May 2023 arXiv papers — page 101

Shokhrukh Kholmatov, Paolo Piovano

The existence and the regularity results obtained in [37] for the variational model introduced in [36] to study the optimal shape of crystalline materials in the setting of stress-driven rearrangement instabilities (SDRI) are extended from two dimensions to any dimensions $n\geq2$. The energy is the sum of the elastic and the surface energy contributions, wh

Takayuki Kubo

We explore critical field and critical current behavior in inhomogeneous narrow thin-film superconducting strips. Formulations are developed to calculate free energy, critical field, and critical current for strips with inhomogeneous Pearl length distributions. Our findings show that inhomogeneities, specifically a shorter Pearl length in the middle of the s

Marine Desprez, Vincent Miele, Olivier Gimenez

Due to their high predictive performance and flexibility, machine learning models are an appropriate and efficient tool for ecologists. However, implementing a machine learning model is not yet a trivial task and may seem intimidating to ecologists with no previous experience in this area. Here we provide a series of tips to help ecologists in implementing m

Weilin Chen

Backscatter communication is a burgeoning low-power communication technology that has been introduced into the Internet of Things (IoT) due to its excellent self-sustainability. However, conventional backscatter communication (BackCom) technologies often suffer from insufficient data transmission rates and are difficult to meet the increasing bandwidth deman

Srinivas Arigapudi, Yuval Heller, Amnon Schreiber

Coordination games admit two types of equilibria: pure equilibria, where all players successfully coordinate their actions, and mixed equilibria, where players frequently experience miscoordination. The existing literature shows that under many evolutionary dynamics, populations converge to a pure equilibrium from almost any initial distribution of actions.

Junde Wu, Jiayuan Zhu, Yueming Jin, Min Xu

Large foundation models, known for their strong zero-shot generalization, have excelled in visual and language applications. However, applying them to medical image segmentation, a domain with diverse imaging types and target labels, remains an open challenge. Current approaches, such as adapting interactive segmentation models like Segment Anything Model (S

Yuanhao Cai, Yuxin Zheng, Jing Lin, Xin Yuan

Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet

Ganesh Kumar

Lithium-ion batteries are widely used in various applications, including portable electronic devices, electric vehicles, and renewable energy storage systems. Accurately estimating the remaining useful life of these batteries is crucial for ensuring their optimal performance, preventing unexpected failures, and reducing maintenance costs. In this paper, we p

Danqing Hu, Jiangfan Wang, Yi-feng Yang

We propose an auxiliary-bath algorithm for the numerical renormalization group (NRG) method to solve multi-impurity models with shared electron baths. The method allows us to disentangle the electron baths into independent Wilson chains to perform standard NRG procedures beyond the widely adopted independent bath approximation. Its application to the 2-impur

Yuzhe Qin, Huaxiong Huang, Zilong Song, Shixin Xu

In this paper, we propose a Poisson-Nernst-Planck-Navier-Stokes-Cahn-Hillard (PNP-NS-CH)model for an electrically charged droplet suspended in a viscous fluid subjected to an external electric field. Our model incorporates spatial variations of electric permittivity and diffusion constants, as well as interfacial capacitance. Based on a time scale analysis,

Yi-Hui Xing, Lin Zhuang, E. C. Marino, Wu-Ming Liu

Tha quantum electrodynamics of particles constrained to move on a plane is not a fully dimensionally reduced theory because the gauge fields through which they interact live in higher dimensions. By constraining the gauge field to the surface of the bulk, we obtain a fully reduced planar Abelian Chern-Simons Higgs model that can describe the vortex dynamics

Jongho Park, Jinchao Xu

We propose a new training algorithm, named DualFL (Dualized Federated Learning), for solving distributed optimization problems in federated learning. DualFL achieves communication acceleration for very general convex cost functions, thereby providing a solution to an open theoretical problem in federated learning concerning cost functions that may not be smo

Thomas Mensink, Pascal Mettes

Mixup is a widely adopted strategy for training deep networks, where additional samples are augmented by interpolating inputs and labels of training pairs. Mixup has shown to improve classification performance, network calibration, and out-of-distribution generalisation. While effective, a cornerstone of Mixup, namely that networks learn linear behaviour pat

