May 2022 arXiv papers — page 56

Sinéad M. Griffin

Searches for new physics in high-energy physics (HEP) experiments commonly rely on interactions with materials. A burgeoning direction is the accurate calculation and design of materials for HEP applications. In this Snowmass contribution, I briefly motivate the science need for quantum mechanical calculations of materials for HEP and outline the range of qu

Pedro Fagundes, Plamen Koshlukov

In this paper we study the images of multilinear graded polynomials on the graded algebra of upper triangular matrices UT_n. For positive integers q \leq n, we classify these images on UT_n endowed with a particular elementary Z_q-grading. As a consequence, we obtain the images of multilinear graded polynomials on UT_n with the natural Z_n-grading. We apply

Alejandro Schuler, Yi Li, Mark van der Laan

Gradient boosting performs exceptionally in most prediction problems and scales well to large datasets. In this paper we prove that a ``lassoed'' gradient boosted tree algorithm with early stopping achieves faster than $n^{-1/4}$ L2 convergence in the large nonparametric space of cadlag functions of bounded sectional variation. This rate is remarkable becaus

Vladislav Lialin, Kevin Zhao, Namrata Shivagunde, Anna Rumshisky

Existing pre-trained transformer analysis works usually focus only on one or two model families at a time, overlooking the variability of the architecture and pre-training objectives. In our work, we utilize the oLMpics benchmark and psycholinguistic probing datasets for a diverse set of 29 models including T5, BART, and ALBERT. Additionally, we adapt the oL

Jones Yeboah, Victor Adewopo, Sylvia Azumah, Izunna Okpala

Biometric systems involve security assurance to make our system highly secured and robust. Nowadays, biometric technology has been fixed into new systems with the aim of enforcing strong privacy and security. Several innovative system have been introduced, and most of them have biometrics installed to protect military bases, banking machines, and other sophi

Lav Kumar Singh

In this short note, the second dual of generalized group algebra $(\ell^1(G,\mathcal A),\ast)$ equipped with both Arens products is investigated, where $G$ is any discrete group and $\mathcal A$ is a Banach algebra containing a complemented algebraic copy of $(\ell^1(\mathbb N),\bullet)$. We give an explicit family of annihilators(w.r.t both the Arens produc

Luis Espath

This continuum mechanical theory aims at detailing the underlying rational mechanics of dynamic boundary conditions proposed by Fischer, Maass, & Dieterich [1], Goldstein, Miranville, & Schimperna [2], and Knopf, Lam, Liu & Metzger, [3]. As a byproduct, we generalize these theories. These types of dynamic boundary conditions are described by the coupling bet

Lingxi Chen

Motherhood is the main contributor to gender gaps in the labor market. IVF is a method of assisted reproduction that can delay fertility, which results in decreased motherhood income penalty. In this research, I estimate the effects of expanded access to in vitro fertilization (IVF) arising from state insurance mandates. I use a difference-in-differences mod

Vitaliy Bibaev, Alexey Kalina, Vadim Lomshakov, Yaroslav Golubev

In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-base

Vidit Gautam

Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histology slides obtained via biopsies. A major problem that stands in the way is lack of data for a few cancer-types. In this paper, we ascertain that data augmentatio

Leslie M. Morales, Eric L. Sandquist, Gail H. Schaefer, Christopher D. Farrington

We observe the brightest member of the Praesepe cluster, Epsilon Cancri, to precisely measure the characteristics of the stars in this binary system, en route to a new measurement of the cluster's age. We present spectroscopic radial velocity measurements and interferometric observations of the sky-projected orbit to derive the masses, which we find to be M_

Kexin Yin, Xiao Fang, Bintong Chen, Olivia Sheng

Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference, although social psychology theories suggest the critical

Zhiquan Wang, Bedrich Benes, Ahmed H. Qureshi, Christos Mousas

We introduce a novel co-design method for autonomous moving agents' shape attributes and locomotion by combining deep reinforcement learning and evolution with user control. Our main inspiration comes from evolution, which has led to wide variability and adaptation in Nature and has the potential to significantly improve design and behavior simultaneously. O

Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid

There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language. This work concerns addressing two major problems in existing Arabic PLMs which constraint progress of the Arabic NLU and NLG fields.First, existing Arabic PLMs are not well-explored and their pre-trainig can be improved significantly using a

Shawn Shan, Wenxin Ding, Emily Wenger, Haitao Zheng

Server breaches are an unfortunate reality on today's Internet. In the context of deep neural network (DNN) models, they are particularly harmful, because a leaked model gives an attacker "white-box" access to generate adversarial examples, a threat model that has no practical robust defenses. For practitioners who have invested years and millions into propr

Jessica Mégane, Nuno Lourenço, Penousal Machado

The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution

Sravan Kumar Ankireddy, Hyeji Kim

In decoding linear block codes, it was shown that noticeable reliability gains can be achieved by introducing learnable parameters to the Belief Propagation (BP) decoder. Despite the success of these methods, there are two key open problems. The first is the lack of interpretation of the learned weights, and the other is the lack of analysis for non-AWGN cha

Zhiqi Bu, Jialin Mao, Shiyun Xu

Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a computationally expensive operation, known as the per-sample gradient clipping. We propose an efficient and scalable implementation of this clipping on convolutional layers, termed as the mixed ghost clipp

Jin Xu, Weiqi Wang, Zheming Gao, Haochen Luo

Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop a chi-square test to show that the delay evolution over stations follows a first-order Markov chain. We then propose

Jing Guo, Raghu G. Raj, David J. Love, Christopher G. Brinton

Signal classification problems arise in a wide variety of applications, and their demand is only expected to grow. In this paper, we focus on the wireless sensor network signal classification setting, where each sensor forwards quantized signals to a fusion center to be classified. Our primary goal is to train a decision function and quantizers across the se

Lorenzo Rovigatti, John Russo, Flavio Romano, Michael Matthies

The self-assembly of colloidal diamond (CD) crystals is considered as one of the most coveted goals of nanotechnology, both from the technological and fundamental points of view. For applications, colloidal diamond is a photonic crystal which can open new possibilities of manipulating light for information processing. From a fundamental point of view, its un

Lukasz Czekaj, Tomasz Biegus, Robert Kitlowski, Stanislaw Raczynski

This paper covers automated settlement of receivables in non-governmental organizations. We tackle the problem with entity matching techniques. We consider setup, where base algorithm is used for preliminary ranking of matches, then we apply several novel methods to increase matching quality of base algorithm: score post processing, cascade model and chain m

Anthony L. Corso, Sydney M. Katz, Craig Innes, Xin Du

Modern autonomous systems rely on perception modules to process complex sensor measurements into state estimates. These estimates are then passed to a controller, which uses them to make safety-critical decisions. It is therefore important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-dri

Michael Howard

Developing a software service requires a strict software development life cycle and process. This process demands controlling all application code through source control management as well as a rigorous versioning and branching strategy. However, the platform and infrastructure also benefit from this rigor. Software services must be deployed to a target run

Shahram Panahiyan, Carlos Sánchez Muñoz, Maria V. Chekhova, Frank Schlawin

We discuss how two-photon absorption (TPA) of squeezed and coherent states of light can be detected in measurements of the transmitted light fields. Such measurements typically suffer from competing loss mechanisms such as experimental imperfections and linear scattering losses inside the sample itself, which can lead to incorrect assessments of the two-phot

Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia

Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional text, and multi-step reasoning. To overcome this limitation, we p

A M Ishtiaque Mahbub, Viet-Anh Le, Andreas A. Malikopoulos

Platoon formation with connected and automated vehicles (CAVs) in a mixed traffic environment poses significant challenges due to the presence of human-driven vehicles (HDVs) with unknown dynamics and control actions. In this paper, we develop a safety-prioritized receding horizon control framework for creating platoons of HDVs preceded by a CAV. Our framewo

Jingfei Zhang, Yi Li

Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size. Model fitting is typically carried out via separate node-wise lasso regressions, ignoring the network-induced structure among these re

Gene Li, Cong Ma, Nathan Srebro

We present a family $\{\hat{\pi}\}_{p\ge 1}$ of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell_p$ norms, where $\hat{\pi}_2$ corresponds to Bellman-consistent pessimism (BCP), while $\hat{\pi}_\infty$ is a novel generalization of lower confidence bound (LCB) to the line

