Reviewerly/exHarmony huggingface dataset:
GraphPlan: Story Generation by Planning with Event Graph

Story generation is a task that aims to automatically produce multiple sentences to make up a meaningful story. This task is challenging because it requires high-level understanding of semantic meaning of sentences and causality of story events. Naive sequence-to-sequence models generally fail to acquire such knowledge, as the logical correctness can hardly be guaranteed in a text generation model without the strategic planning. In this paper, we focus on planning a sequence of events assisted by event graphs, and use the events to guide the generator. Instead of using a sequence-to-sequence model to output a storyline as in some existing works, we propose to generate an event sequence by walking on an event graph. The event graphs are built automatically based on the corpus. To evaluate the proposed approach, we conduct human evaluation both on event planning and story generation. Based on large-scale human annotation results, our proposed approach is shown to produce more logically correct event sequences and stories.
Medical Datasets Collections for Artificial Intelligence-based Medical  Image Analysis

We collected 32 public datasets, of which 28 for medical imaging and 4 for natural images, to conduct study. The images of these datasets are captured by different cameras, thus vary from each other in modality, frame size and capacity. For data accessibility, we also provide the websites of most datasets and hope this will help the readers reach the datasets.
[What are possible applications of virtual reality for forensic psychiatry?]

Virtual reality (VR) has been on the rise in recent years due to the better quality and accessibility of VR glasses and software. Despite the fact that VR is being used more often in psychiatric care, little is known about the possible applications of VR in forensic psychiatry.To investigate which possible VR-applications already exist that can also be used in forensic psychiatry and what the possible risks are.Scientific literature was consulted in PubMed, Web of Knowledge, Embase en Cochrane Library to search for immersive VR-applications for aggression, motivation and the most prevalent psychopathologies in forensic psychiatry.Several relevant VR-applications were found that can be used in the diagnosis or treatment of forensic psychiatric patients. Despite the limited number of empirical studies, several authors emphasize the potential benefits of VR for this target group.Due to the low number of studies of good quality on the use of VR in psychiatric care, it is currently not yet possible to draw clear conclusions about efficiency, deployability and specific applications. However, there is enough potential within forensic psychiatry to integrate VR into various parts of the care process, such as treatment, diagnosis and risk assessment.
SELF-EDUCATIONAL ACTIVITY IN THE STUDENT AGE AS AN INDICATOR OF MENTAL WELL-BEING OF PERSONALITY.

The aim: The aim of the article is to highlight the results of an experimental study of the indicators of students' mental health depending on their involvement in self-educational activities.Materials and methods: We conducted a comparative experimental research. There was a block of questionnaires, which required to choose one of the answers offered in the list and evaluate it on a given scale. The research collected data on respondents' self-educational activities and indicators of their mental health: meaningfulness of life, reflexivity, internal motivation for learning, stress resistance, components of self-actualization.Results: Respondents engaged in self-educational activities in the profession they acquire have significantly higher rates of internal motivation for learning, time orientation and autonomy than respondents who are not engaged in self-educational activities. Respondents engaged in self-educational activities which are not related to the profession they acquire have significantly higher rates of internal motivation for learning, time orientation, cognition and creativity needs than non-self-educated respondents.Conclusions: The research helps us to understand the progress of students' mental well-being. The results of the research help to find the ways of providing the mental health to students. In particular, encouragement of students' self-educational activities will help to improve the mental well-being of their personality.
The duality-character Solution-Information-Carrying (SIC) unitary  propagators

