October 2023 arXiv papers — page 8

Charles Marx, Sofian Zalouk, Stefano Ermon

Calibration ensures that probabilistic forecasts meaningfully capture uncertainty by requiring that predicted probabilities align with empirical frequencies. However, many existing calibration methods are specialized for post-hoc recalibration, which can worsen the sharpness of forecasts. Drawing on the insight that calibration can be viewed as a distributio

Weiwen Chen, Yingtie Lei, Shenghong Luo, Ziyang Zhou

Underwater images often exhibit poor quality, distorted color balance and low contrast due to the complex and intricate interplay of light, water, and objects. Despite the significant contributions of previous underwater enhancement techniques, there exist several problems that demand further improvement: (i) The current deep learning methods rely on Convolu

Junyeol Ryu, Jeongyoon Eo

With continuous advances in deep learning, distributed training is becoming common in GPU clusters. Specifically, for emerging workloads with diverse amounts, ratios, and patterns of communication, we observe that network contention can significantly degrade training throughput. However, widely used scheduling policies often face limitations as they are agno

Hongfeng Li, Yong Wang

In this paper, we define the spectral Einstein functional associated with the sub-signature operator for manifolds with boundary. Motivated by the spectral Einstein functional and the sub-signature operator, we relate them to the noncommutative residue for manifolds with boundary. And we give the proof of the Dabrowski-Sitarz-Zalecki type theorems for the sp

Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang

Recent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic similarity between camouflaged objects and their background, objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To t

Marios Costa, Herodotos Herodotou

We determine the fine-tuning of the Yukawa couplings of supersymmetric QCD, discretized on a lattice. We use perturbation theory at one-loop level. The Modified Minimal Subtraction scheme ($\overline{{\rm MS}}$) is employed; by its definition, this scheme requires perturbative calculations, in the continuum and/or on the lattice. On the lattice, we utilize t

Jiahui Li, Stefano Chesi

We study the complex nonlinear dynamics of the two-photon Dicke model in the semiclassical limit by considering cavity and qubit dissipation. In addition to the normal and super-radiant phases, another phase that contains abundant chaos-related phenomena is found under balanced rotating and counter-rotating couplings. In particular, chaos may manifest itself

Kirpa Garg, Sartaj Ul Hasan, Pantelimon Stanica

Finding functions, particularly permutations, with good differential properties has received a lot of attention due to their varied applications. For instance, in combinatorial design theory, a correspondence of perfect $c$-nonlinear functions and difference sets in some quasigroups was recently shown by Anbar et al. (J. Comb. Des. 31(12):1-24, 2023). Additi

Itumeleng M. Monageng, Vanessa A. McBride, Julia Alfonso-Garzon, Lee J. Townsend

4U 1210-64 is a peculiar X-ray binary that was first discovered in 1978 by the Uhuru satellite. The analysis of the X-ray data revealed a 6.7-day orbital period and an additional long-term modulation that is manifested as low and high flux states. Based on the previous classification of the donor star from the analysis of its optical spectra, the system has

Junu Kim, Chaeeun Shim, Bosco Seong Kyu Yang, Chami Im

Machine learning (ML) has recently shown promising results in medical predictions using electronic health records (EHRs). However, since ML models typically have a limited capability in terms of input sizes, selecting specific medical events from EHRs for use as input is necessary. This selection process, often relying on expert opinion, can cause bottleneck

Suman Sapkota, Binod Bhattarai

Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the existing methods either use activation or gradient information or both, which demands abundant labelled examples. In this work, we use heuristics to derive importance estimation si

Yao Xiao

We define an equivariant Lagrangian Floer theory for Lagrangian torus fibers in a compact symplectic toric manifold equipped with a subtorus action. We show that the set of all Lagrangian torus fibers with weak bounding cochain data whose equivariant Lagrangian Floer cohomology is non-zero can be identified with a rigid analytic space. We prove that the set

Yihang Li, Shuichiro Shimizu, Chenhui Chu, Sadao Kurohashi

Existing multimodal machine translation (MMT) datasets consist of images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity, making visual information ineffective in generating appropriate translations. Recent work has constructed an ambiguous subtitles dataset to alleviate this problem but is still limited to the