Liyan Xu, Jinho D. Choi

This paper suggests a direction of coreference resolution for online decoding on actively generated input such as dialogue, where the model accepts an utterance and its past context, then finds mentions in the current utterance as well as their referents, upon each dialogue turn. A baseline and four incremental-updated models adapted from the mention-linking

E. Kaan Ulgen, Sinan Alis, Christophe Benoist, F. Korhan Yelkenci

We present a catalogue of isolated field elliptical (IfE) galaxies drawn from the W1 field of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). 228 IfEs were identified from a flux-limited (r<21.8) galaxy catalogue which corresponds to a density of 3 IfE/sq.deg. For comparison we consider a sample of elliptical galaxies living in dense environments,

Luis Olsina, Philip Lew, Guido Tebes

This preprint specifies supplementary material and the results of a comparative, syntactic, and semantic study of terms for three software testing glossaries. The three software testing glossaries are: the ISO 29119-1, Concepts and definitions standard, the Standard Glossary of Terms used in Software Testing (version 3.5) by the International Software Testin

Arsenii A. Onuchin, Oleg N. Kachan

Traditionally, extracting patterns from eye movement data relies on statistics of different macro-events such as fixations and saccades. This requires an additional preprocessing step to separate the eye movement subtypes, often with a number of parameters on which the classification results depend. Besides that, definitions of such macro events are formulat

Saket Gurukar, Nikil Pancha, Andrew Zhai, Eric Kim

Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several tasks such as recommendation and search. At Pinterest, we have developed and deployed PinSage, a data-efficient GCN that learns pin embeddings from the Pin-Board graph. The Pin-Boa

Jingwei Liu

We propose a general framework of European power option pricing under two different market assumptions about extended Vasic\v{e}k interest rate process and exponential Ornstein-Uhlenbeck asset process with continuous dividend as underlying, in which the Brownian motions involved in Vasic\v{e}k interest rate and exponential Ornstein-Uhlenbeck process are time

Guangji Bai, Chen Ling, Liang Zhao

Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change. The advancement of this area is challenged by: 1) characterizing data distribution drift and its impacts on models, 2) expre

Ugur Demir, Zheyuan Zhang, Bin Wang, Matthew Antalek

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers based architec

Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom

Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research. Recent works on mesh processing have concentrated on various kinds of natural symmetries, including translations, rotations, scaling, node permutations, and gauge transformations. To date, no existing architecture is equivariant to all of these transformations. I

Jiarui Zhang, Filip Ilievski, Kaixin Ma, Jonathan Francis

Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models, in zero-shot evaluation on various downstream language reasoning tasks. Since these improvements are reported in aggregate, however, little is known about (i) how to select the appropriate knowledge for solid perfor

Ethan Huynh

The recent advances in image transformers have shown impressive results and have largely closed the gap between traditional CNN architectures. The standard procedure is to train on large datasets like ImageNet-21k and then finetune on ImageNet-1k. After finetuning, researches will often consider the transfer learning performance on smaller datasets such as C

Viktor Moskvin

A billiard is a dynamical system in which a particle alternates between motion in a straight line and specular reflection from a boundary. For billiards in non-convex areas bounded by segments of confocal quadrics are studied. The topology of 3-dimmensional bifurcations of Liouville foliations is described.

Salem Alqahtani, Murat Demirbas

In this paper, we present BunchBFT Byzantine fault-tolerant state-machine replication for high performance and scalability. At the heart of BunchBFT is a novel design called the cluster-based approach that divides the replicas into clusters of replicas. By combining this cluster-based approach with hierarchical communications across clusters, piggybacking te

Ekaterina Kompantseva, Askar Tuganbaev

For an Abelian group $G$, any homomorphism $\mu\colon G\otimes G\rightarrow G$ is called a \textsf{multiplication} on $G$. The set $\text{Mult}\,G$ of all multiplications on an Abelian group $G$ itself is an Abelian group with respect to addition; the group is called the \textsf{multiplication group} of $G$. Let $\mathcal{A}_0$ be the class of all reduced bl

