April 2023 arXiv papers — page 24

Yuke Zhang, Dake Chen, Souvik Kundu, Haomei Liu

Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer from high computational and communication costs due to the expensive multi-party computation (MPC) protocols. Existing literature has developed lighter MPC protocols to yield more eff

Nikita Dvornik, Isma Hadji, Ran Zhang, Konstantinos G. Derpanis

Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, with most of the video being irrelevant to the procedure. This motivates the need to temporally localize the instruction steps in such videos, i.e. the task called key-step localizat

Shing Yiu Fu, Kareem J. Garriga Francis, Mervin Lim Pac Chong, Yiwen E

We report a systematic investigation into the processes behind a near hundredfold enhanced second harmonic wave generated from a laser-induced air plasma, by examining the temporal dynamics of the frequency conversion processes, and the polarization of the emitted second harmonic beam. Contrary to typical nonlinear optical processes, the enhanced second harm

Ruanjing Zhang, Jiliang Jing, Zhipeng Peng, Qihong Huang

The strong gravitational lensing of a regular and rotating magnetic black hole in non-minimally coupled Einstein-Yang-Mills theory is studied. We find that, with the increase of any characteristic parameters of this black hole, such as the rotating parameter, magnetic charge and EYM parameter, the angular image position and relative magnification decrease wh

Edgar Pacheco

Since transition to higher education emerged as a research topic in the early 1970s, scholarly inquiry has focused on students without impairments and, what is more, little attention has been paid to the role of digital technologies. This article seeks to address this knowledge gap by looking at the university experiences of a group of first-year students wi

Tingting Bi, Boming Xia, Zhenchang Xing, Qinghua Lu

The Software Bill of Materials (SBOM) has emerged as a promising solution, providing a machine-readable inventory of software components used, thus bolstering supply chain security. This paper presents an extensive study concerning the practical aspects of SBOM practice. Leveraging an analysis of 4,786 GitHub discussions from 510 SBOM-related projects, our r

Junyan Zhao

A general curve $C$ of genus six is canonically embedded into the smooth del Pezzo surface $\Sigma \subseteq \mathbb{P}^1 \times \mathbb{P}^2$ of degree $5$ as a divisor in the class $\mathcal{O}_{\Sigma}(2,2)$. In this article, we study the variation of geometric invariant theory (VGIT) for such pairs $(\Sigma,C)$, and relate the VGIT moduli spaces to the K

Michele Dall'Arno

Designs, and in particular symmetric, informationally complete (SIC) structures, play an important role in the quantum tomographic reconstruction process and, by extension, in certain interpretations of quantum theory focusing on such a process. This fact is due to the symmetry of the reconstruction formula that designs lead to. However, it is also known tha

Mustafa Bal, Akshay A. Murthy, Shaojiang Zhu, Francesco Crisa

We present a novel transmon qubit fabrication technique that yields systematic improvements in T$_1$ relaxation times. We fabricate devices using an encapsulation strategy that involves passivating the surface of niobium and thereby preventing the formation of its lossy surface oxide. By maintaining the same superconducting metal and only varying the surface

Peter Buser, Eran Makover, Bjoern Muetzel

We present four counterexamples in surface homology. The first example shows that even if the loops inducing a homology basis intersect each other at most once, they still may separate the surface into two parts. The other three examples show some difficulties in working with minimal homology bases.

Mohammed Abuhamad, Changhun Jung, David Mohaisen, DaeHun Nyang

Authorship attribution has become increasingly accurate, posing a serious privacy risk for programmers who wish to remain anonymous. In this paper, we introduce SHIELD to examine the robustness of different code authorship attribution approaches against adversarial code examples. We define four attacks on attribution techniques, which include targeted and no

Maximiliano Isi, Will M. Farr, Vijay Varma

We demonstrate how to constrain the degree of absolute alignment of the total angular momenta of LIGO-Virgo binary black holes, looking for a special direction in space that would break isotropy. We also allow for inhomogeneities in the distribution of black holes over the sky. Making use of dipolar models for the spatial distribution and orientation of the

