Miracle-dz/UniFAR-FTUs huggingface dataset:
GAN with autoencoder and importance sampling

Deep generative model such as generative adversarial networks (GAN) has shown impressive achievements in computer graphics applications. GAN is trained to learn the distribution of target data and is able to generate new samples similar to the original target data. However, most GAN based networks encounter mode collapse problem resulting in the generation of samples only from a single or a few modes of target data distribution. In order to address mode collapse problem, we propose to adopt autoencoder to learn target data distribution in encoded space. An importance sampling scheme is used to collect fake and real data samples in the encoded latent space and calculate the similarity of two distributions in real data space. Experimental evaluation compared to state-of-the-art method on synthetic and MNIST datasets shows the potential of our approach in reducing mode collapse problem and generating samples from diverse aspect of target data.
Social facilitation with virtual jogging companion on smartglasses

Jogging is a fundamental activity integrated various sports. It is known familiarly as one of daily exercises. Being able to do alone anytime and anywhere is important, and motivation is a key issue for initiating and maintaining such exercise. Presence of others tends to affect physical activity. It is called Social Facilitation. Individuals perform better on simple or well-rehearsed task with the social facilitation. Presence of attentive spectator makes the jogger's pace faster than no-spectator condition [Strube et al. 1981]. Furthermore, competition with a superior partner causes the jogger to get motivated and increase effort, which is called Köhler effect. Presence of others is effective for exercise; however, it is difficult to find such partner. Some virtual reality exercises have been researched to overcome this gap between individual and pair/group exercise. Software-generated virtual partners have already applied for stationary bike, plank exercise, rowing exercise with an immersive head-mounted or mere stationary display. People prefer change in their surroundings while jogging. Mueller et al. tried to realize outdoor jogging experience with a drone [Mueller and Muirhead 2015] and a distant jogger felt through his/her voice [Mueller et al. 2010]. Jogging with an autonomous robot and a runner participating from other place could connect with appropriate partner anytime and anywhere. However, whole control of the drone in crowded environment with people and buildings is difficult and voice communication gets much attention from the environment.
Interaction of a stereoscopic 3DCG image with motion parallax displayed in mid-air

We propose a novel system that enables a user to see stereoscopic 3DCG images in mid-air and interact with them directly as shown in Figure 1. This system displays 3DCG objects with motion parallax. Thus the user can observe them in mid-air while feeling a stereoscopic effect by the motion parallax. It is also possible to interact with the mid-air 3DCG objects by fingers. The user can move, deform and draw 3DCG objects as if they were there.
Shape and texture reconstruction for insects by using X-ray CT and focus stack imaging

Because of their variety in shapes, textures, and ecologies, insects have been an important subject of natural science for a long time. Reconstructing three dimensional (3D) shapes and textures of insects in digital format has various benefits such as deterioration free, high space efficiency and high accessibility. In addition to the scientific purpose, the highly realistic insect models are also useful for creating graphics scenes, since they are commonly seen in our daily life.
Structure-Preserving Constrained Optimal Trajectory Planning of a Wheeled Inverted Pendulum

The wheeled inverted pendulum (WIP) is an underactuated, nonholonomic mechatronic system, and has been popularized commercially as the Segway. Designing a control law for motion planning, that incorporates the state and control constraints, while respecting the configuration manifold, is a challenging problem. In this article, we derive a discrete-time model of the WIP system using discrete mechanics and generate optimal trajectories for the WIP system by solving a discrete-time constrained optimal control problem. Furthermore, we describe a nonlinear continuous-time model with parameters for designing a closed-loop linear–quadratic regulator (LQR). A dual control architecture is implemented in which the designed optimal trajectory is, then, provided as a reference to the robot with the optimal control trajectory as a feedforward control action, and an LQR in the feedback mode is employed to mitigate noise and disturbances for ensuing stable motion of the WIP system. While performing experiments on the WIP system involving aggressive maneuvers with fairly sharp turns, we found a high degree of congruence in the designed optimal trajectories and the path traced by the robot while tracking these trajectories. This corroborates the validity of the nonlinear model and the control scheme. Finally, these experiments demonstrate the highly nonlinear nature of the WIP system and robustness of the control scheme.
Contingencies of Self-Worth on Positive and Negative Events and Their Relationships to Depression

