March 2024 arXiv papers — page 69

Shan-Ping Wu, Shao-Wen Wei

Among the study of black hole thermodynamics, topology offers a novel approach and perspective for classifying black hole systems. In this work, we explore the thermodynamical topology of the quantum BTZ black hole by employing the concept of the generalized free energy. To fully characterize the thermodynamics, we introduce two distinct topological numbers.

Guangchi Fang, Bing Wang

In this study, we explore the challenge of efficiently representing scenes with a constrained number of Gaussians. Our analysis shifts from traditional graphics and 2D computer vision to the perspective of point clouds, highlighting the inefficient spatial distribution of Gaussian representation as a key limitation in model performance. To address this, we i

Fenfen Yin, Jiacheng Ding, Limin Lai, Wei Zhang

The $\beta$-skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This method plays a key role in establishing the three-dimensional structure of the Universe and serves as a tool for quantitatively characterizing the nature of the cosmic web. This st

Ke Chen, Shao-Wen Wei

In the curved spacetime background, the trajectory of a spinning test particle will deviate from the geodesic. Using the effective potential method, we study the motion of a spinning test particle on the equatorial plane of a polymer black hole in loop quantum gravity described by the Mathisson-Papapetrou-Dixon equations with minimal spin-gravity interaction

Leyuan Sun, Asako Kanezaki, Guillaume Caron, Yusuke Yoshiyasu

Object-goal navigation is a crucial engineering task for the community of embodied navigation; it involves navigating to an instance of a specified object category within unseen environments. Although extensive investigations have been conducted on both end-to-end and modular-based, data-driven approaches, fully enabling an agent to comprehend the environmen

Alifu Xiafukaiti, Devanshu Garg, Aruto Hosaka, Koichi Yanagisawa

Convolutional neural networks (CNNs) have gained widespread usage across various fields such as weather forecasting, computer vision, autonomous driving, and medical image analysis due to its exceptional ability to extract spatial information, share parameters, and learn local features. However, the practical implementation and commercialization of CNNs in t

S. Sivaprasad Kumar, Pooja Yadav

In this paper, we introduce and explore a new class of starlike functions denoted by $\mathcal{S}^*_{\mathfrak{B}}$, defined as follows: $$\mathcal{S}^*_{\mathfrak{B}}=\{f\in \mathcal{A}:zf'(z)/f(z)\prec \sqrt{1+\tanh{z}}=:\mathfrak{B}(z)\}.$$ Here, $\mathfrak{B}(z)$ represents a mapping from the unit disk onto a bean-shaped domain. Our study focuses on unde

Shun Niijima, Atsushi Suzuki, Ryoichi Tsuzaki, Masaya Kinoshita

Mobile robots equipped with multiple light detection and ranging (LiDARs) and capable of recognizing their surroundings are increasing due to the minitualization and cost reduction of LiDAR. This paper proposes a target-less extrinsic calibration method of multiple LiDARs with non-overlapping field of view (FoV). The proposed method uses accumulated point cl

Kazuo Hongo, Takashi Kito, Yasuhisa Kamikawa, Masaya Kinoshita

This paper introduces the Passive Transformable Omni-Ball (PTOB), an advanced omnidirectional wheel engineered to enhance step-climbing performance, incorporate built-in actuators, diminish vibrations, and fortify structural integrity. By modifying the omni-ball's structure from two to three segments, we have achieved improved in-wheel actuation and a reduct

Sotaro Katayama, Noriaki Takasugi, Mitsuhisa Kaneko, Norio Nagatsuka

This paper presents a stochastic/robust nonlinear model predictive control (NMPC) to enhance the robustness of model-based legged locomotion against contact uncertainties. We integrate the contact uncertainties into the covariance propagation of stochastic/robust NMPC framework by leveraging the guard saltation matrix and an extended Kalman filter-like covar

