April 2024 arXiv papers — page 172

Behnam Ghavami, Amin Kamjoo, Lesley Shannon, Steve Wilton

The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent. To facilitate the transition from cloud to edge computing, this paper introduces a technique that effectively reduces the memory footprint of DNNs, accommodating the limitations of resource-constrained

Ofir Yesharim, Guy Tshuva, Ady Arie

Quantum metrology leverages quantum correlations for enhanced parameter estimation. Recently, structured light enabled increased resolution and sensitivity in quantum metrology systems. However, lossy and complex setups impacting photon flux, hinder true quantum advantage while using high dimensional structured light. We introduce a straightforward mechanica

Adnan Malik, Tayyaba Naz, Abdul Qadeer, M. Farasat Shamir

The objective of this manuscript is to investigate the traversable wormhole solutions in the background of the $f(R, \phi)$ theory of gravity, where $R$ is the Ricci scalar and $\phi$ is the scalar potential respectively. For this reason, we use the Karmarkar criterion for traversable static wormhole geometry to create a wormhole shape function. The suggeste

Muxin Han, Dongxue Qu, Cong Zhang

It has been conjectured that quantum gravity effects may cause the black-to-white hole transition due to quantum tunneling. The transition amplitude of this process is explored within the framework of the spin foam model on a 2-complex containing 56 vertices. We develop a systematic way to construct the bulk triangulation from the boundary triangulation to o

Julius Jankowski, Lara Brudermüller, Nick Hawes, Sylvain Calinon

Non-prehensile manipulation such as pushing is typically subject to uncertain, non-smooth dynamics. However, modeling the uncertainty of the dynamics typically results in intractable belief dynamics, making data-efficient planning under uncertainty difficult. This article focuses on the problem of efficiently generating robust open-loop pushing plans. First,

Donald Yau

A central question in equivariant algebraic K-theory asks whether there exists an equivariant K-theory machine from genuine symmetric monoidal G-categories to orthogonal G-spectra that preserves equivariant algebraic structures. We answer this question positively by constructing an enriched multifunctor K from the G-categorically enriched multicategory of O-

Manasvee Saraf, Luca Cortese, O. Ivy Wong, Barbara Catinella

Empirical studies of the relationship between baryonic matter in galaxies and the gravitational potential of their host halos are important to constrain our theoretical framework for galaxy formation and evolution. One such relation, between the atomic hydrogen (HI) mass of central galaxies ($M_{\rm{HI,c}}$) and the total mass of their host halos ($M_{\rm{ha

Takami Kuroda, Masaru Shibata

We investigate impacts of stellar rotation and magnetic fields on black hole (BH) formation and its subsequent explosive activities, by conducting axisymmetric radiation-magnetohydrodynamics simulations of gravitational collapse of a 70 $M_\odot$ star with two-moment multi energy neutrino transport in numerical relativity. Due to its dense stellar structure,

Benjamin Qureshi, Jenny M. Poulton, Thomas E. Ouldridge

Cells produce RNA and proteins via molecular templating networks. We show that information transmission in such networks is bounded by functions of a simple thermodynamic property of the network, regardless of complexity. Surprisingly, putative systems operating at this bound do not have a high flux around the network. Instead, they have low entropy producti

Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Fei Chen

Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering, scene layout conditioning, or image editing techniques which often require hand drawn masks. Nonetheless, pre-existing works struggle to tak

Giovanni Bussi, Massimiliano Bonomi, Paraskevi Gkeka, Michael Sattler

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational

Xiangyue Liu, Han Xue, Kunming Luo, Ping Tan

We present GenN2N, a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc. Unlike previous methods designed for individual translation tasks with task-specific schemes, GenN2N achieves all these NeRF editing tasks by employing a plug-and-play image-to-i

Xiangjie Li, Yuanbin Cheng, Xingbo Pan, Yunrong Zhang

Quantum secure direct communication (QSDC) guarantees both the security and reliability of information transmission using quantum states. One-photon-interference QSDC (OPI-QSDC) is a technique that enhances the transmission distance and ensures secure point-to-point information transmission, but it requires complex phase locking technology. This paper propos

