March 2024 arXiv papers — page 136

Hengyuan Zhang, Zitao Liu, Chenming Shang, Dawei Li

Knowledge tracing (KT) plays a crucial role in predicting students' future performance by analyzing their historical learning processes. Deep neural networks (DNNs) have shown great potential in solving the KT problem. However, there still exist some important challenges when applying deep learning techniques to model the KT process. The first challenge lies

Zubair Qazi, William Shiao, Evangelos E. Papalexakis

As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance

Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design involves transforming the source to a latent vector, which is then rounded to integers and entropy coded. While this approach has been shown to be optimal on a few specific sources

Zongsheng Yue, Jianyi Wang, Chen Change Loy

While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps. Existing acceleration sampling techniques, though seeking to expedite the process, inevitably sacrifice performance to some extent, resu

Jianghao Li, Shizhe Hong, Zhenzhen Niu, Zhidong Bai

In this paper, we investigate hypothesis testing for the linear combination of mean vectors across multiple populations through the method of random integration. We have established the asymptotic distributions of the test statistics under both null and alternative hypotheses. Additionally, we provide a theoretical explanation for the special use of our test

Jiawei Tang, Shuang Wu, Bo Lan, Yahui Dong

The configuration of most robotic systems lies in continuous transformation groups. However, in mobile robot trajectory tracking, many recent works still naively utilize optimization methods for elements in vector space without considering the manifold constraint of the robot configuration. In this letter, we propose a geometric model predictive control (MPC

Anton Dochtermann, Ritika Nair, Jay Schweig, Adam Van Tuyl

Suppose $\Delta$ is a pure simplicial complex on $n$ vertices having dimension $d$ and let $c = n-d-1$ be its codimension in the simplex. Terai and Yoshida proved that if the number of facets of $\Delta$ is at least $\binom{n}{c}-2c+1$, then $\Delta$ is Cohen-Macaulay. We improve this result by showing that these hypotheses imply the stronger condition that

Megan A. Witherow, Crystal Butler, Winston J. Shields, Furkan Ilgin

Customizable 3D avatar-based facial expression stimuli may improve user engagement in behavioral biomarker discovery and therapeutic intervention for autism, Alzheimer's disease, facial palsy, and more. However, there is a lack of customizable avatar-based stimuli with Facial Action Coding System (FACS) action unit (AU) labels. Therefore, this study focuses

Ni Emas, Chris Blake, Rossana Ruggeri, Anna Porredon

The ratio of the average tangential shear signal of different weak lensing source populations around the same lens galaxies, also known as a shear ratio, provides an important test of lensing systematics and a potential source of cosmological information. In this paper we measure shear ratios in three current weak lensing surveys -- KiDS, DES, and HSC -- usi

Wenhui Tan, Bei Liu, Junbo Zhang, Ruihua Song

Modeling generalized robot control policies poses ongoing challenges for language-guided robot manipulation tasks. Existing methods often struggle to efficiently utilize cross-dataset resources or rely on resource-intensive vision-language models, thus limiting their multi-task performance and practical applications. In this study, we propose a novel approac

Dong Shu, Tianle Chen, Mingyu Jin, Chong Zhang

The task of multi-hop link prediction within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, as it requires the model to reason through and understand all intermediate connections before making a prediction. In this paper, we introduce the Knowledge Graph Large Language Model (KG-LLM), a novel framework that leverages l

Cesar A. Ipanaque Zapata

A classic problem in analysis is to solve nonlinear equations of the form \begin{equation*} F(x)=0, \end{equation*} where $F:D^n\to \mathbb{R}^m$ is a continuous map of the closed unit disk $D^n\subset\mathbb{R}^n$ in $\mathbb{R}^m$. A topological technique, which exists in the literature, for the existence of solutions of nonlinear equations is the topologi

Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang

Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive. By formulating the group imbalance pr

Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. This paper introduces the POSNEGDM -- ``Reinforcement

Shuoyao Wang, Jiawei Lin, Fangwei Ye

Adaptive video streaming plays a crucial role in ensuring high-quality video streaming services. Despite extensive research efforts devoted to Adaptive BitRate (ABR) techniques, the current reinforcement learning (RL)-based ABR algorithms may benefit the average Quality of Experience (QoE) but suffers from fluctuating performance in individual video sessions

