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1. Contemporary and Emerging MRI Strategies for Assessing Kidney Allograft Complications: Arterial Stenosis and Parenchymal Injury, From the AJR Special Series on Imaging of Fibrosis.

Author

Bane, Octavia, Lewis, Sara, Lim, Ruth, Carney, Benjamin, Shah, Amar, and Fananapazir, Ghaneh

Subjects
MRI, allograft, kidney, transplant, Humans, Constriction, Pathologic, Kidney, Fibrosis, Kidney Diseases, Graft Rejection, Allografts, Magnetic Resonance Imaging
Abstract

MRI plays an important role in the evaluation of kidney allografts for vascular complications as well as parenchymal insults. Transplant renal artery stenosis, the most common vascular complication of kidney transplant, can be evaluated by MRA using gadolinium and nongadolinium contrast agents as well as by unenhanced MRA techniques. Parenchymal injury occurs through a variety of pathways, including graft rejection, acute tubular injury, BK polyomavirus infection, drug-induced interstitial nephritis, and pyelonephritis. Investigational MRI techniques have sought to differentiate among these causes of dysfunction as well as to assess the degree of interstitial fibrosis or tubular atrophy (IFTA)-the common end pathway for all of these processes-which is currently evaluated by invasively obtained core biopsies. Some of these MRI sequences have shown promise in not only assessing the cause of parenchymal injury but also assessing IFTA noninvasively. This review describes current clinically used MRI techniques and previews promising investigational MRI techniques for assessing complications of kidney grafts.

Published
2024

2. An Eager Satisfiability Modulo Theories Solver for Algebraic Datatypes

Author

Shah, Amar, Mora, Federico, and Seshia, Sanjit A.

Subjects
Computer Science - Logic in Computer Science, Computer Science - Artificial Intelligence, Computer Science - Programming Languages
Abstract

Algebraic data types (ADTs) are a construct classically found in functional programming languages that capture data structures like enumerated types, lists, and trees. In recent years, interest in ADTs has increased. For example, popular programming languages, like Python, have added support for ADTs. Automated reasoning about ADTs can be done using satisfiability modulo theories (SMT) solving, an extension of the Boolean satisfiability problem with constraints over first-order structures. Unfortunately, SMT solvers that support ADTs do not scale as state-of-the-art approaches all use variations of the same \emph{lazy} approach. In this paper, we present an SMT solver that takes a fundamentally different approach, an \emph{eager} approach. Specifically, our solver reduces ADT queries to a simpler logical theory, uninterpreted functions (UF), and then uses an existing solver on the reduced query. We prove the soundness and completeness of our approach and demonstrate that it outperforms the state-of-theart on existing benchmarks, as well as a new, more challenging benchmark set from the planning domain.

Published
2023

3. Optimal control for sampling the transition path process and estimating rates

Author

Yuan, Jiaxin, Shah, Amar, Bentz, Channing, and Cameron, Maria

Subjects
Mathematics - Optimization and Control
Abstract

Many processes in nature such as conformal changes in biomolecules and clusters of interacting particles, genetic switches, mechanical or electromechanical oscillators with added noise, and many others are modeled using stochastic differential equations with small white noise. The study of rare transitions between metastable states in such systems is of great interest and importance. The direct simulation of rare transitions is difficult due to long waiting times. Transition path theory is a mathematical framework for the quantitative description of rare events. Its crucial component is the committor function, the solution to a boundary value problem for the backward Kolmogorov equation. The key fact exploited in this work is that the optimal controller constructed from the committor leads to the generation of transition trajectories exclusively. We prove this fact for a broad class of stochastic differential equations. Moreover, we demonstrate that the committor computed for a dimensionally reduced system and then lifted to the original phase space still allows us to construct an effective controller and estimate the transition rate with reasonable accuracy. Furthermore, we propose an all-the-way-through scheme for computing the committor via neural networks, sampling the transition trajectories, and estimating the transition rate without meshing the space. We apply the proposed methodology to four test problems: the overdamped Langevin dynamics with Mueller's potential and the rugged Mueller potential in 10D, the noisy bistable Duffing oscillator, and Lennard-Jones-7 in 2D.

Published
2023

7. Recovery of Meteorites Using an Autonomous Drone and Machine Learning

Author

Citron, Robert I., Jenniskens, Peter, Watkins, Christopher, Sinha, Sravanthi, Shah, Amar, Raissi, Chedy, Devillepoix, Hadrien, and Albers, Jim

Subjects
Astrophysics - Earth and Planetary Astrophysics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. However, locating meteorite fragments in strewn fields remains a challenge with very few meteorites being recovered from the meteors triangulated in past and ongoing meteor camera networks. We examined if locating meteorites can be automated using machine learning and an autonomous drone. Drones can be programmed to fly a grid search pattern and take systematic pictures of the ground over a large survey area. Those images can be analyzed using a machine learning classifier to identify meteorites in the field among many other features. Here, we describe a proof-of-concept meteorite classifier that deploys off-line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada., Comment: 16 pages, 9 Figures

