Miracle-dz/UniFAR-FTUs huggingface dataset:
Rap2 and TNIK control Plexin-dependent tiled synaptic innervation in C. elegans

During development, neurons form synapses with their fate-determined targets. While we begin to elucidate the mechanisms by which extracellular ligand-receptor interactions enhance synapse specificity by inhibiting synaptogenesis, our knowledge about their intracellular mechanisms remains limited. Here we show that Rap2 GTPase (rap-2) and its effector, TNIK (mig-15), act genetically downstream of Plexin (plx-1) to restrict presynaptic assembly and to form tiled synaptic innervation in C. elegans. Both constitutively GTP- and GDP-forms of rap-2 mutants exhibit synaptic tiling defects as plx-1 mutants, suggesting that cycling of the RAP-2 nucleotide state is critical for synapse inhibition. Consistently, PLX-1 suppresses local RAP-2 activity. Excessive ectopic synapse formation in mig-15 mutants causes a severe synaptic tiling defect. Conversely, overexpression of mig-15 strongly inhibited synapse formation, suggesting that mig-15 is a negative regulator of synapse formation. These results reveal that subcellular regulation of small GTPase activity by Plexin shapes proper synapse patterning in vivo.
Towards Real-Time Privacy Preservation: A Streaming Location Anonymous Method Based on Distributed Framework

In order to better serve users, several location-based services rely on the real-time spatio-temporal information. Existing location privacy- preserving methods traverse the whole dataset to anonymize k locations together, and do not utilize parallel computing technology. The anonymization for big volume of location data may result in huge computing cost. We propose a new method called Never Wait for Long (NW4L), which protects the privacy of big-volume location data in parallel and real-time. Instead of linear structure, a k-d tree structure is adopted for the nearest location search to speed up computation. To further improve efficiency, locations are pre- classified in several groups, so that each group can be anonymized in parallel with support from the distributed stream computation framework. In this paper, we implemented NW4L based on Spark and used real-world dataset for performance evaluation. Experimental results show that 100,000 location samples can be processed in 2 minutes, which is feasible for real-time computation.
The Pros and Cons of the Use of Laser Ablation Synthesis for the Production of Silver Nano-Antimicrobials

Silver nanoparticles (AgNPs) are well-known for their antimicrobial effects and several groups are proposing them as active agents to fight antimicrobial resistance. A wide variety of methods is available for nanoparticle synthesis, affording a broad spectrum of chemical and physical properties. In this work, we report on AgNPs produced by laser ablation synthesis in solution (LASiS), discussing the major features of this approach. Laser ablation synthesis is one of the best candidates, as compared to wet-chemical syntheses, for preparing Ag nano-antimicrobials. In fact, this method allows the preparation of stable Ag colloids in pure solvents without using either capping and stabilizing agents or reductants. LASiS produces AgNPs, which can be more suitable for medical and food-related applications where it is important to use non-toxic chemicals and materials for humans. In addition, laser ablation allows for achieving nanoparticles with different properties according to experimental laser parameters, thus influencing antibacterial mechanisms. However, the concentration obtained by laser-generated AgNP colloids is often low, and it is hard to implement them on an industrial scale. To obtain interesting concentrations for final applications, it is necessary to exploit high-energy lasers, which are quite expensive. In this review, we discuss the pros and cons of the use of laser ablation synthesis for the production of Ag antimicrobial colloids, taking into account applications in the food packaging field.
CNN for Text-Based Multiple Choice Question Answering

The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.
Industrial Robot : An International Journal Manipulator residual estimation and its application in collision detection

Purpose – This paper aims to present a method to improve the sensitive and low probabilities of false alarm of a manipulator in a human–robot interaction environment, which can improve the performance of the system owing to non-linear uncertainty in the model of the robot controller. Design/methodology/approach – A novel collision detection method based on adaptive residual estimation is proposed, promoting the detection accuracy of the collision of the manipulator during operation. First, a general momentum residual estimator is designed to incorporate the non-linear factors of the manipulator (e.g. joint friction, speed and acceleration) into the residual-related uncertainty of the model. Second, model parameters are estimated through gradient correction. The residual filter is used to determine the dynamic threshold, resulting in higher detection accuracy. Finally, the performance of the residual estimation scheme is evaluated by comparing the dynamic threshold with residual in real-time experiments where a single Universal Robot 5 robot end–effector collides with the obstacle. Findings – Experimental results demonstrate that the collision detection system can improve sensitivity and lead to low probabilities of false alarm of non-linear uncertainty in the model. Practical implications – The method proposed in this article can be applied to industry and human–robot interaction area. Originality/value – An adaptive collision detection method is proposed in this paper to address non-linear uncertainties of the model in industrial application.
SecPM: a Secure and Persistent Memory System for Non-volatile Memory