Shaoru Chen, Lekan Molu, Mahyar Fazlyab

Barrier functions are a general framework for establishing a safety guarantee for a system. However, there is no general method for finding these functions. To address this shortcoming, recent approaches use self-supervised learning techniques to learn these functions using training data that are periodically generated by a verification procedure, leading to

Min-Seok Seo

The late time behavior of the slow-roll parameter in the stringy quintessence model is studied when axion as well as saxion are allowed to move. Even though the potential is independent of the axion at tree level, the axion can move through its coupling to the saxion and the background geometry. Then the contributions of the axion kinetic energy to the slow-

Chihiro Nakajima

We map the problem of determining flat-foldability of the origami diagram onto the ground-state search problem of spin glass model on random graphs. If the origami diagram is locally flat-foldable around each vertex, a pre-folded diagram, showing the planar-positional relationship of the facet, can be obtained. For remaining combinatorial problem on layer or

Shuoyao Wang, Jiawei Lin

This paper explores the pricing and scheduling strategies of the electric vehicle charging stations in response to the rising demand for cleaner transportation. Most of the existing methods focus on maximizing the energy efficiency or the charging station profit, however, the reputation of EVs is also a key factor for the long-term charging station operation

Li You, Xiaoyu Qiang, Yongxiang Zhu, Fan Jiang

Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide

Yang Jiao, Shaoxiang Chen, Zequn Jie, Jingjing Chen

Large Multimodal Model (LMM) is a hot research topic in the computer vision area and has also demonstrated remarkable potential across multiple disciplinary fields. A recent trend is to further extend and enhance the perception capabilities of LMMs. The current methods follow the paradigm of adapting the visual task outputs to the format of the language mode

Jin Yang, Daniel S. Marcus, Aristeidis Sotiras

U-Net has been widely used for segmenting abdominal organs, achieving promising performance. However, when it is used for multi-organ segmentation, first, it may be limited in exploiting global long-range contextual information due to the implementation of standard convolutions. Second, the use of spatial-wise downsampling (e.g., max pooling or strided convo

Rudeep Gaur

We compute the memory effect due to a gravitational wave striking a Kerr black hole as seen by an observer at null infinity. This is done by working in Bondi--Sachs coordinates. It was shown by Hawking, Perry, and Strominger (HPS) that the memory effect due to a gravitational shockwave is seen as a pure BMS supertranslation from null infinity. Hence, it is o

Chuanqi Zang, Jiji Tang, Rongsheng Zhang, Zeng Zhao

Storytelling aims to generate reasonable and vivid narratives based on an ordered image stream. The fidelity to the image story theme and the divergence of story plots attract readers to keep reading. Previous works iteratively improved the alignment of multiple modalities but ultimately resulted in the generation of simplistic storylines for image streams.

Peiyuan Liu, Hang Guo, Tao Dai, Naiqi Li

Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially wi

Souvik Haldar, Arun Sehrawat, Krishna B. Balasubramanian

Superconducting Nanowire Single Photon Detector (SNSPD) emerges as a potential candidate in the multiple fields requiring sensitive and fast photodetection. While nanowires of low temperature superconducting detectors are mature with commercial solutions, other material options with higher transition temperature and faster responses are currently being explo

Yuzhen Zhang, Zili Gao, Lili He

A Rapid and cost-effective method for detecting bacterial cells on surfaces is critical to protect public health from various aspects, including food safety, clinical hygiene, and pharmacy quality. Herein, we first established an optical detection method based on a gold chip coating with 3-mercaptophenylboronic acid (3-MPBA) to capture bacterial cells, which

MohammadReza Hosseinzadehketilateh, Banafsheh Adami, Nima Karimian

This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardiograms (ECG) from an extensive database comprising 1119 subjects. Previous research on hyperglycemia or glucose detection using ECG has been constrained by challenges related to generalization and scalability, primarily due to using all subjects' ECG in trainin

Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço, Ting Kei Pong

In this paper, without requiring any constraint qualifications, we establish tight error bounds for the log-determinant cone, which is the closure of the hypograph of the perspective function of the log-determinant function. This error bound is obtained using the recently developed framework based on one-step facial residual functions.

