May 2024 arXiv papers — page 123

Bingqing Cheng

Machine learning has recently emerged as a powerful tool for generating new molecular and material structures. The success of state-of-the-art models stems from their ability to incorporate physical symmetries, such as translation, rotation, and periodicity. Here, we present a novel generative method called Response Matching (RM), which leverages the fact th

Kejia Zhang, Lan Zhang, Haiwei Pan, Baolong Yu

In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce

Xin Yi, Shunfan Zheng, Linlin Wang, Xiaoling Wang

The current safeguard mechanisms for large language models (LLMs) are indeed susceptible to jailbreak attacks, making them inherently fragile. Even the process of fine-tuning on apparently benign data for downstream tasks can jeopardize safety. One potential solution is to conduct safety fine-tuning subsequent to downstream fine-tuning. However, there's a ri

Weihua Gao, Wenlong Niu, Wenlong Lu, Pengcheng Wang

The detection and tracking of small targets in passive optical remote sensing (PORS) has broad applications. However, most of the previously proposed methods seldom utilize the abundant temporal features formed by target motion, resulting in poor detection and tracking performance for low signal-to-clutter ratio (SCR) targets. In this article, we analyze the

Zhangjie Peng, Ruijing Liu, Zhaotian Li, Cunhua Pan

In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer exhibit planar wave characteristics but spherical wave characteristics in the near-field channel, which makes the channe

Zetian Mao, Wenwen Li, Jethro Tan

Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development. Existing machine learning models have focused on predicting scalar polycrystalline dielectric constants, neglecting the directional nature of dielectric tensors essential for mat

Patricio Gallardo, Luca Schaffler

The moduli space of hyperplanes in projective space has a family of geometric and modular compactifications that parametrize stable hyperplane arrangements with respect to a weight vector. Among these, there is a toric compactification that generalizes the Losev-Manin moduli space of points on the line. We study the first natural wall crossing that modifies

Mingxiang Chen, Jian Zhang, Boli Zhou, Yang Song

Recent advancements in deep learning for 3D models have propelled breakthroughs in generation, detection, and scene understanding. However, the effectiveness of these algorithms hinges on large training datasets. We address the challenge by introducing Efficient 3D Seam Carving (E3SC), a novel 3D model augmentation method based on seam carving, which progres

Ross Greer, Mohan Trivedi

This study investigates the use of trajectory and dynamic state information for efficient data curation in autonomous driving machine learning tasks. We propose methods for clustering trajectory-states and sampling strategies in an active learning framework, aiming to reduce annotation and data costs while maintaining model performance. Our approach leverage

Zhihao Chen, Xiaonan Ning, Jiucheng Chen, Jianfei Hua

Flat-top beam, known for its ability to generate a consistently even irradiation area, holds vast utility in many fields of scientific and industrial applications. In this paper, a reflective laser beam shaping method based on two axisymmetric aspheric mirrors (AAMs), a polarizing beam splitter (PBS) and two quarter wave plates (QWPs) is proposed to transfor

Guangtai Lu, Yasutomo Ota, Satoshi Iwamoto

Topological photonics shows considerable promise in revolutionizing photonic devices through the use of topological phases, leading to innovations like topological lasers that enhance light control. One of recent breakthroughs is reducing the size of these systems by utilizing lower-dimensional boundary states, notably via higher-order topological phases. Th

Chunyu Tan, Yuxiao Hang, Stephan Haas, Hubert Saleur

The problem of a local impurity in a Luttinger liquid, just like the anisotropic Kondo problem (of which it is technically a cousin), describes many different physical systems. As shown by Kane and Fisher, the presence of interactions profoundly modifies the physics familiar from Fermi liquid theory, and leads to non-intuitive features, best described in the

Zhuofu Tao, Yichen Shi, Yiru Huo, Rui Ye

Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled significant potential across various fields, suggesting their applicability in streamlining large-scale AMS IC design as well. A bottleneck in employing MLLMs for automatic AMS circuit g

