Gradient-tracking Based Differentially Private Distributed Optimization with Enhanced Optimization Accuracy

Privacy protection has become an increasingly pressing requirement in distributed optimization. However, equipping distributed optimization with differential privacy, the state-of-the-art privacy protection mechanism, will unavoidably compromise optimization accuracy. In this paper, we propose an algorithm to achieve rigorous $\epsilon$-differential privacy in gradient-tracking based distributed optimization with enhanced optimization accuracy. More specifically, to suppress the influence of differential-privacy noise, we propose a new robust gradient-tracking based distributed optimization algorithm that allows both stepsize and the variance of injected noise to vary with time. Then, we establish a new analyzing approach that can characterize the convergence of the gradient-tracking based algorithm under both constant and time-varying stespsizes. To our knowledge, this is the first analyzing framework that can treat gradient-tracking based distributed optimization under both constant and time-varying stepsizes in a unified manner. More importantly, the new analyzing approach gives a much less conservative analytical bound on the stepsize compared with existing proof techniques for gradient-tracking based distributed optimization. We also theoretically characterize the influence of differential-privacy design on the accuracy of distributed optimization, which reveals that inter-agent interaction has a significant impact on the final optimization accuracy. The discovery prompts us to optimize inter-agent coupling weights to minimize the optimization error induced by the differential-privacy design. Numerical simulation results confirm the theoretical predictions

Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds

Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approaches that leverage user-provided seeds to discover topic-representative terms, they are less concerned with two factors: (1) the existence of out-of-vocabulary seeds and (2) the power of pre-trained language models (PLMs). In this paper, we generalize the task of seed-guided topic discovery to allow out-of-vocabulary seeds. We propose a novel framework, named SeeTopic, wherein the general knowledge of PLMs and the local semantics learned from the input corpus can mutually benefit each other. Experiments on three real datasets from different domains demonstrate the effectiveness of SeeTopic in terms of topic coherence, accuracy, and diversity.Comment: 12 pages; Accepted to NAACL 202

A normally closed in-channel micro check valve

We present here the first surface-micromachined, normally closed, in-channel, Parylene check valve. This device is fabricated monolithically on a silicon substrate using a five-layer Parylene process. The operating structure of the check valve is a circular sealing plate on top of a ring-shaped valve seat. The sealing plate is center-anchored on top of a chamber diaphragm that is vacuum-collapsed to the bottom of the chamber in order to achieve a normally closed position. A thin gold layer on the roughened valve seat surface is used to reduce stiction between the sealing plate and the valve seat. We have achieved an in-channel check valve with a cracking (opening) pressure of 20/spl sim/40 kPa under forward bias and no measurable leakage under reverse bias up to 270 kPa. Using this design, this valve performs well in two-phase microfluidic systems (i.e. microchannel flows containing gas, liquid, or gas/liquid mixture)

Vibrational analysis of planetary gear trains by finite element method

Planetary gear trains produce several advantages, including high speed reduction, compactness, greater load sharing and higher torque to weight ratio, which are used widely in wind turbine, automobiles, robot and other applications. In some important transmission applications, the noise and vibration are key concerns in design. In this paper, a 3D dynamic contact and impact analysis model of planetary gear trains has been proposed. Tooth surface friction, backlash, tolerance of peg hole, and time-varying stiffness were considered in this dynamic model. The ANSYS / LS-DYNA were utilized to analyze the dynamic responses of gear transmission of the planetary gears. The vibration behavior of an actual gear set under dynamic loading was simulated in the dynamic model. The stiffness and elastic deformation of gear teeth are calculated using the finite element method with actual geometry and positions of the gears. The time-varying position of the carrier defined as the vibration and noise source. After impact analysis, the numerical results of vibration of carrier involved with the transient and steady states. Through the Fast Fourier Transform (FFT) methods, frequency spectrums of the transient and steady states of the calculated vibration of planet carrier are obtained for the gearbox designer to avoid the resonance zone