Reviewerly/exHarmony huggingface dataset:
Resource-efficient quantum algorithm for protein folding

Abstract Predicting the three-dimensional structure of a protein from its primary sequence of amino acids is known as the protein folding problem. Due to the central role of proteins’ structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half a century. Although classical algorithms provide practical solutions for the sampling of the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even when reduced to the simplest Hydrophobic-Polar model. On the other hand, while fault-tolerant quantum computers are beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used in noisy state-of-the-art quantum computers to accelerate energy optimization in frustrated systems. In this work, we present a model Hamiltonian with $${\mathcal{O}}({N}^{4})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a lattice. The model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. In addition, we use a robust and versatile optimization scheme, bringing together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies to simulate the folding of the 10 amino acid Angiotensin on 22 qubits. The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM 20-qubit quantum computer. Bringing together recent advances in building gate-based quantum computers with noise-tolerant hybrid quantum-classical algorithms, this work paves the way towards accessible and relevant scientific experiments on real quantum processors.
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Meetings are a key component of human collaboration.As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics.In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task.QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains.Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task.Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research.Dataset is available at https://github.com/Yale-LILY/QMSum.
A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots

We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task involves matching a response candidate with a conversation context, the challenges for which include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. This motivates us to propose a new matching framework that can sufficiently carry important information in contexts to matching and model relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interact with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) that models relationships among utterances. Context-response matching is then calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experiment results show that both models can significantly outperform state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage important information in contexts for matching.
Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance

In this paper, we study 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the lacking of a representational capability and the training difficulty impede 1-bit CNNs from performing as well as real-valued networks. To this end, we propose Bi-Real net with a novel training algorithm to tackle these two challenges. To enhance the representational capability, we propagate the real-valued activations generated by each 1-bit convolution via a parameter-free shortcut. To address the training difficulty, we propose a training algorithm using a tighter approximation to the derivative of the sign function, a magnitude-aware binarization for weight updating, a better initialization method, and a two-step scheme for training a deep network. Experiments on ImageNet show that an 18-layer Bi-Real net with the proposed training algorithm achieves 56.4% top-1 classification accuracy, which is 10% higher than the state-of-the-arts (e.g., XNOR-Net), with a greater memory saving and a lower computational cost. Bi-Real net is also the first to scale up 1-bit CNNs to an ultra-deep network with 152 layers, and achieves 64.5% top-1 accuracy on ImageNet. A 50-layer Bi-Real net shows comparable performance to a real-valued network on the depth estimation task with merely a 0.3% accuracy gap.
An Encoding Strategy Based Word-Character

Wei Liu, Tongge Xu, Qinghua Xu, Jiayu Song, Yueran Zu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
The Emerging Role of Blockchain Technology Applications in Routine Disease Surveillance Systems to Strengthen Global Health Security

Blockchain technology has an enormous scope to revamp the healthcare system in many ways as it improves the quality of healthcare by data sharing among all the participants, selective privacy and ensuring data safety. This paper explores the basics of blockchain, its applications, quality of experience and advantages in disease surveillance over the other widely used real-time and machine learning techniques. The other real-time surveillance systems lack scalability, security, interoperability, thus making blockchain as a choice for surveillance. Blockchain offers the capability of enhancing global health security and also can ensure the anonymity of patient data thereby aiding in healthcare research. The recent epidemics of re-emerging infections such as Ebola and Zika have raised many concerns regarding health security which resulted in strengthening the surveillance systems. We also discuss how blockchains can help in identifying the threats early and reporting them to health authorities for taking early preventive measures. Since the Global Health Security Agenda addresses global public health threats (both infectious and NCDs); strengthen the workforce and the systems; detect and respond rapidly and effectively to the disease threats; and elevate global health security as a priority. The blockchain has enormous potential to disrupt many current practices in traditional disease surveillance and health care research.
Experimental authentication of quantum key distribution with post-quantum cryptography

Abstract Quantum key distribution (QKD) can provide information theoretically secure key exchange even in the era of quantum computers. However, QKD requires the classical channel to be authenticated, the current method for which is pre-sharing symmetric keys. For a QKD network of n users, this method requires $${C}_{n}^{2}=n(n-1)/2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mi>n</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> pairs of symmetric keys to realize pairwise interconnection. In contrast, with the help of a mature public key infrastructure (PKI) and post-quantum cryptography (PQC) with quantum-resistant security, each user only needs to apply for one digital certificate from a certificate authority (CA) to achieve efficient and secure authentication for QKD. We need to assume only the short-term security of the PQC algorithm to achieve long-term security of the distributed keys. Here, we experimentally verified the feasibility, efficiency, and stability of the PQC algorithm in QKD authentication, and demonstrated the advantages when new users join the QKD network. Using the PQC public-key infrastructure, the nodes need to mutually trust only the CA to authenticate each other. QKD combined with PQC authentication will greatly promote and extend the application prospects of quantum-safe communication.
DetNAS: Backbone Search for Object Detection

