February 2024 arXiv papers — page 97

Zazil Santizo Huerta, Melissa Keranen

The uniform Hamilton-Waterloo Problem (HWP) asks for a resolvable $(C_M, C_N)$-decomposition of $K_v$ into $\alpha$ $C_M$-factors and $\beta$ $C_N$-factors. We denote a solution to the uniform Hamilton Hamilton-Waterloo problem by $\hbox{HWP}(v; M, N; \alpha, \beta)$. Our research concentrates on addressing some of the remaining unresolved cases, which pose

Thomas Place, Marc Zeitoun

We study the class of star-free languages. A long-standing goal is to classify them by the complexity of their descriptions. The most influential research effort involves concatenation hierarchies, which measure alternations between ``complement'' and ``union plus concatenation''. We explore alternative hierarchies that also stratify star-free languages. The

Taesu Kim, Jongho Lee, Daehyun Ahn, Sarang Kim

We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs). QUICK addresses the shared memory bank-conflict problem of state-of-the-art mixed precision matrix multiplication kernels. Our method interleaves the quantized weight matrices of LLMs offline to skip the shared memory write-back

Alisée Bouillon, Eduardo Marin-Bujedo, Matthieu Génévriez

We propose a scheme to directly laser cool Rydberg atoms by laser cooling the residual ion core within the Rydberg-electron orbit. The scheme is detailed for alkaline-earth-metal Rydberg atoms, whose ions can be easily laser cooled. We demonstrate that a closed optical cooling cycle can be found despite the perturbations caused by the Rydberg electron, and t

Chengcheng Yu, Jiapeng Zhu, Xiang Li

Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost. However, most existing algorithms for reinforc

Weixiang Zhao, Zhuojun Li, Shilong Wang, Yang Wang

Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the current large language model (LLM) based conversational general AI assistants. Previous works mainly focus on raising the emotion perception ability of them via naive fine-tuning on

Wenhao Zhuang, Yuyi Mao, Hengtao He, Lei Xie

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical. Although graph neural network (GNN)-based data detectors can achieve decent detection accuracy at reasonable computational cost, they fail to best harness prior informat

Ville Nordstrom

Given a smooth variety $X$ over $\mathbb{C}$, a smooth divisor $i:Y\hookrightarrow X$ and a global function $f$ on $X$ which vanishes on $Y$ and on its critical locus we compute the map induced on Hochschild homology by the pushforward functor $i_*:D^b(Y)\to D^{abs}(MF(X,f))$ in terms of the Hochschild-Kostant-Rosenberg isomorphisms.

Takayuki Koike, Jinichiro Tanaka

Let $X$ be a complex surface obtained as the quotient of the complex Euclidean space $\mathbb{C}^2$ by a discrete subgroup of rank $3$. We investigate the cohomology group $H_0^1(X, E)$ with compact support for a unitary flat line bundle $E$ over $X$. We show the vanishing of $H_0^1(X, E)$ for a certain class of such pairs $(X, E)$, which includes infinitely

Amrutha Muralidhar, Muralidhar Lakkanna

The evolution of cryptocurrency and decentralized finance (DeFi) marks a significant shift in the financial landscape, making it more accessible, inclusive, and participative for various societal groups. However, this transition from traditional financial institutions to DeFi demands a meticulous policy framework that strikes a balance between innovation and

Sadaf Khademi, Anastasia Oikonomou, Konstantinos N. Plataniotis, Arash Mohammadi

Drawing inspiration from the primate brain's intriguing evidence accumulation process, and guided by models from cognitive psychology and neuroscience, the paper introduces the NYCTALE framework, a neuro-inspired and evidence accumulation-based Transformer architecture. The proposed neuro-inspired NYCTALE offers a novel pathway in the domain of Personalized

Achraf Azize, Debabrota Basu

In a Membership Inference (MI) game, an attacker tries to infer whether a target point was included or not in the input of an algorithm. Existing works show that some target points are easier to identify, while others are harder. This paper explains the target-dependent hardness of membership attacks by studying the powers of the optimal attacks in a fixed-t

