March 2024 arXiv papers — page 54

Kun Sun, Rong Wang

The majority of research in computational psycholinguistics has concentrated on the processing of words. This study introduces innovative methods for computing sentence-level metrics using multilingual large language models. The metrics developed sentence surprisal and sentence relevance and then are tested and compared to validate whether they can predict h

David de Castro, Alejandro Cortiñas, Miguel R. Luaces, Oscar Pedreira

Context and motivation: Software Product Lines (SPL) enable the creation of software product families with shared core components using feature models to model variability. Choosing features from a feature model to generate a product may not be sufficient in certain situations because the application engineer may need to be able to decide on configuration ti

Dan Ye, Chen-Xin Jiang, Zi-Xiang Hu

We investigated the behavior of fractional quantum Hall (FQH) states in a two-dimensional electron system with layer thickness and an in-plane magnetic field. Our comparisons across various filling factors within the first Landau level revealed a crucial observation. A slight in-plane magnetic field specifically enhances the nematic order of the $\nu = 7/3$

Jacopo Niedda, Tommaso Tonolo, Giacomo Gradenigo

In this paper we investigate the marginally stable nature of the low-temperature trivial spin glass phase in the spherical $p=2$ spin glass, by perturbing the system with three different kinds of non-linear interactions. In particular, we compare the effect of three additional dense four-body interactions: ferromagnetic couplings, purely disordered couplings

A. Krzyzewska, A. Dyrdal

Bilinear magnetoresistance has been studied theoretically in 2D systems with isotropic cubic form of Rashba spin-orbit interaction. We have derived the effective spin-orbital field due to current-induced spin polarization and discussed its contribution to the unidirectional system response. The analysed model can be applied to the semiconductor quantum wells

Aliaksei Mazheika, Sergey V. Levchenko, Luca M. Ghiringhelli

Symbolic-inference methods have recently found a broad application in materials science. In particular, the Sure-Independence Screening and Sparsifying Operator (SISSO) performs symbolic regression and classification by adopting compressed sensing for the selection of an optimized subset of features and mathematical operators out of a given set of candidates

Mingjin Zhang, Jiannong Cao, Yuvraj Sahni, Xiangchun Chen

Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy, parallel split learning is becoming a practical and popular approach. However, current parallel split learnin

Ian Stewart

Systems of ODEs coupled with the topology of a closed ring are common models in biology, robotics, electrical engineering, and many other areas of science. When the component systems and couplings are identical, the system has a cyclic symmetry group for unidirectional rings and a dihedral symmetry group for bidirectional rings. Hopf bifurcation in equivaria

Yigit Yildirim, Mehmet Suzer, Emre Ugur

Socially compliant navigation is an integral part of safety features in Human-Robot Interaction. Traditional approaches to mobile navigation prioritize physical aspects, such as efficiency, but social behaviors gain traction as robots appear more in daily life. Recent techniques to improve the social compliance of navigation often rely on predefined features

Baris Akbas, Huseyin Taner Yuksel, Aleyna Soylemez, Mazhar Eid Zyada

Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC)

Yosuke Onoue

Stress models are a promising approach for graph drawing. They minimize the weighted sum of the squared errors of the Euclidean and desired distances for each node pair. The desired distance typically uses the graph-theoretic distances obtained from the all-node pair shortest path problem. In a minimized stress function, the obtained coordinates are affected

Aman Saggu, Lennart Ante, Ender Demir

National football teams increasingly issue tradeable blockchain-based fan tokens to strategically enhance fan engagement. This study investigates the impact of 2022 World Cup matches on the dynamic performance of each team's fan token. The event study uncovers fan token returns surged six months before the World Cup, driven by positive anticipation effects.

