May 2024 arXiv papers — page 115

Jihwan Kwak, Sungmin Cha, Taesup Moon

This paper addresses the unrealistic aspect of the commonly adopted Continuous Incremental Semantic Segmentation (CISS) scenario, termed overlapped. We point out that overlapped allows the same image to reappear in future tasks with different pixel labels, which is far from practical incremental learning scenarios. Moreover, we identified that this flawed sc

Dawei Feng, Yihai Zhang, Zhixuan Xu

Pretrained Large Language Models (LLM) such as ChatGPT, Claude, etc. have demonstrated strong capabilities in various fields of natural language generation. However, there are still many problems when using LLM in specialized domain-specific fields. When using generative AI to process downstream tasks, a common approach is to add new knowledge (e.g., private

Antonios Panayotopoulos

For the generation of acyclic biological diagrams, from a graph-theoretical perspective, we introduce the relative diagrams of cyclic permutations with ramphoid and keratoid vertices of degree 2, which correspond to Motzkin and Dyck words/paths. The relation between these two types of diagrams, defines the generation of the first via the permutations of the

Hai-Xia Ma, Tsutomu T. Takeuchi, Suchetha Cooray, Yongda Zhu

A direct approach to studying the galaxy-halo connection is to analyze groups and clusters of galaxies that trace the underlying dark matter halos, emphasizing the importance of identifying galaxy clusters and their associated brightest cluster galaxies (BCGs). In this work, we test and propose a robust density-based clustering algorithm that outperforms the

Aditya Joshi, Jake Renzella, Pushpak Bhattacharyya, Saurav Jha

While deep learning approaches represent the state-of-the-art of natural language processing (NLP) today, classical algorithms and approaches still find a place in NLP textbooks and courses of recent years. This paper discusses the perspectives of conveners of two introductory NLP courses taught in Australia and India, and examines how classical and deep lea

Eason Chen, Jia-En Lee, Jionghao Lin, Kenneth Koedinger

We developed GPTutor, a pioneering web application designed to revolutionize personalized learning by leveraging the capabilities of Generative AI at scale. GPTutor adapts educational content and practice exercises to align with individual students' interests and career goals, enhancing their engagement and understanding of critical academic concepts. The sy

Shakeel Ahmad Sheikh

Speech production is a complex phenomenon, wherein the brain orchestrates a sequence of processes involving thought processing, motor planning, and the execution of articulatory movements. However, this intricate execution of various processes is susceptible to influence and disruption by various neurodegenerative pathological speech disorders, such as Parki

Qiao Yang, Yu-Biao Wu, Wu-Ming Liu

In the context of 2+1 dimensional Dirac materials, we consider electromagnetic interactions alongside a type of spin-dependent Hubbard interaction. The former is described by PQED theory, while the latter corresponds to an effective theory represented by the $SU(N_c)$ Thirring model. Employing Hubbard-Stratonovich transformation and large N expansion in the

Tatiana Strelnikova, Johannes Köhler, Julian Berberich

This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is determined based on linear system identification using a moving window of past measurements, and it serves as a local approximation of the underlying nonlinear dynamics. We prove that

Yi Cui, Yao Li, Jayson R. Miedema, Sharon N. Edmiston

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset

Alexander Valov, Netanel Levi, Baruch Meerson

Thermally activated particle motion in disorder potentials is controlled by the large-$\Delta V$ tail of the distribution of height $\Delta V$ of the potential barriers created by the disorder. We employ the optimal fluctuation method to evaluate this tail for correlated quenched Gaussian potentials in one dimension in the presence of a small bias of the pot

Anand Deopurkar

We compute equivariant fundamental classes of orbits in GL(2)-representations. As applications, we find degrees of the orbit closures corresponding to elliptic fibrations and self-maps of the projective line.

