May 2024 arXiv papers — page 70

Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying

The Shapley value is a prominent tool for interpreting black-box machine learning models thanks to its strong theoretical foundation. However, for models with structured inputs, such as graph neural networks, existing Shapley-based explainability approaches either focus solely on node-wise importance or neglect the graph structure when perturbing the input i

B. R. Rakshith, Kinkar Chandra Das, B. J. Manjunatha

Let G be a simple graph on n vertices with vertex set V(G). The energy of G, denoted by, $\mathcal{E}(G)$ is the sum of all absolute values of the eigenvalues of the adjacency matrix $A(G)$. It is the first eigenvalue-based topological molecular index and is related to the molecular orbital energy levels of ${\pi}$-electrons in conjugated hydrocarbons. Recen

Himanshu Grover, Bhal Chandra Joshi, Jaikhomba Singha, Erbil Gügercinoğlu

The spin-down law of pulsars is generally perturbed by two types of timing irregularities: glitches and timing noise. Glitches are sudden changes in the rotational frequency of pulsars, while timing noise is a discernible stochastic wandering in the phase, period, or spin-down rate of a pulsar. We present the timing results of a sample of glitching pulsars o

Tsugumi Matsumoto, Youichi Yanase, Akito Daido

Thin-film multiphase superconductors are attracting much attention, and it is important to propose how to detect them in experiments. In this work, we study the reciprocal and nonreciprocal paraconductivity of a bilayer model with staggered Rashba-type spin-orbit coupling with and without the potential gradient and Zeeman field. This model shows the Bardeen-

Alexander D. Popov

We consider classical and quantum dynamics of relativistic oscillator in Minkowski space $\mathbb{R}^{3,1}$. It is shown that for a non-zero frequency parameter $\omega$ the covariant phase space of the classical Klein-Gordon oscillator is a homogeneous K\"ahler-Einstein manifold $Z_6=\mathrm{Ad}S_7/\mathrm{U}(1)=\mathrm{U}(3,1)/\mathrm{U}(3)\times \mathrm{U

Jens Hornbostel

We investigate the surjectivity of the real cycle class map from $I$-cohomology to classical intergral cohomology for some real smooth varieties, in particular surfaces. This might be considered as one of several possible incarnations of real integral Hodge theory.

Zonghui Yang, Shijian Gao, Xiang Cheng

Integrated sensing and communication (ISAC) technology is essential for supporting vehicular networks. However, the communication channel in this scenario exhibits time variations, and the potential targets may move rapidly, resulting in double dynamics. This nature poses a challenge for real-time precoder design. While optimization-based solutions are widel

Jérôme Arjonilla, Abdallah Saffidine, Tristan Cazenave

In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure of private information. Disregarding the issue of protecting private information can lead to a highly exploitable performance. Yet, excessive attention to it leads to hesitations t

Benedikt Mayer, Karsten Donnay, Kai Lawonn, Bernhard Preim

Science communication aims at making key research insights accessible to the broad public. If explanatory and exploratory visualization techniques are combined to do so, the approach is also referred to as exploranation. In this context, the audience is usually not required to have domain expertise. However, we show that exploranation can not only support th

Georgy Zinchenko, Vladyslav Pushenko, Joerg Schumacher

Anomalous dissipation is a dissipation mechanism of kinetic energy which is established by a sufficiently spatially rough velocity field. It implies that the rescaled mean kinetic energy dissipation rate becomes constant with respect to Reynolds number ${\rm Re}$, the dimensionless parameter that characterizes the strength of turbulence, given that ${\rm Re}

Lingshun Kong, Jiangxin Dong, Jinhui Tang, Ming-Hsuan Yang

Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. While ViTs generally outperform CNNs by effectively capturing long-range dependencies and input-specific characteristics, their computational complexity increases quadratically with image resolution. This limitation hampers their prac

Zhiheng Feng, Wenhua Wu, Tianchen Deng, Hesheng Wang

Road surface reconstruction plays a crucial role in autonomous driving, which can be used for road lane perception and autolabeling. Recently, mesh-based road surface reconstruction algorithms have shown promising reconstruction results. However, these mesh-based methods suffer from slow speed and poor reconstruction quality. To address these limitations, we

