Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Reliability of Latent Dirichlet Allocation by Assessing Its Stability Using Clustering Techniques on Replicated Runs

For organizing large text corpora topic modeling provides useful tools. A widely used method is Latent Dirichlet Allocation (LDA), a generative probabilistic model which models single texts in a collection of texts as mixtures of latent topics. The assignments of words to topics rely on initial values such that generally the outcome of LDA is not fully reproducible. In addition, the reassignment via Gibbs Sampling is based on conditional distributions, leading to different results in replicated runs on the same text data. This fact is often neglected in everyday practice. We aim to improve the reliability of LDA results. Therefore, we study the stability of LDA by comparing assignments from replicated runs. We propose to quantify the similarity of two generated topics by a modified Jaccard coefficient. Using such similarities, topics can be clustered. A new pruning algorithm for hierarchical clustering results based on the idea that two LDA runs create pairs of similar topics is proposed. This approach leads to the new measure S-CLOP ({\bf S}imilarity of multiple sets by {\bf C}lustering with {\bf LO}cal {\bf P}runing) for quantifying the stability of LDA models. We discuss some characteristics of this measure and illustrate it with an application to real data consisting of newspaper articles from \textit{USA Today}. Our results show that the measure S-CLOP is useful for assessing the stability of LDA models or any other topic modeling procedure that characterize its topics by word distributions. Based on the newly proposed measure for LDA stability, we propose a method to increase the reliability and hence to improve the reproducibility of empirical findings based on topic modeling. This increase in reliability is obtained by running the LDA several times and taking as prototype the most representative run, that is the LDA run with highest average similarity to all other runs.
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection

Commonly, the senses of vision and hearing decrease as the age increase of a human. The most affected organs are hearing and vision due to aging. Elder people consequence a variety of problems while living Activities of Daily Living (ADL) for the reason of age, sense, loneliness and cognitive changes. These cause the risk to ADL which leads to several falls. Getting real life fall data is a difficult process and are not available whereas simulated falls become ubiquitous to evaluate the proposed methodologies. From the literature review, it is investigated that most of the researchers used raw and energy features (time domain features) of the signal data as those are most discriminating. However, in real life situations fall signal may be noisy than the current simulated data. Hence the result using raw feature may dramatically changes when using in a real life scenario. This research is using frequency domain Fourier coefficient features to differentiate various human activities of daily life. The feature vector constructed in this article using that the Fast Fourier Transform are robust to noise, level of detail representation and rotation invariant. In this research, two different supervised classifiers kNN and SVM are used for evaluating the method. Two standard publicly available datasets are used for benchmark analysis. This research shows more discriminating results are obtained applying kNN classifier than the SVM classifier. Various standard measure including Standard Accuracy (SA), Macro Average Accuracy (MAA), Sensitivity (SE) and Specificity (SP) has been accounted. In all cases, the proposed method outperforms energy features whereas competitive results are shown with raw features. It is also noticed that the proposed method performs better than the recently risen deep learning approach in which data augmentation method were not used.
High-dimensional ellipsoids converge to Gaussian spaces

We prove the convergence of (solid) ellipsoids to a Gaussian space in Gromov's concentration/weak topology as the dimension diverges to infinity. This gives the first discovered example of an irreducible nontrivial convergent sequence in the concentration topology, where 'irreducible nontrivial' roughly means to be not constructed from Levy families nor box convergent sequences.
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

The issue of ethnic bias has proven to affect the performance of face recognition in previous works, while it still remains to be vacant in face anti-spoofing. Therefore, in order to study the ethnic bias for face anti-spoofing, we introduce the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1,607 subjects, and 2D plus 3D attack types. Five protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. As our knowledge, CASIA-SURF CeFA is the first dataset including explicit ethnic labels in current released datasets. Then, we propose a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias, which employs a partially shared fusion strategy to learn complementary information from multiple modalities. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability for other existing datasets, i.e., CASIA-SURF, OULU-NPU and SiW datasets. The dataset is available at https://sites.google. com/qq.com/face-anti-spoofing/welcome/challengecvpr2020? authuser=0.
Uncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems

Understanding the fundamentals of human reasoning is central to the development of any system built to closely interact with humans. Cognitive science pursues the goal of modeling human-like intelligence from a theory-driven perspective with a strong focus on explainability. Syllogistic reasoning as one of the core domains of human reasoning research has seen a surge of computational models being developed over the last years. However, recent analyses of models' predictive performances revealed a stagnation in improvement. We believe that most of the problems encountered in cognitive science are not due to the specific models that have been developed but can be traced back to the peculiarities of behavioral data instead. Therefore, we investigate potential data-related reasons for the problems in human reasoning research by comparing model performances on human and artificially generated datasets. In particular, we apply collaborative filtering recommenders to investigate the adversarial effects of inconsistencies and noise in data and illustrate the potential for data-driven methods in a field of research predominantly concerned with gaining high-level theoretical insight into a domain. Our work (i) provides insight into the levels of noise to be expected from human responses in reasoning data, (ii) uncovers evidence for an upper-bound of performance that is close to being reached urging for an extension of the modeling task, and (iii) introduces the tools and presents initial results to pioneer a new paradigm for investigating and modeling reasoning focusing on predicting responses for individual human reasoners.
Grothendieck categories and their tensor product as filtered colimits.

We present two ways of recovering a Grothendieck category as a filtered colimit of small categories by means of the construction of the (2-)filtered (bi)colimit of categories from [9]. The first one, making use of the fact that Grothendieck categories are locally presentable, allows to recover a Grothendieck category as a filtered colimit of its subcategories of alpha-presentable objects, for alpha varying in the family of small regular cardinals. The second one, making use of the fact that Grothendieck categories are precisely the linear topoi, permits to recover a Grothendieck category as a filtered colimit of its linear site presentations. We then show that the tensor product of Grothendieck categories from [18] can be recovered as a filtered colimit of Kelly's alpha-cocomplete tensor product of the categories of alpha-presentable objects with alpha varying in the family of small regular cardinals. We use this construction to translate the functoriality, associativity and simmetry of Kelly's tensor product to the tensor product of Grothendieck categories.
Multiplicative Controller Fusion: A Hybrid Navigation Strategy For Deployment in Unknown Environments

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine tuning. The code for this project is made publicly available at https://sites.google. com/view/mcf-nav/home.
Precision Theory of Electroweak Interactions

As a part of the celebration of 50 years of the Standard Model of particle physics, I present a brief history of the precision theory of electroweak interactions. I emphasize in particular the theoretical preparations for the LEP program and the prediction of m_t and m_h from the electroweak precision data. [to appear in the proceedings of the SM@50 Symposium, Case Western Reserve University, June 1-4, 2018]
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation

Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state - Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. The HEMlets utilize three joint-heatmaps to represent the relative depth information of the end-joints for each skeletal body part. In our approach, a Convolutional Network (ConvNet) is first trained to predict HEMlets from the input image, followed by a volumetric joint-heatmap regression. We leverage on the integral operation to extract the joint locations from the volumetric heatmaps, guaranteeing end-to-end learning. Despite the simplicity of the network design, the quantitative comparisons show a significant performance improvement over the best-of-grade methods (e.g., 20 percent on Human3.6M). The proposed method naturally supports training with “in-the-wild” images, where only weakly-annotated relative depth information of skeletal joints is available. This further improves the generalization ability of our model, as validated by qualitative comparisons on outdoor images. Leveraging the strength of the HEMlets pose estimation, we further design and append a shallow yet effective network module to regress the SMPL parameters of the body pose and shape. We term the entire HEMlets-based human pose and shape recovery pipeline HEMlets PoSh. Extensive quantitative and qualitative experiments on the existing human body recovery benchmarks justify the state-of-the-art results obtained with our HEMlets PoSh approach.
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Social Web Data for Emergency Services

