February 2024 arXiv papers — page 77

Yu Wang, Zeyuan Zhang, Julian McAuley, Zexue He

Enabling large language models (LLMs) to read videos is vital for multimodal LLMs. Existing works show promise on short videos whereas long video (longer than e.g.~1 minute) comprehension remains challenging. The major problem lies in the over-compression of videos, i.e., the encoded video representations are not enough to represent the whole video. To addre

Mandana Kariminejad, David Tormey, Caitríona Ryan, Christopher O'Hara

Minimising cycle time without inducing quality defects is a major challenge in the injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of the IM, however existing methods have limitations, including the need for a large number of experiments and a pre-determined search space. Bayesian adaptive design of exper

Gao Depeng, Gao Yang, Lin Hongwei

Porous structures are intricate solid materials with numerous small pores, extensively used in fields like medicine, chemical engineering, and aerospace. However, the design of such structures using computer-aided tools is a time-consuming and tedious process.In this study, we propose a novel representation method and design approach for porous units that ca

Deijany Rodriguez Linares, Håkan Johansson, Yinan Wang

This letter considers the design of linear-phase finite-length impulse response (FIR) filters for equalization of the frequency responses of digital-to-analog converters (DACs). The letter derives estimates for the filter orders required, as functions of the bandwidth and equalization accuracy, for four DAC pulses that are used in DACs in multiple Nyquist ba

Maurizio Consoli, Alessandro Pluchino

Quantum nonlocality would naturally fit into a version of relativity with a preferred reference system. However, acceptance of this idea has traditionally required experimental evidence. Namely, detecting in laboratory a small angular dependence of the velocity of light correlated to the cosmic motion of the Earth. Here, we summarize a new theoretical framew

Yongquan He, Peng Zhang, Luchen Liu, Qi Liang

In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, the

Jean-Philippe Rolin, Tamara Servi, Patrick Speissegger

Given an o-minimal expansion $\mathbb{R}_{\mathcal{A}}$ of the real ordered field, generated by a generalized quasianalytic class $\mathcal{A}$, we construct an explicit truncation closed ordered differential field embedding of the Hardy field of the expansion $\mathbb{R}_{\mathcal{A},\exp}$ of $\mathbb{R}_{\mathcal{A}}$ by the unrestricted exponential funct

Alexander Auras, Kanchana Vaishnavi Gandikota, Hannah Droege, Michael Moeller

This paper provides an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness o

Sahand Sabour, Siyang Liu, Zheyuan Zhang, June M. Liu

Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglecting essential EI capabilities such as e

Manwook Han, Sun Kwang Kim

We investigate M-ideals of compact operators and two distinct properties in norm-attaining operator theory related with M-ideals of compact operators called the weak maximizing property and the compact perturbation property. For Banach spaces $X$ and $Y$, it is previously known that if $\mathcal{K}(X,Y)$ is an M-ideal or $(X,Y)$ has the weak maximizing prope

Benedetta Morini, Simone Rebegoldi

We study the Inexact Restoration framework with random models for minimizing functions whose evaluation is subject to errors. We propose a constrained formulation that includes well-known stochastic problems and an algorithm applicable when the evaluation of both the function and its gradient is random and a specified accuracy of such evaluations is guarante

Aris Filos-Ratsikas, Yiannis Giannakopoulos, Alexandros Hollender, Charalampos Kokkalis

We study the computational complexity of computing Bayes-Nash equilibria in first-price auctions with discrete value distributions and discrete bidding space, under general subjective beliefs. It is known that such auctions do not always have pure equilibria. In this paper, we prove that the problem of deciding their existence is NP-complete, even for approx

Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen, Laurenz Wiskott

Visual navigation requires a whole range of capabilities. A crucial one of these is the ability of an agent to determine its own location and heading in an environment. Prior works commonly assume this information as given, or use methods which lack a suitable inductive bias and accumulate error over time. In this work, we show how the method of slow feature

Jiawei Xu, Yijie Mao

In this letter, we investigate the performance of Max Minimum Fairness (MMF) for uplink Rate-Splitting Multiple Access (RSMA) in short-packet communications. Specifically, considering a Single-Input Single-Output (SISO) Multiple Access Channel (MAC), we optimize the transmit power allocation between the splitting user messages to maximize the minimum rate am