Melissa Lin, Heer Patel, Medina Lamkin, Hannah Bako

Many toolkit developers seek to streamline the visualization programming process through structured support such as prescribed templates and example galleries. However, few projects examine how users organize their own visualization programs and how their coding choices may deviate from the intents of toolkit developers, impacting visualization prototyping a

Shane Kelly

These are expanded notes from a talk at the RIMS Workshop, Algebraic Number Theory and Related Topics, December 13th, 2023. We discussed Elmanto-Morrow's motivic complex, the procdh sheafification of the classical motivic complex, and their comparison. The procdh topology and the comparison is joint work with Shuji Saito. The comparison was obtained through

Sascha C Burmeister

The use of renewable energies strengthens decarbonization strategies. To integrate volatile renewable sources, energy systems require grid expansion, storage capabilities, or flexible consumption. This study focuses on industries that adapt production to real-time energy markets, offering flexible consumption to the grid. Flexible production considers not on

Jiuming Liu, Jinru Han, Lihao Liu, Angelica I. Aviles-Rivero

Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing world. However, designing an effective 4D backbone remains challenging, mainly due to the irregular and unordered distribution of points and temporal inconsistencies across f

Qing-Hua Zhang, Shao-Ming Fei

Quantum coherence constitutes a foundational characteristic of quantum mechanics and is integral to emerging quantum resource theories. However, quantum coherence is severely restricted by environmental noise in general quantum processing, indicated by the loss of information of a quantum system. Such processing can be described by the trade-offs between the

Meiqin Liu, Chenming Xu, Yukai Gu, Chao Yao

Video compression aims to reconstruct seamless frames by encoding the motion and residual information from existing frames. Previous neural video compression methods necessitate distinct codecs for three types of frames (I-frame, P-frame and B-frame), which hinders a unified approach and generalization across different video contexts. Intra-codec techniques

Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin

This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, select, and learn new, potentially better-performing, policies. Motivated by critical applications, we move beyond point estimators. Instead, we adopt the principle of pessimism where

Yitao Peng, Lianghua He, Die Hu

With the widespread application of deep learning technology in medical image analysis, the effective explanation of model predictions and improvement of diagnostic accuracy have become urgent problems that need to be solved. Attribution methods have become key tools to help doctors better understand the diagnostic basis of models, and are used to explain and

Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren

Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathematical reasoning, their advancement in formal theorem proving is hindered by a lack of training data. To address this issue, we introduce an approach to generate extensive Lean 4 p

Kyohei Mukaida, Hidenaga Watanabe, Masaki Yamada

We present a conceptually simple model to generate asymmetries that are not directly related to baryon nor lepton charges. The model employs a three-Higgs doublet framework, wherein the other two Higgs fields are significantly heavier than the Standard Model (SM) Higgs field. The decay of these heavier Higgs fields generates asymmetry for approximately conse

Mateusz Pach, Dawid Rymarczyk, Koryna Lewandowska, Jacek Tabor

Prototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions. They follow the this looks like that reasoning, representing each prototypical part with patches from training images. However, a single image patch comprises multiple visual features, such as color, shape,

Valery A. Lunts

Let X be a smooth toric variety defined by the fan {\Sigma} . We consider {\Sigma} as a finite set with topology and define a natural sheaf of graded algebras A_{\Sigma} on {\Sigma} . The category of modules over A_{\Sigma} is studied (together with other related categories). This leads to a certain combinatorial Koszul duality equivalence. We describe the e

Nicolas Bouchot

In this paper we consider the simple random walk on $\mathbb{Z}^d$, $d \geq 3$, conditioned to stay in a large domain $D_N$ of typical diameter $N$. Considering the range up to time $t_N \geq N^{2+\delta}$ for some $\delta > 0$, we establish a coupling with what Teixeira (2009) and Li & Sznitman (2014) defined as "tilted random interlacements". This tilted i

Antonia Torres-Cubillo, Alejandro Sánchez-Postigo, Jana Jágerská, J. Gonzalo Wangüemert-Pérez

