December 2023 arXiv papers — page 56

Matthias Borgstede

The Price equation provides a formal account of selection building on a right-total mapping between two classes of individuals, that is usually interpreted as a parent-offspring relation. This paper presents a new formulation of the Price equation in terms of fuzzy set-mappings to account for structures where the targets of selection may vary in the degree t

Uswa Ihsan, Humaira Ashraf, NZ Jhanjhi

In this era of information technology, abundant information is available on the internet in the form of web pages and documents on any given topic. Finding the most relevant and informative content out of these huge number of documents, without spending several hours of reading has become a very challenging task. Various methods of multi-document summarizati

M. R. Sahu, F. J. Matute-Cañadas, M. Benito, P. Krogstrup

We probed a gate-tunable InAs nanowire Josephson weak link by coupling it to a microwave resonator. Tracking the resonator frequency shift when the weak link is close to pinch-off, we observe that the ground state of the latter alternates between a singlet and a doublet when varying either the gate voltage or the superconducting phase difference across it. T

Shuyuan Wang, Qi Li, Huiyuan Luo, Chengkan Lv

Visual anomaly detection aims at classifying and locating the regions that deviate from the normal appearance. Embedding-based methods and reconstruction-based methods are two main approaches for this task. However, they are either not efficient or not precise enough for the industrial detection. To deal with this problem, we derive POUTA (Produce Once Utili

Mario Araujo-Rocha, Alejandro Diaz-Marquez, Guillaume Stirnemann

When applied to binary solutions, thermal gradients lead to the generation of concentration-gradients and thus to inhomogeneous systems. While being known for more than 150 years, the molecular origins for this phenomenon are still debated, and there is no consensus on the underlying physical models or theories that could explain the amplitude of the concent

Sami Raatikainen, Syksy Rasanen, Eemeli Tomberg

We study the formation of primordial black holes (PBH) with ultra-slow-roll inflation when stochastic effects are important. We use the $\Delta N$ formalism and simplify the stochastic equations with an analytical constant-roll approximation. Considering a viable inflation model, we find the spatial profile of the PBH compaction function numerically for each

Faiza Ishaq, Humaira Ashraf, Nz Jhanjhi

With the rapid increase in the Internet of Things (IoT), the amount of data produced and processed is also increased. Cloud Computing facilitates the storage, processing, and analysis of data as needed. However, cloud computing devices are located far away from the IoT devices. Fog computing has emerged as a small cloud computing paradigm that is near to the

Alexander von Bank, Eike-Manuel Edelmann, Laurent Schmalen

We propose an energy-efficient equalizer for IM/DD systems based on spiking neural networks. We optimize a neural spike encoding that boosts the equalizer's performance while decreasing energy consumption.

Pau Torras, Sanket Biswas, Alicia Fornés

Modern-day Optical Music Recognition (OMR) is a fairly fragmented field. Most OMR approaches use datasets that are independent and incompatible between each other, making it difficult to both combine them and compare recognition systems built upon them. In this paper we identify the need of a common music representation language and propose the Music Tree No

Kamiokande Collaboration, K. Abe, C. Bronner, Y. Hayato

An analysis of solar neutrino data from the fourth phase of Super-Kamiokande~(SK-IV) from October 2008 to May 2018 is performed and the results are presented. The observation time of the data set of SK-IV corresponds to $2970$~days and the total live time for all four phases is $5805$~days. For more precise solar neutrino measurements, several improvements a

Antonio Ganfornina-Andrades, J. Enrique Vázquez-Lozano, Iñigo Liberal

In this work we address quantum vacuum amplification effects in time-varying media with an arbitrary time-modulation profile. To this end, we propose a theoretical formalism based on the concept of conjugated harmonic oscillators, evaluating the impact on the transition time in temporal boundaries, shedding light into the practical requirements to observe qu

Alexei Vazquez

A project schedule contains a network of activities, the activity durations, the early and late finish dates for each activity, and the associated total float or slack times, the difference between the late and early dates. Here I show that the distribution of activity durations and total floats of construction project schedules exhibit a power law scaling.