Marcus Nóbrega Gomes, Igor Matheus Benites, Salma M. Elsherif, Ahmad F. Taha

Cost-effective water distribution network (WDN) design with acceptable pressure performance is crucial for the management of drinking water in cities. This paper presents a Microsoft Excel tool to model, simulate, and optimize WDNs with looped pipelines under steady-state incompressible flow simulations. Typically, the hardy-cross method is applied using spr

Michael Deimetry, Timothy C. Petersen, Hamish G. Brown, Matthew Weyland

Differential Phase Contrast (DPC) imaging, in which deviations in the bright field beam are in proportion to the electric field, has been extensively studied in the context of pure elastic scattering. Here we discuss differential phase contrast formed from core-loss scattered electrons, i.e. those that have caused inner shell ionization of atoms in the speci

Zhuoying Li, Bohua Wan, Cong Mu, Ruzhang Zhao

Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adversarial training with source-target domain alignment to enhance generalization capabilities. By pretraining wit

Yi Zhang, Lei Sang, Yiwen Zhang

Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling method is to abstract the intent into unknowable but learnable shared or non-shared parameters. Despite considerable progress, we argue that

Shinnosuke Matsuo, Daiki Suehiro, Seiichi Uchida, Hiroaki Ito

In this paper, we address the segmentation of tumor subtypes in whole slide images (WSI) by utilizing incomplete label proportions. Specifically, we utilize `partial' label proportions, which give the proportions among tumor subtypes but do not give the proportion between tumor and non-tumor. Partial label proportions are recorded as the standard diagnostic

Masahiro Ichihashi, Aya Bamba, Yuichi Kato, Satoru Katsuda

Heating of charged particles via collisionless shocks, while ubiquitous in the universe, is an intriguing yet puzzling plasma phenomenon. One outstanding question is how electrons and ions approach an equilibrium after they were heated to different immediate-postshock temperatures. In order to fill the significant lack of observational information of the dow

Zhihao Yu, Xu Chu, Yujie Jin, Yasha Wang

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obse

Takahiko Matsubara

In Papers I-III [arXiv:2210.10435, arXiv:2210.11085, arXiv:2304.13304], we use the flat-sky and distant-observer approximations to develop a formalism with which the correlation statistics of cosmological tensor fields are calculated by the nonlinear perturbation theory, generalizing the integrated perturbation theory for scalar fields. In this work, the for

Yuan Xu, Fujun Zhou, Weihua Gong, Weijun Wu

Diffusive limit of the non-cutoff Vlasov-Maxwell-Boltzmann system in perturbation framework still remains open. By employing a new weight function and making full use of the anisotropic dissipation property of the non-cutoff linearized Boltzmann operator, we solve this problem with some novel treatments for non-cutoff potentials $\gamma > \max\{-3, -\frac{3}

Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu

In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only t

Masato Arai, Kurando Baba

We construct special Lagrangian submanifolds of the Taub-NUT manifold and the Atiyah-Hitchin manifold by combining the generalized Legendre transform approach and the moment map technique. The generalized Legendre transform approach provides a formulation to construct hyperk\"ahler manifolds and can make their Calabi-Yau structures manifest. In this approach

Jiaxuan Zhang, Zhao-Yun Chen, Yun-Jie Wang, Bin-Han Lu

Fault-tolerant quantum computing (FTQC) is essential for achieving large-scale practical quantum computation. Implementing arbitrary FTQC requires the execution of a universal gate set on logical qubits, which is highly challenging. Particularly, in the superconducting system, two-qubit gates on surface code logical qubits have not been realized. Here, we ex

Mahdi Chehimi, Kenneth Goodenough, Walid Saad, Don Towsley

Quantum networks (QNs) distribute entangled states to enable distributed quantum computing and sensing applications. However, in such QNs, quantum switches (QSs) have limited resources that are highly sensitive to noise and losses and must be carefully allocated to minimize entanglement distribution delay. In this paper, a QS resource allocation framework is

Yusuke Kimura, Shaowen Li, Hiroyuki Sato, Masahiro Fujita

An N-bit quantum state requires a vector of length $2^N$, leading to an exponential increase in the required memory with N in conventional statevector-based quantum simulators. A proposed solution to this issue is the decision diagram-based quantum simulator, which can significantly decrease the necessary memory and is expected to operate faster for specific