Yuya Koda, Yuki Tanaka

A $(g, n)$-decomposition of a link $L$ in a closed orientable $3$-manifold $M$ is a decomposition of $M$ by a closed orientable surface of genus $g$ into two handebodies each intersecting the link $L$ in $n$ trivial arcs. The Goeritz group of that decomposition is then defined to be the group of isotopy classes of orientation-preserving homeomorphisms of the

Daniel Kráľ, Matjaž Krnc, Ander Lamaison

A graph $H$ is common if its Ramsey multiplicity, i.e., the minimum number of monochromatic copies of $H$ contained in any $2$-edge-coloring of $K_n$, is asymptotically the same as the number of monochromatic copies in the random $2$-edge-coloring of $K_n$. Erd\H{o}s conjectured that every complete graph is common, which was disproved by Thomason in the 1980

Viktor Sanca, Anastasia Ailamaki

The rapid growth of machine learning capabilities and the adoption of data processing methods using vector embeddings sparked a great interest in creating systems for vector data management. While the predominant approach of vector data management is to use specialized index structures for fast search over the entirety of the vector embeddings, once combined

Lars Andersen

We discuss $\mathcal{D}$-modules and dynamical systems in the \'etale topology. We introduce the differential scheme associated to a morphism $f: X\to S$ of schemes of the same dimension. We introduce differential inertia group $I_{diff}^i$ which act trivially on the special fibers of these schemes. As an application we discuss the problem of counting points

Muqing Cao, Jiayan Zhao, Xinhang Xu, Lihua Xie

Inspired by the behavior of birds, we present AirCrab, a hybrid aerial ground manipulator (HAGM) with a single active wheel and a 3-degree of freedom (3-DoF) manipulator. AirCrab leverages a single point of contact with the ground to reduce position drift and improve manipulation accuracy. The single active wheel enables locomotion on narrow surfaces without

Max T. M. Ng, Florian Dandl, Hani S. Mahmassani, Klaus Bogenberger

Shared Autonomous Vehicles (SAVs) enable transit agencies to design more agile and responsive services at lower operating costs. This study designs and evaluates a semi-on-demand hybrid route directional service in the public transit network, offering on-demand flexible route service in low-density areas and fixed route service in higher-density areas. We de

Ruige Zong, Tao Wang, Chunwang Li, Xinlin Zhang

Familial cerebral cavernous malformation (FCCM) is a hereditary disorder characterized by abnormal vascular structures within the central nervous system. The FCCM lesions are often numerous and intricate, making quantitative analysis of the lesions a labor-intensive task. Consequently, clinicians face challenges in quantitatively assessing the severity of le

Haeun Moon, Jin-Hong Du, Jing Lei, Kathryn Roeder

Quantitative measurements produced by mass spectrometry proteomics experiments offer a direct way to explore the role of proteins in molecular mechanisms. However, analysis of such data is challenging due to the large proportion of missing values. A common strategy to address this issue is to utilize an imputed dataset, which often introduces systematic bias

Xiaojing Du, Hanjie Zhao, Danyan Xing, Yuxiang Jia

In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records. The challenge in medical NER arises from the complex nested structures and sophisticated medical terminologies, disti

Arkady Leiderman, Santi Spadaro, Stevo Todorcevic

We characterize when the countable power of a Corson compactum has a dense metrizable subspace and construct consistent examples of Corson compacta whose countable power does not have a dense metrizable subspace. We also give several remarks about ccc Corson compacta and, as a byproduct, we obtain a new proof of Kunen and van Mill's characterization of when

Elliott Hauser, Yao-Cheng Chan, Sadanand Modak, Joydeep Biswas

HRI research using autonomous robots in real-world settings can produce results with the highest ecological validity of any study modality, but many difficulties limit such studies' feasibility and effectiveness. We propose Vid2Real HRI, a research framework to maximize real-world insights offered by video-based studies. The Vid2Real HRI framework was used t

Mihir Durve, Jean-Michel Tucny, Deepesh Bhamre, Adriano Tiribocchi

The shape of liquid droplets in air plays an important role in aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such systems. It is desired to have a minimal representation of droplet shapes using as few parameters to automate s

Zhengxiao Du, Aohan Zeng, Yuxiao Dong, Jie Tang

Recent studies have put into question the belief that emergent abilities in language models are exclusive to large models. This skepticism arises from two observations: 1) smaller models can also exhibit high performance on emergent abilities and 2) there is doubt on the discontinuous metrics used to measure these abilities. In this paper, we propose to stud