Amos Azaria, Tom Mitchell

While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. In this paper, we provide evidence that the LLM's internal state can be used to reveal the truthfulness of statements. This includes both statements provided to the

Muhammad Saad, David Mohaisen

Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking, attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting the resources of the website visitors. To understand and counter such attacks, we systematically analyze the static

Ru-Sen Lu, Keiichi Asada, Thomas P. Krichbaum, Jongho Park

The nearby radio galaxy M87 is a prime target for studying black hole accretion and jet formation^{1,2}. Event Horizon Telescope observations of M87 in 2017, at a wavelength of 1.3 mm, revealed a ring-like structure, which was interpreted as gravitationally lensed emission around a central black hole^3. Here we report images of M87 obtained in 2018, at a wav

Sankalp Tiwari, Anindya Chatterjee

The use of global displacement basis functions to solve boundary-value problems in linear elasticity is well established. No prior work uses a global stress tensor basis for such solutions. We present two such methods for solving stress problems in linear elasticity. In both methods, we split the sought stress $\sigma$ into two parts, where neither part is r

Li Zhang, Liam Dugan, Hainiu Xu, Chris Callison-Burch

Recent work has shown that prompting language models with code-like representations of natural language leads to performance improvements on structured reasoning tasks. However, such tasks comprise only a small subset of all natural language tasks. In our work, we seek to answer whether or not code-prompting is the preferred way of interacting with language

Kentaro Ohno, Misato Nakabayashi

We propose a security verification framework for cryptographic protocols using machine learning. In recent years, as cryptographic protocols have become more complex, research on automatic verification techniques has been focused on. The main technique is formal verification. However, the formal verification has two problems: it requires a large amount of co

Luis Verde-Star

For a sequence of polynomials $\{p_k(t)\}$ in one real or complex variable, where $p_k$ has degree $k$, for $k\ge 0$, we find explicit expressions and recurrence relations for infinite matrices whose entries are the coefficients $d(n,m,k)$, called linearization coefficients, that satisfy $$ p_n(t) p_m(t)=\sum_{k=0}^{n+m} d(n,m,k) p_k(t).$$ For any pair of po

Enrique Chávez-Martínez, Daniel Duarte, Takehiko Yasuda

We investigate the relation between essential divisors and F-blowups, in particular, address the problem whether all essential divisors appear on the $e$-th F-blowup for large enough $e$. Focusing on the case of normal affine toric varieties, we establish a simple sufficient condition for a divisor over the given toric variety to appear on the normalized lim

Duong Thuy Anh Nguyen, Jiaming Cheng, Duong Tung Nguyen, Angelia Nedich

Mobile edge computing (MEC) is a promising solution for enhancing the user experience, minimizing content delivery expenses, and reducing backhaul traffic. In this paper, we propose a novel privacy-preserving decentralized game-theoretic framework for resource crowdsourcing in MEC. Our framework models the interactions between a content provider (CP) and mul

Satoko Sawada-Satoh, Nozomu Kawakatu, Kotaro Niinuma, Seiji Kameno

We report dual-frequency very long baseline interferometry (VLBI) observations at 22 and 43 GHz toward the nucleus of a nearby radio galaxy NGC 4261. In particular, we present a VLBI image of the 22 GHz H2O maser line and its location in the circumnuclear region of NGC 4261. H2O maser emission is marginally detected above the three times the rms level within

Haoxiang Luo, Yifan Wu, Gang Sun, Hongfang Yu

UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network. As high-speed moving objects, UAVs not only have extremely strict requirements for communication delay, but also cannot be maliciously controlled as a weapon by the attacker. The

Wenyu Wang, Wu-Long Xu, Jin Min Yang

In direct dark matter (DM) detection via scattering off the electrons, the momentum transfer plays a crucial role. Previous work showed that for self-interacting DM, if the DM particle has a size (the so-called puffy DM), the radius effect could dominate the momentum transfer and become another source of velocity dependence for self-scattering cross section.

