Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

In early stage drug discovery, the hit-to-lead optimization (or “hit expansion”) stage entails starting from a newly-identified active compound, and improving its potency or other properties. Traditionally this process relies on synthesizing and evaluating a series of analogs to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogs with improved potency. Our protocol begins from an inhibitor of the target kinase, and generalizes the synthetic route used to access it. By searching for commercially-available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions – or billions – of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well-described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into a single new compounds, and which are not. Further, the use of the pairwise approximation allows interaction energies to be assigned to each compound in the library, without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFRT790M, and ACK1. In each example, the enumerated library includes additional analogs reported by the original study to have activity, and these analogs are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol. Graphical Abstract
TripRes

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.
Correlating Magneto-Structural Properties to Hyperthermia Performance of Highly Monodisperse Iron Oxide Nanoparticles Prepared by a Seeded-Growth Route

Monodispersecubicspinelironoxidemagneticnanoparticleswithvariable sizes were prepared following a multi-injection seeded-growth approach. As expected from such a well-known synthetic route, all samples were characterized by narrow size distributions, and showed excellent stability in both organic and aqueous media without the presence of aggregates, thus becoming ideal candidates for the study of their hyperthermia performance. Specific Loss Power measurements indicated low heating powers for all samples without a maximum for any specific size, contrary to what theory predicts. The magnetic study showed the formation of size-dependent nonsaturated magnetic regions, which enlarged with the particle size, evidencing a clear discrepancy between the crystal size and the effective magnetic volume. Strain map analysis of high resolution transmission electron micrographs indicated the presence of highly strained crystal areas even if nanoparticles were monocrystalline. The origin of the crystal strain was found to be strictly correlated with the seeded-growth synthetic procedure used for the preparation of the nanoparticles, which turned out to alter their magnetic structure by creating antiphase boundaries. Considering the calculated effective magnetic volumes and their magnetic dispersions in each sample, a reasonable agreement between hyperthermia experiments and theory was obtained.
Extraction of Phenolic Compounds from Fruits of Bitter Melon (Momordica charantia ) with Subcritical Water Extraction and Antioxidant Activities of These

Bitter melon (Momordica charantia) is traditionally known for its medicinal properties such as antidiabetic, anticancer, anti-inflammation, antivirus, and cholesterol lowering effects. It contains many phenolic compounds that may have the potential as antioxidant and antimutagen. Although the value of bitter melon is realized, scientific information on phenolic composition of bitter melon and antioxidant and antimutagenic activities of its extracts from food grade solvents are limited. This study were investigated the total phenolic contents of bitter melon obtained by subcritical water extraction (SCWE) and antioxidant activities of these extracts. The effect of extraction temperature was considered and the results were compared with the extracts obtained by solvent extraction and soxhlet extraction. The total phenolic contents of bitter melon obtained by the SCWE at 130 o C, the SCWE at 150 o C, SCWE at 180 o C, the SCWE at 200 o C, the solvent extraction, and soxhlet extraction were 10.571, 25.219, 42.915, 48.177, 7.743, and 4.992 mg gallic acid equivalents (GAE)/g dry weight (DW), respectively. Overall, the extracts obtained by SCWE were significantly higher than solvent extraction and soxhlet extraction. The main phenolic acid contained in bitter melon was gallic acid. The phenolic acid was calculated from HPLC analysis of the extracts that the gallic acids of the extracts from bitter melon obtained by the SCWE at 130 o C, the SCWE at 150 o C, SCWE at 180 o C, the SCWE at 200 o C, the solvent extraction, and soxhlet extraction were 0.0913, 0.3169, 0.5502, 0.6462, 0.0271, and . 0120 mg/g DW, respectively. Antioxidant was represented by IC 50 index which the IC 50 values of extract obtained with the SCWE at 130 o C, the SCWE at 150 o C, the SCWE at 180 o C, the SCWE at 200 o C, the solvent extraction, and soxhlet extraction were 4.480, 3.970, 5.757, 5.720, 8.426, and 6.107 μg/ml, respectively. The IC 50 values of the SCWE at all temperatures were lower than that of solvent extraction and soxhlet extraction, which means SCWE gave the extracts with higher antioxidant activity.
An Elephant Flows Scheduling Method Based on Feedforward Neural Network

