Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Evaluation and Optimization of Automated Gripper Finger Design for Industrial Robotic Applications

Gripper fingers design is a current and important problem in industrial robotics. Recently, advances have been made to replace the arduous manual trial-and-error design process with optimization methods based on dynamic simulations. In these approaches, the gripper fingers are parametrized and evaluated by simulating multiple grasp sets in order to obtain the quality score, which is subsequently optimized. The computational efficiency of this process depends on: (1) the choice of the scoring function that provides robust evaluation with minimal number of grasps, (2) the choice of optimization algorithm that converges to global optimum quickly and (3) the choice of optimization method and meta-parameters. In this paper, we present considerations pertaining to these three problems. We use the previously proposed gripper finger design and optimization methods for generating a finger cut-out for an asymmetrical object used in industrial assembly tasks. We suggest two new alignment quality scores and compare their efficiency with preexisting methods. In addition, compare the performance of two optimization methods (one local and one global) and find the meta-parameters for the local method.
Dlk1-Dio3 locus-derived lncRNAs perpetuate postmitotic motor neuron cell fate and subtype identity

The mammalian imprinted Dlk1-Dio3 locus produces multiple long non-coding RNAs (lncRNAs) from the maternally inherited allele, including Meg3 (i.e. Gtl2) in the mammalian genome. Although this locus has well-characterized functions in stem cell and tumor contexts, its role during neural development is unknown. By transcriptome profiling cell types at each stage of spinal cord development, we uncovered that lncRNAs expressed from the Dlk1-Dio3 locus are predominantly and gradually enriched in rostral motor neurons (MNs). Mechanistically, Meg3 and other Dlk1-Dio3 locus-derived lncRNAs facilitate Jarid2-Ezh2 interactions. Loss of these lncRNAs compromises the H3K27me3 landscape, leading to aberrant expression of progenitor and caudal Hox genes in postmitotic MNs. Our data illustrate that these lncRNAs in the Dlk1-Dio3 locus play a critical role in maintaining postmitotic MN cell fate by repressing progenitor genes and they shape MN subtype identity by regulating Hox genes, providing strong evidence of how lncRNAs function during embryonic development.
Applying Mechanical Pressure and Skin Stretch Simultaneously for Sensory Feedback in Prosthetic Hands

An effective method of communicating sensory feedback for prosthetics is presented using a combination of mechanical pressure and skin stretch, resulting in a mixture of normal and shear force being applied to the human arm. Stimulations were induced on the subject's forearm by three mechanical cranks, each attached to their own servo motor. Three different crank orientations were tested, each producing a different skin stretch direction, with the results showing that shear force/tangential skin stretch applied longitudinally to the forearm was perceived more easily as it produced the best recognition rate. With minimal training, eighteen able-bodied test subjects were able to recognise six different grips with an accuracy of up to 88%, and achieved an accuracy of 80% when recognising the six grips at two different pressure levels. This sensory feedback mechanism shows potential for a simple, easy to learn stimulation device that could help improve users control and embodiment of their prosthetic device that requires three separate feedback channels.
Discrete Artificial Bee Colony Algorithm based Optical Character Recognition

Recognizing and processing documents, especially historical manuscripts, has been a challenge owing to the difficulty in identifying cursive handwritten characters. This paper presents a simple technique for Optical Character Recognition based on Swarm intelligence to extract textual information from printed or handwritten documents. Primarily, combinations of different filters to remove noise and background stutter is used. After employing frequency domain transforms for feature extraction on the image containing the textual data, the Discrete Artificial Bee Colony algorithm (DABC) is used for selecting useful features. Experimental results show that DABC based feature selection method provides good recognition by removing noise and redundant features. The proposed algorithm works well not only with machine-generated characters, but also with handwritten characters of multiple languages including English, Hindi and Kannada. The technique is comparatively more complete as it works for both machine generated and Handwritten databases, and also aims at achieving language independence.
Unified Probabilistic Modeling of Wind Reserves for Demand Response and Frequency Regulation in Islanded Microgrids

