The resurging interest in Byzantine fault tolerant systems will demand more scalable threshold cryptosystems. Unfortunately, current systems scale poorly, requiring time quadratic in the number of participants. In this paper, we present techniques that help scale threshold signature schemes (TSS), verifiable secret sharing (VSS) and distributed key generation (DKG) protocols to hundreds of thousands of participants and beyond. First, we use efficient algorithms for evaluating polynomials at multiple points to speed up computing Lagrange coefficients when aggregating threshold signatures. As a result, we can aggregate a 130,000 out of 260,000 BLS threshold signature in just 6 seconds (down from 30 minutes). Second, we show how "authenticating" such multipoint evaluations can speed up proving polynomial evaluations, a key step in communication-efficient VSS and DKG protocols. As a result, we reduce the asymptotic (and concrete) computational complexity of VSS and DKG protocols from quadratic time to quasilinear time, at a small increase in communication complexity. For example, using our DKG protocol, we can securely generate a key for the BLS scheme above in 2.3 hours (down from 8 days). Our techniques improve performance for thresholds as small as 255 and generalize to any Lagrange-based threshold scheme, not just threshold signatures. Our work has certain limitations: we require a trusted setup, we focus on synchronous VSS and DKG protocols and we do not address the worst-case complaint overhead in DKGs. Nonetheless, we hope it will spark new interest in designing large-scale distributed systems.

We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns models over small groups of variables and iteratively merges onto a global solution. The structure learning involves combinatorial operations such as minimum spanning tree construction and local recursive grouping; the parameter learning is based on the method of moments and on tensor decompositions. Our method is guaranteed to correctly recover the unknown tree structure and the model parameters with low sample complexity for the class of linear multivariate latent tree models which includes discrete and Gaussian distributions, and Gaussian mixtures. Our bulk asynchronous parallel algorithm is implemented in parallel and the parallel computation complexity increases only logarithmically with the number of variables and linearly with dimensionality of each variable.

Background: We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods. Methods and Results: Using longitudinal electronic health record data, we assessed the relative performance of machine learning models trained to detect a future diagnosis of heart failure in primary care patients. Model performance was assessed in relation to data parameters defined by the combination of different data domains (data diversity), the number of patient records in the training data set (data quantity), the number of encounters per patient (data density), the prediction window length, and the observation window length (ie, the time period before the prediction window that is the source of features for prediction). Data on 4370 incident heart failure cases and 30 132 group-matched controls were used. Recurrent neural network model performance was superior under a variety of conditions that included (1) when data were less diverse (eg, a single data domain like medication or vital signs) given the same training size; (2) as data quantity increased; (3) as density increased; (4) as the observation window length increased; and (5) as the prediction window length decreased. When all data domains were used, the performance of recurrent neural network models increased in relation to the quantity of data used (ie, up to 100% of the data). When data are sparse (ie, fewer features or low dimension), model performance is lower, but a much smaller training set size is required to achieve optimal performance compared with conditions where data are more diverse and includes more features. Conclusions: Recurrent neural networks are effective for predicting a future diagnosis of heart failure given sufficient training set size. Model performance appears to continue to improve in direct relation to training set size.

This paper proposes a receding-horizon, multiobjective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiveness of such plan. We define three objective functions (performance, uncertainty about victim locations, and uncertainty about the environment) and formulate a multi-objective optimization problem employing a combined weighted-sum and ε-constraint method. To handle dynamic scenarios, we employ a receding-horizon approach that allows to dynamically adapt the ε constraint. We illustrate the effectiveness of the proposed method via simulations.

We study the following multi-robot coordination problem: given a graph, where each edge is weighted by the probability of surviving while traversing it, find a set of paths for K robots that maximizes the expected number of nodes collectively visited, subject to constraints on the probabilities that each robot survives to its destination. We call this the Team Surviving Orienteers (TSO) problem, which is motivated by scenarios where a team of robots must traverse a dangerous environment, such as aid delivery in disaster or war zones. We present the TSO problem formally along with several variants, which represent "survivability-aware" counterparts for a wide range of multi-robot coordination problems such as vehicle routing, patrolling, and informative path planning. We propose an approximate greedy approach for selecting paths, and prove that the value of its output is within a factor 1 -e -ps/λ of the optimum where ps is the per-robot survival probability threshold, and 1/λ ≤ 1 is the approximation factor of an oracle routine for the well-known orienteering problem. Our approach has linear time complexity in the team size and polynomial complexity in the graph size. Using numerical simulations, we verify that our approach works well in practice and that it scales to problems with hundreds of nodes and tens of robots.

