February 2025 arXiv papers — page 22

Ademir G. Costa Junior, Fábio S. da Silva, Ricardo Rios

This paper presents a performance comparison among four Convolutional Neural Network architectures (EfficientNet-B3, InceptionV3, ResNet50, and VGG16) for classifying cassava disease images. The images were sourced from an imbalanced dataset from a competition. Appropriate metrics were employed to address class imbalance. The results indicate that EfficientN

Tianrui Mao, Shilun Zhang, Alan Hanjalic, Huijuan Wang

Temporal networks, whose links are activated or deactivated over time, are used to represent complex systems such as social interactions or collaborations occurring at specific times. Such networks facilitate the spread of information and epidemics. The average number of nodes infected via a spreading process on a network starting from a single seed node ove

Amit Kumar, Taoran Ji

Cryptocurrencies fluctuate in markets with high price volatility, posing significant challenges for investors. To aid in informed decision-making, systems predicting cryptocurrency market movements have been developed, typically focusing on historical patterns. However, these methods often overlook three critical factors influencing market dynamics: 1) the m

Andreas Lööw

Despite numerous previous formalisation projects targeting Verilog, the semantics of Verilog defined by the Verilog standard -- Verilog's simulation semantics -- has thus far eluded definitive mathematical formalisation. Previous projects on formalising the semantics have made good progress but no previous project provides a formalisation that can be used to

Diana Marie Schenke, Timo Baumann

LLMs are not generally able to adjust the length of their outputs based on strict length requirements, a capability that would improve their usefulness in applications that require adherence to diverse user and system requirements. We present an approach to train LLMs to acquire this capability by augmenting existing data and applying existing fine-tuning te

L. P. H. Gallagher, M. Mazzanti, Z. E. D. Ackerman, A. Safavi-Naini

Tightly-focused laser beams, or optical tweezers, are essential for analogue and digital quantum simulation with neutral atoms and trapped ions. Despite this, most of the current intuition and theoretical treatment utilizes the paraxial approximation, which breaks down at the focus of optical tweezers. We develop an analytic model, which we use in tandem wit

Frank Schlawin, Manuel Gessner

Stimulated Raman scattering (SRS) is a powerful method for label-free imaging and spectroscopy of materials. Recent experiments have shown that quantum-enhanced Raman scattering can surpass the shot noise limit and improve the sensitivity substantially. Here, we introduce a full theory of quantum-enhanced SRS based on the framework of quantum metrology. Our

Amaury Freslon, Paul Meunier, Pegah Pournajafi

We prove that for every pair of quantum isomorphic graphs, their block trees and their block graphs are isomorphic, and that such an isomorphism can be chosen so that the corresponding blocks are quantum isomorphic -- in particular, 2-connectedness is preserved under quantum isomorphism. We conclude with some corollaries, including obtaining some necessary c

Pascal Mossier, Philipp Oestringer, Jens Keim, Catherine Mavriplis

In this paper, we present an hp-adaptive hybrid Discontinuous Galerkin/Finite Volume method for simulating compressible, turbulent multi-component flows. Building on a previously established hp-adaptive strategy for hyperbolic gas- and droplet-dynamics problems, this study extends the hybrid DG/FV approach to viscous flows with multiple species and incorpora

Siddharth Singh, Tian Yu, Qing Chang, John Karigiannis

Industrial robots are widely used in diverse manufacturing environments. Nonetheless, how to enable robots to automatically plan trajectories for changing tasks presents a considerable challenge. Further complexities arise when robots operate within work cells alongside machines, humans, or other robots. This paper introduces a multi-level hybrid robot motio

Tohida Rehman, Soumabha Ghosh, Kuntal Das, Souvik Bhattacharjee

Text summarization plays a crucial role in natural language processing by condensing large volumes of text into concise and coherent summaries. As digital content continues to grow rapidly and the demand for effective information retrieval increases, text summarization has become a focal point of research in recent years. This study offers a thorough evaluat

Reinaldo Francener, Victor P. Goncalves, Diego R. Gratieri

In this work, we investigate the impact of the $L_\mu - L_\tau$ model, which predicts a new massive gauge boson, $Z'$, on astrophysical neutrino events at the IceCube Observatory. This new gauge boson couples with leptons from the second and third families, and would break the power law of the astrophysical neutrino flux due to the interaction of this flux w