During the onset of a disaster event, filtering relevant information from the social web data is challenging due to its sparse availability and practical limitations in labeling datasets of an ongoing crisis. In this paper, we show that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events, as well as exploit additional web resources for training efficient information filtering models during an ongoing crisis. We present a novel method to classify relevant tweets during an ongoing crisis without seeing any new examples, using the publicly available dataset of TREC incident streams that provides labeled tweets with 4 relevant classes across 10 different crisis events. Additionally, our method addresses a crucial but missing component from current research in web science for crisis data filtering models: interpretability. Specifically, we first identify a standard single-task attention-based neural network architecture and then construct a customized multi-task architecture for the crisis domain: Multi-Task Domain Adversarial Attention Network. This model consists of dedicated attention layers for each task and a domain classifier for gradient reversal. Evaluation of domain adaptation for crisis events is performed by choosing a target event as the test set and training on the rest. Our results show that the multi-task model outperformed its single-task counterpart and also, training with additional web-resources showed further performance boost. Furthermore, we show that the attention layer can be used as a guide to explain the model predictions by showcasing the words in a tweet that are deemed important in the classification process. Our research aims to pave the way towards a fully unsupervised and interpretable domain adaptation of low-resource crisis web data to aid emergency responders quickly and effectively.
Microbiota and Other Preventive Strategies and Non-genetic Risk Factors in Parkinson’s Disease

The exact cause of Parkinson’s disease (PD), the second most prevalent neurodegenerative disease in modern societies, is still unknown. Many scientists point out that PD is caused by a complex interaction between different factors. Although the main risk factor is age, there are other influences, genetic and environmental, that individually or in combination may trigger neurodegenerative changes leading to PD. Nowadays, research remains focused on better understanding which environmental factors are related to the risk of developing PD and why. In line with the knowledge on evidence on exposures that prevent/delay PD onset or that impact on disease progression, the aims of this review were: (i) to comment on the non-genetic risk factors that mainly affect idiopathic PD; and (ii) to comment on seemingly reliable preventive interventions. We discuss both environmental factors that may affect the central nervous system (CNS) or the intestinal tract, and the likely mechanisms underlying noxious or protective actions. Knowledge on risk, protective factors, and mechanisms may help to envisage why nigral dopaminergic neurons are so vulnerable in PD and, eventually, to design new strategies for PD prevention and/or anti-PD therapy. This article reviews the variety of the known and suspected environmental factors, such as lifestyle, gut microbiota or pesticide exposition, and distinguishes between those that are harmful or beneficial for the PD acquisition or progression. In fact, the review covers one of the most novel players in the whole picture, and we address the role of microbiota on keeping a healthy CNS and/or on preventing the “side-effects” related to aging.
Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach

Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing use has assumed full knowledge of the safety barrier functions, however there are cases where the safe regions must be estimated online from sensor measurements. In these cases, the corresponding barrier function must be synthesized online. This paper describes a learning framework for estimating control barrier functions from sensor data. Doing so affords system operation in unknown state space regions without compromising safety. Here, a support vector machine classifier provides the barrier function specification as determined by sets of safe and unsafe states obtained from sensor measurements. Theoretical safety guarantees are provided. Experimental ROS-based simulation results for an omnidirectional robot equipped with LiDAR demonstrate safe operation.
FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow

Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation. In this paper, we present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation as well as the static background reconstructions. Our novelty is using optical flow residuals to highlight the dynamic semantics in the RGB-D point clouds and provide more accurate and efficient dynamic/static segmentation for camera tracking and background reconstruction. The dense reconstruction results on public datasets and real dynamic scenes indicate that the proposed approach achieved accurate and efficient performances in both dynamic and static environments compared to state-of-the-art approaches.
Theta- and Gamma-Band Activity Discriminates Face, Body and Object Perception

