Differentially Private Distributed Optimization

In distributed optimization and iterative consensus literature, a standard problem is for $N$ agents to minimize a function $f$ over a subset of Euclidean space, where the cost function is expressed as a sum $\sum f_i$. In this paper, we study the private distributed optimization (PDOP) problem with the additional requirement that the cost function of the individual agents should remain differentially private. The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to the optimal value. Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm. We observe that to achieve $\epsilon$-differential privacy the accuracy of the algorithm has the order of $O(\frac{1}{\epsilon^2})$

Hybrid Deterministic-Stochastic Methods for Data Fitting

Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements. Incremental gradient algorithms offer inexpensive iterations by sampling a subset of the terms in the sum. These methods can make great progress initially, but often slow as they approach a solution. In contrast, full-gradient methods achieve steady convergence at the expense of evaluating the full objective and gradient on each iteration. We explore hybrid methods that exhibit the benefits of both approaches. Rate-of-convergence analysis shows that by controlling the sample size in an incremental gradient algorithm, it is possible to maintain the steady convergence rates of full-gradient methods. We detail a practical quasi-Newton implementation based on this approach. Numerical experiments illustrate its potential benefits.Comment: 26 pages. Revised proofs of Theorems 2.6 and 3.1, results unchange

Collaborative learning based on a micro-webserver remote test controller

This paper presents a remote test workbench that was developed to support on-line assignments dealing with the IEEE 1149.1 standard test access port and boundary-scan architecture. The remote test controller is based on the DS80C400 networked microcontroller from Maxim-Dallas, which offers a very cost-effective solution to the development of micro-webservers enabling low complexity data acquisition and control tasks. All remote experiments are integrated into Moodle in exactly the same way as the remaining courseware that is made available to the students. The use of Moodle facilitates the implementation of collaborative learning activities based on the remote test workbench, and the development of the workbench itself is the subject of a collaborative learning project involving students from the universities of Porto in Portugal and South Australia at Adelaide

Mirror Descent and Convex Optimization Problems With Non-Smooth Inequality Constraints

We consider the problem of minimization of a convex function on a simple set with convex non-smooth inequality constraint and describe first-order methods to solve such problems in different situations: smooth or non-smooth objective function; convex or strongly convex objective and constraint; deterministic or randomized information about the objective and constraint. We hope that it is convenient for a reader to have all the methods for different settings in one place. Described methods are based on Mirror Descent algorithm and switching subgradient scheme. One of our focus is to propose, for the listed different settings, a Mirror Descent with adaptive stepsizes and adaptive stopping rule. This means that neither stepsize nor stopping rule require to know the Lipschitz constant of the objective or constraint. We also construct Mirror Descent for problems with objective function, which is not Lipschitz continuous, e.g. is a quadratic function. Besides that, we address the problem of recovering the solution of the dual problem

Circle grid fractal plate as a turbulent generator for premixed flame: an overview

This review paper focuses to ascertain a new approach in turbulence generation on the structure of premixed flames and external combustion using a fractal grid pattern. This review paper discusses the relationship between fractal pattern and turbulence flow. Many researchers have explored the fractal pattern as a new concept of turbulence generators, but researchers rarely study fractal turbulence generators on the structure premixed flame. The turbulent flow field characteristics have been studied tand investigated in a premixed combustion application. In terms of turbulence intensity, most researchers used fractal grid that can be tailored so that they can design the characteristic needed in premixed flame. This approach makes it extremely difficult to determine the exact turbulent burning velocity on the velocity fluctuation of the flow. The decision to carry out additional research on the effect circle grid fractal plate as a turbulent generator for premixed flame should depends on the blockage ratio and fractal pattern of the grid. 1

Dermal Sensory Regenerative Peripheral Nerve Interface for Reestablishing Sensory Nerve Feedback in Peripheral Afferents in the Rat