Abstract Introduction Front-line treatment for patients with active chronic lymphocytic leukemia (CLL) often includes an anti-CD20 monoclonal antibody. However, treatment with anti-CD20 therapy has the potential to reactivate latent hepatitis B virus (HBV). Thus, in 2015 an American Society of Clinical Oncology (ASCO) panel advised screening all patients for HBV infection before starting anti-CD20 therapies. Both Hepatitis B surface antigen (HBsAg) and Hepatitis B core antibody (HBcAb) testing are required for proper screening. In addition, the ASCO panel recommended testing for both IgM and IgG isotypes of HBcAb, as patients with remote HBV infection may have undetectable levels of HBcAb IgM. We evaluated guideline adherence for HBV testing in a large, real-world cohort of patients with CLL. Methods This retrospective analysis leveraged data derived from electronic health records (EHRs) in the Flatiron Health database. The database included 7,795 patients with CLL who received at least one order for an antineoplastic treatment between January 1, 2011 and May 31, 2018. We included only patients who received an anti-CD20 therapy (rituximab, ofatumumab, obinutuzumab) and excluded patients whose start of CLL treatment (as captured through chart abstraction) was >30 days before the start of structured EHR data (N=4,288). Using structured EHR data (based on Logical Observation Identifiers Names and Codes [LOINC]), we evaluated the frequency of HBsAg and HBcAb testing, including a separate analysis of HBcAb IgM and IgG testing. Results Of 4,288 patients in the cohort treated with anti-CD20, 2,034 (47%) had evidence of HBV testing at any point during their care, and 1,765 (41%) had evidence of HBV testing at any point prior to and up to 90 days following anti-CD20 therapy initiation. Over time, the proportion of HBV infection testing up to 90 days following anti-CD20 treatment initiation increased steadily, from <15% in early 2011, reaching as high as 70% in 2017 (Figure). Following the ASCO guideline publication in 2015, the proportion of patients tested for both HBsAg and HBcAb increased by 33%, while the proportion of HBsAg-only tested patients dropped by 35% (Table). However, even if unspecified HBcAb tests are assumed to represent testing for both IgG and IgM isotypes, in the most recent timeframe nearly half (46%) of the patients who were tested did not receive the requisite screening tests (i.e., for both isotypes). Additionally, nearly 20% received only one test (HBsAg or HBcAb) and another 25% received both tests but the HBcAb test was for the IgM isotype alone (Table). Discussion In this real-world cohort of patients with CLL treated with anti-CD20 therapies, overall HBV testing increased throughout the study period, in accordance with ASCO guidelines. Although the most recent data points may not yet capture testing up to 90 days following treatment initiation, and HBV testing information for some patients may not be captured in the EHR, our findings suggest that a sizable proportion of patients are not receiving appropriate screening for HBV. In addition, a significant proportion (25%) of patients were tested for both HBsAg and HBcAb, but the HBcAb test was for the IgM isotype alone, which could result in a false negative test and missed cases of latent HBV. These findings suggest a need for additional quality improvement efforts. Further research is needed to determine demographic and clinical characteristics of tested versus untested patients, and whether testing is associated with reduced risk of liver failure in patients undergoing anti-CD20 therapy. Disclosures Hooley: Flatiron Health: Employment. Chen:MedCyclops LLC: Equity Ownership; Nootrobox: Consultancy; Flatiron Health: Employment. Maignan:Flatiron Health: Employment. Carson:Roche: Consultancy; Washington University in St. Louis: Employment; Flatiron Health: Employment.

We present an integrated approach to structure and parameter estimation in latent tree graphical models, where some nodes are hidden. Our overall approach follows a divide-and-conquer strategy that learns models over small groups of variables and iteratively merges into a global solution. The structure learning involves combinatorial operations such as minimum spanning tree construction and local recursive grouping; the parameter learning is based on the method of moments and on tensor decompositions. Our method is guaranteed to correctly recover the unknown tree structure and the model parameters with low sample complexity for the class of linear multivariate latent tree models which includes discrete and Gaussian distributions, and Gaussian mixtures. Our bulk asynchronous parallel algorithm is implemented in parallel using the OpenMP framework and scales logarithmically with the number of variables and linearly with dimensionality of each variable. Our experiments confirm a high degree of efficiency and accuracy on large datasets of electronic health records. The proposed algorithm also generates intuitive and clinically meaningful disease hierarchies.