Stanislav Budzinskiy

Every sufficiently big matrix with small spectral norm has a nearby low-rank matrix if the distance is measured in the maximum norm (Udell & Townsend, SIAM J Math Data Sci, 2019). We use the Hanson--Wright inequality to improve the estimate of the distance for matrices with incoherent column and row spaces. In numerical experiments with several classes of ma

Xiangjuan Li, Feifan Li, Yang Li, Quan Pan

Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generative model for creating effective adversarial examples to attack the agent. Our proposed model can achieve both targeted attacks and untargeted attacks. Considering the specificity o

Yifei Duan, Yongqiang Cai

The universal approximation property (UAP) holds a fundamental position in deep learning, as it provides a theoretical foundation for the expressive power of neural networks. It is widely recognized that a composition of linear and nonlinear functions, such as the rectified linear unit (ReLU) activation function, can approximate continuous functions on compa

Luca Gagliardelli, Domenico Beneventano, Marco Esposito, Luca Zecchini

In this paper, we present the data preparation activities that we performed for the Digital Experience Platform (DXP) project, commissioned and supervised by Doxee S.p.A.. DXP manages the billing data of the users of different companies operating in various sectors (electricity and gas, telephony, pay TV, etc.). This data has to be processed to provide servi

Yi Honghu, Liu Ting, Lan Gongjin

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we

Y. Guan, N. Anfimov, G. Cao, Z. Xie

Optical cross-talk is a critical characteristic of Silicon Photomultipliers (SiPMs) and represents a significant source of the excess noise factor, exerting a substantial influence on detector performance. During the avalanche process of SiPMs, photons generated can give rise to both internal cross-talk within the same SiPM and external cross-talk when photo

Annika Moucha, Oliver Roth, Michael Heins

The central theme of this paper is the holomorphic spectral theory of the canonical Laplace operator of the complement $\Omega := \{(z,w) \in \widehat{\mathbb{C}}^2 \colon z \cdot w \neq 1\}$ of the "complexified unit circle" $\{(z,w) \in \widehat{\mathbb{C}}^2 \colon z \cdot w = 1\}$. We start by singling out a distinguished set of holomorphic eigenfunction

Yuan-Hang Zhang, Chesson Sipling, Erbin Qiu, Ivan K. Schuller

In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed "thermal neuristors." These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical beha

G. Carlà, L. Busoni, S. Esposito, G. Agapito

The upcoming extremely large telescopes will have to deal with the so-called ''pupil fragmentation'' effect: for what concerns the Extremely Large Telescope (ELT), the presence of thick spider legs supporting the secondary mirror may induce unseen phase discontinuities across the pupil sectors that could limit the performance of the adaptive optics correctio

Murad Banaji, Balázs Boros, Josef Hofbauer

We consider local bifurcations of equilibria in dynamical systems arising from chemical reaction networks with mass action kinetics. In particular, given any mass action network admitting a local bifurcation of equilibria, assuming only a general transversality condition, we list some enlargements of the network which preserve its capacity for the bifurcatio

Loïc Méchin, François Médard, Joël Leymarie, Sophie Bouchoule

The polarization discontinuity across interfaces in polar nitride-based heterostructures can lead to the formation of two-dimensional electron and hole gases. In the past, the observation of these electron and hole gases has been achieved through various experimental techniques, most often by electronic measurements but occasionally by optical means. However

Georg Junker

Feynman's path integral approach is studied in the framework of the Wigner-Dunkl deformation of quantum mechanics. We start with reviewing some basics from Dunkl theory and investigate the time evolution of a Gaussian wave packet, which exhibits the same dispersion relation as observed in standard quantum mechanics. Feynman's path integral approach is then e

