October 2023 arXiv papers — page 23

Guangxin Lv, Yaodong Tu, James H. Zhang, Gang Chen

Although water is almost transparent to visible light, we demonstrate that the air-water interface interacts strongly with visible light via what we hypothesize as the photomolecular effect. In this effect, transverse-magnetic polarized photons cleave off water clusters from the air-water interface. We use over 10 different experiments to demonstrate the exi

L. R. Colaço, M. S. Ferreira, R. F. L. Holanda, J. E. Gonzalez

In this work, we constrain the Hubble constant parameter, $H_0$, using a combination of the Pantheon sample and galaxy clusters (GC) measurements from minimal cosmological assumptions. Assuming the validity of the cosmic distance duality relation, an estimator is created for $H_0$ that only depends on simple geometrical distances, which is evaluated from Pan

Corentin Le Bars

This thesis is dedicated to random walks on spaces with non-positive curvature. In particular, we study the case of group actions on CAT(0) spaces that admit contracting elements, that is, whose properties mimic those of loxodromic isometries in Gromov-hyperbolic spaces. In this context, we prove several limit laws, among which the almost sure convergence to

Ruohao Guo, Xianghua Ying, Yaru Chen, Dantong Niu

In this paper, we propose a new multi-modal task, termed audio-visual instance segmentation (AVIS), which aims to simultaneously identify, segment and track individual sounding object instances in audible videos. To facilitate this research, we introduce a high-quality benchmark named AVISeg, containing over 90K instance masks from 26 semantic categories in

Haggai Agmon, Yoram Burak

The storage of continuous variables in working memory is hypothesized to be sustained in the brain by the dynamics of recurrent neural networks (RNNs) whose steady states form continuous manifolds. In some cases, it is thought that the synaptic connectivity supports multiple attractor manifolds, each mapped to a different context or task. For example, in hip

M. V. R. Sudheer, Sarath Chandra Varma, Aloke Kumar, Udita U. Ghosh

Recent investigations into coalescence dynamics of complex fluid droplets revealed the existence of sub-Newtonian behaviour for polymeric fluids (elastic and shear thinning). We hypothesize that such delayed coalescence or sub-Newtonian coalescence dynamics may be extended to the general class of shear thickening fluids. To investigate this droplets of aqueo

Shengkun Wang, YangXiao Bai, Kaiqun Fu, Linhan Wang

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentimen

A. Bañón Navarro, G. T. Roberg-Clark, G. G. Plunk, D. Fernando

We investigate the confinement properties of two recently devised quasi-helically symmetric stellarator configurations, HSK and QSTK. Both have been optimized for large critical gradients of the ion temperature gradient mode, which is an important driver of turbulent transport in magnetic confinement fusion devices. To predict the resulting core plasma profi

Anton Svirsky, Corentin Herbert, Anna Frishman

A remarkable feature of two-dimensional turbulence is the transfer of energy from small to large scales. This process can result in the self-organization of the flow into large, coherent structures due to energy condensation at the largest scales. We investigate the formation of this condensate in a quasi-geostropic flow in the limit of small Rossby deformat

Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging -- with no access to underlying states, no knowledge of environment dynamics, and no allowance for live experimentation. We desire learn

Ricardo Barros Lourenço, Michael J. Smith, Sylvia Smullin, Umangi Jain

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understa

Phillip Pope, David Jacobs

The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts. Recent machine learning work on catalyst modeling has focused on prediction of the energy, but has so far not yet demonstrated significant out-of-distribution generalization. Here we investigate another approach based on the pointwise learning of the Kohn-Sha

Bo Xue, Yimu Wang, Yuanyu Wan, Jinfeng Yi

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose $(1+\epsilon)$-th moment is bounded for some $\epsilon\in (0,1]$. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets

An Zhang, Leheng Sheng, Zhibo Cai, Xiang Wang

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevailing in semi-supervised top-K recommendations. The basic idea is to routinely conduct heuristic-based data augmentation and apply contrastive

Nguyen Tien Dung

In this paper, based on the techniques of Malliavin calculus, we provide some new concentration inequalities for the running supremum of the It\^o stochastic integral with unbounded integrands. Several applications and examples are provided as well.