Published
2021
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8. Tuberculosis treatment outcomes and patient support groups, southern India/Resultats des traitements contre la tuberculose et groupes d'entraide entre patients en Inde du Sud/Desenlaces del tratamiento de la tuberculosis y grupos de apoyo a los pacientes en el sur de la India

Author

Potty, Rajaram Subramanian, Kumarasamy, Karthikeyan, Munjattu, Joseph F., Reddy, Ramesh C., Adepu, Rajesham, Singarajipura, Anil, Lakkappa, Mohan H., Swamickan, Reuben, Shah, Amar, Panibatla, Vikas, and Washington, Reynold

Subjects
Medical research -- Comparative analysis, Medicine, Experimental -- Comparative analysis, Comorbidity -- Risk factors -- Patient outcomes -- Drug therapy, Drug resistance in microorganisms -- Risk factors -- Drug therapy -- Patient outcomes, Immunodeficiency -- Risk factors -- Patient outcomes -- Drug therapy, Tuberculosis -- Patient outcomes -- Drug therapy -- Risk factors, Virus diseases -- Risk factors -- Drug therapy -- Patient outcomes, HIV infection -- Risk factors -- Patient outcomes -- Drug therapy, Health
Abstract

Objective To assess treatment outcomes in tuberculosis patients participating in support group meetings in five districts of Karnataka and Telangana states in southern India. Methods Tuberculosis patients from five selected districts who began treatment in 2019 were offered regular monthly support group meetings, with a focus on patients in urban slum areas with risk factors for adverse outcomes. We tracked the patients' participation in these meetings and extracted treatment outcomes from the Nikshay national tuberculosis database for the same patients in 2021. We compared treatment outcomes based on attendance of the support groups meetings. Findings Of 30 706 tuberculosis patients who started treatment in 2019, 3651 (11.9%) attended support groups meetings. Of patients who attended at least one support meeting, 94.1% (3426/3639) had successful treatment outcomes versus 88.2% (23 745/26 922) of patients who did not attend meetings (adjusted odds ratio, aOR: 2.44; 95% confidence interval, CI: 2.10-2.82). The odds of successful treatment outcomes were higher in meeting participants than non-participants for all variables examined including: age [greater than or equal to] 60 years (aOR: 3.19; 95% CI: 2.26-4.51); female sex (aOR: 3.33; 95% CI: 2.46-4.50); diabetes comorbidity (aOR: 3.03; 95% CI: 1.91-4.81); human immunodeficiency virus infection (aOR: 3.73; 95% CI: 1.76-7.93); tuberculosis retreatment (aOR: 1.69; 1.22-2.33); and drug- resistant tuberculosis (aOR: 1.93; 95% CI: 1.21-3.09). Conclusion Participation in support groups for tuberculosis patients was significantly associated with successful tuberculosis treatment outcomes, especially among high-risk groups. Expanding access to support groups could improve tuberculosis treatment outcomes at the population level. Objectif Evaluer les resultats des traitements contre la tuberculose chez les patients qui assistent aux reunions des groupes d'entraide dans cinq districts des Etats du Karnataka et du Telangana, en Inde du Sud. Methodes Dans les cinq districts selectionnes, des patients tuberculeux ayant entame un traitement en 2019 se sont vu proposer des reunions mensuelles organisees a intervalles reguliers. Ces reunions etaient principalement destinees aux patients vivant dans des bidonvilles urbains et presentant des facteurs augmentant le risque d'issue defavorable. Nous avons suivi leur participation aux reunions et avons extrait des informations therapeutiques de la base de donnees nationale Nikshay sur la tuberculose concernant les memes patients en 2021. Nous avons ensuite compare l'issue des traitements a la presence aux reunions des groupes d'entraide. Resultats Sur 30 706 patients tuberculeux ayant entame un traitement en 2019, 3651 (11,9%) ont rejoi nt des groupes d'entraide. Sur l'ensemble des patients ayant assiste a au moins une reunion, 94,1% (3426/3639) ont connu une issue favorable au traitement, contre 88,2% (23 745/26 922) chez les patients qui n'y ont pas assiste (odds ratio ajuste, ORA: 2,44; intervalle de confiance de 95%, IC: 2,10-2,82). Les probabilites de reussite du traitement etaient plus elevees chez les participants aux reunions que chez les non-participants pour toutes les variables examinees, y compris pour les [greater than or equal to] 60 ans (ORA: 3,19; IC de 95%: 2,26-4,51); les patients de sexe feminin (ORA: 3,33; IC de 95%: 2,46-4,50); celles et ceux presentant une comorbidite liee au diabete (ORA: 3,03; IC de 95%: 1,91-4,81); une infection au virus de l'immunodeficience humaine (ORA: 3,73; IC de 95%: 1,76-7,93); un retraitement contre la tuberculose (ORA: 1,69; 1,22-2,33); et enfin, une tuberculose pharmacoresistante (ORA: 1,93; IC de 95%: 1,21-3,09). Conclusion La participation des patients atteints de tuberculose aux groupes d'entraide allait de pair avec de meilleurs resultats de traitement, surtout au sein des categories a haut risque. Promouvoir l'acces a ces groupes d'entraide pourrait ameliorer l'issue des traitements contre la tuberculose a l'echelle de la population. Objetivo Evaluar los desenlaces del tratamiento en pacientes con tuberculosis que participan en reuniones de grupos de apoyo en cinco distritos de los estados de Karnataka y Telangana en el sur de la India. Metodos Se ofrecieron reuniones mensuales regulares de grupos de apoyo a los pacientes con tuberculosis de cinco distritos seleccionados que comenzaron el tratamiento en 2019, con un enfoque en los pacientes de los suburbios urbanos con factores de riesgo de desenlaces adversos. Se realizo un seguimiento de la participacion de los pacientes en estas reuniones y se extrajeron los desenlaces del tratamiento de la base de datos nacional de tuberculosis Nikshay para los mismos pacientes en 2021. Se compararon los desenlaces del tratamiento en funcion de la asistencia a las reuniones de los grupos de apoyo. Resultados De 30 706 pacientes con tuberculosis que iniciaron el tratamiento en 2019, 3651 (11,9 %) asistieron a reuniones de grupos de apoyo. De los pacientes que asistieron al menos a una reunion de apoyo, el 94,1 % (3426/3639) presentaron desenlaces exitosos del tratamiento frente al 88,2 % (23 745/26 922) de los pacientes que no asistieron a las reuniones (razon de posibilidades ajustada, RPA: 2,44; intervalo de confianza del 95 %, IC: 2,10-2,82). Las posibilidades de obtener un desenlace satisfactorio del tratamiento fueron mayores en los participantes en las reuniones que en los no participantes para todas las variables examinadas, incluyendo: edad [greater than or equal to] 60 anos (RPA: 3,19; IC del 95 %: 2,26-4,51); sexo femenino (RPA: 3,33; IC del 95 %: 2,46-4,50); comorbilidad por diabetes (RPA: 3,03; IC del 95 %: 1,91-4,81); infeccion por el virus de la inmunodeficiencia humana (RPA: 3,73; IC del 95 %: 1,76-7,93); retratamiento de la tuberculosis (RPA: 1,69; IC del 95 %: 1,22-2,33); y tuberculosis resistente (RPA: 1,93; IC del 95 %: 1,21-3,09). Conclusion La participacion en grupos de apoyo para pacientes con tuberculosis se asocio de manera significativa con el exito de los desenlaces del tratamiento de la tuberculosis, en especial entre los grupos de alto riesgo. Ampliar el acceso a los grupos de apoyo podria mejorar los desenlaces del tratamiento de la tuberculosis a nivel de la poblacion., Introduction Estimates indicate that India has the largest number of tuberculosis patients (26%) and tuberculosis-related deaths (36%) in the world. (1) India's success in tackling tuberculosis is critical to achieving [...]