Most integrated evanescent-field photonic sensors measure changes in either the real part or the imaginary part of the complex refractive index of the sample, i.e., refraction or absorption. Here we propose and experimentally demonstrate a near-infrared sensor based on a silicon nitride Mach-Zehnder interferometer which provides a direct measurement of the c

Xiaoling Zhou, Ou Wu, Michael K. Ng, Hao Jiang

Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications. Among diverse data valuation approaches, Shapley value-based methods are predominant due to their strong theoretical grounding. However, the exact computation of Shapley values is often computationally prohibitive, prompting

Guanxiong Luo, Shoujin Huang, Martin Uecker

Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality. However, a persistent challenge lies in balancing image quality with imaging speed. This trade-off is primarily constrained by k-space measurements, which traverse specific trajectories in the spatial Fourier domain (k-space). These measurements are often undersampled to shorten

Kristian Stølevik Olsen, Hartmut Löwen

We investigate the competing effects of simultaneous presence of chirality and generalised tumbles in the dynamics of an active Brownian particle. Chiral active particles perform circular motions that give rise to slow transport at late times. By interrupting these circular trajectories at the right time or by performing a tumble at the correct angle, we sho

Jia Guo, Shuai Lu, Weihang Zhang, Fang Chen

Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models. Our research aims to bridge this substa

Venus Rai, Ivan Titov, Michael P. Adams, Kiyonori Suzuki

We have investigated the magnetic microstructure of two-phase Fe-Nb-B~based Nanoperm alloys using unpolarized small-angle neutron scattering (SANS) and small-angle X-ray scattering (SAXS). Our SANS analysis reveals a significantly large magnetic scattering contribution due to spin misalignment, primarily originating from the substantial jump in the longitudi

Fahim Hasan Khan, Akila de Silva, Gregory Dusek, James Davis

It is undeniable that citizen science contributes to the advancement of various fields of study. There are now software tools that facilitate the development of citizen science apps. However, apps developed with these tools rely on individual human skills to correctly collect useful data. Machine learning (ML)-aided apps provide on-field guidance to citizen

Anthony R. Yeates

The global magnetic field in the solar corona is known to contain free magnetic energy and magnetic helicity above that of a current-free (potential) state. But the strength of this non-potentiality and its evolution over the solar cycle remain uncertain. Here we model the corona over Solar Cycle 24 using a simplified magneto-frictional model that retains th

Rebekka Varum Woldseth, Ole Sigmund, Peter Dørffler Ladegaard Jensen

This work presents an 808-line Matlab educational code for combined multi-scale topology optimisation and phasor-based dehomogenisation titled deHomTop808. The multi-scale formulation utilises homogenisation of optimal microstructures to facilitate efficient coarse-scale optimisation. Dehomogenisation allows for a high-resolution single-scale reconstruction

Dennis Hardt, Reza Doostani, Sebastian Diehl, Nina del Ser

Many-particle systems driven out of thermal equilibrium can show properties qualitatively different from any thermal state. Here, we study a ferrimagnet in a weak oscillating magnetic field. In this model, domain walls are not static, but are shown to move actively in a direction chosen by spontaneous symmetry breaking. Thus they act like self-propelling uni

Mate Toth, Erik Leitinger, Klaus Witrisal

Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar. In this paper, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The

Sverre Holm, Joakim Bergli

Absorption of elastic waves in complex media is commonly found to increase linearly with frequency, for both longitudinal and shear waves. This ubiquitous property is observed in media such as rocks, unconsolidated sediments, and human tissue. Absorption is due to relaxation processes at the level of atomic scales and up to the sub-micron scale of biological

Haoran Chen, Micah Goldblum, Zuxuan Wu, Yu-Gang Jiang

Continual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common problem in continual learning is the classification layer's bias towards the most recent task. Traditionally, methods have relied on incorporating data from past tasks during training to

John Panickacheril John, Jörg Schumacher

The impact of variable material properties, such as temperature-dependent thermal conductivity and dynamical viscosity, on the dynamics of a fully compressible turbulent convection flow beyond the anelastic limit are studied in the present work by two series of three-dimensional direct numerical simulations in a layer of aspect ratio 4 with periodic boundary

