December 2023 arXiv papers — page 78

Luis Balderas, Miguel Lastra, José M. Benítez

Convolutional Neural Networks (CNN) are widely used to face challenging tasks like speech recognition, natural language processing or computer vision. As CNN architectures get larger and more complex, their computational requirements increase, incurring significant energetic costs and challenging their deployment on resource-restricted devices. In this paper

Markus Kloimwieder, Christoph Gadermaier

The object oriented programming paradigm is widely used in science and engineering. Many open and commercial libraries are written in C++ and increasingly provide bindings to Python, which is much easier to learn, but still partly encourages the use of object oriented programming. However, scientific ideas are much more directly and meaningfully expressed in

Y. Krasnikova, A. A. Murthy, F. Crisa, M. Bal

Using a spin-polarized muon beam we were able to capture magnetic dynamics in an amorphous niobium pentoxide thin film. Muons are used to probe internal magnetic fields produced by defects. Magnetic fluctuations could be described by the dynamical Kubo-Toyabe model considering a time-dependent local magnetic field. We state that observed fluctuations result

Faysal Mahmud, Md. Mahin Mahfiz, Md. Zobayer Ibna Kabir, Yusha Abdullah

Skin cancer is a serious worldwide health issue, precise and early detection is essential for better patient outcomes and effective treatment. In this research, we use modern deep learning methods and explainable artificial intelligence (XAI) approaches to address the problem of skin cancer detection. To categorize skin lesions, we employ four cutting-edge p

Sam Ganzfried

We present a nonparametric statistical test for determining whether an agent is following a given mixed strategy in a repeated strategic-form game given samples of the agent's play. This involves two components: determining whether the agent's frequencies of pure strategies are sufficiently close to the target frequencies, and determining whether the pure st

Gaurab Pokharel, Sanmay Das, Patrick J. Fowler

Street-level bureaucrats interact directly with people on behalf of government agencies to perform a wide range of functions, including, for example, administering social services and policing. A key feature of street-level bureaucracy is that the civil servants, while tasked with implementing agency policy, are also granted significant discretion in how the

Himanshu Singh

Gaussian Radial Basis Function (RBF) Kernels are the most-often-employed kernels in artificial intelligence and machine learning routines for providing optimally-best results in contrast to their respective counter-parts. However, a little is known about the application of the Generalized Gaussian Radial Basis Function on various machine learning algorithms

Xiao Wang, Jiandong Jin, Chenglong Li, Jin Tang

Existing pedestrian attribute recognition (PAR) algorithms adopt pre-trained CNN (e.g., ResNet) as their backbone network for visual feature learning, which might obtain sub-optimal results due to the insufficient employment of the relations between pedestrian images and attribute labels. In this paper, we formulate PAR as a vision-language fusion problem an

Yuya Sasaki

In this paper, we compute the number of real forms of Fermat hypersurfaces for degree $d \ge 2$ except the degree 4 surface case, and give explicit descriptions of them.

Adam R. Swietek

Financial criteria in architectural design evaluation are limited to cost performance. Here, I introduce a method, Automated Design Appraisal (ADA), to predict the market price of a generated building design concept within a local urban context. Integrating ADA with 3D building performance simulations enables financial impact assessment that exceeds the spat

Swati Shukla, Subhra Sankar Dhar, Shalabh

We propose and study M-estimation to estimate the parameters in the censored regression model in the presence of endogeneity, i.e., the Tobit model. In the course of this study, we follow two-stage procedures: the first stage consists of applying control function procedures to address the issue of endogeneity using instrumental variables, and the second stag

Yuto Nakajima, Hideo Suganuma

The deconfinement transition in non-Abelian gauge theory is understood as spontaneous breaking of $\mathbb{Z}_N$ symmetry at high temperatures. Accordingly, quark-gluon plasma generally includes some partial cells called center domains, each with a homogeneous Polyakov-loop expectation value. In this work, constructing an effective action describing the deco

