Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Adaptive Guidance: Modeling the Variety of User Behaviors in Continuous-Skill-Improving Experiences of Machine Operation Tasks

An adaptive guidance system that supports equipment operators requires a comprehensive model of task and user behavior that considers different skill and knowledge levels as well as diverse situations. In this study, we investigated the relationships between user behaviors and skill levels under operational conditions. We captured sixty samples of two sewing tasks performed by five operators using a head-mounted RGB-d camera and a static gaze tracker. We examined the operators' gaze and head movements, and hand interactions to essential regions (hotspots on machine surface) to determine behavioral differences among continuous skill improving experiences. We modeled the variety of user behaviors to an extensive task model with a two-step automatic approach, baseline model selection and experience integration. The experimental results indicate that some features, such as task execution time and user head movements, are good indexes for skill level and provide valuable information that can be applied to obtain an effective task model. Operators with varying knowledge and operating habits demonstrate different operational features, which can contribute to the design of user-specific guidance.
Opinion Dynamics under Voter and Majority Rule Models with Biased and Stubborn Agents

We study binary opinion dynamics in a fully connected network of interacting agents. The agents are assumed to interact according to one of the following rules: (1) Voter rule: An updating agent simply copies the opinion of another randomly sampled agent; (2) Majority rule: An updating agent samples multiple agents and adopts the majority opinion in the selected group. We focus on the scenario where the agents are biased towards one of the opinions called the {\em preferred opinion}. Using suitably constructed branching processes, we show that under both rules the mean time to reach consensus is $\Theta(\log N)$, where $N$ is the number of agents in the network. Furthermore, under the majority rule model, we show that consensus can be achieved on the preferred opinion with high probability even if it is initially the opinion of the minority. We also study the majority rule model when stubborn agents with fixed opinions are present. We find that the stationary distribution of opinions in the network in the large system limit using mean field techniques.
DA4AD: End-to-end Deep Attention Aware Features Aided Visual Localization for Autonomous Driving

We present a visual localization framework aided by novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either prone to unstable matching caused by severe appearance or lighting changes, or too scarce to deliver constant and robust localization results in challenging scenarios. In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural network. Furthermore, our learned feature descriptors are demonstrated to be competent to establish robust matches and therefore successfully estimate the optimal camera poses with high precision. We comprehensively validate the effectiveness of our method using a freshly collected dataset with high-quality ground truth trajectories and hardware synchronization between sensors. Results demonstrate that our method achieves a competitive localization accuracy when compared to the LiDAR-based localization solutions under various challenging circumstances, leading to a potential low-cost localization solution for autonomous driving.
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles

Modern machine learning models usually do not extrapolate well, i.e., they often have high prediction errors in the regions of sample space lying far from the training data. In high dimensional spaces detecting out-of-distribution points becomes a non-trivial problem. Thus, uncertainty estimation for model predictions becomes crucial for the successful application of machine learning models in many applications. In this work, we show that increasing the diversity of realizations sampled from a neural network with dropout helps to improve the quality of uncertainty estimation. In a series of experiments on simulated and real-world data, we demonstrate that diversification via determinantal point processes-based sampling allows achieving state-of-the-art results in uncertainty estimation for regression and classification tasks. Importantly, our approach does not require any modification to the models or training procedures, allowing for straightforward application to any deep learning model with dropout layers.
Effects of neuronal variability on phase synchronization of neural networks

An important idea in neural information processing is the communication-through-coherence hypothesis, according to which communication between two brain regions is effective only if they are phase-locked. Also of importance is neuronal variability, a phenomenon in which a single neuron's inter-firing times may be highly variable. In this work, we aim to connect these two ideas by studying the effects of that variability on the capability of neurons to reach phase synchronization. We simulate a network of modified-Hodgkin-Huxley-bursting neurons possessing a small-world topology. First, variability is shown to be correlated with the average degree of phase synchronization of the network. Next, restricting to spatial variability - which measures the deviation of firing times between all neurons in the network - we show that it is positively correlated to a behavior we call promiscuity, which is the tendency of neurons to to have their relative phases change with time. This relation is observed in all cases we tested, regardless of the degree of synchronization or the strength of the inter-neuronal coupling: high variability implies high promiscuity (low duration of phase-locking), even if the network as a whole is synchronized and the coupling is strong. We argue that spatial variability actually generates promiscuity. Therefore, we conclude that variability has a strong influence on both the degree and the manner in which neurons phase synchronize, which is another reason for its relevance in neural communication.
Distributional Robustness and Regularization in Reinforcement Learning

