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Bayesian nonparametric hierarchical priors provide flexible models for sharing of information within and across groups. We focus on latent feature allocation models, where the data structures correspond to multisets or unbounded sparse matrices. The fundamental development in this regard is the Hierarchical Indian Buffet process (HIBP), devised by Thibaux and Jordan (2007). However, little is known in terms of explicit tractable descriptions of the joint, marginal, posterior and predictive distributions of the HIBP. We provide explicit novel descriptions of these quantities, in the Bernoulli HIBP and general spike and slab HIBP settings, which allows for exact sampling and simpler practical implementation. We then extend these results to the more complex setting of hierarchies of general HIBP (HHIBP). The generality of our framework allows one to recognize important structure that may otherwise be masked in the Bernoulli setting, and involves characterizations via dynamic mixed Poisson random count matrices. Our analysis shows that the standard choice of hierarchical Beta processes for modeling across group sharing is not ideal in the classic Bernoulli HIBP setting proposed by Thibaux and Jordan (2007), or other spike and slab HIBP settings, and we thus indicate tractable alternative priors., This is an extensive re-write and extension of arXiv:2103.11407 where variations of the results for the HIBP (but not HHIBP) were established

The accurate modeling and control of nonlinear dynamical effects are crucial for numerous robotic systems. The Koopman formalism emerges as a valuable tool for linear control design in nonlinear systems within unknown environments. However, it still remains a challenging task to learn the Koopman operator with control from data, and in particular, the simultaneous identification of the Koopman linear dynamics and the mapping between the state and Koopman spaces. Conventional approaches, based on single-level unconstrained optimization, may lack model robustness, training efficiency, and long-term predictive accuracy. This paper presents a bi-level optimization framework that jointly learns the Koopman embedding mapping and Koopman dynamics with explicit multi-step dynamical constraints, eliminating the need for heuristically-tuned loss terms. Leveraging implicit differentiation, our formulation allows back-propagation in standard learning framework and the use of state-of-the-art optimizers, yielding more stable and robust system performance over various applications compared to conventional methods., Comment: Accepted by 2023 IEEE 62nd Conference on Decision and Control (CDC)

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than $10\times$ speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation., Project Website: https://pypose.org Documentation: https://pypose.org/docs/ Tutorial: https://pypose.org/tutorials/ Source code: https://github.com/pypose/pypose

The inspection/maintenance programmes exist in most countries, aiming at vehicular emission reduction through exhaust emission monitoring and compliance policy to the extant norms. However, considering the absence of an intra-vehicle approach, the higher success rate of vehicles towards compliance policy, remains a grey area. The paper attempts to examine this issue through the application of an exhaust emission index (EEI) for petrol-driven cars. The study observed two different scales finding that the Bharat Stage emission norm scale method reports lower ranges of EEI compared with the linear scale (LS) method (EEImin-BSNS = 1.12 and EEImin-LS = 1.25; EEImax-BSNS = 20.70 and EEImax-LS = 29.54). The LS method and the maximum operator form of aggregation are recommended as these can find the highest number of non-compliant cars (21.81% and 12.03% of the ‘poor’ class, respectively) in the whole fleet tested. The EEI give a more scientific approach to vehicular emission evaluation, like what the air quality index does in the case of the ambient air quality. It helps vehicle owners know their car's emission status as a quick reference index (EEI). The accurate status of such emission further helps the policymakers affect the better phasing-out norms.

The Canopy (CNPY) family consists of four members predicted to be soluble proteins localized to the endoplasmic reticulum (ER). They are involved in a wide array of processes, including angiogenesis, cell adhesion, and host defense. CNPYs are thought to do so via regulation of secretory transport of a diverse group of proteins, such as immunoglobulin M, growth factor receptors, toll‐like receptors, and the low‐density lipoprotein receptor. Thus far, a comparative analysis of the mammalian CNPY family is missing. Bioinformatic analysis shows that mammalian CNPYs, except the CNPY1 homolog, have N‐terminal signal sequences and C‐terminal ER‐retention signals and that mammals have an additional member CNPY5, also known as plasma cell‐induced ER protein 1/marginal zone B cell‐specific protein 1. Canopy proteins are particularly homologous in four hydrophobic alpha‐helical regions and contain three conserved disulfide bonds. This sequence signature is characteristic for the saposin‐like superfamily and strongly argues that CNPYs share this common saposin fold. We showed that CNPY2, 3, 4, and 5 (termed CNPYs) localize to the ER. In radioactive pulse‐chase experiments, we found that CNPYs rapidly form disulfide bonds and fold within minutes into their native forms. Disulfide bonds in native CNPYs remain sensitive to low concentrations of dithiothreitol (DTT) suggesting that the cysteine residues forming them are relatively accessible to solutes. Possible roles of CNPYs in the folding of secretory proteins in the ER are discussed.

