Miracle-dz/UniFAR-FTUs huggingface dataset:
MIMO Millimeter-Wave Channel Estimation Using Coalitional Games

In millimeter-wave massive multiple input multiple output (MIMO) antenna systems, channel estimation is a crucial component. In this paper, we propose a virtual channel representation channel estimation method using out-of-band spatial information to reduce training overheads and a cooperative channel allocation method based on coalitional game framework. The proposed cooperative channel allocation method enhances throughput performance in mm-wave small cell networks.
Nature‐Inspired Emerging Chiral Liquid Crystal Nanostructures: From Molecular Self‐Assembly to DNA Mesophase and Nanocolloids

Liquid crystals (LCs) are omnipresent in living matter, whose chirality is an elegant and distinct feature in certain plant tissues, the cuticles of crabs, beetles, arthropods, and beyond. Taking inspiration from nature, researchers have recently devoted extensive efforts toward developing chiral liquid crystalline materials with self‐organized nanostructures and exploring their potential applications in diverse fields ranging from dynamic photonics to energy and safety issues. In this review, an account on the state of the art of emerging chiral liquid crystalline nanostructured materials and their technological applications is provided. First, an overview on the significance of chiral liquid crystalline architectures in various living systems is given. Then, the recent significant progress in different chiral liquid crystalline systems including thermotropic LCs (cholesteric LCs, cubic blue phases, achiral bent‐core LCs, etc.) and lyotropic LCs (DNA LCs, nanocellulose LCs, and graphene oxide LCs) is showcased. The review concludes with a perspective on the future scope, opportunities, and challenges in these truly advanced functional soft materials and their promising applications.
Estimation of Aggregate Reserve With Point-of-Load Voltage Control

Voltage dependent loads can collectively provide a certain amount of power reserve (by virtue of the ability to change their power consumption within the stipulated voltage tolerance) which could be exploited for grid frequency regulation through voltage control at the substation/feeder or at the point of load. The amount of such power reserve would vary with time of the day depending on the incidence of different types of voltage dependent loads and also the voltage profile across the feeders. It is important for the grid operators to know the aggregate power reserve from the voltage dependent loads during different times of the day in order to schedule other forms of reserves accordingly. This paper presents a methodology to estimate such power reserve from the measured power and voltage at the bulk supply points without knowing the actual distribution network topology and/or load profile of individual customers. The proposed method is applied to estimate the time variation of the aggregate reserve offered by the voltage dependent loads within the domestic sector in Great Britain. Studies on a standard IEEE distribution network are presented to validate the estimated reserve margins under typical voltage profiles across the distribution feeders.
IoT in 5G Aeon: An Inevitable Fortuity of Next Generation Healthcare

Connected wellness program is a standard for healthcare provision that practice tools to deliver healthcare impeccably through various suppliers. It can deliver novel and exceptional prospects for patients to participate in health workforce to accomplish their precaution. Also, it influences the evolving tools to permit precaution inside/outside sickbay, over the capability for portable and wearable technologies that links to cloud-centered context aware healthcare devices. Incorporation of the high speed 5G network and cloud computing tools to the wellbeing-care structure can significantly advance the patient examination quality and provision level for architecture comprising operators, androids and health care interconnected systems. This broadsheet discusses the Internet of Things (loT) in healthcare and likewise reveals the tools tangled in it. We tentatively described the context, which is the paramount endeavor in this research, and understanding the conduct of the cloud enabled loT in healthcare.
SIMPE: A Simulation Platform for Multi-Player Pursuit-Evasion Problems

The pursuit-evasion problem has been studied extensively in the literature for decades. However, there are currently very limited interactive platforms that can assist auser in simulating different scenarios that may have practical relevance in certain applications. This paper presents SIMPE, an interactive simulation platform for simulating Pursuit-Evasion problems with many pursuers and one evader. This platform allows the user to interactively explore various scenarios of these types of problems by varying not only the parameters of the game but also the strategies that are available to the pursuers and evader. The exploratory nature of SIMPE allows the user to quickly and interactively determine the outcome of a particular complex scenario in a short period of time. Three different strategies that represent cooperating and/or not cooperating pursuers are available as options. In addition to various graphs and performance measures, the platform produces a visual display of the movement of the pursuers andevader which will terminate either by the evader escaping or being captured.
Sentiment Analysis of Indian Languages using Convolutional Neural Networks

