October 2022 arXiv papers — page 84

Misa Ogata, Hirotada Okawa, Kotaro Fujisawa, Nobutoshi Yasutake

We present a new formulation for numerically obtaining axisymmetric equilibrium structures of rotating stars in two spatial dimensions. With a view to apply it to the secular evolution of rotating stars, we base it on the Lagrangian description, i.e., we solve the force-balance equations to find the spatial positions of fluid elements endowed individually wi

Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes

We deal with the problem of localized in-video taxonomic human annotation in the video content moderation domain, where the goal is to identify video segments that violate granular policies, e.g., community guidelines on an online video platform. High quality human labeling is critical for enforcement in content moderation. This is challenging due to the pro

Md Mahmudur Rahman, Mahta Mousavi, Peri Tarr, Mohammad Arif Ul Alam

Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research. However, many domain adaptation algorithms suffer with failure to operate adaptation in presence of target domain heterogeneity (which is always present in reality) and presence of multiple inhabitants dramatically hinders their generalizability

Jonathan Dearlove, Prasanna Pakkiam, Arkady Fedorov

Superconducting qubits provide a promising platform for physically realising quantum computers at scale. Such devices require precision control at microwave frequencies. Common practice is to synthesise such control signals using IQ modulation, requiring calibration of a in-phase (I) and quadrature (Q) signals alongside two DC offsets to generate pure tones.

Qing Chen, Huaqiao Wang, Guochun Wu

In this paper, we are concerned with the instability and stability of a quasi-linear hyperbolic-parabolic system modeling vascular networks. Under the assumption that the pressure satisfies $\frac{\nu P'(\bar\rho)}{\gamma \bar\rho} < \beta$, we first show that the steady-state is linear unstable (i.e., the linear solution grows in time in $L^2$) by construct

Kai Yan, Alexander G. Schwing, Yu-Xiong Wang

Although reinforcement learning has found widespread use in dense reward settings, training autonomous agents with sparse rewards remains challenging. To address this difficulty, prior work has shown promising results when using not only task-specific demonstrations but also task-agnostic albeit somewhat related demonstrations. In most cases, the available d

Noriaki Ota, Shingo Yokoi, Shinsuke Yamaoka

In this paper, we present our solution, which placed 5th in the kaggle Google Universal Image Embedding Competition in 2022. We use the ViT-H visual encoder of CLIP from the openclip repository as a backbone and train a head model composed of BatchNormalization and Linear layers using ArcFace. The dataset used was a subset of products10K, GLDv2, GPR1200, and

Giacomo Pantaleoni, Ben Q. Baragiola, Nicolas C. Menicucci

The Gottesman-Kitaev-Preskill (GKP) code encodes a qubit into a bosonic mode using periodic wavefunctions. This periodicity makes the GKP code a natural setting for the Zak transform, which is tailor-made to provide a simple description for periodic functions. We review the Zak transform and its connection to a Zak basis of states in Hilbert space, decompose

Veronica Dike, Daniel Gilman, Tommaso Treu

Dark matter could comprise, at least in part, primordial black holes (PBH). To test this hypothesis, we present an approach to constrain the PBH mass ($M_{\rm{PBH}}$) and mass fraction ($f_{\rm{PBH}}$) from the flux ratios of quadruply imaged quasars. Our approach uses an approximate Bayesian computation (ABC) forward modeling technique to directly sample th

Rimple Sandhu, Brandon Robinson, Mohammad Khalil, Chris L. Pettit

This paper investigates the applicability of a recently-proposed nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly

Siyan Li, Riley Carlson, Christopher Potts

Levin et al. (2019) show experimentally that the interpretations of novel English noun compounds (e.g., stew skillet), while not fully compositional, are highly predictable based on whether the modifier and head refer to artifacts or natural kinds. Is the large language model GPT-3 governed by the same interpretive principles? To address this question, we fi

Zhihao Lan, Menglin L. N. Chen, Jian Wei You, Wei E. I. Sha

Topological photonic edge states are conventionally formed at the interface between two domains of topologically trivial and nontrivial photonic crystals. Recent works exploiting photonic quantum Hall and quantum valley Hall effects have shown that large-area topological waveguide states could be created in a three-layer topological heterostructure that cons

