March 2024 arXiv papers — page 12

Luchang Li, Sheng Qian, Jie Lu, Lunxi Yuan

The Large Language Model (LLM) is widely employed for tasks such as intelligent assistants, text summarization, translation, and multi-modality on mobile phones. However, the current methods for on-device LLM deployment maintain slow inference speed, which causes poor user experience. To facilitate high-efficiency LLM deployment on device GPUs, we propose fo

Mirantsoa Aimé Rasolofomanana, Romain Le Tellier, Hervé Henry

The coupling between mass transfer and hydrodynamic phenomena in two-phase flow is not necessarily straightforward due to the different effects that can be encountered. The treatment of such coupling is complex and requires particular efforts, especially in the modelling of the interface between phases. In this paper, we consider the case of a droplet compos

Didarul Islam, Mohammad Abdullah Al Faisal

This study explored the variability in Aggregate Personal Income (PI) across 13 major industrial sectors in the US during the COVID-19 pandemic. Utilizing time-series data from 2010 Q1 to 2019 Q4, we employed Autoregressive Integrated Moving Average (ARIMA) models to establish baseline trends in Personal Income (PI) before the pandemic. We then extended thes

Wei-Cheng Long, Juan Wu

The AMS02 experiment has published the periodic spectra of proton, helium and helium isotopes across the majority of the 24 solar cycle. These precise data exhibit temporal structures that correlate with solar modulation. In this study, we utilize these data to probe three analytic solar modulation models, including the force-field approximation, the convect

Takafumi Miyazaki, István Pink

It is conjectured that for any fixed relatively prime positive integers $a,b$ and $c$ all greater than 1 there is at most one solution to the equation $a^x+b^y=c^z$ in positive integers $x,y$ and $z$, except for specific cases. We develop the methods in our previous work which rely on a variety from Baker's theory and thoroughly study the conjecture for case

T. B. Mazitov, A. A. Katanin

We study formation and screening of local magnetic moments in the two-band Hubbard model in the presence of Hund exchange interaction using dynamic mean field theory approach. The characteristic temperatures of the formation, beginning and full screening of local magnetic moments are obtained from the analysis of temperature dependencies of the orbital, char

Renkai Wu, Yinghao Liu, Pengchen Liang, Qing Chang

Traditionally for improving the segmentation performance of models, most approaches prefer to use adding more complex modules. And this is not suitable for the medical field, especially for mobile medical devices, where computationally loaded models are not suitable for real clinical environments due to computational resource constraints. Recently, state-spa

Tianchen Deng, Yanbo Wang, Hongle Xie, Hesheng Wang

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obta

Amir Eshaghi Chaleshtori

Price prediction algorithms propose prices for every product or service according to market trends, projected demand, and other characteristics, including government rules, international transactions, and speculation and expectation. As the dependent variable in price prediction, it is affected by several independent and correlated variables which may challe

Zhuopeng Li, Yilin Zhang, Chenming Wu, Jianke Zhu

The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings. However, the previous 3DGS-based methods have limitations in urban scenes due to reliance on initial Structure-from-Motion(SfM) points and difficulties in rendering distant, sky and low-texture areas. To overcome the

Yiteng Xu, Kecheng Ye, Xiao Han, Yiming Ren

Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds, further advancing downstream human-centric tasks and applications. Previous works usually focus on tackling one specific task and rely on huge labeled data, which has poor generalization

Si Xiao, Xianmin Xu

In this paper, we introduce a new approach to solving the porous medium equation using a moving mesh finite element method that leverages the Onsager variational principle as an approximation tool. Both the continuous and discrete problems are formulated based on the Onsager principle. The energy dissipation structure is maintained in the semi-discrete and f

Shoichiro Kitada, Taishi Kotsuka, Yutaka Hori

Molecular communication (MC) is a concept in communication engineering, where diffusive molecules are used to transmit information between nano or micro-scale chemical reaction systems. Engineering MC to control the reaction systems in cells is expected for many applications such as targeted drug delivery and biocomputing. Toward control of the reaction syst