Irit Chelly, Roy Uziel, Oren Freifeld, Ari Pakman

Neural models for amortized probabilistic clustering yield samples of cluster labels given a set-structured input, while avoiding lengthy Markov chain runs and the need for explicit data likelihoods. Existing methods which label each data point sequentially, like the Neural Clustering Process, often lead to cluster assignments highly dependent on the data or

Jørgen Ellegaard Andersen, Shan Shan

Gaussian Boson Sampling (GBS) have shown advantages over classical methods for performing some specific sampling tasks. To fully harness the computational power of GBS, there has been great interest in identifying their practical applications. In this study, we explore the use of GBS samples for computing a numerical approximation to the Gaussian expectation

Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha, Petra Poklukar

Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to offload complex tasks. However, existing approaches inadequat

Qi Yu, Zhichen Zeng, Yuchen Yan, Lei Ying

Network alignment, which aims to find node correspondence across different networks, is the cornerstone of various downstream multi-network and Web mining tasks. Most of the embedding-based methods indirectly model cross-network node relationships by contrasting positive and negative node pairs sampled from hand-crafted strategies, which are vulnerable to gr

Guillermo Lorenzo, David A. Hormuth, Chengyue Wu, Graham Pash

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been sho

Che Liu, Yingji Zhang, Dong Zhang, Weijie Zhang

This work proposes an industry-level omni-modal large language model (LLM) pipeline that integrates auditory, visual, and linguistic modalities to overcome challenges such as limited tri-modal datasets, high computational costs, and complex feature alignments. Our pipeline consists of three main components: First, a modular framework enabling flexible config

M. Baradaran, L. M. Nieto, S. Zarrinkamar

Motivated by the great interest in studying quantum and gravitational phenomena in a unified way, scalar bosons are considered in a cosmic string spacetime and in a non-inertial frame, with a generalized Coulomb-type interaction containing both inverse quadratic and inverse cubic corrections. Solutions for this generalized interaction are shown for an arbitr

Lucas Galvão, Ana Clara das Neves, Maron Anka, Clebson Cruz

Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantu

Magnus Johansson

We consider a one-dimensional discrete nonlinear Schr\"odinger (DNLS) model with Kerr-type on-site nonlinearity, where the nearest-neighbor coupling constants take two different values ordered in a three-periodic sequence. The existence of localized edge states in the linear limit (Su-Schrieffer-Heeger trimer, SSH3) is known to depend on the precise location

Pablo Encarnación, Albert Feijoo, Eulogio Oset

We have addressed here the problem of calculating the correlation function of a stable particle with a resonance, in particular one resonance that qualifies as a molecular state of two components. The formalism used requires to evaluate the scattering matrix of the stable particle with the molecule, a nuclear problem which we address by means of the fixed ce

Hao Peng, Yunjia Qi, Xiaozhi Wang, Zijun Yao

Reward models (RMs) are crucial for the training and inference-time scaling up of large language models (LLMs). However, existing reward models primarily focus on human preferences, neglecting verifiable correctness signals which have shown strong potential in training LLMs. In this paper, we propose agentic reward modeling, a reward system that combines rew

Fabrizio Murgana, Giorgio Comitini, Marco Ruggieri

We test non-perturbative gluon propagators recently studied in the literature, by computing the topological susceptibility, $\chi$, of the superconductive phases of Quantum Chromodynamics at high density. We formulate the problem within the High-Density Effective Theory, and use the 2-particle irreducible formalism to compute the effective potential of the d

Amílcar Branquinho, Ana Foulquié-Moreno, Assil Fradi, Manuel Mañas

We consider matrix orthogonal polynomials related to Bessel type matrices of weights that can be defined in terms of a given matrix Pearson equation. From a Riemann-Hilbert problem we derive first and second order differential relations for the matrix orthogonal polynomials and functions of second kind. It is shown that the corresponding matrix recurrence co

Joel Veness, Marcus Hutter, Andras Gyorgy, Jordi Grau-Moya

This paper considers a generalisation of universal source coding for interaction data, namely data streams that have actions interleaved with observations. Our goal will be to construct a coding distribution that is both universal \emph{and} can be used as a control policy. Allowing for action generation needs careful treatment, as naive approaches which do

