February 2024 arXiv papers — page 96

Sara A. Elrafei, Lennart M. Heijnen, Rasmus H. Godiksen, Alberto G. Curto

Optical absorption plays a central role in optoelectronic and photonic technologies. Strongly absorbing materials are thus needed for efficient and miniaturized devices. There exists, however, a fundamental limit of 50% absorptance for any ultrathin film in a symmetric environment. Although deviating from these conditions allows higher absorption, finding th

Yaoxiang Wang, Zhiyong Wu, Junfeng Yao, Jinsong Su

The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents often struggle during task execution due to methodological constraints, such as error propagation and limited adaptability. To address this issue, we propose a multi-agent framework

Benedikt Schesch, Marco Caserta

Given a network, allocating resources at clusters level, rather than at each node, enhances efficiency in resource allocation and usage. In this paper, we study the problem of finding fully connected disjoint clusters to minimize the intra-cluster distances and maximize the number of nodes assigned to the clusters, while also ensuring that no two nodes withi

Michaël Bensimhoun

A convenient framework for dealing with asymptotic limit problems of probabilistic nature is provided. These problems include questions such as finding the asymptotic proportion of terms of a sequence falling inside a given interval, or the limit of the arithmetic mean of its partial sums; but several classes of problems are examined in a much more general s

Shubham Toshniwal, Ivan Moshkov, Sean Narenthiran, Daria Gitman

Recent work has shown the immense potential of synthetically generated datasets for training large language models (LLMs), especially for acquiring targeted skills. Current large-scale math instruction tuning datasets such as MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed using outputs from closed-source LLMs with commercially re

Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier

Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks. When it comes to long-form text generation, there has been a growing interest in generation from a discourse coherence perspective. However, existing lexical or semantic metrics such as BLEU, ROUGE, BertScore cannot effe

Petra Hozzová, Jaroslav Bendík, Alexander Nutz, Yoav Rodeh

The need to solve non-linear arithmetic constraints presents a major obstacle to the automatic verification of smart contracts. In this case study we focus on the two overapproximation techniques used by the industry verification tool Certora Prover: overapproximation of non-linear integer arithmetic using linear integer arithmetic and using non-linear real

C. Codella, L. Podio, M. De Simone, C. Ceccarelli

The ALMA interferometer has played a key role in revealing a new component of the Sun-like star forming process: the molecular streamers, i.e. structures up to thousands of au long funneling material non-axisymmetrically to disks. In the context of the FAUST ALMA LP, the archetypical VLA1623-2417 protostellar cluster has been imaged at 1.3 mm in the SO(5$_6$

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar, Fengqi You

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptabili

Eric Aspling, Michael Lawler

A set of universal quantum gates is a vital part of the theory of quantum computing, but is absent in the developing theory of Relativistic Quantum Information (RQI). Yet, the Unruh--DeWitt (UDW) detector formalism can be elevated to unitary gates between qubits and quantum fields and has allowed RQI applications in quantum Shannon theory, such as mutual inf

Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell

Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. This pa

Yao Fu, Rameswar Panda, Xinyao Niu, Xiang Yue

We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular \textit{the ability to utilize information at arbitrary input locations}, is a capability that is mostly already acquired through large-scale pretraining, and that this capa

Elvis Aponte, Lourival Lima, José Sanabria

In this paper, we introduce and study the spectral property $(E_A)$. This property means that the difference between the approximate point spectrum and the upper semi-Fredholm spectrum coincides with the difference between the approximate point spectrum and the upper semi-Weyl spectrum. Together with local spectral theory, we explore the behavior of this pro

T. Vrignaud, A. Lecavelier des Etangs, F. Kiefer, A. -M. Lagrange

This study introduces the exocomet curve of growth, a new method to analyse the variable absorptions observed in $\beta$ Pictoris spectrum and link them to the physical properties of the transiting cometary tails. We show that the absorption depth of a comet in a set of lines arising from similar excitation levels of a given chemical species follows a simple