Akihisa Okada, Daisuke Inoue, Shihori Koyama, Tadayoshi Matsumori

We consider a Schelling-like segregation model, in which the behavior of individual agents is determined by a mixed individual and global utility. With a high ratio of global utility being incorporated, the agents are cooperative in order to realize a homogenized state, otherwise the agents are less cooperative, leading to an undesired Nash equilibrium with

Zhengchang Kou, Andrew C. Singer, Michael L. Oelze

Wireless capsule endoscopy (WCE) has been widely adopted as complementary to traditional wired gastroendoscopy, especially for small bowel diseases which are beyond the latter's reach. However, both the video resolution and frame rates are limited in current WCE solutions due to the limited wireless data rate. The reasons behind this are that the electromagn

Joaquín Puig, Jazmín Aragón Sánchez, Gladys Nieva, Alejandro B. Kolton

We use vortex matter in type-II superconductors as a playground to study how different types of disorder affect the long wavelength density fluctuations of the system. We find that irrespective of the vortex-vortex interaction, in the case of samples with weak and dense point defects the system presents the hidden order of hyperuniformity characterized by an

Mawson W. Sammons, Clancy W. James, Cathryn M. Trott, Mark Walker

Fast cosmological transients such as fast radio bursts (FRBs) and gamma-ray bursts (GRBs) represent a class of sources more compact than any other cosmological object. As such they are sensitive to significant magnification via gravitational lensing from a class of lenses which are not well-constrained by observations today. Low-mass primordial black holes a

Md Mahmudur Rahman, Rameswar Panda, Mohammad Arif Ul Alam

We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models. Our framework holds strong distribution matching property by training both source and target auto-encoders using a novel

Jiahui Chen, Rui Wang, Yuta Hozumi, Gengzhuo Liu

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artif

Darren Lyles, Patricia Gonzalez-Guerrero, Meriam Gay Bautista, George Michelogiannakis

Temporal computing promises to mitigate the stringent area constraints and clock distribution overheads of traditional superconducting digital computing. To design a scalable, area- and power-efficient superconducting network on chip (NoC), we propose packet-switched superconducting temporal NoC (PaST-NoC). PaST-NoC operates its control path in the temporal

Yazhou Chen, Yi Peng, Qiaolin He, Xiaoding Shi

In this paper, the compressible immiscible two-phase flow with relaxation is investigated, this model can be regarded as a natural modification of Jin-Xin relaxation scheme proposed and developed by S.Jin and Z.P.Xin([Comm.Pure Appl.Math., 48,1995]) in view of the numerical approximation of conservation laws. Given any entropy solution consists of two differ

Yulong Cao, S. Hrushikesh Bhupathiraju, Pirouz Naghavi, Takeshi Sugawara

Autonomous Vehicles (AVs) increasingly use LiDAR-based object detection systems to perceive other vehicles and pedestrians on the road. While existing attacks on LiDAR-based autonomous driving architectures focus on lowering the confidence score of AV object detection models to induce obstacle misdetection, our research discovers how to leverage laser-based

Ramchander Rao Bhaskara, Manoranjan Majji

Estimation of rigid transformation between two point clouds is a computationally challenging problem in vision-based relative navigation. Targeting a real-time navigation solution utilizing point-cloud and image registration algorithms, this paper develops high-performance avionics for power and resource constrained pose estimation framework. A Field-Program

Levent Batakci, Abigail Branson, Bryan Castillo, Candace Todd

Graphs drawn in the plane are ubiquitous, arising from data sets through a variety of methods ranging from GIS analysis to image classification to shape analysis. A fundamental problem in this type of data is comparison: given a set of such graphs, can we rank how similar they are, in such a way that we capture their geometric &#34;shape&#34; in the plane? I

