October 2022 arXiv papers — page 68

Haotian Ye, James Zou, Linjun Zhang

The existence of spurious correlations such as image backgrounds in the training environment can make empirical risk minimization (ERM) perform badly in the test environment. To address this problem, Kirichenko et al. (2022) empirically found that the core features that are related to the outcome can still be learned well even with the presence of spurious c

Naoki Takeishi, Masaya Santo, Naoto Yokoyama, Shigeo Wada

Biomembranes consisting of two opposing phospholipid monolayers, which comprise the so-called lipid bilayer, are largely responsible for the dual solid-fluid behavior of individual cells and viruses. Quantifying the mechanical characteristics of biomembrane, including the dynamics of their in-plane fluidity, can provide insight not only into active or passiv

Ralph Brinks

This article describes a method to estimate the mortality rate ratio R from current status data with duration in a chronic condition in case the general mortality of the overall population is known. Apart from the general mortality, the method requires four pieces of information from the study participants: age and time at the survey/interview, whether the c

Philip Zechmann, Ehud Altman, Michael Knap, Johannes Feldmeier

Quantum many-body systems with fracton constraints are widely conjectured to exhibit unconventional low-energy phases of matter. In this work, we demonstrate the existence of a variety of such exotic quantum phases in the ground states of a dipole-moment conserving Bose-Hubbard model in one dimension. For integer boson fillings, we perform a mapping of the s

Anthony V. Petyuk

A graph $G=(V,E)$ is word-representable if there exists a word $w$ over the alphabet $V$ such that letters $x$ and $y$ alternate in $w$ if and only if $xy\in E$. Word-representable graphs generalize several important classes of graphs such as $3$-colorable graphs, circle graphs, and comparability graphs. There is a long line of research in the literature ded

O. P. Solovtsova, V. I. Lashkevich, L. P. Kaptari

The contributions to the anomalous magnetic moment of the lepton $L$ ($L=e\ , \mu $ or $\tau$) generated by a specific class of QED diagrams are evaluated analytically up to the eighth order of the electromagnetic coupling constant. The considered class of the Feynman diagrams involves the vacuum polarization insertions into the electromagnetic vertex of the

I. D. Karachentsev, E. I. Kaisina

We undertook a search for new dwarf galaxies in the vicinity of relatively isolated nearby galaxies with distances $D < 12$ Mpc and stellar masses in the $2\times10^{11}-3\times10^8~M_{\odot}$ interval, using the data from the DESI Legacy Imaging Surveys. Around the 46 considered Local Volume galaxies, $67$ new candidates for satellites of these galaxies wer

Marzieh Hashemipour-Nazari, Yuqing Ren, Kees Goossens, Alexios Balatsoukas-Stimming

The recently proposed recursive projection-aggregation (RPA) decoding algorithm for Reed-Muller codes has received significant attention as it provides near-ML decoding performance at reasonable complexity for short codes. However, its complicated structure makes it unsuitable for hardware implementation. Iterative projection-aggregation (IPA) decoding is a

YeongHyeon Park, Myung Jin Kim, Won Seok Park

Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. This study proposes a concise and highly useful method for improving the precision of the event extraction that is hindered by extra noise such as wind noise, which is difficult to ch

Tetsuya Ito

A knot $K$ is called $(m,n)$-fertile if for every prime knot $K'$ whose crossing number is less than or equal to $m$, there exists an $n$-crossing diagram of $K$ such that one can get $K'$ from the diagram by changing its over-under information. We give an obstruction for knot to be $(m,n)$-fertile. As application, we prove the finiteness of $(c(K)+f,c(K)+p)

Robert de Mello Koch, Garreth Kemp

The principle of the holography of information states that in a theory of quantum gravity a copy of all the information available on a Cauchy slice is also available near the boundary of the Cauchy slice. This redundancy in the theory is already present at low energy. In the context of the AdS/CFT correspondence, this principle can be translated into a state

