March 2024 arXiv papers — page 43

Víctor C. da S. Campos, Armando A. Neto, Douglas G. Macharet

Efficient path planning for autonomous mobile robots is a critical problem across numerous domains, where optimizing both time and energy consumption is paramount. This paper introduces a novel methodology that considers the dynamic influence of an environmental flow field and considers geometric constraints, including obstacles and forbidden zones, enrichin

Zerui Wang, Yan Liu, Abishek Arumugam Thiruselvi, Abdelwahab Hamou-Lhadj

In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compati

Nguyen Bich Ngoc, Thiem Hoang, Pham Ngoc Diep, Le Ngoc Tram

Polarization of starlight and thermal dust emission caused by aligned dust grains is a valuable tool to characterize magnetic fields (B-fields) and constrain dust properties. However, the physics of grain alignment is not fully understood. To test the popular paradigm of radiative torque (RAT) theory, including RAT alignment (RAT-A) and disruption (RAT-D), w

Yun-Feng Wei, Tong Liu, Cui-Ying Song

Neutrino-dominated accretion flows (NDAFs) are one of the important MeV neutrino sources and significantly contribute to the cosmic diffuse neutrino background. In this paper, we investigate the spectrum of diffuse NDAF neutrino background (DNNB) by fully considering the effects of the progenitor properties and initial explosion energies based on core-collap

Jiping Luo, Nikolaos Pappas

This paper studies the remote estimation of multiple Markov sources over a lossy and rate-constrained channel. Unlike most existing studies that treat all source states equally, we exploit the \emph{semantics of information} and consider that the remote actuator has different tolerances for the estimation errors. We aim to find an optimal scheduling policy t

Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang

We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special expert tokens within the vocabulary of a meta LLM. The meta LLM can route to an expert LLM like generating new tokens. Expert-Token-Routing not only supports learning the implicit exper

Soobin Cho

We study weak Harnack inequality and a priori H\"older regularity of harmonic functions for symmetric nonlocal Dirichlet forms on metric measure spaces with volume doubling condition. Our analysis relies on three main assumptions: the existence of a strongly local Dirichlet form with sub-Gaussian heat kernel estimates, a tail estimate of the jump measure out

Josef Valvoda, Ryan Cotterell

Current legal outcome prediction models - a staple of legal NLP - do not explain their reasoning. However, to employ these models in the real world, human legal actors need to be able to understand the model's decisions. In the case of common law, legal practitioners reason towards the outcome of a case by referring to past case law, known as precedent. We c

Er-Te Zheng, Hui-Zhen Fu, Mike Thelwall, Zhichao Fang

Timely detection of problematic research is essential for safeguarding scientific integrity. To explore whether social media commentary can serve as an early indicator of potentially problematic articles, this study analysed 3,815 tweets referencing 604 retracted articles and 3,373 tweets referencing 668 comparable non-retracted articles. Tweets critical of

Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang

We show that thermal states of local Hamiltonians are separable above a constant temperature. Specifically, for a local Hamiltonian $H$ on a graph with degree $\mathfrak{d}$, its Gibbs state at inverse temperature $\beta$, denoted by $\rho = e^{-\beta H}/ \operatorname{tr}(e^{-\beta H})$, is a classical distribution over product states for all $\beta < 1/(c\

Leif Seute, Eric Hartmann, Jan Stühmer, Frauke Gräter

Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) for

Stephen M. Walk, Rochelle E. Tractenberg

Tractenberg, Piercey, and Buell 2024 presented a list of 44 proto-Guidelines for Ethical Mathematical Practice, developed through examination of codes of ethics of adjacent disciplines and consultation with members of the mathematics community, and gave justifications for the use of these proto-Guidelines. We propose formatting the list as a deck of 44 cards

Ruopeng Gao, Ji Qi, Limin Wang

Multi-Object Tracking (MOT) has been a long-standing challenge in video understanding. A natural and intuitive approach is to split this task into two parts: object detection and association. Most mainstream methods employ meticulously crafted heuristic techniques to maintain trajectory information and compute cost matrices for object matching. Although thes