Ioannis Caragiannis, Sebastian Homrighausen

We consider the assignment problem, where $n$ agents have to be matched to $n$ items. Each agent has a preference order over the items. In the serial dictatorship (SD) mechanism the agents act in a particular order and pick their most preferred available item when it is their turn to act. Applying SD using a uniformly random permutation as agent ordering res

Wojciech Krauze, Martyna Mazur, Arkadiusz Kuś

Transmission optical coherence tomography (OCT) enables analysis of biological specimens in vitro through detection of forward scattered light. Up to now, transmission OCT was considered as a technique that cannot directly retrieve quantitative phase and is thus a qualitative method. In this paper, we present qtOCT, a novel quantitative transmission optical

Yang Zhang, Shixin Yang, Chenjia Bai, Fei Wu

Grounding the reasoning ability of large language models (LLMs) for embodied tasks is challenging due to the complexity of the physical world. Especially, LLM planning for multi-agent collaboration requires communication of agents or credit assignment as the feedback to re-adjust the proposed plans and achieve effective coordination. However, existing method

Nikolaos Karaliolios, Hervé Le Borgne, Florian Chabot

Semi-Supervised Learning (SSL) seeks to leverage large amounts of non-annotated data along with the smallest amount possible of annotated data in order to achieve the same level of performance as if all data were annotated. A fruitful method in SSL is Pseudo-Labeling (PL), which, however, suffers from the important drawback that the associated loss function

Jinhui Ye, Xing Wang, Wenxiang Jiao, Junwei Liang

Gloss-free sign language translation (SLT) aims to develop well-performing SLT systems with no requirement for the costly gloss annotations, but currently still lags behind gloss-based approaches significantly. In this paper, we identify a representation density problem that could be a bottleneck in restricting the performance of gloss-free SLT. Specifically

Jahez Abraham Johny, Vinod P., Asmitha K. A., G. Radhamani

Malware has become a formidable threat as it has been growing exponentially in number and sophistication, thus, it is imperative to have a solution that is easy to implement, reliable, and effective. While recent research has introduced deep learning multi-feature fusion algorithms, they lack a proper explanation. In this work, we investigate the power of fu

Michele N. Notarnicola, Stefano Olivares

We investigate the role of weak-field homodyne (WF) measurement for quantum communications over a lossy bosonic channel with coherent state encoding. This kind of receiver employs photon-number resolving (PNR) detectors with finite resolution and low-intensity local oscillator. As a figure of merit, we consider the mutual information for a Gaussian input mod

Jun Guo, Zhaofeng Kang, Ji-Gang Zhao

In Ref.~\cite{Guo:2021rre}, we proposed to replace the final dark matter (DM) particle in the semi-annihilation mode $\rm DM+DM\to antiDM+Higgs~boson$ with its $Z_{N\geq 3}$ companion, thus reducing DM number density without DM-nucleon scattering. In this work, we study the indirect detection signals from DM annihilation, the Higgs boson pair with one of the

Debajyoti Choudhuri, Leandro S. Tavares, Dušan D. Repovš

We consider a multiphase spectral problem on a stratified Lie group. We prove the existence of an eigenfunction of $(2,q)$-eigenvalue problem on a bounded domain. Furthermore, we also establish a Pohozaev-like identity corresponding to the problem on the Heisenberg group.

Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang

Graph Neural Networks (GNNs) have revolutionized graph-based machine learning, but their heavy computational demands pose challenges for latency-sensitive edge devices in practical industrial applications. In response, a new wave of methods, collectively known as GNN-to-MLP Knowledge Distillation, has emerged. They aim to transfer GNN-learned knowledge to a

M. Bordag, G. L. Klimchitskaya, V. M. Mostepanenko

In this paper, we consider the convergence properties of the polarization tensor of graphene obtained in the framework of thermal quantum field theory in three-dimensional space-time. During the last years, this problem attracted much attention in connection with calculation of the Casimir force in graphene systems and investigation of the electrical conduct