Digbalay Bose, Rajat Hebbar, Krishna Somandepalli, Haoyang Zhang

Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes. Domain specific challenges associated with visual scenes in movies include transitions, person coverage, and a wide array of real-life and fictional scenarios. Existing visual scene datasets in movies have limited taxonomies and don'

Zeinab Salehi, Yijun Chen, Elizabeth L. Ratnam, Ian R. Petersen

In this paper, we consider dynamic multi-agent systems (MAS) for decentralized resource allocation. The MAS operates at a competitive equilibrium to ensure supply and demand are balanced. First, we investigate the MAS over a finite horizon. The utility functions of agents are parameterized to incorporate individual preferences. We shape individual preference

Vinícius F. Dal Poggetto, Federico Bosia, Marco Miniaci, Nicola M. Pugno

The quest for novel designs for lightweight phononic crystals and elastic metamaterials with wide lowfrequency band gaps has proven to be a significant challenge in recent years. In this context, lattice-type materials represent a promising solution, providing both lightweight properties and significant possibilities of tailoring mechanical and dynamic prope

Yiren Wang, Liangjun Su, Yichong Zhang

In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimat

Min Cao, Cong Ding, Chen Chen, Junchi Yan

Most existing person re-identification methods compute the matching relations between person images across camera views based on the ranking of the pairwise similarities. This matching strategy with the lack of the global viewpoint and the context's consideration inevitably leads to ambiguous matching results and sub-optimal performance. Based on a natural a

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Enrique Tomás Martínez Beltrán, Daniel Demeter

Federated learning (FL) allows participants to collaboratively train machine and deep learning models while protecting data privacy. However, the FL paradigm still presents drawbacks affecting its trustworthiness since malicious participants could launch adversarial attacks against the training process. Related work has studied the robustness of horizontal F

Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun

This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations. This is of particular interest for companies and organizations that own a large number of manuals or instruction books. Despite its potential, the nature of our task poses several challenges: (1) documents contain various structures that h

Chihiro Watanabe, Hirokazu Kameoka

Voice conversion is a task to convert a non-linguistic feature of a given utterance. Since naturalness of speech strongly depends on its pitch pattern, in some applications, it would be desirable to keep the original rise/fall pitch pattern while changing the speaker identity. Some of the existing methods address this problem by either using a source-filter

Jeremias Traub

Diffusion models (DMs) have achieved state-of-the-art results for image synthesis tasks as well as density estimation. Applied in the latent space of a powerful pretrained autoencoder (LDM), their immense computational requirements can be significantly reduced without sacrificing sampling quality. However, DMs and LDMs lack a semantically meaningful represen

Matt Visser, Stefano Liberati

We discuss the tension between the possible existence of Painleve-Gullstrand coordinate systems versus the explicit geometrical features of the Kerr spacetime; a subject of interest to Professor Thanu Padmanabhan in the weeks immediately preceding his unexpected death. We shall carefully distinguish strong and weak Painleve-Gullstrand coordinate systems, and

Wai Hong Ronald Chan, Ken Hara, Jonathan M. Wang, Suhas S. Jain

Large-amplitude current-driven instabilities in hollow cathode plumes can generate energetic ions responsible for cathode sputtering and spacecraft degradation. A 2D2V (two dimensions each in configuration [D] and velocity [V] spaces) grid-based Vlasov--Poisson (direct kinetic) solver is used to study their growth and saturation, which comprises four stages:

Anna Fujioka, Masaki Ogura, Naoki Wakamiya

The problem of guiding a flock of agents to a destination by the repulsion forces exerted by a smaller number of external agents is called the shepherding problem. This problem has attracted attention due to its potential applications, including diverting birds away for preventing airplane accidents, recovering spilled oil in the ocean, and guiding a swarm o

An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua

Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategies are prone to playing a trade-off game between head and tail performance, thus inevitably degrading the overall recommendation accuracy. To reduce the negative impact of popularit

Soledad Le Clainche, Esteban Ferrer, Sam Gibson, Elisabeth Cross

This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of ML methods

Edward Vendrow, Duy Tho Le, Jianfei Cai, Hamid Rezatofighi

Autonomous robotic systems operating in human environments must understand their surroundings to make accurate and safe decisions. In crowded human scenes with close-up human-robot interaction and robot navigation, a deep understanding requires reasoning about human motion and body dynamics over time with human body pose estimation and tracking. However, exi

