October 2023 arXiv papers — page 17

Chanik Kang, Dongjin Seo, Svetlana V Boriskina, Haejun Chung

Innovative machine learning techniques have facilitated the inverse design of photonic structures for numerous practical applications. Nevertheless, within these approaches, the quantity of data and the initial data distribution are paramount for the discovery of highly efficient photonic devices. These devices often require simulated data ranging from thous

Abdullah Zafar, Christoph Busch

Facial images in passports are designated as primary identifiers for the verification of travelers according to the International Civil Aviation Organization (ICAO). Hence, it is important to ascertain the sanctity of the facial images stored in the electronic Machine-Readable Travel Document (eMRTD). With the introduction of automated border control (ABC) s

Yang Lin

Multi-horizon time series forecasting, crucial across diverse domains, demands high accuracy and speed. While AutoRegressive (AR) models excel in short-term predictions, they suffer speed and error issues as the horizon extends. Non-AutoRegressive (NAR) models suit long-term predictions but struggle with interdependence, yielding unrealistic results. We intr

Yi Xiao, Qiangqiang Yuan, Kui Jiang, Jiang He

Recently, convolutional networks have achieved remarkable development in remote sensing image Super-Resoltuion (SR) by minimizing the regression objectives, e.g., MSE loss. However, despite achieving impressive performance, these methods often suffer from poor visual quality with over-smooth issues. Generative adversarial networks have the potential to infer

Ajay Kumar Shrestha, Faijan Ahamad Khan, Mohammed Afaan Shaikh, Amir Jaberzadeh

The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficie

Jingyi Wang, Cosmin G. Petra

A sequential quadratic programming (SQP) algorithm is designed for nonsmooth optimization problems with upper-C^2 objective functions. Upper-C^2 functions are locally equivalent to difference-of-convex (DC) functions with smooth convex parts. They arise naturally in many applications such as certain classes of solutions to parametric optimization problems, e

Cai Zhou, Xiyuan Wang, Muhan Zhang

Node-level random walk has been widely used to improve Graph Neural Networks. However, there is limited attention to random walk on edge and, more generally, on $k$-simplices. This paper systematically analyzes how random walk on different orders of simplicial complexes (SC) facilitates GNNs in their theoretical expressivity. First, on $0$-simplices or node

Sareh Eslamzadeh, Kourosh Nozari, J. T. Firouzjaee

We study cosmological Lemaitre-Tolman-Bondi (LTB) black hole thermodynamics immersed in a quintom universe. We investigate some thermodynamic aspects of such a black hole in detail. We apply two methods of treating particles' tunneling from the apparent horizons and calculate the black hole's temperature in each method; the results of which are the same. In

Yu Enokibori

Although many deep learning (DL) algorithms have been proposed for the IMU-based HAR domain, traditional machine learning that utilizes handcrafted time series features (TSFs) still often performs well. It is not rare that combinations among DL and TSFs show better accuracy than DL-only approaches. However, there is a problem with time series features in IMU

D A Gorlova, I N Tsymbalov, I P Tsygvintsev, A B Savelev

Direct laser electron acceleration in near-critical density plasma produces collimated electron beams with high charge $Q$ (up to $\mu$C). This regime could be of interest for high energy THz radiation generation, as many of the mechanisms have a scaling $\propto Q^2$. In this work we focused specifically on challenges that arise during numerical investigati

Maurice H. P. M. van Putten

Black holes evolve by evaporation of their event horizon. While this process is believed to be unitary, there is no consensus on the recovery of information in black hole entropy. A missing link is a unit of information in black hole evaporation. Distinct from Hawking radiation, we identify evaporation in entangled pairs by $\mathbb{P}^2$ topology of the eve

Takashi U. Ito, Ryosuke Kadono

We propose a model to describe the fluctuations in the internal magnetic field due to ion dynamics observed in the muon spin relaxation ($\mu$SR) by an Edwards-Anderson type autocorrelation function that separates the quasi-static and dynamic components of the correlation by a parameter $Q$ (where $0\le Q\le1$). Our Monte Carlo simulations for this model sho

