Reviewerly/exHarmony huggingface dataset:
Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation

It is a general assumption in deep learning that more training data leads to better performance, and that models will learn to generalize well across heterogeneous input data as long as that variety is represented in the training set. Segmentation of brain tumors is a well-investigated topic in medical image computing, owing primarily to the availability of a large publicly-available dataset arising from the long-running yearly Multimodal Brain Tumor Segmentation (BraTS) challenge. Research efforts and publications addressing this dataset focus predominantly on technical improvements of model architectures and less on properties of the underlying data. Using the dataset and the method ranked third in the BraTS 2018 challenge, we performed experiments to examine the impact of tumor type on segmentation performance. We propose to stratify the training dataset into high-grade glioma (HGG) and low-grade glioma (LGG) subjects and train two separate models. Although we observed only minor gains in overall mean dice scores by this stratification, examining case-wise rankings of individual subjects revealed statistically significant improvements. Compared to a baseline model trained on both HGG and LGG cases, two separately trained models led to better performance in 64.9% of cases (p < 0.0001) for the tumor core. An analysis of subjects which did not profit from stratified training revealed that cases were missegmented which had poor image quality, or which presented clinically particularly challenging cases (e.g., underrepresented subtypes such as IDH1-mutant tumors), underlining the importance of such latent variables in the context of tumor segmentation. In summary, we found that segmentation models trained on the BraTS 2018 dataset, stratified according to tumor type, lead to a significant increase in segmentation performance. Furthermore, we demonstrated that this gain in segmentation performance is evident in the case-wise ranking of individual subjects but not in summary statistics. We conclude that it may be useful to consider the segmentation of brain tumors of different types or grades as separate tasks, rather than developing one tool to segment them all. Consequently, making this information available for the test data should be considered, potentially leading to a more clinically relevant BraTS competition.
Temporal convolutional networks allow early prediction of events in critical care

Abstract Objective Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients’ longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action. Materials and Methods We use a temporal convolutional network to encode longitudinal data and a feedforward neural network to encode demographic data from 4713 ICU admissions in 2014–2018. For each hour of each admission, we predict events in the subsequent 1–6 hours. We compare performance with other models including a recurrent neural network. Results Our model performed similarly to the recurrent neural network for some events and outperformed it for others. This performance increase was more evident in a sensitivity analysis where the prediction timeframe was varied. Average positive predictive value (95% CI) was 0.786 (0.781–0.790) and 0.738 (0.732–0.743) for up- and down-titrating FiO2, 0.574 (0.519–0.625) for extubation, 0.139 (0.117–0.162) for intubation, 0.533 (0.492–0.572) for starting noradrenaline, 0.441 (0.433–0.448) for fluid challenge, and 0.315 (0.282–0.352) for death. Discussion Events were better predicted where their important determinants were captured in structured electronic health data, and where they occurred in homogeneous circumstances. We produce partial dependence plots that show our model learns clinically-plausible associations between its inputs and predictions. Conclusion Temporal convolutional networks improve prediction of clinical events when used to represent longitudinal ICU data.
Characterizing photon number statistics using conjugate optical homodyne detection

We study the problem of determining the photon number statistics of an unknown quantum state using conjugate optical homodyne detection. We quantify the information gain in a single-shot measurement and show that the photon number statistics can be recovered in repeated measurements on an ensemble of identical input states without scanning the phase of the input state or randomizing the phase of the local oscillator used in homodyne detection. We demonstrate how the expectation maximization algorithm and Bayesian inference can be utilized to facilitate the reconstruction and illustrate our approach by conducting experiments to study the photon number distributions of a weak coherent state and a thermal state source.
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Parallel Batch-Dynamic Graphs: Algorithms and Lower BoundsLaxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, and Xiaorui SunLaxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, and Xiaorui Sunpp.1300 - 1319Chapter DOI:https://doi.org/10.1137/1.9781611975994.79PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. In this setting, the graph is stored on a number of machines, each having space strongly sublinear with respect to the number of vertices, that is, nϵ for some constant 0 < ϵ < 1. Our goal is to handle batches of updates and queries where the data for each batch fits onto one machine in constant rounds of parallel computation, as well as to reduce the total communication between the machines. This objective corresponds to the gradual buildup of databases over time, while the goal of obtaining constant rounds of communication for problems in the static setting has been elusive for problems as simple as undirected graph connectivity. We give an algorithm for dynamic graph connectivity in this setting with constant communication rounds and communication cost almost linear in terms of the batch size. Our techniques combine a new graph contraction technique, an independent random sample extractor from correlated samples, as well as distributed data structures supporting parallel updates and queries in batches. We also illustrate the power of dynamic algorithms in the MPC model by showing that the batched version of the adaptive connectivity problem is P-complete in the centralized setting, but sub-linear sized batches can be handled in a constant number of rounds. Due to the wide applicability of our approaches, we believe it represents a practically-motivated workaround to the current difficulties in designing more efficient massively parallel static graph algorithms. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Blockchain Technology Facing Socioeconomic Challenges. Promise versus Probability

