April 2023 arXiv papers — page 92

Thomas Feller, Tim S. Lyon, Piotr Ostropolski-Nalewaja, Sebastian Rudolph

We propose a generic framework for establishing the decidability of a wide range of logical entailment problems (briefly called querying), based on the existence of countermodels that are structurally simple, gauged by certain types of width measures (with treewidth and cliquewidth as popular examples). As an important special case of our framework, we ident

Chen Jiang, Yu Zou

For a real number $0<\epsilon<1/3$, we show that the anti-canonical volume of an $\epsilon$-klt Fano $3$-fold is at most $3200/\epsilon^4$ and the order $O(1/\epsilon^4)$ is sharp.

Kangliang Liu, Xiangcheng Du, Sijie Liu, Yingbin Zheng

Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases. However, directly applying the transformer structure to remove noise is challenging because its complexity grows quadratically with the spatial resolution. In this paper, we propose an efficie

Shanshan Zhong, Zhongzhan Huang, Wushao Wen, Jinghui Qin

The structural re-parameterization (SRP) technique is a novel deep learning technique that achieves interconversion between different network architectures through equivalent parameter transformations. This technique enables the mitigation of the extra costs for performance improvement during training, such as parameter size and inference time, through these

Jonathan Hans Soeseno, Sergio González, Trista Pei-Chun Chen

This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models. We start with the datasets of different issues and apply

Davi R. Rodrigues, Rayan Moukhader, Yanxiang Luo, Bin Fang

Spiking neural networks aim to emulate the brain's properties to achieve similar parallelism and high-processing power. A caveat of these neural networks is the high computational cost to emulate, while current proposals for analogue implementations are energy inefficient and not scalable. We propose a device based on a single magnetic tunnel junction to per

Akshay Gadi Patil, Yiming Qian, Shan Yang, Brian Jackson

The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures. We present a learning framework to recover the shape interiors (RoSI) of existing 3D models with only their exteriors from multi-view and

Michael Rodler, David Paaßen, Wenting Li, Lukas Bernhard

Smart contracts are increasingly being used to manage large numbers of high-value cryptocurrency accounts. There is a strong demand for automated, efficient, and comprehensive methods to detect security vulnerabilities in a given contract. While the literature features a plethora of analysis methods for smart contracts, the existing proposals do not address

Kazuki Koga

A novel algorithm is proposed for quantitative comparisons between compact surfaces embedded in the three-dimensional Euclidian space. The key idea is to identify those objects with the associated surface measures and compute a weak distance between them using the Fourier transform on the ambient space. In particular, the inhomogeneous Sobolev norm of negati

Jordan Wu, Weiyao Ke, Xin-Nian Wang

A simple model for medium modification of the jet function can be used to extract the jet energy loss distribution through a parameterized form. We carry out a comprehensive Bayesian analysis of the world data on single inclusive jet spectra in heavy-ion collisions at both RHIC and LHC energies. We extract the average jet energy loss $\langle \Delta E\rangle

Dennis Wuhrer, Levente Rózsa, Ulrich Nowak, Wolfgang Belzig

We investigate squeezing of magnons in a conical spin spiral configuration. We find that while the energy of magnons propagating along the $\boldsymbol{k}$ and the $-\boldsymbol{k}$ directions can be different due to the non-reciprocal dispersion, these two modes are connected by the squeezing, hence can be described by the same squeezing parameter. The sque

Nina Tahmasebi, Haim Dubossarsky

In this chapter we provide an overview of computational modeling for semantic change using large and semi-large textual corpora. We aim to provide a key for the interpretation of relevant methods and evaluation techniques, and also provide insights into important aspects of the computational study of semantic change. We discuss the pros and cons of different

Zhaoliang Chen, Zhihao Wu, Luying Zhong, Claudia Plant

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings.Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Netw

