October 2024 arXiv papers — page 115

Weiyuan Gong, Jonas Haferkamp, Qi Ye, Zhihan Zhang

We study the sample complexity of the prototypical tasks quantum purity estimation and quantum inner product estimation. In purity estimation, we are to estimate $tr(\rho^2)$ of an unknown quantum state $\rho$ to additive error $\epsilon$. Meanwhile, for quantum inner product estimation, Alice and Bob are to estimate $tr(\rho\sigma)$ to additive error $\epsi

Owen K. Henry, Timothy A. D. Paglione, Yuzhe Song, Joshua Tan

We present evidence for $\gamma$-ray emission from a stacked population of 39 high-latitude globular clusters (GCs) not detected in the Fermi Point Source Catalog, likely attributable to populations of millisecond pulsars within them. In this work, we use 13 years of data collected by the Large Area Telescope aboard the Fermi Gamma-Ray Space Telescope to sea

Dinesh Kumar Panda, Colin Benjamin

Quantum states exhibiting single-particle entanglement (SPE) can encode and process quantum information more robustly than their multi-particle analogs. Understanding the vulnerability and resilience of SPE to disorder is therefore crucial. This letter investigates phase, coin, and position disorder via discrete-time quantum walks on odd and even cyclic grap

Robert F. Phillips, Benjamin D. Williams

We study the interactive effects (IE) model as an extension of the conventional additive effects (AE) model. For the AE model, the fixed effects estimator can be obtained by applying least squares to a regression that adds a linear projection of the fixed effect on the explanatory variables (Mundlak, 1978; Chamberlain, 1984). In this paper, we develop a nove

Sadaf Syed, Michael Joham, Wolfgang Utschick

Rate splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) are two prospective technologies for improving the spectral and energy efficiency in future wireless communication systems. In this work, we investigate a rate splitting (RS) technique for an RIS-aided system in the presence of only statistical channel knowledge. We propose an

Zhenheng Tang, Xueze Kang, Yiming Yin, Xinglin Pan

To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system designed and implemented for training DNNs using geo-distributed GPUs across different computing clusters or individual devices. Decentralized training faces significant challenges rega

Ke Wang, Jiahui Zhu, Minjie Ren, Zeming Liu

The success of Large Language Models (LLMs) is inherently linked to the availability of vast, diverse, and high-quality data for training and evaluation. However, the growth rate of high-quality data is significantly outpaced by the expansion of training datasets, leading to a looming data exhaustion crisis. This underscores the urgent need to enhance data e

Adam Bouland, Tudor Giurgica-Tiron, John Wright

We study a generalization of entanglement testing which we call the "hidden cut problem." Taking as input copies of an $n$-qubit pure state which is product across an unknown bipartition, the goal is to learn precisely where the state is unentangled, i.e. to determine which of the exponentially many possible cuts separates the state. We give a polynomial-tim

Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchm

Lazar Đoković, Marko Robnik-Šikonja

The sarcasm detection task in natural language processing tries to classify whether an utterance is sarcastic or not. It is related to sentiment analysis since it often inverts surface sentiment. Because sarcastic sentences are highly dependent on context, and they are often accompanied by various non-verbal cues, the task is challenging. Most of related wor

Matteo Stoisa, Federica Paganelli Azza, Luca Romanelli, Mattia Varile

Autonomous Rendezvous and Docking (RVD) have been extensively studied in recent years, addressing the stringent requirements of spacecraft dynamics variations and the limitations of GNC systems. This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) for autonomous spacecraft guidance

Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Chenyun Pan, Elias Kougianos

Sustainable advancement is being made to improve the efficiency of the generation, transmission, and distribution of renewable energy resources, as well as managing them to ensure the reliable operation of the smart grid. Supervisory control and data acquisition (SCADA) enables sustainable management of grid communication flow through its real-time data sens

Andrés Rubiano, Armando Reyes

In this paper, we study the differential smoothness of diffusion algebras.

