October 2023 arXiv papers — page 38

Ramesh Johari, Orrie B. Page, Gabriel Y. Weintraub

In this paper, we examine the biases that arise when firms run A/B tests on continuous parameters to estimate global treatment effects on performance metrics of interest; we particularly focus on price experiments to measure the price impact on quantity demanded, and on profit. In canonical A/B experimental estimators, biases emerge due to interference betwe

Long-Huei Chen, Mohana Prasad Sathya Moorthy, Pratyaksh Sharma

The protein-protein interaction (PPI) network provides an overview of the complex biological reactions vital to an organism's metabolism and survival. Even though in the past PPI network were compared across organisms in detail, there has not been large-scale research on how individual PPI networks reflect on the species relationships. In this study we aim t

Yingwen Wu, Tao Li, Xinwen Cheng, Jie Yang

Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominantly investigated the disparity between in-distribution (ID) and OOD data through forward information analysis, the discrepancy in parameter gradients during the backward process of

Liwei Lin, Gus Xia, Junyan Jiang, Yixiao Zhang

Recent years have witnessed a rapid growth of large-scale language models in the domain of music audio. Such models enable end-to-end generation of higher-quality music, and some allow conditioned generation using text descriptions. However, the control power of text controls on music is intrinsically limited, as they can only describe music indirectly throu

F. Priante, N. Oinonen, Y. Tian, D. Guan

The interaction of water with surfaces is crucially important in a wide range of natural and technological settings. In particular, at low temperatures, unveiling the atomistic structure of adsorbed water clusters would provide valuable data for understanding the ice nucleation process. Using high-resolution Atomic Force Microscopy (AFM) and Scanning Tunnell

Jicheng Liu, Chenao He, Yin-Hui Peng, Zhihao Zhen

Two entangled qubits emerge as an essential resource for quantum control, which are normally quantum confined with atomic precision. It seems inhibitive that in the macroscopic scope collective qubit pairs manifest long coherence and quantum entanglement, especially at high temperature. Here, we report this exotic ensemble effect in solid-state sintering lea

Dexter Neo, Stefan Winkler, Tsuhan Chen

We present a new loss function that addresses the out-of-distribution (OOD) calibration problem. While many objective functions have been proposed to effectively calibrate models in-distribution, our findings show that they do not always fare well OOD. Based on the Principle of Maximum Entropy, we incorporate helpful statistical constraints observed during t

Daniel S. Roll, Zeyneb Kurt, Wai Lok Woo

The Kessler syndrome refers to the escalating space debris from frequent space activities, threatening future space exploration. Addressing this issue is vital. Several AI models, including Convolutional Neural Networks, Kernel Principal Component Analysis, and Model-Agnostic Meta- Learning have been assessed with various data types. Earlier studies highligh

Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou

Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM's in-context learning ability, or do not yield wal

Zhenwei Luo

Learning single image depth estimation model from monocular video sequence is a very challenging problem. In this paper, we propose a novel training loss which enables us to include more images for supervision during the training process. We propose a simple yet effective model to account the frame to frame pixel motion. We also design a novel network archit

W. Zhu, H. D. Tuan, E. Dutkiewicz, H. V. Poor

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relyi

Jiabin Tang, Lianghao Xia, Jie Hu, Chao Huang

Spatio-temporal prediction is crucial in numerous real-world applications, including traffic forecasting and crime prediction, which aim to improve public transportation and safety management. Many state-of-the-art models demonstrate the strong capability of spatio-temporal graph neural networks (STGNN) to capture complex spatio-temporal correlations. Howeve

Haipeng Xiong, Angela Yao

Regression tasks in computer vision, such as age estimation or counting, are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range. By selecting the class qu

Longlin Yu, Tianyu Xie, Yu Zhu, Tong Yang

Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner. However, the single-layer architecture commonly used in current SIVI methods can be insufficient when the target posterior has complicated structures. In this paper, we propo