Canh V. Pham, Tan D. Tran, Dung T. K. Ha, My T. Thai

This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size $n$ subject to a knapsack constraint, $\mathsf{DLA}$ and $\mathsf{RLA}$. $\mathsf{DLA}$ is a deterministic algorithm that provides an approximation factor of $6+\epsilon$ while

Rodrigo Robles Montero

Given an entire transcendental function f with a non-completely invariant Baker domain, we define a Baker lamination on geodesics to study the divergence and convergence of a pinching process of curves in U. If the boundary of some curve in the Baker lamination of f contains infinity, then the pinching deformation does not converge. On the other hand, if f i

Lele Liu, Bo Ning

Spectral graph theory is a captivating area of graph theory that employs the eigenvalues and eigenvectors of matrices associated with graphs to study them. In this paper, we present a collection of $20$ topics in spectral graph theory, covering a range of open problems and conjectures. Our focus is primarily on the adjacency matrix of graphs, and for each to

Ao Sun, Pingchuan Ma, Yuanyuan Yuan, Shuai Wang

EXplainable AI (XAI) is an essential topic to improve human understanding of deep neural networks (DNNs) given their black-box internals. For computer vision tasks, mainstream pixel-based XAI methods explain DNN decisions by identifying important pixels, and emerging concept-based XAI explore forming explanations with concepts (e.g., a head in an image). How

Néstor G. Gracia, André H. Hoang, Vicent Mateu

Recently, it was demonstrated that the discrepancy between the fixed-order (FOPT) and contour-improved (CIPT) perturbative expansions for $\tau$-lepton decay hadronic spectral function moments, which had been affecting the precision of $\alpha_s$ determinations for many years, is related to the CIPT expansion being inconsistent with the standard formulation

Tomás E. Gonzalo

In this conference paper I present the results from a few global studies of Dark Matter (DM) models, in light of recent constraints from direct detection, indirect detection and collider experiments. I show the most recent analysis of models of singlet Higgs-portal DM, where the DM particle is a scalar, vector, Majorana or Dirac fermion. I also present the r

Felix Brandt, Matthias Greger, Erel Segal-Halevi, Warut Suksompong

We consider the problem of funding public goods that are complementary in nature. Examples include charities handling different needs (e.g., protecting animals vs. providing healthcare), charitable donations to different individuals, or municipal units handling different issues (e.g., security vs. transportation). We model these complementarities by assuming

Abdelkader El Makouri, Abdallah Slaoui, Rachid Ahl Laamara

Recently, measurement-based quantum thermal machines have drawn more attention in the field of quantum thermodynamics. However, the previous results on quantum Otto heat engines were either limited to special unital and non-unital channels in the bath stages, or a specific driving protocol at the work strokes and assuming the cycle being time-reversal symmet

Anas Himmi, Ekhine Irurozki, Nathan Noiry, Stephan Clemencon

The evaluation of natural language processing (NLP) systems is crucial for advancing the field, but current benchmarking approaches often assume that all systems have scores available for all tasks, which is not always practical. In reality, several factors such as the cost of running baseline, private systems, computational limitations, or incomplete data m

Takuro Abe

We prove the addition-deletion theorems for the Solomon-Terao polynomials, which have two important specializations. Namely, one is to the characteristic polynomials of hyperplane arangements, and the other to the Poincar\`{e} polynomials of the regular nilpotent Hessenberg varieties. One of the main tools to show them is the free surjection theorem which co

Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi

This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment. A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset and enable effective policy fine-tuning. Leverag

Moharram Aghapournahr, Tran Tuan Nam, Nguyen Thanh Nam, Nguyen Minh Tri

Let $R$ be a commutative Noetherian ring, $\Phi$ a system of ideals of $R$ and $M,X$ two $R$-modules. In this paper, we study the Artinianness and cofiniteness of the module $H^i_{\Phi}(M,X)$ which is an extension of the generalized local cohomology modules of Herzog.

