October 2023 arXiv papers — page 161

Francesca Gladiali, Massimo Grossi

In this paper we are concerned with the number of critical points of solutions of nonlinear elliptic equations. We will deal with the case of non-convex, contractile and non-contractile planar domains. We will prove results on the estimate of their number as well as their index. In some cases we will provide the exact calculation. The toy problem concerns th

Hong Su

Redundancy represents a strategy for achieving high availability. However, various factors, known as singleness factors, necessitate corresponding redundancy measures. The absence of a systematic approach for identifying these singleness factors and the lack of a quantifiable method to assess system redundancy degrees are notable challenges. In this paper, w

Silja Pohjolainen, Nasrin Talebpour Sheshvan, Christian Monstein

On 8 November 2013 a halo-type coronal mass ejection (CME) was observed, together with flares and type II radio bursts, but the association between the flares, radio bursts, and the CME was not clear. Our aim is to identify the origin of the CME and its direction of propagation, and to exclude features that were not connected to it. On the Earth-facing side,

Radu Iosif, Florian Zuleger

We give a characterization of the sets of graphs that are both definable in Counting Monadic Second Order Logic (CMSO) and context-free, i.e., least solutions of Hyperedge-Replacement (HR) grammars introduced by Courcelle and Engelfriet. We prove the equivalence of these sets with: (a) recognizable sets (in the algebra of graphs with HR-operations) of bounde

Xiaobai Ning, A. Pezo, Kyoung-Whan Kim, Weisheng Zhao

We propose a general theory of charge, spin, and orbital diffusion based on Keldysh formalism. Our findings indicate that the diffusivity of orbital angular momentum in metals is much lower than that of spin or charge due to the strong orbital intermixing in crystals. Furthermore, our theory introduces the concept of spin-orbit polarization by which a pure o

V. Nirmala, V. Vijayalakshmi, R. Shanmugapriya

This article introduces the idea of implicative filters in quasi ordered RL-Wajsberg algebras and uses examples to explore some of its features.

Zhi-Yong Wang, Hing Cheung So, Abdelhak M. Zoubir

This paper presents a novel loss function referred to as hybrid ordinary-Welsch (HOW) and a new sparsity-inducing regularizer associated with HOW. We theoretically show that the regularizer is quasiconvex and that the corresponding Moreau envelope is convex. Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived

Adrian Langer

We prove a new version of Bogomolov's inequality on normal proper surfaces. This allows to construct Bridgeland's stability condition on such surfaces. In particular, this gives the first known examples of stability conditions on non-projective, proper schemes.

Ze Li, Yuke Lin, Ning Jiang, Xiaoyi Qin

Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labe

Sohan Kumar Jha

We consider Loop Quantum Gravity(LQG) motivated $4D$ polymerized black hole and study shadow, quasinormal modes, and Hawking radiation. We obtain analytical expressions of photonsphere radius and shadow radius and study their qualitative and quantitative nature of variation with respect to the LQG parameter $\alpha$. We also show shadows of the black hole fo

Santanu Das, Sumedha

We report on reentrance in the random field Ising and Blume-Capel models, induced by an asymmetric bimodal random field distribution. The conventional continuous line of transitions between the paramagnetic and ferromagnetic phases, the {\lambda}-line, is wiped away by the asymmetry. The phase diagram, then, consists of only first order transition lines that

Dennis Ritter, Mike Hemberger, Marc Hönig, Volker Stopp

In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting. In contrast to standard natural object benchmarks existing in the field, our results highlight the most important design choices when only category-labeled CAD models are available but classification needs to be don

Farzaneh Pourahmadi, Jalal Kazempour

The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation s

V. V. Gligorov, V. Reković

We review the status of, and prospects for, real-time data processing for collider experiments in experimental High Energy Physics. We discuss the historical evolution of data rates and volumes in the field and place them in the context of data in other scientific domains and commercial applications. We review the requirements for real-time processing of the