In the non-volatile memory, ensuring the security and correctness of persistent data is fundamental. However, the security and persistence issues are usually studied independently in existing work. To achieve both data security and persistence, simply combining existing persistence schemes with memory encryption is inefficient due to crash inconsistency and significant performance degradation. To bridge the gap between security and persistence, this paper proposes SecPM, a Secure and Persistent Memory system, which consists of a counter cache write-through (CWT) scheme and a locality-aware counter write reduction (CWR) scheme. Specifically, SecPM leverages the CWT scheme to guarantee the crash consistency via ensuring both the data and its counter are durable before the data flush completes, and leverages the CWR scheme to improve the performance via exploiting the spatial locality of counter storage, log and data writes. Preliminary experimental results demonstrate that SecPM significantly reduces the number of write requests and improves the transaction throughput by using the CWR scheme.
Inverse Kinematics Techniques in Computer Graphics: A Survey

Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of a manipulator so that the end effector moves to a desired position; IK can be applied in many areas, including robotics, engineering, computer graphics and video games. In this survey, we present a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view. The paper starts with the definition of forward and IK, their mathematical formulations and explains how to distinguish the unsolvable cases, indicating when a solution is available. The IK literature in this report is divided into four main categories: the analytical, the numerical, the data‐driven and the hybrid methods. A timeline illustrating key methods is presented, explaining how the IK approaches have progressed over the years. The most popular IK methods are discussed with regard to their performance, computational cost and the smoothness of their resulting postures, while we suggest which IK family of solvers is best suited for particular problems. Finally, we indicate the limitations of the current IK methodologies and propose future research directions.
An Adaptive Independence Sampler MCMC Algorithm for Bayesian Inferences of Functions

Many scientific and engineering problems require one to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, many standard Markov chain Monte Carlo (MCMC) algorithms become arbitrarily slow under the mesh refinement, which is referred to as being dimension dependent. In this work we develop an independence sampler based MCMC method for the Bayesian inferences of functions. We represent the proposal distribution as a mixture of a finite number of specially parametrized Gaussian measures. We also design an efficient adaptive algorithm to adjust the parameter values of the mixtures from the previous samples. Finally we provide numerical examples to demonstrate the efficiency and robustness of the proposed method, even for problems with multimodal posterior distributions.
Estimating Bicycle Route Attractivity from Image Data

This master thesis focuses on practical application of Convolutional Neural Network models on the task of road labeling with bike attractivity score. We start with an abstraction of real world locations into nodes and scored edges in partially annotated dataset. We enhance information available about each edge with photographic data from Google Street View service and with additional neighborhood information from Open Street Map database. We teach a model on this enhanced dataset and experiment with ImageNet Large Scale Visual Recognition Competition. We try different dataset enhancing techniques as well as various model architectures to improve road scoring. We also make use of transfer learning to use features from a task with rich dataset of ImageNet into our task with smaller number of images, to prevent model overfitting.
MQTT-Driven Sustainable Node Discovery for Internet of Things-Fog Environments

Consolidation of Internet of Things and Fog computing paradigms requires effective and efficient application-layer protocols between service seekers and providers. As most of these nodes run on batteries, discovering service providing devices in an IoT-Fog environment has to be performed in an energy-efficient way. In this paper, we propose Power Efficient Node Discovery (PEND), which is an MQTT-driven IoT-fog integration solution for the sustainability of object discovery in a publish/subscribe environment. By enabling the MQTT broker to serve as a fog node to trigger turning on/off of the Bluetooth interfaces of subscriber objects, Bluetooth Low Energy Scanner (BLE-S), and to monitor the trajectories of publishers/advertisers, Bluetooth low Energy Advertiser (BLE-A), we introduce significant reduction in the Bluetooth, CPU, and process- specific power consumption of the mobile devices. The reduction in the battery drain by the BLE interface under the proposed scheme can be as low as 10%-20% of a naive, locality based discovery benchmark whereas the process specific battery drain of the proposed scheme can be as low as 55%-62% of the node discovery benchmark. Furthermore, with the synchronization of the publishers and subscribers at the fog layer entity, i.e., MQTT broker, 100% node discovery can be achieved by the scanning/service subscriber devices.
General drawdown-based de Finetti optimization for spectrally negative Lévy risk processes