Background: Without meaningful, intuitive sensory feedback, even the most advanced myoelectric devices require significant cognitive demand to control. The dermal sensory regenerative peripheral nerve interface (DS-RPNI) is a biological interface designed to establish high-fidelity sensory feedback from prosthetic limbs. Methods: DS-RPNIs were constructed in rats by securing fascicles of residual sensory peripheral nerves into autologous dermal grafts, with the objectives of confirming regeneration of sensory afferents within DS-RPNIs and establishing the reliability of afferent neural response generation with either mechanical or electrical stimulation. Results: Two months after implantation, DS-RPNIs were healthy and displayed well-vascularized dermis with organized axonal collaterals throughout and no evidence of neuroma. Electrophysiologic signals were recorded proximal from DS-RPNI's sural nerve in response to both mechanical and electrical stimuli and compared with (1) full-thickness skin, (2) deepithelialized skin, and (3) transected sural nerves without DS-RPNI. Mechanical indentation of DS-RPNIs evoked compound sensory nerve action potentials (CSNAPs) that were like those evoked during indentation of full-thickness skin. CSNAP firing rates and waveform amplitudes increased in a graded fashion with increased mechanical indentation. Electrical stimuli delivered to DS-RPNIs reliably elicited CSNAPs at low current thresholds, and CSNAPs gradually increased in amplitude with increasing stimulation current. Conclusions: These findings suggest that afferent nerve fibers successfully reinnervate DS-RPNIs, and that graded stimuli applied to DS-RPNIs produce proximal sensory afferent responses similar to those evoked from normal skin. This confirmation of graded afferent signal transduction through DS-RPNI neural interfaces validate DS-RPNI's potential role of facilitating sensation in human-machine interfacing. Clinical Relevance Statement: The DS-RPNI is a novel biotic-abiotic neural interface that allows for transduction of sensory stimuli into neural signals. It is expected to advance the restoration of natural sensation and development of sensorimotor control in prosthetics.</p

Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand

Abstract Introduction Regenerative peripheral nerve interfaces (RPNIs) are biological constructs which amplify neural signals and have shown long-term stability in rat models. Real-time control of a neuroprosthesis in rat models has not yet been demonstrated. The purpose of this study was to: a) design and validate a system for translating electromyography (EMG) signals from an RPNI in a rat model into real-time control of a neuroprosthetic hand, and; b) use the system to demonstrate RPNI proportional neuroprosthesis control. Methods Animals were randomly assigned to three experimental groups: (1) Control; (2) Denervated, and; (3) RPNI. In the RPNI group, the extensor digitorum longus (EDL) muscle was dissected free, denervated, transferred to the lateral thigh and neurotized with the residual end of the transected common peroneal nerve. Rats received tactile stimuli to the hind-limb via monofilaments, and electrodes were used to record EMG. Signals were filtered, rectified and integrated using a moving sample window. Processed EMG signals (iEMG) from RPNIs were validated against Control and Denervated group outputs. Results Voluntary reflexive rat movements produced signaling that activated the prosthesis in both the Control and RPNI groups, but produced no activation in the Denervated group. Signal-to-Noise ratio between hind-limb movement and resting iEMG was 3.55 for Controls and 3.81 for RPNIs. Both Control and RPNI groups exhibited a logarithmic iEMG increase with increased monofilament pressure, allowing graded prosthetic hand speed control (R2 = 0.758 and R2 = 0.802, respectively). Conclusion EMG signals were successfully acquired from RPNIs and translated into real-time neuroprosthetic control. Signal contamination from muscles adjacent to the RPNI was minimal. RPNI constructs provided reliable proportional prosthetic hand control.https://deepblue.lib.umich.edu/bitstream/2027.42/146521/1/12984_2018_Article_452.pd

Nebulin and Titin Modulate Cross-bridge Cycling and Length-dependent Calcium Sensitivity

Various mutations in the structural proteins nebulin and titin that are present in human disease are known to affect the contractility of striated muscle. Loss of nebulin is associated with reduced actin filament length and impairment of myosin binding to actin, whereas titin is thought to regulate muscle passive elasticity and is likely involved in length-dependent activation. Here, we sought to assess the modulation of muscle function by these sarcomeric proteins by using the computational platform muscle simulation code (MUSICO) to quantitatively separate the effects of structural changes, kinetics of cross-bridge cycling, and calcium sensitivity of the thin filaments. The simulations show that variation in thin filament length cannot by itself account for experimental observations of the contractility in nebulin-deficient muscle, but instead must be accompanied by a decreased myosin binding rate. Additionally, to match the observed calcium sensitivity, the rate of TnI detachment from actin needed to be increased. Simulations for cardiac muscle provided quantitative estimates of the effects of different titin-based passive elasticities on muscle force and activation in response to changes in sarcomere length and interfilament lattice spacing. Predicted force–pCa relations showed a decrease in both active tension and sensitivity to calcium with a decrease in passive tension and sarcomere length. We conclude that this behavior is caused by partial redistribution of the muscle load between active muscle force and titin-dependent passive force, and also by redistribution of stretch along the thin filament, which together modulate the release of TnI from actin. These data help advance understanding of how nebulin and titin mutations affect muscle function