Rui Liu, Wenguan Wang, Yi Yang

Vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views direct

Haoren Xiong, Hang Xu

We extend the direct approach to the semiclassical asymptotics for Bergman projections, developed by Deleporte--Hitrik--Sj\"ostrand for real analytic exponential weights and Hitrik--Stone for smooth exponential weights, to the case of Gevrey weights. We prove that the amplitude of the asymptotic Bergman projection forms a Gevrey symbol whose asymptotic coeff

Kimon Protopapas, Anas Barakat

Policy Mirror Descent (PMD) stands as a versatile algorithmic framework encompassing several seminal policy gradient algorithms such as natural policy gradient, with connections with state-of-the-art reinforcement learning (RL) algorithms such as TRPO and PPO. PMD can be seen as a soft Policy Iteration algorithm implementing regularized 1-step greedy policy

Yeji Song, Jimyeong Kim, Wonhark Park, Wonsik Shin

In a surge of text-to-image (T2I) models and their customization methods that generate new images of a user-provided subject, current works focus on alleviating the costs incurred by a lengthy per-subject optimization. These zero-shot customization methods encode the image of a specified subject into a visual embedding which is then utilized alongside the te

Sooyeob Jung, Seongah Jeong, Jinkyu Kang, Gyeongrae Im

This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising nonterrestrial network (NTN) solution to provide high-traffic and low-latency data transfer services to IoT devices in

David Garofalo, Chandra B. Singh, Eddie Harmon, Michael Williams

The counter-rotation between black holes and accretion disk configuration was introduced over a decade ago to elucidate the nature of the radio loud/radio-quiet dichotomy and the jet-disk connection, but has since been applied to a plethora of observations across space and time. We briefly review the paradigm in which counter-rotation is key for the triggeri

Siyu Heng, Elaine K. Chiu, Hyunseung Kang

To ensure reliable causal conclusions from observational studies, researchers routinely conduct sensitivity analysis to assess robustness to unmeasured confounding. In matched observational studies (one of the most popular observational study designs), two foundational concepts, design sensitivity and Bahadur-Rosenbaum efficiency, are used to quantify the ro

Wei Chen, Yuanshao Zhu, Yanchuan Chang, Kang Luo

Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical applications such as location services, urban traffic, and public safety. Traditional methods, focusing on simplistic spatio-temporal features, face challenges of complex calculations, limite

Yin Liu

On the Boolean domain, there is a class of symmetric signatures called ``Fibonacci gates'' for which a beautiful P-time combinatorial algorithm has been designed for the corresponding $\operatorname{Holant}$ problems. In this work, I give a combinatorial view for $\operatorname{Holant}(\mathcal{F})$ problems on a domain of size 3 where $\mathcal{F}$ is a set

Jaroslav Albert

The first-passage time (FPT) is the time it takes a system variable to cross a given boundary for the first time. In the context of Markov networks, the FPT is the time a random walker takes to reach a particular node (target) by hopping from one node to another. If the walker pauses at each node for a period of time drawn from a continuous distribution, the

Sihyun Yu, Weili Nie, De-An Huang, Boyi Li

Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process high-dimensional videos directly. To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient exten

Jocelyn A. Castro-Echeverría, Fernando Verduzco, Jorge X. Velasco-Hernández

We analyze an epidemiological model with treatment and recruitment considering the risk perception. In this model, we consider an exponential function as a recruitment rate. We have found that this model undergoes the transcritical Bogdanov-Takens bifurcation with boundary, where the system experiences the transcritical bifurcation between the disease-free e

Yifan He, Claus Aranha

In this study, we use Genetic Programming (GP) to compose new optimization benchmark functions. Optimization benchmarks have the important role of showing the differences between evolutionary algorithms, making it possible for further analysis and comparisons. We show that the benchmarks generated by GP are able to differentiate algorithms better than human-

