April 2024 arXiv papers — page 191

Taeckyung Lee, Sorn Chottananurak, Taesik Gong, Sung-Ju Lee

Test-time adaptation (TTA) has emerged as a viable solution to adapt pre-trained models to domain shifts using unlabeled test data. However, TTA faces challenges of adaptation failures due to its reliance on blind adaptation to unknown test samples in dynamic scenarios. Traditional methods for out-of-distribution performance estimation are limited by unreali

Rongjie Li, Songyang Zhang, Dahua Lin, Kai Chen

Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks. Despite recent advancements, existing methods struggle to generate scene graphs with novel visual relation concepts. To address this challenge, we introduce a new open-vocabulary SGG framework based on sequence generation. Our f

Zhaoyu Liu, Xuetao Ma, John Cenker, Jiaqi Cai

Uniaxial strain has been widely used as a powerful tool for investigating and controlling the properties of quantum materials. However, existing strain techniques have so far mostly been limited to use with bulk crystals. Although recent progress has been made in extending the application of strain to two-dimensional van der Waals (vdW) heterostructures, the

Shenshen Luan, Luyuan Wang, Yepeng Liu, Ninghan Sun

The real-time routing for satellite communication of the mega-constellations is being challenged due to the large-scale of network nodes, especially on devices with limited computation such as onboard embedded systems. In this paper, a fast routing method is proposed for mega-constellation backbone networks. Firstly, inspired by the regularity and sparse cha

Chenxi Shi, Penghao Liang, Yichao Wu, Tong Zhan

The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities and challenges for enterprises, requiring specialized teams to navigate the complexity of engineering technology while prioritizing data security and model interpretability. By lev

Mohamed Abuella, Hadi Fanaee, M. Amine Atou, Slawomir Nowaczyk

To meet the urgent requirements for the climate change mitigation, several proactive measures of energy efficiency have been implemented in maritime industry. Many of these practices depend highly on the onboard data of vessel's operation and environmental conditions. In this paper, a high resolution onboard data from passenger vessels in short-sea shipping

Nuredin Ali, Charles Chuankai Zhang, Ned Mayo, Stevie Chancellor

Social media data has been used for detecting users with mental disorders, such as depression. Despite the global significance of cross-cultural representation and its potential impact on model performance, publicly available datasets often lack crucial metadata related to this aspect. In this work, we evaluate the generalization of benchmark datasets to bui

Jian Jiao, Yu Dai, Hefei Mei, Heqian Qiu

Recent video class-incremental learning usually excessively pursues the accuracy of the newly seen classes and relies on memory sets to mitigate catastrophic forgetting of the old classes. However, limited storage only allows storing a few representative videos. So we propose SNRO, which slightly shifts the features of new classes to remember old classes. Sp

Adrian Miranda

Fuhrmann introduced Abstract Kleisli structures to model call-by-value programming languages with side effects, and showed that they correspond to monads satisfying a certain equalising condition on the unit. We first extend this theory to non-strict morphisms of monads, and to incorporate 2-cells of monads. We then further extend this to a theory of abstrac

Xiaoyan Qu, Xiangfeng Meng

With the increasing prevalence of text generated by large language models (LLMs), there is a growing concern about distinguishing between LLM-generated and human-written texts in order to prevent the misuse of LLMs, such as the dissemination of misleading information and academic dishonesty. Previous research has primarily focused on classifying text as eith

Tien-Yu Chang, Hao Dai, Vincent S. Tseng

Data Augmentation is a common technique used to enhance the performance of deep learning models by expanding the training dataset. Automatic Data Augmentation (ADA) methods are getting popular because of their capacity to generate policies for various datasets. However, existing ADA methods primarily focused on overall performance improvement, neglecting the

Houssem Ben Braiek, Foutse Khomh

This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental

R. M. L. S. Ramanayake, D. C. Dammage, I. Z. M. Zumri, K. A. R. S. Rodrigo

Conventional manual lithological mapping (MLM) through field surveys are resource-extensive and time-consuming. Digital lithological mapping (DLM), harnessing remotely sensed spectral imaging techniques, provides an effective strategy to streamline target locations for MLM or an efficient alternative to MLM. DLM relies on laboratory-generated generic end-mem