Eaman Jahani, Dean Eckles, Alex 'Sandy' Pentland

Social networks affect the diffusion of information, and thus have the potential to reduce or amplify inequality in access to opportunity. We show empirically that social networks often exhibit a much larger potential for unequal diffusion across groups along paths of length 2 and 3 than expected by our random graph models. We argue that homophily alone cann

Jingwei Dong, Dongbin Shin, Ernest Pastor, Tobias Ritschel

Here we perform angle and time-resolved photoelectron spectroscopy on the commensurate Charge Density Wave (CDW) phase of 1T-TaS$_2$. Data with different probe pulse polarization are employed to map the dispersion of electronic states below and above the chemical potential. Upon photoexcitation, the fluctuations of CDW order erase the band dispersion near to

J. -M. Deshouillers, S. Gun, O. Ramaré, J. Sivaraman

We prove that, for every modulus $\mathfrak{q}$, every class of the narrow ray class group $H_{\mathfrak{q}}(\mathbf{K})$ of an arbitrary number field $\mathbf{K}$ contains a product of three unramified prime ideals $\mathfrak{p}$ of degree one with $\mathfrak{N}\mathfrak{p}\le (t(\mathbf{K})\mathfrak{N}\mathfrak{q})^3$, where $t(\mathbf{K})$ is an explicit

Zeyu Cao, Zhipeng Liang, Shu Zhang, Hangyu Li

In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over different departments. This problem remains largely unexplored in the research community. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask

Guangsheng Zhang, Bo Liu, Huan Tian, Tianqing Zhu

As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks

Clémentine Lemarié--Rieusset

By using motivic homotopy theory, we introduce a counterpart in algebraic geometry to oriented links and their linking numbers. After constructing the (ambient) quadratic linking degree -- our analogue of the linking number which takes values in the Witt group of the ground field -- and exploring some of its properties, we give a method to explicitly compute

Adhokshaj Mishra, Manjesh Kumar Hanawal

Software piracy is one of the concerns in the IT sector. Pirates leverage the debugger tools to reverse engineer the logic that verifies the license keys or bypass the entire verification process. Anti-debugging techniques are used to defeat piracy using self-healing codes. However, anti-debugging methods can be defeated when the licensing protections are li

Dominique Mias-Lucquin, Isaure Chauvot de Beauchene

We explored the Protein DataBank (PDB) to collect protein-ssDNA structures and create a multiconformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of

Tony C. W. Mok, Albert C. S. Chung

Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are

Ramona Anton, Nicolae Mihalache, François Vigneron

We propose an algorithm for quickly evaluating polynomials. It pre-conditions a complex polynomial $P$ of degree $d$ in time $O(d\log d)$, with a low multiplicative constant independent of the precision. Subsequent evaluations of $P$ computed with a fixed precision of $p$ bits are performed in average arithmetic complexity $O\big(\sqrt{d(p+\log d)}\big)$ and

Brian D. O. Anderson, Mengbin Ye

This paper considers a deterministic Susceptible-Infected-Susceptible (SIS) networked bivirus epidemic model (termed the bivirus model for short), in which two competing viruses spread through a set of populations (nodes) connected by two graphs, which may be different if the two viruses have different transmission pathways. The networked dynamics can give r

Oleksandra Khokhliuk, Sergiy Maksymenko

Let $T= S^1\times D^2$ be the solid torus, $\mathcal{F}$ the Morse-Bott foliation on $T$ into $2$-tori parallel to the boundary and one singular circle $S^1\times 0$, which is the central circle of the torus $T$, and $\mathcal{D}(\mathcal{F},\partial T)$ the group of diffeomorphisms of $T$ fixed on $\partial T$ and leaving each leaf of the foliation $\mathca