Paulo Sergio Pereira da Silva, Pierre Rouchon

We present a monotonic numerical algorithm including time optimization for generating quantum gates for open systems. Such systems are assumed to be governed by Lindblad master equations for the density operators on a large Hilbert-space whereas the quantum gates are relative to a sub-space of small dimension. Starting from an initial seed of the control inp

Amit Srivastava

Yager[5] proposed a transformation for opposing(negating) the occurence of an event that is not certain using the idea that one can oppose the occurence of any uncertain event by allocating its probability among the other outcomes in the sample space without preference to any particular outcome \textit{i.e.} the probability of every event in the sample space

Venkat Venkatasubramanian, N Sanjeevrajan, Manasi Khandekar, Abhishek Sivaram

Despite the stunning progress recently in large-scale deep neural network applications, our understanding of their microstructure, 'energy' functions, and optimal design remains incomplete. Here, we present a new game-theoretic framework, called statistical teleodynamics, that reveals important insights into these key properties. The optimally robust design

Shuang Li, Jiahua Wang, Lijie Wen

Multi-modal reasoning plays a vital role in bridging the gap between textual and visual information, enabling a deeper understanding of the context. This paper presents the Feature Swapping Multi-modal Reasoning (FSMR) model, designed to enhance multi-modal reasoning through feature swapping. FSMR leverages a pre-trained visual-language model as an encoder,

Jingze Ding, Zijian Zhou, Chenbo Wang, Wenyao Li

This paper investigates physical layer security (PLS) in a movable antenna (MA)-assisted full-duplex (FD) system. In this system, an FD base station (BS) with multiple MAs for transmission and reception provides services for an uplink (UL) user and a downlink (DL) user. Each user operates in half-duplex (HD) mode and is equipped with a single fixed-position

Piotr Pokora, Xavier Roulleau

We construct new examples of free curve arrangements in the complex projective plane using point-line operators recently defined by the second author. In particular, we construct a new example of a conic-line arrangement with ordinary quasi-homogeneous singularities that has non-trivial monodromy.

Yasutaka Hanada, Akira Shudo

Quantum tunneling in a two-dimensional integrable map is studied. The orbits of the map are all confined to the curves specified by the one-dimensional Hamiltonian. It is found that the behavior of tunneling splitting for the integrable map and the associated Hamiltonian system is qualitatively the same, with only a slight difference in magnitude. However, t

Ruijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma

Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface. The inherent variability in brain function between individuals leads existing literature to focus on acquiring separate models for each individual using their respective brain signal data, ignoring commonalities between these dat

Chunbo Lin, Han Zheng

Polymers, integral to advancements in high-tech fields, necessitate the study of their thermal conductivity (TC) to enhance material attributes and energy efficiency. The TC of polymers obtained by molecular dynamics (MD) calculations and experimental measurements is slow, and it is difficult to screen polymers with specific TC in a wide range. Existing mach

Yuki Akiyama, Minh Vu, Konstantinos Slavakis

This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL). The proposed mappings benefit from the rich approximating properties of RKHSs, adopt no assumptions on the statistics of the data owing to their nonparametric nature, require no knowledge on transition probabilities of Markov

Yu Zhang, Haozhe Wang, Jie Cui, Tao He

The growing demand for real-time ultraviolet (UV) monitoring calls for a simple, rapid, and low-cost strategy to prepare UV photodetectors. We prepare a wearable real-time UV photodetector based on hydrogenated titanium dioxide film synthesized by radio frequency atmospheric pressure plasma. The conductivity of our hydrogenated titanium dioxide is improved t

Yunhao Li, Xiaodong Wang, Ping Wang, Xin Yuan

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene representation from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging

Nanako Kato, Luca Maxia, Cristian Pisano

Within the framework of transverse momentum dependent factorization in combination with nonrelativistic QCD, we study charmonium and bottomonium production in hadronic collisions. We focus on quarkonium states with even charge conjugation, for which the color-singlet production mechanism is expected to be also dominant in the small transverse momentum region

Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Jade Freeman

Autonomous robots operating in complex environments face the critical challenge of identifying and utilizing environmental cover for covert navigation to minimize exposure to potential threats. We propose EnCoMP, an enhanced navigation framework that integrates offline reinforcement learning and our novel Adaptive Threat-Aware Visibility Estimation (ATAVE) a

Juhwan Choi, YoungBin Kim

In the field of text data augmentation, rule-based methods are widely adopted for real-world applications owing to their cost-efficiency. However, conventional rule-based approaches suffer from the possibility of losing the original semantics of the given text. We propose a novel text data augmentation strategy that avoids such phenomena through a straightfo

Tonghui Ren, Yuankai Fan, Zhenying He, Ren Huang

Large Language Model (LLM) techniques play an increasingly important role in Natural Language to SQL (NL2SQL) translation. LLMs trained by extensive corpora have strong natural language understanding and basic SQL generation abilities without additional tuning specific to NL2SQL tasks. Existing LLMs-based NL2SQL approaches try to improve the translation by e

Yunhao Li, Jing Wu, Lingzhe Zhao, Peidong Liu

When capturing images through the glass during rainy or snowy weather conditions, the resulting images often contain waterdrops adhered on the glass surface, and these waterdrops significantly degrade the image quality and performance of many computer vision algorithms. To tackle these limitations, we propose a method to reconstruct the clear 3D scene implic

Juhwan Choi, YoungBin Kim

Data augmentation is one of the regularization strategies for the training of deep learning models, which enhances generalizability and prevents overfitting, leading to performance improvement. Although researchers have proposed various data augmentation techniques, they often lack consideration for the difficulty of augmented data. Recently, another line of

S. K. Maurya, Ksh. Newton Singh, G. Mustafa, M. Govender

In this study we explore the astrophysical implications of pressure anisotropy on the physical characteristics of millisecond pulsars within the framework of $f(Q)$ gravity, {in particular $f(Q)=-\alpha\, Q - \beta$, where $\alpha$ and $\beta$ are constants.} Starting off with the field equations for anisotropic matter configurations, we adopt the physically

Clément Erignoux, Brune Massoulié

We introduce a new particle system that we call the SSEP with traps, which is non reversible, attractive, and has a transient regime. We study its \emph{transience time} $\theta_K$, meaning the time after which the system is no longer in a transient state with high probability, on the ring with $K$ sites. We first show that $\theta_K$ is of order $K^2 \log K

Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu

Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics, an area not previously covered in studies on hallucinations. We are able to conduct this analysis via two key ide

Fatima Zaidouni, Dahlia Veyrat, Kelly A. Douglass, Segev BenZvi

We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: V2, a Python implementation of ZOBOV, and VoidFinder, an algorithm which grows and merges spherical void regions. Using the Sloan Digital Sky Survey D

Benoit Liquet, Sarat Moka, Samuel Muller

The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on two multivariate statistical methods: principal components analysis and partial least squares. Both approache

Amir Eshaghi Chaleshtori, Abdollah Aghaie

Several methods have been proposed to identify which sensor sets are optimal for finding and localizing faults under different conditions for mechanical equipment. In order to preserve acceptable performance while minimizing costs, it is crucial to identify the most effective set of sensors available. Nevertheless, some sensor sets can behave differently in

Shuyao Xu, Long Qin, Tianyang Chen, Zhenzhou Zha

In second language learning, scenario-based conversation practice is important for language learners to achieve fluency in speaking, but students often lack sufficient opportunities to practice their conversational skills with qualified instructors or native speakers. To bridge this gap, we propose situational dialogue models for students to engage in conver

Rixin Xua, Zuojie Huanga, Wenchao Gonga, Wu Zhoua

The Depth from Defocus (DFD) imaging technique for measuring the size and number concentration of particles in a dispersed two-phase flow has up to now been restricted to relatively sparse particle densities and to identifying only spherical particles. The present study examines two advancements to the technique, widening its range of application significant