M. Sharif, M. Zeeshan Gul, Nusrat Fatima

The main objective of this manuscript is to investigate the bouncing cosmology in the background of $f(\mathcal{Q})$ gravity, where $\mathcal{Q}$ defines the non-metricity. For this purpose, we use the reconstruction approach and consider a flat Friedmann-Robertson-Walker spacetime with perfect matter configuration. We examine how the first contracting phase

Zhenkun Yuan, Geoffroy Hautier

Calcium oxide (CaO) is a promising host for quantum defects because of its ultrawide band gap and potential for long spin coherence times. Using hybrid functional calculations, we investigate the intrinsic point defects and how they limit Fermi-level positions and doping in CaO. Our results reveal calcium and oxygen vacancies to be the most common intrinsic

Kailu Wu, Xing Liu, Feiyu Bian, Yizhai Zhang

Due to the uncertainty of traffic participants' intentions, generating safe but not overly cautious behavior in interactive driving scenarios remains a formidable challenge for autonomous driving. In this paper, we address this issue by combining a deep learning-based trajectory prediction model with risk potential field-based motion planning. In order to co

Kento Kawaharazuka, Kei Okada, Masayuki Inaba

In this study, seated walk, a movement of walking while sitting on a chair with casters, is realized on a musculoskeletal humanoid from human teaching. The body is balanced by using buttock-contact sensors implemented on the planar interskeletal structure of the human mimetic musculoskeletal robot. Also, we develop a constrained teaching method in which one-

Ronghan Chen, Yang Cong, Yu Ren

Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific training. Recent NeRF-based methods provide a promising solution by directly optimizing the pose from pixel loss between rendered and target images. However, during inference, they requi

Hwa Jeong Lee, Alexander Stoimenow, Gyo Taek Jin

A knot is a closed loop in space without self-intersection. Two knots are equivalent if there is a self homeomorphism of space bringing one onto the other. An arc presentation is an embedding of a knot in the union of finitely many half planes with a common boundary line such that each half plane contains a simple arc of the knot. The minimal number of such

Moritaka Onitsuka, Manabu Nishiura, Kento Kawaharazuka, Kei Tsuzuki

Musculoskeletal humanoids have been developed by imitating humans and expected to perform natural and dynamic motions as well as humans. To achieve desired motions stably in current musculoskeletal humanoids is not easy because they cannot maintain the sufficient moment arm of muscles in various postures. In this research, we discuss planar structures that s

Chenxi Zheng, Thierry Roudier, Brigitte Schmieder, Guiping Ruan

Aims. The aim of this paper is to consider relationship between the decay of sunspots and convection via the motion of the family of granules and how the diffusion mechanism of magnetic field operates in a decaying sunspot. Methods. We report the decay of a sunspot observed by the 1.6m Goode Solar Telescope (GST) with the TiO Broadband Filter Imager (BFI) an

Kou Fujimori, Koji Tsukuda

We consider the sparse estimation for stochastic processes with possibly infinite-dimensional nuisance parameters, by using the Dantzig selector which is a sparse estimation method similar to $Z$-estimation. When a consistent estimator for a nuisance parameter is obtained, it is possible to construct an asymptotically normal estimator for the parameter of in

Zdenek Sekanina

I present the results of the first comprehensive effort aimed at modeling a major component of the stream of SOHO sungrazers, 5000 of which have been detected by the onboard coronagraphs since 1996. The stream of Population I of the Kreutz system, investigated by a Monte Carlo simulation technique, is treated as a product of cascading fragmentation due to un

Liwen Zhu, Peixi Peng, Zongqing Lu, Yonghong Tian

Traffic signal control has a great impact on alleviating traffic congestion in modern cities. Deep reinforcement learning (RL) has been widely used for this task in recent years, demonstrating promising performance but also facing many challenges such as limited performances and sample inefficiency. To handle these challenges, MTLight is proposed to enhance

Xiangming Xi, Feng Gao, Jun Xu, Fangtai Guo

Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task. While previous research has primarily focused on feature-level or parameter-level task relatedness, and proposed various model architectures and learning algorithms to improve learnin

Wei He, Shichun Liu, Jun Zhao, Yiwen Ding

Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot methods heavily depend on high-quality, query-specific demos, which are often lacking. When faced with out-of-demonstration (OOD) queries, methods that rely on hand-crafted demos

Facundo Fainstein, Gabriel B. Mindlin, Pablo Groisman

We show how to train an autoencoder to reconstruct an attractor from recorded footage, preserving the topology of the underlying phase space. This is explicitly demonstrated for the classic finite-amplitude Lorenz atmospheric convection problem.