Xingqi Wang, Xiaoyuan Yi, Xing Xie, Jia Jia

Recent advancements in diffusion models trained on large-scale data have enabled the generation of indistinguishable human-level images, yet they often produce harmful content misaligned with human values, e.g., social bias, and offensive content. Despite extensive research on Large Language Models (LLMs), the challenge of Text-to-Image (T2I) model alignment

Kenny Peng, James Grimmelmann

Social media has a misinformation problem, and counterspeech -- fighting bad speech with more speech -- has been an ineffective solution. Here, we argue that bridging-based ranking -- an algorithmic approach to promoting content favored by users of diverse viewpoints -- is a promising approach to helping counterspeech combat misinformation. By identifying co

Haochuan Yu, Suzanne Aigrain, Baptiste Klein, Michael Cretignier

Stellar active regions like spots and faculae can distort the shapes of spectral lines, inducing variations in the radial velocities that are often orders of magnitude larger than the signals from Earth-like planets. Efforts to mitigate these activity signals have hitherto focused on either the time or the velocity (wavelength) domains. We present a physics-

Felix L. Schwenninger, Alexander A. Wierzba

We study the question of bounded-input bounded-output (BIBO) stability of a class of 1-D hyperbolic boundary control systems, which, in particular, contains distributed port-Hamiltonian systems. Exploiting the particular structure of the transfer function of these systems, we derive several sufficient conditions for BIBO stability.

DuoSheng Chen, Binghui Chen, Yifeng Geng, Liefeng Bo

Recently, several point-based image editing methods (e.g., DragDiffusion, FreeDrag, DragNoise) have emerged, yielding precise and high-quality results based on user instructions. However, these methods often make insufficient use of semantic information, leading to less desirable results. In this paper, we proposed a novel mask-free point-based image editing

Phoenix Yu, Tilo Burghardt, Andrew W Dowsey, Neill W Campbell

We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101,3

Lingxiao Luo, Bingda Tang, Xuanzhong Chen, Rong Han

Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable promise in generating visually grounded responses. However, their application in the medical domain is hindered by unique challenges. For instance, most VLMs rely on a single method of visual grounding, whereas complex medical tasks demand more versatile approaches. Additionall

Manan Bhatia, Ewain Gwynne, Jinwoo Sung

We study Liouville quantum gravity (LQG) in the supercritical (a.k.a. strongly coupled) phase, which has background charge $Q \in (0,2)$ and central charge $\mathbf{c}_{\mathrm{L}} = 1+6Q^2 \in (1,25)$. Recent works have shown how to define LQG in this phase as a planar random geometry associated with a variant of the Gaussian free field, which exhibits "inf

Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee

Brain tumor detection and classification are critical tasks in medical image analysis, particularly in early-stage diagnosis, where accurate and timely detection can significantly improve treatment outcomes. In this study, we apply various statistical and machine learning models to detect and classify brain tumors using brain MRI images. We explore a variety

Nedjma Ousidhoum, Meriem Beloucif, Saif M. Mohammad

Language is a form of symbolic capital that affects people's lives in many ways (Bourdieu1977,1991). As a powerful means of communication, it reflects identities, cultures, traditions, and societies more broadly. Therefore, data in a given language should be regarded as more than just a collection of tokens. Rigorous data collection and labeling practices ar

Xinming Wang, Simon Mak, John Miller, Jianguo Wu

A critical bottleneck for scientific progress is the costly nature of computer simulations for complex systems. Surrogate models provide an appealing solution: such models are trained on simulator evaluations, then used to emulate and quantify uncertainty on the expensive simulator at unexplored inputs. In many applications, one often has available data on r

David King

To many Chomsky's debates with Quine and Skinner are an updated version of the Rationalist Empiricist debates of the 17th century. The consensus being that Chomsky's Rationalism was victorious. This dispute has reemerged with the advent of Large Language Models. With some arguing that LLMs vindicate rationalism because of the necessity of building in innate

Antony R. Lee, Peter Tino, Iain Bruce Styles

Motivated by information geometry, a distance function on the space of stochastic matrices is advocated. Starting with sequences of Markov chains the Bhattacharyya angle is advocated as the natural tool for comparing both short and long term Markov chain runs. Bounds on the convergence of the distance and mixing times are derived. Guided by the desire to com

Omar Eldaghar, Yu Zhu, David F. Gleich

We introduce a spatial graph and hypergraph model that smoothly interpolates between a graph with purely pairwise edges and a graph where all connections are in large hyperedges. The key component is a spatial clustering resolution parameter that varies between assigning all the vertices in a spatial region to individual clusters, resulting in the pairwise c

