April 2023 arXiv papers — page 43

Tejaswanth Reddy Maram, Ria Barnwal, Bindu B

The objective of this study is to illustrate the process of training a Deep Neural Network (DNN) within a Resistive RAM (ReRAM) Crossbar-based simulation environment using CrossSim, an Application Programming Interface (API) developed for this purpose. The CrossSim API is designed to simulate neural networks while taking into account factors that may affect

Benedikt Groß, Gerhard Wunder

Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution.

Bohai Gu, Heng Fan, Libo Zhang

Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded by introducing UniST, a Unified Style Tr

Ivan Cheltsov, Kento Fujita, Takashi Kishimoto, Jihun Park

We prove that smooth Fano 3-folds in the families 2.18 and 3.4 are K-stable.

Ivan Cheltsov, Arman Sarikyan, Ziquan Zhuang

We prove that for every $\epsilon>0$, there is a birationally super-rigid Fano variety $X$ such that $\frac{1}{2}\leqslant\alpha(X)\leqslant \frac{1}{2}+\epsilon$. Also we show that for every $\epsilon>0$, there is a Fano variety $X$ and a finite subgroup $G\subset\mathrm{Aut}(X)$ such that $X$ is $G$-birationally super-rigid, and $\alpha_G(X)<\epsilon$.

Yizhe Zhang, Tao Zhou, Shuo Wang, Peixian Liang

The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partition

Oguzhan Kasikci, Mehmet Ozkan, Yi Pang

We propose that models with spacetime dipole symmetry are connected to Lorentz invariant models via the Carrollian limit. In this way, a recently proposed model with spacetime dipole symmetry was readily reproduced together with its conserved charges. We then couple this model to a dynamical Abelian gauge field and Carroll gravity. Our procedure can be appli

Anthony G Cohn, Jose Hernandez-Orallo

Language models have become very popular recently and many claims have been made about their abilities, including for commonsense reasoning. Given the increasingly better results of current language models on previous static benchmarks for commonsense reasoning, we explore an alternative dialectical evaluation. The goal of this kind of evaluation is not to o

Shaoteng Liu, Xiangyu Zhang, Tao Hu, Jiaya Jia

We present view-synthesis autoencoders (VSA) in this paper, which is a self-supervised learning framework designed for vision transformers. Different from traditional 2D pretraining methods, VSA can be pre-trained with multi-view data. In each iteration, the input to VSA is one view (or multiple views) of a 3D object and the output is a synthesized image in

Lisa Julia Nebel, Oliver Sander, Mircea Bîrsan, Patrizio Neff

We investigate discretizations of a geometrically nonlinear elastic Cosserat shell with nonplanar reference configuration originally introduced by B\^irsan, Ghiba, Martin, and Neff in 2019. The shell model includes curvature terms up to order 5 in the shell thickness, which are crucial to reliably simulate high-curvature deformations such as near-folds or cr

Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn

A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE). In existing samplers, the coefficients of the ODE solvers are pre-determined by the ODE formulation, the reverse discrete timesteps, and the employed ODE methods. In this paper, we consider accelerating several popular ODE-based sampling proce

Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian

A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead of invariant features. However, several recent studies challenged this explanation and found that deep networks may have already learned sufficiently good features for OOD generaliz

Dániel Gerbner, Balázs Keszegh, Kartal Nagy, Balázs Patkós

In the game theoretical approach of the basic problem in Combinatorial Search an adversary thinks of a defective element $d$ of an $n$-element pool $X$, and the questioner needs to find $x$ by asking questions of type is $d\in Q$? for certain subsets $Q$ of $X$. We study cooperative versions of this problem, where there are multiple questioners, but not all

Pranjal Dutta, Prateek Dwivedi, Nitin Saxena

Polynomial Identity Testing (PIT) is a fundamental computational problem. The famous depth-$4$ reduction result by Agrawal and Vinay (FOCS 2008) has made PIT for depth-$4$ circuits an enticing pursuit. A restricted depth-4 circuit computing a $n$-variate degree-$d$ polynomial of the form $\sum_{i = 1}^{k} \prod_{j} g_{ij}$, where $\deg g_{ij} \leq \delta$ is

Chen Wu, Xingjie Hao, Meiqi Hu, Chengguqiu Dai

Wearing face mask is an effective measure to reduce the risk of COVID-19 infections and control its transmission, thus its usage survey is important for better policy decision to mitigate the epidemic spread. Current existing worldwide surveys are mostly self-reported, whose accuracies are hard to guaranteed, and may exaggerate the percentage of face mask we

