February 2024 arXiv papers — page 76

Ioannis Dimitriou

In this work, we consider extensions of the dual risk model with proportional gains by introducing a dependence structure between gain sizes and gain interrarrival times. Among others, we further consider the case where the proportional parameter is randomly chosen, the case where it is a uniformly random variable, as well as the case where we may have upwar

Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber

Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involves a pre-trained embedding model, which converts queries and passages into vectors to capture their semantics. However, a standard pre-trained embedding model may exhibit sub-optim

Jacob Finkenrath

State-of-the-art simulations of discrete gauge theories are based on Markov chains with local changes in the field space, which however at very fine lattice spacings are notoriously difficult due to separated topological sectors of the gauge field. Hybrid Monte Carlo (HMC) algorithms, which are very efficient at coarser lattice spacings, suffer from increasi

Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman

Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evidence. From an eXplainable AI (XAI) perspective, such models are interesting as they tend to be compact. Moreover, captured relations can be directly inspected by domain experts. In

Jiajie Jin, Yutao Zhu, Yujia Zhou, Zhicheng Dou

Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy. However, inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM's answer quality. This paper introduc

Haifan Chen, Guozhi Dong, José A. Iglesias, Wei Liu

This paper contributes to the exploration of a recently introduced computational paradigm known as second-order flows, which are characterized by novel dissipative hyperbolic partial differential equations extending accelerated gradient flows to energy functionals defined on Sobolev spaces, and exhibiting significant performance particularly for the minimiza

Matteo Beccaria, Alejandro Cabo-Bizet

We consider the refined Schur superconformal index of 4d $\mathcal N=4$ $U(N)$ SYM and the first term of its giant-graviton expansion, first predicted in arXiv:2001.11667 using indirect superconformal algebra considerations and analytic continuation of fugacities. This correction is the leading non-perturbative correction to the index at large $N$ and we rep

Ashish Patel, John C. Whittaker, Stephen Burgess

Colocalization analyses assess whether two traits are affected by the same or distinct causal genetic variants in a single gene region. A class of Bayesian colocalization tests are now routinely used in practice; for example, for genetic analyses in drug development pipelines. In this work, we consider an alternative frequentist approach to colocalization te

Shuai Zhao, Leilei Gan, Luu Anh Tuan, Jie Fu

Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to language models have been proposed and successfully implemented. However, this raises the question of whether PEFT, which only updates a limited set of model parameters, constitutes security vulnerabilities when confronted with weight-poisoning backdoor attacks. In this st

Raul A. Briceño, Andrew Jackura, Dimitra A. Pefkou, Fernando Romero-López

Recently, formalism has been derived for studying electroweak transition amplitudes for three-body systems both in infinite and finite volumes. The formalism provides exact relations that the infinite-volume amplitudes must satisfy, as well as a relationship between physical amplitudes and finite-volume matrix elements, which can be constrained from lattice

Yoshiki Matsushita

In this paper, we deal with plane curves with cusps. It is well known that there are various types of cusps. Among them, we investigate criteria for $(n, n+1)$ cusps with respect to several differential conditions and relations between these singularities and evolutes of fronts. We give complete classifications with respect to $(4, 5)$-cusps.

Julian Adamek, Chris Clarkson, Ruth Durrer, Asta Heinesen

Anisotropies in the distance-redshift relation of cosmological sources are expected due to large-scale inhomogeneities in the local Universe. When the observed sources are tracing a large-scale matter flow in a general spacetime geometry, the distance-redshift relation with its anisotropies can be described with a geometrical prediction that generalises the

Qi Ju, Falin Hei, Yuxuan Liu, Zhemei Fang

Constructing effective algorithms to converge to Nash Equilibrium (NE) is an important problem in algorithmic game theory. Prior research generally posits that the upper bound on the convergence rate for games is $O\left(T^{-1/2}\right)$. This paper introduces a novel perspective, positing that the key to accelerating convergence in game theory is rationalit

Yaqi Chen, Xianyi Zeng, Ben Niu

In this paper, we study the Rosenzweig-MacArthur predator-prey model with predator-taxis and time delay defined on a disk. Theoretically, we studied the equivariant Hopf bifurcation around the positive constant steady-state solution. Standing and rotating waves have been investigated through the theory of isotropic subgroups and Lyapunov-Schmidt reduction. T