L. Ram, D. Rout, S. Sarkhel

The impact of a rarest solar wind phenomenon [disappearing solar wind (DSW) event during 26-28 December 2022] on the Martian nightside ionosphere is investigated, for the first time, using MAVEN datasets. During an extremely low solar wind density, the Martian top nightside ionospheric species underwent significant enhancements. At a given altitude, the dens

Zelin Zhao, Fenglei Fan, Wenlong Liao, Junchi Yan

Many contemporary studies utilize grid-based models for neural field representation, but a systematic analysis of grid-based models is still missing, hindering the improvement of those models. Therefore, this paper introduces a theoretical framework for grid-based models. This framework points out that these models' approximation and generalization behaviors

Boyuan Liang, Lingfeng Sun, Xinghao Zhu, Bike Zhang

In reinforcement learning for legged robot locomotion, crafting effective reward strategies is crucial. Pre-defined gait patterns and complex reward systems are widely used to stabilize policy training. Drawing from the natural locomotion behaviors of humans and animals, which adapt their gaits to minimize energy consumption, we propose a simplified, energy-

Jian-Ci Xiao

Luo, Rao and Xiong [Topol. Appl. 322 (2022), 108271] conjectured that if a planar self-similar iterated function system with the open set condition does not involve rotations or reflections, then every connected component of the attractor is locally connected. We create a homogeneous counterexample of Lalley-Gatzouras type, which disproves this conjecture.

Mikiya Tomikawa, Ryo Araki, Atsutoshi Ikeda, Ai Nakamura

Piezomagnetism, linear response between strain and magnetic field, is relatively unexplored cross-correlation but has promising potential as a novel probe of time-reversal-symmetry breaking in various classes of materials. Interestingly, there has been no report of piezomagnetism in ferromagnets, most archetypal time-reversal-symmetry-broken materials. This

Jaehee Lee, Jeongun Lee, Seounghee Yun, Sanha Kim

Elastomeric materials display a complicated set of stretchability and fracture properties that strongly depend on the flaw size, which has long been of interest to engineers and materials scientists. Here, we combine experiments and numerical simulations for a comprehensive understanding of the nonlocal, size-dependent features of fracture in elastomers. We

Muhammad Sakib Khan Inan, Kewen Liao, Haifeng Shen, Prem Prakash Jayaraman

Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc. Such presented sequence data heterogeneity makes it difficult for traditional time series classification algorithms to perform well. Therefore, addressing the heterogeneity challenge demands learning not

Prasanna Vijayaraghavan, Jeffrey Frederic Queisser, Sergio Verduzco Flores, Jun Tani

Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic. "How can linguistic compositionality be developed concomitant

Xing Qin, Xu Liu, Shuangge Ma, Mengyun Wu

Heterogeneity is a fundamental characteristic of cancer. To accommodate heterogeneity, subgroup identification has been extensively studied and broadly categorized into unsupervised and supervised analysis. Compared to unsupervised analysis, supervised approaches potentially hold greater clinical implications. Under the unsupervised analysis framework, sever

P. Flock, A. Laguna-Salina, F. James, G. Blossom

This paper argues why FLAMINGO (Fast Light Atmospheric Monitoring and Imaging Novel Gamma-ray Observatory) is the perfect name for an array of very-high-energy Cherenkov telescopes. Studies which indicate pink is the most suitable pigment for the structures of Cherenkov telescopes have passed with flying colors. Pink optimizes the absorption and reflectivity

Maha Nawaz, Abdul Basit, Muhammad Shafique

Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current tech

Sonwabile Mafunda, Jonathan L. Merzel, K. E. Perry, Anna Varvak

We determine the paint cost spectrum for perfect $k$-ary trees. A coloring of the vertices of a graph $G$ with $d$ colors is said to be \emph{$d$-distinguishing} if only the trivial automorphism preserves the color classes. The smallest such $d$ is the distinguishing number of $G$ and is denoted $\mbox{dist}(G).$ The \emph{paint cost of $d$-distinguishing $G

