Maximum a Posteriori Estimation in Graphical Models Using Local Linear Approximation

Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally encodes the conditional independence relationship among variables. However, maximum a posteriori (MAP) estimation is challenging under hierarchical prior models, and traditional numerical optimization routines or expectation--maximization algorithms are difficult to implement. To this end, our contribution is a novel local linear approximation scheme that circumvents this issue using a very simple computational algorithm. Most importantly, the condition under which our algorithm is guaranteed to converge to the MAP estimate is explicitly stated and is shown to cover a broad class of completely monotone priors, including the graphical horseshoe. Further, the resulting MAP estimate is shown to be sparse and consistent in the $\ell_2$-norm. Numerical results validate the speed, scalability, and statistical performance of the proposed method

Efficient Graphics Representation with Differentiable Indirection

We introduce differentiable indirection -- a novel learned primitive that employs differentiable multi-scale lookup tables as an effective substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.Comment: Project website: https://sayan1an.github.io/din.htm

Parametric Optimization of Re-refining of Waste Lubricating Oil Using Bio-flocculant via Taguchi Approach

Over the past few decades, recycling used lubricants have drawn much attention as a cleaner technique. The current study focuses on the fabrication and application of bio flocculant (sodium alginate) from brown algae (Sargassum Muticum) for the refining of waste lubricating oil. Further the work illustrates on the optimization of the four process parameters like refining time, refining temperature, solvent-to-waste oil ratio, and flocculant dosage at three different levels (low, intermediate and high) using Taguchi approach during the process of refining of waste lubricating oil by clean and environmental friendly extraction flocculation method. The optimized parameters for maximization of the yield (91.31 %) were observed at refining time of 60 minutes, refining temperature of 80 ?, a solvent-to-waste oil ratio of 3:1, and a flocculant dosage of 1 g/kg of solvent. A good fit of the model could be achieved with a R2 of 0.9938 and p value of 0.018. The re-refined lubricating oil had a flash point, pour point, kinematic viscosity@40 ? and 100 ? of 234 ?, -33 ?,155.21 cSt and 17.11 cSt which are comparable to the virgin lubricating oil and hence refined oil can remarkably be used for specific purpose in automotive engine after addition of requisite amount of additives

A numerical study of frictional contact

Friction is a complex phenomenon resulting from elastic and plastic deformations coupled with molecular interaction along the contact boundary. When two surfaces touch, their roughness, and normal force determines the actual area under contact, governing the process of deformation and molecular interaction. A typical macroscopic interaction may involve millions of microscopic contacts and the aggregate of these forces give rise to the phenomenon of friction. In this thesis, our goal is to simulate the phenomenon of friction assuming unlubricated contact and elastic deformation at the contact asperities. We collect data by varying many parameters that affect friction between two surfaces and build a function approximator exploiting the correlation in data. Such an approximator is a computationally inexpensive, versatile and more accurate substitute for friction coefficient tables currently in use with various physically based simulators.La friction est un phénomène complexe résultant de déformations élastiques et plastiques couplées à une interaction moléculaire le long de la frontière de contact. Lorsque deux surfaces se touchent, leur rugosité et leur force normale déterminent la surface réelle en contact qui régit le processus de déformation et d'interaction moléculaire. Une interaction macroscopique typique peut impliquer des millions de contacts microscopiques, c'est l'agrégation de ces forces qui provoque le phénomène de friction. Dans cette thèse, notre objectif est de simuler le phénomène de frottement en supposant un contact non lubrifié ainsi qu'une déformation élastique aux aspérités de contact. Nous recueillons des données en faisant varier de nombreux paramètres qui affectent le frottement entre deux surfaces et construisons une approximation exploitant la corrélation des données. Un tel approximation est peu coûteux, polyvalent et plus précis que les tables de coefficients de frottement actuellement utilisées dans divers simulateurs physiques

Development and calibration of a cost-effective piezo-electric industrial dynomometer

Force is one of the major key parameters in a cutting process. The measurement of this parameter allows for assessment of the machining operation as well as to monitor tool condition (wear, breakage, etc.) and machine errors (looseness, backlash, etc.). The low cost option for measuring force in manufacturing is to use a load cell which is based on strain gauges. This technology has two drawbacks; firstly, strain gauges are not sensitive enough compared to other technologies and will not detect subtle changes in the cutting forces. Secondly, due to their design, load cells are mounted far from the cutting tool which means that the measured force will be not be representative of the actual cutting force. In the research environment, dynamometers are the most common technology that is used for measuring cutting forces in machine tools due to their high sensitivity. These dynamometers can also be mounted directly to the tool holders. They are based on four piezo-electric sensors which are preloaded between top and bottom plates like a sandwich. This dynamometer technology has not found its way into the production line, as it is relatively expensive (around 50,000 Singapore Dollars). Research is going on to develop a similar design of these dynamometers based on a single force sensor. The use of one sensor means that a calibration with a real dynamometer should be performed. In this project a force is applied using a robot arm on the customized dyno and then the readings recorded arc utilized to calibrate and compare them. In this research study, calibration is achieved using the least squares method and artificial neural networks followed by further analyses.Master of Science (Aerospace Engineering