Ximeng Li, Jing Ren

The rapid progress in gravitational wave astronomy has provided an opportunity for investigating the presence of long-range scalar forces that exclusively manifest around astrophysical black holes. In this paper, we explore a new possibility in this context, particularly in connection to the hypothesis that astrophysical black holes might be horizonless ultr

Peter Erwin

Using a volume- and mass-limited (D < 30 Mpc, log (M_star/M_sun) $\geq 9.75$) sample of 155 barred S0-Sd galaxies, I determine the fraction with secondary structures within their bars. Some 20 +/- 3% have a separate inner bar, making them double-barred; an identical fraction have nuclear rings, with 11^{+3}_{-2}% hosting both. The inner-bar frequency is a st

Yinan Fang

Charge noise has been one of the main issues in realizing high fidelity two-qubit quantum gates in semiconductor based qubits. Here, we study the influence of quasistatic noise in quantum dot detuning on the controlled phase gate for spin qubits that defined on a double quantum dot. Analytical expressions for the noise averaged Hamiltonian, exchange interact

William Hill, Ireton Liu, Anita De Mello Koch, Damion Harvey

We propose a new benchmark for planning tasks based on the Minecraft game. Our benchmark contains 45 tasks overall, but also provides support for creating both propositional and numeric instances of new Minecraft tasks automatically. We benchmark numeric and propositional planning systems on these tasks, with results demonstrating that state-of-the-art plann

Thomas F. Bloom, Jared Duker Lichtman

We give a concise and accessible introduction to the real-analytic determinant method for counting integral points on algebraic curves, based on the classic 1989 paper of Bombieri and Pila.

Woohyun Cho, Yoon-Gu Kang, Jaehun Cha, Dong Hyun David Lee

Topological quantum phases have been largely understood in weakly correlated systems, which have identified various quantum phenomena such as spin Hall effect, protected transport of helical fermions, and topological superconductivity. Robust ferromagnetic order in correlated topological materials particularly attracts attention, as it can provide a versatil

Stephen A. Wells, Joseph D. Taylor, Paul G. Morris, Alain Nogaret

We construct neuron models from data by transferring information from an observed time series to the state variables and parameters of Hodgkin-Huxley models. When the learning period completes, the model will predict additional observations and its parameters uniquely characterise the complement of ion channels. However, the assimilation of biological data,

Yoshiki Fukusumi

We propose a theoretical formulation of protected edge modes in the language of quantum field theories based on the contemporary understanding of the renormalization group. We use bulk and boundary renormalization arguments which have never captured enough attention in condensed matter physics and related fields. We revisit various exotic bulk and boundary p

Astrid Klipfel, Yaël Fregier, Adlane Sayede, Zied Bouraoui

Discovering crystal structures with specific chemical properties has become an increasingly important focus in material science. However, current models are limited in their ability to generate new crystal lattices, as they only consider atomic positions or chemical composition. To address this issue, we propose a probabilistic diffusion model that utilizes

Rahul Vishwakarma, Subhankar Mishra

Theorem proving is a fundamental task in mathematics. With the advent of large language models (LLMs) and interactive theorem provers (ITPs) like Lean, there has been growing interest in integrating LLMs and ITPs to automate theorem proving. In this approach, the LLM generates proof steps (tactics), and the ITP checks the applicability of the tactics at the

Alexander Keimer, Lukas Pflug

We study the singular limit problem for nonlocal conservation laws in which the sign of the initial datum is unrestricted and the velocity of the conservation law depends on a nonlocal approximation of the absolute value of the density. We demonstrate that the nonlocal solutions converge to the local entropy solution when the nonlocal kernel tends to a Dirac

Philipp Böer, Thorsten Feldmann

We review recent progress in the computation of structure-dependent QED corrections to exclusive $B$ decays in the factorization approach.

