Reviewerly/exHarmony huggingface dataset:
A structural model for contextual code changes

We address the problem of predicting edit completions based on a learned model that was trained on past edits. Given a code snippet that is partially edited, our goal is to predict a completion of the edit for the rest of the snippet . We refer to this task as the EditCompletion task and present a novel approach for tackling it. The main idea is to directly represent structural edits. This allows us to model the likelihood of the edit itself, rather than learning the likelihood of the edited code. We represent an edit operation as a path in the program’s Abstract Syntax Tree (AST), originating from the source of the edit to the target of the edit. Using this representation, we present a powerful and lightweight neural model for the EditCompletion task. We conduct a thorough evaluation, comparing our approach to a variety of representation and modeling approaches that are driven by multiple strong models such as LSTMs, Transformers, and neural CRFs. Our experiments show that our model achieves a 28% relative gain over state-of-the-art sequential models and 2× higher accuracy than syntactic models that learn to generate the edited code , as opposed to modeling the edits directly. Our code, dataset, and trained models are publicly available at &lt;a&gt;https://github.com/tech-srl/c3po/&lt;/a&gt; .
Machine Learning model for Breast Cancer Prediction

The work which has been presented here mainly concentrates on the prediction of breast cancer. For this purpose, convolution neural network is used. In this work, previous records of breast cancer were taken in to account. Convolutional neural network has been used in the identification of breast cancer. In this method first of all pictures are organized. After that this organized picture is separated on the basis of its qualities. In the next step these pictures are developed in a new form and in the end prediction work is done. For the reduction of comparison time, space edge based pictures are taken. Because of that performance improves. In the introduction part of this work, fundamental ideology related to breast cancer prediction system is explained. In the next part of this work those researches are highlighted in which a lot of work is already done in the determination of breast cancer. Inspiration in addition to problem related research has been highlighted afterward. In the end, results of computerized calculation related to this research are shown. It has been clearly comes out of results that when edge based pictures are treated in convolution neural network, time and space reduced Which makes the performance of work better.
Evolution speed of open quantum dynamics

The space of density matrices is embedded in a Euclidean space to deduce the dynamical equation satisfied by the state of an open quantum system. The Euclidean norm is used to obtain an explicit expression for the speed of the evolution of the state. The unitary contribution to the evolution speed is given by the modified skew information of the Hamiltonian, while the radial component of the evolution speed, connected to the rate at which the purity of the state changes, is shown to be determined by the modified skew information of the Lindblad operators. An open-system analogue of the quantum navigation problem is posed, and a perturbative analysis is presented to identify the amount of change on the speed. Properties of the evolution speed are examined further through example systems, showing that the evolution speed need not be a decreasing function of time.
An Efficient Quantum Computing technique for cracking RSA using Shor’s Algorithm

Quantum Computing is a prominent word in this era as it allows computation to be performed in no time. The motive of using Quantum Computing (QC) is that even exponentially large number of problems can be solved using it which was earlier difficult with the classical computing. Conventional methods are based on usage of bits which consist of 0's and 1's while QC works with qubits. The main issue is that conventional computing has issue of storage as well as computation even when parallel computation is performed on it. Concept of quantum parallelism allows the computation to be performed in exponentially very low time as compared to conventional method. This paper will discuss about Quantum Computing Algorithms and how Shor's algorithm is able to break RSA algorithms is discussed. Entanglement and superposition of qubits helps fast computation. The demonstration of the applicability has been evaluated based on Computation time, storage capacity, accuracy, confidentiality, efficiency, integrity, and availability. Among various algorithms Shor's technique is able to break various encryption algorithm with more supremacy as compared to conventional computing methods. In nutshell, paper will discuss about various QC algorithms and will illustrate how shor's algorithm is able to crack RSA.
VR Sickness Versus VR Presence: A Statistical Prediction Model