Zhanyu Liu, Chaolv Zeng, Guanjie Zheng

With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase. It aims to condense the original large-scale graph to a much smaller synthetic graph while preserving the essential information necessary for e

Suhua Lai, Jiahong Wu, Jianwen Zhang, Xiaokui Zhao

This paper is concerned with the stability and large-time behavior of 3D incompressible MHD equations with only vertical dissipation near a background magnetic field. By making full use of the dissipation generated by the background magnetic field, we first establish the global stability of the solutions in $H^3$-norm. Then, the optimal decay rates of the so

De Cheng, Yanling Ji, Dong Gong, Yan Li

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-world environments constantly encounter adverse weather conditions that are not previously observed. Therefore, it practically requires adverse weather removal models to continually le

John M. Campbell

Using an infinite family of generalizations of the Chudnovsky brothers' series recently obtained via the analytic continuation of the Borwein brothers' formula for Ramanujan-type series of level 1, we apply the Gauss-Salamin-Brent iteration for $\pi$ to obtain a new, Ramanujan-type series that yields more digits per term relative to current world record give

Runmin Cong, Ronghui Sheng, Hao Wu, Yulan Guo

Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network

Qiufu Li, Xi Jia, Jiancan Zhou, Linlin Shen

This paper introduces the concept of uniform classification, which employs a unified threshold to classify all samples rather than adaptive threshold classifying each individual sample. We also propose the uniform classification accuracy as a metric to measure the model's performance in uniform classification. Furthermore, begin with a naive loss, we mathema

Qinglong Tian, Jiwei Zhao

Protecting individual privacy is crucial when releasing sensitive data for public use. While data de-identification helps, it is not enough. This paper addresses parameter estimation in scenarios where data are perturbed using the Post-Randomization Method (PRAM) to enhance privacy. Existing methods for parameter estimation under PRAM data suffer from limita

Michael Gekhtman, Anton Izosimov

We review several constructions of integrable systems with an underlying cluster algebra structure, in particular the Gekhtman-Shapiro-Tabachnikov-Vainshtein construction based on perfect networks and the Goncharov-Kenyon approach based on the dimer model. We also discuss results of Galashin and Pylyavskyy on integrability of T-systems.

Hsin-Ju Lin, Tsu-Chun Chung, Ching-Chun Hsiao, Pin-Yu Chen

Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task. For instance, delivery robots in multilingual cities need to be capable of doing multilingual text detection so that the robots can read traffic signs and road markings. Moreover, the target languages change from re

Rahmat Heidari, Frederik Geth

This paper discusses the modeling of inverters used in distributed energy resources in steady state. Modeling the interaction between distribution grids and inverter-based resources is crucial to understand the consequences for the network's operational and planning processes. This work highlights the limitations of existing models and emphasizes the need fo

Hongcheng Zhang, Liu Liang, Pengxin Zeng, Xiao Song

Sparse 3D detectors have received significant attention since the query-based paradigm embraces low latency without explicit dense BEV feature construction. However, these detectors achieve worse performance than their dense counterparts. In this paper, we find the key to bridging the performance gap is to enhance the awareness of rich representations in two

Zhiyu Chen, Yu Li, Suochao Zhang, Jingbo Zhou

As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services. Nonetheless, in some specific domains, there are still concerns regarding cost and trade-offs between privacy issues and accuracy. In this study, we introduce a cost-effec

Hyungi Lee, Giung Nam, Edwin Fong, Juho Lee

Transfer learning has recently shown significant performance across various tasks involving deep neural networks. In these transfer learning scenarios, the prior distribution for downstream data becomes crucial in Bayesian model averaging (BMA). While previous works proposed the prior over the neural network parameters centered around the pre-trained solutio

Shanwei Ding, Guanghan Li

In this paper, we uncover an intriguing algebra property of an element symmetric polynomial. By this property, we establish the longtime existence and convergence of a locally constrained flow, thereby some families of geometric inequalities in sphere can be derived. Meanwhile, a new family of ``three terms'' geometric inequalities involving two weighted cur

Ishan Banerjee

Let $X$ be an algebraic surface with $\mathcal{L}$ an ample line bundle on $X$. Let $\Gamma(X, \mathcal{L})$ be the \emph{geometric monodromy} group associated to family of nonsingular curves in $X$ that are zero loci of sections of $\mathcal{L}$. We provide obstructions to $\Gamma(X, \mathcal{L})$ being finite index in the mapping class group. We also show

Yanhong Bai, Jiabao Zhao, Tingjiang Wei, Qing Cai

With the long term accumulation of high quality educational data, artificial intelligence has shown excellent performance in knowledge tracing. However, due to the lack of interpretability and transparency of some algorithms, this approach will result in reduced stakeholder trust and a decreased acceptance of intelligent decisions. Therefore, algorithms need