Ethan Baron, Bram Janssens, Matthias Bogaert

Vector embeddings have been successfully applied in several domains to obtain effective representations of non-numeric data which can then be used in various downstream tasks. We present a novel application of vector embeddings in professional road cycling by demonstrating a method to learn representations for riders and races based on historical results. We

Driss Bennis, Brahim El Alaoui, Raja L'hamri

Let $R$ be a commutative ring with identity $1\neq 0$. In this paper, we continue the study started in [10] concerning when the extended zero-divisor graph of $R$, $\overline{\Gamma}(R)$, is complemented. We also study when $\overline{\Gamma}(R)$ is uniquely complemented. We give a complete characterization of when $\overline{\Gamma}(R)$ of a finite ring is

Ahammad Abdullah, Md. Jubair Mia

Bangladesh, situated in the foothills of the Himalayas in South Asia, is a nation characterized by its extensive river network. This riverine state comprises various features such as small hill ranges, meandering seasonal creeks, muddy canals, picturesque rivers, their tributaries, and branching streams. Numerous cities and ports have been established along

Steven Jöns, Christoph Müller, Johanna Hintz, Andrea Beck

The ghost fluid method allows a propagating interface to remain sharp during a numerical simulation. The solution of the Riemann problem at the interface provides proper information to determine interfacial fluxes as well as the velocity of the phase boundary. Then considering two-material problems, the initial states of the Riemann problem belong to differe

Saeed Mehraban, Mehrdad Tahmasbi

The approximate stabilizer rank of a quantum state is the minimum number of terms in any approximate decomposition of that state into stabilizer states. Bravyi and Gosset showed that the approximate stabilizer rank of a so-called "magic" state like $|T\rangle^{\otimes n}$, up to polynomial factors, is an upper bound on the number of classical operations requ

Hanxu Hu, Hongyuan Lu, Huajian Zhang, Yun-Ze Song

In this paper, we take the initiative to investigate the performance of LLMs on complex planning tasks that require LLMs to understand a virtual spatial environment simulated via natural language and act correspondingly in text. We propose a benchmark named Natural Language Planning and Action (Natala) composed of a set of novel tasks: Brick World, NLVR-base

Swetha Pemma, Rebecca Janisch, Gerhard Dehm, Tobias Brink

The migration of grain boundaries leads to grain growth in polycrystals and is one mechanism of grain-boundary-mediated plasticity, especially in nanocrystalline metals. This migration is due to the movement of dislocation-like defects, called disconnections, which couple to externally applied shear stresses. While this has been studied in detail in recent y

Stephane Bajeot, Sergio Benvenuti, Matteo Sacchi

We propose new classes of $4d$ $\mathcal{N}\!=\!1$ S-confining gauge theories, with a simple gauge group, rank-two matter and cubic superpotentials. The gauge group can be symplectic, orthogonal or special unitary. In some cases we derive the dualities via the deconfinement technique that uses iteratively known, more fundamental, dualities. In the symplectic

Hayder Al-Hraishawi, Madyan Alsenwi, Junaid ur Rehman, Eva Lagunas

This paper investigates the transformative potential of digital twin (DT) technology for non-terrestrial networks (NTNs). NTNs, comprising airborne and space-borne elements, face unique challenges in network control, management, and optimization. DT technology provides a novel framework for designing and managing complex cyber-physical systems with enhanced

Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson

Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to workforce fluctuations. The past few years have seen increased interest in automating such repeated tasks but mostly in controlled settings. Tasks such as picking objects

Zi Li, Wei Guo, Tingting Zhang, Jianhui Song

We report the epitaxial growth of Ruddlesden-Popper nickelates, $ \mathrm{La}_{n+1}\mathrm{Ni}_{n}\mathrm{O}_{3n+1} $, with $ n $ up to 5 by reactive molecular beam epitaxy (MBE). X-ray diffractions indicate high crystalline quality of these films and transport measurements show strong dependence on the $ n $ values. Angle-resolved photoemission spectroscopy

Michael Guerzhoy

We introduce the problem of phone classification in the context of speech recognition, and explore several sets of local spectro-temporal features that can be used for phone classification. In particular, we present some preliminary results for phone classification using two sets of features that are commonly used for object detection: Haar features and SVM-

