April 2023 arXiv papers — page 116

Yuzong Chen, Mohamed S. Abdelfattah

Deep neural network (DNN) inference using reduced integer precision has been shown to achieve significant improvements in memory utilization and compute throughput with little or no accuracy loss compared to full-precision floating-point. Modern FPGA-based DNN inference relies heavily on the on-chip block RAM (BRAM) for model storage and the digital signal p

Alberto Marchisio, Antonio De Marco, Alessio Colucci, Maurizio Martina

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships between multiple objects for image classification tasks. Other than achieving high accuracy, another relevant factor in deploying CapsNets in safety-critical applications is the robustness against input transformations and malicious adversarial attacks. In this paper, we s

Olivier Bordellès, László Tóth

In this note, we extend to a composite modulo a recent result of Chan (2016) dealing with mean values of the product of an integer and its multiplicative inverse modulo a prime number.

Wen-Feng Wu, Han-Yu Wang, Wei-Hua Wang, Da-Yong Liu

The Mn-Bi-Te family displaying magnetism and non-trivial topological properties has received extensive attention. Here, we predict that the antiferromagnetic structure of Mn$_{3}$Bi$_{2}$Te$_{6}$ with three MnTe layers is energetically stable and the magnetic coupling strength of Mn-Mn is enhanced four times compared with that in the single MnTe layer of MnB

Jesse Pajwani

We reinterpret a result of Pop and Stix on the $p$-adic section conjecture in terms of Berkovich spaces and fixed points. In doing this, we see a version of the result extends to larger classes of fields, which in turn allows us to prove a valuative section conjecture type result for a larger class of varieties. This adds to the programme to reinterpret anab

Karan Pathak, L Shalini

Water crisis is a crucial concern around the globe. Appropriate and timely maintenance of water pumps in drought-hit countries is vital for communities relying on the well. In this paper, we analyze and apply a sequential attentive deep neural architecture, TabNet, for predicting water pump repair status in Tanzania. The model combines the valuable benefits

Yisong Xiao, Tianyuan Zhang, Shunchang Liu, Haotong Qin

Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities when exposed to various noises in real-world applications. Despite the importance of evaluating the impact of quantization on robustness, existing research on this topic is limited a

Kumar Gautam

A quantum unitary gate is realized in this paper by perturbing a free charged particle in a one-dimensional box with a time- and position-varying electric field. The perturbed Hamiltonian is composed of a free particle Hamiltonian plus a perturbing electric potential such that the Schr$\ddot{o}$dinger evolution in time $T$, the unitary evolution operator of

Xiaochi Ding, Xinwei Shen, Qiuwei Wu, Liming Wang

With the development of offshore wind farms (OWFs) in far-offshore and deep-sea areas, each OWF could contain more and more wind turbines and cables, making it imperative to study high-reliability electrical collector system (ECS) for OWF. Enlightened by active distribution network, for OWF, we propose an ECS switch configuration that enables post-fault netw

Jinchuan Cui, Xiaoya Li

The $n$-vehicle exploration problem (NVEP) is a nonlinear unconstrained optimization problem. Given a fleet of $n$ vehicles with mid-trip refueling technique, the NVEP tries to find a sequence of $n$ vehicles to make one of the vehicles travel the farthest, and at last all the vehicles return to the start point. NVEP has a fractional form of objective functi

Nathan Jones, Francesco Pappalardi, Peter Stevenhagen

Under GRH, any element in the multiplicative group of a number field $K$ that is globally primitive (i.e., not a perfect power in $K^*$) is a primitive root modulo a set of primes of $K$ of positive density. For elliptic curves $E/K$ that are known to have infinitely many primes $\mathfrak p$ of cyclic reduction, possibly under GRH, a globally primitive poin