Yarden Mazor

We analytically and numerically study the nonreciprocal surface waves guided by a magnetized metasurface tube. When applying the magnetic bias perpendicularly to the cylinder axis, the conductivity profile cross-section is nonuniform, which enables us to obtain pronounced nonreciprocal modes with different field distributions for propagation in opposite dire

Shuaishuai Liu, Yan Tian, Yu Zhang, Zhenguo Lu

Communication and sensing technology play a significant role in various aspects of modern society. A seamless combination of the communication and the sensing systems is desired and have attracted great interests in recent years. Here, we propose and demonstrate a network architecture that integrating the downstream quantum access network (DQAN) and vibratio

Zhihua Zhong, Hideki Tayakasu, Misako Takayasu

Human mobility in an urban area is complicated; the origins, destinations, and transport methods of each person differ. The quantitative description of urban human mobility has recently attracted the attention of researchers, and it highly related to urban science problems. Herein, combined with physics inspiration, we introduce a revised electric circuit mo

Mengjie Zhang, Yong Lin, Yunyan Yang

Nowadays a great attention has been focused on the discrete fractional Laplace operator as the natural counterpart of the continuous one. In this paper, we discretize the fractional Laplace operator $(-\Delta)^{s}$ for an arbitrary finite graph and any positive real number $s$. It is shown that $(-\Delta)^{s}$ can be explicitly represented by eigenvalues and

R. Jansen, S. Yuasa

Employing spins in quantum dots for fault-tolerant quantum computing in large-scale qubit arrays with on-chip control electronics requires high-fidelity qubit operation at elevated temperature. This poses a challenge for single spin initialization and readout. Existing schemes rely on Zeeman splitting or Pauli spin blockade with typical energy scales of 0.1

Byeongin Joung, Byeong-Uk Lee, Jaesung Choe, Ukcheol Shin

This paper proposes an algorithm for synthesizing novel views under few-shot setup. The main concept is to develop a stable surface regularization technique called Annealing Signed Distance Function (ASDF), which anneals the surface in a coarse-to-fine manner to accelerate convergence speed. We observe that the Eikonal loss - which is a widely known geometri

Shigeyuki Ishida, Yoshihisa Kamiya, Yoshinori Tsuchiya, Pavan Kumar Naik Sugali

Grain alignment is a key factor that determines the performance of a superconducting bulk. In this study, the spark plasma texturing (SPT) technique was used to fabricate a CaKFe$_4$As$_4$ superconducting bulk. X-ray diffraction and electron backscatter diffraction revealed that the $c$-axes of the CaKFe$_4$As$_4$ grains in the SPT bulk are aligned, demonstr

Hongzhi Liu, Guicheng Li, Jiacheng Nie, Hui Tang

Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the correspondi

Youlin Li, Zhengyi Zhou

We execute Avdek's algorithm to find many algebraically overtwisted and tight $3$-manifolds by contact $+1$ surgeries. In particular, we show that a contact $1/k$ surgery on the standard contact $3$-sphere along any positive torus knot with the maximum Thurston-Bennequin invariant yields an algebraically overtwisted and tight $3$-manifold, where $k$ is a pos

Shigeyuki Ishida, S. Pavan Kumar Naik, Yoshinori Tsuchiya, Yasunori Mawatari

A high density CaKFe$_4$As$_4$ bulk sample was successfully synthesized using a spark plasma sintering (SPS) technique. The density of the synthesized sample was 5.02 g cm$^{-3}$, corresponding to 96.2% of the theoretical density of CaKFe$_4$As$_4$. Moreover, a reasonably high Vickers hardness of 1 GPa was measured. The electrical resistivity of the SPS bulk

Jiayu Li, Xuechao Zou, Shiying Wang, Ben Chen

Cattle face recognition holds paramount significance in domains such as animal husbandry and behavioral research. Despite significant progress in confined environments, applying these accomplishments in wild settings remains challenging. Thus, we create the first large-scale cattle face recognition dataset, ICRWE, for wild environments. It encompasses 483 ca

