in skinned characters. Existing approaches often overlook body geometries or add a geometry correction stage after skeletal motion retargeting. This results in conflicts between skeleton interaction and geometry correction, leading to issu

Yanliang Jin, Yifan Wu, Yuan Gao, Shunqing Zhang

The emergence of 6th generation (6G) mobile networks brings new challenges in supporting high-mobility communications, particularly in addressing the issue of channel aging. While existing channel prediction methods offer improved accuracy at the expense of increased computational complexity, limiting their practical application in mobile networks. To addres

Mattia Calzi

Given a bounded symmetric domain $D$ in $\mathbb C^n$, we consider the Clark measures $\mu_\alpha$, $\alpha\in \mathbb T$, associated with a rational inner function $\varphi$ from $D$ into the unit disc in $\mathbb C$. We show that $\mu_\alpha=c|\nabla \varphi|^{-1}\chi_{\mathrm b D \cap \varphi^{-1}(\alpha)}\cdot \mathcal H^{m-1}$, where $m$ is the dimensio

Piotr Sowinski, Ignacio Lacalle, Rafael Vano, Carlos E. Palau

The landscape of computing technologies is changing rapidly, straining existing software engineering practices and tools. The growing need to produce and maintain increasingly complex multi-architecture applications makes it crucial to effectively accelerate and automate software engineering processes. At the same time, artificial intelligence (AI) tools are

A. Pylypets, F. Borodavka, I. Rafalovskyi, I. Gregora

Polarized Raman spectra of piezoelectric bismuth germanate single crystal were recorded using vortex light in order to verify the predicted symmetry selection rules for Raman scattering of photons with nonzero orbital angular momentum. At first, the phonon spectrum is fully characterized by the infrared reflectivity and Raman scattering with ordinary light.

Kai Chenga, Iason Papaioannou, Daniel Straub

Sequential directional importance sampling (SDIS) is an efficient adaptive simulation method for estimating failure probabilities. It expresses the failure probability as the product of a group of integrals that are easy to estimate, wherein the first one is estimated with Monte Carlo simulation (MCS), and all the subsequent ones are estimated with direction

Philipp Denter

We study how motivated reasoning affects the provision of climate policy in an electoral competition framework. Voters experience anticipatory disutility when future outcomes appear grim and may therefore distort beliefs in response to adverse information. We develop a game-theoretic model in which voters and politicians receive signals about the severity of

Xin Xiang, Wenhui Zhou, Guojun Dai

Electroencephalography (EEG)-based visual perception reconstruction has become an important area of research. Neuroscientific studies indicate that humans can decode imagined 3D objects by perceiving or imagining various visual information, such as color, shape, and rotation. Existing EEG-based visual decoding methods typically focus only on the reconstructi

Joachim Breternitz

Until recently, all known ordered II-IV-N2 materials crystallised in the $\beta$-NaFeO$_2$-type in space group Pna2$_1$. BeGeN$_2$, however, crystallises in space group Pmc2$_1$, with a different cation ordering motif long anticipated for this materials class. Chemical bonding analysis is used to rationalise this particular behaviour.

Emanuel Willert

Based on the superposition of incremental frictional surface tractions that, in the case of an incompressible elastic half-space, correspond to a rigid tangential translation of a circular contact domain, the stress and displacement fields in the surface are determined in closed analytic form for an elastic half-space under the action of a constant shear tra

Mingyang Cai, Thomas Klausch, Mark A. van de Wiel

Machine learning models used in medical applications often face challenges due to the covariate shift, which occurs when there are discrepancies between the distributions of training and target data. This can lead to decreased predictive accuracy, especially with unknown outcomes in the target data. This paper introduces a semi-supervised classification and

Ewa Bednarczuk, The Hung Tran, Monika Syga

We investigate the convergence of the primal-dual algorithm for composite optimization problems when the objective functions are weakly convex. We introduce a modified duality gap function, which is a lower bound of the standard duality gap function. Under the sharpness condition of this new function, we identify the area around the set of saddle points wher