Chien-Pin Liu, Ju-Hsuan Li, En-Ping Chu, Chia-Yeh Hsieh

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to th

Luigi Piccinelli, Christos Sakaridis, Fisher Yu

Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of full geometric constraints. Although a scene can consist of millions of pixels, there are fewer high-level patterns. We propose iDisc to learn those patterns with

Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira

When choosing between competing symbolic models for a data set, a human will naturally prefer the "simpler" expression or the one which more closely resembles equations previously seen in a similar context. This suggests a non-uniform prior on functions, which is, however, rarely considered within a symbolic regression (SR) framework. In this paper we develo

Jiebin Peng, Zi Wang, Jie Ren

The magic angle twisted bilayer systems give rise to many exotic phenomena in two-dimensional electronic or photonic platforms. Here, we study the twisted near-field energy radiation between graphene metasurfaces with nonequilibrium drifted Dirac electrons. Anomalously, we find unconventional radiative flux that directs heat from cold to hot. This far-from-e

Brenda Elizabeth Olivas-Padilla, Alina Glushkova, Sotiris Manitsaris

The analysis of human movements has been extensively studied due to its wide variety of practical applications, such as human-robot interaction, human learning applications, or clinical diagnosis. Nevertheless, the state-of-the-art still faces scientific challenges when modeling human movements. To begin, new models must account for the stochasticity of huma

Juan Carlos Ruiz-Sicilia, Marco Di Renzo, Merouane Debbah, H. Vincent Poor

The synergy of metasurface-based holographic surfaces (HoloS) and reconfigurable intelligent surfaces (RIS) is considered a key aspect for future communication networks. However, the optimization of dynamic metasurfaces requires the use of numerical algorithms, for example, based on the singular value decomposition (SVD) and gradient descent methods, which a

C. R. Argüelles, E. A. Becerra-Vergara, J. A. Rueda, R. Ruffini

The nature of dark matter (DM) is one of the most relevant questions in modern astrophysics. We present a brief overview of recent results that inquire into a possible fermionic quantum nature of the DM particles, focusing mainly on the interconnection between the microphysics of the neutral fermions and the macrophysical structure of galactic halos, includi

Yuliya Mishura, Kostiantyn Ralchenko

We introduce fractional diffusion Bessel process with Hurst index $H\in(0,\frac12)$, derive a stochastic differential equation for it, and study the asymptotic properties of its sample paths.

Martí Caro, Hamid Tabani, Jaume Abella, Francesc Moll

The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in

Teng Zhang, Kang Li

Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks. This paper investigates adversarial training and data augmentation with noise in the context of regularized regression in a reproducing kernel Hilbert space (RKHS). We establish the limiting formula for these techniques as the at

Mohammed Barhoush, Louis Salvail

Signing quantum messages has long been considered impossible even under computational assumptions. In this work, we challenge this notion and provide three innovative approaches to sign quantum messages that are the first to ensure authenticity with public verifiability. Our contributions can be summarized as follows: 1) We introduce the concept of time-depe

Jia Zhao, Senrong Xu, Yu Qiao

In this paper, we introduce the notions of relative Rota-Baxter operators of weight $1$ on Lie-Yamaguti algebras, and post-\LYA s, which is an underlying algebraic structure of relative Rota-Baxter operators of weight $1$. We give the relationship between these two algebraic structures. Besides, we establish the cohomology theory of relative Rota-Baxter oper

Sam Bachhuber, Aaron Benda, Benjamin Christophel, Tamás Darvas

In previous work, Darvas-George-Smith obtained inequalities between the large scale asymptotic of the $J$ functional with respect to the $d_1$ metric on the space of toric K\"ahler metrics/rays. In this work we prove sharpness of these inequalities on all toric K\"ahler manifolds, and study the extremizing potentials/rays. On general K\"ahler manifolds we sh

Jinglei Shi, Yihong Xu, Christine Guillemot

Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this context, spatial information is defined as features that remain static regardless of perspectives, while angular information refers to features that vary between viewpoints. We propose a

Anant Jain, Lalan Kumar

Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding

Masahiro Teraoka, Yuzuki Ono, Hojun Im

We have demonstrated a simple and accurate method for characterizing the capacitance of Graphene/n-Si Schottky junction solar cells (GSSCs) which embed the metal-oxide-semiconductor (MOS) capacitor. We measured two types of GSSCs, one with thermal annealing treatments (w-a) and one without (wo-a). It was found that the wo-a GSSC exhibits a two-step feature i

Xavier Cucurull, Anaís Garrell

In this paper, we present an efficient method to incrementally learn to classify static hand gestures. This method allows users to teach a robot to recognize new symbols in an incremental manner. Contrary to other works which use special sensors or external devices such as color or data gloves, our proposed approach makes use of a single RGB camera to perfor

Justus Bruckamp, Markus Chimani, Martina Juhnke

In this paper, we study the connected blocks polytope, which, apart from its own merits, can be seen as the generalization of certain connectivity based or Eulerian subgraph polytopes. We provide a complete facet description of this polytope, characterize its edges and show that it is Hirsch. We also show that connected blocks polytopes admit a regular unimo

Yi-Jun Chang, Oren Hecht, Dean Leitersdorf

In theoretical computer science, it is a common practice to show existential lower bounds for problems, meaning there is a family of pathological inputs on which no algorithm can do better. However, most inputs of interest can be solved much more efficiently, giving rise to the notion of universally optimal algorithms, which run as fast as possible on every

Helen W. J. Zhang, Ying Zhong

In this paper, we obtain asymptotic formulas for $k$-crank of $k$-colored partitions. Let $M_k(a, c; n)$ denote the number of $k$-colored partitions of $n$ with a $k$-crank congruent to $a$ mod $c$. For the cases $k=2,3,4$, Fu and Tang derived several inequality relations for $M_k(a, c; n)$ using generating functions. We employ the Hardy-Ramanujan Circle Met

Prerna Singh, Ayush Tripathi, Lalan Kumar, Tapan Kumar Gandhi

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case

Anthony Arfaux, Panayotis Lavvas

We present a parameterization for the eddy diffusion profile of gas giant exoplanets based on physical phenomena and we explore how the parameterized eddy profile impacts the chemical composition, the thermal structure, the haze microphysics, and the transit spectra of 8 hot-Jupiters. Our eddy parameterization depends on the planetary intrinsic temperature (

Jonathan Gal, Maytal B Szabo, Ori Rottenstreich

In the last few years several papers investigated selfish mine attacks, most of which assumed that every miner that is not part of the selfish mine pool will continue to mine honestly. However, in reality, remaining honest is not always incentivized, particularly when another pool is employing selfish mining or other deviant strategies. In this work we explo

Mariella Dreissig, Dominik Scheuble, Florian Piewak, Joschka Boedecker

Autonomous vehicles rely on a variety of sensors to gather information about their surrounding. The vehicle's behavior is planned based on the environment perception, making its reliability crucial for safety reasons. The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception f

Dominic Breit

We consider the steady Stokes equations in a bounded domain with forcing in divergence form supplemented with no-slip boundary conditions. We provide a maximal regularity theory in Campanato spaces (inlcuding $\mathrm{BMO}$ and $C^{0,\alpha}$ for $0<\alpha <1$ as special cases) under minimal assumptions on the regularity of the underlying domain. Our approac

Xiaoxiong Lu, Eric Yong Xie, Chengqing Li

Periodicity analysis of sequences generated by a deterministic system is a long-standing challenge in both theoretical research and engineering applications. To overcome the inevitable degradation of the Logistic map on a finite-precision circuit, its numerical domain is commonly converted from a real number field to a ring or a finite field. This paper stud

Anders T. Sandnes, Bjarne Grimstad, Odd Kolbjørnsen

Soft-sensors are gaining popularity due to their ability to provide estimates of key process variables with little intervention required on the asset and at a low cost. In oil and gas production, virtual flow metering (VFM) is a popular soft-sensor that attempts to estimate multiphase flow rates in real time. VFMs are based on models, and these models requir