Joshua J. P. Thompson, Mateusz Dyksik, Paulina Peksa, Katarzyna Posmyk

Layered halide perovskites exhibit remarkable optoelectronic properties and technological promise, driven by strongly bound excitons. The interplay of spin-orbit and exchange coupling creates a rich excitonic landscape, determining their optical signatures and exciton dynamics. Despite the dark excitonic ground state, surprisingly efficient emission from hig

Wenhao Guan, Yishuang Li, Tao Li, Hukai Huang

The style transfer task in Text-to-Speech refers to the process of transferring style information into text content to generate corresponding speech with a specific style. However, most existing style transfer approaches are either based on fixed emotional labels or reference speech clips, which cannot achieve flexible style transfer. Recently, some methods

Wenjun Miao, Guansong Pang, Tianqi Li, Xiao Bai

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/p

Gen Ye, Mian Zhu, Yong Cai

Recently, evidence of stochastic gravitational wave background (SGWB) signals observed by pulsar timing array (PTA) collaborations, has prompted investigations into their origins. We explore the compatibility of a proposed inflationary scenario, incorporating an intermediate null energy condition (NEC)-violating phase, with the PTA observations. The NEC viol

Harris Papadopoulos, Nestoras Georgiou, Charalambos Eliades, Andreas Konstantinidis

The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, without howev

Soulaimane Berkane, Dionysis Theodosis, Tarek Hamel, Dimos V. Dimarogonas

This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of aux

Ryosuke Ota

In this paper, the concordance of Morse functions is defined, and a necessary and sufficient condition for given two Morse functions to be concordant is presented and is compared with the cobordism criterion. Cobordism of Morse functions on smooth closed manifolds is an equivalence relation defined by using cobordisms of manifolds and fold maps. Given two Mo

Isanka Garli Hevage, Akif Ibraguimov, Zeev Sobol

Our Recent advancements in stochastic processes have illuminated a paradox associated with the Einstein model of Brownian motion. The model predicts an infinite propagation speed, conflicting with the second law of thermodynamics. The modified model successfully resolves the issue, establishing a finite propagation speed by introducing a concentration-depend

Samarth Hawaldar, Prakriti Shahi, Allison L. Carter, Ana Maria Rey

Trapped ion systems are a leading platform for quantum information processing, but they are currently limited to 1D and 2D arrays, which imposes restrictions on both their scalability and their range of applications. Here, we propose a path to overcome this limitation by demonstrating that Penning traps can be used to realize remarkably clean bilayer crystal

Qingxuan Lv, Yuezun Li, Junyu Dong, Sheng Chen

Recent DeepFake detection methods have shown excellent performance on public datasets but are significantly degraded on new forgeries. Solving this problem is important, as new forgeries emerge daily with the continuously evolving generative techniques. Many efforts have been made for this issue by seeking the commonly existing traces empirically on data lev

Mehedi Hasan, Mohammad Jahid Ibna Basher, Md. Tanvir Rouf Shawon

Intent classification is a fundamental task in natural language understanding, aiming to categorize user queries or sentences into predefined classes to understand user intent. The most challenging aspect of this particular task lies in effectively incorporating all possible classes of intent into a dataset while ensuring adequate linguistic variation. Plent

Husanboy Hoshimov, Odil Yunusov, Farruh Atamurotov, Mubasher Jamil

In this work, we have studied weak gravitational lensing effect around black hole and determine shadow radius in GUP-corrected-Schwarzschild spacetime (S-GUP) in presence of plasma environment. We started with orbits of photons around black hole in S-GUP. In addition, we have studied shadow and gravitational weak lensing around such black hole. By using obse

Kyosuke Tomonari

We establish the mathematical fundamentals for a unified description of curvature, torsion, and non-metricity 2-forms in the way extending the so-called M\"{o}bius representation of the affine group, which is the method to convert the semi-direct product into the ordinary matrix product, to revive the fertility of gauge theories of gravity. First of all, we

Mehran Dehpour

There is no evidence that the universe must have been homogeneous and isotropic before the big bang nucleosynthesis. The Bianchi type-I cosmology is the simplest homogeneous but anisotropic cosmology. In this work, we investigate thermal leptogenesis, as a baryogenesis scenario, in the Bianchi type-I cosmology. Our results show that for specific values of th

