May 2024 arXiv papers — page 66

Michele Coti Zelati, Matias G. Delgadino

We consider variational problem related to entropy maximization in the two-dimensional Euler equations, in order to investigate the long-time dynamics of solutions with bounded vorticity. Using variations on the classical min-max principle and borrowing ideas from optimal transportation and quantitative rearrangement inequalities, we prove results on the str

Azzurra Ciliberti

Let $\mathbf{P}_{2n+2}$ be the regular polygon with $2n+2$ vertices, and let $\theta$ be the rotation of 180$^\circ$. Fomin and Zelevinsky proved that $\theta$-invariant triangulations of $\mathbf{P}_{2n+2}$ are in bijection with the clusters of cluster algebras of type $B_n$ or $C_n$. Furthermore, cluster variables correspond to the orbits of the action of

Hao Fu, Naman Patel, Prashanth Krishnamurthy, Farshad Khorrami

Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models. Recent advances in vision language foundation models have made them capable of detecting OOD samples without requiring in-distribution (ID) images. However, these zero-shot methods often underperform as they do not adequately consider ID class

Xinyi Shang, Peng Sun, Tao Lin

Recent advancements in dataset distillation have demonstrated the significant benefits of employing soft labels generated by pre-trained teacher models. In this paper, we introduce a novel perspective by emphasizing the full utilization of labels. We first conduct a comprehensive comparison of various loss functions for soft label utilization in dataset dist

Baiqiao Chen, Qi Jia, Rui Feng, Fangkui Sun

Euler's formula, an extraordinary mathematical formula, establishes a vital link between complex-valued operations and trigonometric functions, finding widespread application in various fields. With the end of Moore's Law, electronic computing methods are encountering developmental bottlenecks. With its enviable potential, optical computing has successfully

Yu Meng, Mengzhou Xia, Danqi Chen

Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that reparameterizes reward functions in reinforcement learning from human feedback (RLHF) to enhance simplicity and training stability. In this work, we propose SimPO, a simpler yet more effective approach. The effectiveness of SimPO is attributed to a key design

Thibault Leduque, Eric Barthélemy, Hervé Michallet, Joël Sommeria

We describe laboratory experiments in a 2D wave tank that aim at building up and monitor 2D shallow water soliton gas. The water surface elevation is obtained over a large ($\sim 100\,\text{m}^2$) domain, with centimetre-resolution, by stereoscopic vision using two cameras. Floating particles are seeded to get surface texture and determine the wave field by

J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder

The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintil

Yang Zhou, Long Quang, Carlos Nieto-Granda, Giuseppe Loianno

In the past decade, although single-robot perception has made significant advancements, the exploration of multi-robot collaborative perception remains largely unexplored. This involves fusing compressed, intermittent, limited, heterogeneous, and asynchronous environmental information across multiple robots to enhance overall perception, despite challenges l

Luke McDermott

Real world re-identfication (ReID) algorithms aim to map new observations of an object to previously recorded instances. These systems are often constrained by quantity and size of the stored embeddings. To combat this scaling problem, we attempt to shrink the size of these vectors by using a variety of compression techniques. In this paper, we benchmark qua

Raúl Fuentes-Azcatl

Currently, the study of systems confined within various materials, such as graphene and graphene oxide, for diverse applications such as water desalination, metal separation from water, battery cells, and high-efficiency capacitors, is very common. Among these systems, water is a prominent subject of investigation. Understanding the impact of the number of m

Sainyam Galhotra, Joseph Y. Halpern

Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine

Hyun Kyu Kim

In 1980's H. Verlinde suggested to construct and use a quantization of Teichm\"uller spaces to construct spaces of conformal blocks for the Liouville conformal field theory. This suggestion led to a mathematical formulation by Fock in 1990's and later by Fock, Goncharov and Shen, called the modular functor conjecture, based on the Chekhov-Fock quantum Teichm

Young Kyun Jang, Donghyun Kim, Ser-nam Lim

``Learning to hash'' is a practical solution for efficient retrieval, offering fast search speed and low storage cost. It is widely applied in various applications, such as image-text cross-modal search. In this paper, we explore the potential of enhancing the performance of learning to hash with the proliferation of powerful large pre-trained models, such a

