Machine learning in quantum computers via general Boltzmann Machines: Generative and Discriminative training through annealing

We present a Hybrid-Quantum-classical method for learning Boltzmann machines (BM) for generative and discriminative tasks. Boltzmann machines are undirected graphs that form the building block of many learning architectures such as Restricted Boltzmann machines (RBM's) and Deep Boltzmann machines (DBM's). They have a network of visible and hidden nodes where the former are used as the reading sites while the latter are used to manipulate the probability of the visible states. BM's are versatile machines that can be used for both learning distributions as a generative task as well as for performing classification or function approximation as a discriminative task. We show that minimizing KL-divergence works best for training BM for applications of function approximation. In our approach, we use Quantum annealers for sampling Boltzmann states. These states are used to approximate gradients in a stochastic gradient descent scheme. The approach is used to demonstrate logic circuits in the discriminative sense and a specialized two-phase distribution using generative BM

Stochastic Design Optimization of Microstructures with Utilization of a Linear Solver

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143035/1/6.2017-1939.pd

Graph Theoretic Algorithms Adaptable to Quantum Computing

Computational methods are rapidly emerging as an essential tool for understanding and solving complex engineering problems, which complement the traditional tools of experimentation and theory. When considered in a discrete computational setting, many engineering problems can be reduced to a graph coloring problem. Examples range from systems design, airline scheduling, image segmentation to pattern recognition, where energy cost functions with discrete variables are extremized. However, using discrete variables over continuous variables introduces some complications when defining differential quantities, such as gradients and Hessians involved in scientific computations within solid and fluid mechanics. Consequently, graph techniques are under-utilized in this important domain. However, we have recently witnessed great developments in quantum computing where physical devices can solve discrete optimization problems faster than most well-known classical algorithms. This warrants further investigation into the re-formulation of scientific computation problems into graph-theoretic problems, thus enabling rapid engineering simulations in a soon-to-be quantum computing world. The computational techniques developed in this thesis allow the representation of surface scalars, such as perimeter and area, using discrete variables in a graph. Results from integral geometry, specifically Cauchy-Crofton relations, are used to estimate these scalars via submodular functions. With this framework, several quantities important to engineering applications can be represented in graph-based algorithms. These include the surface energy of cracks for fracture prediction, grain boundary energy to model microstructure evolution, and surface area estimates (of grains and fibers) for generating conformal meshes. Combinatorial optimization problems for these applications are presented first. The last two chapters describe two new graph coloring algorithms implemented on a physical quantum computing device: the D-wave quantum annealer. The first algorithm describes a functional minimization approach to solve differential equations. The second algorithm describes a realization of the Boltzmann machine learning algorithm on a quantum annealer. The latter allows generative and discriminative learning of data, which has vast applications in many fields. Theoretical aspects and the implementation of these problems are outlined with a focus on engineering applications.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168116/1/sidsriva_1.pd

FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes

Inverse Reinforcement Learning (IRL) is a compelling technique for revealing the rationale underlying the behavior of autonomous agents. IRL seeks to estimate the unknown reward function of a Markov decision process (MDP) from observed agent trajectories. However, IRL needs a transition function, and most algorithms assume it is known or can be estimated in advance from data. It therefore becomes even more challenging when such transition dynamics is not known a-priori, since it enters the estimation of the policy in addition to determining the system's evolution. When the dynamics of these agents in the state-action space is described by stochastic differential equations (SDE) in It^{o} calculus, these transitions can be inferred from the mean-field theory described by the Fokker-Planck (FP) equation. We conjecture there exists an isomorphism between the time-discrete FP and MDP that extends beyond the minimization of free energy (in FP) and maximization of the reward (in MDP). We identify specific manifestations of this isomorphism and use them to create a novel physics-aware IRL algorithm, FP-IRL, which can simultaneously infer the transition and reward functions using only observed trajectories. We employ variational system identification to infer the potential function in FP, which consequently allows the evaluation of reward, transition, and policy by leveraging the conjecture. We demonstrate the effectiveness of FP-IRL by applying it to a synthetic benchmark and a biological problem of cancer cell dynamics, where the transition function is inaccessible

Effect of trimethoprim-sulfamethoxazole vs. norfloxacin on fecal E. coli resistance pattern and efficacy in patients receiving prophylaxis for spontaneous bacterial peritonitis