Rui Wang, Jing Li, Quanxue Gao, Cheng Deng

The clustering method based on the anchor graph has gained significant attention due to its exceptional clustering performance and ability to process large-scale data. One common approach is to learn bipartite graphs with K-connected components, helping avoid the need for post-processing. However, this method has strict parameter requirements and may not alw

Ethan King, James Kotary, Ferdinando Fioretto, Jan Drgona

Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of approaches, based on distinct strategies. This work proposes a n

Shuo Liu, Wei Xiao, Calin A. Belta

Recent work has shown that stabilizing an affine control system while optimizing a quadratic cost subject to state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this method is that the QPs could easily become infeasibl

Quincy Hershey, Randy Paffenroth, Harsh Pathak, Simon Tavener

Neural networks (NN) can be divided into two broad categories, recurrent and non-recurrent. Both types of neural networks are popular and extensively studied, but they are often treated as distinct families of machine learning algorithms. In this position paper, we argue that there is a closer relationship between these two types of neural networks than is n

Jeeyung Kim, Ze Wang, Qiang Qiu

Efficient text-to-image generation remains a challenging task due to the high computational costs associated with the multi-step sampling in diffusion models. Although distillation of pre-trained diffusion models has been successful in reducing sampling steps, low-step image generation often falls short in terms of quality. In this study, we propose a novel

Jiazheng Xing, Chao Xu, Yijie Qian, Yang Liu

Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the clothing identity uncontrollability and training inefficiency of existing diffusion-based methods, which struggle to maintain the identity even with full parameter training, are sign

Ling-Yan Hung, Yikun Jiang

Is it possible to read off the quantum gravity dual of a CFT directly from its operator algebra? In this essay, we present a step-by-step recipe synthesizing results and techniques from conformal bootstrap, topological symmetries, tensor networks, a novel symmetry-preserving real-space renormalization algorithm devised originally in lattice models, and the a

Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang

Currently, high-definition (HD) map construction leans towards a lightweight online generation tendency, which aims to preserve timely and reliable road scene information. However, map elements contain strong shape priors. Subtle and sparse annotations make current detection-based frameworks ambiguous in locating relevant feature scopes and cause the loss of

Fenggen Yu, Yiming Qian, Xu Zhang, Francisca Gil-Ureta

We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object. By leveraging differentiable volume rendering, our method does not require 3D supervision. Architecturally, our network follows the general pipeline of an image-conditioned neural radiance field

Jie Long Lee, Chen Li, Gim Hee Lee

We present DiSR-NeRF, a diffusion-guided framework for view-consistent super-resolution (SR) NeRF. Unlike prior works, we circumvent the requirement for high-resolution (HR) reference images by leveraging existing powerful 2D super-resolution models. Nonetheless, independent SR 2D images are often inconsistent across different views. We thus propose Iterativ

Kai Zhao, Xiao-Dong Zhang

This paper focuses on extensions of the classic Erd\H{o}s-Gallai Theorem for the set of weighted function of each edge in a graph. The weighted function of an edge $e$ of an $n$-vertex uniform hypergraph $\mathcal{H}$ is defined to a special function with respect to the number of edges of the longest Berge path containing $e$. We prove that the summation of

Xusheng Zhu, Qingqing Wu, Wen Chen

This paper explores the performance of reconfigurable intelligent surface (RIS) assisted spatial modulation (SM) downlink communication systems, focusing on the average bit error probability (ABEP). Notably, in scenarios with a large number of reflecting units, the composite channel can be approximated by a Gaussian distribution using the central limit theor

Mrunal Kamble, Jiaxuan Wang, Girish S. Agarwal

Absorption and gain processes are fundamental to any light-matter interaction and a precise measurement of these parameters is important for various scientific and technological applications. Quantum probes, specifically the squeezed states have proved very successful, particularly in the applications that deal with phase shift and force measurements. In thi

Shubham Dwivedi

We formulate and study the negative gradient flow of an energy functional of Spin(7)-structures on compact $8$-manifolds. The energy functional is the $L^2$-norm of the torsion of the Spin(7)-structure. Our main result is the short-time existence and uniqueness of solutions to the flow. We also explain how this negative gradient flow contains, as the highest