Xin Lu, Feng Chen, W. Zhu, D. N. Sheng

The square-lattice Hubbard and closely related $t$-$J$ models are considered as basic paradigms for understanding strong correlation effects and unconventional superconductivity (SC). Recent large-scale density matrix renormalization group (DMRG) simulations on the extended $t$-$J$ model have identified $d$-wave SC on the electron-doped side (with the next-n

Hans De Raedt, Mikhail I. Katsnelson, Manpreet S. Jattana, Vrinda Mehta

We take the point of view that building a one-way bridge from experimental data to mathematical models instead of the other way around avoids running into controversies resulting from attaching meaning to the symbols used in the latter. In particular, we show that adopting this view offers new perspectives for constructing mathematical models for and interpr

Atsuo Maki, Yuuki Maruyama, Yaliu Liu, Leo Dostal

Numerous accidents caused by parametric rolling have been reported on container ships and pure car carriers (PCCs). A number of theoretical studies have been performed to estimate the occurrence condition of parametric rolling in both regular and irregular seas. Some studies in random wave conditions have been the approximate extension of the occurrence cond

E. B. Menshikova, B. N. Khabibullin

Let $D$ be a domain in a finite-dimensional Euclidean space, and $H$ be a convex subcone in the convex cone of all subharmonic functions on $D$. We obtain a criterion for the existence of a lower envelope from $H$ for an arbitrary function from $H-H$.

David Ponarovsky

We show that given two arbitrary states $\ket{\psi},\ket{\phi}$ it is impossible to compute the transformation: $ \ket{\psi}\ket{\phi} \mapsto \ket{\psi}\left( \mathbb{I} - 2 \ket{\psi}\bra{\psi} \right)\ket{\phi} $ The contradiction of the existence of such operator follows by showing that using it, two players can compute the disjoints of their sets in a s

Francesco Ragusa, Giovanni Maria Farinella, Antonino Furnari

Anticipation problem has been studied considering different aspects such as predicting humans' locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture

Soumyatattwa Kar, Abhishek Bamotra, Bhavya Duvvuri, Radhika Mohanan

Cyber attacks has always been of a great concern. Websites and services with poor security layers are the most vulnerable to such cyber attacks. The attackers can easily access sensitive data like credit card details and social security number from such vulnerable services. Currently to stop cyber attacks, various different methods are opted from using two-s

Gilda Rech Bansimba, Regis Freguin Babindamana, Basile Guy R. Bossoto

In this paper we present new arithmetical and algebraic results following the work of Babindamana and al. on hyperbolas and describe in the new results an approach to attacking a RSA-type modulus based on continued fractions, independent and not bounded by the size of the private key $d$ nor the public exponent $e$ compared to Wiener's attack. When successfu

Kaito Kimura

In this paper, sufficient conditions for finitely generated modules over a commutative noetherian ring to be projective are given in terms of vanishing of Ext modules. One of the main results of this paper asserts that the Auslander--Reiten conjecture holds true for every normal ring.

Dashan Gao, Yunce Zhao, Yinghua Yao, Zeqi Zhang

Deep learning models can be fooled by small $l_p$-norm adversarial perturbations and natural perturbations in terms of attributes. Although the robustness against each perturbation has been explored, it remains a challenge to address the robustness against joint perturbations effectively. In this paper, we study the robustness of deep learning models against

James Fullwood

While in relativity theory space evolves over time into a single entity known as spacetime, quantum theory lacks a standard notion of how to encapsulate the dynamical evolution of a quantum state into a single "state over time". Recently it was emphasized in the work of Fitzsimons, Jones and Vedral that if such a state over time is to encode not only spatial

Mohammad Golshani

We state a new generic absoluteness principle, and use Shelah's memory iteration technique to show that it is consistent with the large continuum.

