February 2025 arXiv papers — page 16

Supratim Ghosh, Nupur Jain, Raghavan B. Sunoj

Developing machine learning (ML) models for yield prediction of chemical reactions has emerged as an important use case scenario in very recent years. In this space, reaction datasets present a range of challenges mostly stemming from imbalance and sparsity. Herein, we consider chemical language representations for reactions to tap into the potential of natu

Tommaso Bevilacqua, Axel Klawonn, Martin Lanser

A thermo-elastoplastic finite element approach is used to perform the simulation of a laser beam welding (LBW) process. This results in a nonlinear, nonsymmetric saddle point multiphysics system, for which the nonlinearity is handled via the Newton method. The iterative solution of the arising linear system is accelerated by using robust and highly scalable,

Muhammad Shahzad Javed, Karin Fossheim, Paola Velasco-Herrejón, Nikolai Elias Koop

Environmental movements and climate strikes have made it apparent that youth feel excluded from the ongoing energy transformation process, highlighting the crucial need for their engagement to achieve a socially accepted transition. This interdisciplinary study focuses on the Norwegian electricity system and involves conducting educational workshops with hig

Chao Wang, Luning Zhang, Zheng Wang, Yang Zhou

Combining multiple perceptual inputs and performing combinatorial reasoning in complex scenarios is a sophisticated cognitive function in humans. With advancements in multi-modal large language models, recent benchmarks tend to evaluate visual understanding across multiple images. However, they often overlook the necessity of combinatorial reasoning across m

Takanori Ayano, Victor M. Buchstaber

In this paper, a theory of hyperelliptic functions based on multidimensional sigma functions is developed and explicit formulas for hyperelliptic solutions to the Kadomtsev-Petviashvili equations KP-I and KP-II are obtained. The long-standing problem of describing the dependence of these solutions on the variation of the coefficients of the defining equation

Gengyuan Hu, Wanli Ouyang, Chao-Yang Lu, Chen Lin

Scaling quantum computing to practical applications necessitates reliable quantum error correction. Although numerous correction codes have been proposed, the overall correction efficiency critically limited by the decode algorithms. We introduce GraphQEC, a code-agnostic decoder leveraging machine-learning on the graph structure of stabilizer codes with lin

Diego H. Useche, Sergio Quiroga-Sandoval, Sebastian L. Molina, Vladimir Vargas-Calderón

Classification can be performed using either a discriminative or a generative learning approach. Discriminative learning consists of constructing the conditional probability of the outputs given the inputs, while generative learning consists of constructing the joint probability density of the inputs and outputs. Although most classical and quantum methods a

Frank G. Schröder

Radio antennas have become a standard tool for the detection of cosmic-ray air showers in the energy range above $10^{16}\,$eV. The radio signal of these air showers is generated mostly due to the deflection of electrons and positrons in the geomagnetic field, and contains information about the energy and the depth of the maximum of the air showers. Unlike t

Spyridon Afentoulidis-Almpanis, Gang Liu

We provide an explicit direct integral decomposition for the tensor product representation $\pi_1\widehat{\otimes}\pi_2$ of the rank one spin group $\mathrm{Spin}(n,1)$ whenever $\pi_1$ is a unitary principal series representation and $\pi_2$ is an arbitrary irreducible unitary representation of $\mathrm{Spin}(n,1)$.

Vimala Soundarapandian, Kartik Nagar, Aseem Rastogi, KC Sivaramakrishnan

Data replication is crucial for enabling fault tolerance and uniform low latency in modern decentralized applications. Replicated Data Types (RDTs) have emerged as a principled approach for developing replicated implementations of basic data structures such as counter, flag, set, map, etc. While the correctness of RDTs is generally specified using the notion

Farshad Rostami Ghadi, Masoud Kaveh, Riku Jantti, F. Javier Lopez-Martinez

This paper investigates the impact of deploying the fluid antenna system (FAS) on the performance of covert communications. In particular, we focus on a scenario where a transmitter seeks to covertly communicate with a receiver, while a warden attempts to detect the transmission. Both the receiver and the warden are assumed to utilize planar FAS. We derive c

Javier Coronado-Blázquez

Large Language Models (LLMs) have transformed text generation through inherently probabilistic context-aware mechanisms, mimicking human natural language. In this paper, we systematically investigate the performance of various LLMs when generating random numbers, considering diverse configurations such as different model architectures, numerical ranges, temp