Consider a scenario where robots traverse a graph, but crossing each edge bears a risk of failure. A team operator seeks a set of paths for the smallest team which guarantee the probabilities that at least one robot visits each node satisfy specified per-node visit thresholds, and the probabilities each robot reaches its destination satisfy a per-robot survival threshold. We present the Risk-Sensitive Coverage (RSC) problem formally as an instance of the submodular set cover problem and propose an efficient cost-benefit greedy algorithm for finding a feasible set of paths. We prove that the number of robots deployed by our algorithm is no more than (λ/ps)(1 + log(λΔκ/ps)) times the smallest team, where Δκ quantifies the relative benefit of the first and last paths, ps is the per-robot survival probability threshold and 1/λ ≤ 1 is the approximation factor of an oracle routine for the well-known orienteering problem. We demonstrate the quality of our solutions by comparing to optimal solutions computed for special cases of the RSC and the efficiency of our approach by applying it to a search and rescue scenario where 225 sites must be visited, each with probability at least 0.95.

This paper addresses the problem of state estimation of a linear time-invariant system when some of the sensors or/and actuators are under adversarial attack. In our set-up, the adversarial agent attacks a sensor (actuator) by manipulating its measurement (input), and we impose no constraint on how the measurements (inputs) are corrupted. We introduce the notion of “sparse strong observability” to characterize systems for which the state estimation is possible, given bounds on the number of attacked sensors and actuators. Furthermore, we develop a secure state estimator based on Satisfiability Modulo Theory (SMT) solvers.

Introduction Heart failure (HF) is a diverse syndrome associated with multiple risk factors and diseases. Heart failure affects millions of adults in the United States annually, and for some subtypes of patients with HF, 5-year mortality is higher than 50%. In order to intervene earlier to reduce morbidity and mortality with targeted therapies it is important to identify patients at high risk for HF. Electronic health records (EHR) provide extensive information on patients that one can use as features for outcome prediction. Many machine learning (ML) methods are able to deal with large feature sets and complete the prediction task successfully. However, there are few efficient algorithms that also yield results that are easily interpretable. Furthermore, when applied to EHR data, such methods (e.g. logistic regression) will struggle from over-fitting due to low number of events per variable. Recent applications of non-negative tensor factorization (NTF) to EHR. Recent applications of non-negative tensor factorization (NTF) to EHR records show potential compromise between sparseness of EHR data and ease of comprehension of the model. Here, we assessed the performance of a novel ML method for detecting patients at risk of HF. In the first step, the NTF algorithm generates phenotypes of patients specific to patient diagnosis and pharmacological profiles prior to HF. Each patient is assigned to its own “fingerprint” label according to his membership to the different phenotypes. The fingerprints were then used as a feature set to predict HF. Ease of comprehension is achieved through transforming the high dimensional space (raw features) to a reduced clinical phenotype space (“fingerprints”).

Well-timed traffic light can tremendously decrease vehicles’ time spent at red lights, but timing the lights well is a very complex, difficult problem to solve. In this paper, we explore swarm optimization methods for optimizing traffic light behavior, which allow us to minimize a function with knowing its analytic form. We apply particle swarm optimization (PSO) and a novel formulation of ant colony optimization (ACO). In our simulation, both these methods yield significant decreases in average total travel time for vehicles, an improvement of 7% over baselines. Our adaptation of ACO performs particularly well, suggesting its potential application in solving optimization problems in a variety of other fields.

Consider a setting where robots must visit sites represented as nodes in a graph, but each robot may fail when traversing an edge. The goal is to find a set of paths for a team of robots which maximizes the expected number of nodes collectively visited, while guaranteeing that the paths satisfy a notion of “independence” formalized by a matroid (e.g. limits on team size, number of visits to regions), and that the probabilities that each robot survives to its destination are above a given threshold. We call this problem the Matroid Team Surviving Orienteers (MTSO) problem, which has broad applications such as environmental monitoring in risky regions and search and rescue in dangerous conditions. We present the MTSO formally and detail numerous examples of matroids in a path planning context. We then propose an approximate greedy algorithm for selecting a feasible set of paths and prove that the value of the output is within a factor p s /p s + λ of the optimum, where p s is the per-robot survival probability threshold and 1/λ ≤ 1 is the approximation factor of an oracle routine for the well known orienteering problem. We demonstrate the efficiency of our approach by applying it to a scenario where a team of robots must gather information while avoiding pirates in the Coral Triangle.

