Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Pyramid Pooling of Selective Convolutional Features for Vein Recognition

Deep neural network (DNN) has demonstrated astounding performance in large-scale image recognition task, and pre-trained DNN models trained for one task have also been applied to domains different from their original purposes. Following such an idea, a novel hand-dorsa vein recognition model is constructed by adopting DNN pre-trained on a large-scale database as a universal feature descriptor. Unlike most of these studies which adopt activations of the fully connected layer of DNN as the image representation, we adopt convolutional activations as the region representation. However, not local features of all regions are equally important for final classification. Thus, to solve this issue, a novel selective convolutional feature model based on spatial weighting is proposed to acquire more robust and discriminative feature representation. In specific, a spatial weighting scheme is applied on the convolutional activations to weigh the importance of local features at different regions for classification, which further enhances the discriminability of feature representation. Besides, to take full advantage of the spatial information of the convolutional activations, spatial pyramid pooling is introduced to obtain feature representation with rich spatial information. The final image representation is formed by concatenating the local features of different level of the spatial pyramid. Series rigorous experiments on the lab-made database are conducted to evidence the effectiveness and feasibility of the proposed model. What is more, an additional experiment with subset of PolyU database illustrates its generalization ability and robustness.
Testing Weak form Market Efficiency: Empirical Evidence from Selected Asian Stock Markets

This empirical study examines stock market efficiency of selected fifteen countries from the Asian region using weekly stock returns from the year 2001 to 2017. In order to test the market efficiency, the following statistical methods were conducted on the realized returns: Auto Correlation, Q Statistics, Correlation Matrix, Unit Root Test, and Run Test. It is revealed that the weekly return is not normally distributed as the historical returns from the considering markets are negatively skewed. We came up to a conclusion that weekly returns do not follow the random walk as it rejects the null hypothesis. Therefore, it may be possible for the investors to gain an arbitrage profit by investing in any of the markets in consideration.
Use of Posterior Hamstring Harvest During Anterior Cruciate Ligament Reconstruction in the Pediatric and Adolescent Population

Background: Posterior hamstring harvest has been described in the adult population in a limited fashion, but no study is available describing the use of posterior hamstring harvest in an active pediatric and adolescent cohort. At times, surgeons may be faced with a challenging anterior harvest due to patient anatomic characteristics, particularly the anatomic features and size of the pes tendons. Clinicians need to have multiple harvest approaches at their disposal. Complications with hamstring harvest such as premature graft transection are more problematic in this population due to higher failure rates with allograft tissue. The posterior harvest via its more proximal location may allow for easier tendon identification, visualization of the accessory attachments, and longer preserved tendon length if transection error occurs when the anterior approach is avoided based on surgical technique, patient anatomic characteristics, and surgeon and patient preference. Purpose: To describe the technique of a posterior hamstring harvest in pediatric and adolescent patients and to analyze complications. Study Design: Case series; Level of evidence, 4. Methods: This study was a retrospective review of a consecutive series of pediatric and adolescent patients who underwent posterior hamstring harvest. During surgery, the patient’s leg was abducted and externally rotated to expose the posteromedial aspect of the knee. A 2-cm incision was made overlying the palpable medial hamstring at the popliteal crease. The posterior hamstring tendons were first harvested proximally with an open tendon stripper and distally with a closed stripper. Preoperative, intraoperative, and postoperative findings and complications were analyzed. Results: A total of 214 patients (mean ± SD age, 15.7 ± 4.1 years; range, 8.0-19.8 years) underwent posterior harvest, with a mean ± SD follow-up of 1.83 ± 1.05 years. No complications occurred in our series related to graft harvest—no graft transections, neurovascular injuries, secondary procedures for wound healing or closure, cosmetic concerns, or limitations in return to activity due to the posterior incision. Conclusion: The posterior hamstring harvest is a safe and reliable technique to harvest autograft tendon in pediatric and adolescent anterior cruciate ligament reconstructions. The posterior technique entailed no complications related to harvest. No patients expressed any cosmetic concerns about their incision or had limitations in return to sport due to the posterior harvest.
PARD3 dysfunction in conjunction with dynamic HIPPO signaling drives cortical enlargement with massive heterotopia

In this study, Liu et al. investigated the molecular mechanisms regulating the proper organization and orderly mitosis of radial glial progenitors (RGPs), which drive the formation of a laminated mammalian cortex in the correct size. They show that RGP behavior and cortical development are controlled by partitioning-defective 3 (PARD3) in concert with dynamic HIPPO and NOTCH signaling regulation, thus providing new insights into mammalian cortical development.
Comparison of GMF and Graphiti based on experiences from the development of the PREDIQT tool

