Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative

Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity

The problem of estimating an unknown signal, x_0 ϵ R^n, from a vector y ϵ R^m consisting of m magnitude-only measurements of the form y_i = |a_ix_o|, where a_i’s are the rows of a known measurement matrix A is a classical problem known as phase retrieval. This problem arises when measuring the phase is costly or altogether infeasible. In many applications in machine learning, signal processing, statistics, etc., the underlying signal has certain structure (sparse, low-rank, finite alphabet, etc.), opening of up the possibility of recovering x_0 from a number of measurements smaller than the ambient dimension, i.e., m < n. Ideally, one would like to recover the signal from a number of phaseless measurements that is on the order of the "degrees of freedom" of the structured x_0. To this end, inspired by the PhaseMax algorithm, we formulate a convex optimization problem, where the objective function relies on an initial estimate of the true signal and also includes an additive regularization term to encourage structure. The new formulation is referred to as regularized PhaseMax. We analyze the performance of regularized PhaseMax to find the minimum number of phaseless measurements required for perfect signal recovery. The results are asymptotic and are in terms of the geometrical properties (such as the Gaussian width) of certain convex cones. When the measurement matrix has i.i.d. Gaussian entries, we show that our proposed method is indeed order-wise optimal, allowing perfect recovery from a number of phaseless measurements that is only a constant factor away from the degrees of freedom. We explicitly compute this constant factor, in terms of the quality of the initial estimate, by deriving the exact phase transition. The theory well matches empirical results from numerical simulations

Quadrotor UAV Guidence For Ground Moving Target Tracking

The studies in aerial vehicles modeling and control have been increased rapidly recently. In this paper , a coordination of two types of heterogeneous robots , namely unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) is considered. In this paper the UAV plays the role of a virtual leader for the UGVs. The system consists of a vision- based target detection algorithm that uses the color and image moment of a given target. The modeling of the vertical take off and landing vehicle will be described by using Euler - Newton equations. All of flight controller commands are directly generated based on the offset of the target from the image frame. The image processing and intelligent control algorithms such a Kalman filter and so on have been implemented on a latest computer. Matlab Simulink software has been used to test, analyze and compare the performance of the controllers in simulations

Universality Laws and Performance Analysis of the Generalized Linear Models

In the past couple of decades, non-smooth convex optimization has emerged as a powerful tool for the recovery of structured signals (sparse, low rank, etc.) from noisy linear or non-linear measurements in a variety of applications in genomics, signal processing, wireless communications, machine learning, etc.. Taking advantage of the particular structure of the unknown signal of interest is critical since in most of these applications, the dimension p of the signal to be estimated is comparable, or even larger than the number of observations n. With the advent of Compressive Sensing there has been a very large number of theoretical results that study the estimation performance of non-smooth convex optimization in such a high-dimensional setting. A popular approach for estimating an unknown signal β₀ ϵ ℝᵖ in a generalized linear model, with observations y = g(Xβ₀) ϵ ℝⁿ, is via solving the estimator β&#x0302; = arg minβ L(y, Xβ + λf(β). Here, L(•,•) is a loss function which is convex with respect to its second argument, and f(•) is a regularizer that enforces the structure of the unknown β₀. We first analyze the generalization error performance of this estimator, for the case where the entries of X are drawn independently from real standard Gaussian distribution. The precise nature of our analysis permits an accurate performance comparison between different instances of these estimators, and allows to optimally tune the hyperparameters based on the model parameters. We apply our result to some of the most popular cases of generalized linear models, such as M-estimators in linear regression, logistic regression and generalized margin maximizers in binary classification problems, and Poisson regression in count data models. The key ingredient of our proof is the Convex Gaussian Min-max Theorem (CGMT), which is a tight version of the Gaussian comparison inequality proved by Gordon in 1988. Unfortunately, having real iid entries in the features matrix X is crucial in this theorem, and it cannot be naturally extended to other cases. But for some special cases, we prove some universality properties and indirectly extend these results to more general designs of the features matrix X, where the entries are not necessarily real, independent, or identically distributed. This extension, enables us to analyze problems that CGMT was incapable of, such as models with quadratic measurements, phase-lift in phase retrieval, and data recovery in massive MIMO, and help us settle a few long standing open problems in these areas.</p

Neutralization of Lethal Potency of Tetanus Toxin using Phage Display Produced scFv Antibody

Background and Aim: Phage display technology provides a new approach for making human antibody fragments that could be applicable in passive immune therapy. We applied the use of this technology to make human single-chain variable fragments (scFvs) specific for tetanus toxin. Tetanus toxin is a neurotoxin constituted by the association of two subunits, mediates its lethal action by blocking neuromuscular vesicle docking. Methods: We previously found that six Human scFv clones inhibit toxin binding to ganglioside GT1b. This is the final report of human tetanus scFvs (scFv 8 and scFv 13) isolated from an immunized library of more than 106 scFv clones with in vivo neutralizing activity. Results: Only scFv 13 can reduce the in vivo toxicity induced by tetanus toxin. Also, scFv 8 has a weak capability of reducing the in vivo toxicity of the toxin. Conclusion: These selected ScFvs can be considered as a possible option to substitute the human tetanus immunoglobulin (HTIG) which is extensively current immunotherapy for tetanus patients. Taken together, our results suggest that the use of human tetanus scFvs may lead to a less aggressive passive immune therapy against tetanus. *Corresponding Author: Mahdi Aminian; Email: [email protected] Please cite this article as: Khalili E, Abbasi E, Aminian M. Neutralization of Lethal Potency of Tetanus Toxin using Phage Display Produced ScFv Antibody.Arch Med Lab Sci. 2021;7:(e3). https://doi.org/10.22037/amls.v7.3378

The Performance Analysis of Generalized Margin Maximizer (GMM) on Separable Data

Logistic models are commonly used for binary classification tasks. The success of such models has often been attributed to their connection to maximum-likelihood estimators. It has been shown that gradient descent algorithm, when applied on the logistic loss, converges to the max-margin classifier (a.k.a. hard-margin SVM). The performance of the max-margin classifier has been recently analyzed. Inspired by these results, in this paper, we present and study a more general setting, where the underlying parameters of the logistic model possess certain structures (sparse, block-sparse, low-rank, etc.) and introduce a more general framework (which is referred to as "Generalized Margin Maximizer", GMM). While classical max-margin classifiers minimize the $2$-norm of the parameter vector subject to linearly separating the data, GMM minimizes any arbitrary convex function of the parameter vector. We provide a precise analysis of the performance of GMM via the solution of a system of nonlinear equations. We also provide a detailed study for three special cases: ($1$) $\ell_2$-GMM that is the max-margin classifier, ($2$) $\ell_1$-GMM which encourages sparsity, and ($3$) $\ell_{\infty}$-GMM which is often used when the parameter vector has binary entries. Our theoretical results are validated by extensive simulation results across a range of parameter values, problem instances, and model structures.Comment: ICML 2020 (submitted February 2020