Reviewerly/exHarmony huggingface dataset:
Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Modeling variable-sized regions of interest (ROIs) in whole slide images using deep convolutional networks is a challenging task, as these networks typically require fixed-sized inputs that should contain sufficient structural and contextual information for classification. We propose a deep feature extraction framework that builds an ROI-level feature representation via weighted aggregation of the representations of variable numbers of fixed-sized patches sampled from nuclei-dense regions in breast histopathology images. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> First, the initial patch-level feature representations are extracted from both fully-connected layer activations and pixel-level convolutional layer activations of a deep network, and the weights are obtained from the class predictions of the same network trained on patch samples. Then, the final patch-level feature representations are computed by concatenation of weighted instances of the extracted feature activations. Finally, the ROI-level representation is obtained by fusion of the patch-level representations by average pooling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Experiments using a well-characterized data set of 240 slides containing 437 ROIs marked by experienced pathologists with variable sizes and shapes result in an accuracy score of 72.65% in classifying ROIs into four diagnostic categories that cover the whole histologic spectrum. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> The results show that the proposed feature representations are superior to existing approaches and provide accuracies that are higher than the average accuracy of another set of pathologists. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> The proposed generic representation that can be extracted from any type of deep convolutional architecture combines the patch appearance information captured by the network activations and the diagnostic relevance predicted by the class-specific scoring of patches for effective modeling of variable-sized ROIs.
Design of Attendance System Based on Face Recognition and Android Platform

Aiming at the disadvantages of traditional manual attendance, this paper proposes a face recognition based attendance scheme. Through mobile platform and face recognition technology to optimize the manual attendance process. This design divides into the face recognition system of check on work attendance information input, attendance sign-in and attendance record three function module, and introduces a principle of face detection and classification, analyses the process of the construction of the face recognition classifier, the last on the Android platform design and implement a face recognition system of check on work attendance, by comparing the experiment results of face recognition accuracy, verify the feasibility of this scheme.
Machine Learning Technique Using the Signature Method for Automated Quality Control of Argo Profiles

A profile from the Argo ocean observation array is a sequence of three-dimensional vectors composed of pressure, salinity, and temperature, appearing as a continuous curve in three-dimensional space. The shape of this curve is faithfully represented by a path signature, which is a collection of all the iterated integrals. Moreover, the product of two terms of the signature of a path can be expressed as the sum of higher-order terms. Thanks to this algebraic property, a nonlinear function of profile shape can always be represented by a weighted linear combination of the iterated integrals, which enables machine learning of a complicated function of the profile shape. In this study, we performed supervised learning for existing Argo data with quality control flags by using the signature method, and demonstrated the estimation performance by cross-validation. Unlike rule-based approaches, which require several complicated and possibly subjective rules, this method is simple and objective in nature because it relies only on past knowledge regarding the shape of profiles. This technique should be critical to realizing automatic quality control for Argo profile data.
Multi-Frame Feature Aggregation for Real-Time Instrument Segmentation in Endoscopic Video

Deep learning-based methods have achieved promising results on surgical instrument segmentation. However, the high computation cost may limit the application of deep models to time-sensitive tasks such as online surgical video analysis for robotic-assisted surgery. Moreover, current methods may still suffer from challenging conditions in surgical images such as various lighting conditions and the presence of blood. We propose a novel Multi-frame Feature Aggregation (MFFA) module to aggregate video frame features temporally and spatially in a recurrent mode. By distributing the computation load of deep feature extraction over sequential frames, we can use a lightweight encoder to reduce the computation costs at each time step. Moreover, public surgical videos usually are not labeled frame by frame, so we develop a method that can randomly synthesize a surgical frame sequence from a single labeled frame to assist network training. We demonstrate that our approach achieves superior performance to corresponding deeper segmentation models on two public surgery datasets.
Towards Efficient Full Pose Omnidirectionality with Overactuated MAVs

Omnidirectional MAVs are a growing field, with demonstrated advantages for aerial interaction and uninhibited observation. While systems with complete pose omnidirectionality and high hover efficiency have been developed independently, a robust system that combines the two has not been demonstrated to date. This paper presents VoliroX: a novel omnidirectional vehicle that can exert a wrench in any orientation while maintaining efficient flight configurations. The system design is presented, and a 6 DOF geometric control that is robust to singularities. Flight experiments further demonstrate and verify its capabilities.
Dependency-based syntax-aware word representations