Aurea Bílá, Jonas Hansen, Peter Müller, Alexander J. Summers

Iteration is a programming operation that traditionally refers to visiting the elements of a data structure in sequence. However, modern programming systems such as Rust, Java, and C# generalise iteration far beyond the traditional use case. They allow iterators to be parameterised with (potentially side-effectful) closures and support the composition of ite

Jun Wang, Taran C. Driver, Felix Allum, Christina C. Papadopoulou

We present an approach that combines photon spectrum correlation analysis with the reconstruction of three-dimensional momentum distribution from velocity map images in an efficient, single-step procedure. We demonstrate its efficacy with the results from the photoionization of the $2p$-shell of argon using the FLASH free-electron laser~(FEL). Distinct spect

Shawn Priore, Christopher Petersen, Meeko Oishi

We propose an open loop control scheme for linear time invariant systems perturbed by multivariate $t$ disturbances through the use of quantile reformulations. The multivariate $t$ disturbance is motivated by heavy tailed phenomena that arise in multi-vehicle planning planning problems through unmodeled perturbation forces, linearization effects, or faulty a

Joel Martis, Sandhya Susarla, Archith Rayabharam, Cong Su

Four-dimensional scanning transmission electron microscopy (4D-STEM) has recently gained widespread attention for its ability to image atomic electric fields with sub-{\AA}ngstrom spatial resolution. These electric field maps represent the integrated effect of the nucleus, core electrons and valence electrons, and separating their contributions is non-trivia

Dani Valevski, Matan Kalman, Eyal Molad, Eyal Segalis

Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Text-driven image editing methods usually need edit masks, struggle with edits that require significant visual c

Nasos Evangelou-Oost, Callum Bannister, Ian J. Hayes

We present a lattice of distributed program specifications, whose ordering represents implementability/refinement. Specifications are modelled by families of subsets of relative execution traces, which encode the local orderings of state transitions, rather than their absolute timing according to a global clock. This is to overcome fundamental physical diffi

Syed Asad Rizvi, Nazreen Pallikkavaliyaveetil, David Zhang, Zhuoyang Lyu

Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstructured data, making the development of graph foundation models challenging. In this work, we propose Foundation-Informed Message Passing (FIMP), a Graph Neural Network (GNN) messag

Seongmin Park, Dongchan Shin, Jihwa Lee

To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The suggested transformations also retain desir

Patrick Jefferson, Washington Taylor, Andrew P. Turner

We use the recently developed methods of 2108.07810 to analyze vertical flux backgrounds and associated chiral matter spectra in the 4D universal $(\text{SU}(3) \times \text{SU}(2) \times \text{U}(1))/\mathbb{Z}_{6}$ model introduced in 1912.10991, which is believed to describe the most general generic family of F-theory vacua with tuned $(\text{SU}(3) \time

Huihui Xu, Kevin Ashley

In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level t

Arthur G. Suvorov, Kostas Glampedakis

The high-mass X-ray binary LS I +61{\deg}303 is also cataloged as a gamma-ray binary as a result of frequent outbursts at TeV photon energies. The system has released two soft-gamma flares in the past, suggesting a magnetar interpretation for the compact primary. This inference has recently gained significant traction following the discovery of transient rad

Wei-Hsiang Lin

For a biological system to grow and expand, mass must be transferred from the environment to the system and be assimilated into its reaction network. Here, I characterize the biomass transfer process for growing autocatalytic systems. By track biomass along reaction pathways, an n-dimensional ordinary differential equation (ODE) of the reaction network can b

Calvin McPhail-Snyder

Classical $\operatorname{SL}_2(\mathbb{C})$-Chern-Simons theory assigns a $3$-manifold $M$ with representation $\rho : \pi_1(M) \to \operatorname{SL}_2(\mathbb{C})$ its complex volume $\operatorname{V}(M, \rho) \in \mathbb{C} / 2 \pi^2 i \mathbb{Z}$, with real part the volume and imaginary part the Chern-Simons invariant. The existing literature focuses on c

Shawn Priore, Ali Bidram, Meeko Oishi

We propose an open loop control scheme for linear systems with time-varying random elements in the plant's state matrix. This paper focuses on joint chance constraints for potentially time-varying target sets. Under assumption of finite and known expectation and variance, we use the one-sided Vysochanskij-Petunin inequality to reformulate joint chance constr