Qiliang Fang, Keiichi Maeda, Hanindyo Kuncarayakti, Takashi Nagao

It is widely believed that asphericity in the explosion is the crucial ingredient leading to successful core-collapse (CC) supernovae. However, direct observational evidence for the explosion geometry and for the connection with the progenitor properties are still missing. Based on the thus-far largest late-phase spectroscopic sample of stripped-envelope CC

Uday S. Goteti, Shane A. Cybart, Robert C. Dynes

Complex systems of many interacting components exhibit patterns of recurrence and emergent behaviors in their time evolution that can be understood from a new perspective of physics of information dynamics, modeled after one such system, our brains. A generic brain-like network model is derived from a system of disordered superconducting loops with Josephson

Robinson Onyemechi Oturugbum

Daily, massive volume of data are produced due to the internet of things' rapid development, which has now permeated the healthcare industry. Recent advances in data mining have spawned a new field of a study dubbed privacy-preserving data mining (PPDM). PPDM technique or approach enables the extraction of actionable insight from enormous volume of data whil

Zhaoyu Wang, Xuan Zhou, Engui Fan

In this paper, we investigate the Painlev\'e asymptotics in a transition zone for the solutions to the Cauchy problem of the Novikov equation under a nonzero background \begin{align} &u_{t}-u_{txx}+4 u_{x}=3uu_xu_{xx}+u^2u_{xxx}, \nonumber &u(x, 0)=u_{0}(x),\nonumber \end{align} where $u_0(x)\rightarrow \kappa>0, \ x\rightarrow \pm \infty$ and $u_0(x)-\kappa

Meixin Xiong, Liuhong Chen, Ju Ming, Xingchen Pan

In this work, a cut high-dimensional model representation (cut-HDMR) expansion based on multiple anchors is constructed via the clustering method. Specifically, a set of random input realizations is drawn from the parameter space and grouped by the centroidal Voronoi tessellation (CVT) method. Then for each cluster, the centroid is set as the reference, ther

Eugene Choi, Rak-Kyeong Seong

We present a collection of explicit formulas for the minimum volume of Sasaki-Einstein 5-manifolds. The cone over these 5-manifolds is a toric Calabi-Yau 3-fold. These toric Calabi-Yau 3-folds are associated with an infinite class of 4d N=1 supersymmetric gauge theories, which are realized as worldvolume theories of D3-branes probing the toric Calabi-Yau 3-f

Grace Li, Tao Long, Lydia B. Chilton

Current research has explored how Generative AI can support the brainstorming process for content creators, but a gap remains in exploring support-tools for the pre-writing process. Specifically, our research is focused on supporting users in finding topics at the right level of specificity for their audience. This process is called topic scoping. Topic scop

Jaehong Chung, Rasool Ahmad, WaiChing Sun, Wei Cai

This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images. We use the Mapper algorithm to transform 3D digital rock images into graph datasets, encapsulating essential geometrical information. These graphs, after training, prove effective in predicting elastic modu

Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff

Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory-Perturbation Equation (MPE) which relates model's sensitivity to perturbation in its training data. Derived using Bayesian principles, the MPE unifies existing sensitivity measure

Saurav Jha, Dong Gong, He Zhao, Lina Yao

Continual learning (CL) aims to train deep neural networks efficiently on streaming data while limiting the forgetting caused by new tasks. However, learning transferable knowledge with less interference between tasks is difficult, and real-world deployment of CL models is limited by their inability to measure predictive uncertainties. To address these issue

Michael John Ilagan

Chatbots have the risk of generating offensive utterances, which must be avoided. Post-deployment, one way for a chatbot to continuously improve is to source utterance/label pairs from feedback by live users. However, among users are trolls, who provide training examples with incorrect labels. To de-troll training data, previous work removed training example

Nathael Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega, Salem Said

This work aims to prove that the classical Gaussian kernel, when defined on a non-Euclidean symmetric space, is never positive-definite for any choice of parameter. To achieve this goal, the paper develops new geometric and analytical arguments. These provide a rigorous characterization of the positive-definiteness of the Gaussian kernel, which is complete b