Mingming Qiu, Elie Najm, Rémi Sharrock, Bruno Traverson

A smart home is realized by setting up various services. Several methods have been proposed to create smart home services, which can be divided into knowledge-based and data-driven approaches. However, knowledge-based approaches usually require manual input from the inhabitant, which can be complicated if the physical phenomena of the concerned environment s

Sathwik Tejaswi Madhusudhan, Girish Chowdhary

A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a melodic framework for compositions and improvisations alike. Raga Recognition is an important music information retrieval task in ICM as it can aid numerous downstream applications ranging from music recommendations to organizing huge music collections. In this work, we propose a deep

Henrique A. Lima, Edwin E. Mozo Luis, Ismael S. S. Carrasco, Alex Hansen

We develop the hypothesis that the dynamics of a given system may lead to the activity being constricted to a subset of space, characterized by a fractal dimension smaller than the space dimension. We also address how the response function might be sensitive to this change in dimensionality. We discuss how this phenomenon is observable in growth processes an

Diogo H. Silva, Francisco A. Rodrigues, Silvio C. Ferreira

Discrete- and continuous-time approaches are frequently used to model the role of heterogeneity on dynamical interacting agents on the top of complex networks. While, on the one hand, one does not expect drastic differences between these approaches, and the choice is usually based on one's expertise or methodological convenience, on the other hand, a detaile

Renxing Wan, Xiaoyu Xu, Wenyuan Yang

This paper presents a study of the well-known marked length spectrum rigidity problem in the coarse-geometric setting. For any two (possibly non-proper) group actions $G\curvearrowright X_1$ and $G\curvearrowright X_2$ with contracting property, we prove that if the two actions have the same marked length spectrum, then the orbit map $Go_1\to Go_2$ must be a

Fabián Aguirre-López, Silvio Franz, Mauro Pastore

Random features models play a distinguished role in the theory of deep learning, describing the behavior of neural networks close to their infinite-width limit. In this work, we present a thorough analysis of the generalization performance of random features models for generic supervised learning problems with Gaussian data. Our approach, built with tools fr

Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann

Traveling waves are a fundamental phenomenon in the brain, playing a crucial role in short-term information storage. In this study, we leverage the concept of traveling wave dynamics within a neural lattice to formulate a theoretical model of neural working memory, study its properties, and its real world implications in AI. The proposed model diverges from

Jorrit Kruthoff, Adam Levine

We explore dilaton gravity with general dilaton potentials in the semi-classical limit viewed both as a gas of blunt defects and also as a semi-classical theory in its own right. We compare the exact defect gas picture with that obtained by naively canonically quantizing the theory in geodesic gauge. We find a subtlety in the canonical approach due to a non-

Aamodh Suresh, Carlos Nieto-Granda, Sonia Martinez

This work presents and evaluates a novel strategy for robotic exploration that leverages human models of uncertainty perception. To do this, we introduce a measure of uncertainty that we term "Behavioral entropy", which builds on Prelec's probability weighting from Behavioral Economics. We show that the new operator is an admissible generalized entropy, anal

Christoph Borschensky, Fausto Frisenna, Wojciech Kotlarski, Anna Kulesza

The Minimal R-symmetric Supersymmetric Standard Model (MRSSM) provides a realisation of supersymmetry in which the parameter space is less constrained by the current LHC data than in the simplest supersymmetric scenarios. In the present paper, we obtain the most precise theoretical predictions in the MRSSM for squark production at the LHC, enabling accurate

Larissa Antunes Amaral, James Munday, Maja Vučković, Ingrid Pelisoli

Approximately 150 low-mass white dwarfs, with masses below 0.4Msun, have been discovered. The majority of these low-mass WDs are observed in binary systems as they cannot be formed through single-star evolution within the Hubble time. In this study, we present a comprehensive analysis of the double low-mass WD eclipsing binary system J2102-4145. Our investig

