October 2024 arXiv papers — page 64

Xin He, Shunkang Zhang, Kaijie Tang, Shaohuai Shi

Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy in memory-constrained environments such as single-GPU devices. Offloading allevi

Eduardo D. Sontag

This paper defines antifragility for dynamical systems as convexity of a newly introduced "logarithmic rate". It shows how to compute this rate for positive linear systems, and it interprets antifragility in terms of pulsed alternations of extreme strategies in comparison to average uniform strategies.

Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai

Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-purpose RAG systems to specialized fields such as science and medicine poses unique challenges due to distribution shifts and limited access to domain-specific data. To tackle this, w

Marco P. M. de Souza, Cristiane M. de Oliveira, Rhakny P. P. Araújo

Currently, the benefits of using computer simulations in the school environment are widely recognized by educators. The encouragement to use these simulations in classrooms is supported by the National Common Curricular Base, a normative document that defines essential learning for basic education. This article focuses on the exploration of the Mechanical En

Nirav Bhan, Shival Gupta, Sai Manaswini, Ritik Baba

Large Language Models (LLMs) have shown remarkable capabilities in various domains, yet their economic impact has been limited by challenges in tool use and function calling. This paper introduces ThorV2, a novel architecture that significantly enhances LLMs' function calling abilities. We develop a comprehensive benchmark focused on HubSpot CRM operations t

Julio González-Díaz, Brais González-Rodríguez, Iria Rodríguez-Acevedo

In this paper we extend the core branch-and-bound algorithm of an RLT-based solver for continuous polynomial optimization, RAPOSa, to handle mixed-integer problems. We do so by a direct adaptation, in which LP relaxations are replaced with MILP ones and, therefore, the additional burden caused by the discrete variables is taken care of by the auxiliary MILP

Diego Marcondes, Ulisses Braga-Neto

We propose generalized resubstitution error estimators for regression, a broad family of estimators, each corresponding to a choice of empirical probability measures and loss function. The usual sum of squares criterion is a special case corresponding to the standard empirical probability measure and the quadratic loss. Other choices of empirical probability

Haozhe Yang, Gang He, Eric Masanet, Ranjit Deshmukh

Green hydrogen has the potential to address two pressing problems in a zero-carbon energy system: balancing seasonal variability of solar and wind in the electricity sector, and replacing fossil fuels in hard-to-abate sectors. However, the previous research only separately modeled the electricity and hard-to-abate sectors, which is unable to capture how the

Antoine Etesse

The paper describes the algebraic structure of the graded algebra of differentially homogeneous polynomials of fixed finite order. We show that it is a finitely generated algebra, and we exhibit a minimal set of generators. Along the way, we provide a simpler proof of the so-called Schmidt--Kolchin conjecture (proved in a previous paper) . From the algebraic

Ankur Nath, Alan Kuhnle

Modern instances of combinatorial optimization problems often exhibit billion-scale ground sets, which have many uninformative or redundant elements. In this work, we develop light-weight pruning algorithms to quickly discard elements that are unlikely to be part of an optimal solution. Under mild assumptions on the instance, we prove theoretical guarantees

Antti Käenmäki, Alex Rutar

Non-autonomous self-similar sets are a family of compact sets which are, in some sense, highly homogeneous in space but highly inhomogeneous in scale. The main purpose of this note is to clarify various regularity properties and separation conditions relevant for the fine local scaling properties of these sets. A simple application of our results is a precis

Biman Barua, M. Shamim Kaiser

The objective of this research is how an implementation of AI algorithms in the microservices architecture enhances travel itineraries by cost, time, user preferences, and environmental sustainability. It uses machine learning models for both cost forecasting and personalization, genetic algorithm for optimization of the itinerary, and heuristics for sustain

Stewart Wallace, Yoann Altmann, Brian D. Gerardot, Erik M. Gauger

We present a Bayesian algorithm to identify generators of open quantum system dynamics, described by a Lindblad master equation, that are compatible with measured experimental data. The algorithm, based on a Markov Chain Monte Carlo approach, assumes the energy levels of the system are known and outputs a ranked list of interpretable master equation models t