Sreehari Sankar, Zhihang Dong

Question generation has recently gained a lot of research interest, especially with the advent of large language models. In and of itself, question generation can be considered 'AI-hard', as there is a lack of unanimously agreed sense of what makes a question 'good' or 'bad'. In this paper, we tackle two fundamental problems in parallel: on one hand, we try

Mohammad Farazi, Wenhui Zhu, Zhangsihao Yang, Yalin Wang

This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. We introduce a novel hybrid geometric deep learning-based model that learns geometrically meaningful and discretization-independent features with a U-Net model as the primary node feature extraction module, followed by a successive spectral-bas

Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts

Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still have difficulty generalizing to minority classes and their decisions are difficult to interpret. These problems are related because the method by which CNNs generalize to minority classes, which requires improvement, is wrapped in a blackbox. To dem

Graham V. Weinberg

This paper is concerned with introducing a framework in which the operational effectiveness of a dismounted combat team can be assessed. It has been motivated by studies at Defence Science and Technology Group which have focused on the structure of the future dismounted fighting force. In order to determine the operational effectiveness of different combat t

Zhan Xu, Yang Zhou, Li Yi, Evangelos Kalogerakis

We present MoRig, a method that automatically rigs character meshes driven by single-view point cloud streams capturing the motion of performing characters. Our method is also able to animate the 3D meshes according to the captured point cloud motion. MoRig's neural network encodes motion cues from the point clouds into features that are informative about th

Luca Rizzi

Let $f\colon X\to Y$ be a semistable fibration between smooth complex varieties of dimension $n$ and $m$. This paper contains an analysis of the local systems of de Rham closed relative one forms and top forms on the fibers. In particular the latter recovers the local system of the second Fujita decomposition of $f_*\omega_{X/Y}$ over higher dimensional base

Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang

We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transformer using a general and light-weight matching algorithm that is as fast as pruning while being more accurate. Off-the-shelf, ToMe can 2x the throughput of state-of-the-art ViT-L @ 5

Matthew Sotoudeh

Modern operating systems, browsers, and office suites have become megasystems built on millions of lines of code. Their sheer size can intimidate even experienced users and programmers away from attempting to understand and modify the software running on their machines. This paper introduces system-specific interpreters (SSIs) as a tool to help users regain

Yuhong Li, Jiajie Li, Cong Han, Pan Li

Neural Architecture Search (NAS) has become a de facto approach in the recent trend of AutoML to design deep neural networks (DNNs). Efficient or near-zero-cost NAS proxies are further proposed to address the demanding computational issues of NAS, where each candidate architecture network only requires one iteration of backpropagation. The values obtained fr

Amol Aggarwal, Charles Bordenave, Patrick Lopatto

L\'evy matrices are symmetric random matrices whose entry distributions lie in the domain of attraction of an $\alpha$-stable law. For $\alpha < 1$, predictions from the physics literature suggest that high-dimensional L\'{e}vy matrices should display the following phase transition at a point $E_{\mathrm{mob}}$. Eigenvectors corresponding to eigenvalues in $

Kazuki Doi, Jonathan Harper, Ali Mollabashi, Tadashi Takayanagi

We study holographic entanglement entropy in dS/CFT and introduce time-like entanglement entropy in CFTs. Both of them take complex values in general and are related with each other via an analytical continuation. We argue that they are correctly understood as pseudo entropy. We find that the imaginary part of pseudo entropy implies an emergence of time in d

Felipe Lopes Oliveira, Conor Cleeton, Rodrigo Neumann Barros Ferreira, Binquan Luan

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture application. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of th

Jinkun Cao, Hao Wu, Kris Kitani

In this work, we propose a novel paradigm to encode the position of targets for target tracking in videos using transformers. The proposed paradigm, Dense Spatio-Temporal (DST) position encoding, encodes spatio-temporal position information in a pixel-wise dense fashion. The provided position encoding provides location information to associate targets across