Ioannis Kleftogiannis, Ilias Amanatidis

We show that classical states can emerge as pure ground state solutions of a quantum many-body system. We use a simple Hubbard model in 1D with strong short-range interactions and a second nearest neighbor hopping with N particles arranged among M sites. We show that the ground state of this Hubbard chain for M=2N-1 consists of a single many-body state where

K. A. Bronnikov, M. I. Kalinin, V. V. Khruschov

We discuss the heat history of the early Universe and its further evolution in the framework of modern cosmological models of general relativity (GR) and alternative theories of gravity. Of great importance are the new puzzling forms of matter comprising parts of our Universe, namely, dark energy and dark matter that crucially affect the Universe structure a

Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi

Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendation research. Among various losses, we find Softmax loss (SL) stands out for not only achieving remarkable accuracy but also better robustness and fairness. Nevertheless, the curren

Stanisław Giziński, Paulina Kaczyńska, Hubert Ruczyński, Emilia Wiśnios

There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication v

José Guilherme de Almeida, Nuno M. Rodrigues, Sara Silva, Nickolas Papanikolaou

Deep learning models trained with large amounts of data have become a recent and effective approach to predictive problem solving -- these have become known as "foundation models" as they can be used as fundamental tools for other applications. While the paramount examples of image classification (earlier) and large language models (more recently) led the wa

Tommaso Bianchi, Alessandro Brighente, Mauro Conti

Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving thanks to a dedicated road infrastructure. DWPT's capabilities in automatically establishing charging sessions and billing without users' intervention make it prone to cybersecurity attacks. Hence, security is essential in preventing fraud, imp

Sara El Mekkaoui, Loubna Benabbou, Abdelaziz Berrado

Motivated by the interpretability question in ML models as a crucial element for the successful deployment of AI systems, this paper focuses on rule extraction as a means for neural networks interpretability. Through a systematic literature review, different approaches for extracting rules from feedforward neural networks, an important block in deep learning

Wenbin Lin, Chengwei Zheng, Jun-Hai Yong, Feng Xu

Lightweight creation of 3D digital avatars is a highly desirable but challenging task. With only sparse videos of a person under unknown illumination, we propose a method to create relightable and animatable neural avatars, which can be used to synthesize photorealistic images of humans under novel viewpoints, body poses, and lighting. The key challenge here

Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi

The contagious and pandemic COVID-19 disease is currently considered as the main health concern and posed widespread panic across human-beings. It affects the human respiratory tract and lungs intensely. So that it has imposed significant threats for premature death. Although, its early diagnosis can play a vital role in revival phase, the radiography tests

H. George E. Hentschel, Anna Pomyalov, Itamar Procaccia, Oran Szachter

In recent work, it was shown that elasticity theory can break down in amorphous solids subjected to nonuniform {\em static} loads. The elastic fields are screened by geometric dipoles; these stem from gradients of the quadrupole field associated with plastic responses. Here we study the dynamical responses induced by {\em oscillatory} loads. The required mod

Tariq Berrada, Jakob Verbeek, Camille Couprie, Karteek Alahari

Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling, the iterative nature of their inference

Gangle Sun, Wenjin Wang, Wei Xu, Christoph Studer

Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection o

Kanniah Balamuralikrishna, John Y. H. Soo, Norhaslinda Mohamed Tahrin, Abdul Halim Abdul Aziz

Magnetic reconnection is a fundamental mechanism through which energy stored in magnetic fields is released explosively on a massive scale, they could be presented as eruptive or confined flares, depending on their association with coronal mass ejections (CMEs). Several previous works have concluded that there is no correlation between flare duration and fla

Bo Liu, Liqiang Yu, Chang Che, Qunwei Lin

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vi

Yu Liu, Runzhe Wan, James McQueen, Doug Hains

The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency. Traditionally, experimenters determine AES based on domain knowledge. However, this method becomes impractical for online experimentation services managing numerous experiments, and a more automated approach is hence of g