Robin Karlsson, Alexander Carballo, Francisco Lepe-Salazar, Keisuke Fujii

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically

Aiying Duan, Chaowei Jiang, ZhenJun Zhou, Xueshang Feng

In this paper we study the initiation mechanism of the first on-disk X-class eruptive flare in solar cycle 25. Coronal magnetic field reconstructions reveal a magnetic flux rope (MFR) with configuration highly consistent with a filament existing for a long period before the flare, and the eruption of the whole filament indicates that the MFR erupted during t

Meixuan Qiao, Jun Wang, Junfu Xiang, Qiyu Hou

Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improving the efficiency of various financial applications. First, we proposed a new method for recognizing structure diagrams in financial announcements, which can better detect and ext

Mitchell Rimerman, Nate Strawn

Andrews plots provide aesthetically pleasant visualizations of high-dimensional datasets. This work proves that Andrews plots (when defined in terms of the principal component scores of a dataset) are optimally ``smooth'' on average, and solve an infinite-dimensional quadratic minimization program over the set of linear isometries from the Euclidean data spa

Timothy B. Mahoney, Po-Chen Liu, David B Grayden, Sam E. John

Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and the risks associated with intracranial electrodes, and has shown promise in long-term seizure monitoring. Howe

Diego Martinez-Taboada, Aaditya Ramdas, Edward H. Kennedy

The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects beyond the mean, for instance decreasing or increasing the variance. We propose a new kernel-based test for distributional effects of the treat

Chaebin Kim, Sujin Kim, Pyeongjae Park, Taehun Kim

The Kitaev model, a honeycomb network of spins with bond-dependent anisotropic interactions, is a rare example of having a quantum spin liquid ground state. Although most Kitaev model candidate materials eventually order magnetically due to inevitable non-Kitaev terms, their bond-dependent anisotropy manifests in unusual spin dynamics. It has recently been s

Shang Liu

Realizing topological orders and topological quantum computation is a central task of modern physics. An important but notoriously hard question in this endeavor is how to diagnose topological orders that lack conventional order parameters. A breakthrough in this problem is the discovery of topological entanglement entropy, which can be used to detect nontri

L. Kriskovics, Zs. Kővári, B. Seli, K. Oláh

We use our photometric time series of more than forty years to analyze the long-term behaviour of EI Eri. Flare activity is investigated using space-borne photometric data obtained with TESS. The MUSICOS campaign aimed to achieve high-resolution spectroscopic observations from many sites around the globe, so that uninterrupted phase coverage of EI Eri became

Huangkai Wu, Youjing Wang, Yumiao Wang, Xiangai Deng

Active target time projection chambers are important tools in low energy radioactive ion beams or gamma rays related researches. In this work, we present the application of machine learning methods to the analysis of data obtained from an active target time projection chamber. Specifically, we investigate the effectiveness of Visual Geometry Group (VGG) and

Amin Sarihi, Peter Jamieson, Ahmad Patooghy, Abdel-Hameed A. Badawy

Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserted according to various design criteria, e.g., nets switching activity, observability, controllability, etc. However, to our knowledge, most HT detection methods are only based on a

Yige Hong, Ziv Scully

How should we schedule jobs to minimize mean queue length? In the preemptive M/G/1 queue, we know the optimal policy is the Gittins policy, which uses any available information about jobs' remaining service times to dynamically prioritize jobs. For models more complex than the M/G/1, optimal scheduling is generally intractable. This leads us to ask: beyond t

Muyu Yang, Jian Ma

The spatial positioning of chromosomes relative to functional nuclear bodies is intertwined with genome functions such as transcription. However, the sequence patterns and epigenomic features that collectively influence chromatin spatial positioning in a genome-wide manner are not well understood. Here, we develop a new transformer-based deep learning model

Anh Bui, Trung Le, He Zhao, Quan Tran

Deep learning models, even the-state-of-the-art ones, are highly vulnerable to adversarial examples. Adversarial training is one of the most efficient methods to improve the model's robustness. The key factor for the success of adversarial training is the capability to generate qualified and divergent adversarial examples which satisfy some objectives/goals