Zhengyang Hu, Song Kang, Qunsong Zeng, Kaibin Huang

Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for r

Tanya Veeravalli, Maxim Raginsky

This paper considers the problem of constructing finite-dimensional state space realizations for stochastic processes that can be represented as the outputs of a certain type of a causal system driven by a continuous semimartingale input process. The main assumption is that the output process is infinitely differentiable, where the notion of differentiabilit

Fernando Pires Hartwig, Kate Tilling, George Davey Smith

The possibility of unmeasured confounding is one of the main limitations for causal inference from observational studies. There are different methods for (partially) empirically assessing the plausibility of unconfoundedness. However, most currently available methods require (at least partial) assumptions about the confounding structure, which may be difficu

Rabindra Basnet, Taksh Patel, Jian Wang, Dinesh Upreti

Recent development in two-dimensional (2D) magnetic materials have motivated the search for new van der Waals magnetic materials, especially Ising-type magnets with strong magnetic anisotropy. Fe-based MPX3 (M = transition metal, X = chalcogen) compounds such as FePS3 and FePSe3 both exhibit an Ising-type magnetic order, but FePSe3 receives much less attenti

Víctor Castillo, Claudio Muñoz, Felipe Poblete, Vicente Salinas

We consider the PDE flow associated to Riemann zeta and general Dirichlet $L$-functions. These are models characterized by nonlinearities appearing in classical number theory problems, and generalizing the classical holomorphic Riemann flow studied by Broughan and Barnett. Each zero of a Dirichlet $L$-function is an exact solution of the model. In this paper

Mahyar Abbasian, Zhongqi Yang, Elahe Khatibi, Pengfei Zhang

Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating their efficacy. However, current LLM-based approaches are limited by their dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate

Shihao Liu, Tingting Li, Ziqiang Cheng, Yan Jiang

In this paper, we design a new kind of high order inverse Lax-Wendroff (ILW) boundary treatment for solving hyperbolic conservation laws with finite difference method on a Cartesian mesh. This new ILW method decomposes the construction of ghost point values near inflow boundary into two steps: interpolation and extrapolation. At first, we impose values of so

Yixuan Weng, Shizhu He, Kang Liu, Shengping Liu

As language models continue to scale in size and capability, they display an array of emerging behaviors, both beneficial and concerning. This heightens the need to control model behaviors. We hope to be able to control the personality traits of language models at the inference-time so as to have various character features, on top of which the requirements o

Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo

In self-supervised contrastive learning, a widely-adopted objective function is InfoNCE, which uses the heuristic cosine similarity for the representation comparison, and is closely related to maximizing the Kullback-Leibler (KL)-based mutual information. In this paper, we aim at answering two intriguing questions: (1) Can we go beyond the KL-based objective

Rohit Ghosh, Jishnu Adhikary, Rezki Chemlal

A cellular automaton (CA)-based modeling approach to simulate wildfire spread, emphasizing its strengths in capturing complex fire dynamics and its integration with geographic information systems (GIS). The model introduces an enhanced CA-based methodology for wildfire prediction, emphasizing interactions between neighboring cells and incorporating major det

Callum Birch-Sykes, Brian Le, Yvonne Peters, Ethan Simpson

In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many final-state jets, such as the all-hadronic decay of top-antitop quark pairs, is challenging. We present HyPER: Hypergra

Sebastian Milster, Fabian Koch, Christoph Widder, Tanja Schilling

Tracer diffusion in polymer networks and hydrogels is relevant in biology and technology, while it also constitutes an interesting model process for the dynamics of molecules in fluctuating, heterogeneous soft matter. Here, we study systematically the time-dependent dynamics and (non-Markovian) memory effects of tracers in polymer networks based on (Markovia