Han Hu, Haolan Zhan, Yujin Huang, Di Liu

In the current landscape of pervasive smartphones and tablets, apps frequently exist across both platforms. Although apps share most graphic user interfaces (GUIs) and functionalities across phones and tablets, developers often rebuild from scratch for tablet versions, escalating costs and squandering existing design resources. Researchers are attempting to

Aaqid Bhat, Sanjay Mandal, P. K. Sahoo

In this article, we explore the concept of cosmological inflation within the framework of the $f (T,\mathcal{T})$ theory of gravity, where $f$ is a general function of the Torsion scalar $T$ and the trace, $\mathcal{T}$, of the energy-momentum tensor. It is assumed that the conditions of slow-roll inflation are applicable in$f (T,\mathcal{T})$ gravity. To de

Nan Li, Junming Guo, Sateng Li, Haoliang Liu

ScaleLat (Scale Lattice) is a computer program written in C for performing the atomic structure analysis of multi-phase system or high entropy alloys (HEAs). The program implements an atomic cluster extraction algorithm to obtain all independent and symmetry-reduced characteristic chemical structures for the complex atomic configurations which are usually ob

Andreas Voskou, Konstantinos P. Panousis, Harris Partaourides, Kyriakos Tolias

Automatic Sign Language Translation (SLT) is a research avenue of great societal impact. End-to-End SLT facilitates the interaction of Hard-of-Hearing (HoH) with hearing people, thus improving their social life and opportunities for participation in social life. However, research within this frame of reference is still in its infancy, and current resources a

Zitai Wang, Qianqian Xu, Zhiyong Yang, Zhikang Xu

Due to the inherent imbalance in real-world datasets, na\"ive Empirical Risk Minimization (ERM) tends to bias the learning process towards the majority classes, hindering generalization to minority classes. To rebalance the learning process, one straightforward yet effective approach is to modify the loss function via class-dependent terms, such as re-weight

Nan Li, Xinyu Gao, Xianghui Feng, Kai Wu

Field emission coupled with molecular dynamics simulation (FEcMD) software package is a computational tool for studying the electron emission characteristics and the atomic structure evolution of micro- and nano-protrusions made of pure metals or multi-component alloys by means of multi-physics and multi-scale methodology. The implementations of molecular dy

Wenhao Li, Xiu Su, Shan You, Fei Wang

Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencoder, primarily predicting noises that need to be eliminated step by step. Consequently, it is crucial to employ a model that aligns with the expected budgets to facilitate superior

Zhenyu Tao, Wei Xu, Xiaohu You

The proliferation of diverse wireless services in 5G and beyond has led to the emergence of network slicing technologies. Among these, admission control plays a crucial role in achieving service-oriented optimization goals through the selective acceptance of service requests. Although deep reinforcement learning (DRL) forms the foundation in many admission c

Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez, Yash Raj Shrestha

Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. This study addresses the automation of resource-intensive and time-consuming process of manually detecting stenotic lesions in coronary arteries in X-ray coronary angiography images.

Wenfeng Liu, Shahram Janbaz, David Dykstra, Bernard Ennis

The ideal shock absorber combines high stiffness with high energy absorption whilst retaining structural integrity after impact and is scalable for industrial production. So far no structure meets all of these criteria. Here, we introduce a special occurrence of plastic buckling as a design concept for mechanical metamaterials that combine all the elements r

Jingyi Pan, Sihang Li, Yucheng Chen, Jinjing Zhu

Nighttime semantic segmentation plays a crucial role in practical applications, such as autonomous driving, where it frequently encounters difficulties caused by inadequate illumination conditions and the absence of well-annotated datasets. Moreover, semantic segmentation models trained on daytime datasets often face difficulties in generalizing effectively