Hao Wang, Peng Fan, Jing Chen, Lili Jiang

Artificial quantum systems have emerged as indispensable platforms to realize exotic topological matter in a well-controlled manner. Here, we demonstrate topological quantum Heisenberg spin lattices, engineered with spin chains and two-dimensional spin arrays using spin 1/2 atoms on insulating films in a scanning tunnelling microscope (STM). We engineered wi

Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang

Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for treating CTR prediction as a binary classification problem, recent advancements have shown that combining BCE loss with an auxiliary ranking loss can significantly improve performance

Frankie J. Encalada, Leslie W. Looney, Shigehisa Takakuwa, John J. Tobin

Young protostellar binary systems, with expected ages less than $\sim$10$^5$ years, are little modified since birth, providing key clues to binary formation and evolution. We present a first look at the young, Class 0 binary protostellar system R CrA IRAS 32 from the Early Planet Formation in Embedded Disks (eDisk) ALMA large program, which observed the syst

Yuki Takeuchi, Akihiro Mizutani

Verification of quantum computation is a task to efficiently check whether an output given from a quantum computer is correct. Existing verification protocols conducted between a quantum computer to be verified and a verifier necessitate quantum communication to unconditionally detect any malicious behavior of the quantum computer solving any promise problem

Yuqi Yang, Peng-Tao Jiang, Jing Wang, Hao Zhang

Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability. In this paper, we extend MLLMs' output by empowering MLLMs with the segmentation ability. The extended MLLMs can both output language responses to the image-language prompts and segment the regions that the complex question or query i

Jinyung Hong, Eun Som Jeon, Changhoon Kim, Keun Hee Park

Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit their capabilities for out-of-distribution (OOD) generalization. Although many debiasing approaches have been proposed to ensure correct predictions from biased datasets, few studies

Ben Cravens, Andrew Lensen, Paula Maddigan, Bing Xue

Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thus enhancing both the efficiency and interpretability of data analysis by transforming the data into a lower-dimensional representation. However, a notable challenge with current manifold learning methods is their lac

Junyoung Kim, Junwon Seo, Jihong Min

Robotic mapping with Bayesian Kernel Inference (BKI) has shown promise in creating semantic maps by effectively leveraging local spatial information. However, existing semantic mapping methods face challenges in constructing reliable maps in unstructured outdoor scenarios due to unreliable semantic predictions. To address this issue, we propose an evidential

Ye Xu, Ya Gao, Xiaorong Qiu, Yang Chen

To address the issues of MixUp and its variants (e.g., Manifold MixUp) in image classification tasks-namely, their neglect of mixing within the same class (intra-class mixup) and their inadequacy in enhancing intra-class cohesion through their mixing operations-we propose a novel mixup method named SynerMix-Intra and, building upon this, introduce a synergis

Ramcharan Meena, Rajendra S. Dhaka

We investigate the structural, dielectric relaxation, electric modulus and impedance behavior of Ni-doped NASICON ceramic Na$_{3+2x}$Zr$_{2-x}$Ni$_{x}$Si$_2$PO$_{\rm 12}$ ($x=$ 0.05--0.2) prepared using the solid-state reaction method. The increase in dielectric constant with temperature and decrease with frequency is explained on the basis of space charge p

Shilv Cai, Xiaoguo Liang, Shuning Cao, Luxin Yan

Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method; and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss bet

Toshitaka Aoki, Yingying Zhang

For Brauer graph algebras, tilting mutation is compatible with flip of Brauer graphs. The aim of this paper is to generalize this result to the class of Brauer configuration algebras introduced by Green and Schroll recently. More precisely, under a certain condition, we introduce flip of Brauer configurations and prove that it is compatible with tilting muta

Haoran Hou, Mingtao Feng, Zijie Wu, Weisheng Dong

3D object detection is a fundamental task in scene understanding. Numerous research efforts have been dedicated to better incorporate Hough voting into the 3D object detection pipeline. However, due to the noisy, cluttered, and partial nature of real 3D scans, existing voting-based methods often receive votes from the partial surfaces of individual objects t

