May 2023 arXiv papers — page 23

Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed

Generative models have had a profound impact on vision and language, paving the way for a new era of multimodal generative applications. While these successes have inspired researchers to explore using generative models in science and engineering to accelerate the design process and reduce the reliance on iterative optimization, challenges remain. Specifical

Marvin Bredlau, Alexander Weber, Alexander Knoll

We present a forest fire firefighting simulation tool named FORFIS that is implemented in Python. Unlike other existing software, we focus on a user-friendly software interface with an easy-to-modify software engine. Our tool is published under GNU GPLv3 license and comes with a GUI as well as additional output functionality. The used wildfire model is based

BAQIS Quafu Group

With the rapid advent of quantum computing, hybrid quantum-classical machine learning has shown promising computational advantages in many key fields. Quantum reinforcement learning, as one of the most challenging tasks, has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical

Luyao Ye, Zikang Zhou, Jianping Wang

Predicting the future trajectories of nearby objects plays a pivotal role in Robotics and Automation such as autonomous driving. While learning-based trajectory prediction methods have achieved remarkable performance on public benchmarks, the generalization ability of these approaches remains questionable. The poor generalizability on unseen domains, a well-

Wei Sun

In this paper, we shall extend the definition of $\mathcal{C}$-subsolution condition and adapt the argument of Guo-Phong-Tong[18] to replace Alexandroff-Bakelman-Pucci estimate in complex cases. As an application, we shall define and study the viscosity solutions to uniformly elliptic complex equations and prove the H\"older regularity, following the argumen

Calum Heggan, Tim Hospedales, Sam Budgett, Mehrdad Yaghoobi

Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets. A key reason that these powerful features enable data-efficient learning of downstream tasks is that they provide augmentation invariance, which is often a useful inductive bias. However, the amount and type of inv

Fei Zheng, Chaochao Chen, Lingjuan Lyu, Binhui Yao

Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, includ

Xiaobing Li, Yuming Zhong, Ranran Guo, Mingmei Xu

Using the single-spin flipping dynamics, we study the nonequilibrium evolution near the entire phase boundary of the 3D Ising model, and find that the average of relaxation time (RT) near the first-order phase transition line (1st-PTL) is significantly larger than that near the critical point (CP). As the system size increases, the average of RT near the 1st

Réka Könyves-Tóth, Bálint Seli

In this study, we analyze the post-maximum spectra of a sample of 27 Type I superluminous supernovae (SLSNe-I) in order to search for physical differences between the so-called Type W and Type 15bn sub-types. This paper is a continuation of \citet{ktr21} and \citet{ktr22}. In the former, it was revealed that not all SLSNe-I show the W-shaped absorption featu

Claire Gilson, Shi-Hao Li, Ying Shi

This paper is devoted to revealing the relationship between matrix-valued $\theta$-deformed bi-orthogonal polynomials and non-commutative Toda-type hierarchies. In this procedure, Wronski quasi-determinants are widely used and play the role of non-commutative $\tau$-functions. At the same time, B\"acklund transformations are realized by using a moment modifi

Fabiano Locatelli, Konstantinos Christodoulopoulos, Camille Delezoide, Josep M. Fàbrega

Superchannels leverage the flexibility of elastic optical networks and pave the way to higher capacity channels in space division multiplexing (SDM) networks. A superchannel consists of subchannels to which continuous spectral grid slots are assigned. To guarantee superchannel operation, we need to account for soft failures, e.g., laser drifts causing interf

Karen Lee-Waddell, Clancy W. James, Stuart D. Ryder, Elizabeth K. Mahony

The putative host galaxy of FRB 20171020A was first identified as ESO 601-G036 in 2018, but as no repeat bursts have been detected, direct confirmation of the host remains elusive. In light of recent developments in the field, we re-examine this host and determine a new association confidence level of 98%. At 37 Mpc, this makes ESO 601-G036 the third closest