A. Rod Gover, Jarosław Kopiński, Andrew Waldron

We develop the mathematics needed to treat the interaction of geometry and stress at any isotropic spacetime singularity. This enables us to handle the Einstein equations at the initial singularity and characterize allowed general relativistic stress-energy tensors. Their leading behaviors are dictated by an initial hypersurface conformal embedding. We also

Ruijie Xi, Munindar P. Singh

There is increasing interest in building computational models of moral reasoning by people to enable effective interaction by Artificial Intelligence (AI) agents. We examine interactions on social media to understand human moral judgments in real-life ethical scenarios. Specifically, we examine posts from a popular Reddit subreddit (i.e., a subcommunity) cal

Megha Sundriyal, Md Shad Akhtar, Tanmoy Chakraborty

A significant increase in content creation and information exchange has been made possible by the quick development of online social media platforms, which has been very advantageous. However, these platforms have also become a haven for those who disseminate false information, propaganda, and fake news. Claims are essential in forming our perceptions of the

Sanghee Lee, Yuta Notsu, Bun'ei Sato

We report intensive monitoring of the activity variability in the H$\alpha$ line for 10 Sun-like stars using the 1.88-m reflector at Okayama Branch Office, Subaru Telescope, during the last four years 2019-2022. Our aim was to investigate features of the stellar magnetic activity behaviors. We correlated the H$\alpha$ line variability of each star with the s

Jaeyeon Bae, Seokhoon Jeong, Seokun Kang, Namgi Han

Storytelling is multi-modal in the real world. When one tells a story, one may use all of the visualizations and sounds along with the story itself. However, prior studies on storytelling datasets and tasks have paid little attention to sound even though sound also conveys meaningful semantics of the story. Therefore, we propose to extend story understanding

Ting Wei Li, Qiaozhu Mei, Jiaqi Ma

Graph Neural Networks (GNNs) have achieved remarkable success in various applications, but their performance can be sensitive to specific data properties of the graph datasets they operate on. Current literature on understanding the limitations of GNNs has primarily employed a $\textit{model-driven}$ approach that leverage heuristics and domain knowledge fro

P A M Guichon, J R Stone, A W Thomas

We present, for the first time, a detailed theory of high density matter including the entire baryon octet at finite temperature, based on a fully relativistic mean field model with a consistent treatment of exchange (Fock) terms, using the quark-meson-coupling model (QMC). It has been already demonstrated that the QMC equation of state is applicable in ther

Daesol Cho, Seungjae Lee, H. Jin Kim

Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics of the environment or external rewards. To tackle these challenges, this work proposes a new approach for curriculum RL called Diversify for Disagreement & Conquer (D2C). Unlike pre

Tingyan Ma, Ligong Wang

Let $G=(V(G), E(G))$ be a graph with vertex set $V(G)$ and edge set $E(G)$. A graph is $ID$-factor-critical if for every independent set $I$ of $G$ whose size has the same parity as $|V(G)|$, $G-I$ has a perfect matching. For two positive integers $a$ and $b$ with $a\leq b$, let $h$: $E(G)\rightarrow [0, 1]$ be a function on $E(G)$ satisfying $a\leq\sum _{e\

Xiangyu Shi, Yanyuan Qiao, Qi Wu, Lingqiao Liu

Effective object detection in autonomous vehicles is challenged by deployment in diverse and unfamiliar environments. Online Source-Free Domain Adaptation (O-SFDA) offers model adaptation using a stream of unlabeled data from a target domain in an online manner. However, not all captured frames contain information beneficial for adaptation, especially in the

Qianqian Shen, Yunhan Zhao, Nahyun Kwon, Jeeeun Kim

Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that

Nicholas Rober, Karan Mahesh, Tyler M. Paine, Max L. Greene

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reach

Yiming Meng, N. Sri. Namachchivaya, Nicolas Perkowski

A better understanding of the instability margin will eventually optimize the operational range for safety-critical industries. In this paper, we investigate the almost-sure exponential asymptotic stability of the trivial solution of a parabolic semilinear stochastic partial differential equation (SPDE) driven by multiplicative noise near the deterministic H