Xinyun Zhang, Lanqing Hong

Few-shot learning (FSL) via customization of a deep learning network with limited data has emerged as a promising technique to achieve personalized user experiences on edge devices. However, existing FSL methods primarily assume independent and identically distributed (IID) data and utilize either computational backpropagation updates for each task or a comm

Lei Zu, Chi Zhang, Hou-Zun Chen, Wei Wang

We explore the potential of precision cosmological data to study non-minimal dark sectors by updating the cosmological constraint on the mirror twin Higgs model (MTH). The MTH model addresses the Higgs little hierarchy problem by introducing dark sector particles. In this work, we perform a Bayesian global analysis that includes the latest cosmic shear measu

Jens Dörpinghaus, David Samray, Robert Helmrich

As a result of transformation processes, the German labor market is highly dependent on vocational training, retraining and continuing education. To match training seekers and offers, we present a novel approach towards the automated detection of access to education and training in German training offers and advertisements. We will in particular focus on (a)

Yaowei Li, Ruijie Quan, Linchao Zhu, Yi Yang

Large-scale pre-training has brought unimodal fields such as computer vision and natural language processing to a new era. Following this trend, the size of multi-modal learning models constantly increases, leading to an urgent need to reduce the massive computational cost of finetuning these models for downstream tasks. In this paper, we propose an efficien

Zhuo Su, Jiehua Zhang, Tianpeng Liu, Zhen Liu

This paper proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution. It explores the broad "middle spectrum" area between channel pruning and conventional grouped convolution. Compared with channel pruning, MSGC can retain most of the informati

Haoran Xue, Z. Y. Chen, Zheyu Cheng, J. X. Dai

Band topology of materials describes the extent Bloch wavefunctions are twisted in momentum space. Such descriptions rely on a set of topological invariants, generally referred to as topological charges, which form a characteristic class in the mathematical structure of fiber bundles associated with the Bloch wavefunctions. For example, the celebrated Chern

Renu Raman Sahu, Alwar Samy Ramasamy, Santosh Bhonsle S, Mark Vailshery D C

Dynamically tunable nanoengineered structures for coloration show promising applications in sensing, displays, and communication. However, their potential challenge remains in having a scalable manufacturing process over large scales in tens of cm of area. For the first time, we report a novel approach for fabricating chromogenic nanostructures that respond

Denise J. Erb, Daniel A. Pearson, Tomáš Škereň, Martin Engler

We investigate the morphologies of the Ge(001) surface that are produced by bombardment with a normally incident, broad argon ion beam at sample temperatures above the recrystallization temperature. Two previously-observed kinds of topographies are seen, i.e., patterns consisting of upright and inverted rectangular pyramids, as well as patterns composed of s

G. Valle, M. Dell'Omodarme, P. G. Prada Moroni, S. Degl'Innocenti

Relying on precise observations for the CPD-54 810 binary system, we investigate the robustness of the estimated age and convective core overshooting for a system with both stars on the main sequence (MS). [...] We adopt the SCEPtER pipeline, based on grids of stellar models computed for a different initial chemical composition and convective core overshooti

Hongguang Sun, Linyi Zhang, Tony Q. S. Quek, Xijun Wang

In this paper, we study the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in the downlink cellular-connected UAV networks with the coexistence of aerial users (AUs) and terrestrial users (TUs). Based on the comparison of the desired signal strength to the dominant interference strength, the A

Jingkai Hong, Arash Fayyazi, Amirhossein Esmaili, Mahdi Nazemi

Recent efforts to improve the performance of neural network (NN) accelerators that meet today's application requirements have given rise to a new trend of logic-based NN inference relying on fixed-function combinational logic (FFCL). This paper presents an innovative optimization methodology for compiling and mapping NNs utilizing FFCL into a logic processor