Ilias Keftakis, Vassilios V. Dimakopoulos

Programming a distributed system, such as a cluster, requires extended use of low-level communication libraries and can often become cumbersome and error prone for the average developer. In this work, we consider each node of a cluster as a separate OpenMP device, able to run code with OpenMP directives in parallel. We make use of the OpenMP device model to

Alankar Kotwal, Anat Levin, Ioannis Gkioulekas

We present a new imaging technique, swept-angle synthetic wavelength interferometry, for full-field micron-scale 3D sensing. As in conventional synthetic wavelength interferometry, our technique uses light consisting of two narrowly-separated optical wavelengths, resulting in per-pixel interferometric measurements whose phase encodes scene depth. Our techniq

Yier Lin

In this paper, we study the stationary distributions for the stochastic vertex models. Our main focus is the stochastic six vertex (S6V) model. We show that the extremal stationary distributions of the S6V model are given by product Bernoulli measures. Moreover, for the S6V model under a moving frame of speed $1$, we show that the extremal stationary distrib

Dominika Przewlocka-Rus, Tomasz Kryjak

Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPUs. Easy to implement, this approach is e

Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang

Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completion (KGC) has become one of the most crucial tasks for KG research. Recently, considerable literature in this space has centered around the use of Message Passing (Graph) Neural Netw

S. V. Vintskevich, N. Bao, A. Nomerotski, P. Stankus

We apply the support vector machine (SVM) algorithm to derive a set of entanglement witnesses (EW) to identify entanglement patterns in families of four-qubit states. The effectiveness of SVM for practical EW implementations stems from the coarse-grained description of families of equivalent entangled quantum states. The equivalence criteria in our work is b

Ryan Solgi, Zichang He, William Jiahua Liang, Zheng Zhang

Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the tensor may affect the error and the compression ratio. In this work, we study the effect of the tensor shape on the tensor decomposition and

Mark S Graham, Petru-Daniel Tudosiu, Paul Wright, Walter Hugo Lopez Pinaya

In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are fa

Lucas Althoff, Alessandro Rodrigues, Mylène C. Q. Farias

The optimization of viewers' quality of experience (QoE) in 360 videos faces two major roadblocks: inaccurate adaptive streaming and viewers missing the plot of a story. Alignment edit emerged as a promising mechanism to avoid both issues at once. Alignment edits act on the content, matching the users' viewport with a region of interest in the video content.

Thuy T. Le

We propose a global convergent numerical method to reconstruct the initial condition of a nonlinear parabolic equation from the measurement of both Dirichlet and Neumann data on the boundary of a bounded domain. The first step in our method is to derive, from the nonlinear governing parabolic equation, a nonlinear systems of elliptic partial differential equ

David Liu, Tina Eliassi-Rad

Are the embeddings of a graph's degenerate core stable? What happens to the embeddings of nodes in the degenerate core as we systematically remove periphery nodes (by repeated peeling off $k$-cores)? We discover three patterns w.r.t. instability in degenerate-core embeddings across a variety of popular graph embedding algorithms and datasets. We use regressi

Elisa Kreiss, Cynthia Bennett, Shayan Hooshmand, Eric Zelikman

Few images on the Web receive alt-text descriptions that would make them accessible to blind and low vision (BLV) users. Image-based NLG systems have progressed to the point where they can begin to address this persistent societal problem, but these systems will not be fully successful unless we evaluate them on metrics that guide their development correctly

Nithin Kavi

In this project, the goal was to use the Julia programming language and parallelization to write a fast map reduce algorithm to count word frequencies across large numbers of documents. We first implement the word frequency counter algorithm on a CPU using two processes with MPI. Then, we create another implementation, but on a GPU using the Julia CUDA libra

Víctor J. Maciá

Given a Galton-Watson process conditioned to have total progeny equal to $n$, we study the asymptotic probability that this conditioned Galton-Watson process has distance to the border bigger or equal than $k$, as the number of nodes $n \rightarrow \infty$. A problem which is akin to this one was solved by R\'enyi and Szekeres for Cayley trees, de Bruijn, Kn