Li Sun, Zhenhao Huang, Qiqi Wan, Hao Peng

Graph neural networks (GNNs) have become the dominant solution for learning on graphs, the typical non-Euclidean structures. Conventional GNNs, constructed with the Artificial Neuron Network (ANN), have achieved impressive performance at the cost of high computation and energy consumption. In parallel, spiking GNNs with brain-like spiking neurons are drawing

Jia-Chen Tang, Xu-Yang Hou, Hao Guo

Dynamical quantum phase transitions (DQPTs) probe the nonequilibrium evolution of quantum systems, unveiling their geometric and topological characteristics. In this study, we introduce the concepts of parallel quench and dynamic geometrical order parameter (DGOP) for non-band models, where these quantities capture the geometric shifts associated with DQPTs.

Vivian Kuperberg, Matilde Lalín

In [arXiv:2212.04969], the authors stated some conjectures on the variance of certain sums of the divisor function $d_k(n)$ over number fields, which were inspired by analogous results over function fields proven in [arXiv:2107.01437]. These problems are related to certain symplectic matrix integrals. While the function field results can be directly related

Evie Nielen, Oliver Tse, Karen Veroy

Configuration Optimization Problems (COPs), which involve minimizing a loss function over a set of discrete points $\boldsymbol{\gamma} \subset P$, are common in areas like Model Order Reduction, Active Learning, and Optimal Experimental Design. While exact solutions are often infeasible, heuristic methods such as the Greedy Sampling Method (GSM) provide pra

Lisa Linville, Chengping Chai, Nathan Marthindale, Jacob Smith

This work builds on recent advances in foundation models in the language and image domains to explore similar approaches for seismic source characterization. We rely on an architecture called Barlow Twins, borrowed from an understanding of the human visual cortical system and originally envisioned for the image domain and adapt it for learning path invarianc

Jeff Denis, Frederic Laberge, Jean-Sebastien Plante, Alexandre Girard

Wearable and legged robot designers face multiple challenges when choosing actuation. Traditional fully actuated designs using electric motors are multifunctional but oversized and inefficient for bearing conservative loads and for being backdrivable. Alternatively, quasi-passive and underactuated designs reduce the amount of motorization and energy storage,

Shiyue Zhang, Ziheng Cheng, Cheng Zhang

Particle-based variational inference methods (ParVIs) use nonparametric variational families represented by particles to approximate the target distribution according to the kernelized Wasserstein gradient flow for the Kullback-Leibler (KL) divergence. Although functional gradient flows have been introduced to expand the kernel space for better flexibility,

Zhihao Liu, Simon Filhol, Désirée Treichler

Estimating the variability of seasonal snow cover, in particular snow depth in remote areas, poses significant challenges due to limited spatial and temporal data availability. This study uses snow depth measurements from the ICESat-2 satellite laser altimeter, which are sparse in both space and time, and incorporates them with climate reanalysis data into a

Rui Sun, Zhipeng Wang, Hengrui Zhang, Ming Jiang

One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausting, costly, and sometimes impossible. In this paper, we propose a framework for global healthcare modelling using datasets

Linus Franke, Laura Fink, Marc Stamminger

Recent advances in novel view synthesis have demonstrated impressive results in fast photorealistic scene rendering through differentiable point rendering, either via Gaussian Splatting (3DGS) [Kerbl and Kopanas et al. 2023] or neural point rendering [Aliev et al. 2020]. Unfortunately, these directions require either a large number of small Gaussians or expe

Mohammad Sabri, Marc Riera, Antonio González

Processing Using Memory (PUM) accelerators have the potential to perform Deep Neural Network (DNN) inference by using arrays of memory cells as computation engines. Among various memory technologies, ReRAM crossbars show promising performance in computing dot-product operations in the analog domain. Nevertheless, the expensive writing procedure of ReRAM cell

Nicholas Walker

Causal decoder-only transformer models used for generative language modelling, such as Generative Pre-trained Transformers (GPT), are trained to predict the next token in a sequence based only on its previous tokens. Despite this simple training objective, they have proved to be powerful AI tools. However, only predicting the next token results in top layer