Guangmin Zheng, Jin Wang, Xiaobing Zhou, Xuejie Zhang

Chain of thought (CoT) has proven useful for problems requiring complex reasoning. Many of these problems are both textual and multimodal. Given the inputs in different modalities, a model generates a rationale and then uses it to answer a question. Because of the hallucination issue, the generated soft negative rationales with high textual quality but illog

Chandrasheel Bhagwat, Kaustabh Mondal, Gunja Sachdeva

Let $(\tau, V_{\tau})$ be a finite dimensional representation of a maximal compact subgroup $K$ of a connected non-compact semisimple Lie group $G$, and let $\Gamma$ be a uniform torsion-free lattice in $G$. We obtain an infinitesimal version of the celebrated Matsushima-Murakami formula, which relates the dimension of the space of automorphic forms associat

Franz Gmeineder, Peter Lewintan, Jean Van Schaftingen

We give sharp conditions for the limiting Korn-Maxwell-Sobolev inequalities \begin{align*} \lVert P\rVert_{{\dot{W}}{^{k-1,\frac{n}{n-1}}}(\mathbb{R}^n)}\le c\big(\lVert\mathscr{A}[P]\rVert_{{\dot{W}}{^{k-1,\frac{n}{n-1}}}(\mathbb{R}^n)}+\lVert\mathbb{B}P\rVert_{L^{1}(\mathbb{R}^n)}\big) \end{align*} to hold for all $P\in C_{c}^{\infty}(\mathbb{R}^{n};V)$, w

Milo Bechtloff Weising

We construct $\Delta$-operators $F[\Delta]$ on the space of almost symmetric functions $\mathscr{P}_{as}^{+}$. These operators extend the usual $\Delta$-operators on the space of symmetric functions $\Lambda \subset \mathscr{P}_{as}^{+}$ central to Macdonald theory. The $F[\Delta]$ operators are constructed as certain limits of symmetric functions in the Che

Amjad Sohail, Rizwan Ahmed, Hazrat Ali

In this paper, we analytically present the phenomena of nanomechanically induced transparency (NMIT) and transmission rate in a parity-time-symmetric ($\mathcal{PT}$-symmetric) opto-nanomechanical system (ONMS) where a levitated dielectric nanospheres is trapped near the antinodes closest to right mirror of passive cavity which further coupled to an active c

Zhiyao Ma, Tian Tian, Yuxuan Liao, Xue Feng

Metasurfaces with tunable functionalities are greatly desired for modern optical system and various applications. To increase the operating channels of polarization-multiplexed metasurfaces, we proposed a structure of N cascaded dual-channel metasurfaces to achieve 2^N electrically switchable functional channels without intrinsic noise or cross-talk. As proo

Lucas Böttcher, Ronald Klingebiel

Budgetary constraints force organizations to pursue only a subset of possible innovation projects. Identifying which subset is most promising is an error-prone exercise, and involving multiple decision makers may be prudent. This raises the question of how to most effectively aggregate their collective nous. Our model of organizational portfolio selection pr

Zhe Wang, Clare C. Yu, Ruqian Wu

Superconducting qubits are a key contender for quantum computing elements, but they often face challenges like noise and decoherence from two-level systems (TLS). Tantalum (Ta) qubits are notable for their long T$_1$ coherence times nearing milliseconds, mainly due to fewer TLS, though the cause was unclear. Our research explored this by analyzing the air-ox

Dapeng Shi, Tiandong Wang, Zhiliang Ying

Exploring and detecting community structures hold significant importance in genetics, social sciences, neuroscience, and finance. Especially in graphical models, community detection can encourage the exploration of sets of variables with group-like properties. In this paper, within the framework of Gaussian graphical models, we introduce a novel decompositio

Taiki Matsushita, Naoyuki Kimura, Takeshi Mizushima, Ilya Vekhter

We investigate the anomalous thermal Hall effect (ATHE) in Weyl superconductors realized by the $E_{1u}$ ($p$-wave and $f$-wave) chiral superconducting order for the point group $D_{6h}$. Using the quasiclassical transport theory, we analyze the influence of the impurity scatterings and the line-nodal excitations on the ATHE. We compare the extrinsic (impuri

Chuan Chen, Tianchi Liao, Xiaojun Deng, Zihou Wu

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of

Issei Saito, Tomoaki Nakamura, Toshiyuki Hatta, Wataru Fujita

Evolving consumer demands and market trends have led to businesses increasingly embracing a production approach that prioritizes flexibility and customization. Consequently, factory workers must engage in tasks that are more complex than before. Thus, productivity depends on each worker's skills in assembling products. Therefore, analyzing the behavior of a

