March 2025 arXiv papers — page 170

Bingchen Liu, Jingchen Li, Yuanyuan Fang, Xin Li

Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer.Current applications often fail to accurately infer and handle new relations orentities involving unseen categories, severely limiting their scalability and prac-ticality in open-domain scenarios. ZL

Rahil N. Valani, David M. Paganin

Active particles are non-equilibrium entities that uptake energy and convert it into self-propulsion. A dynamically rich class of inertial active particles having features of wave-particle coupling and wave memory are walking/superwalking droplets. Such classical, active wave-particle entities (WPEs) have previously been shown to exhibit hydrodynamic analogs

Grzegorz Fabiański, Rafał Stefański, Orfeas Stefanos Thyfronitis Litos

In this work we use formal verification to prove that the Lightning Network (LN), the most prominent scaling technique for Bitcoin, always safeguards the funds of honest users. We provide a custom implementation of (a simplification of) LN, express the desired security goals and, for the first time, we provide a machine checkable proof that they are upheld u

Amit Keinan, Moshe Shenfeld, Katrina Ligett

Recent methods for auditing the privacy of machine learning algorithms have improved computational efficiency by simultaneously intervening on multiple training examples in a single training run. Steinke et al. (2024) prove that one-run auditing indeed lower bounds the true privacy parameter of the audited algorithm, and give impressive empirical results. Th

Gonçalo Hora de Carvalho

Understanding and manipulating bioelectric signaling could present a new wave of progress in developmental biology, regenerative medicine, and synthetic biology. Bioelectric signals, defined as voltage gradients across cell membranes caused by ionic movements, play a role in regulating crucial processes including cellular differentiation, proliferation, apop

Zebin You, Jingyang Ou, Xiaolu Zhang, Jun Hu

Although masked image generation models and masked diffusion models are designed with different motivations and objectives, we observe that they can be unified within a single framework. Building upon this insight, we carefully explore the design space of training and sampling, identifying key factors that contribute to both performance and efficiency. Based

Shule Hao, Junpeng Bao, Chuncheng Lu

Time series analysis is crucial in fields like finance, transportation, and industry. However, traditional models often focus solely on temporal features, limiting their ability to capture underlying information. This paper proposes a novel time series multitask framework, called LTM, which integrates temporal features with textual descriptions to enhance an

Javier Blanco-Romero, Pedro Otero García, Daniel Sobral-Blanco, Florina Almenares Mendoza

Quantum Key Distribution (QKD) promises information-theoretic security, yet integrating QKD into existing protocols like TLS remains challenging due to its fundamentally different operational model. In this paper, we propose a hybrid QKD-KEM protocol with two distinct integration approaches: a client-initiated flow compatible with both ETSI 004 and 014 speci

Lia Shahnazaryan, Patrick Simianer, Joern Wuebker

We explore the impact of multi-source input strategies on machine translation (MT) quality, comparing GPT-4o, a large language model (LLM), with a traditional multilingual neural machine translation (NMT) system. Using intermediate language translations as contextual cues, we evaluate their effectiveness in enhancing English and Chinese translations into Por

Henning Krause

It is shown that any localisation of triangulated categories induces (up to an equivalence) a localisation of abelian categories when one passes to their abelianisations. From this one obtains for any enlargement of Grothendieck universes an example of an abelian category and a Serre subcategory within the smaller universe such that the corresponding quotien

Ronika Sarkar, Suraj S. Hegde, Awadhesh Narayan, Tobias Meng

We study the dynamical consequences of combining the non-Hermitian skin effect with topological edge states. Focusing on the paradigmatic dissipative Hofstadter model, we find that the time-dependent particle density exhibits both chiral damping (due to the non-Hermitian skin effect) and edge-selective extremal damping (rooted in persistent topological edge

Cesare Tonola, Marco Faroni, Saeed Abdolshah, Mazin Hamad

This paper addresses motion replanning in human-robot collaborative scenarios, emphasizing reactivity and safety-compliant efficiency. While existing human-aware motion planners are effective in structured environments, they often struggle with unpredictable human behavior, leading to safety measures that limit robot performance and throughput. In this study

Yan Ren, Shilin Lu, Adams Wai-Kin Kong

Recent advances in 3D Gaussian Splatting (3DGS) have revolutionized scene reconstruction, opening new possibilities for 3D steganography by hiding 3D secrets within 3D covers. The key challenge in steganography is ensuring imperceptibility while maintaining high-fidelity reconstruction. However, existing methods often suffer from detectability risks and util