Moritz Mühlenthaler, Mark H. Siggers, Thomas Suzan

Given a graph $G$ and two graph homomorphisms $\alpha$ and $\beta$ from $G$ to a fixed graph $H$, the problem $H$-Recoloring asks whether there is a transformation from $\alpha$ to $\beta$ that changes the image of a single vertex at each step and keeps a graph homomorphism throughout. The complexity of the problem depends among other things on the presence

Mohamad Abdi, Gerardo Hermosillo Valadez, Halid Ziya Yerebakan

Anatomical landmarks are vital in medical imaging for navigation and anomaly detection. Modern large language models (LLMs), like Llama-2, offer promise for automating the mapping of these landmarks in free-text radiology reports to corresponding positions in image data. Recent studies propose LLMs may develop coherent representations of generative processes

Ines Sorrentino, Giulio Romualdi, Fabio Bergonti, Giuseppe ĽErario

This paper presents a scalable method for friction identification in robots equipped with electric motors and high-ratio harmonic drives, utilizing Physics-Informed Neural Networks (PINN). This approach eliminates the need for dedicated setups and joint torque sensors by leveraging the robo\v{t}s intrinsic model and state data. We present a comprehensive pip

Srinivasan Arunachalam, Louis Schatzki

We consider the task of distributed inner product estimation when allowed limited quantum communication. Here, Alice and Bob are given $k$ copies of an unknown $n$-qubit quantum states $\vert \psi \rangle,\vert \phi \rangle$ respectively. They are allowed to communicate $q$ qubits and unlimited classical communication, and their goal is to estimate $|\langle

Archis S. Joglekar

Partial-differential-equation (PDE)-constrained optimization is a well-worn technique for acquiring optimal parameters of systems governed by PDEs. However, this approach is limited to providing a single set of optimal parameters per optimization. Given a differentiable PDE solver, if the free parameters are reparameterized as the output of a neural network,

Pia Steinmeyer, Jan Mölter, Christian Kuehn

Polyadic (or higher-order) interactions can significantly impact the dynamics of interacting particle systems. However, previous studies have often assumed group sizes to be relatively small. In this work, we examine the influence of multiscale polyadic group interactions, where some groups are small and others are very large. We consider two paradigmatic ex

Paulo Sampaio

We establish a compactness result for solutions of a certain class of hypoelliptic equations. This result allows us to show the existence of global weak solutions to the non-homogeneous Landau-Fermi-Dirac equation with Coulomb potential.

Yang Yu, Zheng Chen, Yu-Min Hu, Xian Gao

We propose a novel method to construct ghost-free multiple scalar-tensor theories. The key idea is to use the geometric quantities of hypersurfaces defined by the scalar fields, rather than the covariant derivatives of scalar fields or spacetime curvature, to build the theory. This approach has proven effective in developing ghost-free scalar-tensor theories

Francesco Evangelisti, Francesco Rossi, Tobia Giani, Ilaria Bloise

Accurate satellite pose estimation is crucial for autonomous guidance, navigation, and control (GNC) systems in in-orbit servicing (IOS) missions. This paper explores the impact of different tasks within a multi-task learning (MTL) framework for satellite pose estimation using monocular images. By integrating tasks such as direct pose estimation, keypoint pr

Matteo Brunelli, Fuqi Liang, Jafar Rezaei

Preference disaggregation methods in Multi-Criteria Decision-Making (MCDM) often encounter challenges related to inconsistency and cognitive biases when deriving a value function from experts' holistic preferences. This paper introduces the Best-Worst Disaggregation (BWD) method, a novel approach that integrates the principles of the Best-Worst Method (BWM)

Antônio H. RIbeiro, Thomas B. Schön, Dave Zahariah, Francis Bach

Adversarial training can be used to learn models that are robust against perturbations. For linear models, it can be formulated as a convex optimization problem. Compared to methods proposed in the context of deep learning, leveraging the optimization structure allows significantly faster convergence rates. Still, the use of generic convex solvers can be ine

