Predictive Runtime Monitoring for Safe Autonomy

As autonomous systems are increasingly prevalent, detecting and preventing imminent safety violations becomes an important problem for the safe operation of these systems in highly dynamic environments involving humans and other systems. Such violations include collisions between multiple agents, failure to respond to events, and robots entering restricted areas. However, detecting such violations at design time is often impractical as behaviors are dependent on unknown environmental conditions. The space of possible behaviors is either excessively large or incompletely known to the designers. Thus, predictive runtime monitoring approaches have recently gained popularity to detect and prevent property violations in real-time. In this thesis, we present predictive runtime monitoring approaches starting with data-driven models for estimating uncertainty in physical models of robots based on observed trajectories. Once the models are learned, we forecast the future behavior of robots using them. However, this approach assumes that guidance algorithms of robots are known to quantify the uncertainty associated with physical models of robots. We will relax this assumption by extrapolating the dynamics of the robot based on past trajectories to infer possible future trajectories. However, such an approach is unable to reason beyond relatively short time horizons. We will extend this approach by adding a notion of the user's intent which is represented using temporal logic formulae. We will begin with simple ``reach-while-avoid'' temporal logic specifications that talk about near term intent and show how Bayesian inference can be used to derive posterior probabilities from the evidence of the agent's actions. We will then extend to a hierarchical approach that combines short and longer term intent monitoring to predict beyond the near-term (immediate) goals of the agent. We will discuss results on some case-studies involving trajectories of human and autonomous agents to predict their intents and possible future behaviors. Finally, we will present predictive viability monitoring that is different from safety monitoring. Viability monitoring does not predict future positions; rather, it determines whether agents are capable of avoiding impending safety violations

Quantitative and qualitative investments in internal control personnel and firm operational efficiency: Evidence from Korea

Although internal control systems in firms aim to provide reasonable assurance regarding objectives related to operations, reporting, and compliance, research focusing on operational efficiency is limited. This study investigates the impact of both quantitative and qualitative investments in internal control personnel on a firm’s operational efficiency. Utilizing a fixed-effect regression model, the Heckman (1979) two-stage model, and a two-stage least squares procedure, this study analyzes 4,471 firm-year observations from Korean listed firms from 2018 to 2020. The findings indicate a positive association between investment in internal control personnel and operational efficiency. This relationship remains robust even under sensitivity tests and concerns of potential endogeneity, as confirmed by the Heckman and two-stage least squares models. Specifically, the Heckman model shows that the ratio of the number of employees (coef = 0.023, t-value = 5.20) and certified public accountants (coef = 0.256, t-value = 5.43) responsible for internal control is positively associated with operational efficiency. Average work experience (coef = 0.002, t-value = 1.84) of internal control personnel is also positively related to operational efficiency. This study provides empirical evidence for the significance of investing in internal control personnel to boost operational efficiency and suggests that firms should consider both quantitative and qualitative aspects of internal control

No radiographic index predicts difficult intubation using the Optiscope™ in cervical spine surgery patients: a retrospective study

Background The Optiscope™ can be used for intubation with minimal neck motion. We retrospectively investigated radiographic predictors of difficult intubation using the Optiscope™ by analyzing preoperative radiographic images. Methods One hundred eighty-four patients who were intubated with the Optiscope™ under manual in-line cervical stabilization for cervical spine surgery were enrolled. Radiographic indices were measured on preoperative cervical spine lateral X-ray and magnetic resonance imaging images. Difficult intubation was defined as failure or time consumption more than 90 s on the first attempt. To identify significant predictors of difficult intubation using the Optiscope™ and evaluate their diagnostic value, multivariable logistic regression and receiver operating characteristic analyses were used. Results Fourty-seven patients showed difficult intubation. There was no significant difference in radiographic indices between the difficult and easy intubation groups, but higher body mass index (BMI) (26.5 [3.0] vs. 24.6 [3.5] kg/m2, P = 0.001), shorter sternomental distance (SMD) (122.0 [104.0 to 150.0] vs. 150.0 [130.0 to 170.0] mm, P = 0.001), shorter interincisor gap (40.0 [35.0 to 45.0] vs. 43.0 [40.0 to 50.0] mm, P = 0.006), and higher incidence of excessive oral secretions (10.6% vs. 2.9%, P = 0.049) were observed in patients with difficult intubation. In multivariable analysis, BMI (odds ratio [95% confidence interval]; 1.15 [1.03 to 1.28], P = 0.011) and SMD (odds ratio [95% confidence interval]; 0.98 [0.97 to 1.00], P = 0.008) were associated with difficult intubation with the Optiscope™. In receiver operating characterstic analysis, the area under the curve for body mass index was 0.68 (95% confidence interval; 0.60 to 0.77, P < 0.001) and that for sternomental distance was 0.66 (95% confience interval; 0.57 to 0.75, P = 0.001). Conclusions The incidence of difficult intubation using the Optiscope™ under manual in-line cervical stabilization was 25.5% in cervical spine surgery patients. No significant predictor of difficult intubation with the Optiscope™ was identified among the measured radiographic indices. Although high BMI and short SMD were predictive of difficult intubation with the Optiscope™, their discrimination power was weak

Regional TMPRSS2 V197M Allele Frequencies Are Correlated with COVID-19 Case Fatality Rates.

Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing

The first reported hepatitis E outbreak in a food manufacturing factory: Korea, 2022

Objectives On February 16, 2022, 12 cases of hepatitis E virus (HEV) infection were reported in a food manufacturing factory in Korea. The aim of this study was to identify additional cases and to determine the source of this HEV outbreak. Methods This study was an in-depth investigation of 12 HEV immunoglobulin M (IgM)-positive cases and their demographic, clinical, and epidemiological characteristics. On-site specimens were collected from the environment and from humans, and a follow-up investigation was conducted 2 to 3 months after the outbreak. Results Among 80 production workers in the factory, 12 (15.0%) had acute HEV infection, all of whom were asymptomatic. The follow-up investigation showed that 3 cases were HEV IgM-positive, while 6 were HEV IgG-positive. HEV genes were not detected in the HEV IgM-positive specimens. HEV genes were not detected in the food products or environmental specimens collected on-site. HEV was presumed to be the causative pathogen. However, it could not be confirmed that the source of infection was common consumption inside the factory. Conclusion This was the first domestic case of an HEV infection outbreak in a food manufacturing factory in Korea. Our results provide information for the future control of outbreaks and for the preparation of measures to prevent domestic outbreaks of HEV infection

Hyperpolarized [1-13C] pyruvate MR spectroscopy detect altered glycolysis in the brain of a cognitively impaired mouse model fed high-fat diet.

Higher dietary intakes of saturated fatty acid increase the risk of developing Alzheimer's disease and dementia, and even in people without diabetes higher glucose levels may be a risk factor for dementia. The mechanisms causing neuronal dysfunction and dementia by consuming high-fat diet degrading the integrity of the blood-brain barrier (BBB) has been suggested but are not yet fully understood, and metabolic state of the brain by this type of insult is still veiled. The objective of this study was to investigate the effect of high-fat diet on the brain metabolism by a multimodal imaging method using the hyperpolarizedcarbon 13 (13C)-pyruvate magnetic resonance (MR) spectroscopy and dynamic contrast-enhanced MR imaging in conjunction with the biochemical assay and the behavior test in a mouse model fed high-fat diet (HFD). In mice were fed 60% HFD for 6 months, hyperpolarized [1-13C] pyruvate MR spectroscopy showed decreased perfusion (p < 0.01) and increased conversion from pyruvate to lactate (p < 0.001) in the brain. The hippocampus and striatum showed the highest conversion ratio. The functional integrity of the blood-brain barrier tested by dynamic contrast-enhanced MR imaging showed no difference to the control. Lactate was increased in the cortex (p < 0.01) and striatum (p < 0.05), while PDH activity was decreased in the cortex (p < 0.01) and striatum (p < 0.001) and the phosphorylated PDH was increased in the striatum (p < 0.05). Mice fed HFD showed less efficiency in learning memory compared with control (p < 0.05). To determine whether hyperpolarized 13C-pyruvate magnetic resonance (MR) spectroscopy could detect a much earier event in the brain. Mice fed HFD for 3 months did not show a detectable cognitive decline in water maze based learning memory. Hyperpolarized [1-13C] pyruvate MR spectroscopy showed increased lactate conversion (P < .001), but no difference in cerebral perfusion. These results suggest that the increased hyperpolarized [1-13C] lactate signal in the brain of HFD-fed mice represent that altered metabolic alteration toward to glycolysis and hypoperfusion by the long-term metabolic stress by HFD further promote to glycolysis. The hyperpolarized [1-13C] pyruvate MR spectroscopy can be used to monitor the brain metabolism and will provide information helpful to understand the disease process

Wavefront shaping: A versatile tool to conquer multiple scattering in multidisciplinary fields

Optical techniques offer a wide variety of applications as light-matter interactions provide extremely sensitive mechanisms to probe or treat target media. Most of these implementations rely on the usage of ballistic or quasi-ballistic photons to achieve high spatial resolution. However, the inherent scattering nature of light in biological tissues or tissue-like scattering media constitutes a critical obstacle that has restricted the penetration depth of non-scattered photons and hence limited the implementation of most optical techniques for wider applications. In addition, the components of an optical system are usually designed and manufactured for a fixed function or performance. Recent advances in wavefront shaping have demonstrated that scattering- or component-induced phase distortions can be compensated by optimizing the wavefront of the input light pattern through iteration or by conjugating the transmission matrix of the scattering medium. This offers unprecedented opportunities in many applications to achieve controllable optical delivery or detection at depths or dynamically configurable functionalities by using scattering media to substitute conventional optical components. In this article, the recent progress of wavefront shaping in multidisciplinary fields is reviewed, from optical focusing and imaging with scattering media, functionalized devices, modulation of mode coupling, and nonlinearity in multimode fiber to multimode fiber-based applications. Apart from insights into the underlying principles and recent advances in wavefront shaping implementations, practical limitations and roadmap for future development are discussed in depth. Looking back and looking forward, it is believed that wavefront shaping holds a bright future that will open new avenues for noninvasive or minimally invasive optical interactions and arbitrary control inside deep tissues. The high degree of freedom with multiple scattering will also provide unprecedented opportunities to develop novel optical devices based on a single scattering medium (generic or customized) that can outperform traditional optical components

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Mathematical Models of Human Drivers Using Artificial Risk Fields

In this paper, we use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations. A risk field assigns a non-negative risk measure to the state of the system in order to model how close that state is to violating a safety property, such as hitting an obstacle or exiting the road. Using risk fields, we construct a stochastic model of the operator that maps from states to likely actions. We demonstrate our approach on a driving task wherein human subjects are asked to drive a car inside a realistic driving simulator while avoiding obstacles placed on the road. We show that the most likely risk field given the driving data is obtained by solving a convex optimization problem. Next, we apply the inferred risk fields to generate distinct driving behaviors while comparing predicted trajectories against ground truth measurements. We observe that the risk fields are excellent at predicting future trajectory distributions with high prediction accuracy for up to twenty seconds prediction horizons. At the same time, we observe some challenges such as the inability to account for how drivers choose to accelerate/decelerate based on the road conditions.Comment: 8 pages, 4 figures, submitted to Intelligent Transportation Systems Conferenc