Reviewerly/exHarmony huggingface dataset:
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video

We present Accel, a novel semantic video segmentation system that achieves high accuracy at low inference cost by combining the predictions of two network branches: (1) a reference branch that extracts high-detail features on a reference keyframe, and warps these features forward using frame-to-frame optical flow estimates, and (2) an update branch that computes features of adjustable quality on the current frame, performing a temporal update at each video frame. The modularity of the update branch, where feature subnetworks of varying layer depth can be inserted (e.g. ResNet-18 to ResNet-101), enables operation over a new, state-of-the-art accuracy-throughput trade-off spectrum. Over this curve, Accel models achieve both higher accuracy and faster inference times than the closest comparable single-frame segmentation networks. In general, Accel significantly outperforms previous work on efficient semantic video segmentation, correcting warping-related error that compounds on datasets with complex dynamics. Accel is end-to-end trainable and highly modular: the reference network, the optical flow network, and the update network can each be selected independently, depending on application requirements, and then jointly fine-tuned. The result is a robust, general system for fast, high-accuracy semantic segmentation on video.
Low resolution face recognition using a two-branch deep convolutional neural network architecture

We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality.
A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

We develop Markov categories as a framework for synthetic probability and statistics, following work of Golubtsov as well as Cho and Jacobs. This means that we treat the following concepts in purely abstract categorical terms: conditioning and disintegration; various versions of conditional independence and its standard properties; conditional products; almost surely; sufficient statistics; versions of theorems on sufficient statistics due to Fisher--Neyman, Basu, and Bahadur. Besides the conceptual clarity offered by our categorical setup, its main advantage is that it provides a uniform treatment of various types of probability theory, including discrete probability theory, measure-theoretic probability with general measurable spaces, Gaussian probability, stochastic processes of either of these kinds, and many others.
Multi-Sensor Multi-Object Tracking With the Generalized Labeled Multi-Bernoulli Filter

This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the multi-sensor case require solving multi-dimensional ranked assignment problems, which are NP-hard. The proposed implementation exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a quadratic complexity in the number of hypothesized objects and linear in the total number of measurements from all sensors.
Mitigating Measurement Errors in Quantum Computers by Exploiting State-Dependent Bias

Quantum computers are susceptible to errors. While quantum computers can be guarded against errors using error correction codes, near-term quantum computers will not have sufficient number of qubits to implement error correction and must perform their computation in the presence of errors. Qubit measurement is typically the most error-prone operation on a quantum computer, with measurement errors ranging from 8% to 30% reported on current machines. This goal of this paper is to mitigate measurement errors by exploiting the state-dependent bias of measurement errors.
Time series prediction for output of multi-region solar power plants

Solar energy, as a renewable and clean energy source, has developed rapidly and has attracted considerable attention. The integration of solar energy into a power grid requires precise prediction of the power output of solar plants. Accurate solar power output prediction can promote power dispatch, maintaining the normal operation of power systems. However, research on multi-region solar power is still rare. In this study, long short-term memory and a particle swarm optimization algorithm contribute to solar power prediction considering time series. In order to improve the prediction accuracy, particle swarm optimization is used to optimize the parameters of the long short-term memory model. In addition, different long short-term memory structures are illustrated to determine the final prediction model with sensitivity analysis. Experiments are carried out to verify the effectiveness of the proposed method. The mean absolute error and root mean square error of the proposed method is the smallest among the prediction methods in four cases containing different seasons. In terms of prediction accuracy, results indicate that the proposed prediction model outperforms basic long short-term memory, artificial neural network, and extreme gradient boosting.
Anchor Diffusion for Unsupervised Video Object Segmentation

Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approach tend to favour short-term temporal dependencies and are thus prone to accumulating inaccuracies, which cause drift over time. Moreover, simple (static) image segmentation models, alone, can perform competitively against these methods, which further suggests that the way temporal dependencies are modelled should be reconsidered. Motivated by these observations, in this paper we explore simple yet effective strategies to model long-term temporal dependencies. Inspired by the non-local operators, we introduce a technique to establish dense correspondences between pixel embeddings of a reference "anchor" frame and the current one. This allows the learning of pairwise dependencies at arbitrarily long distances without conditioning on intermediate frames. Without online supervision, our approach can suppress the background and precisely segment the foreground object even in challenging scenarios, while maintaining consistent performance over time. With a mean IoU of 81.7%, our method ranks first on the DAVIS-2016 leaderboard of unsupervised methods, while still being competitive against state-of-the-art online semi-supervised approaches. We further evaluate our method on the FBMS dataset and the video saliency dataset ViSal, showing results competitive with the state of the art.
Learning Relationships for Multi-View 3D Object Recognition

