Reviewerly/exHarmony huggingface dataset:
FR-NET: Focal Loss Constrained Deep Residual Networks for Segmentation of Cardiac MRI

Accurate segmentation of the left ventricle is an important step in evaluation of cardiac function. We have proposed a framework combining skip connection and focal loss together for left ventricle segmentation from cardiac MRI images. Residual neural networks (ResNet) have been used as the backbone of our method and have been shown to improve not only the segmentation accuracy of the left ventricle (LV) but also the network optimization process, thereby increasing the convergence rate of training due to improved gradient backpropagation. In addition, dice loss is trained with focal cross entropy loss by an alternative training strategy. Experiments show that our method achieves significant performance on the Sunnybrook public dataset.
Cross-Domain Generalization of Neural Constituency Parsers

Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing-but to what degree do they generalize to other domains?We present three results about the generalization of neural parsers in a zero-shot setting: training on trees from one corpus and evaluating on out-of-domain corpora.First, neural and non-neural parsers generalize comparably to new domains.Second, incorporating pre-trained encoder representations into neural parsers substantially improves their performance across all domains, but does not give a larger relative improvement for out-of-domain treebanks.Finally, despite the rich input representations they learn, neural parsers still benefit from structured output prediction of output trees, yielding higher exact match accuracy and stronger generalization both to larger text spans and to out-of-domain corpora.We analyze generalization on English and Chinese corpora, and in the process obtain state-of-the-art parsing results for the Brown, Genia, and English Web treebanks.
QUOTA: The Quantile Option Architecture for Reinforcement Learning

In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making is based on quantiles of a value distribution, not only the mean. QUOTA provides a new dimension for exploration via making use of both optimism and pessimism of a value distribution. We demonstrate the performance advantage of QUOTA in both challenging video games and physical robot simulators.
A novel active learning framework for classification: Using weighted rank aggregation to achieve multiple query criteria

Multiple query criteria active learning methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sample selection. A central issue related to multiple query criteria active learning methods concerns the development of an integration criteria strategy that makes full use of all criteria. The conventional integration criteria strategies adopted in relevant research facilitate the desired effects, but several limitations still must be addressed. For instance, some of the strategies are not sufficiently scalable during the design process, and the number and type of criteria involved are dictated. Thus, it is challenging for the user to integrate other criteria into the original process unless modifications are made to the algorithm. Other strategies are too dependent on empirical parameters, which can be acquired only by experience or cross-validation and thus lack generality; additionally, these strategies are counter to the intention of active learning, as samples need to be labeled in the validation set before the active learning process can begin. To address these limitations, we propose a novel multiple query criteria active learning method for classification tasks that employs a third strategy via weighted rank aggregation. The proposed method serves as a heuristic means to select high-value samples of high scalability and generality and is implemented through a three-step process: (1) the transformation of the sample selection to sample ranking and scoring, (2) the computation of the self-adaptive weights of each criterion, and (3) the weighted aggregation of each sample rank list. Ultimately, the sample at the top of the aggregated ranking list is the most comprehensively valuable and must be labeled. Several experiments generating 419 wins, 226 ties and 55 losses against other state-of-the-art multiple query criteria-based methods are conducted to verify that the proposed method can achieve superior results.
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called “If-Then recipes.” We develop a hierarchical reinforcement learning (HRL) based agent that significantly improves the parsing performance with minimal questions to the user. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.1
Guiding the One-to-One Mapping in CycleGAN via Optimal Transport

CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation. However, there is no theoretical guarantee on the property of the learned one-to-one mapping in CycleGAN. In this paper, we experimentally find that, under some circumstances, the one-to-one mapping learned by CycleGAN is just a random one within the large feasible solution space. Based on this observation, we explore to add extra constraints such that the one-to-one mapping is controllable and satisfies more properties related to specific tasks. We propose to solve an optimal transport mapping restrained by a task-specific cost function that reflects the desired properties, and use the barycenters of optimal transport mapping to serve as references for CycleGAN. Our experiments indicate that the proposed algorithm is capable of learning a one-to-one mapping with the desired properties.
Syntax-aware entity representations for neural relation extraction

Distantly supervised relation extraction has been widely used to find novel relational facts between entities from text, and can be easily scaled to very large corpora. Previous studies on neural relation extraction treat this task as a multi-instance learning problem, and encode the sentences in low-dimensional spaces via neural networks. Although great progress has been made, they seldom consider the information represented by entities, which are of great significance to relation extraction. In this article, we propose several methods based on different tree-based models to learn syntax-aware entity representations for neural relation extraction. First, we encode the context of entities on dependency trees as sentence-level entity embedding based on tree-structured neural network models. Then, we utilize inter-sentence attention mechanism to obtain sentence bag level entity embedding over all sentences containing the specified entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. Experimental results on a widely used real-world dataset indicate that our system performs better than the state-of-the-art systems of relation extraction.
Long Short-Term Memory with Dynamic Skip Connections