Simone Rossoni, Günter Sigl

The surprising isotropy of the ultra-high-energy cosmic ray (UHECR) sky makes it difficult to identify their sources. Observables such as energy spectrum, mass composition and arrival directions are affected by interactions with background photon fields and by deflection in the extragalactic and galactic magnetic fields (EGMF and GMF). In this work, we simul

Nailya Ganiyeva, João Luís Rosa, Francisco S. N. Lobo

We explore the properties of traversable wormhole spacetimes within the framework of energy-momentum squared gravity, also known as $f(R,T^2)$ gravity, where $R$ represents the Ricci scalar, $T_{ab}$ is the energy-momentum tensor, and $T^2 = T_{ab}T^{ab}$. Adopting a linear functional form $f(R,T^2) = R + \gamma T^2$, we demonstrate the existence of a wide r

Suvankar Dutta, Shruti Menon, Aayush Srivastav

When the boundary dynamics of \(AdS_3\) gravity is governed by the collective field theory Hamiltonian proposed by Jevicki and Sakita, its asymptotic symmetry algebra becomes the centerless \(U(1)\) Kac-Moody algebra. We quantize this system using the quantum bosonization of relativistic free fermions and relate these to the dynamical fields of \(AdS_3\) gra

Johan Kon, Roland Tóth, Jeroen van de Wijdeven, Marcel Heertjes

A minimal state-space (SS) realization of an identified linear parameter-varying (LPV) input-output (IO) model usually introduces dynamic and nonlinear dependency of the state-space coefficient functions, complicating stability analysis and controller synthesis. The aim of this paper is to introduce and analyze a direct SS realization of this IO model that a

Cornelius Emde, Alasdair Paren, Preetham Arvind, Maxime Kayser

Large language models (LLMs) are often deployed to perform constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their broad language understanding and capabilities to enhance performance. However, these LLMs are adversarially susceptible, potentially generating outputs outside the intended domain.

Zohreh Aminifard, Geovani Nunes Grapiglia

In this paper, we propose a new non-monotone line-search method for smooth unconstrained optimization problems with objective functions that have many non-global local minimizers. The method is based on a relaxed Armijo condition that allows a controllable increase in the objective function between consecutive iterations. This property helps the iterates esc

Adam Celarek, George Kopanas, George Drettakis, Michael Wimmer

Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real-time novel-view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray-marching approach for volumetric renderi

Gagan Aggarwal, Anupam Gupta, Xizhi Tan, Mingfei Zhao

We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. The competition on a platform is captured by a value and a cost function, mapping bidding strategies to value and cost respectively. We assume a diminishing returns property, whereby the marginal cost is increasing in value. The advertiser uses

Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong

In this paper, we investigate a challenging unsupervised domain adaptation setting -- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy iss

Chandrashekhar Savant, Thai-Son Nguyen, Kazuki Nomoto, Saurabh Vishwakarma

We report a polarization-induced 2D electron gas (2DEG) at an epitaxial AlBN/GaN heterojunction grown on a SiC substrate. Using this 2DEG in a long conducting channel, we realize ultra-thin barrier AlBN/GaN high electron mobility transistors that exhibit current densities of more than 0.25 A/mm, clean current saturation, a low pinch-off voltage of -0.43 V, a

Delong Yang, Shaohui Zhang, Jiasong Sun, Chao Zuo

Label-free tomographic microscopy offers a compelling means to visualize three-dimensional (3D) refractive index (RI) distributions from two-dimensional (2D) intensity measurements. However, limited forward-model accuracy and the ill-posed nature of the inverse problem hamper artifact-free reconstructions. Meanwhile, artificial neural networks excel at model

Zhe Wang, Shaocong Xu, Xucai Zhuang, Tongda Xu

Cooperative perception enhances the individual perception capabilities of autonomous vehicles (AVs) by providing a comprehensive view of the environment. However, balancing perception performance and transmission costs remains a significant challenge. Current approaches that transmit region-level features across agents are limited in interpretability and dem

Anikait Singh, Sheryl Hsu, Kyle Hsu, Eric Mitchell

Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context capabilities of LLMs, we propose few-shot preference optimization (FSPO), an algorithm for LLM personalization that reframes reward modeling as a meta-learning problem. Under FSPO,

Christoph Benzmüller

Deep and shallow embeddings of non-classical logics in classical higher-order logic have been explored, implemented, and used in various reasoning tools in recent years. This paper presents a method for the simultaneous deployment of deep and shallow embeddings of various degrees in classical higher-order logic. This enables flexible, interactive and automat