Yuanhang Zhang, Nicolas K. Fontaine

Multi-plane light conversion (MPLC) has recently been developed as a versatile tool for manipulating spatial distributions of the optical field through repeated phase modulations. An MPLC Device consists of a series of phase masks separated by free-space propagation. It can convert one orthogonal set of beams into another orthogonal set through unitary trans

Riya Ghosh, A. Antony Selvan

The frame set of a window $\phi\in L^2(\mathbb{R})$ is the subset of all lattice parameters $(\alpha, \beta)\in \mathbb{R}^2_+$ such that $\mathcal{G}(\phi,\alpha,\beta)=\{e^{2\pi i\beta m\cdot}\phi(\cdot-\alpha k) : k, m\in\mathbb{Z}\}$ forms a frame for $L^2(\mathbb{R})$. In this paper, we investigate the frame set of B-splines, totally positive functions,

Ruiyuan Kang, Dimitrios Kyritsis, Panos Liatsis

A network-based optimization approach, EEE, is proposed for the purpose of providing validation-viable state estimations to remediate the failure of pretrained models. To improve optimization efficiency and convergence, the most important metrics in the context of this research, we follow a three-faceted approach based on the error from the validation proces

Lin Qi, Xuewen Qin, Feng Gao, Junyu Dong

Hyperspectral unmixing is a critical yet challenging task in hyperspectral image interpretation. Recently, great efforts have been made to solve the hyperspectral unmixing task via deep autoencoders. However, existing networks mainly focus on extracting spectral features from mixed pixels, and the employment of spatial feature prior knowledge is still insuff

Chen Zhao, Wei-Ling Cai, Zheng Yuan

Existing image-to-image (I2I) translation methods achieve state-of-the-art performance by incorporating the patch-wise contrastive learning into Generative Adversarial Networks. However, patch-wise contrastive learning only focuses on the local content similarity but neglects the global structure constraint, which affects the quality of the generated images.

Yueyang Liu, Zois Boukouvalas, Nathalie Japkowicz

The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest. Curtailing its prevalence is of great importance and machine learning has shown significant promise. However, there are two main challenges when applying machine learning to this problem. First, while much too prevalen

Dhawal Buaria, Katepalli R. Sreenivasan

Turbulent flows consist of a wide range of interacting scales. Since the scale range increases as some power of the flow Reynolds number, a faithful simulation of the entire scale range is prohibitively expensive at high Reynolds numbers. The most expensive aspect concerns the small scale motions; thus, major emphasis is placed on understanding and modeling

Huan Zhao, Xiao-Qian Wang, Chao Gao, Zhuo Yu

We present a new technique, iterative fluctuation ghost imaging (IFGI) which dramatically enhances the resolution of ghost imaging (GI). It is shown that, by the fluctuation characteristics of the second-order correlation function, the imaging information with the narrower point spread function (PSF) than the original information can be got. The effects aris

Rajesh Kumar, Rajesh Kumar Yadav, Avinash Khare

We consider the rationally extended harmonic oscillator potential which is isospectral to the conventional one and whose solutions are associated with the exceptional, $X_m$- Hermite polynomials and discuss its various important properties for different even codimension of $m$. The uncertainty relations are obtained for different $m$ and it is shown that for

Tsung-Han Kuo, Zhenge Jia, Tei-Wei Kuo, Jingtong Hu

With the increased accuracy of modern computer vision technology, many access control systems are equipped with face recognition functions for faster identification. In order to maintain high recognition accuracy, it is necessary to keep the face database up-to-date. However, it is impractical to collect the latest facial picture of the system's user through

Naixin Zhu

In my dissertation, I will analyze how the product market position of a mobile app affects its pricing strategies, which in turn impacts an app's monetization process. Using natural language processing and k-mean clustering on apps' text descriptions, I created a new variable that measures the distinctiveness of an app as compared to its peers. I created fou

Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn

We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward process. A typical DPM first obtains an estimate of the original d

Katie Seaborn, Shruti Chandra, Thibault Fabre

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic

Jie Xin, Alexander G. Gavriliuk, Jia-Wei Hu, Jian-Bo Zhang

The in-plane superexchange interaction $J$ of cuprate superconductors has long been suggested to be an important parameter for exploring their high-temperature superconductivity. The bilayer Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ is the most studied system with high-quality single crystals in the wide doping range with the the same structure and phase. So far, t