Xiaohanwen Lin, Fan WU, Nicolas Ubrig, Menghan Liao

In all van der Waals layered antiferromagnetic semiconductors investigated so far a negative magnetoresistance has been observed in vertical transport measurements, with characteristic trends that do not depend on applied bias. Here we report vertical transport measurements on layered antiferromagnetic semiconductor CrPS$_4$ that exhibit a drastically differ

Heather E. Logan

As a university professor, one of my most important responsibilities is mentoring the junior members of my research group and creating an inclusive environment in which they can thrive. Since my autism diagnosis two years ago, colleagues have asked me how they can make their research groups more welcoming to autistic trainees. This short guide, based on conv

Stephan Bauroth

Heap-based exploits that leverage memory management errors continue to pose a significant threat to application security. The root cause of these vulnerabilities are the memory management errors within the applications, however various hardened allocator designs have been proposed as mitigation. A common feature of these designs is the strategic decision to

Jiajun Zhang, Christian Kazoleas, Weidong Zhu, Kai Zhou

Large deployable mesh reflectors are essential for space applications, providing precise reflecting surfaces for high-gain antennas used in satellite communications, Earth observation, and deep-space missions. During on-orbit missions, active shape adjustment and attitude control are crucial for maintaining surface accuracy and proper orientation for these r

Haowei Xu, Ju Li

In the Letter [Physical Review Letters 120, 117702 (2018)], Ferraro \emph{et al.} claimed a quantum advantage in the Dicke quantum battery (QB), whereby $N$ two-level systems (TLS) are coupled to a common photonic mode of a cavity. They argued that compared with the so-called Rabi QB, the Dicke QB exhibits a $\sqrt{N}$ quantum enhancement in the charging pow

P. Gonçalves, B. Salvador

We investigate the two-points correlation function for several boundary-driven interacting particle systems. Our goal is to show that the time evolution of that correlation function is solution to a partial differential equation that can be written in terms of the generator of a two-dimensional random walk, whose jump rates are model dependent. From this, we

Quentin Michaud, Yohan Pipereau, Olivier Levillain, Dhouha Ayed

WebAssembly is an instruction set architecture and binary format standard, designed for secure execution by an interpreter. Previous work has shown that WebAssembly is vulnerable to buffer overflow due to the lack of effective protection mechanisms. In this paper, we evaluate the implementation of Stack Smashing Protection (SSP) in WebAssembly standalone run

Hans Schamel, Efim Pelinovsky, Marcelo V Flamarion

This paper contributes in the first part to the correct understanding of the linear limit in the Schamel equation (S-equation) from the perspective of structure formation in collisionless plasmas. The corresponding modes near equilibrium turn out to be nonlinear modes of the underlying microscopic Vlasov-Poisson (VP) system for which particle trapping is res

Weidi Luo, He Cao, Zijing Liu, Yu Wang

With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key issues: (i) inadequate defense capabilities, particularly in domain-specific scenarios like chemistry, where a lack of specialized knowledge can lead to the generation of harmful

Francesco Artibani, Francesco Clozza, Massimiliano Bazzi, Cesidio Capoccia

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possib

Leila Khaertdinova, Ilya Pershin, Tatiana Shmykova, Bulat Ibragimov

The annotation of patient organs is a crucial part of various diagnostic and treatment procedures, such as radiotherapy planning. Manual annotation is extremely time-consuming, while its automation using modern image analysis techniques has not yet reached levels sufficient for clinical adoption. This paper investigates the idea of semi-supervised medical im

Rodrigo B. Alves, Yuri F. Saporito, Luiz M. Carvalho

Phylogenetic trees constitute an interesting class of objects for stochastic processes due to the non-standard nature of the space they inhabit. In particular, many statistical applications require the construction of Markov processes on the space of trees, whose cardinality grows superexponentially with the number of leaves considered. We investigate whethe

Wenfang Yao, Chen Liu, Kejing Yin, William K. Cheung

Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost

Elizaveta Surzhikova, Jonny Proppe

Increasingly more research areas rely on machine learning methods to accelerate discovery while saving resources. Machine learning models, however, usually require large datasets of experimental or computational results, which in certain fields, such as (bio)chemistry, materials science, or medicine, are rarely given and often prohibitively expensive to obta