Zhihong He, Yangping Luo, Kun Wang, Anbing Ren

Despite having data for over 10^9 stars from Gaia, only less than 10^4 star clusters and candidates have been discovered. Particularly, distant star clusters are rarely identified, due to the challenges posed by heavy extinction and great distance. However, Gaia data has continued to improve, enabling even fainter cluster members to be distinguished from fie

Giacomo Della Posta, Matteo Blandino, Davide Modesti, Francesco Salvadore

Microvortex generators are passive control devices smaller than the boundary layer thickness that energise the boundary layer to prevent flow separation with limited induced drag. In this work, we use direct numerical simulations (DNSs) to investigate the effect of the Reynolds number in a supersonic turbulent boundary layer over a microramp vortex generator

Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations. While numerous traditional manifold-based techniques exist for dimensionality reduction, their application in self-supervised learning has witnessed slow progress. The

Eleftheria Briakou, Colin Cherry, George Foster

Large, multilingual language models exhibit surprisingly good zero- or few-shot machine translation capabilities, despite having never seen the intentionally-included translation examples provided to typical neural translation systems. We investigate the role of incidental bilingualism -- the unintentional consumption of bilingual signals, including translat

Firas Rassoul-Agha, Timo Seppäläinen, Xiao Shen

We show that two semi-infinite positive temperature polymers coalesce on the scale predicted by KPZ (Kardar-Parisi-Zhang) universality. The two polymer paths have the same asymptotic direction and evolve in the same environment, independently until coalescence. If they start at distance $k$ apart, their coalescence occurs on the scale $k^{3/2}$. It follows t

Viktoria Rudykh, Nikita Shulga

For an irrational number $\alpha\in\mathbb{R}$ we consider its irrationality measure function $$ \psi_\alpha(x) = \min_{1\le q\le x,\, q\in\mathbb{Z}} \| q\alpha \|. $$ It is known for all irrational numbers $\alpha$ and $\beta$ satisfying $\alpha\pm\beta\not\in\mathbb{Z}$, there exist arbitrary large values of $t$ with \begin{equation*} | \psi_\alpha(t) - \

Chuang Liu, Renren Jin, Yuqi Ren, Linhao Yu

Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capability of large language models in multiple tasks is of great importance. In this paper, we propose M3KE, a Massive Multi-Level Multi-Subject Knowledge Evaluation benchmark, which is

Quang Nguyen, Ronald Yurko, Gregory J. Matthews

In American football, a pass rush is an attempt by the defensive team to disrupt the offense and prevent the quarterback (QB) from completing a pass. Existing metrics for assessing pass rush performance are either discrete-time quantities or based on subjective judgment. Using player tracking data, we propose STRAIN, a novel metric for evaluating pass rusher

Freeman J. Dyson

A gravitational machine is defined as an arrangement of gravitating masses from which useful energy can be extracted. It is shown that such machines may exist if the masses are of normal astronomical size. A simple example of a gravitational machine, consisting of a double star with smaller masses orbiting around it, is described. It is shown that an efficie

Zhaohua Guo, Yuan Yuan, Rui Miao, Jin-Li Guo

In the past two decades, a series of important results have been established in the empirical and theoretical modeling of complex networks, although considered are mainly pairwise networks. However, with the development of science and technology, an increasing number of higher-order networks with many-body interactions have gradually moved to the center stag

Harry Schmidt, Immanuel van Santen

We describe the Zariski-closure of sets of torsion points in connected algebraic groups. This is a generalization of the Manin-Mumford conjecture for commutative algebraic groups proved by Hindry. He proved that every subset with Zariski-dense torsion points is the finite union of torsion-translates of algebraic subgroups. We formulate and prove an analogous

Jianfeng Dong, Xiaoman Peng, Zhe Ma, Daizong Liu

Attribute-specific fashion retrieval (ASFR) is a challenging information retrieval task, which has attracted increasing attention in recent years. Different from traditional fashion retrieval which mainly focuses on optimizing holistic similarity, the ASFR task concentrates on attribute-specific similarity, resulting in more fine-grained and interpretable re

Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle, Sebastian Will

In single-objective optimization, it is well known that evolutionary algorithms also without further adjustments can tolerate a certain amount of noise in the evaluation of the objective function. In contrast, this question is not at all understood for multi-objective optimization. In this work, we conduct the first mathematical runtime analysis of a simple

Koji Nuida

Zorn's Lemma is a well-known equivalent of the Axiom of Choice. It is usually regarded as a topic in axiomatic set theory, and its historically standard proof (from the Axiom of Choice) relies on transfinite recursion, a non-elementary set-theoretic machinery. However, the statement of Zorn's Lemma itself uses only elementary terminology for partially ordere

Safiye Ghasemi, Amin Zarei

Link prediction problem has increasingly become prominent in many domains such as social network analyses, bioinformatics experiments, transportation networks, criminal investigations and so forth. A variety of techniques has been developed for link prediction problem, categorized into 1) similarity based approaches which study a set of features to extract s

Samuel N. Cohen, Giulia Mantoan, Lars Nesheim, Áureo de Paula

We introduce a new method of nowcasting using regression on path signatures. Path signatures capture the geometric properties of sequential data. Because signatures embed observations in continuous time, they naturally handle mixed frequencies and missing data. We prove theoretically, and with simulations, that regression on signatures subsumes the linear Ka

Pablo M. Piaggi, Annabella Selloni, Athanassios Z. Panagiotopoulos, Roberto Car

The formation of ice in the atmosphere affects precipitation and cloud properties, and plays a key role in the climate of our planet. Although ice can form directly from liquid water at deeply supercooled conditions, the presence of foreign particles can aid ice formation at much warmer temperatures. Over the past decade, experiments have highlighted the rem

Xiao Yang, Haixing Dai, Zihao Wu, Ramesh Bist

In recent years, the agricultural industry has witnessed significant advancements in artificial intelligence (AI), particularly with the development of large-scale foundational models. Among these foundation models, the Segment Anything Model (SAM), introduced by Meta AI Research, stands out as a groundbreaking solution for object segmentation tasks. While S

Yasutaka Tobe, Anne Robertson, Mehdi Ramezanpour, Juan Cebral

Deviation of blood flow from an optimal range is known to be associated with the initiation and progression of vascular pathologies. Important open questions remain about how the abnormal flow drives specific wall changes in pathologies such as cerebral aneurysms where the flow is highly heterogeneous and complex. This knowledge gap precludes the clinical us

Ngoc N. Tran, Son Duong, Hoang Phan, Tung Pham

Self-supervised learning aims to extract meaningful features from unlabeled data for further downstream tasks. In this paper, we consider classification as a downstream task in phase 2 and develop rigorous theories to realize the factors that implicitly influence the general loss of this classification task. Our theories signify that sharpness-aware feature

G. Giacinti, D. Semikoz

We present a new model of anisotropic cosmic ray propagation in the Milky Way, where cosmic rays are injected at discrete transient sources in the disc and propagated in the Galactic magnetic field. In the framework of our model, we show that the cosmic ray spectrum is time-dependent and space-dependent around the energy of the knee. It has a major contribut

Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye

Revolutionary advancements in Large Language Models have drastically reshaped our interactions with artificial intelligence systems. Despite this, a notable hindrance remains-the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion

Jingxuan Yang, Patrick G. J. Irwin, Joanna K. Barstow

Spectroscopic phase curves of transiting hot Jupiters are spectral measurements at multiple orbital phases, giving a set of disc-averaged spectra that probe multiple hemispheres. By fitting model phase curves to observations, we can constrain the atmospheric properties of hot Jupiters such as molecular abundance, aerosol distribution and thermal structure, w

Inbar Hurvitz, Aviv Karnieli, Ady Arie

Multimode bright squeezed vacuum is a non-classical state of light hosting a macroscopic photon number while offering promising capacity for encoding quantum information in its spectral degree of freedom. Here, we employ an accurate model for parametric downconversion in the high-gain regime and use nonlinear holography to design quantum correlations of brig