Rupsa Bhattacharjee, Zehra Akkaya, Johanna Luitjens, Pan Su

In the current study, our purpose is to evaluate the feasibility of applying deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in healthy controls scanned at 0.55T and compared with 3.0T. The current study assesses the performance of standard in-practice bone, and cartilage segmentation algorithms at 0.55T, both qualitatively and qu

David O'Connell

In this paper we introduce and study the basic properties of de Rham cohomology for a certain class of non-Hausdorff manifolds. After a careful discussion of non-Hausdorff differential forms, we provide a description of de Rham cohomology via Mayer-Vietoris sequences. We then use these sequences to prove both de Rham's Theorem and the Gauss-Bonnet theorem fo

Hugo Ferretti, Y. Batuhan Yilmaz, Kent Bonsma-Fisher, Aaron Z. Goldberg

It is often thought that the super-sensitivity of a quantum state to an observable comes at the cost of a decreased sensitivity to other non-commuting observables. For example, a squeezed state squeezed in position quadrature is super-sensitive to position displacements, but very insensitive to momentum displacements. This misconception was cleared with the

Jiabin Tang, Lianghao Xia, Chao Huang

Spatio-temporal graph neural networks (STGNNs) have gained popularity as a powerful tool for effectively modeling spatio-temporal dependencies in diverse real-world urban applications, including intelligent transportation and public safety. However, the black-box nature of STGNNs limits their interpretability, hindering their application in scenarios related

Naoto Tsuji, Ippei Danshita, Shunji Tsuchiya

Collective dynamics of many particle systems is tightly linked to their underlying symmetry and phase transitions. Higgs and Nambu-Goldstone modes are, respectively, collective amplitude and phase modes of the order parameter that are widely observed in various physical systems at different energy scales, ranging from magnets, superfluids, superconductors to

Zicheng Zhang, Yingjie Zhou, Wei Sun, Xiongkuo Min

Point clouds are widely used in 3D content representation and have various applications in multimedia. However, compression and simplification processes inevitably result in the loss of quality-aware information under storage and bandwidth constraints. Therefore, there is an increasing need for effective methods to quantify the degree of distortion in point

Shengpu Tang, Jenna Wiens

In applying reinforcement learning (RL) to high-stakes domains, quantitative and qualitative evaluation using observational data can help practitioners understand the generalization performance of new policies. However, this type of off-policy evaluation (OPE) is inherently limited since offline data may not reflect the distribution shifts resulting from the

Xiaoyu Yan, Pengfei Zhai, Chen Yang, Shiwei Zhao

Single-event burnout and single-event leakage current (SELC) in SiC power devices induced by heavy ions severely limit their space application, and the underlying mechanism is still unclear. One fundamental problem is lack of high-resolution characterization of radiation damage in the irradiated SiC power devices, which is a crucial indicator of the related

Ding Zou, Wei Lu, Zhibo Zhu, Xingyu Lu

Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. The primary objective of autoscaling is to regulate resource utilization at a desired level, effectively bal

Xiyuan Wang, Zhining Wei, Pan Yan, Shaoyun Yi

We establish several refined strong multiplicity one results for paramodular cusp forms by using automorphic and Galois-theoretic methods. We also give an application to distinguishing eigenforms by the twisted central values of the spinor $L$-functions, which is based on a result in Radziwi{\l}{\l} and Yang 2023 (arXiv:2304.09171).

Sania Gul, Muhammad Salman Khan, Muhammad Fazeel

Speech enhancement concerns the processes required to remove unwanted background sounds from the target speech to improve its quality and intelligibility. In this paper, a novel approach for single-channel speech enhancement is presented, using colored spectrograms. We propose the use of a deep neural network (DNN) architecture adapted from the pix2pix gener

Masato Shirasaki, Shiro Ikeda

We propose a new generative model of projected cosmic mass density maps inferred from weak gravitational lensing observations of distant galaxies (weak lensing mass maps). We construct the model based on a neural style transfer so that it can transform Gaussian weak lensing mass maps into deeply non-Gaussian counterparts as predicted in ray-tracing lensing s