Distributionally Robust Optimization (DRO) has enabled to prove the equivalence between robustness and regularization in classification and regression, thus providing an analytical reason why regularization generalizes well in statistical learning. Although DRO's extension to sequential decision-making overcomes $\textit{external uncertainty}$ through the robust Markov Decision Process (MDP) setting, the resulting formulation is hard to solve, especially on large domains. On the other hand, existing regularization methods in reinforcement learning only address $\textit{internal uncertainty}$ due to stochasticity. Our study aims to facilitate robust reinforcement learning by establishing a dual relation between robust MDPs and regularization. We introduce Wasserstein distributionally robust MDPs and prove that they hold out-of-sample performance guarantees. Then, we introduce a new regularizer for empirical value functions and show that it lower bounds the Wasserstein distributionally robust value function. We extend the result to linear value function approximation for large state spaces. Our approach provides an alternative formulation of robustness with guaranteed finite-sample performance. Moreover, it suggests using regularization as a practical tool for dealing with $\textit{external uncertainty}$ in reinforcement learning methods.
Centrality Graph Convolutional Networks for Skeleton-based Action Recognition

The topological structure of skeleton data plays a significant role in human action recognition. Combining the topological structure with graph convolutional networks has achieved remarkable performance. In existing methods, modeling the topological structure of skeleton data only considered the connections between the joints and bones, and directly use physical information. However, there exists an unknown problem to investigate the key joints, bones and body parts in every human action. In this paper, we propose the centrality graph convolutional networks to uncover the overlooked topological information, and best take advantage of the information to distinguish key joints, bones, and body parts. A novel centrality graph convolutional network firstly highlights the effects of the key joints and bones to bring a definite improvement. Besides, the topological information of the skeleton sequence is explored and combined to further enhance the performance in a four-channel framework. Moreover, the reconstructed graph is implemented by the adaptive methods on the training process, which further yields improvements. Our model is validated by two large-scale datasets, NTU-RGB+D and Kinetics, and outperforms the state-of-the-art methods.
Adsorption property of fatty acid on iron surface with $\Sigma$3(111) grain boundary

Reducing the coefficient of boundary friction on steel surfaces is one of key technologies to improve the efficiency of machines such as automotive engines. It has been shown that the boundary friction on nanostructured steel surfaces in the sliding test using hydrocarbon lubricant molecules is smaller than the friction of normal steel surfaces. The main difference between the nanostructured and a normal surfaces is the density of grain boundaries and other surface defects. The surface defect can attract lubricant molecules and enhance lubricating film formation on metal surfaces. This can be one of the mechanisms that induce the friction reduction on the nanosructured steel surface. In this work, using first principles calculations, the adsorbability of a lubricant molecule, a fatty acid, on a defected iron surfaces has been studied. Adsorbability of the Fe(110) surface with symmetrical tilt {\Sigma}3(111) grain boundary was compared to that of the clean Fe(110) surface. As a result, we found that the molecule is adsorbed on sites close to the grain boundary more strongly (0.77 eV in average) than on the Fe surface without grain boundary.
Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar

Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.
Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages

This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages. Two approaches are considered. The first constructs four separate bilingual automatic speech recognisers (ASRs) corresponding to four different language pairs between which speakers switch frequently. The second uses a single, unified, five-lingual ASR system that represents all the languages (English, isiZulu, isiXhosa, Setswana and Sesotho). We evaluate the effectiveness of these two approaches when used to add additional data to our extremely sparse training sets. Results indicate that batch-wise semi-supervised training yields better results than a non-batch-wise approach. Furthermore, while the separate bilingual systems achieved better recognition performance than the unified system, they benefited more from pseudolabels generated by the five-lingual system than from those generated by the bilingual systems.
A Hybrid Approach for Tracking Individual Players in Broadcast Match Videos