Yusuke Kobayashi, Takashi Noguchi

The $2$-Edge-Connected Spanning Subgraph problem (2-ECSS) is one of the most fundamental and well-studied problems in the context of network design. In the problem, we are given an undirected graph $G$, and the objective is to find a $2$-edge-connected spanning subgraph $H$ of $G$ with the minimum number of edges. For this problem, a lot of approximation alg

A. El Kass, C. T. Jin, J. D. Watson, G. C. Gardner

We demonstrate a radio-frequency parametric amplifier that exploits the gate-tunable quantum capacitance of an ultra high mobility two dimensional electron gas (2DEG) in a GaAs heterostructure at cryogenic temperatures. The prototype narrowband amplifier exhibits a gain greater than 20 dB up to an input power of - 66 dBm (1 dB compression), and a noise tempe

Bingqian Lin, Zicong Chen, Mingjie Li, Haokun Lin

Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks, which is very practical in the medical domain. It can significantly reduce the requirement of large amounts of task-specific data by sufficiently sharing medical knowledge among different tasks. However, due to the challenges of designing strongly ge

Hao Zhou, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas

Energy efficiency (EE) is one of the most important metrics for envisioned 6G networks, and sleep control, as a cost-efficient approach, can significantly lower power consumption by switching off network devices selectively. Meanwhile, the reconfigurable intelligent surface (RIS) has emerged as a promising technique to enhance the EE of future wireless netwo

Ellie Q. Y. Ong, Edward Doddridge, Navid C. Constantinou, Andrew McC. Hogg

The structure of the Antarctic Slope Current at the continental shelf is crucial in governing the poleward transport of warm water. Canyons on the continental slope may provide a pathway for warm water to cross the slope current and intrude onto the continental shelf underneath ice shelves, which can increase rates of ice shelf melting, leading to reduced bu

Zihao Wang

The proposed BSDE-based diffusion model represents a novel approach to diffusion modeling, which extends the application of stochastic differential equations (SDEs) in machine learning. Unlike traditional SDE-based diffusion models, our model can determine the initial conditions necessary to reach a desired terminal distribution by adapting an existing score

Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence, Michael G. Forbes

We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from both model-free RL and model-based control. It uses incomplete information from a partial model and retains RL's

T. P. McCarthy, S. L. Breen, J. F. Kaczmarek, X. Chen

We report the detection of ammonia masers in the non-metastable (6, 3), (7, 5) and (6, 5) transitions, the latter is the first unambiguous maser detection of that transition ever made. Our observations include the first VLBI detection of ammonia maser emission, which allowed effective constrain of the (6, 5) maser brightness temperature. The masers were dete

Samuel Lanthaler, Zongyi Li, Andrew M. Stuart

Neural operator architectures approximate operators between infinite-dimensional Banach spaces of functions. They are gaining increased attention in computational science and engineering, due to their potential both to accelerate traditional numerical methods and to enable data-driven discovery. As the field is in its infancy basic questions about minimal re

Ying Lv, Wendi Bao, Lili Xing, Weiguo Li

In this paper, we construct a class of nonlinear greedy average block Kaczmarz methods to solve nonlinear problems without computing the Moore-Penrose pseudoinverse. This kind of methods adopts the average technique of Gaussian Kaczmarz method and combines with the greedy strategy, which greatly reduces the amount of computation. The convergence analysis and

Isaac Corley, Jonathan Lwowski, Peyman Najafirad

A crucial part of any home is the roof over our heads to protect us from the elements. In this paper we present the Zeitview Rooftop Geometry (ZRG) dataset for residential rooftop understanding. ZRG is a large-scale residential rooftop dataset of over 20k properties collected through roof inspections from across the U.S. and contains multiple modalities incl

A. Noronha, W. de Paula, J. H. de Alvarenga Nogueira, T. Frederico

The homogeneous Bethe-Salpeter equation (BSE) of a (1/2)$^+$ bound system, that has both fermionic and bosonic degrees of freedom, that we call a {\em mock nucleon}, is studied in Minkowski space, in order to analyse the chiral limit in covariant gauges. After adopting an interaction kernel built with a one-particle exchange, the $\chi$-BSE is numerically so