Karima Makhlouf, Tamara Stefanovic, Heber H. Arcolezi, Catuscia Palamidessi

Machine learning (ML) algorithms rely primarily on the availability of training data, and, depending on the domain, these data may include sensitive information about the data providers, thus leading to significant privacy issues. Differential privacy (DP) is the predominant solution for privacy-preserving ML, and the local model of DP is the preferred choic

Jie Chen, Xianbin Wang

Due to the distinct objectives and multipath utilization mechanisms between the communication module and radar module, the system design of integrated sensing and communication (ISAC) necessitates two types of channel state information (CSI), i.e., communication CSI representing the whole channel gain and phase shifts, and radar CSI exclusively focused on ta

Janko Gravner, David Sivakoff

We consider three-state cellular automata in two dimensions in which two colored states, blue and red, compete for control of the empty background, starting from low initial densities $p$ and $q$. When the dynamics of both colored types are one-dimensional, the dynamics has three distinct phases, characterized by a power relationship between $p$ and $q$: two

Chuanyang Zheng, Yihang Gao, Han Shi, Minbin Huang

Positional encoding plays a crucial role in transformers, significantly impacting model performance and length generalization. Prior research has introduced absolute positional encoding (APE) and relative positional encoding (RPE) to distinguish token positions in given sequences. However, both APE and RPE remain fixed after model training regardless of inpu

Camille Coron, Olivier Hénard

We consider a periodic extension of the classical Kingman non-linear model (Kingman, 1978) for the balance between selection and mutation in a large population. In the original model, the fitness distribution of the population is modeled by a probability measure on the unit interval evolving through a simple dynamical system in discrete time: selection acts

Aditya Jonnalagadda, Bruno B. Barufaldi, Andrew D. A. Maidment, Susan P. Weinstein

Model observers are computational tools to evaluate and optimize task-based medical image quality. Linear model observers, such as the Channelized Hotelling Observer (CHO), predict human accuracy in detection tasks with a few possible signal locations in clinical phantoms or real anatomic backgrounds. In recent years, Convolutional Neural Networks (CNNs) hav

BaoYu Tan

Reissner-Nordstrom anti de Sitter (RN-AdS) black hole, characterized by electric charge and negative cosmological constant,exhibits a rich thermodynamics structure. In this paper, we consider the influence of quintessence, a hypothetical dark energy component with negative pressure. we have computed the extended generalized uncertainty principle (EGUP) corre

Egon Peršak, Miguel F. Anjos

Most decision-focused learning work has focused on single stage problems whereas many real-world decision problems are more appropriately modelled using multistage optimisation. In multistage problems contextual information is revealed over time, decisions have to be taken sequentially, and decisions now have an intertemporal effect on future decisions. Deci

Dominik Eckert, Christopher Syben, Christian Hümmer, Ludwig Ritschl

The progression of X-ray technology introduces diverse image styles that need to be adapted to the preferences of radiologists. To support this task, we introduce a novel deep learning-based metric that quantifies style differences of non-matching image pairs. At the heart of our metric is an encoder capable of generating X-ray image style representations. T

Leon Schumm, Hazem Abdel-Khalek, Tom Brown, Falko Ueckerdt

As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and tempor

Momin N. Siddiqui, Adit Gupta, Jennifer M. Reddig, Christopher J. MacLellan

Intelligent tutors have shown success in delivering a personalized and adaptive learning experience. However, there exist challenges regarding the granularity of knowledge in existing frameworks and the resulting instructions they can provide. To address these issues, we propose HTN-based tutors, a new intelligent tutoring framework that represents expert mo

Young Kyun Jang, Ser-nam Lim

Modern retrieval systems often struggle with upgrading to new and more powerful models due to the incompatibility of embeddings between the old and new models. This necessitates a costly process known as backfilling, which involves re-computing the embeddings for a large number of data samples. In vision, Backward-compatible Training (BT) has been proposed t

Raghul Parthipan, Mohit Anand, Hannah M. Christensen, J. Scott Hosking

Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulation in their forecasts is a key problem. However, there is no clear definition of what `error accumulation' actually entails. In this paper, we propose a definition and an associate