W. Dzik, S. Kost, P. Wojtylak

Following a characterization [10] of locally tabular logics with finitary (or unitary) unification by their Kripke models we determine the unification types of some intermediate logics (extensions of {\sf INT}). There are exactly four maximal logics with nullary unification ${\mathsf L}(\mathfrak R_{2}+)$, ${\mathsf L}(\mathfrak R_{2})\cap{\mathsf L}(\mathfr

Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt, Jakob D. Havtorn

Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a sin

Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep neural networks (DNNs) based schedulers h

Murray Moinester, Joel Kronfeld

Drylands forestation offers the potential for significant long-term sequestration of atmospheric CO$_2$. We consider sequestration of organic and inorganic carbon by a planted semi-arid forest, based on carbon that originates from atmospheric CO$_2$. Measurements at Israels Yatir forest give a sequestration rate of $\sim$550 g CO$_2$ m$^{-2}$ yr$^{-1}$ as or

Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effect

Ganchao Wei, Ian H. Stevenson, Xiaojing Wang

With advances in neural recording techniques, neuroscientists are now able to record the spiking activity of many hundreds of neurons simultaneously, and new statistical methods are needed to understand the structure of this large-scale neural population activity. Although previous work has tried to summarize neural activity within and between known populati

Noureddine Karim, Otmane Benchiheb, Mohamed Amouch

A Furstenberg family $\mathcal{F}$ is a collection of infinite subsets of the set of positive integers such that if $A\subset B$ and $A\in \mathcal{F}$, then $B\in \mathcal{F}$. For a Furstenberg family $\mathcal{F}$, finitely many operators $T_1,...,T_N$ acting on a common topological vector space $X$ are said to be disjoint $\mathcal{F}$-transitive if for

Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu

Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows parameters to travel a large distance on the loss level set, in order to improve the convergence speed in subsequent steps. Teleportation exploits symmetries in the loss la

Xingzhe He, Bastian Wandt, Helge Rhodin

Structured representations such as keypoints are widely used in pose transfer, conditional image generation, animation, and 3D reconstruction. However, their supervised learning requires expensive annotation for each target domain. We propose a self-supervised method that learns to disentangle object structure from the appearance with a graph of 2D keypoints

Suryakanta Swain, Debasis Sahu, Debabrata Dwivedee, Gourishankar Sahoo

The recently observed accelerated expansion of the universe has put a challenge for its theoretical understanding. As a possible explanation of this, it is considered that the most part of the present universe is filled with a form of energy that exerts a negative pressure called dark energy, which drives the acceleration. In the present work, we assume a dy

Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

In recent years, deep learning models have become ubiquitous in industry and academia alike. Deep neural networks can solve some of the most complex pattern-recognition problems today, but come with the price of massive compute and memory requirements. This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mo

Guilmer González Flores, Pablo Barrera Sánchez

In this paper, we review some grid quality metrics Robinson1987, Lo1989, Field2000, Knupp2001, Remacle2012 and define some new quality measures for quadrilateral elements. Usually, a maximum value of a quality measure corresponds to the minimum value of the energy density over the grid Ivanenko2000. We also define new discrete functionals which are implement

Rabab Elarabi, Mouhssine El-Arabi, Mohamed Rhoudaf

This paper introduces the notion of $N^*-$function and gives a generalization of $L^p,$ for $0<p<1$ denoted by $L_\Phi$ where $\Phi$ is an $N^*-$function. As well as, this paper examines some properties regarding to this generalized spaces and its linear forms, including some analogies and common features to some other well known spaces. As well as, we prove

Filip D. Jevtić, Slobodan Vujošević

In his ontological argument G\"{o}del says nothing about its underlying logic. The argument is modal and at least of second-order and since S5 axiom is used so it is widely accepted that the logic of the argument is the S5 second-order modal logic. However, there is a step in the proof in which G\"{o}del applies the necessitation rule on the assumptions of t

Andong Wang, Wei-Ning Lee

To interpret deep learning models, one mainstream is to explore the learned concepts by networks. Testing with Concept Activation Vector (TCAV) presents a powerful tool to quantify the contribution of query concepts (represented by user-defined guidance images) to a target class. For example, we can quantitatively evaluate whether and to what extent concept