Luping Xiang, Yao Zeng, Jie Hu, Kun Yang

A novel Physical Layer Security (PLS) framework is conceived for enhancing the security of the wireless communication systems by exploiting multi-domain polarization in Multiple-Input Multiple-Output (MIMO) systems. We design a sophisticated key generation scheme based on multi-domain polarization, and the corresponding receivers. An in-depth analysis of the

Sudarson Roy Pratihar, Subhadip Paul, Pranab Kumar Dash, Amartya Kumar Das

Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining and machine learning techniques (apart from rules oriented approach) have been used in past, but efficiency has been low as fraud pattern changes very rapidly. This paper presents an industrialized solution approach with self adaptive data mining technique and application of big data

Zixin Wang, Yadan Luo, Liang Zheng, Zhuoxiao Chen

This article presents a comprehensive survey of online test-time adaptation (OTTA), focusing on effectively adapting machine learning models to distributionally different target data upon batch arrival. Despite the recent proliferation of OTTA methods, conclusions from previous studies are inconsistent due to ambiguous settings, outdated backbones, and incon

Aditya Wadaskar, Ding Zhao, Ibrahim Pehlivan, Danijela Cabric

Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users. It is desirable to orthogonalize users across sub-bands by deploying frequency-dependent beams with a sub-band-specific spatial response. True-Time-Delay (TTD) antenna arrays are a promising wideband architecture to imp

Takashi Tanaka

A simple method to reduce the numerical cost in free electron laser (FEL) simulations is presented, which is based on retrieving a spatially-coherent component of microbunching to suppress artifact effects that can potentially overestimate the FEL gain; this significantly reduces the number of macroparticles to reach the numerical convergence and enables the

Herman L. Marshall

An event-based maximum likelihood method for handling X-ray polarimetry data is extended to include the effects of background and nonuniform sampling of the possible position angle space. While nonuniform sampling in position angle space generally introduces cross terms in the uncertainties of polarization parameters that could create degeneracies, there are

Rhitabrat Pokharel, Ameeta Agrawal

The ability to process idiomatic or literal multiword expressions is a crucial aspect of understanding and generating any language. The task of generating contextually relevant continuations for narratives containing idiomatic (or literal) expressions can allow us to test the ability of generative language models (LMs) in understanding nuanced language conta

Zilin Yuan, Anping Huang, Wen-Hao Zhou, Guo-Liang Ma

Built upon the state-of-the-art model a multiphase transport (AMPT), we develop a new module of chiral anomaly transport (CAT), which can trace the evolution of the initial topological charge of gauge field created through sphaleron transition at finite temperature and external magnetic field in heavy ion collisions. The eventual experimental signals of chir

Lin Wang, Zhichao Wang, Xi Leng, Xiaoying Tang

Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protecting privacy by avoiding data exchange between clients and servers, it has been shown that the server can infer user ratings based on updated non-zero gradients obtained from two c

Yen-Shao Chen, Tauhid Zaman

The proliferation of harmful content and misinformation on social networks necessitates content moderation policies to maintain platform health. One such policy is shadow banning, which limits content visibility. The danger of shadow banning is that it can be misused by social media platforms to manipulate opinions. Here we present an optimization based appr

Kelly Ann Pawlak, Jeffrey M. Epstein, Daniel Crow, Srilekha Gandhari

We demonstrate that it is possible to construct operators that stabilize the constraint-satisfying subspaces of computational problems in their Ising representations. We provide an explicit recipe to construct unitaries and associated measurements given a set of constraints. The stabilizer measurements allow the detection of constraint violations, and provid

Md Azim Khan

Several visual tasks, such as pedestrian detection and image-to-image translation, are challenging to accomplish in low light using RGB images. Heat variation of objects in thermal images can be used to overcome this. In this work, an end-to-end framework, which consists of a generative network and a detector network, is proposed to translate RGB image into