Melvin Reka, Tessa Pulli, Markus Vincze

6D object pose estimation for unseen objects is essential in robotics but traditionally relies on trained models that require large datasets, high computational costs, and struggle to generalize. Zero-shot approaches eliminate the need for training but depend on pre-existing 3D object models, which are often impractical to obtain. To address this, we propose

Yizhuo Li, Jiakang Zheng, Bokai Xu, Yiyang Zhu

Reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technology for further improving spectral efficiency (SE) with low cost and power consumption. However, conventional RIS has inevitable limitations due to its diagonal scattering matrix. In contrast, beyond-diagonal RIS (BD-RIS) has gai

ZhiYong Hu, JingFu Hu, YiPing Ao

In this study, we used the 13.7m telescope at Qinghai Station to observe CO data in 48 galaxies in Virgo clusters, of which 41 sources observed CO signals. The properties of molecular gas are deduced by co-to-$H_2$ factor. We also collected and investigated the relationship between $M_{H_2}$ and other galactic properties ($M_B$, $L_K$, sfr, and $def_{HI}$).

Manuel Ladra, Andrés Pérez-Rodríguez

The main goal of this note is to show that subalgebras of regular evolution algebras are themselves evolution algebras. This allows us to assume, without loss of generality, that every subalgebra in the regular setting has a basis consisting of vectors with disjoint supports. Finally, we use this result to characterise the existence of codimension-one subalg

Riccardo Galafassi, Fabien Vialla, V. Rajaji, Alexis Forestier

Due to their unique dimensionality, the physical properties of two-dimensional materials are deeply impacted by their surroundings, calling for a thorough understanding and control of these effects. We investigated the influence of the substrate and the pressure transmitting medium on bilayer graphene in a unique high-pressure environment where the sample is

Vasiliki Sideri-Lampretsa, Daniel Rueckert, Huaqi Qiu

Evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity. However, most existing DIR works either address this trade-off inadequately or overlook it altogether. In this paper, we highlight the issues with existing practices and propose an evalu

Karthik Adimurthi

We prove local H\"older regularity for bounded and sign-changing weak solutions to nonlocal Trudinger equations of the form \[ (|u|^{p-2}u)_t + \text{P.V.} \int_{\mathbb{R}^n} \frac{|u(x,t) - u(y,t)|^{p-2}(u(x,t)-u(y,t))}{|x-y|^{n+sp}} = 0, \] in the range $1< p<\infty$ and $s \in (0,1)$. One of the main difficulties in extending the local theory to the nonl

Brian-Frederik Jahnke, Rebecca Schmook, Falk Howar

In the evolving landscape of cloud computing, optimizing energy efficiency across the edge-cloud continuum is crucial for sustainability and cost-effectiveness. We introduce GMB-ECC, a framework for measuring and benchmarking energy consumption across the software and hardware layers of the edge-cloud continuum. GMB-ECC enables energy assessments in diverse

Antonio Cammi, Lorenzo Loi, Andrea Missaglia, Ludovica Tumminelli

The management of Spent Nuclear Fuel (SNF) is one of the main challenges in the decommissioning of nuclear power plants. Thermal reactors, such as Light Water Reactors (LWRs), produce significant amounts of minor actinides (MAs) such as Americium, Curium, and Neptunium, which are key contributors to the long-term radiotoxicity and decay heat in SNF. Currentl

Milan Maksimovic, Ivan S. Maksymov

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech indust

Maxime Maria, Valentin Guillaume, Simon Guionniere, Nicolas Dacquay

VTX is an open-source molecular visualization software designed to overcome the scaling limitations of existing real-time molecular visualization software when handling massive molecular datasets. VTX employs a meshless molecular graphics engine utilizing impostor-based techniques and adaptive level-of-detail (LOD) rendering. This approach significantly redu

Dmitry Nikolaev, Sean Papay

Analysis of parliamentary speeches and political-party manifestos has become an integral area of computational study of political texts. While speeches have been overwhelmingly analysed using unsupervised methods, a large corpus of manifestos with by-statement political-stance labels has been created by the participants of the MARPOR project. It has been rec

