August 2022 arXiv papers — page 53

Alexandre Belsley, Jonathan C. F. Matthews

The concentration of a chiral solution is a key parameter in many scientific fields and industrial processes. This parameter can be estimated to high precision by exploiting circular birefringence or circular dichroism present in optically active media. Using the Quantum Fisher information formalism, we quantify the performance of Gaussian probes in estimati

Igor Volkov, Oleg Kargaltsev

We present a convenient tool (ChaSES) which allows to search for extended structures in Chandra X-ray Observatory Advanced CCD Imaging Spectrometer (ACIS) images. The tool relies on DBSCAN clustering algorithm to detect regions with overdensity of photons compared to the background. Here we describe the design and functionality of the tool which we make publ

Sia Gholami, Saba Khashe

Airlines are critical today for carrying people and commodities on time. Any delay in the schedule of these planes can potentially disrupt the business and trade of thousands of employees at any given time. Therefore, precise flight delay prediction is beneficial for the aviation industry and passenger travel. Recent research has focused on using artificial

Cheuk-Yin Wong

If we approximate light quarks as massless and apply the Schwinger confinement mechanism to light quarks, we will reach the conclusion that a light quark $q$ and its antiquark $\bar q$ will be confined as a $q\bar q$ boson in the Abelian U(1) QED gauge interaction in (1+1)D, as in an open string. From the work of Coleman, Jackiw, and Susskind, we can infer f

Fengwu Zhu, Wei Liu

In this paper, we present sufficient conditions and criteria to establish the large and moderate deviation principle of multivalued McKean-Vlasov stochastic differential equation by means of the weak convergence method.

Agelos Georgakopoulos, George Kontogeorgiou

We prove that a group $\Gamma$ admits a discrete topological (equivalently, smooth) action on some simply-connected 3-manifold if and only if $\Gamma$ has a Cayley complex embeddable -- with certain natural restrictions -- in one of the following four 3-manifolds: (i) $\mathbb{S}^3$, (ii) $\mathbb{R}^3$, (iii) $\mathbb{S}^2 \times \mathbb{R}$, (iv) the compl

Yu-Kuo Hsiao

We study the semileptonic $B\to{\bf B\bar B'}L\bar L'$ decays with $\bf B\bar B'$ ($L\bar L'$) representing a baryon (lepton) pair. Using the new determination of the $B\to{\bf B\bar B'}$ transition form factors, we obtain ${\cal B}(B^-\to p\bar p \mu^-\bar \nu_\mu) =(5.4\pm 2.0)\times 10^{-6}$ agreeing with the current data. Besides, ${\cal B}(B^-\to \Lambd

Amtul Haq Ayesha, Donghao Qiao, Farhana Zulkernine

With a surge in online medical advising remote monitoring of patient vitals is required. This can be facilitated with the Remote Photoplethysmography (rPPG) techniques that compute vital signs from facial videos. It involves processing video frames to obtain skin pixels, extracting the cardiac data from it and applying signal processing filters to extract th

Jan Jezabek, Akash Singh

Protecting NLP models against misspellings whether accidental or adversarial has been the object of research interest for the past few years. Existing remediations have typically either compromised accuracy or required full model re-training with each new class of attacks. We propose a novel method of retroactively adding resilience to misspellings to transf

Elton Pinto

We present a space-efficient implementation of the quantum verification of matrix products (QVMP) algorithm and demonstrate its functionality by running it on the Aer simulator with two simulation methods: statevector and matrix product state (MPS). We report circuit metrics (gate count, qubit count, circuit depth), transpilation time, simulation time, and a

Chanwoo Park, Sangdoo Yun, Sanghyuk Chun

We propose the first unified theoretical analysis of mixed sample data augmentation (MSDA), such as Mixup and CutMix. Our theoretical results show that regardless of the choice of the mixing strategy, MSDA behaves as a pixel-level regularization of the underlying training loss and a regularization of the first layer parameters. Similarly, our theoretical res

Paterne Gahungu, Jean Remy Kubwimana, Lionel Jean Marie Benjamin Muhimpundu, Egide Ndamuzi