Siyi Xu, Sherry Yeh, Laura. K. Rogers, Amy Steele

The chemical composition of an extrasolar planet is fundamental to its formation, evolution and habitability. In this study, we explore a new way to measure the chemical composition of the building blocks of extrasolar planets, by measuring the gas composition of the disrupted planetesimals around white dwarf stars. As a first attempt, we used the photo-ioni

Bin Gao, Zhuomin He, Puru Sharma, Qingxuan Kang

Interacting with humans through multi-turn conversations is a fundamental feature of large language models (LLMs). However, existing LLM serving engines executing multi-turn conversations are inefficient due to the need to repeatedly compute the key-value (KV) caches of historical tokens, incurring high serving costs. To address the problem, this paper propo

Cesar E. P. Villegas, Alexandre R. Rocha

The optical response of quasi-one-dimensional systems is often dominated by tightly bound excitons, that significantly influence their basic electronic properties. Despite their importance for device performance, accurately predicting their excitonic effects typically requires computationally demanding many-body approaches. Here, we present a simplified mode

Taras Bodnar, Nestor Parolya

In this paper, we derive high-dimensional asymptotic properties of the Moore-Penrose inverse and, as a byproduct, of various ridge-type inverses of the sample covariance matrix. In particular, the analytical expressions of the asymptotic behavior of the weighted sample trace moments of generalized inverse matrices are deduced in terms of the partial exponent

Mu-Yi Shen, Chia-Chi Hsu, Hao-Yu Hou, Yu-Chen Huang

In this study, we introduce the DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomous driving agents in a targeted real-world scene. Standard simulator-based rendering often fails to accurately reflect real-world performance due to the sim-to-real gap, which repr

Samuel Stocksieker, Denys Pommeret, Arthur Charpentier

The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image datasets. This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary fo

He Guo, Zixuan Ye, Zhiguo Cao, Hao Lu

We introduce in-context matting, a novel task setting of image matting. Given a reference image of a certain foreground and guided priors such as points, scribbles, and masks, in-context matting enables automatic alpha estimation on a batch of target images of the same foreground category, without additional auxiliary input. This setting marries good perform

Fahad Al Saadi, Edgar Knobloch, Mark Nelson, Hannes Uecker

Numerical continuation is used to compute solution branches in a two-component reaction-diffusion model of Leslie--Gower type. %in the vicinity of a Turing-Hopf interaction. Two regimes are studied in detail. In the first, the homogeneous state loses stability to supercritical spatially uniform oscillations, followed by a subcritical steady state bifurcation

Masaya Kotani, Takeru Oba, Norimichi Ukita

This paper proposes a depth estimation method using radar-image fusion by addressing the uncertain vertical directions of sparse radar measurements. In prior radar-image fusion work, image features are merged with the uncertain sparse depths measured by radar through convolutional layers. This approach is disturbed by the features computed with the uncertain

Kaiwen Wang, Yinzhe Shen, Martin Lauer

Existing object detectors encounter challenges in handling domain shifts between training and real-world data, particularly under poor visibility conditions like fog and night. Cutting-edge cross-domain object detection methods use teacher-student frameworks and compel teacher and student models to produce consistent predictions under weak and strong augment

Alonso Hernández-Antón, Fernando Luis, Alberto Castro

Quantum optimal control theory (QOCT) can be used to design the shape of electromagnetic pulses that implement operations on quantum devices. By using non-trivially shaped waveforms, gates can be made significantly faster than those built by concatenating monochromatic pulses. Recently, we applied this technique to the control of molecular spin qudits modell

Longhui Li, Bochen Liu

In this paper we come up with a dual version of the Furstenberg problem and obtain partial results via $L^p$ estimates of orthogonal projections. Examples are also discussed. Moreover, compared with general sets, we find that special structure like Cartesian product has better $L^p$-behavior. This leads to improvement on some discretized sum-product estimate

Harpal Singh, Arbaz Khan

This study introduces the divergence-conforming discontinuous Galerkin finite element method (DGFEM) for numerically approximating optimal control problems with distributed constraints, specifically those governed by stationary generalized Oseen equations. We provide optimal a priori error estimates in energy norms for such problems using the divergence-conf

Simon Schnake, Dirk Krücker, Kerstin Borras

The simulation of calorimeter showers presents a significant computational challenge, impacting the efficiency and accuracy of particle physics experiments. While generative ML models have been effective in enhancing and accelerating the conventional physics simulation processes, their application has predominantly been constrained to fixed detector readout