Abstract For spectrally negative Lévy risk processes we consider a general version of de Finetti's optimal dividend problem in which the ruin time is replaced with a general drawdown time from the running maximum in its value function. We identify a condition under which a barrier dividend strategy is optimal among all admissible strategies if the underlying process does not belong to a small class of compound Poisson processes with drift, for which the take-the-money-and-run dividend strategy is optimal. It generalizes the previous results on dividend optimization from ruin time based to drawdown time based. The associated drawdown functions are discussed in detail for examples of spectrally negative Lévy processes.
Effects of medicines used to treat gastrointestinal diseases on the pharmacokinetics of coadministered drugs: a PEARRL Review

Drugs used to treat gastrointestinal diseases (GI drugs) are widely used either as prescription or over‐the‐counter (OTC) medications and belong to both the 10 most prescribed and 10 most sold OTC medications worldwide. The objective of this review article is to discuss the most frequent interactions between GI and other drugs, including identification of the mechanisms behind these interactions, where possible.
Discrete Spatial Compression beyond Beamspace Channel Sparsity Based on Branch-and-Bound

One of the most challenging issues in deploying massive multiple-input multiple-output (MIMO) systems is the significant radio-frequency (RF) front-end complexity, hardware cost and power consumption. Towards this end, the beamspace-MIMO based approach is a promising solution. In this paper, we first show that traditional beamspace-MIMO approaches suffer from spatial power leakage and imperfect channel statistics estimation. A beam combination module is hence proposed, which consists of a small number (compared with the number of antenna elements) of low-resolution (possibly one- bit) digital (discrete) phase shifters after beamspace transformation to further compress the beamspace signal dimensionality, such that the number of RF chains can be reduced beyond beamspace transformation and beam selection. The optimum discrete beam combination weights are obtained based on the branch-and-bound (BB) approach. The key to the BB-based solution is to solve the embodied sub- problem, whose solution is derived in a closed-form. Link-level simulation results based on realistic channel models and LTE parameters are presented which show that the proposed schemes can reduce the number of RF chains by up to 25% with a one-bit phase-shifter-network.
Selecting suitable virtual machine migrations for optimal provisioning of virtual data centers

Maximizing the number of virtual infrastructures spawned out of a data center is a prime concern of cloud service providers to improve their revenue and the customers' quality-of-experience. Optimal placement of topology sensitive virtual data centers on its physical counterparts increases the resource utilization and thereby the revenue. However, provisioning, scaling, and de-provisioning of virtual data centers, over the time, leaves the cloud data center fragmented. Embedding new virtual data centers on a fragmented data center would require migration of virtual machines and virtual network, already mapped to it. However, these migrations are costly in terms of the resource usage and possible violations of the terms of service level agreement. The costs of these migrations associated with a new embedding should be controlled to increase the profit. In this paper, the problem of finding virtual machine migrations with minimum cost is formally defined and two meta-heuristic solutions are proposed for it. We consider the memory size, the rate of page dirtying, the class of application running on the VM, and the bandwidth of the migration path for modeling the cost of migration. The objective is to find a pattern of virtual machine migrations so that the total cost for embedding a new virtual data center is minimized. Experimental results show that the proposed technique can reduce the average migration time upto 30% and the penalty for corresponding service level agreement violation by 200%.
Establishing Normal Values for Shear-Wave Elastography of the Renal Cortex in Healthy Adults

Background: Shear-wave elastography of the kidney has emerged as a potential clinical application of this novel imaging tool. However, normal velocity values for shear-wave elastography involving the cortex of healthy kidneys have not been definitively established, and both inter- and intraobserver reliability has yet to be comprehensively evaluated. Methods: This prospective study involved ultrasound examination of 11 healthy adults. Shear-wave velocity values were obtained at the renal cortex in the longitudinal and transverse planes by both junior (fellow) and senior (attending) radiologists. Results: The mean shear-wave velocity values ranged between 2.82 and 2.9 m/s, which did not vary significantly between observers (junior vs. senior) or method of measurement (longitudinal vs. transverse planes), P = 0.533. However, there was a wide variation for these measurements (0.51–4.99 m/s). Separate analysis of the measurement depth demonstrated no statistically significant association with the shear-wave velocity values, P = 0.477. Conclusion: Our results agree with previous publications and help establish normal shear-wave velocity values and their range for the renal cortex in adults.
Clustering and Centralized Resource Scheduling of 3D In-Building Small Cells for Intra MAC Functional Split Control-/User-Plane Decoupled CRAN