Arun S. Kannan

The Steinberg tensor product theorem is a fundamental result in the modular representation theory of reductive algebraic groups. It describes any finite-dimensional simple module of highest weight $\lambda$ over such a group as the tensor product of Frobenius twists of simple modules with highest weights the weights appearing in a $p$-adic decomposition of $

Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt

Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications. This limitation stems from the common assumption that training and testing data share the same distribution--an assumption frequent

Klaus Heeger, Danny Hermelin, Michael L. Pinedo, Dvir Shabtay

This paper resolves a long-standing open question in bicriteria scheduling regarding the complexity of a single machine scheduling problem which combines the number of tardy jobs and the maximal tardiness criteria. We use the lexicographic approach with the maximal tardiness being the primary criterion. Accordingly, the objective is to find, among all soluti

Thomas Montandon, Oliver Hahn, Clément Stahl

Ultra-large scales close to the cosmological horizon will be probed by the upcoming observational campaigns. They hold the promise to constrain single-field inflation as well as general relativity, but in order to include them in the forthcoming analyses, their modelling has to be robust. In particular, general relativistic effects may be mistaken for primor

Shyam Prakash V P, Vivek K. Agrawal

We present the spectral and timing study of the bright NS-LMXB GX 5-1 using \textit{\textit{AstroSat}/LAXPC} and \textit{SXT} observations conducted in the year 2018. During the observation, the source traces out the complete HB and NB of the Z-track in the HID. Understanding the spectral and temporal evolution of the source along the 'Z' track can probe the

Jaehyeon Kim, Keon Lee, Seungjun Chung, Jaewoong Cho

With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach for zero-shot Text-to-Speech (TTS) synthesis. Despite the ongoing rush towards scaling paradigms, audio tokenization ironically amplifies the scalability challenge, stemming from

Xiangjie Li, Min Wang, Xingbo Pan, Yunrong Zhang

Quantum secure direct communication (QSDC) is a quantum communication paradigm that transmits confidential messages directly using quantum states. Measurement-device-independent (MDI) QSDC protocols can eliminate the security loopholes associated with measurement devices. To enhance the practicality and performance of MDI-QSDC protocols, we propose a one-pho

René Schwermer, Ruben Mayer, Hans-Arno Jacobsen

In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling collaborative processing without compromising individual data privacy. This is achieved through a decentralized network of

Gail Robertson, Amy L Wilson, Jim Q Smith

Graphical models and likelihood ratios can be used by forensic scientists to compare support given by evidence to propositions put forward by competing parties during court proceedings. Such models can also be used to evaluate support for activity-level propositions, i.e. propositions that refer to the nature of activities associated with evidence and how th

Peter Danchev, Esther García, Miguel Gómez Lozano

We study the problem of when a periodic square matrix of order $n$ over an arbitrary field $\mathbb{F}$ is decomposable into the sum of a square-zero matrix and a torsion matrix, and show that this decomposition can always be obtained for matrices of rank at least $\frac n2$ when $\mathbb{F}$ is either a field of prime characteristic, or the field of rationa

Laurent Côté, Yusuf Barış Kartal

We associate an invariant called the completed Tate cohomology to a filtered circle-equivariant spectrum and a complex oriented cohomology theory. We show that when the filtered spectrum is the spectral symplectic cohomology of a Liouville manifold, this invariant depends only on the stable homotopy type of the underlying manifold. We make explicit computati

Sanjib Kumar Agarwalla, Mauricio Bustamante, Masoom Singh, Pragyanprasu Swain

Upcoming neutrino experiments will soon search for new neutrino interactions more thoroughly than ever before, boosting the prospects of extending the Standard Model. In anticipation of this, we forecast the capability of two of the leading long-baseline neutrino oscillation experiments, DUNE and T2HK, to look for new flavor-dependent neutrino interactions w