Justin T. Chiu, Wenting Zhao, Derek Chen, Saujas Vaduguru

Large language models (LLMs) excel at processing and generating both text and code. However, LLMs have had limited applicability in grounded task-oriented dialogue as they are difficult to steer toward task objectives and fail to handle novel grounding. We present a modular and interpretable grounded dialogue system that addresses these shortcomings by compo

Hongyu Zang, Xin Li, Leiji Zhang, Yang Liu

While bisimulation-based approaches hold promise for learning robust state representations for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up to par. In some instances, their performance has even significantly underperformed alternative methods. We aim to understand why bisimulation methods succeed in online settings, b

Anand Sharma, A. G. Ramakrishnan

A classifier is developed that defines a joint distribution of global character features, number of sub-units and local sub-unit features to model Hindi online handwritten characters. The classifier uses latent variables to model the structure of sub-units. The classifier uses histograms of points, orientations, and dynamics of orientations (HPOD) features t

Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss

Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative methods, like conjugate gradients (CG). However, as datasets increase in magnitude, the kernel matrices become increasingly ill-conditioned a

Takehide Soh, Tomoya Tanjo, Yoshio Okamoto, Takehiro Ito

This paper collects all descriptions of solvers and ISR instances submitted to CoRe Challenge 2023.

Rafael Pires de Lima, Behzad Vahedi, Morteza Karimzadeh

Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models still need to undergo scrutiny as higher metrics performance not always translate to adequate outputs. Sea ice ty

Yuanyuan Qiao

We study the global existence of classical solutions for two-dimensional incompressible MHD system with only magnetic diffusion. By using the time-weighted lower-order energy and uniformly bounded higher-order energy estimates, we prove the global existence result under the assumption that the initial magnetic field is close enough to a constant magnetic vor

Yuxin Zuo, Bei Li, Chuanhao Lv, Tong Zheng

This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete. Instead, we attribute this phenomenon to insufficient cross-modal interaction, rather than image information redundancy. A novel approach is p

Shuai Zheng, Zhizhe Liu, Zhenfeng Zhu, Xingxing Zhang

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of

Junhong Gou, Bo Zhang, Li Niu, Jianfu Zhang

The virtual try-on task refers to fitting the clothes from one image onto another portrait image. In this paper, we focus on virtual accessory try-on, which fits accessory (e.g., glasses, ties) onto a face or portrait image. Unlike clothing try-on, which relies on human silhouette as guidance, accessory try-on warps the accessory into an appropriate location

Hongcheng Guo, Boyang Wang, Jiaqi Bai, Jiaheng Liu

Multimodal manga analysis focuses on enhancing manga understanding with visual and textual features, which has attracted considerable attention from both natural language processing and computer vision communities. Currently, most comics are hand-drawn and prone to problems such as missing pages, text contamination, and aging, resulting in missing comic text

Mohana Prasad Sathya Moorthy, Manoj Kumar Sure, Krishna M. Sivalingam

This paper deals with congestion control in a software defined network (SDN) setting. Presently, explicit router schemes, such as Explicit Congestion Notification (ECN), work in conjunction with the TCP protocol to handle congestion in a distributed manner. With the emergence of SDN and centralized control, it is possible to leverage the global view of the n

Rachana Sathish, Rahul Venkataramani, K S Shriram, Prasad Sudhakar

Promptable foundation models, particularly Segment Anything Model (SAM), have emerged as a promising alternative to the traditional task-specific supervised learning for image segmentation. However, many evaluation studies have found that their performance on medical imaging modalities to be underwhelming compared to conventional deep learning methods. In th

Henry Grasshorn Gebhardt, Olivier Doré

Tuning into the bass notes of the large-scale structure requires careful attention to geometrical effects arising from wide angles. The spherical Fourier-Bessel (SFB) basis provides a harmonic-space coordinate system that fully accounts for all wide-angle effects. To demonstrate the feasibility of the SFB power spectrum, in this paper we validate our SFB pip