Wolfhard Janke, Suman Majumder, Martin Marenz, Subhajit Paul

Semiflexible polymers are widely used as a paradigm for understanding structural phases in biomolecules including folding of proteins. Here, we compare bead-spring and bead-stick variants of coarse-grained semiflexible polymer models that cover the whole range from flexible to stiff by conducting extensive replica-exchange Monte Carlo computer simulations. I

Justen R Geddes, Amanda Randles

Continuously measured arterial blood velocity can provide insight into physiological parameters and potential disease states. The efficient and effective description of the temporal profiles of arterial velocity is crucial for both clinical practice and research. We propose a pipeline to identify the minimum number of points of interest to adequately describ

Irina Arévalo, Jose L. Salmeron

Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a m

Arkady Pikovsky, Michael Rosenblum

We tackle the quantification of synchrony in globally coupled populations. Furthermore, we treat the problem of incomplete observations when the population mean field is unavailable, but only a small subset of units is observed. We introduce a new order parameter and demonstrate its efficiency for quantifying synchrony via monitoring general observables, reg

J. Guo, D. Hill, V. Lauter, L. Stingaciu

The ubiquitous domain wall kinetics under magnetic field or current application describes the dynamic properties in nanostructured magnets. However, when the geometrical size of a nanomagnetic system is constricted to the limiting domain wall length scale, the competing energetics between anisotropy, exchange and dipolar interactions can cause emergent kinet

Omid Madani

Consider a predictor, a learner, whose input is a stream of discrete items. The predictor's task, at every time point, is probabilistic multiclass prediction, i.e. to predict which item may occur next by outputting zero or more candidate items, each with a probability, after which the actual item is revealed and the predictor updates. To output probabilities

Giulio Virginio Clemente, Diego Garlaschelli

Temporal Networks, and more specifically, Markovian Temporal Networks, present a unique challenge regarding the community discovery task. The inherent dynamism of these systems requires an intricate understanding of memory effects and structural heterogeneity, which are often key drivers of network evolution. In this study, we address these aspects by introd

Dheeraj R. Pasham, Francesco Tombesi, Petra Sukova, Michal Zajacek

Binaries containing a compact object orbiting a supermassive black hole are thought to be precursors of gravitational wave events, but their identification has been extremely challenging. Here, we report quasi-periodic variability in X-ray absorption which we interpret as quasi-periodic outflows (QPOuts) from a previously low-luminosity active galactic nucle

Pascal Giorgi, Bruno Grenet, Armelle Perret du Cray, Daniel S. Roche

We consider the classical problems of interpolating a polynomial given a black box for evaluation, and of multiplying two polynomials, in the setting where the bit-lengths of the coefficients may vary widely, so-called unbalanced polynomials. Writing s for the total bit-length and D for the degree, our new algorithms have expected running time $\tilde{O}(s \

Mustafa Doger, Sennur Ulukus

We analyze how secure a block is after the block becomes $k$-deep, i.e., security-latency, for Nakamoto consensus under an exponential network delay model. We provide the fault tolerance and extensive bounds on safety violation probabilities given mining rate, delay rate and confirmation rules. Next, modeling the blockchain system as a batch service queue wi

Pratanu Roy, Stephen Castonguay

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been

Yinhong Liu, Yimai Fang, David Vandyke, Nigel Collier

In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios. However, the creation of high-quality annotated data for Task-Oriented Dialog (TOD) is recognized to be slow and costly. To address these challenges, we intro

Mirko Mauri, Joaquín Moraga

We introduce the notion of birational complexity of a log Calabi-Yau pair. This invariant measures how far the log Calabi-Yau pair is to being birational to a toric pair. We study fundamental properties of the new invariant, with a particular focus on the geometry of dual complexes.