Jianhua Zhu, Liangcai Gao, Wenqi Zhao

Significant progress has been made in the field of handwritten mathematical expression recognition, while existing encoder-decoder methods are usually difficult to model global information in $LaTeX$. Therefore, this paper introduces a novel approach, Implicit Character-Aided Learning (ICAL), to mine the global expression information and enhance handwritten

Shuang Liu, Yuan Lou, Maolin Zhou

The paper is concerned with the principal eigenvalue of some linear elliptic operators with drift in two dimensional space. We provide a refined description of the asymptotic behavior for the principal eigenvalue as the drift rate approaches infinity. Under some non-degeneracy assumptions, our results illustrate that these asymptotic behaviors are completely

Hajime Sotani, Bernhard Müller, Tomoya Takiwaki

Gravitational wave signals from core-collapse supernovae are one of the important observables for extracting the information of dense matter. To extract the properties of proto-neutron stars produced via core-collapse supernovae by asteroseismology, we perform a linear perturbation analysis using data obtained from two-dimensional numerical simulations. We e

Nanase Harada, Toshiki Saito, Yuri Nishimura, Yoshimasa Watanabe

The HNC/HCN ratio is observationally known as a thermometer in Galactic interstellar molecular clouds. A recent study has alternatively suggested that the HNC/HCN ratio is affected by the ultraviolet (UV) field, not by the temperature. We aim to study this ratio on the scale of giant molecular clouds in the barred spiral galaxy M83 towards the southwestern b

Junyuan Guo, Li Fang

This work is devoted to establish an improved blow-up criterion for strong solutions to a three-dimensional compressible non-Newtonian fluid with vacuum. The considered system is the Power Law model in a bounded periodic domain in R^3.We establish a blow-up criterion for the local strong solutions in terms of the L^4(0,T;L^{\infty}({\Omega}))norm of the grad

Changxin Ding

Csikv\'{a}ri constructed a poset on trees to prove that several graph functions attain extreme values at the star and the path among the trees on a fixed number of vertices. Reiner and Smith proved that the Tutte polynomials $T(1,y)$ of cones over trees, which are the graphs obtained by attaching a cone vertex to a tree, have the described extreme behavior.

Murad Badshah, Muhammad Waqas, Muhammad Ajaz, Wolfgang Bietenholz

In this study, we systematically investigate the dynamics of various hadrons namely \( \pi^+ \), \( \pi^- \), \( K^+ \), \( K^- \), \( p \), \( \bar{p} \), \( \Lambda \), \( \bar{\Lambda} \), \( \Xi^- \) and \( \bar{\Xi}^+ \) produced in central Au-Au collisions. We analyze data of AGS and RHIC, which span a broad range of collision energies, ranging from \(

Yasmeen S. Baki

Historically, the study of graded (twisted or otherwise) Calabi--Yau algebras has meant the study of such algebras under an $\mathbb{N}$-grading. In this paper, we propose a suitable definition for a twisted $G$-graded Calabi-Yau algebra, for $G$ an arbitrary abelian group. Building on the work of Reyes and Rogalski, we show that a $G$-graded algebra is twis

Guozhang Liu, Ting Liu, Mengke Yuan, Tao Pang

The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented

Rubing Li, Arun Sundararajan

The emergence of the branded recommerce channel - digitally enabled and branded marketplaces that facilitate purchasing pre-owned items directly from a manufacturer's e-commerce site - leads to new variants of classic IS and economic questions relating to secondary markets. Such branded recommerce is increasingly platform-enabled, creating opportunities for

Chunwei Meng, Zhiqing Wei, Dingyou Ma, Wanli Ni

Integrated sensing and communication (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this paper, we investigate transmit beamforming design for multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and comm

Yazhou Xie

This article surveys the growing interest in utilizing Deep Learning (DL) as a powerful tool to address challenging problems in earthquake engineering. Despite decades of advancement in domain knowledge, issues such as uncertainty in earthquake occurrence, unpredictable seismic loads, nonlinear structural responses, and community engagement remain difficult

