October 2023 arXiv papers — page 84

Mikhail Lebyodkin, Jamieson Brechtl, Tatiana Lebedkina, Kangkang Wen

Recent observations of jerky flow in high-entropy alloys (HEA) revealed a high role of self-organization of dislocations in their plasticity. The present work reports first results of investigation of stress fluctuations during plastic deformation of a FeCoNiTiAl alloy, examined in a wide temperature range covering both smooth and jerky flow. These fluctuati

Indranil Biswas, Hisashi Kasuya

We prove that a compact Sasakian manifolds whose first and second basic Chern classes vanish is locally isomorphic to the real Heisenberg group equipped with the standard left invariant Sasakian structure up to deformation associated to a basic $1$-from.

Mikhail Panov, Vasily Zubkov, Anna Solomnikova, Igor Klepikov

The intensive development of technology for fabrication semiconducting CVD diamond layers poses an important task of developing a precise and non-destructive method for estimation the boron content in thin epitaxial layers. For bulk and uniformly doped diamond samples, the infrared optical spectroscopy successfully performs such a role. Here we propose a cor

Pyae Hein Htet, Eric Lauga

The Drosophila melanogaster embryo, an elongated multi-nucleated cell, is a classical model system for eukaryotic development and morphogenesis. Recent work has shown that bulk cytoplasmic flows, driven by cortical contractions along the walls of the embryo, enable the uniform spreading of nuclei along the anterior-posterior (AP) axis necessary for proper em

Jungbin Son, Alice Oh

Time is one of the crucial factors in real-world question answering (QA) problems. However, language models have difficulty understanding the relationships between time specifiers, such as 'after' and 'before', and numbers, since existing QA datasets do not include sufficient time expressions. To address this issue, we propose a Time-Context aware Question A

Samuel Pautrel, Florent Malabat, Louis Waquier, Martin Colombano

We explore new methods for coupling light to on-chip gallium arsenide nanophotonic structures using etched conical optical fibers. With a single-sided conical fiber taper, we demonstrate efficient coupling to an on-chip photonic bus waveguide in a liquid environment. We then show that it is possible to replace such on-chip bus waveguide by two joined conical

Mariia Zameshina, Olivier Teytaud, Laurent Najman

Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about the lack of diversity in the generated imagery. To tackle this, we introduce Diverse Diffusion, a method for boosting image diversity beyond gender and ethnicity, spanning into richer realms, including color diversity.Diverse Diffusion is a general unsupervis

Jichang Xiao, Xiaoqun Wang

Deep learning algorithms have been successfully applied to numerically solve linear Kolmogorov partial differential equations~(PDEs). A recent research shows that if the initial functions are bounded, the empirical risk minimization (ERM) over clipped ReLU networks generalizes well for solving the linear Kolmogorov PDE. In this paper, we propose to use a tru

Rui Chen, Lu Gao, Yutian Liu, Yong Liang Guan

Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time traffic data for vehicle-road cooperation. However, the full potential of smart roads in improving the

Yuma Fujimoto, Hisashi Ohtsuki

Indirect reciprocity is a mechanism that explains large-scale cooperation in human societies. In indirect reciprocity, an individual chooses whether or not to cooperate with another based on reputation information, and others evaluate the action as good or bad. Under what evaluation rule (called ``social norm'') cooperation evolves has long been of central i

Tao Zou, Le Yu, Yifei Huang, Leilei Sun

In many real-world scenarios (e.g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships, which can be abstracted as Text-Attributed Heterogeneous Graphs (TAHGs). Current pretraining tasks for Language Models (LMs) primarily focus on separately learning the textual in

Lorenzo Gigli, Alexander Goscinski, Michele Ceriotti, Gareth A. Tribello

The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the

Takuma Isobe, Tsuneya Yoshida, Yasuhiro Hatsugai

Although topological phenomena attract growing interest not only in linear systems but also in nonlinear systems, the bulk-edge correspondence under the nonlinearity of eigenvalues has not been established so far. We address this issue by introducing auxiliary eigenvalues. We reveal that the topological edge states of auxiliary eigenstates are topologically

Aye Chan May, Adisak Seesanea

We study Dirichlet problems for fractional Laplace equations of the form $(-\Delta)^{\frac{\alpha}{2}} u = f(x,u)$ in $\mathbb{R}^{n}$ for $0<\alpha<n$ where the nonlinearity $f(x,u) = \sum_{i=1}^{M} \sigma_{i} u^{q_i} + \omega$ involves sublinear terms with $0<q_{i}<1$ and the coefficients $\sigma_{i}, \omega$ are nonnegative locally finite Borel measures o