Mohamed S. Darwish, Hazem Badreldin, Nasser M. Ahmed, Mostafa Morsy

A multi-parameter analysis was conducted to evaluate the impact of meteorological parameters, night sky brightness and seismic hazard on proposed sites for the new optical/infrared Egyptian astronomical telescope. The ERA5 reanalysis data set is used to get the following meteorological parameters: Total cloud coverage fraction, precipitable water vapor, rela

Yuxuan Jiang, Guo-Yang Li, Zhaoyi Zhang, Shiyu Ma

Shear wave elastography (SWE) is a promising imaging modality for mechanical characterization of tissues, offering biomarkers with potential for early and precise diagnosis. While various methods have been developed to extract mechanical parameters from shear wave characteristics, their relationships in viscoelastic materials under prestress remain poorly un

Bogusław Broda

A general structure of unitary evolution (evaporation) of the black hole, respecting causality imposed by the event horizon (semicausality), has been derived and presented in the language of quantum circuits. The resulting consequences for the evolution of the corresponding entanglement entropy and the entropy curve have been determined. As an illustration o

Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi

Chain-of-thought (CoT) prompting, which offers step-by-step problem-solving rationales, has impressively unlocked the reasoning potential of large language models (LLMs). Yet, the standard CoT is less effective in problems demanding multiple reasoning steps. This limitation arises from the complex reasoning process in multi-step problems: later stages often

Anke Tang, Li Shen, Yong Luo, Yibing Zhan

Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven to be powerful and scalable to incorporate fine-tuned weights from different tasks into a multi-task model. However, efficiently fine-tuning large pre-trained models on multiple do

Daniel Anthes, Sushrut Thorat, Peter König, Tim C. Kietzmann

Continual learning algorithms strive to acquire new knowledge while preserving prior information. Often, these algorithms emphasise stability and restrict network updates upon learning new tasks. In many cases, such restrictions come at a cost to the model's plasticity, i.e. the model's ability to adapt to the requirements of a new task. But is all change de

Yong Siah Teo

With a finite amount of measurement data acquired in variational quantum algorithms, the statistical benefits of several optimized numerical estimation schemes, including the scaled parameter-shift (SPS) rule and finite-difference (FD) method, for estimating gradient and Hessian functions over analytical schemes~[unscaled parameter-shift (PS) rule] were repo

Katie Seaborn

Games allow us to construct and explore identities and offer us role models, good and bad. Game characters are a reflection of us -- players and creators alike -- or could be. But do games also encode identities, values, and orientations that transcend diegetic categories and player self-insertion? I explore the notion of game characters as conduits of trans

Yong-Chang Zhang, Thomas Pohl, Fabian Maucher

In this paper we study metastable states in single- and two-component dipolar Bose-Einstein condensates. We show that this system supports a rich spectrum of symmetries that are remarkably stable despite not being ground states. In a parameter region where striped phases are ground states, we find such metastable states that are energetically favourable comp

Katie Seaborn

Games user research is a-booming -- or maybe a-goomba-ing -- with a boundless parade of papers popping up from every nook and pipe. We may need a super power -- or super method -- from another world. I outline three motivations for jump-starting research synthesis in games user research. I argue that: research synthesis will validate this field of study and

P. von Neumann-Cosel, V. O. Nesterenko, I. Brandherm, P. I. Vishnevskiy

Dipole toroidal modes appear in many fields of physics. In nuclei, such a mode was predicted more than 50 years ago, but clear experimental evidence was lacking so far. Using a combination of high-resolution inelastic scattering experiments with photons, electrons and protons, we identify for the first time candidates for toroidal dipole excitations in the n

Beining Wang, Ruizhe Zhang, Yueyue Wu, Qingyao Ai

Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand. Previous studies on user behavior analysis have shown that information retrieval (IR) systems can significantly influence users' decisions by presenting results in varying orders and formats. However, whether such influence exists i