Hang Gao, Wei Yang, Muming Zhang

This paper concerns with the hierarchical control of the semilinear parabolic equations with interior degeneracy. By a Stackelberg-Nash strategy, we consider the linear and semilinear system with one leader and two followers. First, for any given leader, we analyze a Nash equilibrium corresponding to a bi-objective optimal control problem. The existence and

Chen Xu, Jonghyeok Lee, Xiuyuan Cheng, Yao Xie

We present a computationally efficient framework, called $\texttt{FlowDRO}$, for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty sets while aiming to find continuous worst-case distribution (also called the Least Favorable Distribution, LFD) and sample from it. The requirement for LFD to be continuous is so

Zifu Wang, Maxim Berman, Amal Rannen-Triki, Philip H. S. Torr

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional evaluation metrics to be biased towards \textit{majority classes} (e.g. overall pixel-wise accuracy) and \t

Ziqian Lin, Hao Ding, Nghia Trong Hoang, Branislav Kveton

Recent studies on pre-trained vision/language models have demonstrated the practical benefit of a new, promising solution-building paradigm in AI where models can be pre-trained on broad data describing a generic task space and then adapted successfully to solve a wide range of downstream tasks, even when training data is severely limited (e.g., in zero- or

Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee, Rahul Dodhia

Differentially private (DP) synthetic data sets are a solution for sharing data while preserving the privacy of individual data providers. Understanding the effects of utilizing DP synthetic data in end-to-end machine learning pipelines impacts areas such as health care and humanitarian action, where data is scarce and regulated by restrictive privacy laws.

Anh Q. Do, Duy V. Nguyen

We analyze the physics of a type of homopolar motor comprising an AA battery with two cylindrical neodymium magnets on each end that roll inside a metal cylindrical tube. The motion of the motor results from the interaction between the magnetic field of the magnets and the magnetic field created by the current inside the magnets. We develop a model to descri

Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia

Diffusion-based image generation models, such as Stable Diffusion or DALL-E 2, are able to learn from given images and generate high-quality samples following the guidance from prompts. For instance, they can be used to create artistic images that mimic the style of an artist based on his/her original artworks or to maliciously edit the original images for f

Jiaqian Ren, Hao Peng, Lei Jiang, Zhiwei Liu

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of cla

Ryan Balshaw, P. Stephan Heyns, Daniel N. Wilke, Stephan Schmidt

Latent variable models (LVMs) are commonly used to capture the underlying dependencies, patterns, and hidden structure in observed data. Source duplication is a by-product of the data hankelisation pre-processing step common to single channel LVM applications, which hinders practical LVM utilisation. In this article, a Python package titled spectrally-regula

Logan Bell, Nikhil Devanathan, Stephen Boyd

We consider the performance of a least-squares regression model, as judged by out-of-sample $R^2$. Shapley values give a fair attribution of the performance of a model to its input features, taking into account interdependencies between features. Evaluating the Shapley values exactly requires solving a number of regression problems that is exponential in the

Philippe Rigollet, Jan-Christian Hütter

These lecture notes were written for the course 18.657, High Dimensional Statistics at MIT. They build on a set of notes that was prepared at Princeton University in 2013-14 that was modified (and hopefully improved) over the years.

Wayne Polyzou

Background: Phenomenological Poincar\'e invariant quantum mechanical models can provide an efficient description of the dynamics of strongly interacting particles that is consistent with spectral and scattering observables. These models are representation dependent and in order to apply them to reactions with electromagnetic probes it is necessary to have a

Anant Asthana, Shreev Goyal

Let $G$ be a connected multigraph with $n$ vertices, and suppose $G$ has been edge-colored with $n-1$ colors so that each color class induces a spanning tree. Rota's Basis Conjecture for graphic matroids posits that one can find $n-1$ mutually edge-disjoint rainbow spanning trees. In a recent paper, Maezawa and Yazawa have shown that the conjecture holds if