In order to solve the problem of unbalanced network load caused by elephant flows in the data center network, a dynamic multi-path load balancing method based on feedforward neural network (FNN-LB) was proposed. In this method, topological perception and traffic information monitoring are carried out first, and the elephant flows are marked. The collected network traffic information is then used as input to estimate the load of each link through the feedforward neural network. Finally, the optimized ant colony algorithm is used to find the optimal paths for transmitting the elephant flows, so that the optimal paths are selected according to the real-time status of the links. Simulation results show that the proposed scheme can effectively reduce network transmission delay and packet loss rate, and improve link utilization.
Overview of PDF Malware Classifiers

PDF means Portable Document Format. It is a commonly used format for data exchange nowadays. Though, it turns out to be the most striking to the target for misuse by malware designer and vulnerability researcher. PDF which contains malicious based attacks mainly targeting individuals, Financial sectors and government official. The existing tools like IDS mean Intrusion Detection System and packages like antivirus are inefficient to overcome these kinds of attacks. In this paper, we discussed about PDF structure file structure and its working and we have discussed some classifiers related to malware detection.
Computational Approach to guide Mind Controlled Robotic Arm using BCI – A Review

This paper reports a review of computational approach to guide mind controlled robotic arm using Brain Computer Interface (BCI). BCI controlled robotic arm provides an individual to use brain signals, Electroencephalogram (EEG) or Electrooculogram (EOG) for their physical activity. For assisting physically challenged people robotic arm can be a major rehabilitation device, which consists of five stages, such as Data acquisition, signal preprocessing, feature extraction, classification and interfacing robotic arm. This method is clinically important since it paves a way for making a fully dependent patient into a partially dependent person, which in turn supports their mental health and stability.
Design of a Scheduling Algorithm in 3D MIMO Beamforming 5G Systems for Interference Mitigation

The combination of beamforming antenna array with Massive multiple-input multiple-output (MIMO) systems plays a major role in wireless communication systems especially in fifth generation (5G) systems. These hybrid antennas offers increased diversity for the user equipment or base stations with increased capacity and reduction of channel interference and multipath fading. It is often realized by focusing the radiation in projected direction or varying the traffic scenarios using suitable beamforming techniques. Several beamforming techniques often undergoes analog beamforming in the frequency band and wideband phase shifts are not harmonious to scheduled users. On other hand, beamforming techniques reduces the possible interference by redirecting the transmission towards the desired users but the condition may worsen with dense deployment. In this paper, we model the interference mitigation between beamforming antenna arrays using a scheduling algorithm that maintains optimally the complexity of beamforming. The experimental results show that the proposed scheduling algorithm is efficient enough to reduce the interference or mitigate the interference in MIMO systems.
Formation Maintenance of Large-Scale Unmanned Aerial Vehicles Under Wireless Channels

This paper focuses on the formation maintenance problem of large-scale multi-UAV system under wireless channels. In contrast to the previous studies on multi-agent consensus protocol, the controller we proposed can effectively deal with communication limitations. Specifically, we develop a communication model for multi-UAV systems to analyze the communication graph, which is determined by the communication parameters and the formation pattern of system. Then we provide several distance-based sufficient conditions of UAVs for having a spanning tree in the communication graph. With these sufficient conditions, we provide a control law of the formation maintenance for the large-scale multi-UAV system under wireless channel. Finally, our simulation results corroborate the effectiveness of the proposed theoretical results.
Supervised and Unsupervised Machine Learning based Review on Diabetes Care

