October 2022 arXiv papers — page 120

Sungil Kwon, M. S. Barrueta, L. Castellano, J. M. Lyles

The front end of Los Alamos Neutron Science Center (LANSCE) linear accelerator uses four 201.25-MHz Drift-Tube Linacs (DTLs) to accelerate the H+ and H- beams to 100 MeV. Three of the 201.25-MHz DTLs are powered by diacrodes and the first DTL is powered by a tetrode. A 20-kW solid-state power amplifier (SSPA) is used to provide ~15 kW drive power to the tetr

Lauren Eyler Dang, Jens Magelund Tarp, Trine Julie Abrahamsen, Kajsa Kvist

Augmenting a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. To select and analyze the experiment (RCT alone or combined with external data) with the optimal bias-variance tradeoff, we develop a novel experiment-selector cross-validated targeted maximum likelihood estimator for randomized-external data

Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu

Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood data dependency limits GNNs in practical deployments. Conversely, the known efficient MLPs are much less effective than GNNs due to the lack of relational knowledge. In this work, to

Juncheng Wei, Qidi Zhang, Yifu Zhou

We construct finite time blow-up solutions to the Landau-Lifshitz-Gilbert equation (LLG) from ${\mathbb R}^2$ into $S^2$ \begin{equation*} \begin{cases} u_t= a(\Delta u+|\nabla u|^2u) -b u\wedge \Delta u &\ \mbox{ in }\ {\mathbb R}^2\times(0,T), u(\cdot,0) = u_0\in S^2 &\ \mbox{ in }\ {\mathbb R}^2, \end{cases} \end{equation*} where $a^2+b^2=1,~a > 0,~ b\in

B. J. Frei, A. C. D. Hoffmann, P. Ricci, S. Brunner

This work reports on the development and numerical implementation of the linear electromagnetic gyrokinetic (GK) model in a tokamak flux-tube geometry using a moment approach based on the expansion of the perturbed distribution function on a velocity-space Hermite-Laguerre polynomials basis. A hierarchy of equations of the expansion coefficients, referred to

Sungil Kwon, A. Archuleta, L. Castellano, C. Marchwinski

The linear accelerator (LINAC) at the Los Alamos Neu-tron Science Center (LANSCE) consists of Pre-buncher, Main-Buncher, low-energy beam transport (LEBT), four 201.25-MHz Drift Tube Linacs (DTLs) and forty-four 805-MHz Coupled Cavity Linacs (CCLs). As a part of the upcoming LANSCE Modernization project, low-level RF (LLRF) systems of four 201-MHz DTLs and tw

Jingjing Zou, Chi-Hua Chen, John A. D. Aston

We propose a framework for jointly modeling the geometry and functionality in high dimensional functional surfaces. The proposed mixed effects model characterizes effects of subject-specific covariates and exogenous stimuli on functional surfaces while accounting for potential mutual-influence of their geometry and functionality. This is achieved through a c

Renato Calleja, Alessandra Celletti, Joan Gimeno, Rafael de la Llave

We consider a Celestial Mechanics model: the spin-orbit problem with a dissipative tidal torque, which is a singular perturbation of a conservative system. The goal of this paper is to show that it is possible to compute quasi-periodic attractors accurately and reliably for parameter values extremely close to the breakdown. Therefore, it is possible to obtai

Matthew Eichhorn, Siddhartha Banerjee, David Kempe

Team formation is ubiquitous in many sectors: education, labor markets, sports, etc. A team's success depends on its members' latent types, which are not directly observable but can be (partially) inferred from past performances. From the viewpoint of a principal trying to select teams, this leads to a natural exploration-exploitation trade-off: retain succe

Jawad Ettayb

In this paper, the structured pseudospectrum of non-archimedean matrices and structured pseudospectrum of non-archimedean matrix pencils are introduced. Many results are proved about them and we give a few examples.

Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen

Recent advances in Transformer architectures have empowered their empirical success in a variety of tasks across different domains. However, existing works mainly focus on predictive accuracy and computational cost, without considering other practical issues, such as robustness to contaminated samples. Recent work by Nguyen et al., (2022) has shown that the

Dongseong Hwang, Khe Chai Sim, Yu Zhang, Trevor Strohman

Knowledge distillation is an effective machine learning technique to transfer knowledge from a teacher model to a smaller student model, especially with unlabeled data. In this paper, we focus on knowledge distillation for the RNN-T model, which is widely used in state-of-the-art (SoTA) automatic speech recognition (ASR). Specifically, we compared using soft

Xuanjie Shao, Arnab Hazra, Jordan Richards, Raphaël Huser

Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Max-stable processes are common choices for modeling spatially-indexed block maxima, where an assumption of stationarity is usual to make inference feasible. However, this assumption is often unrealistic for data observed over a large or complex domain. We propose a

Renee Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon

Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified

Guilherme Lourenço de Toledo, Ricardo Marcondes Marcacini

Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current studies show that exploring other modalities (e.g., images) increases sentiment analysis performance. State-of-the-art multimodal models, such

Damon Falck, Thomas Orton

In this work we consider a variant of adversarial online learning where in each round one picks $B$ out of $N$ arms and incurs cost equal to the $\textit{minimum}$ of the costs of each arm chosen. We propose an algorithm called Follow the Perturbed Multiple Leaders (FPML) for this problem, which we show (by adapting the techniques of Kalai and Vempala [2005]

Mark de Berg

Let $P$ be a set of $n$ points in the plane, where each point $p\in P$ has a transmission radius $r(p)>0$. The transmission graph defined by $P$ and the given radii, denoted by $\mathcal{G}_{\mathrm{tr}}(P)$, is the directed graph whose nodes are the points in $P$ and that contains the arcs $(p,q)$ such that $|pq|\leq r(p)$. An and Oh [Algorithmica 2022] pre

Satoshi Hayakawa, Harald Oberhauser, Terry Lyons

Given a probability measure $\mu$ on a set $\mathcal{X}$ and a vector-valued function $\varphi$, a common problem is to construct a discrete probability measure on $\mathcal{X}$ such that the push-forward of these two probability measures under $\varphi$ is the same. This construction is at the heart of numerical integration methods that run under various na

Galia Dafni, Chun Ho Lau, Tiago Picon, Claudio Vasconcelos

In this work we present necessary cancellation conditions for the continuity of linear operators in $h^p(\mathbb{R}^n)$, $0<p\leq 1$, that map atoms into pseudo-molecules. Our necessary condition, expressed in terms of the $T^{\ast}$ condition, is the same as the one recently proved sufficient in [3], thus providing a necessary and sufficient cancellation co

Ke Hu, Bo Li, Tara N. Sainath

Multilingual end-to-end automatic speech recognition models are attractive due to its simplicity in training and deployment. Recent work on large-scale training of such models has shown promising results compared to monolingual models. However, the work often focuses on multilingual models themselves in a single-pass setup. In this work, we investigate secon

Yusuke Tanaka, Ankur Mehta

This paper introduces REMS, a robotics middleware and control framework that is designed to introduce the Zen of Python to robotics and to improve robotics education and development flow. Although existing middleware can serve hardware abstraction and modularity, setting up environments and learning middleware-specific syntax and procedures are less viable i

Karim Guirguis, Mohamed Abdelsamad, George Eskandar, Ahmed Hendawy

Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learning novel classes given only a few samples. While competitive results have been achieved using two-stage FSOD detectors, typically one-stage FSODs underperform compared to them. We

Meng Liu, Haoran Liu, Shuiwang Ji

Learning energy-based models (EBMs) is known to be difficult especially on discrete data where gradient-based learning strategies cannot be applied directly. Although ratio matching is a sound method to learn discrete EBMs, it suffers from expensive computation and excessive memory requirements, thereby resulting in difficulties in learning EBMs on high-dime

Ghadeer Abuoda, Daniele Dell'Aglio, Arthur Keen, Katja Hose

RDF and property graph models have many similarities, such as using basic graph concepts like nodes and edges. However, such models differ in their modeling approach, expressivity, serialization, and the nature of applications. RDF is the de-facto standard model for knowledge graphs on the Semantic Web and supported by a rich ecosystem for inference and proc

Ben Zhou, Dian Yu, Dong Yu, Dan Roth

Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-based features to directly approach this task, but these approaches come with significant drawbacks, such as lack of contextual reasoning and poor cross-lingual generalization. In