Although it is well-known that the negative effects of VR sickness, and the desirable sense of presence are important determinants of a user's immersive VR experience, there remains a lack of definitive research outcomes to enable the creation of methods to predict and/or optimize the trade-offs between them. Most VR sickness assessment (VRSA) and VR presence assessment (VRPA) studies reported to date have utilized simple image patterns as probes, hence their results are difficult to apply to the highly diverse contents encountered in general, real-world VR environments. To help fill this void, we have constructed a large, dedicated VR sickness/presence (VR-SP) database, which contains 100 VR videos with associated human subjective ratings. Using this new resource, we developed a statistical model of spatio-temporal and rotational frame difference maps to predict VR sickness. We also designed an exceptional motion feature, which is expressed as the correlation between an instantaneous change feature and averaged temporal features. By adding additional features (visual activity, content features) to capture the sense of presence, we use the new data resource to explore the relationship between VRSA and VRPA. We also show the aggregate VR-SP model is able to predict VR sickness with an accuracy of 90% and VR presence with an accuracy of 75% using the new VR-SP dataset.
Fiber-coupled EPR-state generation using a single temporally multiplexed squeezed light source

A prerequisite for universal quantum computation and other large-scale quantum information processors is the careful preparation of quantum states in massive numbers or of massive dimension. For continuous variable approaches to quantum information processing (QIP), squeezed states are the natural quantum resources, but most demonstrations have been based on a limited number of squeezed states due to the experimental complexity in up-scaling. The number of physical resources can however be significantly reduced by employing the technique of temporal multiplexing. Here, we demonstrate an application to continuous variable QIP of temporal multiplexing in fiber: Using just a single source of squeezed states in combination with active optical switching and a 200 m fiber delay line, we generate fiber-coupled Einstein-Podolsky-Rosen entangled quantum states. Our demonstration is a critical enabler for the construction of an in-fiber, all-purpose quantum information processor based on a single or few squeezed state quantum resources.
TMMF: Temporal Multi-Modal Fusion for Single-Stage Continuous Gesture Recognition

Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction. Current gesture recognition methods have focused on recognising isolated gestures, and existing continuous gesture recognition methods are limited to two-stage approaches where independent models are required for detection and classification, with the performance of the latter being constrained by detection performance. In contrast, we introduce a single-stage continuous gesture recognition framework, called Temporal Multi-Modal Fusion (TMMF), that can detect and classify multiple gestures in a video via a single model. This approach learns the natural transitions between gestures and non-gestures without the need for a pre-processing segmentation step to detect individual gestures. To achieve this, we introduce a multi-modal fusion mechanism to support the integration of important information that flows from multi-modal inputs, and is scalable to any number of modes. Additionally, we propose Unimodal Feature Mapping (UFM) and Multi-modal Feature Mapping (MFM) models to map uni-modal features and the fused multi-modal features respectively. To further enhance performance, we propose a mid-point based loss function that encourages smooth alignment between the ground truth and the prediction, helping the model to learn natural gesture transitions. We demonstrate the utility of our proposed framework, which can handle variable-length input videos, and outperforms the state-of-the-art on three challenging datasets: EgoGesture, IPN hand and ChaLearn LAP Continuous Gesture Dataset (ConGD). Furthermore, ablation experiments show the importance of different components of the proposed framework.
Real-time calibration with spectator qubits

Abstract Accurate control of quantum systems requires a precise measurement of the parameters that govern the dynamics, including control fields and interactions with the environment. Parameters will drift in time and experiments interleave protocols that perform parameter estimation, with protocols that measure the dynamics of interest. Here we specialize to a system made of qubits where the dynamics correspond to a quantum computation. We propose setting aside some qubits, which we call spectator qubits, to be measured periodically during the computation, to act as probes of the changing experimental and environmental parameters. By using control strategies that minimize the sensitivity of the qubits involved in the computation, we can acquire sufficient information from the spectator qubits to update our estimates of the parameters and improve our control. As a result, we can increase the length of experiments where the dynamics of the data qubits are highly reliable. In particular, we simulate how spectator qubits can keep the error level of operations on data qubits below a 10 −4 threshold in two scenarios involving coherent errors: a classical magnetic field gradient dynamically decoupled with sequences of two or four π -pulses, and laser beam instability detected via crosstalk with neighboring atoms in an ion trap.
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text

Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.However, it performs much worse in specific domains such as the medical field due to the lack of extensive training data and professional structural knowledge neglect.As an effort, we first collect a large scale medical multi-choice question dataset (more than 21k instances) for the National Licensed Pharmacist Examination in China.It is a challenging medical examination with a passing rate of less than 14.2% in 2018.Then we propose a novel reading comprehension model KMQA, which can fully exploit the structural medical knowledge (i.e., medical knowledge graph) and the reference medical plain text (i.e., text snippets retrieved from reference books).The experimental results indicate that the KMQA outperforms existing competitive models with a large margin and passes the exam with 61.8% accuracy rate on the test set.
On-off sketch

Approximate stream processing has attracted much attention recently. Prior art mostly focuses on characteristics like frequency, cardinality, and quantile. Persistence, as a new characteristic, is getting increasing attention. Unlike frequency, persistence highlights behaviors where an item appears recurrently in many time windows of a data stream. There are two typical problems with persistence - persistence estimation and finding persistent items. In this paper, we propose the On-Off sketch to address both problems. For persistence estimation, using the characteristic that the persistence of an item is increased periodically, we compress increments when multiple items are mapped to the same counter, which significantly reduces the error. Compared with the Count-Min sketch, 1) in theory, we prove that the error of the On-Off sketch is always smaller; 2) in experiments, the On-Off sketch achieves around 6.17 times smaller error and 2.2 times higher throughput. For finding persistent items, we propose a technique to separate persistent and non-persistent items, further improving the accuracy. We show that the space complexity of our On-Off sketch is much better than the state-of-the-art (PIE), and it reduces the error up to 4 orders of magnitude and achieves 2.84 times higher throughput than prior algorithms in experiments.
Virtual to Real Adaptation of Pedestrian Detectors

Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there was an increasing interest in Convolutional Neural Network-based architectures for the execution of such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics. A suitably labeled dataset is essential to achieve this purpose. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is costly. To this end, we introduce ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images collected with the highly photo-realistic graphical engine of the video game GTA V - Grand Theft Auto V, where annotations are automatically acquired. However, when training solely on the synthetic dataset, the model experiences a Synthetic2Real Domain Shift leading to a performance drop when applied to real-world images. To mitigate this gap, we propose two different Domain Adaptation techniques suitable for the pedestrian detection task, but possibly applicable to general object detection. Experiments show that the network trained with ViPeD can generalize over unseen real-world scenarios better than the detector trained over real-world data, exploiting the variety of our synthetic dataset. Furthermore, we demonstrate that with our Domain Adaptation techniques, we can reduce the Synthetic2Real Domain Shift, making closer the two domains and obtaining a performance improvement when testing the network over the real-world images. The code, the models, and the dataset are made freely available at https://ciampluca.github.io/viped/
Protecting Quantum Fisher Information in Correlated Quantum Channels

Abstract Quantum Fisher information (QFI) has potential applications in quantum metrology tasks. QFI is investigated when the consecutive actions of a quantum channel on the sequence of qubits have partial classical correlations. The results showed that while the decoherence effect is detrimental to QFI, effects of such classical correlations on QFI are channel‐dependent. For the Bell‐type probe states, the classical correlations on consecutive actions of the depolarizing and phase flip channels can be harnessed to improve QFI, while the classical correlations in the bit flip and bit‐phase flip channels induce a slight decrease of QFI. For a more general parameterization form of the probe states, the advantage of using the initial correlated system on improving QFI can also remain in a wide regime of the correlated quantum channels.
Survey of XAI in Digital Pathology

Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes with many challenges. The algorithms should have high prediction accuracy but also be transparent, understandable and reliable. Thus, explainable artificial intelligence (XAI) is highly relevant for this domain. We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. The review includes several contributions. Firstly, we give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging, and categorise them from three different aspects. In doing so, we incorporate uncertainty estimation methods as an integral part of the XAI landscape. We also connect the technical methods to the specific prerequisites in digital pathology and present findings to guide future research efforts. The survey is intended for both technical researchers and medical professionals, one of the objectives being to establish a common ground for cross-disciplinary discussions.
Improving the outlier detection method in concrete mix design by combining the isolation forest and local outlier factor