Christopher Blech, Harikrishnan K. Sreekumar, Yannik Hüpel, Sabine C. Langer

For the purpose of high-fidelity aircraft cabin noise simulations during early design phases, we study three efficient solving approaches for the fully coupled finite element model of an aircraft fuselage segment. Obtaining an efficient solution with respect to consumed computational time and resources is challenging within a conventional simulation pipeline

Katie Seaborn, Satoru Iseya

Maldaimonia is a new experiential concept that refers to self-actualization and self-expression through egocentric, destructive, and/or exploitative activities. Still, it is unclear whether maldaimonia is an actual facet of real experience. As a subversive orientation, it may be rare or socially challenging to discuss openly. However, video games provide a s

Xinling Li, Yu Shen, Chi Xie, Xiaohu Zhang

To enhance the service quality of bikesharing programs, bike fleet relocation is widely applied to redistribute bikes from bike sufficient areas to bike shortage areas thereby making a better bike-rider balance across different areas. In this study, a network flow model is proposed to solve the optimal relocation problem of shared bikes, and is implemented w

Katie Seaborn, Katja Rogers, Somang Name, Miu Kojima

Kawaii is the Japanese concept of cute++, a global export with local characteristics. Recent work has explored kawaii as a feature of user experience (UX) with social robots, virtual characters, and voice assistants, i.e., kawaii vocalics. Games have a long history of incorporating characters that use voice as a means of expressing kawaii. Nevertheless, no w

Juan Li, Claudia Bank

Dominance is usually considered a constant value that describes the relative difference in fitness or phenotype between heterozygotes and the average of homozygotes at a focal polymorphic locus. However, the observed dominance can vary with the genetic background of the focal locus. Here, alleles at other loci modify the observed phenotype through position e

Olga Sambataro, Riccardo Costanzi, Joao Alves, Andrea Caiti

This paper presents a comprehensive review of the current state of the art in quantum navigation systems, with a specific focus on their application in maritime navigation. Quantum technologies have the potential to revolutionise navigation and positioning systems due to their ability to provide highly accurate and secure information. The review covers the p

Muze Ren

The Baxterization process for the dynamical Yang-Baxter equation is studied. We introduce the local dynamical Hecke ,Temperley-Lieb and Birman-Murakami-Wenzl operators, then by inserting spectral parameters, from each representation of these operators, we get dynamical R matrix under some conditions. As applications, we reformulate trigonometric degeneration

Arvind Renganathan, Rahul Ghosh, Ankush Khandelwal, Vipin Kumar

We introduce TAM-RL (Task Aware Modulation using Representation Learning), a novel multimodal meta-learning framework for few-shot learning in heterogeneous systems, designed for science and engineering problems where entities share a common underlying forward model but exhibit heterogeneity due to entity-specific characteristics. TAM-RL leverages an amortiz

Yuyang Zhang, Xiaofeng Han, Baojun Wang

Recently, although pre-trained language models have achieved great success on multilingual NLP (Natural Language Processing) tasks, the lack of training data on many tasks in low-resource languages still limits their performance. One effective way of solving that problem is to transfer knowledge from rich-resource languages to low-resource languages. However

Stefano Longhi

In classical mechanics, a particle cannot escape from an unbounded potential well. Naively, one would expect a similar result to hold in wave mechanics, since high barriers make tunneling difficult. However, this is not always the case and it is known that wave delocalization can arise in certain models with incommensurate unbounded potentials sustaining cri

Chaoqi Chen, Luyao Tang, Leitian Tao, Hong-Yu Zhou

Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel domains and object classes often occur. In this paper, we study a practical problem of Domain Generalization under Category Shift (DGCS), which aims to simultaneously detect unknown-

Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun

Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Although current methods achieve target joint distribution identifiability by enforcing minimal changes across domains, they often necessitate stringent conditions, such as an adequate number of domains, monotonic transf

Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li

This paper aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos. To derive quality evaluation systems, the study uses subjective questionnai