Junyi Liu

Recommender systems are essential information technologies today, and recommendation algorithms combined with deep learning have become a research hotspot in this field. The recommendation model known as LFM (Latent Factor Model), which captures latent features through matrix factorization and gradient descent to fit user preferences, has given rise to vario

Bartosz Malman

We demonstrate a phenomenon of condensation of the Fourier transform $\widehat{f}$ of a function $f$ defined on the real line $\mathbb{R}$ which decreases rapidly on one half of the line. For instance, we prove that if $f$ is square-integrable on $\mathbb{R}$, then a one-sided estimate of the form \[\rho_f(x) := \int_x^{\infty} |f(t)| \,dt = \mathcal{O}\big(

Sojung An, Junha Lee, Jiyeon Jang, Inchae Na

Obtaining a sufficient forecast lead time for local precipitation is essential in preventing hazardous weather events. Global warming-induced climate change increases the challenge of accurately predicting severe precipitation events, such as heavy rainfall. In this paper, we propose a deep learning-based precipitation post-processor for numerical weather pr

Yunpu Zhang, Changsheng You

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks by exploiting massive number of antennas for generating pencil-like beamforming. This also leads to a fundamental paradigm shift from conventional far-field communications towards the new near-fiel

Hani Mavalizadeh, Mads Almassalkhi

This paper uses convex inner approximations (CIA) of the AC power flow to tackle the optimization problem of quantifying a three-phase distribution feeder's capacity to host distributed energy resources (DERs). This is often connoted hosting capacity (HC), but herein we consider separative bounds for each node on positive and negative DER injections, which e

Sophia-Gisela Strey, Alexander Castronovo, Kailash Elumalai

Numerical simulations and observations show that galaxies are not uniformly distributed. In cosmology, the largest known structures in the universe are galaxy filaments formed from the hierarchical clustering of galaxies due to gravitational forces. These structures consist of walls and bridges that connect clusters. Here, we use graph theory to model the st

Zihui Ma, Lingyao Li, John C. E. Johnson

In March 2020, college campuses underwent a sudden transformation to online learning due to the COVID-19 outbreak. To understand the impact of COVID-19 on students' expectations, this study conducted a three-year survey from ten core courses within the Project Management Center for Excellence at the University of Maryland. The study involved two main steps:

Shunichiro Orihara

In causal inference, the Inverse Probability Weighting (IPW) estimator is commonly used to estimate causal effects for estimands within the class of Weighted Average Treatment Effect (WATE). When constructing confidence intervals (CIs), robust sandwich variance estimators are frequently used for practical reasons. Although these estimators are easy to calcul

Weizhu Bao, Chushan Wang

We propose and analyze a novel symmetric Gautschi-type exponential wave integrator (sEWI) for the nonlinear Schr\"odinger equation (NLSE) with low regularity potential and typical power-type nonlinearity of the form $ |\psi|^{2\sigma}\psi $ with $ \psi $ being the wave function and $ \sigma > 0 $ being the exponent of the nonlinearity. The sEWI is explicit a

Madan Mohan Mahana, Sankar Davuluri, Tarak Nath Dey

This article proposes a new method to increase the efficiency of stimulated Raman adiabatic passage (STIRAP) in superconducting circuits using a shortcut to the adiabaticity (STA) method. The STA speeds up the adiabatic process before decoherence has a significant effect, thus leading to increased efficiency. This method achieves fast, high-fidelity coherent

Zhonghua Sun, Ling Li, Shixin Zhu

Cyclic codes are an interesting family of linear codes since they have efficient decoding algorithms and contain optimal codes as subfamilies. Constructing infinite families of cyclic codes with good parameters is important in both theory and practice. Recently, Tang and Ding [IEEE Trans. Inf. Theory, vol. 68, no. 12, pp. 7842--7849, 2022] proposed an infini

Hyunseung Kim, Byungkun Lee, Hojoon Lee, Dongyoon Hwang

In the field of unsupervised skill discovery (USD), a major challenge is limited exploration, primarily due to substantial penalties when skills deviate from their initial trajectories. To enhance exploration, recent methodologies employ auxiliary rewards to maximize the epistemic uncertainty or entropy of states. However, we have identified that the effecti