Yves Deville

Profile likelihood provides a general framework to infer on a scalar parameter of a statistical model. A confidence interval is obtained by numerically finding the two abscissas where the profile log-likelihood curve intersects an horizontal line. An alternative derivation for this interval can be obtained by solving a constrained optimisation problem which

Hongyu Liu, Zhi-Qiang Miao, Guang-Hui Zheng

In this paper, we develop a general mathematical framework for the electro-osmosis problem to design simultaneous microscale electric and hydrodynamic cloaking in a Hele-Shaw configuration. A novel approach to achieving simultaneously cloaking both the electric and flow fields through a combination of scattering-cancellation technology and an electro-osmosis

Ziyang Wang, Sanwoo Lee, Hsiu-Yuan Huang, Yunfang Wu

Prompt-based methods have achieved promising results in most few-shot text classification tasks. However, for readability assessment tasks, traditional prompt methods lackcrucial linguistic knowledge, which has already been proven to be essential. Moreover, previous studies on utilizing linguistic features have shown non-robust performance in few-shot settin

Christopher Hahn, Jonas Harbig, Peter Kling

In the arbitrary pattern formation problem, $n$ autonomous, mobile robots must form an arbitrary pattern $P \subseteq \mathbb{R}^2$. The (deterministic) robots are typically assumed to be indistinguishable, disoriented, and unable to communicate. An important distinction is whether robots have memory and/or a limited viewing range. Previous work managed to f

Richard Seeber

This paper considers the implicit Euler discretization of Levant's arbitrary order robust exact differentiator in presence of sampled measurements. Existing implicit discretizations of that differentiator are shown to exhibit either unbounded bias errors or, surprisingly, discretization chattering despite the use of the implicit discretization. A new, proper

Renwick J. Hudspith, Matthias F. M. Lutz, Daniel Mohler

In this paper we determine the masses of $I(J^P)=0\left(3/2^+\right)$ and $0\left(3/2^-\right)$ $\Omega$-baryon ground states using lattice QCD. We utilise Wilson-clover ensembles with $2+1$ dynamical quark flavours generated by the CLS consortium along a trajectory with a constant trace of the quark-mass matrix. We show that N$^3$LO $\text{SU}(3)_f$ chiral

Carsten Carstensen, Ngoc Tien Tran

The hybrid-high order (HHO) scheme has many successful applications including linear elasticity as the first step towards computational solid mechanics. The striking advantage is the simplicity among other higher-order nonconforming schemes and its geometric flexibility as a polytopal method on the expanse of a parameter-free refined stabilization. This pape

Sahara Ali, Uzma Hasan, Xingyan Li, Omar Faruque

This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science. More specifically, the paper presents an overview of causal discovery and causal inference, explains the underlying causal assumptions, and enlists evaluation techniques and key terminologies of the domain area. The pap

Junheng Peng, Yong Li, Yingtian Liu, Zhangquan Liao

Noise is one of the primary sources of interference in seismic exploration. Many authors have proposed various methods to remove noise from seismic data; however, in the face of strong noise conditions, satisfactory results are often not achievable. In recent years, methods based on diffusion models have been applied to the task of strong noise processing in

Bastian Heinlein, Lukas Brand, Malcolm Egan, Maximilian Schäfer

In the context of the Internet of Bio-Nano Things (IoBNT), nano-devices are envisioned to perform complex tasks collaboratively, i.e., by communicating with each other. One candidate for the implementation of such devices are engineered cells due to their inherent biocompatibility. However, because each engineered cell has only little computational capabilit

Jana Jurečková, Jan Picek, Jan Kalina

Various indicators and measures of the real life procedures rise up as functionals of the quantile process of a parent random variable Z. However, Z can be observed only through a response in a linear model whose covariates are not under our control and the probability distribution of error terms is generally unknown. The problem is that of nonparametric est