Raquel Fernández de Cabo, David González-Andrade, Pavel Cheben, Aitor V. Velasco

Power splitters play a crucial role in virtually all photonic circuits, enabling precise control of on-chip signal distribution. However, state-of-the-art solutions typically present trade-offs in terms of loss, bandwidth, and fabrication robustness, especially when targeting multimode operation. Here, we present a novel multimode 3-dB power splitter based o

Junliang Luo, Stefan Kitzler, Pietro Saggese

We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination with

Matteo Cederle, Luca Vittorio Piron, Marina Ceccon, Federico Chiariotti

As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in smart mobility. Addressing this gap, our study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services providing a novel

Youyang Qu, Ming Ding, Nan Sun, Kanchana Thilakarathna

Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information from their vast datasets. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns, offering

Donato Riccio, Fabrizio Maturo, Elvira Romano

Many conventional statistical and machine learning methods face challenges when applied directly to high dimensional temporal observations. In recent decades, Functional Data Analysis (FDA) has gained widespread popularity as a framework for modeling and analyzing data that are, by their nature, functions in the domain of time. Although supervised classifica

Min An

Let $\left(X_n, d_n\right)$ be a sequence of metric spaces and let $\mathcal{F}=\left\{f_n\right\}_{n \in \mathbb{Z}}$ be a sequence of continuous and onto maps $f_n: X_n \rightarrow X_{n+1}, n \in \mathbb{Z}_{+}$. In this paper, we prove that if the compression ratio meets $\prod \lambda_i=0$, then there exists $\delta_n>0$ such that any $\delta_n$ - pseudo

Minghui Xu, Hao Fei, Fei Li, Shengqiong Wu

Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which is challenging due to the lengthy and background information-rich na ture of the texts. In this work, We propose using a unified semantic discourse structure (S3) to represent docu

Kenichi Konishi, Stefano Bolognesi, Andrea Luzio

We review recent developments in our understanding of the dynamics of strongly-coupled chiral $SU(N)$ gauge theories in four dimensions, problems which are potentially important in our quest to go beyond the standard $SU(3)_{QCD} \times (SU(2) \times U(1))_{GWS}$ model of the fundamental interactions. The generalized symmetries and associated new 't Hooft an

María Blanco-Rey, Rodrigo Castrillo, Khadiza Ali, Pierluigi Gargiani

Magnetic anisotropy and magnetic exchange interactions are crucial parameters that characterize the hybrid metal-organic interface, key component of an organic spintronic device. We show that the incorporation of 4$f$ RE atoms to hybrid metal-organic interfaces of CuPc/REAu$_2$ type (RE= Gd, Ho) constitutes a feasible approach towards on-demand magnetic prop

Andrea Montessori, Luiz A. Hegele, Marco Lauricella

In this work an optimized multicomponent lattice Boltzmann (LB) model is deployed to simulate axisymmetric turbulent jets of a fluid evolving in a quiescent, immiscible environment over a wide range of dynamic regimes. The implementation of the multicomponent lattice Boltzmann code achieves peak performances on graphic processing units with a significant red

Xunlong Wang, Bin Wu

Opinion dynamics is of paramount importance as it provides insights into the complex dynamics of opinion propagation and social relationship adjustment. It is assumed in most of the previous works that social relationships evolve much faster than opinions. This is not always true in reality. We propose an analytical approximation to study this issue for arbi

M. Koussour, N. Myrzakulov, Javlon Rayimbaev, Alnadhief H. A. Alfedeel

In this work, we investigate the bouncing behavior of the universe within the framework of $f(R,L_m)$ gravity, using a simple form of $f(R,L_m)=\frac{R}{2}+L_m^\gamma$ (where $\gamma$ is a free model parameter) as previously studied. The model predicts a vanishing Hubble parameter in the early and late times, with the deceleration parameter approaching a spe

Mohamed Abdalmoaty, Roy S. Smith

We revisit the problem of non-parametric closed-loop identification in frequency domain; we give a brief survey of the literature and provide a small noise analysis of the direct, indirect, and joint input-output methods when two independent experiments with identical excitation are used. The analysis is asymptotic in the noise variance (i.e., as the standar