Islanded microgrids provide unique challenges due to the lack of transmission support, requiring local energy supply and grid regulation architecture. This paper presents a unified probabilistic assessment of wind reserves for demand response (DR) and frequency regulation in islanded microgrids. A multivariate nonparametric kernel density estimation algorithm is used to generate the probabilistic models of the wind resource, electrical demand, and predicted performance of wind generation. These models are numerically combined to evaluate the capability of wind generation to act as a dynamic reserve and predict its performance for DR, secondary generation, and frequency regulation in an islanded system. The probabilistic model captures multivariate cross-correlation, nonstationary environmental and load behavior, as well as multimodality in their underlying probability distributions. A case study is conducted to validate the proposed model, which predicts wind generation effectiveness for varying load profiles, wind profiles, and generation capacities. PLEXIM simulation software is used to implement a model microgrid to demonstrate the integration of wind generation and its regulatory capabilities. The proposed algorithm has applications in power system planning and operation, and it provides a method using probabilistic data set for long term energy management and optimization of islanded microgrids.
Multi-typed Community Discovery in Dynamic Heterogeneous Information Networks through Tensor Method

Community discovery in a dynamic heterogeneous information network is a challenging topic and quite more difficult than that in a traditional static homogeneous information network. Community in heterogeneous information network, named multi-typed community, contains multiple types of dynamic objects and links, which brings three challenges. Firstly, the multi-typed communities are heterogeneous. Secondly, The communities are constantly changing along time. Finally, the network schemas for different heterogeneous information networks are various. To overcome these challenges, we propose a multi-typed community discovery method for dynamic heterogeneous information networks through tensor method without the restriction of network schema. A tensor decomposition framework is designed to model the multi-typed community and address the community evolution. Experimental result on a real-world dataset demonstrates the efficiency of our framework.
Adaptive Control of Apache Spark's Data Caching Mechanism Based on Workload Characteristics

Apache Spark caches reusable data into memory/disk. From our preliminary evaluation, we have found that a memory-and-disk caching is ineffective compared to disk-only caching when memory usage has reached its limit. This is because a thrashing state involving frequent data move between the memory and the disk occurs for a memory-and-disk caching. Spark has introduced a thrashing avoidance method for a single RDD (Resilient Distributed Dataset), but it cannot be applied to workloads using multiple RDDs because prior detection of the dependencies between the RDDs is difficult due to unpredictable access pattern. In this paper, we propose a thrashing avoidance method for such workloads. Our method adaptively modifies the cache I/O behavior depending on characteristics of the workload. In particular, caching data are directly written to the disk instead of the memory if cached data are frequently moved from the memory to the disk. Further, cached data are directly returned to the execution-memory instead of the storage-memory if cached data in the disk are required. Our method can adaptively select the optimal cache I/O behavior by observing workload characteristics at runtime instead of analyzing the dependence among RDDs. Evaluation results showed that execution time was reduced by 33% for KMeans using the modified Spark memory-and-disk caching rather than the original.
Time Domain Measurements of Signals Backscattered by Wideband RFID Tags

Passive wideband RFID is increasing interest for its capability of providing high-accuracy tag localization in addition to identification and tag-reader communication. The measurement of backscattering capabilities of wideband antennas is usually conducted in the frequency domain by using network analyzers, which does not allow for the extraction of the antenna mode component of the backscattered signal when the antenna load is time variant. To overcome this issue, in this paper we present a novel setup for time domain measurements of signals backscattered by wideband RFID tags. Experimental evaluations are presented for comparing different wideband antennas and show the effects of the setup characteristics and of the processing schemes on the achievable measurement results.
Binary Variable-to-Fixed Length Balancing Scheme With Simple Encoding/Decoding

We present a systematic variable-to-fixed length scheme encoding binary information sequences into binary balanced sequences. The redundancy of the proposed scheme is larger than the redundancy of the best fixed-to-fixed length schemes in the case of long codes, but it is smaller in the case of short codes. The biggest advantage comes from the simplicity of the scheme: encoding only requires one to keep track of the sequence weight, while decoding requires only one extremely simple step, irrespective of the sequence length.
Evolution and Optimization of BEOL MOM Capacitors Across Advanced CMOS Nodes

Metal-oxide-metal (MOM) capacitors fabricated in CMOS back-end-of-line (BEOL) interconnect layers are widely used for advanced digital, RF and mm-wave applications. This paper describes the impact of CMOS scaling trends on key MOM figures of merit across advanced CMOS nodes. A methodology for maximizing the operating voltage by trading-off footprint for increased working voltage during MOM development is presented. In addition, the impact of BEOL scaling on MOM figures of merit is presented across GlobalFoundries ™ 0.13-μm to 14-nm nodes. It is shown that capacitance density stops increasing beyond 28nm. However, RF characterization data for MOM capacitors fabricated in 22FDX® (22-nm) technology showing a high-Q of 110 for a 63-fF capacitor at 30GHz and Q of 80 for a 28-fF capacitor at 77GHz are presented. Capacitor Q-factor is also shown to be weakly dependent on scaling at a constant capacitance for advanced nodes.
Portable Lumipen: Dynamic SAR in Your Hand