Jose L. Salmeron, Irina Arévalo, Antonio Ruiz-Celma

The increasing requirements for data protection and privacy has attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learn

Álvaro Vergara, Deheng Wei, Raúl Fuentes

The nature and behaviour of the drag coefficient of irregularly shaped grains within a wide range of Reynolds numbers is discussed. Using computational fluid dynamics (CFD) tools, the behaviour of the boundary layer at high Re has been determined by applying the Reynolds Averaged Navier-Stokes turbulence model (RANS). The dependence of the mesh size and the

Davor Hafnar, Jure Demšar

Procedural content generation uses algorithmic techniques to create large amounts of new content for games at much lower production costs. In newer approaches, procedural content generation utilizes machine learning. However, these methods usually require expensive collection of large amounts of data, as well as the development and training of fairly complex

Anupam Prakash, Yue Sun, Shouvanik Chakrabarti, Charlie Che

We consider the problem of pricing discretely monitored Asian options over $T$ monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide two quantum algorithms with complexity poly-logarithmic in $T$ and polynomial in $1/\epsilon$, where $\epsilon$ is the additive approximation error. Our algorithms are obtained respe

Anuj Jakhar, Ravi Kalwaniya, Prabhakar Yadav

Let $\theta$ be a root of a monic polynomial $h(x) \in \Z[x]$ of degree $n \geq 2$. We say $h(x)$ is monogenic if it is irreducible over $\Q$ and $\{ 1, \theta, \theta^2, \ldots, \theta^{n-1} \}$ is a basis for the ring $\Z_K$ of integers of $K = \Q(\theta)$. In this article, we study about the monogenity of number fields generated by a root of composition o

Theodora Kontogianni, Yuanwen Yue, Siyu Tang, Konrad Schindler

Despite continual learning's long and well-established academic history, its application in real-world scenarios remains rather limited. This paper contends that this gap is attributable to a misalignment between the actual challenges of continual learning and the evaluation protocols in use, rendering proposed solutions ineffective for addressing the comple

Chi-Ming Chang, Ying-Hsuan Lin

We propose a classification of BPS states in holographic CFTs into monotone and fortuitous, based on their behaviors in the large $N$ limit. Intuitively, monotone BPS states form infinite sequences with increasing rank $N$, while fortuitous ones exist within finite ranges of consecutive ranks. A precise definition is formulated using supercharge cohomology.

Abdullah Hamdi, Luke Melas-Kyriazi, Jinjie Mai, Guocheng Qian

Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However, it may require a large number of Gaussians, which creates a substantial memory footprint. This paper introduces GES (Generalized Exponential Splatting), a novel representation that employs Generalized Exponential Function (GEF) to model 3D scenes,

Zhichao Wang, Denny Wu, Zhou Fan

Many recent works have studied the eigenvalue spectrum of the Conjugate Kernel (CK) defined by the nonlinear feature map of a feedforward neural network. However, existing results only establish weak convergence of the empirical eigenvalue distribution, and fall short of providing precise quantitative characterizations of the ''spike'' eigenvalues and eigenv

Sandra Fortini, Sonia Petrone

There is currently a renewed interest in the Bayesian predictive approach to statistics. This paper offers a review on foundational concepts and focuses on predictive modeling, which by directly reasoning on prediction, bypasses inferential models or may characterize them. We detail predictive characterizations in exchangeable and partially exchangeable sett

D. Capocci, P. Johnson, S. Oughton, L. Biferale

In hydrodynamic (HD) turbulence an exact decomposition of the energy flux across scales has been derived that identifies the contributions associated with vortex stretching and strain self-amplification (P. Johnson, Phys. Rev. Lett., 124, 104501 (2020), J. Fluid Mech. 922, A3 (2021)) to the energy flux across scales. Here we extend this methodology to genera

Katy Craig, Karthik Elamvazhuthi, Harlin Lee

In the present work, we develop a novel particle method for a general class of mean field control problems, with source and terminal constraints. Specific examples of the problems we consider include the dynamic formulation of the p-Wasserstein metric, optimal transport around an obstacle, and measure transport subject to acceleration controls. Unlike existi