Robert Brandenberger, Jürg Fröhlich, Hao Jiao

We propose a mechanism for the generation of magnetic fields on cosmological scales that is operative after recombination. An essential ingredient is an instability (of parametric resonance type) of the electromagnetic field driven by an oscillating pseudo-scalar dark matter field, $\phi$, that is coupled to the electromagnetic field tensor via a $\phi F \we

Liuquan Wang, Wentao Zeng

Let $A$ be a $r\times r$ rational nonzero symmetric matrix, $B$ a rational column vector, $C$ a rational scalar. For any integer lattice $L$ and vector $v$ of $\mathbb{Z}^r$, we define Nahm sum on the lattice coset $v+L\in \mathbb{Z}^r/L$: \begin{align*}\label{eq-lattice-sum} f_{A,B,C,v+L}(q):=\sum_{n=(n_1,\dots,n_r)^\mathrm{T} \in v+L} \frac{q^{\frac{1}{2}n

William Solow, Sandhya Saisubramanian, Alan Fern

We introduce WOFOSTGym, a novel crop simulation environment designed to train reinforcement learning (RL) agents to optimize agromanagement decisions for annual and perennial crops in single and multi-farm settings. Effective crop management requires optimizing yield and economic returns while minimizing environmental impact, a complex sequential decision-ma

Michael Somma, Thomas Gallien, Branka Stojanovic

Anomaly detection in complex dynamical systems is essential for ensuring reliability, safety, and efficiency in industrial and cyber-physical infrastructures. Predictive maintenance helps prevent costly failures, while cybersecurity monitoring has become critical as digitized systems face growing threats. Many of these systems exhibit oscillatory behaviors a

Daniele Nantes-Sobrinho, Manfred Schmidt-Schauss, Alexander Baumgartner, Temur Kutsia

Generalization problems in languages with binders involve computing the most common structure between expressions while respecting bound variable renaming and freshness constraints. These problems often lack a least general solution. However, leveraging nominal techniques, we previously demonstrated that a semantic approach with atom-variables enables the el

Shiqi Wang, Zhibo Zhang, Libing Fang, Cam-Tu Nguyen

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph da

Tasnim Ahmed, Salimur Choudhury

The adoption of unmanned aerial vehicles to monitor critical infrastructure is gaining momentum in various industrial domains. Organizational imperatives drive this progression to minimize expenses, accelerate processes, and mitigate hazards faced by inspection personnel. However, traditional infrastructure monitoring systems face critical bottlenecks-5G net

Y. Abraham, B. A. van Tiggelen, N. Benech, C. Negreira

We demonstrate that the diffusion coefficient, $D$, for ultrasound propagating in a multiple scattering medium, such as a dense granular suspension, can be measured using a time reversal experiment. This requires an unprecedented experimental setup in which a piezoelectric transducer, acting as a Time-Reversal Mirror (TRM), is embedded within the granular su

J. Trey Diulus, Ashley R. Head, Jorge Anibal Boscoboinik, Andrei Kolmakov

Advancements in differential pumping and electron optics over the past few decades have enabled x-ray photoelectron spectroscopy (XPS) measurements at (near-)ambient pressures, bridging the pressure gap for characterizing realistic sample chemistries. Recently, we demonstrated the capabilities of an ambient pressure XPS (APXPS) setup for in-situ plasma envir

Alex Iosevich, Ben Krause, Hamed Mousavi

In this note we provide a quick proof that maximal truncations of oscillatory singular integrals are bounded from $L^1(\mathbb{R})$ to $L^{1,\infty}(\mathbb{R})$. The methods we use are entirely elementary, and rely only on pigeonholing and stationary phase considerations.