Ryotaro Okabe, Zack West, Abhijatmedhi Chotrattanapituk, Mouyang Cheng

The synthesis of inorganic crystalline materials is essential for modern technology, especially in quantum materials development. However, designing efficient synthesis workflows remains a significant challenge due to the precise experimental conditions and extensive trial and error. Here, we present a framework using large language models (LLMs) to predict

Shuyang Hou, Anqi Zhao, Jianyuan Liang, Zhangxiao Shen

The rise of spatiotemporal data and the need for efficient geospatial modeling have spurred interest in automating these tasks with large language models (LLMs). However, general LLMs often generate errors in geospatial code due to a lack of domain-specific knowledge on functions and operators. To address this, a retrieval-augmented generation (RAG) approach

Di Qiu, Zheng Chen, Rui Wang, Mingyuan Fan

Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time applicability. To address these challenges, we propose a simple yet effective tuning-free framework for character video synthesis, named MovieCharacter, designed to streamline the synth

Veronica Fantini, Campbell Wheeler

We prove conjectures of Garoufalidis-Gu-Mari\~no that perturbative series associated with the hyperbolic knots $4_1$ and $5_2$ are resurgent and Borel summable. In the process, we give an algorithm that can be used to explicitly compute the Borel-Laplace resummation as a combination of state integrals of Andersen-Kashaev. This gives a complete description of

Arash Marioriyad, Mohammadali Banayeeanzade, Reza Abbasi, Mohammad Hossein Rohban

Text-to-image diffusion models, such as Stable Diffusion and DALL-E, are capable of generating high-quality, diverse, and realistic images from textual prompts. However, they sometimes struggle to accurately depict specific entities described in prompts, a limitation known as the entity missing problem in compositional generation. While prior studies suggest

Yunhan Zhao, Xiang Zheng, Lin Luo, Yige Li

In this paper, we focus on black-box defense for VLMs against jailbreak attacks. Existing black-box defense methods are either unimodal or bimodal. Unimodal methods enhance either the vision or language module of the VLM, while bimodal methods robustify the model through text-image representation realignment. However, these methods suffer from two limitation

Max R. P. Grossmann

When is autonomy granted to a decision-maker based on their knowledge, and if no autonomy is granted, what form will the intervention take? A parsimonious theoretical framework shows how policymakers can exploit decision-maker mistakes and use them as a justification for intervention. In two experiments, policymakers ("Choice Architects") can intervene in a

Mehdi Hosseinzadeh, Ian Reid

In the field of autonomous driving and mobile robotics, there has been a significant shift in the methods used to create Bird's Eye View (BEV) representations. This shift is characterised by using transformers and learning to fuse measurements from disparate vision sensors, mainly lidar and cameras, into a 2D planar ground-based representation. However, thes

Shuyang Zhu, Ziqing Zhu

In response to the increasing complexity of electricity markets due to low-carbon requirements and the integration of sustainable energy sources, this paper proposes a dynamic quantum computing enhanced bilevel optimization model for electricity market operations. The upper level focuses on market mechanism optimization using Reinforcement Learning (RL), spe

Emma Albertini, Daniel Platt, Toby Wiseman

Kaluza-Klein theory, by which we mean vacuum gravity in 5-dimensions, with asymptotics that are a product of a circle with Minkowski spacetime, has a variety of different static black hole solutions; localized black holes and the homogeneous and inhomogeneous black strings. There is currently no uniqueness theorem for the solutions, and for fixed circle size

Nouman Ahmad

Lately, the continuous development of deep learning models by many researchers in the area of computer vision has attracted more researchers to further improve the accuracy of these models. FasterRCNN [32] has already provided a state-of-the-art approach to improve the accuracy and detection of 80 different objects given in the COCO dataset. To further impro

Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein

In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, the models are capable of mapping users' protected attributes (e.g., gender or ethnicity) from these user embeddings even without explicit access to them, resulting in models that m

Xun Guo, Shan Zhang, Yongxin He, Ting Zhang

Current techniques for detecting AI-generated text are largely confined to manual feature crafting and supervised binary classification paradigms. These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability. Consequently, these methods are often inapplicable for out-of-distribution (OOD) data and newly emerged large lang