Social media has become an important part of human life; not only has it become a platform for people to interact with each other, it has become the news portal, a stage for people to express themselves, and even for heinous tasks such as cyber bullying, stalking etc. India being a country with the world's second largest population, not to mention the lingual diversity, the usage of social media in all its forms, is at its heights. This resulted in the evolution of code-mixed data, which is a combination of more than one language. The Bengali-English code mixed data used in this work is provided by the NLP Tool Contest, SAIL @ ICON 2017. The Convolutional Neural Network has been used to classify the data as positive, negative or neutral. Later to analyze performance of the system in the native script of Indian languages, the same procedure has been applied on Telugu dataset which is created manually from various source of online movie reviews and the results are compared.
Heterogeneous distributed computing based on high‐level abstractions

The rise of heterogeneous systems has given place to great challenges for users as they involve new concepts, restrictions, and frameworks. Their exploitation is further complicated in the context of distributed memory systems, which require the usage of additional different programming paradigms and tools. In this paper, we propose a novel approach to program heterogeneous clusters that is based on high‐level abstractions such as tiles and hierarchical decomposition combined with the powerful APIs that data types and embedded languages can provide in languages such as C++. Rather than building our proposal from scratch, we have implemented it as a natural integration of the existing Hierarchically Tiled Arrays (HTA) and Heterogeneous Programming Library (HPL) projects, ie, the first one being focused on distributed computing and the second one on heterogeneous processing. The result, called Heterogeneous Hierarchically Tiled Arrays (H2TA), is very intuitive and easy to use thanks to the global view of the data and the single‐threaded view of the execution that it provides at cluster level together with the transparency it provides with respect to the management of the heterogeneous devices. An evaluation comparing our proposal with MPI‐based implementations shows its large programmability advantages and the reasonable overhead incurred.
Vehicle Re-Identification Based on Coupled Dictionary Learning

Vehicle re-identification technology is a key technology to identify unlicensed vehicles in the cross-view of video. Existing vehicle re-identification methods often focuses on the extraction of vehicle license information. There is not a good way to re-identify a vehicle without a license plate or a vehicle with a blocked license plate. In this paper, a method based on feature fusion and coupled dictionary learning is proposed for vehicle re-identification in the absence of a license plate or license block. In dictionary learning, a new least squares coupled dictionary learning method (nLSCDL) is proposed. We design and join the label matrix and mapping matrix to reduce the intra-class disparity of the same vehicle and widen the inter-class disparity of different vehicles across the field of vision. The method has good robustness to illumination change, perspective change and the parameters of the camera itself. our method has good recognition rate under three different vehicle data sets and GRID data sets.
Vision-Based Lane Keeping - A Survey

Ahstract- Several intelligent transportation system applications, such as automatic steering systems, lane departure warning systems, and driver performance monitoring systems, depend on obtaining accurate estimates of the vehicle's trajectory with respect to the lane the vehicle is driving in. However, lane marking detection and tracking using video or laser sensors tend to be extremely noisy, requiring robust methods for lane marking detection and tracking. Therefore, it has been an active field of research for decades. In this paper, we survey methods for detecting lane marking as well as tracking lane boundaries in real time focusing on vision-based system. We present a generic problem and elaborate the broad ranged proposed method in vision-based system. Finally, we identify these gaps and recommend research direction that may bridge them.
Iterative Deep Learning for Road Topology Extraction

This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity among the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the road network, inspired by a human delineating a complex network with the tip of their finger. We perform an extensive and comprehensive qualitative and quantitative evaluation on the road network estimation task, and show that our method also generalizes well when moving to networks of retinal vessels.
Fenofibrate Reduces the Asthma-Related Fibroblast-To-Myofibroblast Transition by TGF-Β/Smad2/3 Signaling Attenuation and Connexin 43-Dependent Phenotype Destabilization

The activation of human bronchial fibroblasts by transforming growth factor-β1 (TGF-β1) leads to the formation of highly contractile myofibroblasts in the process of the fibroblast–myofibroblast transition (FMT). This process is crucial for subepithelial fibrosis and bronchial wall remodeling in asthma. However, this process evades current therapeutic asthma treatment strategies. Since our previous studies showed the attenuation of the TGF-β1-induced FMT in response to lipid-lowering agents (e.g., statins), we were interested to see whether a corresponding effect could be obtained upon administration of hypolipidemic agents. In this study, we investigated the effect of fenofibrate on FMT efficiency in populations of bronchial fibroblasts derived from asthmatic patients. Fenofibrate exerted a dose-dependent inhibitory effect on the FMT, even though it did not efficiently affect the expression of α-smooth muscle actin (α-SMA; marker of myofibroblasts); however, it considerably reduced its incorporation into stress fibers through connexin 43 regulation. This effect was accompanied by disturbances in the actin cytoskeleton architecture, impairments in the maturation of focal adhesions, and the fenofibrate-induced deactivation of TGF-β1/Smad2/3 signaling. These data suggest that fenofibrate interferes with myofibroblastic differentiation during asthma-related subepithelial fibrosis. The data indicate the potential application of fenofibrate in the therapy and prevention of bronchial remodeling during the asthmatic process.
Atomistic Modelling and Simulation of Transmission Electron Microscopy Images: Application to Intrinsic Defects of Graphene