Zhongsen Li, Wenxuan Chen, Shuai Wang, Chuyu Liu

The inductive bias of the convolutional neural network (CNN) can be a strong prior for image restoration, which is known as the Deep Image Prior (DIP). Recently, DIP is utilized in unsupervised dynamic MRI reconstruction, which adopts a generative model from the latent space to the image space. However, existing methods usually use a pyramid-shaped CNN gener

Nishant Kumar, Ziyan Tao, Jaikirat Singh, Yang Li

Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagnostic decisions, as the fusion mechanism blends features from source images, thereby making it difficult to interpret the underlying tumor pa

Sauro Succi, Andrea Montessori

We present a mathematical and computational framework to couple the Keldysh non equilibrium quantum transport formalism with a nanoscale lattice Boltzmann method for the computational design of quantum-engineered nanofluidic devices.

Baptiste Serraille

In this paper, we present a $C^0$-fragmentation property for Hamiltonian diffeomorphisms. More precisely, it is known that for a given open covering $\mathcal{U}$ of a compact symplectic surface we can write each $C^0$-small enough Hamiltonian diffeomorphism as the composition of Hamiltonian diffeomorphisms compactly supported inside the open sets of the cov

Jia Wei, Xingjun Zhang, Witold Pedrycz

Bagging has achieved great success in the field of machine learning by integrating multiple base classifiers to build a single strong classifier to reduce model variance. The performance improvement of bagging mainly relies on the number and diversity of base classifiers. However, traditional deep learning model training methods are expensive to train indivi

Hao Wang, Tang Li, Chenhui Chu, Nengjun Zhu

Key-value relations are prevalent in Visually-Rich Documents (VRDs), often depicted in distinct spatial regions accompanied by specific color and font styles. These non-textual cues serve as important indicators that greatly enhance human comprehension and acquisition of such relation triplets. However, current document AI approaches often fail to consider t

Mengxi Chen, Zhixing Ling, Mingjun Liu, Qinyu Wu

Complementary metal-oxide-semiconductor (CMOS) detectors are a competitive choice for current and upcoming astronomical missions. To understand the performance variations of CMOS detectors in space environment, we investigate the total ionizing dose effects on custom-made large-format X-ray CMOS detectors. Three CMOS detector samples were irradiated with a C

Nobuo Iida, Masaki Taniguchi

We present a framework for studying transverse knots and symplectic surfaces utilizing the Seiberg-Witten monopole equation. Our primary approach involves investigating an equivariant Seiberg-Witten theory introduced by Baraglia-Hekmati on branched covers, incorporating invariant contact/symplectic structures. Within this framework, we introduce a novel slic

Shourie S. Grama, Mahdi Javadi, Shivesh Kumar, Hossein Zamani Boroujeni

This paper presents the design, analysis, and performance evaluation of RicMonk, a novel three-link brachiation robot equipped with passive hook-shaped grippers. Brachiation, an agile and energy-efficient mode of locomotion observed in primates, has inspired the development of RicMonk to explore versatile locomotion and maneuvers on ladder-like structures. T

Zekun Zhao, Qingqian Kang, Huan Zhang, Teng Zhao

The research focused on enhancing the measurement accuracy through the use of non-Gaussian states has garnered increasing attention. In this study, we propose a scheme to input the coherent state mixed with photon-catalyzed squeezed vacuum state into the Mach-Zender interferometer to enhance phase measurement accuracy. The findings demonstrate that photon ca

Jianqing Zhang, Yang Liu, Yang Hua, Jian Cao

Heterogeneous Federated Learning (HtFL) enables task-specific knowledge sharing among clients with different model architectures while preserving privacy. Despite recent research progress, transferring knowledge in HtFL is still difficult due to data and model heterogeneity. To tackle this, we introduce a public pre-trained generator (e.g., StyleGAN or Stabl

E. Leung, J. Guan, KO. Kwok, CT. Hung

Introduction: The current study investigates the complex association between COVID-19 and the studied districts' socioecology (e.g. internal and external built environment, sociodemographic profiles, etc.) to quantify their contributions to the early outbreaks and epidemic resurgence of COVID-19. Methods: We aligned the analytic model's architecture with the

