CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion

Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learning models, and powerful hardware platforms to perceive and safely operate in real-time. However, in many contexts, some sensing modalities negatively impact perception while increasing the system's overall energy consumption. Since AS are often energy-constrained edge devices, energy-efficient sensor fusion methods have been proposed. However, existing methods either fail to adapt to changing scenario conditions or to optimize energy efficiency system-wide. We propose CARMA: a context-aware sensor fusion approach that uses context to dynamically reconfigure the computation flow on a Field-Programmable Gate Array (FPGA) at runtime. By clock-gating unused sensors and model sub-components, CARMA significantly reduces the energy used by a multi-sensory object detector without compromising performance. We use a Deep-learning Processor Unit (DPU) based reconfiguration approach to minimize the latency of model reconfiguration. We evaluate multiple context-identification strategies, propose a novel system-wide energy-performance joint optimization, and evaluate scenario-specific perception performance. Across challenging real-world sensing contexts, CARMA outperforms state-of-the-art methods with up to 1.3x speedup and 73% lower energy consumption.Comment: Accepted to be published in the 2023 ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED 2023

Longitudinal trajectory of acidosis and mortality in acute kidney injury requiring continuous renal replacement therapy

Abstract Background Acidosis frequently occurs in severe acute kidney injury (AKI), and continuous renal replacement therapy (CRRT) can control this pathologic condition. Nevertheless, acidosis may be aggravated; thus, monitoring is essential after starting CRRT. Herein, we addressed the longitudinal trajectory of acidosis on CRRT and its relationship with worse outcomes. Methods The latent growth mixture model was applied to classify the trajectories of pH during the first 24 hours and those of C-reactive protein (CRP) after 24 hours on CRRT due to AKI (n = 1815). Cox proportional hazard models were used to calculate hazard ratios of all-cause mortality after adjusting multiple variables or matching their propensity scores. Results The patients could be classified into 5 clusters, including the normally maintained groups (1st cluster, pH = 7.4; and 2nd cluster, pH = 7.3), recovering group (3rd cluster with pH values from 7.2 to 7.3), aggravating group (4th cluster with pH values from 7.3 to 7.2), and ill-being group (5th cluster, pH < 7.2). The pH clusters had different trends of C-reactive protein (CRP) after 24 hours; the 1st and 2nd pH clusters had lower levels, but the 3rd to 5th pH clusters had an increasing trend of CRP. The 1st pH cluster had the best survival rates, and the 3rd to 5th pH clusters had the worst survival rates. This survival difference was significant despite adjusting for other variables or matching propensity scores. Conclusions Initial trajectories of acidosis determine subsequent worse outcomes, such as mortality and inflammation, in patients undergoing CRRT due to AKI

Risk of ventricular tachycardia and its outcomes in patients undergoing continuous renal replacement therapy due to acute kidney injury

Background Despite efforts to treat critically ill patients who require continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI), their mortality risk remains high. This condition may be attributable to complications of CRRT, such as arrhythmias. Here, we addressed the occurrence of ventricular tachycardia (VT) during CRRT and its relationship with patient outcomes. Methods This study retrospectively enrolled 2,397 patients who started CRRT due to AKI from 2010 to 2020 at Seoul National University Hospital in Korea. The occurrence of VT was evaluated from the initiation of CRRT until weaning from CRRT. The odds ratios (ORs) of mortality outcomes were measured using logistic regression models after adjustment for multiple variables. Results VT occurred in 150 patients (6.3%) after starting CRRT. Among them, 95 cases were defined as sustained VT (i.e., lasting ≥30 seconds), and the other 55 cases were defined as non-sustained VT (i.e., lasting <30 seconds). The occurrence of sustained VT was associated with a higher mortality rate than a nonoccurrence (OR, 2.04 and 95% confidence interval [CI], 1.23–3.39 for the 30-day mortality; OR, 4.06 and 95% CI, 2.04–8.08 for the 90-day mortality). The mortality risk did not differ between patients with non-sustained VT and nonoccurrence. A history of myocardial infarction, vasopressor use, and certain trends of blood laboratory findings (such as acidosis and hyperkalemia) were associated with the subsequent risk of sustained VT. Conclusion Sustained VT occurrence after starting CRRT is associated with increased patient mortality. The monitoring of electrolytes and acid-base status during CRRT is essential because of its relationship with the risk of VT

Clinical application of the Panbio™ COVID-19 Ag rapid test device and SSf-COVID19 kit for the detection of SARS-CoV-2 infection

