Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressibility of Network Opinion and Spread States in the Laplacian-Eigenvector Basis

Using three case studies, we examine whether snapshot data from network opinion-evolution and spread processes are compressible in the Laplacian-eigenvector basis, in the sense that each snapshot can be approximated well using a (possibly different) small set of basis vectors. The first case study is concerned with a linear consensus model that is subject to a stochastic input at an unknown location; both empirical and formal analyses are used to characterize compressibility. Second, compressibility of state snapshots for a stochastic voter model is assessed via an empirical study. Finally, compressibility is studied for state-level daily COVID-19 positivity-rate data. The three case studies indicate that state snapshots from opinion-evolution and spread processes allow terse representations, which nevertheless capture their rich propagative dynamics.
"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?

AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go,"Starcraft"and poker), and protein structure prediction. However, at the same time, AI is also bearing serious controversies. Many researchers argue that little substantial progress has been made for AI in recent decades. In this paper, the author (1) explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed"weak AI"and"strong AI"(a.k.a. artificial general intelligence); (3) clarifies how to judge which paradigm a research work should be classified into; (4) discusses what is the greatest value of"weak AI"if it has no chance to develop into"strong AI".
IEEE 802.11bf: Toward Ubiquitous Wi-Fi Sensing

Wi-Fi is among the most successful wireless technologies ever invented. As Wi-Fi becomes more and more present in public and private spaces, it becomes natural to leverage its ubiquitousness to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking, just to name a few. This paper reports ongoing efforts by the IEEE 802.11bf Task Group (TGbf), which is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant waveforms. We summarize objectives and timeline of TGbf, and discuss some of the most interesting proposed technical features discussed so far. We also introduce a roadmap of research challenges pertaining to Wi-Fi sensing and its integration with future Wi-Fi technologies and emerging spectrum bands, hoping to elicit further activities by both the research community and TGbf.
Theta, alpha and gamma traveling waves in a multi-item working memory model

Brain oscillations are believed to be involved in the different operations necessary to manipulate information during working memory tasks. We propose a modular variant of the Lisman-Idiart model for short-term memory, where theta, alpha and gamma oscillations are traveling waves. Using this model we show that the interactions between theta and gamma determine the allocation of multiple information in distinct modules, while the interference between theta and alpha disrupts the maintenance of current stored information. The effect of alpha in erasing or blocking storage is robust and seems fairly independent of the frequency, as long as it stays within the alpha range. The model help us to understand why the alpha and theta oscillations, which have close frequency bands, could have opposite roles in working memory.
Low-temperature structure of lithium nesosilicate, Li4SiO4, and its Li1s and O1s X-ray photoelectron spectrum

Li 4 siO 4 , M r =119.84, λ(Mo Kα)=0.71073 A 150 K, monoclinic, P2 1 /m, a=11.532 (1), b=6.075 (1), c=16.678 (1) A, β=99.04 (4) o , V=1153.9 (2) A 3 , μ(Mo Kα)=5.3 cm -1 , Z=14, F(000)=812, D x =2.414 Mg m -3 , R=0.028, wR=0.025 for 2255 observed reflections with I>2. 5σ(I). The crystal structure of lithium nesosilicate has been reinvestigated at 150 K and its room-temperature Li 1s and O 1s XPS spectrum has been measured
Outlier-Robust Subsampling Techniques for Persistent Homology

In recent years, persistent homology (PH) has been successfully applied to real-world data in many different settings. Despite significant computational advances, PH algorithms do not yet scale to large datasets preventing interesting applications. One approach to address computational issues posed by PH is to select a set of landmarks by subsampling from the data. Currently, these landmark points are chosen either at random or using the maxmin algorithm. Neither is ideal as random selection tends to favour dense areas of the data while the maxmin algorithm is very sensitive to noise. Here, we propose a novel approach to select landmarks specifically for PH that preserves coarse topological information of the original dataset. Our method is motivated by the Mayer-Vietoris sequence and requires only local PH computation thus enabling efficient computation. We test our landmarks on artificial datasets which contain different levels of noise and compare them to standard landmark selection techniques. We demonstrate that our landmark selection outperforms standard methods as well as a subsampling technique based on an outlier-robust version of the $k$--means algorithm for low sampling densities in noisy data with respect to robustness to outliers.
LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration

Probabilistic point cloud registration methods are becoming more popular because of their robustness. However, unlike point-to-plane variants of iterative closest point (ICP) which incorporate local surface geometric information such as surface normals, most probabilistic methods (e.g., coherent point drift (CPD)) ignore such information and build Gaussian mixture models (GMMs) with isotropic Gaussian covariances. This results in sphere-like GMM components which only penalize the point-to-point distance between the two point clouds. In this paper, we propose a novel method called CPD with Local Surface Geometry (LSG-CPD) for rigid point cloud registration. Our method adaptively adds different levels of point-to-plane penalization on top of the point-to-point penalization based on the flatness of the local surface. This results in GMM components with anisotropic covariances. We formulate point cloud registration as a maximum likelihood estimation (MLE) problem and solve it with the Expectation-Maximization (EM) algorithm. In the E step, we demonstrate that the computation can be recast into simple matrix manipulations and efficiently computed on a GPU. In the M step, we perform an unconstrained optimization on a matrix Lie group to efficiently update the rigid transformation of the registration. The proposed method outperforms state-of-the-art algorithms in terms of accuracy and robustness on various datasets captured with range scanners, RGBD cameras, and LiDARs. Also, it is significantly faster than modern implementations of CPD. The source code is available at https://github.com/ChirikjianLab/LSG-CPD.git.
On the Hierarchical Community Structure of Practical SAT Formulas

Modern CDCL SAT solvers easily solve industrial instances containing tens of millions of variables and clauses, despite the theoretical intractability of the SAT problem. This gap between practice and theory is a central problem in solver research. It is believed that SAT solvers exploit structure inherent in industrial instances, and hence there have been numerous attempts over the last 25 years at characterizing this structure via parameters. These can be classiﬁed as rigorous , i.e., they serve as a basis for complexity-theoretic upper bounds (e.g., backdoors), or correlative , i.e., they correlate well with solver run time and are observed in industrial instances (e.g., community structure). Unfortunately, no parameter proposed to date has been shown to be both strongly correlative and rigorous over a large fraction of industrial instances. Given the sheer diﬃculty of the problem, we aim for an intermediate goal of proposing a set of parameters that is strongly correlative and has good theoretical properties. Speciﬁcally, we propose parameters based on a graph partitioning called Hierarchical Community Structure (HCS), which captures the recursive community structure of a graph of a Boolean formula. We show that HCS parameters are strongly correlative with solver run time using an Empirical Hardness Model, and further build a classiﬁer based on HCS parameters that distinguishes between easy industrial and hard random/crafted instances with very high accuracy. We further strengthen our hypotheses via scaling studies. On the theoretical side, we show that counterexamples which plagued community structure do not apply to HCS, and that there is a subset of HCS parameters such that restricting them limits the size of embeddable expanders.
Mining Latent Classes for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e., potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we introduce an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and fore-ground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5i and 7.0% mIOU on COCO-20i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://github.com/LiheYoung/MiningFSS.
A Note on Global Optimization for Max-Plus Linear Systems

This note further addresses the global optimization problem for max-plus linear systems considered in [Automatica 119 (2020) 109104]. Firstly, the operations between infinity elemens and real numbers involved in the formulas of solving global optimization problems are explained explicitly. Secondly, the formula of the greatest lower bound and the criterion of solvability of globally optimal solutions are simplified. Thirdly, the criterion of uniqueness of globally optimal solutions and the set of all globally optimal solutions are presented.
A hybrid controller for safe and efficient collision avoidance control

We design and experimentally evaluate a hybrid safe-by-construction collision avoidance controller for autonomous vehicles. The controller combines into a single architecture the respective advantages of an adaptive controller and a discrete safe controller. The adaptive controller relies on model predictive control to achieve optimal efficiency in nominal conditions. The safe controller avoids collision by applying two different policies, for nominal and out-of-nominal conditions, respectively. We present design principles for both the adaptive and the safe controller and show how each one can contribute in the hybrid architecture to improve performance, road occupancy and passenger comfort while preserving safety. The experimental results confirm the feasibility of the approach and the practical relevance of hybrid controllers for safe and efficient driving.
Selfgait: A Spatiotemporal Representation Learning Method for Self-Supervised Gait Recognition

Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the performance of gait recognition suffers from insufficient labeled data, especially in some practical scenarios associated with short gait sequences or various clothing styles. It is unpractical to label the numerous gait data. In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones. Specifically, we employ the horizontal pyramid mapping (HPM) and micro-motion template builder (MTB) as our spatiotemporal backbones to capture the multi-scale spatiotemporal representations. Experiments on CASIA-B and OU-MVLP benchmark gait datasets demonstrate the effectiveness of the proposed SelfGait compared with four state-of-the-art gait recognition methods. The source code has been released at https://github.com/EchoItLiu/SelfGait.
Factor Analysis of Seattle Fine Particles

Ambient particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) samples were collected at a centrally located urban monitoring site in Seattle, WA on Wednesdays and Saturdays using Interagency Monitoring of Protected Visual Environments (IMPROVE) samplers. Particulate carbon was analyzed using the thermal optical reflectance method that divides carbon into four organic carbon (OC), pyrolyzed organic carbon (OP), and three elemental carbon (EC) fractions. A total of 384 samples that were analyzed for 36 species were collected between March 1996 and February 2000. These data were analyzed with the standard factor analysis model using the Multilinear Engine (ME). Eleven sources were identified: sulfate-rich secondary aerosol (26%), diesel emissions (22%), wood smoke (16%), gasoline vehicle (10%), aged sea salt (8%), airborne soil (7%), nitrate-rich secondary aerosol (5%), sea salt (4%), oil combustion (3%), paper mill (2%), and ferrous metal processing (1%). The use of ME provided enhanced source separations, including the nitrate-rich aerosol source and two industrial sources that were not deduced in a previous PMF2 solution. Conditional probability functions using surface wind data and resolved source contributions aid in the identifications of local sources. Potential source contribution function analysis tentatively shows southern Washington State, along the Canadian border, and southwestern British Colombia, Canada as the possible source areas and pathways that give rise to the high contribution of the sulfate-rich secondary aerosol.
Face Transformer for Recognition

Recently there has been a growing interest in Transformer not only in NLP but also in computer vision. We wonder if transformer can be used in face recognition and whether it is better than CNNs. Therefore, we investigate the performance of Transformer models in face recognition. Considering the original Transformer may neglect the inter-patch information, we modify the patch generation process and make the tokens with sliding patches which overlaps with each others. The models are trained on CASIA-WebFace and MS-Celeb-1M databases, and evaluated on several mainstream benchmarks, including LFW, SLLFW, CALFW, CPLFW, TALFW, CFP-FP, AGEDB and IJB-C databases. We demonstrate that Face Transformer models trained on a large-scale database, MS-Celeb-1M, achieve comparable performance as CNN with similar number of parameters and MACs. To facilitate further researches, Face Transformer models and codes are available at https://github.com/zhongyy/Face-Transformer.
Compact 3D Map-Based Monocular Localization Using Semantic Edge Alignment

Accurate localization is fundamental to a variety of applications, such as navigation, robotics, autonomous driving, and Augmented Reality (AR). Different from incremental localization, global localization has no drift caused by error accumulation, which is desired in many application scenarios. In addition to GPS used in the open air, 3D maps are also widely used as alternative global localization references. In this paper, we propose a compact 3D map-based global localization system using a low-cost monocular camera and an IMU (Inertial Measurement Unit). The proposed compact map consists of two types of simplified elements with multiple semantic labels, which is well adaptive to various man-made environments like urban environments. Also, semantic edge features are used for the key image-map registration, which is robust against occlusion and long-term appearance changes in the environments. To further improve the localization performance, the key semantic edge alignment is formulated as an optimization problem based on initial poses predicted by an independent VIO (Visual-Inertial Odometry) module. The localization system is realized with modular design in real time. We evaluate the localization accuracy through real-world experimental results compared with ground truth, long-term localization performance is also demonstrated.
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

We present Picasso, a CUDA-based library comprising novel modules for deep learning over complex real-world 3D meshes. Hierarchical neural architectures have proved effective in multi-scale feature extraction which signifies the need for fast mesh decimation. However, existing methods rely on CPU-based implementations to obtain multi-resolution meshes. We design GPU-accelerated mesh decimation to facilitate network resolution reduction efficiently on-the-fly. Pooling and unpooling modules are defined on the vertex clusters gathered during decimation. For feature learning over meshes, Picasso contains three types of novel convolutions namely, facet2vertex, vertex2facet, and facet2facet convolution. Hence, it treats a mesh as a geometric structure comprising vertices and facets, rather than a spatial graph with edges as previous methods do. Picasso also incorporates a fuzzy mechanism in its filters for robustness to mesh sampling (vertex density). It exploits Gaussian mixtures to define fuzzy coefficients for the facet2vertex convolution, and barycentric interpolation to define the coefficients for the remaining two convolutions. In this release, we demonstrate the effectiveness of the proposed modules with competitive segmentation results on S3DIS. The library will be made public through github.
The Mass and Age Distribution of Halo White Dwarfs in the Canada–France Imaging Survey