Shaoqing Zhang, Wei Xu, Shi Jin, Xiaohu You

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually co

Yuxiang Qin, Sreedhar Balu, J. Stuart B. Wyithe

Using a semi-analytic galaxy-formation model, we study analogues of 8 recently discovered JWST galaxies at $z>{\sim}12$. We select analogues from a cosmological simulation with a $(311{\rm cMpc})^3$ volume and an effective particle number of $10^{12}$ enabling resolution of every atomic-cooling galaxy at $z{\le}20$. We vary model parameters to reproduce the

Lu Xu, Qin Yang

Chemists have been pursuing the general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure-activity relationship (QSAR) models have limited application domains, e.g., they tend to have poor generalization performance when applied to molecules with parent structures different

Michael Stimson, William Reid, Aneta Neumann, Simon Ratcliffe

Mine planning is a complex task that involves many uncertainties. During early stage feasibility, available mineral resources can only be estimated based on limited sampling of ore grades from sparse drilling, leading to large uncertainty in under-sampled parts of the deposit. Planning the extraction schedule of ore over the life of a mine is crucial for its

Harshit Kumar Choudhary, I. Vinod Reddy

A \emph{star coloring} of a graph $G$ is a proper vertex-coloring such that no path on four vertices is $2$-colored. The minimum number of colors required to obtain a star coloring of a graph $G$ is called star chromatic number and it is denoted by $\chi_s(G)$. A graph $G$ is called $k$-critical if $\chi_s(G)=k$ and $\chi_s(G -e) < \chi_s(G)$ for every edge

Hezhe Qiao, Guansong Pang

We reveal a one-class homophily phenomenon, which is one prevalent property we find empirically in real-world graph anomaly detection (GAD) datasets, i.e., normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker than normal nodes. However, this anomaly-discriminative property is igno

Yinji Li, Zhiwei Wang, Xiangyu Zhou

We show the existence and uniqueness of bounded solutions to the degenerate complex Monge-Amp\`ere type equations on compact Hermitian manifolds. We also study the asymptotics of these solutions. As applications, we give partial answers to the Tosatti-Weinkove conjecture and Demailly-P\u aun conjecture.

Timothe Presles, Cyrille Enderli, Gilles Burel, El Houssain Baghious

In image processing, image segmentation is the process of partitioning a digital image into multiple image segment. Among state-of-the-art methods, Markov Random Fields (MRF) can be used to model dependencies between pixels, and achieve a segmentation by minimizing an associated cost function. Currently, finding the optimal set of segments for a given image

Timothe Presles, Cyrille Enderli, Gilles Burel, El Houssain Baghious

The Integrated Side Lobe Ratio (ISLR) problem we consider here consists in finding optimal sequences of phase shifts in order to minimize the mean squared cross-correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP-hard. I

Garima Mishra, Somendra M. Bhattacharjee

We determine the phase diagram of DNA with inter- and intra-strand native-pair interactions that mimic the compaction of DNA. We show that DNA takes an overall sheet-like structure in the region where an incipient transition to a compact phase would have occurred. The stability of this phase is due to the extra entropy from the folding of the sheet, which is

Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro

This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider convolutional MNNs and GNNs where the manifold and the graph convolutions are respectively defined in terms of the Laplace-B

Amirhossein Layegh, Amir H. Payberah, Ahmet Soylu, Dumitru Roman

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-

JunTao He, Ji Lin

With the discovery of various matter wave solitons in spin-orbit-coupled Bose-Einstein condensates (BECs), exploring their properties has become increasingly significant. We mainly study stationary and moving bright solitons in spin-orbit-coupled spin-1 BECs with or without a Zeeman field. The bright solitons correspond to the plane wave (PW) and standing wa

Enrico Picotti, Enrico Mion, Alberto Dalla Libera, Josip Pavlovic

Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique. However, accurate control models for real vehicles could require costly and time-demanding specific measurements. To address this problem, the exploitation of system data to complement or deriv

