May 2023 arXiv papers — page 122

Yi Han

Consider the random Schr\"odinger operator $H_n$ defined on $\{0,1,\cdots,n\}\subset\mathbb{Z}$ $$ (H_n\psi)_\ell=\psi_{\ell-1,n}+\psi_{\ell+1,n}+\sigma\frac{\omega_\ell}{a_{\ell,n}}\psi_{\ell,n},\quad \psi_0=\psi_{n+1}=0, $$ where $\sigma>0$, $\omega_\ell$ are i.i.d. random variables and $a_{\ell,n}$ typically has order $\sqrt{n}$ for $\ell\in[\epsilon n,(1

Talat Nazir, Sergei Silvestrov

Using the setting of ordered metric spaces, we obtain common end point of two multivalued mappings satisfying a generalized $(\psi,\varphi)$-weak contractive condition. Under comparative condition on the set of end points of multivalued mappings, our results assure the uniqueness of the end point. These results generalize and improve several recent results o

Hillary M. Heiling, Naim U. Rashid, Quefeng Li, Joseph G. Ibrahim

Generalized linear mixed models (GLMMs) are widely used in research for their ability to model correlated outcomes with non-Gaussian conditional distributions. The proper selection of fixed and random effects is a critical part of the modeling process since model misspecification may lead to significant bias. However, the joint selection of fixed and random

Aolan Ding, Rick Durrett

In 2002, Chung and Lu introduced a version of the Erdos-Renyi model which an edge between $i$ and $j$ is present with probability $p(i,j)$. They applied this model to compute the diameter of power-law random graphs, with yielded easier proofs than those for the configuration model. In 2007 their model was brought and integrated under the umbrella of Bolobas,

Oscar García-Prada

We explore the role of Vinberg pairs defined by cyclic gradings of a semisimple complex Lie algebra in the context of Higgs bundle theory.

Hillary M. Heiling, Naim U. Rashid, Quefeng Li, Xianlu L. Peng

Modern biomedical datasets are increasingly high dimensional and exhibit complex correlation structures. Generalized Linear Mixed Models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with

Jiyue Jiang, Sheng Wang, Qintong Li, Lingpeng Kong

When communicating with elders with cognitive impairment, cognitive stimulation (CS) help to maintain the cognitive health of elders. Data sparsity is the main challenge in building CS-based dialogue systems, particularly in the Chinese language. To fill this gap, we construct a Chinese CS conversation (CSConv) dataset, which contains about 2.6K groups of di

Jonas Bremer, Pratika Dayal

We couple the DELPHI framework for galaxy formation with a model for the escape of ionizing photons to study both its variability with galaxy assembly and the resulting key reionization sources. In this model, leakage either occurs through a fully ionized gas distribution (ionization bounded) or additionally through channels cleared of gas by supernova explo

N. -P. Liu, S. -B. Qian, W. -P. Liao, Y. Huang

High-precision light curves were extracted from TESScut images. Together with APOGEE and LAMOST medium resolution spectra, a joint study was made for six early K-type contact binary candidates selected unbiasedly with orbital periods around 0.268 days. It is found that all of them (RV CVn, EK Com, V384 Ser, V1038 Her, EH CVn, and CSS$\_$J125403.7+503945) are

Ayman Elhalwagy, Tatiana Kalganova

The generalisation of Neural Networks (NN) to multiple datasets is often overlooked in literature due to NNs typically being optimised for specific data sources. This becomes especially challenging in time-series-based multi-dataset models due to difficulties in fusing sequential data from different sensors and collection specifications. In a commercial envi

Chunhui Zhang, Li Liu, Yawen Cui, Guanjie Huang

Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence similar to that of a human being. This is in contrast to narrow or specialized AI, which is designed to perform specific tasks with a high degree of efficiency. Ther

Hao Yan, Saurabh Srivastava, Yintao Tai, Sida I. Wang

Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively exp

Michael Poluektov, Andrew Polar

It is known that any continuous multivariate function can be represented exactly by a composition functions of a single variable - the so-called Kolmogorov-Arnold representation. It can be a convenient tool for tasks where it is required to obtain a predictive model that maps some vector input of a black box system into a scalar output. In this case, the rep