F. Cianfrani, G. Montani

Turbulent transport provides the main contribution to particle and energy losses in tokamak plasmas, which control is of paramount importance for forthcoming reactors such as the Divertor-Tokamak-Test (DTT) facility under construction at ENEA Frascati. In this work we investigate the characteristic features of drift turbulence at the plasma edge through 3D e

Xiang Luo, Zhiwen Tang, Jin Wang, Xuejie Zhang

User Simulators play a pivotal role in training and evaluating task-oriented dialogue systems. Traditional user simulators typically rely on human-engineered agendas, resulting in generated responses that often lack diversity and spontaneity. Although large language models (LLMs) exhibit a remarkable capacity for generating coherent and contextually appropri

Anargyros Katsabekis, Apostolos Thoma

Let $G$ be a simple graph on the vertex set $\{v_{1},\ldots,v_{n}\}$. An algebraic object attached to $G$ is the toric ideal $I_G$. We say that $I_G$ is subgraph splittable if there exist subgraphs $G_1$ and $G_2$ of $G$ such that $I_G=I_{G_1}+I_{G_2}$, where both $I_{G_1}$ and $I_{G_2}$ are not equal to $I_G$. We show that $I_G$ is subgraph splittable if an

Jiajie Chen, Joseph Nakao, Jing-Mei Qiu, Yang Yang

We present a class of high-order Eulerian-Lagrangian Runge-Kutta finite volume methods that can numerically solve Burgers' equation with shock formations, which could be extended to general scalar conservation laws. Eulerian-Lagrangian (EL) and semi-Lagrangian (SL) methods have recently seen increased development and have become a staple for allowing large t

Dalmin Bae, Junyoung Park, Myeonghyeon Kim, Haneul Kwak

We present a Floquet framework for controlling topological features of a one-dimensional optical lattice system with dual-mode resonant driving, in which both the amplitude and phase of the lattice potential are modulated simultaneously. We investigate a three-band model consisting of the three lowest orbitals and elucidate the formation of a cross-linked tw

Victor Godet

We revisit pure quantum cosmology in three dimensions. The Wheeler-DeWitt equation can be solved perturbatively and the dynamics reduces to a particle on moduli space. Its time evolution is equivalent to the $T\overline{T}$ deformation. Focusing on spacetimes with torus slices, we show that inflationary cosmologies correspond to particle trajectories in Arti

Jan Pablo Burgard, Maria Eduarda Pinheiro, Martin Schmidt

Random forests are among the most famous algorithms for solving classification problems, in particular for large-scale data sets. Considering a set of labeled points and several decision trees, the method takes the majority vote to classify a new given point. In some scenarios, however, labels are only accessible for a proper subset of the given points. More

Joongkyu Lee, Min-hwan Oh

In this paper, we study the contextual multinomial logit (MNL) bandit problem in which a learning agent sequentially selects an assortment based on contextual information, and user feedback follows an MNL choice model. There has been a significant discrepancy between lower and upper regret bounds, particularly regarding the maximum assortment size $K$. Addit

Koyal Suman Samantaray, Sourabh Kumar, P Maneesha, Dilip Sasmal

A new study explores the distinct roles of spontaneous polarization and piezoelectric polarization in piezo-phototronic coupling. This investigation focuses on differences in photocatalytic and piezo-photocatalytic performance using sodium bismuth titanate (NBT), a key ferroelectric material. The research aims to identify which type of polarization has a gre

A. Kreuzkamp, C. Wetterich

Probabilistic cellular automata with deterministic updating are quantum systems. We employ the quantum formalism for an investigation of random probabilistic cellular automata, which start with a probability distribution over initial configurations. The properties of the deterministic updating are randomly distributed over space and time. We are interested i

Runjie Yan, Kailu Wu, Kaisheng Ma

Recent advancements in Text-to-3D generation have yielded remarkable progress, particularly through methods that rely on Score Distillation Sampling (SDS). While SDS exhibits the capability to create impressive 3D assets, it is hindered by its inherent maximum-likelihood-seeking essence, resulting in limited diversity in generation outcomes. In this paper, w