Jijing Zhao, Huangxin Chen, Mingchao Cai, Shuyu Sun

This paper presents a novel parallel splitting algorithm for solving quasi-static multiple-network poroelasticity (MPET) equations. By introducing a total pressure variable, the MPET system can be reformulated into a coupled Stokes-parabolic system. To efficiently solve this system, we propose a parallel splitting approach. In the first time step, a monolith

Wietske A. P. Bastiaansen, Melek Rousian, Anton H. J. Koning, Wiro J. Niessen

Early brain development is crucial for lifelong neurodevelopmental health. However, current clinical practice offers limited knowledge of normal embryonic brain anatomy on ultrasound, despite the brain undergoing rapid changes within the time-span of days. To provide detailed insights into normal brain development and identify deviations, we created the 4D H

Valentine Maris, Filip Požar, Jean-Christophe Wallet

Poisson structures of the Poincar\'e group can be linked to deformations of the Minkowski space-time, classified some time ago by Zakrewski. Based on this classification, various quantum Minkowski space-times with coordinates Lie algebras and specific Poincare Hopf algebras have been exhibited by Mercati and called T-Minkowski space-times. Here we construct

Abhinav Sinha, Trideb Shit, Avinash Tetarwal, Diptiman Sen

The interplay of topology and disorder in quantum dynamics has recently attracted significant attention across diverse platforms, including solid-state devices, ultracold atoms, and photonic systems. Here, we report on a topological Anderson transition caused by quasiperiodic modulation of the stronger intra-cell couplings in photonic Su-Schrieffer-Heeger la

Yongqiang Yao, Jingru Tan, Kaihuan Liang, Feizhao Zhang

Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communication overhead. This paper proposes Hierarchical Balance Pac

Patrick Pilch, Laur Peedu, Urmas Nagel, Toomas Rõõm

We present terahertz spectroscopic measurements of quantum spin dynamics in the honeycomb magnet K$_2$Co$_2$TeO$_6$ as a function of temperature, polarization and in an external magnetic field applied in the honeycomb plane. Magnetic excitations are resolved below the magnetic ordering temperature of $T_\text{N}$ = 12 K. In the applied magnetic field, we rev

Kazuya Nishimura, Ryoma Bise, Yasuhiro Kojima

Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely related to pathological features such as disease subtypes, it may be valuable to augment image representation with pathological information. However, there are no attempts to leverage S

L. Thomas van Binsbergen, Marten C. Steketee, Milen G. Kebede, Heleen L. Janssen

Compliance with the GDPR privacy regulation places a significant burden on organisations regarding the handling of personal data. The perceived efforts and risks of complying with the GDPR further increase when data processing activities span across organisational boundaries, as is the case in both small-scale data sharing settings and in large-scale interna

Elena Tonucci, Tim van Kempen, Jean-Philippe Beaulieu, Lilou Bernard

The ESA space mission Ariel requires bright sources that are stable at the level of 100ppm over 6 hours in order to accurately measure exoplanet atmospheres through transmission spectroscopy. To ensure this, in-flight instrument calibration can be performed by observing stellar calibrators. In this study, a stellar calibrator candidate list distributed over

Ming Wang, Fang Wang, Minghao Hu, Li He

Long-form article generation (LFAG) presents challenges such as maintaining logical consistency, comprehensive topic coverage, and narrative coherence across extended articles. Existing datasets often lack both the hierarchical structure and fine-grained annotation needed to effectively decompose tasks, resulting in shallow, disorganized article generation.

Anders Sundelin, Javier Gonzalez-Huerta, Krzysztof Wnuk

Public cloud services are integral to modern software development, offering scalability and flexibility to organizations. Based on customer requests, a large product development organization considered migrating the microservice-based product deployments of a large customer to a public cloud provider. We conducted an exploratory single-case study, utilizing

Jing Yang, Sen Yang, Xiao Tan, Hanli Wang

As an essential component of autonomous driving systems, high-definition (HD) maps provide rich and precise environmental information for auto-driving scenarios; however, existing methods, which primarily rely on query-based detection frameworks to directly model map elements or implicitly propagate queries over time, often struggle to maintain consistent te

Ziliang Miao, Runjian Chen, Yixi Cai, Buwei He

Moving object segmentation (MOS) on LiDAR point clouds is crucial for autonomous systems like self-driving vehicles. Previous supervised approaches rely heavily on costly manual annotations, while LiDAR sequences naturally capture temporal motion cues that can be leveraged for self-supervised learning. In this paper, we propose Temporal Overlapping Predictio