Thibault Scoquart, Igor V. Gornyi, Alexander D. Mirlin

Models of many-body localization (MBL) can be represented as tight-binding models in the many-body Hilbert space (Fock space). We explore the role of correlations between matrix elements of the effective Fock-space Hamiltonians in the scaling of MBL critical disorder $W_c(n)$ with the size $n$ of the system. For this purpose, we consider five models, which a

Betania Silva C Campello, Guilherme Dean Pelegrina, Renata Pelissari, Ricardo Suyama

Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine mult

Daniel G. Zhu

For a positive integer $k$, let $m(k)$ be the minimum positive integer $m$ such that $mx$ can be written as an integer linear combination of $k$th powers of integer polynomials. We correct an error in a 1976 formula of Chinburg and Henriksen for $m(k)$.

Matthew D. Dickers, Gennady B. Sushko, Andrei V. Korol, Nigel J. Mason

In this study, we conduct atomistic-level molecular dynamics simulations on fixed-sized silicon-germanium (Si$_{1-x}$Ge$_{x}$) crystals to elucidate the effects of dopant concentration and temperature on the crystalline inter-planar distances. Our calculations consider a range of Ge dopant concentrations between pure Si (0%) and 15%, and for both the optimis

Tomás Domínguez-Bolaño, Valentín Barral, Carlos J. Escudero, José A. García-Naya

This article discusses the development of an IoT system for monitoring and controlling various devices and systems from different vendors. The authors considered key challenges in IoT projects, such as interoperability and integration, scalability, and data storage, processing, and visualization, during the design and deployment phases. In addition to these

Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons

Solving nonlinear optimal control problems is a challenging task, particularly for high-dimensional problems. We propose algorithms for model-based policy iterations to solve nonlinear optimal control problems with convergence guarantees. The main component of our approach is an iterative procedure that utilizes neural approximations to solve linear partial

Christodoulos Peltekis, Kosmas Alexandridis, Giorgos Dimitrakopoulos

Transformers have improved drastically the performance of natural language processing (NLP) and computer vision applications. The computation of transformers involves matrix multiplications and non-linear activation functions such as softmax and GELU (Gaussion Error Linear Unit) that are accelerated directly in hardware. Currently, function evaluation is don

Andy Regensky, Fabian Brand, André Kaup

With the increasing efforts of bringing high-quality virtual reality technologies into the market, efficient 360-degree video compression gains in importance. As such, the state-of-the-art H.266/VVC video coding standard integrates dedicated tools for 360-degree video, and considerable efforts have been put into designing 360-degree projection formats with i

Florian Poydenot, Bruno Andreotti

Rain drops form in clouds by collision of submillimetric droplets falling under gravity: larger drops fall faster than smaller ones and collect them on their path. The puzzling stability of fogs and non-precipitating warm clouds with respect to this avalanching mechanism has been a longstanding problem. How to explain that droplets of diameter around $10~{\r

Luca Fabbri

On differential manifolds with spinor structure, it is possible to express the Euler and Pontryagin currents in terms of tensors that also appear as source in the Dirac equation. It is hence possible to tie concepts rooted in geometry and topology to dynamical characters of quantum matter.

Victor Dubach

We propose a new approach to conjugation-invariant random permutations. Namely, we explain how to construct uniform permutations in given conjugacy classes from certain point processes in the plane. This enables the use of geometric tools to study various statistics of such permutations. For their longest decreasing subsequences, we prove universality of the

Rahul Mishra, Arnav Bhavsar

In this study, we tackle a modern research challenge within the field of perceptual brain decoding, which revolves around synthesizing images from EEG signals using an adversarial deep learning framework. The specific objective is to recreate images belonging to various object categories by leveraging EEG recordings obtained while subjects view those images.