Xusheng Li, Chengliang Wang, Shumao Wang, Zhuo Zeng

The multi-line LiDAR is widely used in autonomous vehicles, so point cloud-based 3D detectors are essential for autonomous driving. Extracting rich multi-scale features is crucial for point cloud-based 3D detectors in autonomous driving due to significant differences in the size of different types of objects. However, because of the real-time requirements, l

Huibing Wang, Mingze Yao, Yawei Chen, Yunqiu Xu

Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsuperv

Alexander Lappe, Anna Bognár, Ghazaleh Ghamkhari Nejad, Albert Mukovskiy

High-level visual brain regions contain subareas in which neurons appear to respond more strongly to examples of a particular semantic category, like faces or bodies, rather than objects. However, recent work has shown that while this finding holds on average, some out-of-category stimuli also activate neurons in these regions. This may be due to visual feat

Jicheng Yu, Yuqiang Feng

In this paper, Lie symmetry analysis method is applied to the (2+1)-dimensional time fractional Kadomtsev-Petviashvili (KP) equation with the mixed derivative of Riemann-Liouville time-fractional derivative and integer-order $x$-derivative. We obtained all the Lie symmetries admitted by the KP equation and used them to reduce the (2+1)-dimensional fractional

Hao-Bin Liu, Wei-Min Gu, Zhi-Xiang Zhang, Tuan Yi

The stellar spectra from LAMOST Medium Resolution Survey can be used to search for compact objects in binaries. The LAMOST DR10 catalog includes > 980, 000 targets with multiple medium resolution spectra. We select the targets with large or rapid radial velocity variation, and obtained an input-sample of 1822 sources. We use light curves and spectra to ident

Sumanta Das

We classify all $\pi_1$-injective proper maps between non-compact surfaces up to proper homotopy.

Adimurthi, Prosenjit Roy, Vivek Sahu

Classical boundary Hardy inequality, that goes back to 1988, states that if $1 < p < \infty, \ ~\Omega$ is bounded Lipschitz domain, then for all $u \in C^{\infty}_{c}(\Omega)$, $$\int_{\Omega} \frac{|u(x)|^{p}}{\delta^{p}_{\Omega}(x)} dx \leq C\int_{\Omega} |\nabla u(x) |^{p}dx,$$ where $\delta_\Omega(x)$ is the distance function from $\Omega^c$. In this ar

Muhammad Fadhil Ginting, Sung-Kyun Kim, David D. Fan, Matteo Palieri

This paper addresses the problem of object-goal navigation in autonomous inspections in real-world environments. Object-goal navigation is crucial to enable effective inspections in various settings, often requiring the robot to identify the target object within a large search space. Current object inspection methods fall short of human efficiency because th

Valdemar Švábenský, Mélina Verger, Maria Mercedes T. Rodrigo, Clarence James G. Monterozo

Algorithmic bias is a major issue in machine learning models in educational contexts. However, it has not yet been studied thoroughly in Asian learning contexts, and only limited work has considered algorithmic bias based on regional (sub-national) background. As a step towards addressing this gap, this paper examines the population of 5,986 students at a la

Zenglin Shi, Pei Liu, Tong Su, Yunpeng Wu

Continual learning, involving sequential training on diverse tasks, often faces catastrophic forgetting. While knowledge distillation-based approaches exhibit notable success in preventing forgetting, we pinpoint a limitation in their ability to distill the cumulative knowledge of all the previous tasks. To remedy this, we propose Dense Knowledge Distillatio

Penghao Liang, Bo Song, Xiaoan Zhan, Zhou Chen

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper propos

Chameleon Team

We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evalua

Maxim Ziatdinov

Active learning optimizes the exploration of large parameter spaces by strategically selecting which experiments or simulations to conduct, thus reducing resource consumption and potentially accelerating scientific discovery. A key component of this approach is a probabilistic surrogate model, typically a Gaussian Process (GP), which approximates an unknown

Huaiyu Zhang, Jiangwei Zhang

In this paper, we study scalar curvature rigidity of non-smooth metrics on smooth manifolds with non-positive Yamabe invariant. We prove that if the scalar curvature is not less than the Yamabe invariant in distributional sense, then the manifold must be isometric to an Einstein manifold. This result extends Theorem 1.4 in Jiang, Sheng and the first author (

Aida Asgarova, Vugar Ismailov

We consider the problem of approximation of a continuous function $f$ defined on a compact metric space $X$ by elements from a sum of two algebras. We prove a de la Vall\'{e}e Poussin type theorem, which estimates the approximation error $E(f)$ from below. We also obtain a duality formula for the precise computation of $E(f)$.

