Miracle-dz/UniFAR-FTUs huggingface dataset:
Inertial Gait-based Person Authentication Using Siamese Networks

Gait-based person authentication using smartphones and wearable devices has attracted a lot of attention in recent years due to its unobtrusive nature and the widespread inertial sensors embedded in such devices. While traditional methods of authentication relied on hand-crafted feature extraction tailored for specific conditions, recent deep learning algorithms have been used to automatically discover hidden features and patterns in the raw data. However, current deep learning methods of gait authentication are not portable. They train models to classify a specific user as legitimate and the rest as imposters without being able to change that user to a different one without retraining the model from scratch. Furthermore, they are impractical, since they require a large volume of data for each class to train properly. In this work, we propose using a Siamese Network-based model which (1) Can be trained using only a few samples per subject. (2) Automatically extract features from raw inertial data. (3) Once trained, can authenticate any new user given only one gait instance of that new user. We apply our model to three publicly available datasets and show that it can achieve very promising results. For example, our model achieves 3.42% EER on the world's largest inertial gait dataset (OU-ISIR) after training on raw data of 592 subjects and testing on the raw data of completely different 78 subjects, where each subject provided an average of 5.9 seconds of gait data only. We show some performance analysis on different architectural variations. and finally, we show the ability of our model to generalize to new unseen datasets using transfer learning.
Lugus Lucorum Detection by Improved Convolutional Neural Network

To improve the detection accuracy of Lugus Lucorum in natural environment, a Lugus Lucorum detection method is proposed based on improved convolution neural network (CNN). First, based on YOLO-v3, a new training data set labeling strategy is designed to make the target have a higher effective pixel occupation rate in Ground Truth. Two DenseBlock structures are integrated to effectively alleviate gradient disappearance, reduce the number of parameters, and save computational power. Feature reuse can also play an anti-overfitting role. The proposed method is validated on the dataset of Lugus Lucorum images. The experiment results show that the method can effectively detect the Lugus Lucorum.
Improvement of Efficiency in Evolutionary Pruning

Filter pruning has been widely applied on compression of deep convolutional neural networks. Especially, the filter pruning approach using evolutionary algorithms has exhibited powerful capabilities of automatic model compression and global optimal solution search. However, the exponential increasing of search space brought by the extremely large scale of deep networks makes it difficult to achieve a well-compressed model with limited time and energy. In this paper, we design an effective method aiming at improving the efficiency in evolutionary pruning, called IEEPruner. Multiple pruning criteria are first utilized to initialize an efficient search space, and then a novel encoding scheme is proposed to further reduce the search space. After that, we formulate the search of the optimal pruned structure as a multiobjective optimization problem, and integrate a multiobjective evolutionary algorithm to achieve a good balance between model size and accuracy in an automatic manner. Thus, we automatically search for the optimal pruned model in this search space. Extensive experiments on widely used convolutional neural networks including VGGNet, ResNet, and LeNet have demonstrated the efficiency of the proposed evolutionary pruning method.
Diversified Point Cloud Classification Using Personalized Federated Learning

As a comprehensive and unstructured data source describing 3 dimensional (3D) objects, point cloud data has attracted wide interest from researchers. Neural networks (NNs) provide flexible, end-to-end solutions to point cloud processing tasks. Yet, one of the unavoidable problems in training the NNs is the difficulty in collecting and labeling high-quality point cloud data. Existing point cloud datasets are often collected at different scales and densities. Directly combining highly heterogeneous datasets might violate data protection regulations, but also lead to degradation of model performance. In our experiments, naive horizontal federated learning has been applied to alleviate the data privacy concern. However, when the datasets are not identically distributed, the federated model's performance falls far behind the state-of-the-art of centralized models. This paper investigates the union of highly heterogeneous point cloud datasets for the object classification task. In the proposed framework, the federated model is split into shared and personalized modules. The former improves generalizability by bringing multiple data sources into play, while the latter adapts local distribution and avoids convergence at false optima. Experiments show that the proposed framework FedPCN achieves 35.1% higher accuracy than FedAvg. At local test samples, FedPCN also outperforms the locally trained model by 0.5%.
A Proposal to Ensure Social Distancing with Deep Learning-based Object Detection

Social distancing is becoming really important in last month as a vehicle to limit the COVID-19 Coronavirus pandemic. Generally speaking, it is effective to control the spread of contagious diseases. In this context there is the need to monitor social distancing violations: for this reason in this paper we propose a social distancing detector able to count the violations by analysing video streams. Preliminary results show that the proposed method can be employed to guarantee social distancing. Moreover we discuss several suggestions aimed to improve the following proposal.
Network Mechanism Supporting Long-Distance-Dependencies