Mohammad Eslami, Tara Ghasempouri, Samuel Pagliarini

The globalization of the semiconductor industry has introduced security challenges to Integrated Circuits (ICs), particularly those related to the threat of Hardware Trojans (HTs) - malicious logic that can be introduced during IC fabrication. While significant efforts are directed towards verifying the correctness and reliability of ICs, their security is o

Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang

Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. Similar to pre-trained language models, PGMs also inherit biases from human society, resulting in discriminatory behavior in downstream applications. The debiasing process of existing fair methods is generally coupled with

C. J. Hao, Y. Xu, L. G. Hou, S. B. Bian

Unraveling the internal kinematics of open clusters is crucial for understanding their formation and evolution. However, there is a dearth of research on this topic, primarily due to the lack of high-quality kinematic data. Using the exquisite-precision astrometric parameters and radial velocities provided by Gaia data release 3, we investigate the internal

Chao Li

Geometric/arithmetic theta correspondences provide correspondences between automorphic forms and cohomology classes/algebraic cycles on Shimura varieties. We give an introduction focusing on the example of unitary groups and highlight recent advances in the arithmetic theory (also known as the Kudla program) and their applications. These are expanded lecture

Abhisha Garg, Suraj Srivastava, Nimish Yadav, Aditya K. Jagannatham

Bayesian learning aided massive antenna array based THz MIMO systems are designed for spatial-wideband and frequency-wideband scenarios, collectively termed as the dual-wideband channels. Essentially, numerous antenna modules of the THz system result in a significant delay in the transmission/ reception of signals in the time-domain across the antennas, whic

Michail Athanasakis-Kaklamanakis, Gerda Neyens

The rotational constant parametrizes the relative spacing between a molecule's rotational energy levels. It depends on the molecule's classical moments of inertia, which, in all studies, are expressed by treating the constituent nuclei as point masses separated by the bond length. We point out that treating the finite nuclear size leads to a correction to th

Willian Cintra, Zhigui Lin, Carlos Alberto Santos, Phyu Phyu Win

This paper deals with an impulsive degenerate logistic model, where pulses are introduced for modeling interventions or disturbances, and degenerate logistic term may describe refugees or protections zones for the species. Firstly, the principal eigenvalue depending on impulse rate, which is regarded as a threshold value, is introduced and characterized. Sec

Takashi Kagaya, Kenkichi Tsunoda

We discuss the sharp interface limit, leading to a mean curvature flow energy, for the rate function of the large deviation principle of a Glauber+Kawasaki process with speed change. We provide an explicit formula of the limiting functional given by the mobility and the transport coefficient.

Meng Gao

In this paper, we use the transference principle to investigate the representation of sufficiently large positive integers as the sum of prime powers and integer powers, where the primes are drawn from a positive density subset of the set of all primes , and the integer powers are drawn from a positive density subset of k-th powers.

Lars Schall, Christian Bespin, Ivan Caicedo, Jochen Dingfelder

Monolithic pixel detectors combine readout electronics and sensor in a single entity of silicon, which simplifies the production procedure and lowers the material budget compared to conventional hybrid pixel detector concepts. Benefiting from the advances in commercial CMOS processes towards large biasing voltage capabilities and the increasing availability

Guozhi Dong, Hailong Guo, Shuo Yang

In this paper, we propose novel algorithms integrated projection-free techniques with accelerated gradient flows to minimize bending energies for nonlinear plates with non-convex metric constraints. We discuss the stability and constraint consistency in a semi-discrete setting for both bilayer and prestrained plates. The proposed algorithms exhibit substanti

Xinbo Wu, Lav R. Varshney

Even though large language models (LLMs) have demonstrated remarkable capability in solving various natural language tasks, the capability of an LLM to follow human instructions is still a concern. Recent works have shown great improvements in the instruction-following capability via additional training for instruction-following tasks. However, the mechanism