Atmospheric pollution remains one of the major public health threat worldwide with an estimated 7 millions deaths annually. In Africa, rapid urbanization and poor transport infrastructure are worsening the problem. In this paper, we have analysed spatio-temporal variations of PM2.5 across different geographical regions in Africa. The West African region rema

Avinash Madasu, Anvesh Rao Vijjini

A well formed query is defined as a query which is formulated in the manner of an inquiry, and with correct interrogatives, spelling and grammar. While identifying well formed queries is an important task, few works have attempted to address it. In this paper we propose transformer based language model - Bidirectional Encoder Representations from Transformer

BoGwang Jeon

Let $M$ be a $2$-cusped hyperbolic $3$-manifold. By the work of Thurston, the product of the derivatives of the holonomies of core geodesics of each Dehn filling of $M$ is an invariant of it. In this paper, we classify Dehn fillings of $M$ with sufficiently large coefficients using this invariant. Further, for any given two Dehn fillings of $M$ (with suffici

Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version. Intriguingly, we observe that such a simple pipeline already achieves competitive results against recent advanced works, when

Zhihui Zou, Ping Zhu, Charlson C. Kim, Wei Deng

In the presence of energetic particles (EPs), the Long-Lived Mode (LLM) frequency multiplication with n = 1,2,3 or higher is often observed on HL-2A, where n is the toroidal mode number. Hybrid kinetic-MHD model simulations of the energetic particle (EP) driven kink/fishbone modes on a static HL-2A-like tokamak using NIMROD code find that, when the backgroun

Ernst-Ulrich Gekeler

The coefficient forms \( {}_{a} \ell_{k} \) and the para-Eisenstein series \(\alpha_{k}\) are simplicial Drinfeld modular forms. We study the attached simplicial complexes \(\mathcal{BT}^{r}( {}_{a} \ell_{k})\) and \(\mathcal{BT}^{r}(\alpha_{k})\), which are full subcomplexes of the Bruhat-Tits building \(\mathcal{BT}^{r}\) of \( \mathrm{PGL}(r, K_{\infty})\

Mario Liu

Thermodynamics of superfluids is revisited, clarifying two points. First, the density and pressure distribution for given equilibrium velocities is obtained, with the finding that counter heat currents give rise to a pressure depression and a centripetal force. Second, it is shown that the ideal two-fluid hydro\-dynamics is simply an assembly of \textit{equi

A. Bravar, A. Buonaura, S. Corrodi, A. Damyanova

We present and discuss the development and performance of a compact scintillating fiber (SciFi) detector for timing to be used in the Mu3e experiment at very high particle rates. The SciFi detector is read out with multichannel silicon photomuiltipliers (SiPM) arrays at both ends to achieve the best timing performance. Mu3e is a new experiment under preparat

Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han

Graph pre-training strategies have been attracting a surge of attention in the graph mining community, due to their flexibility in parameterizing graph neural networks (GNNs) without any label information. The key idea lies in encoding valuable information into the backbone GNNs, by predicting the masked graph signals extracted from the input graphs. In orde

Mario Liu

Grains are widely assumed to be characterized by a single temperature -- derived either from the configurational entropy, or employing the kinetic theory. Yet granular media do have two temperatures, $T_g$ and $T$, pertaining to the grains and atoms. It is argued here that a two-temperature plasma yields a more useful analogy for grains than a molecular gas:

Yihao Ding, Hongfeng Zhang

Motivated by the $(\mathfrak{g},K)$-cohomology and Dirac cohomology, we determine Dirac series of $\mathrm{GL}(n,\mathbb{H})$, and show that the spin lowest $K$-type of any Dirac series, which determines the Dirac cohomology, is unique and multiplicity-free for both $\mathrm{GL}(n,\mathbb{H})$ and $\mathrm{GL}(n,\mathbb{R})$. This verifies a conjecture about

Masataka Kawano, Chisa Hotta

We clarify the ground state phase diagram of the half-filled square-lattice Hubbard model with Rashba spin-orbit coupling (SOC) characterized by the spin-split energy bands due to broken inversion symmetry. Although the Rashba metals and insulating magnets have been studied well, the intermediate interaction strength of the system remained elusive due to the