Vladimir Spokoiny

Nonlinear regression problem is one of the most popular and important statistical tasks. The first methods like least squares estimation go back to Gauss and Legendre. Recent models and developments in statistics and machine learning like Deep Neuronal Networks (DNN) or nonlinear PDE stimulate new research in this direction which has to address the important

Jianqi Chen, Hao Chen, Keyan Chen, Yilan Zhang

Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space. Despite some achievements in transferability and attack success rate, the crafted adversarial examples are easily perceived by human eyes. Towards visual imperceptibility, some recent works explore unrestricted attacks without $L_p$-norm constraints, yet lacking transfera

Antoine Mercier, Guillaume Berger, Sunny Panchal, Florian Letsch

End-to-end learning has taken hold of many computer vision tasks, in particular, related to still images, with task-specific optimization yielding very strong performance. Nevertheless, human-centric action recognition is still largely dominated by hand-crafted pipelines, and only individual components are replaced by neural networks that typically operate o

Yunong Wu, Thomas Gilles, Bogdan Stanciulescu, Fabien Moutarde

Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Scene Graph Neural Networks with projected vectorized representations for multi-agent trajectory prediction. Projected vectorized representation models the traffic scene as a graph wh

Elisa Tsai, Deepak Kumar, Ram Sundara Raman, Gavin Li

DNS manipulation is an increasingly common technique used by censors and other network adversaries to prevent users from accessing restricted Internet resources and hijack their connections. Prior work in detecting DNS manipulation relies largely on comparing DNS resolutions with trusted control results to identify inconsistencies. However, the emergence of

Emmanuel Briand, Mercedes Rosas, Stefan Trandafir

The $\mathit{SU}(3)$ tensor multiplicities are piecewise polynomial of degree $1$ in their labels. The pieces are the chambers of a complex of cones. We describe in detail this chamber complex and determine the group of all linear symmetries (of order $144$) for these tensor multiplicities. We represent the cells by diagrams showing clearly the inclusions as

Gaurish Thakkar, Nives Mikelic Preradović, Marko Tadić

This article presents a sentence-level sentiment dataset for the Croatian news domain. In addition to the 3K annotated texts already present, our dataset contains 14.5K annotated sentence occurrences that have been tagged with 5 classes. We provide baseline scores in addition to the annotation process and inter-annotator agreement.

Lehao Yang, Cui Zhu, Tian Feng

The demand for high-quality city street layouts has persisted for an extended period presenting notable challenges. Conventional methods are yet to effectively address the integration of both natural and socioeconomic factors in this complex task. In this study, we propose a novel conditional adversarial learning-based method for city street layout generatio

Matteo Sacchi, Orr Sela, Gabi Zafrir

Much insight into the dynamics of quantum field theories can be gained by studying the relationship between field theories in different dimensions. An interesting observation is that when two theories are related by dimensional reduction on a compact surface, their 't Hooft anomalies corresponding to continuous symmetries are also related: the anomaly polyno

Seunghoon Lee, Huanchen Zhai, Garnet Kin-Lic Chan

We describe an ab initio approach to simulate L-edge X-ray absorption (XAS) and 2p3d resonant inelastic X-ray scattering (RIXS) spectroscopies. We model the strongly correlated electronic structure within a restricted active space and employ a correction vector formulation instead of sum-over-states expressions for the spectra, thus eliminating the need to c

Wei Yuan, Shilong Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen

Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns recommendation models by sharing model public parameters with clients, thereby offering a privacy-preserving solution. Unfortunately, the exposure of model parameters leaves a backdo

S. Jahedi, F. Javadi, M. J. Mehdipour

Let $\{\frak{M} _k \} _{ k \in \mathbb{Z}} $ be a sequence of closed subspaces of Hilbert space $H$, and let $\{\Theta_k\}_{k \in \mathbb{Z}}$ be a sequence of linear operators from $H$ into $\frak{M}_k$, $k \in \mathbb{Z}$. In the definition of fusion frames, we replace the orthogonal projections on $\frak{M} _k$ by $\Theta_k$ and find a slight generalizati

Roope Anttila, Balázs Bárány, Antti Käenmäki

We show that the Hausdorff dimension of any slice of the graph of the Takagi function is bounded above by the Assouad dimension of the graph minus one, and that the bound is sharp. The result is deduced from a statement on more general self-affine sets, which is of independent interest. We also prove that the Marstrand's slicing theorem on the graph of the T

Erlan D. Nursultanov, Durvudkhan Suragan

In this paper, we present a refined version of the (classical) Stein inequality for the Fourier transform, elevating it to a new level of accuracy. Furthermore, we establish extended analogues of a more precise version of the Stein inequality for the Fourier transform, broadening its applicability from the range $1<p<2$ to $2\leq p<\infty$.