Tianlin Li, Xiaoyu Zhang, Chao Du, Tianyu Pang

The widespread adoption of large language models (LLMs) underscores the urgent need to ensure their fairness. However, LLMs frequently present dominant viewpoints while ignoring alternative perspectives from minority parties, resulting in potential biases. We hypothesize that these fairness-violating behaviors occur because LLMs express their viewpoints usin

Ruixin Peng, Ziqing Li

Tennis is so popular that coaches and players are curious about factors other than skill, such as momentum. This article will try to define and quantify momentum, providing a basis for real-time analysis of tennis matches. Based on the tennis Grand Slam men's singles match data in recent years, we built two models, one is to build a model based on data-drive

Nicolas Bousquet, Linda Cook, Laurent Feuilloley, Théo Pierron

Detecting specific structures in a network has been a very active theme of research in distributed computing for at least a decade. In this paper, we start the study of subgraph detection from the perspective of local certification. Remember that a local certification is a distributed mechanism enabling the nodes of a network to check the correctness of the

Tianlin Li, Qian Liu, Tianyu Pang, Chao Du

The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources. Despite substantial efforts devoted to data cleaning and curation, well-constructed LLMs have been reported to suffer from copyright infringement, data poisoning, and/or privacy violations, which would impede practical d

Vinay Setty

In this paper, we explore the challenges associated with establishing an end-to-end fact-checking pipeline in a real-world context, covering over 90 languages. Our real-world experimental benchmarks demonstrate that fine-tuning Transformer models specifically for fact-checking tasks, such as claim detection and veracity prediction, provide superior performan

Zijun Liu, Boqun Kou, Peng Li, Ming Yan

Despite the strong performance of large language models (LLMs) across a wide range of tasks, they still have reliability issues. Previous studies indicate that strong LLMs like GPT-4-turbo excel in evaluating the reliability of responses from LLMs, but face efficiency and local deployment issues. Thus, to enable weak LLMs to effectively assess the reliabilit

Pierluigi Colli, Shunsuke Kurima, Luca Scarpa

This paper deals with a nonlocal model for a hyperbolic phase field system coupling the standard energy balance equation for temperature with a dynamic for the phase variable: the latter includes an inertial term and a nonlocal convolution-type operator where the family of kernels depends on a small parameter. We rigorously study the asymptotic convergence o

Raphaël Mouravieff, Benjamin Piwowarski, Sylvain Lamprier

Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information. In this context, many methods have highlighted the benefits of intermediate pre-training from SQL queries. However, while most approaches aim at generating final answers from inputs directly,

Asaf Petruschka, Shay Sapir, Elad Tzalik

Fault-tolerant connectivity labelings are schemes that, given an $n$-vertex graph $G=(V,E)$ and $f\geq 1$, produce succinct yet informative labels for the elements of the graph. Given only the labels of two vertices $u,v$ and of the elements in a faulty-set $F$ with $|F|\leq f$, one can determine if $u,v$ are connected in $G-F$, the surviving graph after rem

Murray Shanahan

The advent of conversational agents with increasingly human-like behaviour throws old philosophical questions into new light. Does it, or could it, ever make sense to speak of AI agents built out of generative language models in terms of consciousness, given that they are "mere" simulacra of human behaviour, and that what they do can be seen as "merely" role

Mingang Yuan, Wenzhe Zhang, Gaofei Huang

This paper investigates a wireless-powered hybrid reflecting intelligent surface (hybrid RIS)-assisted multiple access system, where the RIS can harvest energy from energy station (ES) transmitted radio frequency signal (RF), and each reflecting element can flexibly switch between active mode, passive mode, and idle mode. The objective is to minimize the max

Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo

Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. Ensemble-based learning works by training multiple (weak) classifiers whose output is aggregated. Federated ensembles are ensembles applied to a federated setting, where each classifier in the ensemble is trained o

Knut Salomonsson, Eric Oldgren, Emanuel Ström, Ozan Öktem

A major challenge in computed tomography is reconstructing objects from incomplete data. An increasingly popular solution for these problems is to incorporate deep learning models into reconstruction algorithms. This study introduces a novel approach by integrating a Fourier neural operator (FNO) into the Filtered Backprojection (FBP) reconstruction method,