Qizhou Wang, Jin Peng Zhou, Zhanke Zhou, Saebyeol Shin

Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are crucial to remove undesirable responses, ensuring legal and safe model usage. It has spurred recent research into LLM unlearning, focusing on erasing targeted undesirable knowledge w

Kang-Sin Choi

We study the self-similar structure of loop amplitudes in quantum field theory and apply it to amplitude generation and renormalization. A renormalized amplitude can be regarded as an effective coupling that recursively appears within another loop. It is best described as a vertex function from the effective action. It is a scale-dependent, finite, parametri

Alexis Anagnostakis

We establish the representation of general regular diffusions on star-shaped graphs as time-changed Walsh Brownian motions. These are regular continuous Markov processes described locally by a family generalized second order differential operators defined on every edge and a gluing condition at the junction vertex. This allows us to prove two additional resu

Evangelos Kanoulas, Panagiotis Eustratiadis, Yongkang Li, Yougang Lyu

As large language models (LLMs) become more specialized, we envision a future where millions of expert LLMs exist, each trained on proprietary data and excelling in specific domains. In such a system, answering a query requires selecting a small subset of relevant models, querying them efficiently, and synthesizing their responses. This paper introduces a fr

Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel, Sriraam Natarajan

Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environments. This results in notable sample inefficiency and hinders generalization across diverse tasks. The complexity is further pronounced in relational settings, where domain knowledg

Laure Flapan, Sarah Frei, Lisa Marquand

We study the equivariant Kuznetsov component $\mathrm{Ku}_G(X)$ of a general cubic fourfold $X$ with a symplectic involution. We show that $\mathrm{Ku}_G(X)$ is equivalent to the derived category $D^b(S)$ of a $K3$ surface $S$, where $S$ is given as a component of the fixed locus of the induced symplectic action on the Fano variety of lines on $X$.

Hongyi Ling, Shubham Parashar, Sambhav Khurana, Blake Olson

We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on unreliable self-verification or external verifiers to evaluate these steps, which demand significant data and computations. Here, we propose automated heuristics discovery (AutoHD)

Massimo D'Elia, Lorenzo Maio, Kevin Zambello, Giuseppe Zanichelli

We investigate how a magnetic background field influences the location and the nature of the Roberge-Weiss (RW) finite temperature transition for $N_f = 2+1$ QCD with physical quark masses. To that purpose, we perform numerical simulations of the finite temperature theory, discretized through stout staggered quarks and the tree-level improved Symanzik pure g

Ruben T. Lucassen, Sander P. J. Moonemans, Tijn van de Luijtgaarden, Gerben E. Breimer

Millions of melanocytic skin lesions are examined by pathologists each year, the majority of which concern common nevi (i.e., ordinary moles). While most of these lesions can be diagnosed in seconds, writing the corresponding pathology report is much more time-consuming. Automating part of the report writing could, therefore, alleviate the increasing workloa

Iker Atienza-Diez, Gabriel Rodriguez-Maroto, Saúl Ares, Susanna Manrubia

The limited availability of COVID-19 vaccines has prompted extensive research on optimal vaccination strategies. Previous studies have considered various non-pharmaceutical interventions, vaccine efficacy, and distribution strategies. In this work, we address the combined effects of inter-group contacts and vaccination rates under contact reduction, analyzin

Guoqing Chao, Kaixin Xu, Xijiong Xie, Yongyong Chen

Incomplete multi-view clustering has become one of the important research problems due to the extensive missing multi-view data in the real world. Although the existing methods have made great progress, there are still some problems: 1) most methods cannot effectively mine the information hidden in the missing data; 2) most methods typically divide represent

Zhenyi Zhu, Yuchen Huang, Liu Liu

Time-dependent partial differential equations are a significant class of equations that describe the evolution of various physical phenomena over time. One of the open problems in scientific computing is predicting the behaviour of the solution outside the given temporal region. Most traditional numerical methods are applied to a given time-space region and

Andrea De Girolamo, Paolo Facchi, Peter Rabl, Saverio Pascazio

We optimize matrix-product state-based algorithms for simulating quantum circuits with finite fidelity, specifically the time-evolving block decimation (TEBD) and the density-matrix renormalization group (DMRG) algorithms, by exploiting the irregular arrangement of entangling operations in circuits. We introduce a variation of the standard TEBD algorithm, we

Yonggang Hu, Linyu Ma, Xintian Wang

This paper studies the tensor product of flat cotorsion modules. Let~$R$~and $S$ be~$k$-algebras. We prove that both~$R$-module\ $M$ and~$S$-module\ $N$ are flat cotorsion modules if and only if~$M\otimes_{k} N$ is a flat cotorsion~$R\otimes_{k} S $-module. Based on this conclusion, we provide a lower bound for the global cotorsion dimension of the tensor pr