Gökhan Demirel, Simon Grafenhorst, Kevin Förderer, Veit Hagenmeyer

This work analyzes the impact of varying concentrations mini-photovoltaic (MPV) systems, often referred to as balcony power plants, on the stability and control of the low-voltage (LV) grid. By local energy use and potentially reversing meter operation, we focus on how these MPV systems transform grid dynamics and elucidate consumer participation in the ener

Victor J. B. Jung, Alessio Burrello, Moritz Scherer, Francesco Conti

Transformer networks are rapidly becoming SotA in many fields, such as NLP and CV. Similarly to CNN, there is a strong push for deploying Transformer models at the extreme edge, ultimately fitting the tiny power budget and memory footprint of MCUs. However, the early approaches in this direction are mostly ad-hoc, platform, and model-specific. This work aims

Charlotte Helliar, Xizhi Liu

For an integer $r \ge 3$ and a subset $L \subset [0,r-1]$, a graph $G$ is $(K_{r}, L)$-intersecting if the number of vertices in the intersection of every pair of $K_r$ in $G$ belongs to $L$. We study the maximum number of $K_r$ in an $n$-vertex $(K_{r}, L)$-intersecting graphs. The celebrated Ruzsa--Szemer\'{e}di Theorem corresponds to the case $r=3$ and $L

Maike Behrendt, Stefan Sylvius Wagner, Marc Ziegele, Lena Wilms

Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, automating these measures has become feasible. While some studies focus on analyzing specific quality indicators,

Yuhao Wang, Wenyu Song, Zhan Cao, Zehao Yu

Degeneracy and symmetry have a profound relation in quantum systems. Here, we report gate-tunable subband degeneracy in PbTe nanowires with a nearly symmetric cross-sectional shape. The degeneracy is revealed in electron transport by the absence of a quantized plateau. Utilizing a dual gate design, we can apply an electric field to lift the degeneracy, refle

Amine Ouasfi, Adnane Boukhayma

Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio. Within the realm of 3D shape representation, Neural Signed Distance Functions (SDF) have demonstrated remarkable potential in faithfully encoding intricate shape geometry. However,

Frédéric Nataf, Emile Parolin

Domain decomposition (DD) methods are a natural way to take advantage of parallel computers when solving large scale linear systems. Their scalability depends on the design of the coarse space used in the two-level method. The analysis of adaptive coarse spaces we present here is quite general since it applies to symmetric and non-symmetric problems, to symm

Paul Oh, Sean Kwon

The 5th generation (5G) new radio (NR) access technology and the beyond-5G future wireless communication require extremely high data rate and spectrum efficiency. Energy-efficient transmission/reception schemes are also regarded as an important component. The polarization domain has attracted substantial attention in this aspects. This paper is the first to

Zhuo Chen, Yun Liu

This paper proposes a dynamic research contest, namely chasing contest, in which two asymmetric contestants exert costly effort to accomplish two breakthroughs. The contestants are asymmetric in that one of them is present-biased and has already achieved one breakthrough (the leader), whereas the other is time-consistent and needs to achieve two breakthrough

Hao Wu, Huabin Liu, Yu Qiao, Xiao Sun

We present Dive Into the BoundarieS (DIBS), a novel pretraining framework for dense video captioning (DVC), that elaborates on improving the quality of the generated event captions and their associated pseudo event boundaries from unlabeled videos. By leveraging the capabilities of diverse large language models (LLMs), we generate rich DVC-oriented caption c

Liwei Kang, Wee Sun Lee

Continual learning, an important aspect of artificial intelligence and machine learning research, focuses on developing models that learn and adapt to new tasks while retaining previously acquired knowledge. Existing continual learning algorithms usually involve a small number of tasks with uniform sizes and may not accurately represent real-world learning s

Xiaoyu Hu

Suppose that the adjusted Brill-Noether number is zero, we prove that there exists a family of twice-marked smooth projective curves such that the family of linear series with two imposed ramification conditions is irreducible. Moreover, under certain conditions, we show that the monodromy group contains the alternating group. In the case $r=1$, the monodrom