Yelleti Vivek, P. S. V. S. Sai Prasad

While building machine learning models, Feature selection (FS) stands out as an essential preprocessing step used to handle the uncertainty and vagueness in the data. Recently, the minimum Redundancy and Maximum Relevance (mRMR) approach has proven to be effective in obtaining the irredundant feature subset. Owing to the generation of voluminous datasets, it

Bohan Wang, Yushun Zhang, Huishuai Zhang, Qi Meng

Adam is widely adopted in practical applications due to its fast convergence. However, its theoretical analysis is still far from satisfactory. Existing convergence analyses for Adam rely on the bounded smoothness assumption, referred to as the \emph{L-smooth condition}. Unfortunately, this assumption does not hold for many deep learning tasks. Moreover, we

Kathryn Lund

With the recent realization of exascale performace by Oak Ridge National Laboratory's Frontier supercomputer, reducing communication in kernels like QR factorization has become even more imperative. Low-synchronization Gram-Schmidt methods, first introduced in [K. \'{S}wirydowicz, J. Langou, S. Ananthan, U. Yang, and S. Thomas, Low Synchronization Gram-Schmi

William Busching, Delphine Hintz, Oleksii Mostovyi, Alexey Pozdnyakov

We consider the problem of fair pricing and hedging under small perturbations of the num\'eraire. We show that for replicable claims, the change of num\'eraire affects neither the fair price nor the hedging strategy. For non-replicable claims, we demonstrate that is not the case. By reformulating the key stochastic control problem in a more tractable form, w

Xuran Meng, Jianfeng Yao, Yuan Cao

Recent works have demonstrated a double descent phenomenon in over-parameterized learning. Although this phenomenon has been investigated by recent works, it has not been fully understood in theory. In this paper, we investigate the multiple descent phenomenon in a class of multi-component prediction models. We first consider a ''double random feature model'

Shuai Su, Zhongkai Zhao, Yixin Fei, Shuda Li

Correspondence matching is a fundamental problem in computer vision and robotics applications. Solving correspondence matching problems using neural networks has been on the rise recently. Rotation-equivariance and scale-equivariance are both critical in correspondence matching applications. Classical correspondence matching approaches are designed to withst

Michael Monoyios, Oleksii Mostovyi

We investigate the stability of the Epstein-Zin problem with respect to small distortions in the dynamics of the traded securities. We work in incomplete market model settings, where our parametrization of perturbations allows for joint distortions in returns and volatility of the risky assets and the interest rate. Considering empirically the most relevant

Kerem Ozfatura, Emre Ozfatura, Alptekin Kupcu, Deniz Gunduz

The increasing popularity of the federated learning (FL) framework due to its success in a wide range of collaborative learning tasks also induces certain security concerns. Among many vulnerabilities, the risk of Byzantine attacks is of particular concern, which refers to the possibility of malicious clients participating in the learning process. Hence, a c

Piotr T. Chruściel, Gregory J. Galloway

We prove uniqueness, existence, and regularity results for maximal hypersurfaces in spacetimes with a conformal completion at timelike infinity and asymptotically constant scalar curvature, as relevant for asymptotically AdS spacetimes. This work is dedicated to Yvonne Choquet-Bruhat on the occasion of her upcoming 99th birthday.

Seyed Naseh Sajadi, Ali Hajilou, Seyed Hossein Hendi

In this paper, we obtain analytical approximate black hole solutions in the framework of $f(R)$ gravity and the absence of a cosmological constant. In this area, we apply the equations of motion of the theory to a spherically symmetric spacetime with one unknown function and derive black hole solutions without any constraints on the Ricci scalar. To do so, f

David S. Berman, Tancredi Schettini Gherardini

We examine a generalisation of the usual self-duality equations for Yang-Mills theory when the colour space admits a non-trivial involution. This involution allows us to construct a non-trivial twist which may be combined with the Hodge star to form a twisted self-dual curvature. We will construct a simple example of twisted self-duality for $su(2) \oplus su