Andrew Hillier

The ambipolar diffusion approximation is used to model partially ionised plasma dynamics in a single fluid setting. To correctly apply the commonly used version of ambipolar diffusion, a set of criteria should be satisfied including the requirement that the difference in velocity between charges and neutral species (known as drift velocity) is much smaller t

Zhan Qu, Daniel Gomm, Michael Färber

Temporal Graph Neural Networks (TGNNs) are widely used to model dynamic systems where relationships and features evolve over time. Although TGNNs demonstrate strong predictive capabilities in these domains, their complex architectures pose significant challenges for explainability. Counterfactual explanation methods provide a promising solution by illustrati

Andrew K. Gillespie, Cuikun Lin, Django Jones, R. V. Duncan

Materials used to study nuclear fusion can retain atmospheric helium unless pretreated before an experiment. Understanding helium outgassing is important for accurate diagnostics in experiments surrounding nuclear fusion. The presence of helium is often cited as the primary evidence that a nuclear reaction has occurred, so it is imperative that known sources

Jacob J. Richardson, Mats Vermeeren

We present four types of discrete Lagrangian 2-form associated to the integrable quad equations of the ABS list. These include the triangle Lagrangian that has traditionally been used in the Lagrangian multiform description of ABS equations, the trident Lagrangian that was central to Part I of this paper, and two Lagrangians that have not been studied in the

Jens Klooster, Mikhail Zhelonkin

The classical tests in the instrumental variable model can behave arbitrarily if the data is contaminated. For instance, one outlying observation can be enough to change the outcome of a test. We develop a framework to construct testing procedures that are robust to weak instruments, outliers and heavy-tailed errors in the instrumental variable model. The fr

Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar, Kaiqing Zhang

Large language models (LLMs) have been increasingly employed for (interactive) decision-making, via the development of LLM-based autonomous agents. Despite their emerging successes, the performance of LLM agents in decision-making has not been fully investigated through quantitative metrics, especially in the multi-agent setting when they interact with each

Barbora Bezděková, Oleg Yu. Tsupko, Christian Pfeifer

In most analytical studies of light ray propagation in curved spacetimes around a gravitating object surrounded by a medium, it is assumed that the medium is a cold nonmagnetized plasma. The distinctive feature of this environment is that the equations of motion of the rays are independent of the plasma velocity, which, however, is not the case in other medi

Ido Lavi, Ricard Alert, Jean-François Joanny, Jaume Casademunt

Active nematics exhibit spontaneous flows through a well-known linear instability of the uniformly-aligned quiescent state. Here we show that even a linearly stable uniform state can experience a nonlinear instability, resulting in a discontinuous transition to spontaneous flows. In this case, quiescent and flowing states may coexist. Through a weakly nonlin

Taehyeong Kim

For any diagonal element $a$ with two eigenvalues, we construct a sequence of $a$-invariant probability measures on the space of unimodular lattices with high entropy but converging to the zero measure. This extends the result of Kadyrov [Ergodic Theory Dynam. Systems, 32(1) (2012)].

Stefan Steinerberger

Erd\H{o}s and Graham proposed to determine the number of subsets $S \subseteq \left\{1,2,\dots,n\right\}$ with $\sum_{s \in S} 1/s = 1$ and asked, among other things, whether that number could be as large as $2^{n - o(n)}$. We show that the number of subsets $S \subseteq \left\{1,2,\dots,n\right\}$ with $\sum_{s \in S} 1/s \leq 1$ is smaller than $2^{0.93n}$

Yang Wang, Durga Bhaktavatsala Rao Dasari, Jörg Wrachtrup

We present a protocol for the ground-state cooling of a tripartite hybrid quantum system, in which a macroscopic oscillator acts as a mediator between a single probe spin and a remote spin ensemble. In the presence of weak dispersive coupling between the spins and the oscillator, cooling of the oscillator and the ensemble spins can be achieved by exploiting

Vyacheslav Krushkal, Louisa Liles, Yangxiao Luo

We extend a construction of Jones to associate $(n, n)$-tangles with elements of Thompson's group $F$ and prove that it is asymptotically faithful as $n \to\infty$. Using this construction we show that the oriented Thompson group $\vec F$ admits a lax group action on a category of Khovanov's chain complexes.