Guoen Hu, Xudong Lai, Xiangxing Tao, Qingying Xue

In this paper, the authors consider the endpoint estimates for the maximal Calder\'on commutator defined by $$T_{\Omega,\,a}^*f(x)=\sup_{\epsilon>0}\Big|\int_{|x-y|>\epsilon}\frac{\Omega(x-y)}{|x-y|^{d+1}} \big(a(x)-a(y)\big)f(y)dy\Big|,$$ where $\Omega$ is homogeneous of degree zero, integrable on $S^{d-1}$ and has vanishing moment of order one, $a$ be a fu

Ryoma Sato

Users are dissatisfied with services. Since the service is not tailor-made for a user, it is natural for dissatisfaction to arise. The problem is, that even if users are dissatisfied, they often do not have the means to resolve their dissatisfaction. The user cannot alter the source code of the service, nor can they force the service provider to change. The

Matteo Esposito, Francesco Palagiano

Preliminary security risk analysis (PSRA) provides a quick approach to identify, evaluate and propose remeditation to potential risks in specific scenarios. The extensive expertise required for an effective PSRA and the substantial ammount of textual-related tasks hinder quick assessments in mission-critical contexts, where timely and prompt actions are esse

Abdulrahman A. Ahmed, M. Amin Rahimian, Mark S. Roberts

Agent-based simulation with a synthetic population can help us compare different treatment conditions while keeping everything else constant within the same population (i.e., as digital twins). Such population-scale simulations require large computational power (i.e., CPU resources) to get accurate estimates for treatment effects. We can use meta models of t

Sajad Faramarzi, Hosein Zarini, Sepideh Javadi, Mohammad Robat Mili

In this paper, we consider the downlink transmission of a multi-antenna base station (BS) supported by an active simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS) to serve single-antenna users via simultaneous wireless information and power transfer (SWIPT). In this context, we formulate an energy efficiency maximisation problem t

Ruolan Xiong

Let $X$ be a compact metric space and $\Phi=\{\varphi_t\}_{t\in\mathbb{R}}$ be a continuous flow on $X$. We introduce two types of topological pressure for family of discontinuous potentials $a=\{a_t\}_{t>0}$. First, define the topological pressure of family of measurable potentials $a=\{a_t\}_{t>0}$ on a subset $Z$ for flow and proof its invariant principle

Yingchun Leng, Yiming Chen, Rui Li, Lihua Wang

The precise measurement of the gravity of the earth plays a pivotal role in various fundamental research and application fields. Although a few gravimeters have been reported to achieve this goal, miniaturization of high-precision gravimetry remains a challenge. In this work, we have proposed and demonstrated a miniaturized gravimetry operating at room tempe

Huiping Zhuang, Yuchen Liu, Run He, Kai Tong

Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is Catastrophic Forgetting, i.e., the loss of previously learned knowledge. Among existing baselines, replay-based methods show competitive results but requires extra memory for storin

Jiacheng Ruan, Jingsheng Gao, Mingye Xie, Daize Dong

Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better adaptation to downstream tasks. This paper proposes a distillation framework for the AT method instead of crafting a carefully designed adapter module, which aims to improve fine-

Adrian S. Lewis, Genaro Lopez-Acedo, Adriana Nicolae

To explore convex optimization on Hadamard spaces, we consider an iteration in the style of a subgradient algorithm. Traditionally, such methods assume that the underlying spaces are manifolds and that the objectives are geodesically convex: the methods are described using tangent spaces and exponential maps. By contrast, our iteration applies in a general H

Vladislav Rykhlov

This paper is devoted to the construction of semiclassical spectrum and efficient (simple to implement) explicit semiclassical asymptotic eigenfunctions of the Dirac operator for relatively high-energy bound states in graphene in magnetic field, considering the effect of trigonal warping [11, 16] to be small. It turns out that the asymptotic spectrum of the

Rui Xie, Zhengran Zeng, Zhuohao Yu, Chang Gao

Code large language models mark a pivotal breakthrough in artificial intelligence. They are specifically crafted to understand and generate programming languages, significantly boosting the efficiency of coding development workflows. In this technical report, we present CodeShell-Base, a seven billion-parameter foundation model with 8K context length, showca