Lovis Heindrich, Philip Torr, Fazl Barez, Veronika Thost

Sparse autoencoders (SAEs) have emerged as a promising approach in language model interpretability, offering unsupervised extraction of sparse features. For interpretability methods to succeed, they must identify abstract features across domains, and these features can often manifest differently in each context. We examine this through "answerability" - a mo

Gabriele Masina, Roberto Sebastiani

Optimization Modulo Theories (OMT) extends Satisfiability Modulo Theories (SMT) with the task of optimizing some objective function(s). In OMT solvers, a CDCL-based SMT solver enumerates theory-satisfiable total truth assignments, and a theory-specific procedure finds an optimum model for each of them; the current optimum is then used to tighten the search s

Quanxing Zha, Xin Liu, Shu-Juan Peng, Yiu-ming Cheung

Can we accurately identify the true correspondences from multimodal datasets containing mismatched data pairs? Existing methods primarily emphasize the similarity matching between the representations of objects across modalities, potentially neglecting the crucial relation consistency within modalities that are particularly important for distinguishing the t

Jean Hayoz, Markus Johannes Bonse, Felix Dannert, Emily Omaya Garvin

The Enhanced Resolution Imager and Spectrograph (ERIS) is the new Adaptive-Optics (AO) assisted Infrared instrument at the Very Large Telescope (VLT). Its refurbished Integral Field Spectrograph (IFS) SPIFFIER leverages a new AO module, enabling high-contrast imaging applications and giving access to the orbital and atmospheric characterisation of super-Jovi

Xinghao Wang, Feng Liu, Rui Su, Zhihui Wang

Recent advances in deep learning have revolutionized seismic monitoring, yet developing a foundation model that performs well across multiple complex tasks remains challenging, particularly when dealing with degraded signals or data scarcity. This work presents SeisMoLLM, the first foundation model that utilizes cross-modal transfer for seismic monitoring, t

Jiaqi Chen, Bo Feng, Liang Zhang

Recently, a new approach for high loop integrals has been proposed in \cite{Huang:2024nij}, where the whole parameter integration has been divided into two parts: a one-loop-like integration and the remaining parameter integration. In this paper, we systematically study the one-loop-like integrals. We establish the IBP relations for the integral family and s

Ke Niu, Haiyang Yu, Mengyang Zhao, Teng Fu

Person re-identification (Re-ID) is a crucial task in computer vision, aiming to recognize individuals across non-overlapping camera views. While recent advanced vision-language models (VLMs) excel in logical reasoning and multi-task generalization, their applications in Re-ID tasks remain limited. They either struggle to perform accurate matching based on i

Kevin Teo, Naomi Arnold, Andrew Hone, Michael Coulon

The global shipping network, which moves over 80% of the world's goods, is not only a vital backbone of the global economy but also one of the most polluting industries. Studying how this network operates is crucial for improving its efficiency and sustainability. While the transport of solid goods like packaged products and raw materials has been extensivel

ALICE Collaboration

We present the measurement of the $p_{\rm T}$-differential production cross section of $\omega$ mesons in pp and p$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV at midrapidity by ALICE. In addition, the first measurement of the nuclear modification factor $R_{\text{pPb}}$ for $\omega$ mesons at LHC energies is presented, complementing the existing measur

Thibaut Loiseau, Guillaume Bourmaud

Camera pose estimation is crucial for many computer vision applications, yet existing benchmarks offer limited insight into method limitations across different geometric challenges. We introduce RUBIK, a novel benchmark that systematically evaluates image matching methods across well-defined geometric difficulty levels. Using three complementary criteria - o

Yu Yan, Sheng Sun, Zixiang Tang, Teli Liu

Stance detection on social media aims to identify attitudes expressed in tweets towards specific targets. Current studies prioritize Large Language Models (LLMs) over Small Language Models (SLMs) due to the overwhelming performance improving provided by LLMs. However, heavily relying on LLMs for stance detection, regardless of the cost, is impractical for re

Yujie Feng, Liming Zhan, Zexin Lu, Yongxin Xu

Regular updates are essential for maintaining up-to-date knowledge in large language models (LLMs). Consequently, various model editing methods have been developed to update specific knowledge within LLMs. However, training-based approaches often struggle to effectively incorporate new knowledge while preserving unrelated general knowledge. To address this c