Sergey V. Uchaikin, Boris I. Ivanov, Jinmyeong Kim, Çağlar Kutlu

The axion is expected to solve the strong CP problem of quantum chromodynamics and is one of the leading candidates for dark matter. CAPP in South Korea has several axion search experiments based on cavity haloscopes in the frequency range of 1-6 GHz. The main effort focuses on operation of the experiments with the highest possible sensitivity. It requires m

Arindam Das, Sanjoy Mandal, Sujay Shil

We investigate the possibilities of probing the electroweak scale seesaw scenarios such as type-I, type-II and type-III seesaw at $e^-\gamma$ and $\gamma\gamma$ colliders. For the case of type-I seesaw, the heavy neutrinos can be produced at $e^{-}\gamma$ colliders in association with a $W$ boson. We study a variety of final states in this case including sin

Hongchen Tan, Baocai Yin, Kun Wei, Xiuping Liu

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure

J. P. van Soest, C. A. Potts, S. Peiter, A. Sanz Mora

Environmental noise coupling to mechanical experiments often introduces low-frequency fluctuations to the resonators, adding noise to measurements and reducing signal to noise. To counter these fluctuations, we demonstrate a dynamic feedback system implemented by the locking of a microwave drive to the noisy cavity. A homodyne interferometer scheme monitors

Metin Gürses, Aslı Pekcan

Bilinearization of a given nonlinear partial differential equation is very important not only to find soliton solutions but also to obtain other solutions such as the complexitons, positons, negatons, and lump solutions. In this work we study the bilinearization of nonlinear partial differential equations in $(2+1)$-dimensions. We write the most general sixt

Balder ten Cate, Víctor Dalmau, Phokion G. Kolaitis, Wei-Lin Wu

A query algorithm based on homomorphism counts is a procedure for determining whether a given instance satisfies a property by counting homomorphisms between the given instance and finitely many predetermined instances. In a left query algorithm, we count homomorphisms from the predetermined instances to the given instance, while in a right query algorithm w

Yuanyuan Feng, Lei Li

We study in this paper the monotonicity properties of the numerical solutions to Volterra integral equations with nonincreasing completely positive kernels on nonuniform meshes. There is a duality between the complete positivity and the properties of the complementary kernel being nonnegative and nonincreasing. Based on this, we propose the ``complementary m

Qianhan Zeng, Yingqiu Zhu, Xuening Zhu, Feifei Wang

Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model seriously. To address this issue, we propose an improved Naive Bayes method for text classification. It is analytically simple and free of subjective judgements on the correct and incor

Jeonghee Rho, Aravind P. Ravi, Le Ngoc Tram, Thiem Hoang

We present polarization observations of the young supernova remnant (SNR) Cas A using the High-resolution Airborne Wideband Camera-Plus (HAWC+) instrument onboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). The polarization map at 154 microns reveals dust grains with strong polarization fractions (5 - 30 percent), supporting previous measur

Jinwon Choi, Jooyeon Park

In this paper, we determine the graphs which have the minimal spectral radius among all the connected graphs of order $n$ and the independence number $\lceil\frac{n}{2}\rceil-1.$

Jonathan Breuer, Hynek Kovařík

It is well-known that, due to the interaction between the spin and the magnetic field, the two-dimensional Pauli operator has an eigenvalue $0$ at the threshold of its essential spectrum. We show that when perturbed by an effectively positive perturbation, $V$, coupled with a small parameter $\varepsilon$, these eigenvalues become resonances. Moreover, we de

Luis Iván Hernández Ruíz, Kouji Yano

A cluster representation for a Hawkes process with renewal immigration is obtained. The centre and satellite processes are indicated as a renewal process and generalized branching processes respectively. It is confirmed that the proposed construction indeed represents a cluster process and it is verified that it admits the desired intensity. Finally, the pro

Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan

We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, we utilize the holistic priors, includin

Jielin Qiu, Jiacheng Zhu, Shiqi Liu, William Han

Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies. Despite the growing interest, most current studies focus solely on classification or regression tasks, which overlook a crucial aspect of clinical cardio-disease diagnosis: the diagnostic report generated by experie