Belle Collaboration, J. H. Yin, Y. B. Li, E. Won

We measure the cross section of $e^+e^-\rightarrow\eta_c J/\psi$ at the $\Upsilon(nS) (n=1$ -- $5)$ on-resonance and 10.52 GeV off-resonance energy points using the full data sample collected by the Belle detector with an integrated luminosity of $955~\rm fb^{-1}$. We also search for double charmonium production in $e^+e^-\rightarrow\eta_c J/\psi$ via initia

Chengchang Liu, Lesi Chen, Luo Luo, John C. S. Lui

We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires $\mathcal{O}(d)$ communication complexity, where $d$ is the problem dimension. We also provide theoretical analysis to show the proposed method has the similar convergence rate as the classical second-order optim

James Gardner, Scott Habershon, Reinhard J. Maurer

Mixed-quantum classical (MQC) methods for simulating the dynamics of molecules at metal surfaces have the potential to accurately and efficiently provide mechanistic insight into reactive processes. Here, we introduce simple two-dimensional models for the scattering of diatomic molecules at metal surfaces based on recently published electronic structure data

Florian Nill

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal bundle, A ~ A/G x G, then the normalized dynamics only depends on the quotient A/G. In this way, the dynamics of fraction

Shunichiro Kinoshita, Tomohiro Kozuka, Keiju Murata, Keita Sugawara

We study the quasinormal mode (QNM) spectrum of an asymptotically AdS black hole with the Robin boundary condition at infinity. We consider the Schwarzshild-AdS$_4$ with the flat event horizon as the background spacetime and study its scalar field perturbation. Denoting leading coefficients of slow- and fast-decay modes of the scalar field at infinity as $\p

Dimia Iberraken, Lounis Adouane

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and comp

Qingsheng Wang, Lingqiao Liu, Chenchen Jing, Hao Chen

Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute ``wet" in ``wet apple" and ``wet cat" is different. As a solution, we provide analysis and a

Nariki Tanaka, Hiroshi Kera, Kazuhiko Kawamoto

Using Fourier analysis, we explore the robustness and vulnerability of graph convolutional neural networks (GCNs) for skeleton-based action recognition. We adopt a joint Fourier transform (JFT), a combination of the graph Fourier transform (GFT) and the discrete Fourier transform (DFT), to examine the robustness of adversarially-trained GCNs against adversar

Moritz Hardt, Yu Sun

Many recent efforts augment language models with retrieval, by adding retrieved data to the input context. For this approach to succeed, the retrieved data must be added at both training and test time. Moreover, as input length grows linearly with the size of retrieved data, cost in computation and memory grows quadratically for modern Transformers. To avoid

Xu Chen, Zhiyong Feng, J. Andrew Zhang, Feifei Gao

In this paper, we propose a novel complex convolutional neural network (CNN) CSI enhancer for integrated sensing and communications (ISAC), which exploits the correlation between the sensing parameters (such as angle-of-arrival and range) and the channel state information (CSI) to significantly improve the CSI estimation accuracy and further enhance the sens

Yutong Xie, Bing Yang, Qingbiao Guan, Jianpeng Zhang

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we re

Harshal Potdar, Ignacio Pagonabarraga, Sudipto Muhuri

Experiments performed using micro-patterned one dimensional collision assays have allowed a precise quantitative analysis of the collective manifestation of contact inhibition locomotion (CIL) wherein, individual migrating cells reorient their direction of motion when they come in contact with other cells. Inspired by these experiments, we present a discrete

Raven Beutner, Bernd Finkbeiner, Hadar Frenkel, Niklas Metzger

We introduce Hyper$^2$LTL, a temporal logic for the specification of hyperproperties that allows for second-order quantification over sets of traces. Unlike first-order temporal logics for hyperproperties, such as HyperLTL, Hyper$^2$LTL can express complex epistemic properties like common knowledge, Mazurkiewicz trace theory, and asynchronous hyperproperties

Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo

We train language models (LMs) with federated learning (FL) and differential privacy (DP) in the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP-FTRL)~\citep{kairouz21b} algorithm to achieve meaningfully formal DP guarantees without requiring uniform sampling of client devices. To provide favorable privacy-utility trade-offs, we i

Jianqiu Chen, Zikun Zhou, Mingshan Sun, Tianpeng Bao

Many robotics and industry applications have a high demand for the capability to estimate the 6D pose of novel objects from the cluttered scene. However, existing classic pose estimation methods are object-specific, which can only handle the specific objects seen during training. When applied to a novel object, these methods necessitate a cumbersome onboardi

Haoran He, Peilin Wu, Chenjia Bai, Hang Lai

Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most works in RL operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available. Conversely, in real-world scenarios, robot agents usually rely solely on local states (e.g., proprioceptive feedback of r

Martin Balko, Marian Poljak

For graphs $G^<$ and $H^<$ with linearly ordered vertex sets, the \ordered Ramsey number $r_<(G^<,H^<)$ is the smallest positive integer $N$ such that any red-blue coloring of the edges of the complete ordered graph $K^<_N$ on $N$ vertices contains either a blue copy of $G^<$ or a red copy of $H^<$. Motivated by a problem of Conlon, Fox, Lee, and Sudakov (20

Zhongxi Chen, Ke Sun, Xianming Lin, Rongrong Ji

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods primarily employ semantic segmentation, which suffers from overconfident incorrect predictions. In this paper, we propose a new paradigm that treats COD as a conditional mask-generatio

Yuexiong Ding, Xiaowei Luo

Accident of struck-by machines is one of the leading causes of casualties on construction sites. Monitoring workers' proximities to avoid human-machine collisions has aroused great concern in construction safety management. Existing methods are either too laborious and costly to apply extensively, or lacking spatial perception for accurate monitoring. Theref

Eleonora Amoroso, Ángel Crespo-Blanco, Patrizia Pucci, Patrick Winkert

In this paper we first introduce an innovative equivalent norm in the Musielak-Orlicz Sobolev spaces in a very general setting and we then present a new result on the boundedness of the solutions of a wide class of nonlinear Neumann problems, both of independent interest. Moreover, we study a variable exponent double phase problem with a nonlinear boundary c

Yue Fan, Ningjing Fan, Ivan Skorokhodov, Oleg Voynov

We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists of three stages. In the first stage, we reconstruct the scen

Bin Li, Zhen Qian, Lei Liu, Yuan Wu

In the paper, we investigate the coordination process of sensing and computation offloading in a reconfigurable intelligent surface (RIS)-aided base station (BS)-centric symbiotic radio (SR) systems. Specifically, the Internet-of-Things (IoT) devices first sense data from environment and then tackle the data locally or offload the data to BS for remote compu

Yuexiong Ding, Xiaowei Luo

Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligence (AI) applications only applicable to some scenarios that only require 2D information. However, most advanced applications usually require AI agents to perceive 3D spatial inform

Peiyi Wang, Lei Li, Liang Chen, Zefan Cai

In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models. We find that the quality ranking of candidate responses can be easily hacked by simply altering their order of appearance in the context. This m

Gintaras Valiukevičius

We are studying properties of the name appointment in various categories of enriched graphs. The Yoneda lemma is generalized for continuous transforms between transports of enriched original graphs.