The HSSS quantum search process owns the dual character that it obeys both the unitary quantum dynamics and the mathematical-logical principle of the unstructured search problem. It is essentially different from a conventional quantum search algorithm. It is constructed with the duality-character oracle operations of unstructured search problem. It consists of the two consecutive steps: (1) the search-space dynamical reduction and (2) the dynamical quantum-state-difference amplification (QUANSDAM). The QUANSDAM process is directly constructed with the SIC unitary propagators, while the latter each are prepared with the basic SIC unitary operators. Here the preparation for the SIC unitary propagators of a single-atom system is concretely carried out by starting from the basic SIC unitary operators. The SIC unitary propagator of a quantum system may reflect the quantum symmetry of the quantum system, while the basic SIC unitary operators may not. The quantum symmetry is considered as the fundamental quantum-computing-speedup resource in the quantum-computing speedup theory. The purpose for the preparation is ultimately to employ the quantum symmetry to speed up the QUANSDAM process. The preparation is a solution-information transfer process. It is unitary and deterministic. It obeys the information conservation law. In methodology it is based on the energy eigenfunction expansion and the multiple-quantum operator algebra space. Furthermore, a general theory mainly based on the Feynman path integration technique and also the energy eigenfunction expansion method is established to treat theoretically and calculate a SIC unitary propagator of any quantum system in the coordinate representation, which may be further used to construct theoretically an exponential QUANSDAM process in future.
Memory-Efficient Hierarchical Neural Architecture Search for Image  Restoration

Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition tasks. Inspired by the success, here we attempt to leverage NAS techniques to automatically design efficient network architectures for low-level image restoration tasks. In particular, we propose a memory-efficient hierarchical NAS (termed HiNAS) and apply it to two such tasks: image denoising and image super-resolution. HiNAS adopts gradient based search strategies and builds a flexible hierarchical search space, including the inner search space and outer search space. They are in charge of designing cell architectures and deciding cell widths, respectively. For the inner search space, we propose a layer-wise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance. For the outer search space, we design a cell-sharing strategy to save memory, and considerably accelerate the search speed. The proposed HiNAS method is both memory and computation efficient. With a single GTX1080Ti GPU, it takes only about 1 hour for searching for denoising network on the BSD-500 dataset and 3.5 hours for searching for the super-resolution structure on the DIV2K dataset. Experiments show that the architectures found by HiNAS have fewer parameters and enjoy a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. Code is available at: https://github.com/hkzhang91/HiNAS
Frequency Consistent Adaptation for Real World Super Resolution

Recent deep-learning based Super-Resolution (SR) methods have achieved remarkable performance on images with known degradation. However, these methods always fail in real-world scene, since the Low-Resolution (LR) images after the ideal degradation (e.g., bicubic down-sampling) deviate from real source domain. The domain gap between the LR images and the real-world images can be observed clearly on frequency density, which inspires us to explictly narrow the undesired gap caused by incorrect degradation. From this point of view, we design a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying existing SR methods to the real scene. We estimate degradation kernels from unsupervised images and generate the corresponding LR images. To provide useful gradient information for kernel estimation, we propose Frequency Density Comparator (FDC) by distinguishing the frequency density of images on different scales. Based on the domain-consistent LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR models. Extensive experiments show that the proposed FCA improves the performance of the SR model under real-world setting achieving state-of-the-art results with high fidelity and plausible perception, thus providing a novel effective framework for real-world SR application.
Exploring wav2vec 2.0 on speaker verification and language  identification

Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation learning. It follows a two-stage training process of pre-training and fine-tuning, and performs well in speech recognition tasks especially ultra-low resource cases. In this work, we attempt to extend self-supervised framework to speaker verification and language identification. First, we use some preliminary experiments to indicate that wav2vec 2.0 can capture the information about the speaker and language. Then we demonstrate the effectiveness of wav2vec 2.0 on the two tasks respectively. For speaker verification, we obtain a new state-of-the-art result, Equal Error Rate (EER) of 3.61% on the VoxCeleb1 dataset. For language identification, we obtain an EER of 12.02% on 1 second condition and an EER of 3.47% on full-length condition of the AP17-OLR dataset. Finally, we utilize one model to achieve the unified modeling by the multi-task learning for the two tasks.
Effective Label Propagation for Discriminative Semi-Supervised Domain  Adaptation

Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks when massive labeled data are available in the source domain but very few labeled samples are provided in the target domain. Existing solutions usually focus on feature alignment between the two domains while paying little attention to the discrimination capability of learned representations in the target domain. In this paper, we present a novel and effective method, namely Effective Label Propagation (ELP), to tackle this problem by using effective inter-domain and intra-domain semantic information propagation. For inter-domain propagation, we propose a new cycle discrepancy loss to encourage consistency of semantic information between the two domains. For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain. As a general method, our ELP can be easily applied to various domain adaptation approaches and can facilitate their feature discrimination in the target domain. Experiments on Office-Home and DomainNet benchmarks show ELP consistently improves the classification accuracy of mainstream SSDA methods by 2%~3%. Additionally, ELP also improves the performance of UDA methods as well (81.5% vs 86.1%), based on UDA experiments on the VisDA-2017 benchmark. Our source code and pre-trained models will be released soon.
Graph Geometry Interaction Learning

While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the flexibility to model intrinsic complex graph geometry. To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph. GIL captures a more informative internal structural features with low dimensions while maintaining conformal invariance of each space. Furthermore, our method endows each node the freedom to determine the importance of each geometry space via a flexible dual feature interaction learning and probability assembling mechanism. Promising experimental results are presented for five benchmark datasets on node classification and link prediction tasks.
Efficient Projection-Free Algorithms for Saddle Point Problems

The Frank-Wolfe algorithm is a classic method for constrained optimization problems. It has recently been popular in many machine learning applications because its projection-free property leads to more efficient iterations. In this paper, we study projection-free algorithms for convex-strongly-concave saddle point problems with complicated constraints. Our method combines Conditional Gradient Sliding with Mirror-Prox and shows that it only requires $\tilde{O}(1/\sqrt{\epsilon})$ gradient evaluations and $\tilde{O}(1/\epsilon^2)$ linear optimizations in the batch setting. We also extend our method to the stochastic setting and propose first stochastic projection-free algorithms for saddle point problems. Experimental results demonstrate the effectiveness of our algorithms and verify our theoretical guarantees.
Incorporating Commonsense Knowledge into Abstractive Dialogue  Summarization via Heterogeneous Graph Networks

Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our model can outperform various methods. We also conduct zero-shot setting experiments on the Argumentative Dialogue Summary Corpus, the results show that our model can better generalized to the new domain.
Learning to Profile: User Meta-Profile Network for Few-Shot Learning

Meta-learning approaches have shown great success in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications. Although e-commerce companies have spent many efforts on learning representations to provide a better user experience, we argue that such efforts cannot be stopped at this step. In addition to learning a strong profile, the challenging question about how to effectively transfer the learned representation is raised simultaneously. This paper introduces the contributions that we made to address these challenges from three aspects. 1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems. Moreover, we argue that an industrial model should not only have good performance in terms of accuracy, but also have better robustness and uncertainty performance under extreme conditions. We evaluate the performance of our model with extensive control experiments in various extreme scenarios, i.e. out-of-distribution detection, data insufficiency and class imbalance scenarios. The Meta-Profile Network shows significant improvement in the model performance when compared to baseline models.
SOCRATES: Towards a Unified Platform for Neural Network Analysis

Studies show that neural networks, not unlike traditional programs, are subject to bugs, e.g., adversarial samples that cause classification errors and discriminatory instances that demonstrate the lack of fairness. Given that neural networks are increasingly applied in critical applications (e.g., self-driving cars, face recognition systems and personal credit rating systems), it is desirable that systematic methods are developed to analyze (e.g., test or verify) neural networks against desirable properties. Recently, a number of approaches have been developed for analyzing neural networks. These efforts are however scattered (i.e., each approach tackles some restricted classes of neural networks against certain particular properties), incomparable (i.e., each approach has its own assumptions and input format) and thus hard to apply, reuse or extend. In this project, we aim to build a unified framework for developing techniques to analyze neural networks. Towards this goal, we develop a platform called SOCRATES which supports a standardized format for a variety of neural network models, an assertion language for property specification as well as multiple neural network analysis algorithms including two novel ones for falsifying and probabilistic verification of neural network models. SOCRATES is extensible and thus existing approaches can be easily integrated. Experiment results show that our platform can handle a wide range of networks models and properties. More importantly, it provides a platform for synergistic research on neural network analysis.
Structured Policy Iteration for Linear Quadratic Regulator