Melody Sepahpour-Fard, Michael Quayle, Padraig MacCarron, Shane Mannion

This study investigates the emergence of collective identity among individuals critical of vaccination policies in France during the COVID-19 pandemic. As concerns grew over mandated health measures, a loose collective formed on Twitter to assert autonomy over vaccination decisions. Using analyses of pronoun usage, outgroup labeling, and tweet similarity, we

Imran E Kibria, Donald S. Williamson

Research in modeling subjective metrics for quality assessment has led to the development of no-reference speech models that directly operate on utterance waveforms to predict the Mean Opinion Score (MOS). These models often rely on convolutional layers for local feature extraction and embeddings from impractically large pretrained networks to enhance genera

Mahnaz Asghari, Ahmad Sheykhi

In this paper, we ponder observational constraints on the modified $f(R,T)$ gravity, where the gravitational action is a function of Ricci scalar $R$ plus the trace of the energy-momentum tensor $T$, regarding the functional form $f(R,T)=R+2f(T)$ with $f(T)=8\pi G\lambda T$. For this purpose, we utilize recently available data, including cosmic microwave bac

Sameep Chattopadhyay, Pulkit Paliwal, Sai Shankar Narasimhan, Shubhankar Agarwal

Time series forecasts are often influenced by exogenous contextual features in addition to their corresponding history. For example, in financial settings, it is hard to accurately predict a stock price without considering public sentiments and policy decisions in the form of news articles, tweets, etc. Though this is common knowledge, the current state-of-t

Yanyun Wang, Li Liu, Zi Liang, Yi R.

Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accuracy-robustness trade-off. Previous works have studied this issue under the current AT paradigm, but still face over 10% accuracy reduction without significant robustness improvement

Antoine Soulas

We propose a novel approach to quantify quantum coherence which, contrary to the previous ones, does not rely on resource theory but rather on ontological considerations. In this framework, coherence is understood as the ability for a quantum system's statistics to deviate from the total probability formula. After motivating the importance of the total proba

Dewei Zhou, Ji Xie, Zongxin Yang, Yi Yang

The increasing demand for controllable outputs in text-to-image generation has spurred advancements in multi-instance generation (MIG), allowing users to define both instance layouts and attributes. However, unlike image-conditional generation methods such as ControlNet, MIG techniques have not been widely adopted in state-of-the-art models like SD2 and SDXL

Stanisław Kacprzak, Konrad Kowalczyk

Prediction of speaker's height is of interest for voice forensics, surveillance, and automatic speaker profiling. Until now, TIMIT has been the most popular dataset for training and evaluation of the height estimation methods. In this paper, we introduce HeightCeleb, an extension to VoxCeleb, which is the dataset commonly used in speaker recognition tasks. T

Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan

Active reconfigurable intelligent surfaces (RISs) can improve the performance of integrated sensing and communication (ISAC), and therefore enable simultaneous data transmission and target sensing. However, when the line-of-sight (LoS) link between the base station and the sensing target is blocked, the sensing signals suffer from severe path loss, resulting

Niels Jakob Laustsen, James Smith

We study the lattice of closed ideals of bounded operators on two families of Banach spaces: the Baernstein spaces $B_p$ for $1<p<\infty$ and the Schreier spaces $S_p$ for $1\le p<\infty$. Our main conclusion is that there are $2^{\mathfrak{c}}$ many closed ideals that lie between the ideals of compact and strictly singular operators on each of these spaces,

Vishaal Krishnan, Sumit Sinha, L. Mahadevan

Patterns arise spontaneously in a range of systems spanning the sciences, and their study typically focuses on mechanisms to understand their evolution in space-time. Increasingly, there has been a transition towards controlling these patterns in various functional settings, with implications for engineering. Here, we combine our knowledge of a general class

Ryuhei Hibi, Hiroaki Hashida, Yuichi Kawamoto, Nei Kato

Intelligent reflecting surfaces (IRSs) are gaining attention as a low-cost solution to the coverage reduction in high-frequency bands used in next-generation communications. IRSs achieve low costs by controlling only the reflection of radio waves. However, to improve further the propagation environment, larger IRS sizes are required owing to their inability

ZeQiang Wang

The anomalous magnetic moments of leptons represent excellent probes of the Standard Model and therefore also of possible new physics effects. In particular, the persisting hint of new physics in the muon $g$-2 motivates the investigation of similar effects also in the other leptonic dipoles. In this work, we examine the new physics sensitivity of the tau $g