Weizhu Bao, Bo Lin, Ying Ma, Chushan Wang

We propose and analyze an extended Fourier pseudospectral (eFP) method for the spatial discretization of the Gross-Pitaevskii equation (GPE) with low regularity potential by treating the potential in an extended window for its discrete Fourier transform. The proposed eFP method maintains optimal convergence rates with respect to the regularity of the exact s

Jie Su, Jiancheng Wang, Yigong Zhang, Xiangming Cheng

Currently astrometric microlensing is the only tool that can directly measure the mass of a single star, it can also help us to detect compact objects like isolated neutron stars and black holes. The number of microlensing events that are being predicted and reported is increasing. In the paper, the potential lens stars are selected from three types of stars

Kunyu Wang, Juluan Shi, Wenxuan Wang

Deep neural networks are susceptible to adversarial attacks, which pose a significant threat to their security and reliability in real-world applications. The most notable adversarial attacks are transfer-based attacks, where an adversary crafts an adversarial example to fool one model, which can also fool other models. While previous research has made progr

Pallavi Banerjee, Satyaki Chakraborty

In this paper we introduce a novel framework for trajectory prediction of geospatial sequences using GraphTransformers. When viewed across several sequences, we observed that a graph structure automatically emerges between different geospatial points that is often not taken into account for such sequence modeling tasks. We show that by leveraging this graph

Bohan Li, Junyi Guo, Xiaoqing Liang

This paper explores an optimal investment and reinsurance problem involving both ordinary and catastrophe insurance businesses. The catastrophic events are modeled as following a compound Poisson process, impacting the ordinary insurance business. The claim intensity for the ordinary insurance business is described using a Cox process with a shot-noise inten

Ruijun Shi, Yue Zhou, Tianyu Zhao, Zhoujian Cao

Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems. At present, deep learning methods have not been widely explored for GW waveform generation and extrapolation. To solve the data processing difficulty and the increasing waveform compl

Lucy W. Will, Aimee A. Norton, Jon Todd Hoeksema

Data from the Michelson Doppler Imager (MDI) and Helioseismic and Magnetic Imager (HMI) are analyzed from 1996 to 2023 to investigate tilt angles ($\gamma$) of bipolar magnetic regions and Joy's Law for Cycles 23, 24, and a portion of 25. The HMI radial magnetic field ($B_{r}$) and MDI magnetogram ($B_{los}$) data are used to calculate ($\gamma$) using the f

Wenting Zhao, Ye Liu, Tong Niu, Yao Wan

Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially when answering questions that require less commonly known information. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge. Nonetheless,

Francesco Maggi, Michael Novack, Daniel Restrepo

We provide, in the setting of Gauss' capillarity theory, a rigorous derivation of the equilibrium law for the three dimensional structures known as Plateau borders which arise in "wet" soap films and foams. A key step in our analysis is a complete measure-theoretic overhaul of the homotopic spanning condition introduced by Harrison and Pugh in the study of P

T. B. Keesom, P. P. Popov, P. Dhyani, G. B. Jacobs

A method to infer and synthetically extrapolate roughness fields from electron microscope scans of additively manufactured surfaces using an adaptation of Rogallo's synthetic turbulence method [R. S. Rogallo, NASA Technical Memorandum 81315, 1981] based on Fourier modes is presented. The resulting synthetic roughness fields are smooth and are compatible with

Justus C. Will, Andrea M. Jenney, Kara D. Lamb, Michael S. Pritchard

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involv

Yunfei Yu, Mo Cheng, Zicheng Tao, Wuxiao Han

Two-dimensional (2D) layered magnets, such as iron chalcogenides, have emerged these years as a new family of unconventional superconductor and provided the key insights to understand the phonon-electron interaction and pairing mechanism. Their mechanical properties are of strategic importance for the potential applications in spintronics and optoelectronics

Konstantin Rips

Polaritonic lattice configurations in dimensions $D=2$ are used as simulators of topological phases, based on symmetry class A Hamiltonians. Numerical and topological studies are performed in order to characterise the bulk topology of insulating phases, which is predicted to be connected to non-trivial edge mode states on the boundary. By using spectral flat

