October 2023 arXiv papers — page 45

Camille Bourgaux, Ana Ozaki, Rafael Peñaloza

We investigate semiring provenance--a successful framework originally defined in the relational database setting--for description logics. In this context, the ontology axioms are annotated with elements of a commutative semiring and these annotations are propagated to the ontology consequences in a way that reflects how they are derived. We define a provenan

Kosei Hatakeyama, Daisuke Miki, Masaki Tani, Yuuki Yamasaki

We investigate the violation of the Leggett-Garg inequalities for a harmonic oscillator in various quantum states. We focus on the two-time quasi-probability distribution function with a dichotomic variable constructed with the position operator of a harmonic oscillator. First, we developed a new formula to compute the two-time quasi-probability distribution

Kota Nagasaki, Toru Seo

Traffic congestion at urban-scale levels occurs when road network supply is insufficient compared with demand. Therefore, the relationship between supply and demand has been extensively investigated in the literature. Especially the impact of a network's topology (i.e., connectivity) on traffic congestion has been widely studied. On the other hand, only a fe

Kan Chen

Based on the hadronic resonance picture, we propose a possible framework to simultaneously describe the resonance parameters of the observed $Z_{cs}$ and $Z_{c}$ states that are close to the thresholds of the $D^{(*)}\bar{D}^{(*)}_s$ and $D^{(*)}\bar{D}^{(*)}$ systems, respectively. We construct the effective potentials of the $Z_{cs}$ and $Z_{c}$ states by

Jesko Hecking-Harbusch, Jochen Quante, Maximilian Schlund

Modern automotive software is highly complex and consists of millions lines of code. For safety-relevant automotive software, it is recommended to use sound static program analysis to prove the absence of runtime errors. However, the analysis is often perceived as burdensome by developers because it runs for a long time and produces many false alarms. If the

Ram Brustein, A. J. M. Medved

The Kerr metric is a vacuum solution of the Einstein equations outside of a rotating black hole, but what interior matter is actually rotating and sourcing the Kerr geometry? Here, we describe a rotating exotic matter which can source the Kerr geometry for the entire acceptable range of its spin parameter and be shown to saturate the radial null-energy condi

Jiaxu Cui, Bingyi Sun, Jiming Liu, Bo Yang

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However, most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectivene

Hyeonwoo Kim, Seungeun Oh, Seongsoo Lee, Kwang suk Lee

Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explores label-free imaging techniques' applications in LD research. We discuss holotomography and vibrational spectr

Bhaskar Arya, Aseem Paranjape, Tirthankar Roy Choudhury

We investigate the nature of correlations in the small-scale flux statistics of the Lyman-$\alpha$ (Ly$\alpha$) forest across redshift bins. Understanding these correlations is important for unbiased cosmological and astrophysical parameter inference using the Ly$\alpha$ forest. We focus on the 1-dimensional flux power spectrum (FPS) and mean flux ($\bar F$)

Chenxu Yang, Ping Li, Yaping Mao, Eddie Cheng

Let $G$ be a graph and $S\subseteq V(G)$ with $|S|\geq 2$. Then the trees $T_1, T_2, \cdots, T_\ell$ in $G$ are \emph{internally disjoint Steiner trees} connecting $S$ (or $S$-Steiner trees) if $E(T_i) \cap E(T_j )=\emptyset$ and $V(T_i)\cap V(T_j)=S$ for every pair of distinct integers $i,j$, $1 \leq i, j \leq \ell$. Similarly, if we only have the condition

Sofiane Schaack, Etienne Mangaud, Erika Fallacara, Simon Huppert

Anhydrous sodium hydroxide, a common and structurally simple compound, shows spectacular isotope effects: NaOD undergoes a first-order transition, which is absent in NaOH. By combining ab initio electronic structure calculations with path integrals, we show that NaOH is an unusual example of a quantum paraelectric: zero-point quantum fluctuations stretch the

Yunping Wang, Ercai Chen, Kexiang Yang

It is well-known that the relativized variational principle established by Bogenschutz and Kifer connects the fiber topological entropy and fiber measure-theoretic entropy. In context of random dynamical systems, metric mean dimension was introduced to characterize infinite fiber entropy systems. We give four types of measure-theoretic $\epsilon$-entropies,

