October 2022 arXiv papers — page 98

Ludovic Sibille, Xinrui Zhan, Lei Xiang

Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical rou

Joseph R. Burger, Jordan G. Okie, Ian Hatton, Vanessa P. Weinberger

Understanding scaling relations of social and environmental attributes of urban systems is necessary for effectively managing cities. Urban scaling theory (UST) has assumed that population density scales positively with city size. We present a new global analysis using a publicly available database of 933 cities from 38 countries. Our results showed that (18

Qianying Liu, Chaitanya Kaul, Jun Wang, Christos Anagnostopoulos

For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations. However, existing research uses off-the-shelf vision Transformer blocks based on linear projections and feature processing which lack spatial and local co

Lev Grossman, Brian Plancher

Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors. But training DRL models is incredibly compute and memory intensive, requiring large training datasets and replay buffers to achieve performant results. This poses a challenge for the next generation of field robots that will need to learn on the ed

Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong

Semantic segmentation of LiDAR point clouds is an important task in autonomous driving. However, training deep models via conventional supervised methods requires large datasets which are costly to label. It is critical to have label-efficient segmentation approaches to scale up the model to new operational domains or to improve performance on rare cases. Wh

Barbara Kaltenbacher, William Rundell

This paper considers the Westervelt equation, one of the most widely used models in nonlinear acoustics, and seeks to recover two spatially-dependent parameters of physical importance from time-trace boundary measurements. Specifically, these are the nonlinearity parameter $\kappa(x)$ often referred to as $B/A$ in the acoustics literature and the wave speed

Marc W. Pound, Mark G. Wolfire

The PhotoDissociation Region Toolbox provides comprehensive, easy-to-use, public software tools and models that enable an understanding of the interaction of the light of young, luminous, massive stars with the gas and dust in the Milky Way and in other galaxies. It consists of an open-source Python toolkit and photodissociation region models for analysis of

Mahyar Najibi, Jingwei Ji, Yin Zhou, Charles R. Qi

Learning-based perception and prediction modules in modern autonomous driving systems typically rely on expensive human annotation and are designed to perceive only a handful of predefined object categories. This closed-set paradigm is insufficient for the safety-critical autonomous driving task, where the autonomous vehicle needs to process arbitrarily many

Mehrdad Tavassoli, Sunny Katyara, Maria Pozzi, Nikhil Deshpande

The uses of robots are changing from static environments in factories to encompass novel concepts such as Human-Robot Collaboration in unstructured settings. Pre-programming all the functionalities for robots becomes impractical, and hence, robots need to learn how to react to new events autonomously, just like humans. However, humans, unlike machines, are n

Rebecca M. M. Hicke, Maanya Goenka, Eric Alexander

Word clouds are frequently used to analyze and communicate text data in many domains. In order to help guide research on improving the legibility of word clouds, we have conducted a survey of their usage in Digital Humanities academia and journalism. Using a modified grounded theory approach, we sought to identify the most common purposes for which word clou

Zarina Banoo, Ramni Gupta

The multiplicity fluctuations are sensitive to QCD phase transition and to the presence of critical point in QCD phase diagram. At critical point a system undergoing phase transition is characterized by large fluctuations in the observables which is an important tool to understand the dynamics of particle production in heavy-ion interactions and phase change

Ghazal Alinezhad Noghre, Vinit Katariya, Armin Danesh Pazho, Christopher Neff

Path prediction is an essential task for many real-world Cyber-Physical Systems (CPS) applications, from autonomous driving and traffic monitoring/management to pedestrian/worker safety. These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for multiple subjects (e.g., pedestrians and v

Eder M. Correa, Lino Grama

In this paper, we describe invariant twisted K\"ahler-Einstein (tKE) metrics on flag varieties. We also explore some applications of the ideas involved in the proof of our main result to the existence of invariant twisted constant scalar curvature K\"{a}hler metrics. Also, we provide a precise description for the greatest Ricci lower bound of an arbitrary K\

Dan Guyer, Thomas Sachen

Continuing the work of Zemke, Livingston and Allen, we consider when linear combinations of torus knots are concordant to $L$-space knots. We begin by proving Allen's conjecture for alternating torus knots. That is, we prove that a linear combination of alternating torus knots is concordant to an $L$-space knot if and only if the connected sum is a single to