Xuesong Nie, Xi Chen, Haoyuan Jin, Zhihang Zhu

Spatiotemporal predictive learning offers a self-supervised learning paradigm that enables models to learn both spatial and temporal patterns by predicting future sequences based on historical sequences. Mainstream methods are dominated by recurrent units, yet they are limited by their lack of parallelization and often underperform in real-world scenarios. T

Fang Bai, Kanzhi Wu, Adrien Bartoli

We study the generalized Procrustes analysis (GPA), as a minimal formulation to the simultaneous localization and mapping (SLAM) problem. We propose KernelGPA, a novel global registration technique to solve SLAM in the deformable environment. We propose the concept of deformable transformation which encodes the entangled pose and deformation. We define defor

Giulio Starace, Konstantinos Papakostas, Rochelle Choenni, Apostolos Panagiotopoulos

Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. Existing model interpretability research (Tenney et al., 2019) suggests that a linguistic hierarchy emerges in the LLM layers, with lower layers better suited to solving syntactic tasks and higher l

Hokuto Konno

For a simply-connected closed manifold $X$ of $\dim X \neq 4$, the mapping class group $\pi_0(\mathrm{Diff}(X))$ is known to be finitely generated. We prove that analogous finite generation fails in dimension 4. Namely, we show that there exist simply-connected closed smooth 4-manifolds whose mapping class groups are not finitely generated. More generally, f

R. Nolte, B. Lutz

The primary reference instruments for neutron fluence measurements used at the Physikalisch-Technische Bundesanstalt (PTB) are based on the primary standard for neutron measurements which is the differential neutron-proton scattering cross section. Such instruments require considerable effort for their operation and analysis. Therefore, routine measurements

Kenji Fukushima, Chengpeng Yu

We analyze the helicity conservation following from the axial Ward identity for the massless fermion. We discuss the pair production of a fermion, $\ell$, and an anti-fermion, $\bar{\ell}$, from the annihilation process of photons, $\gamma$'s and conclude that the chirality production is prohibited in any annihilation process of on-shell $\gamma$'s. We demon

Rahul Mukerjee

Augmented block designs for unreplicated test treatments are investigated under the A- and MV-criteria with respect to control versus control, test versus test and control versus test comparisons. We derive design-independent lower bounds on these criteria over a wide class of competing designs. These bounds are useful benchmarks and the resulting expression

Angana Bhattacharya, Bhagwat S. Chouhan, Kajal Sharma, Gagan Kumar

The applications of terahertz metamaterials are being actively explored in recent times for applications in high-speed communication devices, miniature photonic circuits, and bio-chemical devices because of their wide advantages. The toroidal resonance, a new type of metasurface resonance, has been examined with great interest to utilize its properties in te

Kui Wu, Sebastian Kindel, Thomas Descamps, Tobias Hangleiter

Efficient interconnection between distant semiconductor spin qubits with the help of photonic qubits would offer exciting new prospects for future quantum communication applications. In this paper, we optimize the extraction efficiency of a novel interface between a singlet-triplet spin qubit and a photonic qubit. The interface is based on a 220 nm thick GaA

Bobby Azad, Reza Azad, Sania Eskandari, Afshin Bozorgpour

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models. Trained on large-scale dataset to bridge the gap between different modalities, foundation models facilitate contextual

Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail design choices and interactions among c

Hao Hu, Yiqin Yang, Jianing Ye, Ziqing Mai

Reward-free data is abundant and contains rich prior knowledge of human behaviors, but it is not well exploited by offline reinforcement learning (RL) algorithms. In this paper, we propose UBER, an unsupervised approach to extract useful behaviors from offline reward-free datasets via diversified rewards. UBER assigns different pseudo-rewards sampled from a

Yeong-Bok Bae, Young-Hwan Hyun, Gungwon Kang

We have numerically investigated close scattering processes of two black holes (BHs). Our careful analysis shows for the first time a non-merging ringdown gravitational wave coming from dynamical tidal deformations of individual BHs during their close encounter. The ringdown wave frequencies turn out to agree well with the quasi-normal ones of a single BH in

Sandeep Kumar, Tirthankar Ghosal, Asif Ekbal

To this date, the efficacy of the scientific publishing enterprise fundamentally rests on the strength of the peer review process. The journal editor or the conference chair primarily relies on the expert reviewers' assessment, identify points of agreement and disagreement and try to reach a consensus to make a fair and informed decision on whether to accept