Qinhao Zhou, Yuwen Tan, Boqing Gong, Xiang Xiang

Class-incremental learning (CIL) aims to enable models to continuously learn new classes while overcoming catastrophic forgetting. The introduction of pre-trained models has brought new tuning paradigms to CIL. In this paper, we revisit different parameter-efficient tuning (PET) methods within the context of continual learning. We observe that adapter tuning

Simon M. Huttegger, Sean Walsh, Francesca Zaffora Blando

L\'evy's Upward Theorem says that the conditional expectation of an integrable random variable converges with probability one to its true value with increasing information. In this paper, we use methods from effective probability theory to characterise the probability one set along which convergence to the truth occurs, and the rate at which the convergence

A. Winter, A. Winter, A. Winter, A. Winter

We critically examine the often-made observation that "quantum winter [or some other winter] is coming", and the related admonition to prepare for this or that winter, inevitably bound to arrive. What we find based on even the most superficial look at the available evidence is that such statements not only are overblown hype, but are also factually wrong: Wi

Zhiyu Li, Xikui Ma, Zoé-Lise Deck-Léger, Amir Bahrami

Space-time modulation systems have garnered significant attention due to their resemblance to moving-matter systems and promising applications. Unlike conventional moving-matter systems, modulation systems do not involve net motion of matter, and are therefore easier to implement and not restricted to subluminal velocities. However, canonical wave-medium int

Aayush Atul Verma, Bharatesh Chakravarthi, Arpitsinh Vaghela, Hua Wei

Event cameras, with their high temporal and dynamic range and minimal memory usage, have found applications in various fields. However, their potential in static traffic monitoring remains largely unexplored. To facilitate this exploration, we present eTraM - a first-of-its-kind, fully event-based traffic monitoring dataset. eTraM offers 10 hr of data from d

Yanyan Shao, Shuting He, Qi Ye, Yuchao Feng

Tracking by natural language specification (TNL) aims to consistently localize a target in a video sequence given a linguistic description in the initial frame. Existing methodologies perform language-based and template-based matching for target reasoning separately and merge the matching results from two sources, which suffer from tracking drift when langua

Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy, Noman Bashir

Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper studies the online carbon-aware resource scaling problem with unknown job lengths (OCSU) and applies it to carbon-aware resource scaling for executing computing workloads. The task is to dynamically scale resources (e.g., the number of servers) assigned to a job of un

Toshiro Hiranouchi

Our investigation focuses on an additive analogue of the Bloch-Gabber-Kato theorem which establishes a relation between the Milnor $K$-group of a field of positive characteristic and a Galois cohomology group of the field. Extending the Aritin-Schreier-Witt theory, we present an isomorphism from the Mackey product associated with the Witt group and the multi

Akshita Abrol, Purnima Murali Mohan, Tram Truong-Huu

In next-generation networks, achieving Round-trip Time (RTT) fairness is essential for ensuring fair bandwidth distribution among diverse flow types, enhancing overall network utilization. The TCP congestion control algorithm -- BBR, was proposed by Google to dynamically adjust sending rates in response to changing network conditions. While BBRv2 was impleme

Jun Li, Ziluo Zhang, Zhanglin Hou, Kento Yasuda

We discuss and compare the statistical properties of two stochastic three-sphere micromachines, i.e., odd micromachine and thermal micromachine. We calculate the steady state time-correlation functions for these micromachines and decompose them into the symmetric and antisymmetric parts. In both cases, the cross-correlation between the two spring extensions

Yafeng Chen, Siqi Zheng, Hui Wang, Luyao Cheng

We introduce 3D-Speaker-Toolkit, an open-source toolkit for multimodal speaker verification and diarization, designed for meeting the needs of academic researchers and industrial practitioners. The 3D-Speaker-Toolkit adeptly leverages the combined strengths of acoustic, semantic, and visual data, seamlessly fusing these modalities to offer robust speaker rec

Jacob Sherson, Florent Vinchon

This study investigates the optimization of Generative AI (GenAI) systems through human feedback, focusing on how varying feedback mechanisms influence the quality of GenAI outputs. We devised a Human-AI training loop where 32 students, divided into two groups, evaluated AI-generated responses based on a single prompt. One group assessed a single output, whi