Lijun Zhu, Daniel C Ralph

Spin-orbit torques (SOTs) have been widely understood as an interfacial transfer of spin that is independent of the bulk properties of the magnetic layer. Here, we report that SOTs acting on ferrimagnetic FexTb1-x layers decrease and vanish upon approaching the magnetic compensation point because the rate of spin transfer to the magnetization becomes slower

Maya Sankar

We show that there is a constant $c$ such that any 3-uniform hypergraph $\mathcal H$ with $n$ vertices and at least $cn^{5/2}$ edges contains a triangulation of the real projective plane as a subgraph. This resolves a conjecture of Kupavskii, Polyanskii, Tomon, and Zakharov. Furthermore, our work, combined with prior results, asymptotically determines the Tu

Mohd Harun, Saurabh Kumar Singh

In this paper, we will prove the non-trivial bound for the weighted average version of shifted convolution sum for $GL(3)\times GL(2)$, i.e. for any $\epsilon >0$ and $X^{1/4+\delta} \leq H \leq X$ with $\delta >0$, \[ \frac{1}{H}\sum_{h=1}^\infty \lambda_f(h) V\left( \frac{h}{H}\right)\sum_{n=1}^\infty \lambda_{\pi}(1,n) \lambda_g (n+h) W\left( \frac{n}{X}

Hao Wang, Zhichao Chen, Zhaoran Liu, Haozhe Li

Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure $\rightarrow$ click $\rightarrow$ conver

Justin Cheigh, Guilherme Zeus Dantas e Moura, Ryan Jeong, Jacob Lehmann Duke

Zeckendorf proved that any positive integer has a unique decomposition as a sum of non-consecutive Fibonacci numbers, indexed by $F_1 = 1, F_2 = 2, F_{n+1} = F_n + F_{n-1}$. Motivated by this result, Baird, Epstein, Flint, and Miller defined the two-player Zeckendorf game, where two players take turns acting on a multiset of Fibonacci numbers that always sum

Xiutao Zhu, Ervin Győri, Zhen He, Zequn Lv

Let $f_k(n,H)$ denote the maximum number of edges not contained in any monochromatic copy of~$H$ in a $k$-coloring of the edges of $K_n$, and let $ex(n,H)$ denote the Tur\'an number of $H$. In place of $f_2(n,H)$ we simply write $f(n,H)$. Keevash and Sudakov proved that $f(n,H)=ex(n,H)$ if $H$ is an edge-critical graph or $C_4$ and asked if this equality hol

Kavin R, Ananta K Majee

In this paper, we establish large deviation principle for the strong solution of evolutionary p-Laplace equation driven by small multiplicative Brownian noise, where the weak convergence approach plays a key role. Moreover, by using Girsanov transformation along with $L^1$-contraction approach, we show the quadratic transportation cost inequality for the str

Jing Tan, Xiaotong Zhao, Xintian Shi, Bin Kang

Traditional temporal action detection (TAD) usually handles untrimmed videos with small number of action instances from a single label (e.g., ActivityNet, THUMOS). However, this setting might be unrealistic as different classes of actions often co-occur in practice. In this paper, we focus on the task of multi-label temporal action detection that aims to loc

Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo

Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize the information from a single representation that resides in the penultimate layer to determine whether the input is anomalous or not. Altho

Abir De, Soumen Chakrabarti

Submodular functions and variants, through their ability to characterize diversity and coverage, have emerged as a key tool for data selection and summarization. Many recent approaches to learn submodular functions suffer from limited expressiveness. In this work, we propose FLEXSUBNET, a family of flexible neural models for both monotone and non-monotone su

Yair Caro, Balázs Patkós, Zsolt Tuza, Máté Vizer

Motivated by the theorem of Gy\H ori and Lov\'asz, we consider the following problem. For a connected graph $G$ on $n$ vertices and $m$ edges determine the number $P(G,k)$ of unordered solutions of positive integers $\sum_{i=1}^k m_i = m$ such that every $m_i$ is realized by a connected subgraph $H_i$ of $G$ with $m_i$ edges such that $\cup_{i=1}^kE(H_i)=E(G