Yuxuan Yue, Zhihang Yuan, Haojie Duanmu, Sifan Zhou

Large Language Models (LLMs) face significant deployment challenges due to their substantial memory requirements and the computational demands of auto-regressive text generation process. This paper addresses these challenges by focusing on the quantization of LLMs, a technique that reduces memory consumption by converting model parameters and activations int

Aluna Rizzoli, Donna Testerman

Let G be a simple algebraic group defined over an algebraically closed field k of characteristic p>0. Here we classify all irreducible kG-modules for which the principal A1 has no repeated composition factors, extending the work of Liebeck-Seitz-Testerman which treated the same question when k is replaced by an algebraically closed field of characteristic ze

XueGuang Zhang

Sizes of narrow emission line regions (NLRs) of AGN could be estimated by [O~{\sc iii}] line luminosity $L_{O3}$ through the known $R_{NLRs}-L_{O3}$ empirical relations. Unfortunately, it is not convenient to test the $R_{NLRs}-L_{O3}$ empirical relations through structure properties of spatially resolved NLRs of large samples of AGN. In this manuscript, a m

Marcello Di Bello, Nicolò Cangiotti, Michele Loi

By combining the philosophical literature on statistical evidence and the interdisciplinary literature on algorithmic fairness, we revisit recent objections against classification parity in light of causal analyses of algorithmic fairness and the distinction between predictive and diagnostic evidence. We focus on trial proceedings as a black-box classificati

Zhixun Chen, Yali Du, David Mguni

Many leading language models (LMs) use high-intensity computational resources both during training and execution. This poses the challenge of lowering resource costs for deployment and faster execution of decision-making tasks among others. We introduce a novel plug-and-play LM framework named Language Optimising Network Distribution (LONDI) framework. LONDI

Alexandre L. Ratschat, Rubén Martín-Rodríguez, Yasemin Vardar, Gerard M. Ribbers

Object properties perceived through the tactile sense, such as weight, friction, and slip, greatly influence motor control during manipulation tasks. However, the provision of tactile information during robotic training in neurorehabilitation has not been well explored. Therefore, we designed and evaluated a tactile interface based on a two-degrees-of-freedo

Paola Ferrari, Isabella Furci, Stefano Serra-Capizzano

Motivated by a recent work on a preconditioned MINRES for flipped linear systems in imaging, in this note we extend the scope of that research for including more precise boundary conditions such as reflective and anti-reflective ones. We prove spectral results for the matrix-sequences associated to the original problem, which justify the use of the MINRES in

Xuanyu Lei, Zonghan Yang, Xinrui Chen, Peng Li

State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional capabilities in vision-language tasks. Despite their advanced functionalities, the performances of LMMs are still limited in challenging scenarios that require complex reasoning with multiple levels of visual information. Existing prompting techniques for LMMs focus on either impr

Haoxiang Zong, Chen Zhang, Xu Cai, Marta Molinas

Loop gain-based eigen-sensitivity (LGES) is a useful frequency-domain tool for oscillation diagnosis of converter-based system. However, the existing theory is still scant in two aspects: participation factor (PF) is bound up with the frequency-domain modal characteristic that does not necessarily point to the stability as that of the time-domain eigen-sensi

Fabio Bugini, Michele Coghi, Torstein Nilssen

In this work we show that rough stochastic differential equations (RSDEs), as introduced by Friz, Hocquet, and L\^e (2021), are Malliavin differentiable. We use this to prove existence of a density when the diffusion coefficients satisfies standard ellipticity assumptions. Moreover, when the coefficients are smooth and the diffusion coefficients satisfies a

Xinyu Hu, Mingqi Gao, Sen Hu, Yang Zhang

Some prior work has shown that LLMs perform well in NLG evaluation for different tasks. However, we discover that LLMs seem to confuse different evaluation criteria, which reduces their reliability. For further verification, we first consider avoiding issues of inconsistent conceptualization and vague expression in existing NLG quality criteria themselves. S