Michal Zajaček, Bożena Czerny, Narayan Khadka, Mary Loli Martínez-Aldama

In Khadka et al. (2023), a sample of X-ray-detected reverberation-mapped quasars was presented and applied for the comparison of cosmological constraints inferred using two well-established relations in AGN -- the X-ray/UV luminosity ($L_{X}-L_{UV}$) relation and the broad-line region radius-luminosity ($R-L$) relation. $L_{X}-L_{UV}$ and $R-L$ luminosity di

Xiaoyu Wang, Kangyao Huang, Xinyu Zhang, Honglin Sun

An innovative sort of mobility platform that can both drive and fly is the air-ground robot. The need for an agile flight cannot be satisfied by traditional path planning techniques for air-ground robots. Prior studies had mostly focused on improving the energy efficiency of paths, seldom taking the seeking speed and optimizing take-off and landing places in

Takuya Inoue, Yusuke Nakamura

The purpose of this paper is to extend the scope of the Ehrhart theory to periodic graphs. We give sufficient conditions for the growth sequences of periodic graphs to be a quasi-polynomial and to satisfy the reciprocity laws. Furthermore, we apply our theory to determine the growth series in several new examples.

Anubhav Dogra, Srikant Sekhar Padhee, Ekta Singla

This paper presents an optimization approach for generating custom manipulator configurations using a proposed unconventional modular library. An end-to-end solution is presented in which the resulting optimal models of the modular compositions can be integrated directly with the Robot Operating System platform. The approach utilizes an unconventional modula

Yesom Park, Chang hoon Song, Jooyoung Hahn, Myungjoo Kang

This paper proposes a deep-learning-based method for recovering a signed distance function (SDF) of a given hypersurface represented by an implicit level set function. Using the flexibility of constructing a neural network, we use an augmented network by defining an auxiliary output to represent the gradient of the SDF. There are three advantages of the augm

Gaurish Thakkar, Nives Mikelic Preradovic, Marko Tadić

This paper introduces Cro-FiReDa, a sentiment-annotated dataset for Croatian in the domain of movie reviews. The dataset, which contains over 10,000 sentences, has been annotated at the sentence level. In addition to presenting the overall annotation process, we also present benchmark results based on the transformer-based fine-tuning approach

Wei Zhang, Fan Wang, Fang Yao

Research on the localization of the genetic basis associated with diseases or traits has been widely conducted in the last a few decades. Scan methods have been developed for region-based analysis in whole-genome association studies, helping us better understand how genetics influences human diseases or traits, especially when the aggregated effects of multi

Keshav Singh, Aaron Chew, Jonah Herzog-Arbeitman, B. Andrei Bernevig

The recently introduced topological heavy fermion model (THFM) provides a means for interpreting the low-energy electronic degrees of freedom of the magic angle twisted bilayer graphene as hybridization amidst highly dispersing topological conduction and weakly dispersing localized heavy fermions. In order to understand the Landau quantization of the ensuing

GRAVITY Collaboration, A. Soulain, K. Perraut, J. Bouvier

Aims: We aim at spatially and spectrally resolving the innermost scale of the young stellar object CI Tau to constrain the inner disk properties and better understand the magnetospheric accretion phenomenon. Methods: The high sensitivity offered by the combination of the four 8-m telescopes of the VLTI allied with the spectral resolution of the K-band beam c

Xiaobing Dai, Armin Lederer, Zewen Yang, Sandra Hirche

When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly popular learning method for this purpose due to the existence of prediction error bounds. Moreover, GP models can be efficiently updated online, such that event-triggered online l