Qinxiu Sun, Qianwen Zhu

In this paper, we explore non-abelian extensions of relative Rota-Baxter Lie algebras and classify the non-abelian extensions by introducing the non-abelian second cohomology group. We also study the inducibility of a pair of automorphisms about a non-abelian extension of relative Rota-Baxter Lie algebras and derive the Wells type exact sequences. Finally, w

Thore Gerlach, Sascha Mücke

Combinatorial optimization problems, integral to various scientific and industrial applications, often vary significantly in their complexity and computational difficulty. Transforming such problems into Quadratic Unconstrained Binary Optimization (QUBO) has regained considerable research attention in recent decades due to the central role of QUBO in Quantum

Lars Willas Dreyer, Jean Rabault, Atle Jensen, Ingrid Dæhlen

Ocean waves propagating through the Marginal Ice Zone (MIZ) and the pack ice are strongly attenuated. This attenuation is critical for protecting sea ice from energetic wave events that could otherwise lead to sea ice break-up and dislocation over large areas. Despite the importance of waves-in-ice attenuation, the exact physical mechanisms involved, and the

S. Nath, I. Turan, L. Desvignes, L. Largeau

Superconductivity in ultra-doped $Si_{1-x}Ge_{x}:B$ epilayers is demonstrated by nanosecond laser doping, which allows introducing substitutional B concentrations well above the solubility limit and up to $7\,at.\%$. A Ge fraction $x$ ranging from 0 to 0.21 is incorporated in $Si:B$ : 1) through a precursor gas by Gas Immersion Laser Doping; 2) by ion implan

Haozhe Liu, Wentian Zhang, Jinheng Xie, Francesco Faccio

We explore the role of attention mechanism during inference in text-conditional diffusion models. Empirical observations suggest that cross-attention outputs converge to a fixed point after several inference steps. The convergence time naturally divides the entire inference process into two phases: an initial phase for planning text-oriented visual semantics

Victor P. Goncalves, Bruno D. Moreira, Luana Santana

The coherent production of vector mesons in photon - hadron interactions is considered one of the most promising observables to probe the QCD dynamics at high energies and the transverse spatial distribution of the gluons in the hadron wave function. In this paper, we perform an exploratory study about the dependence of the transverse momentum distributions,

Nikolaos K. Efremidis, Demetrios N. Christodoulides

Statistical mechanics can provide a versatile theoretical framework for investigating the collective dynamics of weakly nonlinear waves-settings that can be utterly complex to describe otherwise. In optics, composite systems arise due to interactions between different frequencies and/or polarizations. The purpose of this work is to develop a thermodynamic th

Jiatong Li, Gang Li, Nan Su Su Win, Ling Lin

Multispectral transmission imaging provides strong benefits for early breast cancer screening. The frame accumulation method addresses the challenge of low grayscale and signal-to-noise ratio resulting from the strong absorption and scattering of light by breast tissue. This method introduces redundancy in data while improving the grayscale and signal-to-noi

Benjamin Bach, Fanny Chevalier, Helen-Nicole Kostis, Mark Subbaro

This first workshop on visualization for climate action and sustainability aims to explore and consolidate the role of data visualization in accelerating action towards addressing the current environmental crisis. Given the urgency and impact of the environmental crisis, we ask how our skills, research methods, and innovations can help by empowering people a

Zehan Zheng, Fan Lu, Weiyi Xue, Guang Chen

Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS), LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this, we propose LiDAR4D, a differenti

Pablo A. Morales, Pavel Castro-Villarreal

Temperature constraints are highly desirable in the experimental setup when seeking the synthesis of new carbon structures. Fluctuations of the Dirac field result in temperature-dependent corrections to the Helfrich-Canham formulation, which governs the classical elasticity of the graphene membrane at equilibrium. Here, we examine the emergent shapes allowed

Sebastiano Bontorin, Simone Centellegher, Riccardo Gallotti, Luca Pappalardo

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviours. Our study introduces an approac

Kostiantyn Drach

We generalize the classical Blaschke Rolling Theorem to convex domains in Riemannian manifolds of bounded sectional curvature and arbitrary dimension. Our results are sharp and, in this sharp form, are new even in the model spaces of constant curvature.