Shicheng Xu, Liang Pang, Yunchang Zhu, Huawei Shen

Vision-language alignment in Large Vision-Language Models (LVLMs) successfully enables LLMs to understand visual input. However, we find that existing vision-language alignment methods fail to transfer the existing safety mechanism for text in LLMs to vision, which leads to vulnerabilities in toxic image. To explore the cause of this problem, we give the ins

Lukas Mecke, Alia Saad, Sarah Prange, Uwe Gruenefeld

In this paper we assess how well users know biometric authentication methods, how they perceive them, and if they have misconceptions about them. We present the results of an online survey that we conducted in two rounds (2019, N=57; and 2023, N=47) to understand the impact of the increasing availability of biometrics on their use and perception. The survey

Alessio Cicero, Mohammad Ali Maleki, Muhammad Waqar Azhar, Anton Frisk Kockum

Quantum computing has the potential to revolutionize multiple fields by solving complex problems that can not be solved in reasonable time with current classical computers. Nevertheless, the development of quantum computers is still in its early stages and the available systems have still very limited resources. As such, currently, the most practical way to

Lotte Rassaerts, Eke Suichies, Bram van de Vrande, Marco Alonso

This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied

Vincent Pilaud, Germain Poullot

We provide a piecewise linear isomorphism from the normal fan of the pivot polytope of a product of simplices to the normal fan of a shuffle of associahedra.

Zhuomin Chen, Jingchao Ni, Hojat Allah Salehi, Xu Zheng

Self-supervised graph representation learning (GRL) typically generates paired graph augmentations from each graph to infer similar representations for augmentations of the same graph, but distinguishable representations for different graphs. While effective augmentation requires both semantics-preservation and data-perturbation, most existing GRL methods fo

Mete Ismayilzada, Defne Circi, Jonne Sälevä, Hale Sirin

Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabilities remain questionable, raising doubts about whether these models learn language similarly to humans. While humans exhibit compositional generalization and linguistic creativity

Sarwan Ali, Taslim Murad, Prakash Chourasia, Haris Mansoor

Understanding the structural and functional characteristics of proteins are crucial for developing preventative and curative strategies that impact fields from drug discovery to policy development. An important and popular technique for examining how amino acids make up these characteristics of the protein sequences with position-specific scoring (PSS). Whil

John M. Hanna, Pavlos Varsos, Jérôme Kowalski, Lorenzo Sala

Zero-dimensional (0D) cardiovascular models are reduced-order models used to study global circulation dynamics and transport. They provide estimates of biomarkers (such as pressure, flow rates, and concentrations) for surgery planning and boundary conditions for high-fidelity 3D models. Although their computational cost is low, tasks like parameter estimatio

Aaron D. Ratschow, Alexander J. Wagner, Mathijs Janssen, Steffen Hardt

Charge transport in porous electrodes is foundational for modern energy storage technologies like supercapacitors, fuel cells, and batteries. Supercapacitors in particular rely solely on storing energy in charged pores. Here, we simulate the charging of a single electrolyte-filled pore using the modified Poisson-Nernst-Planck and Navier-Stokes equations. We

Sai Shankar Narasimhan, Shubhankar Agarwal, Litu Rout, Sanjay Shakkottai

Generating realistic time series samples is crucial for stress-testing models and protecting user privacy by using synthetic data. In engineering and safety-critical applications, these samples must meet certain hard constraints that are domain-specific or naturally imposed by physics or nature. Consider, for example, generating electricity demand patterns w

Koen de Vos, Elena Torta, Herman Bruyninckx, Cesar Lopez Martinez

This paper presents a framework for multi-agent navigation in structured but dynamic environments, integrating three key components: a shared semantic map encoding metric and semantic environmental knowledge, a claim policy for coordinating access to areas within the environment, and a Model Predictive Controller for generating motion trajectories that respe

Andrea Delgado, Diego Venegas-Vargas, Adam Huynh, Kevon Carroll

Generative modeling for high-resolution images in Liquid Argon Time Projection Chambers (LArTPC), used in neutrino physics experiments, presents significant challenges due to the complexity and sparsity of the data. This work explores the application of quantum-enhanced generative networks to address these challenges, focusing on the scaling of models to han

