128 research outputs found
Vizard: A Metadata-hiding Data Analytic System with End-to-End Policy Controls
Owner-centric control is a widely adopted method for easing owners\u27 concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners\u27 data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce , a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of for large-scale and metadata-hiding analytics over data streams
Building a digital twin of EDFA: a grey-box modeling approach
To enable intelligent and self-driving optical networks, high-accuracy
physical layer models are required. The dynamic wavelength-dependent gain
effects of non-constant-pump erbium-doped fiber amplifiers (EDFAs) remain a
crucial problem in terms of modeling, as it determines optical-to-signal noise
ratio as well as the magnitude of fiber nonlinearities. Black-box data-driven
models have been widely studied, but it requires a large size of data for
training and suffers from poor generalizability. In this paper, we derive the
gain spectra of EDFAs as a simple univariable linear function, and then based
on it we propose a grey-box EDFA gain modeling scheme. Experimental results
show that for both automatic gain control (AGC) and automatic power control
(APC) EDFAs, our model built with 8 data samples can achieve better performance
than the neural network (NN) based model built with 900 data samples, which
means the required data size for modeling can be reduced by at least two orders
of magnitude. Moreover, in the experiment the proposed model demonstrates
superior generalizability to unseen scenarios since it is based on the
underlying physics of EDFAs. The results indicate that building a customized
digital twin of each EDFA in optical networks become feasible, which is
essential especially for next generation multi-band network operations
Force-modulated reductive elimination from platinum(ii) diaryl complexes
Coupled mechanical forces are known to drive a range of covalent chemical reactions, but the effect of mechanical force applied to a spectator ligand on transition metal reactivity is relatively unexplored. Here we quantify the rate of C(sp(2))–C(sp(2)) reductive elimination from platinum(ii) diaryl complexes containing macrocyclic bis(phosphine) ligands as a function of mechanical force applied to these ligands. DFT computations reveal complex dependence of mechanochemical kinetics on the structure of the force-transducing ligand. We validated experimentally the computational finding for the most sensitive of the ligand designs, based on MeOBiphep, by coupling it to a macrocyclic force probe ligand. Consistent with the computations, compressive forces decreased the rate of reductive elimination whereas extension forces increased the rate relative to the strain-free MeOBiphep complex with a 3.4-fold change in rate over a ∼290 pN range of restoring forces. The calculated natural bite angle of the free macrocyclic ligand changes with force, but (31)P NMR analysis and calculations strongly suggest no significant force-induced perturbation of ground state geometry within the first coordination sphere of the (P–P)PtAr(2) complexes. Rather, the force/rate behavior observed across this range of forces is attributed to the coupling of force to the elongation of the O⋯O distance in the transition state for reductive elimination. The results suggest opportunities to experimentally map geometry changes associated with reactions in transition metal complexes and potential strategies for force-modulated catalysis
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
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