Yi Shuen C. Lee, Margaret Millhouse, Andrew Melatos

Detection confidence of the source-agnostic gravitational-wave burst search pipeline BayesWave is quantified by the log signal-versus-glitch Bayes factor, $\ln\mathcal{B}_{\mathcal{S},\mathcal{G}}$. A recent study shows that $\ln\mathcal{B}_{\mathcal{S},\mathcal{G}}$ increases with the number of detectors. However, the increasing frequency of non-Gaussian no

Dayu Shi, Xun Zhang, Ziqi Liu, Xuanbang Chen

This paper introduces a novel approach to optimize energy efficiency in wavelength division multiplexing (WDM) Visible Light Communication (VLC) systems designed for indoor broadcasting networks. A physics-based LED model is integrated into system energy efficiency optimization, enabling quantitative analysis of the critical issue of VLC energy efficiency: t

Clemens Kirisits, Noemi Naujoks, Otmar Scherzer

Diffraction tomography is an inverse scattering technique used to reconstruct the spatial distribution of the material properties of a weakly scattering object. The object is exposed to radiation, typically light or ultrasound, and the scattered waves induced from different incident field angles are recorded. In conventional diffraction tomography, the incid

Yang Luo, Xiqing Guo, Hao Li

Due to the complementary nature of visible light and thermal infrared modalities, object tracking based on the fusion of visible light images and thermal images (referred to as RGB-T tracking) has received increasing attention from researchers in recent years. How to achieve more comprehensive fusion of information from the two modalities at a lower cost has

Ashutosh Singh, Tulay Yildirim, Om Prakash

In this study, in order to get better codes, we focus on double skew cyclic codes over the ring $\mathrm{R}= \mathbb{F}_q+v\mathbb{F}_q, ~v^2=v$ where $q$ is a prime power. We investigate the generator polynomials, minimal spanning sets, generator matrices, and the dual codes over the ring $\mathrm{R}$. As an implementation, the obtained results are illustra

Tsz Pang Yuen, Eni Musta

In order to estimate the proportion of `immune' or `cured' subjects who will never experience failure, a sufficiently long follow-up period is required. Several statistical tests have been proposed in the literature for assessing the assumption of sufficient follow-up, meaning that the study duration is longer than the support of the survival times for the u

Xixuan Hao, Wei Chen, Yibo Yan, Siru Zhong

Urban socioeconomic indicator prediction aims to infer various metrics related to sustainable development in diverse urban landscapes using data-driven methods. However, prevalent pretrained models, particularly those reliant on satellite imagery, face dual challenges. Firstly, concentrating solely on macro-level patterns from satellite data may introduce bi

Xiang Ma, Yuan Zhou, Hanwen Zhang, Qun Wang

As public awareness of environmental protection continues to grow, the trend of integrating more electric vehicles (EVs) into the transportation sector is rising. Unlike conventional internal combustion engine (ICE) vehicles, EVs can minimize carbon emissions and potentially achieve autonomous driving. However, several obstacles hinder the widespread adoptio

Titouan Renard, Andreas Schlaginhaufen, Tingting Ni, Maryam Kamgarpour

Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve entropy-regularized IRL problem. In particular, we employ a stochastic gradient descent update for the reward and a stochastic soft policy iteration update for the policy.

Samuele Garelli, Fabrizio Leisen, Luca Pratelli, Pietro Rigo

Let $\alpha_n(\cdot)=P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)$ be the predictive distributions of a sequence $(X_1,X_2,\ldots)$ of $p$-dimensional random vectors. Suppose $$\alpha_n= \mathcal{N} _p (M_n,Q_n)$$ where $M_n=\frac{1}{n}\sum_{i=1}^nX_i$ and $Q_n=\frac{1}{n}\sum_{i=1}^n(X_i-M_n)(X_i-M_n)^t$. Then, there is a random probability measure $\alp

Reed Milewicz, Jon Bisila, Miranda Mundt, Joshua Teves

Evidence-based practice (EBP) in software engineering aims to improve decision-making in software development by complementing practitioners' professional judgment with high-quality evidence from research. We believe the use of EBP techniques may be helpful for research software engineers (RSEs) in their work to bring software engineering best practices to s