Dependency syntax has been demonstrated highly useful for a number of natural language processing (NLP) tasks. Typical approaches of utilizing dependency syntax include Tree-RNN and Tree-Linearization, both of which exploit explicit 1-best tree outputs from a well-trained parser as inputs. However, these approaches may suffer from error propagation due to the inevitable errors contained in the 1-best tree outputs. In this work, we propose a novel approach to integrate dependency syntax without using the discrete tree outputs. The key idea is to use the intermediate hidden representations of a well-trained encoder-decoder dependency parser, which are referred to as Dependency-based Syntax-Aware Word Representations (Dep-SAWRs). Then, we simply concatenate such Dep-SAWRs with the conventional context-insensitive word embeddings to compose input word representations, without requiring to modify the model architecture of the downstream tasks. We evaluate the proposed method on four kinds of typical NLP tasks, including sentence classification, sentence matching, sequence labeling and machine translation. Experimental results show that the proposed approach is highly promising. On the one hand, it can utilize dependency syntax effectively, bringing consistently better performance on the four tasks compared with baselines without using syntax. On the other hand, the proposed method can outperform the Tree-RNN and Tree-Linearization approaches in most settings, and meanwhile are highly efficient in syntax integration. In addition, the proposed method would be easily extendable to encoding other structural attributes of language.
Medical image processing with contextual style transfer

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.
A negative transfer approach to person re-identification via domain augmentation

Despite recent remarkable progress, person re-identification (ReID) still suffers from a shortage of annotated training data. To deal with this problem, there has been a boost of interest in developing various data augmentation methods. In this paper, we are devoted to developing an end-to-end joint representation learning framework for the ReID task on the basis of a novel data augmentation strategy. Specifically, we regard the original training dataset as a source domain and generate the counterpart augmented domains through image channel shuffling. Accordingly, we design a symmetric classification network for ReID learning. By investigating the domain-level and identity-level relationship between domains, we use the idea of negative transfer and structural consistency to optimize the network for learning discriminative feature embeddings. Comprehensive experiments on some benchmark datasets demonstrate the effectiveness and robustness of our proposed approach. Source code is released at: https://github.com/flychen321/negative_transfer_reid.
Disambiguating Monocular Depth Estimation with a Single Transient

Monocular depth estimation algorithms successfully predict the relative depth order of objects in a scene. However, because of the fundamental scale ambiguity associated with monocular images, these algorithms fail at correctly predicting true metric depth. In this work, we demonstrate how a depth histogram of the scene, which can be readily captured using a single-pixel time-resolved detector, can be fused with the output of existing monocular depth estimation algorithms to resolve the depth ambiguity problem. We validate this novel sensor fusion technique experimentally and in extensive simulation. We show that it significantly improves the performance of several state-of-the-art monocular depth estimation algorithms.
CuttleSys: Data-Driven Resource Management for Interactive Services on Reconfigurable Multicores

Multi-tenancy for latency-critical applications leads to resource interference and unpredictable performance. Core reconfiguration opens up more opportunities for application colocation, as it allows the hardware to adjust to the dynamic performance and power needs of a specific mix of co-scheduled services. However, reconfigurability also introduces challenges, as even for a small number of reconfigurable cores, exploring the design space becomes more time- and resource-demanding.We present CuttleSys, a runtime for reconfigurable multicores that leverages scalable and lightweight data mining to quickly identify suitable core and cache configurations for a set of co-scheduled applications. The runtime combines collaborative filtering to infer the behavior of each job on every core and cache configuration, with Dynamically Dimensioned Search to efficiently explore the configuration space. We evaluate CuttleSys on multicores with tens of reconfigurable cores and show up to 2.46× and 1.55× performance improvements compared to core-level gating and oracle-like asymmetric multicores respectively, under stringent power constraints.
Exploring User Centered Design in Healthcare: A Literature Review

Recently User Centered Design (UCD) has been frequently used in software development to place the user's needs and goals in the centre of the development process to deliver a highly usable system. This paper aims to capture the current usage of UCD methods in healthcare system development and briefly discusses the best usage of these methods. We conducted a systematic review of the literature using selection criteria and resulted in retrieving 20 relevant publications. The results were analyzed to identify the most common use of UCD methods in the healthcare system. We hope this paper will contribute to the body of knowledge on healthcare software development,by providing a broad overview of the existing works on UCD in healthcare along with identifying the best practices of the most common method in UCD.
Iterative Knowledge Distillation for Automatic Check-Out