Face and body perception is mediated by configural mechanisms, which allow the perception of these stimuli as a whole, rather than the sum of individual parts. Indirect measures of configural processing in visual cognition are the face and body inversion effects (FIE and BIE), which refer to the drop in performance when these stimuli are perceived upside-down. Albeit FIE and BIE have been well characterized at the behavioral level, much still needs to be understood in terms of the neurophysiological correlates of these effects. Thus, in the current study, the brain’s electrical activity has been recorded by a 128 channel electroencephalogram (EEG) in 24 healthy participants while perceiving (upright and inverted) faces, bodies and houses. EEG data were analyzed in both the time domain (i.e., event-related potentials—ERPs) and the frequency domain [i.e., induced theta (5–7 Hz) and gamma (28–45 Hz) oscillations]. ERPs amplitude results showed increased N170 amplitude for inverted faces and bodies (compared to the same stimuli presented in canonical position) but not for houses. ERPs latency results showed delayed N170 components for inverted (vs. upright) faces, houses, but not bodies. Spectral analysis of induced oscillations indicated physiological FIE and BIE; that is decreased gamma-band synchronization over right occipito-temporal electrodes for inverted (vs. upright) faces, and increased bilateral frontoparietal theta-band synchronization for inverted (vs. upright) faces. Furthermore, increased left occipito-temporal and right frontal theta-band synchronization for upright (vs. inverted) bodies was found. Our findings, thus, demonstrate clear differences in the neurophysiological correlates of face and body perception. The neurophysiological FIE suggests disruption of feature binding processes (decrease in occipital gamma oscillations for inverted faces), together with enhanced feature-based attention (increase in frontoparietal theta oscillations for inverted faces). In contrast, the BIE may suggest that structural encoding for bodies is mediated by the first stages of configural processing (decrease in occipital theta oscillations for inverted bodies).
ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology

Current text visualization techniques typically provide overviews of document content and structure using intrinsic properties such as term frequencies, co-occurrences, and sentence structures. Such visualizations lack conceptual overviews incorporating domain-relevant knowledge, needed when examining documents such as research articles or technical reports. To address this shortcoming, we present ConceptScope, a technique that utilizes a domain ontology to represent the conceptual relationships in a document in the form of a Bubble Treemap visualization. Multiple coordinated views of document structure and concept hierarchy with text overviews further aid document analysis. ConceptScope facilitates exploration and comparison of single and multiple documents respectively. We demonstrate ConceptScope by visualizing research articles and transcripts of technical presentations in computer science. In a comparative study with DocuBurst, a popular document visualization tool, ConceptScope was found to be more informative in exploring and comparing domain-specific documents, but less so when it came to documents that spanned multiple disciplines.
Role of the Glucagon and Glucagon-like Peptides in Pancreatic Beta Cell and Cardiovascular Function

Glucagon is inappropriately elevated in diabetes, and inhibition of glucagon receptor (Gcgr) signaling is beneficial for glycemic control. However, the mechanism through which inhibition of Gcgr signaling leads to improved glycemic control is unknown. Additionally, Gcgr is expressed in the cardiovascular system; however the role of Gcgr signaling in the cardiovascular system is unexplored. In order to develop therapies targeting Gcgr signaling for the treatment of type 2 diabetes (T2D) it is vital to understand Gcgr’ s role in regulation of glucose homeostasis and cardiovascular function. In mice, genetic deletion of the glucagon receptor results in increased levels of the insulinotropic hormone, glucagon-like peptide-1 (GLP-1). I hypothesized Glp1r signaling contributed substantially to the improved glucose tolerance observed in Gcgr-/mice. I have generated and characterized the Gcgr and Glp-1 receptor double knockout (Gcgr-/-:Glp1r-/-) mice. My studies demonstrate that Glp1r substantially contributes to the delayed gastric emptying and improved intraperitoneal glucose tolerance in Gcgr-/mice, but it did not contribute to the improved oral glucose tolerance seen in the Gcgr-/mice. Interestingly, expression of nonclassical incretin receptors and sensitivity to their exogenous agonists were increased in Gcgr-/-:Glp1r-/mice, suggesting that in the absence of the classical incretin receptors the non-classical incretin receptors compensate to maintain the enteroinsular axis.