Yehong Zhang, Jun Wu, Hui Xu

Fuzzing is a popular bug detection technique achieved by testing software executables with random inputs. This technique can also be extended to libraries by constructing executables that call library APIs, known as fuzz drivers. Automated fuzz driver synthesis has been an important research topic in recent years since it can facilitate the library fuzzing p

Cristian F. Jimenez-Varon, Hao Lee, Marc G. Genton, Ying Sun

Visualization and assessment of copula structures are crucial for accurately understanding and modeling the dependencies in multivariate data analysis. In this paper, we introduce an innovative method that employs functional boxplots and rank-based testing procedures to evaluate copula symmetry. This approach is specifically designed to assess key characteri

Ran Chen, Xingjian Yi, Jing Zhao, Yueheng He

The development of generative design driven by artificial intelligence algorithms is speedy. There are two research gaps in the current research: 1) Most studies only focus on the relationship between design elements and pay little attention to the external information of the site; 2) GAN and other traditional generative algorithms generate results with low

J. Berges

I review developments of how compact table-top setups with ultracold atoms can help us to understand the more complex real-time dynamics of QCD probed in heavy-ion collision experiments.

Namhoon Cho, Hyo-Sang Shin

We propose automatic optimisation methods considering the geometry of matrix manifold for the normalised parameters of neural networks. Layerwise weight normalisation with respect to Frobenius norm is utilised to bound the Lipschitz constant and to enhance gradient reliability so that the trained networks are suitable for control applications. Our approach f

Phuc D. A. Nguyen, Tuan Duc Ngo, Evangelos Kalogerakis, Chuang Gan

We introduce Open3DIS, a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes, scales, and colors, making precise instance-level identification a challenging task. Recent advancements in Open-Vocabulary scene understanding have made significant strides i

Negin Bagherpour, Mahdi Sharifzadeh

Mixed integer nonlinear programming (MINLP) problems are encountered in modeling a physical/industrial process consisting both nonlinearity and discrete selective parameters. There are variety of algorithms for solving MINLP problems most of which solve large-scale MINLP problems very slowly. In this research two parallelization scheme are suggested for solv

K. P. Santoso, F. A. Madany, H. Suryotrisongko

This study proposes the implementation of Deep Generative Adversarial Networks (GANs) for augmenting the NSL-KDD dataset. The primary objective is to enhance the efficacy of eXtreme Gradient Boosting (XGBoost) in the classification of cyber-attacks on the NSL-KDD dataset. As a result, the method proposed in this research achieved an accuracy of 99.53% using

Luca Brandolini, Bianca Gariboldi, Giacomo Gigante, Alessandro Monguzzi

In this paper, we prove that some renowned lower bounds in discrepancy theory admit a discrete analogue. Namely, we prove that the lower bound of the discrepancy for corners in the unit cube due to Roth holds true also for a suitable finite family of corners. We also prove two analogous results for the discrepancy on the torus with respect to squares and bal

Zubeyir Cinkir

Wolstenholme's type summations involve certain powers of all residues $k$ modulo some prime number $p$. We first consider the sums of double or triple products of certain powers of all residues, e.g., the sums of the terms $(a+k)^m(b+k)^n$ or $(a+k)^m(b+k)^n(c+k)^s$ as $k$ ranges over all residues modulo $p$. We consider the sums of double or triple ratios o

Elisa Alboni, Gianluigi Grandesso, Gastone Pietro Rosati Papini, Justin Carpentier

Trajectory Optimization (TO) and Reinforcement Learning (RL) are powerful and complementary tools to solve optimal control problems. On the one hand, TO can efficiently compute locally-optimal solutions, but it tends to get stuck in local minima if the problem is not convex. On the other hand, RL is typically less sensitive to non-convexity, but it requires

Lei Li, Zhihui Xie, Mukai Li, Shunian Chen

This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context. We first build a vision-language feedback (VLFeedback) dataset utilizing AI annotation. Specifically, responses are generated by models sampled from 12 LVLMs, conditioned on mul

