Miracle-dz/UniFAR-FTUs huggingface dataset:
The CAT Effect: Exploring the Impact of Casual Affective Triggers on Online Surveys’ Response Rates

We explore the impact of Casual Affective Triggers (CAT) on response rates of online surveys. As CAT, we refer to objects that can be included in survey participation invitations and trigger participants’ affect. The hypothesis is that participants who receive CAT-enriched invitations are more likely to respond to a survey. We conducted a study where the control condition received invitations without affective triggers, and the experimental condition received CAT-enriched invitations. We differentiated the triggers within the experimental condition: one-third of the population received a personalized invitation, one-third received a picture of the surveyor’s cat, and one-third received both. We followed up with a survey to validate our findings. Our results suggest that CATs have a positive impact on response rates. We did not find CATs to induce response bias.
A Variational Hierarchical Model for Neural Cross-Lingual Summarization

The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese). The CLS task is essentially the combination of machine translation (MT) and monolingual summarization (MS), and thus there exists the hierarchical relationship between MT&MS and CLS. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. However, it is very challenging for the model to directly conduct CLS as it requires both the abilities to translate and summarize. To address this issue, we propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder. The hierarchical model contains two kinds of latent variables at the local and global levels, respectively. At the local level, there are two latent variables, one for translation and the other for summarization. As for the global level, there is another latent variable for cross-lingual summarization conditioned on the two local-level variables. Experiments on two language directions (English-Chinese) verify the effectiveness and superiority of the proposed approach. In addition, we show that our model is able to generate better cross-lingual summaries than comparison models in the few-shot setting.
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap

Semantic analyses of object point clouds are largely driven by releasing of benchmarking datasets, including synthetic ones whose instances are sampled from object CAD models. However, learning from synthetic data may not generalize to practical scenarios, where point clouds are typically incomplete, non-uniformly distributed, and noisy. Such a challenge of Simulation-to-Reality (Sim2Real) domain gap could be mitigated via learning algorithms of domain adaptation; however, we argue that generation of synthetic point clouds via more physically realistic rendering is a powerful alternative, as systematic non-uniform noise patterns can be captured. To this end, we propose an integrated scheme consisting of physically realistic synthesis of object point clouds via rendering stereo images via projection of speckle patterns onto CAD models and a novel quasi-balanced self-training designed for more balanced data distribution by sparsity-driven selection of pseudo labeled samples for long tailed classes. Experiment results can verify the effectiveness of our method as well as both of its modules for unsupervised domain adaptation on point cloud classification, achieving the state-of-the-art performance. Source codes and the SpeckleNet synthetic dataset are available at https://github.com/Gorilla-Lab-SCUT/QS3.
Computational Benchmarks with Optimal Multilevel Argyris FEM

The main drawback for the application of the conforming Argyris FEM is the labourious implementation on the one hand and the low convergence rates on the other. If no appropriate adaptive meshes are utilised, only the convergence rate caused by corner singularities [Blum and Rannacher, 1980], far below the approximation order for smooth functions, can be achieved. The ﬁne approximation with the Argyris FEM produces high-dimensional linear systems and for a long time an optimal preconditioned scheme was not available for unstructured grids. This paper presents numerical benchmarks to conﬁrm that the adaptive multilevel solver for the hierarchical Argyris FEM from [Carstensen and Hu, 2021] is in fact highly eﬃcient and of linear time complexity. Moreover, the very ﬁrst display of optimal convergence rates in practically relevant benchmarks with corner singularities and general boundary conditions lead to the rehabilitation of the Argyris ﬁnite element from the computational perspective.
A New Fault-Tolerant Synchronization Scheme with Anonymous Pulses