Loc Gia Nguyen, Kohei Watabe

A Network Intrusion Detection System (NIDS) is a tool that identifies potential threats to a network. Recently, different flow-based NIDS designs utilizing Machine Learning (ML) algorithms have been proposed as solutions to detect intrusions efficiently. However, conventional ML-based classifiers have not seen widespread adoption in the real world due to the

Morteza Karimzadeh, Rafael Pires de Lima

Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming more common for mapping purposes. One such case is sea ice, which is highly dynamic and rapidly changes as a result of the combined effect of wind, temperature, and ocean currents. Therefore, frequent mapping of sea ice is necessary to ensure safe marine navigation. However, there is

Wei-Hao Han, Ye Xing, Ji Xu

In this paper, we study the production of charmonium pentaquark $c \bar c q q q$ from bottom baryon and B-meson decays under the flavor SU(3) symmetry. Decay amplitudes for various processes are parametrized in terms of the SU(3) irreducible nonperturbative amplitudes. A number of relations between decay widths have been deduced. Moreover, the strong decays

Yang Sui, Xin He, Yang Bai

In this paper, we design a regularization-free algorithm for high-dimensional support vector machines (SVMs) by integrating over-parameterization with Nesterov's smoothing method, and provide theoretical guarantees for the induced implicit regularization phenomenon. In particular, we construct an over-parameterized hinge loss function and estimate the true p

Alejandro F. Ramírez, Santiago Saglietti, Lingyun Shao

We introduce and study a non-oriented first passage percolation model having a property of statistical invariance by time reversal. This model is defined in a graph having directed edges and the passage times associated with each set of outgoing edges from a given vertex are distributed according to a generalized Bernoulli-Exponential law and i.i.d. among ve

Rafael Pires de Lima, Behzad Vahedi, Nick Hughes, Andrew P. Barrett

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the amb

Go Kamoda, Benjamin Heinzerling, Keisuke Sakaguchi, Kentaro Inui

Factual probing is a method that uses prompts to test if a language model "knows" certain world knowledge facts. A problem in factual probing is that small changes to the prompt can lead to large changes in model output. Previous work aimed to alleviate this problem by optimizing prompts via text mining or fine-tuning. However, such approaches are relation-s

Reshmi Ghosh, Harjeet Singh Kajal, Sharanya Kamath, Dhuri Shrivastava

Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks. However, current works on topic segmentation often focus on segmentation of structured texts. In this paper, we comprehensively analyze the generalization capabili

Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang

Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions. LLMs' inability to attribute their claims to external knowledge and their tendency to hallucinate makes it difficult to rely on their responses. Humans, too, are prone to factual errors in their wri

Ryosuke Nakahama

The non-commutative harmonic oscillator (NCHO) was introduced as a specific Hamiltonian operator on $L^2(\mathbb{R})\otimes\mathbb{C}^2$ by Parmeggiani and Wakayama. Then it was proved by Ochiai and Wakayama that the eigenvalue problem for NCHO is reduced to a Heun differential equation. In this article, we consider some generalization of NCHO for $L^2(\math

Chenghua Duan, Chun Liu, Xingye Yue

In this paper, we propose a numerical method to uniformly handle the random genetic drift model for pure drift with or without natural selection and mutation. For pure drift and natural selection case, the Dirac $\delta$ singularity will develop at two boundary ends and the mass lumped at the two ends stands for the fixation probability. For the one-way muta

Yang Yi Poh, Ethan Grooby, Kenneth Tan, Lindsay Zhou

Auscultation for neonates is a simple and non-invasive method of providing diagnosis for cardiovascular and respiratory disease. Such diagnosis often requires high-quality heart and lung sounds to be captured during auscultation. However, in most cases, obtaining such high-quality sounds is non-trivial due to the chest sounds containing a mixture of heart, l

