Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, we propose a novel AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM integrates pose features over time as dynamic gait feature while canonical features are averaged as static gait feature. Both of them are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency. We further compare our GaitNet with state of the art face recognition to demonstrate the advantages of gait biometrics identification under certain scenarios, e.g., long distance/ lower resolutions, cross view angles.

Background: Volatile pyrethroid insecticides, such as transfluthrin, have received increasing attention for their potent repellent activities in recent years for controlling human disease vectors. It has been long understood that pyrethroids kill insects by promoting activation and inhibiting inactivation of voltage-gated sodium channels. However, the mechanism of pyrethroid repellency remains poorly understood and controversial. Methodology/Principal findings: Here, we show that transfluthrin repels Aedes aegypti in a hand-in-cage assay at nonlethal concentrations as low as 1 ppm. Contrary to a previous report, transfluthrin does not elicit any electroantennogram (EAG) responses, indicating that it does not activate olfactory receptor neurons (ORNs). The 1S-cis isomer of transfluthrin, which does not activate sodium channels, does not elicit repellency. Mutations in the sodium channel gene that reduce the potency of transfluthrin on sodium channels decrease transfluthrin repellency but do not affect repellency by DEET. Furthermore, transfluthrin enhances DEET repellency. Conclusions/Significance: These results provide a surprising example that sodium channel activation alone is sufficient to potently repel mosquitoes. Our findings of sodium channel activation as the principal mechanism of transfluthrin repellency and potentiation of DEET repellency have broad implications in future development of a new generation of dual-target repellent formulations to more effectively repel a variety of human disease vectors.

Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.