Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applicationsComment: ICSE 201

Design and evaluation of a low-cost sensor node for near-background methane measurement

We developed a low-cost methane sensing node incorporating two metal oxide (MOx) sensors (Figaro Engineering TGS2611-E00 and TGS2600), humidity and temperature sensing, data storage, and telemetry. We deployed the prototype sensor alongside a reference methane analyzer at two sites: one outdoors and one indoors. We collected data at each site for several months across a range of environmental conditions (particularly temperature and humidity) and methane levels. We explored calibration models to investigate the performance of our system and its suitability for methane background monitoring and enhancement detection, first selecting a linear regression to fit a sensor baseline response and then fitting methane response by the sensor deviation from baseline. We achieved moderate accuracy in a 2 to 10 ppm methane range compared to data from the reference analyzer (RMSE &lt; 0.6 ppm), but we found that the sensor response varied over time, possibly as a result of changes in non-targeted gas concentrations. We suggest that this cross sensitivity may be responsible for mixed results in previous studies. We discuss the implications of our results for the use of these and similar inexpensive MOx sensors for methane monitoring in the 2 to 10 ppm range.</p

Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring

Assessing the intracity spatial distribution and temporal variability in air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and hybrid random forest–linear regression models. Using data collected by almost 70 RAMP monitors over periods ranging up to 18 months, we recommend the use of limited quadratic regression calibration models for CO, neural network models for NO, and hybrid models for NO2 and O3 for any low-cost monitor using electrochemical sensors similar to those of the RAMP. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. Generalized models also transfer better when the RAMP is deployed to other locations. For long-term deployments, it is recommended that model performance be re-evaluated and new models developed periodically, due to the noticeable change in performance over periods of a year or more. This makes generalized calibration models even more useful since only a subset of deployed monitors are needed to build these new models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.</p

Asymmetric effects of false positive and false negative indications on the verification of alerts in different risk conditions

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Indications from alerts or alarm systems can be the trigger for decisions, or they can elicit further information search. We report an experiment on the tendency to collect additional information after receiving system indications. We varied the proclivity of the alarm system towards false positive or false negative indications and the perceived risk of the situation. Results showed that false alarm-prone systems led to more frequent re-checking following both alarms and non-alarms in the high risk condition, whereas miss-prone systems led to high re-checking rates only for non-alarms, representing an asymmetry effect. Increasing the risk led to more re-checks with all alarm systems, but it had a stronger impact in the false alarm-prone condition. Results regarding the relation of risk and the asymmetry effect of false negative and false positive indications are discussed

Vapor−Wall Deposition in Chambers: Theoretical Considerations

In order to constrain the effects of vapor–wall deposition on measured secondary organic aerosol (SOA) yields in laboratory chambers, researchers recently varied the seed aerosol surface area in toluene oxidation and observed a clear increase in the SOA yield with increasing seed surface area (Zhang, X.; et al. Proc. Natl. Acad. Sci. U.S.A. 2014, 111, 5802). Using a coupled vapor–particle dynamics model, we examine the extent to which this increase is the result of vapor–wall deposition versus kinetic limitations arising from imperfect accommodation of organic species into the particle phase. We show that a seed surface area dependence of the SOA yield is present only when condensation of vapors onto particles is kinetically limited. The existence of kinetic limitation can be predicted by comparing the characteristic time scales of gas-phase reaction, vapor–wall deposition, and gas–particle equilibration. The gas–particle equilibration time scale depends on the gas–particle accommodation coefficient α_p. Regardless of the extent of kinetic limitation, vapor–wall deposition depresses the SOA yield from that in its absence since vapor molecules that might otherwise condense on particles deposit on the walls. To accurately extrapolate chamber-derived yields to atmospheric conditions, both vapor–wall deposition and kinetic limitations must be taken into account

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

Background Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA). Methods Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC). Results We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. Conclusion The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement.Neuro Imaging Researc

Accuracy of CTA evaluations in daily clinical practice for large and medium vessel occlusion detection in suspected stroke patients

Introduction: Early detection of large vessel occlusion (LVO) is essential to facilitate fast endovascular treatment. CT angiography (CTA) is used to detect LVO in suspected stroke patients. We aimed to assess the accuracy of CTA evaluations in daily clinical practice in a large cohort of suspected stroke patients. Patients and methods: We used data from the PRESTO study, a multicenter prospective observational cohort study that included suspected stroke patients between August 2018 and September 2019. Baseline CTAs were re-evaluated by an imaging core laboratory and compared to the local assessment. LVO was defined as an occlusion of the intracranial internal carotid artery, M1 segment, or basilar artery. Medium vessel occlusion (MeVO) was defined as an A1, A2, or M2 occlusion. We calculated the accuracy, sensitivity, and specificity to detect LVO and LVO+MeVO, using the core laboratory evaluation as reference standard. Results: We included 656 patients. The core laboratory detected 89 LVOs and 74 MeVOs in 155 patients. Local observers missed 6 LVOs (7%) and 28 MeVOs (38%), of which 23 M2 occlusions. Accuracy of LVO detection was 99% (95% CI: 98-100%), sensitivity 93% (95% CI: 86-97%), and specificity 100% (95% CI: 99-100%). Accuracy of LVO+MeVO detection was 95% (95% CI: 93-96%), sensitivity 79% (95% CI: 72-85%), and specificity 99% (95% CI: 98-100%). Discussion and Conclusion: CTA evaluations in daily clinical practice are highly accurate and LVOs are adequately recognized. The detection of MeVOs seems more challenging. The evolving EVT possibilities emphasize the need to improve CTA evaluations in the acute setting.Cardiovascular Aspects of Radiolog