Zirui Yuan, Minglai Shao, Zhiqian Chen

Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on individual information propagation, neglecting the simultaneous and interactive dissemination of multiple information items. In reality, wh

Tuofeng Lei, Qu Luo, Shuyan Ni, Shimiao Chen

Sparse code multiple access (SCMA) is a promising technique for enabling massive connectivity and high spectrum efficiency in future machine-type communication networks. However, its performance crucially depends on well-designed multi-dimensional codebooks. In this paper, we propose a novel progressive codebook optimization scheme that can achieve near-opti

Blai Bonet, Dominik Drexler, Hector Geffner

Recently, a simple but powerful language for expressing and learning general policies and problem decompositions (sketches) has been introduced in terms of rules defined over a set of Boolean and numerical features. In this work, we consider three extensions of this language aimed at making policies and sketches more flexible and reusable: internal memory st

Han Ji, Xiping Wu

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) are an emerging indoor wireless communication paradigm, which combines the advantages of the capacious optical spectra of LiFi and ubiquitous coverage of WiFi. Meanwhile, load balancing (LB) becomes a key challenge in resource management for such hybrid networks. The existing LB met

Jianfu Chen, Peice Hua, Cai Heng Li, Yanni Wu

A locally primitive 2-design is a 2-design admitting an automorphism group $G$ with primitive local actions. It is proved that $G$ is point-primitive, and either $G$ is an almost simple group, or $G$ acting on the points is an affine group.

Stefano Cassano, Fabrizio Sossan

This paper proposes a novel set of power constraints for Battery Energy Storage Systems (BESSs), referred to as Dynamic Power Constraints (DPCs), that account for the voltage and current limits of the BESS as a function of its State of Charge (SOC). These constraints are formulated for integration into optimization-based BESS scheduling problems, providing a

Huayang Li, Deng Cai, Zhi Qu, Qu Cui

Phrase-level dense retrieval has shown many appealing characteristics in downstream NLP tasks by leveraging the fine-grained information that phrases offer. In our work, we propose a new task formulation of dense retrieval, cross-lingual contextualized phrase retrieval, which aims to augment cross-lingual applications by addressing polysemy using context inf

Chuanxin Wang, Tomoya Naito, Jian Li, Haozhao Liang

We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)], the present method enables consideration of the spin and isospin degrees of freedom by employing a non-fully connected deep neural network and the unsupervised

Zonghan Zhang, Zijian Zhang, Zhiqian Chen

Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snapshot observation is especially relevant due to its prevalence. However, the inherent complexities of this problem, such as limited informatio

Vanessa Cerrone

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment currently under construction in South China, in an underground laboratory with approximately 650 m of rock overburden (1800 m.w.e.). The detector consists of a 20 kton liquid scintillator target, contained inside a 35.4-meter-diameter spherical acrylic vessel. The cen

Fei Gao, Yi Lu, Yu-Xin Liu

We solve the coupled Dyson-Schwinger equations for quark propagator and quark gluon vertex in the Nambu-Gorkov basis which is widely applied to study the color superconductivity. After considering the non-Abelian feature in the off-diagonal part of quark gluon vertex, we acquire a quark pairing gap in chiral limit above the chiral phase transition temperatur

Haoyu Li, Junpeng Wang, Yan Zheng, Liang Wang

Word embedding, a high-dimensional (HD) numerical representation of words generated by machine learning models, has been used for different natural language processing tasks, e.g., translation between two languages. Recently, there has been an increasing trend of transforming the HD embeddings into a latent space (e.g., via autoencoders) for further tasks, e

Rémi Delloque

We investigate the set of (real Dolbeault classes of) balanced metrics $\Theta$ on a balanced manifold $X$ with respect to which a torsion-free coherent sheaf $\mathcal{E}$ on $X$ is slope stable. We prove that the set of all such $[\Theta] \in H^{n - 1,n - 1}(X,\mathbb{R})$ is an open convex cone locally defined by a finite number of linear inequalities. Wh

Anastasios A. Tsiatis, Marie Davidian

The sequential multiple assignment randomized trial (SMART) is the ideal study design for the evaluation of multistage treatment regimes, which comprise sequential decision rules that recommend treatments for a patient at each of a series of decision points based on their evolving characteristics. A common goal is to compare the set of so-called embedded reg