Creating graphical editors can be a difficult undertaking. A graphical editor specialized at a well defined domain will likely be of good value for the end user, as such a tool should support the domain in a more direct way than a general purpose editor would, enabling, among other things, the user to avoid simple mistakes. This thesis evaluates two frameworks aimed at the creation of graphical editors, namely GMF and Graphiti. GMF is considered a mature technology, whereas Graphiti is currently in its incubation phase. Both technologies use GEF, Draw2d and Eclipse in general as underlying technologies. While GMF is fairly well documented, little is written about GMF as compared to Graphiti. Furthermore neither has been evaluated with respect to tree-based methods using value propagation. The main hypothesis is that GMF will outperform Graphiti with respect to the following criteria: • Applicability for supporting tree-based methods using value propagation.
Neuro-Activity-Based Dynamic Path Planner for 3-D Rough Terrain

This paper presents a natural mechanism of the human brain for generating a dynamic path planning in 3-D rough terrain. The proposed paper not only emphasizes the inner state process of the neuron but also the development process of the neurons in the brain. There are two algorithm processes in this proposed model, the forward transmission activity for constructing the neuron connections to find the possible way and the synaptic pruning activity with backward neuron transmission for finding the best pathway from current position to target position and reducing inefficient neuron with its synaptic connections. In order to respond and avoid the unpredictable obstacle, dynamic path planning is also considered in this proposed model. An integrated system for applying the proposed model in the real cases is also presented. In order to prove the effectiveness of the proposed model, we applied it in the pathway of a four-legged robot on rough terrain in both computer simulation and real cases. Unpredictable collision is also performed in those experiments. The model can find the best pathway and facilitate the safe movement of the robot. When the robot found an unpredictable collision, the path planner dynamically changed the pathway. The proposed path planning model is capable to be applied in further advance implementation.
Hop-by-hop interest rate notification and adjustment in named data networks

The transport model of the Named Data Networking (NDN) architecture introduces several new features, especially content can be retrieved from multiple sources and through multiple paths and may be stored in different in-network caches. These distinguished features make the traditional host-to-host TCP/IP congestion control schemes inconsistent with the NDN's communication model. In this paper, we describe and evaluate IRNA: a Hop-By-Hop Interest Rate Notification and Adjustment scheme. To address the above issues, IRNA controls the outgoing Interest queue occupancy at every NDN router. When the queue size exceeds or falls below a specified threshold, IRNA signals it towards downstream nodes by explicitly specifying the suitable Interest receiving rate. Consumers and routers along the path adjust their Interest sending rates according to the available rate specified in this notification. We evaluated IRNA through simulation and our results show that IRNA reacts quickly to congestion events while maintaining a proper bandwidth usage. Moreover, IRNA guarantees bandwidth fair sharing between consumers.
Max-margin generative adversarial networks

Generative Adversarial Networks (GANs) have recently received a lot attention due to the promising performance in image generation, inpainting and style transfer. However, GANs and their variants still face several challenges, including vanishing gradients, mode collapse and unbalanced training between generator and discriminator, which limits further improvement and application of GANs. In this paper, we propose the Max-Margin Generative Adversarial Networks (MMGANs) to approach these challenges by substituting the sigmoid cross-entropy loss of GANs with a max-margin loss. We present the theoretical guarantee regarding merits of max-margin loss to solve the above problems in GANs. Experiments on MNIST and CelebA have shown that MMGANs have three main advantages compared with regular GANs. Firstly, MMGANs is robust to vanishing gradients and mode collapse. Secondly, MMGANs have good stability and strong balance ability during the training process. Thirdly, MMGANs can be easily expanded to multi-class classification tasks.
An S band tracking receiver LNA for satellite communications

A novel implementation of a Pseudomorphic High Electron Mobility Transistor (pHEMT) Low Noise Amplifier (LNA) for use in satellite communications tracking receiver is presented in this paper. The proposed design is of a single stage LNA working between frequency range of 2000 MHz to 2200 MHz. The designed LNA exhibit a Noise Figure (NF) of 0.320 dB which is less than the available pHEMT S Band LNA designs along with a transducer gain greater than 10dB. The Voltage Standing Wave Ratio (VSWR) of the designed and simulated LNA is 1:1.324. Simulation and measurements are taken in Advanced System Design (ADS) software. The circuit is designed and simulated using FR4 PCB laminate.
Frame structure design for massive MIMO systems-PHY perspective