Rendani Mbuvha, David I. Adelani, Tendani Mutavhatsindi, Tshimangadzo Rakhuhu

Named Entity Recognition (NER) plays a vital role in various Natural Language Processing tasks such as information retrieval, text classification, and question answering. However, NER can be challenging, especially in low-resource languages with limited annotated datasets and tools. This paper adds to the effort of addressing these challenges by introducing

Anna Fujioka, Masaki Ogura, Naoki Wakamiya

The problem of guiding a flock of several autonomous agents using repulsion force exerted by a smaller number of agents is called the shepherding problem and has been attracting attention due to its potential engineering applications. Although several works propose methodologies for achieving the shepherding task in this context, most assume that sheep agent

Malak Alnimer, Khaldoun Al-Zoubi, Mohammed Al-Dolat

Let $\Gamma$ be a group, $\Re$ be a $\Gamma$-graded commutative ring with unity $1$ and $\Im$ a graded $\Re$-module. In this paper, we introduce the concept of graded weakly $J_{gr}$-semiprime submodules as a generalization of graded weakly semiprime submodules. We study several results concerning of graded weakly $J_{gr}$% -semiprime submodules. For example

Sijing Wu, Yichao Yan, Yunhao Li, Yuhao Cheng

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head

Sathya Chitturi, Zhurun Ji, Alexander Petsch, Cheng Peng

The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $\omega$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated

Shiyao Peng, Qiao He, Ducheng Peng, Xin Ouyang

Repurposing existing natural gas pipelines is a promising solution for large-scale transportation of mixed hydrogen-methane gas. However, it remains debatable whether gravitational stratification can notably affect hydrogen partial pressure in the gas mixture. To address this issue, we combined molecular dynamics simulation with thermodynamic and diffusion t

Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Guandong Xu

As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large v

Xiaonan Nie, Xupeng Miao, Zilong Wang, Zichao Yang

With the increasing data volume, there is a trend of using large-scale pre-trained models to store the knowledge into an enormous number of model parameters. The training of these models is composed of lots of dense algebras, requiring a huge amount of hardware resources. Recently, sparsely-gated Mixture-of-Experts (MoEs) are becoming more popular and have d

Linqing Li, Zhifeng Wang

Recently, knowledge tracing models have been applied in educational data mining such as the Self-attention knowledge tracing model(SAKT), which models the relationship between exercises and Knowledge concepts(Kcs). However, relation modeling in traditional Knowledge tracing models only considers the static question-knowledge relationship and knowledge-knowle

Sugumi Kanno, Jiro Soda, Akira Taniguchi

We study a scattering problem of gravitational waves (GWs) by an axion domain wall in Chern-Simons (CS) gravity. We find that circular polarization of GWs is produced after passing through the domain wall. It turns out that the circular polarization is sizable if the frequency of the GW is comparable to a critical value determined by the characteristic CS le

Mikhael Shahoud

In this paper there are several results, we prove approximation of periodic function by Fejer means and De La Vallee Poussin means in Lebesgue spaces the estimates are given in terms of function for and in terms of second continuity modulus.

Maxime Giteau, Michela F. Picardi, Georgia T. Papadakis

Heat engines cannot generally operate at maximum power and efficiency, imposing a trade-off between the two. Here, we highlight the exact nature of this trade-off for engines that exchange heat radiatively with a hot source. We derive simple analytical expressions for the performance bounds of reciprocal and nonreciprocal radiative heat engines. We also high

Michelle Iskandar, Harvey Mannering, Zhanxiang Sun, Jacqueline Matthew

Prenatal ultrasound imaging is the first-choice modality to assess fetal health. Medical image datasets for AI and ML methods must be diverse (i.e. diagnoses, diseases, pathologies, scanners, demographics, etc), however there are few public ultrasound fetal imaging datasets due to insufficient amounts of clinical data, patient privacy, rare occurrence of abn

Jeongkyun Park, Kwanghee Choi, Hyunjun Heo, Hyung-Min Park

With the advent of general-purpose speech representations from large-scale self-supervised models, applying a single model to multiple downstream tasks is becoming a de-facto approach. However, the pooling problem remains; the length of speech representations is inherently variable. The naive average pooling is often used, even though it ignores the characte