Alex Manukovsky, Yuriy Shlepnev, Shimon Mordooch

Delay Deviation Exceedance (DDE) and Differential Skew Exceedance (DSE) measures are proposed to quantify delay uncertainty in single-ended and differential PCB interconnects arising from Fiber Weave Effect (FWE). DDE and DSE are constructed with 3D EM analysis of traces over inhomogeneous dielectric with glass fiber bundles in resin. Measurements or 3D EM m

I. Psaradaki, E. Costantini, D. Rogantini, M. Mehdipour

Understanding the chemistry of the interstellar medium (ISM) is fundamental for the comprehension of the Galactic and stellar evolution. X-rays provide an excellent way to study the dust chemical composition and crystallinity along different sight-lines in the Galaxy. In this work we study the dust grain chemistry in the diffuse regions of the interstellar m

B. B. Thompson, R. Smith, K. Kraljic

We conduct hydrodynamical cosmological zoom simulations of fourteen voids to study the ability of haloes to accrete gas at different locations throughout the voids at z = 0. Measuring the relative velocity of haloes with respect to their ambient gas, we find that a tenth of the haloes are expected to be unable to accrete external gas due to its fast flow pas

Danila Puzyrev, Dmitry Skryabin

Octave-wide frequency combs in microresonators are essential for self-referencing. However, it is difficult for the small-size and high-repetition-rate microresonators to achieve perfect soliton modelocking over the broad frequency range due to the detrimental impact of dispersion. Here we examine the stability of the soliton states consisting of one hundred

Huaxiu Yao, Yiping Wang, Linjun Zhang, James Zou

Improving the generalization of deep networks is an important open challenge, particularly in domains without plentiful data. The mixup algorithm improves generalization by linearly interpolating a pair of examples and their corresponding labels. These interpolated examples augment the original training set. Mixup has shown promising results in various class

Peter B. Rau, Ira Wasserman

We study the stability of Hall MHD with strong magnetic fields in which Landau quantization of electrons is important. We find that the strong-field Hall modes can be destabilized by the dependence of the differential magnetic susceptibility on magnetic field strength. This instability is studied using linear perturbation theory, and is found to have typical

Zirong Chen, Haotian Xue

Due to the unfamiliarity to particular words(or proper nouns) for ingredients, non-native English speakers can be extremely confused about the ordering process in restaurants like Subway. Thus, We developed a dialogue system, which supports Chinese(Mandarin)1 and English2 at the same time. In other words, users can switch arbitrarily between Chinese(Mandarin

Zirong Chen

FrameNet( Fillmore and Baker [2009] ) is well-known for its wide use for knowledge representation in the form of inheritance-based ontologies and lexica( Trott et al. [2020] ). Although FrameNet is usually applied to languages like English, Spanish and Italian, there are still plenty of FrameNet data sets available for other languages like Chinese, which dif

Ilias Magoulas, Francesco A. Evangelista

Efficient quantum circuits are necessary for realizing quantum algorithms on noisy intermediate-scale quantum devices. Fermionic excitations entering unitary coupled-cluster (UCC) ans\"atze give rise to quantum circuits containing CNOT "staircases" whose number scales exponentially with the excitation rank. Recently, Yordanov et al. [Phys. Rev. A 102, 062612

Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh

Conventional active learning (AL) frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. However, introducing AL to data hungry deep learning algorithms has been a challenge. Some proposed approaches include uncertainty-based techniques, geometric methods, implicit combination of uncerta

Mark Rofin, Vladislav Mikhailov, Mikhail Florinskiy, Andrey Kravchenko

The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation capabilities from multiple perspectives. Although the paradigm is shifting towards more fine-grained evaluation across diverse tasks, the delicate question of how to aggregate the

Francesco Bascone, Franco Pezzella, Patrizia Vitale

We consider the integrability of a two-parameter deformation of the Wess-Zumino-Witten model, previously introduced in relation with Poisson-Lie T-duality. The resulting family of Poisson-Lie dual models is shown to be integrable by using the Maillet r/s formalism.