Xiangdong Sun, Zhiqiang Miao, Baoyuan Sun, Ang Li

Hyperons are essential constituents in the neutron star interior. The poorly-known hyperonic interaction is a source of uncertainty for studying laboratory hypernuclei and neutron star observations. In this work, we perform Bayesian inference of phenomenological hyperon-nucleon interactions using the tidal-deformability measurement of the GW170817 binary neu

A. C. M. Ran

A result on the structure of expansive matrices in an indefinite inner product space is derived, which exhibits the largest unitary compression of the matrix.

Phillip Swazinna, Steffen Udluft, Thomas Runkler

Offline reinforcement learning algorithms still lack trust in practice due to the risk that the learned policy performs worse than the original policy that generated the dataset or behaves in an unexpected way that is unfamiliar to the user. At the same time, offline RL algorithms are not able to tune their most important hyperparameter - the proximity of th

Valeri Frumkin, David Darrow, Ward Struyve, John W. M. Bush

In certain instances, the particle paths predicted by Bohmian mechanics are thought to be at odds with classical intuition. A striking illustration arises in the interference experiments envisaged by Englert, Scully, S\"ussmann and Walther, which lead the authors to claim that the Bohmian trajectories can not be real and so must be `surreal'. Through a combi

Xiao Chen, Alex Morehead, Jian Liu, Jianlin Cheng

Proteins interact to form complexes to carry out essential biological functions. Computational methods have been developed to predict the structures of protein complexes. However, an important challenge in protein complex structure prediction is to estimate the quality of predicted protein complex structures without any knowledge of the corresponding native

J. Armijos-Abendaño, E. López, M. Llerena, C. H. A. Logan

We present a spectral and temporal analysis of XMM-Newton data from a sample of six galaxies (NGC 3783, Mrk 279, Mrk 766, NGC 3227, NGC 7314, and NGC 3516). Using the hardness-ratio curves, we identify time-intervals in which clouds are eclipsing the central X-ray source in five of the six sources. We detect three occultations in NGC 3227 and one occultation

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei

Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel prompting strategy, least-to-most prompting.

Xuhong Wang, Sirui Chen, Yixuan He, Minjie Wang

Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs.However, most previous works approach the problem in compromised settings, either formulating it as a link predicti

M. Yu. Piotrovich, S. D. Buliga, T. M. Natsvlishvili

We estimated the radiative efficiency and spin value for a number of local active galactic nuclei with z < 0.34 using 3 popular models connecting the radiative efficiency with such parameters of AGNs as mass of supermassive black hole, angle between the line of sight and the axis of the accretion disk and bolometric luminosity. Analysis of the obtained data

Paul Hege, Massimo Moscolari, Stefan Teufel

We present an algorithm for reliably and systematically proving the existence of spectral gaps in Hamiltonians with quasicrystalline order, based on numerical calculations on finite domains. We apply this algorithm to prove that the Hofstadter model on the Ammann-Beenker tiling of the plane has spectral gaps at certain energies, and we are able to prove the

Zifeng Ding, Bailan He, Yunpu Ma, Zhen Han

Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to KGs, TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling. This poses a greater challenge in learning few-shot

Hengtao He, Alva Kosasih, Xianghao Yu, Jun Zhang

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low-complexity graph neural network (

Zihan Zhang, Xiang Xiang, Xuehua Peng, Jianbo Shao

Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning

Abraham Loeb

I show that interstellar films of material thinner than a micron, drift away from the Galactic plane as a result of stellar radiation pressure. Such films, whether produced naturally by dust coagulation in proto-planetary disks or artificially by technological civilizations, would accumulate over the age of the Milky-Way and hover above the Galactic disk at

Sen Pei, Jiaxi Sun, Xiaopeng Zhang, Gaofeng Meng

Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks. However, even the \emph{state-of-the-art} deep learning based classifiers are extremely vulnerable to adversarial examples, resulting in sharp decay of discrimination accuracy in the presence of enormous unknown attacks. Given the fact that neural networks