A. Toubiana, L. Sberna, M. Volonteri, E. Barausse

We develop a parametrised model to describe the formation and evolution of massive black holes, designed for comparisons with both electromagnetic and gravitational wave observations. Using an extended Press-Schechter formalism, we generate dark matter halo merger trees. We then seed and evolve massive black holes through parameterised prescriptions. This ap

Sobhan Kazempour, Amin Rezaei Akbarieh, Sichun Sun, Chengye Yu

In this study, we introduce an extension of the quasi-dilaton massive gravity theory and derive the field equations by varying the action with respect to the metric. This extension elucidates the dynamics of the system and demonstrates how it can encompass and recover previous cosmological models through different parameter values. We present the cosmologica

Olivier Barrois, Julien Aubert

Motivated by recent observations of rapid (interannual) signals in the geomagnetic data, and by advances in numerical simulations approaching the Earth's outer core conditions, we present a study on the dynamics of hydromagnetic waves evolving over a static base state. Under the assumption of timescales separation between the rapid waves and the slow convect

Caroline Tatsuoka, Dongbin Xiu

We present a deep learning framework for correcting existing dynamical system models utilizing only a scarce high-fidelity data set. In many practical situations, one has a low-fidelity model that can capture the dynamics reasonably well but lacks high resolution, due to the inherent limitation of the model and the complexity of the underlying physics. When

Andrea Aiello

Bell's theorem supposedly demonstrates an irreconcilable conflict between quantum mechanics and local, realistic hidden variable theories. Most proofs of Bell's theorem, are based on inequalities. In this paper we present an alternative proof which does not involve inequalities, but only a direct comparison between correlation functions calculated using quan

Blas Durá-Azorín, Alejandro Manjavacas, Antonio I. Fernández-Domínguez

We investigate the directional characteristics of photon statistics in dimers of quantum emitters. For their analysis, we construct a two-point second-order correlation function that allows us to find a new mechanism for photon anticorrelation, termed as geometric antibunching. This phenomenon is completely agnostic to the quantum state of the emitters and e

Wei Qiao, Yebo Feng, Teng Li, Zhuo Ma

Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by their ability to remain undetected within the victim system for extended periods, aiming to exfiltrate sensitive data or disrupt operations. Existing detection approaches often struggle to effectively identify these complex threats, construct the attack chain for defense

Qiuxin Du, Xiaoying Wei, Jiawei Li, Emily Kuang

Inter-generational communication is essential for bridging generational gaps and fostering mutual understanding. However, maintaining it is complex due to cultural, communicative, and geographical differences. Recent research indicated that while Virtual Reality (VR) creates a relaxed atmosphere and promotes companionship, it inadequately addresses the compl

Pranab Sen

We provide a powerful machinery to prove fully smooth one shot multipartite covering, aka convex split, type results for quantum states. In the important case of smooth multipartite convex split for classical quantum states, aka smooth multipartite soft covering, our machinery works even when certain marginals of these states do not satisfy pairwise independ

Matteo Biagiola, Robert Feldt, Paolo Tonella

Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a quadratic number of distance computations as the number of test executions increases, which limits its scalability in scenarios requiring extensive testing to uncover faults. Simu

Lorenzo Monti, Tatiana Muraveva, Gisella Clementini, Alessia Garofalo

Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization

Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy

Real-world applications of reinforcement learning often involve environments where agents operate on complex, high-dimensional observations, but the underlying (''latent'') dynamics are comparatively simple. However, outside of restrictive settings such as small latent spaces, the fundamental statistical requirements and algorithmic principles for reinforcem

Aykut Kayhan

In this paper, we study Lorentzian biconservative hypersurfaces for which the gradient of their mean curvature $H$ is lightlike, i.e. $\langle \gr H,\gr H\rangle=0$. We establish the non-existence of such hypersurfaces in the Minkowski spaces by conducting a rigorous analysis of both the Codazzi and Gauss equations.