Klaas De Kinder, Christophe Caloz

We present a general analytical solution to the problem of electromagnetic scattering at a one-dimensional arbitrarily accelerated space-time engineered-modulation (ASTEM) interface in the subluminal regime. We show that such an interface fundamentally produces chirping, whose profile can be designed according to specifications. This work represents an impor

Harry Cook, Yulia Bezsudnova, Lari M. Koponen, Ole Jensen

We realise an intrinsic optically pumped magnetic gradiometer based on non-linear magneto-optical rotation. We show that our sensor can reach a gradiometric sensitivity of 18 $\text{fT}/\text{cm}/\sqrt{\text{Hz}}$ and can reject common mode homogeneous magnetic field noise with up to 30 dB attenuation. We demonstrate that our magnetic field gradiometer is su

Antonio M. Scielzo

A result by Ornstein and Weiss states that a free and measure-preserving action of an amenable group on a probability space yields a decomposition of the space in disjoint images, up to a small error, analogous to the one given by the Rokhlin lemma in the case of a single transformation. We generalise this result to non-singular actions, and use it to prove

Tristan C. Collins, Freid Tong, Shing-Tung Yau

Let $P$ be a convex body containing the origin in its interior. We study a real Monge-Amp\`ere equation with singularities along $\del P$ which is Legendre dual to a certain free boundary Monge-Amp\`ere equation. This is motivated by the existence problem for complete Calabi-Yau metrics on log Calabi-Yau pairs $(X, D)$ with $D$ an ample, simple normal crossi

Luca Fabbri

We consider the Dirac field in polar formulation, showing that when torsion is taken in effective approximation the theory has the thermodynamic properties of a van der Waals gas, that when the limit of zero chiral angle is taken the theory reduces to that of a Weyssenhoff fluid, and that under the spinless condition it gives the Newtonian particle. This nes

Ming Li, Lichang Chen, Jiuhai Chen, Shwai He

Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the training data quality. Many recent methods focus on improving the data quality but often overlook the compatibility of the data with the student model being finetuned. This paper intr

Denis Jered McInerney, William Dickinson, Lucy C. Flynn, Andrea C. Young

Many diagnostic errors occur because clinicians cannot easily access relevant information in patient Electronic Health Records (EHRs). In this work we propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses; our ultimate aim is to increase access to evidence and reduce di

Mario De Lucia, Gerhard Ulbricht, Eoin Baldwin, Jack Piercy

This paper describes the energy resolution of Microwave Kinetic Inductance Detectors (MKIDs), and models some limiting factors to it. Energy resolution is a measure of the smallest possible difference in energy of the impinging photons, Delta E, that the detector can identify and is of critical importance for many applications. Limits to the energy resolutio

Cheng Kang, Xinye Chen, Yong Hu, Daniel Novak

Improving the controllability, portability, and inference speed of diffusion language models (DLMs) is a key challenge in natural language generation. While recent research has shown significant success in complex text generation with language models, the memory and computational power are still very demanding and fall short of expectations, which naturally

Leonardo F. Cavenaghi, João Marcos do Ó, Llohann D. Sperança

This paper explores the existence and properties of \emph{basic} eigenvalues and eigenfunctions associated with the Riemannian Laplacian on closed, connected Riemannian manifolds featuring an effective isometric action by a compact Lie group. Our primary focus is on investigating the potential existence of homeomorphic yet not diffeomorphic smooth manifolds

Jiaxin Zhang, Zhongzhi Li, Mingliang Zhang, Fei Yin

Recent advancements in large language models (LLMs) and multi-modal models (MMs) have demonstrated their remarkable capabilities in problem-solving. Yet, their proficiency in tackling geometry math problems, which necessitates an integrated understanding of both textual and visual information, has not been thoroughly evaluated. To address this gap, we introd

David E. Bernal Neira, Robin Brown, Pratik Sathe, Filip Wudarski

We discuss guidelines for evaluating the performance of parameterized stochastic solvers for optimization problems, with particular attention to systems that employ novel hardware, such as digital quantum processors running variational algorithms, analog processors performing quantum annealing, or coherent Ising Machines. We illustrate through an example a b