Previous studies have found that the relationship between contingencies of self-worth (CSW) and depression was generally weak. We posited that this is partly because previous studies assumed CSW on positive and negative events as the same construct (one-dimensional CSW), which should be better conceptualized as two distinctive dimensions (two-dimensional CSW) in explaining depression. A total of 393 undergraduates from Taiwan completed the measures regarding one-dimensional CSW, two-dimensional CSW, and depression. After dividing CSW of seven classic domains into two dimensions of positive and negative CSW, the results of confirmatory factor analyses showed that the two-dimensional CSW model had better model fit than the one-dimensional model in all domains. Furthermore, relative to using one-dimensional CSW as a predictor of depression, the variance accounted for largely increased when positive and negative CSW entered simultaneously in the regression equation. The results suggest that CSW on positive and negative events should be seen as two dimensions and this perspective may largely increase the explanatory power of CSW in explaining mental health.
AI Neurotechnology for Aging Societies - Task-load and Dementia EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods

Dementia and especially Alzheimer's disease (AD) are the most common causes of cognitive decline in elderly people. A spread of the above mentioned mental health problems in aging societies is causing a significant medical and economic burden in many countries around the world. According to a recent World Health Organization (WHO) report, it is approximated that currently, worldwide, about 47 million people live with a dementia spectrum of neurocognitive disorders. This number is expected to triple by 2050, which calls for possible application of AI-based technologies to support an early screening for preventive interventions and a subsequent mental wellbeing monitoring as well as maintenance with so-called digital-pharma or beyond a pill therapeutical approaches. This paper discusses our attempt and preliminary results of brainwave (EEG) techniques to develop digital biomarkers for dementia progress detection and monitoring. We present an information geometry-based classification approach for automatic EEG-derived event related responses (ERPs) discrimination of low versus high task-load auditory or tactile stimuli recognition, of which amplitude and latency variabilities are similar to those in dementia. The discussed approach is a step forward to develop AI, and especially machine learning (ML) approaches, for the subsequent application to mild-cognitive impairment (MCI) and AD diagnostics.
Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness

Brain neurons exhibit complex electroresponsive properties - including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset - which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF derives from the GLIF model family and is therefore mono-compartmental, keeps the limited computational load typical of a linear low-dimensional system, admits analytical solutions and can be tuned through gradient-descent algorithms. Importantly, E-GLIF is designed to maintain a correspondence between model parameters and neuronal membrane mechanisms through a minimum set of equations. In order to test its potential, E-GLIF was used to model a specific neuron showing rich and complex electroresponsiveness, the cerebellar Golgi cell, and was validated against experimental electrophysiological data recorded from Golgi cells in acute cerebellar slices. During simulations, E-GLIF was activated by stimulus patterns, including current steps and synaptic inputs, identical to those used for the experiments. The results demonstrate that E-GLIF can reproduce the whole set of complex neuronal dynamics typical of these neurons - including intensity-frequency curves, spike-frequency adaptation, depolarization-induced and post-inhibitory rebound bursting, spontaneous subthreshold oscillations, resonance and phase-reset, - providing a new effective tool to investigate brain dynamics in large-scale simulations.
OpenISS depth camera as a near-realtime broadcast service for performing arts and beyond

Open Illimitable Space System is a suite of configurable tools and the open source core for Illimitable Space System v2, which exhibits multimodal interaction and provides a platform for artists to enhance their performance by leveraging modern day technology. It is an experimental C/C++ framework with wrappers and API for motion capture and other interactions. Recently, we extended the OpenISS by enabling SOAP and REST APIs, which brought us closer to a more flexible and scalable architecture for being available as an interactive broadcast service over the Internet to a wider audience. In April 2018, we did a realtime live streaming demonstration exhibit to a team of Ubisoft representatives along with the ISSv2 project, making as an example Microsoft Kinect-as-a-service prior to Microsoft's announcement for Kinect for Azure. During the demo, we served 43032 requests, up to 1474 requests per min and we had a total of 19 visitors. A subsequent real time-live streaming demonstration was done at the ChineseCHI workshop later in April at the CHI2018 conference as well with a live application of the service in a dance performance.
Optimize deep super-resolution and denoising for compressed textures

Lossy GPU-native compression formats such as BCn, ETC and ASTC have been widely used to reduce video memory footprint of textures. A downloadable GPU application like a mobile 3D game should select the most suitable compression format and resolution per texture, while taking a variety of the target device's capabilities and download volume into consideration.
Adaptation of manga face representation for accurate clustering

Manga character drawing styles differ greatly among artists. To accurately cluster faces within an individual manga, we propose a method to adapt manga face representations to an individual manga. We use deep features trained for generic manga face recognition, and adapt them by deep metric learning (DML) for the target manga volume. DML uses pseudo positive and negative pairs defined by considering page and frame information. We performed experiments using a dataset comprising 104 manga volumes and found that our feature adaptation significantly improved the accuracy of manga face clustering.
Commentary: Comparison of the Protective Effects of Individual Components of Particulated trans-Sialidase (PTCTS), PTC and TS, against High Cholesterol Diet-Induced Atherosclerosis in Rabbits