M Manceau, T E Wall, H Philip, A N Baranov

We evaluate the spectral performance of a novel continuous-wave room-temperature distributed feedback quantum cascade laser operating at the long wavelength of 17 $\mu$m. By demonstrating broadband laser absorption spectroscopy of the $\nu$2 fundamental vibrational mode of N2O molecules, we have determined the spectral range and established the spectroscopic

Cong Wang, Xiaofeng Cao, Lanzhe Guo2, Zenglin Shi

Semi-supervised learning provides an expressive framework for exploiting unlabeled data when labels are insufficient. Previous semi-supervised learning methods typically match model predictions of different data-augmented views in a single-level interaction manner, which highly relies on the quality of pseudo-labels and results in semi-supervised learning no

L. Piersanti, M. Bellaveglia, A. Gallo, R. Magnanimi

SPARC_LAB is a facility designed for the production of FEL radiation and the exploration of advanced acceleration techniques using a high brightness electron photo-injector. Specifically, particle-driven plasma wakefield acceleration (PWFA) necessitates exceptional beam stability, in order to minimize the jitter between the driver and witness beams. This req

Vasileios Arampatzakis, George Pavlidis, Kyriakos Pantoglou, Nikolaos Mitianoudis

The estimation of depth in two-dimensional images has long been a challenging and extensively studied subject in computer vision. Recently, significant progress has been made with the emergence of Deep Learning-based approaches, which have proven highly successful. This paper focuses on the explainability in monocular depth estimation methods, in terms of ho

Chuyue Lou, M. Amine Atoui

At present, decision making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, Convolutional Neural Network (CNN) can learn effective representations o

Yasser Khalafaoui, Basarab Matei, Nistor Grozavu, Martino Lovisetto

Data is increasingly being collected from multiple sources and described by multiple views. These multi-view data provide richer information than traditional single-view data. Fusing the former for specific tasks is an essential component of multi-view clustering. Since the goal of multi-view clustering algorithms is to discover the common latent structure s

Tomáš Maleček, Guillaume Agnus, Thomas Maroutian, Lukáš Horák

We demonstrate the possibility of growing textured La$_{2/3}$Sr$_{1/3}$MnO$_3$ (LSMO) thin films on silicon substrates with magneto-optical and optical properties comparable to high-quality epitaxial layers grown on bulk SrTiO$_3$ (STO). The pulsed laser deposition growth of LSMO is achieved by a two-dimensional nanosheet (NS) seed layer of Ca$_2$Nb$_3$O$_{1

Olivier Raimond, Georgii Riabov

This paper is devoted to the construction of stochastic flows of measurable mappings in a locally compact separable metric space (M, $\rho$). We propose a new construction that produces strong measurable continuous modifications for certain stochastic flows of measurable mappings in metric graphs.

Xin Zhang, Hongbo Lou, Beatrice Ruta, Yuriy Chushkin

Relaxation dynamics, as a key to understand glass formation and glassy properties, remains an elusive and challenging issue in condensed matter physics. In this work, in situ high-pressure synchrotron high-energy x-ray photon correlation spectroscopy has been developed to probe the atomic-scale relaxation dynamics of a cerium-based metallic glass during comp

Torsten Krauß, Jasper Stang, Alexandra Dmitrienko

Due to costly efforts during data acquisition and model training, Deep Neural Networks (DNNs) belong to the intellectual property of the model creator. Hence, unauthorized use, theft, or modification may lead to legal repercussions. Existing DNN watermarking methods for ownership proof are often non-intuitive, embed human-invisible marks, require trust in al

Oriane Thiery, Mira Rizkallah, Clément Bailly, Caroline Bodet-Milin

Diffuse Large B-cell Lymphoma (DLBCL) is a lymphatic cancer involving one or more lymph nodes and extranodal sites. Its diagnostic and follow-up rely on Positron Emission Tomography (PET) and Computed Tomography (CT). After diagnosis, the number of nonresponding patients to standard front-line therapy remains significant (30-40%). This work aims to develop a