A. R. Rajan, S. Sheena, C. S. Preenu

Much study has been done on semigroups which are unions of groups. There are several ways in which a union of groups can be made into a semigroup in which each of the component groups arises as subgroups of the constructed semigroup. An important class of such unions is a semilattice of groups. Group semirings are semirings $(G,+,\cdot )$ where $(G,\cdot )$

Jose L. Salmeron, Irina Arévalo

Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the cu

Zehao Xiao, Jiayi Shen, Mohammad Mahdi Derakhshani, Shengcai Liao

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless, conventional prompt learning methods overfit their training distribution and lose the generalization ability on test distributions. To improve generalization across various distribution shifts, we propose any-shift prompting: a general

Luis A. Saavedra, Hridoy S. Dutta, Alastair R. Beresford, Alice Hutchings

We present the results of the first large-scale study into Android markets that offer modified or modded apps: apps whose features and functionality have been altered by a third-party. We analyse over 146k (thousand) apps obtained from 13 of the most popular modded app markets. Around 90% of apps we collect are altered in some way when compared to the offici

Ignas Gasparavičius, Andrius Grigutis

This survey article is dedicated to the life of the famous American economist H. Markowitz (1927--2023). We do revisit the main statements of the portfolio selection theory in terms of mathematical completeness including all the necessary auxiliary details.

A. A. Habob, H. Tabassum, O. Waqar

This paper considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider non-orthogonal multi-modal information dissemination, which is based on superposed message transmission from RSU and successive inter

Jiaxiang Geng, Yanzhao Hou, Xiaofeng Tao, Juncheng Wang

Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed various client sampling methods, they have limitations in joint system and data heterogeneity design, which may not align with practical heterogeneous wireless networks. In this wo

P. Das, P. Saha, M. Singh, P. Kumar

Kagome metals gain attention as they manifest a spectrum of quantum phenomena, including superconductivity, charge order, frustrated magnetism, and intertwined correlated states of condensed matter. With regard to electronic band structure, several of the them exhibit non-trivial topological characteristics. Here, we present a thorough investigation on the g

Shahar Yadin, Noam Elata, Tomer Michaeli

A prominent family of methods for learning data distributions relies on density ratio estimation (DRE), where a model is trained to $\textit{classify}$ between data samples and samples from some reference distribution. DRE-based models can directly output the likelihood for any given input, a highly desired property that is lacking in most generative techniq

Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter

We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations within MIM models generally reside in intermediate layers. Accordingly, MIM-Refiner leverages multiple contrastive heads that are connected to different intermediate layers. In each hea

Marina Garrote-López, Monroe Stephenson

In this work, we explore Partitioned Independent Component Analysis (PICA), an extension of the well-established Independent Component Analysis (ICA) framework. Traditionally, ICA focuses on extracting a vector of independent source signals from a linear combination of them defined by a mixing matrix. We aim to provide a comprehensive understanding of the id

Stephanie M. Ribet, Georgios Varnavides, Cassio C. S. Pedroso, Bruce E. Cohen

In photon upconverting core-shell nanoparticles, structure strongly dictates performance. Conventional imaging in scanning transmission electron microscopy has sufficient resolution to probe the atomic structure of these nanoparticles, but contrast, dose, and projection limitations make conventional imaging modes insufficient for fully characterizing these s

Ting Fang Tan, Kabilan Elangovan, Liyuan Jin, Yao Jie

Purpose: To assess the alignment of GPT-4-based evaluation to human clinician experts, for the evaluation of responses to ophthalmology-related patient queries generated by fine-tuned LLM chatbots. Methods: 400 ophthalmology questions and paired answers were created by ophthalmologists to represent commonly asked patient questions, divided into fine-tuning (

Enrique Mármol Campos, Aurora González Vidal, José Luis Hernández Ramos, Antonio Skarmeta

Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectivenes