December 2024 arXiv papers — page 164

Qiaolin Qin, Heng Li, Ettore Merlo, Maxime Lamothe

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role in ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues,

Katharina Schuh, Iain Souttar

We establish general conditions under which there exists uniform in time convergence between a stochastic process and its approximated system. These standardised conditions consist of a local in time estimate between the original and the approximated process as well as of a contraction property for one of the processes and a uniform control for the other one

Jarvis Guo, Tuney Zheng, Yuelin Bai, Bo Li

Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These datasets target simplistic tasks, and onl

T. M. Crispim, Marcos V. de S. Silva, G. Alencar, Celio R. Muniz

In this work, we investigate wormhole geometries with multiple throats and anti-throats in general relativity. The existence of these structures is identified through the analysis of minima and maxima in the area of the solution. Using embedding diagrams, we visualize the geometry and demonstrate that these objects exhibit a complex structure, distinct from

Tirtharaj Barman, Swarandeep Sahoo

Convection-driven flows in planetary interiors exhibit rich dynamics owing to multiple spatio-temporally varying forcing conditions and physical constraints. In particular, the churning of liquid metals in the Earth's outer core, responsible for the dynamic geomagnetic field, is subjected to lower mantle thermal heterogeneity. Besides, the plausible existenc

Guanyu Jin, Roger J. A. Laeven, Dick den Hertog, Aharon Ben-Tal

Risk measures, which typically evaluate the impact of extreme losses, are highly sensitive to misspecification in the tails. This paper studies a robust optimization approach to combat tail uncertainty by proposing a unifying framework to construct uncertainty sets for a broad class of risk measures, given a specified nominal model. Our framework is based on

Minji Kim, Tianshu Wen, Kookjin Lee, Youngsoo Choi

This study presents the conditional neural fields for reduced-order modeling (CNF-ROM) framework to approximate solutions of parametrized partial differential equations (PDEs). The approach combines a parametric neural ODE (PNODE) for modeling latent dynamics over time with a decoder that reconstructs PDE solutions from the corresponding latent states. We in

Ardra Edathandel Sasi, Moncy Vilavinal John

The evolution of the Hubble parameter $H(z)$ with redshift $z$ is estimated from the Pantheon+ data of Type Ia supernovae, for the $\Lambda$CDM model and the three special cases of the eternal coasting (EC) cosmological model with three different spatial geometries. The scatter associated with $H(z)$ is seen to grow markedly with redshift. This behaviour, wh

James Beetham, Souradip Chakraborty, Mengdi Wang, Furong Huang

Jailbreak attacks expose vulnerabilities in safety-aligned LLMs by eliciting harmful outputs through carefully crafted prompts. Existing methods rely on discrete optimization or trained adversarial generators, but are slow, compute-intensive, and often impractical. We argue that these inefficiencies stem from a mischaracterization of the problem. Instead, we

Abhishek Raj, Vadim Oganesyan, Antonello Scardicchio

We present a classical kinetically constrained model of interacting particles on a triangular ladder, which displays diffusion and jamming and can be treated by means of a classical-quantum mapping. Interpreted as a theory of interacting fermions, the diffusion coefficient is the inverse of the effective mass of the quasiparticles which can be computed using

Emma Berger, Vivek Maurya, Z. M. McIntyre, Ken Xuan Wei

Numerical gate design typically makes use of high-dimensional parameterizations enabling sophisticated, highly expressive control pulses. Developing efficient experimental calibration methods for such gates is a long-standing challenge in quantum control, as on-device calibration requires the optimization of noisy experimental data over high-dimensional para

Xiaoyu Xu

Any profinite isomorphism between two cusped finite-volume hyperbolic 3-manifolds carries profinite isomorphisms between their Dehn fillings. With this observation, we prove that some cusped finite-volume hyperbolic 3-manifolds are profinitely rigid among all compact, orientable 3-manifolds, through detecting their exceptional Dehn fillings. In addition, we

Ismet Dagli, James Crea, Soner Seckiner, Yuanchao Xu

Shared-memory system-on-chips (SM-SoC) are ubiquitously employed by a wide-range of mobile computing platforms, including edge/IoT devices, autonomous systems and smartphones. In SM-SoCs, system-wide shared physical memory enables a convenient and financially-feasible way to make data accessible by dozens of processing units (PUs), such as CPU cores and doma

Hervé Dutrieux, Joseph Karpie, Kostas Orginos, Savvas Zafeiropoulos

Some calculations of parton distributions from first principles only give access to a limited range of Fourier modes of the function to reconstruct. We present a physically motivated procedure to regularize the inverse integral problem using a Gaussian process as a Bayesian prior. We propose to fix the hyperparameters of the prior in a meaningful physical fa

Carter Swift, Nandini Trivedi

Two hallmarks of quantum non-demolition (QND) measurement are the ensemble-level conservation of the expectation value of the measured observable $A$ and the eventual, inevitable collapse of the system into some eigenstate of $A$. This requires that $A$ commutes with $H$, the system's Hamiltonian. In what we term "Auxiliary Observable QND" measurement, $A$ d

Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti

Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements hinder deployment on constrained resources. To enhance efficiency, binarization and Early Exit (EE) have proved to be effective solutions. However, binarization may lead to performance loss as

Andrii Liashyk, Stanislav Pakuliak, Eric Ragoucy

A new method is introduced to derive general recurrence relations for off-shell Bethe vectors in quantum integrable models with either type $\mathfrak{gl}_n$ or type $\mathfrak{o}_{2n+1}$ symmetries. These recurrence relations describe how to add a single parameter $z$ to specific subsets of Bethe parameters, expressing the resulting Bethe vector as a linear

Michael C. Wood, Adam A. Forbes

The issue of hallucinations in large language models (LLMs) remains a critical barrier to the adoption of AI in enterprise and other high-stakes applications. Despite advancements in retrieval-augmented generation (RAG) systems, current state-of-the-art methods fail to achieve more than 80% accuracy in generating faithful and factually correct outputs, even

Abhishek Raj, Paolo Glorioso, Sarang Gopalakrishnan, Vadim Oganesyan

We consider the relaxation of finite-wavevector density waves in a facilitated classical lattice gas. Linear hydrodynamics predicts that such perturbations should relax exponentially, but nonlinear effects were predicted to cause subexponential relaxation via nonperturbative long-time tails. We present a detailed numerical study of this effect. While our res

I. Bubanja, H. Jung, N. Raicevic, S. Taheri Monfared

Understanding the intrinsic transverse momentum (intrinsic-$k_T$) of partons within colliding hadrons, typically modeled with a Gaussian distribution characterized by a specific width (the intrinsic-$k_T$ width), has been an extremely challenging issue. This difficulty arises because event generators like Pythia require an intrinsic-$k_T$ width that unexpect

Aurelio Amerio, Dan Hooper, Tim Linden

We study the gamma-ray emission from millisecond pulsars within the Milky Way's globular cluster system in order to measure the luminosity function of this source population. We find that these pulsars have a mean luminosity of $\langle L_{\gamma}\rangle \sim (1-8)\times 10^{33}\, {\rm erg/s}$ (integrated between 0.1 and 100 GeV) and a log-normal width of $\

Liam J. Scanlon, Santosh Bhusal, Christina M. Hoffmann, Junhong He

Weyl semimetals have a variety of intriguing physical properties, including topologically protected electronic states that coexist with conducting states. Possible exploitation of topologically protected states in a conducting material is promising for technological applications. Weyl semimetals that form in a noncentrosymmetric structure that also contain m

Jakub Peleška, Gustav Šír

Transformer models have continuously expanded into all machine learning domains convertible to the underlying sequence-to-sequence representation, including tabular data. However, while ubiquitous, this representation restricts their extension to the more general case of relational databases. In this paper, we introduce a modular neural message-passing schem

Peter Gladbach, Jan Maas, Lorenzo Portinale

This paper deals with the large-scale behaviour of nonlinear minimum-cost flow problems on random graphs. In such problems, a random nonlinear cost functional is minimised among all flows (discrete vector-fields) with a prescribed net flux through each vertex. On a stationary random graph embedded in $\mathbb{R}^d$, our main result asserts that these problem

Ayushman Singh, Sharad Prakash, Aniket Das, Nidhi Kushwaha

This study presents an integrated deep learning model for automatic detection and classification of Gastrointestinal bleeding in the frames extracted from Wireless Capsule Endoscopy (WCE) videos. The dataset has been released as part of Auto-WCBleedGen Challenge Version V2 hosted by the MISAHUB team. Our model attained the highest performance among 75 teams

Daniel Scheiermann, Albert Gallemí, Luis Santos

Dipolar Bose-Einstein condensates are excellent platforms for studying supersolidity, characterized by coexisting density modulation and superfluidity. The realization of dipolar mixtures opens intriguing new scenarios, most remarkably the possibility of realizing a double supersolid, composed by two interacting superfluids. We analyze the complex excitation

Sepideh Bazazi, Jurgis Karpus, Taha Yasseri

Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes more integrated into daily life. Research indicates that people are often less willing to cooperate with AI agents than with humans, more readily exploiting AI for personal gain. While prior studies have shown that giving AI agents human-like features influe

Papia Panda, Rukmani Mohanta

In this work, we explore the possibility of probing the mass ordering sensitivity as a function of supernova distance in the context of the ongoing neutrino experiment NO$\nu$A. We provide a detailed study of the active-active and active-sterile mixing frameworks, illustrating how supernova neutrinos can be used to realize the existence of sterile neutrinos.

Tom Clegg, Thilo gross

A key unresolved question in microbial ecology is how the extraordinary diversity of microbiomes emerges from the behaviour of individual populations. This process is driven by the cross-feeding networks that structure these communities, but are hard to untangle due to their inherent complexity. We address this problem using the tools of network science to d

Zixiao Chen, Chentao Wu, Yunfei Gu, Ranhao Jia

Hardware prefetching is one of the most widely-used techniques for hiding long data access latency. To address the challenges faced by hardware prefetching, architects have proposed to detect and exploit the spatial locality at the granularity of spatial region. When a new region is activated, they try to find similar previously accessed regions for footprin

Jian Yang, Jiaxi Yang, Ke Jin, Yibo Miao

Code large language models (codeLLMs) have made significant strides in code generation. Most previous code-related benchmarks, which consist of various programming exercises along with the corresponding test cases, are used as a common measure to evaluate the performance and capabilities of code LLMs. However, the current code LLMs focus on synthesizing the

Luka Medic, Anton Ramšak, Tomaž Rejec

We study a two-terminal Josephson junction with conventional superconductors and a normal region with Rashba spin-orbit interaction, characterized by two Aharonov-Casher (AC) fluxes. When the superconducting phase difference equals $\pi$, the Andreev subgap spectrum may host zero-energy Weyl singularities associated with a vanishing normal-state reflection e

Aditi Singh, Akash Shetty, Abul Ehtesham, Saket Kumar

Text-to-SQL systems facilitate smooth interaction with databases by translating natural language queries into Structured Query Language (SQL), bridging the gap between non-technical users and complex database management systems. This survey provides a comprehensive overview of the evolution of AI-driven text-to-SQL systems, highlighting their foundational co

Carline Biesdorf, Jürgen Schaffner-Bielich, Laura Tolos

We investigate the influence of dark matter on hybrid stars. Using a two-fluid approach, where normal and dark matter components interact only gravitationally, we explore how dark matter can trigger the appearance of quark matter in neutron stars for unprecedented low masses. Our findings reveal that dark matter increases the central pressure of neutron star

Yuping Gao, Songling Shan, Guanghui Wang

Given an integer $k\ge1$, an edge-$k$-coloring of a graph $G$ is an assignment of $k$ colors $1,\ldots,k$ to the edges of $G$ such that no two adjacent edges receive the same color. A vertex-distinguishing (resp. sum-distinguishing) edge-$k$-coloring of $G$ is an edge-$k$-coloring such that for any two distinct vertices $u$ and $v$, the set (resp. sum) of co

Kaustubh D. Dhole, Kai Shu, Eugene Agichtein

Computational argumentation, which involves generating answers or summaries for controversial topics like abortion bans and vaccination, has become increasingly important in today's polarized environment. Sophisticated LLM capabilities offer the potential to provide nuanced, evidence-based answers to such questions through Retrieval-Augmented Argumentation (

Kenneth Chung Tak Chiu

We use the determinant method of Bombieri-Pila and Heath-Brown and its Arakelov reformulation by Chen utilizing Bost's slope method to estimate the number of hypersurfaces required to cover the regular rational points with bounded Arakelov height on a projective variety. Using complex cellular structures introduced by Binyamini-Novikov, we replace the usual

Chen Xu

We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power-$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian-smoothed $f_N$ with stochastic approximations. Under mild conditions on $f$, for any $\delta>0$, we prove that with a su

Ashley S. Dale, Mei Qiu, Foo Bin Che, Thomas Bsaibes

Much effort has been made to explain and improve the success of transfer-based attacks (TBA) on black-box computer vision models. This work provides the first attempt at a priori prediction of attack success by identifying the presence of vulnerable features within target models. Recent work by Chen and Liu (2024) proposed the manifold attack model, a unifyi

Yohann Perron, Vladyslav Sydorov, Adam P. Wijker, Damian Evans

Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai/data

Robin J. Williams, Pablo Ramirez-Espinosa, Olena Semenovska, Petar Popovski

Reconfigurable intelligent surfaces (RISs) are potential enablers of future wireless communications and sensing applications and use-cases. The RIS is envisioned as a dynamically controllable surface that is capable of transforming impinging electromagnetic waves in terms of angles and polarization. Many models has been proposed to predict the wave-transform

Jacob Watson, Fabrício Góes, Marco Volpe, Talles Medeiros

This paper investigates the suitability of frontier Large Language Models (LLMs) for Q&A interactions in science centres, with the aim of boosting visitor engagement while maintaining factual accuracy. Using a dataset of questions collected from the National Space Centre in Leicester (UK), we evaluated responses generated by three leading models: OpenAI's GP

Volkan Sevinc, Michail Tsagris

Not many tests exist for testing the equality for two or more multivariate distributions with compositional data, perhaps due to their constrained sample space. At the moment, there is only one test suggested that relies upon random projections. We propose a novel test termed {\alpha}-Energy Based Test ({\alpha}-EBT) to compare the multivariate distributions

Günter Rote

We construct a probabilistic finite automaton (PFA) with 7 states and an input alphabet of 5 symbols for which the PFA Emptiness Problem is undecidable. The only input for the decision problem is the starting distribution. For the proof, we use reductions from special instances of the Post Correspondence Problem. We also consider some variations: The input a

Yiming Li, Jiacheng Qiu, Sylvain Calinon

Distance functions are crucial in robotics for representing spatial relationships between a robot and its environment. They provide an implicit, continuous, and differentiable representation that integrates seamlessly with control, optimization, and learning. While standard distance fields rely on the Euclidean metric, many robotic tasks inherently involve n

Laurent Orseau, Marcus Hutter, Levi H. S. Lelis

Levin Tree Search (LTS) (Orseau et al., 2018) is a search algorithm for deterministic environments that uses a user-specified policy to guide the search. It comes with a formal guarantee on the number of search steps (node visits) for finding a solution node that depends on the quality of the policy. In this paper, we introduce a new algorithm, called $\sqrt

Ryan Campbell, Jennifer Wadsworth

A recent development in extreme value modeling uses the geometry of the dataset to perform inference on the multivariate tail. A key quantity in this inference is the gauge function, whose values define this geometry. Methodology proposed to date for capturing the gauge function either lacks flexibility due to parametric specifications, or relies on complex

Wael Joudi, Rika Saskia Windisch, Alberto Trentino, Diana Propst

We measure the two-dimensional elastic modulus $E^\text{2D}$ of atomically clean defect-engineered graphene with a known defect distribution and density in correlated ultra-high vacuum experiments. The vacancies are introduced via low-energy (< 200 eV) Ar ion irradiation and the atomic structure is obtained via semi-autonomous scanning transmission electron

Pankaj Kumar, Judith T. Karpen, Joel T. Dahlin

Plasmoids (or magnetic islands) are believed to play an important role in the onset of fast magnetic reconnection and particle acceleration during solar flares and eruptions. Direct imaging of flare current sheets and formation/ejection of multiple plasmoids in extreme ultraviolet (EUV) images, along with simultaneous X-ray and radio observations, offers sig

Rahid Zaman, Ashfaq Adnan

Traumatic brain injuries (TBI) are considered a silent epidemic. It affects many people, from automobiles to sports to service members. In this study, we employed a musculoskeletal head-neck model to understand the effect of impact locations, characteristics, and neck strength on head and neck injury severity. Three types of impact forces were studied: low-v

R. Thiessen, M. Conte, T. L. Stepien, T. Hillen

Go-or-grow approaches represent a specific class of mathematical models used to describe populations where individuals either migrate or reproduce, but not both simultaneously. These models have a wide range of applications in biology and medicine, chiefly among those the modeling of brain cancer spread. The analysis of go-or-grow models has inspired new mat

Zoltán Kovács

We give an alternative proof of the statement, by using elimination from algebraic geometry, that the only set $S\subset\mathbb{R}^2$, $\left|S\right|=6$ such that all subsets that form a triangle are isosceles triangles, is the regular pentagon with its center. Our proof can be extended to answer some related questions raised by Erd\H{o}s.

Alain Bensoussan, Ziyu Huang, Shanjian Tang, Sheung Chi Phillip Yam

In this article, from the viewpoint of control theory, we discuss the relationships among the commonly used monotonicity conditions that ensure the well-posedness of the solutions arising from problems of mean field games (MFGs) and mean field type control (MFTC). We first introduce the well-posedness of general forward-backward stochastic differential equat

Richard D. Chatterjee, Raymond T. Pierrehumbert

Recent James Webb Space Telescope observations of cool, rocky exoplanets reveal a probable lack of thick atmospheres, suggesting prevalent escape of the secondary atmospheres formed after losing primordial hydrogen. Yet, simulations indicate that hydrodynamic escape of secondary atmospheres, composed of nitrogen and carbon dioxide, requires intense fluxes of

Jinlin Wu, Xusheng Liang, Xuexue Bai, Zhen Chen

Surgical interventions, particularly in neurology, represent complex and high-stakes scenarios that impose substantial cognitive burdens on surgical teams. Although deliberate education and practice can enhance cognitive capabilities, surgical training opportunities remain limited due to patient safety concerns. To address these cognitive challenges in surgi

Junyuan Zhang, Songhua Liu, Xinchao Wang

One-shot Federated learning (FL) is a powerful technology facilitating collaborative training of machine learning models in a single round of communication. While its superiority lies in communication efficiency and privacy preservation compared to iterative FL, one-shot FL often compromises model performance. Prior research has primarily focused on employin

Lishuai Gao, Yujie Zhong, Yingsen Zeng, Haoxian Tan

Large Language Models (LLMs) have been widely used in various tasks, motivating us to develop an LLM-based assistant for videos. Instead of training from scratch, we propose a module to transform arbitrary well-trained image-based LLMs into video-LLMs (after being trained on video data). To better adapt image-LLMs for processing videos, we introduce two desi

Junhao Chen, Peng Shu, Yiwei Li, Huaqin Zhao

Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. In this paper, we propose QueEn, a

Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy, Aayush Kapoor

In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This paper introduces the concept of ``privacy drift", an innovative framework that parallels the well-known phenomenon of concept drift. While concept drift addresses the variability i

Mohammed Majthoub Almoghrabi, Martin Skutella, Philipp Warode

An unsplittable multiflow routes the demand of each commodity along a single path from its source to its sink node. As our main result, we prove that in series-parallel digraphs, any given multiflow can be expressed as a convex combination of unsplittable multiflows, where the total flow on any arc deviates from the given flow by less than the maximum demand

Karen Joy, Michelle Liang, Tawfiq Ammari

As a novel virus, COVID introduced considerable uncertainty into the daily lives of people all over the globe since late 2019. Relying on twenty-three semi-structured interviews with parents whose children contracted COVID, we analyzed how the use of social media moderated parental uncertainty about the symptoms, prognosis, long-term potential health ramific

Ali Nour Eldin, Benjamin Dalmas, Walid Gaaloul

Automated process discovery from event logs is a key component of process mining, allowing companies to acquire meaningful insights into their business processes. Despite significant research, present methods struggle to balance important quality dimensions: fitness, precision, generalization, and complexity, but is limited when dealing with complex loop str

Chaitat Utintu, Pinaki Nath Chowdhury, Aneeshan Sain, Subhadeep Koley

Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with temporal artefacts. We present a practical, training-free framework that makes precise video colour editing accessible through an intuitive interface while maintaining professional-qual

Awatif Muflih Alqahtani, Saleem Yousuf, Shahid Ahmad Wani, Roberto S. Costas-Santos

This paper explores the operational principles and monomiality principle that significantly shape the development of various special polynomial families. We argue that applying the monomiality principle yields novel results while remaining consistent with established findings. The primary focus of this study is the introduction of degenerate multidimensional

Thomas Walker, Octave Mariotti, Amir Vaxman, Hakan Bilen

Positional encodings are a common component of neural scene reconstruction methods, and provide a way to bias the learning of neural fields towards coarser or finer representations. Current neural surface reconstruction methods use a "one-size-fits-all" approach to encoding, choosing a fixed set of encoding functions, and therefore bias, across all scenes. C

Richard J. Smith

Let $(M,d)$ be a complete metric space and let $\mathcal{F}(M)$ denote the Lipschitz-free space over $M$. We develop a ``Choquet theory of Lipschitz-free spaces'' that draws from the classical Choquet theory and the De Leeuw representation of elements of $\mathcal{F}(M)$ (and its bidual) by positive Radon measures on $\beta\widetilde{M}$, where $\widetilde{M

Edoardo Loru, Alessandro Galeazzi, Anita Bonetti, Emanuele Sangiorgio

The abundance of information on social media has reshaped public discussions, shifting attention to the mechanisms that drive online discourse. This study analyzes large-scale Twitter (now X) data from three global debates--Climate Change, COVID-19, and the Russo-Ukrainian War--to investigate the structural dynamics of engagement. Our findings reveal that di

Subashree Venkatasubramanian, David A. Barajas-Solano

We present a deep-learning Variational Encoder-Decoder (VED) framework for learning data-driven low-dimensional representations of the relationship between high-dimensional parameters of a physical system and the system's high-dimensional observable response. The framework consists of two deep learning-based probabilistic transformations: An encoder mapping

D. Jaffino Stargen

In treatments of electromagnetism, it is often tacitly assumed that the vector potentials of the field and their conjugate momenta satisfy the canonical Poisson bracket relations, despite the fact that the components of the vector potential are constrained by gauge conditions. Here I explicate how this comes about by imposing Poisson bracket relations on the

Xingxing Liao, Junhao Xie, Jie Zhou

The compound Gaussian (CG) family of distributions has achieved great success in modeling sea clutter. This work develops a flexible-tailed CG model to improve generality in clutter modeling, by introducing the positive tempered $\alpha$-stable (PT$\alpha$S) distribution to model clutter texture. The PT$\alpha$S distribution exhibits widely tunable tails by

Julien Zylberman

While many classical algorithms rely on Laplace transforms, it has remained an open question whether these operations could be implemented efficiently on quantum computers. In this work, we introduce the Quantum Laplace Transform (QLT), which enables the implementation of $N\times N$ discrete Laplace transforms on quantum states encoded in $\lceil \log_2(N)\

Ilka Brunner, Daniel Roggenkamp, Christian P. M. Schneider

We construct defects describing the transition between different phases of gauged linear sigma models with higher rank abelian gauge groups, as well as defects embedding these phases into the GLSMs. Our construction refers entirely to the sector protected by B-type supersymmetry, decoupling the gauge sector. It relies on an abstract characterization of such

Florian K. Unseld, Brennan Undseth, Eline Raymenants, Yuta Matsumoto

Micromagnet-enabled electric-dipole spin resonance (EDSR) is an established method of high-fidelity single-spin control in silicon. However, the resulting architectural limitations have restrained silicon quantum processors to one-dimensional arrays, and heating effects from the associated microwave dissipation exacerbates crosstalk during multi-qubit operat

E. Dionis, B. Peaudecerf, S. Guérin, D. Guéry-Odelin

We numerically study the optimal control of an atomic Bose-Einstein condensate in an optical lattice. We present two generalizations of the gradient-based algorithm, GRAPE, in the non-linear case and for a two-dimensional lattice. We show how to construct such algorithms from Pontryagin's maximum principle. A wide variety of target states can be achieved wit

Samuel Schapiro, Han Zhao

Recently, sharpness-aware minimization (SAM) has emerged as a promising method to improve generalization by minimizing sharpness, which is known to correlate well with generalization ability. Since the original proposal of SAM, many variants of SAM have been proposed to improve its accuracy and efficiency, but comparisons have mainly been restricted to the i