Zeyi Zhang, Tenglong Ao, Yuyao Zhang, Qingzhe Gao

In this work, we present Semantic Gesticulator, a novel framework designed to synthesize realistic gestures accompanying speech with strong semantic correspondence. Semantically meaningful gestures are crucial for effective non-verbal communication, but such gestures often fall within the long tail of the distribution of natural human motion. The sparsity of

Nir Gavish

The competitive exclusion principle in epidemiology implies that when competing strains of a pathogen provide complete protection for each other, the strain with the largest reproduction number outcompetes the other strains and drives them to extinction. The introduction of various trade-off mechanisms may facilitate the coexistence of competing strains, esp

Piper Fowler-Wright

In this thesis we develop methods for many-body open quantum systems and apply them to systems of organic polaritons. The methods employ a mean-field approach to reduce the dimensionality of large-scale problems. Initially assuming the absence of correlations in the many-body state, this approach is built upon in two ways. First, we show how the mean-field a

Junyue Zhang, Yifen Mu

Despite the significant potential for various applications, stochastic games with long-run average payoffs have received limited scholarly attention, particularly concerning the development of learning algorithms for them due to the challenges of mathematical analysis. In this paper, we study the stochastic games with long-run average payoffs and present an

Lanqiu Yao, Thaddeus Tarpey

A core component of precision medicine research involves optimizing individualized treatment rules (ITRs) based on patient characteristics. Many studies used to estimate ITRs are longitudinal in nature, collecting outcomes over time. Yet, to date, methods developed to estimate ITRs often ignore the longitudinal structure of the data. Information available fr

Joshua Pickard, Cooper Stansbury, Amit Surana, Lindsey Muir

Advances in methods of biological data collection are driving the rapid growth of comprehensive datasets across clinical and research settings. These datasets provide the opportunity to monitor biological systems in greater depth and at finer time steps than was achievable in the past. Classically, biomarkers are used to represent and track key aspects of a

Yuhang Lei, Wen Zhao, Liang Cui, Xiaoying Li

Characterizing the temporal-spectral profile of single photons is essential for quantum information protocol utilizing temporal mode for encoding. Based on the phase retrieval algorithm, we present a method to reconstruct the phase spectrum difference between two wave packets from their Hong-Ou-Mandel dip, and intensity spectra. Our confirmatory experiment w

Shimpei Endo, Evgeny Epelbaum, Pascal Naidon, Yusuke Nishida

This review article presents historical developments and recent advances in our understanding on the three-body forces and Efimov physics, from an interdisciplinary viewpoint encompassing nuclear physics and cold atoms. Theoretical attempts to elucidate the three-body force with the chiral effective field theory are explained, followed by an overview of expe

Joseph Cho, Mrudang Mathur, Cyril Zakka, Dhamanpreet Kaur

Deep learning algorithms require extensive data to achieve robust performance. However, data availability is often restricted in the medical domain due to patient privacy concerns. Synthetic data presents a possible solution to these challenges. Image generative models have found increasing use for medical applications, but are often task-specific, thus limi

Abdulrahman Alabdulkareem, Christian M Arnold, Yerim Lee, Pieter M Feenstra

Traditional security mechanisms isolate resources from users who should not access them. We reflect the compositional nature of such security mechanisms back into the structure of LLMs to build a provably secure LLM; that we term SecureLLM. Other approaches to LLM safety attempt to protect against bad actors or bad outcomes, but can only do so to an extent m