Jiawei Wang, Xiang Li, Xin Guo, Ter-Hoe Loh

Silicon photonics, compatible with large-scale silicon manufacturing, is a disruptive photonic platform that has indicated significant implications in industry and research areas (e.g., quantum, neuromorphic computing, LiDAR). Cutting-edge applications such as high-capacity coherent optical communication and heterodyne LiDAR have escalated the demand for int

Haojie Hou, Yan-Xia Ren, Renming Song

In this paper, we study asymptotic behaviors of the tails of extinction time and maximal displacement of a critical branching killed L\'{e}vy process $(Z_t^{(0,\infty)})_{t\ge 0}$ in $\mathbb{R}$, in which all particles (and their descendants) are killed upon exiting $(0, \infty)$. Let $\zeta^{(0,\infty)}$ and $M_t^{(0,\infty)}$ be the extinction time and ma

R. Vogt

A number of new four-quark states containing from one to four charm or anti-charm quarks have been observed recently. Many of these new states have been discovered at the LHC. The production of these states via intrinsic charm in the proton is investigated. The tetraquark masses obtained in this approach, while dependent on the internal transverse momenta of

Masaaki Nagata, Makoto Morishita, Katsuki Chousa, Norihito Yasuda

Using crowdsourcing, we collected more than 10,000 URL pairs (parallel top page pairs) of bilingual websites that contain parallel documents and created a Japanese-Chinese parallel corpus of 4.6M sentence pairs from these websites. We used a Japanese-Chinese bilingual dictionary of 160K word pairs for document and sentence alignment. We then used high-qualit

Nitol Saha, Md Masruk Aulia, Dibakar Das, Md. Mostafizur Rahman

A stability chamber is essential for pharmaceutical facilities to test the stability and quality of products over time by exposing them to different environmental conditions. This paper introduces an IoT-enabled stability chamber designed for the pharmaceutical industry. We constructed four stability chambers by leveraging the existing infrastructure within

B. K. Nally, P. M. R. Brydon

Motivated by the recent discovery of a possible field-mediated parity switch within the superconducting state of CeRh2As2 [Khim et al., Science 373, 1012 (2021)], we thoroughly investigate the dependence of the superconducting state of a strongly-coupled Rashba mono- and bilayer on internal parameters and an applied magnetic field. The role of interlayer pai

Feng Wang, M. Cenk Gursoy, Senem Velipasalar

In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning. Specifically, in the proposed feature-based federated learning, we design the

Jonathan Wang, Huang Huang, Vincent Lim, Harry Zhang

Dynamic manipulation of free-end cables has applications for cable management in homes, warehouses and manufacturing plants. We present a supervised learning approach for dynamic manipulation of free-end cables, focusing on the problem of getting the cable endpoint to a designated target position, which may lie outside the reachable workspace of the robot en

Meng-Hock Koh, Philippe Quentin

A simple and efficient method to treat nuclear pairing correlations within a simple Hartree-Fock--plus-BCS description is proposed and discussed. It relies on the fact that the intensity of pairing correlations depends crucially on level densities around the Fermi surface ($ \rho(e_F)$) and that any fitting of nuclear energies as functions of the nucleon num

Evgeny Sevost'yanov, Valery Targonskii

We study mappings that satisfy the inverse modulus inequality of Poletsky type in a fixed domain. It is shown that, under some additional restrictions, the image of a ball under such mappings contains a fixed ball uniformly over the class. This statement can be interpreted as the well-known analogue of Koebe's theorem for analytic functions. As an applicatio

Édouard Bonnet, Julien Duron, John Sylvester, Viktor Zamaraev

We introduce a dense counterpart of graph degeneracy, which extends the recently-proposed invariant symmetric difference. We say that a graph has sd-degeneracy (for symmetric-difference degeneracy) at most $d$ if it admits an elimination order of its vertices where a vertex $u$ can be removed whenever it has a $d$-twin, i.e., another vertex $v$ such that at