Zhihua Tian, Yuan Ding, Wenjie Qu, Xiang Yu

Money laundering is the process that intends to legalize the income derived from illicit activities, thus facilitating their entry into the monetary flow of the economy without jeopardizing their source. It is crucial to identify such activities accurately and reliably in order to enforce anti-money laundering (AML). Despite considerable efforts to AML, a la

Sandy Mathew, Maëliss Ethis de Corny, Nicolas Chauvet, Laureen Moreaud

The origin of the second harmonic generation (SHG) from plasmonic structures remains a subject of debate. Here, we investigate SHG from gold plasmonic nanostructures by scanning nanostructures at the single-particle level to acquire two-dimensional (2D) SHG maps which are qualitatively and quantitatively compared with numerically simulated SHG maps. This app

Frank G. Schroeder

IceCube-Gen2, the next generation extension of the IceCube Neutrino Observatory at the South Pole, offers a unique scientific potential for cosmic-ray physics at PeV to EeV energies complementing the main science case of neutrino astronomy. The cosmic-ray science case will be enabled by a surface array on top of an extended optical array deep in the polar ic

Mohammad Moulaeifard, Loic Coquelin, Mantas Rinkevičius, Andrius Sološenko

Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly supported by machine learning methods, raising the question of the most appropriate input representation and model choice. Comprehensive comparisons, in particular across different i

Yuan-Chih Yang, Hung-Hsuan Chen

Dropout and DropConnect are well-known techniques that apply a consistent drop rate to randomly deactivate neurons or edges in a neural network layer during training. This paper introduces a novel methodology that assigns dynamic drop rates to each edge within a layer, uniquely tailoring the dropping process without incorporating additional learning paramete

Chenhao Ding, Xinyuan Gao, Songlin Dong, Yuhang He

With the development of visual-language models (VLM) in downstream task applications, test-time adaptation methods based on VLM have attracted increasing attention for their ability to address changes distribution in test-time. Although prior approaches have achieved some progress, they typically either demand substantial computational resources or are const

M. C. Crabb

The Poincar\'e-Hopf theorem for line fields, as described in a paper of Crowley and Grant, is interpreted as a special case of a Poincar\'e-Hopf theorem for $n$-valued sections of a vector bundle over a closed manifold of the same dimension.

Fernando Martin-Maroto, Nabil Abderrahaman, David Mendez, Gonzalo G. de Polavieja

Statistics and Optimization are foundational to modern Machine Learning. Here, we propose an alternative foundation based on Abstract Algebra, with mathematics that facilitates the analysis of learning. In this approach, the goal of the task and the data are encoded as axioms of an algebra, and a model is obtained where only these axioms and their logical co

Tamoghno Nath, Krishna Gopal Benerjee, Adrish Banerjee

Intersymbol Interference (ISI) has a detrimental impact on any Molecular Communication via Diffusion (MCvD) system. Also, the receiver noise can severely degrade the MCvD channel performance. However, the channel codes proposed in the literature for the MCvD system have only addressed one of these two challenges independently. In this paper, we have designed

Malin P. Forsström, Fredrik Viklund

In this note, we discuss a random current expansion and a switching lemma for Ising lattice gauge theory at all choices of inverse temperature $\beta$, leading to summation over surfaces. We also describe couplings of this expansion with other representations, including the high-temperature expansion and the cluster expansion. We deduce some simple consequen

Xiang Geng, Zhejian Lai, Jiajun Chen, Hao Yang

Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task. Due to the data scarcity, synthetic data generation has emerged as a promising solution. However, synthetic QE data often suffers from distribution shift, which can manifest as discrepancies betwee

K. Barkaoui, D. Sebastian, S. Zúñiga-Fernández, A. H. M. J. Triaud

We report the discovery of a transiting brown dwarf orbiting a low-mass star, TOI-6508b. Today, only ~50 transiting brown dwarfs have been discovered. TOI-6508b was first detected with data from the Transiting Exoplanet Survey Satellite (TESS) in Sectors 10, 37, and 63. Ground-based follow-up photometric data were collected with the SPECULOOS-South and LCOGT

Vasudevarao Allu, Dipon Kumar Mondal

In this paper, we establish a compactness criterion for the composition-differentiation operator \( D_\Phi \) in terms of a decay condition of the mean counting function at the boundary of a half-plane. We provide a sufficient condition of the boundedness of the operator \( D_\Phi \) for the symbol \( \Phi \) with zero characteristic. Additionally, we invest