Lingfeng Tao, Jiucai Zhang, Michael Bowman, Xiaoli Zhang

In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and the complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning based approaches mainly focus on training a single robot-structure-specific policy through the centralized learning mechanism, lacking adaptabi

Boris Chen, Amir Ziai, Rebecca Tucker, Yuchen Xie

A match cut is a transition between a pair of shots that uses similar framing, composition, or action to fluidly bring the viewer from one scene to the next. Match cuts are frequently used in film, television, and advertising. However, finding shots that work together is a highly manual and time-consuming process that can take days. We propose a modular and

Alex Lecavalier, Jeff Denis, Jean-Sébastien Plante, Alexandre Girard

Robotic legs have bimodal operations: swing phases when the leg needs to move quickly in the air (high-speed, low-force) and stance phases when the leg bears the weight of the system (low-speed, high-force). Sizing a traditional single-ratio actuation system for such extremum operations leads to oversized heavy electric motor and poor energy efficiency, whic

Rohith Saradhy, Erich Frahm, Eduardo B. S. Mendes, Roger Rusack

We describe a low-cost system designed to monitor wander in digital clocks with a precision of $\le$ 1 ps. With this system we have shown that it is possible to track phase variations at the sub-picosecond level by adding noise to a reference clock. As in many cases where a clock is part of a complex distribution network small changes in temperature and othe

Sean T. Linden, Aaron S. Evans, Lee Armus, Jeffrey A. Rich

We present the results of a {\it James Webb Space Telescope} NIRCam investigation into the young massive star cluster (YMC) population in the luminous infrared galaxy VV 114. We identify 374 compact YMC candidates with a $S/N \geq 3$, 5, and 5 at F150W, F200W, and F356W respectively. A direct comparison with our {\it HST} cluster catalog reveals that $\sim 2

Zong Fan, Ping Gong, Shanshan Tang, Christine U. Lee

Automatic breast lesion detection and classification is an important task in computer-aided diagnosis, in which breast ultrasound (BUS) imaging is a common and frequently used screening tool. Recently, a number of deep learning-based methods have been proposed for joint localization and classification of breast lesions using BUS images. In these methods, fea

Kenneth Beard, Anthony Stefan, Robert Viator, Aaron Welters

Using a Hilbert space framework inspired by the methods of orthogonal projections and Hodge decompositions, we study a general class of problems (called Z-problems) that arise in effective media theory, especially within the theory of composites, for defining the effective operator. A new and unified approach is developed, based on block operator methods, fo

Mark M. Bailey

Efforts by foreign actors to influence public opinion have gained considerable attention because of their potential to impact democratic elections. Thus, the ability to identify and counter sources of disinformation is increasingly becoming a top priority for government entities in order to protect the integrity of democratic processes. This study presents a

Matthias Lienert

In relativistic quantum theory, one sometimes considers integral equations for a wave function $\psi(x_1,x_2)$ depending on two space-time points for two particles. A serious issue with such equations is that, typically, the spatial integral over $|\psi|^2$ is not conserved in time -- which conflicts with the basic probabilistic interpretation of quantum the

Zonglin Li, Ruiqi Guo, Sanjiv Kumar

Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and modularity. In this paper we examined

Nina K. Stein, William H. Kinney

We construct a family of simple single-field inflation models consistent with Planck / BICEP Keck bounds which have a parametrically small tensor amplitude and no running of the scalar spectral index. The construction consists of a constant-roll hilltop inflaton potential with the end of inflation left as a free parameter induced by higher-order operators wh

Piyush Behre, Sharman Tan, Padma Varadharajan, Shuangyu Chang

While speech recognition Word Error Rate (WER) has reached human parity for English, long-form dictation scenarios still suffer from segmentation and punctuation problems resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctu

Manoel Benedito Nirdo da Silva Campos

Presents a differentiated teaching proposal that allows the student to be the agent in the construction of knowledge, overcoming the difficulties that Mathematics presents. Aiming to understand how the use of statistical tools can contribute to the improvement of the teaching-learning process and the construction of statistical knowledge, studied with studen

Mostafa Hassanlou, Ebrahim Abbasi

In this paper, we investigate weighted composition, Volterra and Integral operators on second derivative Hardy spaces. Some equivalent conditions for boundedness of the operators will be given using the boundedness on the Hardy spaces. Also we give a criteria for compactness of weighted composition operators.