Isaac Goldbring, Jennifer Pi

We investigate the problem of elementary equivalence of the free group factors, that is, do all free group factors $L(\mathbb{F}_n)$ share a common first-order theory? We establish a trichotomy of possibilities for their common first-order fundamental group, as well as several possible avenues for establishing a dichotomy in direct analog to the free group f

Lasha Ephremidze, Ilya Spitkovsky

We propose a superfast method for constructing orthogonal matrices $M\in\mathcal{O}(n,q)$ in finite fields $GF(q)$. It can be used to construct $n\times n$ orthogonal matrices in $Z_p$ with very high values of $n$ and $p$, and also orthogonal matrices with a certain circulant structure. Equally well one can construct paraunitary filter banks or wavelet matri

Suo Tang

Observation of efficient Breit-Wheeler electron-positron pair creation is one of the main goals for modern-day strong laser-particle experiments. We propose exploring this process by colliding plasma-generated harmonics, from currently available laser pulses, with a beam of GeV photons. We show that the creation yield is enhanced for more than one order of m

Hubert Klahr, Hans Baehr, Julio David Melon Fuksman

The linear stability analysis of a stratified rotating fluid (see paper I) showed that disks with a baroclinic stratification under the influence of thermal relaxation will become unstable to thermal instabilities. One instability is the Goldreich-Schubert-Fricke instability (GSF), which is the local version of the Vertical Shear Instability (VSI) and the ot

Zenan Huang, Haobo Wang, Junbo Zhao, Nenggan Zheng

Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a.k.a.}, latent processes identification. Driven by the independent assumption, existing works have made great progress in handling single-view data. However, it is a non-trivial problem that extends them to multi-view time ser

Hoang Ky Nguyen

Circa 1962 Hans A. Buchdahl pioneered a program -- and made significant progress -- seeking vacuo configurations in pure $\mathcal{R}^{2}$ gravity [H. A. Buchdahl, Nuovo Cimento 23, 141 (1962)]. Unfortunately, he deemed the final step in his calculations impassable and prematurely suspended his pursuit. Since then, his achievements on this front have faded i

Stefano Canino, Maria Virginia Catalisano, Alessandro Gimigliano, Monica Idà

We consider 0-dimensional schemes supported at a single point in n-space that are m-symmetric, i.e. that intersect any smooth curve passing through the point with length m, and the ones among them that are maximal with respect to inclusion (called m-superfat points). We study properties of such schemes, in particular for n=2. We give a first application of t

Kai Wang, Xin Wang, Yangyao Chen

We investigate the environmental dependence of the gas-phase metallicity for galaxies at $z=0$ to $z\gtrsim 2$ and the underlying physical mechanisms driving this dependence using state-of-the-art cosmological hydrodynamical simulations. We find that, at fixed stellar mass, central galaxies in massive halos have lower gas-phase metallicity than those in low-

C. Royon

We describe the discovery of the colorless $C$-odd gluonic compound, the odderon, by the D0 and TOTEM Collaborations by comparing elastic differential cross sections measured in $pp$ and $p \bar{p}$ interactions at high energies. We also discuss the reach on quartic anomalous couplings and the sensitivity to axion like particle production by using the LHC as

Mahdi Jemmali, Loai Kayed B. Melhim, Wadii Boulila, Hajer Amdouni

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. Du

Yongcheng Yao

Functional Magnetic Resonance Imaging (fMRI) is commonly utilized to study human brain activity, including abnormal functional properties related to neurodegenerative diseases. This study aims to investigate the differences in the topological properties of functional brain networks between individuals with Alzheimer's Disease (AD) and normal controls. A tota

Sam Mayo

We provide a condition for which the supersingular $l$-isogeny graph in characteristic $p$ has only one nontrivial automorphism, given by the action of Frobenius. For a fixed $p$, our condition is known to hold for a density 1 set of primes $l$.

