PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method

In recent years, self-supervised learning has attracted widespread academic debate and addressed many of the key issues of computer vision. The present research focus is on how to construct a good agent task that allows for improved network learning of advanced semantic information on images so that model reasoning is accelerated during pre-training of the current task. In order to solve the problem that existing feature extraction networks are pre-trained on the ImageNet dataset and cannot extract the fine-grained information in pedestrian images well, and the existing pre-task of contrast self-supervised learning may destroy the original properties of pedestrian images, this paper designs a pre-task of mask reconstruction to obtain a pre-training model with strong robustness and uses it for the pedestrian re-identification task. The training optimization of the network is performed by improving the triplet loss based on the centroid, and the mask image is added as an additional sample to the loss calculation, so that the network can better cope with the pedestrian matching in practical applications after the training is completed. This method achieves about 5% higher mAP on Marker1501 and CUHK03 data than existing self-supervised learning pedestrian re-identification methods, and about 1% higher for Rank1, and ablation experiments are conducted to demonstrate the feasibility of this method. Our model code is located at https://github.com/ZJieX/prsnet

Wave transport in parity-time symmetric, time-varying, and quasi-periodic systems

The past several decades have witnessed a rapid growth of research interest in the fields of artificial materials and systems, in particular in the areas of parity-time symmetric systems, time-varying systems and quasi-periodic systems. These newly developed material platforms have been enabling many exotic wave-matter interaction phenomena unavailable in nature. In this context, I investigated a series of wave transport and scattering phenomena in parity-time symmetric, time-varying and quasi periodic systems in this dissertation. First, I proposed a sensing circuit based on sixth-order exceptional point (EP), which supports high sensitivity, resolution, and nondegraded thermal noise performance compared with conventional diabolic point (DP) sensing system. I also studied the influence of thermal noise in a general two-level sensing platform based on EP. Second, I demonstrated a robust microwave tunneling device operating in the extreme case in which the transmission channel is shorted through a small reactance. We observed full restoration of information and overall transparency to an external observer through use of a pair of parity-time-symmetric emitter and absorber. Third, I studied the effects of realistic switching parameters and synchronization in a series of nonreciprocal devices based on synchronized loss modulation. The research showed that the nonreciprocal response of these systems experiences a linear regression of insertion loss and isolation with respect to the timing error among switches. Remarkably, impedance matching, and nonreciprocal phase shifts are immune from synchronization issues, and reasonable levels of synchronization errors still guarantee low insertion loss and good isolation. Fourth, I studied wave scattering phenomena in static and dynamic quasi-periodic LC resonator array, which provides an easily accessible and reconfigurable platform to study fractal energy band and topological edge state. Overall, my explorations have been enabling a new degree of control of waves in circuits and metamaterials, pushing forward the opportunities for extreme wave-matter interactions using gain and time modulations.Electrical and Computer Engineerin

Controlling photonic spin Hall effect via exceptional points

The photonic spin Hall effect (SHE), featured by a spin-dependent transverse shift of an impinging optical beam driven by its polarization handedness, has many applications including precise metrology and spin-based nanophotonic devices. It is highly desirable to control and enhance the photonic SHE. However, such a goal remains elusive, due to the weak spin-orbit interaction of light, especially for systems with optical loss. Here we reveal a flexible way to modulate the photonic SHE via exceptional points, by exploiting the transverse shift in a parity-time (PT) symmetric system with balanced gain and loss. The underlying physics is associated with the near-zero value and abrupt phase jump of the reflection coefficients at exceptional points. We find that the transverse shift is zero at exceptional points, but it is largely enhanced in their vicinity. In addition, the transverse shift switches its sign across the exceptional point, resulting from spontaneous PT-symmetry breaking. Due to the sensitivity of transverse shift at exceptional points, our work also indicates that the photonic SHE can enable a precise way to probe the location of exceptional point in photonic systems.Comment: 14 pages, 4 figure

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multi-task multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory.Comment: In proceedings of KDD'20, Applied Data Science Track; 9 pages, 2 supplementary page

Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation

Photoplethysmography (PPG) provides a low-cost, non-invasive method to continuously monitor various cardiovascular parameters. PPG signals are generated by wearable devices and frequently contain large artifacts caused by external factors, such as motion of the human subject. In order to ensure robust and accurate extraction of physiological parameters, corrupted areas of the signal need to be identified and handled appropriately. Previous methodology relied either on handcrafted feature detectors or signal metrics which yield sub-optimal performance, or relied on machine learning techniques such as deep neural networks (DNN) which lack interpretability and are computationally and memory intensive. In this work, we present a novel method to learn a small set of interpretable convolutional kernels that has performance similar to -- and often better than -- the state-of-the-art DNN approach with several orders of magnitude fewer parameters. This work allows for efficient, robust, and interpretable signal quality assessment and artifact segmentation on low-power devices.Comment: 16 pages, 6 figure