A spatial-temporal topic model with sparse prior and RNN prior for bursty topic discovering in social networks

With the rapid growth of social network users, the social network has accumulated massive social network topics. However, due to the randomness of content, it becomes sparse and noisy, accompanied by many daily chats and meaningless topics, which brings challenges to bursty topics discovery. To deal with these problems, this paper proposes the spatial-temporal topic model with sparse prior and recurrent neural networks (RNN) prior for bursty topic discovering (ST-SRTM). The semantic relationship of words is learned through RNN to alleviate the sparsity. The spatial-temporal areas information is introduced to focus on bursty topics for further weakening the semantic sparsity of social network context. Besides, we introduced the “Spike and Slab” prior to decouple the sparseness and smoothness. Simultaneously, we realized the automatic discovery of social network bursts by introducing the burstiness of words as the prior and binary switching variables. We constructed multiple sets of comparative experiments to verify the performance of ST-SRTM by leveraging different evaluation indicators on real Sina Weibo data sets. The experimental results confirm the superiority of our ST-SRTM.

Capillaric - Field Effect Transistors

Controling fluid flow in capillaric circuits is a key requirement to increase their uptake for assay applications. Capillary action off-valves provide such functionality by pushing an occluding bubble into the channel using a difference in capillary pressure. Previously, we utilised the binary switching mode of this structure to develop a powerful set of fundamental fluidic valving operations. In this work we provide evidence that these structures are in fact functionally complementary to electronic Junction Field Effect Transistors and thus warrant the use of the new term of capillaric- Field Effect Transistor to describe these types of valves. To support this conclusion, we present a theoretical description, experimental characterisation, and practical application of analog flow resistance control. In addition, we demonstrate that the valves can also be re-opened. These are two key capabilities previously missing for a full analogy to electronic transistors. We show modulation of the flow resistance from fully open to pinch-off, determine the flow rate – trigger channel volume relationship and demonstrate that the latter can be modelled using Shockley’s equation for electronic transistors. Finally, we provide a first example of how the valves can be opened and closed repeatedly.

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

Reconfigurable all-dielectric metalens with diffraction-limited performance

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency, especially for non-mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning in the full 2π range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling binary switching of metasurfaces between arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for reconfigurable meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 μm wavelength. The reconfigurable metalens features a record large switching contrast ratio of 29.5 dB. We further validate aberration-free and multi-depth imaging using the metalens, which represents a key experimental demonstration of a non-mechanical tunable metalens with diffraction-limited performance.

Active binary switching of soft colloids: stability and structural properties

We employ reactive dynamical density functional theory (R-DDFT) and reactive Brownian dynamics (R-BD) simulations to study the non-equilibrium structure and phase behavior of an active dispersion of soft Gaussian colloids...

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, including sensing and reacting to environmental cues or randomly adopting a phenotypic state. Memory – a phenomenon often associated with, but not restricted to, higher multicellular organisms – can help when temporal correlations exist. How does memory manifest itself in unicellular organisms? Through a combination of deterministic modelling and stochastic simulations, we describe the population-wide fitness consequences of phenotypic memory in microbial populations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that multiple cellular states capture the empirical observations of lag time distributions, overshoots, and ultimately the phenomenon of phenotypic heterogeneity. We emphasise the implications of our work in understanding antibiotic tolerance, and, in general, survival under fluctuating environments.

Reconfigurable all-dielectric metalens with diffraction-limited performance

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency especially for non mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning covering the full 2π range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling binary switching of metasurfaces between arbitrary phase profiles and propose a new figure-of-merit tailored for active meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 μm wavelength. The metalens is constructed using Ge2Sb2Se4Te1 (GSST), an O-PCM with a large refractive index contrast (Δn > 1) and unique broadband low-loss characteristics in both amorphous and crystalline states. The reconfigurable metalens features focusing efficiencies above 20% at both states for linearly polarized light and a record large switching contrast ratio of 29.5 dB. We further validate aberration-free and multi-depth imaging using the metalens, which represents the first experimental demonstration of a non-mechanical active metalens with diffraction-limited performance.

Moving Horizon Estimation for Uncertain Networked Control Systems with Packet Loss

This paper studies a moving horizon estimation approach to solve the constrained state estimation problem for uncertain networked systems with random packet loss. The system model error range is known, and the packet loss phenomena are modeled by a binary switching random sequence. Taking the model error, the packet loss, the system constraints, and the network transmission noise into account, a time-varying weight matrix is obtained by solving a least-square problem. Then, a robust moving horizon estimator is designed to estimate the system state by minimizing an optimization problem with an arrival cost function. The proposed estimator ensures that the optimal estimated state can be obtained in the worst case. Furthermore, the asymptotic convergence of the estimator is analyzed and some sufficient conditions for convergence are given. Finally, the validity of the proposed approach can be demonstrated by numerical simulations.