Edgar Costa, David Harvey, Andrew V. Sutherland

We present efficient algorithms for counting points on a smooth plane quartic curve $X$ modulo a prime $p$. We address both the case where $X$ is defined over $\mathbb F_p$ and the case where $X$ is defined over $\mathbb Q$ and $p$ is a prime of good reduction. We consider two approaches for computing $\#X(\mathbb F_p)$, one which runs in $O(p\log p\log\log

Enrui Zhang, Bart Spronck, Jay D. Humphrey, George Em Karniadakis

Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a n

Ohad Amosy, Tamuz Danzig, Ely Porat, Gal Chechik

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuristically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the optimization steps, which require many executions of the quantum circuit. Therefore, there is active research focusing on

Norma Rivano, Nicola Marzari, Thibault Sohier

Dimensionality provides a clear fingerprint on the dispersion of infrared-active, polar-optical phonons. For these phonons, the local dipoles parametrized by the Born effective charges drive the LO-TO splitting of bulk materials; this splitting actually breaks down in two-dimensional materials. Here, we extend the existing theory to the one-dimensional (1D)

Michael Schuckers, Sandip Purnapatra, Kaniz Fatima, Daqing Hou

Biometric recognition is used across a variety of applications from cyber security to border security. Recent research has focused on ensuring biometric performance (false negatives and false positives) is fair across demographic groups. While there has been significant progress on the development of metrics, the evaluation of the performance across groups,

Dusan Zigic, Jussi Auvinen, Igor Salom, Pasi Huovinen

QGP tomography aims to constrain the parameters characterizing the properties and evolution of Quark-Gluon Plasma (QGP) formed in heavy-ion collisions, by exploiting low and high-$p_\perp$ theory and data. Higher-order harmonics $v_n$ ($n>2$) are an important -- but seldom explored -- part of this approach. However, to take full advantage of them, several is

Bingchen Li, Xin Li, Yiting Lu, Sen Liu

Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts. Since the complex hybrid distortions, it is hard to restore the distorted image with the simple cooperation of super-resolution and compression artifacts removing. In this paper, we take a step for

Tingting Wu, Xiao Ding, Hao Zhang, Jinglong Gao

Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e.g., easy to hard) sequence. Previous work takes incorrect samples

He Zhang, Ran Zhang, Tao Zhou

In this paper, we present a deep learning-based numerical method for approximating high dimensional stochastic partial differential equations (SPDEs). At each time step, our method relies on a predictor-corrector procedure. More precisely, we decompose the original SPDE into a degenerate SPDE and a deterministic PDE. Then in the prediction step, we solve the

Shane Chern, Dazhao Tang

There are a number of sporadic coefficient-vanishing results associated with theta series, which suggest certain underlying patterns. By expanding theta powers as linear combinations of products of theta functions, we present two strategies that will provide a unified treatment. Our approaches rely on studying the behavior of products of two theta series und

Zehui Lin, Ruobing Huang, Dong Ni, Jiayi Wu

Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information for cancer diagnosis. However, training models for video analysis is non-trivial as it requires a voluminous dataset which is also expensive to annotate. Furthermore, the diagnosi

Yuri Kirita, Takumi Hasada, Masaki Hashida, Yusuke Hirahara

Sub-eV axion-like particles (ALPs) have been searched for by focusing two-color near-infrared pulse lasers into a vacuum along a common optical axis. Within the focused quasi-parallel collision system created by combining a creation field ($2.5\,\mathrm{mJ}/47\,\mathrm{fs}$ Ti:Sapphire laser) and a background inducing field ($1.5\,\mathrm{mJ}/9\,\mathrm{ns}$

Thomas Führer, Michael Karkulik

We present a method for the numerical approximation of distributed optimal control problems constrained by parabolic partial differential equations. We complement the first-order optimality condition by a recently developed space-time variational formulation of parabolic equations which is coercive in the energy norm, and a Lagrangian multiplier. Our final f

Jingyu Lin, Jie Jiang, Yan Yan, Chunchao Guo

The prosperity of deep learning contributes to the rapid progress in scene text detection. Among all the methods with convolutional networks, segmentation-based ones have drawn extensive attention due to their superiority in detecting text instances of arbitrary shapes and extreme aspect ratios. However, the bottom-up methods are limited to the performance o