Srija Anand, Praveen Srinivasa Varadhan, Mehak Singal, Mitesh M. Khapra

Recent advancements in Text-to-Speech (TTS) technology have led to natural-sounding speech for English, primarily due to the availability of large-scale, high-quality web data. However, many other languages lack access to such resources, relying instead on limited studio-quality data. This scarcity results in synthesized speech that often suffers from intell

Seong-Han Kim, Chul-Sik Kee

We propose new electromagnetic surface waves at the interface formed by connecting a perfect electric conductor (PEC) and a perfect magnetic conductor (PMC) parallel plate waveguides containing materials with positive permittivities and permeabilities. This challenges the conventional understanding that surface waves require materials with negative permittiv

Patrícia Gonçalves, Julian Kern, Lu Xu

We consider a class of generalized long-range exclusion processes evolving either on $\mathbb Z$ or on a finite lattice with an open boundary. The jump rates are given in terms of a general kernel depending on both the departure and destination sites, and it is such that the particle displacement has an infinite expectation, but some tail bounds are satisfie

Juan Sosa, Alejandro Urrego-Lopez, Cesar Prieto

This article analyzes a specific bipartite network that shows the relationships between users and anime, examining how the descriptions of anime influence the formation of user communities. In particular, we introduce a new variable that quantifies the frequency with which words from a description appear in specific word clusters. These clusters are generate

Axel Brunnbauer, Julian Lemmel, Zahra Babaiee, Sophie Neubauer

Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing methods often depend on online interactions or access to system dynamics, limiting their practicality in real-world scenarios where such interactions are infeasible or difficult to model. In this paper

Zhanchao Zhou, Tianyi Wu, Zhiyun Jiang, Fares Obeid

While Transformer models have achieved remarkable success in various domains, the effectiveness of information propagation through deep networks remains a critical challenge. Standard hidden state residuals often fail to adequately preserve initial token-level information in deeper layers. This paper introduces ResFormer, a novel architecture that enhances i

Shreya Khisa, Ali Amhaz, Mohamed Elhattab, Chadi Assi

Beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as an innovative and generalized RIS framework that provides greater flexibility in wave manipulation and enhanced coverage. In comparison to conventional RIS, optimization of BD-RIS is more challenging due to the large number of optimization variables associated with it. Typically, opti

Taishi Kurahashi, Kohei Tominaga

We revisit Smullyan's paper ``Truth and Provability'' (2013) for three purposes. First, we introduce the notion of Smullyan models to give a precise definition for Smullyan's framework discussed in that paper. Second, we clarify the relationship between three theorems proved by Smullyan and other newly introduced properties for Smullyan models in terms of bo

Alexandre Benatti, Luciano da F. Costa

A good deal of science and technology concepts and methods rely on comparing and relating entities in quantitative terms. Among the several possible approaches, similarity indices allow some interesting features, especially the ability to quantify how much two entities resemble one another. In this work, the Jaccard similarity for comparing non-zero real-val

Pranab Sen

We prove fully smooth one shot multipartite covering, aka convex split, results as well as fully smooth multipartite decoupling results for quantum states. Fully smooth one shot results for these problems were not known earlier, though the works of Cheng, Gao and Berta (arXiv:2304.12056) for convex split, and Colomer and Winter (arXiv:2304.12114) for decoupl

Kai Ino, Omar Leon Sanchez

We prove that the (elementary) class of differential-difference fields in characteristic $p>0$ admits a model-companion. In the terminology of Chatzidakis-Pillay, this says that the class of differentially closed fields of characteristic $p$ equipped with a generic differential-automorphism is elementary; i.e., DCF$_p$A exists. Along the way, we provide alte

Shansan Gong, Shivam Agarwal, Yizhe Zhang, Jiacheng Ye

Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models. However, current DLMs have been studied at a smaller scale compared to their AR counterparts and lack fair comparison on language modeling benchmarks. Additionally, training diffusion models

D. Hallett, J. Wiercinski, L. Hallacy, S. Sheldon

We present a novel waveguide design that incorporates a split-diode structure, allowing independent electrical control of transition energies of multiple emitters over a wide range with minimal loss in waveguide coupling efficiency. We use this design to systematically map out the transition from superradiant to independent emission from two quantum dots. We