Zhiren Huang, Charalampos Sipetas, Alonso Espinosa Mireles de Villafranca, Tri Quach

Large urban special events significantly contribute to a city's vibrancy and economic growth but concurrently impose challenges on transportation systems due to alterations in mobility patterns. This study aims to shed light on mobility patterns by utilizing a unique, comprehensive dataset collected from the Helsinki public transport mobile application and B

Xinchun Yu, Shuangqing Wei, Shao-Lun Huang, Xiao-Ping Zhang

This paper investigates the asymptotics of the maximal throughput of communication over AWGN channels by $n$ channel uses under a covert constraint in terms of an upper bound $\delta$ of Kullback-Leibler divergence (KL divergence). It is shown that the first and second order asymptotics of the maximal throughput are $\sqrt{n\delta \log e}$ and $(2)^{1/2}(n\d

Ciro Ciliberto, Claudio Fontanari

The canonical degree $C.K_X$ of an integral curve on a smooth projective surface $X$ is conjecturally bounded from above by an expression of the form $A(g-1)+B$, where $g$ is the geometric genus of $C$ and $A$, $B$ are constants depending only on $X$. We prove that this conjecture holds with $A = -1$ under the assumptions $h^0(X, -K_X) = 0$ and $h^0(X, 2K_X

Juho Vepsäläinen

The web is the most prominent application platform globally, thanks to its vast user base. It started as a site platform in the 90s and morphed into an application one over time as interactive web applications became a reality. So-called single-page applications (SPAs) represent the current mainstream way of developing web applications but they come with the

Mario Figueira-Pereira, Xavier Barber, David Conesa, Antonio López-Quílez

In ecology we may find scenarios where the same phenomenon (species occurrence, species abundance, etc.) is observed using two different types of samplers. For instance, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale or bird watching fishery commercial vessels)

Yu Tang, Kaan Ozbay, Li Jin

Connected vehicles (CVs) can provide numerous new data via vehicle-to-vehicle or vehicle-to-infrastructure communication. These data can in turn be used to facilitate real-time traffic state estimation. In this paper, we focus on ramp queue length estimation in a connected vehicle environment, which improves control design and implementation of ramp metering

Amir Weiss, Andrew C. Singer, Gregory W. Wornell

Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenges, recent studies have shown that with a properly designed architecture, neural networks can lead to unprecedented localization capabilities and enhanced accuracy. However, the rob

Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf

Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not, and are widely used for assessing the privacy risks of language models. Most existing attacks rely on the observation that models tend to assign higher probabilities to their training samples than non-training points.

Liangyu Zhao, Arvind Krishnamurthy

We present a strongly polynomial-time algorithm to generate bandwidth optimal allgather/reduce-scatter on any network topology, with or without switches. Our algorithm constructs pipeline schedules achieving provably the best possible bandwidth performance on a given topology. To provide a universal solution, we model the network topology as a directed graph

H. Gharagulyan, Y. Melikyan, V. Hayrapetyan, Kh. Kirakosyan

The colloidal 2D materials based on graphene and its modifications are of great interest when it comes to forming LC phases. These LC phases allow controlling the orientational order of colloidal particles, paving the way for the efficient processing of modified graphene with anisotropic properties. Here, we present the peculiarities of AA functionalization

Ataberk Olgun, Majd Osseiran, Abdullah Giray Ya{ğ}lık{c}ı, Yahya Can Tuğrul

RowHammer (RH) is a significant and worsening security, safety, and reliability issue of modern DRAM chips that can be exploited to break memory isolation. Therefore, it is important to understand real DRAM chips' RH characteristics. Unfortunately, no prior work extensively studies the RH vulnerability of modern 3D-stacked high-bandwidth memory (HBM) chips,

Yoshihiro Sugimoto

In this article, we study the behavior of iterations of symplectomorphisms and Hamiltonian diffeomorphisms on symplectic manifolds. We prove that symplectomorphisms and Hamiltonian diffeomorphisms do not have $C^1$-recurrence on negatively monotone symplectic manifolds. This is a generalization of the results of the study of Polterovich, Ono, Atallah-Shelukh

Kang Han, Wei Xiang, Lu Yu

Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline, neural networks are used to represent a scene independently or transform queried learnable feature vector of a point to the expected color or density. With the aid of geometry guid