Surya Pratap Singh, Billy Mazotti, Dhyey Manish Rajani, Sarvesh Mayilvahanan

This paper presents a detailed examination of low-light visual Simultaneous Localization and Mapping (SLAM) pipelines, focusing on the integration of state-of-the-art (SOTA) low-light image enhancement algorithms with standard and contemporary SLAM frameworks. The primary objective of our work is to address a pivotal question: Does illuminating visual input

Lida Zhu, Shuichi Makita, Junya Tamaoki, Antonia Lichtenegger

Multiple scattering is one of the main factors that limits the penetration depth of optical coherence tomography (OCT) in scattering samples. We propose a method termed multi-focus averaging (MFA) to suppress the multiple-scattering signals and improve the image contrast of OCT in deep regions. The MFA method captures multiple OCT volumes with various focal

Hongfei Zhang, Shu Zhang

In this paper, we consider complex-valued solutions of the planar Schr\"odinger-Newton system, which can be described by minimizers of the constraint minimization problem. It is shown that there exists a critical rotational velocity $0<\Omega^*\leq \infty$, depending on the general trapping potential $V(x)$, such that for any rotational velocity $0\leq\Omega

Guangyu Tan, Yijun Hou, Hui Tian

Supra-arcade downflows (SADs) are dark voids descending towards the post-reconnection flare loops and exhibit obvious variation in properties like width. However, due to the lack of further statistical studies, the mechanism behind such variations hitherto remains elusive. Here we statistically investigated widths of 81 SADs observed in one flare by the Sola

Kuanren Qian, Ashlee S. Liao, Shixuan Gu, Victoria A. Webster-Wood

Neuron growth is a complex, multi-stage process that develops sophisticated morphologies and interwoven neurite networks. Recent advances have enabled us to examine the effects of neuron growth factors and seek causes for neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. A computational tool that

Jia-Wei Hu, Kai Zhang, Yong-Chang Ma, Nan-Lin Wang

Exploring the origin of the pseudogap is important for the understanding of superconductivity in cuprates. Here we report a systematical experimental study on the phonon vibrational properties of Tl$_2$Ba$_2$Ca$_{n-1}$Cu$_n$O$_{2n+4+\delta}$ ($n$=1,2,3) single crystals based on the Raman scattering measurements over the temperature range from 10 to 300 K. Th

Behnam Nikoobakht

The advent of powerful laser sources has made it possible to observe a relatively large cross section of the excited state of Hydrogen atom. This is due to the effect of joint collisions of a linearly polarized $N$-photon and high-energy electron. For such a process, we evaluate the excitation cross section for geometries, in which the laser field is perpend

Kazuya Kato, Chikara Nakayama, Sampei Usui

To advance our log Hodge theory, we introduce log real analytic functions and log $C^{\infty}$ functions, define how to integrate them, and prove the log Poincar\'e lemma. We give better understandings of the degeneration of Hodge structure, including a geometric interpretation of the theory of $SL(2)$-orbits.

Joaquim P. Jossy, Prateek Gupta

Density gradients aligned at an angle to pressure gradients result in baroclinic torque in fluid flows, generating vorticity. In this work, we study the vorticity generated by the baroclinic torque exerted by the interaction of pressure jumps across random two-dimensional shock waves with density gradients. A field of random two-dimensional shock waves has a

Alexander Shlapunov, Nikolai Tarkhanov

Let $X$ be a smooth $n\,$-dimensional manifold and $D$ be an open connected set in $X$ with smooth boundary $\partial D$. Perturbing the Cauchy problem for an elliptic system $Au = f$ in $D$ with data on a closed set $\iG \subset \partial D$ we obtain a family of mixed problems depending on a small parameter $\varepsilon > 0$. Although the mixed problems are

Zilong Lin, Zhengyi Li, Xiaojing Liao, XiaoFeng Wang

As a prominent instance of vandalism edits, Wiki search poisoning for illicit promotion is a cybercrime in which the adversary aims at editing Wiki articles to promote illicit businesses through Wiki search results of relevant queries. In this paper, we report a study that, for the first time, shows that such stealthy blackhat SEO on Wiki can be automated. O