Giacomo Balloccu, Ludovico Boratto, Christian Cancedda, Gianni Fenu

The integration of path reasoning with language modeling in recommender systems has shown promise for enhancing explainability but often struggles with the authenticity of the explanations provided. Traditional models modify their architecture to produce entities and relations alternately--for example, employing separate heads for each in the model--which do

Benjamin Krawciw, Lincoln D. Carr, Cecilia Diniz Behn

Complex network theory has focused on properties of networks with real-valued edge weights. However, in signal transfer networks, such as those representing the transfer of light across an interferometer, complex-valued edge weights are needed to represent the manipulation of the signal in both magnitude and phase. These complex-valued edge weights introduce

Romain Xu-Darme, Georges Quénot, Zakaria Chihani, Marie-Christine Rousset

In this work, we perform an analysis of the visualisation methods implemented in ProtoPNet and ProtoTree, two self-explaining visual classifiers based on prototypes. We show that such methods do not correctly identify the regions of interest inside of the images, and therefore do not reflect the model behaviour, which can create a false sense of bias in the

Guanzheng Chen, Xin Li, Zaiqiao Meng, Shangsong Liang

Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the preset context window of Transformer. Position Embedding (PE) scaling methods, while effective in extending the context window to a specific length, demonstrate either notable limi

Mikhail Shifman, Roman Zwicky

In this note we show that $\beta'_*$, the $\beta$-function slopes in the electric and magnetic theories are equal at the corresponding infrared fixed points. This follows from the scaling of the correlators of the trace of the energy momentum tensors. The slopes $\beta'_*$ determine the scaling dimensions. Our paper can be considered as a commentary to Ansel

Daisei Abe, Tsuyoshi Inoue, Shu-ichiro Inutsuka

The Herschel Gould Belt Survey showed that stars form in dense filaments in nearby molecular clouds. Recent studies suggest that massive filaments are bound by the slow shocks caused by accretion flows onto the filaments. The slow shock is known to be unstable to the corrugation deformation of the shock front. The corrugation instability could convert the ac

Xiaoxuan Ma, Stephan P. Kaufhold, Jiajun Su, Wentao Zhu

Understanding the behavior of non-human primates is crucial for improving animal welfare, modeling social behavior, and gaining insights into distinctively human and phylogenetically shared behaviors. However, the lack of datasets on non-human primate behavior hinders in-depth exploration of primate social interactions, posing challenges to research on our c

Hokeun Yoon, JinYeong Bak

Question generation (QG) from a given context can enhance comprehension, engagement, assessment, and overall efficacy in learning or conversational environments. Despite recent advancements in QG, the challenge of enhancing or measuring the diversity of generated questions often remains unaddressed. In this paper, we introduce a multi-question generation mod

Hiroyuki Fujioka, Ryutaro Tomomatsu, Koichi Takamiya

Background: The existence of a tetraneutron comprising four neutrons has long been debated. Purpose: Motivated by a recent observation of particle-stable tetraneutrons, we investigated potential particle-stable tetraneutron emission in thermal neutron-induced $^{235}$U fission using a nuclear research reactor. Methods: We performed $\gamma$-ray spectroscopy

Wolfgang Maass

Research on neuromorphic computing is driven by the vision that we can emulate brain-like computing capability, learning capability, and energy-efficiency in novel hardware. Unfortunately, this vision has so far been pursued in a half-hearted manner. Most current neuromorphic hardware (NMHW) employs brain-like spiking neurons instead of standard artificial n

Marco Panza

The chapter advances a reformulation of the classical problem of the nature of mathematical objects (if any), here called "Plato's problem," in line with the program of a philosophy of mathematical practice. It then provides a sketch of a platonist solution, following the same perspective. This solution disregards as nonsensical the question of the existence

Kota Sugihara, Ken Hayami

Brown and Walker (1997) showed that GMRES determines a least squares solution of $ A x = b $ where $ A \in {\bf R}^{n \times n} $ without breakdown for arbitrary $ b, x_0 \in {\bf R}^n $ if and only if $A$ is range-symmetric, i.e. $ {\cal R} (A^{\rm T}) = {\cal R} (A) $, where $ A $ may be singular and $ b $ may not be in the range space ${\cal R} A)$ of $A$