D. García-Beltrán, J. C. Ruíz-Pantaleón, E. Velasco-Barreras

For the so-called infinitesimal Poisson algebras encoding first-order jets of Poisson submanifolds, we provide a description of their first cohomology in terms of intrinsic cohomologies of the underlying Poisson submanifold. We establish a natural mapping from their first cohomology to the first Poisson cohomology of the corresponding Poisson submanifold. Mo

Li'ang Li, Yifei Duan, Guanghua Ji, Yongqiang Cai

The study of universal approximation properties (UAP) for neural networks (NN) has a long history. When the network width is unlimited, only a single hidden layer is sufficient for UAP. In contrast, when the depth is unlimited, the width for UAP needs to be not less than the critical width $w^*_{\min}=\max(d_x,d_y)$, where $d_x$ and $d_y$ are the dimensions

Jingyi Shi, Yang Xiao, Yuekang Li, Yeting Li

Deep learning (DL) applications are prevalent nowadays as they can help with multiple tasks. DL libraries are essential for building DL applications. Furthermore, DL operators are the important building blocks of the DL libraries, that compute the multi-dimensional data (tensors). Therefore, bugs in DL operators can have great impacts. Testing is a practical

Koji Kobashi

Solid CO2 phase I was studied using the pressure-constant NPT Monte Carlo simulation and the Kihara core potential in the temperature range below 194 K and the pressure range below 10 GPa. At a pressure of 1 bar, the temperature dependence of the calculated lattice constant agreed reasonably well with experiment. It was found that the random distribution of

Pieter Roffelsen

In the context of $q$-Painlev\'e VI with generic parameter values, the Riemann-Hilbert correspondence induces a one-to-one mapping between solutions of the nonlinear equation and points on an affine Segre surface. Upon fixing a generic point on the surface, we give formulae for the function values of the corresponding solution near the critical points, in th

Nirmalendu Prakash, Ming Shan Hee, Roy Ka-Wei Lee

Total Defence is a defence policy combining and extending the concept of military defence and civil defence. While several countries have adopted total defence as their defence policy, very few studies have investigated its effectiveness. With the rapid proliferation of social media and digitalisation, many social studies have been focused on investigating p

Qian Zhang, Wenming Zou

This paper is concerned with the following logarithmic Schr\"{o}dinger system: $$\left\{\begin{align} \ &\ -\Delta u_1+\omega_1u_1=\mu_1 u_1\log u_1^2+\frac{2p}{p+q}|u_2|^{q}|u_1|^{p-2}u_1,\\ \ &\ -\Delta u_2+\omega_2u_2=\mu_2 u_2\log u_2^2+\frac{2q}{p+q}|u_1|^{p}|u_2|^{q-2}u_2,\\ \ &\ \int_{\Omega}|u_i|^2\,dx=\rho_i,\ \ i=1,2,\\ \ &\ (u_1,u_2)\in H_0^1(\Ome

Safinah Ali, Vishesh Kumar, Cynthia Breazeal

An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. There is little work in using game-based le

Kathrin Bringmann, Zilong He, Ben Kane

In this paper, we investigate class numbers of shifted quadratic lattices $L+\frac{\boldsymbol{u}}{c}$ with $\boldsymbol{u}\in L$ and odd conductor $c\in \mathbb{N}$. For a lattice $L$ whose genus only contains one class, we determine a lower bound for the number of classes in the genus of $L+\frac{\boldsymbol{u}}{c}$ depending on $c$. As a result, we obtain

Junxin Che, Ruquan You, Fei Zeng, Haiwang Li

In the study of rotating channel flow, the key dimensionless parameters typically include the Reynolds number, rotation number, Prandtl number and buoyancy number. Our research focused on comparing the flow characteristics between the enlarged model, analyzed under the rotating similarity theory, and the original channel flow. Significantly different flow be