J. Zare, S. E. Hosseini, M. R. Rastan

The meteorological conditions markedly affect the energy efficiencies and cost/power rate of the wind turbines. This study numerically investigates the performance of the National Renewable Energy Laboratory (NREL) Phase VI wind turbine, designed to be insusceptible to surface roughness, undergoing either clean or dusty air. First, the numerical approach is

Sergey Smirnov

Nonequilibrium states driven by both electric bias voltages $V$ and temperature differences $\Delta T$ (or thermal voltages $eV_T\equiv k_B\Delta T$) are unique probes of various systems. Whereas average currents $I(V,V_T)$ are traditionally measured in majority of experiments, an essential part of nonequilibrium dynamics, stored particularly in fluctuations

Fan Feng, Chen Liang, Dongdong Chen, Ke Du

Based on image moment theory, an approach for space-variant Shack-Hartmann wavefront reconstruction is presented in this article. The relation between the moment of a pair of subimages and the local transformation coefficients is derived. The square guide 'star' is used to obtain a special solution from this relation. The moment-based wavefront reconstructio

Michi-To Suzuki, Takuya Nomoto, Eiaki V. Morooka, Yuki Yanagi

The magnetic structure is crucial in determining the physical properties inherent in magnetic compounds. We present an adequate descriptor for magnetic structure with proper magnetic symmetry and high discrimination performance, which does not depend on artificial choices for coordinate origin, axis, and magnetic unit cell in crystal. We extend the formalism

Wenli Xiao, Yiwei Lyu, John Dolan

Multi-Agent Reinforcement Learning (MARL) discovers policies that maximize reward but do not have safety guarantees during the learning and deployment phases. Although shielding with Linear Temporal Logic (LTL) is a promising formal method to ensure safety in single-agent Reinforcement Learning (RL), it results in conservative behaviors when scaling to multi

Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng

Twitter bot detection has become a crucial task in efforts to combat online misinformation, mitigate election interference, and curb malicious propaganda. However, advanced Twitter bots often attempt to mimic the characteristics of genuine users through feature manipulation and disguise themselves to fit in diverse user communities, posing challenges for exi

Hussel Suriyaarachchi, Alaeddin Nassani, Paul Denny, Suranga Nanayakkara

Knowledge of programming and computing is becoming increasingly valuable in today's world, and thus it is crucial that students from all backgrounds have the opportunity to learn. As the teaching of computing at high-school becomes more common, there is a growing need for approaches and tools that are effective and engaging for all students. Especially for s

Yuan Gao, Riquan Zhang, Hansheng Wang

Bagging is a useful method for large-scale statistical analysis, especially when the computing resources are very limited. We study here the asymptotic properties of bagging estimators for $M$-estimation problems but with massive datasets. We theoretically prove that the resulting estimator is consistent and asymptotically normal under appropriate conditions

Anand Gokul Mahalingam, Aayush Shah, Akshay Gulati, Royston Mascarenhas

Improving performance in multiple domains is a challenging task, and often requires significant amounts of data to train and test models. Active learning techniques provide a promising solution by enabling models to select the most informative samples for labeling, thus reducing the amount of labeled data required to achieve high performance. In this paper,

Mizuki Fukuda, Masaharu Ishikawa

A 2-sphere embedded in the 4-sphere invariant under a circle action is called a branched twist spin. A branched twist spin is constructed from a 1-knot in the 3-sphere and a pair of coprime integers uniquely. In this paper, we study, for each pair of coprime integers, if two different 1-knots yield the same branched twist spin, and prove that such a pair of

Seokju Yun, Youngmin Ro

We introduce Dynamic Mobile-Former(DMF), maximizes the capabilities of dynamic convolution by harmonizing it with efficient operators.Our Dynamic MobileFormer effectively utilizes the advantages of Dynamic MobileNet (MobileNet equipped with dynamic convolution) using global information from light-weight attention.A Transformer in Dynamic Mobile-Former only r

