A Joint Model and Data Driven Method for Distributed Estimation

This paper considers the problem of distributed estimation in wireless sensor networks (WSN), which is anticipated to support a wide range of applications such as the environmental monitoring, weather forecasting, and location estimation. To this end, we propose a joint model and data driven distributed estimation method by designing the optimal quantizers and fusion center (FC) based on the Bayesian and minimum mean square error (MMSE) criterions. First, universal mean square error (MSE) lower bound for the quantization-based distributed estimation is derived and adopted as the design metric for the quantizers. Then, the optimality of the mean-fusion operation for the FC with MMSE criterion is proved. Next, by exploiting different levels of the statistic information of the desired parameter and observation noise, a joint model and data driven method is proposed to train parts of the quantizer and FC modules as deep neural networks (DNNs), and two loss functions derived from the MMSE criterion are adopted for the sequential training scheme. Furthermore, we extend the above results to the case with multi-bit quantizers, considering both the parallel and one-hot quantization schemes. Finally, simulation results reveal that the proposed method outperforms the state-of-the-art schemes in typical scenarios.Comment: in IEEE Internet of Things Journa

Quasi-Periodic Variations in X-ray Emission and Long-Term Radio Observations: Evidence for a Two-Component Jet in Sw J1644+57

The continued observations of Sw J1644+57 in X-ray and radio bands accumulated a rich data set to study the relativistic jet launched in this tidal disruption event. The X-ray light curve of Sw J1644+57 from 5-30 days presents two kinds of quasi-periodic variations: a 200 second quasi-periodic oscillation (QPO) and a 2.7-day quasi-periodic variation. The latter has been interpreted by a precessing jet launched near the Bardeen-Petterson radius of a warped disk. Here we suggest that the $\sim$ 200s QPO could be associated with a second, narrower jet sweeping the observer line-of-sight periodically, which is launched from a spinning black hole in the misaligned direction with respect to the black hole's angular momentum. In addition, we show that this two-component jet model can interpret the radio light curve of the event, especially the re-brightening feature starting $\sim 100$ days after the trigger. From the data we infer that inner jet may have a Lorentz factor of $\Gamma_{\rm j} \sim 5.5$ and a kinetic energy of $E_{\rm k,iso} \sim 3.0 \times 10^{52} {\rm erg}$, while the outer jet may have a Lorentz factor of $\Gamma_{\rm j} \sim 2.5$ and a kinetic energy of $E_{\rm k,iso} \sim 3.0 \times 10^{53} {\rm erg}$.Comment: 11 pages, 7 figures, accepted for publication in Ap

Direct grafting of tetraaniline via perfluorophenylazide photochemistry to create antifouling, low bio-adhesion surfaces.

Conjugated polyaniline has shown anticorrosive, hydrophilic, antibacterial, pH-responsive, and pseudocapacitive properties making it of interest in many fields. However, in situ grafting of polyaniline without harsh chemical treatments is challenging. In this study, we report a simple, fast, and non-destructive surface modification method for grafting tetraaniline (TANI), the smallest conjugated repeat unit of polyaniline, onto several materials via perfluorophenylazide photochemistry. The new materials are characterized by nuclear magnetic resonance (NMR) and electrospray ionization (ESI) mass spectroscopy. TANI is shown to be covalently bonded to important carbon materials including graphite, carbon nanotubes (CNTs), and reduced graphene oxide (rGO), as confirmed by transmission electron microscopy (TEM). Furthermore, large area modifications on polyethylene terephthalate (PET) films through dip-coating or spray-coating demonstrate the potential applicability in biomedical applications where high transparency, patternability, and low bio-adhesion are needed. Another important application is preventing biofouling in membranes for water purification. Here we report the first oligoaniline grafted water filtration membranes by modifying commercially available polyethersulfone (PES) ultrafiltration (UF) membranes. The modified membranes are hydrophilic as demonstrated by captive bubble experiments and exhibit extraordinarily low bovine serum albumin (BSA) and Escherichia coli adhesions. Superior membrane performance in terms of flux, BSA rejection and flux recovery after biofouling are demonstrated using a cross-flow system and dead-end cells, showing excellent fouling resistance produced by the in situ modification

Identifying the Riemann zeros by periodically driving a single qubit

The Riemann hypothesis, one of the most important open problems in pure mathematics, implies the most profound secret of prime numbers. One of the most interesting approaches to solve this hypothesis is to connect the problem with the spectrum of the physical Hamiltonian of a quantum system. However, none of the proposed quantum Hamiltonians have been experimentally feasible.Here, we report the first experiment to identify the first non-trivial zeros of the Riemann zeta function and the first two zeros of P\'olya's fake zeta function, using a novel Floquet method, through properly designed periodically driving functions. According to this method, the zeros of these functions are characterized by the occurrence of crossings of quasi-energies when the dynamics of the system are frozen. The experimentally obtained zeros are in excellent agreement with their exact values. Our study provides the first experimental realization of the Riemann zeros, which may provide new insights into this fundamental mathematical problem.Comment: 5 pages, 7 figure