Junhao Zheng, Ruiyan Wang, Chongzhi Zhang, Huawen Feng

Class-Incremental Learning (CIL) is a practical and challenging problem for achieving general artificial intelligence. Recently, Pre-Trained Models (PTMs) have led to breakthroughs in both visual and natural language processing tasks. Despite recent studies showing PTMs' potential ability to learn sequentially, a plethora of work indicates the necessity of a

Wieland Morgenstern, Niklas Gard, Simon Baumann, Anna Hilsmann

We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras. These dynamic vision sensors are a great fit to be paired with laser projectors for depth estimation in a structured light approach. Our key contributions involve a conversion of the projector time map into a rectified X-map, capturing x

Raphael Seidel, René Zander, Matic Petrič, Niklas Steinmann

The quantum backtracking algorithm proposed by Ashley Montanaro raised considerable interest, as it provides a quantum speed-up for a large class of classical optimization algorithms. It does not suffer from Barren-Plateaus and transfers well into the fault-tolerant era, as it requires only a limited number of arbitrary angle gates. Despite its potential, th

Pierre Civit, Muhammad Ayaz Dzulfikar, Seth Gilbert, Rachid Guerraoui

Byzantine agreement allows n processes to decide on a common value, in spite of arbitrary failures. The seminal Dolev-Reischuk bound states that any deterministic solution to Byzantine agreement exchanges Omega(n^2) bits. In synchronous networks, solutions with optimal O(n^2) bit complexity, optimal fault tolerance, and no cryptography have been established

Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian

The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptional generalizability. However, LLMs often encounter challenges in generating harmful content when faced with problematic prompts. To address this problem, existing work attempted t

Suropriya Saha

We establish the criterion for the phase coexistence in a mixture of nonreciprocally interacting scalar densities. For an arbitrary number of components the active pressure exists for a specific class of interactions, and when the free energy receives no contribution from cross couplings between spatial gradients of two different species. In this case, the p

Aleksei Egorov, Antoine Kraych, Matous Mrovec, Ralf Drautz

We elucidated the core structure of screw dislocations in ordered B2 FeCo using a recent magnetic bond-order potential (BOP) [Egorov et al., Phys. Rev. Mater. 7, 044403 (2023)]. We corroborated that dislocations in B2 FeCo exist in pairs separated by antiphase boundaries. The equilibrium separation is about 50 A, which demands large-scale atomistic simulatio

Siyi Chen, Amir H. Kashani, Ji Yi

The morphology and hierarchy of the vascular systems are essential for perfusion in supporting metabolism. In human retina, one of the most energy-demanding organs, retinal circulation nourishes the entire inner retina by an intricate vasculature emerging and remerging at the optic nerve head (ONH). Thus, tracing the vascular branching from ONH through the v

Yilin Wang

A Jordan curve on the Riemann sphere can be encoded by its conformal welding, a circle homeomorphism. The Loewner energy measures how far a Jordan curve is away from being a circle, or equivalently, how far its welding homeomorphism is away from being a M\"obius transformation. We consider two optimizing problems for the Loewner energy, one under the constra

Tianyi Zhou, Stefan Neumann, Kiran Garimella, Aristides Gionis

Timeline algorithms are key parts of online social networks, but during recent years they have been blamed for increasing polarization and disagreement in our society. Opinion-dynamics models have been used to study a variety of phenomena in online social networks, but an open question remains on how these models can be augmented to take into account the fin

Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramon Huerta

Mitigating the retention of sensitive or private information in large language models is essential for enhancing privacy and safety. Existing unlearning methods, like Gradient Ascent and Negative Preference Optimization, directly tune models to remove unwanted information. However, these methods often become unstable because they fine-tune by maximizing cros

Kaito Ito, Taira Tsuchiya

This paper investigates the problem of controlling a linear system under possibly unbounded stochastic noise with unknown convex cost functions, known as an online control problem. In contrast to the existing work, which assumes the boundedness of noise, we show that an $ \tilde{O}(\sqrt{T}) $ high-probability regret can be achieved under unbounded noise, wh