George Marinescu, Duc-Viet Vu

We prove that the Bergman kernel function associated to a smooth measure supported on a piecewise-smooth maximally totally real submanifold K in C^n is of polynomial growth (e.g, in dimension one, K is a finite union of transverse Jordan arcs in C). Our bounds are sharp when K is smooth. We give an application to equidistribution of zeros of random polynomia

Yeqin Liu

We find an algorithm to compute the cohomology groups of spherical vector bundles on complex projective K3 surfaces, in terms of their Mukai vectors. In many good cases, we give significant simplifications of the algorithm. As an application, when the Picard rank is one, we show a numerical condition that is equivalent to weak Brill-Noether for a spherical v

Sha Lu, Xuecheng Xu, Li Tang, Rong Xiong

LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with large perspective difference. To address the challenge, we propose DeepRING to learn t

H. Khodabakhshi, H. Lu, R. B. Mann

The gravitational Lagrangian can be written as a summation of a bulk and a total derivative term. For some theories of gravity such as Einstein gravity, or more general Lovelock gravities, there are Lagrangian holographic relations between the bulk and the total derivative term such that the latter is fully determined by the former. However at the $D\rightar

Mohammed Abouzaid, Yoel Groman, Umut Varolgunes

We resolve the long-standing problem of constructing the action of the operad of framed (stable) genus-$0$ curves on Hamiltonian Floer theory; this operad is equivalent to the framed $E_2$ operad. We formulate the construction in the following general context: we associate to each compact subset of a closed symplectic manifold a new chain-level model for sym

Iakov Aizenberg, Zvi Citron, Alexander Milov

From first principles, particles with the same quark content and similar masses should have similar kinematic distributions. Transverse mass scaling may be employed to estimate possible differences in the momentum distribution of such particles. Based on this scaling the excited bottomonium states measured at the LHC are found to be significantly different f

Haibo Li

The growing availability and usage of low precision foating point formats has attracts many interests of developing lower or mixed precision algorithms for scientific computing problems. In this paper we investigate the possibility of exploiting lower precision computing in LSQR for solving discrete linear ill-posed problems. We analyze the choice of proper

Naman Goyal

Recently self supervised learning has seen explosive growth and use in variety of machine learning tasks because of its ability to avoid the cost of annotating large-scale datasets. This paper gives an overview for best self supervised learning approaches for multimodal learning. The presented approaches have been aggregated by extensive study of the literat

Yue Hu, Alex Lazarian

Interaction of three-dimensional magnetic fields, turbulence, and self-gravity in the molecular cloud is crucial in understanding star formation but has not been addressed so far. In this work, we target the low-mass star-forming region L1688 and use the spectral emissions of $^{12}$CO, $^{13}$CO, C$^{18}$O, and H I, as well as polarized dust emissions. To o

Rishabh Madan, Rajat Kumar Jenamani, Vy Thuy Nguyen, Ahmed Moustafa

Existing work in physical robot caregiving is limited in its ability to provide long-term assistance. This is majorly due to (i) lack of well-defined problems, (ii) diversity of tasks, and (iii) limited access to stakeholders from the caregiving community. We propose Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loop (SPAR

Haoyue Dai, Peter Spirtes, Kun Zhang

Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of the target variables. Causal discovery under measurement error aims to recover the causal graph among unobser

Indradyumna Roy, Soumen Chakrabarti, Abir De

The graph retrieval problem is to search in a large corpus of graphs for ones that are most similar to a query graph. A common consideration for scoring similarity is the maximum common subgraph (MCS) between the query and corpus graphs, usually counting the number of common edges (i.e., MCES). In some applications, it is also desirable that the common subgr

Bolong Zhang, Juan Chen, Quan Wen

Image super-resolution reconstruction is an important task in the field of image processing technology, which can restore low resolution image to high quality image with high resolution. In recent years, deep learning has been applied in the field of image super-resolution reconstruction. With the continuous development of deep neural network, the quality of