Eddardaa B. Loussaief, Mohammed Ayad, Domenc Puig, Hatem A. Rashwan

The joint utilization of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies. Integrating information from multiple data domains has shown promise in improving model generalizability and adaptability. However, thi

Xin-Xiang Ju, Teng-Zhou Lai, Bo-Hao Liu, Wen-Bin Pan

We investigate a new proposal connecting the geometry at various radial scales in asymptotic AdS spacetime with entanglement structure at corresponding real-space length scales of the boundary theory. With this proposal, the bulk IR geometry encodes the long-scale entanglement structure of the dual quantum system. We consider two distinct types of IR geometr

Theis Bathke

Recent literature has found conditional transition rates to be a useful tool for avoiding Markov assumptions in multi-state models. While the estimation of univariate conditional transition rates has been extensively studied, the intertemporal dependencies captured in the bivariate conditional transition rates still require a consistent estimator. We provide

Adrian Pena, Bogdan Ostahie, Cristian Radu

We theoretically study the 2D Su-Schrieffer-Heeger model in the context of Floquet topological insulators (FTIs). FTIs are systems which undergo topological phase transitions, governed by Chern numbers, as a result of time reversal symmetry (TRS) breaking by a time periodic process. In our proposed model, the condition of TRS breaking is achieved by circular

Upalaparna Banerjee, Joydeep Chakrabortty, Kaanapuli Ramkumar

We outline a proposal, based on the Heat-Kernel method, to compute 1PI effective action up to any loop order for quantum field theory with scalar and fermion fields. We algebraically extract the divergences associated with the composite operators without explicitly performing any momentum loop integral. We perform this analysis explicitly for one and two-loo

Haofan Wang, Matteo Spinelli, Qixun Wang, Xu Bai

Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization. However, despite this notable progress, current models continue to grapple with several complex challenges in producing style-consistent image generation. Firstly, the concept of style is inherently underdetermined, encompassing

James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau, Rebekah Rajiah

The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software app

Luca Benfenati, Daniele Jahier Pagliari, Luca Zanatta, Yhorman Alexander Bedoya Velez

Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use of Transformer neural networks, with a Masked Auto-Encoder architecture, as Foundation Models for SHM. We demo

Robin Haunschild, Lutz Bornmann

Global overlay maps of science use base maps that are overlaid by specific data (from single researchers, institutions, or countries) for visualizing scientific performance such as field-specific paper output. A procedure to create global overlay maps using OpenAlex is proposed. Six different global base maps are provided. Using one of these base maps, examp

Adrian Moldovan, Angel Caţaron, Răzvan Andonie

Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Ne

Yunfan Lu, Yijie Xu, Wenzong Ma, Weiyu Guo

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the

Patrick S. Nairne

In this paper we provide two new constructions that are useful for the theory of projection complexes developed by Bestvina, Bromberg, Fujiwara and Sisto. We prove that there exists a subtree of the projection complex which is quasiisometric to the projection complex. We use this subtree to form a tree of metric spaces, which is a subgraph of the quasi-tree

Yao Lu, Si Wu

The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store predefined pattern sequences and retrieve them robustly. We show that to store arbitrary pattern sequences, it i

Marko Zaric, Jakob Hollenstein, Justus Piater, Erwan Renaudo

Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics. Current state-of-the-art action representations in robotics lack proper effect-driven learning of the robot's actions. Although successful in solving manipulation tasks, deep learning methods also lack this ability, in addition to th

Manuel Klädtke, Moritz Schulze Darup, Daniel E. Quevedo

Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct data-driven predictive control (FCS-DPC) counterpart has received relatively limited attention. In this paper, we introduce a novel reformulation of a commonly used DPC scheme tha