Yung-Peng Hsu, Hung-Hsuan Chen

Clustering is essential in data analysis and machine learning, but traditional algorithms like $k$-means and Gaussian Mixture Models (GMM) often fail with nonconvex clusters. To address the challenge, we introduce the Flexible Bivariate Beta Mixture Model (FBBMM), which utilizes the flexibility of the bivariate beta distribution to handle diverse and irregul

Dingkun Yan, Xinrui Wang, Zhuoru Li, Suguru Saito

Sketch colorization plays an important role in animation and digital illustration production tasks. However, existing methods still meet problems in that text-guided methods fail to provide accurate color and style reference, hint-guided methods still involve manual operation, and image-referenced methods are prone to cause artifacts. To address these limita

Mohamed Jleli, Evgeniy Petrov, Bessem Samet

In this paper, we are concerned with the study of the existence of fixed points for single and multi-valued three-points contractions. Namely, we first introduce a new class of single-valued mappings defined on a metric space equipped with three metrics. A fixed point theorem is established for such mappings. The obtained result recovers that established rec

Niloofar Ranjbar, Hamed Baghbani

This paper presents a novel approach for multi-label emotion detection, where Llama-3 is used to generate explanatory content that clarifies ambiguous emotional expressions, thereby enhancing RoBERTa's emotion classification performance. By incorporating explanatory context, our method improves F1-scores, particularly for emotions like fear, joy, and sadness

Jinchen Jiang, Shunshun Cao, Kejia Lee, Bojun Wang

We use the Five-hundred-meter Aperture Spherical radio Telescope to observe the bright millisecond pulsar PSR B1937+21 (J1939+2134) and record the data in the band from 1.0 to 1.5 GHz. We measure the neutral hydrogen (HI) emission and absorption lines near 1420 MHz ($\lambda \simeq 21$ cm). We derive the kinematic distance of the pulsar with the HI observati

Panpan Zhou, Yueyue Xu, Yibei Li, Bo Wahlberg

This paper investigates the encirclement control problem involving two groups using a non-cooperative differential game approach. The active group seeks to chase and encircle the passive group, while the passive group responds by fleeing cooperatively and simultaneously encircling the active group. Instead of prescribing an expected radius or a predefined pa

E. Palle, F. Yan, G. Morello, M. Stangret

With a mass, radius, and mean density similar to Earth's, the rocky planet GJ 1132 b is the first truly small planet for which an atmosphere detection was proposed. If confirmed, ultra-reduced magma outgassing is the only mechanism capable of producing HCN and H$_2$O in large enough quantities to match the HST observations. The proposed atmosphere detection,

Frank G. Schroeder, Abdul Rehman

Machine learning is a useful tool for identifying radio pulses from cosmic-ray air showers and for cleaning such pulses from radio background. This can lower the detection threshold and increase the accuracy for the pulse time and amplitude. We have trained Convolutional Neural Networks (CNNs) using CoREAS simulations and background recorded by a prototype s

SeonHwa Kim, Jiwon Kim, Soobin Park, Donghoon Ahn

Score distillation sampling (SDS) demonstrates a powerful capability for text-conditioned 2D image and 3D object generation by distilling the knowledge from learned score functions. However, SDS often suffers from blurriness caused by noisy gradients. When SDS meets the image editing, such degradations can be reduced by adjusting bias shifts using reference

Rashid A., Amal A Samad

Optimization techniques are at the core of many scientific and engineering disciplines. The steepest descent methods play a foundational role in this area. In this paper we studied a generalized steepest descent method on Riemannian manifolds, leveraging the geometric structure of manifolds to extend optimization techniques beyond Euclidean spaces. The conve

Mengting Li, Hui Chen, Alireza Pourafzal, Henk Wymeersch

Positioning technology, which aims to determine the geometric information of a device in a global coordinate, is a key component in integrated sensing and communication systems. In addition to traditional active anchor-based positioning systems, reconfigurable intelligent surfaces (RIS) have shown great potential for enhancing system performance. However, th

Alexandre Marthe, Samuel Bounan, Aurélien Garivier, Claire Vernade

Risk-sensitive planning aims to identify policies maximizing some tail-focused metrics in Markov Decision Processes (MDPs). Such an optimization task can be very costly for the most widely used and interpretable metrics such as threshold probabilities or (Conditional) Values at Risk. Indeed, previous work showed that only Entropic Risk Measures (EntRM) can b