M. W. Ochmann, W. Kollatschny, M. A. Probst, E. Romero-Colmenero

NGC 1566 is known for exhibiting recurrent outbursts, which are accompanied by changes in spectral type. The most recent transient event occurred from 2017 to 2019 and was reported to be accompanied by a change in Seyfert classification from Seyfert 1.8 to Seyfert 1.2. We analyze data from an optical spectroscopic variability campaign of NGC 1566 taken with

Michael L. Weber, Giovanni Picogna, Barbara Ercolano

Disc winds and planet-disc interactions are two crucial mechanisms that define the structure, evolution and dispersal of protoplanetary discs. While winds are capable of removing material from discs, eventually leading to their dispersal, massive planets can shape their disc by creating sub-structures such as gaps and spiral arms. We study the interplay betw

Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo

The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies. However, determining the knowledge that an LLM already possesses and the knowledge that requires the help of a search engine remains an unresolved issue. Most existing methods solve this problem through the results of

Juan Climent Vidal, Enric Cosme Llópez, Raúl Ruiz Mora

This ongoing project aims to define and investigate, from the standpoint of category theory, order theory and universal algebra, the notions of higher-order many-sorted rewriting system and of higher-order many-sorted categorial algebra and their relationships, via the higher-order Curry-Howard isomorphisms. The ultimate goal, to be developed in future versi

Julien Langlois, Renaud Gueroult

The signature of light dragging in a rotating unmagnetized plasma is studied analytically. In contrast with previous work which focused exclusively on the drag effects arising from rigid rotation, we examine here the supplemental contribution of inertia to the rest-frame dielectric properties of a rotating medium. We reveal, for the first time, that these so

Andrea Macrì, Fabrizio Lillo

Optimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreover, models with time-varying liquidity typically assume that it is observable, despite the fact that, in reality, it is latent and hard to measure in real time. In this paper we sho

Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks. This paper presents a comprehensive analysis of catastrophic forgetting in MLLMs and introduces a post-training adjustment method c

Igor A. Maia, Maxime Fiore, Romain Gojon

We study the generation of tones by ideally-expanded round jets impinging on a flat plate. Data from large-eddy simulations performed for different nozzle-to-plate distances is explored, and we consider closure of the aeroacoustic feedback loop responsible for the tones by guided jet modes. Allowable frequency ranges for resonance, underpinned by the existen

Jan Philip Wahle, Terry Ruas, Mohamed Abdalla, Bela Gipp

This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023). We put NLP's propensity to cite older work in the context of these 20 other fields to analyze whether NLP shows similar temporal citation patterns to these other fields over time or whether differences can be observed. Our analysis, based on a dataset of

Marco Faverzani, Pietro Campana, Rodolfo Carobene, Marco Gobbo

Superconducting parametric amplifiers offer the capability to amplify feeble signals with extremely low levels of added noise, potentially reaching quantum-limited amplification. This characteristic makes them essential components in the realm of high-fidelity quantum computing and serves to propel advancements in the field of quantum sensing. In particular,

Wei Qin, Adam Miranowicz, Franco Nori

It has been a long-standing goal to improve dispersive qubit readout with squeezed light. However, injected external squeezing (IES) {\it cannot} enable a practically interesting increase in the signal-to-noise ratio (SNR), and simultaneously, the increase of the SNR due to the use of intracavity squeezing (ICS) is even {\it negligible}. Here, we {\it counte

Qunyue Huang, Bin Fang

Existing blind image quality assessment (BIQA) methods focus on designing complicated networks based on convolutional neural networks (CNNs) or transformer. In addition, some BIQA methods enhance the performance of the model in a two-stage training manner. Despite the significant advancements, these methods remarkably raise the parameter count of the model,

Josep Lumbreras, Marco Tomamichel

We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector. We introduce an algorithm for this problem that exhibits a minimax regret scaling as $\log^3(T)$ in the time horizon $T$, in stark contrast the square root scaling of