Kartik Nagpal, Negar Mehr

The optimal robot assembly planning problem is challenging due to the necessity of finding the optimal solution amongst an exponentially vast number of possible plans, all while satisfying a selection of constraints. Traditionally, robotic assembly planning problems have been solved using heuristics, but these methods are specific to a given objective struct

Rahul Mazumder, Haoyue Wang

The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART. In this paper, we investigate the convergence rate of CART under a regression setting. First, we establish an upper bound on the prediction error of CART under a sufficient impurity decrease (

Qi Xu, Chao Yu, Wei Chen, Jianglin Zhao

Free-running InGaAs/InP single-photon detectors (SPDs) based on negative-feedback avalanche diodes (NFADs) are the key components for applications requiring asynchronous single-photon detection in the near-infrared region. From the perspective of practical applications, the features of SPDs in terms of high photon detection efficiency (PDE), low noise, large

Wenxiang Dong, H. Vicky Zhao

During a public health crisis like COVID-19, individuals' adoption of protective behaviors, such as self-isolation and wearing masks, can significantly impact the spread of the disease. In the meanwhile, the spread of the disease can also influence individuals' behavioral choices. Moreover, when facing uncertain losses, individuals' decisions tend to be irra

Liang Hong

The number of active shooter incidents in the US has been increasing alarmingly. It is imperative for the government as well as the public to understand these events. Though both analytic and agent-based models have been proposed for studying active shooter incidents, there are only a few analytic models in the literature, and none incorporate civilian resis

Shiyang Liu, Yang Chen

A novel method for reducing the impact of non-ideal pseudo-random binary sequence (PRBS) on microwave photonic random demodulators (RDs) in a photonics-assisted compressed sensing (CS) system is proposed. Different from the commonly used method that switches the bias point of the optical modulator in the RD between two quadrature transmission points to mix t

Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li

In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data. Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data. Evaluating LLMs' competence in understanding spatial-te

Chau Pham, Truong Vu, Khoi Nguyen

This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. Ho

S. I. Bozhevolnyi, I. M. Suslov

Localization of electrons in 1D disordered systems is usually described in the random phase approximation, when distributions of phases \varphi and \theta, entering the transfer matrix, are considered as uniform. In the general case, the random phase approximation is violated, and the evolution equations are written in terms of the Landauer resistance \rho a

Mike Kotschenreuther, Xing Liu, Swadesh M. Mahajan, David R. Hatch

A fundamental dynamical constraint -- that fluctuation induced charge-weighted particle flux must vanish -- can prevent instabilities from accessing the free energy in the strong gradients characteristic of Transport Barriers (TBs). Density gradients, when larger than a certain threshold, lead to a violation of the constraint and emerge as a stabilizing forc

Chung-Hsien Wang, Nai-Yu Tsai, Yi-Cheng Wang, H. H. Jen

In the study of optical properties of large atomic system, a weak laser driving is often assumed to simplify the system dynamics by linearly coupled equations. Here, we investigate the light scattering properties of atomic ensembles beyond weak-field excitation through the cumulant expansion method. By progressively incorporating higher-order correlations in

Grigorii Monakov

We consider a nonstationary sequence of independent random isometries of a compact metrizable space. Assuming that there are no proper closed subsets with deterministic image we establish a weak-* convergence to the unique invariant under isometries measure, Ergodic Theorem and Large Deviation Type Estimate. We also show that all the results can be carried o

Yike Zhang, Wenting Wang, Jiaxin Han, Xiaohu Yang

The two point correlation function (2PCF) is a powerful statistical tool to measure galaxy clustering. Although 2PCF has also been used to study the clustering of stars on parsec and sub-parsec scales, its physical implication is not clear on such non-linear scales. In this study, we use the Illustris-TNG50 simulation to study the connection between the 2PCF

Francis Gracy Arockiaraj, Agnes Pristy Ignatius Xavier, Shivasubramanian Gopinath, Aravind Simon John Francis Rajeswary