Maria N. Gastiasoro, Maria Eleonora Temperini, Paolo Barone, Jose Lorenzana

SrTiO3 is known for its proximity to a ferroelectric phase and for showing an 'optimal' doping for superconductivity with a characteristic dome-like behaviour resembling systems close to a quantum critical point. Several mechanisms have been proposed to link these phenomena, but the abundance of undetermined parameters prevents a definite assessment. Here, w

George Livadiotis, David J. McComas

The recently developed concept of "entropic defect" is important for understanding the foundations of thermodynamics in space plasma physics, and more generally, for systems with physical correlations among their particles. Using this concept, this paper derives the basic formulation of the distribution function of velocities (or kinetic energies) in space p

Sijia Wang, Yoojin Choi, Junya Chen, Mostafa El-Khamy

Most existing works on continual learning (CL) focus on overcoming the catastrophic forgetting (CF) problem, with dynamic models and replay methods performing exceptionally well. However, since current works tend to assume exclusivity or dissimilarity among learning tasks, these methods require constantly accumulating task-specific knowledge in memory for ea

Yun Kai, Jet Lem, Marcus Ossiander, Maryna L. Meretska

Achieving high repeatability and efficiency in laser-induced strong shock wave excitation remains a significant technical challenge, as evidenced by the extensive efforts undertaken at large-scale national laboratories to optimize the compression of light element pellets. In this study, we propose and model a novel optical design for generating strong shocks

Anthony Trubiano, Michael F. Hagan

The promise of self-assembly to enable bottom-up formation of materials with prescribed architectures and functions has driven intensive efforts to uncover rational design principles for maximizing the yield of a target structure. Yet, despite many successful examples of self-assembly, ensuring kinetic accessibility of the target structure remains an unsolve

Stephen Gillen

Analytic combinatorics in several variables (ACSV) analyzes the asymptotic growth of the coefficients of a meromorphic generating function $F = G/H$ in a direction $\mathbf{r}$. It uses Morse theory on the pole variety $V := \{ H = 0 \} \subseteq (\mathbb{C}^*)^d$ of $F$ to deform the torus $T$ in the multivariate Cauchy Integral Formula via the downward gra

Gabriel E. Monsalve

We give a new algorithmic method of detection of atypical values for 2-variables real polynomial functions with emphasis on the effectivity.

Moritz Weckbecker, Wenkai Xu, Gesine Reinert

Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may depend on the choice of kernels in an underlying reproducing kernel Hilbert space (RKHS). Here we assess the effect of RKHS choice for KSD tests of random networks models, develop

Olivier Deligny

The Pierre Auger Observatory has been detecting ultra-high energy cosmic rays (UHECRs) for more than fifteen years. An essential feature of the Observatory is its hybrid design: cosmic rays above $100~$PeV are detected through the observation of the associated air showers with different and complementary techniques, from surface detector arrays and fluoresce

T. J. Christiansen, K. Datchev

We compute low energy asymptotics for the resolvent of a planar obstacle, and deduce asymptotics for the corresponding scattering matrix, scattering phase, and exterior Dirichlet-to-Neumann operator. We use an identity of Vodev to relate the obstacle resolvent to the free resolvent and an identity of Petkov and Zworski to relate the scattering matrix to the

Constantia Alexandrou, Simone Bacchio, Martha Constantinou, Petros Dimopoulos

We present a calculation of the Mellin moments of the nucleon transverse quark spin densities extracted from the unpolarized and transversity generalized form factors. We use three $N_F=2+1+1$ ensembles of twisted mass fermions with quark masses tuned to their physical values and lattice spacings $a\sim 0.08$~fm, $a\sim 0.07$~fm and $a\sim 0.06$~fm and extra

Juyeop Kim, Junha Park, Songkuk Kim, Jong-Seok Lee

Neural networks with self-attention (a.k.a. Transformers) like ViT and Swin have emerged as a better alternative to traditional convolutional neural networks (CNNs). However, our understanding of how the new architecture works is still limited. In this paper, we focus on the phenomenon that Transformers show higher robustness against corruptions than CNNs, w

Gautam Shetty, Sabir Hossain, Chuan Hu, Xianke Lin

Autonomous vehicles can enhance overall performance and implement safety measures in ways that are impossible with conventional automobiles. These functions are executed through vehicle control systems, which have been the subject of considerable research. Autonomous cars have a distinct advantage as they possess various perception sensors that can predict r

