2,540 research outputs found
LumiGAN: Unconditional Generation of Relightable 3D Human Faces
Unsupervised learning of 3D human faces from unstructured 2D image data is an
active research area. While recent works have achieved an impressive level of
photorealism, they commonly lack control of lighting, which prevents the
generated assets from being deployed in novel environments. To this end, we
introduce LumiGAN, an unconditional Generative Adversarial Network (GAN) for 3D
human faces with a physically based lighting module that enables relighting
under novel illumination at inference time. Unlike prior work, LumiGAN can
create realistic shadow effects using an efficient visibility formulation that
is learned in a self-supervised manner. LumiGAN generates plausible physical
properties for relightable faces, including surface normals, diffuse albedo,
and specular tint without any ground truth data. In addition to relightability,
we demonstrate significantly improved geometry generation compared to
state-of-the-art non-relightable 3D GANs and notably better photorealism than
existing relightable GANs.Comment: Project page: https://boyangdeng.com/projects/lumiga
Improving Resnet-9 Generalization Trained on Small Datasets
This paper presents our proposed approach that won the first prize at the
ICLR competition on Hardware Aware Efficient Training. The challenge is to
achieve the highest possible accuracy in an image classification task in less
than 10 minutes. The training is done on a small dataset of 5000 images picked
randomly from CIFAR-10 dataset. The evaluation is performed by the competition
organizers on a secret dataset with 1000 images of the same size. Our approach
includes applying a series of technique for improving the generalization of
ResNet-9 including: sharpness aware optimization, label smoothing, gradient
centralization, input patch whitening as well as metalearning based training.
Our experiments show that the ResNet-9 can achieve the accuracy of 88% while
trained only on a 10% subset of CIFAR-10 dataset in less than 10 minuet
A geiger-mode APD photon counting system with adjustable dead-time and interchangeable detector
A Geiger-mode avalanche photodiode (GM-APD) photon counting system is presented in this letter. The system provides a maximum counting rate of 35 Mcounts/s and is capable of directly displaying the counting rate and data logging to a PC. In this system, the detector can be easily changed to enhance its usefulness in different applications. A novel active quench and reset integrated circuit (AQR-IC) is designed for the system with adjustable hold-off time from several nanoseconds up to 1.6 μs with a setting resolution of ~6.5 ns. This facilitates optimal performance when using different types of APDs. The AQR-IC also registers each avalanche event as a TTL pulse that is processed by a microcontroller to calculate the photon-counting rate. The microcontroller can be interfaced with a PC over USB to record the measured data and to allow further processing. Software was also written to calculate the photon-counting rate, display the results and save the data to files
Genome comparison reveals inversions and alternative evolutionary history of nutritional endosymbionts in planthoppers (Hemiptera: Fulgoromorpha)
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Photoplethysmography for Quantitative Assessment of Sympathetic Nerve Activity (SNA) During Cold Stress
The differences in the degree of sympathetic nerve activity (SNA) over cutaneous blood vessels, although known to be more prominent in the periphery than the core vasculature, has not been thoroughly investigated quantitatively. Hence, two studies were carried out to investigate the differences in SNA between the periphery and the core during the cold pressor test (CPT) (right-hand immersion in ice water) and cold exposure (whole body exposed to cold air) using photoplethysmography (PPG). Two methods utilizing PPG, namely differential multi-site PTT measurements and low-frequency spectral analysis were explored for quantitative determination of SNA. Each study involved 12 healthy volunteers, and PPG signals were acquired from the right index finger (RIF), left index finger (LIF) (periphery) and the ear canal (core). During CPT, Pulse Transit Time (PTT) was measured to the respective locations and the mean percentage change in PTT during ice immersion at each location was used as an indicator for the extent of SNA. During cold exposure, the low-frequency spectral analysis was performed on the acquired raw PPGs to extract the power of the sympathetic [low-frequency (LF): 0.04–0.15 Hz] and parasympathetic components [high-frequency (HF): 0.15–0.4 Hz]. The ratio of LF/HF components was then used to quantify the differences in the influence of SNA on the peripheral and core circulation. PTT measured from the EC, and the LIF has dropped by 5 and 7%, respectively during ice immersion. The RIF PTT, on the other hand, has dropped significantly (P < 0.05) by 12%. During the cold exposure, the LF/HF power ratio at the finger has increased to 86.4 during the cold exposure from 19.2 at the baseline (statistically significant P = 0.002). While the ear canal LF/HF ratio has decreased to 1.38 during the cold exposure from 1.62 at baseline (P = 0.781). From these observations, it is evident that differential PTT measurements or low-frequency analysis can be used to quantify SNA. The results also demonstrate the effectiveness of the central auto-regulation during both short and long-term stress stimulus as compared to the periphery
Glass transition of an epoxy resin induced by temperature, pressure and chemical conversion: a configurational entropy rationale
A comparative study is reported on the dynamics of a glass-forming epoxy
resin when the glass transition is approached through different paths: cooling,
compression, and polymerization. In particular, the influence of temperature,
pressure and chemical conversion on the dynamics has been investigated by
dielectric spectroscopy. Deep similarities are found in dynamic properties. A
unified reading of our experimental results for the structural relaxation time
is given in the framework of the Adam-Gibbs theory. The quantitative agreement
with the experimental data is remarkable, joined with physical values of the
fitting parameters. In particular, the fitting function of the isothermal
tau(P) data gives a well reasonable prediction for the molar thermal expansion
of the neat system, and the fitting function of the isobaric-isothermal tau(C)
data under step- polymerization conforms to the prediction of diverging tau at
complete conversion of the system.Comment: 16 pages, 8 figures, from the talk given at the 4th International
Discussion Meeting on Relaxations in Complex Systems (IDMRCS), Hersonissos,
Helaklion, Crete (Greece), 17-23 June 200
Visual Analytics for Health Monitoring and Risk Management in CARRE
With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people’s health in such a consistent way that the sensors may become people’s lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people’s health
MyHealthAvatar and CARRE: case studies of interactive visualisation for Internet-enabled sensor-assisted health monitoring and risk analysis
With the progress of wearable sensor technologies, more wearable health sensors have been made available on the market, which enables not only people to monitor their health and lifestyle in a continuous way but also doctors to utilise them to make better diagnoses. Continuous measurement from a variety of wearable sensors implies that a huge amount of data needs to be collected, stored, processed and presented, which cannot be achieved by traditional data processing methods. Visualisation is designed to promote knowledge discovery and utilisation via mature visual paradigms with well-designed user interactions and has become indispensable in data analysis. In this paper we introduce the role of visualisation in wearable sensor-assisted health analysis platforms by case studies of two projects funded by the European Commission: MyHealthAvatar and CARRE. The former focuses on health sensor data collection and lifestyle tracking while the latter aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The roles of visualisation components including timeline, parallel coordinates, map, node-link diagrams, Sankey diagrams, etc. are introduced and discussed
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