Yuanyuan Li

In this paper, we solve the $L_p$ chord Minkowski problem in the case of discrete measures whose supports are in general position for negative $p$ and $q>0.$ As for general Borel measure with a density, we also give a proof but need $p\in(-n,0)$ and $n+1>q\geqslant 1.$ The $L_p$ chord Minkowski problem was recently posed by Lutwak, Xi, Yang and Zhang, which

Fen Zou, Yong Li, Jie-Qiao Liao

Engineering multiphoton resources is of importance in quantum metrology, quantum lithography, and biological sensing. Here we propose a concept of dynamical emission of $N$ strongly-correlated photons. This is realized in a circuit quantum electrodynamical system driven by two Gaussian-pulse sequences. The underlying physical mechanism relies on the stimulat

Changwei Xiong

Let $U\subset \mathbb{R}^n$ ($n\geq 3$) be an exterior Euclidean domain with smooth boundary $\partial U$. We consider the Steklov eigenvalue problem on $U$. First we derive a sharp lower bound for the first eigenvalue in terms of the support function and the distance function to the origin of $\partial U$. Second under various geometric conditions on $\part

Chenkai Liu, Chu Ma, Yun Lai, Nicholas X. Fang

The scattering of waves is a ubiquitous phenomenon in physics, yet there are numerous scenarios, such as the pursuit of invisibility, where suppressing it is of utmost importance. In comparison to prior methods which are restricted by limited bandwidths, here we present a technique to suppress sound scattering across an ultra-broad spectrum by utilizing illu

Mengfei Zhang, Zhiyuan Li, Ziqian Hua Mark R. Morris

We perform three-dimensional numerical simulations to study the hydrodynamic evolution of Sgr A East, the only known supernova remnant (SNR) in the center of our Galaxy, to infer its debated progenitor SN type and its potential impact on the Galactic center environment. Three sets of simulations are performed, each of which represents a represent a certain t

Ding Li, Qichao Zhang, Zhongpu Xia, Kuan Zhang

Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map.

Takashi Shimada, Fumiko Ogushi, Janos Torok, Janos Kertesz

A simple dynamical model of collective edit activity of Wikipedia articles and their content evolution is introduced. Based on the recent empirical findings, each editor in the model is characterized by an ability to make content edit, i.e., improving the article by adding content and a tendency to make maintenance edit, i.e., dealing with formal aspects and

Jacqueline Urakami, Katie Seaborn

Communication between people is characterized by a broad range of nonverbal cues. Transferring these cues into the design of robots and other artificial agents that interact with people may foster more natural, inviting, and accessible experiences. In this position paper, we offer a series of definitive nonverbal codes for human-robot interaction (HRI) that

Noshin Nawal

The energy inefficiency of the apps can be a major issue for the app users which is discussed on App Stores extensively. Previous research has shown the importance of investigating the energy related app reviews to identify the major causes or categories of energy related user feedback. However, there is no study that efficiently extracts the energy related

Noreen Anwar, Philippe Duplessis-Guindon, Guillaume-Alexandre Bilodeau, Wassim Bouachir

This paper presents a novel approach for visible-thermal infrared stereoscopy, focusing on the estimation of disparities of human silhouettes. Visible-thermal infrared stereo poses several challenges, including occlusions and differently textured matching regions in both spectra. Finding matches between two spectra with varying colors, textures, and shapes a

Katie Seaborn, Somang Nam, Julia Keckeis, Tatsuya Itagaki

The Japanese notion of "kawaii" or expressions of cuteness, vulnerability, and/or charm is a global cultural export. Work has explored kawaii-ness as a design feature and factor of user experience in the visual appearance, nonverbal behaviour, and sound of robots and virtual characters. In this initial work, we consider whether voices can be kawaii by explor

Shaoqi Sun, Shijie Sun, Lijun Xu, Jing Zhong

Magnetic particle spectroscopy (MPS) is one of the most versatile methods to characterize the magnetic properties of magnetic nanoparticles (MNPs). The excitation magnetic field is one of the most crucial factors that affects the MPS signal of the MNPs. In this study, a multi-frequency MPS system is developed to investigate the MPS signal of MNPs in differen

Hongchao Zhang, Tao Zhu, Anzhong Wang

We propose a cosmological model in the framework of Poincar\'e gauge gravity, in which cosmological constant, inflaton, and dark matter candidate all naturally originate. Cosmological constant originates in the process of breaking of the Poincar\'e symmetries down to the Lorentz symmetries. We select a gauge Lagrangian without any additional matter fields, w