M. Stanley, Y. Li, J. C. Palmstrom, J. L. Thompson

We use magnetoresistance measurements at high magnetic field (B \leq 65 T) and low temperature (T \geq 500 mK) to gain fresh insights into the behavior of the upper critical field, Hc2, in superconducting ultrathin FeSe films of varying degrees of disorder, grown by molecular beam epitaxy on SrTiO3. Measurements of Hc2 across samples with a widely varying su

A. D. Dolgov, L. A. Panasenko, V. A. Bochko

The suppression of relic gravitational waves due to their conversion into electromagnetic radiation in a cosmological magnetic field is studied. The coupled system of equations describing gravitational and electromagnetic wave propagation in an arbitrary curved space-time and in external magnetic field is derived. The subsequent elimination of photons from t

Wai-Chung Kwan, Xingshan Zeng, Yufei Wang, Yusen Sun

Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systematically evaluate the long-sequence capability of LLMs. One of the reasons is that conventional and widely-used benchmarks mainly consist of short sequences. In this paper, we propos

Daniels Krimans, Seth Putterman

Hydrodynamic equations for a one-component plasma are derived as a generalization of the Euler equations to include the effects of the long-range Coulomb interaction. By using a variational principle, these equations self-consistently unify thermodynamics, dispersion laws, nonlinear motion, and conservation laws. In the moderate and strong coupling limits, i

Hui-Ying Ren, Yue Mao, Ya-Ning Ren, Qing-Feng Sun

Inducing and controlling electrostatic barriers in two-dimensional (2D) quantum materials has shown extraordinary promise to enable control of charge carriers, and is key for the realization of nanoscale electronic and optoelectronic devices1-10. Because of their atomically thin nature, the 2D materials have a congenital advantage to construct the thinnest p

Xuan Zuo, Zhi-Yuan Fan, Hang Qian, Ming-Song Ding

Hybrid quantum systems based on magnons in magnetic materials have made significant progress in the past decade. They are built based on the couplings of magnons with microwave photons, optical photons, vibration phonons, and superconducting qubits. In particular, the interactions among magnons, microwave cavity photons, and vibration phonons form the system

Hyosim Yang, Gangseon Ji, Min Choi, Seondo Park

Nanoconfined waters have been extensively studied within various systems, demonstrating low permittivity under static conditions; however, their dynamics have been largely unexplored due to the lack of a robust platform, particularly in the terahertz (THz) regime where hydrogen bond dynamics occur. We report the THz complex refractive index of nanoconfined w

Umme Hafsa Momy

Coronary artery disease (CAD) is a leading cause of death worldwide. Treatments have evolved, with stenting becoming the primary approach over bypass surgery. This article reviews the evolution of coronary stent technology, starting from the first angioplasty in 1977. Pioneers like Forssmann, Dotter, and Gruentzig established the foundation. The late 1980s s

Aarya Upadhyay

I played youth soccer for 11 years starting age at 5. As I progressed through the age groups and got better at the game, I moved up in club levels under the US Club Soccer system. It was during my time as an ECNL player that I began to realize the magnitude of the travel burden associated with local, regional and out of state games. Most of the travel was by

Fang Wu, Stan Z. Li

Therapeutic antibodies are an essential and rapidly expanding drug modality. The binding specificity between antibodies and antigens is decided by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of fo

Qing-hu Hou, Zhongjie Li

In recent years, Sun has proposed numerous conjectures regarding the log-concavity of root sequences $\{\sqrt[n]{a_n}}_{n\geqslant 1}$. We establish criteria for the asymptotic log-concavity of $\{\sqrt[n]{a_n}}_{n\geqslant 1}$ and the asymptotic ratio log-convexity of $\{\sqrt[n]{a_n}}_{n\geqslant 1}$ for $P$-recursive sequences $\{\sqrt[n]{a_n}}_{n\geqslan

Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN

This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, namely, GPT-4, GPT- 3.5, PaLM-2, and LLaMA-2. Our findings reveal that most closed-source LLMs are generally bette

Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

Adapters are widely popular parameter-efficient transfer learning approaches in natural language processing that insert trainable modules in between layers of a pre-trained language model. Apart from several heuristics, however, there has been a lack of studies analyzing the optimal number of adapter parameters needed for downstream applications. In this pap

Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu, Gulsen Taskin

Carefully curated and annotated datasets are the foundation of machine learning, with particularly data-hungry deep neural networks forming the core of what is often called Artificial Intelligence (AI). Due to the massive success of deep learning applied to Earth Observation (EO) problems, the focus of the community has been largely on the development of eve

Junding Chen, Aleix Gimenez-Grau, Xinan Zhou

We consider correlation functions in 6d $(2,0)$ theories of two $\frac{1}{2}$-BPS operators inserted away from a $\frac{1}{2}$-BPS surface defect. In the large central charge limit the leading connected contribution corresponds to sums of tree-level Witten diagram in AdS$_7\times$S$^4$ in the presence of an AdS$_3$ defect. We show that these correlators can

Thomas D. Cohen, Hyunwoo Oh

We present an efficient approach for preparing ground states in the context of strongly interacting local quantum field theories on quantum computers. The approach produces the vacuum state in a time proportional to the square-root of the volume, which is a square-root improvement in speed compared to traditional approaches. The approach exploits a novel met

Jun Dai, Qingmin Zhang, Ye Qiu, Chuan Li

In this paper, we present the imaging and spectroscopic observations of the simultaneous horizontal and vertical large-amplitude oscillation of a quiescent filament triggered by an EUV wave on 2022 October 02. Particularly, the filament oscillation involved winking phenomenon in Ha images and horizontal motions in EUV images. Originally, a filament and its o

Nissim Fraija, Edilberto Aguilar-Ruiz, Antonio Galván, Jose Antonio de Diego Onsurbe

Since its mission, Fermi Collaboration reported the highest flux observed during July - September 2012 for the BL Lac Markarian 421 (Mrk 421). The integrated flux was eight times greater than the average flux reported in the second Fermi catalog. During April 2013, Mrk 421 exhibited the highest TeV $\gamma$-ray and optical fluxes recorded. The Telescope Arra

Yuhang Hu, Zhizhuo Zhang, Hod Lipson

Building on recent advancements in transformer based approaches for domestic robots performing knolling, the art of organizing scattered items into neat arrangements. This paper introduces Knolling bot 2.0. Recognizing the challenges posed by piles of objects or items situated closely together, this upgraded system incorporates a self-supervised graspability

Matthew J. Felicetti, Dianhui Wang

Neural networks for industrial applications generally have additional constraints such as response speed, memory size and power usage. Randomized learners can address some of these issues. However, hardware solutions can provide better resource reduction whilst maintaining the model's performance. Stochastic configuration networks (SCNs) are a prime choice i

Zitong Chen, Chau Pham, Siqi Wang, Michael Doron

Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has not been a systemic attempt to create and evaluate neural n

Zhaocong Li

While traditional methods relies on depth sensors, the current trend leans towards utilizing cost-effective RGB images, despite their absence of depth cues. This paper introduces an interesting approach to detect grasping pose from a single RGB image. To this end, we propose a modular learning network augmented with grasp detection and semantic segmentation,

Feng Wang, Senem Velipasalar, M. Cenk Gursoy

Federated learning (FL) aims at keeping client data local to preserve privacy. Instead of gathering the data itself, the server only collects aggregated gradient updates from clients. Following the popularity of FL, there has been considerable amount of work, revealing the vulnerability of FL approaches by reconstructing the input data from gradient updates.