Daniel Doehring, Gregor J. Gassner, Manuel Torrilhon

A novel optimization procedure for the generation of stability polynomials of stabilized explicit Runge-Kutta methods is devised. Intended for semidiscretizations of hyperbolic partial differential equations, the herein developed approach allows the optimization of stability polynomials with more than hundred stages. A potential application of these high deg

Markus Hiller, Krista A. Ehinger, Tom Drummond

We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer the drop in performance or limitation to only one input modality seen with other efficient Transformer-based approaches. BiXT is inspired by the Perceiver architectures but replaces

ATLAS Collaboration

This paper presents a search for top-squark pair production in final states with a top quark, a charm quark and missing transverse momentum. The data were collected with the ATLAS detector during LHC Run 2 and corresponds to an integrated luminosity of 139fb$^{-1}$ of proton-proton collisions at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV. The analysis is

Tuncay Aktosun, Ricardo Weder

We present the transformations to remove or add bound states or to decrease or increase the multiplicities of any existing bound states for the half-line matrix-valued Schr\"odinger operator with the general selfadjoint boundary condition, without changing the continuous spectrum of the operator. When the matrix-valued potential is selfadjoint, is integrable

Chenjie Fan, Shumao Wang

We revisit the work of \cite{raphael2011existence} on the minimal mass blow up solution for $iu_{t}+\Delta u=-k(x)|u|^{2}u$, and extend the construction of such a solution to the $k\in C^{2}$ case.

Bruno C. L. Rodrigues, Vinicius V. Santana, Sandris Murins, Idelfonso B. R. Nogueira

This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generati

Ikuya Kaneko

We establish the prime geodesic theorem for the modular surface with exponent $\frac{2}{3}+\varepsilon$, improving upon the long-standing exponent $\frac{25}{36}+\varepsilon$ of Soundararajan-Young (2013). This was previously known conditionally on the generalised Lindel\"{o}f hypothesis for quadratic Dirichlet $L$-functions. Our argument goes through a well

Ruiyi Yang, Flora D. Salim, Hao Xue

Knowledge graphs (KGs) have been increasingly employed for link prediction and recommendation using real-world datasets. However, the majority of current methods rely on static data, neglecting the dynamic nature and the hidden spatio-temporal attributes of real-world scenarios. This often results in suboptimal predictions and recommendations. Although there

Pujian Mao

In this paper, we derive the generic solution of the Newman-Penrose equations in the Newman-Unti gauge with vanishing curvature tensor. The obtained solutions are the vacua of the gravitational theory which are connected to the derivations in metric formalism from exponentiating the infinitesimal BMS generators in the BMS gauge in \cite{Compere:2016jwb,Compe

Piotr Dudek

This paper proposes the design and implementation strategy of a novel computing architecture, the Factor Machine. The work is a step towards a general-purpose parallel system operating in a non-sequential manner, exploiting processing/memory co-integration and replacing the traditional Turing/von Neumann model of a computer system with a framework based on "

Sugirtha T, Pranav S, Nitin Benjamin Dasiah, Sridevi M

Essential tasks in autonomous driving includes environment perception, detection and tracking, path planning and action control. This paper focus on path planning, which is one of the challenging task as it needs to find optimal path in highly complex and dynamic environments. Usually, a driving scenario has large number of obstacles in their route. In this

Zhanqiang Guo, Zimeng Tan, Jianjiang Feng, Jie Zhou

Vascular structure segmentation plays a crucial role in medical analysis and clinical applications. The practical adoption of fully supervised segmentation models is impeded by the intricacy and time-consuming nature of annotating vessels in the 3D space. This has spurred the exploration of weakly-supervised approaches that reduce reliance on expensive segme

Ziwei Liu, Wen Chen, Qingqing Wu, Jinhong Yuan

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service

Rohit Tyagi, A. H. Kelkar

In this paper we report on the development and performance of a cylindrical mirror analyser electron spectrometer for ion atom collision experiments. A low cost data acquisition system using Arduino microcontroller has also been developed and tested. We have measured the Auger emission spectra for various gaseous targets in collision with 1 MeV proton beams.