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.
Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming

While in recent years machine learning (ML) based approaches have been the popular approach in developing endto-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to 18% performance gain when compared to standard MCQ solvers.
Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents

Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this task increases even more if the maximum allowed inference turnaround time is constrained by a threshold. The increased overhead in inference cost, compared to the limited gain in classification capabilities make current multi-scale approaches infeasible in such scenarios. There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model. A depth-wise separable multi-column convolutional network is developed to enable our method. We evaluated the proposed approach on four publicly available, benchmark datasets of visually rich documents. Results suggest that our proposed approach demonstrates robust performance compared to the state-of-the-art methods in both classification accuracy and total inference turnaround.
A Deep Bi-directional Attention Network for Human Motion Recovery

Human motion capture (mocap) data, recording the movement of markers attached to specific joints, has gradually become the most popular solution of animation production. However, the raw motion data are often corrupted due to joint occlusion, marker shedding and the lack of equipment precision, which severely limits the performance in real-world applications. Since human motion is essentially a sequential data, the latest methods resort to variants of long short-time memory network (LSTM) to solve related problems, but most of them tend to obtain visually unreasonable results. This is mainly because these methods hardly capture long-term dependencies and cannot explicitly utilize relevant context, especially in long sequences. To address these issues, we propose a deep bi-directional attention network (BAN) which can not only capture the long-term dependencies but also adaptively extract relevant information at each time step. Moreover, the proposed model, embedded attention mechanism in the bi-directional LSTM (BLSTM) structure at the encoding and decoding stages, can decide where to borrow information and use it to recover corrupted frame effectively. Extensive experiments on CMU database demonstrate that the proposed model consistently outperforms other state-of-the-art methods in terms of recovery accuracy and visualization.
Metric Learning on Healthcare Data with Incomplete Modalities

Utilizing multiple modalities to learn a good distance metric is of vital importance for various clinical applications. However, it is common that modalities are incomplete for some patients due to various technical and practical reasons in healthcare datasets. Existing metric learning methods cannot directly learn the distance metric on such data with missing modalities. Nevertheless, the incomplete data contains valuable information to characterize patient similarity and modality relationships, and they should not be ignored during the learning process. To tackle the aforementioned challenges, we propose a metric learning framework to perform missing modality completion and multi-modal metric learning simultaneously. Employing the generative adversarial networks, we incorporate both complete and incomplete data to learn the mapping relationship between modalities. After completing the missing modalities, we use the nonlinear representations extracted by the discriminator to learn the distance metric among patients. Through jointly training the adversarial generation part and metric learning, the similarity among patients can be learned on data with missing modalities. Experimental results show that the proposed framework learns more accurate distance metric on real-world healthcare datasets with incomplete modalities, comparing with the state-of-the-art approaches. Meanwhile, the quality of the generated modalities can be preserved.
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces

Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (or parameterized) action spaces which is very common in practice. Our work fills this gap by proposing two novel algorithms: Deep Multi-Agent Parameterized Q-Networks (Deep MAPQN) and Deep Multi-Agent Hierarchical Hybrid Q-Networks (Deep MAHHQN). We follow the centralized training but decentralized execution paradigm: different levels of communication between different agents are used to facilitate the training process, while each agent executes its policy independently based on local observations during execution. Our empirical results on several challenging tasks (simulated RoboCup Soccer and game Ghost Story) show that both Deep MAPQN and Deep MAHHQN are effective and significantly outperform existing independent deep parameterized Q-learning method.
Learning to Learn Gradient Aggregation by Gradient Descent

In the big data era, distributed machine learning emerges as an important learning paradigm to mine large volumes of data by taking advantage of distributed computing resources. In this work, motivated by learning to learn, we propose a meta-learning approach to coordinate the learning process in the master-slave type of distributed systems. Specifically, we utilize a recurrent neural network (RNN) in the parameter server (the master) to learn to aggregate the gradients from the workers (the slaves). We design a coordinatewise preprocessing and postprocessing method to make the neural network based aggregator more robust. Besides, to address the fault tolerance, especially the Byzantine attack, in distributed machine learning systems, we propose an RNN aggregator with additional loss information (ARNN) to improve the system resilience. We conduct extensive experiments to demonstrate the effectiveness of the RNN aggregator, and also show that it can be easily generalized and achieve remarkable performance when transferred to other distributed systems. Moreover, under majoritarian Byzantine attacks, the ARNN aggregator outperforms the Krum, the state-of-art fault tolerance aggregation method, by 43.14%. In addition, our RNN aggregator enables the server to aggregate gradients from variant local models, which significantly improve the scalability of distributed learning.
POMHDP: Search-Based Belief Space Planning Using Multiple Heuristics