Fresnel incoherent correlation holography (FINCH) is a well-established digital holography technique for 3D imaging of objects illuminated by spatially incoherent light. FINCH has a higher lateral resolution of 1.5 times that of direct imaging systems with the same numerical aperture. However, the other imaging characteristics of FINCH such as axial resoluti

Shan Lu, Zhicheng Dong, Donghong Cai, Fang Fang

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divi

Philippe Guyenne

Nonlinear deformations of a two-dimensional gas bubble are investigated in the framework of a Hamiltonian formulation involving surface variables alone. The Dirichlet--Neumann operator is introduced to accomplish this dimensional reduction and is expressed via a Taylor series expansion. A recursion formula is derived to determine explicitly each term in this

Xinfa Zhu, Yuke Li, Yi Lei, Ning Jiang

This paper aims to build a multi-speaker expressive TTS system, synthesizing a target speaker's speech with multiple styles and emotions. To this end, we propose a novel contrastive learning-based TTS approach to transfer style and emotion across speakers. Specifically, contrastive learning from different levels, i.e. utterance and category level, is leverag

Donald Loveland, Rajmonda Caceres

Graph Neural Network (GNN) research has produced strategies to modify a graph's edges using gradients from a trained GNN, with the goal of network design. However, the factors which govern gradient-based editing are understudied, obscuring why edges are chosen and if edits are grounded in an edge's importance. Thus, we begin by analyzing the gradient computa

Geoffrey Exoo

The known lower bound for the the classical Ramsey number $R(5,6)$ is improved from $58$ to $59$. The method used to construct the graph is a simple variant of computational methods that have been previously used to construct Ramsey graphs. The new method uses the concurrent programming features of the {\em Go} programming language.

You Lu, Rong Luo, Zhengke Miao, Cun-Quan Zhang

Bermond, Jackson and Jaeger [{\em J. Combin. Theory Ser. B} 35 (1983): 297-308] proved that every bridgeless ordinary graph $G$ has a circuit $4$-cover and Fan [{\em J. Combin. Theory Ser. B} 54 (1992): 113-122] showed that $G$ has a circuit $6$-cover which together implies that $G$ has a circuit $k$-cover for every even integer $k\ge 4$. The only left case

Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau

Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. To address these hurdles, we navigate the uncharted waters of Semi-Super

Jingwen Chen, Pedro Gaspar

In this article, we use Morse-theoretic techniques to construct connections between low energy critical submanifolds of the Allen-Cahn energy functional in the 3-sphere via the negative gradient flow.

Zhisen Meng, Zhao Wang

Fullerenes, including \ce{C60} and \ce{C70}, have been detected in various astronomical environments. Understanding how their structures evolve over time is essential for gaining insights into their life cycle and making further observations. To address this, we conducted reactive molecular dynamics simulations to investigate the evolution of fullerenes in t

Sean Patrick O'Neil, Edmond Jonckheere, Sophie Schirmer

Control of quantum systems via time-varying external fields optimized to maximize a fidelity measure at a given time is a mainstay in modern quantum control. However, save for specific systems, current analysis techniques for such quantum controllers provide no analytical robustness guarantees. In this letter we provide analytical bounds on the differential

Yuan He, Yong-Guo Shi

In this paper, we study the generalized Dedekind-Rademacher sums considered by Hall, Wilson and Zagier. We establish a formula for the products of two Bernoulli functions. The proof relies on Parseval's formula, Hurwitz's formula, and Lerch's functional equation. The result leads to reciprocity formulas for some generalizations of the classical Dedekind sums

Fabiano F. Santos, Moisés Bravo-Gaete, Manoel M. Ferreira, Rodolfo Casana

This work examines the thermodynamics and hydrodynamics behaviors of a five-dimensional black hole under the influence of an external magnetic field. The solution is the gravity dual to the Anti-de Sitter/Boundary Conformal Field Theory correspondence, enabling the study of properties within an anisotropic fluid framework. Utilizing holographic renormalizati