Haomiao Ni, Yuan Xue, Liya Ma, Qian Zhang

General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA, and more importantly, augmenting raw video with infant body parsin

Junhyeon Kwon, Yingcai Zheng, Mikyoung Jun

Hawkes process is one of the most commonly used models for investigating the self-exciting nature of earthquake occurrences. However, seismicity patterns have complicated characteristics due to heterogeneous geology and stresses, for which existing methods with Hawkes process cannot fully capture. This study introduces novel nonparametric Hawkes process mode

Heng Li

Motivation: Protein-to-genome alignment is critical to annotating genes in non-model organisms. While there are a few tools for this purpose, all of them were developed over ten years ago and did not incorporate the latest advances in alignment algorithms. They are inefficient and could not keep up with the rapid production of new genomes and quickly growing

Raúl A. Briceño, Andrew W. Jackura, Arkaitz Rodas, Juan V. Guerrero

The $\gamma^\star \gamma^\star \to \pi \pi$ scattering amplitude plays a key role in a wide range of phenomena, including understanding the inner structure of scalar resonances as well as constraining the hadronic contributions to the anomalous magnetic moment of the muon. In this work, we explain how the infinite-volume Minkowski amplitude can be constraine

Zhaori Guo, Timothy J. Norman, Enrico H. Gerding

Interactive reinforcement learning can effectively facilitate the agent training via human feedback. However, such methods often require the human teacher to know what is the correct action that the agent should take. In other words, if the human teacher is not always reliable, then it will not be consistently able to guide the agent through its training. In

M. S. Aronna, F. Bonnans, B. S. Goh

We deal with a control-affine problem with scalar control subject to bounds, a scalar state constraint and endpoint constraints of equality type. For the numerical solution of this problem, we propose a shooting algorithm and provide a sufficient condition for its local convergence. We exhibit an example that illustrates the theory.

Pok Man Tam, Charles L. Kane

We show that the topology of the Fermi sea of a two-dimensional electron gas (2DEG) is reflected in the ballistic Landauer transport along a long and narrow Josephson $\pi$-junction that proximitizes the 2DEG. The low-energy Andreev states bound to the junction are shown to exhibit a dispersion that is sensitive to the Euler characteristic of the Fermi sea (

Zeren Shui, Daniel S. Karls, Mingjian Wen, Ilia A. Nikiforov

For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurate methods in this domain are rooted in first-principles quantum mechanical calculations such as density functional theory (DFT). Because these methods have remained computationally

Sanghyun Son, Yi-Ling Qiao, Jason Sewall, Ming C. Lin

We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients fo

Koushik Chatterjee, Ramesh Narayan

We evolve two high-resolution general relativistic magnetohydrodynamic (GRMHD) simulations of advection-dominated accretion flows around non-spinning black holes (BHs), each over a duration $\sim 3\times 10^5\,GM_{\rm BH}/c^3$. One model captures the evolution of a weakly magnetized (SANE) disk and the other a magnetically arrested disk (MAD). Magnetic flux

Patrick Yard, Alex E. Jones, Stefano Paesani, Alexandre Maïnos

Multi-photon interference is at the heart of photonic quantum technologies. Arrays of integrated cavities can support bright sources of single-photons with high purity and small footprint, but the inevitable spectral distinguishability between photons generated from non-identical cavities is an obstacle to scaling. In principle, this problem can be alleviate

Matthew E. Quenneville, John P. Blakeslee, Chung-Pei Ma, Jenny E. Greene

We present wide-field, deep $K$-band photometry of 98 luminous early-type galaxies (ETGs) from the MASSIVE survey based on observations taken with the WIRCam instrument on the Canada-France-Hawaii Telescope. Using these images, we extract accurate total $K$-band luminosities ($L_K$) and half-light radii ($R_e$) for this sample of galaxies. We use these new v

Mohammed Istafaul Haque Ansari, Ashish Bhateja, Ishan Sharma

We investigate axial segregation of binary mixtures in a laterally shaken horizontal channel formed by ratchet-like sidewalls that appear as concatenated trapeziums when not offset axially. Grain mixtures shaken in such a channel are observed to segregate in two stages: they first separate rapidly into two vertically arranged layers and, then, these layers m