Shomik Jain, Vinith Suriyakumar, Kathleen Creel, Ashia Wilson

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic p

Adnan A. E. Hajomer, Ivan Derkach, Nitin Jain, Hou-Man Chin

Quantum key distribution (QKD) enables two remote parties to share encryption keys with security based on the laws of physics. Continuous variable (CV) QKD with coherent states and coherent detection integrates well with existing telecommunication networks. However, thus far, long-distance CV-QKD has only been demonstrated using a highly complex scheme where

I. Yu. Rybak

We study the dynamics of Nambu-Goto cosmic string loops coupled to a massive Kalb-Ramond field. This coupling provides a framework for describing the interaction between the cosmic string and the gauge field. Within this setup, we compute the energy flux associated with the radiation of massive Kalb-Ramond modes from oscillating string loops. For loops featu

Bo Li, Chun-Shing Lee, Xin-Yuan Gao, Hai-Yao Deng

The nature of glassy states in realistic finite dimensions is still under fierce debate. Lattice models can offer valuable insights and facilitate deeper theoretical understanding. Recently, a disordered-interacting lattice model with distinguishable particles in two dimensions (2D) has been shown to produce a wide range of dynamical properties of structural

Pedro D. S. Silva, Mario J. Neves, Manoel M. Ferreira

We examine a dielectric medium supporting a magnetic current in connection with optical properties and energy propagation. The dispersion relations, propagating modes, and some optical effects are examined for isotropic and anisotropic magnetic conductivity tensors, with the latter ones implying nonreciprocal permittivities. The eigenvalues of the effective

Yulun Du, Lydia Chilton

Collaborative stories, which are texts created through the collaborative efforts of multiple authors with different writing styles and intentions, pose unique challenges for NLP models. Understanding and generating such stories remains an underexplored area due to the lack of open-domain corpora. To address this, we introduce STORYWARS, a new dataset of over

Geneviève Dusson, Louis Garrigue, Benjamin Stamm

Standard perturbation theory of eigenvalue problems consists of obtaining approximations of eigenmodes in the neighborhood of an operator where the corresponding eigenmode is known. Nevertheless, if the corresponding eigenmodes of several nearby operators are known, standard perturbation theory cannot simultaneously use all this knowledge to provide a better

Grzegorz Chimczak, Anna Kowalewska-Kudłaszyk, Ewelina Lange, Karol Bartkiewicz

We analyse two quantum systems with hidden parity-time (PT) symmetry: one is an optical device, whereas another is a superconducting microwave-frequency device. To investigate their symmetry, we introduce an equilibrium frame, in which loss and gain terms for a given Hamiltonian are balanced. We show that the non-Hermitian Hamiltonians of both systems can be

Min Ju Park, Suik Cheon, Hyun-Woo Lee

Recent studies suggest that the nonlinear transport properties in Weyl semimetal may be a measurable consequence of its chiral anomaly. Nonlinear responses in transport are estimated to be substantial, because in real materials such as TaAs or Bi$_{1-x}$Sb$_x$, the Fermi level resides near the Weyl nodes where the chiral charge pumping is said to diverge. Ho

Cong Jiang, Xiao-Long Hu, Zong-Wen Yu, Xiang-Bin Wang

We present a result of side-channel-secure (SCS) quantum key distribution (QKD) under fully realistic conditions. Our result is not only measurement-device independent but also effective with imperfect (and unstable) source devices including imperfect vacuum and imperfect coherent-state source. Applying the virtual mapping idea, we present a general security

R. M. Causey

We provide a short calculation of the Szlenk index of $C(K)$ spaces using the Grasberg norm.

Jipeng Qiang, Kang Liu, Yun Li, Yunhao Yuan

Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence. Recently, LS methods based on pretrained language models have made remarkable progress, generating potential substitutes for a target word through analysis of its contextual surroundings. However, these methods tend to overlook the preservation of the sentence'

S. A. Narawade, M. Koussour, B. Mishra

In this paper, we have presented an accelerating cosmological model of the Universe in an extended symmetric teleparallel gravity or $f(Q,T)$ gravity. The parametric form of the Hubble parameter is, $H\left(z\right) =H_{0}\left[ \alpha +\left( 1-\alpha \right) \left( 1+z\right) ^{n}\right] ^{\frac{3}{2n}}$, where $H_0$ and $n$ are constants and for $n=3$, th