T. H. Freitas, J. A. Lima

The present paper deals with the investigation of the structure of general fiber product rings $R\times_TS$, where $R$, $S$ and $T$ are local rings with common residue field. We show that the Poincar\'e series of any $R$-module over the fiber product ring $R\times_TS$ is bounded by a rational function. In addition, we give a description of ${\rm depth}(R\tim

Alex V. Plyukhin

We consider a classical Brownian oscillator of mass $m$ driven from an arbitrary initial state by varying the stiffness $k(t)$ of the harmonic potential according to the protocol $k(t)=k_0+a\,\delta(t)$, involving the Dirac delta function. The microscopic work performed on the oscillator is shown to be $W=(a^2/2m)\,q^2-a q v$, where $q$ and $v$ are the coord

Andreas Padalkin, Christian Scheideler

In this paper, we study the computation of shortest paths within the \emph{geometric amoebot model}, a commonly used model for programmable matter. Shortest paths are essential for various tasks and therefore have been heavily investigated in many different contexts. For example, in the programmable matter context, which is the focus of this paper, Kostitsyn

Julian Hofstadler, Krzysztof Latuszynski, Gareth O. Roberts, Daniel Rudolf

We consider adaptive increasingly rare Markov chain Monte Carlo (MCMC) algorithms, which are adaptive MCMC methods, where the adaptation concerning the "past'' happens less and less frequently over time. Under a contraction assumption with respect to a Wasserstein-like function we deduce upper bounds of the convergence rate of Monte Carlo sums taking a renor

Kazuki Hayashi, Kazuma Onishi, Toma Suzuki, Yusuke Ide

Large-scale Vision-Language Models (LVLMs) process both images and text, excelling in multimodal tasks such as image captioning and description generation. However, while these models excel at generating factual content, their ability to generate and evaluate texts reflecting perspectives on the same image, depending on the context, has not been sufficiently

P. Leonardi, V. Nascimbeni, V. Granata, L. Malavolta

The discovery of the first transiting hot Jupiters (HJs; giant planets on orbital periods shorter than $P\sim10$ days) was announced more than twenty years ago. As both ground- and space-based follow-up observations are piling up, we are approaching the temporal baseline required to detect secular variations in their orbital parameters. In particular, severa

S. Jalilvand, M. Soltani, Z. Noorinejad, M. Amini

The concept of topological Fano resonance, characterized by an ultrasharp asymmetric line shape, is a promising candidate for robust sensing applications due to its sensitivity to external parameters and immunity to structural disorder. In this study, the vacancy-induced topological Fano resonance in a nanoribbon made up of a hexagonal lattice with armchair

Galip Ümit Yolcu, Moritz Weckbecker, Thomas Wiegand, Wojciech Samek

Data Attribution (DA) is an emerging approach in the field of eXplainable Artificial Intelligence (XAI), aiming to identify influential training datapoints which determine model outputs. It seeks to provide transparency about the model and individual predictions, e.g. for model debugging, identifying data-related causes of suboptimal performance. However, ex

Yoshiteru Maeno, Shingo Yonezawa, Aline Ramires

This review describes recent significant research developments made on the layered perovskite Sr2RuO4 and discusses current issues from both experimental and theoretical perspectives. Since the discovery of superconductivity in Sr2RuO4 in 1994, studies using high-quality single crystals quickly revealed it to be an archetypal unconventional superconductor am

Lotan Attias, Alex Levchenko, Maxim Khodas

We study the anomalous Hall effect arising from the altermagnetic order and spin-orbit interaction in doped FeSb$_2$. To investigate the anomalous transport, we have constructed a tight-binding model of FeSb$_2$. We separately considered the constraints imposed on the model parameters by the spin symmetry group and magnetic symmetry group at zero and finite

Stefan Ploner, Jungeun Won, Julia Schottenhamml, Jessica Girgis

Optical coherence tomography (OCT) is a non-invasive, micrometer-scale imaging modality that has become a clinical standard in ophthalmology. By raster-scanning the retina, sequential cross-sectional image slices are acquired to generate volumetric data. In-vivo imaging suffers from discontinuities between slices that show up as motion and illumination artif