Tracking people in a video sequence is a challenging task that has been approached from many perspectives. This task becomes even more complicated when the person to track is a player in a broadcasted sport event, the reasons being the existence of difficulties such as frequent camera movements or switches, total and partial occlusions between players, and blurry frames due to the codification algorithm of the video. This paper introduces a player tracking solution which is both fast and accurate. This allows to track a player precisely in real-time. The approach combines several models that are executed concurrently in a relatively modest hardware, and whose accuracy has been validated against hand-labeled broadcast video sequences. Regarding the accuracy, the tests show that the area under curve (AUC) of our approach is around 0.6, which is similar to generic state of the art solutions. As for performance, our proposal can process high definition videos (1920x1080 px) at 80 fps.
Beyond linear coupling in microwave optomechanics

We explore the nonlinear dynamics of a microwave optomechanical system consisting of a drumhead nano-electro-mechanical resonator (NEMS) capacitively coupled to a microwave cavity. Experiments are performed under a strong microwave Stokes pumping which triggers mechanical self-sustained oscillations. We analyze the results in the framework of an extended nonlinear optomechanical theory, and demonstrate that quadratic and cubic coupling terms in the opto-mechanical Hamiltonian have to be considered. Quantitative agreement with the measurements is obtained considering only genuine geometrical nonlinearities: no thermo-optical instabilities are observed, in contrast with laser-driven systems. Based on these results, we describe a method to quantify nonlinear properties of microwave optomechanical systems. This method is clearly a new technique available in the quantum electro-mechanics toolbox, where higher-order coupling terms are proposed as a new resource for specific quantum schemes like quantum non-demolition (QND) measurements. We also find that the motion imprints a wide comb of extremely narrow peaks in the microwave output field, which could also be exploited in specific microwave-based measurements, potentially limited only by the quantum noise of the optical {\it and} the mechanical fields for a ground-state cooled NEMS device.
Driving Skill Modeling Using Neural Networks for Performance-Based Haptic Assistance

This article addresses a data-driven framework, modeling expert driving skills for performance-based haptic assistance using neural networks (NNs). We have built a haptic driving training simulator to collect expert driving data and to provide proper haptic feedback. We establish an expert driving skill model by training NNs with the collected data. Then, the skill model is applied to the performance-based haptic assistance to provide optimized references of the steering/pedaling movements. We evaluate the skill model and its application to the performance-based haptic assistance in two user experiments. The results of the first experiment demonstrate that our skill model has appropriately captured experts’ steering/pedaling skills. The results of the second experiment show that our performance-based haptic assistance can help novice drivers perform steering as expert drivers, but cannot assist their pedaling performance.
Real-time Tone Mapping: A State of the Art Report

The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR's features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Also, display of HDR images/video content on an ordinary display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping operators, have been studied and proposed in the last few decades. In this state of the art report, we present a comprehensive survey of 50+ tone mapping algorithms that have been implemented on hardware for acceleration and real-time performance. These algorithms have been adapted or redesigned to make them hardware-friendly. All real-time application poses strict timing constraints which requires time exact processing of the algorithm. This design challenge require novel solution, and in this report we focus on these issues. In this we survey will discuss those tonemap algorithms which have been implemented on GPU [1-10], FPGA [11-41], and ASIC [42-53] in terms of their hardware specifications and performance. Output image quality is an important metric for tonemap algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the functionality of adapting the algorithm on hardware platform. We have compiled and studied all the metrics used in this survey [54-67]. Finally, in this report we demonstrate the link between hardware cost and image quality thereby illustrating the underlying trade-off which will be useful for the research community.
A Neural Network Based Framework for Archetypical Sound SYnthesis

This paper describes a preliminary approach to algorithmically reproduce the archetypical structure adopted by humans to classify sounds. In particular, we propose an approach to predict the human perceived chaos/order level in a sound and synthesize new timbres that present the desired amount of this feature. We adopted a Neural Network based method, in order to exploit its inner predisposition to model perceptive and abstract features. We finally discuss the obtained accuracy and possible implications in creative contexts.
Apelin Promotes ECM Synthesis by Enhancing Autophagy Flux via TFEB in Human Degenerative NP Cells under Oxidative Stress