Bo Wu, Ruiqi Mao, Yi Liu, Di Sang

Rydberg atom,which exhibits a strong response to weak electric(E) fields,is regarded as a promising atomic receiver to surpass sensitivity of conventional receivers. However, its sensitivity is strongly limited by the noise coming from both classical and quantum levels and how to enhance it significantly remains challenging. Here we experimentally prove that

Xuhe Gong, Jiazi Bi, Xiaobin Liu, Ran Li

The prediction of glass forming ability (GFA) and various properties in bulk metallic glasses (BMGs) pose a challenge due to the unique disordered atomic structure in this type of materials. Machine learning shows the potential ability to find a way out. However, the training set from the experimental data of BMGs faces the issue of data imbalance, including

Mingze Ni, Zhensu Sun, Wei Liu

Recent studies on adversarial examples expose vulnerabilities of natural language processing (NLP) models. Existing techniques for generating adversarial examples are typically driven by deterministic hierarchical rules that are agnostic to the optimal adversarial examples, a strategy that often results in adversarial samples with a suboptimal balance betwee

Honghui Zhao, Hongyuan Shen, Ao You, Yuanqin Yu

Due to the simultaneous presence of two polar functional groups and flexible spatial structure, Aminoethanol (AE) is a model system for investigating the relationship between intramolecular hydrogen bonding and conformational equlibrium. In addition, Aminoethanol and their derivatives exhibit remarkable efficacy in the reversible capture of carbon dioxide. T

Grace Fan, Roee Shraga, Renée J. Miller

We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source table as closely as possible. Unlike query discovery problems like Query-by-Example or by-Target, Table Reclamation focuses on reclaiming the data in the Source Table as fully as possi

Baisen Yu, Shoichi Sato, Masaaki Tanaka, Ryosho Nakane

We have experimentally and theoretically investigated the spin injection/detection polarization in a Si-based ferromagnetic tunnel junction with an amorphous MgO layer, and demonstrated that the experimental features of the spin polarization in a wide bias range can be well explained using our theoretical model based on the band diagram of the junction and t

Seonghyeon Kang, Kawon Han, Songcheol Hong

This paper presents a time-frequency phase-coded sub-Nyquist sampling orthogonal frequency division multiplexing (PC-SNS-OFDM) radar system to reduce the analog-to-digital converter (ADC) sampling rate without any additional hardware or signal processing. The proposed radar divides the transmitted OFDM signal into multiple sub-bands along the frequency axis

Ming Cai, Hisayuki Hara

Several causal discovery algorithms have been proposed. However, when the sample size is small relative to the number of variables, the accuracy of estimating causal graphs using existing methods decreases. And some methods are not feasible when the sample size is smaller than the number of variables. To circumvent these problems, some researchers proposed c

Yong He, Hongshan Yu, Chaoxu Mu, Mingtao Feng

Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTr

Amir Gholami, Zhewei Yao, Sehoon Kim, Coleman Hooper

The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0

Somabha Mukherjee, Tianyu Liu, Bhaswar B. Bhattacharya

Limit theorems for the magnetization in the $p$-spin Curie-Weiss model, for $p \geq 3$, has been derived recently by Mukherjee et al. (2021). In this paper, we strengthen these results by proving Cram\'er-type moderate deviation theorems and Berry-Esseen bounds for the magnetization (suitably centered and scaled). In particular, we show that the rate of conv

Zijie Wu, Mingtao Feng, Yaonan Wang, He Xie

Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries.We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes. SEK conditions the denoising process with a hand-drawn sketch of the target scene and cues from an object relationship

Alicja Chaszczewicz, Raj Sanjay Shah, Ryan Louie, Bruce A Arnow

Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to support the large number of people with

Fazal Muhammad Ali Khan, Hatem Abou-Zeid, Aryan Kaushik, Syed Ali Hassan

The industrial Internet of Things (IIoT) under Industry 4.0 heralds an era of interconnected smart devices where data-driven insights and machine learning (ML) fuse to revolutionize manufacturing. A noteworthy development in IIoT is the integration of federated learning (FL), which addresses data privacy and security among devices. FL enables edge sensors, a

Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark Hasegawa-Johnson

In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the recently proposed test-time prompt tuning for large-scale vision-language models such as CLIP. Unfortunately, these prompts have been mainly developed to improve accuracy, overlooking the importance o

Haofei Zhao, Yilun Liu, Shimin Tao, Weibin Meng

Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evolution, QE has yielded a wealth of results. This article provides a comprehensive overview of QE datasets, anno

Mina Lee, Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through a la

Zicun Li, Jianxing Huang, Xinguo Ren, Jinbin Li

Ensuring solid-state lithium batteries perform well across a wide temperature range is crucial for their practical use. Molecular dynamics (MD) simulations can provide valuable insights into the temperature dependence of the battery materials, however, the high computational cost of ab initio MD poses challenges for simulating ion migration dynamics at low t

Francisco Raverta Capua, Juan Schandin, Pablo De Cristóforis

Remote sensing through unmanned aerial systems (UAS) has been increasing in forestry in recent years, along with using machine learning for data processing. Deep learning architectures, extensively applied in natural language and image processing, have recently been extended to the point cloud domain. However, the availability of point cloud datasets for tra

Kazuki Adachi, Shohei Enomoto, Taku Sasaki, Shin'ya Yamaguchi

Person re-identification (re-id), which aims to retrieve images of the same person in a given image from a database, is one of the most practical image recognition applications. In the real world, however, the environments that the images are taken from change over time. This causes a distribution shift between training and testing and degrades the performan

Sumin Lee, Yooseung Wang, Sangmin Woo, Changick Kim

Panoramic Activity Recognition (PAR) seeks to identify diverse human activities across different scales, from individual actions to social group and global activities in crowded panoramic scenes. PAR presents two major challenges: 1) recognizing the nuanced interactions among numerous individuals and 2) understanding multi-granular human activities. To addre

Jiaxing Sun, Weiquan Huang, Jiang Wu, Chenya Gu

We introduce CHARM, the first benchmark for comprehensively and in-depth evaluating the commonsense reasoning ability of large language models (LLMs) in Chinese, which covers both globally known and Chinese-specific commonsense. We evaluated 7 English and 12 Chinese-oriented LLMs on CHARM, employing 5 representative prompt strategies for improving LLMs' reas

Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin

Transfer learning is a de facto standard method for efficiently training machine learning models for data-scarce problems by adding and fine-tuning new classification layers to a model pre-trained on large datasets. Although numerous previous studies proposed to use homomorphic encryption to resolve the data privacy issue in transfer learning in the machine

Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan

The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the perspectives and experiences of AI practitioners in developing a fair AI/ML system. Understanding AI pract

Kyuwon Choi, Cheolkyun Rho, Taeyoun Kim, Daewoo Choi

This paper presents a novel reinforcement learning (RL) approach called HAAM-RL (Heuristic Algorithm-based Action Masking Reinforcement Learning) for optimizing the color batching re-sequencing problem in automobile painting processes. The existing heuristic algorithms have limitations in adequately reflecting real-world constraints and accurately predicting

Austin Cooper, Sean Meyn

The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place, and identify properties of the post-change behavior. It is shown in this paper that approaches based on reinforcement learning (RL) can be adapted based on any "surrogate information state" that is

Atsuya Hasegawa, Srijita Kundu, Harumichi Nishimura

Quantum nondeterministic distributed computing was recently introduced as dQMA (distributed quantum Merlin-Arthur) protocols by Fraigniaud, Le Gall, Nishimura and Paz (ITCS 2021). In dQMA protocols, with the help of quantum proofs and local communication, nodes on a network verify a global property of the network. Fraigniaud et al. showed that, when the netw