Peter Werner, Thomas Cohn, Rebecca H. Jiang, Tim Seyde

We propose two novel algorithms for constructing convex collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-planning frameworks such as trajectory optimization with Graphs of Convex Sets [1] and is currently a major roadblock in the adoption of these approaches. In this paper, we build upo

Brian Lee Beatty, Shani Kahan, Burcak Bas, Bettina Zou

Fingerprints, otherwise known as dermatoglyphs, are most commonly thought of in the context of identification, but have myriad other roles in human biology. They are formed by the restricted ability of ridges and furrows of the epidermis to flatten. The patterns these ridges and furrows make can be represented as 2D fingerprints, but also as 3D structures wi

Yingpeng Duan, Yujie Tang

This paper investigates distributed zeroth-order feedback optimization in multi-agent systems with coupled constraints, where each agent operates its local action vector and observes only zeroth-order information to minimize a global cost function subject to constraints in which the local actions are coupled. Specifically, we employ two-point zeroth-order gr

Manuel del Pino, Rowan Juneman, Monica Musso

We consider the Ginzburg-Landau equation in the plane linearized around the standard degree-one vortex solution $W(x)=w(r)e^{i\theta}$. Using explicit representation formulae for the Fourier modes in $\theta$, we obtain sharp estimates for the inverse of the linearized operator which hold for a large class of right-hand sides. This theory can be applied, for

Orchid Chetia Phukan, Swarup Ranjan Behera, Girish, Mohd Mujtaba Akhtar

Despite being trained exclusively on speech data, speech foundation models (SFMs) like Whisper have shown impressive performance in non-speech tasks such as audio classification. This is partly because speech shares some common traits with audio, enabling SFMs to transfer effectively. In this study, we push the boundaries by evaluating SFMs on a more challen

Luca Baracco, Olga Bernardi, Alessandra Nardi

We prove that any finitely smooth axially symmetric strictly convex domain, with everywhere positive curvature and sufficiently close to an ellipse is area spectrally rigid. This means that any area-isospectral family of domains in this class is necessarily equi-affine. We use techniques, adapted to symplectic billiards, inspired to the paper by J. De Simoi,

Philippe Nadeau, Hunter Spink, Vasu Tewari

We develop a quasisymmetric analogue of the theory of Schubert cycles, building off of our previous work on a quasisymmetric analogue of Schubert polynomials and divided differences. Our constructions result in a natural geometric interpretation for the ring of quasisymmetric coinvariants.

Yang Zhang, Fa Wang, Xin Huang, Xintao Li

This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and accurate risk assessment. The system utilizes EC2 p3.8xlarge GPU instances to accelerate model training, reducing training time by 93.2% wh

Luca Marsilio, Davide Marzorati, Matteo Rossi, Andrea Moglia

Osteoarthritis is a degenerative condition affecting bones and cartilage, often leading to osteophyte formation, bone density loss, and joint space narrowing. Treatment options to restore normal joint function vary depending on the severity of the condition. This work introduces an innovative deep-learning framework processing shoulder CT scans. It features

Zifan Lin, Saverio Cambioni, Sara Seager

Some exoplanets have much higher densities than expected from stellar abundances of planet-forming elements. There are two theories - metal-rich formation hypothesis and naked core hypothesis - that explain how formation and evolution can alter the compositions and structures of rocky planets to diverge from their primordial building blocks. Here, we revisit

Onur Gilanliogullari, Brahim Mustapha, Pavel Snopok

Flat beams are preferred in high-intensity accelerators and high-energy colliders due to one of the transverse plane emittances being much smaller than the other, which enhances luminosity and beam brightness. However, flat beams are not desirable at low energies due to space charge forces which are significantly enhanced in one plane. The same is true, alth

D. Karavola, M. Petropoulou, D. F. G. Fiorillo, L. Comisso

A ubiquitous feature of accreting black hole systems is their hard X-ray emission which is thought to be produced through Comptonization of soft photons by electrons and positrons in the vicinity of the black hole, in a region with optical depth of order unity. The origin and composition of this Comptonizing region, known as the corona, is a matter open for

