Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Formal methods for motion planning and control in dynamic and partially known environments

This thesis is motivated by time and safety critical applications involving the use of autonomous vehicles to accomplish complex tasks in dynamic and partially known environments. We use temporal logic to formally express such complex tasks. Temporal logic specifications generalize the classical notions of stability and reachability widely studied within the control and hybrid systems communities. Given a model describing the motion of a robotic system in an environment and a formal task specification, the aim is to automatically synthesize a control policy that guarantees the satisfaction of the specification. This thesis presents novel control synthesis algorithms to tackle the problem of motion planning from temporal logic specifications in uncertain environments. For each one of the planning and control synthesis problems addressed in this dissertation, the proposed algorithms are implemented, evaluated, and validated thought experiments and/or simulations. The first part of this thesis focuses on a mobile robot whose success is measured by the completion of temporal logic tasks within a given period of time. In addition to such time constraints, the planning algorithm must also deal with the uncertainty that arises from the changes in the robot's workspace during task execution. In particular, we consider a robot deployed in a partitioned environment subjected to structural changes such as doors that can open and close. The motion of the robot is modeled as a continuous time Markov decision process and the robot's mission is expressed as a Continuous Stochastic Logic (CSL) formula. A complete framework to find a control strategy that satisfies a specification given as a CSL formula is introduced. The second part of this thesis addresses the synthesis of controllers that guarantee the satisfaction of a task specification expressed as a syntactically co-safe Linear Temporal Logic (scLTL) formula. In this case, uncertainty is characterized by the partial knowledge of the robot's environment. Two scenarios are considered. First, a distributed team of robots required to satisfy the specification over a set of service requests occurring at the vertices of a known graph representing the environment is examined. Second, a single agent motion planning problem from the specification over a set of properties known to be satised at the vertices of the known graph environment is studied. In both cases, we exploit the existence of o-the-shelf model checking and runtime verification tools, the efficiency of graph search algorithms, and the efficacy of exploration techniques to solve the motion planning problem constrained by the absence of complete information about the environment. The final part of this thesis extends uncertainty beyond the absence of a complete knowledge of the environment described above by considering a robot equipped with a noisy sensing system. In particular, the robot is tasked with satisfying a scLTL specification over a set of regions of interest known to be present in the environment. In such a case, although the robot is able to measure the properties characterizing such regions of interest, precisely determining the identity of these regions is not feasible. A mixed observability Markov decision process is used to represent the robot's actuation and sensing models. The control synthesis problem from scLTL formulas is then formulated as a maximum probability reachability problem on this model. The integration of dynamic programming, formal methods, and frontier-based exploration tools allow us to derive an algorithm to solve such a reachability problem

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Spectral approaches for identifying kinetic features in molecular dynamics simulations of globular proteins

Proteins live in an environment of random thermal vibrations yet they convert this constant disorder into selective biological function. As data acquisition methods for resolving protein motions improve more of the randomness is also captured; there is thus a parallel need for analysis methods that filter out the disorder and clarify functionally-relevant protein behavior. Few behaviors are more relevant than folding in the first place, and this thesis opens by addressing which conformational states are kinetically relevant for promoting or inhibiting attainment of the folded native state. Our modeling approach discretizes simulation data into a network of nodes and edges representing, respectively, different protein conformations and observed conformational transitions. A perturbative strategy is then invoked to quantify the importance of each node, i.e. conformational substate, with regard to theoretical folding rates. On a test of 10 proteins this framework identifies unique ‘kinetic traps’ and ‘facilitator substates’ that sometimes evade detection with traditional RMSD-based analysis. We then apply spectral approaches and auto-regressive models to (1) address efficiency concerns for more general networks and (2) mimic protein flexibility with compact linear models

The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4

In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.Comment: 230 pages report; 181 pages for main content

Reducing Uncertainties in Conservation Decision-Making for American Alligators

Effective conservation decision-making necessitates monitoring programs that are designed to collect unbiased and precise measurements of relevant attributes deemed to reduce structural uncertainty of the managed resource state. American alligators (Alligator mississippiensis; hereafter alligator) are a keystone species within the southeastern United States that have cascading effects on ecosystem structure and function, and are managed under consumptive use management programs throughout their range. Management of alligator populations in South Carolina is challenging due to pervasive uncertainties regarding the size class distribution, which is only partially observable using the primary monitoring tool (nightlight surveys), a lack of demographic parameter estimates, and identification of measurable attributes that could pose conservation threats (e.g., drought, contaminants). My objective was to develop analytical tools to reduce partial observability in alligator monitoring and identify potential drivers of alligator population dynamics to reduce structural uncertainty. I developed a Bayesian integrated population model (IPM) that produced among the first demographic parameter estimates for alligators in South Carolina and determined that survival probabilities increased greatly among immature size classes, but are relatively similar among adults (\u3e0.90); a pattern that has been previously reported for American crocodiles (Crocodylus acutus). The IPM produced size-class specific abundance estimates for alligators from count data with prolific state uncertainty (\u3e60% unknown size observations). In general, alligator abundance trends were uncertain and appeared to vary spatially, though the mean population growth (λ) estimates for all sites, IPM versions, and the Lefkovtich matrix were \u3c1, indicating a population decline. However, the 95% Bayesian credible intervals for λ at one survey site included 1, indicating some uncertainty. I then used the demographic parameter estimates to simulate virtual alligator populations under varying gradations of initial population density, harvest rate to determine an optimal level of spatiotemporal replication for a monitoring programs. To evaluate the need to obtain size class-specific abundance estimates, the simulated count data from the underlying virtual population was total individuals (of all size classes). Based on fundamental objectives to maximize financial effectiveness and minimize management and ecological uncertainty, all of the harvest and density scenarios (except low density and maximum harvest) selected a monitoring program with six temporal replicates (the maximum) and 320 spatial replicates (1 spatial replicate = 0.5 km river segment). In general, data reliability (precision and accuracy) was more sensitive to increasing temporal, compared to spatial, replication, which has been previously reported in other simulation based studies in which detection probabilities are low (p\u3c 0.10). Moreover, all scenarios and monitoring programs induced changes in alligator size class structure, though the effects were minimized with reduced harvest rate, increase survey effort and population density. In synthesis, the demographic parameter estimates produced by the IPM can and are being used to improve monitoring methodology for alligators in South Carolina, and provide a mechanism to increase the demographic resolution of monitoring data, inform optimal monitoring decisions, and explore further uncertainties associate with harvest decisions. Finally, to better elucidate potential drivers of alligator population status, I evaluated total mercury (THg) concentrations in adult alligator whole blood from a longitudinal mark-recapture study. I determined that THg in whole blood was best described by an interactive effect of sex and predicted age, as calculated by predicted age at first capture using a recently developed growth model for alligators in South Carolina. THg concentrations averaged 0.16 ± 0.05 mg kg-1 ww and were slightly higher in males than female, though the overall average is significantly lower than other estimates reported in the Florida Everglades and the Savannah River Site in South Carolina. The quadratic effect of THg with predicted age, in which older individuals had lower levels than younger individuals is novel, and contrasts with previous assumptions that THg bioaccumulates with age (i.e., does not decrease). We posit that determinate (asymptotic) growth, which could accompany age-related changes in foraging patters and metabolism, could potentially explain the lower THg we detected in the oldest individuals. The results from our study could highlight the need for long-term longitudinal monitoring of sentinel species to further evaluate our hypotheses