Published
2023
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9. Urban Driving with Conditional Imitation Learning

Author

Hawke, Jeffrey, Shen, Richard, Gurau, Corina, Sharma, Siddharth, Reda, Daniele, Nikolov, Nikolay, Mazur, Przemyslaw, Micklethwaite, Sean, Griffiths, Nicolas, Shah, Amar, and Kendall, Alex

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Robotics
Abstract

Hand-crafting generalised decision-making rules for real-world urban autonomous driving is hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations is appealing. Prior work has studied imitation learning (IL) for autonomous driving with a number of limitations. Examples include only performing lane-following rather than following a user-defined route, only using a single camera view or heavily cropped frames lacking state observability, only lateral (steering) control, but not longitudinal (speed) control and a lack of interaction with traffic. Importantly, the majority of such systems have been primarily evaluated in simulation - a simple domain, which lacks real-world complexities. Motivated by these challenges, we focus on learning representations of semantics, geometry and motion with computer vision for IL from human driving demonstrations. As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic. We address inherent dataset bias by data balancing, training our final policy on approximately 30 hours of demonstrations gathered over six months. We evaluate our method on an autonomous vehicle by driving 35km of novel routes in European urban streets., Comment: Under submission; added acknowledgements

Published
2019

10. A Case of S-Variant Hepatitis B Virus: An Immune System Escape Artist

Author

Shah, Amar S, Civelli, Valerie F, Bali, Varun, Johnson, Royce H, and Heidari, Arash

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Digestive Diseases, Hepatitis, HIV/AIDS, Vaccine Related, Liver Disease, Hepatitis - B, Chronic Liver Disease and Cirrhosis, Infectious Diseases, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Infection, Good Health and Well Being, Genetic Variation, Hepatitis B, Hepatitis B Surface Antigens, Hepatitis B virus, Humans, Immune System, Male, Middle Aged, S-variant hepatitis B, hepatitis B viral infection, HBV, preS/S mutant HBV, immune escape HBV
Abstract

Genomic variants of the hepatitis B virus (HBV) preS/S protein are well-known to occur. Typically, immunity is gained through recovered HBV infection or by immunization. Very rarely, there are certain mutations that may enable HBV escape from the immune detection. PreS/S mutants may present with unpredictable pathobiologic, clinical, and transmittable implications. Standard laboratory testing for genomic HBV variants is not routinely performed by reference guidelines. s-variant HBV management remains challenging. Herein is a case of s-variant chronic HBV infection in a 55-year-old man. Diagnosis and treatment are described.