Background: Spontaneous Bacterial Peritonitis (SBP) is an infection of ascitic fluid. It is highly mortal and recurrent condition, so prophylaxis with Norfloxacin (NOR) or Trimethoprim-sulfamethoxazole (TMP-SMX) seems to play an important role in the prevention of further episodes of SBP. Aims of the study were to assess the effect of TMP-SMX/NOR on the sensitivity pattern of fecal E. coli after long term prophylaxis in Spontaneous Bacterial Peritonitis (SBP) and to compare the efficacy of TMP-SMX and NOR in prophylaxis of SBP.Methods: An interventional, prospective, open label, single center study conducted in Maulana Azad medical college, New Delhi, India. 52 patients of SBP or with high risk of SBP were screened and finally 39 patients were recruited. Stool sensitivity testing of fecal E. coli was done and they were divided into TMP-SMX group(n=18) and NOR group(n=21) according to sensitivity. After 45±3 days (7 weeks) their stool sample was re-examined for change sensitivity pattern of E. coli. Efficacy variables like any episode of SBP, fever (FEV) resolution of ascites (ASC), bacteremia (BACT), extraperitoneal infection (EPI), liver transplantation (LT) or death (D) were noted throughout the period of 24 weeks.Results: Resistance developed in 60% vs. 48% in TMP-SMX vs. NOR group(p=0.46) after 45 days of prophylaxis. By the end of 24 weeks, Incidence of SBP (29%vs. 25%, p>0.99), episodes of FEV(P=0.60), EPI(p>0.99), ASC(p>0.99) and death (14% vs. 16%, p>0.99) were almost similar in both the groups (TMP-SMX vs. NOR) respectively.Conclusions: Both TMP-SMX and NOR showed same degree of resistance and found equi-efficacious when administered as long-term prophylactic therapy in SBP. TMP-SMX can be a suitable as well as cost effective alternative to NOR for the prophylaxis of SBP

Sustainability of physical exam skills in a resident-led curriculum in a large internal medicine program with competency based medical education

Background: Competency Based Medical Education (CBME) designates physical examination competency as an Entrustable Professional Activity (EPA). Considerable concern persists regarding the increased time burden CBME may place on educators. We developed a novel physical examination curriculum that shifted the burden of physical examination case preparation and performance assessment from faculty to residents. Our first objective was to determine if participation led to sustainable improvements in physical examination skills. The second objective was to determine if resident peer assessment was comparable to faculty assessment.    Methods: We selected physical exam case topics based on the Objectives of Training in the Specialty of Internal Medicine as prescribed by the Royal College of Physicians and Surgeons of Canada. Internal Medicine residents compiled evidence-based physical exam checklists that faculty reviewed before distribution to all learners. Physical exam practice sessions with whole-group demonstration followed by small-group practice sessions were performed weekly. We evaluated this pilot curriculum with a formative OSCE, during which a resident peer and a faculty member simultaneously observed and assessed examinee performance by .Results: Participation in the novel curriculum practice sessions improved OSCE performance (faculty score mean 78.96 vs. 62.50, p<0.05). Peer assessment overestimated faculty scores (76.2 vs. 65.7, p<0.001), but peer and faculty assessments were highly correlated (R2 = 0.73 (95% CI 0.50-0.87).Conclusion: This novel physical examination curriculum leads to sustainable improvement of physical examination skills. Peer assessment correlated well with the gold standard faculty assessment. This resident-led physical examination curriculum enhanced physical examination skills in a CBME environment, with minimal time commitment from faculty members

Stochastic Design Optimization of Microstructures with Utilization of a Linear Solver

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143075/1/1.J056000.pd

Analysis of gut bacteriome of in utero arsenic-exposed mice using 16S rRNA-based metagenomic approach

IntroductionApproximately 200 million people worldwide are affected by arsenic toxicity emanating from the consumption of drinking water containing inorganic arsenic above the prescribed maximum contaminant level. The current investigation deals with the role of prenatal arsenic exposure in modulating the gut microbial community and functional pathways of the host.Method16S rRNA-based next-generation sequencing was carried out to understand the effects of in utero 0.04 mg/kg (LD) and 0.4 mg/kg (HD) of arsenic exposure. This was carried out from gestational day 15 (GD-15) until the birth of pups to understand the alterations in bacterial diversity.ResultsThe study focused on gestational exposure to arsenic and the altered gut microbial community at phyla and genus levels, along with diversity indices. A significant decrease in firmicutes was observed in the gut microbiome of mice treated with arsenic. Functional analysis revealed that a shift in genes involved in crucial pathways such as insulin signaling and non-alcoholic fatty liver disease pathways may lead to metabolic diseases in the host.DiscussionThe present investigation may hypothesize that in utero arsenic exposure can perturb the gut bacterial composition significantly as well as the functional pathways of the gestationally treated pups. This research paves the way to further investigate the probable mechanistic insights in the field of maternal exposure environments, which may play a key role in epigenetic modulations in developing various disease endpoints in the progeny