The characterization of advanced materials and devices in the nanometer range requires complex tools to understand the precise links between structure and properties. This paper demonstrates that the modelling of graphene-based defects can be obtained efficiently for various atomic arrangements using the Brenner module of the SAMSON software platform. The signatures of all kinds of defects are computed in terms of energy and simulated scanning transmission electron microscopy images. The results are in good agreement with the majority of the available theoretical and experimental data. This original methodology is an excellent compromise between the speed and the precision required by the semiconductor industry and opens the possibility of realistic in-silico research conjugated to the experimental nanocharacterization of these promising materials. We propose a novel approach to compare the agreement between experiment and simulation by using the projected radial distribution function. The maximum projected Euclidian distance between the model and the experiment is always better than 100 pm.
Mumpitz at PARSEME Shared Task 2018: A Bidirectional LSTM for the Identification of Verbal Multiword Expressions

In this paper, we describe Mumpitz, the system we submitted to the PARSEME Shared task on automatic identification of verbal multiword expressions (VMWEs). Mumpitz consists of a Bidirectional Recurrent Neural Network (BRNN) with Long Short-Term Memory (LSTM) units and a heuristic that leverages the dependency information provided in the PARSEME corpus data to differentiate VMWEs in a sentence. We submitted results for seven languages in the closed track of the task and for one language in the open track. For the open track we used the same system, but with pretrained instead of randomly initialized word embeddings to improve the system performance.
Machine Learning Techniques for Automatic Depression Assessment

Depression is one of the most common mood disorder that is inherently related to emotions, involving bad mood, low self-esteem and loss of interest in normal pleasurable activities. The aim of this work is to develop a framework based on the dataset provided by AVEC'14 for depression assessment. The proposed work presents two different motion representation methods: a) Gabor Motion History Image (GMHI), and b) Motion History Image (MHI). Several combinations of appearance-based low level features are extracted from both motion representations. These features were further combined with statistically derived features, and used for training and testing with several machine learning techniques. The proposed approach reached an F1 score of 81.93%, both for MHI and GMHI, with SVM classifier. The achieved performance is comparable to state-of-the-art approaches, while manages to outperform several others. Apart from accomplishing a competitive performance, the proposed work provides an exhaustive exploration of different combinations of the investigated motion representations, descriptors, and classifiers.
Blind PDF Document Watermarking Robust Against PCA and ICA Attacks

Spread Transform Dither Modulation (STDM) is a blind watermarking scheme used for its high robustness against re-quantization and random noise attacks. It has been applied mainly on images, speech, and PDF documents. The key of this scheme is the projection vector aiming at spreading the embedded message over a set of cover elements. However, it has been recently shown that such a key vector can be estimated thanks to Blind Source Separation (BSS) techniques, e.g. Principal Component Analysis (PCA) and Independent Component Analysis (ICA). This security breach can be harnessed by an opponent to copy, remove, or modify the embedded watermark. In this position paper, a CAR-STDM (Component Analysis Resistant-STDM) is designed and its application on PDF documents is presented. The security is guaranteed by the use of a cryptographically secure number generator, preventing algebraic approaches (as BSS ones) from finding the key. First experimental results show that the Secure-STDM achieves the security against the aforementioned BSS techniques. It is further shown that CAR-STDM preserves its robustness against AWGN and Salt&Peper attacks, and keeps furthermore its transparency.
A Simple and Effective Approach for Transmit Antenna Selection in Multiuser Massive MIMO Leveraging Submodularity