Renzo Cavalieri, Tyler L. Kelly, Rob Silversmith

We provide an explicit formula for all primary genus-zero $r$-spin invariants. Our formula is piecewise polynomial in the monodromies at each marked point and in $r$. To deduce the structure of these invariants, we use a tropical realization of the corresponding cohomological field theories. We observe that the collection of all WDVV relations is equivalent

Svanhvít Lilja Ingólfsdóttir, Pétur Orri Ragnarsson, Haukur Páll Jónsson, Haukur Barri Símonarson

Grammatical error correction (GEC) is the task of correcting typos, spelling, punctuation and grammatical issues in text. Approaching the problem as a sequence-to-sequence task, we compare the use of a common subword unit vocabulary and byte-level encoding. Initial synthetic training data is created using an error-generating pipeline, and used for finetuning

Naftali R. Smith

We consider an overdamped particle with a general physical mechanism that creates noisy active movement (e.g., a run-and-tumble particle or active Brownian particle etc.), that is confined by an external potential. Focusing on the limit in which the correlation time $\tau$ of the active noise is small, we find the nonequilibrium steady-state distribution $P_

Longyan Gong

We introduce a family of 1D aperiodic tight-binding models with linearly varying patches of A-type sites with on-site energies $\epsilon_A=0$ connected by single B-type sites with $\epsilon_B=W$. We analytically show such structures have strong spatial correlations. We theoretically find states are extended at resonance levels in the vicinity of $E^\kappa_M=

Xuejing Liu, Wei Tang, Jinghui Lu, Rui Zhao

Recent advancements in multimodal foundation models (e.g., CLIP) have excelled in zero-shot generalization. Prompt tuning involved in the knowledge transfer from foundation models to downstream tasks has gained significant attention recently. Existing prompt-tuning methods in cross-modal learning, however, either solely focus on language branch, or learn vis

Hirakjyoti Sarma, Ritupan Sarmah, Nilakshi Das

The dynamics of a harmonically trapped three-dimensional Yukawa ball of charged dust particles immersed in plasma is investigated as function of external magnetic field and Coulomb coupling parameter using molecular dynamics simulation. It is shown that the harmonically trapped dust particles organize themselves into nested spherical shells. The particles st

Keita Kume, Isao Yamada

We present an adaptive parametrization strategy for optimization problems over the Stiefel manifold by using generalized Cayley transforms to utilize powerful Euclidean optimization algorithms efficiently. The generalized Cayley transform can translate an open dense subset of the Stiefel manifold into a vector space, and the open dense subset is determined a

JinMyong An, YuIl Jo, JinMyong Kim

In this paper, we study the continuous dependence of the Cauchy problem for the inhomogeneous biharmonic nonlinear Schr\"{o}dinger (IBNLS) equation \[iu_{t} +\Delta^{2} u=\lambda |x|^{-b}|u|^{\sigma}u,~u(0)=u_{0} \in H^{s} (\mathbb R^{d}),\] in the standard sense in $H^s$, i.e. in the sense that the local solution flow is continuous $H^s\to H^s$. Here $d\in

Hongyan Guo, Huaimin Li

In this paper, a family of infinite dimensional Lie algebras $\tilde{\mathcal{L}}$ is introduced and investigated, called the extended Heisenberg-Virasoro algebra,denoted by $\tilde{\mathcal{L}}$. These Lie algebras are related to the $N=2$ superconformal algebra and the Bershadsky-Polyakov algebra. We study restricted modules and associated vertex algebras

Deshendran Moodley, Christopher Seebregts

This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. We use the case of public health emergency response to disease outbreaks and epidemic control. There is potential to take advantage of the increasing availability o

Xin Hua, Zhijiang Du, Hongjian Yu, Jixin Maa

Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, existing deep learning-based SR methods p

