Collaborating Authors

Automated analysis of High‐content Microscopy data with Deep Learning


Advances in automated image acquisition and analysis, coupled with the availability of reagents for genome‐scale perturbation, have enabled systematic analyses of cellular and subcellular phenotypes (Mattiazzi Usaj et al, 2016). One powerful application of microscopy‐based assays involves assessment of changes in the subcellular localization or abundance of fluorescently labeled proteins in response to various genetic lesions or environmental insults (Laufer et al, 2013; Ljosa et al, 2013; Chong et al, 2015). Proteins localize to regions of the cell where they are required to carry out specific functions, and a change in protein localization following a genetic or environmental perturbation often reflects a critical role of the protein in a biological response of interest. High‐throughput (HTP) microscopy enables analysis of proteome‐wide changes in protein localization in different conditions, providing data with the spatiotemporal resolution that is needed to understand the dynamics of biological systems. The budding yeast, Saccharomyces cerevisiae, remains a premiere model system for the development of experimental and computational pipelines for HTP phenotypic analysis.

Observation of Anderson localization in disordered nanophotonic structures


Anderson localization is an interference effect crucial to the understanding of waves in disordered media. However, localization is expected to become negligible when the features of the disordered structure are much smaller than the wavelength. Here we experimentally demonstrate the localization of light in a disordered dielectric multilayer with an average layer thickness of 15 nanometers, deep into the subwavelength regime. We observe strong disorder-induced reflections that show that the interplay of localization and evanescence can lead to a substantial decrease in transmission, or the opposite feature of enhanced transmission. This deep-subwavelength Anderson localization exhibits extreme sensitivity: Varying the thickness of a single layer by 2 nanometers changes the reflection appreciably.

'Zelda: Breath Of The Wild' Is Increasingly Looking Like It Will Be Out This Spring

Forbes - Tech

After all the rumors, it seems that according to a recent issue of CoroCoro Comic, it looks like Zelda: Breath of the Wild will be coming out for the Wii U and Nintendo Switch this Spring. Coupled with reports from elsewhere, as covered by Paul Tassi and myself, it is clear that this March release date seems far more credible and lends credence to the launch window of the Nintendo Switch itself. To be honest, without Breath of the Wild it is hard to think of a game that could initially push the Nintendo Switch in terms of its launch. Maybe the rumored update to Pokémon Sun and Moon could do it, but a new mainline Zelda game is definitely a much stronger contender. After all, the launch of the Wii had Twilight Princess and that clearly played a key role in helping the console find its feet.

Probing entanglement in a many-body-localized system


An interacting quantum system that is subject to disorder may cease to thermalize owing to localization of its constituents, thereby marking the breakdown of thermodynamics. The key to understanding this phenomenon lies in the system's entanglement, which is experimentally challenging to measure. We realize such a many-body–localized system in a disordered Bose-Hubbard chain and characterize its entanglement properties through particle fluctuations and correlations. We observe that the particles become localized, suppressing transport and preventing the thermalization of subsystems. Notably, we measure the development of nonlocal correlations, whose evolution is consistent with a logarithmic growth of entanglement entropy, the hallmark of many-body localization.

Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization Machine Learning

Location is key to spatialize internet-of-things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this paper proposes a deep reinforcement learning (DRL) based unsupervised wireless-localization method. The main contributions are as follows. (1) This paper proposes an approach to model a continuous wireless-localization process as a Markov decision process (MDP) and process it within a DRL framework. (2) To alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily-life crowdsourced data), this paper presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS). (3) To ease requirements for model re-training when using DRL for localization, this paper uses RSS measurements together with agent location to construct DRL inputs. The proposed method was tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflected the advantages and challenges for using DRL in wireless localization.