Zhengguang Liu, Yanrong Zhang, Xiaoli Li

In recent years, the scalar auxiliary variable (SAV) approach has become very popular and hot in the design of linear, high-order and unconditional energy stable schemes of gradient flow models. However, the nature of SAV-based numerical schemes preserving modified energy dissipation limits its wider application. A relaxation technique to correct the modifie

Anqi Fu, Vicki T. Taasti, Masoud Zarepisheh

Reducing proton treatment time improves patient comfort and decreases the risk of error from intra-fractional motion, but must be balanced against clinical goals and treatment plan quality. We formulated the proton treatment planning problem as a convex optimization problem with a cost function consisting of a dosimetric plan quality term plus a weighted $l_

Bonhee Ku, Tatsuya Itagaki, Katie Seaborn

Mindfulness meditation is a validated means of helping people manage stress. Voice-based virtual assistants (VAs) in smart speakers, smartphones, and smart environments can assist people in carrying out mindfulness meditation through guided experiences. However, the common fixed location embodiment of VAs makes it difficult to provide intuitive support. In t

Isabell Lederer, Rudolf Mayer, Andreas Rauber

The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains that can build on a solid understanding of the threats, attacks and defenses available to protect their IP, the

Yufan Zhang, Sujit Dey, Yuanyuan Shi

Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers. We model such decision-making processes by a bilevel program, with the power network at th

Xiao-Wei Zhang, Yafei Ren, Chong Wang, Ting Cao

We develop a first-principles quantum scheme to calculate the phonon magnetic moment in solids. As a showcase example, we apply our method to study gated bilayer graphene, a material with strong covalent bonds. According to the classical theory based on the Born effective charge, the phonon magnetic moment in this system should vanish, yet our quantum mechan

Han Zhang, Hao Zhou, Medhat Elsayed, Majid Bavand

Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs). In conventional cell-centric TS strategies, BSs make TS decisions for all user equipment (UEs) in a centralized manner, which focuses more on the overall performan

Zipei Nie, Hang Zhou

We study the unit-demand capacitated vehicle routing problem in the random setting of the Euclidean plane. The objective is to visit $n$ random terminals in a square using a set of tours of minimum total length, such that each tour visits the depot and at most $k$ terminals. We design an elegant algorithm combining the classical sweep heuristic and Arora's f

Mark Swillus, Andy Zaidman

The analysis of sentimental posts about software testing on Stack Overflow reveals that motivation and commitment of developers to use software testing methods is not only influenced by tools and technology. Rather, attitudes are also influenced by socio-technical factors. No prior studies have attempted to talk with Stack Overflow users about the sentimenta

Takane Ueno, Yeongdae Kim, Hiroki Oura, Katie Seaborn

The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no "true" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on platforms like Twitter, is a global pro

Marcus Krellner, The Anh Han

Humans judge each other&#39;s actions, which at least partly functions to detect and deter cheating and to enable helpfulness in an indirect reciprocity fashion. However, most forms of judging do not only concern the action itself, but also the moral status of the receiving individual (to deter cheating it must be morally acceptable to withhold help from che

Leonard Papenmeier, Luigi Nardi, Matthias Poloczek

Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-the-art methods for high-dimensional Bayes

A. D. Alhaidari, T. J. Taiwo

We introduce nonlinear extension of the J-matrix method of scattering. The formulation relies predominantly on the linearization of products of orthogonal polynomials. We present a toy model as an illustrative example and obtain the nonlinear scattering matrix.

Mateus V. Gasparino, Prabhat K. Mishra, Girish Chowdhary

This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure. We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties. Generally, non-parame

Samed Keşir, Sefa Kayraklık, İbrahim Hökelek, Ali Emre Pusane

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical l

Kaustav Bhattacharjee, Aritra Dasgupta

The open data ecosystem is susceptible to vulnerabilities due to disclosure risks. Though the datasets are anonymized during release, the prevalence of the release-and-forget model makes the data defenders blind to privacy issues arising after the dataset release. One such issue can be the disclosure risks in the presence of newly released datasets which may

Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo

It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industr

Hengky Susanto, David James Woo, Kai Guo

The recent advancement in Natural Language Processing (NLP) capability has led to the development of language models (e.g., ChatGPT) that is capable of generating human-like language. In this study, we explore how language models can be utilized to help the ideation aspect of creative writing. Our empirical findings show that language models play different r