Tommaso Calo, Christopher J. MacLellan

Intelligent Tutoring Systems (ITSs) have shown great potential in delivering personalized and adaptive education, but their widespread adoption has been hindered by the need for specialized programming and design skills. Existing approaches overcome the programming limitations with no-code authoring through drag and drop, however they assume that educators p

Luke Strgar, David Matthews, Tyler Hummer, Sam Kriegman

The automatic design of robots has existed for 30 years but has been constricted by serial non-differentiable design evaluations, premature convergence to simple bodies or clumsy behaviors, and a lack of sim2real transfer to physical machines. Thus, here we employ massively-parallel differentiable simulations to rapidly and simultaneously optimize individual

Bastien Batardière, Julien Chiquet, François Gindraud, Mahendra Mariadassou

Analyzing high-dimensional count data is a challenge and statistical model-based approaches provide an adequate and efficient framework that preserves explainability. The (multivariate) Poisson-Log-Normal (PLN) model is one such model: it assumes count data are driven by an underlying structured latent Gaussian variable, so that the dependencies between coun

Jialin Li, Haowu Wang

In this paper we give unified formulas for the numbers of representations of positive integers as sums of four generalized $m$-gonal numbers, and as restricted sums of four squares under a linear condition, respectively. These formulas are given as $\mathbb{Z}$-linear combinations of Hurwitz class numbers. As applications, we prove several Zhi-Wei Sun's conj

Shuheng Ge, Haoyu Xing, Li Zhang, Xiangqian Wu

Creating realistic, natural, and lip-readable talking face videos remains a formidable challenge. Previous research primarily concentrated on generating and aligning single-frame images while overlooking the smoothness of frame-to-frame transitions and temporal dependencies. This often compromised visual quality and effects in practical settings, particularl

M. Kuzuhara, A. Fukui, J. H. Livingston, J. A. Caballero

Recent discoveries of Earth-sized planets transiting nearby M dwarfs have made it possible to characterize the atmospheres of terrestrial planets via follow-up spectroscopic observations. However, the number of such planets receiving low insolation is still small, limiting our ability to understand the diversity of the atmospheric composition and climates of

Aniket Deroy, Naksatra Kumar Bailung, Kripabandhu Ghosh, Saptarshi Ghosh

Despite the availability of vast amounts of data, legal data is often unstructured, making it difficult even for law practitioners to ingest and comprehend the same. It is important to organise the legal information in a way that is useful for practitioners and downstream automation tasks. The word ontology was used by Greek philosophers to discuss concepts

Stefano Carignano, Luca Tagliacozzo

We show how the Loschmidt echo of a product state after a quench to a conformal invariant critical point and its leading finite time corrections can be predicted by using conformal field theories (CFT). We check such predictions with tensor networks, finding excellent agreement. As a result, we can use the Loschmidt echo to extract the universal information

Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li

Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images, they reduce the rich tapestry of human preference to a single overall score. However, the preference results vary when humans evaluate i

Nathan Bowler, Florian Reich

In this series we introduce and investigate the concept of connectoids, which captures the connectivity structure of various discrete objects like undirected graphs, directed graphs, bidirected graphs, hypergraphs or finitary matroids. In this paper we develop a universal end space theory based on connectoids: the end spaces of connectoids unify the existing

Pengyue Jia, Yiding Liu, Xiaopeng Li, Yuhao Wang

Worldwide geolocalization aims to locate the precise location at the coordinate level of photos taken anywhere on the Earth. It is very challenging due to 1) the difficulty of capturing subtle location-aware visual semantics, and 2) the heterogeneous geographical distribution of image data. As a result, existing studies have clear limitations when scaled to

Yibin Wang, Weizhong Zhang, Honghui Xu, Cheng Jin

Scene text synthesis involves rendering specified texts onto arbitrary images. Current methods typically formulate this task in an end-to-end manner but lack effective character-level guidance during training. Besides, their text encoders, pre-trained on a single font type, struggle to adapt to the diverse font styles encountered in practical applications. C