Citation: Higuchi ML (2018) Commentary: Comparison of the Protective Effects of Individual Components of Particulated trans-Sialidase (PTCTS), PTC and TS, against High Cholesterol Diet-Induced Atherosclerosis in Rabbits. Front. Cardiovasc. Med. 5:171. doi: 10.3389/fcvm.2018. 00171 Commentary: Comparison of the Protective Effects of Individual Components of Particulated trans-Sialidase (PTCTS), PTC and TS, against High Cholesterol Diet-Induced Atherosclerosis in Rabbits
Improving Information Service Performance of Family Businesses Through IT Governance

Improving information service performance is central and immensely influences all business activities, run by family enterprises in particular. Contextually, online businesses have not engaged mapping of transparent and accountable information services. This research aimed to obtain more comprehensive results to ease improvement of IT process values in actualizing information service performance. Besides assessing maturity values and managerial implications, the model of IT governance was recommended based on the relationship of processes of Acquire and Implement (AI) Domain, Monitor and Evaluate (ME) Domain, and others through COBIT 4.1 Framework. Results show that averages of AI and ME Domains are respectively 2.788 and 2.417. The lowest scores of these domains are 2.312 (AI4), 2.416 (AI5), 2.356 (ME1), and 2.235 (ME4). A recommended model of IT governance emphasizes the management of changes through horizontal communication and orientation of the seniority level in the family and synchronization and interoperability of improving information service performance for internal and external needs.
Every little thing makes us think of cancer - How patient access to medical records influences role relations

While increasing attention in society is given to openness and visibility enabled by digital technologies, relatively few studies have considered the consequences that visibility of work creates for role relations. Building on a field study of 115 consultations and interviews with 11 physicians and 30 cancer patients who received digital access to their records, we show unexpected consequences of openness for physician-patient relationships: instead of helping patients to inform themselves and take over the tasks of physicians, the portal seems to increase patients' reliance on professional expertise. Our preliminary findings illustrate that role relations are reinforced even though the portals are introduced and actively used by patients with attempts to alter them.
An Overview of On-Chip ESD Protection in Modern Deep Sub-Micron CMOS Technology

Electrostatic discharge (ESD) protection is a field with increasing importance in the era of sub-10-nm technology nodes. Device scaling comes at a price of thinner gate dielectrics and metallization layers, leakage currents management issues, device-to-die area ratio efficiency, etc. On the other hand, CMOS technology is steadily gaining traction in mixed-signal and RF applications which impose their own set of requirements. Parasitic capacitance and latch-up immunity are the major concerns for an ESD device used in high-speed circuits. This paper provides an in-dept systematic overview of optimization methods employed to meet the latest parameter requirements created by emerging sub-micron integrated circuits designs.
Optimization and Characterization of Phosphorus Diffused LPCVD Polysilicon Passivated Contacts with Low Pressure Tunnel Oxide

This work presents the investigation of low pressure in-situ thermal oxidation as the interfacial oxide for n+ polysilicon-oxide passivated contact structure, achieving excellent surface passivation below 1 fA·cm-2 and contact resistivity below 1 mΩ·cm2. The results from the process optimisation are presented in detail, showing the importance of accurate control of oxidation conditions, and presenting the correlation to the electrical properties. Additionally, a method of fabricating contact resistivity structures from symmetrical photoconductance decay lifetime samples, and the extraction of the specific contact resistivity using 3D numerical simulation is presented.
An Improvement for Medical Image Analysis Using Data Enhancement Techniques in Deep Learning

A huge number of applications and algorithms have been suggested to analyze medical images. Recent developments in deep learning especially, deep convolutional neural networks (CNN), improved the performance of medical image classification methods. However, training a deep CNN from scratch with medical images is complicated task as it requires a large amount of labelled data. In this paper, we show the role of using different data augmentation techniques to solve such problems. Firstly, we created a deep CNN model with twelve layers for image classification. Then we trained this network with original computed tomography scan (CT) image dataset and some new datasets which are created by generating new images using our original image data. By comparing the classification results of our network on different datasets, we show that using data augmentation techniques can help to improve the medical image classification results and to boost up the network performance
Yoga Practitioners Uniquely Activate the Superior Parietal Lobule and Supramarginal Gyrus During Emotion Regulation