Nikolai A. Sinitsyn, Vijay Ganesh Sadhasivam, Fumika Suzuki

The passage through a critical point of a many-body quantum system leads to abundant nonadiabatic excitations. Here, we explore a regime, in which the critical point is not crossed although the system is passing slowly very close to it. We show that the leading exponent for the excitation probability then can be obtained by standard arguments of the Dykhne f

Patricia Arroba, José L. Risco-Martín, José M. Moya, José L. Ayala

Data centers handle impressive high figures in terms of energy consumption, and the growing popularity of Cloud applications is intensifying their computational demand. Moreover, the cooling needed to keep the servers within reliable thermal operating conditions also has an impact on the thermal distribution of the data room, thus affecting to servers' power

Ryoga Matsumoto

We construct special idempotents in $\mathrm{End}_{U_q(\mathfrak{sl}_2)}(M(\mu)\otimes V_1^{\otimes n})$ like the Jones Wenzl projector where $M(\mu)$ is Verma module whose highest weight is $\mu$ and $V_1$ is $2$-dimensional irreducible module.

Weihang Su, Qingyao Ai, Xiangsheng Li, Jia Chen

With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Benefiting from the pre-training and fine-tuning paradigm, these models achieve state-of-the-art performance. In previous works, plain texts in Wikipedia have been widely used in the

Jing Xu, Connor Horn, Yu Jiang, Amin Pishehvar

Yttrium iron garnet (YIG) magnonics has garnered significant research interest because of the unique properties of magnons (quasiparticles of collective spin excitation) for signal processing. In particular, hybrid systems based on YIG magnonics show great promise for quantum information science due to their broad frequency tunability and strong compatibilit

Joseph G. Checkelsky, B. Andrei Bernevig, Piers Coleman, Qimiao Si

Flat band materials such as the kagome metals or moir\'e superlattice systems are of intense current interest. Flat bands can result from the electron motion on numerous (special) lattices and usually exhibit topological properties. Their reduced bandwidth proportionally enhances the effect of Coulomb interaction, even when the absolute magnitude of the latt

A. Arbuzov, S. Bondarenko, I. Boyko, Ya. Dydyshka

Results of a simulation of Bhabha small-angle scattering process at center-of-mass energy of 240~GeV and at the $Z$-boson resonance using the Monte Carlo generator {\tt{ReneSANCe}} are presented. For the given accuracy about $10^{-4}$ in estimating luminosity of future electron-positron accelerators, the minimum cutoff angle for simulated events is establish

Bingyin Zhao, Yingjie Lao

Backdoor attacks are emerging threats to deep neural networks, which typically embed malicious behaviors into a victim model by injecting poisoned samples. Adversaries can activate the injected backdoor during inference by presenting the trigger on input images. Prior defensive methods have achieved remarkable success in countering dirty-label backdoor attac

Xirui Li, Chao Ma, Xiaokang Yang, Ming-Hsuan Yang

Diffusion models have made significant advances in generating high-quality images, but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless, existing methods strug

Yingda Yin, Yuzheng Liu, Yang Xiao, Daniel Cohen-Or

Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories. Recent efforts have sought to harness vision-language models like CLIP for open-set semantic reasoning, yet these methods struggle to distinguish between objects of the same

Shengcheng Yu, Chunrong Fang, Mingzhe Du, Yuchen Ling

GUI testing is significant in the SE community. Most existing frameworks are intrusive and only support some specific platforms. With the development of distinct scenarios, diverse embedded systems or customized operating systems on different devices do not support existing intrusive GUI testing frameworks. Some approaches adopt robotic arms to replace the i

Sayan Jana, Lea Sirota

We report a peculiar tunneling phenomenon that occurs in lattices with nonreciprocal couplings. The nonreciprocity holds for an inner portion of the lattice, constituting a non-Hermitian interface between outer Hermitian sections. The couplings are mirrored about the interface center. As a standalone system that was widely studied in recent years, each secti

G. Ramalho

The $\gamma^\ast N \to N(1520)$ transition has a property that differs from the other low-lying nucleon resonance amplitudes: the magnitude of the transverse helicity amplitudes.The transition helicity amplitudes are defined in terms of square-transfer momentum $q^2$, or $Q^2=-q^2$. Near the photon point ($Q^2=0$) there is a significant difference in the mag

Kai Diethelm, Safoura Hashemishahraki, Ha Duc Thai, Hoang The Tuan

This paper is devoted to studying three-dimensional non-commensurate fractional order differential equation systems with Caputo derivatives. Necessary and sufficient conditions are for the asymptotic stability of such systems are obtained.