Sahil Kumar Singh, Abhishek Dhar, Herbert Spohn, Anupam Kundu

We consider the relaxation of an initial non-equilibrium state in a one-dimensional fluid of hard rods. Since it is an interacting integrable system, we expect it to reach the Generalized Gibbs Ensemble (GGE) at long times for generic initial conditions. Here we show that there exist initial conditions for which the system does not reach GGE even at very lon

Shubham Singh, Yashwant Gupta, Kishalay De

We present the results of a high-time resolution polarization study of single pulses from pulsars B0950+08 and B1642-03. Single pulses from pulsar B0950+08 sometimes show isolated micropulses without any significant associated subpulse emission. Assuming that the properties of such micropulses represent the intrinsic nature of micropulse emission, we charact

Jiepeng Fang, Yixin Lan

By introducing $N$-framed quivers, we define the localization of Lusztig's sheaves for $N$-framed quivers and functors $E^{(n)}_{i}, F^{(n)}_{i}, K^{\pm}_i$ for localizations. This gives a categorical realization of tensor products of integrable highest weight modules of the quantized enveloping algebra. The simple perverse sheaves in the localization provid

Munib Mesinovic, Peter Watkinson, Tingting Zhu

Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk prediction by estimating time-to-event distributions. Here, we propose a novel conditional variational autoencoder-based method, DySurv, which uses a combination of static and longitu

Federico J. Furch, Gunnar Arisholm

A simple approach to generate high-energy few-cycle optical vortices with minimized topological charge dispersion is introduced. By means of numerical simulations it is shown that, by leveraging the intrinsic properties of optical parametric chirped pulse amplification (OPCPA), clean transfer of topological charge from a high energy narrowband pump pulse to

Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan, Vineet Gundecha

We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs through an ensemble of critics and the model's own feedback. Drawing inspiration from human behavior, we explore whether LLMs can emulate the self-correction process observed in humans

Bertram Tschiderer

We consider a class of time-homogeneous diffusion processes on $\mathbb{R}^{n}$ with common invariant measure but varying volatility matrices. In Euclidean space, we show via stochastic control of the diffusion coefficient that the corresponding flow of time-marginal distributions admits an entropic gradient flow formulation in the quadratic Wasserstein spac

Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh, Frank C. Park

We present a new method of training energy-based models (EBMs) for anomaly detection that leverages low-dimensional structures within data. The proposed algorithm, Manifold Projection-Diffusion Recovery (MPDR), first perturbs a data point along a low-dimensional manifold that approximates the training dataset. Then, EBM is trained to maximize the probability

Pourya Shamsolmoali, Jocelyn Chanussot, Huiyu Zhou, Yue Lu

Efficient object detection methods have recently received great attention in remote sensing. Although deep convolutional networks often have excellent detection accuracy, their deployment on resource-limited edge devices is difficult. Knowledge distillation (KD) is a strategy for addressing this issue since it makes models lightweight while maintaining accur

Hong-Min Jiang, Min Mao, Zhi-Yong Miao, Shun-Li Yu

Inspired by the recent discovery of a successive evolutions of electronically ordered states, we present a self-consistent theoretical analysis that treats the interactions responsible for the chiral charge order and superconductivity on an equal footing. It is revealed that the self-consistent theory captures the essential features of the successive tempera

Galib Muhammad Shahriar Himel, Md Masudul Islam

This article presents a comprehensive dataset featuring ten distinct hen breeds, sourced from various regions, capturing the unique characteristics and traits of each breed. The dataset encompasses Bielefeld, Blackorpington, Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and Turken breeds, offering a diverse representation of poultry

Stefan Hoeche, Frank Krauss, Peter Meinzinger

We present the first complete simulation framework, in the Sherpa event generator, for resolved photon interactions at next-to leading order accuracy. It includes photon spectra obtained through the equivalent-photon approximation, parton distribution functions to parametrize the hadronic structure of quasi-real photons, the matching of the parton shower to

Juan Orendain, Ruben Maldonado

We treat the problem of lifting bicategories into double categories through categories of vertical morphisms. We consider structures on decorated 2-categories allowing us to formally implement arguments of sliding certain squares along vertical subdivisions in double categories. We call these structures $\pi_2$-indexings. We present a construction associatin

Lorenzo Brusca, Lars C. P. M. Quaedvlieg, Stratis Skoulakis, Grigorios G Chrysos

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly constructs two smaller sub-graphs, predicts the one with the larger MIS, and then uses it in the next recursive