Shizhen Chang

Copy-move forgery detection is a crucial research area within digital image forensics, as it focuses on identifying instances where objects in an image are duplicated and placed in different locations. The detection of such forgeries is particularly important in contexts where they can be exploited for malicious purposes. Recent years have witnessed an incre

Linghao Feng, Dongcheng Zhao, Yi Zeng

Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural networks, offer a closer approximation to brain-like processing due to their rich spatiotemporal dynamics. Howe

Fabio Caceffo, Vincenzo Alba

We build the quasiparticle picture for the tripartite mutual information (TMI) after quantum quenches in spin chains that can be mapped onto free-fermion theories. A nonzero TMI (equivalently, topological entropy) signals quantum correlations between three regions of a quantum many-body system. The TMI is sensitive to entangled multiplets of more than two qu

Majid Rahro Zargar

Let $(R,\fm)$ be a local ring and $C$ be a homologically bounded and finitely generated $R$-complex. Then, we prove that $C$ is a dualizing complex of $R$ if and only if $C$ is a Cohen-Macaulay semidualizing complex of type one or $\mu_R^{\inf C+\dim_R(C) }(\fm,R)=\beta_{\inf C}^R(C)$. Also, we show that a semidualizing complex $C$ is dualizing if and only i

Malte Döntgen

The effect of non-Boltzmann energy distributions on the free propagation of shock waves through a monoatomic gas is investigated via theory and simulation. First, the non-Boltzmann heat capacity ratio $\gamma$, as a key property for describing shock waves, is derived from first principles via microcanonical integration. Second, atomistic molecular dynamics s

Zongwei Wu, Jingjing Wang, Zhuyun Zhou, Zhaochong An

Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contri

Riccardo Della Monica, Ivan de Martino

Dark matter is undoubtedly one of the fundamental, albeit unknown, components of the standard cosmological model. The failure to detect WIMPs, the most promising candidate particle for cold dark matter, actually opens the way for the exploration of viable alternatives, of which ultralight bosonic particles with masses $\sim 10^{-21}$ eV represent one of the

Bo Deng, Moritz Göb, Benjamin A. Stickler, Max Masuhr

Nonlinear mechanical resonators display rich and complex dynamics and are important in many areas of fundamental and applied sciences. In this letter, we show that a particle confined in a funnel-shaped potential features a Duffing-type nonlinearity due to the coupling between its radial and axial motion. Employing an ion trap platform, we study the nonlinea

Lukas Neumann, Jakob S. den Brok, Frank Bigiel, Adam Leroy

Mapping molecular line emission beyond the bright low-J CO transitions is still challenging in extragalactic studies, even with the latest generation of (sub-)mm interferometers, such as ALMA and NOEMA. We summarise and test a spectral stacking method that has been used in the literature to recover low-intensity molecular line emission, such as HCN(1-0), HCO

Lane P. Hughston, Leandro Sánchez-Betancourt

We consider a rational agent who at time $0$ enters into a financial contract for which the payout is determined by a quantum measurement at some time $T>0$. The state of the quantum system is given in the Heisenberg representation by a known density matrix $\hat p$. How much will the agent be willing to pay at time $0$ to enter into such a contract? In the

Ankita Budhraja, Rishi Sharma, Balbeer Singh

We perform a comprehensive analysis of medium modifications on ungroomed jet angularities, $\tau_a$, within the framework of Soft-Collinear Effective Theory with Glauber gluons (SCET$_{\rm G}$). Angularities are a one-parameter family of jet substructure observables with angularity exponent $a < 2$ for infrared safety. Variation of the angularity exponent al

Luca Benedetto

Recent years witnessed an increase in the amount of research on the task of Question Difficulty Estimation from Text QDET with Natural Language Processing (NLP) techniques, with the goal of targeting the limitations of traditional approaches to question calibration. However, almost the entirety of previous research focused on single silos, without performing

Anastasija Nikiforova, Nina Rizun, Magdalena Ciesielska, Charalampos Alexopoulos

The OGD is seen as a political and socio-economic phenomenon that promises to promote civic engagement and stimulate public sector innovations in various areas of public life. To bring the expected benefits, data must be reused and transformed into value-added products or services. This, in turn, sets another precondition for data that are expected to not on