Shahriar Tarvir Nushin, Shadman Shahriar Sharar, Farhan Ishraque Zahin

Leveraging the power of deep learning to design nanophotonic devices has been an area of active research in recent times, with Generative Adversarial Networks (GANs) being a popular choice alongside autoencoder-based methods. However, both approaches typically require large datasets and significant computational resources, which can outweigh the advantages o

Antoine Maillard

Given a sequence of $d \times d$ symmetric matrices $\{\mathbf{W}_i\}_{i=1}^n$, and a margin $\Delta > 0$, we investigate whether it is possible to find signs $(\epsilon_1, \dots, \epsilon_n) \in \{\pm 1\}^n$ such that the operator norm of the signed sum satisfies $\|\sum_{i=1}^n \epsilon_i \mathbf{W}_i\|_{\rm op} \leq \Delta$. Kunisky and Zhang (2023) recen

Kai-Robin Lange, Carsten Jentsch

The application of natural language processing on political texts as well as speeches has become increasingly relevant in political sciences due to the ability to analyze large text corpora which cannot be read by a single person. But such text corpora often lack critical meta information, detailing for instance the party, age or constituency of the speaker,

Linger Deng, Linghao Zhu, Yuliang Liu, Yu Wang

Large Multimodal Models (LMMs) face limitations in geometric reasoning due to insufficient Chain of Thought (CoT) image-text training data. While existing approaches leverage template-based or LLM-assisted methods for geometric CoT data creation, they often face challenges in achieving both diversity and precision. To bridge this gap, we introduce a two-stag

Zohra Mehri, Abdelaali Boudjemaa

We investigate analytically and numerically the Anderson localization of quasiparticles in binary Bose mixtures in the presence of the Lee-Huang-Yang quantum and thermal corrections subjected to correlated disordered potentials. We calculate the density profiles, the Bogoliubov quasiparticles modes, and the localization length in both the mixture and droplet

Filippos Christianos, Georgios Papoudakis, Thomas Coste, Jianye Hao

This paper introduces a novel mobile phone control architecture, Lightweight Multi-modal App Control (LiMAC), for efficient interactions and control across various Android apps. LiMAC takes as input a textual goal and a sequence of past mobile observations, such as screenshots and corresponding UI trees, to generate precise actions. To address the computatio

Congxi Zhang, Yongchun Xie

Learning identifiable representations and models from low-level observations is helpful for an intelligent spacecraft to complete downstream tasks reliably. For temporal observations, to ensure that the data generating process is provably inverted, most existing works either assume the noise variables in the dynamic mechanisms are (conditionally) independent

Yehonathan Refael, Jonathan Svirsky, Boris Shustin, Wasim Huleihel

Training and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the optimizer states. Various techniques have been developed to tackle these challenges, such as low-rank adaptation (LoRA), which involves introducing a parallel trainable low-rank ma

Sander van Cranenburgh, Francisco Garrido-Valenzuela

Residential location choices are traditionally modelled using factors related to accessibility and socioeconomic environments, neglecting the importance of local street-level conditions. Arguably, this neglect is due to data practices. Today, however, street-level images -- which are highly effective at encoding street-level conditions -- are widely availabl

Ángel Díaz Carral, Simon Gravelle, Maria Fyta

Quaternary phases formed in copper alloys are investigated through a combination of quantum-mechanical and classical computer simulations and active machine learning. Focus is given on nickel, silicon, and chromium impurities in a copper matrix. The analysis of the formation enthalpies of candidate quaternary structures leads to the prediction of two novel q

Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael Bronstein

Incorporating equivariance as an inductive bias into deep learning architectures to take advantage of the data symmetry has been successful in multiple applications, such as chemistry and dynamical systems. In particular, roto-translations are crucial for effectively modeling geometric graphs and molecules, where understanding the 3D structures enhances gene