Alexey Gordeev, Klas Markström, Lars-Daniel Öhman

We introduce near triple arrays as binary row-column designs with at most two consecutive values for the replication numbers of symbols, for the intersection sizes of pairs of rows, pairs of columns and pairs of a row and a column. Near triple arrays form a common generalization of such well-studied classes of designs as triple arrays, (near) Youden rectangl

Asim Ullah, Jameel-Un Nabi, Muhammad Riaz

The electron capture plays significant role in the pre supernova and supernova evolutions of massive stars which in turn are of great importance in synthesizing heavy elements beyond iron. In this paper we study the effect of nuclear deformation on the computed electron capture cross section on selected even even chromium isotopes (464850Cr). The nuclear def

Jameel-Un-Nabi, H. V. Klapdor-Kleingrothaus

Proton neutron quasiparticle RPA is used for the first time to calculate weak interaction rates for sd and fp shell nuclei at high temperatures and densities. The calculated rates take into consideration the latest experimental energy levels and ft value compilations. Particle emission processes from excited states are taken into account. The calculation is

Banik Rai, Nitesh Kumar

Recent magnetotransport studies on uniaxial ferromagnets have reported a cusplike feature in Hall resistivity when the magnetic field is tilted away from the conventional orthogonal direction of the Hall measurement. This feature has often been attributed to the topological Hall effect arising from a non-coplanar spin structure. In this article, we have stud

Igor Bogush, Vladimir M. Fomin, Oleksandr V. Dobrovolskiy

The movement of magnetic flux quanta (Abrikosov vortices) in superconductors leads to dissipation and is influenced by various ordering effects arising from vortex-vortex, vortex-defect, and vortex-edge interactions. Under combined dc and ac stimuli, when the distance traveled by fluxons during an ac cycle corresponds to an integer multiple of the vortex lat

Lei Cong, Filip Ficek, Pavel Fadeev, Yevgeny V. Stadnik

We show that atomic antimatter spectroscopy can be used to search for new bosons that carry spin-dependent exotic forces between antifermions. A comparison of a recent precise measurement of the hyperfine splitting of the $1$S and $2$S electronic levels of antihydrogen and bound-state quantum electrodynamics theory yields the first tests of positron-antiprot

Xuanyu Hu, Thomas P. Andert

Bilobed bodies represent a significant class of small extraterrestrial objects in the Solar System. We present a double harmonic-series approach to model the gravity of the lobes separately, thereby allowing their mass distributions to be constrained independently. We study an exemplary candidate contact binary, comet 67P/Churyumov-Gerasimenko, and establish

Igor Ngouagnia Yemeli, Salvatore Perna, Diane Gouéré, Amel Kolli

We study the high power ferromagnetic resonance (FMR) of perpendicularly magnetized BiYIG nanodisks where the uniaxial anisotropy almost compensates the shape anisotropy. We observe a strong saturation of the averaged magnetization upon moderately increasing the amplitude of the rf field and a broadening of the FMR line towards lower and higher magnetic fiel

Longchao Da, Tiejin Chen, Zhuoheng Li, Shreyas Bachiraju

The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure design, policy evaluation, and traffic simulation. However, there is a critical need for a systematic framework to guide the adoption of GenAI in this interdisciplinary domain. In this

Hung Q. Vo, Pengyu Yuan, Zheng Yin, Kelvin K. Wong

Self-supervised learning (SSL) with Vision Transformers (ViT) has shown immense potential in medical image analysis. However, the quadratic complexity ($\mathcal{O}(N^2)$) of standard self-attention poses a severe barrier for high-resolution biomedical tasks, effectively excluding resource-constrained research labs from utilizing state-of-the-art models. To

Elisabetta Brocchieri, Lucilla Corrias

The purpose of this article is to investigate the emergence of cross-diffusion in the time evolution of two slow-fast species in competition. A class of triangular cross-diffusion system is obtained as the singular limit of a fast reaction-diffusion system. We first prove the convergence of the unique strict solution of the fast reaction-diffusion system tow