Peter Skands, Christian T. Preuss

We report on a new formalism for parton showers whose fixed-order expansion can be corrected through next-to-next-to-leading order (NNLO) in QCD. It is the first such formalism we are aware of that has no dependence on any auxiliary scales or external resummations and which is fully differential in all of the relevant phase spaces. Since the shower acts as t

Peng Wei

Numerous industrial processes can be defined using distributed parameter systems (DPSs). This study introduces a two-stage spatial construction approach for real-time modeling of DPSs in cases of limited sensors. Initially, a discrete space-completion approach is created to recuperate the spatiotemporal patterns of non-monitored locations under sparse sensin

Javier Rodrigo Martinez, Santiago del Palacio, Valentí Bosch-Ramon

We aim to interpret the radio emission of stellar bow shocks and assess under which conditions it could be either thermal or non-thermal, and how to use the observational data to infer their physical properties. We used an extended non-thermal emission model for stellar bow shocks for which we incorporated a consistent calculation of the thermal emission fro

Prof N. Jeenath Laila, Dr G. Tamilpavai

In the digital age, it is crucial to make sure that financial transactions are as secure and reliable as possible. This abstract offers a ground-breaking method that combines smart contracts, blockchain technology, FaceNet512 for improved face recognition, and Gaussian Mixture Models (GMM) for speech authentication to create a system for video and audio veri

Magdalena Lesniewska, Nigel Mottram, Oliver Henrich

We study the morphology of the Saturn ring defect and director structure around a colloidal particle with normal anchoring conditions and within the flow of the nematic host phase through a rectangular duct of comparable size to the particle. The changes in the defect structures and director profile influence the advection behaviour of the particle, which we

Zhengyang Lei, Sihong Shao, Yunfeng Xiong

Numerical resolution of high-dimensional nonlinear PDEs remains a huge challenge due to the curse of dimensionality. Starting from the weak formulation of the Lawson-Euler scheme, this paper proposes a stochastic particle method (SPM) by tracking the deterministic motion, random jump, resampling and reweighting of particles. Real-valued weighted particles ar

Lokesh Kumar Sharma, Suresh Parekh, Sanjay Maurya, Kuldeep Singh

This paper examines a power law solution under $f(R,T)$ gravity for an isotropic and homogeneous universe by considering its functional form as $f(R,T) = R + \xi RT$, where $\xi$ is a positive constant. In $f(R,T)$ gravity, we have built the field equation for homogeneous and isotropic spacetime. The developed model's solution is $a = \alpha t^{\beta}$. We h

Pranav S. Page, Anand S. Siyote, Vivek S. Borkar, Gaurav S. Kasbekar

The Internet of Things (IoT) is emerging as a critical technology to connect resource-constrained devices such as sensors and actuators as well as appliances to the Internet. In this paper, we propose a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency IDentification (RFID) systems, which uses the priv

Yotam Maoz

We study smooth linear spectral statistics of twisted Laplacians on random $n$-covers of a fixed compact hyperbolic surface $X$. We consider two aspects of such statistics. The first is the fluctuations of such statistics in a small energy window around a fixed energy level when averaged over the space of all degree $n$ covers of $X$. The second is the energ

Ruisheng Cao, Hanchong Zhang, Hongshen Xu, Jieyu Li

Text-to-SQL aims to generate an executable SQL program given the user utterance and the corresponding database schema. To ensure the well-formedness of output SQLs, one prominent approach adopts a grammar-based recurrent decoder to produce the equivalent SQL abstract syntax tree (AST). However, previous methods mainly utilize an RNN-series decoder, which 1)

H. Souissi, M. Gromovyi, I. Septembre, V. Develay

So far, exciton-polariton (polariton) lasers were mostly single mode lasers based on microcavities. Despite the large repulsive polariton-polariton interaction, pulsed mode-locked polariton laser was never reported. Here, we use a $60 \ \mu m-$long GaN-based waveguide surrounded by distributed Bragg reflectors forming a multi-mode horizontal cavity. We demon

Johannes Jakubik, Sujit Roy, C. E. Phillips, Paolo Fraccaro

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled datasets through self-supervision, and then fine-tuned for various downstream tasks with small labeled datasets. This pa