Ting Liu, Xuyang Liu, Liangtao Shi, Zunnan Xu

Parameter-efficient fine-tuning (PEFT) has emerged as a popular solution for adapting pre-trained Vision Transformer (ViT) models to downstream applications by updating only a small subset of parameters. While current PEFT methods have achieved fine-tuning efficiency, they overlook the efficiency of computation and GPU memory during inference, falling short

A. Ballester-Bolinches, R. Esteban-Romero, P. Jiménez-Seral, V. Pérez-Calabuig

A complete description of all possible multiplicative groups of finite skew left braces whose additive group has trivial centre is shown. As a consequence, some earlier results of Tsang can be improved and an answer to an open question set by Tsang at Ischia Group Theory 2024 Conference is provided.

Pascal Reeck, Vladyslav Shtabovenko, Matthias Steinhauser

We provide details to several technical aspects which are important for the calculation of next-to-next-to-leading order corrections to the mixing of neutral $B$ mesons. This includes the computation of the master integrals for finite charm and bottom quark masses, traces over products of up to 22 $\gamma$ matrices and tensor integrals with up to rank 11.

Farrokh Labib, B. David Clader, Nikitas Stamatopoulos, William J. Zeng

We demonstrate that the problem of amplitude estimation, a core subroutine used in many quantum algorithms, can be mapped directly to a problem in signal processing called direction of arrival (DOA) estimation. The DOA task is to determine the direction of arrival of an incoming wave with the fewest possible measurements. The connection between amplitude est

Chunwei Liu, Matthew Russo, Michael Cafarella, Lei Cao

A long-standing goal of data management systems has been to build systems which can compute quantitative insights over large corpora of unstructured data in a cost-effective manner. Until recently, it was difficult and expensive to extract facts from company documents, data from scientific papers, or metrics from image and video corpora. Today's models can a

Axel Leblanc, Chotivut Tangchingchai, Zahra Sadre Momtaz, Elyjah Kiyooka

Hybrid superconductor-semiconductor Josephson field-effect transistors (JoFETs) function as Josephson junctions with a gate-tunable critical current. Additionally, they can feature a non-sinusoidal current-phase relation (CPR) containing multiple harmonics of the superconducting phase difference, a so-far underutilized property. In this work, we exploit this

Saee A. Joshi, Vinayak M. Sholapurkar

The process of identifying a Dirichlet-type space $D(\mu)$ for a positive, Borel measure $\mu$, supported on the unit circle $\mathbb T,$ with a de Branges-Rovnyak space was initiated by Sarason. A characterization of the symbol for a de Branges-Rovnyak spaces for which the shift operator is a $2$-isometry, was provided in an article by Kellay and Zarrabi. I

Jacob Haqq-Misra, Eric T. Wolf, Thomas J. Fauchez, Ravi K. Kopparapu

This paper highlights methods from geostatistics that are relevant to the interpretation, intercomparison, and synthesis of atmospheric model data, with a specific application to exoplanet atmospheric modeling. Climate models are increasingly used to study theoretical and observational properties of exoplanets, which include a hierarchy of models ranging fro

Abhishek Kumar Mehta

In this paper, Dirac Quantization of $3D$ gravity in the first-order formalism is attempted where instead of quantizing the connection and triad fields, the connection and the triad 1-forms themselves are quantized. The exterior derivative operator on the space of differential forms is treated as the `time' derivative to compute the momenta conjugate to thes

Qinghua Guan, Jinhui Ouyang, Di Wu, Weiren Yu

The spatiotemporal data generated by massive sensors in the Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, and stability) in real-time analysis and decision making for different IoT applications. The complexity of IoT data prevents the common people from gai

Marcos Matabuena, Joe Sartini, Francisco Gude

Postprandial glucose collected through continuous glucose monitoring (CGM) provides critical information for assessing metabolic capacity and guiding dietary recommendations. Traditional approaches summarize these data into scalar measures, such as 2-hour AUC or peak glucose, potentially overlooking temporal dynamics. We propose analyzing entire CGM trajecto