Vitaly Nikolaev, Louis Vervoort

We attempt to make superdeterminism more intuitive, notably by simulating a deterministic model system, a billiard game. In this system an initial 'bang' correlates all events, just as in the superdeterministic universe. We introduce the notions of 'strong' and 'soft' superdeterminism, in order to clarify debates in the literature. Based on the analogy with

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be an analytically unramified Cohen-Macaulay local ring and let $\mathfrak{a}$ be an $\mathfrak{m}$-primary ideal in $A$. If $I$ is an ideal in $A$ then let $I^*$ be the integral closure of $I$ in $A$. Let $G_{\mathfrak{a}}(A)^* = \bigoplus_{n\geq 0 }(\mathfrak{a}^n)^*/(\mathfrak{a}^{n+1})^*$ be the associated graded ring of the integr

Wei Yan, Sian-Jheng Lin

Evolving secret sharing schemes do not require prior knowledge of the number of parties $n$ and $n$ may be infinitely countable. It is known that the evolving $2$-threshold secret sharing scheme and prefix coding of integers have a one-to-one correspondence. However, it is not known what prefix coding of integers to use to construct the scheme better. In thi

Masataka Iwai, Shin-ichi Matsumura

In this paper, for compact K\"ahler manifolds with nef cotangent bundle, we study the abundance conjecture and the associated Iitaka fibrations. We show that, for a minimal compact K\"ahler manifold, the second Chern class vanishes if and only if the cotangent bundle is nef and the canonical bundle has the numerical dimension $0$ or $1$. Additionally, in thi

Luca Lanzilao, Johan Meyers

Large-eddy simulations of the atmospheric boundary layer are often performed using pseudo-spectral methods, which adopt a fringe-region approach to introduce inflow boundary conditions. However, we notice that a standard fringe-region technique excites spurious gravity waves when stratified atmospheres are considered, therefore enhancing the amount of energy

Shiqi Li, Xiang Xiang

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that u

Cheng-Jun Xia, Jian-Feng Xu, Guang-Xiong Peng, Ren-Xin Xu

The interface effects of quark matter play important roles in the properties of compact stars and small nuggets such as strangelets and $ud$QM nuggets. By introducing a density derivative term to the Lagrangian density and adopting Thomas-Fermi approximation, we find it is possible to reproduce the results obtained by solving Dirac equations. Adopting certai

Elia Bruè, Andrea Mondino, Daniele Semola

We solve a conjecture raised by Kapovitch, Lytchak, and Petrunin by showing that the metric measure boundary is vanishing on any ${\rm RCD}(K,N)$ space without boundary. Our result, combined with [Kapovitch-Lytchak-Petrunin '21], settles an open question about the existence of infinite geodesics on Alexandrov spaces without boundary raised by Perelman and Pe

Elias Heftrig, Haya Shulman, Michael Waidner

Cryptographic algorithm agility is an important property for DNSSEC: it allows easy deployment of new algorithms if the existing ones are no longer secure. In this work we show that the cryptographic agility in DNSSEC, although critical for provisioning DNS with strong cryptography, also introduces a vulnerability. We find that under certain conditions, when

Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo

In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. In this paper, we propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents' coll

Tobias Jawecki, Pranav Singh

The exponential function maps the imaginary axis to the unit circle and, for many applications, this unitarity property is also desirable from its approximations. We show that this property is conserved not only by the (k,k)-rational barycentric interpolant of the exponential on the imaginary axis, but also by (k,k)-rational barycentric approximants that min

Li Yang, Zhibin He, Changhe Li, Junwei Han

Since gyri and sulci, two basic anatomical building blocks of cortical folding patterns, were suggested to bear different functional roles, a precise mapping from brain function to gyro-sulcal patterns can provide profound insights into both biological and artificial neural networks. However, there lacks a generic theory and effective computational model so

Henrik Parsamyan, Hovhannes Haroyan, Khachatur Nerkararyan

A metamaterial composed of thin metallic strips as an efficient broadband absorber in the mid-infrared spectrum is investigated. Here the matching between dielectric and geometrical properties of the individual elements is critical to ensure high absorption. Detailed theoretical analysis based on the electric dipole approximation is performed to characterize