Comprehensive analysis of reclamation of spent lubricating oil using green solvent: RSM and ANN approach

Waste lubricating oil (WLO) is the most significant liquid hazardous waste, and indiscriminate disposal of waste lubricating oil creates a high risk to the environment and ecology. Present investigation emphasizes the re-refining of used automobile engine oil using the extraction-flocculation approach to reduce environmental hazards and convert the waste to energy. The extraction-flocculation process was modeled and optimized using response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA). The present study assessed parametric effects of refining time, refining temperature, solvent to waste oil ratio, and flocculant dosage. Experimental findings showed that the percentage of yield of recovered oil is to the tune of 86.13%. With the Central Composite Design approach, the maximum percentage of extracted oil is 85.95%, evaluated with 80 minutes of refining time, 50.17 C refining temperature, 7:1 solvent to waste oil ratio and flocculant dosage of 3 g/kg of solvent and 86.71% with 79.97 minutes refining time, 55.53 C refining temperature, 4.89:1 g/g solvent to waste oil ratio, 2.99 g/kg of flocculant concentration with Artificial Neural Network. A comparison shows that the ANN gives better results than the CCD approach. Physico-chemical properties of the recovered lube oil are comparable with the properties of fresh lubricating oil

Analysis of temperature dependent electrical performance of Al/CuO/ITO Schottky barrier diode and explanation of inhomogeneous barrier heights by double Gaussian distribution

Here temperature dependent electrical properties of two differently synthesized CuO nanoparticles (hydrothermal (CuO (H)) and reflux (CuO (R))) based Schottky barrier diodes (SBDs) have been investigated. Both the materials based devices show an abnormal decrease in the ideality factor (η) and an increase in the barrier height (BH) with the increase in temperature. Experimentally obtained Richardson constant (A*) for both materials based devices show much lower values than the theoretical values. This discrepancy in A* due to the presence of inhomogeneities at the barrier height (BH) of metal-semiconductor (MS) interface has been analyzed by assuming double Gaussian distribution (GD) of the BHs.The mean BH values (ϕb0¯) for CuO (H) based devices obtained as 1.18eV and 1.07eV with standard deviation 0.18V and 0.15 V respectively which are less than the values of its counterpart. The obtained values of A* from the modified plot for both SBDs are in close agreement with the theoretical values. This study portrays a successful explanation of temperature dependent current-voltage characteristics of these two SBDs assuming double GD of the BHs

Assessing sustainability focus across global banks

Global banks play intermediary and even direct roles in achievement of the UN Sustainable Development Goals (SDGs). However, developmental progress is complex to measure due to siloed, varied and even non-transparent, ambiguous, non-standardized ways of calibration and reporting. This research explores public disclosures (sustainability, CSR, and annual reports, press releases, website, and others) of fifty banks across nine geo-segments over five calendar years (2018–2023). Inductive methods, co-word assessments, content analysis are deployed to develop qualitative commentaries indicating geo-specific performances of the banks. The research findings indicate goal-specific attribution and discovery of motives and initiatives. This validates how motives in embracing the SDGs vary and relate to achievement of – (A) core business objectives; (B) support and financing of other industries, organizations, and governments in sustainable initiatives; and (C) corporate citizenship, altruistic and ethical considerations. Methodological approaches to calibrate findings across seventeen SDGs help identify benchmark practices, understand complementary actions, and required focus for banks and other industries. This is relevant to geo-specific sustainability challenges, trade-offs, and requirements. The findings can guide public policies and regulations, empower banks and other institutions to accelerate awareness and evaluate effectiveness towards sustainable developments

Anomalous Dynamics in <i>tert</i>-Butyl Alcohol–Water and Trimethylamine <i>N</i>‑Oxide–Water Binary Mixtures: A Femtosecond Transient Absorption Study

In this article, we have investigated the unusual dynamics of <i>tert</i>-butyl alcohol (TBA)–water and trimethylamine <i>N</i>-oxide (TMAO)–water binary mixtures using solvation dynamics as a tool. For this purpose, femtosecond transient absorption spectroscopy has been employed. Although these two molecules are isosteres to each other, a significant difference in water dynamics has been observed. The solvation times in TBA–water binary mixtures are found to be between 1.5 and 15.5 ps. On the contrary, we have observed very fast dynamics in TMAO–water binary mixtures (between 210 and 600 fs). Interestingly, unusual retardation in dynamics is observed at 0.10 mole fraction of TBA and TMAO in both the binary mixtures