Matthew DeCross, Eli Chertkov, Megan Kohagen, Michael Foss-Feig

A number of commercially available quantum computers, such as those based on trapped-ion or superconducting qubits, can now perform mid-circuit measurements and resets. In addition to being crucial for quantum error correction, this capability can help reduce the number of qubits needed to execute many types of quantum algorithms by measuring qubits as early

Keck Collaboration, P. A. R. Ade, Z. Ahmed, M. Amiri

We present estimates of line-of-sight distortion fields derived from the 95 GHz and 150 GHz data taken by BICEP2, BICEP3, and Keck Array up to the 2018 observing season, leading to cosmological constraints and a study of instrumental and astrophysical systematics. Cosmological constraints are derived from three of the distortion fields concerning gravitation

Jianwei Lyu, George H. Rieke

Polar dust has been found to play an important role in the mid-infrared emission of nearby Seyfert nuclei. If and how often polar dust exists among the quasar population is unknown due to the lack of spatially-resolved observations. In this Letter, we report correlations between the prominence of AGN forbidden line emission (commonly associated with the narr

Po-Sheng Liu, Fu-Jen Tsai, Yan-Tsung Peng, Chung-Chi Tsai

Most previous deblurring methods were built with a generic model trained on blurred images and their sharp counterparts. However, these approaches might have sub-optimal deblurring results due to the domain gap between the training and test sets. This paper proposes a reblur-deblur meta-transferring scheme to realize test-time adaptation without using ground

Francesco Avanzini, Nahuel Freitas, Massimiliano Esposito

We lay the foundation of a circuit theory for chemical reaction networks. Chemical reactions are grouped into chemical modules solely characterized by their current-concentration characteristic, as electrical devices by their current-voltage (I-V) curve in electronic circuit theory. This, combined with the chemical analog of Kirchhoff's current and voltage l

John Musgrave, Alina Campan, Temesguen Messay-Kebede, David Kapp

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composi

Avi Shragai, Florian Theuss, Gael Grissonnanche, B. J. Ramshaw

A solid object's geometry, density, and elastic moduli completely determine its spectrum of normal modes. Solving the inverse problem - determining a material's elastic moduli given a set of resonance frequencies and sample geometry - relies on the ability to compute resonance spectra accurately and efficiently. Established methods for calculating these spec

Johanna Kölbel, Walter Schirmacher, Evgenyi Shalaev, J. Axel Zeitler

The model glass-former glycerol and its aqueous mixtures were investigated with terahertz-time domain spectroscopy (THz-TDS) in the frequency range of 0.3--3.0\,THz at temperatures from 80--305\,K. It was shown that the infrared absorption coefficient measured with THz-TDS can be theoretically related to the reduced Raman intensity ($\propto \alpha/\omega^2$

Nasim Rahaman, Martin Weiss, Francesco Locatello, Chris Pal

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural

Alessandro Peca, Nico Cappelluti, Meg Urry, Stephanie LaMassa

We present X-ray spectral analysis of XMM and Chandra observations in the 31.3 deg$^2$ Stripe-82X (S82X) field. Of the 6181 X-ray sources in this field, we analyze a sample of 2937 active galactic nuclei (AGN) with solid redshifts and sufficient counts determined by simulations. Our results show a population with median values of spectral index $\Gamma=1.94_

Mukul Bhattacharya, Jose Alonso Carpio, Kohta Murase, Shunsaku Horiuchi

Relativistic jets originating from protomagnetar central engines can lead to long duration gamma-ray bursts (GRBs) and are considered potential sources of ultrahigh-energy cosmic rays and secondary neutrinos. We explore the propagation of such jets through a broad range of progenitors, from stars which have shed their envelopes to supergiants which have not.