Qi Li, Jiaxin Cai, Yuanlong Yu, Jason Gu

Amidst the swift advancements in photography and sensor technologies, high-definition cameras have become commonplace in the deployment of Unmanned Aerial Vehicles (UAVs) for diverse operational purposes. Within the domain of UAV imagery analysis, the segmentation of ultra-high resolution images emerges as a substantial and intricate challenge, especially wh

Mei Zhang, Haocheng Zhang, Chengqing Jiang

An accurate measurement of magnetic field is very important for understanding the formation and evolution of solar magnetic fields. Currently there are two types of solar magnetic field measurement instruments: the filter-based magnetographs and the Stokes polarimeters. The former gives high temporal resolution magnetograms and the latter provides more accur

Sajjad Amrollahi Biyouki, Hoon Hwangbo

Image deblurring tries to eliminate degradation elements of an image causing blurriness and improve the quality of an image for better texture and object visualization. Traditionally, prior-based optimization approaches predominated in image deblurring, but deep neural networks recently brought a major breakthrough in the field. In this paper, we comprehensi

Qingzhai Fan, Yutong Wu

Let $\Omega$ be a class of unital $\rm C^{*}$-algebras. The class of ${\rm C^*}$-algebras which are asymptotical tracially in $\Omega$, denoted by ${\rm AT}\Omega$. In this paper, we will show that the following class of ${\rm C^*}$-algebras in the class $\Omega$ are inherited by simple unital ${\rm C^*}$-algebras in the class $\rm T\Omega$ $(1)$ the class o

Vikas Saini, Ajinkya Punjal, Utkarsh Kumar Pandey, Ruturaj Vikrant Puranik

In topological materials, shielding of bulk and surface states by crystalline symmetries has provided hitherto unknown access to electronic states in condensed matter physics. Interestingly, photo-excited carriers relax on an ultrafast timescale, demonstrating large transient mobility that could be harnessed for the development of ultrafast optoelectronic de

Zhizheng Zhang, Wenxuan Xie, Xiaoyi Zhang, Yan Lu

Recent popularity of Large Language Models (LLMs) has opened countless possibilities in automating numerous AI tasks by connecting LLMs to various domain-specific models or APIs, where LLMs serve as dispatchers while domain-specific models or APIs are action executors. Despite the vast numbers of domain-specific models/APIs, they still struggle to comprehens

Zihan Zhang, Jiayao Sun, Xianjun Xia, Ziqian Wang

Deep learning based techniques have been popularly adopted in acoustic echo cancellation (AEC). Utilization of speaker representation has extended the frontier of AEC, thus attracting many researchers' interest in personalized acoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies are widely adopted in speech enhancement. To further explore

Shuang Li, Longhui Yuan, Binhui Xie, Tao Yang

Test-time adaptation (TTA) adapts the pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams, which holds great value for the deployment of models in real-world applications. Numerous studies have achieved promising performance on simplistic test streams, characterized by independently and unifor

N. Zen

In Ref. [1], Minkov et al reported the time dependence of magnetic moment of hydride materials under high pressure in a diamond anvil cell. Here we point out that the straight lines interpolated with the measurement results give the misleading impression that thermal flux creep in superconductors decays linearly with time. To dispel this misconception, we co

Liangang Ma

We introduce the notion of n-mating in this work, which includes the classical mating of polynomials as a special case. The new notion brings further links between the polynomial world and the rational world than the classical one, as well as a natural classification of rational maps according to their n-unmatability. We classify the hyperbolic 2-matings acc

Shuai Yuan, Carlo Tomasi

Both optical flow and stereo disparities are image matches and can therefore benefit from joint training. Depth and 3D motion provide geometric rather than photometric information and can further improve optical flow. Accordingly, we design a first network that estimates flow and disparity jointly and is trained without supervision. A second network, trained