Wenqi Jia, Miao Liu, Hao Jiang, Ishwarya Ananthabhotla

In recent years, the thriving development of research related to egocentric videos has provided a unique perspective for the study of conversational interactions, where both visual and audio signals play a crucial role. While most prior work focus on learning about behaviors that directly involve the camera wearer, we introduce the Ego-Exocentric Conversatio

Théo Vincent, Alberto Maria Metelli, Boris Belousov, Jan Peters

Approximate value iteration (AVI) is a family of algorithms for reinforcement learning (RL) that aims to obtain an approximation of the optimal value function. Generally, AVI algorithms implement an iterated procedure where each step consists of (i) an application of the Bellman operator and (ii) a projection step into a considered function space. Notoriousl

Ardavan S. Nobandegani, Irina Rish, Thomas R. Shultz

Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theoretical analysis of the $\textit{trust game}$, the canonical task for studying trust in behavioral and brain sciences, along with simulation results supporting our analysis. Specifically, leveraging reinforcement learning

Mina Khadem, Maryam Ansarifard, Nader Mokari, Mohammadreza Javan

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) me

Antonio Tavera-Vázquez, Andrés Córdoba, Sam Rubin, Vincenzo Vitelli

The development of synthetic microswimmers has advanced our understanding of the fundamental self-propelled mechanisms of living systems. However, there are scarce studies at the microscale within highly structured anisotropic media, such as bacteria or cellular receptors that swim in concentrated solutions of filamentous proteins or lipids with viscoelastic

Fernando Pérez-García, Sam Bond-Taylor, Pedro P. Sanchez, Boris van Breugel

Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to asses

Dilara Ickecan, Yunus Emre Okyayli, Erdi Ata Bleda, Dogan Erbahar

While Bernal stacked bilayer graphene bears two distinct atom types in its lattice, there exists no analytical framework addressing the number of atomic environments that emerge in twisted bilayer graphene superlattices. Here, we computationally analyze 140 different twisted bilayer superlattices using descriptor functions to study the emergent local environ

Pengchao Han, Shiqiang Wang, Yang Jiao, Jianwei Huang

While providing machine learning model as a service to process users' inference requests, online applications can periodically upgrade the model utilizing newly collected data. Federated learning (FL) is beneficial for enabling the training of models across distributed clients while keeping the data locally. However, existing work has overlooked the coexiste

Gunay Payli, Hicran Bakis, Ebru Aktekin, Hidetoshi Sano

We report the first detection of optical emission from the shell-type Galactic supernova remnant (SNR) G108.2$-$0.6. We obtained H$\alpha$ images and long-slit spectra using the 1.5-m RTT150 telescope to examine the morphological and spectral characteristics of the SNR. We detected several filaments along its north and south regions, which is consistent with

Murad Dawood, Sicong Pan, Nils Dengler, Siqi Zhou

In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning~(MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in hav

Ivan Litvinov, Dan Refaeli, Alex Liberzon, Slava Krylov

We present the findings from an experimental study of a MEMS flow sensor in which an initially curved, double-clamped bistable microbeam is the primary sensing element. Our research explores how the overheat ratio, direct flow loading, and turbulence-induced vibration affect the sequential snap-through (ST) buckling and snap-back (SB) release of an electrost

Hanul Jeon, James Walsh

It is widely claimed that the natural axiom systems$\unicode{x2013}$including the large cardinal axioms$\unicode{x2013}$form a well-ordered hierarchy. Yet, as is well-known, it is possible to exhibit non-linearity and ill-foundedness by means of \emph{ad hoc} constructions. In this paper we formulate notions of proof-theoretic strength based on set-theoretic

Sagy Ephrati, Arnout Franken, Erwin Luesink, Paolo Cifani

A closure model is presented for large-eddy simulation (LES) based on the three-dimensional variational data assimilation algorithm. The approach aims at reconstructing high-fidelity kinetic energy spectra in coarse numerical simulations by including feedback control to represent unresolved dynamics interactions in the flow as stochastic processes. The forci