Noam Levi, Alon Beck, Yohai Bar-Sinai

Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data. We show both analytically and numerically that grokking can surprisingly occur in linear networks performing linear tasks in a simple teacher-student setup with Gaussian inputs. In this setting, the full training dynamics is derived in terms of t

Léo Pouy, Fouad Khenfri, Patrick Leserf, Chokri Mraidha

As we advance in the fast-growing era of Machine Learning, various new and more complex neural architectures are arising to tackle problem more efficiently. On the one hand their efficient usage requires advanced knowledge and expertise, which is most of the time difficult to find on the labor market. On the other hand, searching for an optimized neural arch

Reon Ishii, Tomotaro Namba, Hiroyuki Katsuki, Kenji Ohmori

On the basis of optimal control theory, we numerically study how to optimally manipulate molecular vibrational dynamics by using cycle-averaged polarizability interactions induced by mildly intense non-resonant laser (NR) pulses. As the essential elements to be controlled are the probability amplitudes, namely, the populations and the relative phases of the

Gennadi Glinsky

Two distinct families of pan-primate endogenous retroviruses, namely HERVL and HERVH, infected primates germline, colonized host genomes, and evolved into the global retroviral genomic regulatory dominion (GRD) operating during human embryogenesis (HE). HE retroviral GRD constitutes 8839 highly conserved fixed LTR elements linked to 5444 down-stream target g

Fan Lin, Yong-Liang Ma

We show that the recent construction of $N_f=1$ baryons on the $\eta^\prime$ domain wall can be understood as vortexes of the principal effective theory -- the Chern-Simons-Higgs theory -- on a 2+1-dimensional sheet. This theory has a series of vertex solutions, and the vortex with unit topological charge naturally spins $N_c/2$, which coincides with the spi

Vincent P. Grande, Michael T. Schaub

Persistent Homology is a widely used topological data analysis tool that creates a concise description of the topological properties of a point cloud based on a specified filtration. Most filtrations used for persistent homology depend (implicitly) on a chosen metric, which is typically agnostically chosen as the standard Euclidean metric on $\mathbb{R}^n$.

Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou

A long-standing goal of AI systems is to perform complex multimodal reasoning like humans. Recently, large language models (LLMs) have made remarkable strides in such multi-step reasoning on the language modality solely by leveraging the chain of thought (CoT) to mimic human thinking. However, the transfer of these advancements to multimodal contexts introdu

Mehdi Rafiei, Toby P. Breckon, Alexandros Iosifidis

Anomaly detection methods have demonstrated remarkable success across various applications. However, assessing their performance, particularly at the pixel-level, presents a complex challenge due to the severe imbalance that is most commonly present between normal and abnormal samples. Commonly adopted evaluation metrics designed for pixel-level detection ma

Sarah Timhadjelt

We study the spectral gap behavior of an operator obtained by summing a random permutation $M$ and a deterministic bistochastic matrix $Q$. We are interested in the asymptotic in terms of dimension. In the case where $(M,Q)$ are asymptotically free with amalgamation over the diagonal, we can compute limit operators $(u,q)$ which give the weak limit spectral

Prakhar Sah, Matthew Hicks

Internet of Things (IoT) devices sit at the intersection of unwieldy software complexity and unprecedented attacker access. This unique position comes with a daunting security challenge: how can I protect both proprietary code and confidential data on a device that the attacker has unfettered access to? Trusted Execution Environments (TEEs) promise to solve

Markus Diehl, Florian Fabry, Peter Ploessl

Double parton distributions satisfy the same evolution equations as ordinary single-parton densities, provided that the colours of the two partons are uncorrelated. The situation is different for colour correlated parton pairs, where evolution to higher scales results in a suppression by Sudakov double logarithms. We perform a detailed study of evolution for

Marika Malaspina, Jessica Amianto Barbato, Marco Cremaschi, Francesca Gasparini

In the video game industry, great importance is given to the experience that the user has while playing a game. In particular, this experience benefits from the players' perceived sense of being in the game or immersion. The level of user immersion depends not only on the game's content but also on how the game is displayed, thus on its User Interface (UI) a