Haochen Han, Kaiyao Miao, Qinghua Zheng, Minnan Luo

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such nois

Juncheng Li, Bodong Cheng, Ying Chen, Guangwei Gao

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot

Masod Sadipour, Mohammad Masoud Momeni, Majid Soltani

In this study, a computational simulation is employed to place two essential parameters, the permeability of vessels and hydraulic conductivity, under assessment. These parameters impact the movement of drug particles through vessels, and normal and tumoral tissue to examine the concentration of nanoparticles, interstitial pressure, and velocity. To provide

J. Antonio Garcia-Barreto, Emmanuel Momjian

We report VLA B-configuration observations of the HI 21 cm line on the close disk galaxy pair NGC 5595 and NGC 5597. At the angular resolution of the observations, $\sim7.1'' \times 4.2''$, while most of the HI 21 cm in NGC 5595 and in NGC 5597 has the same extent as the optical disk, we have detected for the first time extended structures (streamers) to the

Ramiro Torres-Escobedo, Hao Zhou, Eduardo de la Fuente, A. U. Abeysekara

We present a short overview of the TeV-Halos objects as a discovery and a relevant contribution of the High Altitude Water \v{C}erenkov (HAWC) observatory to TeV astrophysics. We discuss history, discovery, knowledge, and the next step through a new and more detailed analysis than the original study in 2017. TeV-Halos will contribute to resolving the problem

Yueqiu Sun, Rohitkrishna Nambiar, Vivek Vidyasagaran

Manipulatives used in the right way help improve mathematical concepts leading to better learning outcomes. In this paper, we present a phygital (physical + digital) curriculum inspired teaching system for kids aged 5-8 to learn geometry using shape tile manipulatives. Combining smaller shapes to form larger ones is an important skill kids learn early on whi

Thiago Bergamaschi

We introduce and explicitly construct a quantum code we coin a "Pauli Manipulation Detection" code (or PMD), which detects every Pauli error with high probability. We apply them to construct the first near-optimal codes for two tasks in quantum communication over adversarial channels. Our main application is an approximate quantum code over qubits which can

XuanYang Lai, RenPing Sun, ShaoGang Yu, YanLan Wang

Photoelectron spectroscopy in intense laser fields has proven to be a powerful tool for providing detailed insights into molecular structure. The ionizing molecular orbital, however, has not been reconstructed from the photoelectron spectra, mainly due to the fact that its phase information can be hardly extracted. In this work, we propose a method to retrie

Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing

The release of ChatGPT, Gemini, and other large language model has drawn huge interests on foundations models. There is a broad consensus that foundations models will be the fundamental building blocks for future AI systems. However, there is a lack of systematic guidance on the architecture design. Particularly, the the rapidly growing capabilities of found

Yang Liu, Sen Li

This paper investigates the spatial pricing and fleet management strategies for an integrated platform that provides both ride-sourcing services and intracity parcel delivery services over a transportation network utilizing the idle time of ride-sourcing drivers. Specifically, the integrated platform simultaneously offers on-demand ride-sourcing services for

Vidushi Sharma, Dibakar Datta

Selenium (Se) cathodes are an exciting emerging high energy density storage system for Potassium ion batteries(KIB), where potassiation reactions are less understood. Here, we present an atomic-level investigation of KxSe cathode enclosed in hexagonal lattices of carbon(C) characteristic of multilayered graphene matrix and multiwalled carbon nanotubes (MW-CN

Jennifer Hom, Sungkyung Kang, JungHwan Park

A knot in $S^3$ is topologically slice if it bounds a locally flat disk in $B^4$. A knot in $S^3$ is rationally slice if it bounds a smooth disk in a rational homology ball. We prove that the smooth concordance group of topologically and rationally slice knots admits a $\mathbb{Z}^\infty$ subgroup. All previously known examples of knots that are both topolog