Somnath Sendhil Kumar, Dhruv Jain, Eshaan Agarwal, Raunak Pandey

While Large Language Models (LLMs) have demonstrated enhanced capabilities in function-calling, these advancements primarily rely on accessing the functions' responses. This methodology is practical for simpler APIs but faces scalability issues with irreversible APIs that significantly impact the system, such as a database deletion API. Similarly, processes

Ethan Eddy, Evan Campbell, Scott Bateman, Erik Scheme

While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions. In this work, a novel myoelectric control paradigm -- on-demand myoelectric control -- is proposed, designed, and evaluated, to reduce the

Georgy Chernykh

We prove that if a complex genus $\varphi \colon \varOmega^U \to R$ is rigid on $SU$-manifolds with a torus action then $\varphi$ is the elliptic Krichever genus.

Maurice Chiodo, Dennis Müller

Mathematics has become inescapable in modern, digitized societies: there is hardly any area of life left that isn't affected by it, and we as mathematicians play a central role in this. Our actions affect what others, in particular our students, decide to do with mathematics, and how mathematics affects the world, for better or worse. In return, the study of

Kaisheng Song, Meenu Upadhyay, Markus Meuwly

The reaction dynamics of H$_2$COO to form linear HCOOH and dioxirane as first steps for OH-elimination is quantitatively investigated. Using a machine learned potential energy surface at the CASPT2/aug-cc-pVTZ level of theory vibrational excitation along the CH-normal mode $\nu_{\rm CH}$ with energies up to 40.0 kcal/mol ($\sim 5 \nu_{\rm CH}$) leads almost

Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov

Informally, a model is calibrated if its predictions are correct with a probability that matches the confidence of the prediction. By far the most common method in the literature for measuring calibration is the expected calibration error (ECE). Recent work, however, has pointed out drawbacks of ECE, such as the fact that it is discontinuous in the space of

Benjamin Johnson, Bechir Hamdaoui

RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for enabling secure device identification and authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data collected under one domain performs badly when tested on data collected un

Pascal Pernot

Average calibration of the (variance-based) prediction uncertainties of machine learning regression tasks can be tested in two ways: one is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV); the alternative is to compare the mean squared z-scores (ZMS) to 1. The problem is that both appr

Can Cui, Sebastian Marino, Quentin Kral, Henrik Latter

Mounting observational evidence indicates that cold circumstellar gas is present in debris disk systems. This work focuses on various dynamical processes that debris-disk gas may undergo. We review five mechanisms that can transport angular momentum and their applications to debris disks. These include molecular viscosity, hydrodynamic turbulence, magnetohyd

Zhizhang Yuan, Fanqi Shen, Meng Li, Yuguo Yu

Neural electrical activity is fundamental to brain function, underlying a range of cognitive and behavioral processes, including movement, perception, decision-making, and consciousness. Abnormal patterns of neural signaling often indicate the presence of underlying brain diseases. The variability among individuals, the diverse array of clinical symptoms fro

Marion Mallweger, Milena Guevara-Bertsch, Boyan T. Torosov, Robin Thomm

In this work, we present a method for measuring the motional state of a two-level system coupled to a harmonic oscillator. Our technique uses ultra-narrowband composite pulses on the blue sideband transition to scan through the populations of the different motional states. Our approach does not assume any previous knowledge of the motional state distribution

Darryl D. Holm, Ruiao Hu, Oliver D. Street

The present work investigates the evolution of linear perturbations of time-dependent ideal fluid flows with advected quantities, expressed in terms of the second order variations of the action corresponding to a Lagrangian defined on a semidirect product space. This approach is related to Jacobi fields along geodesics and several examples are given explicit

Chris Hamblin, Thomas Fel, Srijani Saha, Talia Konkle

Efforts to decode neural network vision models necessitate a comprehensive grasp of both the spatial and semantic facets governing feature responses within images. Most research has primarily centered around attribution methods, which provide explanations in the form of heatmaps, showing where the model directs its attention for a given feature. However, gra