Vadim Briaud, Andrey Shkerin, Sergey Sibiryakov

In flat space and at finite temperature, there are two regimes of false vacuum decay in quantum field theory. At low temperature, the decay proceeds through thermally-assisted tunneling described by periodic Euclidean solutions -- bounces -- with non-trivial time dependence. On the other hand, at high temperature the bounces are time-independent and describe

Nils Quetschlich, Lukas Burgholzer, Robert Wille

Any potential application of quantum computing, once encoded as a quantum circuit, needs to be compiled in order to be executed on a quantum computer. Deciding which qubit technology, which device, which compiler, and which corresponding settings are best for the considered problem -- according to a measure of goodness -- requires expert knowledge and is ove

Daniele De Bernardis, Mathieu Jeannin, Jean-Michel Manceau, Raffaele Colombelli

We analyse the effect of a strong perpendicular magnetic field on an intersubband transition in a disordered doped quantum well strongly coupled to an optical cavity. The magnetic field changes the lineshape of the intersubband optical transition due to the interface roughness of the quantum well from a Lorentzian to a Gaussian one. In this regime, a novel f

Adarsh S. Patri, T. Senthil

Recent experiments on multilayer graphene materials have discovered a plethora of correlated phases, including ferromagnetism and superconductivity, in the absence of a moir\'{e} potential. These findings pose an intriguing question of whether an underlying moir\'{e} potential plays a key role in determining the phases realizable in tunable two-dimensional q

Jeong-Gyu Kim, Munan Gong, Chang-Goo Kim, Eve C. Ostriker

We present an efficient heating/cooling method coupled with chemistry and ultraviolet (UV) radiative transfer, which can be applied to numerical simulations of the interstellar medium (ISM). We follow the time-dependent evolution of hydrogen species (H$_2$, H, H$^+$), assume carbon/oxygen species (C, C$^+$, CO, O, and O$^+$) are in formation-destruction bala

Wenbin Lu, Eliot Quataert

We propose that the recently observed quasi-periodic eruptions (QPEs) in galactic nuclei are produced by unstable mass transfer due to Roche lobe overflow of a low-mass main-sequence star in a mildly eccentric (e ~ 0.5) orbit. We argue that the QPE emission is powered by circularization shocks, but not directly by black hole accretion. Our model predicts the

Matteo Nori, Andrea V. Macciò, Marco Baldi

We present the first high-resolution zoom-in simulation of a Milky-way-like halo extracted from the Aquarius Project in the Fuzzy Dark Matter (FDM) framework. We use the N-body code AX-GADGET, based on a particle oriented solution of the Schr\"{o}dinger-Poisson equations, able to detail the complexity of structure formation while keeping track of the quantum

Kevin J. Kelly, Joachim Kopp

The MiniBooNE excess persists as a significant puzzle in particle physics. Given that the MiniBooNE detector cannot discriminate between electron-like signals and backgrounds due to photons, the goal of this work is to study photon backgrounds in MiniBooNE in depth. We first consider a novel single-photon background arising from multi-nucleon scattering with

Kirill Boguslavski, Paul Hotzy, David I. Müller

The complex Langevin (CL) method is a promising approach to overcome the sign problem, which emerges in real-time formulations of quantum field theories. Over the past decade, stabilization techniques for CL have been developed with important applications in finite density QCD. However, they are insufficient for SU($N_c$) gauge theories on a Schwinger-Keldys

Nicolas Regnault, Shuo Liu, B. Andrei Bernevig

Models with Hilbert space fragmentation are characterized by (exponentially) many dynamically disconnected subspaces, not associated with conventional symmetries but captured by nontrivial Krylov subspaces. These subspaces usually exhibit a whole range of thermalization properties, from chaotic to integrable, to quantum many-body scars. However, so far, they

Katie Teixeira, Sarah Ballard

The diversity of dynamical conditions among exoplanets is now well established. Yet, the relevance of orbital dynamical timescales to biological evolutionary timescales is poorly understood. Given that even minor orbital changes may place significant pressure on any organisms living on a planet, dynamical sculpting has important implications for the putative

Fulya Kıroğlu, James C. Lombardi, Kyle Kremer, Giacomo Fragione

We study close encounters of a $1\,M_{\odot}$ middle-age main-sequence star (modeled using MESA) with massive black holes through hydrodynamic simulations, and explore in particular the dependence of the outcomes on the black hole mass. We consider here black holes in the intermediate-mass range, $M_{\rm BH}= 100-10^4\,M_{\odot}$. Possible outcomes vary from

Renan A. Rojas-Gomez, Teck-Yian Lim, Alexander G. Schwing, Minh N. Do

We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be trained end-to-end from data and generalizes existing handcrafted downsampling layers. It is widely applicable as it can be integrated into any convolutional network by replacing down/u

Andreas Helset, Elizabeth E. Jenkins, Aneesh V. Manohar

We formulate the field-space geometry for an effective field theory of scalars and gauge bosons. Geometric invariants such as the field-space curvature enter in both scattering amplitudes and the renormalization group equations, with the scalar and gauge results unified in a single expression.