Automatic Check-Out (ACO) provides an object detection based mechanism for retailers to process the purchases of customers automatically. However, it suffers a lot from the domain shift problem because of different data distribution between the single item in training exemplar images and mixed items in testing checkout images. In this paper, we propose a new iterative knowledge distillation method to solve the domain adaptation problem for this task. First, we develop a new augmentation data strategy to generate synthesized checkout images. It can extract segmented items from the training images by the coarse-to-fine strategy and filter items with unrealistic poses by pose pruning. Second, we propose a dual pyramid scale network (DPSNet) to exploit the multi-scale feature representation in joint detection and counting views. Third, the iterative knowledge distillation training strategy is developed to make full use of both image-level and instance-level samples to narrow the semantic gap between source domain and target domain. Extensive experiments on the large-scale Retail Product Checkout (RPC) dataset show the proposed DPSNet can achieve state-of-the-art performance compared with existing methods. The source codes can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://isrc.iscas.ac.cn/gitlab/research/dpsnet</uri> .
Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this article, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions.
Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem for cloud providers. CPU frequency scaling can be used to reduce power dissipation, but also impacts VM performance and therefore revenue. In this paper, we firstly propose a non-linear power model that estimates power dissipation of a multi-core PM and secondly a pricing model that adjusts the pricing based on the VM's CPU-boundedness characteristics. Finally, we present a cloud controller that uses these models to allocate VMs and scale CPU frequencies of the PMs to achieve energy cost savings that exceed service revenue losses. We evaluate the proposed approach using simulations with realistic VM workloads, electricity price and temperature traces and estimate energy savings of up to 14.57%.
Minimum Variance Embedded Random Vector Functional Link Network

In this paper, we propose an improved randomized based feed forward neural networks, known as Total variance minimization based random vector functional link network (Total-Var-RVFL) and intraclass variance minimization based random vector functional link network (Class-Var-RVFL). Total-Var-RVFL exploits the training data dispersion by minimizing the total variance while as Class-Var-RVFL minimizes the intraclass variance of the training data. The proposed classification models are evaluated on 18 datasets (UCI datasets). From the experimental analysis, one can see that the proposed classification models show better generalization performance as compared to the given baseline models.
Sentiment Analysis Using Twitter

Analysing the sentiment from the tweets of a user can give general insights into how the person is thinking. This paper analyses different groups of people like graduate students, politicians and doctors and aims to study the behavioural patterns. A method was designed to extract sentiment from tweets obtained using Twitter API. The VADER sentiment analysis library was used to gather median sentiment scores for different groups of people. The results show that politicians have a similar propensity for positive and negative tweets. Comedians’ tweets have the best positive median score, while graduate students’ tweets have the least positive median score among the tested groups. This paper extensively compares tweets of different groups of people and provides a novel analysis of general sentiment trends.
The value of geophysical data for geothermal exploration: Examples from empirical, field, and synthetic data

The value of information (VOI) metric requires geophysicists to clearly link their measurements to important subsurface parameters in geothermal exploration, such as temperature, permeability, and fluid information. The metric quantifies the increase in the expected (average) value outcome of a decision. The decision is made with a certain type of additional information, compared to if the decision was made without the information. VOI has challenges that are similar to those faced by machine learning enthusiasts. Labeled data sets are needed in which the labels are the decision variables and the features are the geophysical observables. This paper presents three examples of how the statistical relationship, or reliability, may be calculated for geothermal exploration applications. VOI provides a way to document successes and identify opportunities for improvement in derisking geothermal prospecting with geophysical information.
Fast optimization of parametrized quantum optical circuits

Parametrized quantum optical circuits are a class of quantum circuits in which the carriers of quantum information are photons and the gates are optical transformations. Classically optimizing these circuits is challenging due to the infinite dimensionality of the photon number vector space that is associated to each optical mode. Truncating the space dimension is unavoidable, and it can lead to incorrect results if the gates populate photon number states beyond the cutoff. To tackle this issue, we present an algorithm that is orders of magnitude faster than the current state of the art, to recursively compute the exact matrix elements of Gaussian operators and their gradient with respect to a parametrization. These operators, when augmented with a non-Gaussian transformation such as the Kerr gate, achieve universal quantum computation. Our approach brings two advantages: first, by computing the matrix elements of Gaussian operators directly, we don't need to construct them by combining several other operators; second, we can use any variant of the gradient descent algorithm by plugging our gradients into an automatic differentiation framework such as TensorFlow or PyTorch. Our results will find applications in quantum optical hardware research, quantum machine learning, optical data processing, device discovery and device design.
Outcome-Oriented Deep Temporal Phenotyping of Disease Progression

Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.