Anindya Saha, Anandmayee Tej, Hong-Li Liu, Tie Liu

We present new continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) survey for the two protoclusters, G12.42+0.50 and G19.88-0.53. The 3 mm continuum maps reveal seven cores in each of the two globally contracting protoclusters. These cores satisfy the radius-mass relation and the surface mass

Jian Ding, Yiyang Jiang, Heng Ma

In the shotgun assembly problem for a graph, we are given the empirical profile for rooted neighborhoods of depth $r$ (up to isomorphism) for some $r\geq 1$ and we wish to recover the underlying graph up to isomorphism. When the underlying graph is an Erd\H{o}s-R\'enyi $\mathcal G(n, \frac{\lambda}{n})$, we show that the shotgun assembly threshold $r_* \appr

Mohammad Farazmand, Arvind K. Saibaba

Reconstructing high-resolution flow fields from sparse measurements is a major challenge in fluid dynamics. Existing methods often vectorize the flow by stacking different spatial directions on top of each other, hence confounding the information encoded in different dimensions. Here, we introduce a tensor-based sensor placement and flow reconstruction metho

Namkyeong Lee, Dongmin Hyun, Junseok Lee, Chanyoung Park

Over the past few years, graph representation learning (GRL) has been a powerful strategy for analyzing graph-structured data. Recently, GRL methods have shown promising results by adopting self-supervised learning methods developed for learning representations of images. Despite their success, existing GRL methods tend to overlook an inherent distinction be

Yasuyuki Hamura

In this short note, we consider the problem of estimating multivariate hypergeometric parameters under squared error loss when side information in aggregated data is available. We use the symmetric multinomial prior to obtain Bayes estimators. It is shown that by incorporating the side information, we can construct an improved estimator.

Andrea Palermo, Francesco Becattini, Matteo Buzzegoli, Gabriele Inghirami

The polarization of the $\Lambda$ hyperon has become an important probe of the Quark-Gluon Plasma produced in relativistic heavy-ion collisions. Recently, it has been found that polarization receives a substantial contribution from a local equilibrium term proportional to the symmetric derivative of the four-temperature vector, the thermal shear tensor. We s

Li Huang, Bertrand Meyer

A successful automated program proof is, in software verification, the ultimate triumph. In practice, however, the road to such success is paved with many failed proof attempts. Unlike a failed test, which provides concrete evidence of an actual bug in the program, a failed proof leaves the programmer in the dark. Can we instead learn something useful from i

Li Huang, Bertrand Meyer, Manuel Oriol

In software verification, a successful automated program proof is the ultimate triumph. The road to such success is, however, paved with many failed proof attempts. The message produced by the prover when a proof fails is often obscure, making it very hard to know how to proceed further. The work reported here attempts to help in such cases by providing imme

Zhaodi Zhang, Yiting Wu, Si Liu, Jing Liu

The robustness of deep neural networks is crucial to modern AI-enabled systems and should be formally verified. Sigmoid-like neural networks have been adopted in a wide range of applications. Due to their non-linearity, Sigmoid-like activation functions are usually over-approximated for efficient verification, which inevitably introduces imprecision. Conside

Greta Grassmann, Lorenzo Di Rienzo, Giorgio Gosti, Marco Leonetti

Understanding the molecular mechanisms driving the binding between bio-molecules is a crucial challenge in molecular biology. In this respect, characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions (short range forces), and the consequent shape complementarity between the interacting

Aikaterini Adam, Torsten Sattler, Konstantinos Karantzalos, Tomas Pajdla

AR/VR applications and robots need to know when the scene has changed. An example is when objects are moved, added, or removed from the scene. We propose a 3D object discovery method that is based only on scene changes. Our method does not need to encode any assumptions about what is an object, but rather discovers objects by exploiting their coherent move.