Weinan Dai, Yifeng Jiang, Chengjie Mou, Chongyu Zhang

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher

A. S. Sadovsky, A. P. Filin, S. A. Akimenko, A. V. Artamonov

A high statistics data sample of the $K^{+}$ decays is recorded by the OKA collaboration. A missing mass analysis is performed to search for a light invisible pseudoscalar axion-like particle (ALP) $a$ in the decay $K^{+} \to \pi^{+} \pi^{0} a$. No signal is observed, the upper limits for the branching ratio of the decay are calculated. The $90\%$ confidence

Chen Jason Zhang, Yunrui Liu, Pengcheng Zeng, Ting Wu

The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done solely by machines or conducted by professional experts due to cost constraints. Within the field of social scienc

J. Auler, M. Engler, K. Franz, J. Kahlenberg

The confinement of ultracold neutrons (UCNs) in a three dimensional magnetic field gradient trap allows for a measurement of the free neutron lifetime with superior control over spurious loss channels and can provide a large kinetic energy acceptance to enhance statistical sensitivity. In this paper, we present the first successful implementation of a pulsed

Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai

Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task. However, automating the generation of such expert-level prompts remains elusive. Existing prompt optimiza

Laetitia Mottet, Typhaine Coquard, Marc Prat

Heat and mass transfer inside the porous wick of a capillary evaporator is studied using a mixed pore-network model. The impact of the thermal conductivity of the wick on the overheating limit (defined as the difference between the maximum temperature at the top of the metallic casing and the saturated temperature), breakthrough (which occurs when the vapor

Meinulf Göckeler, Thomas Schücker

In two recent papers Gonuguntla and Singleton claim that the Wu-Yang fiber bundle approach does not lead to to a consistent model for magnetic charge. We point out that this claim is false.

Christophe Dupont, Virgile Tapiero

Let $f$ be a holomorphic map of $\mathbb{C}\mathbb{P}^2$ of degree $d\geq 2$, let $T$ be its Green current and $\mu=T\wedge T$ be its equilibrium measure. We give a new proof of a theorem due to Dujardin asserting that $\mu \ll T\wedge\omega_{\mathbb{P}^2}$ implies $\lambda_2=\frac{1}{2} \log\ d$, where $\lambda_1 \geq \lambda_2$ are the Lyapunov exponents o

Darko Mitrovic

A political party can be considered as a company whose value depends on the voters support i.e. on the percentage of population supporting the party. Dynamics of the support is thus as a stochastic process with a deterministic growth rate perturbed by a white noise modeled through the Wiener process. This is in an analogy with the option modeling where the s

Marek Mozrzymas, Michał Horodecki, Michał Studziński

In this paper, we present the connection of two concepts as induced representation and partially reduced irreducible representations (PRIR) appear in the context of port-based teleportation protocols. Namely, for a given finite group $G$ with arbitrary subgroup $H$, we consider a particular case of matrix irreducible representations, whose restriction to the

Melih İs

In this paper, we deal with the robot motion planning problem in multi-valued function theory. We first enrich the multi-homotopy studies by introducing a multi-homotopy lifting property and a multi-fibration. Then we compute both a topological multi-complexity and a Lusternik-Schnirelmann multi-category of a space or a multi-fibration.

Qinyong Wang, Zhenxiang Gao, Rong Xu

Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs. However, the lack of interpretability and explainability of graph embedding methods has limited their applicability in scenarios requiring explicit reasoning. In this pape

Benjamin De Bruyne, Pierre Le Doussal, Satya N. Majumdar, Gregory Schehr

We consider $N$ classical particles interacting via the Coulomb potential in spatial dimension $d$ and in the presence of an external trap, at equilibrium at inverse temperature $\beta$. In the large $N$ limit, the particles are confined within a droplet of finite size. We study smooth linear statistics, i.e. the fluctuations of sums of the form ${\cal L}_N