Álvaro R. Puente-Uriona, Giulio Pettini, Michele Modugno

We present a direct comparison between the stroboscopic and non-stroboscopic effective approaches for ultracold atoms in shaken honeycomb lattices, focusing specifically on the optimal driving introduced by A. Verdeny and F. Mintert [Phys. Rev. A 92, 063615 (2015)]. In the fast-driving regime, we compare the effective non-stroboscopic Hamiltonian derived thr

Rostyslav O. Serha, Andrey A. Voronov, David Schmoll, Roman Verba

Quantum magnonics investigates the quantum-mechanical properties of magnons such as quantum coherence or entanglement for solid-state quantum information technologies at the nanoscale. The most promising material for quantum magnonics is the ferrimagnetic yttrium iron garnet (YIG), which hosts magnons with the longest lifetimes. YIG films of the highest qual

Mark Klaisoongnoen, Nick Brown, Tim Dykes, Jessica R. Jones

Whilst Field-Programmable Gate Arrays (FPGAs) have been popular in accelerating high-frequency financial workload for many years, their application in quantitative finance, the utilisation of mathematical models to analyse financial markets and securities, is less mature. Nevertheless, recent work has demonstrated the benefits that FPGAs can deliver to quant

Sarita de Berg, Tim Ophelders, Irene Parada, Frank Staals

A geometric $t$-spanner $\mathcal{G}$ on a set $S$ of $n$ point sites in a metric space $P$ is a subgraph of the complete graph on $S$ such that for every pair of sites $p,q$ the distance in $\mathcal{G}$ is a most $t$ times the distance $d(p,q)$ in $P$. We call a connection between two sites a \emph{link}. In some settings, such as when $P$ is a simple poly

Gao Depeng, Zhang Yuanzhi, Lin Hongwei

Triply periodic minimal surfaces (TPMSs) play a vital role in the design of porous structures, with applications in bone tissue engineering, chemical engineering, and the creation of lightweight models. However, fabrication of TPMSs via additive manufacturing is feasible only within a specific range of relative densities, termed the effective relative densit

Hector Gramaglia

Computational interpretations of linear logic allow static control of memory resources: the data produced by the program are endowed through its type with attributes that determine its life cycle, and guarantee safe deallocation. The use of linear types encounters limitations in practice, since linear data, in the traditional sense, do not so often appear in

M. Hukkanen, W. Ryssens, P. Ascher, M. Bender

Precision mass measurements of $^{104}$Y, $^{106}$Zr, $^{104,104m,109}$Nb, and $^{111,112}$Mo have been performed with the JYFLTRAP double Penning trap mass spectrometer at the Ion Guide Isotope Separator On-Line facility. The order of the long-lived states in $^{104}$Nb was unambiguously established. The trend in two-neutron separation energies around the $

U. K. Anandavardhanan, Hengfei Lu, Nadir Matringe, Vincent Sécherre

Let $G$ be a group with subgroup $H$, and let $(\pi,V)$ be a complex representation of $G$. The natural action of the normalizer $N$ of $H$ in $G$ on the space $\mathrm{Hom}_H(\pi,\mathbb{C})$ of $H$-invariant linear forms on $V$, provides a representation $\chi_{\pi}$ of $N$ trivial on $H$, which is a character when $\mathrm{Hom}_H(\pi,\mathbb{C})$ is one d

Clara Ferreira Cores, Kaur Kristjuhan, Mark Nicholas Jones

Despite the advantage quantum computers are expected to deliver when performing simulations compared to their classical counterparts, the current noisy intermediate-scale quantum (NISQ) devices remain limited in their capabilities. The training of parameterized quantum circuits (PQCs) remains a significant practical challenge, exacerbated by the requirement

Tuomas Orponen, Guangzeng Yi

Let $P \subset \mathbb{R}^{2}$ be a Katz-Tao $(\delta,s)$-set, and let $\mathcal{L}$ be a Katz-Tao $(\delta,t)$-set of lines in $\mathbb{R}^{2}$. A recent result of Fu and Ren gives a sharp upper bound for the $\delta$-covering number of the set of incidences $\mathcal{I}(P,\mathcal{L}) = \{(p,\ell) \in P \times \mathcal{L} : p \in \ell\}$. In fact, for $s,t