Xiaowen Liu, Renhua Wang, Hongtao Huo, Xin Yang

The GAN-based infrared and visible image fusion methods have gained ever-increasing attention due to its effectiveness and superiority. However, the existing methods adopt the global pixel distribution of source images as the basis for discrimination, which fails to focus on the key modality information. Moreover, the dual-discriminator based methods suffer

Yafu Li, Leyang Cui, Yongjing Yin, Yue Zhang

Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption. This assumption is further strengthened by cross-entropy loss, which encourages a strict match between the hypothesis and the reference token by token. To alleviate this issue, we propose multi-granularity optim

Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

Although increasingly training-expensive, most self-supervised learning (SSL) models have repeatedly been trained from scratch but not fully utilized, since only a few SOTAs are employed for downstream tasks. In this work, we explore a sustainable SSL framework with two major challenges: i) learning a stronger new SSL model based on the existing pretrained S

Paulo E. Arratia

Many microorganisms live and evolve in complex fluids. Examples include mammalian spermatozoa in cervical mucus, worms (e.g., \textit{C. elegans}) in wet soil, and bacteria (e.g., \textit{H. pylori}) in our stomach lining. Due to the presence of (bio)polymers and/or solids, such fluids often display nonlinear response to (shear) stresses including viscoelast

Víctor Becerril, Marco A. Pérez

We study homological and homotopical aspects of Gorenstein flat modules over a ring with respect to a duality pair $(\mathcal{L,A})$. These modules are defined as cycles of exact chain complexes with components in $\mathcal{L}$ which remain exact after tensoring by objects in $\mathcal{A} \cap {}^\perp\mathcal{A} = \mathcal{A} \cap \Big( \bigcap_{i \in \math

Melissa Middleton, Cattram Nguyen, John B. Carlin, Margarita Moreno-Betancur

Case-cohort studies are conducted within cohort studies, wherein collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis w

Zhongxiang Sun, Jun Xu, Xiao Zhang, Zhenhua Dong

Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal systems. Semantic text matching models have been applied to the task where the source and target legal cases are considered as long-form text documents. These general-purpose matching

Alexander C. Ritter, Raymond R. Volkas

The asymmetric dark matter (ADM) paradigm is motivated by the apparent coincidence between the cosmological mass densities of visible and dark matter, $\Omega_\mathrm{DM} \simeq 5\Omega_\mathrm{VM}$. However, most ADM models only relate the number densities of visible and dark matter, and do not motivate the similarity in their particle masses. One exception

Rubén Loaiza-Maya, Didier Nibbering

Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state space models. This paper proposes a variational approximation that is accurate and fast for any model with a closed-form measurement density function and a state transition distri

Hendrik Meer, Stephan Wust, Christin Schmitt, Paul Herrgen

We demonstrate how the antiferromagnetic order in heterostructures of NiO/Pt thin films can be modified by optical pulses. We irradiate our samples with laser light and identify an optically induced creation of antiferromagnetic domains by imaging the created domain structure utilizing the X-ray magnetic linear dichroism effect. We study the effect of differ

Igor E. Verbitsky

We give a survey of nonlinear potential estimates and their applications obtained recently for positive solutions to sublinear problems of the type \[ u = \mathbf{G}(\sigma u^q) + f \quad \textrm{in} \,\, \Omega, \] where $0 < q < 1$, $\sigma\ge 0$ is a Radon measure in $\Omega$, $ f \ge 0$ is a measurable function, and $\mathbf{G}$ is a linear integral oper

X. Jiang, J. Scott, Mark Friesen, M. Saffman

The fidelity of gate operations on neutral atom qubits is often limited by fluctuations of the laser drive. Here, we quantify the sensitivity of quantum gate fidelities to laser phase and intensity noise. We first develop models to identify features observed in laser self-heterodyne noise spectra, focusing on the effects of white noise and servo bumps. In th

Qin Liu, Zhenlin Xu, Gedas Bertasius, Marc Niethammer

Click-based interactive image segmentation aims at extracting objects with a limited user clicking. A hierarchical backbone is the de-facto architecture for current methods. Recently, the plain, non-hierarchical Vision Transformer (ViT) has emerged as a competitive backbone for dense prediction tasks. This design allows the original ViT to be a foundation mo