Published
2021

11. Bayesian single- and multi-objective optimisation with nonparametric priors

Author

Shah, Amar and Ghahramani, Zoubin

Subjects
machine learning, Bayesian optimisation, Bayesian, optimisation, sequential decision, single objective, multiple objective, Gaussian process
Abstract

Optimisation is integral to all sorts of processes in science, economics and arguably underpins the fruition of human intelligence through millions of years of optimisation, or 'evolution'. Scarce resources make it crucial to maximise their efficient usage. In this thesis, we consider the task of maximising unknown functions which we are able to query point-wise. The function is deemed to be 'costly' to evaluate e.g. larger run time or financial expense, requiring a judicious querying strategy given previous observations. We adopt a probabilistic framework for modelling the unknown function and Bayesian non-parametric modelling. In particular, we focus on the 'Gaussian process' (GP), a popular non-parametric Bayesian prior on functions. We motivate these choices and give an overview of the Gaussian process in the introduction, and its application to 'Bayesian optimisation'. A GP's behaviour is intimately controlled by the choice of 'kernel' or covariance function, typically chosen to be a parametric function. In chapter 2 we instead place a non-parametric Bayesian prior, known as an Inverse Wishart process prior, over a GP kernel function, and show that this may be marginalised analytically leading to a 'Student-t process' (TP). Furthermore we explore a larger class of 'elliptical processes', and show that the TP is the most general for which analytic calculation is possible, and apply it successfully for Bayesian optimisation. The remainder of the thesis focusses on various Bayesian optimisation settings. In chapter 3, we consider a setting where we are able to evaluate a function at multiple locations in parallel. Our approach is to consider a measure of information, 'entropy', to decide which batch of points to evaluate a function at next. We similarly apply information gain for 'multi-objective' Bayesian optimisation in chapter 4. Here, one wishes to find a 'Pareto frontier' of efficient settings with respect to several different objectives through sequential evaluation. Finally, in chapter 5 we exploit the idea that in a multi-objective setting, functions are 'correlated', incorporating this belief in our choice of prior distribution over the multiple objectives.

Published
2020
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13. A role for CT in COVID-19? What data really tell us so far.

Author

Hope, Michael D, Raptis, Constantine A, Shah, Amar, Hammer, Mark M, Henry, Travis S, and six signatories

Subjects
six signatories, Humans, Pneumonia, Viral, Coronavirus Infections, Tomography, X-Ray Computed, Clinical Laboratory Techniques, Pandemics, Real-Time Polymerase Chain Reaction, Betacoronavirus, General & Internal Medicine, Medical and Health Sciences
Published
2020

14. CT on the Diamond Princess: What Might This Tell Us About Sensitivity for COVID-19?

Author

Isikbay, Masis, Hope, Michael D, Raptis, Constantine A, Shah, Amar, Bierhals, Andrew J, Bhalla, Sanjeev, Hammer, Mark M, Kligerman, Seth J, Jeudy, Jean, Filev, Peter D, Henry, Travis S, Inui, Shohei, Fujikawa, Akira, Jitsu, Motoyuki, Kunishima, Naoaki, Watanabe, Sadahiro, Suzuki, Yuhi, Umeda, Satoshi, and Uwabe, Yasuhide

Published
2020

15. The Coincidence of 2 Epidemics, Coccidioidomycosis and SARS-CoV-2: A Case Report

Author

Shah, Amar S, Heidari, Arash, Civelli, Valerie F, Sharma, Ritika, Clark, Charles S, Munoz, Augustine D, Ragland, Alan Scott, and Johnson, Royce H

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Emerging Infectious Diseases, Rare Diseases, Lung, Pneumonia, Prevention, Infectious Diseases, Pneumonia & Influenza, Vaccine Related, Biodefense, Infection, Respiratory, Good Health and Well Being, Betacoronavirus, COVID-19, California, Chronic Disease, Coccidioidomycosis, Coinfection, Coronavirus Infections, Epidemics, Humans, Lung Diseases, Fungal, Male, Middle Aged, Pandemics, Pneumonia, Viral, SARS-CoV-2, Tomography, X-Ray Computed, pulmonary coccidioidomycosis, cavitary coccidioidomycosis, CoV-2, SARS-Cov-2, corona virus, COVID19, pneumonia
Abstract

In the middle of a pandemic, patients with cough and fever are thought to have SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2). It should be remembered that in the desert southwest of the United States, we have an ongoing epidemic of coccidioidomycosis (CM). There are additionally many other respiratory illnesses that could be confused with CoV-2 or overlooked. This is a case report of CoV-2 engrafted on chronic cavitary pulmonary CM. In a time where the coronavirus pandemic is becoming rampant, we demonstrate the case of a coinfection with cavitary pulmonary CM. In this case, the importance of detection of the coronavirus and treatment of the coinfection is explored.