Marcin Wachowiak, André Bourdoux, Sofie Pollin

A frequency-diverse array (FDA) is an alternative array architecture in which each antenna is preceded by a mixer instead of a phase shifter. The mixers introduce a frequency offset between signals transmitted by each antenna, resulting in a time-varying beam pattern. However, time-dependent beamforming is not desirable for communication or sensing. In this

Yuanlong Wu, Mingxing Nie, Tao Zhu, Liming Chen

Class-incremental learning (CIL) for time series data faces critical challenges in balancing stability against catastrophic forgetting and plasticity for new knowledge acquisition, particularly under real-world constraints where historical data access is restricted. While pre-trained models (PTMs) have shown promise in CIL for vision and NLP domains, their p

Yuheng Liu, Xinke Li, Yuning Zhang, Lu Qi

Three-dimensional scene generation is crucial in computer vision, with applications spanning autonomous driving, gaming and the metaverse. Current methods either lack user control or rely on imprecise, non-intuitive conditions. In this work, we propose a method that uses, scene graphs, an accessible, user friendly control format to generate outdoor 3D scenes

Savino Detto

Let n be any odd natural number other than a perfect square, in this article it is demonstrated that this new factorization algorithm is much more efficient than the implementation technique [2,3 p.1470], described in this article, of the Fermat's factorization algorithm [1 p.6,3 p.1470], implementation technique which I call the Fermat's factorization metho

Giulio Ferri, Enzo Marino

We propose a novel approach to the analysis of programmable geometrically exact shear deformable beam systems made of shape memory polymers. The proposed method combines the viscoelastic Generalized Maxwell model with the Williams, Landel and Ferry relaxation principle, enabling the reproduction of the shape memory effect of structural systems featuring comp

Gianni Bianchini, Marco Casini, Milad Gholami

Within the context of renewable energy communities, this paper focuses on optimal operation of producers equipped with energy storage systems in the presence of demand response. A novel strategy for optimal scheduling of the storage systems of the community members under price-volume demand response programs, is devised. The underlying optimization problem i

Ali Baheri, Cecilia O. Alm

We present a hierarchical neuro-symbolic control framework that tightly couples a classical symbolic planner with a transformer-based policy to address long-horizon decision-making under uncertainty. At the high level, the planner assembles an interpretable sequence of operators that guarantees logical coherence with task constraints, while at the low level

Shoham Letzter, Abhishek Methuku, Benny Sudakov

We develop novel methods for constructing nearly Hamilton cycles in sublinear expanders with good regularity properties, as well as new techniques for finding such expanders in general graphs. These methods are of independent interest due to their potential for various applications to embedding problems in sparse graphs. In particular, using these tools, we

Gabriele Pichierri, Konstantin Batygin

The outer solar system is populated by a broad aggregate of minor bodies, which occupy orbits whose dynamical character ranges from long-term stable to rapidly diffusive. We investigate the chaotic properties of known distant trans-Neptunian objects (TNOs) by numerically integrating TNO clones and statistically analyzing their orbital diffusion. Comparing th

Gaoyue Guo, Nicolas Juillet, Wenpin Tang

Tournaments are competitions between a number of teams, the outcome of which determines the relative strength or rank of each team. In many cases, the strength of a team in the tournament is given by a score. Perhaps, the most striking mathematical result on the tournament is Moon's theorem, which provides a necessary and sufficient condition for a feasible

Shengkun Ma, Hao Peng, Lei Hou, Juanzi Li

Machine Reading Comprehension (MRC) is an essential task in evaluating natural language understanding. Existing MRC datasets primarily assess specific aspects of reading comprehension (RC), lacking a comprehensive MRC benchmark. To fill this gap, we first introduce a novel taxonomy that categorizes the key capabilities required for RC. Based on this taxonomy

Changzhi Yu, Shuangping Han, Kai Song, Liantuan Xiao

In this paper, we propose a spatiotemporal deep learning network for photon-level Block Compressed Sensing Imaging, aimed to address challenges such as signal loss, artifacts, and noise interference in large-pixel dynamic imaging and tracking at the photon level. This approach combines information in the time and frequency domains with a U-Net-LSTM deep lear

Guillaume Wisniewski, Ophélie Lacroix

We compare the performance of a transition-based parser in regards to different annotation schemes. We pro-pose to convert some specific syntactic constructions observed in the universal dependency treebanks into a so-called more standard representation and to evaluate parsing performances over all the languages of the project. We show that the ``standard''