Shuai Ma, Qiaoyi Chen, Xinru Wang, Chengbo Zheng

In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely trigger analytical thinking and face difficulties in communicating the nuances of conflicting opinions to the AI when disagreements occur. To tackle this challenge, we propose Human

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The energy-dependent cross section for $e^+e^-\to \eta\psi(2S)$ is measured at eighteen center of mass energies from 4.288 GeV to 4.951 GeV using the BESIII detector. Using the same data samples, we also perform the first search for the reaction $e^+e^-\to\eta\tilde{X}(3872)$, but no evidence is found for the $\tilde{X}(3872)$ in the $\pi^+\pi^- J/\psi$ mass

Ningyi Lyu, Paul Bergold, Micheline B. Soley, Chen Wang

We introduce quantum circuits for simulations of multi-mode state-vectors on 3D cQED processors, using matrix product state representations. The circuits are demonstrated as applied to simulations of molecular docking based on holographic Gaussian boson sampling, as illustrated for binding of a thiol-containing aryl sulfonamide ligand to the tumor necrosis f

Hanqing Yang, Marie Siew, Carlee Joe-Wong

The increasing prevalence of Cyber-Physical Systems and the Internet of Things (CPS-IoT) applications and Foundation Models are enabling new applications that leverage real-time control of the environment. For example, real-time control of Heating, Ventilation and Air-Conditioning (HVAC) systems can reduce its usage when not needed for the comfort of human o

J. Kelly, S. Zafar, L. Heidemann, J. Zacchi

In December 2023, the European Parliament provisionally agreed on the EU AI Act. This unprecedented regulatory framework for AI systems lays out guidelines to ensure the safety, legality, and trustworthiness of AI products. This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems by leveraging product quality mod

M. Glowacki, L. Albrow, T. Reynolds, E. Elson

We report on the serendipitous discovery of 49 HI-rich galaxies in a 2.3 hour Open Time observation with MeerKAT. We present their properties including their HI masses, intensity and velocity maps, and spectra. We determine that at least three HI-rich galaxy groups have been detected, potentially as part of a supergroup. Some members of these galaxy groups s

Anna Negro, Subodh P. Patil

We present the details for the covariant renormalization of the stress tensor for vacuum tensor perturbations at the level of the effective action, adopting Hadamard regularization techniques to isolate short distance divergences and gauge fixing via the Faddeev-Popov procedure. The subsequently derived renormalized stress tensor can be related to more famil

Jeanne Duflot, Margaret Cheney, James A. Given

This paper studies Frequency-Difference-of-Arrival (FDOA) curves for the 2-dimensional, 2-sensor case. The primary focus of this paper is to give a description of curves associated to the FDOA problem from the algebro-geometric point of view. To be more precise, the complex projective picture of the family of FDOA curves for all possible relative velocities

Hugo Sousa, Ricardo Campos, Alípio Jorge

Temporal expression identification is crucial for understanding texts written in natural language. Although highly effective systems such as HeidelTime exist, their limited runtime performance hampers adoption in large-scale applications and production environments. In this paper, we introduce the TEI2GO models, matching HeidelTime's effectiveness but with s

Sicong Pan, Liren Jin, Xuying Huang, Cyrill Stachniss

Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment. A key challenge in such scenarios is planning view configurations to collect informative measurements for reconstructing an initially unknown object. One-shot view planning enables efficient data collection by predicting view configurations and

F. Aharonian, F. Ait Benkhali, J. Aschersleben, H. Ashkar

HESS J1813$-$178 is a very-high-energy $\gamma$-ray source spatially coincident with the young and energetic pulsar PSR J1813$-$1749 and thought to be associated with its pulsar wind nebula (PWN). Recently, evidence for extended high-energy emission in the vicinity of the pulsar has been revealed in the Fermi Large Area Telescope (LAT) data. This motivates r

Takuya Hirose, Hajime Otsuka, Koji Tsumura, Yoshiki Uchida

We consider a $U(1)$ gauge theory on $M^4\times T^4$ with background magnetic fluxes. We show that a theory including arbitrary fluxes can always be studied in a theory involving only diagonal fluxes by appropriate coordinate transformations. It is found that the number of independent magnetic fluxes is equal to the rank of the classical value of the field s

Mimi Dai, Chao Wu

We study a class of active scalar equations with even non-local operator in the drift term. Non-trivial stationary weak solutions in the space $C^{0-}$ are constructed using the iterative convex integration approach.