Laura Abatangelo, Alberto Ferrero, Paolo Luzzini

The Grushin Laplacian $- \Delta_\alpha $ is a degenerate elliptic operator in $\mathbb{R}^{h+k}$ that degenerates on $\{0\} \times \mathbb{R}^k$. We consider weak solutions of $- \Delta_\alpha u= Vu$ in an open bounded connected domain $\Omega$ with $V \in W^{1,\sigma}(\Omega)$ and $\sigma > Q/2$, where $Q = h + (1+\alpha)k$ is the so-called homogeneous dime

Chia-Tso Lai, Carsten Blank, Peter Schmelcher, Rick Mukherjee

Quadratic Unconstrained Binary Optimization (QUBO) sits at the heart of many industries and academic fields such as logistics, supply chain, finance, pharmaceutical science, chemistry, IT, and energy sectors, among others. These problems typically involve optimizing a large number of binary variables, which makes finding exact solutions exponentially more di

Kamaledin Ghiasi-Shirazi, Mohammadreza Qaraei

Kernel methods in machine learning use a kernel function that takes two data points as input and returns their inner product after mapping them to a Hilbert space, implicitly and without actually computing the mapping. For many kernel functions, such as Gaussian and Laplacian kernels, the feature space is known to be infinite-dimensional, making operations i

Ruifeng Zheng, Puxian Wei, Qiaoli Yang

When axion cold dark matter interacts with a static magnetic field, it can be converted to photons with energy near the axion's mass. Classical analysis shows that incorporating a resonant cavity significantly enhances this conversion rate, forming the basis for many experiments aimed at detecting dark matter axions. However, one might ask: Does the axion-ph

Dorothee Sigg, Moritz Hardt, Celestine Mendler-Dünner

Drivers on food delivery platforms often run a loss on low-paying orders. In response, workers on DoorDash started a campaign, #DeclineNow, to purposefully decline orders below a certain pay threshold. For each declined order, the platform returns the request to other available drivers with slightly increased pay. While contributing to overall pay increase t

Nicolas Lazzari, Stefano De Giorgis, Aldo Gangemi, Valentina Presutti

This work explores the integration of ontology-based reasoning and Machine Learning techniques for explainable value classification. By relying on an ontological formalization of moral values as in the Moral Foundations Theory, relying on the DnS Ontology Design Pattern, the \textit{sandra} neuro-symbolic reasoner is used to infer values (fomalized as descri

A. Choplin, L. Siess, S. Goriely, S. Martinet

Asymptotic giant branch (AGB) stars can experience proton ingestion events (PIEs), leading to a rich nucleosynthesis. During a PIE, the intermediate neutron capture process (i-process) develops, leading to the production of trans-iron elements. It is also suggested that lithium is produced during these events. We investigate the production of lithium and tra

Nikolaos Dimitrakopoulos

We briefly summarise the results for the four top-quark production process in the 4$\ell$ decay channel at NLO accuracy in perturbative QCD. We employ the narrow-width approximation for the treatment of the unstable particles, preserving spin correlations to NLO accuracy throughout our computations. The NLO QCD corrections are applied to both the production

Zhiyuan Zhao, Hengrui Kang, Bin Wang, Conghui He

Document Layout Analysis is crucial for real-world document understanding systems, but it encounters a challenging trade-off between speed and accuracy: multimodal methods leveraging both text and visual features achieve higher accuracy but suffer from significant latency, whereas unimodal methods relying solely on visual features offer faster processing spe

Spencer L. Stahl, Stuart I. Benton

The cost of writing, transferring, and storing large data from unsteady simulations limits access to the entire solution, often leaving much of the flow under-sampled or unanalyzed. For example, modeling transient behavior of rare dynamic events requires 3D snapshots at high sampling rates over long periods, generating significant amounts of data and creatin

Leonardo Giusti, Matteo Bresciani, Mattia Dalla Brida, Tim Harris

We present a recently introduced strategy to study non-perturbatively thermal QCD up to temperatures of the order of the electro-weak scale, combining step scaling techniques and shifted boundary conditions. The former allow to renormalize the theory for a range of scales which spans several orders of magnitude with a moderate computational cost. Shifted bou

Sergio López-Ureña

In this work, we propose activation functions for neuronal networks that are refinable and sum the identity. This new class of activation functions allows the insertion of new layers between existing ones and/or the increase of neurons in a layer, both without altering the network outputs. Our approach is grounded in subdivision theory. The proposed activati