Tianyi Li, Mingfeng Shang, Shian Wang, Raphael Stern

With the advent of vehicles equipped with advanced driver-assistance systems, such as adaptive cruise control (ACC) and other automated driving features, the potential for cyberattacks on these automated vehicles (AVs) has emerged. While overt attacks that force vehicles to collide may be easily identified, more insidious attacks, which only slightly alter d

Changxi Liu, Alen Sabu, Akanksha Chaudhari, Qingxuan Kang

High-performance, multi-core processors are the key to accelerating workloads in several application domains. To continue to scale performance at the limit of Moore's Law and Dennard scaling, software and hardware designers have turned to dynamic solutions that adapt to the needs of applications in a transparent, automatic way. For example, modern hardware i

Henry Ling, Philip Richard, Saeed Rahmanian Koshkaki, Michael Kolodrubetz

We study a periodically driven one dimensional Kitaev model in the presence of disorder. In the clean limit our model exhibits four topological phases corresponding to the existence or non-existence of edge modes at zero and pi quasienergy. When disorder is added, the system parameters get renormalized and the system may exhibit a topological phase transitio

Yuqing Wang, Zhenghao Xu, Tuo Zhao, Molei Tao

Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wang et al., 2022), and catapult (Lewkowycz et al., 2020). These phenomena cannot be well explained by classical optimization theory. Though significant theoretical progress has been

Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan

Transformers excel at in-context learning (ICL) -- learning from demonstrations without parameter updates -- but how they do so remains a mystery. Recent work suggests that Transformers may internally run Gradient Descent (GD), a first-order optimization method, to perform ICL. In this paper, we instead demonstrate that Transformers learn to approximate seco

Lauren M. Foster, James E. Taylor, John P. Blakeslee

Dwarf galaxies are important tracers of small-scale cosmological structure, yet much of our knowledge about these systems comes from the limited sample of dwarf galaxies within the Local Group. To make a comprehensive inventory of dwarf populations in the local Universe, we require effective methods for deriving distance estimates for large numbers of faint,

Bingcheng Qing, Long B. Nguyen, Xinyu Liu, Hengjiang Ren

Quantum-limited Josephson parametric amplifiers play a pivotal role in advancing the field of circuit quantum electrodynamics by enabling the fast and high-fidelity measurement of weak microwave signals. Therefore, it is necessary to develop robust parametric amplifiers with low noise, broad bandwidth, and reduced design complexity for microwave detection. H

Luis G. Coelho, Tertuliano Franco, Lael V. Lima, João P. C. de Paula

Intransitive dice $D^{(1)}, \ldots, D^{(\ell)}$ are dice such that $D^{(1)}$ has advantage when played against $D^{(2)}$, dice $D^{(2)}$ has advantage when played against $D^{(3)}$ and so on, up to $D^{(\ell)}$, which has advantage over $D^{(1)}$. In this twofold work, we first present (deterministic) results on the existence of general intransitive dice. Se

Zhen Cao, F. Aharonian, Q. An, Axikegu

For decades, supernova remnants (SNRs) have been considered the prime sources of Galactic Cosmic rays (CRs). But whether SNRs can accelerate CR protons to PeV energies and thus dominate CR flux up to the knee is currently under intensive theoretical and phenomenological debate. The direct test of the ability of SNRs to operate as CR PeVatrons can be provided

A. A. Araújo Filho, Kimet Jusufi, B. Cuadros-Melgar, Genly Leon

This study uses a nonsingular Yukawa--modified potential to obtain a static and spherically symmetric black hole solution with a cosmological constant. Such Yukawa--like corrections are encoded in two parameters, $\alpha$ and $\lambda$, that modify Newton's law of gravity in large distances, and a deformation parameter $\ell_0$, which plays an essential role

Safwen Naimi, Olfa Koubaa, Wassim Bouachir, Guillaume-Alexandre Bilodeau

Lichens are symbiotic organisms composed of fungi, algae, and/or cyanobacteria that thrive in a variety of environments. They play important roles in carbon and nitrogen cycling, and contribute directly and indirectly to biodiversity. Ecologists typically monitor lichens by using them as indicators to assess air quality and habitat conditions. In particular,