Manuel Valiente

We consider one-dimensional, integrable many-body classical and quantum systems in thermal equilibrium. In the classical case, we use the classical limit of the Bethe equations to obtain a self-consistent integral equation whose solution gives the distribution of asymptotic Bethe momenta, or rapidities, as well as the classical partition function in the cano

Liwei Yang, Xinying Wang, Xiaotang Zhou, Zhengchao Wu

Implicit sentiment analysis aims to uncover emotions that are subtly expressed, often obscured by ambiguity and figurative language. To accomplish this task, large language models and multi-step reasoning are needed to identify those sentiments that are not explicitly stated. In this study, we propose a novel Dual Reverse Chain Reasoning (DRCR) framework to

Jianpeng Zou, Zhanfeng Zhong, Jintao Wang, Zheng Shi

In this letter, we investigate a coordinated multiple point (CoMP)-aided integrated sensing and communication (ISAC) system that supports multiple users and targets. Multiple base stations (BSs) employ a coordinated power allocation strategy to serve their associated single-antenna communication users (CUs) while utilizing the echo signals for joint radar ta

Flavie Didier, Salah Laghrouche, Daniel Depernet

The floating wind turbines sector has great energy potential. However, minimizing the movement of the structure under the combined effect of wind and waves while ensuring maximum power extraction over a wide operating range is one of the main challenges for the control of these turbines. This paper presents a review of control methods for floating wind turbi

Siyuan Mu, Sen Lin

Artificial intelligence (AI) has achieved astonishing successes in many domains, especially with the recent breakthroughs in the development of foundational large models. These large models, leveraging their extensive training data, provide versatile solutions for a wide range of downstream tasks. However, as modern datasets become increasingly diverse and c

Bruno Kahn

We point out a relationship between the norm residue isomorphism theorem of Suslin-Voevodsky-Rost and the theory of birational motives, as well as its generalisation to "higher jets".

Hanzhi Chen, Boyang Sun, Anran Zhang, Marc Pollefeys

Future robots are envisioned as versatile systems capable of performing a variety of household tasks. The big question remains, how can we bridge the embodiment gap while minimizing physical robot learning, which fundamentally does not scale well. We argue that learning from in-the-wild human videos offers a promising solution for robotic manipulation tasks,

Nico Tauchnitz

This paper is dedicated to the elementary proof of Pontryagin's maximum principle for problems with free right end point. The proof for the standard problem is taken from the monography of Ioffe and Tichomirov. We assume piecewise continuous controls and the proof turns out to be very simple. We generalize the concept to the problem of optimal multiprocesses

Marcelo Eduardo Pederiva, José Mario De Martino, Alessandro Zimmer

Comprehending the environment and accurately detecting objects in 3D space are essential for advancing autonomous vehicle technologies. Integrating Camera and LIDAR data has emerged as an effective approach for achieving high accuracy in 3D Object Detection models. However, existing methodologies often rely on heavy, traditional backbones that are computatio

Yuhang Pi, Zhifang Zhang

Let $n,q,t,s,p$ be non-negative integers where $n\geq s$ and $q\geq 1$. For $\mathbf{x}\in A_{q}^{n}\triangleq\{ 0,1,\ldots,q-1 \}^{n}$, let the $t$-insertion $s$-deletion $p$-substitution ball of $\mathbf{x}$, denoted by $\mathcal{B}_{t,s,p}(\mathbf{x})$, be the set of sequences in $A_{q}^{n+t-s}$ which can be obtained from $\mathbf{x}$ by performing $t$ in

L. Becchetti, N. Solferino, M. E. Tessitore

We examine the relationship among photovoltaic (PV) investments, energy production, and environmental impact using a dynamic optimization model. Our findings show that increasing investment in renewables supports both energy generation and ecological sustainability, with the optimal path depending on policy priorities. Our analysis demonstrates that the econ

Luis A. Fernández, Isabel Lasheras, Cecilia Pola

Doctors are well aware that sometimes cancer treatments not only fail, but even work backwards, i.e. they make the treated tumor grow. In this work we present a mathematical perspective on this paradox in the case of chemotherapy, by studying a minimally parameterized mathematical model for the system composed of the tumor and the surrounding vasculature. To