This paper presents a novel clustering and centralized resource scheduling algorithm for 3- dimensional (3D) in-building femtocells (FCs) for an intra MAC functional split heterogeneous CRAN with control-/user-plane decoupled architecture to reuse resources in FCs. We consider two levels of FC clustering where level 1 clustering defines an initial 3D FC cluster size such that each FC is allocated statically with an equal number of orthogonal resource blocks subject to interference constraints. Whereas, level 2 clustering defines a cooperating set of FCs per floor per level 1 cluster to enable joint transmission Coordinated Multipoint (CoMP) during off-states of their transmit power. We find the probability of CoMP- state of a femto UE by modeling its traffic activity in non-CoMP-and CoMP-state using the Birth-Death process, derive the CoMP capacity gain, and update the initial level 1 cluster size subject to fronthaul's bandwidth and latency constraints. With numerical and simulation results, we show that the proposed clustering and centralized scheduling algorithm, by applying CoMP in level 1 clusters each consists of 2 floors with 4 FCs per floor in a building, can achieve the maximum capacity gain of 44% per building and 32.15% for the overall system.
Simulation of LoRa in NS-3: Improving LoRa Performance with CSMA

In this paper, we consider two research issues. First, we present a NS-3 module that simulates the behavior of LoRa in an accurate way. To validate the module, we compare its results with measurements on a real-world testbed and with measured values reported by other work. We also show that the module correctly represents the capture effect that lowers the packet drop rate due to collisions. Second, we want to improve the performance of LoRa devices while not impacting energy consumption, the aspect that usually is not taken into account in the literature. We use the simulator to evaluate CSMA, a simple enhancement to LoRaWAN that lowers the collision ratio. The simulation results show that CSMA considerably lowers the collision ratio while only slightly increasing energy consumption. We also observe that CSMA presents lower energy consumption than LoRa for a large number of devices. Another advantage of CSMA consists of increased throughput and larger network capacity because the ETSI restrictions on the duty cycle do not longer apply.
A Connectivity Aware Transmission Quality Guaranteed Geographic Routing in Urban Internet of Vehicles

Internet of Vehicles (IoV) has drawn more and more attention. However, due to high vehicle movement and frequent topology changing, it is quite challenging to design an efficient routing protocol in the complex urban IoV. In this paper, we propose a Connectivity aware Transmission quality guaranteed Geographic Routing in urban IoV (CTGR). First, we give the connectivity model in case of disconnected link and the transmission quality (TQ) model when the network is connected, respectively. Then, using the two models, with the assistance of road weight evaluation scheme (RWE), each road segment can be assigned with a suitable weight. Based on the weight information, the road segment can be dynamically selected one by one to comprise the optimized routing path, avoiding the local maximum and data congestion. An improved next hop selection strategy is further proposed to forward the packet along the selected road segment, guaranteeing fast and reliable packet transmission. Simulation results show that our proposed protocol achieves higher packet delivery ratio and lower transmission delay compared with the existing protocols.
Semi-Blind Channel Estimation and Data-Multiplexing for Massive MIMO Network

In this paper, we analyze the performance of mas- sive MIMO network with two-antenna users. We show that increasing the number of antennas at the user side is possible without increasing the channel estimation overhead linearly with the number of extra antennas. We propose a simple data-aided channel estimation method for the extra antenna. The proposed method exploits the asymptotic orthogonality of the channel vectors in very large MIMO system and takes advantage of the pilot-aided channel estimation performed for the first antenna. The extra antenna is used for data multiplexing and therefore increases the user throughput. We obtain achievable rates when matched filtering detection is used in uplink multi-cell MIMO system. The rate bounds are tight even at finite (not very large) number of antennas. The proposed algorithm is applicable for more than two antennas.
Editorial of the Workshop on Very Large Internet of Things (VLIoT 2018)

The 2nd “Very Large Internet of Things” (VLIoT) workshop in conjunction with the 44th International Conference on Very Large Data Bases (VLDB) taking place in Rio de Janeiro, Brazil in 2018 is a forum for all researchers in the area of Internet of Things especially interested in related data management issues. This editorial of a special issue containing the workshop’s papers provides an overview over the aims and scope of the workshop and the review procedure. Furthermore, we determine and shortly analyze a statistics of the topics addressed by the accepted papers. TYPE OF PAPER AND