Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Protein Structural Fingerprints under the Label-Free Supervision of Domain Knowledge

Finding homologous proteins is the indispensable first step in many protein biology studies. Thus, building highly efficient “search engines” for protein databases is a highly desired function in protein bioinformatics. As of August 2018, there are more than 140,000 protein structures in PDB, and this number is still increasing rapidly. Such a big number introduces a big challenge for scanning the whole structure database with high speeds and high sensitivities at the same time. Unfortunately, classic sequence alignment tools and pairwise structure alignment tools are either not sensitive enough to remote homologous proteins (with low sequence identities) or not fast enough for the task. Therefore, specifically designed computational methods are required for quickly scanning structure databases for homologous proteins. Here, we propose a novel ContactLib-DNN method to quickly scan structure databases for homologous proteins. The core idea is to build structure fingerprints for proteins, and to perform alignment-free comparisons with the fingerprints. Specifically, the fingerprints are low-dimensional vectors representing the contact groups within the proteins. Notably, the Cartesian distance between two fingerprint vectors well matches the RMSD between the two corresponding contact groups. This is done by using RMSD as the domain knowledge to supervise the deep neural network learning. When comparing to existing methods, ContactLib-DNN achieves the highest average AUROC of 0.959. Moreover, the best candidate found by ContactLib-DNN has a probability of 70.0% to be a true positive. This is a significant improvement over 56.2%, the best result produced by existing methods. GitHub: https://github.com/Chenyao2333/contactlib/ Index Terms homologous proteins, protein structures, remote protein homolog detection, alignment-free comparisons
Correlated Attention Networks for Multimodal Emotion Recognition

Emotion is a subjective, conscious experience when people face different kinds of stimuli. In this paper, we propose a new model, Correlated Attention Network (CAN), to make multimodal emotion recognition. Correlated Attention Network is an extension of attention based recurrent neural network with correlation calculated of different gated recurrent units to take correlation of EEG and eye movement extracted signals into attention mechanism and takes advantage of coordinated representation with complementary features. In experiments on 3 real world datasets, we find that our model can significantly contribute to higher emotion classification accuracy when higher correlation is acquired. Our experiment results indicate that the Correlated Attention Network model performs better than the state-of-the-art methods with a mean accuracy of of 94.03% on SEED dataset, 87.71% on SEED IV dataset, 88.51% and 85.62% for four classification and two dichotomies on DEAP dataset, respectively.
DCM-Based Gait Generation for Walking on Moving Support Surfaces

This paper presents a novel walking gait generator that allows the successful traversal of moving support surfaces such as conveyor belts, moving plates and escalators. The gait generator previews all steps of a complete gait sequence, while providing efficient matrix-vector based computations. The moving support surfaces are explicitly taken into account for the trajectory design. Multiple successful simulations of walking on different non-stationary ground surfaces prove the high quality of the proposed walking gait generator.
Unified High-Speed EMT Equivalent and Implementation Method of MMCs With Single-Port Submodules

This paper proposes a unified high-speed electromagnetic transient (EMT) equivalent and implementation method for modular multilevel converter (MMC) with arbitrary single-port submodule (SM) structures. The proposed method, interacts with the users by the incidence matrix, branch admittance matrix and the column vector of the current sources as input, then a nodal admittance matrix is automatically generated for each individual SM and the internal node information of the SMs are transferred to the interfacing nodes. Hence, the internal nodes are eliminated without obtaining the complicated analytical solutions, the reduced-order equivalent circuit of each SM is obtained and all the SMs within each arm are cascaded into one ultimate Thévenin equivalent. At the end of each time step, the EMT solver updates all the previously eliminated internal node information so that nothing is lost under steady and transient states. The proposed approach is validated by EMT models of an enhanced double half-bridge sub-module based MMC-HVdc system on power systems computer aided design (PSCAD)/EMTdc.
Exploiting Symmetries to Design EKFs With Consistency Properties for Navigation and SLAM

The extended Kalman filter (EKF) is both the historical algorithm for multi-sensor fusion and still state of the art in numerous industrial applications. However, it may prove inconsistent in the presence of unobservability under a group of transformations. In this paper, we first build an alternative EKF based on an alternative nonlinear state error. This EKF is intimately related to the theory of the invariant EKF. Then, under a simple compatibility assumption between the error and the transformation group, we prove that the linearized model of the alternative EKF automatically captures the unobservable directions, and many desirable properties of the linear case then directly follow. This provides a novel fundamental result in filtering theory. We apply the theory to multi-sensor fusion for navigation, when all the sensors are attached to the vehicle and do not have access to absolute information, as typically occurs in GPS-denied environments. In the context of simultaneous localization and mapping (SLAM), Monte Carlo runs and comparisons to OC-EKF, robocentric EKF, and optimization-based smoothing algorithms (iSAM) illustrate the results. The proposed EKF is also proved to outperform standard EKF and to achieve comparable performance to iSAM on a publicly available real dataset for multi-robot SLAM.
Deep reinforcement learning from error-related potentials via an EEG-based brain-computer interface