Qi Qi, Jiameng Lyu, Kung sik Chan, Er Wei Bai

Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shift between training and test sets, has received tremendous attention in recent years. In this paper, we propose and analyze stochastic algorithms that apply to both non-convex and convex losses for solving Kullback Leibler divergence constrained DRO

Sahana Kumar, Eric Y. Hsiao, Chris Ashall, Mark M. Phillips

We present multi-wavelength time-series spectroscopy of SN 2013aa and SN 2017cbv, two Type Ia supernovae (SNe Ia) on the outskirts of the same host galaxy, NGC 5643. This work utilizes new nebular-phase near-infrared (NIR) spectra obtained by the Carnegie Supernova Project-II, in addition to previously published optical and NIR spectra. By measuring nebular-

Dustin A. Gilbert, Mi-Young Im, Kai Liu, Peter Fischer

The first order reversal curve (FORC) method is a macroscopic measurement technique which can be used to extract quantitative, microscopic properties of hysteretic systems. Using magnetic transmission X-ray microscopy (MTXM), local element-specific FORC measurements are performed on a 20 nm thick film of CoTb. The FORCs measured with microscopy reveal a step

Xinrui Song, Xuanang Xu, Sheng Xu, Baris Turkbey

In this work, we propose to explicitly use the landmarks of prostate to guide the MR-TRUS image registration. We first train a deep neural network to automatically localize a set of meaningful landmarks, and then directly generate the affine registration matrix from the location of these landmarks. For landmark localization, instead of directly training a ne

Mirosława Łukawska, Anders Fjendbo Jensen, Filipe Rodrigues

The mixed multinomial logit model assumes constant preference parameters of a decision-maker throughout different choice situations, which may be considered too strong for certain choice modelling applications. This paper proposes an effective approach to model context-dependent intra-respondent heterogeneity, thereby introducing the concept of the Context-a

P. Samantray, R. H. J. Peerlings, T. J. Massart, O. Rokoš

In fibrous paper materials, an exposure to a variation in moisture content causes changes in the geometrical and mechanical properties. Such changes are strongly affected by the inter-fibre bonds, which are responsible for the transfer of the hygro-mechanical response from one fibre to its neighbours in the network, resulting in sheet-scale deformation. Most

William Gao, April Wang, Gal Metzer, Raymond A. Yeh

We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes. We represent shapes using an irregular tetrahedral grid which encodes an occupancy and displacement field. Our formulation enables defining tetrahedral convolution, pooling, and upsampling operations to synthesize explicit mesh connectivity with variable topological ge

Priyabrata Maharana, G. K. Ananthasuresh

This paper presents an analytical method to predict the delayed switching dynamics of nonlinear shallow arches while switching from one state to another state for different loading cases. We study an elastic arch subject to static loading and time-dependent loading separately. In particular, we consider a time-dependent loading that evolves linearly with tim

Bowen Yang, Yin Min Goh, Suk Hyun Sung, Gaihua Ye

Layered {\alpha}-RuCl3 is a promising material to potentially realize the long-sought Kitaev quantum spin liquid with fractionalized excitations. While evidence of this exotic state has been reported under a modest in-plane magnetic field, such behavior is largely inconsistent with theoretical expectations of Kitaev phases emerging only in out-of-plane field

Ryan Kozlowski, J. Carter Luketich, Elijah Oshatz, Douglas J. Durian

The flow of granular materials through constricted openings is important in many natural and industrial processes. These complex flows - featuring dense, dissipative flow in the bulk but low-dissipation, low density outpouring in the vicinity of the orifice - have long been characterized empirically by the Beverloo rule and, recently, modeled successfully us

Giuseppe De Nittis, Max Lein, Marcello Seri

In this monograph we develop magnetic pseudodifferential theory for operator-valued and equivariant operator-valued functions and distributions from first principles. These have found plentiful applications in mathematical physics, including in rigorous perturbation theory for slow-fast systems and perturbed periodic operators. Yet, a systematic treatise was

Ernesto Carro, Luis Benet, Isaac Pérez Castillo

In spite of its simplicity, the central limit theorem captures one of the most outstanding phenomena in mathematical physics, that of universality. While this classical result is well understood it is still not very clear what happens to this universal behaviour when the random variables become correlated. A fruitful mathematically laboratory to investigate