César L. Folcia, Josu Ortega, Teresa Sierra, Alejandro Martínez-Bueno

We present a liquid-crystal laser device based on the chiral ferroelectric nematic phase (NF*). The laser medium is obtained by mixing a ferroelectric nematic material with a chiral agent and a small proportion of a fluorescent dye. Notably, in the NF* phase very low electric fields perpendicular to the helical axis are able to reorient the molecules, giving

Daniel Jakab, Brian Michael Deegan, Sushil Sharma, Eoin Martino Grua

In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera syst

Alyzia-Maria Konsta, Gemma Di Federico, Alberto Lluch Lafuente, Andrea Burattin

Attack Trees are a graphical model of security used to study threat scenarios. While visually appealing and supported by solid theories and effective tools, one of their main drawbacks remains the amount of effort required by security experts to design them from scratch. This work aims to remedy this by providing a method for the automatic generation of Atta

Ashwin S. Pande

We use String Field Theory (SFT) to construct a higher analogue of Bunke-Schick's functor $P: \mathbf{Top}^{op} \to \mathbf{Set}$ \cite{BunkeS1} by geometrizing $P.$ We use the projection of SFT onto its massless modes \cite{SFTDiffeo} to construct the category $\C$ whose objects are pairs (which we identify with SFT backgrounds) and whose maps are morphisms

Menglin Li, Kwan Hui Lim

Social geolocation is an important problem of predicting the originating locations of social media posts. However, this task is challenging due to the need for a substantial volume of training data, alongside well-annotated labels. These issues are further exacerbated by new or less popular locations with insufficient labels, further leading to an imbalanced

Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot

Incorporating natural language rationales in the prompt and In-Context Learning (ICL) have led to a significant improvement of Large Language Models (LLMs) performance. However, generating high-quality rationales require human-annotation or the use of auxiliary proxy models. In this work, we propose Self-AMPLIFY to automatically generate rationales from post

A. Yu. Levochkina, H. G. Ahmad, P. Mastrovito, I. Chatterjee

Josephson Traveling Wave Parametric Amplifiers (JTWPAs) are largely exploited in quantum technologies for their broadband and low noise performance in the microwave regime. When one or more microwave tones are applied at the input, such devices show a complex wave-mixing response due to their intrinsic nonlinear nature. Numerical simulations of the JTWPAs no

Anas Belfathi, Ygor Gallina, Nicolas Hernandez, Richard Dufour

Recent advances in pre-trained language modeling have facilitated significant progress across various natural language processing (NLP) tasks. Word masking during model training constitutes a pivotal component of language modeling in architectures like BERT. However, the prevalent method of word masking relies on random selection, potentially disregarding do

Zhongzheng Qiao, Quang Pham, Zhen Cao, Hoang H Le

Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence of new disease classification in healthcare or the addition of new activities in human activity recognition. In such cases, a learning system is required to assimilate novel classe

Davide Mambelli, Stephan Bongers, Onno Zoeter, Matthijs T. J. Spaan

Policy gradient methods are widely adopted reinforcement learning algorithms for tasks with continuous action spaces. These methods succeeded in many application domains, however, because of their notorious sample inefficiency their use remains limited to problems where fast and accurate simulations are available. A common way to improve sample efficiency is

Maria Luisa Buzzo, Duncan A. Forbes, Thomas H. Jarrett, Francine R. Marleau

We use spectral energy distribution (SED) fitting to place constraints on the stellar populations of 59 ultra-diffuse galaxies (UDGs) in the low-to-moderate density fields of the MATLAS survey. We use the routine PROSPECTOR, coupled with archival data in the optical from DECaLS, and near- and mid-infrared imaging from WISE, to recover the stellar masses, age

Thomas Duyckaerts, Phan van Tin

We consider the nonlinear Schr{\"o}dinger equation with double power nonlinearity. We extend the scattering result in [17] for all L 2-supercritical powers, specially, our results adapt to the cases of energy-supercritical nonlinearity.