Yinqiu He

Nonparametric item response models provide a flexible framework in psychological and educational measurements. Douglas (2001) established asymptotic identifiability for a class of models with nonparametric response functions for long assessments. Nevertheless, the model class examined in Douglas (2001) excludes several popular parametric item response models

Yunfei Yu, Guoshuai Du, Shang Chen, Jingjing Zhang

Anisotropy, as an exotic degree of freedom, enables us to discover the emergent two-dimensional (2D) layered nanomaterials with low in-plane symmetry and to explore their outstanding properties and promising applications. 2D black arsenic (b-As) with puckered structure has garnered increasing attention these years owing to its extreme anisotropy with respect

Carter T. Butts

Known by many names and arising in many settings, the forced linear diffusion model is central to the modeling of power and influence within social networks (while also serving as the mechanistic justification for the widely used spatial/network autocorrelation models). The standard equilibrium solution to the diffusion model depends on strict timescale sepa

Leiyu Pan, Supryadi, Deyi Xiong

Robustness, the ability of models to maintain performance in the face of perturbations, is critical for developing reliable NLP systems. Recent studies have shown promising results in improving the robustness of models through adversarial training and data augmentation. However, in machine translation, most of these studies have focused on bilingual machine

Qingxu Li, Juan Li, Siqi Zheng, Junzhi Wang

The isotopic ratios are good tools for probing the stellar nucleosynthesis and chemical evolution. We performed high-sensitivity mapping observations of the J=7-6 rotational transitions of OCS, OC34S, O13CS, and OC33S toward the Galactic Center giant molecular cloud, Sagittarius B2 (Sgr B2) with IRAM 30m telescope. Positions with optically thin and uncontami

Kobi Feldman, Martin Kellogg, Oscar Chaparro

Proponents of software verification have argued that simpler code is easier to verify: that is, that verification tools issue fewer false positives and require less human intervention when analyzing simpler code. We empirically validate this assumption by comparing the number of warnings produced by four state-of-the-art verification tools on 211 snippets of

Deepanway Ghosal, Navonil Majumder, Roy Ka-Wei Lee, Rada Mihalcea

Visual question answering (VQA) is the task of answering questions about an image. The task assumes an understanding of both the image and the question to provide a natural language answer. VQA has gained popularity in recent years due to its potential applications in a wide range of fields, including robotics, education, and healthcare. In this paper, we fo

Daman Arora, Anush Kini, Sayak Ray Chowdhury, Nagarajan Natarajan

Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-based retrieval models, recent work shows promising results on the zero-shot retrieval problem, i.e., no access to labeled data from the target domain. Two such popular paradigms are

Jingwei Ling, Zhengdong Gao, Shixin Xue, Qili Hu

Optical frequency comb underpins a wide range of applications from communication, metrology, to sensing. Its development on a chip-scale platform -- so called soliton microcomb -- provides a promising path towards system miniaturization and functionality integration via photonic integrated circuit (PIC) technology. Although extensively explored in recent yea

Hui Ouyang

This is a continuation of our previous work entitled \enquote{Alternating Proximity Mapping Method for Convex-Concave Saddle-Point Problems}, in which we proposed the alternating proximal mapping method and showed convergence results on the sequence of our iterates, the sequence of averages of our iterates, and the sequence of function values evaluated at th

Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex wo

Nam H. Le, Milan Sonka, Fatima Toor

Quantum image processing is a growing field attracting attention from both the quantum computing and image processing communities. We propose a novel method in combining a graph-theoretic approach for optimal surface segmentation and hybrid quantum-classical optimization of the problem-directed graph. The surface segmentation is modeled classically as a grap

Jiaxin Zhang, Zhuohang Li, Kamalika Das, Sricharan Kumar

Large language models (LLMs) have demonstrated remarkable capabilities in various tasks. However, their suitability for domain-specific tasks, is limited due to their immense scale at deployment, susceptibility to misinformation, and more importantly, high data annotation costs. We propose a novel Interactive Multi-Fidelity Learning (IMFL) framework for the