Harold C. Steinacker, Tung Tran

We study the one-loop effective action of the higher-spin gauge theory induced by the IKKT matrix model on a $\mathcal{M}^{1,3}\times \mathcal{K}$ background, where $\mathcal{M}^{1,3}$ is an FLRW cosmological spacetime brane and $\mathcal{K}$ are compact fuzzy extra dimensions. In particular, we show that all non-abelian ($\mathfrak{hs}$-valued) gauge fields

Ahmed M. Farouk, I. I. Beterov, Peng Xu, I. I. Ryabtsev

Recent progress in quantum computing and quantum simulation of many-body systems with arrays of neutral atoms using Rydberg excitation has provided unforeseen opportunities towards computational advantage in solving various optimization problems. The problem of a maximum-weight independent set of unit-disk graphs is an example of an NP-hard optimization prob

Juyoung Yun, Jungmin Shin

In the era of space exploration, coronal holes on the sun play a significant role due to their impact on satellites and aircraft through their open magnetic fields and increased solar wind emissions. This study employs computer vision techniques to detect coronal hole regions and estimate their sizes using imagery from the Solar Dynamics Observatory (SDO). A

Zhiqi Li, Barnabás Börcsök, Duowen Chen, Yutong Sun

This paper introduces a novel Lagrangian fluid solver based on covector flow maps. We aim to address the challenges of establishing a robust flow-map solver for incompressible fluids under complex boundary conditions. Our key idea is to use particle trajectories to establish precise flow maps and tailor path integrals of physical quantities along these traje

Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta

In this paper, we dive into the reliability concerns of Integrated Gradients (IG), a prevalent feature attribution method for black-box deep learning models. We particularly address two predominant challenges associated with IG: the generation of noisy feature visualizations for vision models and the vulnerability to adversarial attributional attacks. Our ap

Peng Yin, Andrey M. Shirokov, Pieter Maris, Patrick J. Fasano

The calculation of nuclear electromagnetic sum rules by directly diagonalizing the nuclear Hamiltonian in a large basis is numerically challenging and has not been performed for $A>2$ nuclei. With the significant progress of high performance computing, we show that calculating sum rules using numerous discretized continuum states obtained by directly diagona

Yixing Jiang, Jeremy Irvin, Ji Hun Wang, Muhammad Ahmed Chaudhry

Large language models are effective at few-shot in-context learning (ICL). Recent advancements in multimodal foundation models have enabled unprecedentedly long context windows, presenting an opportunity to explore their capability to perform ICL with many more demonstrating examples. In this work, we evaluate the performance of multimodal foundation models

Guilherme Duarte

Despite their cost, randomized controlled trials (RCTs) are widely regarded as gold-standard evidence in disciplines ranging from social science to medicine. In recent decades, researchers have increasingly sought to reduce the resource burden of repeated RCTs with factorial designs that simultaneously test multiple hypotheses, e.g. experiments that evaluate

Jinfu Zhu, Hongli Ding, Haokui Li, Jiahang Shao

Transverse Deflecting Cavities (TDCs) are generally adopted for electron beam diagnosis. Three sets of S-band and two sets of X-band TDCs are planned at Shenzhen Superconducting Soft X-ray Free Electron Laser (S3FEL) to accurately measure the temporal distribution of ultra-short electron bunches. The microwave system of one TDC consisting of a Low-Level Radi

Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang

Protein-protein bindings play a key role in a variety of fundamental biological processes, and thus predicting the effects of amino acid mutations on protein-protein binding is crucial. To tackle the scarcity of annotated mutation data, pre-training with massive unlabeled data has emerged as a promising solution. However, this process faces a series of chall

Liming Sun, Lei Wang

We consider a type of Hardy-Sobolev inequality, whose weight function is singular on the whole domain boundary. We are concerned with the attainability of the best constant of such inequality. In dimension two, we link the inequality to a conformally invariant one using the conformal radius of the domain. The best constant of such inequality on a smooth boun

Andrea Bajcsy, Jaime F. Fisac

Artificial intelligence (AI) is interacting with people at an unprecedented scale, offering new avenues for immense positive impact, but also raising widespread concerns around the potential for individual and societal harm. Today, the predominant paradigm for human--AI safety focuses on fine-tuning the generative model's outputs to better agree with human-p