Based on the concept of spatial augmented reality (SAR), a number of applications have been developed by extending the type of target objects to include dynamically moving objects; however, the systems themselves still remain fixed. In this paper, we propose a new paradigm for dynamic SAR implemented with a mobile device and a practical architecture using a 3D-stacked vision chip and a high-speed optical gaze controller. We evaluated the prototype system that we developed, called “Portable Lumipen”, and confirmed that image capturing at 1000 fps, rapid processing and a 3 ms response time for projection were achieved on a mobile device. We also propose potential applications using this portable dynamic SAR system and demonstrated miniaturization of a high-speed visual feedback system.
Synapse Type-Dependent Expression of Calcium-Permeable AMPA Receptors

Calcium-permeable (CP) AMPA-type glutamate receptors (AMPARs) are known to mediate synaptic plasticity in several different interneuron (IN) types. Recent evidence suggests that CP-AMPARs are synapse-specifically expressed at excitatory connections onto a subset of IN types in hippocampus and neocortex. For example, CP-AMPARs are found at connections from pyramidal cells (PCs) to basket cells (BCs), but not to Martinotti cells (MCs). This synapse type-specific expression of CP-AMPARs suggests that synaptic dynamics as well as learning rules are differentially implemented in local circuits and has important implications not just in health but also in disease states such as epilepsy.
Can Machine Learning Techniques Be Effectively Used in Real Networks against DDoS Attacks?

The threat of distributed denial of service (DDoS) attacks has worsened recently with the proliferation of unsecured Internet of Things (IoT) devices. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are decentralised. We study the effectiveness of using machine learning (ML) to detect DDoS attacks, facilitated by Software-Defined Networking (SDN), a recent paradigm that aims to improve network management by centralising network information and control. In this study, ML algorithms are implemented on nmeta2, an SDN-based traffic classification architecture, and evaluated on a physical network testbed to demonstrate their efficacy during a DDoS attack scenario, especially in accurately classifying non-malicious traffic. This is unlike most approaches that aim to identify/classify malicious traffic but also misclassify non-malicious traffic, inadvertently leading to degraded performance for legitimate network traffic. Furthermore, there is potentially considerable data loss during DDoS attacks that can further degrade classification performance. We examine these issues that arise when using ML to detect DDoS attacks in live network scenarios.
Improved Armchair Hexagonal Graphene Ring Gas Sensor

We investigate density of states and an electric current of an armchair hexagonal graphene ring (AHGR) with adsorbed gas molecules (CO, NO, CO2, and NH3). In order to optimize the structure as a gas sensor, a perpendicular magnetic field and an in-plane electric field are applied. The electronic structure of AHGR is sensitive to adsorbed gas molecules so that it changes to n-type and p-type semiconductor in the presence of NH3 and CO2 gas molecule. In addition, the current passing through graphene ring attached to two semi-infinite armchair graphene nanoribbon leads obtains different values by interaction with the gas molecules at a fixed voltage which may be of relevant to gas sensors. By tuning magnetic or electric field, the difference of current values related to various gas molecules increases because of change in density of states that it provides an appropriate condition to recognize more types of adsorbed gas molecules.
Integrated pathology and radiology learning for a musculoskeletal system module: an example of interdisciplinary integrated form

php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Advances in Medical Education and Practice 2018:9 677–679 Advances in Medical Education and Practice Dovepress
Unsupervised Locality-Preserving Robust Latent Low-Rank Recovery-Based Subspace Clustering for Fault Diagnosis

With the increasing demand for unsupervised learning for fault diagnosis, the subspace clustering has been considered as a promising technique enabling unsupervised fault diagnosis. Although various subspace clustering methods have been developed to deal with high-dimensional and non-linear data, analyzing the intrinsic structure from the data is still challenging. To address this issue, a new subspace clustering method based on locality-preserving robust latent low-rank recovery (L2PLRR) was developed. Unlike conventional subspace clustering methods, the developed method maps the high-dimensional and non-linear data into a low-dimensional latent space by preserving local similarities of the data with the goal of resolving the difficulty in analyzing the high-dimensional data. Likewise, in the developed L2PLRR method, learned features correspond to low-rank coefficients of the data in the latent space, which will be further used for fault diagnosis (e.g., identification of health states of an object system). The efficacy of the developed L2PLRR method was verified with a bearing fault diagnosis application by comparing with conventional and state-of-the-art subspace clustering methods in terms of diagnostic performance.
Narrowband Interference Suppression in a Spreading WFRFT System and Optimal Order Selection