Ilyasse Lamrani, Hanaa Zitane, Delfim F. M. Torres

We study controllability and observability concepts of tempered fractional linear systems in the Caputo sense. First, we formulate a solution for the class of tempered systems under investigation by means of the Laplace transform method. Then, we derive necessary and sufficient conditions for the controllability, as well as for the observability, in terms of

Lars Blatny, Henning Löwe, Johan Gaume

This article presents GRFsaw, an open-source software for generating two-phase (binary) microstructures with user-defined structural properties. Unlike most standard software for microstructure generation, GRFsaw is based on the concept of thresholding Gaussian random fields (GRF). It is designed to be used by researchers or engineers in need of a lightweigh

Kuofeng Gao, Shu-Tao Xia, Ke Xu, Philip Torr

Large Audio-Language Models (LALMs), such as GPT-4o, have recently unlocked audio dialogue capabilities, enabling direct spoken exchanges with humans. The potential of LALMs broadens their applicability across a wide range of practical scenarios supported by audio dialogues. However, given these advancements, a comprehensive benchmark to evaluate the perform

Wei-Ming Chen, Yen-Ting Lin, Chia-Yi Ju

While perturbation theories constitute a significant foundation of modern quantum system analysis, extending them from the Hermitian to the non-Hermitian regime remains a non-trivial task. In this work, we generalize the Rayleigh-Schr\"odinger perturbation theory to the non-Hermitian regime by employing a geometric formalism. This framework allows us to comp

Alessandro Proserpio, Ian A. B. Strachan

The Frobenius manifold structure on the space of rational functions with multiple simple poles is constructed. In particular, the dependence of the Saito-flat coordinates on the flat coordinates of the intersection form is studied. While some of the individual flat coordinates are complicated rational functions, they appear in the prepotential in certain com

Narasimha Raghavan Veeraragavan, Sai Praneeth Karimireddy, Jan Franz Nygård

This paper presents a differentially private approach to Kaplan-Meier estimation that achieves accurate survival probability estimates while safeguarding individual privacy. The Kaplan-Meier estimator is widely used in survival analysis to estimate survival functions over time, yet applying it to sensitive datasets, such as clinical records, risks revealing

Christel Baier, Sascha Klüppelholz, Johannes Lehmann

The enormous growth of the complexity of modern computer systems leads to an increasing demand for techniques that support the comprehensibility of systems. This has motivated the very active research field of formal methods that enhance the understanding of why systems behave the way they do. One important line of research within the verification community

Xinyi Zhang, Naiqi Li, Angela Dai

While remarkable success has been achieved through diffusion-based 3D generative models for shapes, 4D generative modeling remains challenging due to the complexity of object deformations over time. We propose DNF, a new 4D representation for unconditional generative modeling that efficiently models deformable shapes with disentangled shape and motion while

Brian A. Bryce, Kathryn M. Marcotte

The design and performance of wave union TDC implemented in a Lattice CertusPro-NX FPGA is discussed. This FPGA is available for radiation tolerant applications. The TDC is implemented with 16-channels and a 200 MHz reference clock. Each channel is able to record at an event rate of > 1 MHz. The performance of the TDC is assessed over voltage and temperature

Manish Bhattarai, Minh Vu, Javier E. Santos, Ismael Boureima

We introduce a novel method to enhance cross-language code translation from Fortran to C++ by integrating task-specific embedding alignment into a Retrieval-Augmented Generation (RAG) framework. Unlike conventional retrieval approaches that utilize generic embeddings agnostic to the downstream task, our strategy aligns the retrieval model directly with the o

Raul Alfredo de Sousa Silva, Yasmine Belaidouni, Rabah Iguernaissi, Djamal Merad

Understanding the behavior of laboratory animals is a key to find answers about diseases and neurodevelopmental disorders that also affects humans. One behavior of interest is the stopping, as it correlates with exploration, feeding and sleeping habits of individuals. To improve comprehension of animal's behavior, we focus on identifying trait revealing age/

Zuzana Patáková, Micha Sharir

For a set $P$ of $n$ points in $\mathbb R^d$, for any $d\ge 2$, a hyperplane $h$ is called $k$-rich with respect to $P$ if it contains at least $k$ points of $P$. Answering and generalizing a question asked by Peyman Afshani, we show that if the number of $k$-rich hyperplanes in $\mathbb R^d$, $d \geq 3$, is at least $\Omega(n^d/k^\alpha + n/k)$, with a suff

Dirk Schuetz

We show that the $X$-torsion order of a knot, which is defined in terms of a generalised Lee complex, can be calculated using the reduced Bar-Natan--Lee--Turner spectral sequence. We use this for extensive calculations, including an example of $X$-torsion order $4$.