Soeren Ihssen, Simon Geisert, Gabriel Jauma, Patrick Winkel

We present a flip-chip architecture for an array of coupled superconducting qubits, in which circuit components reside inside individual microwave enclosures. In contrast to other flip-chip approaches, the qubit chips in our architecture are electrically floating, which guarantees a simple, fully modular assembly of capacitively coupled circuit components su

Alessandro De Luca, Gabriele Fici, Andrea Frosini

An upward (resp. downward) digitally convex word is a binary word that best approximates from below (resp. from above) an upward (resp. downward) convex curve in the plane. We study these words from the combinatorial point of view, formalizing their geometric properties and highlighting connections with Christoffel words and finite Sturmian words. In particu

Bin Shang, Chao Zhang

We establish a new type of weak Harnack estimates with optimal parabolic tail for the weak supersolutions to a doubly nonlinear nonlocal $p$-Laplace equation, which is modeled on the nonlocal Trudinger equation. Our results are achieved by employing the expansion of positivity and measure theoretical techniques. In particular, the weak Harnack estimates high

Weihao wu, Zhiwei Lin, Yixuan Zhou, Jingbei Li

Conversational speech synthesis (CSS) aims to synthesize both contextually appropriate and expressive speech, and considerable efforts have been made to enhance the understanding of conversational context. However, existing CSS systems are limited to deterministic prediction, overlooking the diversity of potential responses. Moreover, they rarely employ lang

Mahya Karbalaii

We propose a simple yet robust unsupervised model to detect pump-and-dump events on tokens listed on the Poloniex Exchange platform. By combining threshold-based criteria with exponentially weighted moving averages (EWMA) and volatility measures, our approach effectively distinguishes genuine anomalies from minor trading fluctuations, even for tokens with lo

Anton Varonka, Kazuki Watanabe

We study the following reachability problem for piecewise affine maps: Given two vectors $\mathbf{s}, \mathbf{t} \in \mathbb{Q}^d$ and a piecewise affine map $f \colon \mathbb{Q}^d\rightarrow \mathbb{Q}^d$, does there exist $n\in \mathbb{N}$ such that $f^{n}(\mathbf{s}) = \mathbf{t}$? In this work, we focus on this reachability problem for a subclass of piec

Benedikt Tscheschner, Eduardo Veas, Marc Masana

Incremental Learning scenarios do not always represent real-world inference use-cases, which tend to have less strict task boundaries, and exhibit repetition of common classes and concepts in their continual data stream. To better represent these use-cases, new scenarios with partial repetition and mixing of tasks are proposed, where the repetition patterns

Berken Utku Demirel, Christian Holz

Deep learning models lack shift invariance, making them sensitive to input shifts that cause changes in output. While recent techniques seek to address this for images, our findings show that these approaches fail to provide shift-invariance in time series, where the data generation mechanism is more challenging due to the interaction of low and high frequen

Marco Pleines, Daniel Addis, David Rubinstein, Frank Zimmer

Pok\'emon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploration, and a vast array of potential policies. We introduce a simplistic environment and a Deep Reinforcement Learning (DRL) training methodology, demonstrating a baseline agent that

E. T. Akhmedov, M. N. Milovanova

We consider an electric charge uniformly accelerating along $x$ direction and moving with constant velocity along $y$ direction. We show that in the co-accelerating along $x$ direction Rinder's frame this charge creates non-zero Poynting vector, which, however, does not lead to a non-vanishing flux through an infinitely distant surface. Furthermore, we show

Na Du, Yan Zhao, Enting Xu, Jianwei Han

Lithium nitrate LiNO$_3$ is identified to possess a dielectric constant $\epsilon$' larger than 6x10$^6$ at 1 kHz in powder samples above the critical temperature $T$$_W$ = 306 K. For single crystalline samples, $\epsilon$' can be sustained to remain above 10$^5$ and the dissipation factor below 10 in the temperature region of 280-340 K after a simple 'activ

Yuan Sui, Yufei He, Tri Cao, Simeng Han

Large Language Models (LLMs) often struggle with computational efficiency and error propagation in multi-step reasoning tasks. While recent advancements on prompting and post-training have enabled LLMs to perform step-wise reasoning, they still tend to explore unproductive solution paths without effective backtracking or strategy adjustment. In this paper, w