Congcong Fu, Hui Li, Jian Lou, Jiangtao Cui

Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (OLAP) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the fact and dimension tables, even those latest DP efforts specifically designed for join, if directly applied

Jinmin Yi, Weicheng Ye, Daniel Gottesman, Zi-Wen Liu

We establish rigorous connections between quantum circuit complexity and approximate quantum error correction (AQEC) capability, two properties of fundamental importance to the physics and practical use of quantum many-body systems, covering systems with both all-to-all connectivity and geometric scenarios like lattice systems in finite spatial dimensions. T

Søren Wengel Mogensen, Odd O. Aalen, Susanne Strohmaier

We study time-dependent mediators in survival analysis using a treatment separation approach due to Didelez [2019] and based on earlier work by Robins and Richardson [2011]. This approach avoids nested counterfactuals and crossworld assumptions which are otherwise common in mediation analysis. The causal model of treatment, mediators, covariates, confounders

Peiyi Li, Ji Liu, Hrushikesh Pramod Patil, Paul Hovland

Virtual distillation is a technique that aims to mitigate errors in noisy quantum computers. It works by preparing multiple copies of a noisy quantum state, bridging them through a circuit, and conducting measurements. As the number of copies increases, this process allows for the estimation of the expectation value with respect to a state that approaches th

D. Saha, Sachin Kulkarni

Physics based numerical simulation has been carried out to probe the sub-gap density of states (DOS) and underlying electron transport properties of amorphous oxide based thin film transistors (TFTs). The DOS model of TFTs consists of exponential band tails, Gaussian shallow donor levels and deep acceptor states. Electrical transport and various TFT performa

Bowei He, Zexu Sun, Jinxin Liu, Shuai Zhang

In offline imitation learning (IL), an agent aims to learn an optimal expert behavior policy without additional online environment interactions. However, in many real-world scenarios, such as robotics manipulation, the offline dataset is collected from suboptimal behaviors without rewards. Due to the scarce expert data, the agents usually suffer from simply

Wendi Ma, Marlon Bran Lorenzana, Wei Dai, Hongfu Sun

As aliasing artefacts are highly structural and non-local, many MRI reconstruction networks use pooling to enlarge filter coverage and incorporate global context. However, this inadvertently impedes fine detail recovery as downsampling creates a resolution bottleneck. Moreover, real and imaginary features are commonly split into separate channels, discarding

Chen Jiao, Mao Fengjian, Lv Zuohong, Tang Jianhua

Recent transfer learning (TL) approaches in industrial intelligent fault diagnosis (FD) mostly follow the "pre-train and fine-tuning" paradigm to address data drift, which emerges from variable working conditions. However, we find that this approach is prone to the phenomenon known as catastrophic forgetting. Furthermore, performing frequent models fine-tuni

Chung-Soo Ahn, Jagath C. Rajapakse, Rajib Rana

While speech emotion recognition (SER) research has made significant progress, achieving generalization across various corpora continues to pose a problem. We propose a novel domain adaptation technique that embodies a multitask framework with SER as the primary task, and contrastive learning and information maximisation loss as auxiliary tasks, underpinned

Mohamed Ghattassi, Nader Masmoudi

In this paper, we present a new generalized Hughes model designed to intelligently depict pedestrian congestion dynamics, allowing pedestrian groups to either navigate through or circumvent high-density regions. First, we describe the microscopic settings of the model. The corresponding optimization problems are deterministic and can be formulated by a close

Jun Huang, Yang Yang, Hang Yu, Jianguo Li

Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services. Despite its benefits, ensuring the reliability and safety of a microservice system remains highly challenging. Existing anomaly detection algorithms based on a single data modality (i.e., metrics, logs

Zhilong Li, Zepeng Gao, Ling Liu, Yongjia Wang

In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision tree based machine-learning algorithm, is used to study the fusion cross section (CS) of heavy-ion reaction. Several basic quantities (e.g., mass number and proton number of projectile and target) and the CS obtained from phenomenological formula are fed into the LightGB