Armando D. Urbina, Hari Sridhara, Alexis Scholtz, Andrea M. Armani

The following laboratory procedure provides students with a hands-on experience in nanomaterials chemistry and characterization. This three-day protocol is easy to follow for undergraduates with basic chemistry or materials science backgrounds and is suitable for inclusion in upper division courses in inorganic chemistry or materials science. Students use ai

Guanhua Ding, Zexi Ye, Zhen Zhong, Gang Li

Block pruning, which eliminates contiguous blocks of weights, is a structural pruning method that can significantly enhance the performance of neural processing units (NPUs). In industrial applications, an ideal block pruning algorithm should meet three key requirements: (1) maintain high accuracy across diverse models and tasks, as machine learning deployme

Jae-Hwan Choi, Ildoo Kim

We broaden the domain of the Fourier transform to contain all distributions without using the Paley-Wiener theorem and devise a new weak formulation built upon this extension. This formulation is applicable to evolution equations involving pseudo-differential operators, even when the signs of their symbols may vary over time. Notably, our main operator inclu

Xu Ma, Xiyang Dai, Yue Bai, Yizhou Wang

Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design. While intuitive explanations abound, the foundational rationale behind its application remains largely unexplored. Our study attempts to reveal the star operation's ability to map inputs into high-dimensional, non-linear feat

Wanyu Bian, Albert Jang, Fang Liu

Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity among image datasets with different types of contrast leads to suboptimal learning performance. This paper pro

Megumi Niikura, Shinichiro Abe, Shoichiro Kawase, Teiichiro Matsuzaki

We plan to develop a new nuclear database for muon-induced nuclear reactions (muon nuclear data). The database will consist of (1) energies and intensities of the muonic X rays, (2) lifetimes of the muonic atom, (3) production branching ratio of the residual nuclei by muon capture, (4) emission probabilities of the particles after muon capture, and (5) energ

Robik Shrestha, Yang Zou, Qiuyu Chen, Zhiheng Li

Existing text-to-image generative models reflect or even amplify societal biases ingrained in their training data. This is especially concerning for human image generation where models are biased against certain demographic groups. Existing attempts to rectify this issue are hindered by the inherent limitations of the pre-trained models and fail to substanti

J. Beck, W. W. L. Chen, Y. Yang

We study some new dynamical systems where the corresponding piecewise linear flow is neither time reversible nor measure preserving. We create a dissipative system by starting with a finite polysquare translation surface, and then modifying it by including a one-sided barrier on a common vertical edge of two adjacent atomic squares, in the form of a union of

Chuan-Yin Xia, Hua-Bi Zeng

In the framework of the AdS/CFT correspondence, we find a neutral complex scalar field dynamics in a $2+1$ dimensional black hole background which can provide a scheme for studying the pattern formation process in $1+1$ dimensional reaction-diffusion systems. The patterns include plane wave, defect turbulence, phase turbulence, spatio-temporal intermittency

J. Beck, W. W. L. Chen, Y. Yang

We study the relationship between the discrete and the continuous versions of the Kronecker--Weyl equidistribution theorem, as well as their possible extension to manifolds in higher dimensions. We also investigate a way to deduce in some limited way uniformity results in higher dimension from results in lower dimension.

Xu Ma, Xiyang Dai, Jianwei Yang, Bin Xiao

In this work, we present efficient modulation, a novel design for efficient vision networks. We revisit the modulation mechanism, which operates input through convolutional context modeling and feature projection layers, and fuses features via element-wise multiplication and an MLP block. We demonstrate that the modulation mechanism is particularly well suit

Qinhao Zhou, Zihan Zhang, Xiang Xiang, Ke Wang

Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks. However, when used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4. As intelligent ag

H. Nakada, K. Ishida

Based on the variational principle, self-consistent single-particle (s.p.) potentials at positive energies are discussed, which correspond to the real part of the optical potential as the single folding potential (SFP). The nuclear-matter s.p. potential produced by the semi-realistic nucleonic interaction M3Y-P6, which has links to the bare nucleonic interac

J. Beck, W. W. L. Chen, Y. Yang

We extend the famous result of Katok and Zemlyakov on the density of half-infinite geodesics on finite flat rational surfaces to half-infinite geodesics on a finite polycube translation $3$-manifold. We also extend this original result to establish a weak uniformity statement.