A. Bazavov, D. Bollweg, O. Kaczmarek, F. Karsch

We study the nature of charm degrees of freedom in hot strong interaction matter by performing lattice QCD calculations of the second and fourth-order cumulants of charm fluctuations, and their correlations with net baryon number, electric charge and strangeness fluctuations. We show that below the chiral crossover temperature thermodynamics of charm can be

Zhecheng Wang, Rajanie Prabha, Tianyuan Huang, Jiajun Wu

Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs). A key reason is that the large-scale, semantically diverse image-text dataset required for developing VLMs is still abs

Woohyeok Kim, Geonu Kim, Junyong Lee, Seungyong Lee

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated, enabling physically-meaningful RAW-level image processing on input sRGB images. However, existing learning-based ISP methods fail to handle th

Luca Rosignoli, Francesco Ubertosi, Myriam Gitti, Fabrizio Brighenti

We present the analysis of new, deep $Chandra$ observations (130~ks) of the galaxy cluster Abell~2495. This object is known for the presence of a triple offset between the peaks of the intracluster medium (ICM), the brightest cluster galaxy (BCG), and the warm gas glowing in H$\alpha$ line. The new $Chandra$ data confirm that the X-ray emission peak is locat

Hanul Jeon

In this paper, we define constructive analogues of second-order set theories, which we will call $\mathsf{IGB}$, $\mathsf{CGB}$, $\mathsf{IKM}$, and $\mathsf{CKM}$. Each of them can be viewed as $\mathsf{IZF}$- and $\mathsf{CZF}$-analogues of G\"odel-Bernays set theory $\mathsf{GB}$ and Kelley-Morse set theory $\mathsf{KM}$. We also provide their proof-theor

Zhongnian Li, Haotian Ren, Tongfeng Sun, Zhichen Li

Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each instance, which can lead to the leakage of sensitive information (such as smoking, diseases, etc.) about the instances. However, existing MLL suffer from failures in protection for sensitive information. In this paper, we propose a novel setting named Multi-Label Lear

Dan Shi, Chaobin You, Jiantao Huang, Taihao Li

As an indispensable ingredient of intelligence, commonsense reasoning is crucial for large language models (LLMs) in real-world scenarios. In this paper, we propose CORECODE, a dataset that contains abundant commonsense knowledge manually annotated on dyadic dialogues, to evaluate the commonsense reasoning and commonsense conflict detection capabilities of C

Bram Vanroy

Despite the rapid expansion of types of large language models, there remains a notable gap in models specifically designed for the Dutch language. This gap is not only a shortage in terms of pretrained Dutch models but also in terms of data, and benchmarks and leaderboards. This work provides a small step to improve the situation. First, we introduce two fin

Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang

Recent work demonstrates that, after instruction tuning, Code Large Language Models (Code LLMs) can obtain impressive capabilities to address a wide range of code-related tasks. However, current instruction tuning methods for Code LLMs mainly focus on the traditional code generation task, resulting in poor performance in complex multi-task scenarios. In this

N. Li, G. Liao, D. Yan, Y. Xu

Transition-edge sensors (TESs) are sensitive devices for detecting photons from millimeter radiation to gamma rays. Their photon counting efficiency and collecting area benefit from large-array multiplexing scheme, and therefore the development of multiplexing readout system has been an important topic in this field. Among the many multiplex techniques, time

Michael Dalvean

In English place name analysis, meanings are often derived from the resemblance of roots in place names to topographical features, proper names and/or habitation terms in one of the languages that have had an influence on English place names. The problem here is that it is sometimes difficult to determine the base language to use to interpret the roots. The

Frank Nielsen

Exponential families are statistical models which are the workhorses in statistics, information theory, and machine learning among others. An exponential family can either be normalized subtractively by its cumulant or free energy function or equivalently normalized divisively by its partition function. Both subtractive and divisive normalizers are strictly

