The State and the Co-Operative Movement in the Bombay Presidency 1880-1930.

The Deccan Riots of 1875 were believed to have been brought about by the large scale transfer of land, through foreclosure, from the cultivating to the money-lending classes. As a remedy, Sir William Wedderburn suggested the establishment of State-backed capitalistic agricultural banks. But the India Office felt that his scheme would require too much State intervention. The Indian Co-operative Societies Act reflects a more positive attitude to the role of the State, although in 1904 it was hoped that the societies which were to be set up by official 'Registrars' would eventually become entirely independent. Local management and thrift were emphasized. The alternative scheme of agricultural banks remained under consideration, however. The Government of India appear to have dismissed it only when the Egyptian Agricultural Bank did not succeed; what became the non-official Bombay Provincial Co-operative Bank was originally intended to be an agricultural bank. The fostering of democracy through village co-operatives was especially stressed in Bombay. Also, Indian 'Honorary Organizers', and a 'Co-operative Institute', wore given an important role in the movement, although much of the in-initiative remained with the Registrar and his staff. By the later twenties, however, the attempt to foster democracy at village level through co-operatives appeared to have largely failed? villages in Bombay were not 'little Republics'. The honorary workers, many of them now nationalists, were becoming dilettante in their attitudes to co-operation, too. Control therefore increasingly passed into the hands of the State. But at the purely economic level there had been some success in Bombay, The well-managed Provincial Bank was in some areas satisfactorily fulfilling the ryots' credit and marketing needs, although its system of branches, each having dependent societies, was perhaps not completely co-operative. The thesis has been largely based on documents found in the office of the Bombay Registrar, and elsewhere in India

Codistributed Lineages of Feather Lice Show Different Phylogenetic Patterns

Recent molecular phylogenies have suggested that hawks (Accipitridae) and falcons (Falconidae) form 2 distantly related groups within birds. Avian feather lice have often been used as a model for comparing host and parasite phylogenies, and in some cases there is significant congruence between them. Using 1 mitochondrial and 3 nuclear genes, I inferred a phylogeny for the feather louse genus Degeeriella (which are all obligate raptor ectoparasites) and related genera. This phylogeny indicated that Degeeriella is polyphyletic, with lice from falcons and hawks forming 2 distinct clades. Falcon lice were sister to lice from African woodpeckers, while Capraiella, a genus of lice from rollers lice, was embedded within Degeeriella from hawks. This phylogeny showed significant geographic structure, with host geography playing a larger role than host taxonomy in explaining louse phylogeny, particularly within clades of closely related lice. However, the louse phylogeny broadly reflects host phylogeny, for example Accipiter lice form a distinct clade. Unlike most bird species, individual kingfisher species (Aves: Alcidae) are typically parasitized by 1 of 3 genera of lice (Insecta: Phthiraptera). These lice partition hosts by subfamily: Alcedoecus and Emersoniella parasitize Daceloninae whereas Alcedoffula parasitizes both Alcedininae and Cerylinae. While Emersoniella is geographically restricted, Alcedoecus and Alcedoffula are widespread. I used 2 molecular markers, the nuclear gene EF-1α and mitochondrial gene COI to infer phylogenies for both widespread genera of kingfisher lice, Alcedoffula and Alcedoecus. Additinally, I combined published host records with new host records reported here and used ancestral state reconstruction to identify patterns of host parasitism. Lastly, I compared louse phylogenies to host phylogenies to reconstruct their cophylogenetic history. I determined there are 2 distinct clades within Alcedoffula, 1 infesting Alcedininae, and the other infesting Cerylinae. Ancestral state reconstruction of kingfisher lice across the kingfisher phylogeny showed Alcedoecus and Emersoniella parasitize distinct clades within the kingfisher subfamily Daceloninae, and a single host switch by Alcedoecus onto the portion of the Daceloninae clade, which typically hosts Emersoniella. Cophylogenetic analysis indicated that although Alcedoecus and the lineage of Alcedoffula occurring on Alcedininae did not show evidence of cospeciation, the lineage of Alcedoffula occurring on Cerylinae showed strong evidence of cospeciation. The chewing louse genus Colpocephalum parasitizes nearly a dozen distantly related orders of birds. Such a broad host range is uncommon among lice. However, the monophyly of the genus Colpocephalum with respect to a group of morphologically similar genera has never been tested. Using 1 nuclear and 1 mitochondrial gene, I inferred a phylogeny for 54 lice sampled from across the Colpocephalum-complex. The resulting phylogeny demonstrates several lineages were restricted to single host orders. These lineages corresponded to previously described genera. Maddison-Slatkin tests were performed on the resulting phylogeny and showed that host order, host family, and biogeographic region had significant phylogenetic signals when mapped onto the Colpocephalum-complex phylogeny. A PARAFIT analysis comparing the overall Colpocephalum-complex phylogeny to a host phylogeny revealed significant congruence between host and parasite trees. I also compared the cophylogenetic history of Colpocephalum and their hosts to that of a second distantly related feather louse genus, Degeeriella, which also infests diurnal birds of prey. Using PARAFIT to identify individual host-parasite links that contributed to overall congruence, I found no evidence of correlated cophylogenetic patterns between these 2 lice groups, which suggested that their distribution patterns were shaped by divergent evolutionary processes