Dimitrios Bachtis, Giulio Biroli, Aurélien Decelle, Beatriz Seoane

In this paper, we investigate the feature encoding process in a prototypical energy-based generative model, the Restricted Boltzmann Machine (RBM). We start with an analytical investigation using simplified architectures and data structures, and end with numerical analysis of real trainings on real datasets. Our study tracks the evolution of the model's weig

Niclas Vödisch

Holistic scene understanding poses a fundamental contribution to the autonomous operation of a robotic agent in its environment. Key ingredients include a well-defined representation of the surroundings to capture its spatial structure as well as assigning semantic meaning while delineating individual objects. Classic components from the toolbox of roboticis

I. K. Kominis

It was recently demonstrated that a multitude of realizations of several magnetic sensing technologies satisfy the energy resolution limit, which connects a quantity composed by the variance of the magnetic field estimate, the sensor volume and the measurement time, and having units of action, with $\hbar$. A first-principles derivation of this limit is stil

Marc Harary

Reliably measuring the collinearity of bivariate data is crucial in statistics, particularly for time-series analysis or ongoing studies in which incoming observations can significantly impact current collinearity estimates. Leveraging identities from Welford's online algorithm for sample variance, we develop a rigorous theoretical framework for analyzing th

L. M. J. Hall, L. S. Sirkina, A. Morreau, W. Langbein

The interaction of a quantum system with its environment limits its coherence time. This, in particular, restricts the utility of qubits in quantum information processing applications. In this paper, we show that the decoherence of a coupled qubit system can be minimized, or even eliminated, by exploiting the quantum coherence of the bath itself. We investig

Shreya Kumbhakar, Tuhin Kumar Maji, Binita Tongbram, Shinjan Mandal

Electrical resistivity in good metals, particularly noble metals such as gold (Au), silver (Ag), or copper, increases linearly with temperature ($T$) for $T > \Theta_{\mathrm{D}}$, where $\Theta_{\mathrm{D}}$ is the Debye temperature. This is because the coupling ($\lambda$) between the electrons and the lattice vibrations, or phonons, in these metals is rat

Suvo Banik, Partha Sarathi Dutta, Sukriti Manna, Subramanian KRS Sankaranarayanan

Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity and thermody

Vit Piskovsky

In this paper, I prove necessary and sufficient conditions for the existence of Turing instabilities in a general system with three interacting species. Turing instabilities describe situations when a stable steady state of a reaction system (ordinary differential equation) becomes an unstable homogeneous steady state of the corresponding reaction-diffusion

Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif, Yevgeny Seldin

PAC-Bayesian analysis is a frequentist framework for incorporating prior knowledge into learning. It was inspired by Bayesian learning, which allows sequential data processing and naturally turns posteriors from one processing step into priors for the next. However, despite two and a half decades of research, the ability to update priors sequentially without

Duc-Viet Vu

We prove a uniform diameter estimate and a uniform local non-collapsing of volumes for a large family of Kaehler metrics generalizing those obtained recently by Guo-Phong-Song-Sturm. We treat also similar questions in the singular setting.

Hegel Pedroza, Wallace Abreu, Ryan Corey, Iran Roman

Guitar tablature transcription (GTT) aims at automatically generating symbolic representations from real solo guitar performances. Due to its applications in education and musicology, GTT has gained traction in recent years. However, GTT robustness has been limited due to the small size of available datasets. Researchers have recently used synthetic data tha

Zhicheng Sun, Zhenhao Yang, Yang Jin, Haozhe Chi

Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidanc

Laurent Mazet

Following the strategy developed by Chodosh, Li, Minter and Stryker, and using the volume estimate of Antonelli and Xu, we prove that, in $\mathbb R^6$, a complete, two-sided, stable minimal hypersurfaces is flat.