Oktay Cebecioğlu, Ahmet Saban, Salih Kibaroğlu

Inspired by the Maxwell symmetry generalization of general relativity (Maxwell gravity), we have constructed the Maxwell extension of $f(R)$ gravity. We found that the semi-simple extension of the Poincare symmetry allows us to introduce geometrically a cosmological constant term in four-dimensional $f(R)$ gravity. This symmetry also allows the introduction

Hisakazu Minakata

We update and summarize the present status of our understanding of the reparametrization symmetry with $i \leftrightarrow j$ state exchange in neutrino oscillation in matter. We introduce a systematic method called ``Symmetry Finder'' (SF) to uncover such symmetries, demonstrate its efficient hunting capability, and examine their characteristic features. App

Kangning Liu, Weicheng Zhu, Yiqiu Shen, Sheng Liu

Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two different randomly-selected instances. Unfortuna

Fabrizio Zanello

The goal of this note is to provide a general lower bound on the number of even values of the Fourier coefficients of an arbitrary eta-quotient $F$, over any arithmetic progression. Namely, if $g_{a,b}(x)$ denotes the number of even coefficients of $F$ in degrees $n\equiv b$ (mod $a$) such that $n\le x$, then we show that $g_{a,b}(x) / \sqrt{x}$ is unbounded

J. F. Tsoplefack, F. Palmero, J. Cuevas-Maraver, A. Provata

We study systematically the scattering of solitons on localized impurities in the discrete nonlinear Schr\"odinger (DNLS) equation with a saturable nonlinearity. We show that, apart from the generic scenario of the outcome of the scattering process, namely the emergence of a reflected and a transmitted soliton, other effects can occur. In particular, it is f

Pranjal Bhardwaj, Prajjwal Gupta, Thejineaswar Guhan, Kathiravan Srinivasan

Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluate

Christopher V. Cappiello, Neal P. Avis Kozar, Aaron C. Vincent

As a supernova shock expands into space, it may collide with dark matter particles, scattering them up to velocities more than an order of magnitude larger than typical dark matter velocities in the Milky Way. If a supernova remnant is close enough to Earth, and the appropriate age, this flux of high-velocity dark matter could be detectable in direct detecti

Fabrizio Zanello

We investigate log-concavity in the context of level Hilbert functions and pure $O$-sequences, two classes of numerical sequences introduced by Stanley in the late Seventies whose structural properties have since been the object of a remarkable amount of interest in combinatorial commutative algebra. However, a systematic study of the log-concavity of these

Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu, Matteo Figini

Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convolution and introduce a novel layer that allows us to place information in the image selectively and choose the `stroke breadth' at which the image is synthesized, whilst incurring

Hongxiang Xie, Joan Palacios, Nuria González-Prelcic

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dic

Vasco Portilheiro

We prove an impossibility result, which in the context of function learning says the following: under certain conditions, it is impossible to simultaneously learn symmetries and functions equivariant under them using an ansatz consisting of equivariant functions. To formalize this statement, we carefully study notions of approximation for groups and semigrou

Marcin Bownik, David Cruz-Uribe

In this paper we prove the Jones factorization theorem and the Rubio de Francia extrapolation theorem for matrix $\mathcal A_p$ weights. These results answer longstanding open questions in the study of matrix weights. The proof requires the development of the theory of convex-set valued functions and measurable seminorm functions. In particular, we define a

Blair Bilodeau, Yanbo Tang, Alex Stringer

Laplace's method is used to approximate intractable integrals in a statistical problems. The relative error rate of the approximation is not worse than $O_p(n^{-1})$. We provide the first statistical lower bounds showing that the $n^{-1}$ rate is tight.