Gustavo Bramao, Ilia Tarygin

In this paper, we explore a novel combination of supervised learning and quadratic programming to refine dynamic pricing models in the car rental industry. We utilize dynamic modeling of price elasticity, informed by ordinary least squares (OLS) metrics such as p-values, homoscedasticity, error normality. These metrics, when their underlying assumptions hold

Fernando Moya Caceres, Akram Al-Hourani, Saman Atapattu, Michael Aygur

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentio

Baohao Liao, Christof Monz

With the growing size of large language models, the role of quantization becomes increasingly significant. However, outliers present in weights or activations notably influence the performance of quantized models. Recently, \citet{qtransformer} introduced a novel softmax function aimed at pretraining models in an outlier-free manner, thereby enhancing their

Riccardo Schiavone, Gianluigi Liva, Roberto Garello

We analyze the performance of enhanced spread spectrum Aloha (E-SSA) in the framework of unsourced multiple access (UMAC). The asynchronous, unframed transmission of E-SSA is modified to enable a direct comparison with framed UMAC schemes and with Polyanskiy's achievability bound. The design of E-SSA is tailored to the UMAC setting, resorting to short polar

Yi Liu, Guowei Yang, Gelei Deng, Feiyue Chen

With the prevalence of text-to-image generative models, their safety becomes a critical concern. adversarial testing techniques have been developed to probe whether such models can be prompted to produce Not-Safe-For-Work (NSFW) content. However, existing solutions face several challenges, including low success rate and inefficiency. We introduce Groot, the

Haiming Zhu, Yangyang Xu, Jun Yu, Shengfeng He

With the revolution of generative AI, video-related tasks have been widely studied. However, current state-of-the-art video models still lag behind image models in visual quality and user control over generated content. In this paper, we introduce TokenWarping, a novel framework for temporally coherent video translation. Existing diffusion-based video editin

Abhishek Kuriyal, Vaibhav Kumar

Semantic Segmentation (SS) of LiDAR point clouds is essential for many applications, such as urban planning and autonomous driving. While much progress has been made in interpreting SS predictions for images, interpreting point cloud SS predictions remains a challenge. This paper introduces pGS-CAM, a novel gradient-based method for generating saliency maps

Yoon-Seok Choun, Hyeon Jung Kim, Ki-Seok Kim

We construct an emergent holographic dual description in the Luttinger-Ward functional approach, where the renormalization group (RG) flows of collective bi-local fields appear manifestly in the bulk effective action with an emergent extra dimension. This holographic dual effective field theory reproduces $1/N$ quantum corrections in a self-consistent manner

P. Hernandez-Gomez, J. M. Muñoz, M. A. Valente, M. P. F. Graça

Lithium ferrites are well known materials due to their numerous technological applications especially in microwave devices. Mn-doped lithium ferrite nanoparticles were prepared by sol-gel technique by means of Pechini method, and then annealed at different temperatures in 250 to 1000 {\deg}C range. XRD confirms spinel formation with particle size in the 15 t

Alistair Francis, Mikolaj Czerkawski

Deep learning models are increasingly data-hungry, requiring significant resources to collect and compile the datasets needed to train them, with Earth Observation (EO) models being no exception. However, the landscape of datasets in EO is relatively atomised, with interoperability made difficult by diverse formats and data structures. If ever larger dataset

William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro

Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov

Srinivasan Umesh, Leon Cohen, Douglas Nelson

We present a framework for experimentally linking speech production and hearing. Using this approach, we describe experimental results, that lead to the concept that sounds made by different individuals and perceived to be the same can be transformed into each other by a "speech scale". The speech scale is empirically determined using only speech data. We sh

Xiang He, Zuoqin Wang

Let $\Omega \subset \mathbb R^d$ be a bounded Euclidean domain. According to the famous Weyl law, both its Dirichlet eigenvalue $\lambda_k(\Omega)$ and its Neumann eigenvalue $\mu_k(\Omega)$ have the same leading asymptotics $w_k(\Omega)=C(d,\Omega)k^{2/d}$ as $k \to \infty$. G. P\'olya conjectured in 1954 that each Dirichlet eigenvalue $\lambda_k(\Omega)$ i