Sequential behaviors such as language or bird songs are structured in time. This structure relies on the notion of long-distance-dependencies: transitions between words depend on the identity of words produced in the past. Here we propose a network mechanism supporting such dependencies. To do so we trained artificial neural networks to produce a minimal set of sequences exhibiting long-distance-dependencies. By reverse-engineering the trained networks we found this to rely on two superposing neural sequences, one responsible for the production of the motor sequence and another one encoding a contextual memory. We show how these two sequences are supported by neural activity and network connectivity and how they interact with each other to decide on transitions between words. We discuss similarities between the neural activity of our artificial neural networks and neural correlates of long-distance-dependencies that have recently been exposed in songbirds.
Modelling studies of HOM and its contributions to growth of new particles:comparison of boreal forest in Finland and polluted environment in China

Abstract. Highly oxygenated multifunctional compounds (HOM) play a key role in the growth of new particles during new particle formation (NPF) events, but their quantitative roles in different environments of the globe haven’t been well studied yet. In this study, the HOM concentrations and NPF events at a remote boreal forest site in Finland (SMEAR II) and a sub-urban site in the polluted eastern China (SORPES) were simulated with the MALTE-BOX model, and their roles in NPF at the two distinctly different environments are discussed. We found that sulfuric acid and HOM organonitrate concentrations in the gas phase are significantly higher but other HOM monomers and dimers from monoterpene oxidation are lower at SORPES compared to SMEAR II. The model can simulate the NPF events at SMEAR II with a good agreement but underestimate the growth of new particles at SORPES, indicating a dominant role of sulfuric acid and HOM originated from oxidation of aromatics in the polluted environment. This study highlight the needs of molecular-scale measurements in improving the understanding of NPF mechanisms in the polluted areas like eastern China.
Review of Reactions and Molecular Mechanisms in Cellulose Pyrolysis

: Cellulose, which is a polymer of β -1 → 4 linked D -glucose units, comprises about half the components of lignocellulosic biomasses. A better understanding of the chemistry involved in cellulose pyrolysis provides valuable insights into the development of efficient pyrolysis-based conversion technologies of biomass into biofuels, biochemicals and biomaterials. This review focuses on the reactions and molecular mechanisms that determine the reactivity and product selectivity in pyrolysis and the related conversion technologies. This information is useful for understanding the processing technologies conducted at high temperatures, such as wood drying and the production of cellulose-plastic composite materials. Because the cellulose pyrolysis behavior changes drastically in the temperature range of 300–350 °C, this review is divided into low- and high-temperature regimes. The intermediates produced from cellulose pyrolysis are further converted into various gas, liquid or solid products. Hence, these processes are addressed as primary pyrolysis and secondary reactions in this review. The roles of the crystalline cellulose in pyrolysis are noted.
Intragroup sparsity for efficient inference

This work studies intragroup sparsity, a fine-grained structural constraint on network weight parameters. It eliminates the computational inefficiency of fine-grained sparsity due to irregular dataflow, while at the same time achieving high inference accuracy. We present theoretical analysis on how weight group sizes affect sparsification error, and on how the performance of pruned networks changes with sparsity level. Further, we analyze inference-time I/O cost of two different strategies for achieving intragroup sparsity and how the choice of strategies affect I/O cost under mild assumptions on accelerator architecture. Moreover, we present a novel training algorithm that yield models of improved accuracies over the standard training approach under the intragroup sparsity constraint.
Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks

Spiking neural networks offer the potential to drastically reduce energy consumption in edge devices. Unfortunately they are overshadowed by today's common analog neural networks, whose superior backpropagation-based learning algorithms frequently demonstrate superhuman performance on different tasks. The best accuracies in spiking networks are achieved by training analog networks and converting them. Still, during runtime many simulation time steps are needed until they converge. To improve the simulation time we evaluate two inference optimization algorithms and propose an additional method for error minimization. We assess them on Residual Networks of different sizes, up to ResNet101. The combination of all three is evaluated on a large scale with a RetinaNet on the COCO dataset. Our experiments show that all optimization algorithms combined can speed up the inference process by a factor of ten. Additionally, the accuracy loss between the original and the converted network is less than half a percent, which is the lowest on a complex dataset reported to date.
Influence of optically active defects on thermal conductivity of polycrystalline diamond