Yu Nakayama

Can space-time symmetries such as Lorentz, dilatation, or conformal symmetry be recovered at infinite temperature? To address this question, we study correlation functions of generalized free conformal field theories (a.k.a free holographic theories in thermal AdS space-time) at infinite temperature. We show that they are broken at the leading order in boson

Zexi Niu, Ning-Chen Sun, Jifeng Liu

Type IIn supernovae (SNe) exhibit narrow hydrogen lines that arise from the strong interaction between ejecta and circumstellar material. It remains poorly understood, however, what progenitor stars give rise to these explosions. In this work, we perform a detailed analysis of the progenitor and environment of the nearby Type IIn SN 2010jl. With newer images

Chuan-Ning Luo, Shao-Peng Tang, Ming-Zhe Han, Jin-Liang Jiang

In 2019, Neutron star Interior Composition ExploreR (NICER) mission released its findings on the mass and radius of the isolated neutron star (INS) PSR J0030+0451, revealing a mass of approximately 1.4 solar masses ($M_{\odot}$) and a radius near 13 kilometers. However, the recent re-analysis by the NICER collaboration \citep{vinciguerra2024updated} suggests

Zhihao Wang, Yulin Zhou, Ningyu Zhang, Xiaosong Yang

Human motion prediction is consisting in forecasting future body poses from historically observed sequences. It is a longstanding challenge due to motion's complex dynamics and uncertainty. Existing methods focus on building up complicated neural networks to model the motion dynamics. The predicted results are required to be strictly similar to the training

Bin Xie, Hao Tang, Bin Duan, Dawen Cai

Segment Anything Model (SAM), a prompt-driven foundation model for natural image segmentation, has demonstrated impressive zero-shot performance. However, SAM does not work when directly applied to medical image segmentation, since SAM lacks the ability to predict semantic labels, requires additional prompts, and presents suboptimal performance. Following th

Yiquan Chen, Yingchao Lyu, Di Zhang

Deep reinforcement learning has made significant progress in games with imperfect information, but its performance in the card game Doudizhu (Chinese Poker/Fight the Landlord) remains unsatisfactory. Doudizhu is different from conventional games as it involves three players and combines elements of cooperation and confrontation, resulting in a large state an

Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi

Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue, involving the dynamic addition of new classes in the context of federated learning. In this field, Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems. However, prior approaches lack the crucial synergy betwe

Steven Mascaro, Yue Wu, Ross Pearson, Owen Woodberry

COVID-19 appeared abruptly in early 2020, requiring a rapid response amid a context of great uncertainty. Good quality data and knowledge was initially lacking, and many early models had to be developed with causal assumptions and estimations built in to supplement limited data, often with no reliable approach for identifying, validating and documenting thes

Seungsu Hwang, Seoung Dal Jung, Jungwoo Moon

We investigate transverse Ricci solitons, the self-similar solutions of the transverse Ricci flow, on a compact foliated manifold. In particular, we show the relations between a taut Riemannian foliation and a transverse Ricci soliton. Moreover, we find some examples of transverse Ricci solitons.

Gyaneshwar Agrahari, Dalibor Froncek

A graph $G(V,E)$ is $\Gamma$-harmonious when there is an injection $f$ from $V$ to an Abelian group $\Gamma$ such that the induced edge labels defined as $w(xy)=f(x)+f(y)$ form a bijection from $E$ to $\Gamma$. We study $\Gamma$-harmonious labelings of several cycles-related classes of graphs, including Dutch windmills, generalized prisms, generalized closed

Yundong Sun, Dongjie Zhu, Yansong Wang, Zhaoshuo Tian

Recently, Graph Transformers have emerged as a promising solution to alleviate the inherent limitations of Graph Neural Networks (GNNs) and enhance graph representation performance. Unfortunately, Graph Transformers are computationally expensive due to the quadratic complexity inherent in self-attention when applied over large-scale graphs, especially for no

