This paper handles the task of event nugget detection. In fact, deep learning methods were able to manage the extraction of relevant learned features. However, these methods tend to rely on ...NLP-Toolkits, as they feed gradually handcrafted features into their initial model. To alleviate this dependency and offer a deeper semantic understanding of the information encompassed in data, we investigate the use of pre-trained language models. The proposed approach uses the RoBERTa model because it offers a robust context-sensitive and pertinent representation of trends in data. The results demonstrate that our approach significantly outperforms its BERT-based variants and state-of-the-art approaches.
•We present an open-source software for semi-automated analysis of fluorescent calcium imaging.•FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package, enables automated segmentation ...and calcium transient event detections.•Calcium dynamics of single-cells can be used to phenotype neurons.•FluoroSNNAP enables global and local synchronization cluster analysis.•FluoroSNNAP determines functional connectivity and allows graphical visualization.
Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking.
Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge.
We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau.
We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods.
We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease.
With the proliferation of the smart Internet of Things (IoT) devices based on Android system, malicious Android applications targeting for IoT devices have received more and more attention due to the ...concern of privacy leakage and property loss. However, existing malware detection approaches based on static or dynamic analysis are not scalable to the evolvement of malware and cannot extract enough valid semantics in application programming interface (API) level, failing to detect new malware. In this paper, we propose EveDroid, a scalable and event-aware Android malware detection system, which exploits the behavioral patterns in different events to effectively detect new malware based on the insight that events can reflect apps' possible running activities. Unlike existing approaches using API calls as features directly, we propose to use event group to describe apps' behaviors in event level, which can capture higher level of semantics than in API level. In event group, we adopt function clusters to represent behaviors in each event so that behaviors hidden in events can still be captured as time goes on, which enables EveDroid to detect new malware in the event level. The function clusters can generalize API calls into vectors based on their API composition to capture new API calls, which makes EveDroid scalable to malware evolving. Moreover, a neural network is specifically designed to aggregate the multiple events and automatically mine the semantic relationship among them. We train the system and evaluate its <inline-formula> <tex-math notation="LaTeX">{F}1 </tex-math></inline-formula>-measure on a dataset of 14 956 benign and 28 848 malicious Android apps released in different years. The experimental results show that EveDroid outperforms other malware detection systems.
Ahmed and colleagues recently described a novel hybrid lymphocyte expressing both a B and T cell receptor, termed double expresser (DE) cells. DE cells in blood of type 1 diabetes (T1D) subjects were ...present at increased numbers and enriched for a public B cell clonotype. Here, we attempted to reproduce these findings. While we could identify DE cells by flow cytometry, we found no association between DE cell frequency and T1D status. We were unable to identify the reported public B cell clone, or any similar clone, in bulk B cells or sorted DE cells from T1D subjects or controls. We also did not observe increased usage of the public clone VH or DH genes in B cells or in sorted DE cells. Taken together, our findings suggest that DE cells and their alleged public clonotype are not enriched in T1D. This Matters Arising paper is in response to Ahmed et al. (2019), published in Cell. See also the response by Ahmed et al. (2021), published in this issue.
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•DE cells not increased in blood or lymphoid tissue of T1D subjects compared to controls•Failure to replicate results of Ahmed et al. with identical FACS assay•“Public” clone-x BCR sequence not found in blood or spleen of T1D and controls•CDR3 AA sequences similar to clone-x are not enriched in B cells or sorted DE cells
There does not appear to be increased abundance of dual-expresser TCR+/BCR+ lymphocytes in type I diabetes patients, nor a public clonotype, challenging the results of a previous study.
A fundamental challenge of mobile sensor networks is automated active reconfiguration of sensors in response to environmental stimuli in order to maximize their total sensing quality (or minimize ...their total sensing cost) of events occurring over an environment. In this paper, given an event distribution over a convex environment, we consider mobile isotropic sensors with adjustable sensing range and propose a new family of provably correct reactive coverage control algorithms for both continuous- and discrete-time sensor dynamics. The proposed coverage control algorithms constantly (re)configure sensor positions and sensing ranges in order to minimize a statistical distance, in particular, an f-divergence, between the event distribution over the environment and the overall event detection probability of sensors. We show that the standard Voronoi-based coverage control law of homogeneous mobile sensor networks is a special case of our framework where the event detection probability of each sensor has a Gaussian form, the statistical distance is set to be the Kullback-Leibler (KL) divergence and sensor allocation is performed based on Voronoi diagrams. To increase the practicality of our framework, we also present its integration with a Voronoi-based collision avoidance strategy for disk-shaped sensor bodies and its extension to differential drive sensor dynamics, while retaining the stability properties.
The absolute secondary scintillation yield is of paramount importance for modelling dual-phase or high-pressure gas detectors, to be used in contemporary and in future rare event detection ...experiments. In addition, the search for neutrinoless double electron capture complements the search for neutrinoless double beta decay and has been measured for 124Xe in several Dark Matter and Double Beta decay detectors, operating at present. Krypton presents itself as an interesting candidate for double electron capture detection experiments. We have studied the krypton secondary scintillation yield, at room temperature, as a function of electric field in the gas scintillation gap. A large area avalanche photodiode has been used to allow the simultaneous detection of the scintillation pulses as well as the direct interaction of x-rays, the latter being used as a reference for the calculation of the number of charge carriers produced by the scintillation pulses and, thus, the determination of the number of photons impinging the photodiode. An amplification parameter of 113 photons per kV per drifting electron and a scintillation threshold of 2.7 Td (0.7 kVcm−1bar−1 at 293 K) was obtained, in good agreement with the simulation data reported in the literature. On the other hand, the ionisation threshold in krypton was found to be around 13.5 Td (3.4 kVcm−1bar−1), less than what had been obtained by the most recent simulation work-package. The krypton amplification parameter is about 80% and 140% of those measured for xenon and argon, respectively.
A state estimation based approach to obtain an external network equivalent and track topology changes is developed in this work. The proposed approach enables real-time detection and identification ...of topology-based external events. Following an orthogonal parameter estimator, an index related to the measurement residuals is tracked to detect external disturbances, which are classified based on reference values and actual system data. The method makes use of boundary bus voltages, external currents, an initial snapshot of the network topology, as well as the sensitivity of external changes to the equivalent network. Tests on the IEEE 300-bus and Brazilian 6092-bus systems highlight its capabilities in detecting and identifying shunt, single and double line outages and insertion events.
Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to ...independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.
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