Dmitry Nikolaev, Tanise Ceron, Sebastian Padó

Scaling analysis is a technique in computational political science that assigns a political actor (e.g. politician or party) a score on a predefined scale based on a (typically long) body of text (e.g. a parliamentary speech or an election manifesto). For example, political scientists have often used the left--right scale to systematically analyse political

Gia Minh Hoang, Youngjoo Lee, Jae Gwan Kim

Alzheimer's disease is one of the most common types of neurodegenerative disease, characterized by the accumulation of amyloid-beta plaque and tau tangles. Recently, deep learning approaches have shown promise in Alzheimer's disease diagnosis. In this study, we propose a reproducible model that utilizes a 3D convolutional neural network with a dual attention

Surendra Singh

Complex oxide thin films and heterostructures offer a wide range of properties originating from the intrinsic coupling between lattice strain and magnetic/electronic ordering. This article reviews experimental, phenomenological, and theoretical analyses of the coupling of strain with electronic and magnetic properties of mixed valence manganite heterostructu

Mengce Zheng

We point out critical deficiencies in lattice-based cryptanalysis of common prime RSA presented in ``Remarks on the cryptanalysis of common prime RSA for IoT constrained low power devices'' [Information Sciences, 538 (2020) 54--68]. To rectify these flaws, we carefully scrutinize the relevant parameters involved in the analysis during solving a specific triv

Marie Touboul, Bruno Lombard, Raphaël Assier, Sébastien Guenneau

Laminated media with material properties modulated in space and time in the form of travelling waves have long been known to exhibit non-reciprocity. However, when using the method of low frequency homogenisation, it was so far only possible to obtain non-reciprocal effective media when both material properties are modulated in time, in the form of a Willis-

Julian Berberich, Daniel Fink

Quantum computing is a fascinating interdisciplinary research field that promises to revolutionize computing by efficiently solving previously intractable problems. Recent years have seen tremendous progress on both the experimental realization of quantum computing devices as well as the development and implementation of quantum algorithms. Yet, realizing co

Guanqun Sun, Yizhi Pan, Weikun Kong, Zichang Xu

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional Unet architectures and their transformer-integrated variants excel in automated segmentation tasks. However, they lack the ability to harness the intrinsic position and channel features of image. Existing models also struggle with parameter

Bjørnar Gullikstad Hem

In this article, we use concepts and methods from the theory of simplicial sets to study discrete Morse theory. We focus on the discrete flow category introduced by Vidit Nanda, and investigate its properties in the case where it is defined from a discrete Morse function on a regular CW complex. We design an algorithm to efficiently compute the Hom posets of

Sami Hamdan, Shammi More, Leonard Sasse, Vera Komeyer

The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relationships, diagnose diseases, and develop biomarkers using various data sources like magnetic resonance imaging

Jiaming Ji, Borong Zhang, Jiayi Zhou, Xuehai Pan

Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces obstacles due to substantial safety concerns. Safe reinforcement learning (SafeRL) emerges as a solution to optimize policies while simultaneously adhering to multiple constraints, thereby addressing the challenge of integratin

Vera Serganova

We introduce a new way to study representations of the Lie superalgebra $p(n)$. Since the center of the universal enveloping algebra $U$ acts trivially on all irreducible representations, we suggest to study the quotient algebra $\bar{U}$ by the radical of $U$. We show that $\bar{U}$ has a large center which separates typical finite dimensional irreducible r

Marcel Hoffmann, Lukas Galke, Ansgar Scherp

We study the problem of lifelong graph learning in an open-world scenario, where a model needs to deal with new tasks and potentially unknown classes. We utilize Out-of-Distribution (OOD) detection methods to recognize new classes and adapt existing non-graph OOD detection methods to graph data. Crucially, we suggest performing new class detection by combini

Shiryo Owa, Derek B. Leinweber, Anthony W. Thomas, Xuan-Gong Wang

Schemes for describing the light quark mass dependence of the nucleon mass calculated in lattice QCD are compared. The three schemes in consideration include a fully relativistic and Lorentz covariant scheme, one that is fully relativistic but not Lorentz covariant, and a semirelativistic scheme utilizing the heavy baryon approximation. Calculations of obser

Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson, Etienne Boursier

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tis

Haoqing Li, Jinfu Yang, Yifei Xu, Runshi Wang

Infrared Small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotation. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the develop

Thomas Lichtenegger

A novel approach to simulate dynamic, recurrent flows with very large time steps is presented. Data-driven forecasts based on the method of analogues are made employing a set of precomputed time series segments of flow fields. These predictions are then corrected with the evolved deviation between initial flow state and its nearest neighbor in the database.