Jaida Gao, Calab Su, Etai Miller, Kevin Lu

The evolution of Artificial Intelligence (AI) stands as a pivotal force shaping our society, finding applications across diverse domains such as education, sustainability, and safety. Leveraging AI within mobile applications makes it easily accessible to the public, catalyzing its transformative potential. In this paper, we present a methodology for the rapi

Daisuke Mashima, Muhammad M. Roomi, Bennet Ng, Zbigniew Kalbarczyk

Assurance of cybersecurity is crucial to ensure dependability and resilience of smart power grid systems. In order to evaluate the impact of potential cyber attacks, to assess deployability and effectiveness of cybersecurity measures, and to enable hands-on exercise and training of personals, an interactive, virtual environment that emulates the behaviour of

Bruno Kahn

We give a detailed proof of the B\'enabou-Roubaud theorem. As a byproduct it yields a weakening of its hypotheses: the base category does not need fibre products and the Beck-Chevalley condition, in the form of a natural transformation, can be weakened by only requiring the latter to be epi.

Anna Ijjas, Paul J. Steinhardt, David Garfinkle, William G. Cook

In a systematic study, we use an equivalent pair of improved numerical relativity codes based on a tetrad-formulation of the classical Einstein-scalar field equations to examine whether slow contraction or inflation (or both) can resolve the homogeneity, isotropy and flatness problems. Our finding, based on a set of gauge/frame invariant diagnostics, is that

Elynn Chen, Xi Chen, Wenbo Jing

In data-driven decision-making in marketing, healthcare, and education, it is desirable to utilize a large amount of data from existing ventures to navigate high-dimensional feature spaces and address data scarcity in new ventures. We explore knowledge transfer in dynamic decision-making by concentrating on batch stationary environments and formally defining

Dejing Du, Yong Liu, Hua Cai, Danping Chen

To achieve the physics goal of precisely measure the Higgs, Z, W bosons and the top quark, future electron-positron colliders require that their detector system has excellent jet energy resolution. One feasible technical option is the high granular calorimetery based on the particle flow algorithm (PFA). A new high-granularity hadronic calorimeter with glass

Fumihiko Ishiyama

We have developed a nonlinear method of time series analysis that allows us to obtain multiple nonlinear trends without harmonics from a given set of numerical data. We propose to apply the method to recognize the ongoing status of COVID-19 infection with an analytical equation for nonlinear trends. We found that there is only a single nonlinear trend, and t

Ziduo Yang, Yi-Ming Zhao, Xian Wang, Xiaoqing Liu

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computa

Peter Reinhard Hansen, Chen Tong

We introduce a new class of multivariate heavy-tailed distributions that are convolutions of heterogeneous multivariate t-distributions. Unlike commonly used heavy-tailed distributions, the multivariate convolution-t distributions embody cluster structures with flexible nonlinear dependencies and heterogeneous marginal distributions. Importantly, convolution

Ruijie Tao, Zhan Shi, Yidi Jiang, Tianchi Liu

Modern speaker recognition system relies on abundant and balanced datasets for classification training. However, diverse defective datasets, such as partially-labelled, small-scale, and imbalanced datasets, are common in real-world applications. Previous works usually studied specific solutions for each scenario from the algorithm perspective. However, the r

Lung-Chuan Chen, Zong-Ru Li

Large language models (LLMs) have demonstrated exceptional performance in various NLP applications. However, the majority of existing open-source LLMs are pre-trained primarily on English data and little part of other languages. This deficiency in multilingual training data results in suboptimal performance when applied to languages with fewer available reso

Ruijie Tao, Xinyuan Qian, Rohan Kumar Das, Xiaoxue Gao

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in su

Giung Nam, Byeongho Heo, Juho Lee

Large-scale contrastive vision-language pre-trained models provide the zero-shot model achieving competitive performance across a range of image classification tasks without requiring training on downstream data. Recent works have confirmed that while additional fine-tuning of the zero-shot model on the reference data results in enhanced downstream performan

Wilson Wu, John X. Morris, Lionel Levine

Do transformers "think ahead" during inference at a given position? It is known transformers prepare information in the hidden states of the forward pass at time step $t$ that is then used in future forward passes $t+\tau$. We posit two explanations for this phenomenon: pre-caching, in which off-diagonal gradient terms present during training result in the m

S. T. Petcov, M. Tanimoto

We present simple effective theory of quark masses, mixing and CP violation with level $N=3$ ($A_4$) modular symmetry, which provides solution to the strong CP problem without the need for an axion. The vanishing of the strong CP-violating phase $\bar \theta$ is ensured by assuming CP to be a fundamental symmetry of the Lagrangian of the theory. The CP symme