M E Buzikov, A M Mayer

This paper offers a contemporary and comprehensive perspective on the classical algorithms utilized for the solution of minimum-time problem for linear systems (MTPLS). The use of unified notations supported by visual geometric representations serves to highlight the differences between the Neustadt-Eaton and Barr-Gilbert algorithms. Furthermore, these notat

Ricardo Brancas, Vasco Manquinho, Ruben Martins

Logic programs are a powerful approach for solving NP-Hard problems. However, due to their declarative nature, debugging logic programs poses significant challenges. Unlike procedural paradigms, which allow for step-by-step inspection of program state, logic programs require reasoning about logical statements for fault localization. This complexity is amplif

Zs. Kővári

Tidal forces in close binary systems have diverse impacts on magnetic activity. The synchronicity characteristic of close systems counteracts magnetic braking, thereby sustaining rapid rotation-a key factor in increased levels of magnetic activity. Tidal effects can also work against the slowing down of rotation during stellar evolution, when the star inflat

George Zograf, Betül Küçüköz, Alexander Yu. Polyakov, Maria Bancerek

Dielectric metasurfaces that combine high-index materials with optical nonlinearities are widely recognized for their potential in various quantum and classical nanophotonic applications. However, the fabrication of high-quality metasurfaces poses significant material-dependent challenges, as their designs are often susceptible to disorder, defects, and scat

Nick Fischer, Bernhard Haeupler, Rustam Latypov, Antti Roeyskoe

We give the first parallel algorithm with optimal $\tilde{O}(m)$ work for the classical problem of computing Single-Source Shortest Paths in general graphs with negative-weight edges. In graphs without negative edges, Dijkstra's algorithm solves the Single-Source Shortest Paths (SSSP) problem with optimal $\tilde O(m)$ work, but is inherently sequential. A r

Ilja Siroš, Dave Singelée, Bart Preneel

4G and 5G represent the current cellular communication standards utilized daily by billions of users for various applications. Consequently, ensuring the security of 4G and 5G network implementations is critically important. This paper introduces an automated fuzzing framework designed to test the security of 4G and 5G attach procedure implementations. Our f

Zenan Li, Yunpeng Huang, Zhaoyu Li, Yuan Yao

Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This paper proposes a natural framework that fuses neural network training, symbol grounding, and logical constraint synthesis into a coherent and efficient end-to-end learning process

Zeyuan Li, Yangfan He, Lewei He, Jianhui Wang

Recently, large language models (LLMs) have achieved significant progress in automated code generation. Despite their strong instruction-following capabilities, these models frequently struggled to align with user intent in coding scenarios. In particular, they were hampered by datasets that lacked diversity and failed to address specialized tasks or edge ca

Or Kalifa

We bound the volume of thick embeddings of finite graphs into the Heisenberg group, as well as the volume of coarse wirings of finite graphs into groups with polynomial growth. This work follows the work of Kolmogorov-Brazdin, Gromov-Guth and Barret-Hume on thick embeddings of graphs (or complexes) into various spaces. We present here a conjecture of Itai Be

Anjali Bhatnagar, Diganta Borah

We study several intrinsic properties of the Carath\'eodory and Szeg\"o metrics on finitely connected planar domains. Among them are the existence of closed geodesics and geodesic spirals, boundary behaviour of Gaussian curvatures, and $L^2$-cohomology. A formula for the Szeg\"o metric in terms of the Weierstrass $\wp$-function is obtained. Variations of the

Yanyu Liu, Yinghui Pan, Yifeng Zeng, Biyang Ma

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest,

Manjunath D, Prajwal Gurunath, Sumanth Udupa, Aditya Gandhamal

Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details. However, in critical applications like aerial perception, it is essential for DNNs to maintain consistent reliability across all conditions, including low-light scenarios where EO

John Peca-Medlin, Chenyang Zhong

One method to generate random permutations involves using Gaussian elimination with partial pivoting (GEPP) on a random matrix $A$ and storing the permutation matrix factor $P$ from the resulting GEPP factorization $PA=LU$. We are interested in exploring properties of random butterfly permutations, which are generated using GEPP on specific random butterfly