Alessandro Pitanti, Nazim Ashurbekov, Ismael dePedro-Embid, Madeleine Msall

Controlling the symmetry of optical and mechanical waves is pivotal to their full exploitation in technological applications and topology-linked fundamental physics experiments. Leveraging on the control of orbital angular momentum, we introduce here a device forming super-high-frequency-modulated optical vortex beams via acousto-optic interaction of light w

Haemanth Velmurugan, Arnav Das, Turbasu Chatterjee, Amit Saha

Simulating quantum circuits is a computationally intensive task that relies heavily on tensor products and matrix multiplications, which can be inefficient. Recent advancements, eliminate the need for tensor products and matrix multiplications, offering significant improvements in efficiency and parallelization. Extending these optimizations, we adopt a bloc

Xuefei Lin, Guangyu Xu, Lei Peiyao, Bin Xiong

The present article is an empirical study that investigates the learning situation of linear algebra. Research was performed among 60 science and engineering students from different universities in Zhejiang, Jiangsu, Hubei, and Shandong who were taught by three mathematics teachers in the course of a 3-day short-term training consisting of 24 classes; follow

Guangyuan Shi, Zexin Lu, Xiaoyu Dong, Wenlong Zhang

Aligning large language models (LLMs) through supervised fine-tuning is essential for tailoring them to specific applications. Recent studies suggest that alignment primarily adjusts a model's presentation style rather than its foundational knowledge, indicating that only certain components of the model are significantly impacted. To uncover how alignment af

Luis A. Hernández, R. Zamora

In this paper, we study the effects of vorticity on the QCD phase transition using the Linear Sigma Model coupled to quarks. By going beyond the mean-field approximation and incorporating screening effects via ring diagrams, we explore the chiral symmetry restoration in extreme conditions, such as high temperatures, high densities, and large angular velociti

Tim Kunt

How many mutually non-attacking queens can be placed on a d-dimensional chessboard of size n? The n-queens problem in higher dimensions is a generalization of the well-known n-queens problem. We present an integer programming formulation of the n-queens problem in higher dimensions and several strengthenings through additional valid inequalities. Compared to

Zhichao Liu, Zhiming Ma, Guiying Yan

The code spectrum of polar codes is crucial to the performance of polar codes. Based on the lower-triangular affine group (LTA) of decreasing monomial codes and the one-variable descendance (ovd) relation, we define a new subgroup of LTA which can find more cosets with the same weight distribution. Using this algebraic structure, we further reduce the comple

Yanqiu Zou, Huaizhou Jin, Qifei Ma, Zhenrong Zheng

Dynamic surface-enhanced Raman spectroscopy (SERS) is nowadays one of the most interesting applications of SERS, in particular for single molecule studies. In fact, it enables the study of real-time processes at the molecular level. This review summarizes the latest developments in dynamic SERS techniques and their applications, focusing on new instrumentati

Emil Ryd, Vihaan Khandelwal, Hayden So, Jason H. Steffen

We examine the speed of different boarding methods in a proposed Flying Wing aircraft design with four aisles using an agent-based model. We study the effect of various passenger movement variables on the boarding process. We evaluate the impact of these factors on the boarding time when the boarding process runs sequentially and in parallel with the aisles

J. Soenen, A. Yurtman, T. Becker, K. Vanthournout

To enable the transition from fossil fuels towards renewable energy, the low-voltage grid needs to be reinforced at a faster pace and on a larger scale than was historically the case. To efficiently plan reinforcements, one needs to estimate the currents and voltages throughout the grid, which are unknown but can be calculated from the grid layout and the el

Frederic Rendell-Bhatti, Markus Appel, Connor S. Inglis, Melony Dilshad

Barocalorics (BCs) are emerging as promising alternatives to vapour-phase refrigerants, which are problematic as they exacerbate climate change when they inevitably leak into the atmosphere. However, the commercialisation of BC refrigerants is significantly hindered by hysteresis in the solid-solid phase transition that would be exploited in a refrigeration

Lorenzo Peri, Felix-Ekkehard von Horstig, Sylvain Barraud, Christopher J. B. Ford

