Reviewerly/exHarmony huggingface dataset:
Unsupervised Feature Learning for Speech Using Correspondence and Siamese Networks

In zero-resource settings where transcribed speech audio is unavailable, unsupervised feature learning is essential for downstream speech processing tasks. Here we compare two recent methods for frame-level acoustic feature learning. For both methods, unsupervised term discovery is used to find pairs of word examples of the same unknown type. Dynamic programming is then used to align the feature frames between each word pair, serving as weak top-down supervision for the two models. For the correspondence autoencoder (CAE), matching frames are presented as input-output pairs. The Triamese network uses a contrastive loss to reduce the distance between frames of the same predicted word type while increasing the distance between negative examples. For the first time, these feature extractors are compared on the same discrimination tasks using the same weak supervision pairs. We find that, on the two datasets considered here, the CAE outperforms the Triamese network. However, we show that a new hybrid correspondence-Triamese approach (CTriamese), consistently outperforms both the CAE and Triamese models in terms of average precision and ABX error rates on both English and Xitsonga evaluation data.
Conditional Segmentation in Lieu of Image Registration

Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detailed pixel- or voxel-level correspondence within or outside of these ROIs often have little clinical value. In this paper, we propose an alternative paradigm in which the location of corresponding image-specific ROIs, defined in one image, within another image is learnt. This results in replacing image registration by a conditional segmentation algorithm, which can build on typical image segmentation networks and their widely-adopted training strategies. Using the registration of 3D MRI and ultrasound images of the prostate as an example to demonstrate this new approach, we report a median target registration error (TRE) of 2.1 mm between the ground-truth ROIs defined on intraoperative ultrasound images and those propagated from the preoperative MR images. Significantly lower (>34%) TREs were obtained using the proposed conditional segmentation compared with those obtained from a previously-proposed spatial-transformation-predicting registration network trained with the same multiple ROI labels for individual image pairs. We conclude this work by using a quantitative bias-variance analysis to provide one explanation of the observed improvement in registration accuracy.
High-Efficiency Three-Party Quantum Key Agreement Protocol with Quantum Dense Coding and Bell States

We propose a high-efficiency three-party quantum key agreement protocol, by utilizing two-photon polarization-entangled Bell states and a few single-photon polarization states as the information carriers, and we use the quantum dense coding method to improve its efficiency. In this protocol, each participant performs one of four unitary operations to encode their sub-secret key on the passing photons which contain two parts, the first quantum qubits of Bell states and a small number of single-photon states. At the end of this protocol, based on very little information announced by other, all participants involved can deduce the same final shared key simultaneously. We analyze the security and the efficiency of this protocol, showing that it has a high efficiency and can resist both outside attacks and inside attacks. As a consequence, our protocol is a secure and efficient three-party quantum key agreement protocol.
Information-based approach towards a unified resource theory

Resource theories play an important role in quantum information theory, as they identify resourceful states and channels that are potentially useful for the accomplishment of tasks that would be otherwise unreachable. The elementary structure of such theories, which is based on the definition of free states and free operations, successfully accommodates different nonclassical aspects, such as quantum coherence and entanglement, but it is still not clear whether and how far such formal framework can be extended. In this work, by taking information as the most primitive quantum resource and defining resource-destroying operations, we develop a unifying approach that proves able to encompass several nonclassical aspects, including the newly developed concepts of quantum irreality and realism-based nonlocality.
Path integral based convolution and pooling for graph neural networks*

Abstract Graph neural networks (GNNs) extend the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose path integral-based GNNs (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix that we call the maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus leading to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics that we propose to boost applications of GNN in physical sciences.
Discrimination of thermal baths by single-qubit probes

Nonequilibrium states of quantum systems in contact with thermal baths help distinguishing between environments with different temperatures or different statistics. We extend these studies to a more generic problem that consists in discriminating between two baths with disparate constituents at unequal temperatures. Notably there exist temperature regimes in which the presence of coherence in the initial state preparation is beneficial for the discrimination capability. We also find that nonequilibrium states are not universally optimal and detail the conditions in which it becomes advantageous to wait for complete thermalization of the probe. These concepts are illustrated in a linear-optical simulation.1 MoreReceived 6 May 2020Accepted 18 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033497Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systems & decoherenceQuantum channelsQuantum metrologyQuantum thermodynamicsQuantum Information
Search-based adversarial testing and improvement of constrained credit scoring systems