Haodong Zhao, Jinming Hu, Peixuan Li, Fangqi Li

Language models (LMs) have emerged as critical intellectual property (IP) assets that necessitate protection. Although various watermarking strategies have been proposed, they remain vulnerable to Linear Functionality Equivalence Attack (LFEA), which can invalidate most existing white-box watermarks without prior knowledge of the watermarking scheme or train

Shengyao Li, Ya Deng, Dianyi Hu, Chao Zhu

Nonreciprocal electrical transport, characterized by an asymmetric relationship between current and voltage, plays a crucial role in modern electronic industries. Recent studies have extended this phenomenon to superconductors, introducing the concept of the superconducting diode effect (SDE). The SDE is characterized by unequal critical supercurrents along

Youheng Zhu, Yuheng Bu

Recent progress has shown that the generalization error of the Gibbs algorithm can be exactly characterized using the symmetrized KL information between the learned hypothesis and the entire training dataset. However, evaluating such a characterization is cumbersome, as it involves a high-dimensional information measure. In this paper, we address this issue

Lukas Birkenmaier, Matthias Roth, Indira Sen

Computational text classification is a challenging task, especially for multi-dimensional social constructs. Recently, there has been increasing discussion that synthetic training data could enhance classification by offering examples of how these constructs are represented in texts. In this paper, we systematically examine the potential of theory-driven syn

Ruimeng Ye, Yang Xiao, Bo Hui

As large language models (LLMs) continue to advance, ensuring their alignment with human values becomes increasingly critical. Traditional alignment methods heavily rely on human feedback to fine-tune models. With the emergence of superhuman models whose outputs may surpass human understanding, evaluating and aligning these models using human judgments poses

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

Utilizing a data set of $6.7$ fb$^{-1}$ from electron-positron collisions recorded by the BESIII detector at the BEPCII storage ring, a search is conducted for the processes $e^{+}e^{-} \to \phi \chi_{c0}$ and $\phi\eta_{c2}(1D)$ across center-of-mass energies from 4.47 to 4.95 GeV. In the absence of any significant signals, upper limits are set. These inclu

Arianna Burzacchi, Valeria Maria Urbano, Marika Arena, Giovanni Azzone

The analysis of the transportation usage rate provides opportunities for evaluating the efficacy of the transportation service offered by proposing an indicator that integrates actual demand and capacity. This study aims to develop a methodology for analyzing the occupancy rate from large-scale datasets to identify gaps between supply and demand in public tr

Aaron Macks

This paper describes a parsing/generation system for finite verbal forms in Akkadian, with the possible addition of suffixes, implemented in Prolog. The work described provides the framework and engine to interpret the D, N, and G stems along with accusative, dative and ventive endings.

Ikenna Chris-Okoro, Sheilah Cherono, Wisdom Akande, Swapnil Nalawade

This work reports a pulsed laser-assisted synthesis, detailed structural characterization, and study of plasmonic properties of three sets of TiN/TiNO thin films with high electron density. The first two sets of TiN films were grown at 600C and 700C under a high vacuum condition. The third set of TiN film was grown in the presence of 5 mTorr of molecular oxy

Joerg Seiler

A pseudodifferential calculus for parameter-dependent operators on smooth manifolds with boundary in the spirit of Boutet de Monvel's algebra is constructed. The calculus contains, in particular, the resolvents of realizations of differential operators subject to global projection boundary conditions (spectral boundary conditions are a particular example); r

M. Croci, G. N. Wells

In this paper we develop the first fine-grained rounding error analysis of finite element (FE) cell kernels and assembly. The theory includes mixed-precision implementations and accounts for hardware-acceleration via matrix multiplication units, thus providing theoretical guidance for designing reduced- and mixed-precision FE algorithms on CPUs and GPUs. Gui

Yedi Hu, Yunzhi Yao, Ningyu Zhang, Huajun Chen

Model merging has emerged as a key technique for enhancing the capabilities and efficiency of Large Language Models (LLMs). The open-source community has driven model evolution by iteratively merging existing models, yet a principled understanding of the gains and underlying factors in model merging remains limited. In this work, we study model evolution thr