Kishanthan Thangarajah, Noble Mathews, Michael Pu, Meiyappan Nagappan

Many applications are being written in more than one language to take advantage of the features that different languages provide such as native code support, improved performance, and language-specific libraries. However, there are few static analysis tools currently available to analyse the source code of such multilingual applications. Existing work on cro

Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, Adel Bibi

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The

Nicholas Faucher, Michael R. Blanton, Andrea V. Macciò

We present simulated galaxy spectral energy distributions (SEDs) from the far ultraviolet through the far infrared, created using hydrodynamic simulations and radiative transfer calculations, suitable for the validation of SED modeling techniques. SED modeling is an essential tool for inferring star formation histories from nearby galaxy observations, but is

Xiaofeng Liu, Jiaxin Gao, Yaohua Liu, Risheng Liu

Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding. However, there is still a lack of high-quality motion datasets for small bio-robotics, which presents more challenging scenarios for long-term movement prediction and behavior

Libo Qin, Qiguang Chen, Xiao Xu, Yunlong Feng

Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding. Specifically, OpenSL

Seth Bullock, Hiroki Sayama

An existing model of opinion dynamics on an adaptive social network is extended to introduce update policy heterogeneity, representing the fact that individual differences between social animals can affect their tendency to form, and be influenced by, their social bonds with other animals. As in the original model, the opinions and social connections of a po

Yunzhe Zhang, Yao Lu, Qi Xuan

Contrastive learning, a dominant self-supervised technique, emphasizes similarity in representations between augmentations of the same input and dissimilarity for different ones. Although low contrastive loss often correlates with high classification accuracy, recent studies challenge this direct relationship, spotlighting the crucial role of inductive biase

Florian Kreten

We construct the traveling wave solutions of an FKPP growth process of two densities of particles, and prove that the critical traveling waves are locally stable in a space where the perturbations can grow exponentially at the back of the wave. The considered reaction-diffusion system was introduced by Hannezo et al. in the context of branching morphogenesis

Jingqiu Ding, Tommaso d'Orsi, Yiding Hua, David Steurer

We study robust community detection in the context of node-corrupted stochastic block model, where an adversary can arbitrarily modify all the edges incident to a fraction of the $n$ vertices. We present the first polynomial-time algorithm that achieves weak recovery at the Kesten-Stigum threshold even in the presence of a small constant fraction of corrupte

Eric Viklund, David N. Seidman, David Burk, Sam Posen

In this study we will show a new method of polishing for Nb3Sn cavities known as centrifugal barrel polishing (CBP). Using this method, Nb3Sn coated samples are polished to a surface roughness comparable to a traditional Nb cavity after electropolishing (EP). We also investigate different methods of cleaning the Nb3Sn surface after CBP to remove residual abr

Axel Muller, Metod Saniga, Alain Giorgetti, Henri de Boutray

We present algorithms and a C code to reveal quantum contextuality and evaluate the contextuality degree (a way to quantify contextuality) for a variety of point-line geometries located in binary symplectic polar spaces of small rank. With this code we were not only able to recover, in a more efficient way, all the results of a recent paper by de Boutray et

Rafiad Sadat Shahir, Zayed Humayun, Mashrufa Akter Tamim, Shouri Saha

In contrast to biological neural circuits, conventional artificial neural networks are commonly organized as strictly hierarchical architectures that exclude direct connections among neurons within the same layer. Consequently, information flow is primarily confined to feedforward and feedback pathways across layers, which limits lateral interactions and con

Meer Ashwinkumar, Jacob M. Leedom, Masahito Yamazaki

We discuss the interrelations between several ideas in quantum gravity -- holography, the Swampland, and the concept of ensemble averaging. To do so, we study ensemble averages of Narain-type theories associated with general even quadratic forms and their holographic duals. We establish the emergence of global symmetries and discuss their consistency with co

Xiaofeng Liu, Jiaxin Gao, Xin Fan, Risheng Liu

Contemporary Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast, achieving commendable results on specific datasets. Nevertheless, these approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. Insufficient con