Protecting a Client’s Confidences: Recent Developments in Privileged Communication Between Attorneys and Accountants

oai:jlc.law-dev.library.pitt.edu:article/1The attorney-client privilege is one of the foundations of ourjurisprudence. Originally, designed to prevent attorneys from testifying against their clients, the privilege eventually evolved to reflect legal, societal, and financial complexities. This privilege depends on full disclosure and open communication between attorney and the client in order to provide competent and adequate representation. Today, attorneys often require and rely on expert guidance of accountants for various issues pertaining to litigation and transactional work.This article illustrates how the recent cases of Commissioner v. Comcast Corp. and United States v. Textron affect privileged communications in complex tax and transactional matters between attorneys and accountants retained for the purposes of client representation. The article also offers guidance on how to preserve privilege in communication between attorneys and accountants as waiver of such privilege may have significant and costly implications. At conclusion, unresolved issues pertaining to privileged communication are discussed and solutions are offered

Fatigue crack propagation in cylindrical shells

Fatigue crack propagation in cylindrical shells with fluctuating internal pressur

Computational Methods for Bayesian Inference in Complex Systems

Bayesian methods are critical for the complete understanding of complex systems. In this approach, we capture all of our uncertainty about a system’s properties using a probability distribution and update this understanding as new information becomes available. By taking the Bayesian perspective, we are able to effectively incorporate our prior knowledge about a model and to rigorously assess the plausibility of candidate models based upon observed data from the system. We can then make probabilistic predictions that incorporate uncertainties, which allows for better decision making and design. However, while these Bayesian methods are critical, they are often computationally intensive, thus necessitating the development of new approaches and algorithms. In this work, we discuss two approaches to Markov Chain Monte Carlo (MCMC). For many statistical inference and system identification problems, the development of MCMC made the Bayesian approach possible. However, as the size and complexity of inference problems has dramatically increased, improved MCMC methods are required. First, we present Second-Order Langevin MCMC (SOL-MC), a stochastic dynamical system-based MCMC algorithm that uses the damped second-order Langevin stochastic differential equation (SDE) to sample a desired posterior distribution. Since this method is based on an underlying dynamical system, we can utilize existing work in the theory for dynamical systems to develop, implement, and optimize the sampler's performance. Second, we present advances and theoretical results for Sequential Tempered MCMC (ST-MCMC) algorithms. Sequential Tempered MCMC is a family of parallelizable algorithms, based upon Transitional MCMC and Sequential Monte Carlo, that gradually transform a population of samples from the prior to the posterior through a series of intermediate distributions. Since the method is population-based, it can easily be parallelized. In this work, we derive theoretical results to help tune parameters within the algorithm. We also introduce a new sampling algorithm for ST-MCMC called the Rank-One Modified Metropolis Algorithm (ROMMA). This algorithm improves sampling efficiency for inference problems where the prior distribution constrains the posterior. In particular, this is shown to be relevant for problems in geophysics. We also discuss the application of Bayesian methods to state estimation, disturbance detection, and system identification problems in complex systems. We introduce a Bayesian perspective on learning models and properties of physical systems based upon a layered architecture that can learn quickly and flexibly. We then apply this architecture to detecting and characterizing changes in physical systems with applications to power systems and biology. In power systems, we develop a new formulation of the Extended Kalman Filter for estimating dynamic states described by differential algebraic equations. This filter is then used as the basis for sub-second fault detection and classification. In synthetic biology, we use a Bayesian approach to detect and identify unknown chemical inputs in a biosensor system implemented in a cell population. This approach uses the tools of Bayesian model selection.</p

Learning a quantum computer's capability using convolutional neural networks

The computational power of contemporary quantum processors is limited by hardware errors that cause computations to fail. In principle, each quantum processor's computational capabilities can be described with a capability function that quantifies how well a processor can run each possible quantum circuit (i.e., program), as a map from circuits to the processor's success rates on those circuits. However, capability functions are typically unknown and challenging to model, as the particular errors afflicting a specific quantum processor are a priori unknown and difficult to completely characterize. In this work, we investigate using artificial neural networks to learn an approximation to a processor's capability function. We explore how to define the capability function, and we explain how data for training neural networks can be efficiently obtained for a capability function defined using process fidelity. We then investigate using convolutional neural networks to model a quantum computer's capability. Using simulations, we show that convolutional neural networks can accurately model a processor's capability when that processor experiences gate-dependent, time-dependent, and context-dependent stochastic errors. We then discuss some challenges to creating useful neural network capability models for experimental processors, such as generalizing beyond training distributions and modelling the effects of coherent errors. Lastly, we apply our neural networks to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around 2-5%)