Xutong Yue, Xilai Wang, Yuxin He, Zhenkun Zhou

An increasing number of individuals are willing to post states and opinions in social media, which has become a valuable data resource for studying human health. Furthermore, social media has been a crucial research point for healthcare now. This paper outlines the methods in our participation in the #SMM4H 2023 Shared Tasks, including data preprocessing, co

Keren Duer, Eli Galanti, Yohai Kaspi

Jupiter's atmosphere comprises several dynamical regimes: the equatorial eastward flows and surrounding retrograde jets; the midlatitudes, with the eddy-driven, alternating jet-streams and meridional circulation cells; and the jet-free turbulent polar region. Despite intensive research conducted on each of these dynamical regimes over the past decades, they

Md Naimul Hoque, Ayman Mahfuz, Mayukha Kindi, Naeemul Hassan

Large Language Models (LLMs) have created opportunities for designing chatbots that can support complex question-answering (QA) scenarios and improve news audience engagement. However, we still lack an understanding of what roles journalists and readers deem fit for such a chatbot in newsrooms. To address this gap, we first interviewed six journalists to und

Boming Zhao, Luwei Yang, Mao Mao, Hujun Bao

Due to the ability to synthesize high-quality novel views, Neural Radiance Fields (NeRF) have been recently exploited to improve visual localization in a known environment. However, the existing methods mostly utilize NeRFs for data augmentation to improve the regression model training, and the performance on novel viewpoints and appearances is still limited

Patrick Altmeyer, Mojtaba Farmanbar, Arie van Deursen, Cynthia C. S. Liem

Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models to learn how the input data is distributed. This effectively reallocates the task of learning realistic explana

Muhammad Saud Ul Hassan, Christian Hubicki

Open-loop stable limit cycles are foundational to legged robotics, providing inherent self-stabilization that minimizes the need for computationally intensive feedback-based gait correction. While previous methods have primarily targeted specific robotic models, this paper introduces a general framework for rapidly generating limit cycles across various dyna

Naoki Kitazawa

Morse functions with exactly two singular points on homotopy spheres and canonical projections of spheres are generalized as special generic maps. A special generic map is, roughly, a smooth map represented as the composition of a smooth surjection onto a manifold whose preimages are diffeomorphic to a unit sphere in the interior of the manifold and single p

Wei Tang, Zhiqian Wu, Yixin Cao, Yong Liao

Knowledge graph completion (KGC) aims to predict missing facts in knowledge graphs (KGs), which is crucial as modern KGs remain largely incomplete. While training KGC models on multiple aligned KGs can improve performance, previous methods that rely on transferring raw data among KGs raise privacy concerns. To address this challenge, we propose a new federat

Zhuoping Ruan, Ingo Witt

We study first-order symmetrizable hyperbolic $N\times N$ systems in a spacetime cylinder whose lateral boundary is totally characteristic. In local coordinates near the boundary at $x=0$, these systems take the form \[ \partial_t u + \mathcal A(t,x,y,xD_x,D_y) u = f(t,x,y), \quad (t,x,y)\in(0,T)\times\mathbb R_+\times\mathbb R^d, \] where $\mathcal A(t,x,y,

Juan A. Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji

Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation methods have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond path curves. To address these issues, we introduce StarVector, a multimod

Yi Ding, Weiming Song, Kai Zhu

To properly estimate signal significance while accounting for both statistical and systematic uncertainties, we conducted a study to analyze the impact of typical systematic uncertainties, such as background shape, signal shape, and the number of backgrounds, on significance calculation using the continuous test method. Our investigation reveals unexpected a