Yiming Ma, Victor Sanchez, Soodeh Nikan, Devesh Upadhyay

Driver distractions are known to be the dominant cause of road accidents. While monitoring systems can detect non-driving-related activities and facilitate reducing the risks, they must be accurate and efficient to be applicable. Unfortunately, state-of-the-art methods prioritize accuracy while ignoring latency because they leverage cross-view and multimodal

Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji, Vinod Kumar Chauhan

Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP. In this work, we evaluate a broad set of machine learning techniques ranging from simple RNNs to specialised transformers such as BioBERT on a dataset containing clinical notes along with a set of annotations indicatin

Natasha Alkhatib, Maria Mushtaq, Hadi Ghauch, Jean-Luc Danger

Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulnerabilities. Among these protocols, the Co

S. Chion, M. Dajczer

The local classification of Kaehler submanifolds $M^{2n}$ of the hyperbolic space $\mathbb{H}^{2n+p}$ with low codimension $2\leq p\leq n-1$ under only intrinsic assumptions remains a wide open problem. The situation is quite different for submanifolds in the round sphere $\mathbb{S}^{2n+p}$, $2\leq p\leq n-1$, since Florit, Hui and Zheng have shown that the

Yuchen Mao, Zhongkai Tao

We apply a new method with explicit solution operators to construct asymptotically flat initial data sets of the vacuum Einstein equation with new localization properties. Applications include an improvement of the decay rate in Carlotto--Schoen [arXiv:1407.4766] to $\mathcal{O}(|x|^{-(d-2)})$ and a construction of nontrivial asymptotically flat initial data

Chee Sheng Fong

In the work, we derive exact analytic expressions for $(3+N)$-flavor neutrino oscillation probabilities in an arbitrary matter potential in term of matrix elements and eigenvalues of the Hamiltonian. With the analytic expressions, we demonstrate that nonunitary and nonstandard neutrino interaction scenarios are physically distinct: they satisfy different ide

Francesca Bianco, Dimitri Ognibene

In complex environments, where the human sensory system reaches its limits, our behaviour is strongly driven by our beliefs about the state of the world around us. Accessing others' beliefs, intentions, or mental states in general, could thus allow for more effective social interactions in natural contexts. Yet these variables are not directly observable. Th

Yingjin Song, Daniel Beck

Most previous work in music emotion recognition assumes a single or a few song-level labels for the whole song. While it is known that different emotions can vary in intensity within a song, annotated data for this setup is scarce and difficult to obtain. In this work, we propose a method to predict emotion dynamics in song lyrics without song-level supervis

Peter Wardrip, David Gagnon, James Mathews, Jen Scianna

In this paper, we study the various ways 3rd-5th grade educators in Wisconsin utilized Jo Wilder and the Capitol Case, a historical inquiry game, as part of their classroom instruction. The 15 educators involved in the study were all grade school teachers in Wisconsin who took part in the "Doing History Fellowship" program, a professional development opportu

Jian Shi, Dan Wang, Chenye Wu, Zhu Han

The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals. This article aims to establish a carbon-oriented framework to examin

Dumitru Astefanesei, Hyat Huang, Jutta Kunz, Eugen Radu

We consider an Einstein-scalar field model which is a consistent truncation of ${\cal N}=8$ $D=4$ gauged supergravity, the scalar field possessing a potential which is unbounded from below and a tachyonic mass above the Breitenlohner-Freedman bound. We investigate the spherically symmetric asymptotically anti-de Sitter soliton and black hole solutions, with

Catherine Chen, Carsten Eickhoff

As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite recent advances in explainable AI and IR techniques, there is no consensus on the definition of explainability.

Gabriel Gómez, Guillermo Palma, Ángel Rincón, Norman Cruz

Bulk viscosity in cold dark matter is an appealing feature that introduces distinctive phenomenological effects in the cosmological setting as compared to the $\Lambda$CDM model. Under this view, we propose a general parametrization of the bulk viscosity of the form $\xi\sim H^{1-2s} \rho_{m}^{s}$, that covers intriguingly some well-known cases in the Eckart

Thomas Effland, Michael Collins

We present Expected Statistic Regularization (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi-supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present

Luke Swanson, David Gagnon, Jennifer Scianna

Educational games are an increasingly popular teaching tool in modern classrooms. However, the development of complementary tools for teachers facilitating classroom gameplay is lacking. We present the results of a participatory design process for a teacher-facing, real-time game data dashboard. This two-phase process included a workshop to elicit teachers'