Saransh Chopra, Francisco Maturana, K. V. Rashmi

Most large-scale storage systems employ erasure coding to provide resilience against disk failures. Recent work has shown that tuning this redundancy to changes in disk failure rates leads to substantial storage savings. This process requires code conversion, wherein data encoded using an $[n^{I\mskip-2mu},k^{I\mskip-2mu}]$ initial code has to be transformed

Nicholas Kapoor, P. Christopher Staecker

How can we probabilistically predict the winner in a ranked-choice election without all ballots being counted? In this study, we introduce a novel algorithm designed to predict outcomes in Instant Runoff Voting (IRV) elections. The algorithm takes as input a set of discrete probability distributions describing vote totals for each candidate ranking and calcu

Qiang Jia, Piljin Yi

We revisit anomalous phases related to large gauge transformations, such as the Witten anomaly. The latter, known to plague $d=4$ $Sp(k)$ theories, is well-understood in terms of $\pi_4(Sp(k))=\mathbb{Z}_2$, but it also has an oblique relation to the instantons, labeled by $\pi_3(G)=\mathbb{Z}$, via the fermion zero mode counting. We revisit this relation an

Jiaxing Yang, Lihe Zhang, Jiayu Sun, Huchuan Lu

Referring Image Segmentation (RIS) consistently requires language and appearance semantics to more understand each other. The need becomes acute especially under hard situations. To achieve, existing works tend to resort to various trans-representing mechanisms to directly feed forward language semantic along main RGB branch, which however will result in ref

Javier Lopez-Piqueres, Jing Chen

In this study, we introduce a novel family of tensor networks, termed constrained matrix product states (MPS), designed to incorporate exactly arbitrary discrete linear constraints, including inequalities, into sparse block structures. These tensor networks are particularly tailored for modeling distributions with support strictly over the feasible space, of

Yufan Zhang, Honglin Wen, Yuexin Bian, Yuanyuan Shi

Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. The current deterministic clearing approach in the day-ahead (DA) market, where RESs participate based on expected production, has been criticized for causing a lack of coordination between the DA and real-time (RT) markets, leading to high overall oper

Yikun Zhang, Yen-Chi Chen, Alexander Giessing

Existing statistical methods in causal inference often assume the positivity condition, where every individual has some chance of receiving any treatment level regardless of covariates. This assumption could be violated in observational studies with continuous treatments. In this paper, we develop identification and estimation theories for causal effects wit

Katie Seaborn, Iona Gessinger, Suzuka Yoshida, Benjamin R. Cowan

Recent research has begun to assess people's perceptions of voice user interfaces (VUIs) as dialogue partners, termed partner models. Current self-report measures are only available in English, limiting research to English-speaking users. To improve the diversity of user samples and contexts that inform partner modelling research, we translated, localized, a

Xing He, Mithun Ghosh, Ding-Shyue Yang

Metals exhibit nonequilibrium electron and lattice subsystems at transient times following femtosecond laser excitation. In the past four decades, various optical spectroscopy and time-resolved diffraction methods have been used to study electron-phonon coupling and the effects of underlying dynamical processes. Here, we take advantage of the surface specifi

Nandagopal Manoj, Valerio Peri

Extensive research has explored the optical properties of topological insulating materials, driven by their inherent stability and potential applications. In this study, we unveil a novel functionality of three-dimensional integer quantum Hall (3D IQH) states as broad-band filters for circularly polarized light, particularly effective in the terahertz (THz)

Yong Kong

The problem of counting polymer coverings on the rectangular lattices is investigated. In this model, a linear rigid polymer covers $k$ adjacent lattice sites such that no two polymers occupy a common site. Those unoccupied lattice sites are considered as monomers. We prove that for a given number of polymers ($k$-mers), the number of arrangements for the po

Lihong Jin, Wei Dong, Wenshan Wang, Michael Kaess

We introduce BEVRender, a novel learning based approach for the localization of ground vehicles in Global Navigation Satellite System(GNSS)-denied off-road scenarios. These environments are typically challenging for conventional vision-based state estimation due to the lack of distinct visual landmarks and the instability of vehicle poses. To address this, B

Yong Yang, Mengxi You

In this paper, we strengthen a result of Seager regarding the number of orbits of a solvable primitive linear group.