Published
2020

17. Learning to Drive in a Day

Author

Kendall, Alex, Hawke, Jeffrey, Janz, David, Mazur, Przemyslaw, Reda, Daniele, Allen, John-Mark, Lam, Vinh-Dieu, Bewley, Alex, and Shah, Amar

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Robotics, Statistics - Machine Learning
Abstract

We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control. We use a continuous, model-free deep reinforcement learning algorithm, with all exploration and optimisation performed on-vehicle. This demonstrates a new framework for autonomous driving which moves away from reliance on defined logical rules, mapping, and direct supervision. We discuss the challenges and opportunities to scale this approach to a broader range of autonomous driving tasks., Comment: Further results and demo videos can be viewed at: https://wayve.ai/blog/l2diad

Published
2018

21. Beyond the Needle: Unmasking Atypical Mycobacteria in Postvaccination Abscesses in Childr.

Author

Shah, Amar, Joshi, Devesh N., and Shah, Anirudh

Subjects
*IMMUNIZATION, *MYCOBACTERIAL diseases, *MEDICAL protocols, *SOFT tissue infections, *MYCOBACTERIUM, *OPPORTUNISTIC infections, *DISEASE duration, *HAND washing, *ASEPSIS & antisepsis, *RETROSPECTIVE studies, *INJECTIONS, *ABSCESSES, *CHILDREN
Abstract

Introduction: Atypical or nontuberculous mycobacteria (NTM) are an environmental organism responsible for opportunistic infection. Rapid-growing NTM are more commonly associated with hospital-acquired infections. Many of the organisms responsible for diseases in immunocompromised patients and hospital-acquired infections originate from tap water, such as Mycobacterium kansasii, Mycobacterium xenopi, Mycobacterium gordonae, Mycobacterium simiae, Mycobacterium mucogenicum, Mycobacterium fortuitum, Mycobacterium chelonae, and Mycobacterium abscessus. NTM is a rare organism responsible for the injection abscess. Considering low incidents, not much clinical data are available for this condition. Here, we discuss such cases which can be helpful to spread awareness and provide data for future policy makers. Materials and Methods: This was a retrospective study. Data on patients with injection abscess were collected from the last 6 years. Detailed history and clinical examination findings were analyzed. Children with injection abscess were operated and their further management and outcome were studied. Results: A total of 13 cases with confirmed culture of NTM were treated over 6 years. The age ranged from 2 1/2 months to 5 3/4 years with male:female ratio of 7:6. All patients hailed from the same geographical area. All children were healthy with no history of any long-term or chronic illness, without additional symptoms and had received Bacillus Calmette-Guérin vaccination at birth. The total duration of illness varied from 1 to 5 months, with a mean of 3 months. All patients had a history of intramuscular age-appropriate vaccination as per the national immunization schedule. All patients were followed up to 6 months after intervention and none of our patients developed relapse. Conclusion: Patient who does not respond with optimum treatment should have a high suspicion of such opportunistic infection, which is crucial to their management. Hospital-acquired NTM infections often result from contaminated instruments or fluids. Adherence to strict aseptic precautions, hand hygiene and environmental precautions are the key to preventing these infections. In case of skin and soft tissue infections / abscesses, surgical intervention plays a significant role for managing the patient. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

Author

Shah, Amar and Ghahramani, Zoubin

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first non-greedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics., Comment: 12 pages in Neural Information Processing Systems 2015

Published
2015

24. Unitary Evolution Recurrent Neural Networks

Author

Arjovsky, Martin, Shah, Amar, and Bengio, Yoshua

Subjects
Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

Recurrent neural networks (RNNs) are notoriously difficult to train. When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies. To circumvent this problem, we propose a new architecture that learns a unitary weight matrix, with eigenvalues of absolute value exactly 1. The challenge we address is that of parametrizing unitary matrices in a way that does not require expensive computations (such as eigendecomposition) after each weight update. We construct an expressive unitary weight matrix by composing several structured matrices that act as building blocks with parameters to be learned. Optimization with this parameterization becomes feasible only when considering hidden states in the complex domain. We demonstrate the potential of this architecture by achieving state of the art results in several hard tasks involving very long-term dependencies.

Published
2015

25. Predictive Entropy Search for Multi-objective Bayesian Optimization

Author

Hernández-Lobato, Daniel, Hernández-Lobato, José Miguel, Shah, Amar, and Adams, Ryan P.