Abdaljalel Alizzi, Zurab K. Silagadze

The Majorana transformation makes it possible to reduce the Thomas-Fermi equation to a first-order differential equation. This reduction is possible due to the special scaling property of the Thomas-Fermi equation under homology transformations. Such reductions are well known in the context of stellar astrophysics, where the use of homology-invariant variabl

Simon Eberle, Hui Yu

We study global solutions to the thin obstacle problem with at most quadratic growth at infinity. We show that every ellipsoid can be realized as the contact set of such a solution. On the other hand, if such a solution has a compact contact set, we show that it must be an ellipsoid.

Juho Leinonen, Paul Denny, Stephen MacNeil, Sami Sarsa

Reasoning about code and explaining its purpose are fundamental skills for computer scientists. There has been extensive research in the field of computing education on the relationship between a student's ability to explain code and other skills such as writing and tracing code. In particular, the ability to describe at a high-level of abstraction how code

Yulin Liu, Haoran Liu, Yingda Yin, Yang Wang

Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data. Rotation, as an important quantity in computer vision, graphics, and robotics, can exhibit many ambiguities when occlusion and symmetry occur and thus dema

Yuzhen Mao, Zhun Deng, Huaxiu Yao, Ting Ye

As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of training method because they don't require accessing sensitive attributes during test time in co

Miranda C. N. Cheng, Ioana Coman, Davide Passaro, Gabriele Sgroi

We study the quantum modular properties of $\widehat Z{}^G$-invariants of closed three-manifolds. Higher depth quantum modular forms are expected to play a central role for general three-manifolds and gauge groups $G$. In particular, we conjecture that for plumbed three-manifolds whose plumbing graphs have $n$ junction nodes with definite signature and for r

Yixuan Qiu, Xiao Wang

Sampling from high-dimensional distributions is a fundamental problem in statistical research and practice. However, great challenges emerge when the target density function is unnormalized and contains isolated modes. We tackle this difficulty by fitting an invertible transformation mapping, called a transport map, between a reference probability measure an

Satya Pratheek Tata, Subhankar Mishra

Generative Adversarial Networks (GANs) have emerged as a significant player in generative modeling by mapping lower-dimensional random noise to higher-dimensional spaces. These networks have been used to generate high-resolution images and 3D objects. The efficient modeling of 3D objects and human faces is crucial in the development process of 3D graphical e

Zhi Gao, Chen Xu, Feng Li, Yunde Jia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this

Jincheng Zhang, Kevin Brink, Andrew R Willis

Infrared thermography has been widely used in several domains to capture and measure temperature distributions across surfaces and objects. This methodology can be further expanded to 3D applications if the spatial distribution of the temperature distribution is available. Structure from Motion (SfM) is a photometric range imaging technique that makes it pos

Baoyi Chen, Bo Tong

We employ the Boltzmann transport model to study the sequential suppression pattern of $\Upsilon(1S,2S,3S)$ states in both small (p-Pb) and the large (Pb-Pb) collision systems at $\sqrt{s_{NN}}=5.02$ TeV. The cold nuclear matter effects happen before the formation of bottomonium, which is the same for different bottomonium states $\Upsilon(1S,2S,3S)$. The se

Hengjie Yu, Dan Luo, Sam F. Y. Li, Maozhen Qu

Crops are constantly challenged by different environmental conditions. Seed treatment by nanomaterials is a cost-effective and environmentally-friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly inc

Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas, Ryan J. Tibshirani

De Finetti's theorem, also called the de Finetti-Hewitt-Savage theorem, is a foundational result in probability and statistics. Roughly, it says that an infinite sequence of exchangeable random variables can always be written as a mixture of independent and identically distributed (i.i.d.) sequences of random variables. In this paper, we consider a weighted

Vladimir Vasilyev, Alexander Vasilyev, Anastasia Mashinets

We study a general discrete boundary value problem in Sobolev--Slobodetskii spaces in a plane quadrant and reduce it to a system of integral equations. We show a solvability of the system for a small size of discreteness starting from a solvability of its continuous analogue.