Yanan Wu, Songhe Feng, Yang Wang

Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label ima

Ryuichi Sai, Mathias Jacquelin, François P. Hamon, Mauricio Araya-Polo

Designing large-scale geological carbon capture and storage projects and ensuring safe long-term CO2 containment - as a climate change mitigation strategy - requires fast and accurate numerical simulations. These simulations involve solving complex PDEs governing subsurface fluid flow using implicit finite-volume schemes widely based on Two-Point Flux Approx

Chahak Mehta, Krishna Kumar

Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata and leverages scientific domain knowledge to synthesize a new dataset from the original to construct knowledge graphs. We demonstrate our approach's effectivenes

Dmitry Zinoviev, Arkapravo Sarkar, Pelin Bicen

Elon Musk has long been known to significantly impact Wall Street through his controversial statements and actions, particularly through his own use of social media. An innovator and visionary entrepreneur, Musk is often considered a poster boy for all entrepreneurs worldwide. It is, thus, interesting to examine the effect that Musk might have on Main Street

Bill Tomlinson, Andrew W. Torrance, William J. Ripple

In the past several years, scientists have issued a series of warnings about the threats of climate change and other forms of environmental disruption. Here, we provide a scientists' warning on how technology affects these issues. Technology simultaneously provides substantial benefits for humanity, and also profound costs. Current technological systems are

Wan-Jin Yeo, Yao-Rui Yeo, Aaron Miller, Samu Taulu

We introduce a formulation where individual line segments of a current loop have translationally non-invariant contributions to the electro-quasi-static magnetic scalar potential and magnetic field in source-free regions. While closed current loops composed of these open segments have translationally invariant contributions, our formulation indicates that th

Sara Amani, Lance White, Trini Balart, Laksha Arora

ChatGPT is a natural language processing tool that can engage in human-like conversations and generate coherent and contextually relevant responses to various prompts. ChatGPT is capable of understanding natural text that is input by a user and generating appropriate responses in various forms. This tool represents a major step in how humans are interacting

Nikola Faltová, Michal Prišegen, Klaus Bernhard, Stefan Hümmerich

Context. The second subclass of chemically peculiar stars, the CP2 stars, are early-type stars exhibiting anomalous abundances with variable line strengths possibly also accompanied by photometric variability that typically belong to the Galactic disk. However, a small fraction of these objects were recently found to be located far from the Galactic plane an

Atoosa Kasirzadeh

The allure of emerging AI technologies is undoubtedly thrilling. However, the promise that AI technologies will benefit all of humanity is empty so long as we lack a nuanced understanding of what humanity is supposed to be in the face of widening global inequality and pressing existential threats. Going forward, it is crucial to invest in rigorous and collab

Cheolmin Kim, Youngseok Kim, Diego Klabjan

We introduce a mini-batch stochastic variance-reduced algorithm to solve finite-sum scale invariant problems which cover several examples in machine learning and statistics such as principal component analysis (PCA) and estimation of mixture proportions. The algorithm is a stochastic generalization of scale invariant power iteration, specializing to power it

Yu-Hui Chen, Raman Sarokin, Juhyun Lee, Jiuqiang Tang

The rapid development and application of foundation models have revolutionized the field of artificial intelligence. Large diffusion models have gained significant attention for their ability to generate photorealistic images and support various tasks. On-device deployment of these models provides benefits such as lower server costs, offline functionality, a

Tianyu Yuan, Hao Yan, Mary Lou P. Bailey, Jessica F. Williams

Many researchers have been encouraged to describe the dynamics of chromosomal loci in chromatin using the classical Rouse model of polymer dynamics by the agreement between the measured mean square displacement (MSD) versus time of fluorescently-labelled loci and the Rouse-model predictions. However, the discovery of intermediate-scale chromatin organization

Cedric Donié, Neha Das, Satoshi Endo, Sandra Hirche

Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex

J. Osorno, N. Nagar, T. Richtler, P. Humire

The stellar dynamic-based black hole mass measurements of M87 are twice that determined via ionized gas kinematics; the former is closer to the estimation from the diameter of the gravitationally-lensed ring around the black hole. Using deeper and more comprehensive ionized gas kinematic data, we aim to better constrain the morphology and kinematics of the n

Aaditya Singh, Kartik Sarangmath, Prithvijit Chattopadhyay, Judy Hoffman

Robustness to natural distribution shifts has seen remarkable progress thanks to recent pre-training strategies combined with better fine-tuning methods. However, such fine-tuning assumes access to large amounts of labelled data, and the extent to which the observations hold when the amount of training data is not as high remains unknown. We address this gap