Ben Pineau, Mitchell A. Taylor

We present a novel method for establishing large data local well-posedness in low regularity Sobolev spaces for general quasilinear Schr\"odinger equations with non-degenerate and nontrapping metrics. Our result represents a definitive improvement over the landmark results of Kenig, Ponce, Rolvung and Vega, as it weakens the regularity and decay assumptions

Titing Cui, Su Jia, Thomas Lavastida

Dynamic pricing models often posit that a $\textbf{stream}$ of customer interactions occur sequentially, where customers' valuations are drawn independently. However, this model is not entirely reflective of the real world, as it overlooks a critical aspect, the law of diminishing marginal utility, which states that a customer's marginal utility from each ad

Faezeh Khodabandehlou, Christian Maes, Karel Netočný

We study the solution $V$ of the Poisson equation $LV + f=0$ where $L$ is the backward generator of an irreducible (finite) Markov jump process and $f$ is a given centered state function. Bounds on $V$ are obtained using a graphical representation derived from the Matrix Forest Theorem and using a relation with mean first-passage times. Applications include

Yang Zhao, Jiaxi Yang, Yiling Tao, Lixu Wang

The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems. However, developing federated unlearning methods presents challenges, particularly in balancing three often conflicting objectives: privacy, accuracy, and efficiency.

J. Racker

We study washout processes in post-sphaleron baryogenesis, a mechanism where the matter-antimatter asymmetry is generated in the decay of exotic particles after the electroweak phase transition. In particular we focus, in a quite model independent way, on those scattering processes that have an amplitude proportional to the CP asymmetry. We find that when th

Chao Xu, Xinyan Zhang, Howard H. Yang, Xijun Wang

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) ca

Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha

Recent advances have substantially improved the accuracy, memory cost, and training speed of differentially private (DP) deep learning, especially on large vision and language models with millions to billions of parameters. In this work, we thoroughly study the per-sample gradient clipping style, a key component in DP optimization. We show that different cli

Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd

We consider multilevel low rank (MLR) matrices, defined as a row and column permutation of a sum of matrices, each one a block diagonal refinement of the previous one, with all blocks low rank given in factored form. MLR matrices extend low rank matrices but share many of their properties, such as the total storage required and complexity of matrix-vector mu

Jia-Xin Lin, Jia-Ting Li, Wei-Hong Liang, Hua-Xing Chen

We perform a theoretical study of the $J/\psi \to \omega (\phi) K^* \bar{K} + c.c. \to \omega (\phi) K^0 \pi^+ K^-$ reactions with the assumption that the $f_1(1285)$ is dynamically generated from a single channel $K^* \bar{K} + c.c$ interaction in the chiral unitary approach. Two peaks in the $K^0 \pi^+ K^-$ invariant mass distribution are observed, one cle

Zihao Zhang, Zonghai Yao, Huixue Zhou, Feiyun ouyang

Large language models have shown success as a tutor in education in various fields. Educating patients about their clinical visits plays a pivotal role in patients' adherence to their treatment plans post-discharge. This paper presents EHRTutor, an innovative multi-component framework leveraging the Large Language Model (LLM) for patient education through co

Shashika R. Muramudalige, Benjamin W. K. Hung, Rosanne Libretti, Jytte Klausen

Law-enforcement investigations aimed at preventing attacks by violent extremists have become increasingly important for public safety. The problem is exacerbated by the massive data volumes that need to be scanned to identify complex behaviors of extremists and groups. Automated tools are required to extract information to respond queries from analysts, cont

Xiangli Yang, Xinglin Pan, Irwin King, Zenglin Xu

Generalized category discovery (GCD) is a recently proposed open-world task. Given a set of images consisting of labeled and unlabeled instances, the goal of GCD is to automatically cluster the unlabeled samples using information transferred from the labeled dataset. The unlabeled dataset comprises both known and novel classes. The main challenge is that unl

Gabriel Gonzalez Contreras

In this paper the factorization method introduced by Rosu \& Cornejo-P\'erez for second order non linear differential equations is generalized by adding a parameter in order to obtain the general solutions for the mixed quadratic and linear Li\'enard type equation. The new parametric factorization is used to obtain complete analytic solutions for nonlinear s

Xin Liu, Muhammad Khalifa, Lu Wang

A model is considered well-calibrated when its probability estimate aligns with the actual likelihood of the output being correct. Calibrating language models (LMs) is crucial, as it plays a vital role in detecting and mitigating hallucinations of LMs as well as building more trustworthy models. However, standard calibration techniques may not be suited for