Stanisław Kaźmierowski, Marcin Dziubiński

We propose an algorithm for computing Nash equilibria (NE) in a class of conflicts with multiple battlefields with uniform battlefield values and a non-linear aggregation function. By expanding the symmetrization idea of Hart [9], proposed for the Colonel Blotto game, to the wider class of symmetric conflicts with multiple battlefields, we reduce the number

Bilal Faye, Hanane Azzag, Mustapha Lebbah, Djamel Bouchaffra

Deep learning models face persistent challenges in training, particularly due to internal covariate shift and label shift. While single-mode normalization methods like Batch Normalization partially address these issues, they are constrained by batch size dependencies and limiting distributional assumptions. Multi-mode normalization techniques mitigate these

Wenhao Lin, Yuqing Ni, Wen Yang, Chao Yang

In this paper, we study a cooperative linear quadratic Gaussian (LQG) control system with a single user and a server. In this system, the user runs a process and employs the server to meet the needs of computation. However, the user regards its state trajectories as privacy. Therefore, we propose a privacy scheme, in which the user sends data to the server i

Onnop Srivannavit, Rakesh Joshi, Weibin Zhu, Bin Gong

This study presents a novel thermocouple instrument designed for precise temperature monitoring within biological tissues and cells, addressing a significant gap in biological research. Constructed on a Silicon-On-Insulator (SOI) substrate, the instrument employs doped silicon and chromium/gold junctions, achieving a Seebeck coefficient of up to 447 uV/K, ra

Atakan Coban, Mert Buyukdede

In the study, analyzes were made for one-dimensional constant acceleration motion using the Arduino microcontroller and distance sensor, using the position and time values obtained for the movement of an object thrown from bottom to top until it falls to the ground. Within the scope of the study, the concepts of displacement, distance traveled, average speed

Shreya Shankar, Rolando Garcia, Joseph M Hellerstein, Aditya G Parameswaran

Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationalization of machine learning, or MLOps, requires MLEs to have proficiency in data science and engineering. When considered holistically, the job seems staggering -- how do MLEs do MLO

Guoyang Zhao, Fulong Ma, Weiqing Qi, Yuxuan Liu

Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road. However, the complexity of road environments makes curb detection challenging. This paper introduces CurbNet, a novel framework for curb detection utilizing point cloud segmentation. To address the lack of comprehensive curb datasets with 3D ann

Zeyu Liu, Pengfei Zhang

The rapid development of quantum science and technology is leading us into an era where quantum many-body systems can be comprehended through quantum simulations. Holographic duality, which states gravity and spacetime can emerge from strongly interacting systems, then offers a natural avenue for the experimental study of gravity physics without delving into

Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang

Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing project-specific information. As much of this project-specific context cannot fit into the prompts of LLMs, we must find ways to allow the model to explore the project-level code

Albert J. Pool, Alejandro D. Somoza, Conor Mc Keever, Michael Lubasch

For the solution of time-dependent nonlinear differential equations, we present variational quantum algorithms (VQAs) that encode both space and time in qubit registers. The spacetime encoding enables us to obtain the entire time evolution from a single ground-state computation. We describe a general procedure to construct efficient quantum circuits for the

Shakthi Perera, Dilum Fernando, H. L. P. Malshan, H. M. P. S. Madushan

Denoising Diffusion Probabilistic Models (DDPMs) have significantly advanced generative AI, achieving impressive results in high-quality image and data generation. However, enhancing fidelity without compromising semantic content remains a key challenge in the field. Recent diffusion research in multiple disciplines has introduced objectives and architectura

Petr Hliněný, Jan Jedelský

We introduce H-clique-width, a new structural measure of graphs that aims to provide a hereditary analogue of the traditional graph product structure. The definition naturally generalises the ordinary clique-width concept. As a result, for a class H of graphs (such as the class of paths), the H-clique-width of a graph G equals the least integer t such that G