Paul Brunet

In this report, we introduce observation algebras, constructed by considering the downclosed subsets of a coherence space ordered by reverse inclusion. These may be interpreted as specifications of sets of events via some predicates with some extra structure. We provide syntax for these algebras, as well as axiomatisations. We establish completeness of these

Deuksin Kwon, Jiwon Hae, Emma Clift, Daniel Shamsoddini

Negotiation requires dynamically balancing self-interest and cooperation within the flow of conversation to maximize one's own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by

Thomas Giletti, Luca Rossi

In this paper, we study the large time behaviour of solutions of multistable reaction-diffusion equations in $\mathbb{R}^N$, with a spatially periodic heterogeneity. By multistable, we mean that the problem admits a finite -- but arbitrarily large -- number of stable, periodic steady states. In contrast with the more classical monostable and bistable framewo

Albert Gassol Puigjaner, Manish Prajapat, Andrea Carron, Andreas Krause

A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we propose a novel method, COAT-MPC, Constrained Optimal Auto-Tuner for MPC. With every tuning iteration, COAT-MPC gathers performance data and learns by updating i

Zhiping Kou, Xiaohu Li, Sheng-Li Qin, Tie Liu

Hot cores, as a stage of massive star formation, exhibit abundant line emissions of COMs. We present a deep line survey of two isomers of C$_2$H$_6$O: ethanol (C$_2$H$_5$OH; EA), and dimethyl ether (CH$_3$OCH$_3$; DE) as well as their possible precursor CH$_3$OH towards 60 hot cores by using the ALMA 3 mm line observations. EA is detected in 40 hot cores and

Sihao Lin, Daqi Liu, Ruochong Fu, Dongrui Liu

Estimating the 3D world from 2D monocular images is a fundamental yet challenging task due to the labour-intensive nature of 3D annotations. To simplify label acquisition, this work proposes a novel approach that bridges 2D vision foundation models (VFMs) with 3D tasks by decoupling 3D supervision into an ensemble of image-level primitives, e.g., semantic an

Guillaume Fieni, Romain Rouvoy, Lionel Seinturier

The energy consumption analysis and optimization of data centers have been an increasingly popular topic over the past few years. It is widely recognized that several effective metrics exist to capture the efficiency of hardware and/or software hosted in these infrastructures. Unfortunately, choosing the corresponding metrics for specific infrastructure and

Zelei Cheng, Xin-Qiang Cai, Yuting Tang, Pushi Zhang

Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone for aligning large language models (LLMs) with human values. However, existing approaches struggle to capture the multi-dimensional, distributional nuances of human preferences. Methods such as RiC that directly inject raw reward values into prompts face significant numerical sensitiv

Saranya P., Sunoj S. M.

This study explores information measures based on extropy, introducing dynamic relative extropy measures for residual and past lifetimes, and investigating their various properties. Furthermore, the study analyzes the relationships between extropy-based divergence with dynamic relative extropy and other extropy measures. A nonparametric estimator for relativ

Jonathan Junné, Alexandre Rege

We investigate the stability of solutions to the Vlasov-Poisson system using the unifying framework of the kinetic Wasserstein distance, introduced by Iacobelli in (Section 4 in Arch. Ration. Mech. Anal. 244 (2022), no. 1, 27-50). This allows us to treat both macroscopic densities that lie in a Yudovich space, as recently considered by Crippa et al. (Theorem

Rakesh Khanna A., Raymond E. Goldstein, Adriana I. Pesci, Nir Gov

We report the first quantitative study of the onset of dawn choruses of cicadas in several natural habitats. A time-frequency analysis of the acoustical signals is used to define an order parameter for the development of collective singing. The ensemble of recordings reveals that the chorus onset times accurately track the changing sunrise times over the cou

Zhen Zou, Feng Zhao

Diffusion Transformer (DiT) has exhibited impressive generation capabilities but faces great challenges due to its high computational complexity. To address this issue, various methods, notably feature caching, have been introduced. However, these approaches focus on aligning non-cache diffusion without analyzing why caching damage the generation processes.