Hadi Nemati, Pedro Sánchez-Martín, Álvaro Ortega, Lukas Sigrist

In this paper, a novel approach to define the optimal bidding of renewable-only virtual power plants (RVPPs) in the day-ahead, secondary reserve, and intra-day markets is proposed. To this aim, a robust optimization algorithm is developed to account for the asymmetric nature of the uncertainties that characterize the market prices, as well as the energy prod

Sung Rak Choi, Chuyu Zhou

For Fano fibrations with $\epsilon$-lc singularities of a fixed dimension, we show the existence of bounded relative-global complements. If the base of the fibration is of dimension one, we even show the existence of bounded relative-global klt complements.

Nicolas Boizard, Kevin El Haddad, Céline Hudelot, Pierre Colombo

Deploying large language models (LLMs) of several billion parameters can be impractical in most industrial use cases due to constraints such as cost, latency limitations, and hardware accessibility. Knowledge distillation (KD) offers a solution by compressing knowledge from resource-intensive large models to smaller ones. Various strategies exist, some relyi

Hengxuan Jiang, Xiumei Wang, Jie Chen, Xingping Zhou

We investigate the emergence of unconventional corner mode in a two-dimensional topolectrical circuits induced by asymmetric couplings. The non-Hermitian skin effect of two kinked one-dimensional lattices with multiple asymmetric couplings are explored. Then we extend to the two-dimensional model, derive conditions for the non-Hermitian hybrid skin effect an

Xavier Piulachs, Klaus Langohr, Mireia Besalú, Natalia Pallarès

Two Cox-based multistate modeling approaches are compared for analyzing a complex multicohort event history process. The first approach incorporates cohort information as a fixed covariate, thereby providing a direct estimation of the cohort-specific effects. The second approach includes the cohort as stratum variable, thus giving an extra flexibility in est

Zongru Wu, Zhuosheng Zhang, Pengzhou Cheng, Gongshen Liu

Despite the notable success of language models (LMs) in various natural language processing (NLP) tasks, the reliability of LMs is susceptible to backdoor attacks. Prior research attempts to mitigate backdoor learning while training the LMs on the poisoned dataset, yet struggles against complex backdoor attacks in real-world scenarios. In this paper, we inve

Marco Gaido, Sara Papi, Matteo Negri, Luisa Bentivogli

The field of natural language processing (NLP) has recently witnessed a transformative shift with the emergence of foundation models, particularly Large Language Models (LLMs) that have revolutionized text-based NLP. This paradigm has extended to other modalities, including speech, where researchers are actively exploring the combination of Speech Foundation

Gustave Monce, Thomas Couturou, Yasmine Hamdaoui, Thomas Degueule

Designing an effective API is essential for library developers as it is the lens through which clients will judge its usability and benefits, as well as the main friction point when the library evolves. Despite its importance, defining the boundaries of an API is a challenging task, mainly due to the diverse mechanisms provided by programming languages that

Yuying Du, Xueyan Tang

Smart contracts, as a key component of blockchain technology, play a crucial role in ensuring the automation of transactions and adherence to protocol rules. However, smart contracts are susceptible to security vulnerabilities, which, if exploited, can lead to significant asset losses. This study explores the potential of enhancing smart contract security au

Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu

Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which are scarce or even unavailable in many applications. Large language models (LLMs) have recently demonstrated remarkable capabilities in few-shot and zero-shot TAG learning, but th

Pierre-Jean Bénard, Yann Traonmilin, Jean François Aujol

We consider the problem of recovering off-the-grid spikes from linear measurements. The state of the art Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with Projected Gradient Descent (PGD) successfully recovers those signals. In most cases, the main computational cost lies in a unique global descent on all parameters (positions and ampli

Ma Luo

Periodical corrugation in dielectric slab transfers the two waveguide modes at zero Bloch wave number into a leaky resonant mode and a symmetry protected bound states in the continuum (BIC) with small frequency detune. The leaky resonant mode can be directly excited by weak linearly polarized normally incident optical field. In the presence of Kerr nonlinear

Duncan Adamson, Nathan Flaherty, Igor Potapov, Paul Spirakis

Robots are becoming an increasingly common part of scientific work within laboratory environments. In this paper, we investigate the problem of designing \emph{schedules} for completing a set of tasks at fixed locations with multiple robots in a laboratory. We represent the laboratory as a graph with tasks placed on fixed vertices and robots represented as a