Toru Okuda, Chandra B. Singh, Ramiz Aktar

We examine the time delay between radio and X-ray and between narrow radio frequency flares in Sagittarius A* (Sgr A*), from analyses of the synchrotron, bremsstrahlung and monochromatic luminosity curves. Using the results of 2D relativistic radiation magnetohydrodynamic (MHD) simulations based on the shock oscillation model, we find three types of time del

Hiroki Ogata, Luis Iván Hernández Ruíz, Kouji Yano

The asymptotic normality in multi-dimension of the nonparametric estimator of the transition probabilities of a Markov renewal chain is proved, and is applied to that of other nonparametric estimators involved with the associated semi-Markov chain.

Verena Ingrid Prantl, Torsten Moeller, Laura Koesten

In contemporary discourse, logos (reason) and, more recently, ethos (credibility) in data communication have been discussed extensively. While the concept of Pathos has enjoyed great interest in the VIS community over the past few years, its connection to similar but relevant concepts like aesthetics and rhetoric remains unexplored. In this paper, we provide

Florian Effenberg, Shota Abe, Gregory Sinclair, Tyler Abrams

Experiments have been conducted in the DIII-D tokamak to explore the in-situ growth of silicon-rich layers as a potential technique for real-time replenishment of surface coatings on plasma-facing components (PFCs) during steady-state long-pulse reactor operation. Silicon (Si) pellets of 1 mm diameter were injected into low- and high-confinement (L-mode and

Xinzhu Li, Hailin Wang

We report experimental studies of a driven spin-mechanical system, in which a nitrogen vacancy (NV) center couples to out-of-plane vibrations of a diamond cantilever through the excited-state deformation potential. Photoluminescence excitation studies show that in the unresolved sideband regime and under strong resonant mechanical driving, the excitation spe

Sawani Datta, Ram Prakash Pandeya, Arka Bikash Dey, A. Gloskovskii

We investigate the electronic structure of an antiferromagnetic Kondo lattice system CeAgAs2 employing hard x-ray photoemission spectroscopy. CeAgAs2, an orthorhombic variant of HfCuSi2 structure, exhibits antiferromagnetic ground state, Kondo like resistivity upturn and compensation of magnetic moments at low temperatures. The photoemission spectra obtained

Ritesh Goenka, Kenneth Moore, Ethan Patrick White

We obtain new upper and lower bounds on the number of unit perimeter triangles spanned by points in the plane. We also establish improved bounds in the special case where the point set is a section of the integer grid.

Jialin Gong, Guangqian Ding, Chengwu Xie, Jianhua Wang

When the spin-orbit coupling (SOC) is absent, almost all the proposed half-metals with the twofold degenerate nodal points at the K (or K') in two-dimensional (2D) materials are misclassified as "Dirac half-metals" owing to the way graphene was utilized in the earliest studies. Actually, each band crossing point at K or K' is described by a 2D Weyl Hamiltoni

Sawani Datta, Ram Prakash Pandeya, Arka Bikash Dey, A. Gloskovskii

We investigate the electronic structure of a novel Kondo lattice system CeCuX2 (X = As/Sb) employing high resolution depth-resolved photoemission spectroscopy of high quality single crystalline materials. CeCuSb2 and CeCuAs2 represent different regimes of the Doniach phase diagram exhibiting Kondo-like transport properties and CeCuSb2 is antiferromagnetic (T

Zhimin Zhu, Jianguo Zhao, Tong Mu, Yuliang Yang

In deep learning, Multi-Layer Perceptrons (MLPs) have once again garnered attention from researchers. This paper introduces MC-MLP, a general MLP-like backbone for computer vision that is composed of a series of fully-connected (FC) layers. In MC-MLP, we propose that the same semantic information has varying levels of difficulty in learning, depending on the

Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman

Spurious correlations that degrade model generalization or lead the model to be right for the wrong reasons are one of the main robustness concerns for real-world deployments. However, mitigating these correlations during pre-training for large-scale models can be costly and impractical, particularly for those without access to high-performance computing res

Peter Moeck

The reader is informed about a method for the objective identification of the plane symmetry group of a "noisy" crystal pattern. Without giving numerical details, this information theory based method is applied to two beautiful pieces of graphic art. The plane symmetry group identifications distinguish between genuine symmetries and pseudosymmetries as a byp

Subhayan Maity, Sujayita Bakra

Emergent scenario of cosmic evolution is a topic of great interest in recent cosmology, especially because it describes a non-singular origin of the Universe unlike the Big-Bang models. This types of cosmic evolution pattern have already been established through the non-equilibrium thermodynamic prescription. But those models are phenomenological and require

Heng-Jin Liu, Hui-Gan Cheng, Zhao-Qing Feng

Within the framework of the quantum molecular dynamics transport model, the collective flows of clusters and pions in heavy-ion collisions have been systematically investigated. The clusters are recognized by the Wigner phase-space density approach at the stage of freeze out in nuclear collisions, i.e., deuteron, triton, $^{3}$He and $\alpha$. The directed a

Jie Zhou

The generating series of Gromov-Witten invariants of elliptic curves can be expressed in terms of multi-variable elliptic functions by works of Bloch-Okounkov and Okounkov-Pandharipande. In this work we give new sum-over-partitions formulas for these generating series and show that they are configuration space integrals of cohomology classes constructed from

Hao Zhang, Yu-Chen Wang, Tong-Jie Zhang, Ting-ting Zhang

Gaussian Process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner. In this study, we compare two methods for GP kernel selection: Approximate Bayesian Computation (ABC) Rejection and nested sampling. We analyze three types of data: cosmic Chronometer data (CC), Type Ia Supernovae (S

Gensheng Pei, Yazhou Yao, Fumin Shen, Dan Huang

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of introducing motion information, such as o

Z. -H. Shang, S. -Y. Mu, K. -H Ji, Z. -P. Qiang

To address the problem of the low accuracy of transverse velocity field measurements for small targets in high-resolution solar images, we proposed a novel velocity field measurement method for high-resolution solar images based on PWCNet. This method transforms the transverse velocity field measurements into an optical flow field prediction problem. We eval

Mathav Murugan

We study reflected diffusion on uniform domains where the underlying space admits a symmetric diffusion that satisfies sub-Gaussian heat kernel estimates. A celebrated theorem of Jones (Acta Math. 1981) states that uniform domains in Euclidean space are extension domains for Sobolev spaces. In this work, we obtain a similar extension property for metric spac

Zhaolin Ren, Tongzheng Ren, Haitong Ma, Na Li

This paper proposes an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear stochastic systems. This method reveals an infinite-dimensional feature representation induced by the system's nonlinear stochastic dynamics, enabling a linear representation of the state-action value function. For practical implementation, this repr

Fang Wu, Shuting Jin, Siyuan Li, Stan Z. Li

Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical data is extremely laborious. To surmount this data sparsity challenge, we present an instructive learning algorithm named InstructMol to measure pseudo-labels' reliability and help t

Takaaki Nomura, Hiroshi Okada

We discuss radiative neutrino mass models with a general lepton flavor dependent $U(1)$ gauge symmetry. The scotogenic model is adopted for neutrino mass generation in which $Z_2$ odd singlet fermions and an inert scalar doublet are introduced. A lepton flavor dependent local $U(1)$ symmetry is applied to realize two-zero texture of a Majorana mass matrix of