The ability for optically active media to rotate the polarization of light is the basis of polarimetry, an illustrious technique responsible for many breakthroughs in fields as varied as astronomy, medicine and material science. Here, we recast the primary mechanism for spin readout in semiconductor-based quantum computers, Pauli spin-blockade (PSB), as the

Moritz Gubler, Moritz R. Schäfer, Jörg Behler, Stefan Goedecker

Accurate charge densities are essential for reliable electronic structure calculations because they significantly impact predictions of various chemical properties and in particular, according to the Hellmann-Feynman theorem, atomic forces. This study examines the accuracy of charge densities obtained from different DFT exchange-correlation functionals in co

Stanisław Łaniewski, Robert Ślepaczuk

This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and embedding techniques-with state-of-the-ar

Flavio S. Correa da Silva, Simon Sawhney

Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare expenditures can be minimised by followup planning grounded on predictive models and machine learning. Since AKI is multi-fac

Zhichao Jiang, Eli Ben-Michael, D. James Greiner, Ryan Halen

Dropout poses a significant challenge to causal inference in longitudinal studies with time-varying treatments. However, existing research does not simultaneously address dropout and time-varying treatments. We examine selective eligibility, an important yet overlooked source of non-ignorable dropout in such settings. This problem arises when a unit's prior

K V Srinanda, M Manvith Prabhu, Shyam Lal

This manuscript summarizes work on the Capsule Vision Challenge 2024 by MISAHUB. To address the multi-class disease classification task, which is challenging due to the complexity and imbalance in the Capsule Vision challenge dataset, this paper proposes CASCRNet (Capsule endoscopy-Aspp-SCR-Network), a parameter-efficient and novel model that uses Shared Cha

Yongmin Yoon, Jae-Woo Kim, Jongwan Ko

The most massive early-type galaxies (ETGs) are known to form through numerous galaxy mergers. Thus, it is intriguing to study whether their formation in low-density environments, where nearby companions are almost absent, is associated with mergers, which are directly traced by tidal features. Using the 436 most massive ETGs with $M_\mathrm{star}>10^{11.2}\

Vivina Barutello, Mattia G. Bergomi, Gian Marco Canneori, Roberto Ciccarelli

In this paper we present \texttt{SymOrb.jl}, a software which combines group representation theory and variational methods to provide numerical solutions of singular dynamical systems of paramount relevance in Celestial Mechanics and other interacting particles models. Among all, it prepares for large-scale search of symmetric periodic orbits for the classic

Lukas Bertsch, Ádám Gyenge, Balázs Szendrői

We review the relationship between discrete groups of symmetries of Euclidean three-space, constructions in algebraic geometry around Kleinian singularities including versions of Hilbert and Quot schemes, and their relationship to finite-dimensional and affine Lie algebras via the McKay correspondence. We focus on combinatorial aspects, such as the enumerati

Yajing Yang, Qian Liu, Min-Yen Kan

We introduce DataTales, a novel benchmark designed to assess the proficiency of language models in data narration, a task crucial for transforming complex tabular data into accessible narratives. Existing benchmarks often fall short in capturing the requisite analytical complexity for practical applications. DataTales addresses this gap by offering 4.9k fina

Vladimir Guzov, Ilya A. Petrov, Gerard Pons-Moll

With the rapid growth of the volume of research fields like computer vision and computer graphics, researchers require effective and user-friendly rendering tools to visualize results. While advanced tools like Blender offer powerful capabilities, they also require a significant effort to master. This technical report introduces Blendify, a lightweight Pytho

Ivan Bioli, Yannis Voet

In structural dynamics, mass lumping techniques are commonly employed for improving the efficiency of explicit time integration schemes and increasing their critical time step constrained by the largest discrete frequency of the system. For immersogeometric methods, Leidinger \cite{leidinger2020explicit} first showed in 2020 that for sufficiently smooth spli

Shaofei Cai, Zihao Wang, Kewei Lian, Zhancun Mu

Vision-language models (VLMs) have excelled in multimodal tasks, but adapting them to embodied decision-making in open-world environments presents challenges. One critical issue is bridging the gap between discrete entities in low-level observations and the abstract concepts required for effective planning. A common solution is building hierarchical agents,