Janssen effect and the stability of quasi 2-D sandpiles

We present the results of three dimensional molecular dynamics study of global normal stresses in quasi two dimensional sandpiles formed by pouring mono dispersed cohesionless spherical grains into a vertical granular Hele-Shaw cell. We observe Janssen effect which is the phenomenon of pressure saturation at the bottom of the container. Simulation of cells with different thicknesses shows that the Janssen coefficient $\kappa$ is a function of the cell thickness. Dependence of global normal stresses as well as $\kappa$ on the friction coefficients between the grains ($\mu_p$) and with walls ($\mu_w$) are also studied. The results show that in the range of our simulations $\kappa$ usually increases with wall-grain friction coefficient. Meanwhile by increasing $\mu_p$ while the other system parameters are fixed, we witness a gradual increase in $\kappa$ to a parameter dependent maximal value

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Exponential martingales and changes of measure for counting processes

We give sufficient criteria for the Dol\'eans-Dade exponential of a stochastic integral with respect to a counting process local martingale to be a true martingale. The criteria are adapted particularly to the case of counting processes and are sufficiently weak to be useful and verifiable, as we illustrate by several examples. In particular, the criteria allow for the construction of for example nonexplosive Hawkes processes as well as counting processes with stochastic intensities depending on diffusion processes

Towards Viewpoint Invariant 3D Human Pose Estimation

We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100 K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

Outsourcing an image classification task raises privacy concerns, both from the image provider\u27s perspective, who wishes to keep their images confidential, and from the classification algorithm provider\u27s perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, which is secure against malicious adversaries. The novelty of EPIC is that it builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Secure Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX 2018) --the state-of-the-art in private image classification-- and it improves the total communication cost by 50 times, while achieving a 7\% higher accuracy on CIFAR-10 dataset. When benchmarked for performance, while maintaining the same CIFAR-10 accuracy as Gazelle, EPIC is 700 times faster and the communication cost is reduced by 500 times

Foam-Mat Freeze-Drying of Blueberry Juice by Using Trehalose-β-Lactoglobulin and Trehalose-Bovine Serum Albumin as Matrices

This study aimed to evaluate the effect of pure protein compounds and trehalose incorporated into blueberry juice for foam-mat freeze-drying on the foam and powder properties. Foam-mat freeze-drying (FMFD) of blueberry juice was tested at − 55 °C for 24 h. Matrices used were trehalose + β-lactoglobulin (T3BL1) and trehalose + bovine serum albumin (T3A1) and compared with maltodextrin + whey protein isolate (M3W1). Physicochemical properties of foam and powder, e.g., foam stability, foam density, moisture, rehydration time, color, particle morphology, total phenolic, and anthocyanins (total and individuals), were investigated. T3BL1 and T3A1 had more stable foam than M3W1. However, overrun of T3BL1 and T3A1 foamed were inferior to the M3W1 sample. The M3W1 sample recovered 79% powder (dry weight) and was superior to others. Rehydration time of powdered T3BL1 and T3A1, with bulk densities of 0.55–0.60 g cm−3, was the fastest (34–36 s). The blueberry powders of M3W1 showed more irregular particle size and shape, while the samples with trehalose and pure proteins generated particles of more uniform size with obvious pores. T3BL1 and T3A1 showed less redness (a*) values than the M3W1 product. All samples were considered pure red due to hue values < 90. M3W1 was superior in total phenolic content (TPC) and total monomeric anthocyanins (TMA) compared with both samples made with trehalose + β-lactoglobulin and trehalose+bovine serum albumin. Delphinidin-3-glucoside (Del3Gl) concentration was found to be higher in M3W1. Also, M3W1 had higher cyanidin-3-glucoside (Cyn3Gl) and malvidin-3-glucoside (Mal3Gl) concentration. M3W1 also prevented the degradation of these bioactive compounds better than the other FMFD samples. The use of pure proteins and trehalose as matrices in the FMFD process had little advantage compared with maltodextrin/whey protein isolate. Thus, maltodextrin/whey protein isolate seems an ideal matrix for the manufacture of FMFD blueberry