I. Zanon, E. Clément, A. Goasduff, J. Menéndez

The excited states of unstable $^{20}$O were investigated via $\gamma$-ray spectroscopy following the $^{19}$O$(d,p)^{20}$O reaction at 8 $A$MeV. By exploiting the Doppler Shift Attenuation Method, the lifetime of the 2$^+_2$ and 3$^+_1$ states were firmly established. From the $\gamma$-ray branching and E2/M1 mixing ratios for transitions deexciting the 2$^

Mojtaba Bemana, Thomas Leimkühler, Karol Myszkowski, Hans-Peter Seidel

We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Relying on a pre-trained LDR generative diffusion models is vital as, first, there is no sufficiently large HDR image dataset available to re-train them, and, second, even if it was, re-training such models is impossible for most com

Jianqing Song, Jianguo Huang, Wenyu Jiang, Baoming Zhang

Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal prediction methods provide a theoretical guarantee for node classification tasks, ensuring that the conformal prediction set contains the ground-truth label with a desired probability (e.g

Michael Kerber, Florian Russold

We introduce graphcodes, a novel multi-scale summary of the topological properties of a dataset that is based on the well-established theory of persistent homology. Graphcodes handle datasets that are filtered along two real-valued scale parameters. Such multi-parameter topological summaries are usually based on complicated theoretical foundations and diffic

Yi Xiong, Ningyuan Chen, Xuefeng Gao

When two players are engaged in a repeated game with unknown payoff matrices, they may use single-agent multi-armed bandit algorithms to choose the actions independent of each other. We show that when the players use Thompson sampling, the game dynamics converges to the Nash equilibrium under a mild assumption on the payoff matrices. Therefore, algorithmic c

Paulina Tomaszewska, Mateusz Sperkowski, Przemysław Biecek

The development of Artificial Intelligence for healthcare is of great importance. Models can sometimes achieve even superior performance to human experts, however, they can reason based on spurious features. This is not acceptable to the experts as it is expected that the models catch the valid patterns in the data following domain expertise. In the work, we

Hejun Huang, Zuguo Chen, Yi Huang, Guangqiang Luo

Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis. By harnessing cardiac MRI images and necessitating o

Freddie Illingworth, David R. Wood

A $\textit{dominating $K_t$-model}$ in a graph $G$ is a sequence $(T_1,\dots,T_t)$ of pairwise disjoint non-empty connected subgraphs of $G$, such that for $1 \leqslant i<j \leqslant t$ every vertex in $T_j$ has a neighbour in $T_i$. Replacing "every vertex in $T_j$" by "some vertex in $T_j$" retrieves the standard definition of $K_t$-model, which is equival

Hoel Queffelec

These are lecture notes from a lecture series given at CIRM in the Fall 2023. They give a down-to-earth introduction to Khovanov and Seidel's categorical representation of Artin-Tits groups, emphasizing the fact that it is all explicitly computable. Several prospective applications to (geometric) group theory are mentioned.

Yongxin Guo, Zhenglin Cheng, Xiaoying Tang, Zhaopeng Tu

The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results.However, the performance of SMoE heavily depends on the choice of hyper-parameters, such as the number of experts and the number of experts to be activated (referred to as top-

Kazuhiro Ichihara, Gakuto Kato

We give a visual construction of stable maps from the $3$-sphere into the real plane enjoying the following properties; the set of definite fold points coincides with a given two-bridge link and the map only admits certain types of fibers containing two indefinite fold points. As a corollary, we determine the stable map complexities defined by Koda and Ishik

Chenglong Liu, Haoran Wei, Jinyue Chen, Lingyu Kong

Modern LVLMs still struggle to achieve fine-grained document understanding, such as OCR/translation/caption for regions of interest to the user, tasks that require the context of the entire page, or even multiple pages. Accordingly, this paper proposes Fox, an effective pipeline, hybrid data, and tuning strategy, that catalyzes LVLMs to focus anywhere on sin

Xiaobo Yang, Xiaojin Gong

This work presents a tuning-free semantic segmentation framework based on classifying SAM masks by CLIP, which is universally applicable to various types of supervision. Initially, we utilize CLIP's zero-shot classification ability to generate pseudo-labels or perform open-vocabulary segmentation. However, the misalignment between mask and CLIP text embeddin