Robust pulse synchronization is fundamental in constructing reliable synchronous applications in wired and wireless distributed systems. In wired systems, self-stabilizing Byzantine pulse synchronization aims for synchronizing fault-prone distributed components with arbitrary initial states in bounded-delay message-passing systems. In wireless systems, fault-tolerant synchronization of pulse-coupled oscillators is also built for a similar goal but often works under specific system restrictions, such as low computation power, low message complexity, and anonymous physical pulses whose senders cannot be identified by the receivers. These restrictions often prevent us from constructing high-reliable wireless synchronous applications. This paper tries to break barriers between bounded-delay message-passing systems and classical pulse-coupled oscillators by introducing a new fault-tolerant synchronization scheme for the so-called anonymous bounded-delay pulsing systems in the presence of indeterministic communication delays and inconsistent faults. For low computation power and low message complexity, instead of involving in consensus-based primitives, the proposed synchronization scheme integrates the so-called discrete mean-fields, planar random walks, and some additional easy operations in utilizing only sparsely generated anonymous pulses. For fault-tolerance, we show that a square-root number of faulty oscillators can be tolerated by utilizing planar random walks in anonymous pulse synchronization. For self-stabilization, we show that the stabilization can be reached in an expected constant number of observing windows in anonymous bounded-delay pulsing systems with the pulsing-frequency restriction.
ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments

Long-term scene changes pose challenges to localization systems using a pre-built map. This paper presents a LiDAR-based system that provides robust localization against those challenges. Our method starts with activation of a mapping process temporarily when global matching towards the pre-built map is unreliable. The temporary map will be merged onto the pre-built map for later localization sessions once reliable matching is obtained again. We further integrate a LiDAR inertial odometry (LIO) to provide motion-compensated LiDAR scans and a reliable pose initial estimate for the global matching module. To generate a smooth real-time trajectory for navigation purposes, we fuse poses from odometry and global matching by solving a pose graph optimization problem. We evaluate our localization system with extensive experiments on the NCLT dataset including a variety of changing indoor and outdoor environments, and the results demonstrate a robust and accurate long-term localization performance. The implementations are open sourced on GitHub11https://github.com/HaisenbergPeng/ROLL.
Identifying the Deviator

A group of players are supposed to follow a prescribed profile of strategies. If they follow this profile, they will reach a given target. We show that if the target is not reached because some player deviates, then an outside observer can identify the deviator. We also construct identification methods in two nontrivial cases.
High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection

Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle. To date, a Symmetric and Non-negative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing to its high interpretability and scalability. However, most existing SNMF-based community detection methods neglect the high-order connection patterns in a network. Motivated by this discovery, in this paper, we propose a High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regularized NMF (HSGN) model that adopts the following three-fold ideas: a) adopting a weighted pointwise mutual information (PMI)-based approach to measure the HOP indices among nodes in a network; b) leveraging an iterative reconstruction scheme to encode the captured HOP into the network; and c) introducing a symmetry and graph-regularized NMF algorithm to detect communities accurately. Extensive empirical studies on eight real-world networks demonstrate that an HSGN-based community detector significantly outperforms both benchmark and state-of-the-art community detectors in providing highly-accurate community detection results.
Trie-based ranking of quantum many-body states

Ranking bit patterns—ﬁnding the index of a given pattern in an ordered sequence—is a major bottleneck scaling up numerical quantum many-body calculations, as fermionic and hard-core bosonic states translate naturally to bit patterns. Traditionally, ranking is done by bisectioning search, which has poor cache performance on modern machines. We instead propose to use tries (preﬁx trees), thereby achieving a two- to ten-fold speed-up in numerical experiments with only moderate memory overhead. For the important problem of ranking permutations, the corresponding tries can be compressed. These compressed “staggered” lookups allow for a considerable speed-up while retaining the memory requirements of prior algorithms based on the combinatorial number system.
Contrastive Conditional Neural Processes

Conditional Neural Processes (CNPs) bridge neural net-works with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-i.i. d function instantiations, CNPs are jointly optimized for in-instantiation observation prediction and cross-instantiation meta-representation adaptation within a generative reconstruction pipeline. There can be a challenge in tying together such two targets when the distri-bution of function observations scales to high-dimensional and noisy spaces. Instead, noise contrastive estimation might be able to provide more robust representations by learning distributional matching objectives to combat such inherent limitation of generative models. In light of this, we propose to equip CNPs by 1) aligning prediction with en-coded ground-truth observation, and 2) decoupling meta-representation adaptation from generative reconstruction. Specifically, two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning (TCL) and cross-instantiation function contrastive learning (FCL), to facilitate local predictive alignment and global function consistency, respectively. We empirically show that TCL captures high-level abstraction of obser-vations, whereas FCL helps identify underlying functions, which in turn provides more efficient representations. Our model outperforms other CNPs variants when evaluating function distribution reconstruction and parameter identifi-cation across 1D, 2D and high-dimensional time-series.
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows

To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution. Using isometries separates manifold learning and density estimation and enables training of both parts to high accuracy. Thus, model selection and tuning are simplified compared to existing injective normalizing flows. Applied to data sets on (approximately) flat manifolds, the combined approach generates high-quality data.
Complete affine manifolds with Anosov holonomy groups

Let $N$ be a complete affine manifold $A^n/\Gamma$ of dimension $n$ where $\Gamma$ is an affine transformation group acting on the complete affine space $A^n$ and $K(\Gamma, 1)$ is realized as a finite CW-complex. $N$ has a $k$-partially hyperbolic holonomy group if the tangent bundle pulled over the unit tangent bundle over a sufficiently large compact part splits into expanding, neutral, and contracting subbundles along the geodesic flow where the expanding and contracting subbundles have the dimension $k$, $k<n/2$. In part 1, we will show that the complete affine $n$-manifold has a $P$-Anosov linear holonomy group for a parabolic subgroup $P$ of $\mathrm{GL}(n, \mathrm{R})$ if and only if it has a partially hyperbolic linear holonomy group. This was never been done over the full general linear group prior to this paper. The part 1 will be where the techniques are mostly representation theoretical. In part 2, we show that if the holonomy group is partially hyperbolic of index $k$, $k<n/2$, then $\mathrm{cd}(\Gamma) \leq n-k$. Moreover, if a finitely-presented affine group $\Gamma$ acts on $A^n$ properly discontinuously and freely with the $k$-Anosov linear group for $k \leq n/2$, then $\mathrm{cd}(\Gamma) \leq n-k$. Also, there exists a compact collection of $n-k$-dimensional affine subspaces where $\Gamma$ acts on. The techniques here are mostly from coarse geometry. Canary and Tsouvalis proved the same result before using the powerful method of Bestvina and Mess for word hyperbolic groups; however, our techniques are different in that our method is to project the holonomy cover to a stable affine subspace, and we plan to generalize to relative Anosov groups.
Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering

This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (CS) theory to obtain a robust controller for general nonlinear systems. The proposed Covariance-Controlled Model Predictive Path Integral (CC-MPPI) controller addresses the performance degradation observed in some MPPI implementations owing to unexpected disturbances and uncertainties. Namely, in cases where the environment changes too fast or the simulated dynamics during the MPPI rollouts do not capture the noise and uncertainty in the actual dynamics, the baseline MPPI implementation may lead to divergence. The proposed CC-MPPI controller avoids divergence by controlling the dispersion of the rollout trajectories at the end of the prediction horizon. Furthermore, the CC-MPPI has adjustable trajectory sampling distributions that can be changed according to the environment to achieve efficient sampling. Numerical examples using a ground vehicle navigating in challenging environments demonstrate the proposed approach.
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT

Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical. It has applications in several use cases like Question-Answering, Natural Language Generation, Neural Machine Translation, where grammatical correctness is crucial. In this paper we aim to understand the decision-making process of BERT (Devlin et al., 2019) in distinguishing between Linguistically Acceptable sentences (LA) and Linguistically Unacceptable sentences (LUA). We leverage Layer Integrated Gradients Attribution Scores (LIG) to explain the Linguistic Acceptability criteria that are learnt by BERT on the Corpus of Linguistic Acceptability (CoLA) (Warstadt et al., 2018) benchmark dataset. Our experiments on 5 categories of sentences lead to the following interesting findings: 1) LIG for LA are significantly smaller in comparison to LUA, 2) There are specific subtrees of the Constituency Parse Tree (CPT) for LA and LUA which contribute larger LIG, 3) Across the different categories of sentences we observed around 88% to 100% of the Correctly classified sentences had positive LIG, indicating a strong positive relationship to the prediction confidence of the model, and 4) Around 43% of the Misclassified sentences had negative LIG, which we believe can become correctly classified sentences if the LIG are parameterized in the loss function of the model.
A Novel Blind Source Separation Framework Towards Maximum Signal-To-Interference Ratio