Ali K. Caires-Santos, Maribel Fernández, Daniele Nantes-Sobrinho

Many formal languages include binders as well as operators that satisfy equational axioms, such as commutativity. Here we consider the nominal language, a general formal framework which provides support for the representation of binders, freshness conditions and $\alpha$-renaming. Rather than relying on the usual freshness constraints, we introduce a nominal

Edoardo Bocchi, Ángel Castro, Francisco Gancedo

In this paper, we study the dynamics of a two-dimensional viscous fluid evolving through a porous medium or a Hele-Shaw cell, driven by gravity and surface tension. A key feature of this study is that the fluid is confined within a vessel with vertical walls and below a dry region. Consequently, the dynamics of the contact points between the vessel, the flui

Ruben T. Lucassen, Tijn van de Luijtgaarden, Sander P. J. Moonemans, Gerben E. Breimer

Vision-language models in pathology enable multimodal case retrieval and automated report generation. Many of the models developed so far, however, have been trained on pathology reports that include information which cannot be inferred from paired whole slide images (e.g., patient history), potentially leading to hallucinated sentences in generated reports.

Kobe Bergmans, Karl Meerbergen, Raf Vandebril

The sparse matrix-vector (SpMV) multiplication is an important computational kernel, but it is notoriously difficult to execute efficiently. This paper investigates algorithm performance for unstructured sparse matrices, which are more common than ever because of the trend towards large-scale data collection. The development of an SpMV multiplication algorit

Yan Su, Qiulin Wu, Weizhen Li, Chengchang Pan

The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target disea

Marcel G. Clerc, Claudio Falcón, René G. Rojas

We present an experimental and theoretical study of the effect of spatio-temporal fluctuations in quasi-reversible systems displaying a spatial quintic supercritical bifurcation. The saturation mechanism is drastically changed by the inclusion of fluctuations. Experimentally, we observe the modification of the bifurcation diagram of parametrically amplified

Bernd R. Schuh

The Diophantine equation 4/n=1/x+1/y+1/z for a Pythagorean prime n is split into two independent Diophantine equations, which correspond to two different types of solution. The solvability of these equations forces certain restrictions on allowed Pythagorean primes. Empirical evidence suggests that these restrictions hold for all Pythagorean primes, which I

Saeed Mohammadzadeh, Mostafa Rahmani, Kanapathippillai Cumanan, Alister Burr

This paper introduces a novel iterative algorithm for optimizing pilot and data power control (PC) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems, aiming to enhance system performance under real-time channel conditions. The approach begins by deriving the signal-to-interference-plus-noise ratio (SINR) using a matched filtering receive

Jiyuan Wang, Weishan Ye, Jialin He, Li Zhang

With the rapid advancement of deep learning, attention mechanisms have become indispensable in electroencephalography (EEG) signal analysis, significantly enhancing Brain-Computer Interface (BCI) applications. This paper presents a comprehensive review of traditional and Transformer-based attention mechanisms, their embedding strategies, and their applicatio

Akash Dhasade, Rachid Guerraoui, Anne-Marie Kermarrec, Diana Petrescu

Large language models (LLMs) achieve remarkable performance across domains but remain prone to hallucinations and inconsistencies. Retrieval-augmented generation (RAG) mitigates these issues by augmenting model inputs with relevant documents retrieved from external sources. In many real-world scenarios, relevant knowledge is fragmented across organizations o

Honglin Guo, Kai Lv, Qipeng Guo, Tianyi Liang

Language model heavily depends on high-quality data for optimal performance. Existing approaches rely on manually designed heuristics, the perplexity of existing models, training classifiers, or careful prompt engineering, which require significant expert experience and human annotation effort while introduce biases. We introduce CritiQ, a novel data selecti

Anderson A. Tomaz, Rafael S. Mattos, Mario Barbatti

Left on its own, a quantum state evolves deterministically under the Schr\"odinger Equation, forming superpositions. Upon measurement, however, a stochastic process governed by the Born rule collapses it to a single outcome. This dual evolution of quantum states -- the core of the Measurement Problem -- has puzzled physicists and philosophers for nearly a ce

Klaus Deckelnick, Robert Nürnberg

We prove optimal error bounds for a second order in time finite element approximation of curve shortening flow in possibly higher codimension. In addition, we introduce a second order in time method for curve diffusion. Both schemes are based on variational formulations of strictly parabolic systems of partial differential equations that feature a tangential