Danyang Zhang, Zhennan Shen, Rui Xie, Situo Zhang

The Graphical User Interface (GUI) is pivotal for human interaction with the digital world, enabling efficient device control and the completion of complex tasks. Recent progress in Large Language Models (LLMs) and Vision Language Models (VLMs) offers the chance to create advanced GUI agents. To ensure their effectiveness, there's a pressing need for qualifi

Saurabh Band, Florian Stechmann, Malavika Unnikrishnan, Shadi Attarha

Among the various required resources for this civilization, the habitat is one of the crucial resources to live on Mars. Such an extraterrestrial habitat is designed to provide a safe place to live during the initial missions. It is equipped with monitoring and life support systems to ensure the astronauts' safety. In this work, we present a robust monitorin

Abdusattar Kurban, Xia Zhou, Na Wang, Yong-Feng Huang

Tidal disruption events are common in the Universe, which may occur in various compact star systems and could account for many astrophysical phenomena. Depending on the separation between the central compact star and its companion, either a full disruption or a partial disruption may occur. The partial disruption of a rocky planet around a neutron star can p

Zhong-Yi Wang, Da-Yong Liu, Liang-Jian Zou

In this paper we have systematically studied the electronic instability of pressured black phosphorous (BP) under strong magnetic field. We first present an effective model Hamiltonian for pressured BP near the Lifshitz point. We show that when the magnetic field exceeds a certain critical value, the nodal-line semimetal (NLSM) state of BP with a small band

Jiahui Liu, Jesper Byggmastar, Zheyong Fan, Ping Qian

Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning based interatomic potentials have shown sufficiently high accuracy for radiation damage simulations, but most existing ones are still not efficient enough to model high-energy colli

Eitam Sheetrit, Menachem Brief, Oren Elisha

A hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a significant problem in the healthcare domain, as they lead to increased hospitalization costs, decreased patient satisfaction, and increased risk of adverse outcomes such as infectio

Prashant Agrawal, Abhinav Nakarmi, Mahavir Prasad Jhawar, Subodh Sharma

We introduce the notion of \emph{traceable mixnets}. In a traditional mixnet, multiple mix-servers jointly permute and decrypt a list of ciphertexts to produce a list of plaintexts, along with a proof of correctness, such that the association between individual ciphertexts and plaintexts remains completely hidden. However, in many applications, the privacy-u

Roee M. Francos, Alfred M. Bruckstein

Consider a given planar circular region, in which there is an unknown number of smart mobile evaders. We wish to detect evaders using a line formation of sweeping agents whose total sensing length is predetermined. We propose procedures for designing spiral sweeping protocols that ensure the successful completion of the task, thus deriving conditions on the

Kouji Tahata, Kohei Matsuda

Previously, the diagonals-parameter symmetry model based on $f$-divergence (denoted by DPS[$f$]) was reported to be equivalent to the diagonals-parameter symmetry model regardless of the function $f$, but the proof was omitted. Here, we derive the DPS[$f$] model and the proof of the relation between the two models. We can obtain various interpretations of th

Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang

Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases. However, limited by the properties of retrieved knowledge, they still have trouble benefiting from both the knowledge relevance and distinguishment simultaneously. To address the challenge, we propose CPACE,

Emel Gürbüz, Biplab Sanyal

Although 2D solids and their vdW heterostructures have garnered much attention recently, their amorphous forms have been less explored. We present a detailed analysis of the structural, electronic, and thermal properties of 2D amorphous graphene (A-Gra), silicene (A-Si), and silicon carbide (A-SiC) using Classical Molecular Dynamics (MD) simulations and firs

Abhijit Mathad

Semileptonic charged current $b \to c l^- \overline{\nu}_l$ decays offer a compelling avenue to probe the limits of the Standard Model and investigate lepton flavor universality (LFU). While the SM maintains flavor universality, new physics models can introduce interactions that violate this universality, particularly for decays involving third-generation qu

Byung-Hak Hwang

Skewing operators play a central role in the symmetric function theory because of the importance of the product structure of the symmetric function space. The theory of noncommutative symmetric functions is a useful tool for studying expansions of a given symmetric function in terms of various bases. In this paper, we establish a further development of the t