Hassan Nagib, Ricardo Vinuesa, Sergio Hoyas

Indicator functions of the streamwise normal-stress profiles (NSP), based on careful differentiation of some of the best direct numerical simulations (DNS) data from channel and pipe flows, over the range $550<Re_\tau<16,000$, are examined to establish the existence and range in wall distances of either a logarithmic-trend segment or a $1/4$-power region. Fo

Zhechao Wang, Peirui Cheng, Mingxin Chen, Pengju Tian

Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information. However, it encounters two main challenges in multi-drone collaboration settings. The expansive aerial observations make it difficult to generate precise Bird's Eye View (BEV) representations. Besides, excessive interact

Reiner Brüning, Jasmin Bedow, Roberto Lo Conte, Kirsten von Bergmann

Combining spin textures in ultra-thin films with conventional superconductors has emerged as a powerful and versatile platform for designing topologically non-trivial superconducting phases as well as spin-triplet Cooper pairs. As a consequence, two-dimensional magnet-superconductor hybrids (2D MSHs) are promising candidate systems to realize devices for top

Hanrong Zhang, Zhenting Wang, Boheng Li, Fulin Lin

Self-supervised learning (SSL) models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in SSL often involve noticeable triggers, like colored patches or visible noise, which are vulnerable to human inspection. This paper proposes an imperceptible and effective backdoor attack against self-supervised models. We first find that

Rupam Samanta, Jean-Yves Ollitrault

A collision between a proton and a heavy nucleus at ultrarelativistic energy creates particles whose rapidity distribution is asymmetric, with more particles emitted in the direction of the nucleus than in the direction of the proton. This asymmetry becomes more pronounced as the centrality estimator, defined from the energy deposited in a calorimeter, incre

Rachid Guerraoui, Rafael Pinot, Geovani Rizk, John Stephan

Batch normalization has proven to be a very beneficial mechanism to accelerate the training and improve the accuracy of deep neural networks in centralized environments. Yet, the scheme faces significant challenges in federated learning, especially under high data heterogeneity. Essentially, the main challenges arise from external covariate shifts and incons

Peng Sun, Yi Jiang, Tao Lin

Data, the seminal opportunity and challenge in modern machine learning, currently constrains the scalability of representation learning and impedes the pace of model evolution. In this work, we investigate the efficiency properties of data from both optimization and generalization perspectives. Our theoretical and empirical analysis reveals an unexpected fin

Sagar Sethi, Agnieszka Kuźmicz, Marek Jamrozy, Lyuba Slavcheva-Mihova

We report the discovery of an S-shaped morphology of the radio galaxy J0644$+$1043 imaged with a 30 $\mu$Jy sensitive 525 MHz broadband (band 3 $+$ 4) uGMRT map. Dedicated spectroscopic observations of the host galaxy carried out with the 2-meter Rozhen telescope yielded a redshift of 0.0488, giving a projected linear size of the peculiar radio structure of

Ahmed O. Elmeligy, Ioannis Psaromiligkos, Au Minh

The use of cellular networks for massive machine-type communications (mMTC) is an appealing solution due to the wide availability of cellular infrastructure. Estimating the number of devices (network load) is vital for efficient allocation of the available resources, especially for managing the random access channel (RACH) of the network. This paper consider

Andreu Benavent-Claró, Yara Alvarez-Braña, Fernando Benito-Lopez, Lourdes Basabe-Desmonts

Air-permeable porous media hosts air within their pores. Upon removal from the interior of the material, these porous media have the tendency to reabsorb air from the surrounding, acting as a suction pump. Therefore, the technique used to convert porous media into a pump, consists of degassing the material to remove their air inside. The suction property whe

Simone Di Cataldo, Maria Rescigno, Lorenzo Monacelli, Umbertoluca Ranieri

Hydrogen hydrates present a rich phase diagram influenced by both pressure and temperature, with the so-called C$_2$ phase emerging prominently above 2.5 GPa. In this phase, hydrogen molecules are densely packed within a cubic ice-like lattice and the interaction with the surrounding water molecules profoundly affects their quantum rotational dynamics. Herei

Oscar Davis, Samuel Kessler, Mircea Petrache, İsmail İlkan Ceylan

Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured molecular data. The predominant generative modeling paradigm for discrete data is still autoregressive, with more recent alternatives based on diffusion or flow-matching falling shor

Charlotte Myers, Kishalay De, Lin Yan, Jacob E. Jencson

While core-collapse supernovae (SNe) often show early and consistent signs of circumstellar (CSM) interaction, some exhibit delayed signatures due to interaction with distant material around the progenitor star. Here we present the discovery in NEOWISE data of WTP19aalnxx, a luminous mid-infrared (IR) transient in the outskirts of the galaxy KUG 0022-007 at