Maryam Bahrami, Zeyad Khashroum

In the rapidly advancing landscape of contemporary technology, power electronics assume a pivotal role across diverse applications, ranging from renewable energy systems to electric vehicles and consumer electronics. The efficacy and precision of these power electronics systems stand as cornerstones of their functionality. Within this context, the integratio

Siqi Du, Shengjun Tang, Weixi Wang, Xiaoming Li

This paper introduces a novel framework, Tree-GPT, which incorporates Large Language Models (LLMs) into the forestry remote sensing data workflow, thereby enhancing the efficiency of data analysis. Currently, LLMs are unable to extract or comprehend information from images and may generate inaccurate text due to a lack of domain knowledge, limiting their use

Ding-qiang Su, Hua Bai, Xiangyan Yuan, Xiangqun Cui

This article presents research work on a spectroscopic survey telescope. Our idea is as follows: for such a telescope, a pure reflecting optical system is designed, which should have an aperture and a field of view (FOV) both as large as possible and excellent image quality, and then a strip lensm (lens-prism) atmospheric dispersion corrector (S-ADC) is adde

Lixi Zhou, Qi Lin, Kanchan Chowdhury, Saif Masood

Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of representative architectures to address the requirement. We highlight three pivotal paradigms: The state-of-the-art DL

Jianmin Chen, Shiquan Ruan, Hongxia Zhang

We give a geometric model for the category of coherent sheaves over the weighted projective line of type $(p,q)$ in terms of an annulus with marked points on its boundary. We establish a bijection between indecomposable sheaves over the weighted projective line and certain homotopy classes of oriented curves in the annulus, and prove that the dimension of ex

Olgica Milenkovic, Chao Pan

In this review paper, we delve into the nascent field of molecular data storage, focusing on system implementations and code constructions. We start by providing an overview of basic concepts in synthetic and computational biology. Afterwards, we proceed with a review of the diverse approaches followed to implement such systems. In the process, we identify n

Zexu Sun, Bowei He, Ming Ma, Jiakai Tang

Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this paper, we first present a possible explanation for the above phenomenon. We verify that there is a feature sensitivity problem in online marketing using different real-world datasets,

Michela Eleuteri, Antonia Passarelli di Napoli

We establish the Lipschitz regularity of the a priori bounded local minimizers of integral functionals with non autonomous energy densities satisfying non standard growth conditions under a sharp bound on the gap between the growth and the ellipticity exponent.

Siyu Ren, Zhiyong Wu, Kenny Q. Zhu

Neural language models are probabilistic models of human text. They are predominantly trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the forward cross-entropy between the empirical data distribution and the model distribution. However, various degeneration phenomena are still widely observed when decoding from the distri

Agnimitra Dasgupta, Dhruv V Patel, Deep Ray, Erik A Johnson

We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posterior distribution of physics-based Bayesian inverse problems framed in high-dimensional ambient spaces. We dub the proposed framework GAN-Flow. The proposed method leverages the intr

Evgeni Lozitsky

We are studying fractional linear recursions of second and third orders and finding periodic recursions with periods of eight and twelve, which, apparently, were not known before.

Pengfei Zhou, Weiqing Min, Yang Zhang, Jiajun Song

Food detection is becoming a fundamental task in food computing that supports various multimedia applications, including food recommendation and dietary monitoring. To deal with real-world scenarios, food detection needs to localize and recognize novel food objects that are not seen during training, demanding Zero-Shot Detection (ZSD). However, the complexit

Jian Wang, Yue Zhuo

The visual anomaly diagnosis can automatically analyze the defective products, which has been widely applied in industrial quality inspection. The anomaly classification can classify the defective products into different categories. However, the anomaly samples are hard to access in practice, which impedes the training of canonical machine learning models. T