Daniel T. Chang

The conventional definition of hypergraph has two major issues: (1) there is not a standard definition of directed hypergraph and (2) there is not a formal definition of nested hypergraph. To resolve these issues, we propose a new definition of hypergraph that unifies the concepts of undirected, directed and nested hypergraphs, and that is uniform in using h

Lorenzo Mauri, Giacomo Zanella

Stochastic Gradient (SG) Markov Chain Monte Carlo algorithms (MCMC) are popular algorithms for Bayesian sampling in the presence of large datasets. However, they come with little theoretical guarantees and assessing their empirical performances is non-trivial. In such context, it is crucial to develop algorithms that are robust to the choice of hyperparamete

Bo Peng, Diana Valencia

The recent advancements in exoplanet observations enable the potential detection of exo-Venuses, rocky planets with carbon-rich atmospheres. How extended these atmospheres can be, given high carbon abundances, has not been studied. To answer this, we present a model for a theoretical class of exoplanets - puffy Venuses - characterized by thick, carbon-domina

Xinyu Shen, Qimin Zhang, Huili Zheng, Weiwei Qi

This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study, with a focus on predicting obsessive-compulsive disorder scales. We simulated a dataset to mimic the correlation structures commonly found in imaging data and eva

Jared Coleman, Bhaskar Krishnamachari, Khalil Iskarous, Ruben Rosales

We propose a new paradigm for machine translation that is particularly useful for no-resource languages (those without any publicly available bilingual or monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation). Using the LLM-RBMT paradigm, we design the first language education/revitalization-oriented machine translator for Owens Valley

Priya Sundaresan, Aditya Ganapathi, Harry Zhang, Shivin Devgon

We investigate the problem of pixelwise correspondence for deformable objects, namely cloth and rope, by comparing both classical and learning-based methods. We choose cloth and rope because they are traditionally some of the most difficult deformable objects to analytically model with their large configuration space, and they are meaningful in the context o

Jiyong Cheon, Kyu Hwan Choi, Kevin J. Modica, Robert J. Mitchell

We study the partitioning of motile bacteria in an aqueous two-phase mixture of dextran (DEX) and polyethylene glycol (PEG), which can phase separate into DEX-rich and PEG-rich phases. While non-motile bacteria partition exclusively into the DEX-rich phase in all conditions tested, we observed that motile bacteria penetrate the soft DEX/PEG interface and par

Dolly Taparia, Taisuke T. Sasaki, Tomoya Nakatani, Hirofumi Suto

To utilize half-metallic Heusler alloys in practical spintronic devices, such as magnetic sensors and magnetic memories, the key is to realize highly textured and structurally ordered polycrystalline thin films. In this study, we fabricated polycrystalline Co2FeGa0.5Ge0.5 (CFGG) Heusler alloy films deposited on a [001]-oriented Ag buffer layer, which was ach

David Popović

This paper classifies the chain homotopy equivalence types of knot Floer complexes $CFK_{\mathbb{F}[U,V]}(K)$ of knot Floer width 2. They have no nontrivial local systems. As an application, this shows that all Montesinos knots admit a basis that can be simultaneously horizontally and vertically simplified.

Yasaman Etesam, Özge Nilay Yalçın, Chuxuan Zhang, Angelica Lim

"How does the person in the bounding box feel?" Achieving human-level recognition of the apparent emotion of a person in real world situations remains an unsolved task in computer vision. Facial expressions are not enough: body pose, contextual knowledge, and commonsense reasoning all contribute to how humans perform this emotional theory of mind task. In th

David Jin, Harry Zhang, Kai Chang

We perform detailed theoretical analysis of an expectation-maximization-based algorithm recently proposed in for solving a variation of the 3D registration problem, named multi-model 3D registration. Despite having shown superior empirical results, did not theoretically justify the conditions under which the EM approach converges to the ground truth. In this

J. Olivares Carvajal, M. Zoccali, M. De Leo, R. Contreras Ramos

The structure and kinematics of the old component of the Galactic bulge are still a matter of debate. The bulk of the bulge as traced by red clump stars includes two main components, which are usually identified as the metal-rich and metal-poor components. They have different shapes, kinematics, mean metallicities, and alpha-element abundances. It is our cur