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next generation wireless communication systems and for green communications. There are two operation modes: time-division duplex (TDD) and frequency-division duplex (FDD) for massive MIMO systems, and the debate on the two modes is still discussed in the literature. The originator of massive MIMO systems, Marzetta, has presented the basic frame structures for both the TDD and the FDD modes. However, the basic frame structures do not carefully take the hardware impairments such as phase noise (PN) into account. In this paper, by taking the PN as the key hardware impairment into account, we recommend new and efficient frame structures for both the TDD and the FDD modes. The corresponding performance comparison between the two modes is also provided by using the data transmission efficiency (DTE) as the performance measurement. The comparison may help determine which mode should be used for massive MIMO systems.
Discrete noise-eliminating second order generalized integrator based control strategy applied to grid-integrated solar PV synchronization converter

This work introduces a novel discrete noise-eliminating second order generalized integrator (DNSOGI) based control technique for synchronization converter of single phase two stage grid-connected solar photovoltaic (PV) system. The role of DNSOGI in control technique is fundamental component extraction from the grid voltage for unit template generation and fundamental component extraction from the load current, which enhances the performances of the control technique. In this work, DNSOGI based control technique is tested for nonlinear loading, unbalanced load condition as well as for solar insolation variation condition. The satisfactory dynamic, as well as the steady-state performance of DNSOGI, shows that the energy management is successfully implemented in all conditions. Moreover, the total harmonic distortion of grid current satisfies the objective of the proposed system and an IEEE-519 standard.
Parameters tuning of snake robots sidewinding gait using Bayesian optimization

In this paper, Bayesian optimization has been employed to maximize the forward velocity of a developed snake robot, when performing side-winding motion. Taking into account the snake body shape, the optimization has been performed in the high dimensional space of gait parameters by imposing appropriate bounds on the optimization parameters, which guarantees that each parameter to be evaluated will result a valid motion. Experimental results, which show 34 percent faster motion with necessary practical consideration are included to show the effectiveness of the proposed method.
Rate-Adaptive Protograph LDPC Codes for Multi-Level-Cell NAND Flash Memory

The multi-level-cell (MLC) NAND flash memory exhibits a diversity of the raw bit error rate (BER) over different program/erase (P/E) cycles and different types of bits within a memory cell. In this letter, we first apply a protograph-based extrinsic information transfer chart analysis to the MLC flash channel and design novel rate-adaptive protograph low-density parity-check (RAP-LDPC) codes by using a code extension approach. The proposed RAP-LDPC code has multiple code rates, which can be adapted dynamically to different P/E cycles. To mitigate the unbalanced raw BERs between different types of bits within a memory cell, we further propose an optimum mapping between the variable nodes of the protograph and different types of bits of the memory cell. Since the proposed RAP-LDPC codes are based on the same parity-check matrix with specific structure, a single protograph encoder/decoder is sufficient to handle all the code rates. Simulation results demonstrate that the proposed RAP-LDPC codes with optimum mapping outperform the irregular LDPC codes for all the code rates with a faster decoding convergence speed for the MLC flash channel.
Quantifying the Impact of Video Quality and Hardware / Software Stabilization on Facial Detection and Recognition in a Mobile Robot System

This paper empirically explores the relationship between the performance of real time facial detection/recognition and software/hardware camera stabilization techniques in mobile robots​. ​ Since the 1960’s, computer vision (CV) has grown in a number of ways including but not limited to, transportation, security systems and mobile service robots​. ​ As the demand for CV has increased, so too have the capabilities of these complex systems to include advanced functions like facial detection and recognition​. ​ However, without a stable image for these systems to process, new developments in CV will not be as applicable for mobile robots​. While facial recognition systems that rely on stationary cameras are already in use, there is a drop in their performance in CV applications when implemented in moving systems (Jung et al​.​ 2004)​. ​ One issue that we have experienced during experiments at Union College (N​.​Y​.​) is the inability to maintain a “smooth” video feed required for detecting and identifying individuals​. ​ In CSC-325: Introduction to Robotics, my group’s robot relied on facial recognition for movement and would lose track of its target each time it began to move due to camera shake​. ​ In response to this problem, this study explored the benefit of camera stabilization hardware and software solutions​. ​ For the test four solutions were used: 1. No solution 2. Camera stabilizing ​hardware 3. Camera stabilizing ​software 4. Both ​camera stabilizing ​hardware​ ​and​ ​software
The magnetostatic multipole expansion in biomagnetism: applications and implications