Tuan Anh Pham, Seoung-Hun Kang, Yasemin Ozbek, Mina Yoon

In this work, we demonstrate the formation and electronic influence of lateral heterointerfaces in FeSn containing Kagome and honeycomb layers. Lateral heterostructures offer spatially resolved property control, enabling the integration of dissimilar materials and promoting phenomena not typically observed in vertical heterostructures. Using the molecular be

Prakhar Kaushik, Adam Kortylewski, Alan Yuille

An important and unsolved problem in computer vision is to ensure that the algorithms are robust to changes in image domains. We address this problem in the scenario where we have access to images from the target domains but no annotations. Motivated by the challenges of the OOD-CV benchmark where we encounter real world Out-of-Domain (OOD) nuisances and occ

T. Kubo, K. Kojima, N. Katayama, T. Runčevski

In {\delta}-Ag2/3V2O5 with charge degrees of freedom in V, it is known that the charge ordering state and physical properties of V that appear at low temperatures depend strongly on the ordering state of Ag. In this study, we focused on the Ag ions in the interlayer and studied the structure using synchrotron radiation powder diffraction in dependence on tem

Daichi Takeda

In black hole thermodynamics, defining coarse-grained entropy for dynamical black holes has long been a challenge, and various proposals, such as generalized entropy, have been explored. Guided by the AdS/CFT, we introduce a new definition of coarse-grained entropy for a dynamical black hole in Lorentzian Einstein gravity. On each time slice, this entropy is

Kaizhe Xu, Jiajia Guo, Jun Zhang, Shi Jin

Reconfigurable intelligent surface (RIS) is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic waves. In the prior literature, the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are deployed. In thi

D. Q. Wang, D. Reuter, A. D. Wieck, A. R. Hamilton

We present a new double-layer design for 2D surface superlattice systems in GaAs-AlGaAs heterostructures. Unlike previous studies, our device (1) uses an in-situ gate, which allows very short period superlattice in high mobility, shallow heterostructures; (2) enables independent control of the carrier density and the superlattice modulation potential amplitu

Ishan Banerjee

In this paper we prove under some simplifying hypotheses questions of Picoco and Levinson-Ullery on Cayley-Bacharach sets. Our results imply that, under suitable hypotheses Cayley-Bacharach sets lie on curves of low degree. We then use these results to estimate error terms to the normalized motive of the space of smooth degree $d$ hypersurfaces in $\mathbb{P

Kexin Li

Iteratively reweighted L1 (IRL1) algorithm is a common algorithm for solving sparse optimization problems with nonconvex and nonsmooth regularization. The development of its acceleration algorithm, often employing Nesterov acceleration, has sparked significant interest. Nevertheless, the convergence and complexity analysis of these acceleration algorithms co

Joaquim Dias Garcia, Alexandre Street, Mario Veiga Pereira

Simulating long-term hydrothermal bid-based markets considering strategic agents is a challenging task. The representation of strategic agents considering intertemporal constraints within a stochastic framework brings additional complexity to the already difficult single-period bilevel, thus, non-convex, optimal bidding problem. Thus, we propose a simulation

Francisco M. F. R. Gonçalves, Ryan M. Bena, Konstantin I. Matveev, Néstor O. Pérez-Arancibia

We present model predictive selection (MPS), a new method for selecting the stable closed-loop (CL) equilibrium attitude-error quaternion (AEQ) of an uncrewed aerial vehicle (UAV) during the execution of high-speed yaw maneuvers. In this approach, we minimize the cost of yawing measured with a performance figure of merit (PFM) that takes into account both th

Weiwei Luo, Jiang Fan, Alexey B. Kuzmenko, Wei Cai

In topological photonics, artificial photonic structures are constructed for realizing nontrivial unidirectional propagation of photonic information. On the other hand, moir\'e superlattices are emerging as an important avenue for engineering quantum materials with novel properties. In this paper, we combine these two aspects and demonstrate theoretically th

Ngo Phuoc Nguyen Ngoc, Huynh Anh Thi, Nguyen Van Vinh

In 2002, Chowdhury et al. introduced a simplified model aimed at depicting the dynamics of single-lane unidirectional ant traffic. Despite efforts, an exact solution for the stationary state of this ant-trail model remains elusive. The primary challenge arises from inherent fluctuations in the total amount of pheromone along the ant-trail. These fluctuations