Roberto Nesci, Mariateresa Fiocchi, Antonio Vagnozzi

We discuss the optical light curves of two Be X-ray Binaries, IGR J06074+2205 and SAX J2103.5+4545 recovered from the ATLAS, ZTF and ASAS-SN databases. Both sources show long term optical variability of 620 and 420 days respectively, with color redder when brighter. We suggest that this is due to the precession of the circumstellar disk. Another possibility

Susie Lu, Ji Liu

This paper characterizes the graphical properties of an optimal topology with minimal Laplacian energy under the constraint of fixed numbers of vertices and edges, and devises an algorithm to construct such connected optimal graphs. These constructed graphs possess maximum vertex and edge connectivity, and more importantly, exhibit large algebraic connectivi

Abhishek Ghose, Emma Thuong Nguyen

Active learning (AL) techniques optimally utilize a labeling budget by iteratively selecting instances that are most valuable for learning. However, they lack ``prerequisite checks'', i.e., there are no prescribed criteria to pick an AL algorithm best suited for a dataset. A practitioner must pick a technique they \emph{trust} would beat random sampling, bas

Ming Li, Zhiyong Sun

In this paper, we demonstrate that controllers designed by artificial potential fields (APFs) can be derived from reciprocal control barrier function quadratic program (RCBF-QP) safety filters. By integrating APFs within the RCBF-QP framework, we explicitly establish the relationship between these two approaches. Specifically, we first introduce the concepts

Qingmian Wan, Hongli Peng, Xing Liao, Kuayue Liu

This paper introduces in detail the effective method of comprehensive target judgment by using radar RA map and point cloud map. Different output of radar can effectively judge the road boundary of target and the relative coordinates of target, avoid the error of output caused by excessive processing information, and greatly improve the processing efficiency

Pablo Tejerina-Pérez, Daniele Bertacca, Raul Jimenez

We propose a possible quantum signature of the early Universe that could lead to observational imprints of the quantum nature of the inflationary period. Graviton production in the presence of an inflaton scalar field results in entangled states in polarization. This is because of a non-trivial effect due to the derivatives on two scalar fluctuations and it

Artur Kawalec

In article, we explore the secondary zeta function $Z(s)$, which is defined as a generalized zeta type of series over imaginary parts of non-trivial zeros of the Riemann zeta function $\zeta(s)$. This function has been analytically continued as a meromorphic function in $\mathbb{C}$ with one double pole and an infinity of simple poles. The secondary zeta fun

Shuai Zhao, Linchao Zhu, Ruijie Quan, Yi Yang

A primary concern regarding training large language models (LLMs) is whether they abuse copyrighted online text. With the increasing training data scale and the prevalence of LLMs in daily lives, two problems arise: \textbf{1)} false positive membership inference results misled by similar examples; \textbf{2)} membership inference methods are usually too com

M. Lozhkin, A. Skopenkov

In this expository note we show how combinatorial Nullstellensatz by N. Alon naturally appears in solutions of elementary problems. Simple ideas gradually and naturally appear in such solutions, thus bringing a reader to generalizations. The note is accessible to mathematicians not specialized in the area, and to students familiar with polynomials.

Ruiqi Kong, He Chen

This work introduces DeepCRF, a deep learning framework designed for channel state information-based radio frequency fingerprinting (CSI-RFF). The considered CSI-RFF is built on micro-CSI, a recently discovered radio-frequency (RF) fingerprint that manifests as micro-signals appearing on the channel state information (CSI) curves of commercial WiFi devices.