Recognizing 3D object has attracted plenty of attention recently, and view-based methods have achieved best results until now. However, previous view-based methods ignore the region-to-region and view-to-view relationships between different view images, which are crucial for multi-view 3D object representation. To tackle this problem, we propose a Relation Network to effectively connect corresponding regions from different viewpoints, and therefore reinforce the information of individual view image. In addition, the Relation Network exploits the inter-relationships over a group of views, and integrates those views to obtain a discriminative 3D object representation. Systematic experiments conducted on ModelNet dataset demonstrate the effectiveness of our proposed methods for both 3D object recognition and retrieval tasks.
Guided Image-to-Image Translation With Bi-Directional Feature Transformation

We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image. Various types of conditioning mechanisms for leveraging the given guidance image have been explored, including input concatenation, feature concatenation, and conditional affine transformation of feature activations. All these conditioning mechanisms, however, are uni-directional, i.e., no information flow from the input image back to the guidance. To better utilize the constraints of the guidance image, we present a bi-directional feature transformation (bFT) scheme. We show that our novel bFT scheme outperforms other conditioning schemes and has comparable results to state-of-the-art methods on different tasks.
No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques

We show that for human-object interaction detection a relatively simple factorized model with appearance and layout encodings constructed from pre-trained object detectors outperforms more sophisticated approaches. Our model includes factors for detection scores, human and object appearance, and coarse (box-pair configuration) and optionally fine-grained layout (human pose). We also develop training techniques that improve learning efficiency by: (1) eliminating a train-inference mismatch; (2) rejecting easy negatives during mini-batch training; and (3) using a ratio of negatives to positives that is two orders of magnitude larger than existing approaches. We conduct a thorough ablation study to understand the importance of different factors and training techniques using the challenging HICO-Det dataset.
Semantic Stereo Matching With Pyramid Cost Volumes

The accuracy of stereo matching has been greatly improved by using deep learning with convolutional neural networks. To further capture the details of disparity maps, in this paper, we propose a novel semantic stereo network named SSPCV-Net, which includes newly designed pyramid cost volumes for describing semantic and spatial information on multiple levels. The semantic features are inferred by a semantic segmentation subnetwork while the spatial features are derived by hierarchical spatial pooling. In the end, we design a 3D multi-cost aggregation module to integrate the extracted multilevel features and perform regression for accurate disparity maps. We conduct comprehensive experiments and comparisons with some recent stereo matching networks on Scene Flow, KITTI 2015 and 2012, and Cityscapes benchmark datasets, and the results show that the proposed SSPCV-Net significantly promotes the state-of-the-art stereo-matching performance.
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

In this paper, we propose a novel approach for the rank minimization problem, termed rank residual constraint (RRC). Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observation, we progressively approximate (approach) the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Toward this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image in each iteration are improved gradually and jointly. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual qualities.
MAGNet: A Modular Accelerator Generator for Neural Networks

Deep neural networks have been adopted in a wide range of application domains, leading to high demand for inference accelerators. However, the high cost associated with ASIC hardware design makes it challenging to build custom accelerators for different targets. To lower design cost, we propose MAGNet, a modular accelerator generator for neural networks. MAGNet takes a target application consisting of one or more neural networks along with hardware constraints as input and produces synthesizable RTL for a neural network accelerator ASIC as well as valid mappings for running the target networks on the generated hardware. MAGNet consists of three key components: (i) MAGNet Designer, a highly configurable architectural template designed in C++ and synthesizable by high-level synthesis tools. MAGNet Designer supports a wide range of design-time parameters such as different data formats, diverse memory hierarchies, and dataflows. (ii) MAGNet Mapper, an automated framework for exploring different software mappings for executing a neural network on the generated hardware. (iii) MAGNet Tuner, a design space exploration framework encompassing the designer, the mapper, and a deep learning framework to enable fast design space exploration and co-optimization of architecture and application. We demonstrate the utility of MAGNet by designing an inference accelerator optimized for image classification application using three different neural networks-AlexNet, ResNet, and DriveNet. MAGNet-generated hardware is highly efficient and leverages a novel multi-level dataflow to achieve 40 fJ/op and 2.8 TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in a 16nm technology node for the ResNet-50 benchmark with <; 1% accuracy loss on the ImageNet dataset.
A reduced universum twin support vector machine for class imbalance learning