Zhenyu Liu, Yunxin Li, Baotian Hu, Wenhan Luo

To improve Multimodal Large Language Models' (MLLMs) ability to process images and complex instructions, researchers predominantly curate large-scale visual instruction tuning datasets, which are either sourced from existing vision tasks or synthetically generated using LLMs and image descriptions. However, they often suffer from critical flaws, including mi

Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut

This short paper presents two open problems on the widely used Polyak's Heavy-Ball algorithm. The first problem is the method's ability to exactly \textit{accelerate} in dimension one exactly. The second question regards the behavior of the method for parameters for which it seems that neither a Lyapunov nor a cycle exists. For both problems, we provide a de

Jiahui Cen, Jianghao Lin, Weixuan Zhong, Dong Zhou

Knowledge Tracing (KT) is a fundamental technology in intelligent tutoring systems used to simulate changes in students' knowledge state during learning, track personalized knowledge mastery, and predict performance. However, current KT models face three major challenges: (1) When encountering new questions, models face cold-start problems due to sparse inte

Basit Auyoob Mir, Fouzul Atik, Priti Prasanna Mondal

The power graph \( \mathcal{G}_G \) of a group \( G \) is a graph whose vertex set is \( G \), and two elements \( x, y \in G \) are adjacent if one is an integral power of the other. In this paper, we determine the adjacency, Laplacian, and signless Laplacian spectra of the power graph of the dihedral group \( D_{2pq} \), where \( p \) and \( q \) are disti

Nikolay Blagoev, Lydia Yiyu Chen, Oğuzhan Ersoy

Data and pipeline parallelism are ubiquitous for training of Large Language Models (LLM) on distributed nodes. Driven by the need for cost-effective training, recent work explores efficient communication arrangement for end to end training. Motivated by LLM's resistance to layer skipping and layer reordering, in this paper, we explore stage (several consecut

Dong Liu, Juan S. Giraldo, Peter Palensky, Pedro P. Vergara

Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and onl

Simon Seibel, Josef Kiendl

We present a finite element modelling approach for unidirectional Fused Filament Fabrication (FFF)-printed specimens under tensile loading. In this study, the focus is on the fracture behaviour, the goal is to simulate the mechanical behaviour of specimens with different strand orientations until final failure of the specimens. In particular, the aim is to r

Marco Artusa

We extend Tate duality for Galois cohomology of abelian varieties to $1$-motives over a $p$-adic field, improving a result of Harari and Szamuely. To do this, we replace Galois cohomology with the condensed cohomology of the Weil group. This is a topological cohomology theory defined in a previous work, which keeps track of the topology of the $p$-adic field

Yajuan Liu, Han Cai, Xiaohu Tang

In this paper, we consider the multiple failures in the distributed storage systems under the cooperative repair model. We introduce a new cooperative repair scheme for the (n,k,d,N) minimum storage regenerating (MSR) codes proposed by Ye and Barg (IEEE Transactions on Information Theory, vol. 64, no. 4, 2017), which is capable of repairing any h failed node

Dongkun Zhang, Jiaming Liang, Ke Guo, Sha Lu

Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement learning (RL), have shown promise in specific scenarios, RL planners struggle with training inefficiencies and managing large-scale, real-world driving scenarios. In this paper, w

Qianxi He, Qianyu He, Jiaqing Liang, Yanghua Xiao

Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to generalize across logically equivalent transformations. LLMs often rely on fixed sequential patterns rather than true logical understanding. To address this issue, we introduce an order-

Zizhen Lin, Junyu Wang, Ruili Li, Fei Shen

Single-channel speech enhancement is a challenging ill-posed problem focused on estimating clean speech from degraded signals. Existing studies have demonstrated the competitive performance of combining convolutional neural networks (CNNs) with Transformers in speech enhancement tasks. However, existing frameworks have not sufficiently addressed computationa

Yuichiro Toma

We consider moments of higher powers of quadratic Dirichlet character sums. In a restricted region, we give their asymptotic behavior by using de la Bret\`{e}che's multivariable Tauberian theorem. We also give the lower bound of the exponent of $\log$ factor in the conjecture of Jutila. As an application, we give a lower bound of a weighted average of shifte

Pavel Exner, Olaf Post

In this article we discuss the convergence of first order operators on a thickened graph (a graph-like space) towards a similar operator on the underlying metric graph. On the graph-like space, the first order operator is of the form exterior derivative (the gradient) on functions and its adjoint (the negative divergence) on closed 1-forms (irrotational vect