Adriana Lleras-Muney, Matthew Miller, Shuyang Sheng, Veronica Sovero

We investigate the returns to adolescent friendships on earnings in adulthood using data from the National Longitudinal Study of Adolescent to Adult Health. Because both education and friendships are jointly determined in adolescence, OLS estimates of their returns are likely biased. We implement a novel procedure to obtain bounds on the causal returns to fr

Bikram Keshari Pradhan, Aditya Vijaykumar, Debarati Chatterjee

Neutron star (NS) equation of state (EoS) insensitive relations or universal relations (UR) involving neutron star bulk properties play a crucial role in gravitational-wave astronomy. Considering a wide range of equations of state originating from (i) phenomenological relativistic mean field models, (ii) realistic EoS models based on different physical motiv

Yifan Du, Mengze Wang, Tamer A. Zaki

The state of turbulent, minimal-channel flow is estimated from spatio-temporal sparse observations of the velocity, using both a physics-informed neural network (PINN) and adjoint-variational data assimilation (4DVar). The performance of PINN is assessed against the benchmark results from 4DVar. The PINN is efficient to implement, takes advantage of automati

Daniel Severt

The finite-volume energy levels corresponding to the Roper resonance based on a two-flavor chiral effective Lagrangian at leading one-loop order are investigated. It is shown that the Roper mass can be extracted from these levels for not too large lattice volumes. Further, to include three-body $N \pi \pi$ dynamics, a non-relativistic effective field theory

D. M. Riffe, Richard B. Wilson

A semiempirical theory for the excitation and subsequent relaxation of nonthermal electrons is described. The theory, which is applicable to ultrafast-laser excited metals, is based on the Boltzmann transport equation for the carrier distribution function $f(\epsilon,t)$ and includes electron-phonon, electron-electron, and electron-photon scattering integral

Jiameng Pu, Zain Sarwar, Sifat Muhammad Abdullah, Abdullah Rehman

Recent advances in generative models for language have enabled the creation of convincing synthetic text or deepfake text. Prior work has demonstrated the potential for misuse of deepfake text to mislead content consumers. Therefore, deepfake text detection, the task of discriminating between human and machine-generated text, is becoming increasingly critica

Simon Le Cleac'h, Hong-Xing Yu, Michelle Guo, Taylor A. Howell

We present a differentiable pipeline for simulating the motion of objects that represent their geometry as a continuous density field parameterized as a deep network. This includes Neural Radiance Fields (NeRFs), and other related models. From the density field, we estimate the dynamical properties of the object, including its mass, center of mass, and inert

Zineb Garroussi, Abdoul Wassi Badirou, Mathieu D'amours, André Girard

The massive increase of data traffic, the widespread proliferation of wireless applications and the full-scale deployment of 5G and the IoT, imply a steep increase in cellular networks energy use, resulting in a significant carbon footprint. This paper presents a comprehensive model to show the interaction between the networking and energy features of the pr

Igor Pak, David Soukup

We prove that congruences of the cogrowth sequence in a unitriangular group UT$(m, \Bbb Z)$ are undecidable. This is in contrast with abelian groups, where the congruences of the cogrowth sequence are decidable. As an application, we conclude that there is no algorithm to present the cogrowth series as the diagonal of a rational function.

Alejandro Diaz

Despite the rapid progression in our understanding of neutrinos over the last half century, much is left unknown about their properties. This leaves neutrinos as the most promising portal for Beyond Standard Model (BSM) physics, and neutrinos have already provided fruitful surprises. A number of neutrino experiments in the last three decades have observed an

Tamás Kovács, Máté Pszota, Emese Kővári, Emese Forgács-Dajka

The long-term dynamical evolution is a crucial point in recent planetary research. Although the amount of observational data is continuously growing and the precision allows us to obtain accurate planetary orbits, the canonical stability analysis still requires N-body simulations and phase space trajectory investigations. We propose a method for stability an