Kolade Adjibi, Allan Martinez, Miguel Mascorro, Carlos Montes

We will present exact solutions for three variations of stochastic Korteweg de Vries-Burgers (KdV-Burgers) equation featuring variable coefficients. In each variant, white noise exhibits spatial uniformity, and the three categories include additive, multiplicative, and advection noise. Across all cases, the coefficients are time-dependent functions. Our disc

J. Beck, W. W. L. Chen, Y. Yang

Almost nothing is known concerning the extension of $3$-dimensional Kronecker--Weyl equidistribution theorem on geodesic flow from the unit torus $[0,1)^3$ to non-integrable finite polycube translation $3$-manifolds. In the special case when a finite polycube translation $3$-manifold is the cartesian product of a finite polysquare translation surface with th

Earl Patrick Bellinger, Jakob Stegmann, Tom Wagg

Local and distant measurements of the Hubble constant are in significant tension: local measurements of the Hubble constant appear to show a Universe that is significantly contracted when compared to distant measurements. From the point of view of an observer, a passing gravitational wave could cause the Universe to appear locally contracted and expanded in

Yanhui Zhang, Caisheng Wei, Yifan Zhang, Congcong Tian

This paper studies quadcopters obstacle avoidance trajectory control (OATC) problem for express delivery. A new nonlinear adaptive learning controller that is low-cost and portable to different wheelbase sizes is proposed to adapt to large-angle maneuvers and load changes in UAV delivery missions. The controller consists of a nonlinear variable gain (NLVG) f

Zhenyao He, Jindan Xu, Hong Shen, Wei Xu

Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting

J. Beck, W. W. L. Chen, Y. Yang

The class of 2-dimensional non-integrable flat dynamical systems has a rather extensive literature with many deep results, but the methods developed for this type of problems, both the traditional approach via Teichm\"{u}ller geometry and our recent shortline-ancestor method, appear to be exclusively plane-specific. Thus we know very little of any real signi

Sutapa Mandal, Snehashish Sarkar, Pinaki Pal

Numerous study on natural and man made systems including rotating convection report the phenomena of supercritical and subcritical transitions from one state to another with the variation of relevant control parameters. However, the complexity of the rotating convection system even under the idealized Rayleigh-B\'enard geometry, hindered the simplest possibl

Xuhai Hong, Lang Su, Jie Li

The optical absorption spectra of cyclo[n]carbons (n=10, 14, 18) are investigated in the framework of time-dependent density functional theory. The collective plasmon excitations well develop as the increases of the ring size and the symmetry group of cyclo[n]carbons. An increase in intensity for the main peaks with the growing number of atoms in cyclo[n]car

Zhiwen Zhou, Zhiqiang Xiao, Yong Zeng

Delay alignment modulation (DAM) is a novel transmission technique for wireless systems with high spatial resolution by leveraging delay compensation and path-based beamforming, to mitigate the inter-symbol interference (ISI) without resorting to complex channel equalization or multi-carrier transmission. However, most existing studies on DAM consider a simp

Xin Zou, Weiwei Liu

Out-of-distribution (OOD) generalization has attracted increasing research attention in recent years, due to its promising experimental results in real-world applications. In this paper,we study the confidence set prediction problem in the OOD generalization setting. Split conformal prediction (SCP) is an efficient framework for handling the confidence set p

Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal

Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain, the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this re

André Yuji Yasutomi, Toshiaki Hatano, Kanta Hamasaki, Makoto Hattori

Fixation of structural parts to concrete is a repetitive, heavy-duty, and time-consuming task that requires automation due to the lack of skilled construction workers. Previously developed automation techniques have not achieved the complete fixation of structural parts and are difficult to implement in narrow construction environments. In this study, we pro