Adrian B. Culver, Pratik Sathe, Rahul Roy

We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. As an example application, we

Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz

Differentiable neural architecture search (DARTS) is a popular method for neural architecture search (NAS), which performs cell-search and utilizes continuous relaxation to improve the search efficiency via gradient-based optimization. The main shortcoming of DARTS is performance collapse, where the discovered architecture suffers from a pattern of declining

Sayandip Dhara, Garry Goldstein, Claudio Chamon, Eduardo R. Mucciolo

We present a scheme to use physical Majorana quasi-zero modes at each junction of a two-dimensional nanowire network to build a logical Majorana zero mode, the location of which is controllable through gate voltages. The wire-network is a way to realize a proposal by Yang et al. arXiv:1808.04825 to imprint a Kekul\'e vortex pattern on a honeycomb lattice via

Jiashuo Jiang, Will Ma, Jiawei Zhang

We study the classical Network Revenue Management (NRM) problem with accept/reject decisions and $T$ IID arrivals. We consider a distributional form where each arrival must fall under a finite number of possible categories, each with a deterministic resource consumption vector, but a random value distributed continuously over an interval. We develop an onlin

Jian-Wei Qiu, Zhite Yu

Generalized parton distributions (GPDs) are important nonperturbative functions that provide tomographic images of partonic structures of hadrons. We introduce a type of exclusive processes, to be referred to as single diffractive hard exclusive processes (SDHEPs). We discuss the necessary and sufficient conditions for SDHEPs to be factorized into GPDs. We d

Andreea Palade, Andra M. Voicu, Petri Mähönen, Ljiljana Simić

We study whether realistic 5G mm-wave cellular networks would cause harmful out-of-band interference to weather satellites sensing in the 23.8 GHz band. We estimate uplink and downlink interference from a single interferer and a network of interferers in New York City, using real 3D building data and realistic antenna patterns. We perform detailed ray-tracin

Iulia-Maria Comsa, Julian Martin Eisenschlos, Srini Narayanan

We propose a benchmark to assess the capability of large language models to reason with conventional metaphors. Our benchmark combines the previously isolated topics of metaphor detection and commonsense reasoning into a single task that requires a model to make inferences by accurately selecting between the literal and metaphorical register. We examine the

Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth

Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy function or a reward. GFNs are typically trained using a flow matching or trajectory balance objective, which matches forward and backward transition models over trajectories. In thi

Shimian Zhang, Skanda Bharadwaj, Keaton Kraiger, Yashasvi Asthana

We demonstrate the utility of recurring pattern discovery from a single image for spatial understanding of a 3D scene in terms of (1) vanishing point detection, (2) hypothesizing 3D translation symmetry and (3) counting the number of RP instances in the image. Furthermore, we illustrate the feasibility of leveraging RP discovery output to form a more precise

Jiayi Sun, Wensheng Gan, Han-Chieh Chao, Philip S. Yu

As a fusion of various emerging digital technologies, the Metaverse aims to build a virtual shared digital space. It is closely related to extended reality, digital twin, blockchain, and other technologies. Its goal is to build a digital space based on the real world, form a virtual economic system, and expand the space of human activities, which injects new

Dexuan Hu, Sławomir Solecki

We give a method of producing a Polish module over an arbitrary subring of $\mathbb Q$ from an ideal of subsets of $\mathbb N$ and a sequence in $\mathbb N$. The method allows us to construct two Polish $\mathbb Q$-vector spaces, $U$ and $V$, such that -- both $U$ and $V$ embed into $\mathbb R$ but -- $U$ does not embed into $V$ and $V$ does not embed into $