Robots operating in the real world encounter substantial uncertainty that cannot be modeled deterministically before the actual execution. This gives rise to the necessity of robust motion planning under uncertainty also known as belief space planning. Belief space planning can be formulated as Partially Observable Markov Decision Processes (POMDPs). However, computing optimal policies for non-trivial POMDPs is computationally intractable. Building upon recent progress from the search community, we propose a novel anytime POMDP solver, Partially Observable Multi-Heuristic Dynamic Programming (POMHDP), that leverages multiple heuristics to efficiently compute high-quality solutions while guaranteeing asymptotic convergence to an optimal policy. Through iterative forward search, POMHDP utilizes domain knowledge to solve POMDPs with specific goals and an infinite horizon. We demonstrate the efficacy of our proposed framework on a real-world, highly-complex, truck unloading application.
Discriminative Sample Generation for Deep Imbalanced Learning

In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions. DVAE is designed to alleviate the class imbalance by explicitly learning class boundaries between training samples, and uses learned class boundaries to guide the feature learning and sample generation. To learn class boundaries, DVAE learns a latent two-component mixture distributor, conditioned by the class labels, so the latent features can help differentiate minority class vs. majority class samples. In order to balance the training data for deep learning to emphasize on the minority class, we combine DVAE and generative adversarial networks (GAN) to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.
Active Regression via Linear-Sample Sparsification

We present an approach that improves the sample complexity for a variety of curve fitting problems, including active learning for linear regression, polynomial regression, and continuous sparse Fourier transforms. In the active linear regression problem, one would like to estimate the least squares solution $\beta^*$ minimizing $\|X\beta - y\|_2$ given the entire unlabeled dataset $X \in \mathbb{R}^{n \times d}$ but only observing a small number of labels $y_i$. We show that $O(d)$ labels suffice to find a constant factor approximation $\tilde{\beta}$: \[ \mathbb{E}[\|X\tilde{\beta} - y\|_2^2] \leq 2 \mathbb{E}[\|X \beta^* - y\|_2^2]. \] This improves on the best previous result of $O(d \log d)$ from leverage score sampling. We also present results for the \emph{inductive} setting, showing when $\tilde{\beta}$ will generalize to fresh samples; these apply to continuous settings such as polynomial regression. Finally, we show how the techniques yield improved results for the non-linear sparse Fourier transform setting.
Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm

The flower pollination algorithm is a new metaheuristic optimization technique that simulates the pollination behavior of flowers in nature. The global and local search processes of the algorithm are performed by simulating the self-pollination and cross-pollination of flowers. However, the conventional flower pollination algorithm has several limitations. To overcome the problem of slow convergence and prevent the algorithm from becoming stuck around local optimum, this paper describes an enhanced metaheuristic wind-driven flower pollination algorithm (WDFPA). Experiments are conducted using 29 benchmark test functions and two engineering design problems, and the proposed WDFPA is compared against other metaheuristic optimization algorithms and several classical optimization approaches. The results show that WDFPA achieves better performance than the conventional flower pollination algorithm, especially in high-dimensional optimization problems. The convergence speed and accuracy of WDFPA exhibit significant improvements over other metaheuristic algorithms in many of the test cases. Additionally, WDFPA produces optimal results for engineering design problems involving a welded beam and a spring structure.
A RGBD-Based Interactive System for Gaming-Driven Rehabilitation of Upper Limbs

Current physiotherapy services may not be effective or suitable for certain patients due to lack of motivation, poor adherence to exercises, insufficient supervision and feedback or, in the worst case, refusal to continue with the rehabilitation plan. This paper introduces a novel approach for rehabilitation of upper limbs through KineActiv, a platform based on Microsoft Kinect v2 and developed in Unity Engine. KineActiv proposes exergames to encourage patients to perform rehabilitation exercises prescribed by a specialist, controls the patient′s performance, and corrects execution errors on the fly. KineActiv comprises a web platform where the physiotherapist can review session results, monitor patient health, and adjust rehabilitation routines. We recruited 10 patients for assessing the system usability as well as the system performance. Results show that KineActiv is a usable, enjoyable and reliable system, that does not cause any negative feelings.
The removal mechanism and performance of tetrabromobisphenol A with a novel multi-group activated carbon from recycling long-root <i>Eichhornia crassipes</i> plants

MGAC showed excellent adsorption performance for TBBPA and provide an ecologically friendly way to deal with eutrophic plants.
Radar Chart for Estimation Performance Evaluation

Comprehensive measures for the estimation performance evaluation (EPE) has become increasingly prominent.This paper proposed a new radar chart evaluation method to measure the estimation performance.Firstly, the new radar chart index, which is composed of several popular incomprehensive measures, are presented, and the method of the weight of the each index is calculated based on vector ranking method.Secondly, the new comprehensive measures for the EPE is designed according to the fan area and the fan arc length.Finally, two cases study are provided to verify the effectiveness of this method.
Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.