R. Prashanth

Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective treatment regimens and patient management. For instance, a group of subjects termed SWEDD who are clinically diagnosed as PD, but show normal Single Photon Emission Computed Tomography (SPECT) scans, change their di

Recep Firat Cekinel, Pinar Karagoz, Cagri Coltekin

This study evaluates the effectiveness of Vision Language Models (VLMs) in representing and utilizing multimodal content for fact-checking. To be more specific, we investigate whether incorporating multimodal content improves performance compared to text-only models and how well VLMs utilize text and image information to enhance misinformation detection. Fur

Chaoda Zheng, Feng Wang, Naiyan Wang, Shuguang Cui

While 3D object bounding box (bbox) representation has been widely used in autonomous driving perception, it lacks the ability to capture the precise details of an object's intrinsic geometry. Recently, occupancy has emerged as a promising alternative for 3D scene perception. However, constructing a high-resolution occupancy map remains infeasible for large

Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert

Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and high costs. To overcome these limitations, synthetic patient data has emerged as an alternative. However, synthet

David Steinmann, Felix Divo, Maurice Kraus, Antonia Wüst

Shortcuts, also described as Clever Hans behavior, spurious correlations, or confounders, present a significant challenge in machine learning and AI, critically affecting model generalization and robustness. Research in this area, however, remains fragmented across various terminologies, hindering the progress of the field as a whole. Consequently, we introd

Tobias Lojewski, Loïc Le Guyader, Naman Agarwal, Christine Boeglin

Time-resolved X-ray absorption spectroscopy (tr-XAS) has been shown to be a versatile measurement technique for investigating non-equilibrium dynamics. Novel X-ray free electron laser (XFEL) facilities like the European XFEL offer increased repetition rates for stroboscopic XAS experiments through a burst operation mode, which enables measurements with up to

Tiago Roxo, Joana C. Costa, Pedro R. M. Inácio, Hugo Proença

State-of-the-art Active Speaker Detection (ASD) approaches heavily rely on audio and facial features to perform, which is not a sustainable approach in wild scenarios. Although these methods achieve good results in the standard AVA-ActiveSpeaker set, a recent wilder ASD dataset (WASD) showed the limitations of such models and raised the need for new approach

Michael Y. Hu, Aaron Mueller, Candace Ross, Adina Williams

The BabyLM Challenge is a community effort to close the data-efficiency gap between human and computational language learners. Participants compete to optimize language model training on a fixed language data budget of 100 million words or less. This year, we released improved text corpora, as well as a vision-and-language corpus to facilitate research into

Donald Shenaj, Ondrej Bohdal, Mete Ozay, Pietro Zanuttigh

Recent advancements in image generation models have enabled personalized image creation with both user-defined subjects (content) and styles. Prior works achieved personalization by merging corresponding low-rank adapters (LoRAs) through optimization-based methods, which are computationally demanding and unsuitable for real-time use on resource-constrained d

Yuan Yao, Weiwei Zhang, Soojeong Yoo, Callum Parker

Selfie taking is a popular social pastime, and is an important part of socialising online. This activity is popular with young people but is also becoming more prevalent with older generations. Despite this, there are a number of accessibility issues when taking selfies. In this research, we investigate preferences from elderly citizens when taking a selfie,

Yeqing Qiu, Ye Xue, Akang Wang, Yiheng Wang

The Max-k-Cut problem is a fundamental combinatorial optimization challenge that generalizes the classic NP-complete Max-Cut problem. While relaxation techniques are commonly employed to tackle Max-k-Cut, they often lack guarantees of equivalence between the solutions of the original problem and its relaxation. To address this issue, we introduce the Relax-O

Alexandra Zytek, Sara Pido, Sarah Alnegheimish, Laure Berti-Equille

Explanations of machine learning (ML) model predictions generated by Explainable AI (XAI) techniques such as SHAP are essential for people using ML outputs for decision-making. We explore the potential of Large Language Models (LLMs) to transform these explanations into human-readable, narrative formats that align with natural communication. We address two k