Masahiro Naritsuka, Tadashi Machida, Shun Asano, Youichi Yanase

The superconducting properties of layered materials can be controlled by thinning, stacking, and twisting, demanding investigation of electronic states by spectroscopic means at the nanometer scale. Here, we reveal the spatial variations of the electronic states in heterostructures of the superconducting monolayer NbSe$_2$/graphene using spectroscopic-imagin

Shun Asano, Youichi Yanase

The recent advent of artificial structures has triggered the emergence of fascinating phenomena that could not exist in natural compounds. A prime example is twisted multilayers, i.e., moir\'e superlattices represented by magic-angle twisted bilayer graphene (MATBG). As in the case of MATBG, unconventional band hybridization can induce a new type of supercon

Zhuowei Li, Zihao Xu, Ligong Han, Yunhe Gao

In-context Learning (ICL) empowers large language models (LLMs) to swiftly adapt to unseen tasks at inference-time by prefixing a few demonstration examples before queries. Despite its versatility, ICL incurs substantial computational and memory overheads compared to zero-shot learning and is sensitive to the selection and order of demonstration examples. In

Songbo Ling, Xin Lü

Let $S$ be a minimal irregular surface of general type, whose Albanese map induces a fibration $f:\,S \to C$ of genus $g$.We prove a linear upper bound on the genus $g$ if $K_S^2\leq 4\chi(\mathcal{O}_S)$. Examples are constructed showing that the above linear upper bound is sharp. We also give a characterization of the Albanese fibrations reaching the above

Fei Ma

The degree-degree correlation is crucial in understanding the structural properties of and dynamics occurring upon network, and is often measured by the assortativity coefficient $r$. In this paper, we first study this measure in detail and conclude that $r$ belongs to an asymmetric range $[-1,1)$ rather than the widely-cited $[-1,1]$. Among which, we verify

Jean-Philippe Burelle, Neža Žager Korenjak

We define for every positive Anosov representation of a nonabelian free group into $\mathrm{SO}(2n,2n-1)$ a family of $\mathbb{R}^{4n-1}$-valued cocycles which induce proper affine actions on $\mathbb{R}^{4n-1}$. We construct fundamental domains in $\mathbb{R}^{4n-1}$ bounded by generalized crooked planes for these affine actions, and deduce that the quotien

A. J. D. Farias Junior, A. Smirnov, Herondy F. Santana Mota, E. R. Bezerra de Mello

In this paper the vacuum energy density and generation of topological mass are investigated for a system of a real and complex scalar fields interacting with each other. In addition to that, it is also included the quartic self-interaction for each one of the fields. The condition imposed on the real field is the periodic condition, while the complex field o

Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang

Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to demonstrate remarkable abilities in various tasks. Existing methods work by emulating the preferences at the single decision (turn) level, limiting their capabilities in settings that require pl

Julien Khlaut, Corentin Dancette, Elodie Ferreres, Alaedine Bennani

In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. Large language models, such as GPT-4, obtain reasonable scores on medical question answering tasks, but smaller models are far behind. In this work, we introduce a method to improve the proficien

Manuel Gonzalez-Espinoza, Ramon Herrera

We study the possibility that inflation is driven by a scalar field together with a vector field minimally coupled to gravity. By assuming an effective potential that incorporates both fields into the action, we explore two distinct scenarios: one where the fields interact and another where they do not. In this context, we find different analytical solutions

Leheng Cai, Xu Guo, Heng Lian, Liping Zhu

High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this critical issue, high-dimensional convoluted rank regression has been recently proposed, introducing penalized c

Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson

This paper explores the use of reverberation chambers for TRP measurements of beamformed radiation by phased arrays at mmWave frequencies. First, the received power was verified by the one-sample K-S GoF test to follow the exponential probability distribution. Different numbers of samples and stirrers' positions were considered. Second, we showed that the ef