Ruizhe Chen, Jianfei Yang, Huimin Xiong, Jianhong Bai

Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face recognition dataset CelebA tend to predict blonde hair for females and black hair for males. Such biases not only jeopardize the robustness of models but also perpetuate and amplify

Hirotoshi Hirai

Since the introduction of the quantum neural network model, it has been widely studied due to its strong expressive power and robustness to overfitting. To date, the model has been evaluated primarily in classification tasks, but its performance in practical multivariate regression problems has not been thoroughly examined. In this study, the Auto-MPG data s

Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao

Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right decisions about the information they get, LLMs should not only provide information but also help users fact-check it. Our experiments with 80 crowdworkers compare language models w

Shuaiyi Li, Yang Deng, Wai Lam

Spatial reasoning in text plays a crucial role in various real-world applications. Existing approaches for spatial reasoning typically infer spatial relations from pure text, which overlooks the gap between natural language and symbolic structures. Graph neural networks (GNNs) have showcased exceptional proficiency in inducing and aggregating symbolic struct

Mohammad Gholami, Zahra Gholami

In this paper, a new method is given for counting cycles in the Tanner graph of a (Type-I) quasi-cyclic (QC) low-density parity-check (LDPC) code which the complexity mainly is dependent on the base matrix, independent from the CPM-size of the constructed code. Interestingly, for large CPM-sizes, in comparison of the existing methods, this algorithm is the f

Yue Hu, A. Lazarian, Yan Wu, Chengcheng Fu

Observing 3D magnetic fields, including orientation and strength, within the interstellar medium is vital but notoriously difficult. However, recent advances in our understanding of anisotropic magnetohydrodynamic (MHD) turbulence demonstrate that MHD turbulence and 3D magnetic fields leave their imprints on the intensity features of spectroscopic observatio

Weixi Zhu, Alan L. Cox, Scott Rixner

This paper presents GMEM, generalized memory management, for peripheral devices. GMEM provides OS support for centralized memory management of both CPU and devices. GMEM provides a high-level interface that decouples MMU-specific functions. Device drivers can thus attach themselves to a process's address space and let the OS take charge of their memory manag

Yuya Yoshikawa, Tomoharu Iwata

The quality of explanations for the predictions made by complex machine learning predictors is often measured using insertion and deletion metrics, which assess the faithfulness of the explanations, i.e., how accurately the explanations reflect the predictor's behavior. To improve the faithfulness, we propose insertion/deletion metric-aware explanation-based

Jingwei Song, Keke Yang, Zheng Zhang, Meng Li

This paper reports on a new real-time robot-centered 3D-2D vascular image alignment algorithm, which is robust to outliers and can align nonrigid shapes. Few works have managed to achieve both real-time and accurate performance for vascular intervention robots. This work bridges high-accuracy 3D-2D registration techniques and computational efficiency require

Massimo Borelli

This short note proposes two additive corrections to a pair of relations published in Wan et al. in order to extend them to a small sample size condition. In particular we focus the interest on the possibility to provide an estimate to the sample standard deviation when knowing only the sample size, the range and/or the quartiles. Our results allow to explic

D. I. Borisov, D. A. Zezyulin

It has been recently shown that complex two-dimensional (2D) potentials $V_\varepsilon(x,y)=V(y+\mathrm{i}\varepsilon\eta(x))$ can be used to emulate non-Hermitian matrix gauge fields in optical waveguides. Here $x$ and $y$ are the transverse coordinates, $V(y)$ and $\eta(x)$ are real functions, $\varepsilon>0$ is a small parameter, and $\mathrm{i}$ is the i

George A. Elliott, Qingzhai Fan, Xiaochun Fang

Let $\Omega$ be a class of ${\rm C^*}$-algebras. In this paper, we study a class of not necessarily unital generalized tracial approximation ${\rm C^*}$-algebras, and the class of simple ${\rm C^*}$-algebras which can be generally tracially approximated by ${\rm C^*}$-algebras in $\Omega$, denoted by ${\rm gTA}\Omega$. Let $\Omega$ be a class of unital ${\rm