We present optical spectroscopy for 18 halo white dwarfs identified using photometry from the Canada–France Imaging Survey and Pan-STARRS1 DR1 3π survey combined with astrometry from Gaia DR2. The sample contains 13 DA, 1 DZ, 2 DC, and 2 potentially exotic types of white dwarf. We fit both the spectrum and the spectral energy distribution in order to obtain the temperature and surface gravity, which we then convert into mass and then age using stellar isochrones and the initial-to-final mass relation. We find a large spread in ages that is not consistent with expected formation scenarios for the Galactic halo. We find a mean age of 9.03−2.03+2.13 Gyr and a dispersion of 4.21−1.58+2.33 Gyr for the inner halo using a maximum-likelihood method. This result suggests an extended star formation history within the local halo population.
Should College Dropout Prediction Models Include Protected Attributes?

Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged about whether including protected attributes in these prediction models discriminates against underrepresented student groups and exacerbates existing inequities. We examine this issue in the context of a large U.S. research university with both residential and fully online degree-seeking students. Based on comprehensive institutional records for the entire student population across multiple years (N = 93,457), we build machine learning models to predict student dropout after one academic year of study and compare the overall performance and fairness of model predictions with or without four protected attributes (gender, URM, first-generation student, and high financial need). We find that including protected attributes does not impact the overall prediction performance and it only marginally improves the algorithmic fairness of predictions. These findings suggest that including protected attributes is preferable. We offer guidance on how to evaluate the impact of including protected attributes in a local context, where institutional stakeholders seek to leverage predictive analytics to support student success.
Stiff Neural Ordinary Differential Equations

Neural Ordinary Differential Equations (ODEs) are a promising approach to learn dynamical models from time-series data in science and engineering applications. This work aims at learning neural ODEs for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODEs in the classical stiff ODE systems of Robertson's problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems. We then present successful demonstrations in stiff systems of Robertson's problem and an air pollution problem. The demonstrations show that the usage of deep networks with rectified activations, proper scaling of the network outputs as well as loss functions, and stabilized gradient calculations are the key techniques enabling the learning of stiff neural ODEs. The success of learning stiff neural ODEs opens up possibilities of using neural ODEs in applications with widely varying time-scales, such as chemical dynamics in energy conversion, environmental engineering, and life sciences.
Urban aerosol number size distributions

Abstract. Aerosol number size distributions have been measured since 5 May 1997 in Helsinki, Finland. The presented aerosol data represents size distributions within the particle diameter size range 8-400nm during the period from May 1997 to March 2003. The daily, monthly and annual patterns of the aerosol particle number concentrations were investigated. The temporal variation of the particle number concentration showed close correlations with traffic activities. The highest total number concentrations were observed during workdays; especially on Fridays, and the lowest concentrations occurred during weekends; especially Sundays. Seasonally, the highest total number concentrations were observed during winter and spring and lower concentrations were observed during June and July. More than 80% of the number size distributions had three modes: nucleation mode ( 30nm), Aitken mode (20-100nm) and accumulation mode ( }$">90nm). Less than 20% of the number size distributions had either two modes or consisted of more than three modes. Two different measurement sites were used; in the first (Siltavuori, 5.5.1997-5.3.2001), the arithmetic means of the particle number concentrations were 7000cm , 6500cm , and 1000cm respectively for nucleation, Aitken, and accumulation modes. In the second site (Kumpula, 6.3.2001-28.2.2003) they were 5500cm , 4000cm , and 1000cm . The total number concentration in nucleation and Aitken modes were usually significantly higher during workdays than during weekends. The temporal variations in the accumulation mode were less pronounced. The lower concentrations at Kumpula were mainly due to building construction and also the slight overall decreasing trend during these years. During the site changing a period of simultaneous measurements over two weeks were performed showing nice correlation at both sites.