Reinforcement learning (RL) from human preferences suffered from temporal and interaction environments’ limitations, which rule out real-time and real-world robotic applications of deep RL. To overcome the limitations, this study introduced the electroencephalography (EEG)-measured error-related potentials (ErrPs) to train a robotic RL system based on a brain-computer interface (BCI). We decoded ErrP signals by selecting human preferences in real-time to train robotic behavior by deep RL during a binary object selection task. Twelve healthy subjects participated in the ErrP experiments, in which they were asked to select and adjust self-favored behavior after a machine’s random selections. The decoded ErrP signals classified by a convolutional neural network (CNN)architecture to achieve an average classification accuracy and an area under the ROC curve of 67.49% and 0.639, respectively. By using the well-trained ErrP signals classifier to train the deep RL system, our final results for training robotic behavior through ErrP-based preferences showed an average of 15.21% improvement in efficiency while obtaining acceptable rewards in RL. Thus, the work used brain signals instead of pressing or clicking buttons as the rewards of RL, and constructed a real-time and free from interaction interference intuitive RL system.
Learning Effective Embeddings for Machine Generated Emails with Applications to Email Category Prediction

Machine generated business-to-consumer (B2C) emails such as receipts, newsletters, and promotions constitute a large portion of users’ inboxes today. These emails reflect the users’ interests and often are sequentially correlated, e.g., users interested in relocating may receive a sequence of messages on housing, moving, job availability, etc. We aim to infer (and eventually serve) the users’ future interests by predicting the categories of their future emails. There are many useful methods, such as recurrent neural networks, that can be applied for such predictions, but in all cases the key to better performance is an effective representation of emails and users. To this end, we propose a general framework for learning embeddings for emails and users, using as input only the sequence of B2C templates users receive and open. (A template is a B2C email stripped of all transient information related to specific users.) These learned embeddings allow us to identify both sequentially correlated emails and users with similar sequential interests. We can also use the learned embeddings either as input features or embedding initializers for email category prediction tasks. Extensive experiments with millions of fully anonymized B2C emails demonstrate that the learned embeddings can significantly improve the prediction accuracy for future email categories. We hope that this effective yet simple embedding learning framework will inspire new machine intelligence applications that will improve the users’ email experience.
Impact of Two-Way Communication of Traffic Light Signal-to-Vehicle on the Electric Vehicle State of Charge

Given the ongoing concerns on the emissions of greenhouse gases that contribute to global warming, the electric vehicle is considered as a promising technology solution for the reduction of these emissions in the transportation sector. Despite the numerous advantages of electric vehicles, the limited driving range is one of the prominent drawbacks that need to be addressed. Since the driving range is related to the battery’s state of charge, which is, in turn, related to power consumption, there is a need to minimize the vehicle power consumption. Increased speeds and accelerations lead to a decrease of the state of charge. In this paper, we utilized vehicular networks that allow traffic light signals to communicate with approaching vehicles to avoid unnecessary high speeds and accelerations at an isolated intersection. This communication can be bidirectional, that is, from the vehicle to the traffic light signal, and vice versa. This enables the traffic light signal to adapt its green duration according to vehicular information and then send back its information to vehicles that in turn adapt their speeds based on the received information. We propose an optimization model that determines the optimum green duration and optimum vehicular speed, which ensures that the vehicle will cross the traffic light signal with maximum battery’s state of charge. The analytical results indicate that the proposed approach which is adaptive in both speed and traffic light signal can achieve an improvement of the state of charge compared to other two approaches, which are adaptive in either speed or traffic signal.
Measuring Bending Angle and Hallucinating Shape of Elongated Deformable Objects

