First Multicolor Photometry of 1SWASP J130111.22+420214.0 and 1SWASP J231839.72+352848.2

The first multicolor observations and light curve solutions of the stars 1SWASP J130111.22+420214.0 and 1SWASP J231839.72+352848.2 are presented. The estimated physical parameters of 1SWASP J130111.22+420214.0 are $M_1=1.0M_{\odot}$, $M_2=1.63M_{\odot}$, $R_1=0.77R_{\odot}$, $R_2=0.91R_{\odot}$, $L_1=0.59L_{\odot}$, and $L_2=0.68L_{\odot}$. While a binary model could not be fitted to the light curve of 1SWASP J231839.72+352848.2, its Fourier series representation indicates that this system could well be a rotating ellipsoidal variable. The components of 1SWASP J130111.22+420214.0 are also compared to other well--known binary systems and their position in mass--radius plane and the Hertzsprung--Russel diagram are argued and possible evolutionary statuses are discussed.Comment: 17 pages, 5 figures. Accepted for publication in New Astronom

Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 201

When Queueing Meets Coding: Optimal-Latency Data Retrieving Scheme in Storage Clouds

In this paper, we study the problem of reducing the delay of downloading data from cloud storage systems by leveraging multiple parallel threads, assuming that the data has been encoded and stored in the clouds using fixed rate forward error correction (FEC) codes with parameters (n, k). That is, each file is divided into k equal-sized chunks, which are then expanded into n chunks such that any k chunks out of the n are sufficient to successfully restore the original file. The model can be depicted as a multiple-server queue with arrivals of data retrieving requests and a server corresponding to a thread. However, this is not a typical queueing model because a server can terminate its operation, depending on when other servers complete their service (due to the redundancy that is spread across the threads). Hence, to the best of our knowledge, the analysis of this queueing model remains quite uncharted. Recent traces from Amazon S3 show that the time to retrieve a fixed size chunk is random and can be approximated as a constant delay plus an i.i.d. exponentially distributed random variable. For the tractability of the theoretical analysis, we assume that the chunk downloading time is i.i.d. exponentially distributed. Under this assumption, we show that any work-conserving scheme is delay-optimal among all on-line scheduling schemes when k = 1. When k > 1, we find that a simple greedy scheme, which allocates all available threads to the head of line request, is delay optimal among all on-line scheduling schemes. We also provide some numerical results that point to the limitations of the exponential assumption, and suggest further research directions.Comment: Original accepted by IEEE Infocom 2014, 9 pages. Some statements in the Infocom paper are correcte

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Wiring of Photosystem II to Hydrogenase for Photoelectrochemical Water Splitting.

In natural photosynthesis, light is used for the production of chemical energy carriers to fuel biological activity. The re-engineering of natural photosynthetic pathways can provide inspiration for sustainable fuel production and insights for understanding the process itself. Here, we employ a semiartificial approach to study photobiological water splitting via a pathway unavailable to nature: the direct coupling of the water oxidation enzyme, photosystem II, to the H2 evolving enzyme, hydrogenase. Essential to this approach is the integration of the isolated enzymes into the artificial circuit of a photoelectrochemical cell. We therefore developed a tailor-made hierarchically structured indium-tin oxide electrode that gives rise to the excellent integration of both photosystem II and hydrogenase for performing the anodic and cathodic half-reactions, respectively. When connected together with the aid of an applied bias, the semiartificial cell demonstrated quantitative electron flow from photosystem II to the hydrogenase with the production of H2 and O2 being in the expected two-to-one ratio and a light-to-hydrogen conversion efficiency of 5.4% under low-intensity red-light irradiation. We thereby demonstrate efficient light-driven water splitting using a pathway inaccessible to biology and report on a widely applicable in vitro platform for the controlled coupling of enzymatic redox processes to meaningfully study photocatalytic reactions.This work was supported by the U.K. Engineering and Physical Sciences Research Council (EP/H00338X/2 to E.R. and EP/G037221/1, nanoDTC, to D.M.), the UK Biology and Biotechnological Sciences Research Council (BB/K002627/1 to A.W.R. and BB/K010220/1 to E.R.), a Marie Curie Intra-European Fellowship (PIEF-GA-2013-625034 to C.Y.L), a Marie Curie International Incoming Fellowship (PIIF-GA-2012-328085 RPSII to J.J.Z) and the CEA and the CNRS (to J.C.F.C.). A.W.R. holds a Wolfson Merit Award from the Royal Society.This is the final version of the article. It first appeared from ACS Publications via http://dx.doi.org/10.1021/jacs.5b0373

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.acceptedVersio