Jacqueline Harding, Nathaniel Sharadin

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion

Oscar H. Ibarra, Ian McQuillan

There are many types of automata and grammar models that have been studied in the literature, and for these models, it is common to determine whether certain problems are decidable. One problem that has been difficult to answer throughout the history of automata and formal language theory is to decide whether a given system $M$ accepts a bounded language (wh

Stephanie N. Kramer, Jeanpun Antarasen, Cole R. Reinholt, Lydia Kisley

We present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam p

Giovanni Colombo, Boris S. Mordukhovich, Dao Nguyen, Trang Nguyen

This paper primarily focuses on the practical applications of optimal control theory for perturbed sweeping processes within the realm of robotics dynamics. By describing these models as controlled sweeping processes with pointwise control and state constraints and by employing necessary optimality conditions for such systems, we formulate optimal control pr

Naomi Andrew, Edgar A. Bering, Ilya Kapovich, Peter Shalen

We prove that if $G_\phi=\langle F, t| t x t^{-1} =\phi(x), x\in F\rangle$ is the mapping torus group of an injective endomorphism $\phi: F\to F$ of a free group $F$ (of possibly infinite rank), then every two-generator subgroup $H$ of $G_\phi$ is either free or a (finitary) sub-mapping torus. As an application we show that if $\phi\in \mathrm{Out}(F_r)$ (wh

J. Shen, M. Chen, V. Korostelev, H. Kim

Charge transfer is a fundamental interface process that can be harnessed for light detection, photovoltaics, and photosynthesis. Recently, charge transfer was exploited in nanophotonics to alter plasmon polaritons by involving additional non-polaritonic materials to activate the charge transfer. Yet, direct charge transfer between polaritonic materials hasn'

Thomas Sayer, Andrés Montoya-Castillo

Multitime quantum correlation functions are central objects in physical science, offering a direct link between experimental observables and the dynamics of an underlying model. While experiments such as 2D spectroscopy and quantum control can now measure such quantities, the accurate simulation of such responses remains computationally expensive and sometim

Chaithanya Naik Mude, Satvik Maurya, Benjamin Lienhard, Swamit Tannu

Realizing the full potential of quantum computing requires large-scale quantum computers capable of running quantum error correction (QEC) to mitigate hardware errors and maintain quantum data coherence. While quantum computers operate within a two-level computational subspace, many processor modalities are inherently multi-level systems. This leads to occas

Parvin Emami, Yue Jiang, Zixin Guo, Luis A. Leiva

Modeling visual saliency in graphical user interfaces (GUIs) allows to understand how people perceive GUI designs and what elements attract their attention. One aspect that is often overlooked is the fact that computational models depend on a series of design parameters that are not straightforward to decide. We systematically analyze how different design pa

Jesús A. Toalá, Omaira González-Martín, Andrea Sacchi, Diego A. Vásquez-Torres

We present the analysis of publicly available NuSTAR, Suzaku and XMM-Newton observations of the symbiotic recurrent nova T CrB covering the 2006.77-2022.66 yr period. The X-ray spectra are analysed by adopting a model that includes a reflection component produced by the presence of a disk that mimics the accretion disk and the immediate surrounding medium. O

Yoshitaka Inoue, Hunmin Lee, Tianfan Fu, Rui Kuang

For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present drGT, a heterogeneous graph deep learning model over drugs, genes, and cell lines that couples prediction with mechanism-oriented interpretability via attention coefficients (ACs). We assess both predictive generalization

Octave Duros, Amélie Juhin, Hebatalla Elnaggar, Gheorghe Sorin Chiuzbăian

Several definitions of the crystal field have been used over time and their variety has lead to many misunderstandings, in both theoretical and experimental literature. Two categories of definitions can be mentioned, the first being the operators equivalents introduced by Stevens in 1952 and the second being the crystal-field operators, introduced by differe