Junghyun Kim, Gi-Cheon Kang, Jaein Kim, Seoyun Yang

Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that comprehend and grasp objects based on natural language instructions. While the ability to understand personal objects like my wallet facilitates more natural interaction with human users, current LCRG systems only allow generic language instructions, e.g., the black-colored wallet next

Rodrigo Cezar de Campos Ferreira, Amandeep Sagwal, Jiří Doležal, Sofia Canola

Single-molecule tip-enhanced Raman spectroscopy (TERS) under ultra-high vacuum (UHV) and cryogenic conditions enables exploration of the relations between the adsorption geometry, electronic state, and vibrational fingerprints of individual molecules. TERS capability of reflecting spin states in open-shell molecular configurations is yet unexplored. Here we

Ariel Neufeld, Sizhou Wu

In this paper we introduce a multilevel Picard approximation algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities whose coefficient functions do not need to be constant. We also provide a full convergence and complexity analysis of our algorithm. To obtain our main results, we consider a particular stochastic fixed-point equ

Luke O'Loughlin, John Maclean, Andrew Black

Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data

Takato Inoue, Hiroyuki Yamane

Let $\Gamma({\mathcal{W}})$ be the Cayley graph of a finite Weyl groupoid ${\mathcal{W}}$. In this paper, we show an existence of a Hamitonian cycle of $\Gamma({\mathcal{W}})$ for any ${\mathcal{W}}$. We exatctly draw a Hamiltonian cycle of $\Gamma({\mathcal{W}})$ for any (resp. some) irreducible ${\mathcal{W}}$ of rank three (resp. four). Moreover for the i

Kotaro Shinmyo, Tadashi Takayanagi, Kenya Tasuki

We compute the pseudo entropy in two-dimensional holographic and free Dirac fermion CFTs for excited states under joining local quenches. Our analysis reveals two of its characteristic properties that are missing in the conventional entanglement entropy. One is that, under time evolution, the pseudo entropy exhibits a dip behavior as the excitations propagat

Fei Liu, Xi Lin, Zhenkun Wang, Shunyu Yao

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with le

Denis Cousineau, Gilles Dowek

The lambda-Pi-calculus allows to express proofs of minimal predicate logic. It can be extended, in a very simple way, by adding computation rules. This leads to the lambda-Pi-calculus modulo. We show in this paper that this simple extension is surprisingly expressive and, in particular, that all functional Pure Type Systems, such as the system F, or the Calc

Neill Lambert, Mauro Cirio, Jhen-dong Lin, Paul Menczel

Dissipative state engineering is a general term for a protocol which prepares the ground state of a complex many-body Hamiltonian using engineered dissipation or engineered environments. Recently, it was shown that a version of this protocol, where the engineered environment consists of one or more dissipative qubit ancillas tuned to be resonant with the low

Huan Zhang, Jinliang Ding, Liang Feng, Kay Chen Tan

Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computational resources to track the optimal solutions. Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optim

Zhiwei Huang, Juan Li, Long Jin, Junjie Wang

As the development of academic conferences fosters global scholarly communication, researchers consistently need to obtain accurate and up-to-date information about academic conferences. Since the information is scattered, using an intelligent question-answering system to efficiently handle researchers' queries and ensure awareness of the latest advancements

Alexander Brinkmann, Roee Shraga, Christian Bizer

E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. However, vendors often provide unstructured product descriptions, necessitating the extraction of attribute-value pairs from these texts. BERT-based extraction methods require large

Nicky Zimmerman, Hanna Müller, Michele Magno, Luca Benini

Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for

Junji Zhang, Joshua Pagotto, Tim Gould, Timothy T. Duignan

Electrolyte solutions play critical role in a vast range of important applications, yet an accurate and scalable method of predicting their properties without fitting to experiment has remained out of reach, despite over a century of effort. Here, we combine state-of-the-art density functional theory and equivariant neural network potentials to demonstrate t

Oleksandr Zaitsev, François Vendel, Etienne Delay

Cormas is an agent-based simulation platform developed in the late 90s by the Green research at CIRAD unit to support the management of natural resources and understand the interactions between natural and social dynamics. This platform is well-suited for a participatory simulation approach that empowers local stakeholders by including them in all modelling