Haoxin Lin, Hongqiu Wu, Jiaji Zhang, Yihao Sun

Real-world decision-making problems are usually accompanied by delayed rewards, which affects the sample efficiency of Reinforcement Learning, especially in the extremely delayed case where the only feedback is the episodic reward obtained at the end of an episode. Episodic return decomposition is a promising way to deal with the episodic-reward setting. Sev

Yiqiu Wang, Meixia Tao, Shu Sun

This paper studies transmit beamforming design in an integrated sensing and communication (ISAC) system, where a base station sends symbols to perform downlink multi-user communication and sense an extended target simultaneously. We first model the extended target contour with truncated Fourier series. By considering echo signals as reflections from the vali

Chia-Yi Lee, Yanwu Liu, Yinke Cheng, Cheng-Hao Lao

Many key functionalities of optical frequency combs such as self-referencing and broad spectral access rely on coherent supercontinuum generation (SCG). While nanophotonic waveguides have emerged as a compact and power-efficient platform for SCG, their geometric degrees of freedom have not been fully utilized due to the underlying nonlinear and stochastic ph

Maksim Makarenko, Qizhou Wang, Arturo Burguete-Lopez, Silvio Giancola

Foundation models, exemplified by GPT technology, are discovering new horizons in artificial intelligence by executing tasks beyond their designers' expectations. While the present generation provides fundamental advances in understanding language and images, the next frontier is video comprehension. Progress in this area must overcome the 1 Tb/s data rate d

Tarek Saier, Mayumi Ohta, Takuto Asakura, Michael Färber

Automatic extraction of information from publications is key to making scientific knowledge machine readable at a large scale. The extracted information can, for example, facilitate academic search, decision making, and knowledge graph construction. An important type of information not covered by existing approaches is hyperparameters. In this paper, we form

Mengchen Liu, Chongyan Chen, Danna Gurari

While there is much excitement about the potential of large multimodal models (LMM), a comprehensive evaluation is critical to establish their true capabilities and limitations. In support of this aim, we evaluate two state-of-the-art LMMs, GPT-4V and Gemini, on a new visual question answering dataset sourced from an authentic online question answering commu

Jing Wu, Lin Wang, Qirui Jin, Fangming Liu

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks, yet their ever-increasing computational demands are hindering their deployment on resource-constrained mobile devices. Hybrid deep learning partitions a DNN into two parts and deploys them across the mobile device and a server, aiming to reduce inference latency or prolo

Kyung-bin Kwon, Sayak Mukherjee, Thanh Long Vu, Hao Zhu

This paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constrai

Jaehui Hwang, Junghyuk Lee, Jong-Seok Lee

With the advancement of generative models, the assessment of generated images becomes more and more important. Previous methods measure distances between features of reference and generated images from trained vision models. In this paper, we conduct an extensive investigation into the relationship between the representation space and input space around gene

Negin Bagherpour

Stock price prediction is a complicated and interesting task. Noisy trends make stock pricing sensitive and complicated while the economical motivation behind, keeps it interesting for researchers and investors. In this paper we are to outline two novel ideas for stock pricing. We also test each of our suggested algorithms for predicting the price of 6 stock

Jinghui Wang, Stavros Melemenidis, Rakesh Manjappa, Vignesh Viswanathan

We characterized the dosimetric properties of a clinical linear accelerator configured to deliver ultra-high dose rate (UHDR) irradiation to mice and cell-culture FLASH radiobiology experiments. UHDR electron beams were controlled by a microcontroller and relay interfaced with the respiratory gating system. We produced beam collimators with indexed stereotac

Yang Hong, Jun Wu, Rosario Morello

Beyond 5G networks provide solutions for next-generation communications, especially digital twins networks (DTNs) have gained increasing popularity for bridging physical space and digital space. However, current DTNs networking frameworks pose a number of challenges especially when applied in scenarios that require high communication efficiency and multimoda

Malik Al Matwi

We study scaling symmetry in a class of non-minimally coupled scalar field in a background of Friedmann-Robertson-Walker (FRW) spacetime. We use a non-minimally coupling $R L^{(\varphi)}$. We find the corresponding conserved charge of that symmetry and see its role in cosmology, and search for its possible breaking down and its outcomes. A suitable potential