Michael C Sachs, Erin E Gabriel, Alessio Crippa, Michael J Daniels

Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial level surrogate evaluation, but none, to our

Yu Gao, Hao Liu, Tak Kwong Wong

In this paper we study the large time asymptotic behavior of (energy) conservative solutions to the Hunter-Saxton equation in a generalized framework that consists of the evolutions of solution and its energy measure. We describe the large time asymptotic expansions of the conservative solutions, and rigorously verify the validity of the leading order term i

Xiaolu Wang, Ziqi Ding, Liangyu Chen

Automatic tagging of knowledge points for practice problems is the basis for managing question bases and improving the automation and intelligence of education. Therefore, it is of great practical significance to study the automatic tagging technology for practice problems. However, there are few studies on the automatic tagging of knowledge points for math

Alexander Sherman

We study ghost distributions on supersymmetric spaces for the case of basic classical Lie superalgebras. We introduce the notion of interlaced pairs, which are those for which both $(\mathfrak{g},\mathfrak{k})$ and $(\mathfrak{g},\mathfrak{k}')$ admit Iwasawa decompositions. For such pairs we define a ghost algebra, generalizing the subalgebra of $\mathcal{U

V. E. Sándor Szabó

We give a complete investigation of Morley's trisector theorem. If the intersections of the half lines starting from the adjacent vertices of a triangle form an equilateral triangle for an arbitrary triangle, then the half lines are the angle trisectors. To derive the result we use elementary trigonometry, Taylor series expansions, and solve systems of polyn

Saurav Agarwal, Srinivas Akella

The area coverage problem is the task of efficiently servicing a given two-dimensional surface using sensors mounted on robots such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). We present a novel formulation for generating coverage routes for multiple capacity-constrained robots, where capacity can be specified in terms of battery

Ryoma Sato

Traditionally, recommendation algorithms have been designed for service developers. However, recently, a new paradigm called user-side recommender systems has been proposed and they enable web service users to construct their own recommender systems without access to trade-secret data. This approach opens the door to user-defined fair systems even if the off

Mojtaba Dehmollaian, Guillaume Lavigne, Christophe Caloz

Cloaking is typically reciprocal. We introduce here the concept of \emph{transmittable nonreciprocal cloaking} whereby the cloaking system operates as a standard omnidirectional cloak for external illumination, but can transmit light from its center outwards at will. We demonstrate a specific implementation of such cloaking that consists in a set of concentr

Ryoma Sato, Makoto Yamada, Hisashi Kashima

The research process includes many decisions, e.g., how to entitle and where to publish the paper. In this paper, we introduce a general framework for investigating the effects of such decisions. The main difficulty in investigating the effects is that we need to know counterfactual results, which are not available in reality. The key insight of our framewor

Saurav Agarwal, Srinivas Akella

Line coverage is the task of servicing a given set of one-dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a gra

A. Chakraborty, A. Bhattacharjee, M. S. Brotherton, R. Chatterjee

It has been inferred from large unbiased samples that $10\%$-$15\%$ of all quasars are radio-loud (RL). Using the quasar catalog from the Sloan Digital Sky Survey, we show that the radio-loud fraction (RLF) for high broad line (HBL) quasars, containing H$\beta$ FWHM greater than $15,000$ km s$^{-1}$, is $\sim 57 \%$. While there is no significant difference

Ziqiao Zhang, Yatao Bian, Ailin Xie, Pengju Han

Self-supervised pre-training is gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pre-trained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations have not been fully explored. In this work, inspired by the two phenomena of Activity Clif

Chan Li, Haiping Huang

Large-scale deep neural networks consume expensive training costs, but the training results in less-interpretable weight matrices constructing the networks. Here, we propose a mode decomposition learning that can interpret the weight matrices as a hierarchy of latent modes. These modes are akin to patterns in physics studies of memory networks, but the least

Christoph Schiel, Philipp Rahe, Philipp Maass

We present a theory for analyzing residence times of single molecules in a fixed detection area of a scanning tunneling microscope (STM). The approach is developed for one-dimensional molecule diffusion and can be extended to two dimensions by using the same methodology. Explicit results are derived for an harmonic attractive and repulsive tip-molecule inter

Byung-Hak Hwang

For a natural unit interval order $P$, we describe proper colorings of the incomparability graph of $P$ in the language of heaps. We also introduce a combinatorial operation, called a \emph{local flip}, on the heaps. This operation defines an equivalence relation on the proper colorings, and the equivalence relation refines the ascent statistic introduced by