We systematically studied the influence of optically active defects on thermal conductivity for polycrystalline diamonds (PCDs) with different colour, crystalline quality and impurity concentrations. The thermal conductivities of PCDs on the growth (top) and nucleation (bottom) surfaces were characterized with 3 ω technique. It is found that the bottom surface shows lower thermal conductivity as compared to the top surface. This could be due to the higher defect density in the bottom surface. Defects analyzed includes non-diamond carbon phase, C–H stretching vibration, Si vacancy, and substitutional nitrogen (Ns 0 ). Our results suggest that, for the top surface, the heat transport is mainly controlled by the concentration of Ns 0 . For the bottom surface, non-diamond carbon phase, Si vacancy, C–H stretch and Ns 0 defects all lead to an obvious reduction in the thermal conductivity. Most importantly, we derived a well fitted equation that estimates the thermal conductivity by optical transmittance, and the equation was demonstrated to be valid at any wavelength in visible region.
Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

This paper investigates the decision-making characteristics of the driver in the overtaking on the highway road. For the research purpose, a novel method was proposed by introducing a logistic regression model accompanied by the statistical test technique, which does not require prior knowledge about the explanatory variables. This study hypothesizes that the driver’s gazing behavior is crucial for the decision-making process in driving and hence, the line-of-sight information was introduced to estimate driver’s gazing behavior in the model of driver’s decision specifically for reproducing the overtaking driving behavior accurately. Consequently, the proposed model realized a high describability on the decision of the driver when performing the overtaking driving task, which is one of the significant advancements of the present study with respect to the past similar studies. This study integrates the perspectives of intelligent vehicle design and cognitive science by revealing which factor the driver pays attention to in a changeable driving environment due to various observable factors. In experiments based on the driving simulator with six human subjects, the overtaking behavior was successfully estimated by specifying a set of variables to reconstruct the driver’s behavior and then the proposed model provided a minimum set of necessary variables accompanied with key coefficients. In conclusion, the proposed approach based on a simple logistic regression model demonstrated driving behaviors with an accurate estimation of the driver’s intention without the need for prior knowledge, and it may contribute to higher describability for various driving actions in a dynamic environment.
Biomaterials of Poly(vinyl alcohol) and Natural Polymers

ABSTRACT Polysaccharides and proteins are abundantly found in nature and are highly recommended for developing eco-friendly materials due to their special properties (biodegradability, biocompatibility, non-toxicity, low cost, etc.). However, they sometimes fail to meet specific requirements due to poor mechanical and physical properties. Poly(vinyl alcohol) (PVA) is one of the promising synthetic polymers with superior properties that can be blended with natural polymers for obtaining novel biomaterials with improved performances. This review addresses recent advance in PVA/polysaccharides and PVA/proteins biocomposites design and fabrication, mainly for the past two decades.
Crystal Structure of DMF-Intermediate Phases Uncovers the Link Between CH3NH3PbI3 Morphology and Precursor Stoichiometry

We found for the first time a new origin of selection of perovskite crystallization pathways from DMF solutions containing MAI and PbI2 to present here a comprehensive study of a full set of essential intermediate phases determining the perovskite’s morphology. For all three discovered structurally different intermediate phases forming at a given precursor ratio, we refined their crystal structures by synchrotron X-ray radiation and investigated dynamics and phase assemblage in the course of decomposition. As a result, we revealed a clear correlation between the composition of the intermediate phases, peculiarities of their crystal structure, and the morphology of the final perovskite films. Using the DFT method we calculated formation enthalpies of these intermediate phases and explained the preferential precipitation of DMSO-adduct rather than DMF-adduct in an antisolvent approach. This finding opens up a possibility of design-on-demand of perovskite materials using simple soft chemistry approaches.
Interpretable Decision-Making for Autonomous Vehicles at Highway On-Ramps With Latent Space Reinforcement Learning

This paper presents a latent space reinforcement learning method for interpretable decision-making of autonomous vehicles at highway on-ramps. This method is based on the latent model and the combination model of the hidden Markov model and Gaussian mixture regression (HMM-GMR). It is difficult for the traditional decision-making method to understand the environment because its input is high-dimensional and lacks an understanding of the task. By utilizing the HMM-GMR model, we can obtain the interpretable state providing semantic information and environment understanding. A framework is proposed to unify representation learning with the deep reinforcement learning (DRL) approach, in which the latent model is used to reduce the dimension of interpretable state by extracting underlying task-relevant information. Experimental results are presented and the results show the right balance between driving safety and efficiency in the challenging scenarios of highway on-ramps merging.
Spatial and Temporal Aware Graph Convolutional Network for Flood Forecasting