Murali Raghu Babu Balusu, Yangfeng Ji, Jacob Eisenstein

Implicit discourse relations bind smaller linguistic units into coherent texts. Automatic sense prediction for implicit relations is hard, because it requires understanding the semantics of the linked arguments. Furthermore, annotated datasets contain relatively few labeled examples, due to the scale of the phenomenon: on average each discourse relation enco

Ilja Doršner, Ajla Lejlić, Shaikh Saad

We investigate asymmetric leptoquark pair production mechanism at the Large Hadron Collider to advocate its potential relevance to establish reliable constraints on the leptoquark parameter space and its ability to aid in correct identification of these attractive sources of new physics. The main feature of asymmetric pair production that genuinely distingui

Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis

We propose a generalization of the synthetic control and interventions methods to the setting with dynamic treatment effects. We consider the estimation of unit-specific treatment effects from panel data collected under a general treatment sequence. Here, each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a l

Dylan Cant

We construct a contactomorphism of the standard sphere which does not have any translated points, providing a negative answer to a conjecture posed by Sandon.

Kantaro Ohmori, Shutaro Shimamura

There has been proposed two continuum descriptions of fracton systems: foliated quantum field theories (FQFTs) and exotic quantum field theories. Certain fracton systems are believed to admit descriptions by both, and hence a duality is expected between such a class of FQFTs and exotic QFTs. In this paper we study this duality in detail for concrete examples

Mohamed Afham, Ranga Rodrigo

Few-Shot learning aims to train and optimize a model that can adapt to unseen visual classes with only a few labeled examples. The existing few-shot learning (FSL) methods, heavily rely only on visual data, thus fail to capture the semantic attributes to learn a more generalized version of the visual concept from very few examples. However, it is a known fac

Vaibhav Bajaj, Guni Sharon, Peter Stone

Applying reinforcement learning (RL) to sparse reward domains is notoriously challenging due to insufficient guiding signals. Common RL techniques for addressing such domains include (1) learning from demonstrations and (2) curriculum learning. While these two approaches have been studied in detail, they have rarely been considered together. This paper aims

Zilu Tang, Muhammed Yusuf Kocyigit, Derry Wijaya

Data augmentation techniques have been proven useful in many applications in NLP fields. Most augmentations are task-specific, and cannot be used as a general-purpose tool. In our work, we present AugCSE, a unified framework to utilize diverse sets of data augmentations to achieve a better, general purpose, sentence embedding model. Building upon the latest

Jinman Wei, Jinkun Yao, Guoshan Zhanga, Bin Guan

The thighbone is the largest bone supporting the lower body. If the thighbone fracture is not treated in time, it will lead to lifelong inability to walk. Correct diagnosis of thighbone disease is very important in orthopedic medicine. Deep learning is promoting the development of fracture detection technology. However, the existing computer aided diagnosis

Xiaoyu Sun, Xiao Chen, Li Li, Haipeng Cai

Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malware writers regularly update their attack mechanisms to hide malicious behavior implementation. This poses two problems to current research techniques: static analysis approaches, g

Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation predictio

Miguel Castroviejo Fernandez, Jordan Leung, Ilya Kolmanovsky

In this paper, a control scheme is developed based on an input constrained Model Predictive Controller (MPC) and the idea of modifying the reference command to enforce constraints, usual of Reference Governors (RG). The proposed scheme, referred to as the RGMPC, requires optimization for MPC with input constraints for which fast algorithms exist, and can han

Yisi Sang, Xiangyang Mou, Mo Yu, Dakuo Wang

An NLP model that understands stories should be able to understand the characters in them. To support the development of neural models for this purpose, we construct a benchmark, Story2Personality. The task is to predict a movie character's MBTI or Big 5 personality types based on the narratives of the character. Experiments show that our task is challenging

Jonathan S. Kent

Neural networks, being susceptible to adversarial attacks, should face a strict level of scrutiny before being deployed in critical or adversarial applications. This paper uses ideas from Chaos Theory to explain, analyze, and quantify the degree to which neural networks are susceptible to or robust against adversarial attacks. To this end, we present a new m