This letter proposes a new blind source separation (BSS) framework termed minimum variance independent component analysis (MVICA), which can potentially achieve the maximum output signal-to-interference ratio (SIR) while also allowing more flexibility in real implementations. The statistical independence assumption has been the foundation of the most dominant BSS techniques in recent decades. However, this assumption does not always hold true and the accurate probabilistic modeling of source is inherently difficult. To overcome these limitations and improve the separation performance, the MVICA framework is rigorously derived by optimizing the design of these independence-based BSS algorithms with the maximum SIR criterion. A deep neural network-supported implementation of MVICA is subsequently described. Experimental results under various conditions show the superiority of MVICA over the state-of-the-art BSS algorithms, in terms of not only SIR but also signal-to-distortion ratio and automatic speech recognition rate.
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning. This is especially relevant in the context of online learning platforms. In this paper, we present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms (n=199 participants): A MOOC platform following a traditional model delivering content using lecture videos and multiple-choice quizzes, and the Korbit learning platform providing a highly personalized, active and practical learning experience. We observe a huge and statistically significant increase in the learning outcomes, with students on the Korbit platform providing full feedback resulting in higher course completion rates and achieving learning gains 2 to 2.5 times higher than both students on the MOOC platform and students in a control group who don't receive personalized feedback on the Korbit platform. The results demonstrate the tremendous impact that can be achieved with a personalized, active learning AI-powered system. Making this technology and learning experience available to millions of learners around the world will represent a significant leap forward towards the democratization of education.
A Method of Automatic Pedestrian Counting in Metro Station Based on Machine Vision

Now it is difficult to use the method of automatic counting pedestrians in metro station based on machine vision because of its low precision and poor stability.To solve this problem,we proposed a novel method of automatic counting pedestrians common in metro station.Our method achieved the function of pedestrian recognition suitable for short-shot and non-direct downward view for the general layout of cameras in metro stations.First,this method collects the Haar features from pedestrian head samples and detects the pedestrian head using strong classifier trained by AdaBoost algorithm.Second,considering different locations in image,this method removed the mistake detection which is too big or too small using bilinear interpolation algorithm according to the principle of put quality before quantity.Finally,according to the detections of pedestrian head,we designed the pedestrian tracking algorithm which includes pedestrian object clustering,break frame-sequence merging and pedestrian leaving.The experimental result shows that using our method in metro station to count people automatically is very effective.Its accuracy reached 90%,so it can be applied in practice.
Oriented Diameter of Planar Triangulations

The diameter of an undirected or a directed graph is defined to be the maximum shortest path distance over all pairs of vertices in the graph. Given an undirected graph $G$, we examine the problem of assigning directions to each edge of $G$ such that the diameter of the resulting oriented graph is minimized. The minimum diameter over all strongly connected orientations is called the oriented diameter of $G$. The problem of determining the oriented diameter of a graph is known to be NP-hard, but the time-complexity question is open for planar graphs. In this paper we compute the exact value of the oriented diameter for triangular grid graphs. We then prove an $n/3$ lower bound and an $n/2+O(\sqrt{n})$ upper bound on the oriented diameter of planar triangulations. It is known that given a planar graph $G$ with bounded treewidth and a fixed positive integer $k$, one can determine in linear time whether the oriented diameter of $G$ is at most $k$. In contrast, we consider a weighted version of the oriented diameter problem and show it to be is weakly NP-complete for planar graphs with bounded pathwidth.
A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text

Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are highly integrated with deep learning techniques and thus require massive annotated linguistic data. This study presents an engineering framework of medical entity recognition, relation extraction and attribute extraction, which are unified in annotation, modeling and evaluation. Specifically, the annotation scheme is comprehensive, and compatible between tasks, especially for the medical relations. The resulted annotated corpus includes 1,200 full medical records (or 18,039 broken-down documents), and achieves inter-annotator agreements (IAAs) of 94.53%, 73.73% and 91.98% F 1 scores for the three tasks. Three task-specific neural network models are developed within a shared structure, and enhanced by SOTA NLP techniques, i.e., pre-trained language models. Experimental results show that the system can retrieve medical entities, relations and attributes with F 1 scores of 93.47%, 67.14% and 90.89%, respectively. This study, in addition to our publicly released annotation scheme and code, provides solid and practical engineering experience of developing an integrated medical information extraction system.