Yusuke Kobayashi, Ryoga Mahara, Tamás Schwarcz

An arborescence in a digraph is an acyclic arc subset in which every vertex execpt a root has exactly one incoming arc. In this paper, we reveal the reconfigurability of the union of $k$ arborescences for fixed $k$ in the following sense: for any pair of arc subsets that can be partitioned into $k$ arborescences, one can be transformed into the other by exch

Sourabh Prakash, Priyanshi Shah, Ashrya Agrawal

In this paper, we present a comprehensive study on semantic segmentation with the Pascal VOC dataset. Here, we have to label each pixel with a class which in turn segments the entire image based on the objects/entities present. To tackle this, we firstly use a Fully Convolution Network (FCN) baseline which gave 71.31% pixel accuracy and 0.0527 mean IoU. We a

Suhyeong Choi, Jinwook Jung, Andrew B. Kahng, Minsoo Kim

We propose a systematic framework to conduct design-technology pathfinding for PPAC in advanced nodes. Our goal is to provide configurable, scalable generation of process design kit (PDK) and standard-cell library, spanning key scaling boosters (backside PDN and buried power rail), to explore PPAC across given technology and design parameters. We build on PR

Isaac Corley, Peyman Najafirad

Digital Surface Models (DSM) offer a wealth of height information for understanding the Earth's surface as well as monitoring the existence or change in natural and man-made structures. Classical height estimation requires multi-view geospatial imagery or LiDAR point clouds which can be expensive to acquire. Single-view height estimation using neural network

Chi Hoi Yip

Let $q$ be an odd power of a prime $p$, and $S \subset \mathbb{F}_q^*$ such that $S=-S$ and $S/S \neq \mathbb{F}_q^*$. We show that the clique number of the Cayley graph $\operatorname{Cay}(\mathbb{F}_q^+,S)$ is at most $\sqrt{|S/S|}+\sqrt{q/p}$, improving the best-known $\sqrt{q}$ upper bound for many families of such graphs substantially. Such a new bound

J. I. Campos Rozo, S. Vargas Domínguez, D. Utz, A. M. Veronig

The Sun's magnetic fields play an important role in various solar phenomena. Solar pores are regions of intensified magnetic field strength compared to the surrounding photospheric environment, and their study can help us better understand the properties and behaviour of magnetic fields in the Sun. Up to now, there exists only a single study on magnetic fiel

Renata Kallosh

We study supergravities with "enhanced UV divergence cancellations". We show that all these cancellations are explained by a simple dimensional analysis of nonlinear local supersymmetry (NLS). We also show that in all cases where E7-type duality was used in the past via vanishing single scalar limit (SSL) to explain/predict UV cancellations one could have us

Xinzhong Er, Ue-Li Pen, Xiaohui Sun, Dongzi Li

Plasma lensing displays interesting characteristics that set it apart from gravitational lensing. The magnetised medium induces birefringence in the two polarisation modes. As the lensing deflection grows stronger, e.g. when images form near the critical curve, the geometric delay of the signal can cause rotation in linear polarisation, in addition to Farada

Stephen Cantrell, Eduardo Reyes

We introduce a new method for studying length spectrum rigidity problems based on a combination of ideas from dynamical systems and geometric group theory. This allows us to compare the marked length spectrum of metrics and distance-like functions coming from various geometric origins. Using our new perspective, we provide concise proofs of well-known length

Ellis Kennedy, Emily Hollingworth, Alejandro Ceballos, Daisy O'Mahoney

Amorphous thin films of Tb$_{17}$Co$_{83}$ (a-Tb-Co) grown by magnetron co-sputtering exhibit changes in magnetic anisotropy with varying growth and annealing temperatures. The magnetic anisotropy constant increases with increasing growth temperature, which is reduced or vanishes upon annealing at temperatures above the growth temperature. The proposed expla