NOvA Collaboration, M. A. Acero, B. Acharya, P. Adamson

This Letter reports a search for charge-parity ($CP$) symmetry violating nonstandard interactions (NSI) of neutrinos with matter using the NOvA Experiment, and examines their effects on the determination of the standard oscillation parameters. Data from $\nu_{\mu}(\bar{\nu}_{\mu})\rightarrow\nu_{\mu}(\bar{\nu}_{\mu})$ and $\nu_{\mu}(\bar{\nu}_{\mu})\rightarr

Shipeng Song, Bin Liu, Fei Teng, Tianrui Li

Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering. However, the presence of false-positive and false-negative examples in recommendation systems hampers accurate preferen

Yutong Wang, Rishi Sonthalia, Wei Hu

We study the generalization capability of nearly-interpolating linear regressors: $\boldsymbol{\beta}$'s whose training error $\tau$ is positive but small, i.e., below the noise floor. Under a random matrix theoretic assumption on the data distribution and an eigendecay assumption on the data covariance matrix $\boldsymbol{\Sigma}$, we demonstrate that any n

Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick

Quantifying a model's predictive uncertainty is essential for safety-critical applications such as autonomous driving. We consider quantifying such uncertainty for multi-object detection. In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes. One challenge in doing so is that boundi

Yunpeng Qing, Shunyu liu, Jingyuan Cong, Kaixuan Chen

Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution problem. However, existing works often suffer from the constraint conflict issue when offline datasets are colle

Chengxing Jia, Fuxiang Zhang, Yi-Chen Li, Chen-Xiao Gao

Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identification, existing OMRL research suggests learning separate task representations that be incorporated with policy input, thus forming a context-based meta-policy. A major approach to trai

Jason Liu

A critical component when developing question-answering AIs is an adversarial dataset that challenges models to adapt to the complex syntax and reasoning underlying our natural language. Present techniques for procedurally generating adversarial texts are not robust enough for training on complex tasks such as answering multi-sentence trivia questions. We in

Yumeng Fu, Junjie Wu, Zhongjie Wang, Meishan Zhang

Emotion recognition in conversation (ERC), the task of discerning human emotions for each utterance within a conversation, has garnered significant attention in human-computer interaction systems. Previous ERC studies focus on speaker-specific information that predominantly stems from relationships among utterances, which lacks sufficient information around

Jiuniu Wang, Zehua Du, Yuyuan Zhao, Bo Yuan

The Agent and AIGC (Artificial Intelligence Generated Content) technologies have recently made significant progress. We propose AesopAgent, an Agent-driven Evolutionary System on Story-to-Video Production. AesopAgent is a practical application of agent technology for multimodal content generation. The system integrates multiple generative capabilities within

Tyler Chen, Gérard Meurant

We establish a near-optimality guarantee for the full orthogonalization method (FOM), showing that the overall convergence of FOM is nearly as good as GMRES. In particular, we prove that at every iteration $k$, there exists an iteration $j\leq k$ for which the FOM residual norm at iteration $j$ is no more than $\sqrt{k+1}$ times larger than the GMRES residua

Hitoshi Fujioka

Given a tuple of holomorphic differentials on a Riemann surface, one can define a Higgs bundle in the Hitchin section and a natural symmetric pairing of the Higgs bundle. We study whether a Higgs bundle of rank 3 in the Hitchin section has a compatible harmonic metric when the spectral curve is a 2-sheeted branched covering of the Riemann surface. In particu

Yiran Wang, Li Xiao

It has been shown that traditional deep learning methods for electronic microscopy segmentation usually suffer from low transferability when samples and annotations are limited, while large-scale vision foundation models are more robust when transferring between different domains but facing sub-optimal improvement under fine-tuning. In this work, we present

Lei Chen, Yiqi Chen, Zhufei Chu, Wenji Fang

Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique

Sarai Hernandez-Torres, Xinyi Li, Daisuke Shiraishi

In this work, we consider the scaling limit of loop-erased random walk (LERW) in three dimensions and prove that the limiting occupation measure is equivalent to its $\beta$-dimensional Minkowski content, where $\beta \in (1, 5/3]$ is its Hausdorff dimension. In doing this we also establish the existence of the two-point function and provide some sharp estim

