This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace.

Cloud computing opens a new door for designing the next-generation video distribution platform. As video services move to the cloud, some related data-intensive applications, such as recommender system, automatic scoring mechanism, and prediction algorithm, should also be cloud-based. Since traditional cloud file systems like MapReduce/Hadoop exhibit cost disadvantages in data accessing, a Cache A Replica On Modification (CAROM) cloud file system is designed to achieve high data availability and low storage cost, which provides resiliency in cloud file systems with high efficiency. In this paper, we focus on moving data-intensive applications to the CAROM cloud file system, for optimizing access latencies while maintaining the benefit of low storage cost. To achieve this, we propose a novel scheduling mechanism as a lubricant between CAROM and data-intensive applications. Our scheme consists of three parts. First, tripartite graph is employed to describe the relationships among tasks, computation nodes, and data nodes. Second, we give a 1:1:1 framework based on the situation that the data of task have been stored in the cache, and introduce two variant frameworks 1:1:M and 1:N:M with the consideration of limitations of cache size and performance of task. Finally, a k-list algorithm is proposed as an approximation algorithm, and its mathematical definitions and proofs are given in detail. We conduct simulations to evaluate our scheme and the results prove that the performance of our algorithm is significantly better than that of the general two-layer algorithm.

In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.

In modern society, vehicle theft has become an increasing problem to the general public. Deploying onboard anti-theft systems could relieve this problem, but it often requires extra investment for vehicle owners. In this paper, we propose the idea of PhoneInside, which does not need a special device but leverages an obsolete smartphone to build a low-cost vehicle anti-theft system. After being fixed in the vehicle body with a car charger, the smartphone can detect vehicle movement and adaptively use GPS, cellular/WiFi localization, and dead reckoning to locate the vehicle during driving. Especially, a novel Velocity-Aware Dead Reckoning (VA-DR) method is presented, which utilizes map knowledge and vehicle’s turns at road curves and intersections to estimate velocity for trajectory computation. Compared to traditional dead reckoning, it reduces accumulated errors and achieves great improvement in localization accuracy. Furthermore, based on the learning of the driving history, our system can establish individual mobility model for a vehicle and distinguish abnormal driving behaviors by a Long Short Term Memory (LSTM) network. With the help of ad hoc authentication, the system can identify vehicle theft and send out timely alarming and tracking messages for rapid recovery. The realistic experiments running on Android smartphones prove that our system can detect vehicle theft effectively and locate a stolen vehicle accurately, with average errors less than the sight range.

Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature predominantly focuses on pair-based and proxy-based methods to maximize inter-class discrepancy and minimize intra-class diversity. However, these methods tend to suffer from the collapse of the embedding space due to their over-reliance on label information. This leads to sub-optimal feature representation and inferior model performance. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Specifically, our proposed loss primarily draws inspiration from the principle of Maximal Coding Rate Reduction. It promotes the sparseness of feature clusters in the embedding space to prevent collapse by maximizing the average coding rate of sample features or class proxies. Moreover, we integrate our proposed loss with pair-based and proxy-based methods, resulting in notable performance improvement. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of our method in preventing embedding space collapse and promoting generalization performance. Our code has been anonymously available at: https://github.com/NUST-Machine-Intelligence-Laboratory/Anti-Collapse-Loss .

Mobile charge problem describes a fleet of mobile chargers delivers energy to the sensor nodes periodically in wireless sensor networks (WSNs). In it, every sensor node has an asynchronous rest working time during a charging round. We consider the asynchronous rest working time and give the lower and upper bounds of the recharging cycle by the suitable total serving rate, to give the definition of time windows for the sensor nodes. In this paper, we model this problem in 2-dimensional WSNs as a vehicle routing problem with time windows (VRPTW). For solving the problem of multiple mobile chargers with different routing paths, we propose to transform the multiple routing problems into a single routing problem, by duplicating the sink into multiple virtual sinks. To optimize the routing path, we propose a local optimization algorithm by considering the collaborative charging among the mobile chargers. Through the simulations, we compare our proposed algorithm with the $\mathrm{H}\mathrm{\eta }\mathrm{C}\mathrm{l}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}(\beta )$ algorithm. We demonstrate the advantages of our collaborative scheduling algorithm in this problem.