Shahrokh Heidari, Michael J. Dinneen, Patrice Delmas

Computer Vision (CV) labelling algorithms play a pivotal role in the domain of low-level vision. For decades, it has been known that these problems can be elegantly formulated as discrete energy minimization problems derived from probabilistic graphical models (such as Markov Random Fields). Despite recent advances in inference algorithms (such as graph-cut

Yong Han, Yanqi Qiu, Zipeng Wang

We obtain the asymptotic growth rate of the moments of the Mandelbrot random cascades at critical exponents. The key ingredient is a $q$ to $q/2$ reduction method for the moment-estimation, which is obtained by combining the martingale inequalities due to Burkholder and Burkholder-Rosenthal.

Rui-lian Du, Changpin Li, Zhi-zhong Sun

A novel H3N3-2$_\sigma$ interpolation approximation for the Caputo fractional derivative of order $\alpha\in(1,2)$ is derived in this paper, which improves the popular L2C formula with (3-$\alpha$)-order accuracy. By an interpolation technique, the second-order accuracy of the truncation error is skillfully estimated. Based on this formula, a finite differen

Satyam Guragain, Ravi Srivastava, Bishal Sonar

For unsigned graphs G and H, the characteristic polynomial of different graph matrices for edge corona, subdivision vertex neighbourhood corona and subdivision edge neighbourhood corona has already been studied using the concept of coronal. However, till date no work regarding the spectrum of these products has been studied for signed graphs. In our work, we

Naiyu Yin, Tian Gao, Yue Yu, Qiang Ji

Capturing the underlying structural causal relations represented by Directed Acyclic Graphs (DAGs) has been a fundamental task in various AI disciplines. Causal DAG learning via the continuous optimization framework has recently achieved promising performance in terms of both accuracy and efficiency. However, most methods make strong assumptions of homosceda

Bishal Sonar, Ravi Srivastava

This paper introduces the concept of $\mu$-signed and duplication signed graphs and shows that both are always structurally balanced. Using the duplication signed graph, we define the corona product of the duplication signed graph (Duplication add vertex corona product and duplication vertex corona product) and explore their structural properties. Additional

C. Beaufort, O. Guillaudin, D. Santos, N. Sauzet

Directional detection is the dedicated strategy to demonstrate that DM-like signals measured by direct detectors are indeed produced by DM particles from the galactic halo. The experimental challenge of measuring the direction of DM-induced nuclear recoils with (sub-)millimeter tracks has limited, so far, the maximal directional reach to DM masses around $10

I. Cabria, A. Lebon, M. B. Torres, L. J. Gallego

In the quest of new absorbent for hydrogen storage, we investigate the capacities of slit pores formed by two BC3 sheets decorated with Li atoms. Their hydrogen storage capacities are determined using density-functional theory in conjunction with a quantum-thermodynamic model that allows to simulate real operating conditions, i.e., finite temperatures and di

Ravi Shankar Jaiswal

We prove optimal estimates of the Bergman and Szeg\H{o} kernels on the diagonal, and the Bergman metric near the boundary of bounded smooth generalized decoupled pseudoconvex domains in $\mathbb{C}^n$. The generalized decoupled domains we consider allow the following possibilities: (a) complex tangential directions need not be decoupled separately, and (b) b

Hannah Blocher, Georg Schollmeyer, Malte Nalenz, Christoph Jansen

We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial

Nannan Wu, Zhaobin Sun, Zengqiang Yan, Li Yu

Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the performance ceilings of FL. In this paper, we, for the first time

Minhao Zou, Hao Geng, Rong Ma, Wei Chen

Nonreciprocal transport in uniform systems has attracted great research interest recently and the existing theories mainly focus on the diffusive regime. In this study, we uncover a novel scenario for nonreciprocal charge transport in the ballistic regime enabled by asymmetric band structures of the system. The asymmetry of the bands induces unequal Coulomb

