Voluntary lane-change policy synthesis with reactive control improvisation

In this paper, we propose reactive control improvisation to synthesize voluntary lane-change policy that meets human preferences under given traffic environments. We first train Markov models to describe traffic patterns and the motion of vehicles responding to such patterns using traffic data. The trained parameters are calibrated using control improvisation to ensure the traffic scenario assumptions are satisfied. Based on the traffic pattern, vehicle response models, and Bayesian switching rules, the lane-change environment for an automated vehicle is modeled as a Markov decision process. Based on human lane-change behaviors, we train a voluntary lane-change policy using explicit-duration Markov decision process. Parameters in the lane-change policy are calibrated through reactive control improvisation to allow an automated car to pursue faster speed while maintaining desired frequency of lane-change maneuvers in various traffic environments

Voluntary lane-change policy synthesis with reactive control improvisation

In this paper, we propose reactive control improvisation to synthesize voluntary lane-change policy that meets human preferences under given traffic environments. We first train Markov models to describe traffic patterns and the motion of vehicles responding to such patterns using traffic data. The trained parameters are calibrated using control improvisation to ensure the traffic scenario assumptions are satisfied. Based on the traffic pattern, vehicle response models, and Bayesian switching rules, the lane-change environment for an automated vehicle is modeled as a Markov decision process. Based on human lane-change behaviors, we train a voluntary lane-change policy using explicit-duration Markov decision process. Parameters in the lane-change policy are calibrated through reactive control improvisation to allow an automated car to pursue faster speed while maintaining desired frequency of lane-change maneuvers in various traffic environments

Connected cruise control design using probabilistic model checking

In this paper, we synthesize a connected cruise controller with performance guarantee using probabilistic model checking, for a vehicle that receives motion information from several vehicles ahead through wireless vehicle-to-vehicle communication. We model the car-following dynamics of the preceding vehicles as Markov chains and synthesize the connected cruise controller as a Markov decision process. We show through simulations that such a design is robust against imperfections in communication

Understanding the risks for post-disaster infectious disease outbreaks: a systematic review protocol

Introduction: Disasters have many forms, including those related to natural hazards and armed conflict. Human-induced global change, such as climate change, may alter hazard parameters of these disasters. These alterations can have serious consequences for vulnerable populations, which often experience post-disaster infectious disease outbreaks, leading to morbidity and mortality. The risks and drivers for these outbreaks and their ability to form cascades are somewhat contested. Despite evidence for post-disaster outbreaks, reviews quantifying them have been on short time scales, specific geographic areas or specific hazards. This review aims to fill this gap and gain a greater understanding of the risk factors involved in these contextual outbreaks on a global level. Methods and analysis: Using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist and Khan’s methodological framework, a systematic search strategy will be created and carried out in August 2020. The strategy will search MEDLINE, Embase and GlobalHealth electronic databases and reference lists of selected literature will also be screened. Eligible studies will include any retrospective cross-sectional, case–control or cohort studies investigating an infectious disease outbreak in a local disaster affected population. Studies will not be excluded based on geographic area or publication date. Excluded papers will include non-English studies, reviews, single case studies and research discussing general risk factors, international refugee camps, public health, mental health and other non-communicable diseases, pathogen genetics or economics. Following selection, data will be extracted into a data charting form, that will be reviewed by other members of the team. The data will then be analysed both numerically and narratively. Ethics and dissemination: Only secondary data will be used and there will be no public or patient involvement; therefore, no ethical approval is needed. Our findings will aim to be disseminated through a peer-reviewed journal. The authors intend to use the results to inform future mathematical modelling studies

Risk-aware motion planning for automated vehicle among human-driven cars

We consider the maneuver planning problem for automated vehicles when they share the road with human-driven cars and interact with each other using a finite set of maneuvers. Each maneuver is calculated considering input constraints, actuator disturbances and sensor noise, so that we can use a maneuver automaton to perform higher-level planning that is robust against lower-level effects. In order to model the behavior of human-driven cars in response to the intent of the automated vehicle, we use control improvisation to build a probabilistic model. To accommodate for potential mismatches between the learned human model and human driving behaviors, we use a conditional value-at-risk objective function to obtain the optimal policy for the automated vehicle. We demonstrate through simulations that our motion planning framework consisting of an interactive human driving model and risk-aware motion planning strategy makes it possible to adapt to different traffic conditions and confidence levels

On cardinal invariants and generators for von Neumann algebras

We demonstrate how virtually all common cardinal invariants associated to a von Neumann algebra M can be computed from the decomposability number, dec(M), and the minimal cardinality of a generating set, gen(M). Applications include the equivalence of the well-known generator problem, "Is every separably-acting von Neumann algebra singly-generated?", with the formally stronger questions, "Is every countably-generated von Neumann algebra singly-generated?" and "Is the gen invariant monotone?" Modulo the generator problem, we determine the range of the invariant (gen(M), dec(M)), which is mostly governed by the inequality dec(M) leq c^{gen(M)}.Comment: 22 pages; the main additions are Theorem 3.8 and Section

Cryo-EM of full-length α-synuclein reveals fibril polymorphs with a common structural kernel.

α-Synuclein (aSyn) fibrillar polymorphs have distinct in vitro and in vivo seeding activities, contributing differently to synucleinopathies. Despite numerous prior attempts, how polymorphic aSyn fibrils differ in atomic structure remains elusive. Here, we present fibril polymorphs from the full-length recombinant human aSyn and their seeding capacity and cytotoxicity in vitro. By cryo-electron microscopy helical reconstruction, we determine the structures of the two predominant species, a rod and a twister, both at 3.7 Å resolution. Our atomic models reveal that both polymorphs share a kernel structure of a bent β-arch, but differ in their inter-protofilament interfaces. Thus, different packing of the same kernel structure gives rise to distinct fibril polymorphs. Analyses of disease-related familial mutations suggest their potential contribution to the pathogenesis of synucleinopathies by altering population distribution of the fibril polymorphs. Drug design targeting amyloid fibrils in neurodegenerative diseases should consider the formation and distribution of concurrent fibril polymorphs

LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about