Massive MIMO systems are expected to enable great improvements in spectral and energy efficiency. Realizing these benefits in practice, however, is hindered by the cost and complexity of implementing large-scale antenna systems. A potential solution is to use transmit antenna selection for reducing the number of radio-frequency (RF) chains at the base station. In this paper, we consider the NP-hard discrete optimization problem of performing transmit antenna selection in the downlink of a single cell, multiuser massive MIMO system by maximizing the downlink sum-rate capacity with fixed user power allocation subject to various RF switching constraints. Whereas prior work has focused on using convex relaxation based schemes, which lack theoretical performance guarantees and can be computationally demanding, we adopt a very different approach. We establish that the objective function of this antenna selection problem is monotone and satisfies an important property known as submodularity, while the RF switching constraints are expressible as the independent sets of a matroid. This implies that a simple greedy algorithm can be used to guarantee a constant-factor approximation for all problem instances. Simulations indicate that greedy selection yields a near-optimal solution in practice and captures a significant fraction of the total downlink channel capacity at substantially lower complexity relative to convex relaxation based approaches, even with very few RF chains. This paves the way for substantial reduction in hardware complexity of massive MIMO systems while using very simple algorithms.
Secure Optimal k-NN on Encrypted Cloud Data using Homomorphic Encryption with Query Users

In cloud computing, research on security issues among outsourced encrypted data is trending topic. It has broad applications in area-based management, classification, and clustering. As any other normal utilized query for online applications, secure k-Nearest Neighbors (k-NN) calculation on encrypted cloud data is highly being considered now a days, and a few advanced answers have been produced. This paper proposed an innovative plan for encrypting the outsourced database and query points. The new plan can adequately support k-Nearest Neighbor (KNN) computation while preserving data privacy and query privacy. To improve the performance of the system, the Opposition-based Particle Swarm Optimization (OPSO) optimization algorithm is utilized to secure the data by Homomorphic Encryption (HE) method. The broad hypothetical and test assessments exhibit the adequacy of our plan with regards to security and performance.
Vibrio cholerae Transmits Through Water Among the Household Contacts of Cholera Patients in Cholera Endemic Coastal Villages of Bangladesh, 2015–2016 (CHoBI7 Trial)

Recurrent cholera causes significant morbidity and mortality in cholera endemic estuarine areas of Bangladesh. There have been limited studies to investigate the transmission patterns of V. cholerae associated with cholera in Bangladesh. In this study, we characterized V. cholerae serogroup O1 isolated from 30 cholera patients, 76 household contacts, 119 stored drinking water samples, and 119 water source samples in Bakerganj and Mathbaria, two cholera endemic coastal regions in Bangladesh. Results of phenotypic and molecular characterization of V. cholerae isolates (n = 56) confirmed them to be toxigenic belonging to serogroup O1 biotype El Tor (ET), and possessing cholera toxin of the classical biotype (altered ET). Molecular fingerprinting of the V. cholerae O1 of clinical and water origins determined by PFGE of Not-I- digested genomic DNA showed them to be closely related, as the PFGE banding patterns were highly homogenous. Phylogenetic analysis using dendrogram of cholera patients, household contacts, and household groundwater sources showed isolates within households to be clonally linked, suggesting water as an important vehicle of transmission of cholera in the coastal villages of Bangladesh. Transmission of toxigenic V. cholerae O1 through drinking water in cholera endemic rural settings underscores the urgent need for evidence based water, sanitation, and hygiene interventions promoting safe drinking water to prevent morbidity and mortality related to cholera and other enteric infections in Bangladesh.
The technical aspects of a feasible new technique for ipsilateral percutaneous transhepatic portal vein embolization.

OBJECTIVE: To devise a simple new technique wherein absolute ethanol is injected via a sheath under proximal balloon occlusion of the right portal vein using a single-balloon catheter and to examine its feasibility and safety for ipsilateral portal vein embolization (PVE). METHODS: Between 2010 and 2016, PVE was performed in 19 patients prior to undergoing extended right hepatectomy. PVE was performed via a percutaneous transhepatic ipsilateral approach, the right portal branch was embolized under ultrasound guidance, and a balloon catheter was placed in the proximal site of the main right portal branch. Absolute ethanol was injected through a sheath under proximal balloon occlusion of the right portal vein using a double-lumen catheter. We evaluated its technical success and complications following PVE and changes in liver enzyme levels. Furthermore, we calculated changes in future liver remnant (FLR) and FLR/total functional liver volume (TFLV) ratio and assessed complications following hepatic resection. RESULTS: PVE was successfully performed in all patients. Mean FLR and FLR/TFLV significantly increased following PVE (p < 0.01). The change in the FLR and FLR/TFLV ratio was 39.6 ± 16.2%. One patient (6.5%) developed procedure-related complications following PVE (perihepatic hematoma). CONCLUSION: The new technique for ipsilateral right PVE is safe, effective, and convenient. ADVANCES IN KNOWLEDGE: This is the first study to investigate the efficacy of injecting ethanol via a sheath under proximal balloon occlusion of the right portal vein using a single-balloon catheter.