Linear quadratic regulator (LQR) is one of the most popular frameworks to tackle continuous Markov decision process tasks. With its fundamental theory and tractable optimal policy, LQR has been revisited and analyzed in recent years, in terms of reinforcement learning scenarios such as the model-free or model-based setting. In this paper, we introduce the \textit{Structured Policy Iteration} (S-PI) for LQR, a method capable of deriving a structured linear policy. Such a structured policy with (block) sparsity or low-rank can have significant advantages over the standard LQR policy: more interpretable, memory-efficient, and well-suited for the distributed setting. In order to derive such a policy, we first cast a regularized LQR problem when the model is known. Then, our Structured Policy Iteration (S-PI) algorithm, which takes a policy evaluation step and a policy improvement step in an iterative manner, can solve this regularized LQR efficiently. We further extend the S-PI algorithm to the model-free setting where a smoothing procedure is adopted to estimate the gradient. In both the known-model and model-free setting, we prove convergence analysis under the proper choice of parameters. Finally, the experiments demonstrate the advantages of S-PI in terms of balancing the LQR performance and level of structure by varying the weight parameter.
Auto-CASH: Autonomous Classification Algorithm Selection with Deep  Q-Network

The great amount of datasets generated by various data sources have posed the challenge to machine learning algorithm selection and hyperparameter configuration. For a specific machine learning task, it usually takes domain experts plenty of time to select an appropriate algorithm and configure its hyperparameters. If the problem of algorithm selection and hyperparameter optimization can be solved automatically, the task will be executed more efficiently with performance guarantee. Such problem is also known as the CASH problem. Early work either requires a large amount of human labor, or suffers from high time or space complexity. In our work, we present Auto-CASH, a pre-trained model based on meta-learning, to solve the CASH problem more efficiently. Auto-CASH is the first approach that utilizes Deep Q-Network to automatically select the meta-features for each dataset, thus reducing the time cost tremendously without introducing too much human labor. To demonstrate the effectiveness of our model, we conduct extensive experiments on 120 real-world classification datasets. Compared with classical and the state-of-art CASH approaches, experimental results show that Auto-CASH achieves better performance within shorter time.
Weakly Supervised Temporal Action Localization with Segment-Level Labels

Temporal action localization presents a trade-off between test performance and annotation-time cost. Fully supervised methods achieve good performance with time-consuming boundary annotations. Weakly supervised methods with cheaper video-level category label annotations result in worse performance. In this paper, we introduce a new segment-level supervision setting: segments are labeled when annotators observe actions happening here. We incorporate this segment-level supervision along with a novel localization module in the training. Specifically, we devise a partial segment loss regarded as a loss sampling to learn integral action parts from labeled segments. Since the labeled segments are only parts of actions, the model tends to overfit along with the training process. To tackle this problem, we first obtain a similarity matrix from discriminative features guided by a sphere loss. Then, a propagation loss is devised based on the matrix to act as a regularization term, allowing implicit unlabeled segments propagation during training. Experiments validate that our method can outperform the video-level supervision methods with almost same the annotation time.
Single Image Deraining with Continuous Rain Density Estimation

Single image deraining (SIDR) often suffers from over/under deraining due to the nonuniformity of rain densities and the variety of raindrop scales. In this paper, we propose a \textbf{\it co}ntinuous \textbf{\it de}nsity guided network (CODE-Net) for SIDR. Particularly, it is composed of { a rain {\color{black}streak} extractor and a denoiser}, where the convolutional sparse coding (CSC) is exploited to filter out noises from the extracted rain streaks. Inspired by the reweighted iterative soft-threshold for CSC, we address the problem of continuous rain density estimation by learning the weights with channel attention blocks from sparse codes. We further {\color{black}develop} a multiscale strategy to depict rain streaks appearing at different scales. Experiments on synthetic and real-world data demonstrate the superiority of our methods over recent {\color{black}state of the arts}, in terms of both quantitative and qualitative results. Additionally, instead of quantizing rain density with several levels, our CODE-Net can provide continuous-valued estimations of rain densities, which is more desirable in real applications.