Background: There is a paucity of literature about the status of therapeutic footwear and their role in prevention of diabetic foot ulcers in Nigeria. The purpose of this study is thus 2-fold. (1) To determine the perceived role of therapeutic footwear in the prevention of foot ulcers among patients with diabetes mellitus, (2) to establish strategies that will encourage the use of therapeutic footwear in the prevention of diabetic foot ulcers. Materials and Methods: This cross-sectional study was carried out among patients with diabetes mellitus in Kaduna state, between December 2012 and March 2013. All the participants in this study had a history of foot ulceration. Exclusion criteria were patients with amputations and non-ambulatory status. Pre-tested questionnaires were used to collect data. The questionnaire was divided into four sections. The first section illustrates the demographics of the respondents. The second section explores the anatomic location of diabetic foot ulcers. The third section evaluates the type of regular footwear worn and experience of participants. The fourth section explores the awareness of respondents regarding therapeutic footwear features. Simple descriptive statistics were used; frequency with percentage distribution for categorised variables. Results: The anatomic subunit in the plantar surface with the highest number of ulcer was the phalanges 23% in males and 26% in females. In the dorsolateral surface, the phalanges 22% and 17% were the most common location in males and females, respectively. Slippers were regularly worn by 71% of respondents, whereas only 1% of respondents were reported to wear therapeutic footwear. More than 75% of respondents were willing to use footwear, as well as buy therapeutic footwear. Conclusion: Majority of the patients are reported to have foot ulcers located on the phalanges and these are related to the wearing of inappropriate footwear. However, they are willing to use therapeutic footwear if recommended by a physician.

We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker method, which aims to compute a tree-structured factorization of an input data set that may be readily interpreted by a domain expert. However, Sparse H-Tucker uses a nested sampling technique to overcome a key scalability problem in Hierarchical Tucker, which is the creation of an unwieldy intermediate dense core tensor, the result of our approach is a faster, more space-efficient, and more accurate method. We test our method on a real healthcare dataset, which is collected from 30K patients and results in an 18th order sparse data tensor. Unlike competing methods, Sparse H-Tucker can analyze the full data set on a single multi-threaded machine. It can also do so more accurately and in less time than the state-of-the-art: on a 12th order subset of the input data, Sparse H-Tucker is 18x more accurate and 7.5x faster than a previously state-of-the-art method. Moreover, we observe that Sparse H-Tucker scales nearly linearly in the number of non-zero tensor elements. The resulting model also provides an interpretable disease hierarchy, which is confirmed by a clinical expert.

With the development of technology, we see more and more possibilities in the methods of conservation and application for traditional culture. Currently, the content in the data base of digital archives information in Taiwan is quite extensive and versatile. It is necessary to study future applications to add to its value. As cultural innovation industry is the trend of economic development of many countries, the distinctive native cultural features and prosperous digitally archived information of cultural instruments in Taiwan are of great value. This study explored the procedure of value-added design on the basis of “digital archives” transformed into “cultural innovation”. The research includes two phases. In the first phase, we collected theories of product semantics combined with the analysis of culture distinctiveness in order to create the transforming model of cultural product design. In the second phase, we carried out the design and application to review the possibility of digitally archived cultural instruments as applied to the design.

Taipei has officially become the World Design Capital 2016 with the slogan of “Adaptive City”. Taiwan economic development is a fusion of Dechnology (Design-Technology) and Humart (Humanity-Art) which is also a process of design evolution showing an “Adaptive Design” in Taiwan design development. Therefore, this study proposes a design conceptual approach not only for Taipei to meet the requirements of WDC 2016, but also for Taiwan to establish a design strategy for the future. Hence, the purpose of this paper is to provide designers, companies, organizations as well as Taipei City with an approach for applying design thinking and with an idea for how to direct their efforts to meet the requirements of a new proposed design strategy.