Shuvozit Ghose, Yang Wang

Point cloud classification refers to the process of assigning semantic labels or categories to individual points within a point cloud data structure. Recent works have explored the extension of pre-trained CLIP to 3D recognition. In this direction, CLIP-based point cloud models like PointCLIP, CLIP2Point have become state-of-the-art methods in the few-shot s

Injune Hwang, Kyogu Lee

Recently, there have been efforts to encode the linguistic information of speech using a self-supervised framework for speech synthesis. However, predicting representations from surrounding representations can inadvertently entangle speaker information in the speech representation. This paper aims to remove speaker information by exploiting the structured na

Pignge Hu, Xiaoteng Zhang, Mengmeng Li, Yingjie Zhu

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and its Retina-OT-Rt visual circuit is highly sensitive to capturing the motion informa

Saleh O. Allehabi, V. A. Dzuba, V. V. Flambaum

We identify two metastable excited states in Sm$ ^{10+} $ highly charged ion as candidates for high accuracy optical clocks. Several atomic properties relevant to optical clock development are calculated using relativistic many-body methods. This includes energy levels, transition amplitudes, lifetimes, scalar polarizabilities, black body radiation shift, an

Masato Fujita, Tomohiro Kawakami

A $G$-invariant version of definable Tietze extension theorem for definably complete structures is proved when a definably compact definable topological group $G$ acts definably and continuously on the definable set.

Hieu Nguyen, Cong-Hoang Ta, Phuong-Thuy Le-Nguyen, Minh-Triet Tran

This paper presents a simple yet efficient ensemble learning framework for Vietnamese scene text spotting. Leveraging the power of ensemble learning, which combines multiple models to yield more accurate predictions, our approach aims to significantly enhance the performance of scene text spotting in challenging urban settings. Through experimental evaluatio

Jinyoung Park, Juyeon Ko, Hyunwoo J. Kim

Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of downstream tasks. However, most existing prompt learning methods suffer from task overf

Christoforos Somarakis, Raman Goyal, Erfaun Noorani, Shantanu Rane

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking i

Tao Hu, Qingsen Yan, Yuankai Qi, Yanning Zhang

Recovering ghost-free High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit saturation and significant motion. Recent Diffusion Models (DMs) have been introduced in HDR imaging field, demonstrating promising performance, particularly in achieving visually perceptible results compared to p

Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu

Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservable factors. These sources of uncertainty

Qi He, Yunwei Mao, Ju Li

PinFi is a class of novel protocols for decentralized pricing of dissipative assets, whose value naturally declines over time. Central to the protocol's functionality and its market efficiency is the role of liquidity providers (LPs). This study addresses critical stability and sustainability challenges within the protocol, namely: the propensity of LPs to p

Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar

Unsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity recently due to its practical real-world applications. As surveillance videos are privacy sensitive and the availability of large-scale video data may enable better US-VAD systems, collaborative learning can be highly rewarding in this setting. However, du

Kartik Gupta, Rahul Vippala, Sahima Srivastava

Point Transformers are near state-of-the-art models for classification, segmentation, and detection tasks on Point Cloud data. They utilize a self attention based mechanism to model large range spatial dependencies between multiple point sets. In this project we explore two things: classification performance of these attention based networks on ModelNet10 da

Leran Wang, Alejandro O. Leon, Wenqing He, Zhongyu Liang

Owing to the unique features of low Gilbert damping, long spin-diffusion lengths and zero Ohmic losses, magnetic insulators are promising candidate materials for next-generation spintronic applications. However, due to the localized magnetic moments and the complex metal-oxide interface between magnetic insulators and heavy metals, spin-functional Dzyaloshin

Feng Bao, Hristo G. Chipilski, Siming Liang, Guannan Zhang

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the pro

Melvyn B. Nathanson

By H\" older's inequality, if $\mathbf{x} \in \ell^p$, then $\mathbf{x}\mathbf{y} \in \ell^1$ for all $\mathbf{y} \in \ell^q$. Landau proved the converse result: If $\mathbf{x}\mathbf{y} \in \ell^1$ for all $\mathbf{y} \in \ell^q$, then $\mathbf{x} \in \ell^p$. This paper proves Landau's theorem and considers a related problem for Hilbert's inequality.