Background Apelin alleviates oxidative stress which contributes to the development of aging. IVDD is a disease closely correlated to aging and oxidative stress which is known to be harmful to NP cells' matrix synthesis. The purpose of the present study was to investigate the role and underlying mechanism of Apelin in NP cells' matrix degradation under oxidative stress. Methods First, the mRNA and protein expressions of Apelin were checked by RT-PCR and Western blot in NP from normal and degenerative IVD to explore the relationship between Apelin and IVDD preliminarily. Then, H2O2 was used to mimic oxidative stress of NP cells. After treated with Apelin 13 and CQ, the GAG content was assessed by DMMB and the mRNA/protein expressions of NP matrix macromolecules (Collagen II and Aggrecan) and autophagy-related markers (LC3 and p62) were assessed by RT-PCR/Western blot. Finally, TFEB was knocked down by esiRNA-TFEB transfection and the nucleoprotein expression of TFEB and autophagy-related markers (LC3 and p62) were assessed by Western blot to discuss whether TFEB is involved in Apelin regulating autophagy flux in NP cells under oxidative stress. Results Our data first confirmed that the mRNA and protein expressions of Apelin were decreased with IVDD. Furthermore, Apelin increased GAG content of NP cells and mRNA/protein expressions of NP matrix macromolecules (Collagen II and Aggrecan) and promoted autophagic flux (LC3II/I increased and p62 decreased) under oxidative stress. Finally, after transfected with esiRNA-TFEB, Apelin cannot promote autophagic flux any more in human degenerative NP cells. Conclusion Our data indicated that Apelin promotes ECM synthesis by enhancing autophagy flux via TFEB in human degenerative NP cells under oxidative stress. This viewpoint may provide a new therapeutic idea for IVDD.
Thunder-Fire Moxibustion for Cervical Spondylosis: A Systematic Review and Meta-Analysis

Background Cervical spondylosis (CS) refers to the degenerative changes in the cervical spinal column, which affect the majority of middle-aged and elderly people. Thunder-fire moxibustion originated from thunder-fire miraculous needle, which has been applied widely for treating pain syndromes for thousands of years. The aim of our research is to provide evidence to assess the efficacy and safety of thunder-fire moxibustion in treating CS. Methods and analysis. Retrieved literature databases included Cochrane Library, MEDLINE, Web of Science, EBSCO, EBASE, Springer, PubMed, WFDP, CNKI, VIP, and CBM. The period of retrieval was from the establishment of the database to December 2018. Randomized controlled trials which compared thunder-fire moxibustion and other therapies in CS were included. The quality of inclusive trials was accessed though a Cochrane risk of bias tool. According to the test results of heterogeneity, a random effect model or fixed effect model was used to analyze the data. Results Meta-analysis was conducted for the total effective rate of thunder-fire moxibustion, traditional Chinese medicine syndrome score, pain score, satisfaction score, and score of the symptoms and functional rehabilitation of cervical vertigo. The analysis results were as follows: compared with other therapies, the efficacy of thunder-fire moxibustion was statistically significant, total effective rate increased (OR = 2.48; 95% CI [1.80, 3.41]; P < 0.00001), traditional Chinese medicine syndrome score decreased (SMD = −3.05; 95% CI[−4.18, −1.93]; P < 0.00001), traditional Chinese medicine syndrome score decreased (SMD = −3.05; 95% CI[−4.18, −1.93]; P < 0.00001), traditional Chinese medicine syndrome score decreased (SMD = −3.05; 95% CI[−4.18, −1.93]; P < 0.00001), traditional Chinese medicine syndrome score decreased (SMD = −3.05; 95% CI[−4.18, −1.93]; P < 0.00001), traditional Chinese medicine syndrome score decreased (SMD = −3.05; 95% CI[−4.18, −1.93]; Conclusion Based on the existing evidence, the curative effect and safety of thunder-fire moxibustion on CS were statistically significant. We should interpret the results scrupulously because of the low evidence level. Large-scale, high-quality, rigorous RCTs with long-term follow-up should be performed in the future.