Stepan Stehlik, Stepan Potocky, Katerina Aubrechtova Dragounova, Petr Belsky

Boron-doped diamond (BDD) films are becoming increasingly popular as electrode materials due to their broad potential window and stability in harsh conditions and environments. Therefore, optimizing the crystal quality and minimizing defect density to maximize electronic properties (e.g. conductivity) of BDD is of great importance. This study investigates th

Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen

Building an agent that can mimic human behavior patterns to accomplish various open-world tasks is a long-term goal. To enable agents to effectively learn behavioral patterns across diverse tasks, a key challenge lies in modeling the intricate relationships among observations, actions, and language. To this end, we propose Optimus-2, a novel Minecraft agent

Zicheng Cai, Yaohua Tang, Yutao Lai, Hua Wang

We introduce SEKI, a novel large language model (LLM)-based neural architecture search (NAS) method. Inspired by the chain-of-thought (CoT) paradigm in modern LLMs, SEKI operates in two key stages: self-evolution and knowledge distillation. In the self-evolution stage, LLMs initially lack sufficient reference examples, so we implement an iterative refinement

Mario Abundo

\noindent We address some direct and inverse problems, for the first-exit time (FET) $\tau $ of a drifted Brownian motion with Poissonian resetting ${\cal X}(t)$ from an interval $(0,b)$ and the first-exit area (FEA) $A,$ namely the area swept out by ${\cal X}(t)$ till the time $\tau $; this type of diffusion process ${\cal X}(t)$ is characterized by the fac

M. Farnir, M. -A. Dupret, G. Buldgen

Context. When leaving the main sequence (MS) for the red-giant branch (RGB), subgiant stars undergo fast structural changes. Consequently, their observed oscillation spectra mirror these changes, constituting key tracers of stellar structure and evolution. However, the complexity of their spectra makes their modelling an arduous task, which few authors have

Megha Srivastava, Reihaneh Iranmanesh, Yuchen Cui, Deepak Gopinath

Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for specialized tasks, such as high performance racing. Several recent works have leveraged AI-assistance to improve instruction of tasks ranging from rehabilitation to surgical robot

Shunxin Yao

This study utilizes a simulated dataset to establish Python code for Double Machine Learning (DML) using Anaconda's Jupyter Notebook and the DML software package from GitHub. The research focuses on causal inference experiments for both binary and continuous treatment variables. The findings reveal that the DML model demonstrates relatively stable performanc

Yuxuan Yan, Na Lu, Difei Mei, Ruofan Yan

Traditional clustering methods typically focus on either cluster-wise global clustering or point-wise local clustering to reveal the intrinsic structures in unlabeled data. Global clustering optimizes an objective function to explore the relationships between clusters, but this approach may inevitably lead to coarse partition. In contrast, local clustering h

An Li, Zhe Zhu, Mingqiang Wei

Existing point cloud completion methods, which typically depend on predefined synthetic training datasets, encounter significant challenges when applied to out-of-distribution, real-world scans. To overcome this limitation, we introduce a zero-shot completion framework, termed GenPC, designed to reconstruct high-quality real-world scans by leveraging explici

Shahin Momenzadeh, Rojiar Pir Mohammadiani

Community detection in network analysis has become more intricate due to the recent hike in social networks (Cai et al., 2024). This paper suggests a new approach named ELPMeans that strives to address this challenge. For community detection in the whole network, ELPMeans combines Laplacian, Hierarchical Clustering as well as K-means algorithms. Our techniqu

Mingtao Guo, Guanyu Xing, Yanli Liu

Relightable portrait animation aims to animate a static reference portrait to match the head movements and expressions of a driving video while adapting to user-specified or reference lighting conditions. Existing portrait animation methods fail to achieve relightable portraits because they do not separate and manipulate intrinsic (identity and appearance) a

Tianzheng Lu, Lili Ju, Liyong Zhu

The transferable neural network (TransNet) is a two-layer shallow neural network with pre-determined and uniformly distributed neurons in the hidden layer, and the least-squares solvers can be particularly used to compute the parameters of its output layer when applied to the solution of partial differential equations. In this paper, we integrate the TransNe

Jinghao Xin, Zhichao Liang, Zihuan Zhang, Peng Wang

Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments. To address these challenges, this study proposes the ColorDynamic framework. First, an end-to-end DRL formulation is established, which maps raw sensor data directly