Chronic stress contributes to both mental and physical illness. A high prevalence and cost of stress-related illnesses North America warrants investigation into alternative or complementary therapies which may help reduce adverse reactions to stressful stimuli. Emotion regulation is the process of monitoring and adjusting emotional responses to environmental stimuli and stressors. Individuals who participate in physical activity are less likely to have adverse responses to potentially stressful situations, potentially due to adaptions in emotion regulation. Yoga is a form of physical activity involving stretching exercises and meditation, that may lessen individuals’ levels of stress and anxiety and improve emotion regulation. High-frequency heart rate variability (HF-HRV) is considered a measure of parasympathetic nervous system (PNS) activity during the emotion regulation. Measuring HRV and brain activity using functional magnetic resonance imaging (fMRI) offers a useful, noninvasive approach to evaluating “neurovisceral” components of emotion regulation. We aimed to determine whether yoga practitioners (YP) exhibit different patterns of brain activation compared to recreational athletes (RA) without current yoga experience, while viewing emotionally arousing visual stimuli. Our secondary aim was to examine potential differences across groups in HRV throughout the presentation of these stimuli. Analysis of fMRI data during exposure to emotion-evoking (EE) stimuli revealed that the YP group activated two unique brain areas, namely the superior parietal lobule and the supramarginal gyrus. These areas have been associated with attentional awareness and reduced egocentric bias, processes that have been implicated in emotion regulation by others. The RA group activated the inferior middle frontal cortex, an area associated with cognitive reappraisal during emotion regulation. The YP group also demonstrated a trend towards a higher ratio of low- to high-frequency HRV compared to the RA group. The present findings support the presence of experience-dependent neurovisceral mechanisms associated with emotion regulation. Individuals who practice yoga regulate their neurovisceral responses to potentially stressful external stimuli in a different manner than recreational athletes who do not engage in yoga practice. The present study had a small sample size (RA: n = 12; YP: n = 19), which should be taken into account when interpreting the results.
Effects of age and extraction solvent on phytochemical content and antioxidant activity of fresh Moringa oleifera L. leaves

Abstract Antioxidant activity (AOA) and phytochemical content of Moringa oleifera Lam leaves were determined as a function of their age and extraction solvent. Fresh Moringa leaves aged 30, 45, and 60 days were harvested and extracted with three solvents; methanol, ethanol, and water. AOA of leaf extracts was measured using radical scavenging assays (DPPH, ABTS, antiperoxide activity [APA]) and reducing assays (FRAP and total antioxidant capacity [TAC]), and these were correlated with total polyphenols (TPC), total flavonoids (TFC), and chlorophyll contents of leaves. Significant variability (p < 0.05) in TPC and AOA of Moringa leaf extracts was observed with age and extraction solvent as well as their interaction. TPC and TFC increased with maturity, except in aqueous extract. The 60‐day‐old leaves showed highest TPC, TFC, and tocopherol contents with highest DPPH activity. On their part, 30‐day‐old leaves recorded better vitamin C, chlorophyll, and carotenoids with highest ABTS activity and APA. Methanol was best extraction solvent for TPC (4.6 g GAE/100 g DM) while ethanol was for flavonoids (1.8 g CE/100 g DM). Ethanol extracts exhibited the highest DPPH activity (53.3%–71.1%), while both ethanolic and methanolic extracts had similar and higher ABTS + activity (3.83–3.86 g AAE/100 g DM). Strong positive correlations (r ≥ 0.8; p < 0.05) were observed between chlorophyll content and DPPH, ABTS, and APA, suggesting that chlorophyll was the major contributor to AOA. TAC was highest in aqueous solvent. Free radical scavenging activity in Moringa leaves is positively correlated to chlorophyll, TFC, and TPC while reducing power is positively correlated to chlorophyll and TPC. AOA of fresh Moringa leaf extract is a function of its phytochemical content and is influenced by both the age of the leaves and the extraction solvent used. Methanolic and ethanolic extracts of 45‐day‐old Moringa leaves exhibited best antioxidant potentials.
Integration of modern technologies for solving territory patroling problems with the use of heterogeneous autonomous robotic systems

The approach to complex automation of patrolling of the territory is proposed. The generalized structural scheme of a complex of hardware-software means for solving the task of patrolling with the use of autonomous mobile robots is considered. The main requirements to the functionality of individual robotic agents and the principles of building the infrastructure subsystems of the complex are considered.
Compact Multibeam Fully Metallic Geodesic Luneburg Lens Antenna Based on Non-Euclidean Transformation Optics

Non-Euclidean transformations have been recently proposed to produce a link between 3-D homogeneous surfaces and 2-D dielectric lenses. Therefore, the propagation in a geometrical surface has the same response of an equivalent refractive index distribution. By using this concept, we propose here a fully metallic Luneburg lens where the propagation is only in the air. Two metallic plates, following a curved shape, are employed to support the propagation mimicking the designed curvature. To reduce the height of the required curvature, the surface has been mirrored twice with respect to two $z$ constant planes. The lens is fed by 11 waveguide ports spaced with an angle of 12.5° providing 1-D beam scanning over an angular range of ±62.5°. A prototype is manufactured and measured with a good agreement with the simulated results between 25 and 36 GHz to demonstrate the concept.