The rapid development in construction industry, induce a large amounts of concrete data that are usually measured and analyzed everyday naming that concrete is the second usable material on earth. Concrete is made from numerous ingredients that have huge variability either at the design stage or at the testing stage. The main goal of this paper is to quantify the anomalies and outliers during the design phase of concrete mixtures. Concrete mixtures have various percentages of ingredients such as cement, slag, fly ash, water, superplasticizer, fine and coarse aggregates. Machine learning and data mining is considered a very thriving topic in many research fields and its implementation in the construction industry still limited. Concrete community is in need for such a tool to produce an efficient way to efficiently design concrete mixtures. Outliers could occur during the evaluation of samples' measurements that might include human or system errors. The Local Outlier Factor (LOF) algorithm is the most common method used to determine outliers, however, the LOF has some challenges. In this paper, an anomaly-based outlier detection algorithm called Isolation Forest based on a Sliding window for the Local Outlier Factor (IFS-LOF) algorithm, is proposed to solve the limitations of the LOF in evaluating 1030 concrete mixtures. The proposed algorithm works without any previous knowledge of data distribution and executes the process within limited memory and with minimal computational effort. The evaluation of results proved that the IFS-LOF algorithm is more efficient in detecting the sequence of outliers and provided more efficient accuracy that other state of the art LOF algorithms.
KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations

Syntactic parsers have dominated natural language understanding for decades.Yet, their syntactic interpretations are losing centrality in downstream tasks due to the success of large-scale textual representation learners.In this paper, we propose KERMIT (Kernelinspired Encoder with Recursive Mechanism for Interpretable Trees) to embed symbolic syntactic parse trees into artificial neural networks and to visualize how syntax is used in inference.We experimented with KERMIT paired with two state-of-the-art transformerbased universal sentence encoders (BERT and XLNet) and we showed that KERMIT can indeed boost their performance by effectively embedding human-coded universal syntactic representations in neural networks.
Exploiting the Matching Information in the Support Set for Few Shot Event Classification

The existing event classification (EC) work primarily focuseson the traditional supervised learning setting in which models are unableto extract event mentions of new/unseen event types. Few-shot learninghas not been investigated in this area although it enables EC models toextend their operation to unobserved event types. To fill in this gap, inthis work, we investigate event classification under the few-shot learningsetting. We propose a novel training method for this problem that exten-sively exploit the support set during the training process of a few-shotlearning model. In particular, in addition to matching the query exam-ple with those in the support set for training, we seek to further matchthe examples within the support set themselves. This method providesmore training signals for the models and can be applied to every metric-learning-based few-shot learning methods. Our extensive experiments ontwo benchmark EC datasets show that the proposed method can improvethe best reported few-shot learning models by up to 10% on accuracyfor event classification
Outlier Detection in High Dimensional Data

High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on data set of small size with a large number of features. In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. The proposed method is designed to address the challenges of dealing with high-dimensional data by projecting the original data onto a smaller space and using the innate structure of the data to calculate anomaly scores for each data point. Numerical experiments on synthetic and real-life data show that our method performs well on high-dimensional data. In particular, the proposed method outperforms the benchmark methods as measured by the $F_1$-score. Our method also produces better-than-average execution times compared to the benchmark methods.
Skeleton-based action recognition using sparse spatio-temporal GCN with edge effective resistance

Graph convolutional neural networks have established significant success in solving various machine learning and computer vision problems. For skeleton-based action recognition, graph convolutional neural networks are the most suitable choice since human skeleton resembles to a graph. Stacking body skeletons over the length of video sequence results in a very complex spatio-temporal graph of many nodes and edges. Modeling the graph convolutional network directly with such a complex graph curtails the performance due to the redundancy of insignificant nodes and edges in the graph. Also for skeleton-based action recognition, the long-term contextual information is of central importance and many current architectures may fail to capture such contextual information. Therefore in order to alleviate these problems, we propose graph sparsification technique using edge effective resistance to better model the global context information and to eliminate redundant nodes and edges in the graph. Furthermore, we incorporate self-attention graph pooling to retain local properties and graph structures while pooling operation. To the best of our knowledge, we are the first to apply graph sparsification using edge effective resistance for skeleton-based action recognition and our proposed method is confirmed to be effective on action recognition, which achieves state-of-the-art results on publicly available datasets: UTD-MHAD, J-HMDB, NTU-RGB + D-60, NTU-RGB + D-120 and Kinetics dataset.