Kaustav K. Das, Mansi M. Kasliwal, Christoffer Fremling, Sheng Yang

The fate of stars in the zero-age main-sequence (ZAMS) range $\approx 8-12$ Msun is unclear. They could evolve to form white dwarfs or explode as electron-capture supernovae (SNe) or iron core-collapse SNe (CCSNe). Even though the initial mass function indicates that this mass range should account for over 40% of all CCSNe progenitors, few have been observat

Nikolas Lamb, Sean Banerjee, Natasha Kholgade Banerjee

We present DeepMend, a novel approach to reconstruct restorations to fractured shapes using learned occupancy functions. Existing shape repair approaches predict low-resolution voxelized restorations, or require symmetries or access to a pre-existing complete oracle. We represent the occupancy of a fractured shape as the conjunction of the occupancy of an un

A. Hiramoto, T. Masuda, D. G. Ang, C. Meisenhelder

This report shows the design and the performance of a large area Silicon Photomultiplier (SiPM) module developed detection of fluorescent light emitted from a 10 cm scale volume. The module was optimized for the planned ACME III electron electric dipole moment (eEDM) search, which will be a powerful probe for the existence of physics beyond the Standard Mode

Anastasia Safonova, Tatiana Yudina, Emil Nadimanov, Cydnie Davenport

The following paper presents a project focused on the research and creation of a new Automatic Speech Recognition (ASR) based in the Chukchi language. There is no one complete corpus of the Chukchi language, so most of the work consisted in collecting audio and texts in the Chukchi language from open sources and processing them. We managed to collect 21:34:2

Seungju Han, Beomsu Kim, Buru Chang

Response diversity has become an important criterion for evaluating the quality of open-domain dialogue generation models. However, current evaluation metrics for response diversity often fail to capture the semantic diversity of generated responses, as they mainly consider lexical aspects of the generated responses. In this paper, we introduce a new automat

Christos Karathanasis, Benoit Revenu, Suvodip Mukherjee, Federico Stachurski

Precision cosmology with gravitational wave (GW) sources requires a deeper understanding of the interplay between GW source population and cosmological parameters governing the dynamics of the Universe. With the swift increase in GW detections, it is necessary to develop a tool for exploring many aspects of cosmology and fundamental physics; this tools allow

Steven Schockaert

Various neural network architectures rely on pooling operators to aggregate information coming from different sources. It is often implicitly assumed in such contexts that vectors encode epistemic states, i.e. that vectors capture the evidence that has been obtained about some properties of interest, and that pooling these vectors yields a vector that combin

Lucia A. Perez, Sangeeta Malhotra, James E. Rhoads, Peter Laursen

We probe what constraints for the global ionized hydrogen fraction the Void Probability Function (VPF) clustering can give for the Lyman-Alpha Galaxies in the Epoch of Reionization (LAGER) narrowband survey as a function of area. Neutral hydrogen acts like a fog for Lyman-alpha emission, and measuring the drop in the luminosity function of Lyman-$\alpha$ emi

César González-Gutiérrez, Audi Primadhanty, Francesco Cazzaro, Ariadna Quattoni

Annotating large collections of textual data can be time consuming and expensive. That is why the ability to train models with limited annotation budgets is of great importance. In this context, it has been shown that under tight annotation budgets the choice of data representation is key. The goal of this paper is to better understand why this is so. With t

V. S. Beskin

We draw attention to two unanswered questions in radio pulsars' theory. The first one concerns the inclination angles between magnetic and rotation axes. We show that the very existence of interpulse pulsars indicates that all possible angles are realized. The second one is the question of the break of the death line on the PPdot-diagram. We show that this b

Vyacheslav Klyukhin

The Compact Muon Solenoid (CMS) detector is a general-purpose experimental setup at the Large Hadron Collider (LHC) at CERN to investigate the production of new particles in the proton-proton collisions at a centre of mass energy 13 TeV. The third run of the data taken is started in April 2022 and will continue till the end of 2025. Then, during a long shutd

Alice Garoffolo

The stochastic gravitational wave background (SGWB) is a rich resource of cosmological information, encoded both in its source statistics and anisotropies induced by propagation effects. We provide a theoretical description of it, without employing theoretical tools which rely on the geometric optics limit. Our formalism is based on the so-called {\it classi

