Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis

We study finite-sum distributed optimization problems involving a master node and $n-1$ local nodes under the popular $\delta$-similarity and $\mu$-strong convexity conditions. We propose two new algorithms, SVRS and AccSVRS, motivated by previous works. The non-accelerated SVRS method combines the techniques of gradient sliding and variance reduction and achieves a better communication complexity of $\tilde{\mathcal{O}}(n {+} \sqrt{n}\delta/\mu)$ compared to existing non-accelerated algorithms. Applying the framework proposed in Katyusha X, we also develop a directly accelerated version named AccSVRS with the $\tilde{\mathcal{O}}(n {+} n^{3/4}\sqrt{\delta/\mu})$ communication complexity. In contrast to existing results, our complexity bounds are entirely smoothness-free and exhibit superiority in ill-conditioned cases. Furthermore, we establish a nearly matched lower bound to verify the tightness of our AccSVRS method.Comment: Camera-ready version for NeurIPS 202

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

Mesoporous silica nanoparticles (MSNs) containing vinyl-, propyl-, isobutyl- and phenyl functionalized monolayers were reported. These functionalized MSNs were prepared via molecular self-assembly of organosilanes on the mesoporous supports. The relative surface coverage of the organic monolayers can reach up to 100% (about 5.06 silanes/nm(2)). These monolayer functionalize MSNs were analyzed by a number of techniques including transmission electron microscope, fourier transform infrared spectroscopy, X-ray diffraction pattern, cross-polarized Si(29) MAS NMR spectroscopy, and nitrogen sorption measurement. The main elements (i.e., the number of absorbed water, the reactivity of organosilanes, and the stereochemistry of organosilane) that greatly affected the surface coverage and the quality of the organic functionalized monolayers on MSNs were fully discussed. The results show that the proper amount of physically absorbed water, the use of high active trichlorosilanes, and the functional groups with less steric hindrance are essential to generate MSNs with high surface coverage of monolayers

Development of A 16:1 serializer for data transmission at 5 Gbps

Radiation tolerant, high speed and low power serializer ASIC is critical for optical link systems in particle physics experiments. Based on a commercial 0.25 μm silicon-onsapphire CMOS technology, we design a 16:1 serializer with 5 Gbps serial data rate. This ASIC has been submitted for fabrication. The post-layout simulation indicates the deterministic jitter is 54 ps (pk-pk) and random jitter is 3 ps (rms). The power consumption of the serializer is 500 mW. The design details and post layout simulation results are presented in this paper