Bayesian nonparametric hierarchical priors are highly effective in providing flexible models for latent data structures exhibiting sharing of information between and across groups. Most prominent is the Hierarchical Dirichlet Process (HDP), and its subsequent variants, which model latent clustering between and across groups. The HDP, may be viewed as a more flexible extension of Latent Dirichlet Allocation models (LDA), and has been applied to, for example, topic modelling, natural language processing, and datasets arising in health-care. We focus on analogous latent feature allocation models, where the data structures correspond to multisets or unbounded sparse matrices. The fundamental development in this regard is the Hierarchical Indian Buffet process (HIBP), which utilizes a hierarchy of Beta processes over J groups, where each group generates binary random matrices, reflecting within group sharing of features, according to beta-Bernoulli IBP priors. To encompass HIBP versions of non-Bernoulli extensions of the IBP, we introduce hierarchical versions of general spike and slab IBP. We provide explicit novel descriptions of the marginal, posterior and predictive distributions of the HIBP and its generalizations which allow for exact sampling and simpler practical implementation. We highlight common structural properties of these processes and establish relationships to existing IBP type and related models arising in the literature. Examples of potential applications may involve topic models, Poisson factorization models, random count matrix priors and neural network models, 4 figures

Parkinson’s disease is one of the most common neurodegenerative disorders and several mutations in different genes have been identified to contribute to the disease. A loss of function parkin RING1 domain mutant (C289G) is associated with autosomal-recessive juvenile-onset Parkinsonism (AR-JP) and displays altered solubility and sequesters into aggregates. Single overexpression of almost each individual member of the Hsp40 (DNAJ) family of chaperones efficiently reduces parkin C289G aggregation and requires interaction with and activity of endogenously expressed Hsp70 s. For DNAJB6 and DNAJB8, potent suppressors of aggregation of polyglutamine proteins for which they rely mainly on an S/T-rich region, it was found that the S/T-rich region was dispensable for suppression of parkin C289G aggregation. Our data implies that different disease-causing proteins pose different challenges to the protein homeostasis system and that DNAJB6 and DNAJB8 are highly versatile members of the DNAJ protein family with multiple partially non-overlapping modes of action with respect to handling disease-causing proteins, making them interesting potential therapeutic targets.

Cloud computing refers to a paradigm shift to overall IT solutions while raising the accessibility, scalability and effectiveness through its enabling technologies. However, migrated cloud platforms and services cost benefits as well as performances are neither clear nor summarized. Globalization and the recessionary economic times have not only raised the bar of a better IT delivery models but also have given access to technology enabled services via internet. Cloud computing has vast potential in terms of lean Retail methodologies that can minimize the operational cost by using the third party based IT capabilities, as a service. It will not only increase the ROI but will also help in lowering the total cost of ownership. In this paper we have tried to compare the cloud computing cost benefits with the actual premise cost which an organization incurs normally. However, in spite of the cost benefits, many IT professional believe that the latest model i.e. "cloud computing" has risks and security concerns. This report demonstrates how to answer the following questions: (1) Idea behind cloud computing. (2) Monetary cost benefits of using cloud with respect to traditional premise computing. (3) What are the various security issues? We have tried to find out the cost benefit by comparing the Microsoft Azure cloud cost with the prevalent premise cost., 9 pages, 7 figures, Paper accepted for the 2010 International Conference on Informatics, Cybernetics, and Computer Applications (ICICCA 2010)