Ray Maresca

In this paper, we will present the author's interpretation and embellishment of five lectures on cluster theory given by Kiyoshi Igusa during the Spring semester of 2022 at Brandeis University. They are meant to be used as an introduction to cluster theory from a representation-theoretic point of view. It is assumed that the reader has some background in rep

Basabendu Barman, Simon Cléry, Raymond T. Co, Yann Mambrini

We show that a minimal scenario, utilizing only the graviton as an intermediate messenger between the inflaton, the dark sector and the Standard Model (SM), is able to generate $simultaneously$ the observed relic density of dark matter (DM), the baryon asymmetry through leptogenesis, as well as a sufficiently hot thermal bath after inflation. We assume an in

Joseba Fernandez de Landa, Rodrigo Agerri

The large majority of the research performed on stance detection has been focused on developing more or less sophisticated text classification systems, even when many benchmarks are based on social network data such as Twitter. This paper aims to take on the stance detection task by placing the emphasis not so much on the text itself but on the interaction d

Chenguang Huang, Oier Mees, Andy Zeng, Wolfram Burgard

Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g., image captions). While this is useful for matching images to natural language descriptions of object goals, it remains disjoint from the process of mapping the environment, so that it lacks t

William Held, Diyi Yang

Multilingual transformer-based models demonstrate remarkable zero and few-shot transfer across languages by learning and reusing language-agnostic features. However, as a fixed-size model acquires more languages, its performance across all languages degrades, a phenomenon termed interference. Often attributed to limited model capacity, interference is common

Ella Peltonen, Nitinder Mohan, Peter Zdankin, Tanya Shreedhar

Not all research leads to fruitful results; trying new ways or methods may surpass the state of the art, but sometimes the hypothesis is not proven or the improvement is insignificant. In a systems discipline like pervasive computing, there are many sources of errors, from hardware issues over communication channels to heterogeneous software environments. Ho

Dae Gwan Lee, Goetz E. Pfander, David Walnut

The complex exponentials with integer frequencies form a basis for the space of square integrable functions on the unit interval. We analyze whether the basis property is maintained if the support of the complex exponentials is restricted to possibly overlapping subsets of the unit interval. We show, for example, that if $S_1, \ldots, S_K \subset [0,1]$ are

Naveen Dukiya, Kuntal Misra, Bikram Pradhan, Vibhore Negi

The International Liquid Mirror Telescope (ILMT) is a 4-meter class survey telescope that has recently achieved first light and is expected to swing into full operations by 1st January 2023. It scans the sky in a fixed 22' wide strip centered at the declination of $+29^o21'41''$ and works in Time Delay Integration (TDI) mode. We present a full catalog of sou

Saurabh Bansal, Gil Paz, Alexey Petrov, Michele Tammaro

We point out that neutrinos can have enhanced couplings to photons, if light (pseudo)scalar mediators are present, resulting in potentially measurable neutrino polarizability. We show that the expected suppression from small neutrino masses can be compensated by the light mediator mass, generating dimension 7 Rayleigh operators at low scales. We explore the

Santiago Casas, Isabella P. Carucci, Valeria Pettorino, Stefano Camera

In this work we present updated forecasts on parameterised modifications of gravity that can capture deviations of the behaviour of cosmological density perturbations beyond $\Lambda$CDM. For these forecasts we adopt the SKA Observatory (SKAO) as a benchmark for future cosmological surveys at radio frequencies, combining a continuum survey for weak lensing a

Martin Khannouz, Tristan Glatard

Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases. However, this assumption is unrealistic when data comes in the form of infinite data streams, or when learning algorithms are deployed on devices with reduced amounts of memory. Such memory constraints impact the model b

Fabiola Fortuna, Alejandro Ibarra, Xabier Marcano, Marcela Marín

We perform an effective field theory analysis to correlate the charged lepton flavor violating processes $\ell_i\to\ell_j\gamma\gamma$ and $\ell_i\to\ell_j\gamma$. Using the current upper bounds on the rate for $\ell_i\to\ell_j\gamma$, we derive model-independent upper limits on the rates for $\ell_i\to\ell_j\gamma\gamma$. Our indirect limits are about three