A. Arhrib, R. Benbrik, M. Krab, B. Manaut

A search for charged Higgs may yield clear and direct signs of new physics outside the realm of the Standard Model (SM). In the Two-Higgs Doublet Model (2HDM), we investigate two of the main single charged Higgs production channels at the Large Hadron Collider (LHC), assuming that either $h$ or $H$ replicates the detected resonance at $\sim 125~\rm{GeV}$. We

H. Th. J. Steiger, E. Theisen, L. Oberauer, O. Pilarczyk

Neutrino experiments often use large volumes of water, organic scintillators or noble liquids as active detection material. Due to the large hydrostatic and buoyancy forces involved, precise knowledge of the liquid levels inside the detector tank are mandatory. Here we present the main characteristics of the TruSense S310 Laser Range System. Level measuremen

Paprapee Buason, Sidhant Misra, Samuel Talkington, Daniel K. Molzahn

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. In this paper, we consider a sensor

Damião J. Araújo, Rafayel Teymurazyan, Vardan Voskanyan

We obtain sharp local $C^{1,\alpha}$ regularity of solutions for singular obstacle problems, Euler-Lagrange equation of which is given by $$ \Delta_p u=\gamma(u-\varphi)^{\gamma-1}\,\text{ in }\,\{u>\varphi\}, $$ for $0<\gamma<1$ and $p\ge2$. At the free boundary $\partial\{u>\varphi\}$, we prove optimal $C^{1,\tau}$ regularity of solutions, with $\tau$ give

E. Serabyn, G. Ruane, D. Echeverri

The number of terrestrial exoplanets accessible to high-contrast coronagraphic imaging with large telescopes is limited by the smallest angular offset from bright stars at which coronagraphs can observe. However, it is possible to reach inside a telescopes coronagraphic regime by employing nulling interferometry across a telescopes pupil. Indeed, cross-apert

Kenechukwu C. Mbanisi, Michael A. Gennert

Mobile telepresence robots (MTRs) have become increasingly popular in the expanding world of remote work, providing new avenues for people to actively participate in activities at a distance. However, humans operating MTRs often have difficulty navigating in densely populated environments due to limited situation awareness and narrow field-of-view, which red

Bhargav Narayanan, Corrine Yap

Given a random binary picture $P_n$ of size $n$, i.e., an $n\times n$ grid filled with zeros and ones uniformly at random, when is it possible to reconstruct $P_n$ from its $k$-deck, i.e., the multiset of all its $k\times k$ subgrids? We demonstrate ``two-point concentration'' for the reconstruction threshold by showing that there is an integer $k_c(n) \sim

Fan Lu, Prashant Mehta, Sean Meyn, Gergely Neu

In recent years there has been a collective research effort to find new formulations of reinforcement learning that are simultaneously more efficient and more amenable to analysis. This paper concerns one approach that builds on the linear programming (LP) formulation of optimal control of Manne. A primal version is called logistic Q-learning, and a dual var

Peter Kagey

We classify an algebraic phenomenon on certain families of wreath products that can be seen as coming from a family of puzzles about switches on the corners of a spinning table. Such puzzles have been written about and generalized since they were first popularized by Martin Gardner in 1979. In this paper, we provide perhaps the fullest generalization yet, mo

Erdal Yiğit, Manbharat Dhadly, Alexander S. Medvedev, Brian J. Harding

Using the horizontal neutral wind observations from the MIGHTI instrument onboard NASA's ICON (Ionospheric Connection Explorer) spacecraft with continuous coverage, we determine the climatology of the mean zonal and meridional winds and the associated mean circulation at low- to middle latitudes ($10^\circ$S-40$^{\circ}$N) for Northern Hemisphere {summer} so

B. N. Khabibullin, E. G. Kudasheva

We prove a version of the Beurling-Malliavin multiplier theorem. This version is formulated here in a simplified form. Let $u\not\equiv -\infty$ and $M\not\equiv -\infty$ be a pair of subharmonic functions on the complex plane $\mathbb C$ with positive parts $u^+:=\sup\{u,0\}$ and $M^+$ such that $$ \operatorname{type}[u]:=\limsup_{z\to \infty} \frac{u^+(z)}