Object detectors are usually equipped with backbone networks designed for image classification. It might be sub-optimal because of the gap between the tasks of image classification and object detection. In this work, we present DetNAS to use Neural Architecture Search (NAS) for the design of better backbones for object detection. It is non-trivial because detection training typically needs ImageNetpre-training while NAS systems require accuracies on the target detection task as supervisory signals. Based on the technique of one-shot supernet, which contains all possible networks in the search space, we propose a framework for backbone search on object detection. We train the supernet under the typical detector training schedule: ImageNet pre-training and detection fine-tuning. Then, the architecture search is performed on the trained supernet, using the detection task as the guidance. This framework makes NAS on backbones very efficient. In experiments, we show the effectiveness of DetNAS on various detectors, for instance, one-stage RetinaNetand the two-stage FPN. We empirically find that networks searched on object detection shows consistent superiority compared to those searched on ImageNet classification. The resulting architecture achieves superior performance than hand-crafted networks on COCO with much less FLOPs complexity.
Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images

Objective: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeline to categorize nuclei, leading to cumbersome, low-throughput, and error-prone assessments. To address this issue, we propose a novel deep fully convolutional network for single-stage nucleus recognition. Methods: Instead of conducting direct pixel-wise classification, we formulate nucleus identification as a deep structured regression model. For each input image, it produces multiple proximity maps, each of which corresponds to one nucleus category and exhibits strong responses in central regions of the nuclei. In addition, by taking into consideration the nucleus distribution in histopathology images, we further introduce an auxiliary task, region of interest (ROI) extraction, to assist and boost the nucleus quantification with weak ROI annotation. The proposed network can be learned in an end-to-end, pixel-to-pixel manner for simultaneous nucleus detection and classification. Results: We have evaluated this network on a pancreatic neuroendocrine tumor Ki67 image dataset, and the experiments demonstrate that our method outperforms recent state-of-the-art approaches. Conclusion: We present a new, pixel-to-pixel deep neural network with two sibling branches for effective nucleus recognition and observe that learning with another relevant task, ROI extraction, can further boost individual nucleus localization and classification. Significance: Our method provides a clean, single-stage nucleus recognition pipeline for histopathology image analysis, especially a new perspective for Ki67 image quantification, which would potentially benefit individual object quantification in whole-slide images.
Securing Digital Images through Simple Permutation-Substitution Mechanism in Cloud-Based Smart City Environment

Data security plays a significant role in data transfer in cloud-based smart cities. Chaotic maps are commonly used in designing modern cryptographic applications, in which one-dimensional (1D) chaotic systems are widely used due to their simple design and low computational complexity. However, 1D chaotic maps suffer from different kinds of attacks because of their chaotic discontinuous ranges and small key-space. To own the benefits of 1D chaotic maps and avoid their drawbacks, the cascading of two integrated 1D chaotic systems has been utilized. In this paper, we report an image cryptosystem for data transfer in cloud-based smart cities using the cascading of Logistic-Chebyshev and Logistic-Sine maps. Logistic-Sine map has been utilized to permute the plain image, and Logistic-Chebyshev map has been used to substitute the permuted image, while the cascading of both integrated maps has been utilized in performing XOR procedure on the substituted image. The security analyses of the suggested approach prove that the encryption mechanism has good efficiency as well as lower encryption time compared with other related algorithms.
Real-time object detection based on YOLO-v2 for tiny vehicle object

In Automatic Driving System (ADS) and Driver Assistance System (DAS), object detection plays a vital part. Nevertheless, existing real-time detection models for tiny vehicle objects have the problems of low precision and poor performance. To solve these issues, we propose a novel real-time object detection model based on You Only Look Once Version 2 (YOLO-v2) deep learning framework for tiny vehicle objects, called Optimized You Only Look Once Version 2 (O-YOLO-v2). In the proposed model, a new structure is introduced to strengthen the feature extraction ability of the network by adding convolution layers at different locations. Meanwhile, the problem of gradient disappearance or dispersion caused by increasing network depth is solved by adding residual modules. Furthermore, in order to promote the detection accuracy of tiny vehicle objects, we combine the low-level features and high-level features of the network. The experimental findings and analysis on a KITTI dataset show that the model not only promotes the accuracy of tiny vehicle object detection but also improves the accuracy of vehicle detection (the accuracy reaches 94%) without decreasing the detection speed.
Improvement of the Fast Clustering Algorithm Improved by <i>K</i>-Means in the Big Data

Abstract Clustering as a fundamental unsupervised learning is considered an important method of data analysis, and K -means is demonstrably the most popular clustering algorithm. In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by K -means. Different from the traditional methods, the algorithm guaranteed the consistency of the clustering accuracy before and after descending dimension, accelerated K -means when the clustering centeres and distance functions satisfy certain conditions, completely matched in the preprocessing step and clustering step, and improved the efficiency and accuracy. Experimental results have demonstrated the effectiveness of the proposed algorithm.
An enhanced Predictive heterogeneous ensemble model for breast cancer prediction

