Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28

Incorporation of covariates in simultaneous localization of two linked loci using affected relative pairs

<p>Abstract</p> <p>Background</p> <p>Many dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors. Incorporating these quantitative variables into linkage analysis as well as localizing two linked disease loci simultaneously could therefore improve the efficiency in mapping genes. We extended the robust multipoint Identity-by-Descent (IBD) approach with incorporation of covariates developed previously to simultaneously estimate two linked loci using different types of affected relative pairs (ARPs).</p> <p>Results</p> <p>We showed that the efficiency was enhanced by incorporating a quantitative covariate parametrically or non-parametrically while localizing two disease loci using ARPs. In addition to its help in identifying factors associated with the disease and in improving the efficiency in estimating disease loci, this extension also allows investigators to account for heterogeneity in risk-ratios for different ARPs. Data released from the collaborative study on the genetics of alcoholism (COGA) for Genetic Analysis Workshop 14 (GAW 14) were used to illustrate the application of this extended method.</p> <p>Conclusions</p> <p>The simulation studies and example illustrated that the efficiency in estimating disease loci was demonstratively enhanced by incorporating a quantitative covariate and by using all relative pairs while mapping two linked loci simultaneously.</p

Statistical screening for IC Trojan detection

We present statistical screening of test vectors for detecting a Trojan, malicious circuitry hidden inside an integrated circuit (IC). When applied a test vector, a Trojan-embedded chip draws extra leakage current that is unfortunately too small for the detector in most cases and concealed by process variation related to chip fabrication. To remedy the problem, we formulate a statistical approach that can screen and select test vectors in detecting Trojans. We validate our approach analytically and with gate-level simulations and show that our screening method leads to a substantial reduction in false positives and false negatives when detecting IC Trojans of various sizes.Engineering and Applied Science

A Chip Architecture for Compressive Sensing Based Detection of IC Trojans

We present a chip architecture for a compressive sensing based method that can be used in conjunction with the JTAG standard to detect IC Trojans. The proposed architecture compresses chip output resulting from a large number of test vectors applied to a circuit under test (CUT). We describe our designs in sensing leakage power, computing random linear combinations under compressive sensing, and piggybacking these new functionalities on JTAG. Our architecture achieves approximately a 10× speedup and 1000× reduction in output bandwidth while incurring a small area overhead.Engineering and Applied Science

Virtual Guidance as a Mid-level Representation for Navigation

In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments poses challenges, particularly when the navigation information is multimodal. To address this issue, the paper introduces a novel technique termed "Virtual Guidance," which is designed to visually represent non-visual instructional signals. These visual cues, rendered as colored paths or spheres, are overlaid onto the agent's camera view, serving as easily comprehensible navigational instructions. We evaluate our proposed method through experiments in both simulated and real-world settings. In the simulated environments, our virtual guidance outperforms baseline hybrid approaches in several metrics, including adherence to planned routes and obstacle avoidance. Furthermore, we extend the concept of virtual guidance to transform text-prompt-based instructions into a visually intuitive format for real-world experiments. Our results validate the adaptability of virtual guidance and its efficacy in enabling policy transfer from simulated scenarios to real-world ones.Comment: Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, and Jou-Min Liu contributed equally to this work; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Body Mass Index–Mortality Relationship in Severe Hypoglycemic Patients With Type 2 Diabetes

AbstractBackgroundHypoglycemia is associated with a higher risk of death. This study analyzed various body mass index (BMI) categories and mortalities of severe hypoglycemic patients with type 2 diabetes mellitus (DM) in a hospital emergency department.MethodsThe study included 566 adults with type 2 diabetes who were admitted to 1 medical center in Taiwan between 2008 and 2009 with a diagnosis of severe hypoglycemia. Mortality data, demographics, clinical characteristics and the Charlson’s Comorbidity Index were obtained from the electronic medical records. Patients were stratified into 4 study groups as determined by the National institute of Health (NiH) and World Health organization classification for BMi, and the demographics were compared using the analysis of variance and χ2 test. Kaplan-Meier’s analysis and the Cox proportional-hazards regression model were used for mortality, and adjusted hazard ratios were adjusted for each BMi category among participants.ResultsAfter controlling for other possible confounding variables, BMI <18.5 kg/m2 was independently associated with low survival rates in the Cox regression analysis of the entire cohort of type 2 DM patients who encountered a hypoglycemic event. Compared to patients with normal BMI, the mortality risk was higher (adjusted hazard ratios = 4.9; 95% confidence interval [CI] = 2.4-9.9) in underweight patients. Infection-related causes of death were observed in 101 cases (69.2%) and were the leading cause of death.ConclusionsAn independent association was observed between BMI less than 18.5 kg/m2 and mortality among type 2 DM patient with severe hypoglycemic episode. Deaths were predominantly infection related