Robert Joseph George, Noel Murasko, John C. Bowman

This paper proposes a practical and efficient solution for computing convolutions using hybrid dealiasing. It offers an alternative to explicit or implicit dealiasing and includes an optimized hyperparameter tuning algorithm that uses experience to find the optimal parameters. Machine learning algorithms and efficient heuristics are also developed to estimat

Sharan Mourya, Pavan Reddy, SaiDhiraj Amuru, Kiran Kumar Kuchi

Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get h

Tian-Wei Liu, Jiang-Bo Bai, Nicholas Fantuzzi

The bistable deployable composite boom (Bi-DCB) can achieve bistable function by storing and releasing strain energy, which has a good application prospect in space field. For example, it serves as the main support section of deployable structures (e.g., solar arrays and antennas). This paper investigates the folding stable state of the Bi-DCB through the an

Nirjhar Das, Arpan Chattopadhyay

In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP, given a set of trajectory demonstrations for the optimal policy. In this work, we seek to infer not only the reward fun

Ayush Singh Rajput, Richa Gupta

The extension of legacy business process automation beyond the bounds of specific processes is known as hyperautomation. Hyperautomation provides automation for nearly any repetitive action performed by business users by combining AI tools with RPA. It automates complex IT business processes that a company's top brains might not be able to complete. This is

Yonatan Ossia, Adar Levi, Yossef E. Panfil, Somnath Koley

Colloidal semiconductor quantum dots are robust emitters implemented in numerous prototype and commercial optoelectronic devices. However, active fluorescence color tuning, achieved so far by electric-field induced Stark effect, has been limited to a small spectral range, and accompanied by intensity reduction due to the electron-hole charge separation effec

Bhavna, Mathew A. Johnson, Ashish Kumar Pandey

We study the modulational instability of small-amplitude periodic traveling wave solutions in a dispersion generalized Ostrovsky equation. Specifically, we investigate the invertibility of the associated linearized operator in the vicinity of the origin and derive a modulational instability index that depends on the dispersion and nonlinearity. For the class

Ya-long Ren, Sheng-li Ma, Stefano Zippilli, David Vitali

Coherent long-range interactions between atoms are a prerequisite for numerous applications in the field of quantum information science, but they usually decrease exponentially with the increase in atomic separation. Here we present an appealing method to dramatically enhance the long-range atom-atom interaction mediated by a coupled-cavity array that is sub

Francesco Pase, Szymon Kobus, Deniz Gunduz, Michele Zorzi

We consider the problem of communicating a sequence of concepts, i.e., unknown and potentially stochastic maps, which can be observed only through examples, i.e., the mapping rules are unknown. The transmitter applies a learning algorithm to the available examples, and extracts knowledge from the data by optimizing a probability distribution over a set of mo

Niclas Boehmer, Piotr Faliszewski, Łukasz Janeczko, Andrzej Kaczmarczyk

We study the robustness of approval-based participatory budgeting (PB) rules to random noise in the votes. Our contributions are twofold. First, we study the computational complexity of the #Flip-Bribery problem, where given a PB instance we ask for the number of ways in which we can flip a given number of approvals in the votes, so that a specific project i

Tom M George

Credit assignment problems, for example policy evaluation in RL, often require bootstrapping prediction errors through preceding states \textit{or} maintaining temporally extended memory traces; solutions which are unfavourable or implausible for biological networks of neurons. We propose theta sequences -- chains of neural activity during theta oscillations

Bhilahari Jeevanesan

We analyze the Hilbert space connectivity of the $L$ site PXP-model by constructing the Hamiltonian matrices via a Gray code numbering of basis states. The matrices are all formed out of a single Hamiltonian-path backbone and entries on skew-diagonals. Starting from this observation, we construct an ensemble of related Hamiltonians based on random graphs wit

Samir Marouani

We introduce two notions of hyperbolicity for not necessarily K\"ahler even balanced $n$-dimensional compact complex manifolds $X$. The first, called {\it SKT hyperbolicity}, generalises Gromov's K\"ahler hyperbolicity by means of SKT metrics. The second, called {\it Gauduchon hyperbolicity} by means of Gauduchon metrics. Our first main result in this paper