Boyang Zheng, Chumeng Liang, Xiaoyu Wu

Diffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on adversarial attacks rises to encounter this unauthorized diffusion customization, by adding protective watermarks to images and poisoning diffusion models. However, current protectio

Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou, Yijia Wang

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with

Mohammadreza M. Kalan, Samory Kpotufe

A critical barrier to learning an accurate decision rule for outlier detection is the scarcity of outlier data. As such, practitioners often turn to the use of similar but imperfect outlier data from which they might transfer information to the target outlier detection task. Despite the recent empirical success of transfer learning approaches in outlier dete

Yuhan Liu, Chengcheng Wan, Kuntai Du, Henry Hoffmann

ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API. However, these APIs offer the same pre-trained models regardless of how their output is used by different applications. This can be suboptimal

Wided Ghardallou, Hessamaldin Mohammadi, Elijah Brick, Ali Mili

Great advances in program analysis would be enabled if it were possible to derive the function of a program from inputs to outputs (or from initial states to final states, depending on how we model program semantics). Efforts to do so have always stalled against the difficulty to derive the function of loops; the expedient solution to capture the function of

Timur Dayanov

A new idea for reusing rockets, based on the Energia 2 launch vehicle, using deployable wings is proposed. A mission design is planned with an expected maximum altitude of 500 m. Following that systems engineering was used to manage the design process of separate components, which in turn led to the manufacturing plans for the separate components. The sizing

Jiaqi Gu, Shenghao Feng, Yimin Wei

We propose a tensor product structure that is compatible with the hypergraph structure. We define the algebraic connectivity of the $(m+1)$-uniform hypergraph in this product, and prove the relationship with the vertex connectivity. We introduce some connectivity optimization problem into the hypergraph, and solve them with the algebraic connectivity. We int

Yayun He, Zuheng Kang, Jianzong Wang, Junqing Peng

Speaker verification (SV) performance deteriorates as utterances become shorter. To this end, we propose a new architecture called VoiceExtender which provides a promising solution for improving SV performance when handling short-duration speech signals. We use two guided diffusion models, the built-in and the external speaker embedding (SE) guided diffusion

Meng Li, Yibo Shi, Jing Wang, Yunqi Huang

With the growing demand for video applications, many advanced learned video compression methods have been developed, outperforming traditional methods in terms of objective quality metrics such as PSNR. Existing methods primarily focus on objective quality but tend to overlook perceptual quality. Directly incorporating perceptual loss into a learned video co

Jianyou Wang, Kaicheng Wang, Xiaoyue Wang, Prudhviraj Naidu

In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high cost and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, Scientific DOc

Rongzhao Zhang, Zhian Bai, Ruoying Yu, Wenrao Pang

When delineating lesions from medical images, a human expert can always keep in mind the anatomical structure behind the voxels. However, although high-quality (though not perfect) anatomical information can be retrieved from computed tomography (CT) scans with modern deep learning algorithms, it is still an open problem how these automatically generated org

Samuel Schmidgall, Axel Krieger, Jason Eshraghian

Recent advances in robot-assisted surgery have resulted in progressively more precise, efficient, and minimally invasive procedures, sparking a new era of robotic surgical intervention. This enables doctors, in collaborative interaction with robots, to perform traditional or minimally invasive surgeries with improved outcomes through smaller incisions. Recen

Haojie Shi, Qingxu Zhu, Lei Han, Wanchao Chi

In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which leads to safe and energy-efficient locomotion. Inspired by this observation, we present a novel approach to train a Deep Neural Network (DNN) policy that integrates proprioceptive an

Taicheng Guo, Changsheng Ma, Xiuying Chen, Bozhao Nan

Reaction prediction, a critical task in synthetic chemistry, is to predict the outcome of a reaction based on given reactants. Generative models like Transformer have typically been employed to predict the reaction product. However, these likelihood-maximization models overlooked the inherent stochastic nature of chemical reactions, such as the multiple ways