Sourav Sinha, Mazen Farhood

Decision making in advanced driver assistance systems involves in general the estimated trajectories of the surrounding objects. Multiple object tracking refers to the process of estimating in real time these trajectories, leveraging for this purpose sensors to detect the objects. This paper deals with devising attacks on object tracking in automated vehicle

Michael A. Fedderke, Jedidiah O. Thompson, Raphael Cervantes, Bianca Giaccone

We propose a novel search technique for axions with a $CP$-violating monopole coupling $\tilde{g}_Q$ to bulk Standard Model charges $Q \in \{B,L,B-L\}$. Gradients in the static axion field configurations sourced by matter induce achromatic circular photon birefringence via the axion-photon coupling $g_{\phi\gamma}$. Circularly polarized light fed into an opt

Ali Mokhtari-Jazi, Matthew R. C. Fitzpatrick, Malcolm P. Kennett

There has been considerable interest in the disordered Bose Hubbard model (BHM) in recent years, particularly in the context of thermalization and many-body localization. We develop a two-particle irreducible (2PI) strong-coupling approach to the disordered BHM that allows us to treat both equilibrium and out-of-equilibrium situations. We obtain equations of

Alp Aydinoglu, Adam Wei, Wei-Cheng Huang, Michael Posa

We propose a hybrid model predictive control algorithm, consensus complementarity control (C3), for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks which require initiating contact with the environment, such as locomotion and manipulation, require a priori mode schedules or are too computationally compl

Jiahui Huang, Wei Liu, Xiang Cheng, Alessio Miranda

Precise positioning of single site-controlled inverted pyramidal InGaAs QD at the antinode of a GaAs photonic crystal cavity with nanometer-scale accuracy holds unique advantages compared to self-assembled QDs and offers great promise for practical on-chip photonic quantum information processing. However, the strong coupling regime in this geometry has not y

Felipe Urrutia, Roberto Araya

Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoher

Gagan Bhatia, Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed

We describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where we tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual models under a full supervised setting (subtasks A and B). We also develop models for the zero-shot setting (subtask C). Our approach involves experiment

Vagner Figueredo de Santana, Raquel Zarattini Chebabi, David Millen

Agriculture is impacted by multiple variables such as weather, soil, crop, stocks, socioeconomic context, cultural aspects, supply and demand, just to name a few. Hence, understanding this domain and identifying challenges faced by stakeholders is hard to scale due to its highly localized nature. This work builds upon six months of field research and present

Don Coppersmith, Michael J. Mossinghoff, Danny Scheinerman, Jeffrey M. VanderKam

For given positive integers $m$ and $n$ with $m<n$, the Prouhet-Tarry-Escott problem asks if there exist two disjoint multisets of integers of size $n$ having identical $k$th moments for $1\leq k\leq m$; in the ideal case one requires $m=n-1$, which is maximal. We describe some searches for ideal solutions to the Prouhet-Tarry-Escott problem, especially solu

Shalosh B. Ekhad, Doron Zeilberger

We extract brilliant ideas of Sandi Klavzar, Michel Mollard, and Marko Petkovsek who used them to solve one very specific enumeration problem, namely counting the number of words in the alphabet {0,1} of length n avoiding two consecutive ones, and having exactly k such neighbors, to a much more general setting where one has any (finite) alphabet, and any (fi

Li Chen, Mingquan Ye

The multicommodity flow problem is a classic problem in network flow and combinatorial optimization, with applications in transportation, communication, logistics, and supply chain management, etc. Existing algorithms often focus on low-accuracy approximate solutions, while high-accuracy algorithms typically rely on general linear program solvers. In this pa

Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computational

Julien Barral, Stéphane Seuret

In this article, starting from a Gibbs capacity, we build a new random capacity by applying two simple operators, the first one introducing some redundancy and the second one performing a random sampling. Depending on the values of the two parameters ruling the redundancy and the sampling, the new capacity has very different multifractal behaviors. In partic

Matija Teršek, Lojze Žust, Matej Kristan

Maritime obstacle detection is critical for safe navigation of autonomous surface vehicles (ASVs). While the accuracy of image-based detection methods has advanced substantially, their computational and memory requirements prohibit deployment on embedded devices. In this paper we analyze the currently best-performing maritime obstacle detection network WaSR.