Maniratnam Mandal

The need for fast, effective and accurate surveys have become increasingly necessary. A major part of the research is supported by photographic surveys which are used for capturing expansive natural surfaces using a wide range of sensors -- visual, infrared, ultrasonic, radio, etc. Because of the unpredictability and fast-varying characteristic of natural su

K. Ganguly, A. Patra

The cold start problem is a common challenge in various domains, including media use cases such as predicting viewership for newly launched shows on Over-The-Top (OTT) platforms. In this study, we propose a generic approach to tackle cold start problems by leveraging metadata and employing multi-model ensemble techniques. Our methodology includes feature eng

Jongjun M. Lee, Hyun-Woo Lee, Myung-Joong Hwang

Generating and probing the magnon squeezing is an important challenge in the field of quantum magnonics. In this work, we propose a cavity magnonics setup with an easy-axis ferromagnet to address this challenge. To this end, we first establish a mechanism for the generation of magnon squeezing in the easy-axis ferromagnet and show that the magnon squeezing c

Aleksandr Artemev

Previously it was suggested, motivated by correspondence with JT gravity, that tachyon correlators in $(2,2p+1)$ minimal Liouville gravity (MLG) in the $p\to \infty$ (semiclassical) limit should be interpreted as moduli space volumes for constant curvature surfaces with conical defects. In this work we propose that these volumes are associated with Kahler me

Yunshan Zhong, Yuyao Zhou, Fei Chao, Rongrong Ji

Arbitrary bit-width network quantization has received significant attention due to its high adaptability to various bit-width requirements during runtime. However, in this paper, we investigate existing methods and observe a significant accumulation of quantization errors caused by switching weight and activations bit-widths, leading to limited performance.

Loïck Lhote, Béatrice Markhoff, Arnaud Soulet

Large knowledge graphs combine human knowledge garnered from projects ranging from academia and institutions to enterprises and crowdsourcing. Within such graphs, each relationship between two nodes represents a basic fact involving these two entities. The diversity of the semantics of relationships constitutes the richness of knowledge graphs, leading to th

Samuel Ackerman, Axel Bendavid, Eitan Farchi, Orna Raz

Machine learning (ML) solutions are prevalent in many applications. However, many challenges exist in making these solutions business-grade. For instance, maintaining the error rate of the underlying ML models at an acceptably low level. Typically, the true relationship between feature inputs and the target feature to be predicted is uncertain, and hence sta

Shunsuke Aoki, Issei Yamamoto, Daiki Shiotsuka, Yuichi Inoue

Fully autonomous driving has been widely studied and is becoming increasingly feasible. However, such autonomous driving has yet to be achieved on public roads, because of various uncertainties due to surrounding human drivers and pedestrians. In this paper, we present an end-to-end learningbased autonomous driving system named SuperDriver AI, where Deep Neu

Mandan Naresh, Paresh Saxena, Manik Gupta

Providing a high Quality of Experience (QoE) for video streaming in 5G and beyond 5G (B5G) networks is challenging due to the dynamic nature of the underlying network conditions. Several Adaptive Bit Rate (ABR) algorithms have been developed to improve QoE, but most of them are designed based on fixed rules and unsuitable for a wide range of network conditio

Maniratnam Mandal, Venkatesh K. Subramanian

Surveillance and surveying are two important applications of empirical research. A major part of terrain modelling is supported by photographic surveys which are used for capturing expansive natural surfaces using a wide range of sensors -- visual, infrared, ultrasonic, radio, etc. A natural surface is non-smooth, unpredictable and fast-varying, and it is di

V. I. Yukalov

The review analyzes the fundamental principles which Artificial Intelligence should be based on in order to imitate the realistic process of taking decisions by humans experiencing emotions. Two approaches are compared, one based on quantum theory and the other employing classical terms. Both these approaches have a number of similarities, being principally

Masahiro Fukuda, Taisuke Ozaki

This study investigates the strain-induced structural transitions of $\eta \leftrightarrow \theta$ and the changes in electronic band structures of Au$_2$X (X=S, Se, Te, Si, Ge) and Au$_4$SSe. We focus on Au$_2$S monolayers, which can form multiple meta-stable monolayers theoretically, including $\eta$-Au$_2$S, a buckled penta-monolayer composed of a square