Hongda Sun, Weikai Xu, Wei Liu, Jian Luan

Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior studies have focused on modeling reasoning steps using various thought structures like chains, trees, or graphs. However, LLM-based reasoning still encounters the following challenges: (1)

Weihuan Huang

CoVaR (conditional value-at-risk) is a crucial measure for assessing financial systemic risk, which is defined as a conditional quantile of a random variable, conditioned on other random variables reaching specific quantiles. It enables the measurement of risk associated with a particular node in financial networks, taking into account the simultaneous influ

Binzhou Yang, Ke Han, Wenrui Tu, Qian Ge

This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent q

Haoran Shen, Yifu Zhang, Wenxuan Wang, Chen Chen

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by d

Mike Devereux, Eric D. Boittier, Markus Meuwly

The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (DCM), and solvated K$^+$ and Cl$^-$ ions. For the electrostatics, point charges (PCs) and machine learning (ML)based minimally distributed charges (MDCM) fitted to the molecular electrostatic potential are evaluated tog

Søren Wengel Mogensen, Karin Rathsman, Per Nilsson

Causal discovery outputs a causal structure, represented by a graph, from observed data. For time series data, there is a variety of methods, however, it is difficult to evaluate these on real data as realistic use cases very rarely come with a known causal graph to which output can be compared. In this paper, we present a dataset from an industrial subsyste

Yi Wang, Hugo Hernández Hernández, Conrad M Albrecht, Xiao Xiang Zhu

Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing. However, MAE tends to excessively focus on pixel details, thereby limiting the model's capacity for semantic understanding, in particular for noisy SAR images. In this paper, we explo

Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho

Electronic Health Records (EHRs), which contain patients' medical histories in various multi-modal formats, often overlook the potential for joint reasoning across imaging and table modalities underexplored in current EHR Question Answering (QA) systems. In this paper, we introduce EHRXQA, a novel multi-modal question answering dataset combining structured E

Ali Javidani, Mohammad Amin Sadeghi, Babak Nadjar Araabi

Self-supervised visual representation learning traditionally focuses on image-level instance discrimination. Our study introduces an innovative, fine-grained dimension by integrating patch-level discrimination into these methodologies. This integration allows for the simultaneous analysis of local and global visual features, thereby enriching the quality of

Marzieh Soltani, Arash Atashi

In order to increase energy efficiency in buildings, optimizing the parameters of the facade form can be challenging due to the dynamic nature of solar radiation. One effective solution is the use of kinetic facades as a second skin, which can control energy consumption. This study proposes a parametric kinetic facade to increase building energy efficiency,

Chuhan Sun, Zipeng Wang

We study a family of strong fractional integral operators whose kernels have singularity on every coordinate subspace. We prove a desired two-weight, L^p-norm inequality provided that the corresponding multi-parameter theta-bump characteristic is finite.

Anh Nguyen-Duc, Beatriz Cabrero-Daniel, Adam Przybylek, Chetan Arora

Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent publications have explored and evaluated the applicatio

Mircea-Tudor Lică, David Dinucu-Jianu

This paper aims to explore the separation of the two forward passes in the Forward-Forward algorithm from a biological perspective in the context of sleep. We show the size of the gap between the sleep and awake phase influences the learning capabilities of the algorithm and highlight the importance of negative data in diminishing the devastating effects of

Nhat-Quang Tran, Anna Felipe, Thanh Nguyen Ngoc, Tom Huynh

Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, custo

Gabriela Bayolo Soler, Miraine Dávila Felipe, Ghislaine Gayraud

Under limited available resources, strategies for mitigating the propagation of an epidemic such as random testing and contact tracing become inefficient. Here, we propose to accurately allocate the resources by computing over time an individual risk of infection based on the partial observation of the epidemic spreading on a contact network; this risk is de

Kai Xu, Lijun Liu, Ning-Ning Wang, Chao Zhang

Quantum steering ellipsoid visualizes the set of all qubit states that can be steered by measuring on another correlated qubit in the Bloch picture. Together with local reduced states, it provides a faithful geometric characterization of the underlying two-qubit state so that almost all nonclassical state features can be reflected in its geometric properties

Nelson Hua, Franziska Breitner, Anton Jesche, Shih-Wen Huang

Manipulating the size and orientation of quantum materials is often used to tune emergent phenomena, but precise control of these parameters is also necessary from an experimental point of view. Various synthesis techniques already exist, such as epitaxial thin film growth and chemical etching, that are capable of producing specific sample dimensions with hi

Yiming Li, Chuanming Zong

This paper proves that the simultaneous lattice packing-covering constant of an octahedron is $7/6$. In other words, $7/6$ is the smallest positive number $r$ such that for every octahedron $O$ centered at the origin there is a lattice $\Lambda$ such that $O+\Lambda$ is a packing in $\mathbb{E}^3$ and $rO+\Lambda$ is a covering of $\mathbb{E}^3$.