Zhenkun Yuan, Diana Dahliah, Romain Claes, Andrew Pike

The chalcogenide perovskite BaZrS$_3$ has attracted much attention as a promising solar absorber for thin-film photovoltaics. Here, we use first-principles calculations to evaluate its carrier transport and defect properties. We find that BaZrS$_3$ has a phonon-limited electron mobility of 37 cm$^2$/Vs comparable to that in halide perovskites but lower hole

P. Boontueng, N. Ritjoho, P. Phumara, T. Sanghangthum

In this work, the standard CsI(Tl) scintillator was used to determine the characteristics of a proton beam. By irradiating the scintillator with a proton beam, it was able to subsequently measure the emitted light using a spectrometer. This work presents the dose response of a scintillator and its use to measure the depth-dose profile of a proton beam. The m

Marc Jaikissoon, Çağıl Köroğlu, Jerry A. Yang, Kathryn M. Neilson

Strain engineering has played a key role in modern silicon electronics, having been introduced as a mobility booster in the 1990s and commercialized in the early 2000s. Achieving similar advances with two-dimensional (2D) semiconductors in a CMOS (complementary metal oxide semiconductor) compatible manner would radically improve the industrial viability of 2

Peiran Yin, Rui Li, Chengjiang Yin, Xiangyu Xu

The nature of dark energy is one of the most outstanding problems in physical science, and various theories have been proposed. It is therefore essential to directly verify or rule out these theories experimentally. However, despite substantial efforts in astrophysical observations and laboratory experiments, previous tests have not yet acquired enough accur

Kentaro Kasai, Masahiro Kawasaki, Kai Murai, Shunsuke Neda

We study a primordial black hole (PBH) formation model based on the framework of the inhomogeneous Affleck-Dine (AD) mechanism, which can explain the seeds of supermassive black holes (SMBHs). This model, however, predicts strong clustering of SMBHs that is inconsistent with the observation of angular correlation of quasars. In this paper, we propose a modif

Wentao Jiang, Jing Zhang, Di Wang, Qiming Zhang

Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision Transformers. However, such methods usually obtain sparse tokens by hand-crafted or parallel-unfriendly desig

Fabian Offert, Paul Kim, Qiaoyu Cai

This paper investigates the application of the transformer architecture in protein folding, as exemplified by DeepMind's AlphaFold project, and its implications for the understanding of so-called large language models. The prevailing discourse often assumes a ready-made analogy between proteins, encoded as sequences of amino acids, and natural language, whic

Dominic LaBella, Ujjwal Baid, Omaditya Khanna, Shan McBurney-Lin

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participa

Linshan Hou, Ruili Feng, Zhongyun Hua, Wei Luo

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model training. This paper proposes a simple yet effective input-level backdoor detection (dubbed IBD-PSC) as a `firewall' to filter out malicious testing images. Our method is motivated by

Manman Wang, Yanfeng Li, Hanqing Liu, Haiqiao Ni

Two-photon interference (TPI) lies at the heart of photonic quantum technologies. TPI is generally regarded as quantum interference stemming from the indistinguishability of identical photons, hence a common intuition prevails that TPI would disappear if photons are distinguishable. Here we disprove this perspective and uncover the essence of TPI. We report

Davin Choo, Themis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya

We study the problem of online unweighted bipartite matching with $n$ offline vertices and $n$ online vertices where one wishes to be competitive against the optimal offline algorithm. While the classic RANKING algorithm of Karp et al. [1990] provably attains competitive ratio of $1-1/e > 1/2$, we show that no learning-augmented method can be both 1-consiste

Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun

Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities. However, they encounter challenges in effectively simulating observational feedback and grounding it with language to propel advancements in physical scientific discovery. Conversely, human scientists und

Feiran Li, Qianqian Xu, Shilong Bao, Zhiyong Yang

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because

Navneet Singh, Shiva Raj Pokhrel

In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qiskit with diverse feature mapping techniques for genomic sequence classification.