Nicholas C. Henderson, Zhongzhe Ouyang

Parameter estimation in logistic regression is a well-studied problem with the Newton-Raphson method being one of the most prominent optimization techniques used in practice. A number of monotone optimization methods including minorization-maximization (MM) algorithms, expectation-maximization (EM) algorithms and related variational Bayes approaches offer a

Tingting Liao, Xiaomei Zhang, Yuliang Xiu, Hongwei Yi

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit

C. P. Burgess, F. Quevedo

Recently there has been an interesting revival of the idea to use large extra dimensions to address the dark energy problem, exploiting the (true) observation that towers of states with masses split, by $M^2_N = f(N) m^2,$ with $f$ an unbounded function of the integer $N$, sometimes contribute to the vacuum energy only an amount of order $m^D$ in $D$ dimensi

Alejandra Arias-Salazar, Andrés Gutiérrez, Xavier Mancero, Stalyn Guerrero-Gómez

This paper proposes a methodology to obtain estimates in small domains when the target is a composite indicator. These indicators are of utmost importance for studying multidimensional phenomena, but little research has been done on how to obtain estimates of these indicators under the small area context. Composite indicators are particularly complex for thi

Jun Yu, Shenshen Du, Guochen Xie, Renjie Lu

Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a fundamental task in remote sensing that can enrich the dataset by fusing information from different sources. Recently, many methods have been proposed to tackle this task, but they are still difficult to complete the conversion from low-resolution images to high-resolution images.

Nicolas Moreno, Suzana Nunes, Victor Calo

Isoporous membranes made from diblock copolymers have numerous applications, including water treatment and protein separation, and are successfully produced at a laboratory scale under controlled conditions. However, achieving optimal conditions for membrane preparation remains a challenge due to the complexity of the involved phenomena. Experimental studies

Zhen Wu, Yizhe Lu, Xinyu Dai

Multimodal speech emotion recognition aims to detect speakers' emotions from audio and text. Prior works mainly focus on exploiting advanced networks to model and fuse different modality information to facilitate performance, while neglecting the effect of different fusion strategies on emotion recognition. In this work, we consider a simple yet important pr

Ruiqiang Liu, Qiqiang Zhong, Mengmeng Cui, Hanjie Mai

In recent years, short Text Matching tasks have been widely applied in the fields ofadvertising search and recommendation. The difficulty lies in the lack of semantic information and word ambiguity caused by the short length of the text. Previous works have introduced complement sentences or knowledge bases to provide additional feature information. However,

S Suryavardan, Shreyash Mishra, Parth Patwa, Megha Chakraborty

The internet gives the world an open platform to express their views and share their stories. While this is very valuable, it makes fake news one of our society's most pressing problems. Manual fact checking process is time consuming, which makes it challenging to disprove misleading assertions before they cause significant harm. This is he driving interest

Kosta Dakic, Bassel Al Homssi, Sumeet Walia, Akram Al-Hourani

With the rapid growth of IoT networks, ubiquitous coverage is becoming increasingly necessary. Low Earth Orbit (LEO) satellite constellations for IoT have been proposed to provide coverage to regions where terrestrial systems cannot. However, LEO constellations for uplink communications are severely limited by the high density of user devices, which causes a

Chen Cheng, Qingping Zhou

This paper investigates the application of unsupervised learning methods for computed tomography (CT) reconstruction. To motivate our work, we review several existing priors, namely the truncated Gaussian prior, the $l_1$ prior, the total variation prior, and the deep image prior (DIP). We find that DIP outperforms the other three priors in terms of represen

Amanda Howard, Yucheng Fu, Panos Stinis

We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual learning method shows robust results that l

Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu

This paper demonstrates how OpenAI's ChatGPT can be used in a few-shot setting to convert natural language instructions into a sequence of executable robot actions. The paper proposes easy-to-customize input prompts for ChatGPT that meet common requirements in practical applications, such as easy integration with robot execution systems and applicability to