Subjects
Statistics - Machine Learning
Abstract

We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO is to choose evaluation points so as to maximally reduce the entropy of the posterior distribution over the Pareto set. Critically, the PESMO multi-objective acquisition function can be decomposed as a sum of objective-specific acquisition functions, which enables the algorithm to be used in \emph{decoupled} scenarios in which the objectives can be evaluated separately and perhaps with different costs. This decoupling capability also makes it possible to identify difficult objectives that require more evaluations. PESMO also offers gains in efficiency, as its cost scales linearly with the number of objectives, in comparison to the exponential cost of other methods. We compare PESMO with other related methods for multi-objective Bayesian optimization on synthetic and real-world problems. The results show that PESMO produces better recommendations with a smaller number of evaluations of the objectives, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large.

Published
2015

26. An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

Author

Shah, Amar, Knowles, David A., and Ghahramani, Zoubin

Subjects
Statistics - Machine Learning, Computer Science - Learning, Statistics - Applications, Statistics - Computation, Statistics - Methodology
Abstract

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performance of these methods has been assessed primarily in the context of Bayesian topic models, particularly latent Dirichlet allocation (LDA). Deriving several new algorithms, and using synthetic, image and genomic datasets, we investigate whether the understanding gleaned from LDA applies in the setting of sparse latent factor models, specifically beta process factor analysis (BPFA). We demonstrate that the big picture is consistent: using Gibbs sampling within SVI to maintain certain posterior dependencies is extremely effective. However, we find that different posterior dependencies are important in BPFA relative to LDA. Particularly, approximations able to model intra-local variable dependence perform best., Comment: ICML, 12 pages. Volume 37: Proceedings of The 32nd International Conference on Machine Learning, 2015

Published
2015

27. Intraperitoneal bladder rupture in a 1 year old: A very unusual accident

Author

Shah, Amar and Shah, Anirudh

Subjects
Traffic accidents -- Accidents, Health
Abstract

Byline: Amar. Shah, Anirudh. Shah Intraperitoneal bladder rupture is a rare event in the paediatric population. Road traffic accidents (RTA) and seat belt injuries are considered to be the main [...]

Published
2022

29. The Advancing Mental Health Equality Collaborative: Using Quality Improvement to Advance Equality in Mental Health Care – CORRIGENDUM

Author

de Bono, Adele, primary, Milarski, Matt, additional, Souza, Renata, additional, Akhtar, Saiqa, additional, Bevan, Rosana, additional, Cannon, Emily, additional, Ayers, Tom, additional, Aya, Viviana, additional, Arundell, Laura-Louise, additional, Farmer, Mark, additional, Burgess, Meera, additional, Smith, Lade, additional, Mohan, Rajesh, additional, and Shah, Amar, additional

Published
2023
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30. Student-t Processes as Alternatives to Gaussian Processes

Author

Shah, Amar, Wilson, Andrew Gordon, and Ghahramani, Zoubin

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Learning, Statistics - Methodology
Abstract

We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process -- a nonparametric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels -- but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications like Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes., Comment: 13 pages, 6 figures, 1 table. To appear in "The Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2014."

Published
2014

32. Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Author

Shah, Amar and Ghahramani, Zoubin

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets., Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)

Published
2013

33. Quality of active case-finding for tuberculosis in India: a national level secondary data analysis

Author

Shewade, Hemant Deepak, primary, Kiruthika, G., additional, Ravichandran, Prabhadevi, additional, Iyer, Swati, additional, Chowdhury, Aniket, additional, Kiran Pradeep, S., additional, Jeyashree, Kathiresan, additional, Devika, S., additional, Chadwick, Joshua, additional, Wesley Vivian, Jeromie, additional, Tumu, Dheeraj, additional, Shah, Amar N., additional, Vadera, Bhavin, additional, Roddawar, Venkatesh, additional, Mattoo, Sanjay K., additional, Rade, Kiran, additional, Rao, Raghuram, additional, and Murhekar, Manoj V., additional

Published
2023
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34. Cross-sectional study evaluating the impact of SARS-CoV-2 variants on Long COVID outcomes in UK hospital survivors

Author

Saigal, Anita, primary, Nagoda Niklewicz, Camila, additional, Naidu, Sindhu Bhaarrati, additional, Bintalib, Heba M, additional, Shah, Amar Jitu, additional, Seligmann, George, additional, Hunter, Alan Stewart, additional, Wey, Emmanuel, additional, Abubakar, Ibrahim, additional, Mahungu, Tabitha, additional, Miller, David, additional, Barnett, Joseph, additional, Jain, Neel Gautam, additional, Brill, Simon, additional, Goldring, James, additional, Jarvis, Hannah, additional, Smith, Colette, additional, Ogbonnaya, Chibueze, additional, Hurst, John R, additional, Lipman, Marc C I, additional, and Mandal, Swapna, additional

Published
2023
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36. A mixed methods evaluation of a differentiated care model piloted for TB care in south India