Saeed Khaki, JinJin Li, Lan Ma, Liu Yang

Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct pref

Ioan Baldea

The proposed protocol is an attempt to meet the experimentalists' legitimate desire of reliably and easily extracting microscopic parameters from current-voltage measurements on molecular junctions. It applies to junctions wherein charge transport dominated by a single level (molecular orbital, MO) occurs via off-resonant tunneling. The recipe is simple. The

Anastasios Tsiamis, Aren Karapetyan, Yueshan Li, Efe C. Balta

In this paper, we study the problem of online tracking in linear control systems, where the objective is to follow a moving target. Unlike classical tracking control, the target is unknown, non-stationary, and its state is revealed sequentially, thus, fitting the framework of online non-stochastic control. We consider the case of quadratic costs and propose

Sanskar Amgain, Prashant Shrestha, Sophia Bano, Ignacio del Valle Torres

Purpose: We apply federated learning to train an OCT image classifier simulating a realistic scenario with multiple clients and statistical heterogeneous data distribution where data in the clients lack samples of some categories entirely. Methods: We investigate the effectiveness of FedAvg and FedProx to train an OCT image classification model in a decentra

Erik Bollt, Nan Chen, Stephen Wiggins

Deploying Lagrangian drifters that facilitate the state estimation of the underlying flow field within a future time interval is practically important. However, the uncertainty in estimating the flow field prevents using standard deterministic approaches for designing strategies and applying trajectory-wise skill scores to evaluate performance. In this paper

Deepanshu Verma, Nick Winovich, Lars Ruthotto, Bart van Bloemen Waanders

We consider numerical approaches for deterministic, finite-dimensional optimal control problems whose dynamics depend on unknown or uncertain parameters. We seek to amortize the solution over a set of relevant parameters in an offline stage to enable rapid decision-making and be able to react to changes in the parameter in the online stage. To tackle the cur

Nikita Puchkin, Maxim Rakhuba

Given a sample of i.i.d. high-dimensional centered random vectors, we consider a problem of estimation of their covariance matrix $\Sigma$ with an additional assumption that $\Sigma$ can be represented as a sum of a few Kronecker products of smaller matrices. Under mild conditions, we derive the first non-asymptotic dimension-free high-probability bound on t

T. Figgemeier, M. Ünzelmann, P. Eck, J. Schusser

We report the experimental discovery of orbital vortex lines in the three-dimensional (3D) band structure of a topological semimetal. Combining linear and circular dichroism in soft x-ray angle-resolved photoemission (SX-ARPES) with first-principles theory, we image the winding of atomic orbital angular momentum, thereby revealing - and determining the locat

Muhammad Rusyaidi Zunaidi, Asanka Sayakkara, Mark Scanlon

Cryptography is vital for data security, but cryptographic algorithms can still be vulnerable to side-channel attacks (SCAs), physical assaults exploiting power consumption and EM radiation. SCAs pose a significant threat to cryptographic integrity, compromising device keys. While literature on SCAs focuses on real-world devices, the rise of sophisticated de

Uri Andrews, David Gonzalez, Steffen Lempp, Dino Rossegger

We investigate the descriptive complexity of the set of models of first-order theories. Using classical results of Knight and Solovay, we give a sharp condition for complete theories to have a $\pmb\Pi_\omega^0$-complete set of models. In particular, any sequential theory (a class of foundational theories isolated by Pudl\'ak) has a $\pmb\Pi_\omega^0$-comple

Imad Aouali

Efficient online decision-making in contextual bandits is challenging, as methods without informative priors often suffer from computational or statistical inefficiencies. In this work, we leverage pre-trained diffusion models as expressive priors to capture complex action dependencies and develop a practical algorithm that efficiently approximates posterior

Eleanor Martin, Jean-François Aubry, Mark Schafer, Lennart Verhagen

As transcranial ultrasound stimulation (TUS) advances as a precise, non-invasive neuromodulatory method, there is a need for consistent reporting standards to enable comparison and reproducibility across studies. To this end, the International Transcranial Ultrasonic Stimulation Safety and Standards Consortium (ITRUSST) formed a subcommittee of experts acros