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Jussi Nurminen Name of the doctoral dissertation The magnetostatic multipole expansion in biomagnetism: applications and implications Publisher School of Science Unit Dept. of Biomedical Engineering and Computational Science Series Aalto University publication series DOCTORAL DISSERTATIONS 79/2014 Field of research Biomedical engineering Manuscript submitted 11 November 2013 Date of the defence 10 June 2014 Permission to publish granted (date) 19 May 2014 Language English Monograph Article dissertation (summary + original articles) Abstract Magnetoencephalography (MEG) is a noninvasive functional imaging method, based on measuring neuronally generated magnetic fields outside the head. MEG has a very high temporal resolution, making it possible to track brain events on the millisecond level. Its main advantage over electroencephalography (EEG) is the simplicity of inverse modelling, enabling more accurate source localization with fewer assumptions.Magnetoencephalography (MEG) is a noninvasive functional imaging method, based on measuring neuronally generated magnetic fields outside the head. MEG has a very high temporal resolution, making it possible to track brain events on the millisecond level. Its main advantage over electroencephalography (EEG) is the simplicity of inverse modelling, enabling more accurate source localization with fewer assumptions. Previous research has shown that the extracranial magnetic fields can be completely described in terms of a magnetostatic multipole expansion derived from Laplace's equation. In recent years, the multipole expansion has found several applications in MEG, including efficient interference suppression and movement compensation; however, this approach places rather high requirements on the MEG device, demanding comprehensive spatial sampling and accurate calibration. In this thesis, we clarify the requirements the multipole-based approach places on the MEG device, in terms of calibration accuracy and other parameters. We propose novel sensor array geometries that would significantly enhance the performance of the method. We also demonstrate the effectiveness of this model by building a prototype device with diverse sensor orientations. Our results indicate improved efficiency in interference suppression as well as enhanced signal-to-noise ratio. The new developments also increase the robustness of MEG, which is particularly significant for clinical applications. The results are directly applicable to other biomagnetic measurements, such as magnetocardiography.
Learning from History to Increase Positive Public Reception and Social Value Alignment of Evidence-Based Science Communication

For effective science communication, three general objectives should be taken into consideration: 1) accurate conveyance of the scientific evidence; 2) warm public reception of the communicator; and 3) alignment of the information with social values. An examination of both successful and failed science communication efforts over the course of history can reveal strategies to better meet these objectives. This article looks back at influential moments of science communication over the past two millennia in the context of the objectives and, using lessons learned from these events as a guide, introduces a five-element approach to improve the potential for attaining the objectives.
A comparative study of control techniques for power loss minimization in a distribution network

Power losses in electrical power systems especially, distribution systems, occur due to the several technical factors. These losses are due to inappropriate size of conductor, long distribution lines, low power factor, overloading of lines etc. The power losses cause economic loss and reduce the system reliability. The reliability of electrical power system can be improved by decreasing power loss of network and by improving voltage profile. In this paper, three grid strategies have been executed which are reactive power injection, transformer tap changing and real load sharing. Genetic Algorithm(GA) and Newton Raphson (NR) based power flow method are used on grid strategies to minimize power loss. These grid strategies are implemented on IEEE-30 Bus radial distribution system. A comparative analysis is performed between grid strategies using GA & NR with respect to real loss minimization.
SDN-Enabled Social-Aware Clustering in 5G-VANET Systems

Nowadays, vehicular applications for traffic safety, traffic efficiency, and entertainment have put forward higher requirements to vehicular communication systems. The fifth-generation (5G) cellular networks are potential to provide high-capacity low-latency communications for vehicles in highly mobile environments. In addition, the IEEE 802.11p-based dedicated short-range communication technology is proposed to form vehicular ad hoc networks (VANETs). Integrating cluster-based VANETs with 5G cellular networks is beneficial for saving scarce spectrum resources, preventing network congestion, and reducing packet loss. However, it is a challenging problem to find an effective clustering algorithm that has high stability adapting to dynamic VANETs. Considering the advantages of software-defined networking (SDN), in this paper, we present an SDN-enabled social-aware clustering algorithm in the 5G-VANET system, which exploits a social pattern (i.e., vehicles’ future routes) prediction model in order to enhance the stability of clusters. Each vehicle’s movement is modeled as a discrete time-homogeneous semi-Markov model, where the state transition probability and sojourn time probability distribution are inputs and each vehicle’s social pattern is output. The predicted social patterns are subsequently used to create clusters so that vehicles in the same cluster tend to share the same routes. Cluster heads are selected based on the metrics of inter-vehicle distance, relative speed, and vehicle attributes. We evaluate our algorithm and the results show that it performs better in terms of cluster lifetime and clustering overhead compared with traditional clustering algorithms.