Author

Washington, Reynold, primary, Ramanaik, Satyanarayana, additional, Kumarasamy, Karthikeyan, additional, Sreenivasa, Prarthana B, additional, Adepu, Rajesham, additional, Reddy, Ramesh Chandra, additional, Shah, Amar, additional, Swamickan, Reuben, additional, Maryala, Bala Krishna, additional, Mukherjee, Aparna, additional, Pujar, Ashwini, additional, Panibatla, Vikas, additional, Lakkappa, Mohan Harnahalli, additional, and Potty, Rajaram Subramanian, additional

Published
2023
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39. Is a differentiated care model needed for patients with TB? A cohort analysis of risk factors contributing to unfavourable outcomes among TB patients in two states in South India

Author

Washington, Reynold, Potty, Rajaram Subramanian, Rajesham, A., Seenappa, T., Singarajipura, Anil, Swamickan, Reuben, Shah, Amar, Prakash, K. H., Kar, Arin, Kumaraswamy, Karthikeyan, Prarthana, B. S., Maryala, Bala Krishna, Sushma, J., Dasari, Ramesh, Shetty, Bharath, Panibatla, Vikas, Mohan, H. L., and Becker, Marissa

Published
2020
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40. A study of sperm quality assessment: Morphological defect in patients with normal sperm count.

Author

Maru, Alpeshkumar M., Tank, Yagnik P., Tank, Bansari Y., Shah, Amar R., and Kanasagara, Anandkumar A.

Subjects
SPERM count, SPERMATOZOA, MALE infertility, SEMEN analysis, RECURRENT miscarriage
Abstract

The article "A study of sperm quality assessment: Morphological defect in patients with normal sperm count" examines the connection between sperm morphology and fertility. The study analyzed 70 samples from patients with normal sperm counts and categorized defects in the head, middle piece, and tail of the sperm. The findings suggest that qualitative defects in sperm morphology are significant in infertility workup, emphasizing the importance of assessing semen morphology. The article provides a thorough exploration of the topic and may be useful for individuals researching sperm quality and its impact on fertility. [Extracted from the article]

Published
2023
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46. Measurement of body temperature in postsurgical children: Comparisons of infrared nonskin contact digital thermometer, skin contact digital thermometer, and mercury in glass thermometer

Author

Jasani, Mitul, Jasani, Alpa, Shah, Anirudh, and Shah, Amar

Subjects
Pediatrics -- Analysis, Measuring instruments -- Analysis, Temperature measurements -- Analysis, Skin -- Analysis, Health
Abstract

Byline: Mitul. Jasani, Alpa. Jasani, Anirudh. Shah, Amar. Shah Context: Postoperative fever is known to occur after all surgical procedures irrespective of the type of anesthesia. Thermometry devices that work [...]

Published
2021

47. Evaluation of a results-based financing nutrition intervention for tuberculosis patients in Madhya Pradesh, India, implemented during the COVID-19 pandemic.

Author

Howell, Embry, Dammala, Rama Rao, Pandey, Pratibha, Strouse, Darcy, Sharma, Atul, Rao, Neeta, Nadipally, Sudheer, Shah, Amar, Rai, Varsha, and Dowling, Russell

Subjects
DIETARY supplements, COVID-19 pandemic, FOOD industry
Abstract

Background: Reducing malnutrition through food supplementation is a critical component of the WHO End Tuberculosis (TB) strategy. A results-based financing (RBF) initiative in Madhya Pradesh, India—called Mukti—introduced an intensive nutrition intervention, including home visits, counseling, food basket distribution, and assistance in obtaining government benefits. Phase 1 of the program (Dhar District), implemented by ChildFund India (ChildFund) and funded by USAID, coincided with the COVID-19 lockdown in 2020. Under an RBF reimbursement scheme, ChildFund was paid based on treatment retention for 6 months and weight gain of 6 kg for adults. Methods: The evaluation used a mixed methods approach. Qualitative components included interviews with key informants and focus groups with program participants. Quantitative components included an analysis of program data (i.e., patient demographics, receipt of program services, and weight gain). An impact analysis of retention in treatment used data from a government database. A difference-in-differences model was used to compare results from baseline data and the program period for Dhar District to similar data for the adjacent Jhabua District. Results: The program was well implemented and appreciated by patients and providers. Patients received an average of 10.2 home visits and 6.2 food baskets. While all age and sex groups gained weight significantly over their 6-month treatment period, there was no program impact on treatment retention. Seventy-six percent of patients achieved both outcome goals. And though average program costs were under budget, ChildFund experienced a loss in the results-based financing scheme, which was covered by USAID to continue program expansion. Conclusions: Implementing a nutrition supplementation and education program for TB patients in India is feasible. The intervention improved weight gain despite COVID-19-related lockdowns. The Mukti program did not impact treatment retention, which was already high at baseline. Program costs were modest, but the results-based financing reimbursement scheme resulted in a loss for the implementer. Overall, the RBF model led to an increased focus on outcomes for program staff and other stakeholders, which led to more efficient service delivery. Future research should examine total costs (including donated staff time) more extensively to determine the cost-effectiveness of Mukti and similar interventions. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Kisspeptin modulates sexual and emotional brain processing in humans