In most of the real world datasets, there is an imbalance in the number of samples belonging to different classes. Various pattern classification problems such as fault or disease detection involve class imbalanced data. The support vector machine (SVM) classifier becomes biased towards the majority class due to class imbalance. Moreover, in the existing SVM based techniques for class imbalance, there is no information about the distribution of data. Motivated by the idea of prior information about data distribution, a reduced universum twin support vector machine for class imbalance learning (RUTSVM-CIL) is proposed in this paper. For the first time, universum learning is incorporated with SVM to solve the problem of class imbalance. Oversampling and undersampling of data is performed to remove the imbalance in the classes. The universum data points are used to give prior information about the data. To reduce the computation time of our universum based algorithm, we use a small sized rectangular kernel matrix. The reduced kernel matrix needs less storage space, and thus applicable for large scale imbalanced datasets. Comprehensive experimentation is performed on various synthetic, real world and large scale imbalanced datasets. In comparison to the existing approaches for class imbalance, the proposed RUTSVM-CIL gives better generalization performance for most of the benchmark datasets. Also, the computation cost of RUTSVM-CIL is very less, making it suitable for real world applications.
Using mobile phones to improve community health workers performance in low-and-middle-income countries

In low-and-middle-income countries community health workers are the core component of the PHC system as they act as a liaison between the communities and the healthcare facilities. Evidence suggests that the services offered by these workers have helped in the decline of maternal and child morbidity and mortality rates and the burden of communicable and non-communicable diseases. However, the coverage and the overall progress towards achieving the SDG targets is very sluggish. The recent consensus concerning this current pace of progress, is that it relates to financial and human resources constraints. CHWs are overburdened as they are expected to accomplish more although they may not obtain the required support to perform their duties. The health systems of LMICs, have given very little attention to the work environment of CHWs; which has negatively affected CHWs productivity, and quality of services. This debate is intended to explore the potential of mobile phone technology in LMICs for improving CHWs performance and effectiveness.To improve CHWs productivity, some studies involved the use of mobile phones for data collection and reporting, while other studies used mobile technology for patient to provider communication, patient education, CHWs supervision, and monitoring and evaluation. A wide range of benefits exists for using mobile phones including reduction in CHWs workload, improvement in data collection, reporting and monitoring, provision of quality healthcare services, supportive supervision, better organization of CHWs tasks and improvement in community health outcomes. However, a number of studies suggests that CHWs encounter unique challenges when adopting and using mobile health solutions for health service delivery such as, lack of CHWs training on new mHealth solutions, weak technical support, issues of internet connectivity and other administrative challenges. Future research efforts should be directed to explore health system readiness for adopting sustainable mHealth solutions to improve CHWs workflows in LMICs.Future research efforts and policy dialogue should be directed to explore health system readiness for adopting sustainable mHealth solutions to improve CHWs workflows in LMICs.
Imitation Learning for Human Pose Prediction

Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks. Inspired by the recent success of deep reinforcement learning methods, in this paper we propose a new reinforcement learning formulation for the problem of human pose prediction, and develop an imitation learning algorithm for predicting future poses under this formulation through a combination of behavioral cloning and generative adversarial imitation learning. Our experiments show that our proposed method outperforms all existing state-of-the-art baseline models by large margins on the task of human pose prediction in both short-term predictions and long-term predictions, while also enjoying huge advantage in training speed.
An informative path planning framework for UAV-based terrain monitoring

Abstract Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.
Path Planning for UAV Ground Target Tracking via Deep Reinforcement Learning

In this paper, we focus on the study of UAV ground target tracking under obstacle environments using deep reinforcement learning, and an improved deep deterministic policy gradient (DDPG) algorithm is presented. A reward function based on line of sight and artificial potential field is constructed to guide the behavior of UAV to achieve target tracking, and a penalty term of action makes the trajectory smooth. In order to improve the exploration ability, multiple UAVs, which controlled by the same policy network, are used to perform tasks in each episode. Taking into account that the history observations have a great degree of correlation with the policy, long short-term memory networks are used to approximate the state of environments, which improve the approximation accuracy and the efficiency of data utilization. The simulation results show that the propose method can make the UAV keep target tracking and obstacle avoidance effectively.
Tensorized Multi-view Subspace Representation Learning

Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of multiple views and prior constraint. Accordingly, we establish a novel algorithm termed as Tensorized Multi-view Subspace Representation Learning. To exploit different views, the subspace representation matrices of different views are regarded as a low-rank tensor, which effectively models the high-order correlations of multi-view data. To incorporate prior information, a constraint matrix is devised to guide the subspace representation learning within a unified framework. The subspace representation tensor equipped with a low-rank constraint models elegantly the complementary information among different views, reduces redundancy of subspace representations, and then improves the accuracy of subsequent tasks. We formulate the model with a tensor nuclear norm minimization problem constrained with $$\ell _{2,1}$$ -norm and linear equalities. The minimization problem is efficiently solved by using an Augmented Lagrangian Alternating Direction Minimization method. Extensive experimental results on diverse multi-view datasets demonstrate the effectiveness of our algorithm.