Pierre Fraigniaud, Mael Luce, Frederic Magniez, Ioan Todinca

We show that, for every $k\geq 2$, $C_{2k}$-freeness can be decided in $O(n^{1-1/k})$ rounds in the \CONGEST{} model by a randomized Monte-Carlo distributed algorithm with one-sided error probability $1/3$. This matches the best round-complexities of previously known algorithms for $k\in\{2,3,4,5\}$ by Drucker et al. [PODC'14] and Censor-Hillel et al. [DISC'

Alon Eden, Michal Feldman, Simon Mauras, Divyarthi Mohan

We study auction design within the widely acclaimed model of interdependent values, introduced by Milgrom and Weber [1982]. In this model, every bidder $i$ has a private signal $s_i$ for the item for sale, and a public valuation function $v_i(s_1,\ldots,s_n)$ which maps every vector of private signals (of all bidders) into a real value. A recent line of work

Yong Tian, Chung-Ming Ko, Pengfei Li, Stacy McGaugh

Recent studies reveal a radial acceleration relation (RAR) in galaxies, which illustrates a tight empirical correlation connecting the observational acceleration and the baryonic acceleration with a characteristic acceleration scale. However, a distinct RAR has been revealed on BCG-cluster scales with a seventeen times larger acceleration scale by the gravit

Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant

In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework. This limits the scope of such bounds, as other forms of capacity measures or regularizations are used in algorithms. In this paper, we leverage the framework of disintegrated PAC-Bayes bounds to derive a general genera

Yuhang Hao, Zengfu Wang, Jing Fu, Quan Pan

In solving the non-myopic radar scheduling for multiple smart target tracking within an active and passive radar network, we need to consider both short-term enhanced tracking performance and a higher probability of target maneuvering in the future with active tracking. Acquiring the long-term tracking performance while scheduling the beam resources of activ

Frédéric Havet, Florian Hörsch, Lucas Picasarri-Arrieta

A digraph is $3$-dicritical if it cannot be vertex-partitioned into two sets inducing acyclic digraphs, but each of its proper subdigraphs can. We give a human-readable proof that the number of 3-dicritical semi-complete digraphs is finite. Further, we give a computer-assisted proof of a full characterization of 3-dicritical semi-complete digraphs. There are

Augustin Lafay, Ian Le, Julien Roussillon

We study a homogeneous system of $d+8$ linear partial differential equations (PDEs) in $d$ variables arising from two-dimensional Conformal Field Theories (CFTs) with a $W_3$-symmetry algebra. In the CFT context, $d$ PDEs are third-order and correspond to the null-state equations, whereas the remaining 8 PDEs (five being second-order and three being first-or

Igor G. Korepanov

A new version of the self-similarity spin transform on three-dimensional cubic lattices is proposed that makes possible calculation of nontrivial spin correlations in a "combinatorial" model, in which all permitted spin configurations have equal probabilities.

Francesco Periti, Nina Tahmasebi

Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC). Current evaluations typically focus on a specific task known as Graded Change Detection (GCD). However, performance comparison across work are often misleading due to their reliance on diverse settings. In this paper, we evaluate state-of-the-art models and approache

Shangying Feng, Tian Liang

Given a bounded domain $\Omega \subset {\mathbb R}^{n}$ with $n\ge2$, let $\phi $ is a Young function satisfying the doubling condition with the constant $K_\phi<2^{n}$. If $\Omega$ is a John domain, we show that $\Omega $ supports a $(\phi_{n}, \phi)$-Poincar\'e inequality. Conversely, assume additionally that $\Omega$ is simply connected domain when $n=2$

Mohammed Alswaitti, Roberto Verdecchia, Grégoire Danoy, Pascal Bouvry

The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with

R. Arnau, J. M. Calabuig, Álvaro González, Enrique A. Sánchez Pérez

Index spaces serve as valuable metric models for studying properties relevant to various applications, such as social science or economics. These properties are represented by real Lipschitz functions that describe the degree of association with each element within the underlying metric space. After determining the index value within a given sample subset, t