Yongjeong Oh, Jaehong Jo, Byonghyo Shim, Yo-Seb Jeon

In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs an iterative and parallel interference removal strategy inspired by parallel interference cancellation (PIC), enhanced with deep learning to joi

E. Santos, J. E. Abrao, J. L. Costa, J. G. S. Santos

Our investigation reveals a groundbreaking discovery of a negative inverse orbital Hall effect (IOHE) in Ge thin films. We employed the innovative orbital pumping technique where spin-orbital coupled current is injected into Ge films using YIG/Pt(2)/Ge($t_{Ge}$) and YIG/W(2)/Ge($t_{Ge}$) heterostructures. Through comprehensive analysis, we observe significan

Chu-Hee Cho, Daniel Eceizabarrena

We study the problem of pointwise convegence for the Schr\"odinger operator on $\mathbb R^n$ along time sequences. We show that the sharp counterexample to the sequential Schr\"odinger maximal estimate given recently by Li, Wang and Yan based in the construction by Luc\`a and Rogers can also be achieved with the construction of Bourgain, and we extend it to

Zhe Han, Ping He

Given a torsion pair $(\mathcal{T},\mathcal{F})$ in an abelian category $\mathcal{A}$ and its Happel-Reiten-Smal{\o} tilt $\mathcal{B}$, the equivalence of the realization functor $D^b({\mathcal B})\to D^b({\mathcal A})$ is determined by some properties of the torsion pair [9]. We call $(\mathcal{T},\mathcal{F})$ satisfying such a property effaceable. If $\m

Megan Cowie, Procopios C. Constantinou, Neil J. Curson, Taylor J. Z. Stock

We use electrostatic force microscopy to spatially resolve random telegraph noise at the Si/SiO$_2$ interface. Our measurements demonstrate that two-state fluctuations are localized at interfacial traps, with bias-dependent rates and amplitudes. These two-level systems lead to correlated carrier number and mobility fluctuations with a range of characteristic

Zirui Hu, Zhifu Gao, Haoxuan Sun, Cixing Chen

This paper focuses on renormalizable cosmology based on the Gauss-Bonnet theory with torsion. Within the framework of renormalizable quantum field theory, we study the matter field containing the Gauss-Bonnet correction term. By modifying the gauge model, which includes a charged scalar field and two families of fermions, and introducing torsion and Gauss-Bo

Albert H. Li, Preston Culbertson, Aaron D. Ames

Conventional approaches to grasp planning require perfect knowledge of an object's pose and geometry. Uncertainties in these quantities induce uncertainties in the quality of planned grasps, which can lead to failure. Classically, grasp robustness refers to the ability to resist external disturbances after grasping an object. In contrast, this work studies r

Huaixi Lu, Akshay Jajoo, Kedar S. Namjoshi

A blockchain facilitates secure and atomic transactions between mutually untrusting parties on that chain. Today, there are multiple blockchains with differing interfaces and security properties. Programming in this multi-blockchain world is hindered by the lack of general and convenient abstractions for cross-chain communication and computation. Current cro

Linrui Dai, Rongzhao Zhang, Yongrui Yu, Xiaofan Zhang

The recently emerging conditional diffusion models seem promising for mitigating the labor and expenses in building large 3D medical imaging datasets. However, previous studies on 3D CT generation primarily focus on specific organs characterized by a local structure and fixed contrast and have yet to fully capitalize on the benefits of both semantic and text

Weiying Xue, Qi Liu, Qiwei Xiong, Yuxiao Wang

Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. In this paper, we propose a novel framework, termed Knowledge Integration to HOI (KI2HOI), that effectively integrates t

Dexue Zhang

Real-enriched categories are categories with real numbers as enrichment. Precisely, a real-enriched category is a category enriched over the commutative and unital quantale composed of the unit interval and a continuous t-norm. These notes present a brief introduction to such categories, focusing on the presheaf monad and its submonads in the category of rea

Zhanyu Liu, Ke Hao, Guanjie Zheng, Yanwei Yu

Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a deep neural network. Recently, a technique named \textit{Dataset Condensation} has emerged as a solution to this problem. This technique genera

Shuji Habuchi, Keita Takahashi, Chihiro Tsutake, Toshiaki Fujii

We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method, our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information, which is related to the differenc

Gianni Q. Moretti, Thomas Weber, Thomas Possmayer, Emiliano Cortés

Dielectric metasurfaces supporting quasi-bound states in the continuum (qBICs) enable high field enhancement with narrow-linewidth resonances in the visible and near-infrared ranges. The resonance emerges when distorting the meta-atom's geometry away from a symmetry-protected BIC condition and, usually, a given design can sustain one or two of these states.