Xianjia Yu, Iacopo Catalano, Paola Torrico Morón, Sahar Salimpour

This letter presents a cooperative relative multi-robot localization design and experimental study. We propose a flexible Monte Carlo approach leveraging a particle filter to estimate relative states. The estimation can be based on inter-robot Ultra-Wideband (UWB) ranging and onboard odometry alone or dynamically integrated with cooperative spatial object de

Naoyuki Haba, Yasuhiro Shimizu, Toshifumi Yamada

We study a prediction on neutrino observables in a non-supersymmetric renormalizable $SO(10)$ GUT model that contains a ${\bf 10}$ complex scalar field and a ${\bf 126}$ scalar field whose Yukawa couplings with ${\bf 16}$ matter fields provide the quark and charged lepton Yukawa couplings, neutrino Dirac Yukawa coupling and Majorana mass for the singlet neut

Hengyuan Hu, Dorsa Sadigh

One of the fundamental quests of AI is to produce agents that coordinate well with humans. This problem is challenging, especially in domains that lack high quality human behavioral data, because multi-agent reinforcement learning (RL) often converges to different equilibria from the ones that humans prefer. We propose a novel framework, instructRL, that ena

Anthony Gruber, Irina Tezaur

A method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyp

Kazumasa Narita

We study an eigenvalue problem for the Laplacian on a compact K\"{a}hler manifold. Considering the $k$-th eigenvalue $\lambda_{k}$ as a functional on the space of K\"{a}hler metrics with fixed volume on a compact complex manifold, we introduce the notion of $\lambda_{k}$-extremal K\"{a}hler metric. We deduce a condition for a K\"{a}hler metric to be $\lambda

Motohiko Ezawa

Topological quantum computation by way of braiding of Majorana fermions is not universal quantum computation. There are several attempts to make universal quantum computation by introducing some additional quantum gates or quantum states. However, there is an embedding problem that $M$-qubit gates cannot be embedded straightforwardly in $N$ qubits for $N>M$.

Elena Bunina, Alexey Miasnikov, Eugene Plotkin

In this paper we study the Diophantine problem in Chevalley groups $G_\pi (\Phi,R)$, where $\Phi$ is an indecomposable root system of rank $> 1$, $R$ is an arbitrary commutative ring with $1$. We establish a variant of double centralizer theorem for elementary unipotents $x_\alpha(1)$. This theorem is valid for arbitrary commutative rings with $1$. The resul

Yuanyuan Wei, Roger Tam, Xiaoying Tang

Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability. While most explainable deep learning applications use post hoc methods (such as GradCAM) to generate feature attribution maps, there is a new type of case-based reasoning models, namely ProtoPNet and its variants, which identify prototype

Tatsuya Yamamura, Ismail Arai, Masatoshi Kakiuchi, Arata Endo

Public transportation has been essential in people's lives in recent years. Bus ridership is a factor in people's choice to board the bus. Therefore, from the perspective of improving service quality, it is important to inform passengers who have not boarded the bus yet about future bus ridership. However, there is a concern that providing inaccurate informa

Parameshwaran Pasupathy, John G Georgiadis, Assimina A Pelegri

Traumatic axonal injury occurs when loads experienced on the tissue-scale are transferred to the individual axons. Mechanical characterization of axon deformation especially under dynamic loads however is extremely difficult owing to their viscoelastic properties. The viscoelastic characterization of axon properties that are based on interpretation of result

Asifa Ashraf, Saadia Mumtaz, Faisal Javed, Zhiyue Zhang

The current study explores the generalized embedded wormhole solutions in the background of $f(\mathcal{R},\mathcal{G})$ gravity, where $\mathcal{R}$ represents the Ricci scalar and $\mathcal{G}$ denotes the Gauss-Bonnet invariant. To investigate the necessary structures of the wormhole solutions we thoroughly analyzed the energy conditions under $f(\mathcal

Siqi Chen, Xueming Li, Xianlin Zhang, Mingdao Wang

Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch. We summarize th