Real-time deep hair matting on mobile devices

Augmented reality is an emerging technology in many application domains. Among them is the beauty industry, where live virtual try-on of beauty products is of great importance. In this paper, we address the problem of live hair color augmentation. To achieve this goal, hair needs to be segmented quickly and accurately. We show how a modified MobileNet CNN architecture can be used to segment the hair in real-time. Instead of training this network using large amounts of accurate segmentation data, which is difficult to obtain, we use crowd sourced hair segmentation data. While such data is much simpler to obtain, the segmentations there are noisy and coarse. Despite this, we show how our system can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.Comment: 7 pages, 7 figures, submitted to CRV 201

I-V characteristics and differential conductance fluctuations of Au nanowires

Electronic transport properties of Au nano-structure are investigated using both experimental and theoretical analysis. Experimentally, stable Au nanowires were created using mechanically controllable break junction in air, and simultaneous current-voltage (I-V) and differential conductance $\delta I/\delta V$ data were measured. The atomic device scale structures are mechanically very stable up to bias voltage $V_b\sim0.6V$ and have a life time of a few $minutes$. Facilitated by a shape function data analysis technique which finger-prints electronic properties of the atomic device, our data show clearly differential conductance fluctuations with an amplitude $>1$ at room temperature, and a nonlinear I-V characteristics. To understand the transport features of these atomic scale conductors, we carried out {\it ab initio} calculations on various Au atomic wires. The theoretical results demonstrate that transport properties of these systems crucially depend on the electronic properties of the scattering region, the leads, and most importantly the interaction of the scattering region with the leads. For ideal, clean Au contacts, the theoretical results indicate a linear I-V behavior for bias voltage $V_b<0.5V$. When sulfur impurities exist at the contact junction, nonlinear I-V curves emerge due to a tunnelling barrier established in the presence of the S atom. The most striking observation is that even a single S atom can cause a qualitative change of the I-V curve from linear to nonlinear. A quantitatively favorable comparison between experimental data and theoretical results is obtained. We also report other results concerning quantum transport through Au atomic contacts.Comment: 11 pages and 9 figures, submitted to PR

High efficiency resonance-based spectrum filters with tunable transmission bandwidth fabricated using nanoimprint lithography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98685/1/ApplPhysLett_99_143111.pd

Improved weighting in particle filters applied to precise state estimation in GNSS

In the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%

MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition

Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on face recognition datasets for feature extraction. However, due to the vast scale of face recognition datasets and the high cost associated with collecting facial labels, this pre-training paradigm incurs significant expenses. Towards this end, we propose to pre-train vision Transformers (ViTs) through a self-supervised approach on a mid-scale general image dataset. In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced. Therefore, we propose a contrastive fine-tuning approach to effectively mitigate this domain disparity. Specifically, we introduce a novel FER training paradigm named Mask Image pre-training with MIx Contrastive fine-tuning (MIMIC). In the initial phase, we pre-train the ViT via masked image reconstruction on general images. Subsequently, in the fine-tuning stage, we introduce a mix-supervised contrastive learning process, which enhances the model with a more extensive range of positive samples by the mixing strategy. Through extensive experiments conducted on three benchmark datasets, we demonstrate that our MIMIC outperforms the previous training paradigm, showing its capability to learn better representations. Remarkably, the results indicate that the vanilla ViT can achieve impressive performance without the need for intricate, auxiliary-designed modules. Moreover, when scaling up the model size, MIMIC exhibits no performance saturation and is superior to the current state-of-the-art methods

High-order stochastic integration schemes for the Rosenbluth-Trubnikov collision operator in particle simulations

In this study, we consider a numerical implementation of the nonlinear Rosenbluth-Trubnikov collision operator for particle simulations in plasma physics in the framework of the finite element method (FEM). The relevant particle evolution equations are formulated as stochastic differential equations, both in the Stratonovich and Itô forms, and are then solved with advanced high-order stochastic numerical schemes. Due to its formulation as a stochastic differential equation, both the drift and diffusion components of the collision operator are treated on an equal footing. Our investigation focuses on assessing the accuracy of these schemes. Previous studies on this subject have used the Euler-Maruyama scheme, which, although popular, is of low order, and requires small time steps to achieve satisfactory accuracy. In this work, we compare the performance of the Euler-Maruyama method to other high-order stochastic methods known in the stochastic differential equations literature. Our study reveals advantageous features of these high-order schemes, such as better accuracy and improved conservation properties of the numerical solution. The main test case used in the numerical experiments is the thermalization of isotropic and anisotropic particle distributions

Optimism Based Exploration in Large-Scale Recommender Systems

Bandit learning algorithms have been an increasingly popular design choice for recommender systems. Despite the strong interest in bandit learning from the community, there remains multiple bottlenecks that prevent many bandit learning approaches from productionalization. Two of the most important bottlenecks are scaling to multi-task and A/B testing. Classic bandit algorithms, especially those leveraging contextual information, often requires reward for uncertainty estimation, which hinders their adoptions in multi-task recommender systems. Moreover, different from supervised learning algorithms, bandit learning algorithms emphasize greatly on the data collection process through their explorative nature. Such explorative behavior induces unfair evaluation for bandit learning agents in a classic A/B test setting. In this work, we present a novel design of production bandit learning life-cycle for recommender systems, along with a novel set of metrics to measure their efficiency in user exploration. We show through large-scale production recommender system experiments and in-depth analysis that our bandit agent design improves personalization for the production recommender system and our experiment design fairly evaluates the performance of bandit learning algorithms

How Well Have Practices Followed Guidelines in Prescribing Antihypertensive Drugs: The Role of Health Insurance

BACKGROUND: The US Joint National Committee (JNC) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure issues guidelines on the optimal first-line drug therapy in treating hypertension. Despite broad dissemination of these guidelines, prescribing practices have long remained discrepant with recommendations. The purpose of this study was to examine the role of insurance type in the selection of drugs for hypertension treatment in light of the JNC guidelines. METHODS: Subjects were derived from the 1996 Medical Expenditures Panel Survey who had a diagnosis of essential hypertension and who were prescribed a diuretic, beta-blocker, calcium channel blocker (CCB), or ACE inhibitor (ACEI) as monotherapy. Using the nationally representative sample, this study presents the first estimates of the impact of insurance policies on the choice of antihypertensive drugs while controlling for predisposing, enabling, and need variables in the context of a logistic health-care utilization model. RESULTS: Nationally in 1996, more than twice as many subjects (7.3 million) were taking ACEIs or CCBs compared to diuretics or beta-blockers (3.1 million) as the first-line drug therapy, a sharp contrast to the JNC guidelines. Patients with health maintenance organization (HMO) insurance were much less likely than fee for service (FFS) patients to follow the JNC guidelines in this respect (odds ratio 0.50, P <.01), controlling for all other factors. Individuals with all other public insurance and no insurance were not statistically different from the FFS group in the use of the study drugs. Other significant factors in the regression model were being of African American descent, being unmarried, having higher out-of-pocket payment, being in excellent physical health, having diabetes, and being diagnosed with essential hypertension after 1988. Each was associated with a decreased likelihood of following the JNC recommendations for the use of diuretics or beta-blockers. CONCLUSIONS: After controlling for other predisposing, enabling, and need variables, patients who had HMO coverage were significantly more likely than FFS patients to receive ACEIs or CCBs. Given a popular public perception of HMOs being most cost conscious in providing health care, further research is needed to understand why prescribing patterns associated with HMOs have poorly followed the JNC recommendations