Many objects in a human-made environment have elongated shapes for easy manipulation and grasping. As humanoid robots are working in this environment, they require proper sensing and perception of such objects. Current approaches are providing mainly the perception of rigid objects, but many everyday items are non-rigid and more challenging to track due to their substantial shape variability. We want the robots to be able to grasp and manipulate thin, elongated, deformable objects. We propose a system based on the Deep Neural Network that can predict the bend angle of such objects using the single RGB image only. In our paper, we present the proposed neural network architecture used for prediction of the bending angle and finding the elongated shape in images with a cluttered background together with the dataset used for training. We observed that the proposed system even though it was trained on synthetic data was able to perform well on real data. The proposed architecture also provide us with the ability to hallucinate how the deformable pipe with any initial bend would look like when subjected to the arbitrary bend angle. Our findings have more profound consequences than the above mentioned. We were able to show that the proposed Encoder-Decoder neural network architecture has the interpretable latent vector element for describing a measurable physical bend angle. Moreover, we allow bending arrows to be situated out of the image plane. In the future work, we are planning to extend the current approach with the prediction of the full 3d shape of the elongated object from a single RGB image.
Detecting the Scope of Negation and Speculation in Biomedical Texts by Using Recursive Neural Network

Detecting the scope of speculation and negation inbiomedical texts is an important research topic in biomedical community. Previous work shows that syntax information is crucial for the task, and these work usually models it locally by extracting a set of manually-crafted features such as dependency paths. In this paper, we explore empirically a recursive neural network model, representing whole dependency trees globally and learning automatically syntactic features, to better incorporate dependency-based syntax information. Experiments on BioScope corpus show that our model achieves competitive performance, outperforming previous systems.
DAdam: A Consensus-Based Distributed Adaptive Gradient Method for Online Optimization

Adaptive optimization methods, such as AdaGrad, RMSProp, and Adam, are widely used in solving large-scale machine learning problems. A number of schemes have been proposed in the literature aiming at parallelizing them, based on communications between peripheral nodes with a central node, but incur high communications cost. To address this issue, we develop a novel consensus-based distributed adaptive moment estimation method (DAdam) for online optimization over a decentralized network that enables data parallelization, as well as decentralized computation. The method is particularly useful, since it can accommodate settings where access only to local data is permitted. Further, as established theoretically in this work, it can outperform centralized adaptive algorithms, for certain classes of loss functions used in machine learning applications. We analyze the convergence properties of the proposed algorithm and provide a regret bound on the convergence rate of adaptive moment estimation methods in both online convex and non-convex settings. Empirical results demonstrate that DAdam exhibits also good performance in practice and compares favorably to competing online optimization methods.
Algorithms for Interval-Valued Pythagorean Fuzzy Sets in Emergency Decision Making Based on Multiparametric Similarity Measures and WDBA

Emergency decision making is critically important for countries or communities to enhance the effectiveness and validity of the emergency response, which can greatly lower environmental damage, casualties, and economic loss. In the case of emergency decision evaluation, the essential problems that arise serious inexactness, fuzziness, and ambiguity. Interval-valued Pythagorean fuzzy set (IVPFS), portrayed by membership and non-membership with the interval form, is an effective and flexible way to seize indeterminacy. In this paper, primarily, a novel score function for an interval-valued Pythagorean fuzzy number is initiated for managing some comparative issues. Then, a new distance measure for IVPFSs with multiple parameters is studied for solving the counter-intuitive situations. The interesting properties among the developed similarity measures, distance measures, and entropy have also been derived. Then, the objective weights of diverse attributes are ascertained by a novel entropy approach. Also, we explore the combination weight, which can reveal both objective preference and subjective preference. In addition, two interval-valued Pythagorean fuzzy decision making methods based on weighted distance-based approximation and multiparametric similarity measure are presented. Later, the validity of the algorithms is illustrated by a mine emergency decision making issue with the influence of diverse parameters on the ordering. Finally, a comparison with some existing decision making methods has been executed by the counter-intuitive phenomena and discrimination problems for verifying their effectiveness.
Meek-Based Tor Traffic Identification with Hidden Markov Model

Tor is one of the major technologies of censorship circumvention systems. To protect the privacy of users and the information of first hop to access Tor networks, Tor Browser introduces an obfuscation technology called Meek. Tor traffic is obfuscated by Meek to behave as ordinary cloud service traffic. In order to advance the capability of network monitoring systems, this paper proposes a Mixture of Gaussians based Hidden Markov Model (MGHMM), a new model for identifying Meek-based Tor traffic. The proposed MGHMM has two components: 1) Mixture of Gaussians (MOG) is used to characterize the Inter-Packet Time (IPT) distribution and the Packet Size (PS) density distribution; 2) HMM is used to compute the probability of a traffic observation sequence and identify Meek-based Tor traffic by using two-dimensional observations composed by IPT and PS. The effectiveness of the proposed model is evaluated with real-world traffic. Extensive experiments show that the proposed MGHMM is able to identify Meek-based Tor traffic effectively.
Anticancer Activity of Chitosan, Chitosan Derivatives, and Their Mechanism of Action