Miles McCrory, Spencer A. Thomas

Clustering algorithms are used extensively in data analysis for data exploration and discovery. Technological advancements lead to continually growth of data in terms of volume, dimensionality and complexity. This provides great opportunities in data analytics as the data can be interrogated for many different purposes. This however leads challenges, such as

J. Kuttruff, D. Nabben, A. C. Zimmermann, A. Ryabov

Ultrafast electron microscopy provides a movie-like access to structural dynamics of materials in space and time, but fundamental atomic motions or electron dynamics are, so far, too quick to be resolved. Here we report the all-optical control, compression and characterization of electron pulses in a transmission electron microscope by the single optical cyc

Liejun Shen, Marco Squassina, Xiaoyu Zeng

We study the fractional Schr\"{o}dinger equations coupled with a neutral scalar field $$ (-\Delta)^s u+V(x)u=K(x)\phi u +g(x)|u|^{q-2}u, \quad x\in \mathbb{R}^3,\qquad (I-\Delta)^t \phi=K(x)u^2, \quad x\in \mathbb{R}^3, $$ where $(-\Delta)^s$ and $(I-\Delta)^t$ denote the fractional Laplacian and Bessel operators with $\frac{3}{4} <s<1$ and $0<t<1$, respecti

Jordi De Jonghe, Rony Keppens

We investigate the influence of background shear flow on linear resistive tearing instabilities with Joule heating for two compressible plasma slab configurations: a Harris current sheet and a force-free, shearing magnetic field that varies its direction periodically throughout the slab, possibly resulting in multiple magnetic nullplanes. To do so, we exploi

Song Guo, Fan Wu, Lei Zhang, Xiawu Zheng

Existing methods for fine-tuning sparse LLMs often suffer from resource-intensive requirements and high retraining costs. Additionally, many fine-tuning methods often rely on approximations or heuristic optimization strategies, which may lead to suboptimal solutions. To address these issues, we propose an efficient and fast framework for fine-tuning sparse L

Akash Guna R. T, Arnav Chavan, Deepak Gupta

Conventional scaling of neural networks typically involves designing a base network and growing different dimensions like width, depth, etc. of the same by some predefined scaling factors. We introduce an automated scaling approach leveraging second-order loss landscape information. Our method is flexible towards skip connections a mainstay in modern vision

Kyungmin Lee, Sangkyung Kwak, Kihyuk Sohn, Jinwoo Shin

Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency. However, such fine-tuned models are not robust; they often fail to compose with concepts of pretrained model or other fine-tuned models. To address this, we propose a novel fine-tuning objective, dubbed Direct Consistency Op

Vladimiro Benedetti, Nicolas Perrin, Weihong Xu

We prove that the Schubert structure constants of the quantum K-theory rings of symplectic Grassmannians of lines have signs that alternate with codimension and vanish for degrees at least 3. We also give closed formulas that characterize the multiplicative structure of these rings, including the Seidel representation and a Chevalley formula.

Gabriela Rus, Nadim Al Hajjar, Paul Tucan, Andra Ciocan

The development of advanced surgical systems embedding the Master-Slave control strategy introduced the possibility of remote interaction between the surgeon and the patient, also known as teleoperation. The present paper aims to integrate innovative technologies into the teleoperation process to enhance workflow during surgeries. The proposed system incorpo

Xiaxia Wang, Gong Cheng

With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task. Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for exist

Sebastian Barzaghi, Alice Bordignon, Bianca Gualandi, Silvio Peroni

One of the main goals of Open Science is to make research more reproducible. There is no consensus, however, on what exactly "reproducibility" is, as opposed for example to "replicability", and how it applies to different research fields. After a short review of the literature on reproducibility/replicability with a focus on the humanities, we describe how t

Manon Costa, Sébastien Gadat, Lorick Huang

This article studies and solves the problem of optimal portfolio allocation with CV@R penalty when dealing with imperfectly simulated financial assets. We use a Stochastic biased Mirror Descent to find optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that sati

D. Pugliese, Z. Stuchlik

Our analysis focus on the dragging effects on the accretion flows and jet emission in Kerr super-spinars. These attractors are characterized by peculiar accretion structures as double tori, or special dragged tori in the ergoregion, produced by the balance of the hydrodynamic and centrifugal forces and also effects of super-spinars repulsive gravity. We inve