The Vertical Distribution of Ice-Nucleating Particles over the North China Plain: A Case of Cold Front Passage

Ice-nucleating particles (INPs) are crucial for cloud freezing processes in the atmosphere. Given the limited knowledge about the vertical distribution of INPs and its relation to aerosols in China, we present two aircraft observations of INPs over the North China Plain on 23 October 2019 and 25 October 2019, before and after a cold front passage. We used a well-established method to identify the INPs on a silicon wafer and then performed single-particle chemical composition analysis using an environmental scanning electron microscope-energy dispersive spectrometer (ESEM-EDS). The INP concentrations range from 0.1 to 9.2 L$^{−1}$ within activation temperatures from −20 to −29 °C. INPs are mostly concentrated within the boundary layer, and their concentration shows a decreasing trend with height (0.5~6 km) before the cold front passage. However, the highest INP concentration always appears at higher altitudes (4~5 km) after the cold front passage. The cold front passage also significantly weakens the correlations between the concentrations of INPs and aerosol particles at different sizes. The activated fraction (AF) of total aerosols increases from 10$^{−6}$ to 10$^{−4}$ with height from near ground to 6 km, reflecting a better nucleating capacity of the aerosols at higher altitudes. There is no obvious variation in AF after the cold front passage. Chemical analysis reveals that the INPs containing mineral dust components comprise the majority of total INPs during both flights. The proportion of pure mineral dust declines from 52.2% to 43.5% after the cold front passage while the proportion of mixed mineral dust increases from 23.9% to 45.7%, suggesting that an increased probability of aging or coating of INPs is introduced by the cold front during their long-distance transport. In addition, 88% of INPs have a diameter larger than 1 μm. This indicates that larger aerosols (>1 μm) are the major contributors to INPs at high altitudes despite their relatively low abundance. Our results demonstrate a significant impact of transport events on the sources and vertical distribution of INPs in the atmosphere

Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation

Spatial organization of different epigenomic marks was used to infer functions of the epigenome. It remains unclear what can be learned from the temporal changes of the epigenome. Here, we developed a probabilistic model to cluster genomic sequences based on the similarity of temporal changes of multiple epigenomic marks during a cellular differentiation process. We differentiated mouse embryonic stem (ES) cells into mesendoderm cells. At three time points during this differentiation process, we used high-throughput sequencing to measure seven histone modifications and variants—H3K4me1/2/3, H3K27ac, H3K27me3, H3K36me3, and H2A.Z; two DNA modifications—5-mC and 5-hmC; and transcribed mRNAs and noncoding RNAs (ncRNAs). Genomic sequences were clustered based on the spatiotemporal epigenomic information. These clusters not only clearly distinguished gene bodies, promoters, and enhancers, but also were predictive of bidirectional promoters, miRNA promoters, and piRNAs. This suggests specific epigenomic patterns exist on piRNA genes much earlier than germ cell development. Temporal changes of H3K4me2, unmethylated CpG, and H2A.Z were predictive of 5-hmC changes, suggesting unmethylated CpG and H3K4me2 as potential upstream signals guiding TETs to specific sequences. Several rules on combinatorial epigenomic changes and their effects on mRNA expression and ncRNA expression were derived, including a simple rule governing the relationship between 5-hmC and gene expression levels. A Sox17 enhancer containing a FOXA2 binding site and a Foxa2 enhancer containing a SOX17 binding site were identified, suggesting a positive feedback loop between the two mesendoderm transcription factors. These data illustrate the power of using epigenome dynamics to investigate regulatory functions

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference noises produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure. One challenge in microseismic data processing is separating weak seismic signals from varying noisy data. To address this issue, we proposed an ambient-noise-assisted multivariate empirical mode decomposition (ANA-MEMD) method for adaptively suppressing noise in low signal-to-noise (S/N) microseismic data. In the proposed method, a new multi-channel record is produced by combining the noisy microseismic signal with preceding ambient noises. The multi-channel record is then decomposed using multivariate empirical mode decomposition (MEMD) into multivariate intrinsic mode functions (MIMFs). Then, the MIMFs corresponding to the main ambient noises can be identified by calculating and sorting energy percentage in descending order. Finally, the IMFs associated with strong interference noise, high-frequency and low-frequency noise are filtered out and suppressed by the energy percentage and frequency range. We investigate the feasibility and reliability of the proposed method using both synthetic data and field data. The results demonstrate that the proposed method can mitigate the mode mixing problem and clarify the main noise contributors by adding additional ambient-noise-assisted channels, hence separating the microseismic signal and ambient noise effectively and enhancing the S/Ns of microseismic signals