Objective We evaluated the sensitivity and specificity of the Panbio™ COVID-19 Ag rapid test device using nasal swabs and those of the SSf-COVID19 kit, one of RT-PCR tests, using saliva specimens. These tests were compared with RT-PCR tests using nasopharyngeal swabs for the diagnosis of SARS-CoV-2 infection. The three diagnostic tests were simultaneously conducted for patients aged ≥ 18 years, who were about to be hospitalized or had been admitted for COVID-19 confirmed by RT-PCR in two research hospitals from August 20 to October 29, 2021. Nasal swabs were tested using the Panbio™ COVID-19 Ag rapid test device. More than 1 mL of saliva was self-collected and tested using the SSf-COVID19 kit. Results In total, 157 patients were investigated; 124 patients who were about to be hospitalized and 33 patients already admitted for COVID-19. The overall sensitivity and specificity of the Panbio™ COVID-19 Ag rapid test device with nasal swabs were 64.7% (95% confidence interval [CI] 47.9–78.5%) and 100.0% (95% CI 97.0–100.0%), respectively. The median time to confirm a positive result was 180 s (interquartile range 60–255 s). The overall sensitivity and specificity of the SSf-COVID19 kit with saliva specimens were 94.1% (95% CI 80.9–98.4%) and 100.0% (95% CI 97.0–100.0%), respectively.This work was supported by a grant from research fund of Seoul National University Hospital (Grant No. 2021–3148

HARMer: cyber-attacks automation and evaluation

With the increasing growth of cyber-attack incidences, it is important to develop innovative and effective techniques to assess and defend networked systems against cyber attacks. One of the well-known techniques for this is performing penetration testing which is carried by a group of security professionals (i.e, red team). Penetration testing is also known to be effective to find existing and new vulnerabilities, however, the quality of security assessment can be depending on the quality of the red team members and their time and devotion to the penetration testing. In this paper, we propose a novel automation framework for cyber-attacks generation named ‘HARMer’ to address the challenges with respect to manual attack execution by the red team. Our novel proposed framework, design, and implementation is based on a scalable graphical security model called Hierarchical Attack Representation Model (HARM). (1) We propose the requirements and the key phases for the automation framework. (2) We propose security metrics-based attack planning strategies along with their algorithms. (3) We conduct experiments in a real enterprise network and Amazon Web Services. The results show how the different phases of the framework interact to model the attackers’ operations. This framework will allow security administrators to automatically assess the impact of various threats and attacks in an automated manner

Integrative Modeling of Housing Recovery as a Physical, Economic, and Social Process

This paper presents a novel approach to modeling housing recovery through the formulation of recovery-based fragility functions built on empirical data collected longitudinally after a recent flood disaster. Previous community resilience frameworks have not addressed social and economic considerations in engineering-based recovery modeling. In doing so, this work takes an important step forward, advancing the use of probability and statistics in civil engineering applications and facilitating their role in interdisciplinary analysis of post-disaster recovery. To address community housing recovery after a flood event, two recovery-based limit states were analyzed: repair completion and re-occupancy. Two least squares regression models identified the variables most strongly associated with each limit state. These variables included household race and ethnicity, whether the household received post-disaster financial recovery assistance, and physical damage to the home. The analyses provide evidence of the simultaneous and interconnected social, economic, and physical processes that take place in a community and influence recovery progress, further demonstrating the need for multidisciplinary teams and analytic approaches in modeling resilience and recovery.This conference presentation is published as Sutley, E.J., Hamideh, S., Dillard, M.K., Gu, D., Seong, K., van de Lindt, J.W. Integrative Modeling of Housing Recovery as a Physical, Economic, and Social Process. at the 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 2019. Posted with permission. </p

Safety assessment and potential of Cecidophyes rouhollahi (Acari, Eriophyidae) for biological control of Galium spurium (Rubiaceae in North America

Évaluation de la sûreté et dupotentielde Cecidophyes rouhollahi (Acari: Eriophyidae) pour la lutte biologique contre Galium spurium (Rubiaceae) eu Amérique du Nord Galium spurium L. (Rubiaceae), originaire d'Europe, est une mauvaise herbe annuelle qui pose de plus en plus de problèmes sur les cultures des provinces des prairies canadiennes. L'acarien gallicole Cecidophyes rouhollahi Craemer (Acari: Eriophyidae), trouvé originellement sur l'espèce Galium aparine L. dans le Sud de la France, a été évalué comme un agent de contrôle biologique potentiel de G. spurium. Dans des essais en serre C. rouhollahi a causé d'importants retards de développement et une absence totale de production de graines de G. spurium. Des expériences de spécificité d'hôte ont montré que C. rouhollahi ne pouvait se développer que sur trois espèces annuelles proches appartenant au genre Galium, section Kolgyda. Aucune espèce de Galium originaire d'Amérique du Nord n'a été attaquée à l'exception de G. aparine. Une étude des informations disponibles sur G. aparine suggérait qu'il est peu probable que cette espèce soit indigène en Amérique du Nord. Dans une publication, il était suggéré que l'acarien gallicole attaquant G. aparine pouvait être associé à un virus. Cependant, une série d'essais en serre avec des colonies de G. spurium infestées par C. rouhollahi n'a pas montré d'infection virale. Sur la base de ces résultats, C. rouhollahi a été approuvé pour des lâchers sur le terrain contre G. spurium au Canada. (Résumé d'auteur

Image_1_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).tif

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p

Image_4_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).tif

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p