Lin Dai, Chul Kim, Adam K. Leibovich

In this paper, we investigate the heavy quark (HQ) mass effects on the transverse momentum dependent fragmentation function (TMDFF). We first calculate the one-loop TMDFF initiated by a heavy quark. We then investigate the HQ TMDFF in the limit where the transverse momentum, $q_\perp$ is small compared to the heavy quark mass, $q_\perp \ll m$ and also in the

Songlin Xu, Xinyu Zhang

Student simulation presents a transformative approach to enhance learning outcomes, advance educational research, and ultimately shape the future of effective pedagogy. We explore the feasibility of using large language models (LLMs), a remarkable achievement in AI, to simulate student learning behaviors. Unlike conventional machine learning based prediction

Zihao Liu

When $a_n$ is the $n$'th coefficient of some holomorphic cusp form, we prove a variety of Omega results for the twisted sum $\sum_{n\le x}a_ne^{2\pi in\alpha}$ and discuss their applications to the Ramanujan $\tau$-function and sums of $a_n$ over arithmetic progressions.

R. A. Konoplya, A. Zhidenko

Recently it was shown that small deformations of the asymptotically flat black-hole geometry in some region near its horizon which do not alter considerably the fundamental mode, nevertheless, strongly affect the first several (and higher) overtones which deviate at an increasing rate from their nondeformed limits. Here we show that, despite the quasinormal

Quang-Hung Luu, Huai Liu, Tsong Yueh Chen

While ChatGPT is a well-known artificial intelligence chatbot being used to answer human's questions, one may want to discover its potential in advancing software testing. We examine the capability of ChatGPT in advancing the intelligence of software testing through a case study on metamorphic testing (MT), a state-of-the-art software testing technique. We a

Manuel R. Ciosici, Alex Hedges, Yash Kankanampati, Justin Martin

We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empirical work claimed that large language models (LLMs) are a poor fit (Wang et al., 2022) compared to reinforceme

Daniel Carter, Zach Hunter, Kevin O'Bryant

We prove that the diameter of a Sidon set (also known as a Babcock sequence, Golomb ruler, or $B_2$ set) with $k$ elements is at least $k^2-b k^{3/2}-O(k)$ where $b\le 1.96365$, a comparatively large improvement on past results. Equivalently, a Sidon set with diameter $n$ has at most $n^{1/2}+0.98183n^{1/4}+O(1)$ elements. The proof is conceptually simple bu

Adam Johnston, Ulises Felix-Rendon, Yu-En Wong, Songtao Chen

Atomic defects in solid-state materials are promising candidates for quantum interconnect and networking applications. Recently, a series of atomic defects have been identified in the silicon platform, where scalable device integration can be enabled by mature silicon photonics and electronics technologies. In particular, T centers hold great promise due to

Óscar Domínguez, Mario Milman

We introduce sparse versions of function spaces that are relevant to characterize the solutions of Euler equations without concentration. The standard Sobolev space $H^{-1}$ is given a sparse structure that allows to measure the degree of compactness of embeddings into $H^{-1}$ and provides new quantitative general criteria for $H^{-1}$-stability. Indices of

Brendan Harding, Yvonne M. Stokes, Rahil N. Valani

Inertial focusing in curved microfluidic ducts exploits the interaction of drag force from the Dean flow with the inertial lift force to separate particles or cells laterally across the cross-section width according to their size. Experimental work has identified that using a trapezoidal cross-section, as opposed to a rectangular one, can enhance the sized b

M. Sallah, C. A. Margeanu, N. Elbassiony, M. Mitwalli

The radiobiological effect on the human health of CANDU spent fuel is assessed using Monte Carlo shielding estimates. The examination of spent fuel occurs after it has been discharged from the reactor. A specific cooling interval is considered, with the radiation dose rates that characterize the used fuel being of interest. Two kinds of fuel were studied in

Xiong Xiong, Ying Wang, Tianyuan Song, Jinguo Huang

As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However,

Joshua Tan, Tara Merk, Sarah Hubbard, Eliza R. Oak

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the