Alok Ranjan Sahoo, Pavan Chakraborty

Hyper spectral images have drawn the attention of the researchers for its complexity to classify. It has nonlinear relation between the materials and the spectral information provided by the HSI image. Deep learning methods have shown superiority in learning this nonlinearity in comparison to traditional machine learning methods. Use of 3-D CNN along with 2-

Daniel G. Zhu

Improving a 2003 result of Bohman and Holzman, we show that for $n \geq 1$, the Shannon capacity of the complement of the $2n+1$-cycle is at least $(2^{r_n} + 1)^{1/r_n} = 2 + \Omega(2^{-r_n}/r_n)$, where $r_n = \exp(O((\log n)^2))$ is the number of partitions of $2(n-1)$ into powers of $2$. We also discuss a connection between this result and work by Day an

Yaoyiran Li, Anna Korhonen, Ivan Vulić

Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of 'traditional' mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource lan

Eleftheria Malami

CP violation in the field of $B$ physics is a crucial topic for exploring the quark sector and search for New Physics, both for theorists and experimentalists. CP violation manifests itself in various ways and in this presentation, we will categorise the decays based on their different dynamics. We aim to present highlights related to the studies of CP viola

Rachel Alpern, Ido Lazer, Issar Tzachor, Hanit Hakim

Naming is very important in software development, as names are often the only vehicle of meaning about what the code is intended to do. A recent study on how developers choose names collected the names given by different developers for the same objects. This enabled a study of these names' diversity and structure, and the construction of a model of how names

Khandaker Foysal Haque, Francesca Meneghello, Md. Ebtidaul Karim, Francesco Restuccia

Emerging mobile virtual reality (VR) systems will require to continuously perform complex computer vision tasks on ultra-high-resolution video frames through the execution of deep neural networks (DNNs)-based algorithms. Since state-of-the-art DNNs require computational power that is excessive for mobile devices, techniques based on wireless edge computing (

Nika Kralj, Miha Ravnik, Žiga Kos

Various active materials exhibit strong spatio-temporal variability of their orientational order known as active turbulence, characterised by irregular and chaotic motion of topological defects, including colloidal suspensions, biofilaments, and bacterial colonies.In particular in three dimensions, it has not yet been explored how active turbulence responds

N. D. Zhigadlo

In materials science, selecting the right synthesis technique for specific compounds is one of the most important steps. High-pressure conditions have a significant effect on the crystal growth processes, leading to the creation of unique structures and properties that usually are not possible under normal conditions. The prime objective of this article is t

Ayelet C. Portnoy, Amit Solomon, Alejandro Cohen

In this paper, we propose an efficient two-stage decoding algorithm for non-adaptive Group Testing (GT) with general correlated prior statistics. The proposed solution can be applied to any correlated statistical prior represented in trellis, e.g., finite state machines and Markov processes. We introduce a variation of List Viterbi Algorithm (LVA) to enable

Saeid Alikhani, Fatemeh Aghaei

Let $G=(V,E)$ be a simple graph. A function $\phi:V\rightarrow \mathbb{N}\cup \{0\}$ is called a configuration of pebbles on the vertices of $G$ and the quantity $\sum_{u\in V}\phi(u)$ is called the size of $\phi$ which is just the total number of pebbles assigned to vertices. A pebbling step from a vertex $u$ to one of its neighbors $v$ reduces $\phi(u)$ by

Tomáš Procházka

This is a short non-technical review focusing on the $\mathcal{W}_N$ family of $\mathcal{W}$-algebras and on their relation to quantum integrability. It is a summary of recently given seminars and workshop contributions.

Katie Clinch, Serge Gaspers, Zixu He, Abdallah Saffidine

To analyze the worst-case running time of branching algorithms, the majority of work in exponential time algorithms focuses on designing complicated branching rules over developing better analysis methods for simple algorithms. In the mid-$2000$s, Fomin et al. [2005] introduced measure & conquer, an advanced general analysis method, sparking widespread adopt

Domagoj Majstorovic, Simon Hoffmann, Frank Diermeyer

Despite the rapid technological progress, autonomous vehicles still face a wide range of complex driving situations that require human intervention. Teleoperation technology offers a versatile and effective way to address these challenges. The following work puts existing ideas into a modern context and introduces a novel technical implementation of the traj

Nur Lan, Emmanuel Chemla, Roni Katzir

Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be expressed by certain architectures. Using the task of formal language learning, we focus on one simple formal language and show that the theoretically correct solution is in fact not

Sutanoya Chakraborty, Arijit Ghosh, Soumi Nandi

We provide a geometric proof of the $(\aleph_{0}, d+1)$-theorem for piercing compact connected sets by hyperplanes. Our argument uses only elementary properties of convex sets and clarifies the core geometric structure behind the theorem.

Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré

We introduce Clifford Group Equivariant Simplicial Message Passing Networks, a method for steerable E(n)-equivariant message passing on simplicial complexes. Our method integrates the expressivity of Clifford group-equivariant layers with simplicial message passing, which is topologically more intricate than regular graph message passing. Clifford algebras i

Megan Lantz, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan

Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issu

Hila Manor, Tomer Michaeli

Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion with pre-trained diffusion models. The first, which we coin ZEro-shot Text

Anna Negro, Subodh P. Patil

Many cosmological observables of interest derive from primordial vacuum fluctuations evolved to late times. These observables represent statistical draws from some underlying quantum or statistical field theoretic framework where infinities arise and require regularization. After subtracting divergences, renormalization conditions must be imposed by measurem

Raffaella Landi, Loredana Bassani, Gabriele Bruni, Manuela Molina

The International Gamma-ray Astrophysics Laboratory (INTEGRAL) has been surveying the sky above 20 keV since its launch in 2002 providing new insights into the nature of the sources that populate our Universe at soft gamma-ray energies. The latest IBIS/ISGRI survey lists 929 hard X-ray sources, of which 113 are reported as unidentified, i.e. lacking a lower

Iosif Iulian Petrila

The informational synthesis of neural structures, processes, parameters and characteristics that allow a unified description and modeling as neural machines of natural and artificial neural systems is presented. The general informational parameters as the global quantitative measure of the neural systems computing potential as absolute and relative neural po

Fernando de Ávila Silva, Marco Cappiello

In this paper we consider a class of evolution operators with coefficients depending on time and space variables $(t,x) \in \mathbb{T} \times \mathbb{R}^n$, where $\mathbb{T}$ is the one-dimensional torus and prove necessary and sufficient conditions for their global solvability in (time-periodic) Gelfand-Shilov spaces. The argument of the proof is based on

Michele Lenzi, Carlos F. Sopuerta

Perturbation theory of vacuum spherically-symmetric spacetimes (including the cosmological constant) has greatly contributed to the understanding of black holes, relativistic compact stars and even inhomogeneous cosmological models. The perturbative equations can be decoupled in terms of (gauge-invariant) master functions satisfying $1+1$ wave equations. In

Marianna Chatzakou, Michael Ruzhansky, Anjali Shriwastawa

In this paper, we prove the anisotropic Shannon inequality for the Renyi entropy with the best constant on Folland-Stein homogeneous Lie groups. As a consequence, we also prove the optimal Shannon inequality in the same setting. Using a logarithmic Sobolev inequality in the setting of stratified groups, we prove a Heisenberg-type uncertainty principle in the

Hai-Tao Yu, Mofei Song

In perception, multiple sensory information is integrated to map visual information from 2D views onto 3D objects, which is beneficial for understanding in 3D environments. But in terms of a single 2D view rendered from different angles, only limited partial information can be provided.The richness and value of Multi-view 2D information can provide superior

Abdellah El Mrini, Edwige Cyffers, Aurélien Bellet

Decentralized Gradient Descent (D-GD) allows a set of users to perform collaborative learning without sharing their data by iteratively averaging local model updates with their neighbors in a network graph. The absence of direct communication between non-neighbor nodes might lead to the belief that users cannot infer precise information about the data of oth

Dan Hedlin

Suppose the lifetime of a large sample of batteries in routine use is measured. A confidence interval is computed to 394 plus/minus 1.96 times 4.6 days. The standard interpretation is that if we repeatedly draw samples and compute confidence intervals, about 95% of the intervals will cover the unknown true lifetime. What can be said about the particular inte