Danil Kuzin, Olga Isupova, Steven Reece, Brooke D Simmons

Ensembling in deep learning improves accuracy and calibration over single networks. The traditional aggregation approach, ensemble averaging, treats all individual networks equally by averaging their outputs. Inspired by crowdsourcing we propose an aggregation method called soft Dawid Skene for deep ensembles that estimates confusion matrices of ensemble mem

Jeffrey W. Doser, Malcolm S. Itter, Grant M. Domke, Andrew O. Finley

National Forest Inventories (NFIs) provide statistically reliable information on forest resources at national and other large spatial scales. As forest management and conservation needs become increasingly complex, NFIs are being called upon to provide forest parameter estimates at spatial scales smaller than current design-based estimation procedures can pr

Yihao Xu, Jesús Salas, Youjin Deng

In finite-size scaling analyses of critical phenomena, proper consideration of correction terms, which can come from different sources, plays an important role. For the Fortuin-Kasteleyn representation of the $Q$-state Potts model in two dimensions, although the subleading magnetic scaling field, with exactly known exponent, is theoretically expected to give

Paul Zheng, Navid Keshtiarast, Pradyumna Kumar Bishoyi, Yao Zhu

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traf

Hanqing Guo, Xiuxiu Lin, Shiyu Zhao

Vision-based drone-to-drone detection has attracted increasing attention due to its importance in numerous tasks such as vision-based swarming, aerial see-and-avoid, and malicious drone detection. However, existing methods often encounter failures when the background is complex or the target is tiny. This paper proposes a novel end-to-end framework that accu

David Treeby, Edward Wang

We present an extension of the Prouhet-Tarry-Escott problem by demonstrating that signed sums of noninteger powers of consecutive integers can be made arbitrarily close to zero.

Menghao Waiyan William Zhu, Pengcheng Hao, Ercan Engin Kuruoğlu

Continual learning in neural networks aims to learn new tasks without forgetting old tasks. Sequential function-space variational inference (SFSVI) uses a Gaussian variational distribution to approximate the distribution of the outputs of the neural network corresponding to a finite number of selected inducing points. Since the posterior distribution of a ne

Shuangping Han, Kai Song, Pengyu Zan, Changzhi Yu

In this work we introduce an innovative approach to single-molecule quantum coherence (SMQC)-based disposable micro-optical anti-counterfeiting labels. This method facilitates the editing and reading of anti-counterfeiting with single molecules used as the anti-counterfeiting information labels. The label is meticulously crafted through inkjet printing techn

Suraj Deshmukh, Sougata Guha, Basudha Roy, Shivprasad Patil

Designing a miniature microscale engine that can override the role of thermal fluctuations has remained elusive and is an important open challenge. Here we provide the design and theoretical framework for a unique information-based engine - a work-to-work converter - comprising a sub-micron size bead and motor protein-microtubule (MT) complex in an optical t

Sagar Mandal

Does $14$ have a friend? Until now, this has been an open question. In this note, we prove that a potential friend $F$ of $14$ is an odd, non-square positive integer. $7$ appears in the prime factorization of $F$ with an even exponent while at most two prime divisors of $F$ can have odd exponents in the prime factorization of $F$. If $p\mid F$ such that $p$

Alan Dao, Dinh Bach Vu, Tuan Le Duc Anh, Bui Quang Huy

This paper introduces PoseLess, a novel framework for robot hand control that eliminates the need for explicit pose estimation by directly mapping 2D images to joint angles using projected representations. Our approach leverages synthetic training data generated through randomized joint configurations, enabling zero-shot generalization to real-world scenario

Chaoran E, Chenghan Chen, Yuyang Shi, Haiyun Wang

Methods: A method of the pulsation for a pVAD is proposed (AP-pVAD Model). AP-pVAD Model consists of two parts: NPQ Model and LSTM-Transformer Model. (1)The NPQ Model determines the mathematical relationship between motor speed, pressure, and flow rate for the pVAD. (2)The Attention module of Transformer neural network is integrated into the LSTM neural netw

Merve Cigdem Ipek, Sevil Sen

With the escalating threat of malware, particularly on mobile devices, the demand for effective analysis methods has never been higher. While existing security solutions, including AI-based approaches, offer promise, their lack of transparency constraints the understanding of detected threats. Manual analysis remains time-consuming and reliant on scarce expe

Yuta Furusho, Tomonari Meguro, Kentaro Nomura

Fermi arcs are one of the characteristic features of Weyl semimetals, appearing as surface states that connect Weyl points with opposite chiralities. It has also been suggested that Fermi arcs can emerge in the bulk due to the interplay between magnetic textures and Weyl physics. We focus on Ti$_2$MnAl which is an ideal magnetic Weyl semimetal with a compens