Camilla Beneduce, Francesco Sciortino, Petr Sulc, John Russo

The goal of inverse self-assembly is to design inter-particle interactions capable of assembling the units into a desired target structure. The effective assembly of complex structures often requires the use of multiple components, each new component increasing the thermodynamic degrees of freedom and hence the complexity of the self-assembly pathway. In thi

Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima

This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-iterate convergence property in both full and noisy feedback settings. In the former, players observe their exact gradient vectors of the utility functions. In the latter, they only

G. G. L. Nashed, Shin'ichi Nojiri

The Chern-Simons amended gravity theory appears as a low-energy effective theory of string theory. The effective theory includes an anomaly-cancelation correction to the Einstein-Hilbert action. The Chern-Simons expression consists of the product $\varphi R \tilde R $ of the Pontryagin density $R \tilde R $ with a scalar field $\varphi$, where the latter is

Askandar H. Amin, Botan K. Ahmed, Bestan B. Maaroof, Tarik A. Rashid

The prior detection of a heart attack could lead to the saving of one's life. Putting specific criteria into a system that provides an early warning of an imminent at-tack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient

Vera Fischer, Corey Bacal Switzer

We provide a general preservation theorem for preserving selective independent families along countable support iterations. The theorem gives a general framework for a number of results in the literature concerning models in which the independence number $\mathfrak{i}$ is strictly below $\mathfrak{c}$, including iterations of Sacks forcing, Miller partition

Malcolm Hillebrand, Sebastian Zimper, Arnold Ngapasare, Matthaios Katsanikas

We present and validate simple and efficient methods to estimate the chaoticity of orbits in low dimensional dynamical systems from computations of Lagrangian descriptors (LDs) on short time scales. Two quantities are proposed for determining the chaotic or regular nature of orbits in a system's phase space, which are based on the values of the LDs of these

Giorgio Sonnino, Alberto Sonnino

We propose a protocol able to show publicly addition and multiplication on secretly shared values. To this aim we developed a protocol based on the use of masks and on the FMPC (Fourier Multi-Party Computation). FMPC is a novel multiparty computation protocol of arithmetic circuits based on secret-sharing, capable to compute addition and multiplication of se

Anindya Bhattacharya, Debapriya Sen

We study some problems of collective choice when individuals can have expressive preferences, that is, where a decision-maker may care not only about the material benefit from choosing an action but also about some intrinsic morality of the action or whether the action conforms to some identity-marker of the decision-maker. We construct a simple framework fo

Francesca Tonolo, François Lique, Mattia Melosso, Cristina Puzzarini

The formyl cation (HCO+) is one of the most abundant ions in molecular clouds and plays a major role in the interstellar chemistry. For this reason, accurate collisional rate coefficients for the rotational excitation of HCO+ and its isotopes due to the most abundant perturbing species in interstellar environments are crucial for non-local thermal equilibriu

Shaotian Cai, Liping Qiu, Xiaojun Chen, Qin Zhang

Image clustering is an important and open-challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate

Renzhi He, Hualin Hong, Boya Fu, Fei Liu

Monocular depth estimation and defocus estimation are two fundamental tasks in computer vision. Most existing methods treat depth estimation and defocus estimation as two separate tasks, ignoring the strong connection between them. In this work, we propose a multi-task learning network consisting of an encoder with two decoders to estimate the depth and defo

Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan

Pre-training and fine-tuning have achieved significant advances in the information retrieval (IR). A typical approach is to fine-tune all the parameters of large-scale pre-trained models (PTMs) on downstream tasks. As the model size and the number of tasks increase greatly, such approach becomes less feasible and prohibitively expensive. Recently, a variety

Xinyu Ma, Ruqing Zhang, Jiafeng Guo, Yixing Fan

Dense retrieval (DR) has shown promising results in information retrieval. In essence, DR requires high-quality text representations to support effective search in the representation space. Recent studies have shown that pre-trained autoencoder-based language models with a weak decoder can provide high-quality text representations, boosting the effectiveness