Feng Wu, T. Frederico, R. Higa, U. van Kolck

Near two-body unitarity, the three-boson system is characterized by an approximate discrete scale invariance manifest in a geometric tower of bound states (the Efimov effect). In the absence of a strong four-body force, the four-boson system has two states associated with each Efimov state, one very nearly unstable, the other several times deeper. We study c

Safwen Naimi, Wassim Bouachir, Guillaume-Alexandre Bilodeau

In this paper, we propose a novel deep learning method based on a new Hybrid ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD) from gait data. We adopt a two-step approach by dividing the problem into two sub-problems. Our Hybrid ConvNet-Transformer model first distinguishes healthy versus parkinsonian patients. If the patient is

Minh N. Dao, Mareike Dressler, Hongzhi Liao, Vera Roshchina

We prove that the Douglas--Rachford method applied to two closed convex cones in the Euclidean plane converges in finitely many steps if and only if the set of fixed points of the Douglas--Rachford operator is nontrivial. We analyze this special case using circle dynamics. We also construct explicit examples for a broad family of projection methods for which

Alaric Sanders, Claudio Castelnovo

Recent excitement in the quantum spin ice community has come from the experimental discovery of pseudospin-$1/2$ breathing pyrochlores, including Ba$_3$Yb$_2$Zn$_5$O$_{11}$, in which inversion symmetry is broken by the `up' and `down' tetrahedra taking different physical sizes. We show here that the often-neglected $J_{z\pm}$ coupling between Kramers ions, i

Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire

We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach are: (i) a dynamic hypernetwork, which learns a smooth mapping from text token embeddings to the space of NeRFs; (ii) NeRF distillation training, which distills scenes encoded in i

Miao Lu, Beining Wu, Xiaodong Yang, Difan Zou

In this work, we theoretically investigate the generalization properties of neural networks (NN) trained by stochastic gradient descent (SGD) algorithm with large learning rates. Under such a training regime, our finding is that, the oscillation of the NN weights caused by the large learning rate SGD training turns out to be beneficial to the generalization

W. Luo, A. Akbarzadeh, Y. Nahas, S. Prokhorenko

Quantum fluctuations (QFs) caused by zero-point phonon vibrations (ZPPVs) are known to prevent the occurrence of polar phases in bulk incipient ferroelectrics down to 0K1-3. On the other hand, little is known about the effects of QFs on the recently discovered topological patterns in ferroelectric nanostructures4-9. Here, by using an atomistic effective Hami

Yonghyeon Lee

Motion Manifold Primitives (MMP), a manifold-based approach for encoding basic motion skills, can produce diverse trajectories, enabling the system to adapt to unseen constraints. Nonetheless, we argue that current MMP models lack crucial functionalities of movement primitives, such as temporal and via-points modulation, found in traditional approaches. This

B. Serenellini, M. Bellaveglia, F. Cardelli, A. Gallo

SPARC_LAB is a high-brightness electron photoinjector dedicated to FEL radiation production and research on novel acceleration techniques. It has been in operation at LNF since 2005. It is composed of a newly designed brazeless 1.6-cell S-band RF gun, two 3 meter long travelling wave S-band accelerating structures, and a 1.4 meter C-band structure that acts

Shuyang Cao, Daniel Boyanovsky

We introduce an effective field theory to study \emph{indirect} mixing of two fields induced by their couplings to a common decay channel in a medium. The extension of the method of Lee, Oehme and Yang, the cornerstone of analysis of CP violation in flavored mesons, to include mixing of particles with different masses provides a guide to and benchmark for th

Kelsey Nelson, Javad Mohammadi, Yu Chen, Erik Blasch

Electric vehicles (EVs) are becoming more popular within the United States, making up an increasingly large portion of the US's electricity consumption. Hence, there is much attention has been directed on how to manage EVs within the power sector. A well-investigated strategy for managing the increase in electricity demand from EV charging is aggregation, wh