Dongjie Wang, Chang-Tien Lu, Xinyue Ye, Tan Yigitcanlar

The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental p

Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user prefe

Jiajun Huang, Sheng Di, Xiaodong Yu, Yujia Zhai

With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a critical bottleneck in large-scale distributed and parallel processing. The large message size in MPI collectives is particularly concerning because it can significantly degrade the ove

Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stärk

Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulated protein-small molecule docking as a generative problem with significant performance boosts over both traditional and deep learning baselines. In this work, we propose a similar

Pavel Sekatski, Florian Giraud, Roope Uola, Nicolas Brunner

This work explores the asymmetry of quantum steering in a setup using high-dimensional entanglement. We construct entangled states with the following properties: $(i)$ one party (Alice) can never steer the state of the other party (Bob), considering the most general measurements, and $(ii)$ Bob can strongly steer the state of Alice, demonstrating genuine hig

David Cruz-Uribe

In this article we give an overview of the problem of finding sharp constants in matrix weighted norm inequalities for singular integrals, the so-called matrix A2 conjecture. We begin by reviewing the history of the problem in the scalar case, including a sketch of the proof of the scalar A2 conjecture. We then discuss the original, qualitative results for s

Mengmou Li, Khaled Laib, Takeshi Hatanaka, Ioannis Lestas

This paper presents a comprehensive convergence analysis for the mirror descent (MD) method, a widely used algorithm in convex optimization. The key feature of this algorithm is that it provides a generalization of classical gradient-based methods via the use of generalized distance-like functions, which are formulated using the Bregman divergence. Establish

Lihua Dong, Fulong Wang, Buyun Chen, Chenliang Xia

Plasmonic microbubbles produced by laser irradiated gold nanoparticles (GNPs) in various liquids have emerged in numerous innovative applications. The nucleation of these bubbles inherently involves rich phenomena. In this paper, we systematically investigate the physicochemical hydrodynamics of plasmonic bubbles upon irradiation of a continuous wave (CW) la

Aditi Kar Gangopadhyay, Mansi, Bimal Mandal, Aleksandr Kutsenko

The Walsh--Hadamard spectrum of a bent function uniquely determines a dual function. The dual of a bent function is also bent. A bent function that is equal to its dual is called a self-dual function. The Hamming distance between a bent function and its dual is related to its Rayleigh quotient. Carlet, Danielsen, Parker, and Sole studied Rayleigh quotients o

A. Belhaj, H. Belmahi, M. Benali, Y. Hassouni

Motivated by M-theory compactifications, we investigate optical properties of black holes in the Starobinsky-Bel-Robinsion gravity. Precisely, we study the shadows and the deflection angle of light rays by non-rotating and rotating black holes in such a novel gravity. We start by discussing the shadows of the Schwarzschild-type solutions. As expected, we obt

Alexander A. Milner, V. A. Apkarian, Valery Milner

Molecules immersed in liquid helium are excellent probes of superfluidity. Their electronic, vibrational and rotational dynamics provide valuable clues about the superfluid at the nanoscale. Here we report on the experimental study of the laser-induced rotation of helium dimers inside the superfluid $^4\mathrm{He}$ bath at variable temperature. The coherent

ALICE TPC Collaboration

To operate the ALICE Time Projection Chamber in continuous mode during the Run~3 and Run~4 data-taking periods of the Large Hadron Collider, the multi-wire proportional chamber-based readout was replaced with gas-electron multipliers. As expected, the detector performance is affected by the so-called common-mode effect, which leads to significant baseline fl

Abdelrahman S. Elgamal, Osama Z. Aletri, Barzan A. Yosuf, Ahmad Adnan Qidan

Visible light communication (VLC) is a promising solution to satisfy the extreme demands of emerging applications. VLC offers bandwidth that is orders of magnitude higher than what is offered by the radio spectrum, hence making best use of the resources is not a trivial matter. There is a growing interest to make next generation communication networks intell

Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong

Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capa