Jiangfei Duan, Ziang Song, Xupeng Miao, Xiaoli Xi

Deep neural networks (DNNs) are becoming progressively large and costly to train. This paper aims to reduce DNN training costs by leveraging preemptible instances on modern clouds, which can be allocated at a much lower price when idle but may be preempted by the cloud provider at any time. Prior work that supports DNN training on preemptive instances employ

Shogo Yamashita, Akimasa Sakuma

We investigated the magnetic properties of Sendust (Fe-Al-Si) alloys not only at 0 K but also at finite-temperatures by means of the first-principles calculations assuming A2, B2, and DO3 structures. We confirmed that the itinerant characteristics of 3d electrons of Fe are not negligible for A2 and B2 structures and a significantly small exchange stiffness c

Benjamin S. Savino, Wen Wu

DNS is performed for flow separation over a bump in a turbulent channel. Comparisons are made between a smooth bump configuration and one where the lee side is covered with replicas of complete shark denticles. As flow over the bump is under an adverse pressure gradient (APG), a reversed pore flow (RPF) is formed in the porous cavities underneath the crowns

Yuanan Diao, Claus Ernst, Gabor Hetyei

The determination of the braid index of an oriented link is generally a hard problem. In the case of alternating links, some significant progresses have been made in recent years which made explicit and precise braid index computations possible for links from various families of alternating links, including the family of all alternating Montesinos links. How

Daryl Mupupuni, Anupama Guntu, Liang Hong, Kamrul Hasan

The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments, involving substantial investments in data collection and model training. Rapid application of AI necessitates evaluating the feasibility of utilizing pre-trained models in unobserved

Soumyendu Sarkar, Avisek Naug, Ricardo Luna, Antonio Guillen

As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the

Yuanzhe Jiang, Xue-Bing Wu, Qinchun Ma, Huapeng Gu

The continuum reverberation mapping is widely used in studying accretion disk of active galactic nuclei (AGN). While some indirect evidence and simulations indicated that the diffuse continuum, especially the strong Balmer continuum from the broad line region (BLR), may contribute to the continuum in the u/U band. Here, we present direct evidence for this co

Sina M Koehlenbeck, Lance Lee, Mario D Balcazar, Ying Chen

The past decades have witnessed the development of new X-ray beam sources with brightness growing at a rate surpassing Moore's law. Current and upcoming diffraction limited and fully coherent X-ray beam sources, including multi-bend achromat based synchrotron sources and high repetition rate X-ray free electron lasers, puts increasingly stringent requirement

Shogo Sato, Takuhiro Kaneko, Kazuhiko Murasaki, Taiga Yoshida

Unsupervised intrinsic image decomposition (IID) is the process of separating a natural image into albedo and shade without these ground truths. A recent model employing light detection and ranging (LiDAR) intensity demonstrated impressive performance, though the necessity of LiDAR intensity during inference restricts its practicality. Thus, IID models emplo

Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang

The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes. Traditional physics-based computational methods, such as molecular dynamics (MD) simulations, suffer from rare event sampling and long equilibration time problems, hindering their applications in general protein systems. Recently, deep ge

Amit Chakrabarti, Andrew McGregor, Anthony Wirth

The maximum coverage problem is to select $k$ sets from a collection of sets such that the cardinality of the union of the selected sets is maximized. We consider $(1-1/e-\epsilon)$-approximation algorithms for this NP-hard problem in three standard data stream models. 1. {\em Dynamic Model.} The stream consists of a sequence of sets being inserted and delet

Meng Li, Ke Wang, Nan Wang

A multitude of substances exist as mixtures comprising multiple chemical components in the natural world. These substances undergo morphological changes under external influences. the phase field model coupled with fluid flow, the dynamic movement and evolution of the phase interface intricately interact with the fluid motion. This article focuses on the N-c

Xidong Wu, Shangqian Gao, Zeyu Zhang, Zhenzhen Li

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging. To address the limitation, the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a ge

Hui Tian, Kai Xu

Surface reconstruction from point clouds is a crucial task in the fields of computer vision and computer graphics. SDF-based methods excel at reconstructing smooth meshes with minimal error and artefacts but struggle with representing open surfaces. On the other hand, UDF-based methods can effectively represent open surfaces but often introduce noise, leadin