Mohammad Reza Karimi Dastjerdi, Jonathan Eisenmann, Yannick Hold-Geoffroy, Jean-François Lalonde

Because of the diversity in lighting environments, existing illumination estimation techniques have been designed explicitly on indoor or outdoor environments. Methods have focused specifically on capturing accurate energy (e.g., through parametric lighting models), which emphasizes shading and strong cast shadows; or producing plausible texture (e.g., with

Liang Chen, Juan Jose Dolado, Jesus Gonzalo, Haozi Pan

This paper studies the estimation of characteristic-based quantile factor models where the factor loadings are unknown functions of observed individual characteristics while the idiosyncratic error terms are subject to conditional quantile restrictions. We propose a three-stage estimation procedure that is easily implementable in practice and has nice proper

Simin Shekarpaz, Fanhai Zeng, George Karniadakis

We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks (PINNs). The proposed method, splitting PINN, effectively addresses the challenge of applying PINNs to forward dynamical systems and demonstrates improved accuracy through its application to neuron mo

Yupeng Zhang, Yueyang Shen, Rongqian Zhang, Yuyao Liu

We propose a numerical method to spline-interpolate discrete signals and then apply the integral transforms to the corresponding analytical spline functions. This represents a robust and computationally efficient technique for estimating the Laplace transform for noisy data. We revisited a Meijer-G symbolic approach to compute the Laplace transform and alter

Petter Brändén, Jonathan Leake

Inspired by the theory of hyperbolic polynomials and Hodge theory, we develop the theory of Lorentzian polynomials on cones. This notion captures the Hodge-Riemann relations of degree zero and one. Motivated by fundamental properties of volume polynomials of Chow rings of simplicial fans, we define a class of multivariate polynomials which we call hereditary

Pau Batlle, Matthieu Darcy, Bamdad Hosseini, Houman Owhadi

We present a general kernel-based framework for learning operators between Banach spaces along with a priori error analysis and comprehensive numerical comparisons with popular neural net (NN) approaches such as Deep Operator Net (DeepONet) [Lu et al.] and Fourier Neural Operator (FNO) [Li et al.]. We consider the setting where the input/output spaces of tar

Negar Nejatishahidin, Will Hutchcroft, Manjunath Narayana, Ivaylo Boyadzhiev

In this paper, we address the problem of wide-baseline camera pose estimation from a group of 360$^\circ$ panoramas under upright-camera assumption. Recent work has demonstrated the merit of deep-learning for end-to-end direct relative pose regression in 360$^\circ$ panorama pairs [11]. To exploit the benefits of multi-view logic in a learning-based framewor

Akshay Bansal, Jamie Sikora

In the last two decades, there has been much effort in finding secure protocols for two-party cryptographic tasks. It has since been discovered that even with quantum mechanics, many such protocols are limited in their security promises. In this work, we use stochastic selection, an idea from stochastic programming, to circumvent such limitations. For exampl

Yandi Li, Jianxiong Guo, Yupeng Li, Tian Wang

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limit

Bruno Suzuki

We show that the Lie potential on the minimal semisimple adjoint orbit $\mathcal{O}_n$ of $\mathfrak{sl}(n+1,\mathbb{C})$ coincides with toric potential on $T^*\mathbb P^{n}$. We then study the corresponding Landau-Ginzburg models in deformation families and give some examples of how the deformations affect the mirrors.

Liang Chen, Minyuan Zhang

This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects (CCE) framework. The proposed two-step estimation method involves applying principal component analysis to estimate latent factors based on cross-sectional aver

Khanh Duy Nguyen, Nima Shahbazi, Abolfazl Asudeh

Historical systematic exclusionary tactics based on race have forced people of certain demographic groups to congregate in specific urban areas. Aside from the ethical aspects of such segregation, these policies have implications for the allocation of urban resources including public transportation, healthcare, and education within the cities. The initial st

Jacob Hauser, Yaodong Li, Sagar Vijay, Matthew P. A. Fisher

Adaptive quantum circuits, in which unitary operations, measurements, and feedback are used to steer quantum many-body systems, provide an exciting opportunity to generate new dynamical steady states. We introduce an adaptive quantum dynamics with continuous symmetry where unitary operations, measurements, and local unitary feedback are used to drive orderin