Márcio Rocha Ferreira, Tadeu Moreira de Classe, Sean Wolfgand Matsui Siqueira

Interactive narratives offer a novel approach to presenting business process models, making them more accessible and collaborative. These narratives create a hyper-textual environment that facilitates knowledge exchange and comprehension for ordinary individuals. However, designing such narratives is complex, as business process modelers must accurately iden

Linglin Jing, Yiming Ding, Yunpeng Gao, Zhigang Wang

Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions, which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data, previous approaches rely on event-to-image reconstruction to obtain pseudo labels for training. However, t

Kennedy A. G. Araújo, Ernesto G. Birgin, Débora P. Ronconi

The flexible job shop scheduling problem with sequencing flexibility and position-based learning effect is considered in the present work. In [K. A. G. Araujo, E. G. Birgin, and D. P. Ronconi, Technical Report MCDO02022024, 2024], models, constructive heuristics, and benchmark instances for the same problem were introduced. In the present work, we are concer

Yichuan Li, Junkai Zhao, Yixiao Li, Zheng Wu

Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations, operational inefficiencies, and process unreliability. This paper introduces a Dynamic Bin-Picking Framework

Simon Jacobsson, Raf Vandebril, Joeri van der Veken, Nick Vannieuwenhoven

Many interesting functions arising in applications map into Riemannian manifolds. We present an algorithm, using the manifold exponential and logarithm, for approximating such functions. Our approach extends approximation techniques for functions into linear spaces in such a way that we can upper bound the forward error in terms of a lower bound on the manif

Tao Zhang, Shabeeb Ameen, Sounok Ghosh, Kyungeun Kim

Tumor spheroids are in vitro three-dimensional, cellular collectives consisting of cancerous cells. Embedding these spheroids in an in vitro fibrous environment, such as a collagen network, to mimic the extracellular matrix (ECM) provides an essential platform to quantitatively investigate the biophysical mechanisms leading to tumor invasion of the ECM. To u

Shrey Aryan, Michael B. Law

We establish a concavity principle for solutions to elliptic and parabolic equations on locally symmetric spaces with nonnegative sectional curvature, extending the results of Langford and Scheuer. To the best of our knowledge, this is the first general concavity principle established on spaces with non-constant sectional curvature.

Georgii Mikriukov, Gesina Schwalbe, Franz Motzkus, Korinna Bade

Adversarial attacks (AAs) pose a significant threat to the reliability and robustness of deep neural networks. While the impact of these attacks on model predictions has been extensively studied, their effect on the learned representations and concepts within these models remains largely unexplored. In this work, we perform an in-depth analysis of the influe

Martina Lippi, Michael C. Welle, Marco Moletta, Alessandro Marino

Visual planning methods are promising to handle complex settings where extracting the system state is challenging. However, none of the existing works tackles the case of multiple heterogeneous agents which are characterized by different capabilities and/or embodiment. In this work, we propose a method to realize visual action planning in multi-agent setting

Raymond A. Mencia, Wei-Ju Lin, Hyunheung Cho, Maxim G. Vavilov

We describe a superconducting qubit derived from operating a properly designed fluxonium circuit in a zero magnetic field. The qubit has a frequency of about 4 GHz and an energy relaxation quality factor $Q \approx 0.7\times 10^7$, even though the dielectric loss quality factor of the circuit components is in the low $10^5$ range. The Ramsey coherence time e

Jordan M. Slagowski

Image-guided navigation of catheter devices to anatomic targets within large 3D cardiac chambers and vessels is challenging in the interventional setting due to the limitations of a conventional 2D x-ray projection format. Scanning-beam digital x-ray (SBDX) is a low-dose inverse geometry x-ray fluoroscopy technology capable of real-time 3D catheter tracking.