Junjie Zheng, Xu Ge, Bin Li, Dengji Zhao

This paper examines the problem of distributing rewards on social networks to improve the efficiency of crowdsourcing tasks for sponsors. To complete the tasks efficiently, we aim to design reward mechanisms that incentivize early-joining agents to invite more participants to the tasks. Nonetheless, participants could potentially engage in strategic behavior

Pengfei Li, Ziyue Ma, Hong Wang, Juan Deng

Background and Objective: In neurosurgery, fusing clinical images and depth images that can improve the information and details is beneficial to surgery. We found that the registration of face depth images was invalid frequently using existing methods. To abundant traditional image methods with depth information, a method in registering with depth images and

Dezhong Yao, Sanmu Li, Yutong Dai, Zhiqiang Xu

Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and varying client classes over time. The constraints of storage limitations and privacy concerns confine local models to exclusively access the present data within each learning cycle.

Takahiro Iwami, Akira Omoto

In this paper, the frequency-domain sound field is regarded as an element of some band-limited function space, and a representation of the field as a linear combination of the reproducing kernel in that space is proposed. This model has the strongest representational capacity of all function systems when we know only the sound pressure information at arbitra

Tobias Galla

This paper provides what is hopefully a self-contained set of notes describing the detailed steps of a generating-functional analysis of systems of generalised Lotka-Volterra equations with random interaction coefficients. Nothing in these notes is original, instead the generating-functional method (also known as the Martin-Siggia-Rose-DeDominic-Janssen form

Viet-Hung Nguyen, Jean-Christophe Charlier

The Green's function method is recognized to be a very powerful tool for modelling quantum transport in nanoscale electronic devices. As atomistic calculations are generally expensive, numerical methods and related algorithms have been developed accordingly to optimize their computation cost. In particular, recursive techniques have been efficiently applied

Martin W. Liebeck, Cheryl E. Praeger

We determine all factorisations $X=AB$, where $X$ is a finite almost simple group and $A,B$ are core-free subgroups such that $A\cap B$ is cyclic or dihedral. As a main application, we classify the graphs $\Gamma$ admitting an almost simple arc-transitive group $X$ of automorphisms, such that $\Gamma$ has a 2-cell embedding as a map on a closed surface admit

Yijia Zheng, Marcel Worring

Hypergraphs are widely being employed to represent complex higher-order relations in real-world applications. Most existing research on hypergraph learning focuses on node-level or edge-level tasks. A practically relevant and more challenging task, edge-dependent node classification (ENC), is still under-explored. In ENC, a node can have different labels acr

Sebastian Allmeier, Nicolas Gast

We study stochastic approximation algorithms with Markovian noise and constant step-size $\alpha$. We develop a method based on infinitesimal generator comparisons to study the bias of the algorithm, which is the expected difference between $\theta_n$ -- the value at iteration $n$ -- and $\theta^*$ -- the unique equilibrium of the corresponding ODE. We show

M. Greta Ruppert, Yvonne Späck-Leigsnering, Herbert De Gersem

Efficient computation of sensitivities is a promising approach for efficiently of designing and optimizing high voltage direct current cable joints. This paper presents the adjoint variable method for coupled nonlinear transient electrothermal problems as an efficient approach to compute sensitivities with respect to a large number of design parameters. The

Joo Yong Shim, Joongheon Kim

Unlike closed systems, where the total energy and information are conserved within the system, open systems interact with the external environment which often leads to complex behaviors not seen in closed systems. The random fluctuations that arise due to the interaction with the external environment cause noise affecting the states of the quantum system, re

Klas Modin, Milo Viviani

Two-dimensional (2-D) incompressible, inviscid fluids produce fascinating patterns of swirling motion. How and why the patterns emerge are long-standing questions, first addressed in the 19th century by Helmholtz, Kirchhoff, and Kelvin. Countless researchers have since contributed to innovative techniques and results. Yet, the overarching problem of swirling