Deepa Anand, Gurunath Reddy M, Vanika Singhal, Dattesh D. Shanbhag

Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their ability to capture rich semantic features of the image have been used for image correspondence tasks on natural images. In this paper, we examine the ability of a variety of pre-trained ViT (DINO, DINOv2, SAM, CLIP) and SD models, trained exclusively on natural images, f

Lu Liu, Ke Yang, Di Lu, Yaozhenghang Ma

We investigate the electronic and magnetic properties of the newly synthesized double perovskites Y$_{2}$NiIrO$_{6}$ and La$_{2}$NiIrO$_{6}$, using density functional calculations, crystal field theory, superexchange pictures, and Monte Carlo simulations. We find that both systems are antiferromagnetic (AFM) Mott insulators, with the high-spin Ni$^{2+}$ $t_{

Jaemin Na, Jung-Woo Ha, Hyung Jin Chang, Dongyoon Han

The teacher-student framework, prevalent in semi-supervised semantic segmentation, mainly employs the exponential moving average (EMA) to update a single teacher's weights based on the student's. However, EMA updates raise a problem in that the weights of the teacher and student are getting coupled, causing a potential performance bottleneck. Furthermore, th

Wenju Sun, Qingyong Li, Wen Wang, Yangli-ao Geng

The dilemma between plasticity and stability presents a significant challenge in Incremental Learning (IL), especially in the exemplar-free scenario where accessing old-task samples is strictly prohibited during the learning of a new task. A straightforward solution to this issue is learning and storing an independent model for each task, known as Single Tas

Andrea Nocera, M. Hashem Pesaran

We investigate the short- and long-term impacts of the Federal Reserve's large-scale asset purchases (LSAPs) on non-financial firms' capital structure using a threshold panel ARDL model. To isolate the effects of LSAPs from other macroeconomic conditions, we interact firm- and industry-specific indicators of debt capacity with measures of LSAPs. We find that

Yotam Maoz

Let $X$ be an orientable hyperbolic surface of genus $g\geq 2$ with a marked point $o$, and let $\Gamma$ be an orientable hyperbolic surface group isomorphic to $\pi_{1}(X,o)$. Consider the space $\text{Hom}(\Gamma,S_{n})$ which corresponds to $n$-sheeted covers of $X$ with labeled fiber. Given $\gamma\in\Gamma$ and a uniformly random $\phi\in\text{Hom}(\Gam

Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann, Bangti Jin

Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of an object from measurements taken on its boundary. It is severely ill-posed, necessitating advanced com

Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have bee

Hang Chen, Xinyu Yang, Keqing Du, Wenya Wang

The cross-pollination between causal discovery and deep learning has led to increasingly extensive interactions. It results in a large number of deep learning data types (such as images, text, etc.) extending into the field of causal discovery, and a multitude of deep learning tasks have begun to utilize causal discovery to explore the internal causal struct

Ruixiang Tang, Jiayi Yuan, Yiming Li, Zirui Liu

In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples. Recent research has exposed the susceptibility of PLMs to backdoor attacks, wherein the adversaries can embed malicious prediction behaviors by manipulating a few training samples. In this study, our objective is to

Haojie Hou, Yan-Xia Ren, Renming Song

Let $N(t)$ be the collection of particles alive at time $t$ in a branching Brownian motion in $\mathbb{R}^d$, and for $u\in N(t)$, let $\mathbf{X}_u(t)$ be the position of particle $u$ at time $t$. For $\theta\in \mathbb{R}^d$, we define the additive measures of the branching Brownian motion by$$\mu_t^\theta (\mathrm{d}\mathbf{x}):= e^{-(1+\frac{\Vert\theta\