Mamadou Keita, Wassim Hamidouche, Hassen Bougueffa, Abdenour Hadid

In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have also raised concerns about the potential misuse of these images, including the creation of misleading content such as fake news and propagand

Qiu-Cheng Song, Travis J. Baker, Howard M. Wiseman

Quantum steering is the phenomenon whereby one party (Alice) proves entanglement by "steering'' the system of another party (Bob) into distinct ensembles of states, by performing different measurements on her subsystem. Here, we investigate steering in a network scenario involving $n$ parties, who each perform local measurements on part of a global quantum s

Hemant Rathi

In the present thesis, we study various models of 2D dilaton gravity known as JT gravity coupled with non-trivial gauge interactions (for instance $SU(2)$ Yang-Mills, quartic interactions between $U(1)$ gauge fields and Modmax interactions etc.). In particular, we investigate the effects of the non-trivial gauge couplings on the thermal properties of black h

Ilya Vorobyev, Christian Deppe, Holger Boche

Many communication applications incorporate event-triggered behavior, where the conventional Shannon capacity may not effectively gauge performance. Consequently, we advocate for the concept of identification capacity as a more suitable metric for assessing these systems. We consider deterministic identification codes for the Gaussian AWGN, the slow fading,

Alessandro Brusaferri, Andrea Ballarino, Luigi Grossi, Fabrizio Laurini

Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles have been recently shown to outperform state of the art PEPF b

Manuel Klädtke, Moritz Schulze Darup

Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer inspection, many of these frameworks are more closely related (or perhaps even equivalent) to each other than it may first appear. We argue for a more formal characterization of thes

Xiu-Lei Ren

We present the light-quark mass dependence of the $\Lambda(1405)$ resonance at leading order in a renormalizable framework of covariant chiral effective field theory. The meson-baryon scattering amplitudes, which are obtained by solving the scattering equation within time-ordered perturbation theory, follow the quark mass trajectory of the Coordinated Lattic

Guglielmo Bonifazi, Iason Chalas, Gian Hess, Jakub Łucki

This paper explores the connection between two recently identified phenomena in deep learning: plasticity loss and neural collapse. We analyze their correlation in different scenarios, revealing a significant association during the initial training phase on the first task. Additionally, we introduce a regularization approach to mitigate neural collapse, demo

Jiale Li, Jiayang Li, Jiahao Chen, Yifan Li

Human-like Agents with diverse and dynamic personalities could serve as an essential design probe in the process of user-centered design, thereby enabling designers to enhance the user experience of interactive applications. In this article, we introduce Evolving Agents, a novel agent architecture that consists of two systems: Personality and Behavior. The P

Viet-Tung Do, Van-Khanh Hoang, Duy-Hung Nguyen, Shahab Sabahi

Large Language Models (LLMs) can perform various natural language processing tasks with suitable instruction prompts. However, designing effective prompts manually is challenging and time-consuming. Existing methods for automatic prompt optimization either lack flexibility or efficiency. In this paper, we propose an effective approach to automatically select

Harsha Miriam Reji, Hemant Shreepad Hegde, R. Prabhu

We study the separability in multiqubit pure and mixed Greenberger-Horne-Zeilinger (GHZ) and W states with an accelerating qubit using the Abe-Rajagopal (AR) $ q $-conditional entropy. We observe that the pure multiqubit GHZ and W states in the inertial : non-inertial bipartition with one of their qubits accelerated will remain non-separable irrespective of

Ziemowit Kostana, Assaf Rinot, Saharon Shelah

We introduce a new weak variation of diamond that is meant to only guess the branches of a Kurepa tree. We demonstrate that this variation is considerably weaker than diamond by proving it is compatible with Martin's axiom. We then prove that this principle is nontrivial by showing it may consistently fail.

Pakawut Jiradilok

We derive some combinatorial formulas related to the diagonal Ramsey numbers $R(k)$. Each formula is a statement of the form "$F(n,k) = 0$ if and only if $n \ge R(k)$," where $F(n,k)$ is a combinatorial expression which depends on $n$ and $k$. Our work is closely related to a recent work by De Loera and Wesley.