This paper has the main purpose to make a critical and balanced analysis about the potential of blockchain technology to face some of the great current socioeconomic challenges, being focus on impact assessment point of view, analyzing the disruptive potential of blockchain to provide solutions at level of different challenges as example, climate change, migrant movements, gender equality, financial inclusion or the cost opportunity of the management of data science. The term blockchain summary a numerous different type of system of distributed ledger, essentially, it is just a record distributed, a ledger of digital events that is distributed or shared among many different parts within an ecosystem (nodes), and chronological in a network. The technology is at an early stage and can be implemented in many ways depending on the objective. The methodological tool for the research is strategic and qualitative SWOT analysis identifying the critical success factors such internal factors (Strengthens and Weakness), and external factors (Opportunities and Threats), summarizes the arguments and counterarguments within the scientific discussion. From the bibliographic review carried out on the finding and disclosure provided by empirical research about business case studies, the research results summarized in the paper confirm that although looks difficulty of give a closed definition to variety of system under the umbrella of blockchain, among the main strengths of technology are its intrinsic characteristics, such as, its ability to store data immutably without relying on a central authority. As weakness, highlight the fact of the need of solve some non-minor inefficiencies as energy consumption and, as result, the difficulty to be scaled. It has the potential to replace the intermediary and central entities or change the way they works, allowing disintermediation and potentially empower people in trade, democratic participation, social interaction and financial inclusion, which represent great opportunities. Although, on the side of threats there is lack of knowledge about the technology, which generates resistance from regulators who are beginning to assess risks and are concerned about how new participants could cannibalize their income models. In addition, it seems clear the importance of assume the fact that the technological changes take time to develop and often require the adaptation of entire ecosystems. Keywords: blockchain, decentralization, democratization, financial inclusion, socioeconomic challenges, tokem traceability, transparency, trust.
Reinforcement learning for semi-autonomous approximate quantum eigensolver

Abstract The characterization of an operator by its eigenvectors and eigenvalues allows us to know its action over any quantum state. Here, we propose a protocol to obtain an approximation of the eigenvectors of an arbitrary Hermitian quantum operator. This protocol is based on measurement and feedback processes, which characterize a reinforcement learning protocol. Our proposal is composed of two systems, a black box named environment and a quantum state named agent. The role of the environment is to change any quantum state by a unitary matrix <?CDATA ${\hat{U}}_{E}={{\rm{e}}}^{-{\rm{i}}\tau {\hat{{ \mathcal O }}}_{E}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">e</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mi mathvariant="normal">i</mml:mi> <mml:mi>τ</mml:mi> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi mathvariant="italic"></mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msup> </mml:math> where <?CDATA ${\hat{{ \mathcal O }}}_{E}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi mathvariant="italic"></mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> </mml:msub> </mml:math> is a Hermitian operator, and τ is a real parameter. The agent is a quantum state which adapts to some eigenvector of <?CDATA ${\hat{{ \mathcal O }}}_{E}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi mathvariant="italic"></mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> </mml:msub> </mml:math> by repeated interactions with the environment, feedback process, and semi-random rotations. With this proposal, we can obtain an approximation of the eigenvectors of a random qubit operator with average fidelity over 90% in less than 10 iterations, and surpass 98% in less than 300 iterations. Moreover, for the two-qubit cases, the four eigenvectors are obtained with fidelities above 89% in 8000 iterations for a random operator, and fidelities of 99% for an operator with the Bell states as eigenvectors. This protocol can be useful to implement semi-autonomous quantum devices which should be capable of extracting information and deciding with minimal resources and without human intervention.
Impact of visuomotor feedback on the embodiment of virtual hands detached from the body

It has been shown that mere observation of body discontinuity leads to diminished body ownership. However, the impact of body discontinuity has mainly been investigated in conditions where participants observe a collocated static virtual body from a first-person perspective. This study explores the influence of body discountinuity on the sense of embodiment, when rich visuomotor correlations between a real and an artificial virtual body are established. In two experiments, we evaluated body ownership and motor performance, when participants interacted in virtual reality either using virtual hands connected or disconnected from a body. We found that even under the presence of congruent visuomotor feedback, mere observation of body discontinuity resulted in diminished embodiment. Contradictory evidence was found in relation to motor performance, where further research is needed to understand the role of visual body discontinuity in motor tasks. Preliminary findings on physiological reactions to a threat were also assessed, indicating that body visual discontinuity does not differently impact threat-related skin conductance responses. The present results are in accordance with past evidence showing that body discontinuity negatively impacts embodiment. However, further research is needed to understand the influence of visuomotor feedback and body morphological congruency on motor performance and threat-related physiological reactions.
Temporal information extraction from mental health records to identify duration of untreated psychosis