Himanshu Beniwal, Dishant Patel, Kowsik Nandagopan D, Hritik Ladia

Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and reason about temporal information remains limited, hindering their application in real-world scenarios where understanding the sequential nature of events is crucial. Our study experiments with 12 state-of-the-art models (ranging from 2B to 70B+ parameters) on a novel n

Shir Kozlovsky, Omkar Joglekar, Dotan Di Castro

In the field of robotics and automation, conventional object recognition and instance segmentation methods face a formidable challenge when it comes to perceiving Deformable Linear Objects (DLOs) like wires, cables, and flexible tubes. This challenge arises primarily from the lack of distinct attributes such as shape, color, and texture, which calls for tail

Pirzada Suhail, Supratik Chakraborty, Amit Sethi

While the deployment of neural networks, yielding impressive results, becomes more prevalent in various applications, their interpretability and understanding remain a critical challenge. Network inversion, a technique that aims to reconstruct the input space from the model's learned internal representations, plays a pivotal role in unraveling the black-box

Jesse Kim

We introduce a new rotation-invariant web basis for a family of Specht modules $S^{(d^3, 1^{n-3d})}$, indexed by normal plabic graphs satisfying a degree condition and resembling $A_2$ webs. We show that the $\mathfrak{S}_n$ action on our basis can be understood combinatorially via a set of skein relations. From this basis, we obtain a cyclic sieving result

Lopamudra Kundu, Xingqin Lin, Rajesh Gadiyar

As 5G deployments continue throughout the world, concerns regarding its energy consumption have gained significant traction. This article focuses on radio access networks (RANs) which account for a major portion of the network energy use. Firstly, we introduce the state-of-the-art 3GPP and O-RAN standardization work on enhancing RAN energy efficiency. Then w

Ezra Brody, Daniele Visioni, Ewa M. Bednarz, Ben Kravitz

Climate change is a prevalent threat, and it is unlikely that current mitigation efforts will be enough to avoid unwanted impacts. One potential option to reduce climate change impacts is the use of stratospheric aerosol injection (SAI). Even if SAI is ultimately deployed, it might be initiated only after some temperature target is exceeded. The consequences

Vincent Le Cam, Daniel-Marc Ducros, Louis-Marie Cottineau

Typical structural health monitoring configuration implies sensors and supervisor installations connected by electric cable for communication. As done in other wireless projects, this one aim at reducing installation and maintenance costsby designing a wireless sensor network. One of the problem when designing wireless sensors, is data tagging: an event has

J. O'Leary, A. Melatos, N. J. O'Neill, P. M. Meyers

The magnetic dipole moment $\mu$ of an accretion-powered pulsar in magnetocentrifugal equilibrium cannot be inferred uniquely from time-averaged pulse period and aperiodic X-ray flux data, because the radiative efficiency $\eta_0$ of the accretion is unknown, as are the mass, radius, and distance of the star. The degeneracy associated with the radiative effi

Pakawut Jiradilok, Elchanan Mossel

Motivated by the classical work on finite noisy automata (Gray 1982, G\'{a}cs 2001, Gray 2001) and by the recent work on broadcasting on grids (Makur, Mossel, and Polyanskiy 2022), we introduce Gaussian variants of these models. These models are defined on graded posets. At time $0$, all nodes begin with $X_0$. At time $k\ge 1$, each node on layer $k$ comput

Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh

Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss. However, the LoRA-adapted LDMs are vulnerable to membership inference (MI) attacks that can judge whether a particular data point belongs to the private dataset, thus leading to th

Mohammed Belkasmi

In this paper we give a complete description of the $h$-amalgamation bases in the class of non trivial abelian groups.

Huiyu Huang, Zhitian Shi, Giuseppe Talli, Maxim Kuschnerov

Photonic integrated circuits utilize various waveguide materials, each excelling in specific metrics like efficient light emission, low propagation loss, high electro-optic efficiency, and potential for mass production. Inherent shortcomings in each platform push exploration of hybrid and heterogeneous integration, which demands specialized designs and extra