Adam Klukowski

We consider the subspace of the homology of a covering space spanned by lifts of simple closed curves. Our main result is the existence of unbranched covers of surfaces where this is a proper subspace. More generally, for a fixed finite solvable quotient of the fundamental group we exhibit a cover whose homology is not generated by the lifts of curves in the

Forrest McKee, David Noever

This study investigates a primary inaudible attack vector on Amazon Alexa voice services using near ultrasound trojans and focuses on characterizing the attack surface and examining the practical implications of issuing inaudible voice commands. The research maps each attack vector to a tactic or technique from the MITRE ATT&CK matrix, covering enterprise, m

Thanh Sang Nguyen, Jooho Lee, Van Thuy Hoang, O-Joun Lee

Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link prediction. Due to its powerful graph data modelling capabilities, various graph embedding models and libraries have been proposed to learn

Michael S. Gilbert, Kamesh Madduri, Erik G. Boman, Sivasankaran Rajamanickam

The multilevel heuristic is the dominant strategy for high-quality sequential and parallel graph partitioning. Partition refinement is a key step of multilevel graph partitioning. In this work, we present Jet, a new parallel algorithm for partition refinement specifically designed for Graphics Processing Units (GPUs). We combine Jet with GPU-aware coarsening

David Jansen, Fabian Heidrich-Meisner

Electron-phonon interactions play a key role in many branches of solid-state physics. Here, our focus is on the transport properties of one-dimensional systems, and we apply efficient real-time matrix-product state methods to compute the optical and thermal conductivities of Holstein chains at finite temperatures and filling. We validate our approach by comp

Siddhartha Kapuria, Tarunraj G. Mohanraj, Nethra Venkatayogi, Ozdemir Can Kara

In this study, toward addressing the over-confident outputs of existing artificial intelligence-based colorectal cancer (CRC) polyp classification techniques, we propose a confidence-calibrated residual neural network. Utilizing a novel vision-based tactile sensing (VS-TS) system and unique CRC polyp phantoms, we demonstrate that traditional metrics such as

Surjodeep Sarkar, Manas Gaur, L. Chen, Muskan Garg

Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral The

Anna Bychek, Helmut Ritsch

The implementation of a superradiant laser as an active frequency standard is predicted to provide better short-term stability and robustness to thermal and mechanical fluctuations when compared to standard passive optical clocks. However, despite significant recent progress, the experimental realization of continuous wave superradiant lasing still remains a

Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder

The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications. Icy moons in our solar system are promising targets for exploration because their liquid oceans make them potential habitats for microscopic life. However, the lack of a precise definition of life poses a fundamental challenge to formulating detect

Dylan Slack, Sameer Singh

Acquiring high-quality data is often a significant challenge in training machine learning (ML) models for tabular prediction, particularly in privacy-sensitive and costly domains like medicine and finance. Providing natural language instructions to large language models (LLMs) offers an alternative solution. However, it is unclear how effectively instruction

Russell A Poldrack, Thomas Lu, Gašper Beguš

Artificial intelligence (AI) tools based on large language models have acheived human-level performance on some computer programming tasks. We report several experiments using GPT-4 to generate computer code. These experiments demonstrate that AI code generation using the current generation of tools, while powerful, requires substantial human validation to e

Valery N. Marachevsky, Arseny A. Sidelnikov

Green functions scattering method is generalized to consider mixing of electromagnetic polarizations after reflection from the plane boundary between different media and applied to derivation of the Casimir-Polder potential in systems with Chern-Simons plane boundary layers. The method is first applied to derive the Casimir-Polder potential of an anisotropic

Jiakuo Zuo, Xidong Mu, Yuanwei Liu

The novel concept of near-field non-orthogonal multiple access (NF-NOMA) communications is proposed. The near-filed beamfocusing enables NOMA to be carried out in both angular and distance domains. Two novel frameworks are proposed, namely, single-location-beamfocusing NF-NOMA (SLB-NF-NOMA) and multiple-location-beamfocusing NF-NOMA (MLB-NF-NOMA). 1) For SLB