Beiyu Xu, Zhiwei Liu, Sophia Ananiadou

The stance detection task aims to categorise the stance regarding specified targets. Current methods face challenges in effectively integrating sentiment information for stance detection. Moreover, the role of highly granular sentiment labelling in stance detection has been largely overlooked. This study presents a novel stance detection framework utilizing

Jiguang Li, Robert Gibbons, Veronika Rockova

Computerized adaptive tests (CATs) play a crucial role in educational assessment and diagnostic screening in behavioral health. Unlike traditional linear tests that administer a fixed set of pre-assembled items, CATs adaptively tailor the test to an examinee's latent trait level by selecting a smaller subset of items based on their previous responses. Existi

Md. Wali Hossain, Afaq Maqsood

We investigate non phantom tracker scalar field models as dynamical dark energy scenario. These models can alleviate the cosmic coincidence problem and transition to a cosmological constant-like behaviour at late times. Focusing on the inverse axionlike and inverse steep exponential potentials, we study their background evolution and perturbations, finding a

G. Di Gennaro, M. Brüggen, E. Moravec, L. Di Mascolo

Diffuse radio emission in galaxy clusters is a tracer of ultra-relativistic particles and $\mu$G-level magnetic fields, and is thought to be triggered by cluster merger events. In the distant Universe (i.e. $z>0.6$), such sources have been observed only in a handful of systems, and their study is important to understand the evolution of large-scale magnetic

Birte Thiede, Michael L. Larsen, Freja Nordsiek, Oliver Schlenczek

The growth, lifetime, number density, and size of water droplets in warm atmospheric clouds determine the evolution, lifetime and light transmission properties of those clouds. These small-scale cloud properties, in addition to precipitation initiation, have strong implications for the Earth's energy budget since warm clouds cover large geographic areas. Spa

Saman Forouzandeh, Pavel N. Krivitsky, Rohitash Chandra

Recommender systems leveraging deep learning models have been crucial for assisting users in selecting items aligned with their preferences and interests. However, a significant challenge persists in single-criteria recommender systems, which often overlook the diverse attributes of items that have been addressed by Multi-Criteria Recommender Systems (MCRS).

Nikolaos Parthenios, Pietro M. Bonetti, Rafael González-Hernández, Warlley H. Campos

Altermagnetism, a recently proposed and experimentally confirmed class of magnetic order, features collinear compensated magnetism with unconventional d-, g-, or i-wave spin order. Here, we show that in a metallic 2D d-wave altermagnet with combined two-fold spin and four-fold lattice rotational symmetry $[C_2||C_4]$, secondary instabilities can arise. Using

Jiawei Kong, Hao Fang, Sihang Guo, Chenxi Qing

While pre-trained Vision-Language Models (VLMs) such as CLIP exhibit impressive representational capabilities for multimodal data, recent studies have revealed their vulnerability to backdoor attacks. To alleviate the threat, existing defense strategies primarily focus on fine-tuning the entire suspicious model. However, the substantial model parameters incr

J. L. Gaona-Reyes, D. G. A. Altamura, A. Bassi

Stochastic unravelings of Lindblad-type master equations, such as stochastic Schr\"odinger equations (SSEs), provide powerful tools to model open quantum systems and continuous measurement processes. The same master equation can be unraveled in different ways; while these unravelings differ at the level of quantum trajectories, by construction they all yield

Gati Aher, Robin Schmucker, Tom Mitchell, Zachary C. Lipton

When executed well, project-based learning (PBL) engages students' intrinsic motivation, encourages students to learn far beyond a course's limited curriculum, and prepares students to think critically and maturely about the skills and tools at their disposal. However, educators experience mixed results when using PBL in their classrooms: some students thriv

Rodrigo A. Dourado, Martin Leijnse, Rubén Seoane Souto

Minimal Kitaev chains, composed of two quantum dots (QDs) connected via a superconductor, have emerged as an attractive platform to realize Majorana bound states (MBSs). These excitations exist when the ground state is degenerate. The additional requirement of isolating the MBS wavefunctions further restricts the parameter space to discrete sweet spots. Whil

Laura Escobar, Patricia Klein, Anna Weigandt

Much of modern Schubert calculus is centered on Schubert varieties in the complete flag variety and on their classes in its integral cohomology ring. Under the Borel isomorphism, these classes are represented by distinguished polynomials called Schubert polynomials, introduced by Lascoux and Sch\"utzenberger. Knutson and Miller showed that Schubert polynomia

