Evidence flow graph methods for validation and verification of expert systems

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could affect the output from a set of rules

Learning to Reconstruct People in Clothing from a Single RGB Camera

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach

Multilateral inversion of A_r, C_r and D_r basic hypergeometric series

In [Electron. J. Combin. 10 (2003), #R10], the author presented a new basic hypergeometric matrix inverse with applications to bilateral basic hypergeometric series. This matrix inversion result was directly extracted from an instance of Bailey's very-well-poised 6-psi-6 summation theorem, and involves two infinite matrices which are not lower-triangular. The present paper features three different multivariable generalizations of the above result. These are extracted from Gustafson's A_r and C_r extensions and of the author's recent A_r extension of Bailey's 6-psi-6 summation formula. By combining these new multidimensional matrix inverses with A_r and D_r extensions of Jackson's 8-phi-7 summation theorem three balanced very-well-poised 8-psi-8 summation theorems associated with the root systems A_r and C_r are derived.Comment: 24 page

“If You Are Old Enough to Die for Your Country, You Should Be Able to Get a Pinch of Snuff”: Views of Tobacco 21 Among Appalachian Youth

Background: Multiple strategies have been utilized in attempts to decrease the prevalence of youth tobacco use. One strategy, raising the minimum legal sale age (MLSA) of tobacco products to 21, known as Tobacco 21, has recently gained popularity. Tobacco 21 legislation targets youth tobacco use by obstructing two main sources of youth tobacco products: stores and older friends. Although these sources are the most common for youth across the nation, regional differences have not been explored. Further, youth perspectives about raising the tobacco MLSA have not been considered. Youth may help identify potential challenges to implementing tobacco control measures, as well as suggest alternatives for intervention, thus helping to shape successful tobacco control policies. Study Aim: This study aimed to 1) examine youth perspectives on raising the tobacco minimum legal sale age to 21 and 2) identify common sources of tobacco products among middle and high school students living in rural, low-income Appalachian communities. Methods: A cross-sectional survey about perceptions and use of tobacco products was conducted with students in the Appalachian regions of Kentucky and North Carolina (N=426). Questions were asked concerning perspectives on the effect of Tobacco 21 implementation. Descriptive statistics characterized participants by Tobacco 21 perspectives. Participants were given the opportunity to further expand upon their opinions in an open-ended format. Results: The majority (58.7%) of participants responded that the same number of youth would use tobacco if the legal purchase age were raised, followed by responses that fewer would use (28.9%) and more would use (12.4%). Significant differences emerged based on tobacco use status (p\u3c.05), friends’ tobacco use (p\u3c.001), and whether participants identified family members as sources of youth tobacco products (p=.047). When given the opportunity to expand upon their views concerning the implementation of Tobacco 21 laws in their communities, many respondents cited poor enforcement of tobacco MLSAs at stores, continued access to tobacco products from family members and friends, and the overall abundance of tobacco in their communities as potential barriers to the successful implementation. Conclusion: Fewer than one-third of participants believed that Tobacco 21 legislation would succeed in reducing the prevalence of youth tobacco use. Perspectives on the effect of Tobacco 21 legislation were related to personal tobacco use, exposure to tobacco users, and beliefs that family members provide tobacco products to youth. Open-ended responses identify potential obstacles in implementing Tobacco 21 legislation in Appalachia. Future research should attempt to include youth perspectives when designing and implementing tobacco control policies and examine family members as sources of tobacco products for youth

Multi-Garment Net: {L}earning to Dress {3D} People from Images

We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments

Recovery of Non-ferrous Metallic Values from Metallurgical Wastes

With the increased industrial expansion and ever incre-asing consumption of non-ferrous metals in India, utili-sation of low grade and complex ores, recovery of metals from waste products like slag, ashes and dross, apart from conservation of the non-ferrous metals not available in the country, by their judicious uses and by substitution, wherever possible, is a matter of great importance

Recovery on non-ferrous metallic values from metallurgical wastes

WITH the increased tempo of industrial expansion and ever-increasing consumption of common nonferrous metals like Al, Cu, Pb, Zn, Sri in India, conservation, substitution and reclamation from waste products and substandard raw materials are of paramount importance for the country, not only to tide over the present crisis but also in the larger interests of economic growth and self-sufficiency. Self-sufficiency can be attained by developing processes for the utilization of low grade and complex ores,recovery of metals from waste products, like slags, ashes, drosses, apart from conservation of the non-ferrous metals not available in the country, by their judicious use and also by substitution wherever possible

{LoopReg}: {S}elf-supervised Learning of Implicit Surface Correspondences, Pose and Shape for {3D} Human Mesh Registration

We address the problem of fitting 3D human models to 3D scans of dressed humans. Classical methods optimize both the data-to-model correspondences and the human model parameters (pose and shape), but are reliable only when initialized close to the solution. Some methods initialize the optimization based on fully supervised correspondence predictors, which is not differentiable end-to-end, and can only process a single scan at a time. Our main contribution is LoopReg, an end-to-end learning framework to register a corpus of scans to a common 3D human model. The key idea is to create a self-supervised loop. A backward map, parameterized by a Neural Network, predicts the correspondence from every scan point to the surface of the human model. A forward map, parameterized by a human model, transforms the corresponding points back to the scan based on the model parameters (pose and shape), thus closing the loop. Formulating this closed loop is not straightforward because it is not trivial to force the output of the NN to be on the surface of the human model - outside this surface the human model is not even defined. To this end, we propose two key innovations. First, we define the canonical surface implicitly as the zero level set of a distance field in R3, which in contrast to morecommon UV parameterizations, does not require cutting the surface, does not have discontinuities, and does not induce distortion. Second, we diffuse the human model to the 3D domain R3. This allows to map the NN predictions forward,even when they slightly deviate from the zero level set. Results demonstrate that we can train LoopRegmainly self-supervised - following a supervised warm-start, the model becomes increasingly more accurate as additional unlabelled raw scans are processed. Our code and pre-trained models can be downloaded for research