Shiyu Shen, Bin Pan, Tianyang Shi, Tao Li

Bayesian Neural Networks (BNNs) have become one of the promising approaches for uncertainty estimation due to the solid theorical foundations. However, the performance of BNNs is affected by the ability of catching uncertainty. Instead of only seeking the distribution of neural network weights by in-distribution (ID) data, in this paper, we propose a new Bay

Zhaozheng Chen, Qianru Sun

The rapid development of deep learning has driven significant progress in image semantic segmentation - a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time-consuming, and labor-intensive. Weakly-supervised semantic segmentation (WSSS

Zhu Cao

Private function evaluation is a task that aims to obtain the output of a function while keeping the function secret. So far its quantum analogue has not yet been articulated. In this study, we initiate the study of quantum private function evaluation, the quantum analogue of classical private function evaluation. We give a formal definition of quantum priva

Koki Kin, Shota Kisaka, Kenji Toma, Shigeo S. Kimura

In the absence of a sufficient amount of plasma injection into the black hole (BH) magnetosphere, the force-free state of the magnetosphere cannot be maintained, leading to the emergence of strong, time-dependent, longitudinal electric field (spark gap). Recent studies of supermassive BH magnetospheres by using analytical methods and particle-in-cell (PIC) s

Guojun Wu

Most multilingual vision-and-language (V&L) research aims to accomplish multilingual and multimodal capabilities within one model. However, the scarcity of multilingual captions for images has hindered the development. To overcome this obstacle, we propose ICU, Image Caption Understanding, which divides a V&L task into two stages: a V&L model performs image

Alex Feiner, Zion Hefty

Given a number field $K$ that is a subfield of the real numbers, we generalize the notion of the classical Frobenius problem to the ring of integers $\mathfrak{O}_K$ of $K$ by describing certain Frobenius semigroups, $\mathrm{Frob}(\alpha_1,\dots,\alpha_n)$, for appropriate elements $\alpha_1,\dots,\alpha_n\in\mathfrak{O}_K$. We construct a partial ordering

Ziyi Zhao, Xiaohua Zhu

In the paper, we analysis the asymptotic behavior of noncompact $\kappa$-noncollapsed steady gradient Ricci soliton $(M, g)$ with nonnegative curvature operator away from a compact set $K$ of $M$. In particular, we prove: any $4d$ noncompact $\kappa$-noncollapsed steady gradient Ricci soliton $(M^4, g)$ with nonnegative sectional curvature must be a Bryant R

D. Huppenkothen, M. Ntampaka, M. Ho, M. Fouesneau

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of

Attila Fazekas, György Kovács

Binary classification is a fundamental task in machine learning, with applications spanning various scientific domains. Whether scientists are conducting fundamental research or refining practical applications, they typically assess and rank classification techniques based on performance metrics such as accuracy, sensitivity, and specificity. However, report

Xiaobin Song, Benben Jiang

Bayesian optimization (BO) is widely used for black-box optimization problems, and have been shown to perform well in various real-world tasks. However, most of the existing BO methods aim to learn the optimal solution, which may become infeasible when the parameter space is extremely large or the problem is time-sensitive. In these contexts, switching to a

Sagbo Marcel Zodji

In this paper, we study a system of PDEs describing the motion of two compressible viscous fluids occupying the whole space $\mathbb R^d\;(d\in \{2,3\}$). The two phases of the mixture are separated by a $\mathscr{C}^{1+\alpha}$-regular sharp interface $\mathcal{C}$ across which the density can experience jumps. We prove the existence of a unique local-in-ti

Amifa Raj, Michael Ekstrand

Many information access systems operationalize their results in terms of rankings, which are then displayed to users in various ranking layouts such as linear lists or grids. User interaction with a retrieved item is highly dependent on the item's position in the layout, and users do not provide similar attention to every position in ranking (under any layou

Imdad Ullah, Najm Hassan, Sukhpal Singh Gill, Basem Suleiman

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical private information such as, context, specific details, identifying information etc. This have raised serious threats to user privacy and reluctance to use such tools. This article propo

Shufan Jiang, Rafael Angarita, Stéphane Cormier, Francis Rousseaux

An important application scenario of precision agriculture is detecting and measuring crop health threats using sensors and data analysis techniques. However, the textual data are still under-explored among the existing solutions due to the lack of labelled data and fine-grained semantic resources. Recent research suggests that the increasing connectivity of