Tae-Geun Kim, Seong Chan Park

We propose a novel framework based on neural network that reformulates classical mechanics as an operator learning problem. A machine directly maps a potential function to its corresponding trajectory in phase space without solving the Hamilton equations. Most notably, while conventional methods tend to accumulate errors over time through iterative time inte

Ilkka Helenius, Joni O. Laulainen, Christian T. Preuss

We introduce multi-jet merging for deep inelastic scattering in the Vincia parton shower in the Monte Carlo event generator Pythia 8. Merging combines event samples of different parton multiplicities with logarithmically enhanced parton-shower radiation. We consider up to five outgoing partons using two different merging algorithms. We vary the relevant scal

Yu Zhou, Hai-Qing Zhang

We compute the effective action for a massive scalar field in (A)dS spacetime using the Euclidean heat kernel method. We highlight that in even-dimensional dS spacetimes, the effective action exhibits a non-trivial imaginary part, reminiscent of the Schwinger effect in quantum electrodynamics. We find consistency between the results obtained from the Euclide

J. Sourd, Y. Skourski, L. Prodan, V. Tsurkan

We investigated the magnetic and magnetoelastic properties of MnSc$_2$Se$_4$ single crystals at low temperature under a magnetic field directed along the crystallographic [111] axis. The magnetization data at low temperature show a linear increase with magnetic field, until saturation is reached above 15 T. In ultrasound, a longitudinal acoustic mode shows a

Rongli Huang, Dayan Wei, Yunhua Ye

In this paper, we consider the existence of constant mean curvature hypersurfaces with prescribed gradient image. Let $\Omega$ and $\tilde{\Omega}$ be uniformly convex bounded domains in $\mathbb{R}^n$ with smooth boundary. We show that there exists unique convex solutions for the second boundary value problem of constant mean curvature equations.

Qi Deng, Zhong-Guo Zhou

We develop a new framework to detect wash trading in crypto assets through real-time liquidity fluctuation. We propose that short-term price jumps in crypto assets results from wash trading-induced liquidity fluctuation, and construct two complementary liquidity measures, liquidity jump (size of fluctuation) and liquidity diffusion (volatility of fluctuation

Nicolò Brizio, Tommaso Morone, Roberto Tateo

The irrelevant composite operator $T\bar{T}$, constructed from components of the stress-energy tensor, exhibits unique properties in two-dimensional quantum field theories and represents a distinctive form of integrable deformation. Significant progress has been made in understanding the bulk aspects of the theory, including its interpretation in terms of co

Zenghua Cai, Chunlan Ma

SrTiO3 (STO) displays a broad spectrum of physical properties, including superconductivity, ferroelectricity, and photoconductivity, making it a standout semiconductor material. Despite extensive researches, the oxygen partial pressure-dependent conductivity in STO has re-mained elusive. This study leverages first-principles calculations, and systematically

Ting-Ting Duan, Sahanaa Büriechin, Hai-Ling Lao, Fu-Hu Liu

In this minireview article, we examine the inconsistent results of thermal parameters derived from various models in high-energy collisions. Through a comprehensive literature review and based on the average transverse momentum or the root-mean-square transverse momentum, we propose model-independent parameters to address these inconsistencies. The relevant

Bikash Patra, Amrita Mukherjee, Bahadur Singh

ATi$_3$Bi$_5$ (A = Cs or Rb) are emerging topological kagome metals that exhibit superconductivity and nematicity without intertwining translational symmetry-breaking charge orders. In this work, we explore the fermiology of their titanium kagome electrons and identify a set of sublattice-pure, high-order van Hove singularities (VHSs) that can suppress charg

Artan Sheshmani, Angel Toledo

In this article we study a relative monoidal version of the Bondal-Orlov reconstruction theorem. We establish an uniqueness result for tensor triangulated category structures $(\boxtimes,\mathbb{1})$ on the derived category $D^{b}(X)$ of a variety $X$ which is smooth projective and faithfully flat over a quasi-compact quasi-separated base scheme $S$ in the c