Author

Comninos, Alexander N., Wall, Matthew B., Demetriou, Lysia, Shah, Amar J., Clarke, Sophie A., Narayanaswamy, Shakunthala, Nesbitt, Alexander, Izzi-Engbeaya, Chioma, Prague, Julia K., Abbara, Ali, Ratnasabapathy, Risheka, Salem, Victoria, Nijher, Gurjinder M., Jayasena, Channa N., Tanner, Mark, Bassett, Paul, Mehta, Amrish, Rabiner, Eugenii A., Honigsperger, Christoph, Silva, Meire Ribeiro, Brandtzaeg, Ole Kristian, Lundanes, Elsa, Wilson, Steven Ray, Brown, Rachel C., Thomas, Sarah A., Bloom, Stephen R., and Dhillo, Waljit S.

Subjects
Brain research, Sex -- Genetic aspects, Neuropeptides -- Properties, Limbic system -- Observations, Health care industry
Abstract

BACKGROUND. Sex, emotion, and reproduction are fundamental and tightly entwined aspects of human behavior. At a population level in humans, both the desire for sexual stimulation and the desire to bond with a partner are important precursors to reproduction. However, the relationships between these processes are incompletely understood. The limbic brain system has key roles in sexual and emotional behaviors, and is a likely candidate system for the integration of behavior with the hormonal reproductive axis. We investigated the effects of kisspeptin, a recently identified key reproductive hormone, on limbic brain activity and behavior. METHODS. Using a combination of functional neuroimaging and hormonal and psychometric analyses, we compared the effects of kisspeptin versus vehicle administration in 29 healthy heterosexual young men. RESULTS. We demonstrated that kisspeptin administration enhanced limbic brain activity specifically in response to sexual and couple-bonding stimuli. Furthermore, kisspeptin's enhancement of limbic brain structures correlated with psychometric measures of reward, drive, mood, and sexual aversion, providing functional significance. In addition, kisspeptin administration attenuated negative mood. CONCLUSIONS. Collectively, our data provide evidence of an undescribed role for kisspeptin in integrating sexual and emotional brain processing with reproduction in humans. These results have important implications for our understanding of reproductive biology and are highly relevant to the current pharmacological development of kisspeptin as a potential therapeutic agent for patients with common disorders of reproductive function. FUNDING. National Institute for Health Research (NIHR), Wellcome Trust (Ref 080268), and the Medical Research Council (MRC)., Introduction Unraveling the intrinsic links among sex, emotion, and reproduction relies on a focused exploration of putative factors. Identifying a factor that unites these fundamental components of human behavior has [...]

Published
2017
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49. Estimation of tuberculosis incidence at subnational level using three methods to monitor progress towards ending TB in India, 2015–2020

Author

Jeyashree, Kathiresan, primary, Thangaraj, Jeromie, additional, Rade, Kiran, additional, Modi, Bhavesh, additional, Selvaraju, Sriram, additional, Velusamy, Saravanakumar, additional, Akhil, Sasidharan, additional, Vijayageetha, Mathavaswami, additional, Sudha Rani, Dhanapal, additional, Sabarinathan, Ramasamy, additional, Manikandanesan, Sakthivel, additional, Elumalai, Rajalakshmi, additional, Natarajan, Meenakumari, additional, Joseph, Bency, additional, Mahapatra, Amarendra, additional, Shamim, Almas, additional, Shah, Amar, additional, Bhardwaj, Ashok, additional, Purty, Anil, additional, Vadera, Bhavin, additional, Sridhar, Anand, additional, Chowdhury, Aniket, additional, Shafie, Asif, additional, Choudhury, Avijit, additional, Dhrubjyoti, Deka, additional, Solanki, Hardik, additional, Sirmanwar, Krushna, additional, Khaparde, Kshitij, additional, Parmar, Malik, additional, Dahiya, Nisha, additional, Debdutta, Parija, additional, Ahmed, Quazi, additional, Ramachandran, Ranjani, additional, Prasad, Ranjeet, additional, Shinde, Rohini, additional, Baruah, Rupali, additional, Chauhan, Sandeep, additional, Bharaswadkar, Sandip, additional, Achanta, Shanta, additional, Sharath, Burugina Nagaraja, additional, Balakrishnan, Shibu, additional, Chandra, Shivani, additional, Khumukcham, Sophia, additional, Mandal, Sudarsan, additional, Chalil, Sumitha, additional, Shah, Vaibhav, additional, Roddawar, Venkatesh, additional, Rao, Raghuram, additional, Sachdeva, Kuldeep, additional, and Murhekar, Manoj, additional

Published
2022
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50. Obituary

Author

Shah, Amar

Subjects
Health
Abstract

Byline: Amar. Shah Dr. Nikhilesh Bhattacharjee was born on April 1, 1945. He completed his MBBS from Gauhati Medical College in 1966. He did his MS General Surgery in 1971 [...]

Published
2020

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