Breast Cancer is one of the most prevalent tumors after lung cancer and is common in both women and men. This disease is mostly asymptomatic in the early stages thus detection is difficult, and it becomes complicated and expensive to be treated in later stages resulting in increased fatality rates. There are comparatively very few pieces of literature that investigated breast cancer employing an ensemble learning for cancer prediction as compared to single classifier approaches. This paper presents a heterogeneous ensemble machine learning approach, to detect breast cancer in the early stages. The proposed approach follows the CRISP-DM process and uses Stacking for building the ensemble model using three different algorithms – K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The performance of this meta classifier is compared with the individual performances of its base classifiers (KNN, SVM, DT) and other single classifiers – Logistic Regression (LR), Artificial Neural Network (ANN), Naïve Bayes (NB), Stochastic Gradient Descent (SGD) and a homogenous ensemble model of Random Forest (RF). The top 5 features – Glucose, Resistin, HOMA, Insulin, and BMI are derived by using Chi-Square. Evaluation of the model helps in estimating its consideration for early breast cancer prediction just by using the anthropometric data of humans. Performances of models are compared using metrics such as accuracy, AUC, ROC Curve, f1-score, precision, recall, log loss, and specificity using K-fold cross-validation of 2, 3, 5, 10, and 20 folds. The proposed ensemble model achieved the greatest accuracy of 78 % with the lowest log-loss of 0.56, at K = 20, thus rejecting the Null hypothesis. The derived p-value is 0.014, from the one-tailed t-test, which provides lower significance at ∝ = 0.05.
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

Proposal of large-scale datasets has facilitated research on deep neural models for news summarization.Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of reallife scenarios including customer service management and medication tracking.To this end, we propose DIALOGSUM, a large-scale labeled dialogue summarization dataset.We conduct empirical analysis on DIALOGSUM using state-of-the-art neural summarizers.Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.(a) Dialogue from DIALOGSUM: #Person_1#: Good morning.I wonder whether you have got an answer from your superior.
Blockchain for Dynamic Spectrum Management

Blockchain is believed to bring new opportunities to dynamic spectrum management (DSM). With features of blockchain, the traditional spectrum management method, such as the spectrum auction, can be improved. It can also help to overcome the challenges about the security or the lack of incentive mechanisms for collaboration in DSM. Moreover, with blockchain, spectrum usage of the DSM system can be recorded in a decentralized manner. In this chapter, we will discuss the potentials of blockchain for spectrum management in a systematic way and using multiple case studies.
An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

To overcome the IMM algorithm is easy divergence and low tracking accuracy when dealing with complex maneuvering situations, this paper proposes an improved interactive multiple model strong tracking square room cubature Kalman filter (IIMM-STSRCKF) algorithm under the idea of real-time dynamic adjustment of gain matrix and transition probability matrix.The algorithm has been improved in two aspects: on the one hand, the algorithm uses the idea of a strong tracking filter to deduce a new method for time-varying fading factor and introduce it into the square root of the state error covariance matrix of the SRCKF, which improves the tracking accuracy for strong maneuver; on the other hand, the probability difference between two consecutive time points in the IMM submodel is used to adjust the Markov probability transfer matrix to adaptively improve the switching speed of the submodel and the rationality of the allocation.By comparing with IMM-CKF algorithm by maneuvering target tracking case and results show that the IIMM-STSRCKF algorithm has better tracking performance in nonmaneuvering, weak maneuvering, and strong maneuvering cases.
Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis

A considerable amount of health record (HR) data has been stored due to recent advances in the digitalization of medical systems. However, it is not always easy to analyze HR data, particularly when the number of persons with a target disease is too small in comparison with the population. This situation is called the imbalanced data problem. Over-sampling and under-sampling are two approaches for redressing an imbalance between minority and majority examples, which can be combined into ensemble algorithms. However, these approaches do not function when the absolute number of minority examples is small, which is called the extremely imbalanced and small minority (EISM) data problem. The present work proposes a new algorithm called boosting combined with heuristic under-sampling and distribution-based sampling (HUSDOS-Boost) to solve the EISM data problem. To make an artificially balanced dataset from the original imbalanced datasets, HUSDOS-Boost uses both under-sampling and over-sampling to eliminate redundant majority examples based on prior boosting results and to generate artificial minority examples by following the minority class distribution. The performance and characteristics of HUSDOS-Boost were evaluated through application to eight imbalanced datasets. In addition, the algorithm was applied to original clinical HR data to detect patients with stomach cancer. These results showed that HUSDOS-Boost outperformed current imbalanced data handling methods, particularly when the data are EISM. Thus, the proposed HUSDOS-Boost is a useful methodology of HR data analysis.