Felix Parker, Fardin Ganjkhanloo, Diego A. Martínez, Kimia Ghobadi

Effective hospital capacity management is pivotal for enhancing patient care quality, operational efficiency, and healthcare system resilience, notably during demand spikes like those seen in the COVID-19 pandemic. However, devising optimal capacity strategies is complicated by fluctuating demand, conflicting objectives, and multifaceted practical constraint

Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda

We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system that can infer \textit{how} to motivate a user effectively. We propose DIIT, a framework that is capable of learning and applying conversation strategies in the form of natural lan

Xin Zhang, Tianjie Ju, Huijia Liang, Ying Fu

The substantial interest in updating Large Language Models (LLMs) without retraining from scratch is accompanied by several challenges. This is particularly true when updating LLMs with datasets that necessitate domain-expert reasoning across extensive texts, despite limited samples. We termed the scenario as the Few-Shot Domain-Expert Reasoning for Updating

Siwei Yang, Xianhang Li, Jieru Mei, Jieneng Chen

Segmenting brain tumors is complex due to their diverse appearances and scales. Brain metastases, the most common type of brain tumor, are a frequent complication of cancer. Therefore, an effective segmentation model for brain metastases must adeptly capture local intricacies to delineate small tumor regions while also integrating global context to understan

Zhendong Cao, Xiaoshan Luo, Jian Lv, Lei Wang

We introduce CrystalFormer, a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. By explicitly incorporating space group symmetry, CrystalFormer greatly reduces the effective complexity of crystal space, which is essential for data-and compute-efficient generative modeling of crystalli

Peisen Li, Yizhe Pang, Junyu Ren

This study presents a new deep learning framework, combining Spatio-Temporal Graph Convolutional Network (STGCN) with a Large Language Model (LLM), for bike demand forecasting. Addressing challenges in transforming discrete datasets and integrating unstructured language data, the framework leverages LLMs to extract insights from Points of Interest (POI) text

Masakazu Teragaito

For a knot in the 3-sphere, the Upsilon invariant is a piecewise linear function defined on the interval [0,2]. For an L-space knot, the Upsilon invariant is determined only by the Alexander polynomial of the knot. We exhibit infinitely many pairs of hyperbolic L-space knots such that two knots of each pair have distinct Alexander polynomials, so they are no

Parvej Khan, Sumana Dutta

Target waves and spiral waves were discovered in the Belousov-Zhabotinsky (BZ) reaction around 50 years ago. Many biological systems demonstrate such rotating spiral patterns. Spiral waves are widely encountered in the glycolytic activity of yeast and cardiac and neuronal tissues. In the cardiac system, the spiral waves and their three dimensional counterpar

Jong Il Park, Deok-Sun Lee, Sang Hoon Lee, Hye Jin Park

Understanding the behaviors of ecological systems is challenging given their multi-faceted complexity. To proceed, theoretical models such as Lotka-Volterra dynamics with random interactions have been investigated by the dynamical mean-field theory to provide insights into underlying principles such as how biodiversity and stability depend on the randomness

Karthik Suresh, Neeltje Kackar, Luke Schleck, Cristiano Fanelli

The complexity and sheer volume of information encompassing documents, papers, data, and other resources from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists. To tackle this issue, a Ret

Ruijie Liu, Tianxiang Zhan, Zhen Li, Yong Deng

In wireless sensor networks (WSNs), coverage and deployment are two most crucial issues when conducting detection tasks. However, the detection information collected from sensors is oftentimes not fully utilized and efficiently integrated. Such sensing model and deployment strategy, thereby, cannot reach the maximum quality of coverage, particularly when the

Jan Kotlarz, Katarzyna Kubiak, Natalia Zalewska

Enceladus is the Saturnian satellite known to have water vapor erupting from its south pole region called Tiger Stripes. Data collected by Cassini Ultraviolet Imaging Spectrograph during Enceladus transiting Saturn allow us to estimate water plume absorption from 1115.35 - 1912.50 Angstrom and compare it to the Mie solutions of Maxwell equations for particle

Tangjun Wang, Chenglong Bao, Zuoqiang Shi

In this paper, we study the partial differential equation models of neural networks. Neural network can be viewed as a map from a simple base model to a complicate function. Based on solid analysis, we show that this map can be formulated by a convection-diffusion equation. This theoretically certified framework gives mathematical foundation and more underst

Brianna MacNider, Haning Xiu, Kai Qian, Ian Frankel

Passive transformation of waves via nonlinear systems is ubiquitous in settings ranging from acoustics to optics and electromagnetics. Passivity is of particular importance for responding rapidly to stimuli and nonlinearity enormously expands signal transformability compared to linear systems due to the breaking of superposition. It is well known that differ