Credit scoring systems are critical FinTech applications that concern the analysis of the creditworthiness of a person or organization. While decisions were previously based on human expertise, they are now increasingly relying on data analysis and machine learning. In this paper, we assess the ability of state-of-the-art adversarial machine learning to craft attacks on a real-world credit scoring system. Interestingly, we find that, while these techniques can generate large numbers of adversarial data, these are practically useless as they all violate domain-specific constraints. In other words, the generated examples are all false positives as they cannot occur in practice. To circumvent this limitation, we propose CoEvA2, a search-based method that generates valid adversarial examples (satisfying the domain constraints). CoEvA2 utilizes multi-objective search in order to simultaneously handle constraints, perform the attack and maximize the overdraft amount requested. We evaluate CoEvA2 on a major bank's real-world system by checking its ability to craft valid attacks. CoEvA2 generates thousands of valid adversarial examples, revealing a high risk for the banking system. Fortunately, by improving the system through adversarial training (based on the produced examples), we increase its robustness and make our attack fail.
Localizing the Common Action Among a Few Videos

This paper strives to localize the temporal extent of an action in a long untrimmed video. Where existing work leverages many examples with their start, their ending, and/or the class of the action during training time, we propose few-shot common action localization. The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label. To address this task, we introduce a new 3D convolutional network architecture able to align representations from the support videos with the relevant query video segments. The network contains: (i) a mutual enhancement module to simultaneously complement the representation of the few trimmed support videos and the untrimmed query video; (ii) a progressive alignment module that iteratively fuses the support videos into the query branch; and (iii) a pairwise matching module to weigh the importance of different support videos. Evaluation of few-shot common action localization in untrimmed videos containing a single or multiple action instances demonstrates the effectiveness and general applicability of our proposal. Code: https://github.com/PengWan-Yang/commonLocalization
Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement

Abstract Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it needs methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare. Results Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover a smaller set of succinct significant patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.
Creative and research competence as a factor of professional training of future teachers: Perspective of learning technology

One of the main current objectives of countries is to achieve a high level of quality in higher education that meets the needs of the labor market problems of industrial-innovative development of the country, the identity and relevant international best practices in the field of education. This study aims to provide a comprehensive review on creative and research competence as a factor of professional training of future teachers according to perspective of learning technology. Document analysis as a qualitative research method was used in the study. It should be noted that in the framework of the Bologna process and the integration of Kazakhstan universities into the European higher education area in recent years, increasing attention is paid to the competence approach which refers to development of key competencies of teacher candidates in the training of future profession. For future teachers, it is necessary to be able to be creative in solving a variety of pedagogical technological problems in addition to mastering the specialized knowledge. Creative competencies of the future teachers play a crucial role in their future professional life. Results are discussed with relevant literature and implications for future research and practices are presented. &#x0D; &#x0D; Keywords: Learning technology, creative competence, research competence, future teachers.
A Compressed-Domain Robust Video Watermarking Against Recompression Attack

With the development of communication networks and the widespread use of mobile terminals, videos are increasingly shared and distributed among mobile users. During the process, the video is recompressed to a certain file size on the sending side and sent to the receiving side via the server. This makes robust video watermark with low complexity to resist recompression attacks become an important issue to address. The proposed video watermarking method in this article is specially designed for resisting recompression attacks when quantization parameter (QP) is greatly increased. In the proposed method, by using the texture information and motion information of video, the invariance of video content under different quantization parameters could be found to help improve the anti-recompression attack ability of video watermark. Moreover, the proposed framework does not use a location map which has security risk to locate the watermark. It aims at finding the optimal location of watermark embedding adaptively according to the feature of video content itself. The experiment results indicate that the proposed video watermarking method can not only achieve greater robustness against recompression attack but also effectively limit the degradation in video perceptual quality.
Multi-source fast transfer learning algorithm based on support vector machine

Knowledge in the source domain can be used in transfer learning to help train and classification tasks within the target domain with fewer available data sets. Therefore, given the situation where the target domain contains only a small number of available unlabeled data sets and multi-source domains contain a large number of labeled data sets, a new Multi-source Fast Transfer Learning algorithm based on support vector machine(MultiFTLSVM) is proposed in this paper. Given the idea of multi-source transfer learning, more source domain knowledge is taken to train the target domain learning task to improve classification effect. At the same time, the representative data set of the source domain is taken to speed up the algorithm training process to improve the efficiency of the algorithm. Experimental results on several real data sets show the effectiveness of MultiFTLSVM, and it also has certain advantages compared with the benchmark algorithm.
Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets

Abstract Background The design used to create labelled data for training prediction models from observational healthcare databases (e.g., case-control and cohort) may impact the clinical usefulness. We aim to investigate hypothetical design issues and determine how the design impacts prediction model performance. Aim To empirically investigate differences between models developed using a case-control design and a cohort design. Methods Using a US claims database, we replicated two published prediction models (dementia and type 2 diabetes) which were developed using a case-control design, and trained models for the same prediction questions using cohort designs. We validated each model on data mimicking the point in time the models would be applied in clinical practice. We calculated the models’ discrimination and calibration-in-the-large performances. Results The dementia models obtained area under the receiver operating characteristics of 0.560 and 0.897 for the case-control and cohort designs respectively. The type 2 diabetes models obtained area under the receiver operating characteristics of 0.733 and 0.727 for the case-control and cohort designs respectively. The dementia and diabetes case-control models were both poorly calibrated, whereas the dementia cohort model achieved good calibration. We show that careful construction of a case-control design can lead to comparable discriminative performance as a cohort design, but case-control designs over-represent the outcome class leading to miscalibration. Conclusions Any case-control design can be converted to a cohort design. We recommend that researchers with observational data use the less subjective and generally better calibrated cohort design when extracting labelled data. However, if a carefully constructed case-control design is used, then the model must be prospectively validated using a cohort design for fair evaluation and be recalibrated.
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering

Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in taskoriented dialogs.In real-world settings with constantly changing services, DST systems must generalize to new domains and unseen slot types.Existing methods for DST do not generalize well to new slot names and many require known ontologies of slot types and values for inference.We introduce a novel ontology-free framework that supports natural language queries for unseen constraints and slots in multi-domain task-oriented dialogs.Our approach is based on generative questionanswering using a conditional language model pre-trained on substantive English sentences.Our model improves joint goal accuracy in zero-shot domain adaptation settings by up to 9% (absolute) over the previous state-of-theart on the MultiWOZ 2.1 dataset.
Weakly supervised temporal model for prediction of breast cancer distant recurrence

Efficient prediction of cancer recurrence in advance may help to recruit high risk breast cancer patients for clinical trial on-time and can guide a proper treatment plan. Several machine learning approaches have been developed for recurrence prediction in previous studies, but most of them use only structured electronic health records and only a small training dataset, with limited success in clinical application. While free-text clinic notes may offer the greatest nuance and detail about a patient's clinical status, they are largely excluded in previous predictive models due to the increase in processing complexity and need for a complex modeling framework. In this study, we developed a weak-supervision framework for breast cancer recurrence prediction in which we trained a deep learning model on a large sample of free-text clinic notes by utilizing a combination of manually curated labels and NLP-generated non-perfect recurrence labels. The model was trained jointly on manually curated data from 670 patients and NLP-curated data of 8062 patients. It was validated on manually annotated data from 224 patients with recurrence and achieved 0.94 AUROC. This weak supervision approach allowed us to learn from a larger dataset using imperfect labels and ultimately provided greater accuracy compared to a smaller hand-curated dataset, with less manual effort invested in curation.
A Supervised Video Hashing Method Based on a Deep 3D Convolutional Neural Network for Large-Scale Video Retrieval

Recently, with the popularization of camera tools such as mobile phones and the rise of various short video platforms, a lot of videos are being uploaded to the Internet at all times, for which a video retrieval system with fast retrieval speed and high precision is very necessary. Therefore, content-based video retrieval (CBVR) has aroused the interest of many researchers. A typical CBVR system mainly contains the following two essential parts: video feature extraction and similarity comparison. Feature extraction of video is very challenging, previous video retrieval methods are mostly based on extracting features from single video frames, while resulting the loss of temporal information in the videos. Hashing methods are extensively used in multimedia information retrieval due to its retrieval efficiency, but most of them are currently only applied to image retrieval. In order to solve these problems in video retrieval, we build an end-to-end framework called deep supervised video hashing (DSVH), which employs a 3D convolutional neural network (CNN) to obtain spatial-temporal features of videos, then train a set of hash functions by supervised hashing to transfer the video features into binary space and get the compact binary codes of videos. Finally, we use triplet loss for network training. We conduct a lot of experiments on three public video datasets UCF-101, JHMDB and HMDB-51, and the results show that the proposed method has advantages over many state-of-the-art video retrieval methods. Compared with the DVH method, the mAP value of UCF-101 dataset is improved by 9.3%, and the minimum improvement on JHMDB dataset is also increased by 0.3%. At the same time, we also demonstrate the stability of the algorithm in the HMDB-51 dataset.