Victor M Afigbo, Matthew M Hedman, Phillip Nicholson, Richard G French

Certain spiral density waves in Saturn's rings are generated through resonances with planetary normal modes, making them valuable probes of Saturn's internal structure. Previous research has primarily focused on the rotation rates of these waves. However, other characteristics of these waves also contain valuable information about the planet's interior. In t

M. Abedlou Ahadi, N. Sadooghi

We determine the chiral vortical conductivities, as well as the orbital and spin moment of inertia of a charged, chiral, and rigidly rotating free Fermi gas. To this purpose, we begin by calculating the vacuum expectation values of a vector and axial vector current using the free fermion propagator in this medium. This propagator is derived by employing the

Sylvain Marcoux, Jean-Sébastien Dessureault

City leaders face critical decisions regarding budget allocation and investment priorities. How can they identify which city districts require revitalization? To address this challenge, a Current Vitality Index and a Long-Term Vitality Index are proposed. These indexes are based on a carefully curated set of indicators. Missing data is handled using K-Neares

Arnavi Chheda-Kothary, Ritesh Kanchi, Chris Sanders, Kevin Xiao

We introduce ArtInsight, a novel AI-powered system to facilitate deeper engagement with child-created artwork in mixed visual-ability families. ArtInsight leverages large language models (LLMs) to craft a respectful and thorough initial description of a child's artwork, and provides: creative AI-generated descriptions for a vivid overview, audio recording to

Udvas Acharjee, N. Uday Kiran

In the study of Ramanujan sums, the so-called regular $A$-function is a set-valued multiplicative function that tracks certain subsets of the divisor sets of natural numbers. McCarthy provided a generalization of the Ramanujan sum using these regular $A$-function based arithmetic convolutions. This approach has recently attracted considerable interest from s

Gengsheng Wang, Huaiqiang Yu, Yubiao Zhang

This paper presents two remarkable phenomena associated with the heat equation with a time delay: namely, the propagation of singularities and periodicity. These are manifested through a distinctive mode of propagation of singularities in the solutions. Precisely, the singularities of the solutions propagate periodically in a bidirectional fashion along the

Taishi Nakamura, Takuya Akiba, Kazuki Fujii, Yusuke Oda

The Mixture of Experts (MoE) architecture reduces the training and inference cost significantly compared to a dense model of equivalent capacity. Upcycling is an approach that initializes and trains an MoE model using a pre-trained dense model. While upcycling leads to initial performance gains, the training progresses slower than when trained from scratch,

Nadya Abdel Madjid, Murad Mebrahtu, Abdulrahman Ahmad, Abdelmoamen Nasser

This paper introduces the Emirates Multi-Task (EMT) dataset, designed to support multi-task benchmarking within a unified framework. It comprises over 30,000 frames from a dash-camera perspective and 570,000 annotated bounding boxes, covering approximately 150 kilometers of driving routes that reflect the distinctive road topology, congestion patterns, and d

Yantao Cao, Akihiro Koda, M. D. Le, V. Pomjakushin

Quantum spin liquid represents an intriguing state where electron spins are highly entangled yet spin fluctuation persists even at 0 K. Recently, the hexaaluminates \textit{R}MgAl$_{11}$O$_{19}$ (\textit{R} = rare earth) have been proposed to be a platform for realizing the quantum spin liquid state with dominant Ising anisotropic correlations. Here, we repo

Anyimadu Daniel Tweneboah, Suleiman Taofik Ahmed, Hossain Mohammad Imran

Medical imaging spans diverse tasks and modalities which play a pivotal role in disease diagnosis, treatment planning, and monitoring. This study presents a novel exploration, being the first to systematically evaluate segmentation, registration, and classification tasks across multiple imaging modalities. Integrating both classical and deep learning (DL) ap

Zhiqiang Wang, Haoyu Wang, Lu Hao

Webshell is a type of backdoor, and web applications are widely exposed to webshell injection attacks. Therefore, it is important to study webshell detection techniques. In this study, we propose a webshell detection method. We first convert PHP source code to opcodes and then extract Opcode Double-Tuples (ODTs). Next, we combine CodeBert and FastText models