Yingjia Li, Koushik Paul, David Novoa, Xi Chen

We implement variational shortcuts to adiabaticity for optical pulse compression in an active nonlinear Kerr medium with distributed amplification and spatially varying dispersion and nonlinearity. Starting with the hyperbolic secant ansatz, we employ a variational approximation to systematically derive dynamical equations, establishing analytical relationsh

Xu Chen, XinXin He, Zhiyong Feng, Zhiqing Wei

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO

Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Birgitte Bak-Jensen

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box approach that combines high accuracy with transparency for wind power forecasting. Specifically, the core is to sum up the feature effec

Hailin Chen, Amrita Saha, Steven Hoi, Shafiq Joty

With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs. Previous distillation methods usually prompt ChatGPT to generate a set of instructions and answers, for the student model to learn. However, such standard distillation approach neglect

Shu-Xuan Wang

Non-Hermitian skin effect is a basic phenomenon in non-Hermitian system, which means that an extensive number of eigenstates can be localized at the boundary. In this Letter, we systematically investigate the constraints from all internal symmetries on the non-Hermitian skin effect in arbitrary dimensions. By adopting the powerful Amoeba formulation, we buil

Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Zachariah Carmichael

We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. Our framework allows users to customize the types of distortions to be optimally applied to images, which helps address the specific distortions relevant to their deployment. The benchmark can generate datasets at various distortion l

Yuto Watanabe, Kazunori Sakurama

This study explores distributed optimization problems with clique-wise coupling via operator splitting and how we can utilize this framework for performance analysis and enhancement. This framework extends beyond conventional pairwise coupled problems (e.g., consensus optimization) and is applicable to broader examples. To this end, we first introduce a new

Endre Csóka

Consider a set $X\subseteq \mathbb{R}^d$ which is 1-dense, namely, it intersects every unit ball. We show that we can get from any point to any other point in $\mathbb{R}^d$ in $n$ steps so that the intermediate points are in $X$, and the discrepancy of the step vectors is at most $2\sqrt{2}$, or formally, $$\sup\limits_{\substack{n\in \mathbb{Z}^+,\ t\in \m

Gianluca Occhetta, Eleonora A. Romano, Luis E. Solá Conde, Jarosław A. Wiśniewski

Given an action of the one-dimensional torus on a projective variety, the associated Chow quotient arises as a natural parameter space of invariant $1$-cycles, which dominates the GIT quotients of the variety. In this paper we explore the relation between the Chow and the GIT quotients of a variety, showing how to construct explicitly the former upon the lat

Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicat

Qi Chen, Kaixun Tu, Qing Wang

While neutrino oscillations have led to attention and research on field mixing arising from quadratic interactions, the field mixing inherent in clothed particles is more fundamental, serving as a significant source of complexity and non-perturbative challenges in quantum field theory. We present an example of an analytical solution for field mixing involvin

Weijia Zhang, Dongnan Liu, Chao Ma, Weidong Cai

Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in autonomous driving due to absence of explicit depth cues in a single RGB image. In this paper, we strive to boost currently underperforming monocular 3D object detectors by leveraging an abundance of unlabelled data via semi-supervised learning. Our proposed ODM3D framew

Nan Xu, Fei Wang, Mingtao Dong, Muhao Chen

Many discriminative natural language understanding (NLU) tasks have large label spaces. Learning such a process of large-space decision making is particularly challenging due to the lack of training instances per label and the difficulty of selection among many fine-grained labels. Inspired by dense retrieval methods for passage finding in open-domain QA, we

Haibo Li

The Bayesian statistical framework provides a systematic approach to enhance the regularization model by incorporating prior information about the desired solution. For the Bayesian linear inverse problems with Gaussian noise and Gaussian prior, we propose a new iterative regularization algorithm that belongs to subspace projection regularization (SPR) metho

Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain

Multi-objective optimization is a type of decision making problems where multiple conflicting objectives are optimized. We study offline optimization of multi-objective policies from data collected by an existing policy. We propose a pessimistic estimator for the multi-objective policy values that can be easily plugged into existing formulas for hypervolume

Pranay Bhardwaj, S. M. Zafaruddin

Current statistical channel models for Terahertz (THz) wireless communication primarily concentrate on the sub-THz band, mostly with $\alpha$-$\mu$ and Gaussian mixture fading distributions for short-term fading and deterministic modeling for atmospheric absorption. In this paper, we develop a generalized statistical model for signal propagation at THz frequ