A Case for a Programmable Edge Storage Middleware

Edge computing is a fast-growing computing paradigm where data is processed at the local site where it is generated, close to the end-devices. This can benefit a set of disruptive applications like autonomous driving, augmented reality, and collaborative machine learning, which produce incredible amounts of data that need to be shared, processed and stored at the edge to meet low latency requirements. However, edge storage poses new challenges due to the scarcity and heterogeneity of edge infrastructures and the diversity of edge applications. In particular, edge applications may impose conflicting constraints and optimizations that are hard to be reconciled on the limited, hard-to-scale edge resources. In this vision paper we argue that a new middleware for constrained edge resources is needed, providing a unified storage service for diverse edge applications. We identify programmability as a critical feature that should be leveraged to optimize the resource sharing while delivering the specialization needed for edge applications. Following this line, we make a case for eBPF and present the design for Griffin - a flexible, lightweight programmable edge storage middleware powered by eBPF

Equilibrium Piezoelectric Potential Distribution in a Deformed ZnO Nanowire

The equilibrium piezoelectric potential distribution in a deformed ZnO semiconductive nanowire has been systematically investigated in order to reveal its dependence on the donor concentration, applied force, and geometric parameters. In particular, the donor concentration markedly affects the magnitude and distribution of the electric potential. At a donor concentration of ND >10 18 cm -3 , the piezopotential is almost entirely screened. Among the other parameters, a variation in the length of the nanowire does not signifi cantly affect the potential distribution

Summary Statistic Privacy in Data Sharing

We study a setting where a data holder wishes to share data with a receiver, without revealing certain summary statistics of the data distribution (e.g., mean, standard deviation). It achieves this by passing the data through a randomization mechanism. We propose summary statistic privacy, a metric for quantifying the privacy risk of such a mechanism based on the worst-case probability of an adversary guessing the distributional secret within some threshold. Defining distortion as a worst-case Wasserstein-1 distance between the real and released data, we prove lower bounds on the tradeoff between privacy and distortion. We then propose a class of quantization mechanisms that can be adapted to different data distributions. We show that the quantization mechanism's privacy-distortion tradeoff matches our lower bounds under certain regimes, up to small constant factors. Finally, we demonstrate on real-world datasets that the proposed quantization mechanisms achieve better privacy-distortion tradeoffs than alternative privacy mechanisms

The effect of add power on distance vision with Acuvue bifocal contact lenses

This study was designed to determine if subjective evaluation of quality of vision could be correlated with reduction in high and low contrast acuity scores. LogMAR visual acuities of 20 non-presbyopic subjects were measured using high and low-contrast Bailey- Lovie charts. Each subject wore an Acuvue Bifocal contact lens with add powers +.1.00, +1.50 , +2.00, and +2.50, each optimized for best distance acuity. Subjects showed significantly decreased acuities with increasing add; a low-contrast target heightened this effect. Subjects reported a reduction in quality of distance vision, increasing fluctuation, ghosting/shadows, and halos around lights that correlated with increasing add power. Our findings suggest that the Acuvue Bifocal contact lens may be expected to perform best for low-to-moderate presbyopes, and that clinicians should anticipate decreased low contrast acuity and reduced overall quality of vision as add powers are increased

CRISPR activation screening of dormant genes to improve secretory capacity in CHO cells

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Community structure of tenebrionid beetles in the Ulan Buh Desert (Inner Mongolia, China) (Coleoptera: Tenebrionidae)

Tenebrionids are a conspicuous faunal component of Central Asian deserts, but little is known about their community ecology. We investigated how tenebrionid community structure varied along a vegetational gradient in the Ulan Buh Desert (Gobi Desert). Sampling was done with pitfall traps in three sites with different vegetation cover. Species abundance distributions were fitted by the geometric series model, which expresses the "niche pre-emption" hypothesis. Community structure was investigated using different measures of diversity (number of species, Margaleff richness and Shannon-Weaner index), dominance (Simpson and Berger-Parker indexes) and evenness (Pielou's index). The observed tenebrionid species richness was similar to that known from other Gobi Desert sites. The three investigated sites have similar species-abundance patterns, but the most dominant species varied among them. This suggests that the local environment operates a filtering action on the same basic fauna, allowing different species to dominate under different conditions. Overall, the highest total abundance was observed in the true desert site, however this site had a community structure similar to that observed in the site with more vegetation. By contrast, the investigated site with intermediate conditions showed a higher diversity and evenness, and a lower dominance. Thus, intermediate conditions of plant cover favour tenebrionid diversity, whereas a dense cover or a very sparse cover increases the dominance

Thermally-reconfigurable metalens

Thanks to the compact design and multi-functional light-manipulation capabilities, reconfigurable metalenses, which consist of arrays of sub-wavelength meta-atoms, offer unique opportunities for advanced optical systems, from microscopy to augmented reality platforms. Although poorly explored in the context of reconfigurable metalens, thermo-optical effects in resonant silicon nanoresonators have recently emerged as a viable strategy to realize tunable meta-atoms. In this work, we report the proof-of-concept design of an ultrathin (300 nm thick) and thermo-optically reconfigurable silicon metalens operating at a fixed, visible wavelength (632 nm). Importantly, we demonstrate continuous, linear modulation of the focal-length up to 21% (from 165 $\mu$m at 20$\deg$C to 135 $\mu$m at 260$\deg$C). Operating under right-circularly polarized light, our metalens exhibits an average conversion efficiency of 26%, close to mechanically modulated devices, and has a diffraction-limited performance. Overall, we envision that, combined with machine-learning algorithms for further optimization of the meta-atoms, thermally-reconfigurable metalenses with improved performance will be possible. Also, the generality of this approach could offer inspiration for the realization of active metasurfaces with other emerging material within field of thermo-nanophotonics