Ke-Jia Chen, Yaming Ji, Youran Qu, Chuhan Xu

Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The existence of negative edges affects the SGNN robustness on two aspects. One is the semantic imbalance as the negative edges are usually hard to obtain though they can provide potentia

Bingchen Liu, Shihao Hou, Weixin Zeng, Xiang Zhao

The construction of large open knowledge bases (OKBs) is integral to many applications in the field of mobile computing. Noun phrases and relational phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. However, in order to meet the requirements of some privacy protection regulations and to ens

Yuki Hattori, Atsufumi Honda, Tatsuya Morimoto

In this paper, generalizing the techniques of Bour's theorem, we prove that every generic cuspidal edge, more generally, generic $n$-type edge, which is invariant under a helicoidal motion in Euclidean $3$-space admits non-trivial isometric deformations. As a corollary, several geometric invariants, such as the limiting normal curvature, the cusp-directional

Kang Kim, Hankyu Cho

Recent years have seen remarkable advances in the field of Simultaneous Machine Translation (SiMT) due to the introduction of innovative policies that dictate whether to READ or WRITE at each step of the translation process. However, a common assumption in many existing studies is that operations are carried out at the subword level, even though the standard

Lu Chen, Hongyu Liu

This paper establishes a complete time-harmonic scattering theory for hyperbolic space, formulating it within the classical Sommerfeld-Rellich paradigm centered on far-field patterns--a foundational framework that has been absent despite the well-developed spectral and time-dependent theories for this geometry. We explicitly construct the ingoing and outgoin

S. P. Dinesh, V. R. Thakar, V. I. Gokul, Arun Bahuleyan

We present a stabilization technique developed to lock and dynamically tune the resonant frequency of a moderate finesse Fabry-P\'erot (FP) cavity used in precision atom-cavity quantum electrodynamics (QED) experiments. Most experimental setups with active stabilization either operate at one fixed resonant frequency or use transfer cavities to achieve the ab

Ruizheng Xu, Guiyu Wei, Zhemin Xie, Sijie Diao

Increasing the efficiency of charge transfer and separation efficiency of photogenerated carriers are still the main challenges in the field of semiconductor-based photocatalysts. Herein, we synthesized g-C3N4@V2C MXene photocatalyst by modifying g-C3N4 using V2C MXene. The prepared photocatalyst exhibited outstanding photocatalytic performance under visible

Josep Martínez-Romeu, Iago Díez, Sebastian Golat, Francisco J. Rodríguez-Fortuño

Optical forces can be chiral when they exhibit opposite signs for the two enantiomeric versions of a chiral molecule or particle. Such forces could be eventually used to separate enantiomers, which could find application in numerous disciplines. Here, we analyze numerically the optical chiral forces arising in the basic element of photonic integrated circuit

Zhuo Huang, Li Shen, Jun Yu, Bo Han

Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. However, most SSL methods are commonly based on instance-wise consistency between different data transformations. Therefore, the label guidance on labeled data is hard to be propagated to unlabeled data. Consequently, the learning

Alon Goldstein, Miriam Havin, Roi Reichart, Ariel Goldstein

This paper investigates the problem-solving capabilities of Large Language Models (LLMs) by evaluating their performance on stumpers, unique single-step intuition problems that pose challenges for human solvers but are easily verifiable. We compare the performance of four state-of-the-art LLMs (Davinci-2, Davinci-3, GPT-3.5-Turbo, GPT-4) to human participant

Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging the hidden knowledge encoded within these highly performant AI systems. Yet, this knowledge is often hard to extract, a

Dui Wang, Xiangyu Hou, Xiaohui Yang, Bo Zhang

Recommendation system (RS) plays significant roles in matching users information needs for Internet applications, and it usually utilizes the vanilla neural network as the backbone to handle embedding details. Recently, the large language model (LLM) has exhibited emergent abilities and achieved great breakthroughs both in the CV and NLP communities. Thus, i

K. Nomura

Octupole deformation and collective excitations are studied within the interacting boson model. By using the results of the self-consistent mean-field calculations with a universal energy density functional, the Hamiltonian of the interacting $s$, $d$, and $f$ boson system is completely determined. A global systematic study confirms that significant octupole