Abstract Background Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research. Natural Language Processing (NLP) is a means to addressing this problem by automatically extracting relevant information in a structured form. As a first step, it is important to identify appropriate documents, i.e., those that are likely to include the information of interest. Next, temporal information extraction methods are needed to identify time references for early psychosis symptoms. This NLP challenge requires solving three different tasks: time expression extraction, symptom extraction, and temporal “linking”. In this study, we focus on the first step, using two relevant EHR datasets. Results We applied a rule-based NLP system for time expression extraction that we had previously adapted to a corpus of mental health EHRs from patients with a diagnosis of schizophrenia (first referrals). We extended this work by applying this NLP system to a larger set of documents and patients, to identify additional texts that would be relevant for our long-term goal, and developed a new corpus from a subset of these new texts (early intervention services). Furthermore, we added normalized value annotations (“2011–05”) to the annotated time expressions (“May 2011”) in both corpora. The finalized corpora were used for further NLP development and evaluation, with promising results (normalization accuracy 71–86%). To highlight the specificities of our annotation task, we also applied the final adapted NLP system to a different temporally annotated clinical corpus. Conclusions Developing domain-specific methods is crucial to address complex NLP tasks such as symptom onset extraction and retrospective calculation of duration of a preclinical syndrome. To the best of our knowledge, this is the first clinical text resource annotated for temporal entities in the mental health domain.
Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres

Cloud computing systems are splitting compute- and data-intensive jobs into smaller tasks to execute them in a parallel manner using clusters to improve execution time. However, such systems at increasing scale are exposed to stragglers, whereby abnormally slow running tasks executing within a job substantially affect job performance completion. Such stragglers are a direct threat towards attaining fast execution of data-intensive jobs within cloud computing. Researchers have proposed an assortment of different mechanisms, frameworks, and management techniques to detect and mitigate stragglers both proactively and reactively. In this paper, we present a comprehensive review of straggler management techniques within large-scale cloud data centres. We provide a detailed taxonomy of straggler causes, as well as proposed management and mitigation techniques based on straggler characteristics and properties. From this systematic review, we outline several outstanding challenges and potential directions of possible future work for straggler research.
ASTS: A Unified Framework for Arbitrary Shape Text Spotting

Arbitrary shape text spotting remains a challenging computer vision task. In this paper, we propose an end-to-end trainable unified framework for arbitrary shape text spotting to overcome the limitations inherent in the existing methods. Specifically, we propose to perceive and understand text based on different levels of semantics, i.e., holistic-, pixel- and sequence-level semantics, and then unify the recognized semantics for robust text spotting. To implement the framework, we customize the detection and mask branches of Mask R-CNN to explore both holistic- and pixel-level semantics for text recognition. According to the recognition results, the text spotting task can then be formulated in the two-dimensional feature space. Then, by feeding the two-dimensional feature maps into an additional text recognition branch, our framework further delivers one-dimensional sequence-level semantics for text recognition based on an attention-based sequence-to-sequence network. Finally, the results from all the three levels of semantics are merged as the final result. Therefore, our framework is capable of simultaneously recognizing texts from both the one- and two-dimensional perspectives, achieving highly comprehensive text recognition. In addition, because some existing datasets lack character-level annotations, the extensive descriptions of texts from our framework further allow us to use only word-level annotations as weak supervision for training a robust text spotting model. Experiments on ICDAR 2013, ICDAR 2015, and Total-Text show that our framework achieves state-of-the-art performance for both detection and recognition.
Multiparameter quantum estimation under dephasing noise

The simultaneous quantum estimation of multiple parameters has recently become an essential aspect of quantum metrology. Although the ultimate sensitivity of a multiparameter quantum estimation in a noiseless environment can overcome the standard quantum limit that every classical sensor is bounded by, it is unclear whether a quantum sensor has an advantage over a classical sensor under a realistic level of noise. In this study, we present the framework of a simultaneous estimation of multiple parameters using quantum sensors under a specific noisy environment. Three components of an external magnetic field are estimated, and we consider the dephasing noise. We show that there is an optimal sensing time in a time-inhomogeneous noisy environment and that its sensitivity can overcome the standard quantum limit.
An efficient Bayesian network for differential diagnosis using experts' knowledge

Purpose This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance. Design/methodology/approach First, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI). Findings The proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods. Practical implications The proposed methodology can be applied to perform disease differential diagnosis analysis. Originality/value This study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.
Conditional Distribution Variability Measures for Causality Detection

In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.