Emma L. Rosenfeld, Connor T. Hann, David I. Schuster, Matthew H. Matheny

We take a bottom-up, first-principles approach to design a two-qubit gate between fluxonium qubits for minimal error, speed, and control simplicity. Our proposed architecture consists of two fluxoniums coupled via a linear resonator. Using a linear coupler introduces the possibility of material optimization for suppressing its loss, enables efficient driving

Chenyu You, Yifei Min, Weicheng Dai, Jasjeet S. Sekhon

Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. However, several pain points persist for this paradigm: (i) directly tuning entire pre-trained models becomes both time-intensive and computationally costly. Additionally, these tuned models tend to become highly specialized, limiting their practicalit

Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis t

Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas

Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learning. However, this problem is poorly understood in the presence of outliers, limiting the use of existing nonconvex algorithms in adversarial settings. In this paper, we study the pr

Michael Lampis

Motivated by the importance of dynamic programming (DP) in parameterized complexity, we consider several fine-grained questions, such as the following examples: (i) can Dominating Set be solved in time $(3-\epsilon)^{pw}n^{O(1)}$? (where $pw$ is the pathwidth) (ii) can Coloring be solved in time $pw^{(1-\epsilon)pw}n^{O(1)}$? (iii) can a short reconfiguratio

Farah Alkhatib, Mostafa Jamshidian, Donatien Le Liepvre, Florian Bernard

Background: For the clinical adoption of stress-based rupture risk estimation in abdominal aortic aneurysms (AAAs), a fully automated pipeline, from clinical imaging to biomechanical stress computation, is essential. To this end, we investigated the impact of AI-based image segmentation methods on stress computation results in the walls of AAAs. We compared

D. Mata-Pacheco

In this thesis we present the cosmological applications of some proposals from Quantum Gravity. Namely, we will explore classical and quantum cosmological implications of the Generalized Uncertainty Principle (GUP), the Ho\v{r}ava-Lifshitz (HL) theory of gravity and the Swampland Conjectures. Furthermore, we will also present a detailed analysis of Lorentzia

Adam Giammarese, Kamal Rana, Erik M. Bollt, Nishant Malik

Model-free forecasting of the temporal evolution of chaotic systems is crucial but challenging. Existing solutions require hyperparameter tuning, significantly hindering their wider adoption. In this work, we introduce a tree-based approach not requiring hyperparameter tuning: TreeDOX. It uses time delay overembedding as explicit short-term memory and Extra-

Sarah Moon

I develop a methodology to partially identify linear combinations of conditional mean outcomes when the researcher only has access to aggregate data. Unlike the existing literature, I only allow for marginal, not joint, distributions of covariates in my model of aggregate data. Bounds are obtained by solving an optimization program and can easily accommodate

Arunima Bhattacharya, Jeremy Wall

In this paper, we prove interior Hessian estimates for shrinkers, expanders, translators, and rotators of the Lagrangian mean curvature flow under the assumption that the Lagrangian phase is hypercritical. We further extend our results to a broader class of Lagrangian mean curvature type equations.

Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri, Aneeshan Sain

This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the process, enabling amateur sketches to generate precise images, living up to the commitment of "what you sketch is what you get". A pilot study underscores the necessity, revealing that

Joshua M. Lewis, Lincoln D. Carr

Fractional evolution equations lack generally accessible and well-converged codes excepting anomalous diffusion. A particular equation of strong interest to the growing intersection of applied mathematics and quantum information science and technology is the fractional Schr\"odinger equation, which describes sub-and super-dispersive behavior of quantum wavef

Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki

Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to

Chandan Kumar, Jansel Herrera-Gerena, John Just, Matthew Darr

The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation. This paper introduces "Learn and Search", a novel approach for object lookup that leverages the power of contrastive learning to enh

Pulkit Pattnaik, Rishabh Maheshwary, Kelechi Ogueji, Vikas Yadav

Direct Preference Optimization (DPO) is an effective technique that leverages pairwise preference data (usually one chosen and rejected response pair per user prompt) to align LLMs to human preferences. In practice, multiple responses can exist for a given prompt with varying quality relative to each other. With availability of such quality ratings for multi