Ravi Teja Ponnaganti, Matthieu Mambrini, Didier Poilblanc

Within the Projected Entangled Pair State (PEPS) tensor network formalism, a simple update (SU) method has been used to investigate the time evolution of a two-dimensional U(1) critical spin-1/2 spin liquid under Hamiltonian quench [Phys. Rev. B 106, 195132 (2022)]. Here we introduce two different variational frameworks to describe the time dynamics of SU(2)

David Lacoste, Olivier Rivoire, David S. Tourigny

Organisms that grow and survive in uncertain environments may need to change their physiological state as the environment changes. When the environment is uncertain, one strategy known as bet-hedging is to make these changes randomly and independently of the environment, to ensure that at least part of the population is well adapted. Organisms that collect i

Ch. Cobollo, A. J. Guirao, V. Montesinos

We provide the explicit formula for the numerical index of any $2$-dimensional Lipschitz-free space, also giving the construction of operators attaining this value as its numerical radius. As a consequence, the numerical index of $2$-dimensional Lipschitz-free spaces can take any value of the interval $[\frac{1}{2},1]$, and this whole range of numerical indi

Marcus Abate, Ariel Schwartz, Xue Iuan Wong, Wangdong Luo

Localization and mapping are key capabilities for self-driving vehicles. In this paper, we build on Kimera and extend it to use multiple cameras as well as external (eg wheel) odometry sensors, to obtain accurate and robust odometry estimates in real-world problems. Additionally, we propose an effective scheme for closing loops that circumvents the drawbacks

Peiqi Wang, Yingcheng Liu, Ching-Yun Ko, William M. Wells

Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One common approach involves contrasting semantically similar (positive) and dissimilar (negative) pairs of data points. Drawing negative samples uniformly from the training data set i

Juraj Vladika, Florian Matthes

With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system

Bruno Ebner, Yvik Swan

We use a Stein identity to define a new class of parametric distributions which we call ``independent additive weighted bias distributions.'' We investigate related $L^2$-type discrepancy measures, empirical versions of which not only encompass traditional ODE-based procedures but also offer novel methods for conducting goodness-of-fit tests in composite hyp

Junghoon Kim, Taejoon Kim, David Love, Christopher Brinton

The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we d

Nguyen Thi Yen Ngoc, Vo Anh Khoa

This paper proposes an explicit Fourier-Klibanov method as a new approximation technique for an age-dependent population PDE of Gompertz type in modeling the evolution of tumor density in a brain tissue. Through suitable nonlinear and linear transformations, the Gompertz model of interest is transformed into an auxiliary third-order nonlinear PDE. Then, a co

Dustin Ross

We introduce the notion of Lorentzian fans, which form a special class of tropical fans that are particularly well-suited for proving Alexandrov-Fenchel type inequalities. To demonstrate the utility of Lorentzian fans, we prove a practical characterization of them in terms of their two-dimensional star fans. We also show that Lorentzian fans are closed under

Maomao Hu, Ram Rajagopal, Jacques A. de Chalendar

Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock th

Xiao-Yang Liu, Ziyi Xia, Hongyang Yang, Jiechao Gao

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of hist

Nir Avni, Chen Meiri

We study widths of conjugacy classes in anisotropic higher rank $S$-arithmetic groups of orthogonal type. Assuming the GRH, we prove that many such groups have bounded conjugacy width. For example, this holds if the degree is greater or equal to 17 and $S$ contains a non-archimedean place. To the best of our knowledge, this is the first boundedness result pr

Yiwei Hu, Paul Guerrero, Miloš Hašan, Holly Rushmeier

Node graph systems are used ubiquitously for material design in computer graphics. They allow the use of visual programming to achieve desired effects without writing code. As high-level design tools they provide convenience and flexibility, but mastering the creation of node graphs usually requires professional training. We propose an algorithm capable of g