A spreading system based on weighted fractional Fourier transform (WFRFT) is proposed over the channel with additive white Gaussian noise (AWGN) and narrowband interference (NBI). The cross-correlation coefficient between pseudo-noise (PN) sequence and NBI (CPI) and the proposed system's bit error rate (BER) are deduced. We proved that the optimal WFRFT order can be obtained to achieve the best BER performance through minimization of CPI. Numerical results show that the optimal WFRFT order can reduce the adverse effect caused by NBI in different distribution states.
A Dual-Band Patch Antenna for Pattern Diversity Application

This paper presents a two-port dual-band pattern diversity patch antenna. The antenna consists of a rectangular patch, a rectangular ground plane, and an upright feeding network. The folding microstrip line and the new coplanar waveguide (CPW) structure are printed on two sides of the single feeding network with a size of <inline-formula> <tex-math notation="LaTeX">$35 \times 14.5 \times 1$ </tex-math></inline-formula> mm<sup>3</sup>. Therefore, two slots of the new CPW are able to generate in phase and out of phase electric fields, respectively, so that the patch is excited by the even and odd mode in lower common band and by the higher-order modes in upper common band. A prototype of the antenna is fabricated and measured. As a result, the lower common operating frequency band is 14% (2.453–2.821 GHz) and the upper common band is 16% (5.876–6.892 GHz). Moreover, the measured maximum peak gains are 4.0 dBi for Port 1 and 8.6 dBi for Port 2 in the lower common frequency band. In the upper common frequency band, 5.6 dBi for Port 1, and 4.5 dBi for Port 2 are obtained. The satisfactory isolation (<inline-formula> <tex-math notation="LaTeX">$\vert S_{12}\vert$ </tex-math></inline-formula> < −20 dB) between the two feeding ports in the common impedance matching bands is achieved. The correlation coefficients and the diversity gains have been calculated, which proves the good diversity performance of the antenna.
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies

Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration. In the case of a robot operating in a real environment the training process can be costly, time-consuming, and even dangerous since failures are common at the start of training. For this reason, it is desirable to be able to leverage simulation and off-policy data to the extent possible to train the robot. In this work, we introduce a robust framework that plans in simulation and transfers well to the real environment. Our model incorporates a gradient-descent based planning module, which, given the initial image and goal image, encodes the images to a lower dimensional latent state and plans a trajectory to reach the goal. The model, consisting of the encoder and planner modules, is first trained through a meta-learning strategy in simulation. We subsequently perform adversarial domain transfer on the encoder by using a bank of unlabelled but random images from the simulation and real environments to enable the encoder to map images from the real and simulated environments to a similarly distributed latent representation. By fine tuning the entire model (encoder + planner) with only a few real world expert demonstrations, we show successful planning performances in different navigation tasks.
Topology-Efficient Discovery: A Topology Discovery Algorithm for Underwater Acoustic Networks

In this paper, we describe topology-efficient discovery (TED), the first designated topology-efficient discovery algorithm for underwater acoustic networks. Topology information is essential for network operations. By knowing the network topology, information sources can determine destination nodes and routing possibilities for their packets and schedule transmissions accordingly. It is therefore of interest to use a schedule for topology discovery to be used at the early stages of network operation. Such schedule must efficiently perform topology discovery and also guarantee a convergence time at the end of which topology discovery is complete and the network switches to its steady-state scheduling protocol. Considering this need, we offer a TED algorithm aimed to discover acoustic links and to assess their reliability. Designed to reduce the time overhead posed by the topology discovery phase, TED allows nodes to share time slots while controlling the number of possible collisions such that the delay of the topology discovery process is minimized. TED also discovers scheduling conflicts by discovering node pairs whose transmissions block one another, often referred to as near–far node pairs (NFNPs). Information about NFNPs can assist for power control or to increase channel utilization if interference cancelation techniques are employed. TED is applicable for underwater networks consisting of modems whose transmission range is on the order of a few kilometer and above, and when the maximal distance between the nodes is known to be large or assumed equal to the modem's transmission range. Numerical results show that under these conditions, TED accurately detects the network topology in a much shorter time compared to benchmark methods. The results also show that, using TED, more packets are received, and thus the accuracy in determining the reliability of communication links increases. We also report results from a sea experiment demonstrating the applicability of TED and its benefits in a real-world scenario.