Satoki Ishikawa, Makoto Yamada, Han Bao, Yuki Takezawa

Predictive coding is a theory which hypothesises that cortex predicts sensory inputs at various levels of abstraction to minimise prediction errors. Inspired by predictive coding, Chen et al. (2024) proposed another theory, temporal prediction hypothesis, to claim that sequence memory residing in hippocampus has emerged through predicting input signals from

Hui Chen, Duncan T. L. Alexander, Cécile Hébert

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap alon

J. I. García-García, R. Tapia-Ramos, A. Vigneron-Tenorio

Let $S\subseteq \mathbb N^p$ be a semigroup, any $P\subseteq S$ is an ideal of $S$ if $P+S\subseteq P$, and an $I(S)$-semigroup is the affine semigroup $P\cup \{0\}$, with $P$ an ideal of $S$. We characterise the $I(S)$-semigroups and the ones that also are $\mathcal C$-semigroups. Moreover, some algorithms are provided to compute all the $I(S)$-semigroups s

Antonio Perez-Calero Yzquierdo, Marco Mascheroni, Edita Kizinevic, Farrukh Aftab Khan

While the computing landscape supporting LHC experiments is currently dominated by x86 processors at WLCG sites, this configuration will evolve in the coming years. LHC collaborations will be increasingly employing HPC and Cloud facilities to process the vast amounts of data expected during the LHC Run 3 and the future HL-LHC phase. These facilities often fe

Yiming Chen, Chen Zhang, Danqing Luo, Luis Fernando D'Haro

The automatic evaluation of natural language generation (NLG) systems presents a long-lasting challenge. Recent studies have highlighted various neural metrics that align well with human evaluations. Yet, the robustness of these evaluators against adversarial perturbations remains largely under-explored due to the unique challenges in obtaining adversarial d

Veera Sundararaghavan, Megna N. Shah, Jeff P. Simmons

There is a growing attention given to utilizing Lagrangian and Hamiltonian mechanics with network training in order to incorporate physics into the network. Most commonly, conservative systems are modeled, in which there are no frictional losses, so the system may be run forward and backward in time without requiring regularization. This work addresses syste

Antonio Perez-Calero Yzquierdo, Marco Mascheroni, Edita Kizinevic, Farrukh Aftab Khan

The CMS Submission Infrastructure (SI) is the main computing resource provisioning system for CMS workloads. A number of HTCondor pools are employed to manage this infrastructure, which aggregates geographically distributed resources from the WLCG and other providers. Historically, the model of authentication among the diverse components of this infrastructu

Huanhuan Yang, Lingling Song, Yunshan Cao, Peng Yan

Recently, topolectrical circuits (TECs) boom in studying the topological states of matter. The resemblance between circuit Laplacians and tight-binding models in condensed matter physics allows for the exploration of exotic topological phases on the circuit platform. In this review, we begin by presenting the basic equations for the circuit elements and unit

Declan Curran, Hira Saleem, Flora Salim

We address the essential role of information retrieval in enhancing climate downscaling, focusing on the need for high-resolution datasets and the application of deep learning models. We explore the requirements for acquiring detailed spatial and temporal climate data, crucial for accurate local forecasts, and discuss how deep learning (DL) techniques can si

Cosmin E. Oancea, Stephen M. Watt

This paper explores practical aspects of using a high-level functional language for GPU-based arithmetic on ``midsize'' integers. By this we mean integers of up to about a quarter million bits, which is sufficient for most practical purposes. The goal is to understand whether it is possible to support efficient nested-parallel programs with a small, flexible

Lan Bo, Xichao Zhang, Masahito Mochizuki, Xuefeng Zhang

Magnetic skyrmions are promising building blocks for future spintronic devices. However, the skyrmion Hall effect (SkHE) remains an obstacle for practical applications based on the in-line transport of skyrmions. Here, we numerically study the static properties and current-driven dynamics of synthetic ferrimagnetic skyrmions. Inspired by graded-index magnoni

Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson

This paper investigates achieving diverse K-factors using a Reverberation Chamber (RC) with a Compact Antenna Test Range (CATR) system. It explores six hybrid "RC plus CATR" configurations involving different excitations of the Rich Isotropic Multipath (RIMP) field and CATR-generated plane waves, with some setups including absorbers. A fixed horn antenna poi