Alexis Plaquet, Hervé Bredin

Since its introduction in 2019, the whole end-to-end neural diarization (EEND) line of work has been addressing speaker diarization as a frame-wise multi-label classification problem with permutation-invariant training. Despite EEND showing great promise, a few recent works took a step back and studied the possible combination of (local) supervised EEND diar

Christelle Tesson, Mohamed Sofiane Bouchetara, Mélanie Ferrien, Suzanne Lesage

Liu et al. recently reported that biallelic mutations in DAGLB are responsible for autosomal recessive early-onset Parkinson's disease. They identified six patients carrying DAGLB mutations, all of Chinese origin and presenting with typical Parkinson disease. No additional cases outside China have been reported so far.To assess the causality of DAGLB in our

Seung-Hyun Kong, Dong-Hee Paek, Sangjae Cho

Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, since Radar measurements are corrupted with invalid components such as noise, interference, and clutter, it is necessary to employ a preprocessing algorithm before the 3D object dete

Xiang Zhang, Senyu Li, Zijun Wu, Ning Shi

Recent advancements in multimodal techniques open exciting possibilities for models excelling in diverse tasks involving text, audio, and image processing. Models like GPT-4V, blending computer vision and language modeling, excel in complex text and image tasks. Numerous prior research endeavors have diligently examined the performance of these Vision Large

Shikui Shang

In this paper, we study the unimodular equivalence of sublattices in an $n$-dimensional lattice. A recursive procedure is given to compute the cardinalities of the unimodular equivalent classes with the indices which are powers of a prime $p$. We also show that these are integral polynomials in $p$. When $n=2$, the explicit formulae of the cardinalities are

Han Yu-Xiao, Bai Benfeng, Zhang Jian-Yu, Huang Jia-Tai

Atomic force microscope (AFM) generally works on the basis of manipulating absolute magnitude of van der Waals (vdW) force between the tip and specimen. The force is, however, less sensitive to alternation of atom species than to tip-sample separations, resulting in the difficulty of compositional identification, even under multi-modal strategies and other A

S. P. Glasby, G. R. Paseman

Let $m,r\in\mathbb{Z}$ and $\omega\in\mathbb{R}$ satisfy $0\leqslant r\leqslant m$ and $\omega\geqslant1$. Our main result is a generalized continued fraction for an expression involving the partial binomial sum $s_m(r) = \sum_{i=0}^r\binom{m}{i}$. We apply this to create new upper and lower bounds for $s_m(r)$ and thus for $g_{\omega,m}(r)=\omega^{-r}s_m(r)

Xiaodong Yu, Hao Cheng, Xiaodong Liu, Dan Roth

Despite remarkable advancements in mitigating hallucinations in large language models (LLMs) by retrieval augmentation, it remains challenging to measure the reliability of LLMs using static question-answering (QA) data. Specifically, given the potential of data contamination (e.g., leading to memorization), good static benchmark performance does not ensure

Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Y. Zhang

Continual pre-training has been urgent for adapting a pre-trained model to a multitude of domains and tasks in the fast-evolving world. In practice, a continually pre-trained model is expected to demonstrate not only greater capacity when fine-tuned on pre-trained domains but also a non-decreasing performance on unseen ones. In this work, we first investigat

Shusaku Sone, Jiaxin Ma, Atsushi Hashimoto, Naoya Chiba

Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and constraints; however, the efficient neural network architecture for matching is underexplored. This paper proposes a novel graph neural network (GNN), \textit{WeaveNet}, designed for b

M. C. Gordillo, J. Boronat

The influence of attractive boron impurities, embedded on a graphene sheet, on the phase diagrams of $^4$He and H$_2$ adsorbed on top was studied using the diffusion Monte Carlo method. The doping of graphene was made by distributing the boron atoms following the same pattern found in an experimentally synthesized substrate. Our results show that while the d

Minh Pham, Xingyuan Lu, Arjun Rana, Stanley Osher

Tomography has had an important impact on the physical, biological, and medical sciences. To date, most tomographic applications have been focused on 3D scalar reconstructions. However, in some crucial applications, vector tomography is required to reconstruct 3D vector fields such as the electric and magnetic fields. Over the years, several vector tomograph