Zheshun Wu, Zenglin Xu, Hongfang Yu, Jie Liu

With the rapid growth of edge intelligence, the deployment of federated learning (FL) over wireless networks has garnered increasing attention, which is called Federated Edge Learning (FEEL). In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trai

Saeif Al-Hazbi, Ahmed Hussain, Savio Sciancalepore, Gabriele Oligeri

Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Lea

Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Kei Okada

Recognition of the current state is indispensable for the operation of a robot. There are various states to be recognized, such as whether an elevator door is open or closed, whether an object has been grasped correctly, and whether the TV is turned on or off. Until now, these states have been recognized by programmatically describing the state of a point cl

X. He, N. J. Huang, Y. P. Fang

The alternating direction method of multipliers (ADMM) has found widespread use in solving separable convex optimization problems. In this paper, by employing Nesterov extrapolation technique, we propose two families of accelerated linearized ADMMs for addressing two-block linearly constrained separable convex optimization problems where each block of the ob

Mahan Mj, Biswajit Nag

We start with a Gromov-hyperbolic surface bundle $E$ over a graph, and drill out essential simple closed curves from fibers to obtain a drilled bundle $F$. We prove that for such drilled bundles $F$, the fundamental group $\pi_1(F)$ is relatively hyperbolic with $(\mathbb{Z}\oplus \mathbb{Z})$ peripheral groups. Combining the relative hyperbolicity of $\pi_1

Ji Jiang, Meng Cao, Tengtao Song, Long Chen

Video Referring Expression Comprehension (REC) aims to localize a target object in videos based on the queried natural language. Recent improvements in video REC have been made using Transformer-based methods with learnable queries. However, we contend that this naive query design is not ideal given the open-world nature of video REC brought by text supervis

See Hian Lee, Feng Ji, Kelin Xia, Wee Peng Tay

Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missing edges, as well as edge weights that provide little informative value. To address these challenges and captu

Tianyi Lu, Xing Zhang, Jiaxi Gu, Renjing Pei

Latent Diffusion Models (LDMs) are renowned for their powerful capabilities in image and video synthesis. Yet, compared to text-to-image (T2I) editing, text-to-video (T2V) editing suffers from a lack of decent temporal consistency and structure, due to insufficient pre-training data, limited model editability, or extensive tuning costs. To address this gap,

Samit Dasgupta, Mahesh Kakde, Jesse Silliman, Jiuya Wang

In this paper we give a complete proof of the Brumer-Stark conjecture over $\mathbf{Z}$.

J. R. Persson

An update is given on the experimental values of the magnetic hyperfine structure and the Bohr-Weisskopf effect in muonic atoms. The need for more measurements and systematic calculations is discussed to allow the differentiation of different models of the Bohr-Weisskopf effect in nuclei.

Chuanbo Hua, Federico Berto, Michael Poli, Stefano Massaroli

While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only recently been tackled by deep learning approaches. In this paper, we present GraphSplineNets, a novel deep-learning method to speed up the forecasting of physical systems by reducing

Samit Dasgupta, Mahesh Kakde, Jesse Silliman, Jiuya Wang

Ribet's method provides a strategy for constructing a nontrivial extension of a $p$-adic Galois representation $\rho_1$ by another such representation $\rho_2$. Suppose we are working over a local ring. An important assumption that occurs throughout literature is that the representations $\rho_i$ are residually distinguishable i.e. are residually non-isomorp

Akihiro Ishibashi, Satoshi Matsumoto, Yuichiro Yoneo

We study no-hair properties of static black holes in four and higher dimensional spacetimes with a cosmological constant. For the vanishing cosmological constant case, we show a no-hair theorem and also a no-short-hair theorem under certain conditions for the energy-momentum of matter fields. For the positive cosmological constant case, we discuss conditions

Mohamed Amazioug, Mohammed Daoud

We explore the quantum correlations, fidelity and quantum thermodynamics of two coupled double quantum dots containing two excess electrons. In this regard, we investigate and compare the evolution of those measures under thermal effects and tunneling coupling. We find the hierarchy of quantum correlations, and one-way steering between the two quantum dots.

Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang

Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation. By leveraging textual features, customized LLMs are also applied for recommendation and demonstrate improvements across diverse recommendation scenarios. Yet the majority of existing methods perform training-free recommendation that heavily re

Vipul Rathore, Rajdeep Dhingra, Parag Singla, Mausam

We tackle the problem of zero-shot cross-lingual transfer in NLP tasks via the use of language adapters (LAs). Most of the earlier works have explored training with adapter of a single source (often English), and testing either using the target LA or LA of another related language. Training target LA requires unlabeled data, which may not be readily availabl

L. Doolittle, S. D. Murthy, A. Benwell, D. Chabot

The LCLS-II RF system physically spans ~700m and has strict requirements -- on the order of 20 fs -- on the phase stability of the accelerating RF fields in its SRF linac. While each LLRF rack is crudely temperature-stabilized, the weather inside the service building as a whole is usually compared to a tin shack in the California sun. A phase-averaging refer

Zhuo Huang, Muyang Li, Li Shen, Jun Yu

Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies based on Lottery Ticket Hypothesis (LTH) address this problem by minimizing the learning target to find some of the parameters that are critical to the task. However, in OOD proble

Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, David Lo

API misuses often lead to software bugs, crashes, and vulnerabilities. While several API misuse detectors have been proposed, there are no automatic repair tools specifically designed for this purpose. In a recent study, test-suite-based automatic program repair (APR) tools were found to be ineffective in repairing API misuses. Still, since the study focused

Qiao Yan, Yihan Wang

4D radar is recognized for its resilience and cost-effectiveness under adverse weather conditions, thus playing a pivotal role in autonomous driving. While cameras and LiDAR are typically the primary sensors used in perception modules for autonomous vehicles, radar serves as a valuable supplementary sensor. Unlike LiDAR and cameras, radar remains unimpaired

Zhe Li, Zhangyang Gao, Cheng Tan, Stan Z. Li

The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any modality can potentially benefit from a large language model, as long as it undergoes vector quantization to become discrete tokens. Inspired by GLM, we propose a General Point Mo

Aagam Bakliwal, Amit D. Joshi

In the dynamic realm of deepfake detection, this work presents an innovative approach to validate video content. The methodology blends advanced 2-dimensional and 3-dimensional Convolutional Neural Networks. The 3D model is uniquely tailored to capture spatiotemporal features via sliding filters, extending through both spatial and temporal dimensions. This c

Han Li, Shaohui Li, Wenrui Dai, Chenglin Li

Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years. However, existing LIC methods are redundant in latent representation due to limitations in capturing anisotropic frequency components and preserving directional details. To overcome these challenges, we propose a novel frequency-aw

Subhendu B. Ghosh, Snehasish Roy Chowdhury, Guruprasad Kar, Arup Roy

Quantum nonlocality, pioneered in Bell's seminal work and subsequently verified through a series of experiments, has drawn substantial attention due to its practical applications in various protocols. Evaluating and comparing the extent of nonlocality within distinct quantum correlations holds significant practical relevance. Within the resource theoretic fr

Ahmad Salmanogli, Amine Bermak

This study unveils a groundbreaking exploration of using semiconductor technology in quantum circuitry. Leveraging the unique operability of 45 nm CMOS technology at deep cryogenic temperatures (~ 300 mK), a novel quantum electronic circuit is meticulously designed. Through the intricate coupling of two matching circuits via a 45 nm nMOS transistor, operatin

Shao-Bo Lin, Xingping Sun, Di Wang

For radial basis function (RBF) kernel interpolation of scattered data, Schaback in 1995 proved that the attainable approximation error and the condition number of the underlying interpolation matrix cannot be made small simultaneously. He referred to this finding as an "uncertainty relation", an undesirable consequence of which is that RBF kernel interpolat

Hao Zhang, Fang Li, Narendra Ahuja

In this paper, we address the challenge of decomposing Neural Radiance Fields (NeRF) into objects from an open vocabulary, a critical task for object manipulation in 3D reconstruction and view synthesis. Current techniques for NeRF decomposition involve a trade-off between the flexibility of processing open-vocabulary queries and the accuracy of 3D segmentat