Sedentary lifestyle, poor diet and work pressure lead the diabetes disease which may cause several fatal health issues like heart attack, strokes, kidney failure, nerve damage etc. Diabetes can be effectively managed when caught early with high accuracy. Machine Learning (ML) approaches are very effective to early detection and prediction of diabetes. The goal of this paper is to offer the inclusive examination of the diagnosis of diabetes by supervised and unsupervised ML algorithms. This survey includes papers on the diagnosis of diabetes from 2018-2020. Decision tree based algorithm such as C4.5, AdaBoost, XGBoost, etc., have predicted the diabetes with high accuracy. Unsupervised learning techniques such as PCA and K-Mean are also useful in the attribute selection and outlier detection from the large dataset. This study reveals that K-Mean and SVM have also diagnosed and evaluated diabetes by high accuracy as an amalgamation of supervised and unsupervised machine learning techniques.
Social Synchrony: An Analytical Contemplation of Contemporary State of Art Frameworks

An appraisal of recent studies conducted under the broad spectrum of social synchrony to measure the information flow and its analytics. There is indeed requirement for social network analysis in static as well as dynamic environment. Social synchrony arises when large number of users mimic for the specific topic in specific time on the given online social networks. Subsequently, time and geographic incorporates dynamicity in social networks which affect the social synchrony. This paper focuses on social synchrony and its pattern using online social networks. The paper reports a systematic review on requirement of social synchrony, its effects, open challenges and future work for further analysis.
ANNs Going Beyond Time Series Forecasting: An Urban Network Perspective

5G is expected to bring forth disruptive indus-trial-societal transformation by enabling a broad catalog of (radically new, highly heterogeneous) applications and services. This scenario has called for zero-touch network and service management (ZSM). With the recent advancements in artificial intelligence, key ZSM capabilities such as the runtime prediction of user demands can be facilitated by data-driven and machine learning methods. In this respect, the article proposes a runtime prediction approach that transforms time series forecasting into a simpler multivariate regression problem with artificial neural networks (ANNs), structurally optimized with a genetic algorithm (GA) metaheuristic. Leveraging on a novel set of input features that capture seasonality and calendar effects, the proposed approach removes the prediction accuracy's dependence on the temporal succession of input data and the forecast horizon. Evaluation results based on real telecommunications data show that the GA-optimized ANN regressor has better prediction performance. It achieved average improvements of ~59 percent and ~86 percent compared to 1-day and 1-hour ahead forecasts obtained with state-of-the-art multi-seasonal time series and long short-term memory forecasting models, respectively. Furthermore, despite its longer training times compared to the baseline models, the proposed ANN regressor relaxes the monitoring requirements in 5G dynamic management systems by allowing less frequent retraining offline.
Design of Semi-circular shaped UWB Antenna for WBAN Applications

An ultra-wide band (UWB) for Wireless Body Area Networks (WBAN) applications is proposed in this paper. The antenna consists of a semi-circular patch with FR4 substrate. The additional features included are C band and Wi-max band rejection. This has been performed in order to utilize the entire UWB band range of 3.1 to 10.6Ghz efficiently without any interference. The rejection is performed by introducing two different types of U-slots in patch and ground. The radiation pattern obtained is bi-directional. The antenna is simulated, and its performance is analyzed using High Frequency Structure Simulator (HFSS).
A Neural Attentive Model Using Human Semantic Knowledge for Clickbait Detection

In this paper, we propose a neural attentive model using human semantic knowledge to detect clickbait. To the best of our knowledge, our work is the first that introduces a data enrichment method and incorporates human semantic knowledge for the purpose of clickbait detection. Experiments show that our approach, on two real-world datasets, not only achieves new state-of-the-art results but significantly outperforms existing systems by a large margin.
Distributed Compressed Sensing of Doubly Selective Channel in Massive MIMO Systems

With the number of antennas and users increases significantly, and the complexity of channel estimation and pilot overhead increase continuously in Massive MIMO systems. To solve this problem, in this paper, a doubly selective channel estimation method based on distributed compressed sensing (DCS) is proposed for large-scale MIMO system. Firstly, the problem of doubly selective channel estimation is formulated under the framework of DCS. Then the joint sparsity of doubly selective channel is theoretically proved by showing that the channel coefficients in transformed domain have the spatial correlation between the adjacent antennas. By exploiting the joint sparsity of transformed domain coefficients corresponding to the channel between different transmit and receive antennas, the channel coefficients is reconstructed by distributed compressed reconstruction algorithm. The simulation results show that the proposed approach can reconstruct the original channel coefficient effectively with significantly reduced pilot overhead.
Application of Computational Techniques in Demand Response: A Review