Edl Schamiloglu, Alexander Figotin

Classical Cherenkov radiation is a celebrated physics phenomenon of electromagnetic (EM) radiation stimulated by an electric charge moving with constant velocity in a three dimensional dielectric medium. Cherenkov radiation has a wide spectrum and a particular distribution in space similar to the Mach cone created by a supersonic source. It is also character

Huaben Chen, Wenkang Ji, Lufeng Xu, Shiyu Zhao

Multi-agent systems driven by large language models (LLMs) have shown promising abilities for solving complex tasks in a collaborative manner. This work considers a fundamental problem in multi-agent collaboration: consensus seeking. When multiple agents work together, we are interested in how they can reach a consensus through inter-agent negotiation. To th

Jiaao Chen, Diyi Yang

Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data, however, this process might suffer from privacy issues and violations of data protection regulations. As a result, the ability to easily remove data related to individual users from such models while not deteriorating their predic

Tommy Grav, Amy K. Mainzer, Joseph R. Masiero, Dar W. Dahlen

The known near-Earth object (NEO) population consists of over 32,000 objects, with a yearly discovery rate of over 3000 NEOs per year. An essential component of the next generation of NEO surveys is an understanding of the population of known objects, including an accounting of the discovery rate per year as a function of size. Using a near-Earth asteroid (N

Xiao Li, Kaiwen Liu, H. Eric Tseng, Anouck Girard

Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic. It is thus crucial to equip autonomous vehicles with artificial reasoning to better comprehend the intentions of the surrounding traffic, thereby facilitating the accomplishments of the tasks. In this work, we propose a behavioral model that encodes drivers' i

Jonathan Engle, Simone Speziale

Spinfoams provide a framework for the dynamics of loop quantum gravity that is manifestly covariant under the full four-dimensional diffeomorphism symmetry group of general relativity. In this way they complete the ideal of three-dimensional diffeomorphism covariance that consistently motivates loop quantum gravity at every step. Specifically, spinfoam model

Hui Ouyang

In this note, we consider the Optimistic Gradient Ascent-Proximal Point Algorithm (OGAProx) proposed by Bo{\c{t}}, Csetnek, and Sedlmayer for solving a saddle-point problem associated with a convex-concave function constructed by a nonsmooth coupling function and one regularizing function. We first provide a counterexample to show that the convergence of the

Lin Yang, Junlong Lyu, Wenlong Lyu, Zhitang Chen

Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optimization process, such as machining errors, execution noise, or contextual variability. This uncertainty deviates the input from the intended va

Gabrielle Cohn, Rishika Agarwal, Deepanshu Gupta, Siddharth Patwardhan

We introduce EELBERT, an approach for compression of transformer-based models (e.g., BERT), with minimal impact on the accuracy of downstream tasks. This is achieved by replacing the input embedding layer of the model with dynamic, i.e. on-the-fly, embedding computations. Since the input embedding layer accounts for a significant fraction of the model size,

Albert Ai, Ovidiu-Neculai Avadanei

In this article we consider the low regularity well-posedness of the surface quasi-geostrophic (SQG) front equation. Recent work on other quasilinear models, including the gravity water waves system and nonlinear waves, have demonstrated that in presence of a null structure, a normal form analysis can substantially improve the low regularity theory. In the c

Hui Ouyang

Inspired by the Optimistic Gradient Ascent-Proximal Point Algorithm (OGAProx) proposed by Bo{\c{t}}, Csetnek, and Sedlmayer for solving a saddle-point problem associated with a convex-concave function with a nonsmooth coupling function and one regularizing function, we introduce the Alternating Proximal Point Algorithm with Gradient Descent and Ascent Steps

Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach

Predicting and reasoning about the future lie at the heart of many time-series questions. For example, goal-conditioned reinforcement learning can be viewed as learning representations to predict which states are likely to be visited in the future. While prior methods have used contrastive predictive coding to model time series data, learning representations

Reza Basiri, Karim Manji, Francois Harton, Alisha Poonja

Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care. However, real DFU datasets are limited, hindering clinical training and research activities. In recent years, generative adversarial networks and diffusion models have emerged as powerful tools for generating synthetic images with remarkable realism and diversity in many applicatio

Ryo Tamura

The tunnel current (TC) and valley current (VC) are crucial in realizing high-speed and energy-saving in next-generation devices. This paper presents the TC and VC link in the partially overlapped graphene. Under the vertical electric field, the two graphene layers have the opposite AB sublattice symmetry, followed by a block on the intravalley transmission.

Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong

Large language models pretrained on a huge amount of data capture rich knowledge and information in the training data. The ability of data memorization and regurgitation in pretrained language models, revealed in previous studies, brings the risk of data leakage. In order to effectively reduce these risks, we propose a framework DEPN to Detect and Edit Priva

Taiki Kawamuro, Claudio Ricci, Satoshi Yamada, Hirofumi Noda

The nearby dwarf galaxy POX 52 at $z = 0.021$ hosts an active galactic nucleus (AGN) with a black-hole (BH) mass of $M_{\rm BH} \sim 10^{5-6} M_\odot$ and an Eddington ratio of $\sim$ 0.1-1. This object provides the rare opportunity to study both AGN and host-galaxy properties in a low-mass highly accreting system. To do so, we collected its multi-wavelength

Xinjuan Cheng, Liangyao Xiao, Xuechao Zhai

We establish a general Rashba Hamiltonian for trilayer graphene (TLG) by introducing an extrinsic layer-dependent Rashba spin-orbit coupling (SOC) arising from the off-plane inversion symmetry breaking. Our results indicate that the band spin splitting depends strongly on the layer-distribution and sign of Rashba SOC as well as the ABA or ABC stacking order

Emanuele Contini, Jinsu Rhee, San Han, Seyoung Jeon

We extend the analysis presented in \cite{contini2023a} to higher redshifts, up to $z=2$, by focusing on the relation between the intracluster light (ICL) fraction and the halo mass, its dependence with redshift, role played by the halo concentration and formation time, in a large sample of simulated galaxy groups/clusters with $13\lesssim \log M_{halo} \les

Bing-Shi Yu, Hai-Jun Wu, Chun-Yu Li, Jia-Qi Jiang

Since the diffraction behavior of a light field is fully determined by its spatial wavefunction, i.e., its spatial complex amplitude (SCA), full characterization of spatial wavefunction, plays a vital role in modern optics from both the fundamental and applied aspects. In this work, we present a novel complex-amplitude profiler based on spatial Stokes tomogr

Pei-Xin Liang, Yasuhiro Oki, Chia-Fu Yu

In this paper we investigate the Tate--Shafarevich group Sha^1(k, T) of a multinorm-one torus $T$ over a global field $k$. We establish a few functorial maps among cohomology groups and explore their relations. Using these properties and relations we obtain a few basic structural results for Sha^1(k, T) and extend a few results of Bayer-Fluckiger--Lee--Parim

Yadi Wei, Jiaxin Wang, Fang-Wei Fu

Linear codes with few weights have significant applications in secret sharing schemes, authentication codes, association schemes, and strongly regular graphs. There are a number of methods to construct linear codes, one of which is based on functions. Furthermore, two generic constructions of linear codes from functions called the first and the second generi

Jiawei Zuo, Ashutosh Bangalore Aravinda Babu, Mo Tian, Jing Bai

In conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state-of-art MMMs a

Jing Gao, Sen Li

This paper compares two prevalent charging strategies for electric vehicles, plug-in charging and battery swapping, to investigate which charging strategy is superior for electric autonomous mobility-on-demand (AMoD) systems. To this end, we use a queueing-theoretic model to characterize the vehicle waiting time at charging stations and battery swapping stat

Norhan M. Eassa, Mahmoud M. Moustafa, Arnab Banerjee, Jeffrey Cohn

Gibbs states (i.e., thermal states) can be used for several applications such as quantum simulation, quantum machine learning, quantum optimization, and the study of open quantum systems. Moreover, semi-definite programming, combinatorial optimization problems, and training quantum Boltzmann machines can all be addressed by sampling from well-prepared Gibbs