Kiichiro Toyoizumi, Kaito Wada, Naoki Yamamoto, Kazuo Hoshino

Quantum algorithms are still challenging to solve linear systems of equations on real devices. This challenge arises from the need for deep circuits and numerous ancilla qubits. We introduce the quantum conjugate gradient (QCG) method using the quantum eigenvalue transformation (QET). The circuit depth of this algorithm depends on the square root of the coef

Valentina Gualtieri, Charles Renshaw-Whitman, Vinicius Hernandes, Eliska Greplova

We introduce QDsim, a python package tailored for the rapid generation of charge stability diagrams in large-scale quantum dot devices, extending beyond traditional double or triple dots. QDsim is founded on the constant interaction model from which we rephrase the task of finding the lowest energy charge configuration as a convex optimization problem. There

CMS Collaboration

Diboson production in association with jets is studied in the fully leptonic final states, pp $\to$ (Z$\gamma^*$)(Z/$\gamma^*$) + jets $\to$ 2$\ell$2$\ell'$ + jets, ($\ell,\ell'$ = e or $\mu$) in proton-proton collisions at a center-of-mass energy of 13 TeV. The data sample corresponds to an integrated luminosity of 138 fb$^{-1}$ collected with the CMS detec

Zheng Yuan, Dorina de Jong, Štefan Beňuš, Noël Nguyen

We introduce the Alternating Reading Task (ART) Corpus, a collection of dyadic sentence reading for studying the entrainment and imitation behaviour in speech communication. The ART corpus features three experimental conditions - solo reading, alternating reading, and deliberate imitation - as well as three sub-corpora encompassing French-, Italian-, and Slo

Gautam Sharma, Chellasamy Jebarathinam, Sk Sazim, Remigiusz Augusiak

Demonstrating contextual correlations in quantum theory through the violation of a non-contextuality inequality necessarily needs some ``contexts" and thus assumes some compatibility relations between the measurements. As a result, any self-testing protocol based on the maximal violation of such inequality is not free from such assumptions. In this work, we

Boris Ryabko

The problem of testing random number generators is considered and it is shown that an approach based on algorithmic information theory allows us to compare the power of different tests in some cases where the available methods of mathematical statistics do not distinguish between the tests. In particular, it is shown that tests based on data compression meth

Pieter Cardinael, Sachin Yadav, Herwig Hahn, Ming Zhao

Fabrication of low-RF loss GaN-on-Si HEMT stacks is critical to enable competitive front-end-modules for 5G and 6G applications. The main contribution to RF losses is the interface between the III-N layer and the HR Si wafer, more specifically the AlN/Si interface. At this interface, a parasitic surface conduction layer exists in Si, which decreases the subs

Zhe Liu, Chunyang Chen, Junjie Wang, Mengzhuo Chen

Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind

Jun'ya Kume, Mark Hindmarsh

In a recent paper, the NANOGrav collaboration studied new physics explanations of the observed pulsar timing residuals consistent with a stochastic gravitational wave background (SGWB), including cosmic strings in the Nambu-Goto (NG) approximation. Analysing one of current models for the loop distribution, it was found that the cosmic string model is disfavo

Shizhan Lu

The concept of interval-valued fuzzy soft $\beta$-covering approximation spaces (IFS$\beta$CASs) is introduced to combine the theories of soft sets, rough sets and interval-valued fuzzy sets, and some fundamental propositions concerning interval-valued fuzzy soft $\beta$-neighborhoods and soft $\beta$-neighborhoods of IFS$\beta$CASs are explored. And then fo

Chen Wang, Yong Li

In this paper, we consider the dynamics of integrable stochastic Hamiltonian systems. Utilizing the Nagaev-Guivarc'h method, we obtain several generalized results of the central limit theorem. Making use of this technique and the Birkhoff ergodic theorem, we prove that the invariant tori persist under stochastic perturbations. Moreover, they asymptotically f