Duong Van Loi, Phung Van Dong, N. T. Duy, Nguyen Huy Thao

The flavor structure of quarks and leptons is not yet fully understood, but it hints a more fundamental theory of non-universal generations. We therefore propose a simple extension of the Standard Model by flipping (i.e., enlarging) the hypercharge $U(1)_Y$ to $U(1)_X\otimes U(1)_N$ for which both $X$ and $N$ depend on generations of both quark and lepton. B

Yuhao Yi, Ronghui You, Hong Liu, Changxin Liu

Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed

Erol Vatansever, Georgi G. Grahovski, Nikolaos G. Fytas

In this paper, we study the thermodynamic properties of spin-$1/2$ antiferromagnetic Heisenberg ladders by means of the stochastic series expansion quantum Monte Carlo technique. This includes the thermal properties of the specific heat, uniform and staggered susceptibilities, spin gap, and structure factor. Our numerical simulations are probed over a large

Hongyuan Wang, Lizhi Wang, Jiang Xu, Chang Chen

Spectral super-resolution that aims to recover hyperspectral image (HSI) from easily obtainable RGB image has drawn increasing interest in the field of computational photography. The crucial aspect of spectral super-resolution lies in exploiting the correlation within HSIs. However, two types of bottlenecks in existing Transformers limit performance improvem

Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan

Large Language Models (LLMs) have performed well on various reasoning tasks, but their inaccessibility and numerous parameters hinder wide application in practice. One promising way is distilling the reasoning ability from LLMs to small models by the generated chain-of-thought reasoning paths. In some cases, however, LLMs may produce incorrect reasoning chai

Takis Angelides, Pranay Naredi, Arianna Crippa, Karl Jansen

We explore the first-order phase transition in the lattice Schwinger model in the presence of a topological $\theta$-term by means of the variational quantum eigensolver (VQE). Using two different fermion discretizations, Wilson and staggered fermions, we develop parametric ansatz circuits suitable for both discretizations, and compare their performance by s

ALICE Collaboration

The first measurements of femtoscopic correlations with the particle pair combinations $\pi^\pm$K$^0_{\rm S}$ in pp collisions at $\sqrt{s}=13$ TeV at the Large Hadron Collider (LHC) are reported by the ALICE experiment. Using the femtoscopic approach, it is shown that it is possible to study the elusive K$^*_0(700)$ particle that has been considered a tetra

Saikat Biswas, Ankur Dey, Boudhayan Paul, Gautam Sengupta

We advance a covariant construction for the holographic odd entanglement entropy (OEE) of time dependent bipartite states in CFT$_2$s dual to bulk AdS$_3$ geometries. In this context we obtain the OEE for bipartite states in zero, finite temperature and finite size CFT$_2$s dual to bulk pure AdS$_3$ and BTZ black hole geometries through appropriate replica t

Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distill

Yuqi Lin, Minghao Chen, Kaipeng Zhang, Hengjia Li

Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions supervised by contrastive loss, making it highly effective for single-label classification. However, it shows poor

Dan Qiu, Jiadong Li, Bo Zhang, Chao Liu

Estimating precise metallicity of M dwarfs is a well-known difficult problem due to their complex spectra. In this work, we empirically calibrate the metallicity using wide binaries with a F, G, or K dwarf and a M dwarf companion. With 1308 FGK+M wide binaries well observed by LAMOST, we calibrated M dwarf's [Fe/H] by using the Stellar LAbel Machine (SLAM) m

Yuhui Wu, Guoqing Wang, Zhiwen Wang, Yang Yang

Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. Despite the success of some conditional methods, previous methods may neglect the importance of a sufficient formulation of task-specific condition strategy, resulting in suboptimal visual outcomes. In this study, we propose JoReS-Diff, a novel ap