Hironori Makino

Theory of the quantal level statistics of classically integrable system, developed by Makino et al. in order to investigate the non-Poissonian behaviors of level-spacing distribution (LSD) and level-number variance (LNV)\cite{MT03,MMT09}, is successfully extended to the study of $E(K,L)$ function which constitutes a fundamental measure to determine most stat

Qiong Wu, Jiaer Xia, Pingyang Dai, Yiyi Zhou

Visible-infrared person re-identification (VI-ReID) is a task of matching the same individuals across the visible and infrared modalities. Its main challenge lies in the modality gap caused by cameras operating on different spectra. Existing VI-ReID methods mainly focus on learning general features across modalities, often at the expense of feature discrimin

Haoran Wang, Dongliang He, Wenhao Wu, Boyang Xia

Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities. Contrastive learning has been adopted by most prior arts. Except for limited amount of negative image-text pairs, the capability of constrastive learning is restricted by manually weighting negative pairs as well as unawareness of external knowledge. In this paper, we propos

Jureeporn Yuennan, Phongpichit Channuie

A natural combination of the first and second derivatives of the scalar potential was achieved in a framework of an alternative refined de Sitter conjecture recently proposed in the literature. In this work, we study various inflation models in which the inflaton is a composite field emerging from various strongly interacting field theories. We then examine

Alex Iosevich, Quy Pham, Thang Pham, Chun-Yen Shen

In this paper we obtain improved dimensional thresholds for dot product sets corresponding to compact subsets of a paraboloid. As a direct application of these estimates, we obtain significant improvements to the best known dimensional thresholds that guarantee that a given compact subset of Euclidean space determines a positive proportion of all possible co

Travis Gagie, Giovanni Manzini, Marinella Sciortino

The Burrows-Wheeler Transform (BWT) is often taught in undergraduate courses on algorithmic bioinformatics, because it underlies the FM-index and thus important tools such as Bowtie and BWA. Its admirers consider the BWT a thing of beauty but, despite thousands of pages being written about it over nearly thirty years, to undergraduates seeing it for the firs

Emmanuele Battista, Vittorio De Falco

We derive the equations of motion for an $N$-body system in the Einstein-Cartan gravity theory at the first post-Newtonian order by exploiting the Weyssenhoff fluid as the spin model. Our approach consists in performing the point-particle limit of the continuous description of the gravitational source. The final equations provide a hint for the validity of t

Junghun Kim, Jihie Kim

Multimodal sentiment analysis has become an increasingly popular research area as the demand for multimodal online content is growing. For multimodal sentiment analysis, words can have different meanings depending on the linguistic context and non-verbal information, so it is crucial to understand the meaning of the words accordingly. In addition, the word m

Padraig X. Lamont

This paper presents Tyche, a Python library to facilitate probabilistic reasoning in uncertain worlds through the construction, querying, and learning of belief models. Tyche uses aleatoric description logic (ADL), which provides computational advantages in its evaluation over other description logics. Tyche belief models can be succinctly created by definin

Nadeem Rao, Mamta Rani, Adem Kiliçman, Pradeep Malik

In the present manuscript, we present a new sequence of operators, $i.e.$, $\alpha$-Bernstein-Schurer-Kantorovich operators depending on two parameters $\alpha\in[0,1]$ and $\rho>0$ for one and two variables to approximate measurable functions on $[0: 1+q], q>0$. Next, we give basic results and discuss the rapidity of convergence and order of approximation f

Zhihui Xie, Tong Yu, Canzhe Zhao, Shuai Li

With the recent advances of conversational recommendations, the recommender system is able to actively and dynamically elicit user preference via conversational interactions. To achieve this, the system periodically queries users' preference on attributes and collects their feedback. However, most existing conversational recommender systems only enable the u

Yael Zaffrani-Reznikov, Onur Afacan, Sila Kurugol, Simon Warfield

Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical utilization. We introduce qDWI-morph, an uns

Junghun Kim, Yoojin An, Jihie Kim

Attention has become one of the most commonly used mechanisms in deep learning approaches. The attention mechanism can help the system focus more on the feature space's critical regions. For example, high amplitude regions can play an important role for Speech Emotion Recognition (SER). In this paper, we identify misalignments between the attention and the s