Aleksandr V. Mosenkov, Andrey D. Panasyuk, Savanah Turner, Crystal-Lynn Bartier

In this paper, we look to analyse the spiral features of grand-design, multiarmed, and flocculent spiral galaxies using deep optical imaging from DESI Legacy Imaging Surveys. We explore the resulting distributions of various characteristics of spiral structure beyond the optical radius, such as the distributions of azimuthal angle, the extent of spiral arms,

N. D. Hari Dass

This chapter explains the concept of \emph{Effective String Theories}(EST), and their success in explaining the results that Yang-Mills flux tubes behave, to a high degree of accuracy, like Bosonic Strings(BST). It describes EST's of L\"uscher and Weisz, and their principal conclusions. It then discusses the Polchinski-Strominger EST's. which are valid in al

Congyi Wang

In this study, we introduce T2M-HiFiGPT, a novel conditional generative framework for synthesizing human motion from textual descriptions. This framework is underpinned by a Residual Vector Quantized Variational AutoEncoder (RVQ-VAE) and a double-tier Generative Pretrained Transformer (GPT) architecture. We demonstrate that our CNN-based RVQ-VAE is capable o

Soumyadip Sahu

This article proposes a new approach to studying the spectral Eisenstein series of weight $k$ on a congruence subgroup of $\text{SL}_2(\mathbb{Z})$ using Hecke's theory of Eisenstein series for the principal congruence subgroups. Our method provides a gateway to analytic and arithmetic properties of the spectral Eisenstein series using corresponding results

Somsubhra De, Shaurya Vats

In the realm of public health, vaccination stands as the cornerstone for mitigating disease risks and controlling their proliferation. The recent COVID-19 pandemic has highlighted how vaccines play a crucial role in keeping us safe. However the situation involves a mix of perspectives, with skepticism towards vaccines prevailing for various reasons such as p

Matthias Scharitzer, Vivek Shende

Under certain hypotheses, we show that Legendrian surfaces related by disk surgery will have q-deformed augmentation spaces that are related by q-deformed cluster transformation. The proof is geometric, via considerations of moduli of holomorphic curves. In fact, our results naturally give a more general HOMFLYPT "skein-valued cluster transformation", of whi

Jie JW Wu

Software companies have widely used online A/B testing to evaluate the impact of a new technology by offering it to groups of users and comparing it against the unmodified product. However, running online A/B testing needs not only efforts in design, implementation, and stakeholders' approval to be served in production but also several weeks to collect the d

Jingyang Xiang, Zhuangzhi Chen, Jianbiao Mei, Siqi Li

Soft filter pruning~(SFP) has emerged as an effective pruning technique for allowing pruned filters to update and the opportunity for them to regrow to the network. However, this pruning strategy applies training and pruning in an alternative manner, which inevitably causes inconsistent representations between the reconstructed network~(R-NN) at the training

Moshi Wei, Nima Shiri Harzevili, Alvine Boaye Belle, Junjie Wang

Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks has gained increasing attention among researchers and developers in recent years. To comprehensively understand this research domain, we have surveyed to analyze API recommendation studies published in the

Shreya Bhatia, Tarushi Gandhi, Dhruv Kumar, Pankaj Jalote

Generating unit tests is a crucial task in software development, demanding substantial time and effort from programmers. The advent of Large Language Models (LLMs) introduces a novel avenue for unit test script generation. This research aims to experimentally investigate the effectiveness of LLMs, specifically exemplified by ChatGPT, for generating unit test

Yonatan Betancur-Ocampo, Guillermo Monsivais

Su-Schrieffer-Heeger (SSH) lattices have helped to understand key concepts of topological insulators. In this paper, we study the transmission properties of $pn$ junctions of bearded SSH lattices forming a bipartite structure. From a tight-binding approach, a Bloch Hamiltonian depicts the electron behavior as a massive pseudo-spin one particle in quasi-one-d