Tianqing Fang, Quyet V. Do, Hongming Zhang, Yangqiu Song

Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge is that it requires out-of-domain generalization ability as the source CSKB for training is of a relatively smaller scale (1M) while the whole candidate space for population is way

Shuyi Li, Michael O'Connor, Shiwei Lan

Regularization is one of the most fundamental topics in optimization, statistics and machine learning. To get sparsity in estimating a parameter $u\in\mathbb{R}^d$, an $\ell_q$ penalty term, $\Vert u\Vert_q$, is usually added to the objective function. What is the probabilistic distribution corresponding to such $\ell_q$ penalty? What is the correct stochast

Gideon Oyibo, Thomas Barrett, Sharadh Jois, Jeffrey Blackburn

Photovoltaics has two main processes: Optical absorption and power conversion. In photosynthesis, the two equivalent processes are optical absorption and chemical conversion. Whereas in the latter, the two processes are carried out by distinct proteins, in conventional photovoltaic diodes, the two processes are convoluted because the optical and transport pa

Andy C. Y. Li, Alexandru Macridin, Stephen Mrenna, Panagiotis Spentzouris

We present a quantum algorithm for implementing $\phi^4$ lattice scalar field theory on qubit computers. The field is represented in the discretized field amplitude basis. The number of qubits and elementary gates required by the implementation of the evolution operator is proportional to the lattice size. The algorithm allows efficient $\phi^4$ state prepar

Salvatore Certo, Andrew Vlasic, Daniel Beaulieu

A new implementation of an adiabatically-trained ensemble model is derived that shows significant improvements over classical methods. In particular, empirical results of this new algorithm show that it offers not just higher performance, but also more stability with less classifiers, an attribute that is critically important in areas like explainability and

Ricardo Montalvo-Lezama, Berenice Montalvo-Lezama, Gibran Fuentes-Pineda

In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we present an extensive evaluation of the transferability of ConvNet and Transformer models pretrained on ImageNet and Kinetics to Trailers12k, a new manually-curated movie trail

E. F. Jiménez-Andrade, S. Cantalupo, B. Magnelli, E. Romano-Díaz

Ly$\alpha$ $\lambda$1216 (Ly$\alpha$) emission extending over $\gtrsim\,\rm 10\,kiloparsec\,(kpc)$ around dusty, massive starbursts at $z\gtrsim3$ might represent a short-lived phase in the evolution of present-day, massive quiescent galaxies. To obtain empirical constraints on this emerging scenario, we present Ly$\alpha$, CIV $\lambda$1550 (CIV), and HeII

Edward W. G. Skevington, Andrew J. Hogg

The collision of a gravitationally-driven horizontal current with a barrier following release from a confining lock is investigated using a shallow water model of the motion, together with a sophisticated boundary condition capturing the local interaction. The boundary condition permits several overtopping modes: supercritical, subcritical, and blocked flow.

Michael Ragone, Paolo Braccia, Quynh T. Nguyen, Louis Schatzki

Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importation of these ideas, combined with an existing rich body of work at the nexus of quantum theory and symmetry, has given rise to the field of Geometric Quantum Machine Learning (GQML).

Yuki Yonemoto, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai

One of the most fundamental method for comparing two given strings $A$ and $B$ is the longest common subsequence (LCS), where the task is to find (the length) of an LCS of $A$ and $B$. In this paper, we deal with the STR-IC-LCS problem which is one of the constrained LCS problems proposed by Chen and Chao [J. Comb. Optim, 2011]. A string $Z$ is said to be an

Kuan-Po Huang, Yu-Kuan Fu, Tsu-Yuan Hsu, Fabian Ritter Gutierrez

Self-supervised learned (SSL) speech pre-trained models perform well across various speech processing tasks. Distilled versions of SSL models have been developed to match the needs of on-device speech applications. Though having similar performance as original SSL models, distilled counterparts suffer from performance degradation even more than their origina

R. Kailasham, Aditya S. Khair

The dynamics of a two dimensional autophoretic disk is quantified as a minimal model for the chaotic trajectories undertaken by active droplets. Via direct numerical simulations, we show that the mean-square displacement of the disk in a quiescent fluid is linear at long times. Surprisingly, however, this apparently diffusive behavior is non-Brownian, owing