Raul Palma, Bogusz Janiak, Luís Moreira de Sousa, Kathi Schleidt

Established in 2012 by members of the Food and Agriculture Organisation (FAO), the Global Soil Partnership (GSP) is a global network of stakeholders promoting sound land and soil management practices towards a sustainable world food system. However, soil survey largely remains a local or regional activity, bound to heterogeneous methods and conventions. Reco

Shaoxiong Ji, Timothee Mickus, Vincent Segonne, Jörg Tiedemann

Multilingual pretraining and fine-tuning have remarkably succeeded in various natural language processing tasks. Transferring representations from one language to another is especially crucial for cross-lingual learning. One can expect machine translation objectives to be well suited to fostering such capabilities, as they involve the explicit alignment of s

Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz, Martin J. Menten

Anatomical atlases are widely used for population studies and analysis. Conditional atlases target a specific sub-population defined via certain conditions, such as demographics or pathologies, and allow for the investigation of fine-grained anatomical differences like morphological changes associated with ageing or disease. Existing approaches use either re

Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch, Peter Ochs

Our work is part of the close link between continuous-time dissipative dynamical systems and optimization algorithms, and more precisely here, in the stochastic setting. We aim to study stochastic convex minimization problems through the lens of stochastic inertial differential inclusions that are driven by the subgradient of a convex objective function. Thi

Oliver Sagi, Alessandro Crippa, Marco Valentini, Marian Janik

Gate-tunable transmons (gatemons) employing semiconductor Josephson junctions have recently emerged as building blocks for hybrid quantum circuits. In this study, we present a gatemon fabricated in planar Germanium. We induce superconductivity in a two-dimensional hole gas by evaporating aluminum atop a thin spacer, which separates the superconductor from th

Danyang Huang, Ziyi Kong, Shuyuan Wu, Hansheng Wang

Spatial autoregressive (SAR) models are important tools for studying network effects. However, with an increasing emphasis on data privacy, data providers often implement privacy protection measures that make classical SAR models inapplicable. In this study, we introduce a privacy-protected SAR model with noise-added response and covariates to meet privacy-p

Norbert J. Mauser, Yifei Wu, Xiaofei Zhao

We consider the cubic nonlinear Schr\"odinger equation with a spatially rough potential, a key equation in the mathematical setup for nonlinear Anderson localization. Our study comprises two main parts: new optimal results on the well-posedness analysis on the PDE level, and subsequently a new efficient numerical method, its convergence analysis and simulati

Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan, Tanmoy Chakraborty

The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity pro

Winston Lindqwister, Manolis Veveakis, Martin Lesueur

Porous media, while ubiquitous across many engineering disciplines, is inherently difficult to characterize due to their innate stochasticity and heterogeneity. The key for predicting porous material behavior comes down to the structuring of its microstructure, where the linkages of microstructural properties to mesoscale effects remain as one of the key que

Pratyush Mishra

We investigate group actions in which certain primitive elements fix a point, while not all group elements possess this property when acting upon some space. Using similar dynamical tools, we introduce the notion of Nielsen girth and prove the existence of groups with infinite girth but having finite Nielsen girth.

Sondess Missaoui, Simos Gerasimou, Nikolaos Matragkas

Despite their unprecedented success, DNNs are notoriously fragile to small shifts in data distribution, demanding effective testing techniques that can assess their dependability. Despite recent advances in DNN testing, there is a lack of systematic testing approaches that assess the DNN's capability to generalise and operate comparably beyond data in their

Arunava Naha, Subhrakanti Dey

In this paper, we investigate a model-free optimal control design that minimizes an infinite horizon average expected quadratic cost of states and control actions subject to a probabilistic risk or chance constraint using input-output data. In particular, we consider linear time-invariant systems and design an optimal controller within the class of linear st

Kennedy A. G. Araújo, Ernesto G. Birgin, Débora P. Ronconi

This paper addresses the flexible job shop scheduling problem with sequencing flexibility and position-based learning effect. In this variant of the flexible job shop scheduling problem, precedence constraints of the operations constituting a job are given by an arbitrary directed acyclic graph, in opposition to the classical case in which a total order is i

Gerardo Barrera, Eyleifur Bjarkason, Sigurdur Hafstein

In this manuscript, we study the stability of the origin for the multivariate geometric Brownian motion. More precisely, under suitable sufficient conditions, we construct a Lyapunov function such that the origin of the multivariate geometric Brownian motion is globally asymptotically stable in probability. Moreover, we show that such conditions can be rewri