Muhammad Hamza, Dominik Siemon, Muhammad Azeem Akbar, Tahsinur Rahman

This paper investigates the dynamics of human AI collaboration in software engineering, focusing on the use of ChatGPT. Through a thematic analysis of a hands on workshop in which 22 professional software engineers collaborated for three hours with ChatGPT, we explore the transition of AI from a mere tool to a collaborative partner. The study identifies key

Keshav Kumar, Dhananjai Pandey

Evidence for a re-entrant quantum paraelectric (QPE) state preceded by a dipole glass (DG) phase with a non-classical exponent in the quantum critical regime of SrFe12O19 is presented. It is shown that the DG transition is accompanied with a spin glass (SG) transition and presence of a biquadratic coupling of two diverse order parameter fields. Further, the

Liyun Zeng, Hao Helen Zhang

Classification and probability estimation are fundamental tasks with broad applications across modern machine learning and data science, spanning fields such as biology, medicine, engineering, and computer science. Recent development of weighted Support Vector Machines (wSVMs) has demonstrated considerable promise in robustly and accurately predicting class

Vikas Kumar, Amisha Bharti, Devanshu Verma, Vasudha Bhatnagar

Generative language models, such as ChatGPT, have garnered attention for their ability to generate human-like writing in various fields, including academic research. The rapid proliferation of generated texts has bolstered the need for automatic identification to uphold transparency and trust in the information. However, these generated texts closely resembl

Sijie Wang, Rui She, Qiyu Kang, Xingchao Jian

The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts. Nonetheless, integrating additional sensors comes with elevated costs and may not be feasible for systems that demand lightweight operation, thereby impacting the practical deployment of VPR. To address thi

Yu Wang, Zexue He, Zhankui He, Hao Xu

Understanding and accurately explaining compatibility relationships between fashion items is a challenging problem in the burgeoning domain of AI-driven outfit recommendations. Present models, while making strides in this area, still occasionally fall short, offering explanations that can be elementary and repetitive. This work aims to address these shortcom

Ying-Ying Chang, Wei-Yao Wang, Wen-Chih Peng

In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in

Yutian Tao, Eftychios Sifakis

Numerical solution of discrete PDEs corresponding to saddle point problems is highly relevant to physical systems such as Stokes flow. However, scaling up numerical solvers for such systems is often met with challenges in efficiency and convergence. Multigrid is an approach with excellent applicability to elliptic problems such as the Stokes equations, and c

Jinbo Yu

We refine a previous work of K. Matsumoto and H. Ishikawa, obtaining an asymptotic formula for the mean square of the product of the Riemann zeta-function and a Dirichlet polynomial in the critical strip (1/4<$\sigma$<1/2), by obtaining an explicit formula of Atkinson type for its error term. This work is closely related to the generalized Dirichlet divisor

Haoyuan Wu, Xinyun Zhang, Peng Xu, Peiyu Liao

Vision-Language models (VLMs) pre-trained on large corpora have demonstrated notable success across a range of downstream tasks. In light of the rapidly increasing size of pre-trained VLMs, parameter-efficient transfer learning (PETL) has garnered attention as a viable alternative to full fine-tuning. One such approach is the adapter, which introduces a few

Wen-Xiang Chen, Yao-Guang Zheng

This paper explores the Kerr-Schild double copy, a duality relating gravity and electromagnetism. We show how Einstein's vacuum solutions in four dimensions can be converted into Maxwell's solutions via a double copy procedure, employing tensor fields. This technique yields novel solutions to Einstein's equations, including Kerr, Schwarzschild, and RN black

Mark N. McDonald, Cameron K. Peterson, Douglas R. Tree

Colloidal particles can create reconfigurable nanomaterials, with applications such as color-changing, self-repairing, and self-regulating materials and reconfigurable drug delivery systems. However, top-down methods for manipulating colloids are limited in the scale they can control. We consider here a new method for using chemical reactions to multiply the

Bing Cao, Junliang Guo, Pengfei Zhu, Qinghua Hu

Due to the rapid development of computer vision, single-modal (RGB) object tracking has made significant progress in recent years. Considering the limitation of single imaging sensor, multi-modal images (RGB, Infrared, etc.) are introduced to compensate for this deficiency for all-weather object tracking in complex environments. However, as acquiring suffici