Lequan Lin, Junbin Gao

Spectral Graph Convolutional Networks (spectral GCNNs), a powerful tool for analyzing and processing graph data, typically apply frequency filtering via Fourier transform to obtain representations with selective information. Although research shows that spectral GCNNs can be enhanced by framelet-based filtering, the massive majority of such research only con

Zeyu Huang, Juzhan Xu, Sisi Dai, Kai Xu

We introduce NIFT, Neural Interaction Field and Template, a descriptive and robust interaction representation of object manipulations to facilitate imitation learning. Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the de

Penghui Fu, Zhiqiang Tan

For multivariate nonparametric regression, doubly penalized ANOVA modeling (DPAM) has recently been proposed, using hierarchical total variations (HTVs) and empirical norms as penalties on the component functions such as main effects and multi-way interactions in a functional ANOVA decomposition of the underlying regression function. The two penalties play c

Zhenyue Zhang, Zhong-Heng Tan

Continuous conformal transformation minimizes the conformal energy. The convergence of minimizing discrete conformal energy when the discrete mesh size tends to zero is an open problem. This paper addresses this problem via a careful error analysis of the discrete conformal energy. Under a weak condition on triangulation, the discrete function minimizing the

Hsien-Kuei Hwang

We prove the curious identity in the sense of formal power series: \[ \int_{-\infty}^{\infty}[y^m] \exp\left(-\frac{t^2}2 +\sum_{j\ge3}\frac{(it)^j}{j!}\, y^{j-2}\right)\mathrm{d} t = \int_{-\infty}^{\infty}[y^m] \exp\left(-\frac{t^2}2+ \sum_{j\ge3}\frac{(it)^j}{j}\, y^{j-2}\right)\mathrm{d} t, \] for $m=0,1,\dots$, where $[y^m]f(y)$ denotes the coefficient

Molly Kate Kreider, Connor Fredrick, Scott A. Diddams, Ryan C. Terrien

The possibility of an Earth-Sun analog beyond our solar system is one of the most longstanding questions in science. At present, answering this question embodies an extremely difficult measurement problem that requires multiple coordinated advances in astronomical telescopes, fiber optics, precision spectrographs, large format detector arrays, and advanced d

G. Grissonnanche, G. A. Pan, H. LaBollita, D. Ferenc Segedin

Superconducting nickelates are a new family of strongly correlated electron materials with a phase diagram closely resembling that of superconducting cuprates. While analogy with the cuprates is natural, very little is known about the metallic state of the nickelates, making these comparisons difficult. We probe the electronic dispersion of thin-film superco

Geng Li, C. P. Sun, Hui Dong

Accelerating controlled thermodynamic processes requires an auxiliary Hamiltonian to steer the system into instantaneous equilibrium states. An extra energy cost is inevitably needed in such finite-time operation. We recently develop a geodesic approach to minimize such energy cost for the shortcut to isothermal process. The auxiliary control typically conta

Jee-weon Jung, Hee-Soo Heo, Bong-Jin Lee, Jaesong Lee

The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two components should be adequately configured to build such a model. First, adequate architecture would be required. We explore several recent state-of-the-art models, including ECAPA-TDNN and MFA-Conformer, as well as other baselines. S

Yuying Hao, Yi Liu, Juncai Peng, Haoyi Xiong

Interactive image segmentation aims at segmenting a target region through a way of human-computer interaction. Recent works based on deep learning have achieved excellent performance, while most of them focus on improving the accuracy of the training set and ignore potential improvement on the test set. In the inference phase, they tend to have a good perfor

Zhicong Huang, Jingwen Zhao, Zhijie Zheng, Dihu Chena

Small object detection for 3D point cloud is a challenging problem because of two limitations: (1) Perceiving small objects is much more diffcult than normal objects due to the lack of valid points. (2) Small objects are easily blocked which breaks the shape of their meshes in 3D point cloud. In this paper, we propose a pillar set abstraction (PSA) and foreg