Dou Li, Kangqi Liu, Linzhen Zhao, Songbai Kang

Light shift and vapor-cell temperature shift are the two most significant factors dominating the long-term instability of compact atomic clocks. Due to the different physical mechanisms, there is not yet a solution that can effectively suppress the frequency shifts induced by these two effects. Here, we propose a 'resonance-offset' locking approach that comp

Yuxuan Liu, Tianchi Yang, Zihan Zhang, Minghui Song

Generative retrieval, a promising new paradigm in information retrieval, employs a seq2seq model to encode document features into parameters and decode relevant document identifiers (IDs) based on search queries. Existing generative retrieval solutions typically rely on a preprocessing stage to pre-define document IDs, which can suffer from a semantic gap be

Kaichen Zhang, Zixuan Yuan, Hui Xiong

In recent years, Optimized Cost Per Click (OCPC) and Optimized Cost Per Mille (OCPM) have emerged as the most widely adopted pricing models in the online advertising industry. However, the existing literature has yet to identify the specific conditions under which these models outperform traditional pricing models like Cost Per Click (CPC) and Cost Per Actio

Kai Yao, Zhaorui Tan, Zixian Su, Xi Yang

Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open unseen domains. Existing OCDA methods solve the intra-domain gaps by a divide-and-conquer strategy, which divides the problem into several individual and parallel domain adaptation

Sabri Boughorbel, MD Rizwan Parvez, Majd Hawasly

Training LLMs for low-resource languages usually utilizes data augmentation from English using machine translation (MT). This, however, brings a number of challenges to LLM training: there are large costs attached to translating and curating huge amounts of content with high-end machine translation solutions; the translated content carries over cultural bias

Joanna Waczyńska, Piotr Borycki, Joanna Kaleta, Sławomir Tadeja

Over the past years, we have observed an abundance of approaches for modeling dynamic 3D scenes using Gaussian Splatting (GS). Such solutions use GS to represent the scene's structure and the neural network to model dynamics. Such approaches allow fast rendering and extracting each element of such a dynamic scene. However, modifying such objects over time is

Gabriel Istrate

We investigate a signed version of the Hammersley process, a discrete process on words related to a property of integer sequences called heapability (Byers et al., ANALCO 2011). The specific version that we investigate corresponds to a version of this property for signed sequences. We give a characterization of the words that can appear as images the signed

Lu Wang, Hong-Yu Chen, Xiangyu Lyu, En-Kun Li

Space-borne gravitational wave detectors like TianQin might encounter data gaps due to factors like micrometeoroid collisions or hardware failures. Such events will cause discontinuity in the data, presenting challenges to the data analysis for TianQin, especially for massive black hole binary mergers. Since the signal-to-noise ratio (SNR) accumulates in a n

Simon Prucker, Lutz Schröder

Data trees serve as an abstraction of structured data, such as XML documents. A number of specification formalisms for languages of data trees have been developed, many of them adhering to the paradigm of register automata, which is based on storing data values encountered on the tree in registers for subsequent comparison with further data values. Already o

Yifan Zhang, Junhui Hou

Contrastive image-to-LiDAR knowledge transfer, commonly used for learning 3D representations with synchronized images and point clouds, often faces a self-conflict dilemma. This issue arises as contrastive losses unintentionally dissociate features of unmatched points and pixels that share semantic labels, compromising the integrity of learned representation

Matthias Chung, Rick Archibald, Paul Atzberger, Jack Michael Solomon

Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalidating artifacts. Drawing on results from compressed sensing and rate-distortion theory, we introduce effective data compression methods by developing autoencoders using high dimens

Rolf Buehler, Julian Schliwinski

Here, we present the 1UVA catalog of time variable ultraviolet (UV) sources. We describe a new analysis pipeline, the VAriable Source Clustering Analysis (VASCA). We apply the pipeline to 10 years of data from the GALaxy Evolution eXplorer (GALEX) satellite. We analyse a sky area of 302 deg$^2$ , resulting in the detection of 4202 time-variable UV sources. W

Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang

Existing genetic programming (GP) methods are typically designed based on a certain representation, such as tree-based or linear representations. These representations show various pros and cons in different domains. However, due to the complicated relationships among representation and fitness landscapes of GP, it is hard to intuitively determine which GP r

Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue

The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance. Despite their potential, effectively utilizing IoT point data within deep-learning frameworks presents significant challenges

Daniel Staško, Filip Hájek, Kristina Vlášková, Jiří Kaštil

Reliable and profound studies of actual magnetic domain structure in rare-earth A2Ir2O7 pyrochlore iridates are frequently limited by insufficient sample quality or lack of single crystals. We report the magnetic properties of the for-the-first-time synthesised Lu2Ir2O7 and Er2Ir2O7 single-crystals. The paper is focused on the robust ferromagnetic component

Brahim Driss, Jérôme Arjonilla, Hui Wang, Abdallah Saffidine

In recent years, much progress has been made in computer Go and most of the results have been obtained thanks to search algorithms (Monte Carlo Tree Search) and Deep Reinforcement Learning (DRL). In this paper, we propose to use and analyze the latest algorithms that use search and DRL (AlphaZero and Descent algorithms) to automatically learn to play an exte

Tomu Tominaga, Naomi Yamashita, Takeshi Kurashima

In this study, we critically examine the foundational premise of algorithmic recourse - a process of generating counterfactual action plans (i.e., recourses) assisting individuals to reverse adverse decisions made by AI systems. The assumption underlying algorithmic recourse is that individuals accept and act on recourses that minimize the gap between their

Ning Ji, Yanzhao Liang, Wanrang Yu, Qiuyu Yin

We perform a numerical and analytical investigation of microwave-to-optical conversion based on four-wave mixing in Rydberg atoms. Our work demonstrates that both all-resonant and off-resonant frequency-mixing configurations achieve near-unit photon conversion efficiencies. We review the conditions that can lead to the presence of two possible dark states. W

Abdul Rahman

This paper investigates the potential of Bayesian optimization (BO) to optimize the atr multiplier and atr period -the parameters of the Supertrend indicator for maximizing trading profits across diverse stock datasets. By employing BO, the thesis aims to automate the identification of optimal parameter settings, leading to a more data-driven and potentially

László Székelyhidi

Recently we introduced the concept of localisability of ideals in the Fourier algebra of locally compact Abelian groups. It turns out that localisability can be used to characterise synthesisability of varieties. Based on this we show that spectral synthesis holds on continuous images of varieties which have spectral synthesis.

Guibin Zhang, Xiangguo Sun, Yanwei Yue, Chonghe Jiang

Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale graphs. One effective approach to mitigate these challenges is graph sparsification, which involves removing non-essential edges to reduce computational overhead. However, previous g

Chan-Jan Hsu, Yi-Chang Chen, Feng-Ting Liao, Pei-Chen Ho

We propose "Generative Fusion Decoding" (GFD), a novel shallow fusion framework designed to integrate large language models (LLMs) into cross-modal text recognition systems for automatic speech recognition (ASR) and optical character recognition (OCR). We derive the necessary formulations to enable GFD to operate across mismatched token spaces of different m

Peter J. Hobson, Chris Morley, Alister Davis, Thomas Smith

We present a target field approach to analytically design magnetic fields using permanent magnets. We assume that their magnetisation is bound to a two-dimensional surface and is composed of a complete basis of surface modes. By posing the Poisson's equation relating the magnetic scalar potential to the magnetisation using Green's functions, we derive simple

Zhixiong Jin, Dimitrios Tsitsokas, Nikolas Geroliminis, Ludovic Leclercq

In large-scale traffic optimization, models based on Macroscopic Fundamental Diagram (MFD) are recognized for their efficiency in broad network analyses. However, they fail to reflect variations in the individual traffic status of each road link, leading to a gap in detailed traffic optimization and analysis. To address the limitation, this study introduces

Yefei He, Luoming Zhang, Weijia Wu, Jing Liu

KV cache stores key and value states from previous tokens to avoid re-computation, yet it demands substantial storage space, especially for long sequences. Adaptive KV cache compression seeks to discern the saliency of tokens, preserving vital information while aggressively compressing those of less importance. However, previous methods of this approach exhi