Anamitro Biswas, Eshita Mazumdar

For a finite abelian group $G,$ the Davenport Constant, denoted by $D(G)$, is defined to be the least positive integer $k$ such that every sequence of length at least $k$ has a non-trivial zero-sum subsequence. A long-standing conjecture is that the Davenport constant of a finite abelian group $G =C_{n_1}\times\cdots\times C_{n_d}$ of rank $d \in \mathbb{N}$

Dan Hefetz, Michael Krivelevich

Given positive integers $k \leq m$ and a graph $G$, a family of lists $L = \{L(v) : v \in V(G)\}$ is said to be a random $(k,m)$-list-assignment if for every $v \in V(G)$ the list $L(v)$ is a subset of $\{1, \ldots, m\}$ of size $k$, chosen uniformly at random and independently of the choices of all other vertices. An $n$-vertex graph $G$ is said to be a.a.s

Sarit Maitra

Inventory management is a fundamental challenge in supply chain management. The challenge is compounded when the associated products have unpredictable demands. This study proposes an innovative optimization approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization. The proposed algorithm is tested with a real-life, unpredictable dem

Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou

Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM). The modular and plug-and-play nature of LoRA enables the integration of diverse domain-specific LoRAs to enhance the capabilities of LLMs. Previous research on exploiting multiple LoRAs either focuses on specific isolated downstream tasks or f

Lucas Monteiro Fernandes, Philipp Rieder, Matthias Neumann, Aude Mulard

We investigate the influence of crystallographic twins on the elastoplastic response of $\gamma$-TiAl intermetallics via full-field FFT-based computations. We first introduce a hierarchical stochastic model, which is used to simulate synthetic polycrystalline microstructures containing twin grains with certain morphologies, and apply it to generate represent

Jugal Garg, Yixin Tao, László A. Végh

We study the relationship between two central concepts in the allocation of divisible goods: competitive equilibrium (CE) and allocations that maximize Nash welfare, i.e., allocations where the weighted geometric mean of the utilities is maximal. When agents have homogeneous concave utility functions, these concepts coincide: the classical Eisenberg-Gale con

Mikel Cortes-Goicoechea, Csaba Kiraly, Dmitriy Ryajov, Jose Luis Muñoz-Tapia

Scalability in blockchain remains a significant challenge, especially when prioritizing decentralization and security. The Ethereum community has proposed comprehensive data-sharding techniques to overcome storage, computational, and network processing limitations. In this context, the propagation and availability of large blocks become the subject of resear

DeBrae Kennedy-Mayo, Jake Gord

In 2019, the paper entitled "Model Cards for Model Reporting" introduced a new tool for documenting model performance and encouraged the practice of transparent reporting for a defined list of categories. One of the categories detailed in that paper is ethical considerations, which includes the subcategories of data, human life, mitigations, risks and harms,

Tobias Enders, James Harrison, Maximilian Schiffer

We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain. In this context, risk-sensitive algorithms promise to learn robust policies. While this field is of general interest to the reinforcement learning commu

Mark Eastwood, John Pocock, Mostafa Jahanifar, Adam Shephard

Digital pathology has gained significant traction in modern healthcare systems. This shift from optical microscopes to digital imagery brings with it the potential for improved diagnosis, efficiency, and the integration of AI tools into the pathologists workflow. A critical aspect of this is visualization. Throughout the development of a machine learning (ML

Jinyuan Li, Han Li, Di Sun, Jiahao Wang

Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions. GMNER task exhibits two challenging properties: 1) The weak correlation between image-text pairs in social media results in a significant portion of named entities being ungroundable. 2)

Fernando Zigunov, John Charonko

In this work, we present a method to perform 2D and 3D omnidirectional pressure integration from velocity measurements with a single-iteration matrix inversion approach. This work builds upon our previous work, where the rotating parallel ray approach was extended to the limit of infinite rays by taking continuous projection integrals of the ray paths and re