Lennart Alexander Van der Goten, Kevin Smith

Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render a scan useless, but can potentially ind

Jonathan Folkerts

For years, earth-based neutrino detectors have been run and operated to detect the elusive neutrino. These have historically been enormous underground detectors. The neutrino Solar Orbiting Laboratory ($\nu$SOL) project is working to design a technical demonstration to show that a much smaller neutrino detector can be operated in near-solar environments for

Xunnian Yang

This paper proposes to generalize linear subdivision schemes to nonlinear subdivision schemes for curve and surface modeling by refining vertex positions together with refinement of unit control normals at the vertices. For each round of subdivision, new control normals are obtained by projections of linearly subdivided normals onto unit circle or sphere whi

Christian Northe, Chunxu Zhang, Rafał Wawrzyńczak, Johannes Gooth

The conformal anomaly indicates the breaking of conformal symmetry (angle-preserving transformations) in the quantum theory by quantum fluctuations and is a close cousin of the gravitational anomaly. We show, for the first time, that the conformal anomaly controls the variance of the local magnetization $M_{loc}$ at finite temperatures in spin chains and spi

Laxmipriya Pati, Daniel Blixt, Maria-Jose Guzman

We present Hamilton's equations for the teleparallel equivalent of general relativity (TEGR), which is a reformulation of general relativity based on a curvatureless, metric compatible, and torsionful connection. For this, we consider the Hamiltonian for TEGR obtained through the vector, antisymmetric, symmetric and trace-free, and trace irreducible decompos

Senan Hogan-Hennessy, Peter Xenopoulos, Claudio Silva

Massively multiplayer online role-playing games often contain sophisticated in-game economies. Many important real-world economic phenomena, such as inflation, economic growth, and business cycles, are also present in these virtual economies. One major difference between real-world and virtual economies is the ease and frequency by which a policymaker, in th

Gabriel P. Martins, Oleg L. Berman, Godfrey Gumbs

We investigated the dynamics of Bose-Einstein condensates (BECs) under an external periodic potential. We consider two such systems, the first being made of exciton-polaritons in a nanoribbon of transition metal dichalcogenides (TMDCs), such as MoSe$_2$, embedded in a microcavity with a special curvature, which serves as the source of the external potential.

Pedro Núñez

We consider differential forms associated to Campana's geometric orbifolds from a new perspective, namely, as a qfh-sheaf on the variety underlying the geometric orbifold. This approach avoids having to choose a covering of the underlying variety, which is one of the drawbacks of a common way to work with these differential forms.

Melanie Mitchell, David C. Krakauer

We survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social situations language encodes -- in any important sense. We describe arguments that have been made for and against such understanding, and key questions for the broader sciences of inte

Arsenio Cutolo, Massimiliano Fraldi, Gaetano Napoli, Giuseppe Puglisi

We study the equilibrium configurations related to the growth of an elastic fibre in a confining flexible ring. This system represents a paradigm for a variety of biological, medical, and engineering problems. We consider a simplified geometry in which initially the container is a circular ring of radius $R$. Quasi-static growth is then studied by solving th

Arnaud Eychenne, Frédéric Valet

We study the solitary waves of fractional Korteweg-de Vries type equations, that are related to the $1$-dimensional semi-linear fractional equations: \begin{align*} \vert D \vert^\alpha u + u -f(u)=0, \end{align*} with $\alpha\in (0,2)$, a prescribed coefficient $p^*(\alpha)$, and a non-linearity $f(u)=\vert u \vert^{p-1}u$ for $p\in(1,p^*(\alpha))$, or $f(u

Jishnu Narayanan S J, Arnab Bachhar, Divya Tripathi, Achintya Kumar Dutta

We have analyzed the low-energy electron attachment to wobble base pairs using the equation motion coupled cluster method and extended basis sets. A doorway mechanism exists for the attachment of the additional electron to the base pairs, where the initially formed dipole-bound anion captures the incoming electron. The doorway dipole-bound anionic state subs