The smart grid is widely considered as the advanced version of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the user’s energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. Demand Response functions as a vital controller for the present electrical framework. From outsider see the interest reaction can be viewed as two connections, one related to customer viewpoint and the other is pertinent to energy supplier or market administrators. But both links are driven by the application of different computational techniques. Throughout the smart grid, the advanced computational technique has been properly utilized to enhance the outcome and capability of learning. The salient feature of this paper is to criticize the applicability of computational techniques for demand response. The paper explores major aspects such as DR issues, different computational techniques, and future extensions of demand response.
Adaptive Memory Event-triggered Control for Multi-agent Systems under External Disturbance

This paper investigates the event-triggered consensus problem of nonlinear multi-agent systems (MASs) with Lipschitz functions under external disturbance in an undirected communication topology. To save communication resources, an adaptive memory event-triggered mechanism (AMETM) is proposed, whose threshold can change dynamically with the state of the system and exclude Zeno behavior. Based on this mechanism, two anti-disturbance control strategies based on disturbance observer and disturbance compensation mechanism are designed, respectively, and the existence of a buffer can maintain a certain performance when the control channel is blocked (such as when cyber attacks occur). Finally, simulation results show the effectiveness of the proposed method.
NykeDB - Strongly Consistent & Fault-Tolerant Distributed Key-Value Store

In this paper, we present a highly consistent, fault-tolerant distributed key-value store. It adapts the raft consensus algorithm broadly in the system and supports concurrent transactions across shards. Each operation on the store is handled by a set of co-ordinators as a raft group. The store’s keys are partitioned across shards, and each shard maintains multiple replicas forming their raft groups.
Digital Twin Enhanced Assembly Based on Deep Reinforcement Learning

Discrete manufacturing is becoming a popular modality, which places a higher demand on the flexibility of the production line. Traditional assembly lines require extensive manual design and cannot meet the need for flexibility. Due to the rise of reinforcement learning, we suspect that modern algorithms are crucial to further improve the flexibility of assembly. In this paper, we propose a digital twin enhanced assembly method with deep reinforcement learning. A digital twin model of the assembly line is first built. Then, the deep deterministic policy gradient based reinforcement learning agent is trained on the digital twin model. The simulation of the reinforcement learning environment is based on a mixture of simulation engine and real signals. Thus, we can balance the training efficiency and the simulation accuracy. Finally, to validate our proposed method, peg-in-hole assembly experiments were conducted and good results were observed.
A Novel Configurable Microwave Passive Component Based on Programmable Bridged-T Coil Array

In this article, a novel configurable microwave passive component is presented, which can be programmed into multiple functions according to the design requirement. The proposed configurable microwave passive component is realized with a 2-D bridged-T coil array, which can be programmed through different wire-bonding configurations to achieve the desired functions. As a design example, a compact configurable microwave passive component implemented using the integrated passive device process is demonstrated. It can be configured into seven different functions, including filters, directional couplers, and power dividers with center frequencies of 2 or 4 GHz, while the circuit size is only 8.7 mm $\times8.1$ mm. The possible extension of the proposed configurable microwave passive component to a reconfigurable one using advanced RF switch technologies is also discussed.
A Deep Learning-Aided Approach to Portfolio Design for Financial Index Tracking

This paper considers the index tracking portfolio (ITP) design problem in financial markets, which aims at reproducing the performance of a financial index by investing in a subset of the assets constituting it. From a regression-based point of view, the ITP design problem is formulated as a mixed-integer programming (MIP). Leveraging the graph convolutional network (GCN), a calibrated GCN is proposed for asset selection followed by a lightweight MIP problem to realize asset allocation. Numerical simulations show that compared to existing methods the proposed learning-aided approach can generate comparable ITP design results and significantly accelerate the computation which is favorable for practical index tracking targets in finance.