I. Ekmark, M. Hoppe, R. A. Tinguely, R. Sweeney

One of the critical challenges in future high current tokamaks is the avoidance of runaway electrons during disruptions. Here, we investigate disruptions mitigated with combined deuterium and noble gas injection in SPARC. We use multi-objective Bayesian optimization of the densities of the injected material, taking into account limits on the maximum runaway

Minseok Kim, Yeongjong Kim, Yeoneung Kim

This work focuses on understanding the minimum eradication time for the controlled Susceptible-Infectious-Recovered (SIR) model in the time-homogeneous setting, where the infection and recovery rates are constant. The eradication time is defined as the earliest time the infectious population drops below a given threshold and remains below it. For time-homoge

Sondre Tesdal Galtung, Katrin Grunert

Following conservative solutions of the nonlinear variational wave equation $u_{tt}-c(u)(c(u)u_x)_x=0$ along forward and backward characteristics, we identify criteria, which guarantee that wave breaking either occurs in the nearby future or occurred recently. Thereafter, we apply the established criteria to show that not every traveling wave solution is a c

Chu Li, Rock Yuren Pang, Delphine Labbé, Yochai Eisenberg

Despite diverse mobility needs worldwide, existing mapping tools fail to address the varied experiences of different mobility device users. This paper presents a large-scale online survey exploring how five mobility groups -- users of canes, walkers, mobility scooters, manual wheelchairs, and motorized wheelchairs -- perceive sidewalk barriers. Using 52 side

Fan Wang, Gang Li, Shi-Dong Liu, Qi Wu

We investigate the radiative decays of the $X(3872)$ to $\gamma V~(V=\rho^0,\, \omega)$ in the molecule scenario, where the $X(3872)$ is regarded as a pure hadronic molecule of the $D\bar{D}^*+c.c$ in an $S$-wave with the quantum numbers $J^{PC}=1^{++}$. The radiative processes were assumed to occur via the triangle hadronic loops, and the relevant calculati

Fucai Li, Jinkai Ni, Man Wu

We investigate the Cauchy problem for a fluid-particle interaction model in $\mathbb{R}^3$. This model consists of the compressible barotropic Navier-Stokes equations and the Vlasov-Fokker-Planck equation coupled together via the density-dependent friction force. Due to the strong coupling caused by the friction force, it is a challenging problem to construc

Zebin Huang, Qun Zhang, Feifan Han, Hao Wang

Terahertz (THz) metalens antennas with compact planar structures have demonstrated significant potential in enhancing gain and aperture efficiency through beam convergence. However, research on THz wireless communication systems utilizing metalens antennas remains limited, primarily due to insufficient collaborative enhancement in gain and bandwidth in THz t

Nguyen Duy Cuong, Alexander Y. Kruger

The conventional definition of extremality of a finite collection of sets is extended by replacing a fixed point (extremal point) in the intersection of the sets by a collection of sequences of points in the individual sets with the distances between the corresponding points tending to zero. This allows one to consider collections of unbounded sets with empt

Sibo Yi, Tianshuo Cong, Xinlei He, Qi Li

Small language models (SLMs) have become increasingly prominent in the deployment on edge devices due to their high efficiency and low computational cost. While researchers continue to advance the capabilities of SLMs through innovative training strategies and model compression techniques, the security risks of SLMs have received considerably less attention

Kristian Løvås Svalland, Maria Teresa Mercaldo, Mario Cuoco

We study topological transitions in one dimensional superconductors that can harbor multiple edge Majorana bound states protected by chiral symmetry. The chiral symmetry arises due to the structure of the internal spin degrees of freedom of the superconductor and it can be guided by the coupling of the superconductor with sources of time-reversal symmetry br

Maxim A. Korolev

Minor corrections to previous version. We study some arithmetical properties of Farey sequences by the method introduced by F.Boca, C.Cobeli and A.Zaharescu (2001). Let $\Phi_{Q}$ be the classical Farey sequence of order $Q$. Having the fixed integers $D\geqslant 2$ and $0\leqslant c\leqslant D-1$, we colour to the red the fractions in $\Phi_{Q}$ with denomi

Yongjun Yun, Jungjai Lee

We reconstruct a holographic dark energy model within a Friedmann cosmology incorporating torsion scalar, assuming no interaction between dark energy and dark matter. Setting the Hubble radius as an infrared (IR) cut-off, we focus on a system dominated by contribution of a time-dependent torsion scalar induced by the spin of matter. In this regime, our resul