Raghav G. Jha, Felix Ringer, George Siopsis, Shane Thompson

We formulate the $O(3)$ non-linear sigma model in 1+1 dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous variable (CV) approach to quantum computing. We construct the ground state and excited states using the coupled-cluster

Minjia Shi, Shitao Li, Tor Helleseth

Determining the weight distribution of a code is an old and fundamental topic in coding theory that has been thoroughly studied. In 1977, Helleseth, Kl{\o}ve, and Mykkeltveit presented a weight enumerator polynomial of the lifted code over $\mathbb{F}_{q^\ell}$ of a $q$-ary linear code with significant combinatorial properties, which can determine the suppor

Zhengyu Tao, Xuejun Guo

We study the Mahler measures of the polynomial family $Q_k(x,y) = x^3+y^3+1-kxy$ using the method previously developed by the authors. An algorithm is implemented to search for CM points with class numbers $\leqslant 3$, we employ these points to derive interesting formulas that link the Mahler measures of $Q_k(x,y)$ to $L$-values of modular forms. As by-pro

Jianping Yao, Son N. Tran, Samantha Sawyer, Saurabh Garg

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been

Seiji Tomita

We constructed a parametrized family of Mordell curves with the rank of at least three.

Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi

Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Traditional methods often depend on fine-tuning with task-specific labels, limiting their effectiveness when labeled data is sca

Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong

With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often suffer limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of

Jinglei Hao, Wukai Li, Binglu Wang, Shunzhou Wang

Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Multi-granularity Backprojection Transformer termed MBT for RSISR. MBT incorporates the backprojection learning strate

Boris Polyakov, Aleksandrs Novikovs, Madara Leimane, Kevon Kadiwala

Spray deposition and inkjet printing of various nanostructures are emerging complementary methods for creating conductive coatings on different substrates. In comparison to established deposition techniques like vacuum metal coating and lithography-based metallization processes, spray deposition and inkjet printing benefit from significantly simplified equip

Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic me

Zhuo Liang, Fengbo Gu, Jiangfeng Zhou, Junhui Liao

A dual-phase TPC (Time Projection Chamber) is more advanced in characterizing an event than a single-phase one because it can, in principle, reconstruct the 3D (X-Y-Z) image of the event, while a single-phase detector can only show a 2D (X-Y) picture. As a result, more enriched physics is expected for a dual-phase detector than a single-phase one. However, t

Tushar Mondal, Gianluca Gregori

We extend the classical magnetohydrodynamics formalism to include nonlocal quantum behavior via the phenomenological Bohm potential. We then solve the quantum magnetohydrodynamics equations to obtain a new analytical form of the dynamic structure factor (DSF), a fundamental quantity linking theory and experiments. Our results show that the three-peak structu

Soniya Kumawat, Vishnu Kumar Sahu, Anurag Tripathi

The empirical and particle force-based models of granular segregation due to density differences among the species are compared in this work. Dependency of the empirical segregation parameters on the initial configuration, the observation time duration, inclination angle, and mixture composition are discussed in detail. The parameters obtained from empirical

Keyu Lu, Yue-Lin Sming Tsai, Qiang Yuan, Le Zhang

We investigate the impact of inelastic collisions between dark matter (DM) and heavy cosmic ray (CR) nuclei on CR propagation. We approximate the fragmentation cross-sections for DM-CR collisions using collider-measured proton-nuclei scattering cross-sections, allowing us to assess how these collisions affect the spectra of CR Boron and Carbon. We derive new

Yanhui Shen

Options, serving as a crucial financial instrument, are used by investors to manage and mitigate their investment risks within the securities market. Precisely predicting the present price of an option enables investors to make informed and efficient decisions. In this paper, we propose a machine learning method for forecasting the prices of SPY (ETF) option

Kenya Murase, Atsushi Nakamoto, Noriyuki Tomiyama

The purpose of this study was to quantitatively evaluate the performance of different simultaneous spatial and temporal regularizers in liver perfusion studies using low-dose dynamic contrast-enhanced computed tomography (DCE-CT). A digital liver phantom was used to simulate chronic liver disease (CLD) and hepatocellular carcinoma (HCC) based on clinical dat

Evan Unit Lim

The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to quantify the dissimilarity between two matrices by combining elements of the Wasserstein Distance with specific transformations. It offers improved time and space complexity compared to the Manhattan Wasserstein Distance (MWD) while maintaining accuracy. QMWD is particularly advantageous