Ling Gao, Daniel Gehrig, Hang Su, Davide Scaramuzza

Event cameras respond primarily to edges--formed by strong gradients--and are thus particularly well-suited for line-based motion estimation. Recent work has shown that events generated by a single line each satisfy a polynomial constraint which describes a manifold in the space-time volume. Multiple such constraints can be solved simultaneously to recover t

Francis Wagner

The following refinement of the Higman embedding theorem is proved: A finitely generated group $R$ is recursively presented if and only if there exists a quasi-isometric malnormal embedding of $R$ into a finitely presented group $H$ such that the image of the embedding enjoys the congruence extension property. Moreover, it is shown that the finitely presente

Naoki Seto

We discuss the possibility of enhancing intelligent life searches toward the Galactic center. From the clockwork orbital motions of stars around the Sgr A${}^*$ black hole, we can determine the distance to the Galactic center at an exceptional accuracy, despite its remoteness $\sim 8.3$kpc. In addition, we can define precise reference epochs by selecting a p

Aleksey Zinger

This informal note collects key results and open problems on the (co)homology of the Deligne-Mumford moduli spaces of real marked rational curves. The open problems are both of topological nature, aiming to investigate the (co)homology of these spaces further, and of algebraic nature, aiming to describe the structure of the homology of these spaces in operad

Yibin Ye, Xichao Teng, Shuo Chen, Yijie Bian

Optical-SAR image matching is a fundamental task for image fusion and visual navigation. However, all large-scale open SAR dataset for methods development are collected from single platform, resulting in limited satellite types and spatial resolutions. Since images captured by different sensors vary significantly in both geometric and radiometric appearance,

Sahan Yoruc Selcuk, Xilin Yang, Bijie Bai, Yijie Zhang

Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Accurate assessment of immunohistochemically (IHC) stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms

Zhonghao Lyu, Yuchen Li, Guangxu Zhu, Jie Xu

In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fine-tuning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. In this system, model pre-training is fi

Lee-Peng Teo

The Brioschi formula expresses the Gaussian curvature $K$ in terms of the functions $E, F$ and $G$ in local coordinates of a surface $S$. This implies the Gauss' theorema egregium, which says that the Gaussian curvature just depends on angles, distances, and their rates of change. In most of the textbooks, the Gauss' theorema egregium was proved as a corolla

Guoqiang Liang, Kanghao Chen, Hangyu Li, Yunfan Lu

Event camera has recently received much attention for low-light image enhancement (LIE) thanks to their distinct advantages, such as high dynamic range. However, current research is prohibitively restricted by the lack of large-scale, real-world, and spatial-temporally aligned event-image datasets. To this end, we propose a real-world (indoor and outdoor) da

Ashkan Shekaari, Mahmoud Jafari

Density functional theory has been applied to investigate the electronic structure and lattice stability of molybdenene monolayer in both its hexagonal and triclinic phases, within ultrasoft pseudopotential approach. In agreement with experimental findings, it has been found that either phase is metallic. Analyzing partial density of states has revealed that

Stephan Weis

The faces of a convex set owe their relevance to an interplay between convexity and topology that is systematically studied in the work of Rockafellar. Infinite-dimensional convex sets are excluded from this theory as their relative interiors may be empty. Shirokov and the present author answered this issue by proving that every point in a convex set lies in

Olympia-Dialekti Vouzina, Alexandros Tafanidis, Nicholas M. Glykos

The effect of mutations on protein structures is usually rather localized and minor. Finding a mutation that can single-handedly change the fold and/or topology of a protein structure is a rare exception. The A31P mutant of the homodimeric Repressor of Primer (Rop) protein is one such exception: This single mutation -- and as demonstrated by two independent

Wang-Yun Gu, Li-Xin Zhang

The arm of this paper is to establish the strong law of large numbers (SLLN) of $m$-dependent random variables under the framework of sub-linear expectations. We establish the SLLN for a sequence of independent, but not necessarily identically distributed random variables. The study further extends the SLLN to $m$-dependent and stationary sequence of random

Zeeshan Rasheed, Muhammad Waseem, Kari Systä, Pekka Abrahamsson

As Large Language Models (LLMs) have become integral to both research and daily operations, rigorous evaluation is crucial. This assessment is important not only for individual tasks but also for understanding their societal impact and potential risks. Despite extensive efforts to examine LLMs from various perspectives, there is a noticeable lack of multi-ag