Mohammadreza Heydarian, Thomas E. Doyle

Human Activity Recognition (HAR) has become a spotlight in recent scientific research because of its applications in various domains such as healthcare, athletic competitions, smart cities, and smart home. While researchers focus on the methodology of processing data, users wonder if the Artificial Intelligence (AI) methods used for HAR can be trusted. Trust

S. Mohanty, S. S. Islam, N. Winterhalter-Stocker, A. Jesche

We delineate quantum magnetism in the strongly spin-orbit coupled, distorted honeycomb-lattice antiferromagnet BiYbGeO$_{5}$. Our magnetization and heat capacity measurements reveal that its low-temperature behavior is well described by an effective $J_{\rm eff}=1/2$ Kramers doublet of Yb$^{3+}$. The ground state is nonmagnetic with a tiny spin gap. Temperat

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be a Gorenstein local ring of dimension $d \geq 1$. Suppose there exists be a non-zero $A$ module $M$ of finite length and finite projective dimension such that $\ell\ell(M)$, the Lowey length of $M$, is equal to $\lambda(M)$, the length of $M$. Then we show that necessarily $A$ is at worst a hypersurface singularity. We also character

Mahdi Shamsi, Soosan Beheshti

Support Vector Machine (SVM) is a robust machine learning algorithm with broad applications in classification, regression, and outlier detection. SVM requires tuning the regularization parameter (RP) which controls the model capacity and the generalization performance. Conventionally, the optimum RP is found by comparison of a range of values through the Cro

Ming Cheng, Xing Cheng, Zhiwu Lin

We consider the compressible Euler-Poisson equations for polytropes $P(\rho)=K\rho^{\gamma}$ with $\gamma\in \left(\frac{6}{5},\frac{4}{3} \right]$ and the white dwarf stars. For $\gamma=\frac{4}{3},$ we establish the existence of a global weak solution for the spherically symmetric initial data with mass less than the mass of the Lane-Emden stars (i.e. non-

Guiyu Zhao, Bo Qiu, A-Li Luo, Xiaoyu Guo

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image

Iraj Abedi, Maryam Zamanian, Hamidreza Bolhasani, Milad Jalilian

Owing to the invasiveness and low accuracy of other tests, including biopsy and ferritin levels, magnetic resonance imaging (T2 and T2*-MRI) has been considered the standard test for patients with thalassemia (THM). Regarding deep learning networks in medical sciences for improving diagnosis and treatment purposes and the existence of minimal resources for t

Zhijie Li, Wenhui Peng, Zelong Yuan, Jianchun Wang

Long-term predictions of nonlinear dynamics of three-dimensional (3D) turbulence are very challenging for machine learning approaches. In this paper, we propose an implicit U-Net enhanced Fourier neural operator (IU-FNO) for stable and efficient predictions on the long-term large-scale dynamics of turbulence. The IU-FNO model employs implicit recurrent Fouri

Anisha Banerjee, Yonatan Yehezkeally, Antonia Wachter-Zeh, Eitan Yaakobi

Nanopore sequencing, superior to other sequencing technologies for DNA storage in multiple aspects, has recently attracted considerable attention. Its high error rates, however, demand thorough research on practical and efficient coding schemes to enable accurate recovery of stored data. To this end, we consider a simplified model of a nanopore sequencer ins

D. Tagliacozzo, A. Marinucci, F. Ursini, G. Matt

We report on the second observation of the radio-quiet active galactic nucleus (AGN) MCG-05-23-16 performed with the Imaging X-ray Polarimetry Explorer (IXPE). The observation started on 2022 November 6 for a net observing time of 640 ks, and was partly simultaneous with NuSTAR (86 ks). After combining these data with those obtained in the first IXPE pointin

Joseph Lindsay, Ramtin Zand

Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enoug

Renato Bellotti, Jonas Willmann, Antony J. Lomax, Andreas Adelmann

Creating high quality treatment plans is crucial for a successful radiotherapy treatment. However, it demands substantial effort and special training for dosimetrists. Existing automated treatment planning systems typically require either an explicit prioritization of planning objectives, human-assigned objective weights, large amounts of historic plans to t