Linhua Jiang, Shuqi Fu, Feige Wang, Sarah E. I. Bosman

We present constraints on the spacetime variation of the fine-structure constant $\alpha$ at redshifts $2.5\le z<9.5$ using JWST emission-line galaxies. The galaxy sample consists of 621 high-quality spectra with strong and narrow [O III] $\lambda\lambda$4959,5007 doublet emission lines from 578 galaxies, including 232 spectra at $z>5$. The [O III] doublet l

Regina Ochonu, Josep Vidal

The 5th generation (5G) and beyond network offers substantial promise as the ideal wireless technology to replace the existing inflexible wired connections in traditional factories of today. 5G network slicing allows for tailored allocation of resources to different network services, each with unique Quality of Service (QoS) requirements. This paper presents

Joaquin Alvarez

We build a valid p-value based on a concentration inequality for bounded random variables introduced by Pelekis, Ramon and Wang. The motivation behind this work is the calibration of predictive algorithms in a distribution-free setting. The super-uniform p-value is tighter than Hoeffding and Bentkus alternatives in certain regions. Even though we are motivat

Jing Li, K. Kreckel, S. Sarbadhicary, Oleg V. Egorov

We present the largest extragalactic survey of supernova remnant (SNR) candidates in nearby star-forming galaxies using exquisite spectroscopic maps from MUSE. Supernova remnants exhibit distinctive emission-line ratios and kinematic signatures, which are apparent in optical spectroscopy. Using optical integral field spectra from the PHANGS-MUSE project, we

Stefan Pricopie, Richard Allmendinger, Manuel Lopez-Ibanez, Clyde Fare

We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where there is a trade-off between evaluating more while maintaining the same setup, or switching and restricting the number o

D. Yu. Sergeeva, D. V. Karlovets, A. A. Tishchenko

Hollow electron beams are today highly desirable for many applications, but are still challenging in view of their detection. In this Letter, we focus on the unique character of the electromagnetic radiation that relativistic hollow electron beams can produce when traveling above a metasurface. We investigate theoretically the specific features of the radiat

Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig

Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with the size of the state space. This is particularly problematic in spatiotemporal regression problems, where the state dimension scales with the number of spatial observations. Exist

Arsalan Shahid, Dana M. Lewis

Background: This study investigates glucose conditions preceding and following various hypoglycemia levels in individuals with type 1 diabetes using open-source automated insulin delivery (AID) systems. It also seeks to evaluate relationships between hypoglycemia and subsequent glycemic variability. Methods: Analysis of continuous glucose monitor (CGM) data

Riyad Bin Rafiq, Weishi Shi, Mark V. Albert

Hand gestures can provide a natural means of human-computer interaction and enable people who cannot speak to communicate efficiently. Existing hand gesture recognition methods heavily depend on pre-defined gestures, however, motor-impaired individuals require new gestures tailored to each individual's gesture motion and style. Gesture samples collected from

Ronald Warzoha, Brian Donovan, Yifei Sun, Elena Cimpoiasu

Recent reports reveal that isothermal chemical doping of hydrogen in correlated complex oxides such as perovskite nickelates (e.g. NdNiO3) can induce a metal-to-insulator transition (MIT) without the need for temperature modulation. In this work, we interrogate the magnitude change in temperature dependence of thermal conductivity upon chemical doping of hyd

Jesus Garcia Fernandez, Sander Keemink, Marcel van Gerven

Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. Howeve

Raagya Arora, Ariel R. Barr, Daniel Bennett, Daniel T. Larson

Ultra-wide bandgap (UWBG) semiconductors are poised to transform power electronics by surpassing the capabilities of established wide bandgap materials, such as GaN and SiC, owing to their capability to operate at higher voltage, frequency, and temperature ranges. While bulk group-III nitrides and their alloys have been extensively studied in the UWBG realm,

Jayanaka L. Dantanarayana, Yiping Kang, Kugesan Sivasothynathan, Christopher Clarke

Software development is shifting from traditional programming to AI-integrated applications that leverage generative AI and large language models (LLMs) during runtime. However, integrating LLMs remains complex, requiring developers to manually craft prompts and process outputs. Existing tools attempt to assist with prompt engineering, but often introduce ad