Tailoring of chitosan through the involvement of its amino, acetamido, and hydroxy groups can give derivatives of enhanced solubility and remarkable anticancer activity. The general mechanism of such activity is associated with the disturbances in normal functioning of cell cycle, interference to the central dogma of biological system from DNA to RNA to protein or enzymatic synthesis, and the disruption of hormonal path to biosynthesis to inhibit the growth of cancer cells. Both chitosan and its various derivatives have been reported to selectively permeate through the cancer cell membranes and show anticancer activity through the cellular enzymatic, antiangiogenic, immunoenhancing, antioxidant defense mechanism, and apoptotic pathways. They get sequestered from noncancer cells and provide their enhanced bioavailability in cancer cells in a sustained release manner. This review presents the putative mechanisms of anticancer activity of chitosan and mechanistic approaches of structure activity relation upon the modification of chitosan through functionalization, complex formation, and graft copolymerization to give different derivatives.
A Pilot Protection Scheme for Transmission Lines in VSC-HVDC Grid Based on Similarity Measure of Traveling Waves

The voltage-source converter (VSC)-based high-voltage direct-current (HVDC) grid is an effective means for large-scale integration of renewable energies. This paper proposes a pilot protection scheme as the backup protection for transmission lines in the VSC-HVDC grid. The traveling wave (TW) propagating processes and waveform characteristics at both ends of the transmission line are analyzed under different fault conditions. Based on the different characteristics between internal and external fault conditions, the protection is formulated utilizing similarity measure of TW waveforms. A four-terminal VSC-HVDC grid is modeled on PSCAD/EMTDC platform to test the performance of the proposed protection. Various simulation results demonstrate that the proposed protection is able to accurately discriminate internal faults from external faults and identify the faulty pole. The protection performance analysis shows that the protection is insensitive to the fault resistance, the protection sampling rate, and the inductance value of the reactors at the end of lines. The protection algorithm is simple with small calculation amount and does not require data synchronism. Furthermore, the proposed protection has superiority in the fault resistance withstand ability compared with other backup protections.
Wavelet Packet-based Kernel Extreme Learning Machine for Sensor Faults Diagnosis of Hypersonic Vehicle

Sensor faults diagnosis is an important research field of the Hypersonic Vehicle (HV). A fast and effective fault diagnosis method can reduce the probability of flight accidents and improve the reliability of vehicle. Aiming at the uncertainty of the HV model and the rapidity of fault diagnosis, wavelet packet decomposition integrated with Biogeography Based Optimization Kernel Extreme Learning Machine (BBO-KELM) is proposed for the first time to identify fault. And the Biogeography Based Optimization (BBO) is used to improve the performance of the KELM firstly. The time-frequency analysis capability of wavelet packet decomposition and the high learning speed of KELM enable fast and accurate sensor faults diagnosis for the HV.
Occlusion Detection for Long Term Visual Tracking

How to detect the occlusion and design a tracker which is robust to occlusion are very difficult in the field of visual tracking. To get a comprehensive understanding about this issue, the trackers or methods which are like to be robust to the occlusion are briefly summarized. Generally, the metrics of occlusion is dependent on the representation of target appearance model. It can be coarsely classified into three types according to the existing literatures, 1) Part-based or structure-based representation; 2) Key-point based representation; 3) Segmentation-based method. The working principles of these three models are introduced. To get a deep insight about the performance of them, evaluation experiments on the occlusion metric are conducted. Finally, some issues for long-term visual tracking are discussed.
Big data storage technologies: a case study for web-based LiDAR visualization

Big data technologies have been growing up quickly during past years. New storage and computing solutions appear while those already established in the market are improved with new features and better performance. Along with this growth also rises the number of applications and fields where the inclusion of big data technologies provides a large number of benefits, from the reduction in computational costs and economic resources to the improvement in the quality of the services provided which has a direct impact on the customers satisfaction. LiDAR (Light Detection and Ranging) data processing is one of the topics that could benefit from the adoption of these kind of technologies due to the massive datasets that are being gathered nowadays, with applications in archaeology, geography, geology or forestry, among many others. An efficient management of this volume of data becomes a key point especially in visualization, computing and analytic processes. In this paper, we analyse how web applications for the visualization of LiDAR data can benefit from the adoption of big data storage technologies, as well as the advantages and disadvantages that may determine the choice of one of them.