Meng-Che Chang, Shi-Yuan Wang, Tuna Erdoğan, Matthieu R. Bloch

We study the information-theoretic limits of joint communication and sensing when the sensing task is modeled as the estimation of a discrete channel state fixed during the transmission of an entire codeword. This setting captures scenarios in which the time scale over which sensing happens is significantly slower than the time scale over which symbol transm

Hector Garcia Martin, Tijana Radivojevic, Jeremy Zucker, Kristofer Bouchard

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields

Luke Duttweiler, Sally W. Thurston, Anthony Almudevar

A Bayesian Network (BN) is a probabilistic model that represents a set of variables using a directed acyclic graph (DAG). Current algorithms for learning BN structures from data focus on estimating the edges of a specific DAG, and often lead to many `likely' network structures. In this paper, we lay the groundwork for an approach that focuses on learning glo

João Costa, Pedro Ribeiro, Andrea de Luca, Tomaž Prosen

We study spectral and steady-state properties of generic Markovian dissipative systems described by quadratic fermionic Liouvillian operators of the Lindblad form. The Hamiltonian dynamics is modeled by a generic random quadratic operator, i.e., as a featureless superconductor of class D, whereas the Markovian dissipation is described by $M$ random linear ju

Thomas E. Doyle, Victoria Tucci, Calvin Zhu, Yifei Zhang

The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature. As cross-discipline teams work together on complex machine learning challenges, finding a consensus of basic definitions in the literature is a more fundamental problem. As a step in the Delphi process to define issues with trus

Farhana Rahman

The research paper aims to analyze the underlying relationship in between the profitability and cost of funds of a firm. A total of twelve companies were selected as a sample for this study which are listed in Dhaka Stock Exchange under Food and Allied Industry. A panel data set of 15 years from 2005 to 2019 was used to conduct the necessary analysis. In thi

Mark Haskins, Ilyas Khan, Alec Payne

We prove a uniqueness result for asymptotically conical (AC) gradient shrinking solitons for the Laplacian flow of closed G_2-structures: If two gradient shrinking solitons to Laplacian flow are asymptotic to the same closed G_2-cone, then their G_2-structures are equivalent, and in particular, the two solitons are isometric. The proof extends Kotschwar and

Jacek Banasiak, David Poka, Sergey K. Shindin

The paper concerns the well-posedness and long-term asymptotics of growth--fragmentation equation with unbounded fragmentation rates and McKendrick--von Foerster boundary conditions. We provide three different methods of proving that there is a strongly continuous semigroup solution to the problem and show that it is a compact perturbation of the correspondi

John Colt, Lucas Nelson, Sykes Cargile, Ted Brzinski

Superellipse sector particles (SeSPs) are segments of superelliptical curves that form a tunable set of hard-particle shapes for granular and colloidal systems. SeSPs allow for continuous parameterization of corner sharpness, aspect ratio, and particle curvature; rods, circles, rectangles, and staples are examples of shapes SeSPs can model. We compare three

D. Ramsey, A. Di Piazza, M. Formanek, P. Franke

The intensity peak of a "flying focus" travels at a programmable velocity over many Rayleigh ranges while maintaining a near-constant profile. Assessing the extent to which these features can enhance laser-based applications requires an accurate description of the electromagnetic fields. Here we present exact analytical solutions to Maxwell's equations for t

Jose I. Vines, James S. Jenkins, Zaira Berdiñas, Maritza G. Soto

Very little is known about the young planet population because the detection of small planets orbiting young stars is obscured by the effects of stellar activity and fast rotation which mask planets within radial velocity and transit data sets. The few planets that have been discovered in young clusters generally orbit stars too faint for any detailed follow

Salman Khurshid Malik, Ramni Gupta

Local density fluctuations are expected to scale as a universal power-law when the system approaches critical point. Such power-law fluctuations are studied within the framework of intermittency through the measurement of normalized factorial moments in ($\eta$, $\phi$) phase space. Observations and results from the intermittency analysis performed for charg

Sudipta Paul, Amit K. Roy-Chowdhury, Anoop Cherian

Recent years have seen embodied visual navigation advance in two distinct directions: (i) in equipping the AI agent to follow natural language instructions, and (ii) in making the navigable world multimodal, e.g., audio-visual navigation. However, the real world is not only multimodal, but also often complex, and thus in spite of these advances, agents still