Dimitrios S. Karachalios, Hossam S. Abbas

In this study, we implement a control method for stabilizing a ballbot that simultaneously follows a reference. A ballbot is a robot balancing on a spherical wheel where the single point of contact with the ground makes it omnidirectional and highly maneuverable but with inherent instability. After introducing the scheduling parameters, we start the analysis

Junbing Yan, Chengyu Wang, Jun Huang, Wei Zhang

Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference. A significant factor contributing to this progress is the benefit of in-context learning and few-shot prompting. However, the reasons behin

Foivos Alimisis

In this work, we state a general conjecture on the solvability of optimization problems via algorithms with linear convergence guarantees. We make a first step towards examining its correctness by fully characterizing the problems that are solvable via Riemannian gradient descent with linear convergence.

Ignacio Soto, Oscar Amador, Manuel Urueña, Maria Calderon

This letter studies the adaptive Decentralized Congestion Control (DCC) algorithm defined in the ETSI TS 102 687 V1.2.1 specification. We provide insights on the parameters used in the algorithm and explore the impact of those parameters on its performance. We show how the algorithm achieves good average medium utilization while protecting against congestion

Mengjie Zhao, Suliang Si, Guanghui Hu

This paper is concerned with an inverse wavenumber/frequency-dependent source problem for the Helmholtz equation. In two and three dimensions, the unknown source term is supposed to be compactly supported in spatial variables but independent on one spatial variable. The dependence of the source function on wavenumber/frequency is supposed to be unknown. Base

Matheus Fabri, Davide Polvara

In this paper we extend the study initiated in arXiv:2302.04709v2 [hep-th] to the computation of one-loop elastic amplitudes. We consider 1+1 dimensional massive bosonic Lagrangians with polynomial-like potentials and absence of inelastic processes at the tree level; starting from these assumptions we show how to write sums of one-loop diagrams as products a

Sonja Radosavljevic, Udita Sanga, Maja Schlüter

Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop

Jiyuan Ke, Minxing Li, Ping He

The principle of multi-critical-points (PMCP) may be a convincing approach to determine the emerging parameter values in different kinds of beyond-standard-model (BSM) models. This could certainly be applied to solve the problem of undetermined new parameters in the ALP-Higgs interaction models. In this paper, we apply this principle to such model and invest

Zhen Yan, Wenfei Yu, Kim L. Page, Jie Lin

Among several dozen known repeating Fast radio bursts (FRBs), those precisely localized offer the best opportunities to explore their multi-wavelength counterparts, which are key to uncovering their origins. Here we report our X-ray, ultraviolet (UV), and optical observations with the $Swift$ satellite of the repeating FRB 20190520B, in coordination with sim

Li Su, Roonak Rezvani, Shaun R. Seaman, Colin Starr

Randomised controlled trials (RCTs) are regarded as the gold standard for estimating causal treatment effects on health outcomes. However, RCTs are not always feasible, because of time, budget or ethical constraints. Observational data such as those from electronic health records (EHRs) offer an alternative way to estimate the causal effects of treatments. R

Tang Li, Maik Kahnt, Thomas L. Sheppard, Runqing Yang

Non-destructive nano-imaging of the internal structure of solid matter is only feasible using hard X-rays due to their high penetration. The highest resolution images are achieved at synchrotron radiation sources (SRF), offering superior spectral brightness and enabling methods such as X-ray ptychography delivering single-digit nm resolution. However the res

Alberto Ulgiati, Simona Paiano, Aldo Treves, Renato Falomo

The fourth-DR3 version (4FGL-DR3) of the Fermi/LAT catalogue of $\gamma$-ray sources contains $\sim$ 1000 objects at a galactic latitude |b| > 10$^{\circ}$ which are not identified with an optical counterpart (UGS). We performed a systematic study of these sources, focusing on 190 objects that have a unique X-ray counterpart in the available Swift/XRT observ

Harshit Sandilya, Peehu Raj, Jainit Sushil Bafna, Srija Mukhopadhyay

Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distrib