Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Earnings Management and the Role of Moral Values in Investing

In this study, we use earnings management to examine (1) how investors regard a CEO’s commitment to honesty and (2) the impact of their perceptions, in light of their own moral values, on their investment decisions. In two laboratory experiments using students as investor proxies, we find that investors perceive a CEO as being more committed to honesty when they believe the CEO has engaged less in earnings management. A one standard deviation increase in a CEO’s perceived commitment to honesty, compared to that of another CEO, leads to a 40% reduction in the importance the investors assigned, when making investment decisions, to differences in the two CEOs’ claimed future returns. This effect is particularly pronounced among investors with a proself value orientation. For prosocial investors, their moral values and those they attribute to the CEO directly influence their investment decisions, with returns playing a secondary role. Our findings contrast with the idea, implicit in the literature on ‘sin’ stocks, that morality is a niche concern. By contrast, we find that moral values play a significant role for distinct types of investors and that they influence investment decisions for both moral and pecuniary reasons

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification

A Wearable Fall Detection System Based on Body Area Networks

Falls can have serious consequences for people, leading to restrictions in mobility or, in the worst case, to traumatic-based cases of death. To provide rapid assistance, a portable fall detection system has been developed that is capable of detecting fall situations and, if necessary, alerting emergency services without any user interaction. The prototype is designed to facilitate reliable fall detection and to classify several fall types and human activities. This solution represents a life-saving service for every person that will significantly improve assistance in the case of fall events, which are a part of daily life. Additionally, this approach facilitates independent system operation, since the system does not depend on sensor or network units located within a building structure. This article also introduces fall analysis. To guarantee functional safety, a hazard analysis method named system-theoretic accident model and processes (STAMP) is applied

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel," but also presents a lightweight solution to otherwise computationally heavy problems.This research was funded by the research support program of Fb2, Frankfurt University of Applied Sciences. The research of D.G.-U. has been supported in part by the Spanish MICINN under grants PGC2018-096504-B-C33 and RTI2018-100754-B-I00, the European Union under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (project FEDER-UCA18-108393). The research of I.M.-B. has been supported in part by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33]

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.This research was partially funded by NHMRC grant 1033452 and was supported by a Victorian Life Sciences Computation Initiative (VLSCI) grant number 0126 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia

Anaerobic methanotrophic communities thrive in deep submarine permafrost

Thawing submarine permafrost is a source of methane to the subsurface biosphere. Methane oxidation in submarine permafrost sediments has been proposed, but the responsible microorganisms remain uncharacterized. We analyzed archaeal communities and identified distinct anaerobic methanotrophic assemblages of marine and terrestrial origin (ANME-2a/b, ANME-2d) both in frozen and completely thawed submarine permafrost sediments. Besides archaea potentially involved in anaerobic oxidation of methane (AOM) we found a large diversity of archaea mainly belonging to Bathyarchaeota, Thaumarchaeota, and Euryarchaeota. Methane concentrations and δ13C-methane signatures distinguish horizons of potential AOM coupled either to sulfate reduction in a sulfate-methane transition zone (SMTZ) or to the reduction of other electron acceptors, such as iron, manganese or nitrate. Analysis of functional marker genes (mcrA) and fluorescence in situ hybridization (FISH) corroborate potential activity of AOM communities in submarine permafrost sediments at low temperatures. Modeled potential AOM consumes 72–100% of submarine permafrost methane and up to 1.2 Tg of carbon per year for the total expected area of submarine permafrost. This is comparable with AOM habitats such as cold seeps. We thus propose that AOM is active where submarine permafrost thaws, which should be included in global methane budgets

Developmental Considerations for Substance Use Interventions From Middle School Through College

This article summarizes a symposium organized by Dr. Elizabeth D’Amico and presented at the 2004 Annual Meeting of the Research Society on Alcoholism in Vancouver, Canada. The four presentations illustrate the importance of creating substance use interventions that are developmentally appropriate for youth. They represent innovative approaches to working with preteens, teenagers, and young adults. Dr. D’Amico’s paper describes her research on the development of a voluntary brief intervention targeting alcohol use among middle school students. Findings indicated that by using school and community input, she was able to obtain a diverse a sample of youth across grades, sex, ethnicity, and substance use status. Dr. Ellickson’s paper describes her research on Project ALERT, a school-based prevention program for middle school youth. Her findings indicate that Project ALERT worked for students at all levels of risk (low, moderate, and high) and for all students combined. Dr. Wagner’s Teen Intervention Project was a ran-domized clinical trial to test the efficacy of a standardized Student Assistance Program for treating middle and high school students with alcohol and other drug problems. The study provided a unique opportunity to begin to examine how development may impact response to an alcohol or other drug intervention. Dr. Turrisi’s paper examined processes underlying the nature of the effects of a parent intervention on college student drinking tendencies. Findings suggested that the parent intervention seems to have its impact on student drinking by reducing the influence of negative communications and decreasing the susceptibility of influences from closest friends. Dr. Kim Fromme provided concluding remarks

Wavelet-Based Angiographic Reconstruction of Computed Tomography Perfusion Data Diagnostic Value in Cerebral Venous Sinus Thrombosis

Objective: The aim of this study was to test the diagnostic value of wavelet-based angiographic reconstruction of CT perfusion data (waveletCTA) to detect cerebral venous sinus thrombosis (CVST) in patients who underwent whole-brain CT perfusion imaging (WB-CTP). Materials and Methods: Datasets were retrospectively selected from an initial cohort of 2863 consecutive patients who had undergone multiparametric CT including WB-CTP. WaveletCTA was reconstructed from WB-CTP: the angiographic signal was generated by voxel-based wavelet transform of time attenuation curves (TACs) from WB-CTP raw data. In a preliminary clinical evaluation, waveletCTA was analyzed by 2 readers with respect to presence and location of CVST. Venous CT and MR angiography (venCTA/venMRA) served as reference standard. Diagnostic confidence for CVST detection and the quality of depiction for venous sections were evaluated on 5-point Likert scales. Thrombus extent was assessed by length measurements. The mean CT attenuation and waveletCTA signal of the thrombus and of flowing blood were quantified. Results: Sixteen patients were included: 10 patients with venCTA-/venMRAconfirmed CVST and 6 patients with arterial single-phase CT angiography (artCTA)-suspected but follow-up-excluded CVST. The reconstruction of waveletCTA was successful in all patients. Among the patients with confirmed CVST, waveletCTA correctly demonstrated presence, location, and extent of the thrombosis in 10/10 cases. In 6 patients with artCTA-suspected but follow-up-excluded CVST, waveletCTA correctly ruled out CVST in 5 patients. Reading waveletCTA in addition to artCTA significantly increased the diagnostic confidence concerning CVST compared with reading artCTA alone (4.4 vs 3.6, P = 0.044). The mean flowing blood-to-thrombus ratio was highest in waveletCTA, followed by venCTA and artCTA (146.2 vs 5.9 vs 2.6, each with P < 0.001). In waveletCTA, the venous sections were depicted better compared with artCTA (4.2 vs 2.6, P < 0.001), and equally well compared with venCTA/venMRA (4.2 vs 4.1, P = 0.374). Conclusions: WaveletCTA was technically feasible in CVST patients and reliably identified CVST in a preliminary clinical evaluation. WaveletCTA might serve as an additional reconstruction to rule out or incidentally detect CVST in patients who undergo WB-CTP