Intelligent flood forecasting systems provide an effective means to forecast flood disaster. Accurate flood flow value prediction is a huge challenge since it's influenced by both spatial and temporal relationship among flood factors. Popular deep learning structures like Long Short-Term Memory (LSTM) network lacks abilities of modeling the spatial correlations of hydrological data, thus cannot yield satisfactory prediction results. Moreover, not all the temporal information is always valuable for flood forecasting. In this paper, we proposed a novel spatial and temporal aware Graph Convolution Network (ST-GCN) for flood prediction, which is capable to extract spatial-temporal information from raw flood data. Moreover, a temporal attention mechanism is introduced to weight the importance of different time steps, thus involving global temporal information to improve flood prediction accuracy. Compared with the existing methods, results on two self-collected datasets show that ST-GCN greatly improves the prediction performance.
ICE-GAN: Identity-Aware and Capsule-Enhanced GAN with Graph-Based Reasoning for Micro-Expression Recognition and Synthesis

Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition. The short detection window, the subtle facial muscle movements, and the limited training samples make micro-expression recognition challenging. To this end, we propose a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network with graph-based reasoning (ICE-GAN), introducing micro-expression synthesis as an auxiliary task to assist recognition. The generator produces synthetic faces with controllable micro-expressions and identity-aware features, whose long-ranged dependencies are captured through the graph reasoning module (GRM), and the discriminator detects the image authenticity and expression classes. Our ICE-GAN was evaluated on Micro-Expression Grand Challenge 2019 (MEGC2019) with a significant improvement (12.9%) over the winner and surpassed other state-of-the-art methods.
Deep Learning-Assisted Energy-Efficient Task Offloading in Vehicular Edge Computing Systems

In this paper, we study an energy-efficient computation offloading for vehicular edge computing systems, where multiple roadside units assist vehicular users to offload computation tasks to edge servers. Our goal is to minimize the users’ energy consumption by optimizing user association, data partition, transmit power, and computation resources, subject to the constraints of partial tasks offloading, user latency, maximum transmit power, outage performance, and computation capacity of edge servers. We utilize deep learning for user association to avoid combinatorial complexity, and develop an efficient optimization algorithm to optimize other variables. The resulting algorithm has scalable complexity with convergence guarantee, as confirmed by our theoretical analysis. Simulation results demonstrate that the introduced resource allocation algorithm can significantly reduce the total energy consumption of users.
Boosting Emotion Recognition in Context using Non-target Subject Information

Recognizing emotions in context from an image has become an emerging topic in the recent past due to its high practical demand in various domains. Performance of the existing works is limited as they predominantly utilized the entire image and the primary human subject as major cues to recognize the emotions from images. However, in addition to the primary human subject, other human subjects in a scene also play a vital role in determining the image's overall emotional state. In this work, a novel visual feature type is introduced based on the features extracted from other human subjects in order to enhance emotion recognition performance. A novel deep learning based hybrid framework is also proposed to effectively integrate the proposed feature with the visual cues from the entire image and primary human subject. The extensive experiments carried out on a subset of the Emotic dataset reveal that the newly introduced visual feature type contributes to the overall emotional state of the scene. Furthermore, the proposed framework achieved 45.11% average accuracy for discrete emotion categories, which is a significant improvement in the emotion recognition performance compared to existing techniques that use emotion information from the entire image and primary human subject.
EEG emotion Enhancement using Task-specific Domain Adversarial Neural Network

Electroencephalogram (EEG) signal has been widely applied in detecting human emotion. Individual differences limit the generalization in cross-subject classification since the release of emotion will be definitely different across persons even with the same emotion stimuli. Previous research utilizes domain adaptation to solve this problem in a leave-one-subject-out training that is learning common emotion-related features from many subjects and testing on a new subject, which requires abundant labeled data. This paper proposed a novel one-to-one domain adaptation method, the Task-specific Domain Adversarial Neural Network (T-DANN) which transfers knowledge from either one subject to predict on another subject or knowledge from one phase to predict on another phase within the same subject. Therefore, T-DANN is more flexible and requires much less data during training. T-DANN is an adversarial training method which adapts the conditional distribution between domains and adapts classification boundaries between classes simultaneously. Compared with data from different subjects, phase data from the same subject has much deeper correlation thus enhances the prediction of emotion in new phase. We evaluated our method on EEG emotion benchmark dataset SEED. The experiments showed that our proposed method outperformed other baseline methods in cross-subject adaptation. By cross-phase adaptation, our method achieved accuracy that approximately 3% lower than the state-of-the-art method but only used 1/14 labeled data, indicating the priority of our proposed method in real-time application.