Fan Wang, Yating Wang, Wing Tat Leung, Zongben Xu

Multiscale problems can usually be approximated through numerical homogenization by an equation with some effective parameters that can capture the macroscopic behavior of the original system on the coarse grid to speed up the simulation. However, this approach usually assumes scale separation and that the heterogeneity of the solution can be approximated by

Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Berrak Sisman

Speech Emotion Recognition (SER) is crucial for enabling computers to understand the emotions conveyed in human communication. With recent advancements in Deep Learning (DL), the performance of SER models has significantly improved. However, designing an optimal DL architecture requires specialised knowledge and experimental assessments. Fortunately, Neural

Xu Zheng, Lin Wang

In this paper, we make the first attempt at achieving the cross-modal (i.e., image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distinct modality gap between images and eve

Paige Hillen

Let $d$ be a square free positive integer and $\mathbb{Q}(\sqrt{d})$ a totally real quadratic field over $\mathbb{Q}$. We show there exists an arithmetic lattice L in $SL(8,\mathbb{R})$ with entries in the ring of integers of $\mathbb{Q}(\sqrt{d})$ and a sequence of lattices $\Gamma_n $ commensurable to L such that the systole of the locally symmetric finite

Alan D. Ogilvie

Google AI systems exhibit patterns mirroring antisocial personality disorder (ASPD), consistent across models from Bard on PaLM to Gemini Advanced, meeting 5 out of 7 ASPD modified criteria. These patterns, along with comparable corporate behaviors, are scrutinized using an ASPD-inspired framework, emphasizing the heuristic value in assessing AI's human impa

Immanuel Ben Porat, Mikaela Iacobelli, Alexandre Rege

We derive the two dimensional incompressible Euler equation as a quasineutral limit of the Vlasov-Poisson equation using a modulated energy approach. We propose a strategy which enables to treat solutions where the gradient of the velocity is merely $\mathrm{BMO}$, in accordance to the celebrated Yudovich theorem.

Yiqun Li, Hong Wang, Xiangcheng Zheng

We investigate complex boundary conditions of the miscible displacement system in two and three space dimensions with the commonly-used Bear-Scheidegger diffusion-dispersion tensor, which describes, e.g., the porous medium flow processes in petroleum reservoir simulation or groundwater contaminant transport. Specifically, we incorporate the no-flux boundary

Qian Li, Hongyun Zhang, Yijie Wang, Wanying Chen

Magic-angle twisted bilayer graphene (MATBG) exhibits correlated phenomena such as superconductivity and Mott insulating state related to the weakly dispersing flat band near the Fermi energy. Beyond its moir\'e period, such flat band is expected to be sensitive to lattice relaxations. Thus, clarifying the evolution of the electronic structure with twist ang

Shan Jia, Reilin Lyu, Kangran Zhao, Yize Chen

DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation. Detecting DeepFakes is currently solved with programmed machine learning algorithms. In this work, we investigate the capabilities of multimodal large language models (LLMs) in DeepFake detection. We conducted qualitative an

Rowan Kelleher, Matthew McEneaney, Anselm Vossen

The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $\Lambda$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generatio

Alexander C. Mark, Russell J. Hemley

Recent reports of superconductivity in the vicinity of room temperature have been the subject of discussion by the community. Specifically, features in the resistance-temperature (R-T) relations have raised questions. We show that many of these features can arise from previously unaccounted-for dynamic effects associated with the AC transport techniques ofte

Yang Bai, Anthony Colas, Christan Grant, Daisy Zhe Wang

In recent research, contrastive learning has proven to be a highly effective method for representation learning and is widely used for dense retrieval. However, we identify that relying solely on contrastive learning can lead to suboptimal retrieval performance. On the other hand, despite many retrieval datasets supporting various learning objectives beyond