T. Xu, X. Wu, J. Zhang

The traditional information security based on cryptosystem is seriously threatened due to the exponential growth of computing capacity. In order to improve for the upper cryptosystem security, the secure transmission at the physical layer is introduced into the wireless communication system. However, considering the openness of wireless channel, the performa

Lirui Xu, Pang Wu, Pan Xia, Fanglin Geng

The local beat-to-beat local pulse pressure (PP) and blood pressure waveform of arteries, especially central arteries, are important indicators of the course of cardiovascular diseases (CVDs). Nevertheless, noninvasive measurement of them remains a challenge in the clinic. This work presents a three-element image-free ultrasound system with a low-computation

Xiang Zhang, Yan Lu, Huan Yan, Jingyang Huang

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. Ho

Alice Desmons, Sarah Brough, Cristina Martínez-Lombilla, Roberto De Propris

We conduct a comparison of the merging galaxy populations detected by a sample of visual identification of tidal features around galaxies as well as spectroscopically-detected close pairs of galaxies to determine whether our method of selecting merging galaxies biases our understanding of galaxy interactions. Our volume-limited parent sample consists of 852

Niklas Lemcke

First we refine the duality theory for Witt divisorial sheaves on smooth projective varieties over a perfect field of positive characteristic. Building on previous work [Lem22], we remove the residual derived limit to obtain a cleaner isomorphism. As an application, we prove a Ramanujam-type vanishing theorem for Witt divisorial sheaves of nef and big diviso

Ao Qu, Xuhuan Huang, Dajiang Suo

Recent advances in sensing and communication have paved the way for collective perception in traffic management, with real-time data sharing among multiple entities. While vehicle-based collective perception has gained traction, infrastructure-based approaches, which entail the real-time sharing and merging of sensing data from different roadside sensors for

Linhao Qu, Xiaoyuan Luo, Kexue Fu, Manning Wang

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly

Nobumitsu Yokoi

Non-equilibrium property of turbulence modifies characteristics of turbulent transport. With the aid of response-function formalism, such non-equilibrium effects in turbulent transport can be represented by the temporal variation of the turbulent energy ($K$) and its dissipation rate ($\varepsilon$) along the mean stream through the advective derivatives of

Chanaprom Cholsuk, Sujin Suwanna, Tobias Vogl

A solid-state quantum emitter is one of the indispensable components for optical quantum technologies. Ideally, an emitter should have a compatible wavelength for efficient coupling to other components in a quantum network. It is therefore essential to understand fluorescent defects that lead to specific emitters. In this work, we employ density functional t

Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang

Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break down at lower bit precision, and investigate quantization aware training for LLMs (LLM-QAT) to push quantization levels even further. We propose a data-free distillation method that

Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang

Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are also powerful in modeling complex policies or trajectories in offline datasets. However, these works have been limited to single-task settings where a generalist agent capable of ad

Anh T Nguyen, Lam Tran, Anh Tong, Tuan-Duy H. Nguyen

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabeled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yiel

Kohei Ueda, Hayato Fujii, Takanori Kida, Masayuki Hagiwara

We report on efficient spin current generation at room temperature in rutile type WO$_{2}$ grown on Al$_{2}$O$_{3}$(0001) substrate. The optimal WO$_{2}$ film has (010)-oriented monoclinically distorted rutile structure with metallic conductivity due to 5$\it{d}$$^2$ electrons, as characterized by x-ray diffraction, electronic transport, and x-ray photoelect

Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game situation and make decisions. Previous studies using deep reinforcement learning have often considered teams as a single agent and valued the teams and players who hold the ball in ea

Hormuzd Bodhanwalla, Dheeraj Raghunathan, Y. Sudhakar

We present a fully-explicit, iteration-free, weakly-compressible method to simulate immiscible incompressible two-phase flows. To update pressure, we circumvent the computationally expensive Poisson equation and use the general pressure equation. In addition, the volume-of-fluid approach is used for interface capturing under the operator-split methodology. O