Roy Aleksan, Luis Oliver, Emmanuel Perez

We show that the angle $\alpha_{ds}$ of the ``flattest'' unitarity triange can be directly measured using the decays $\overline{B}_{d}\to \phi \overline{K}^{(*)0}$ and $\overline{B}_{s}\to \phi{K}^{(*)0}$. Using both $\overline{B}_{d}$ and $\overline{B}_{s}$ enables a further consistency test since the expected time-dependent CP violating asymmetries are ide

Philippe Mounaix

The validity of the instanton analysis approach is tested numerically in the case of the diffraction-amplification problem $\partial_z\psi -\frac{i}{2m}\partial^2_{x^2} \psi =g\vert S\vert^2\, \psi$ for $\ln U\gg 1$, where $U=\vert\psi(0,L)\vert^2$. Here, $S(x,z)$ is a complex Gaussian random field, $z$ and $x$ respectively are the axial and transverse coord

Rangika Peiris, Chao Wang, Richard Gerlach, Minh-Ngoc Tran

A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed. The proposed framework extends the realized exponential GARCH model to be semi-parametrically estimated, via a joint loss function, whilst extending existing quantile time series models to incorporate multiple reali

Ravi Hammond, Dustin Craggs, Mingyu Guo, Jakob Foerster

In dynamic collaborative settings, for artificial intelligence (AI) agents to better align with humans, they must adapt to novel teammates who utilise unforeseen strategies. While adaptation is often simple for humans, it can be challenging for AI agents. Our work introduces symmetry-breaking augmentations (SBA) as a novel approach to this challenge. By appl

Jason Rader, Terry Lyons, Patrick Kidger

We introduce Optimistix: a nonlinear optimisation library built in JAX and Equinox. Optimistix introduces a novel, modular approach for its minimisers and least-squares solvers. This modularity relies on new practical abstractions for optimisation which we call search and descent, and which generalise classical notions of line search, trust-region, and learn

Sourabh Vasant Gothe, Vibhav Agarwal, Sourav Ghosh, Jayesh Rajkumar Vachhani

Generic Event Boundary Detection (GEBD) task aims to recognize generic, taxonomy-free boundaries that segment a video into meaningful events. Current methods typically involve a neural model trained on a large volume of data, demanding substantial computational power and storage space. We explore two pivotal questions pertaining to GEBD: Can non-parametric a

Enrico Randellini, Leonardo Rigutini, Claudio Sacca'

The face expression is the first thing we pay attention to when we want to understand a person's state of mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research field. In this paper, because the small size of available training datasets, we propose a novel data augmentation technique that improves the perfo

Masazumi Honda, Hiroki Matsui, Kazumasa Okabayashi, Takahiro Terada

We revisit the path-integral approach to the wave function of the Universe by utilizing Lefschetz thimble analyses and resurgence theory. The traditional Euclidean path-integral of gravity has the notorious ambiguity of the direction of Wick rotation. In contrast, the Lorentzian method can be formulated concretely with the Picard-Lefschetz theory. Yet, a cha

Jie-Fang Zhang, Zhao Zhang, Meng-yang Zhang, Mei-zhen Jin

There is considerable fundamental theoretical and applicative interest in obtaining two-dimensional rogue wave similar to one-dimensional rogue wave of the nonlinear Schr\"odinger equation. Here, we first time proposes a self-mapping transformation and analytically predict the existence of a family of novel spatio-temporal rogue wave solutions for the Kadomt

A. Endichi, H. Bouhani, O. Baggari, H. Zaari

Gadolinium stand as the favored choice among magnetic refrigerant materials for numerous active magnetic regenerator (AMR) prototypes due to its remarkable ability to exhibit a substantial change in magnetic entropy. This unique characteristic arises from its status as one of the elemental ferromagnets with a high Curie temperature, closely aligning with roo

Xi Chen, Jinyang Sun, Xiumei Wang, Hengxuan Jiang

Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multi-layer perceptron (MLP), and convolutional neural network (CNN) in the field of deep

Marco Cantone, Claudio Marrocco, Alessandro Bria

Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their ability to generalize across diverse networks is a critical factor in their effectiveness and a prerequisite for real-world applications. In this study, we conduct a comprehensive analysis on the generalization of machine-learning-based NIDS through an extensive experim