Xiaoyi Wu, Yushuai Chen, Zhan Li, Ziyang Hong

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the lates

Wansong Liu, Kareem Eltouny, Sibo Tian, Xiao Liang

There has been a growing utilization of industrial robots as complementary collaborators for human workers in re-manufacturing sites. Such a human-robot collaboration (HRC) aims to assist human workers in improving the flexibility and efficiency of labor-intensive tasks. In this paper, we propose a human-aware motion planning framework for HRC to effectively

Boxun Liu, Shijian Gao, Zonghui Yang, Xiang Cheng

Integrated sensing and communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widely utilized hybrid architecture in mmWave systems compromises multiplexing gain due to the constraints of limited radio frequency chains. Moreover, additional sensing functionalities exacerbate the impa

Zhiqiang Shen, Peng Cao, Junming Su, Jinzhu Yang

This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-annotated labeled set, where each labeled image contains only a single-slice annotation, and 2) an unlabeled set comprising numerous unlabeled volumetric images. State-of-the-art BS

B. Yu, L. Huang, J. S. Li, L. Lin

The van der Waals (vdW) layered multiferroics, which host simultaneous ferroelectric and magnetic orders, have attracted attention not only for their potentials to be utilized in nanoelectric devices and spintronics, but also offer alternative opportunities for emergent physical phenomena. To date, the vdW layered multiferroic materials are still very rare.

Oleh Lopushansky

We establish inverse and direct theorems on best approximations in quasi-normed Abelian groups through bilateral Bernstein-Jackson inequalities with exact constants. Using integral representations for quasi-norms of functions $f$ in Lebesgue's spaces by decreasing rearrangements $f^*$ with the help of approximation $E$-functionals, error estimates are found.

Renai Chen, Galen T. Craven

Rectification, the preferential transport of a current in one direction through a system, has garnered significant attention in molecules because of its importance for controlling thermal and electronic currents at the nanoscale. Here, we report the presence of energy storage rectification effects in a molecular chain. This phenomenon is generated by subject

Keanu Nakamura

This review article will offer a qualitative overview of the strongly reversed shear profile for steady-state operation in tokamaks. For a steady-state reactor to be commercially viable, it is necessary to have a large bootstrap fraction. Currently, there appears great potential in an Advanced Tokamak (AT) regime, namely the hollow current profile (strongly

Haifan Zhu, Wei Wang

Based on observations from the Insight-Hard X-ray Modulation Telescope (Insight-HXMT), an analysis of Type-C quasi-periodic oscillations (QPOs) observed during the outburst of the new black hole candidate Swift J1727.8-1613 in 2023 was conducted. This analysis scrutinized the QPO's evolution throughout the outburst, particularly noting its rapid frequency es

Tianyu Cui, Hongxia Li, Jingya Wang, Ye Shi

Federated Prompt Learning (FPL) incorporates large pre-trained Vision-Language models (VLM) into federated learning through prompt tuning. The transferable representations and remarkable generalization capacity of VLM make them highly compatible with the integration of federated learning. Addressing data heterogeneity in federated learning requires personali

Tong Zhan, Chenxi Shi, Yadong Shi, Huixiang Li

With the rapid development of natural language processing (NLP) technology, large-scale pre-trained language models such as GPT-3 have become a popular research object in NLP field. This paper aims to explore sentiment analysis optimization techniques based on large pre-trained language models such as GPT-3 to improve model performance and effect and further

Megan Weiner Mansfield, Michael R. Line, Joost P. Wardenier, Matteo Brogi

Measurements of the carbon-to-oxygen (C/O) ratios of exoplanet atmospheres can reveal details about their formation and evolution. Recently, high-resolution cross-correlation analysis has emerged as a method of precisely constraining the C/O ratios of hot Jupiter atmospheres. We present two transits of the ultra-hot Jupiter WASP-76b observed between 1.4-2.4

Richard Lieu

Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field equations sourced by a topological defect, \ie~a singularity of a very specific form, the result is a localised gravitational field capable of driving flat rotation (\ie~Keplerian circular orbits at a constant speed for all radii) of test mas

Siyang Wang, Éva Székely

Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. These speech language models (SLMs), similarly to their textual counterparts, are scalable, probabilistic, and context-aware. While they can produce diverse and natural outputs, they sometimes face issues such as uninte