Jing Guo, Pengyu Wang, Cheng Huang, Bin-Bin Chen

In the field of correlated electron materials, the relation between the resonating spin singlet and antiferromagnetic states has long been an attractive topic for understanding of the interesting macroscopic quantum phenomena, such as the ones emerging from magnetic frustrated materials, antiferromagnets and high-temperature superconductors. SrCu2(BO3)2 is a

Wei Zhu, Ming Tan

Prompt tuning prepends a soft prompt to the input embeddings or hidden states and only optimizes the prompt to adapt pretrained models (PTMs) to downstream tasks. The previous work manually selects prompt layers which are far from optimal and failed to exploit the potential of prompt tuning. In this work, we propose a novel framework, \underline{S}elective \

Yici Zhong, Kazumi Kashiyama, Shinsuke Takasao, Toshikazu Shigeyama

We numerically construct a series of axisymmetric rotating magnetic wind solutions, aiming at exploring the observation properties of massive white dwarf (WD) merger remnants with a strong magnetic field, a fast spin, and an intense mass loss, as inferred for WD J005311. We investigate the magnetospheric structure and the resultant spin-down torque exerted t

Zechang Sun, Joshua S. Speagle, Song Huang, Yuan-Sen Ting

We present zephyr, a novel method that integrates cutting-edge normalizing flow techniques into a mixture density estimation framework, enabling the effective use of heterogeneous training data for photometric redshift inference. Compared to previous methods, zephyr demonstrates enhanced robustness for both point estimation and distribution reconstruction by

Eduard Looijenga

Allcock constructed a 13-dimensional complex ball quotient of which he conjectured that it admits a natural covering with covering group isomorphic to the Bimonster. This ball quotient contains the moduli space of cubic threefolds as an open dense subset of a 10-dimensional complex subball quotient. We prove that this subball quotient has a neighborhood in t

Sining Wei, Hongfeng Li, Yong Wang

In this paper, we establish the proof of general Kastler-Kalau-Walze type theorems for conformal perturbations of dirac Operators on even dimensional compact manifolds with (respectively without) boundary.

Song Dai, Liuwei Gong, Shaoming Guo, Ruixiang Zhang

We consider Carleson-Sj\"{o}lin operators on Riemannian manifolds that arise naturally from the study of Bochner-Riesz problems on manifolds. They are special cases of H\"{o}rmander-type oscillatory integral operators. We obtain improved $L^p$ bounds of Carleson-Sj\"{o}lin operators in two cases: The case where the underlying manifold has constant sectional

Mohamed Elgaar, Hadi Amiri

We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research to develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks. The novelty of our approach is in the development of linguistic curricula derived from data, existing knowledge about linguisti

Fen Fang, Yi Cheng, Ying Sun, Qianli Xu

In this report, we present our approach to the EPIC-KITCHENS VISOR Hand Object Segmentation Challenge, which focuses on the estimation of the relation between the hands and the objects given a single frame as input. The EPIC-KITCHENS VISOR dataset provides pixel-wise annotations and serves as a benchmark for hand and active object segmentation in egocentric

Yidi Wang

In this manuscript, we present a partial generalization of the field patching technique initially proposed by Harbater-Hartmann to Hensel semi-global fields, i.e., function fields of curves over excellent henselian discretely valued fields. More specifically, we show that patching holds for \'etale algebras over such fields and a suitable set of overfields.

Michael R. Douglas, Liam McAllister

We give a mathematical perspective on string compactifications. Submitted as a chapter in the Encyclopedia of Mathematical Physics.

Hong Danyang, Yu Anzhu, Ji Song, Cao Xuefeng

In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpre

B. R. Ko

We report the first search for the Sagittarius tidal stream of axion dark matter around 4.55 $\mu$eV using CAPP-12TB haloscope data acquired in March of 2022. Our result excluded the Sagittarius tidal stream of Dine-Fischler-Srednicki-Zhitnitskii and Kim-Shifman-Vainshtein-Zakharov axion dark matter densities of $\rho_a\gtrsim0.184$ and $\gtrsim0.025$ GeV/cm