A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation

A constrained L1 minimization method is proposed for estimating a sparse inverse covariance matrix based on a sample of $n$ iid $p$-variate random variables. The resulting estimator is shown to enjoy a number of desirable properties. In particular, it is shown that the rate of convergence between the estimator and the true $s$-sparse precision matrix under the spectral norm is $s\sqrt{\log p/n}$ when the population distribution has either exponential-type tails or polynomial-type tails. Convergence rates under the elementwise $L_{\infty}$ norm and Frobenius norm are also presented. In addition, graphical model selection is considered. The procedure is easily implementable by linear programming. Numerical performance of the estimator is investigated using both simulated and real data. In particular, the procedure is applied to analyze a breast cancer dataset. The procedure performs favorably in comparison to existing methods.Comment: To appear in Journal of the American Statistical Associatio

Explore a probabilistic network model application for biopharma manufacturing process

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LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly Detection Framework Called LightESD. It is an On-Device Learning Method Without the Need for Data Transfer between Edge and Server and is Extremely Lightweight that Most Low-End Edge Devices Can Easily Afford with Negligible Delay, CPU/memory Utilization, and Power Consumption. Yet, It Achieves Highly Competitive Detection Accuracy. Another Salient Feature is that It Can Auto-Adapt to Probably Any Dataset Without Manually Setting or Configuring Model Parameters or Hyperparameters, which is a Drawback of Most Existing Methods. We Focus on Time Series Data Due to its Pervasiveness in Edge Applications Such as IoT. Our Evaluation Demonstrates that LightESD Outperforms Other SOTA Methods on Detection Accuracy, Efficiency, and Resource Consumption. Additionally, its Fully Automated Feature Gives It Another Competitive Advantage in Terms of Practical Usability and Generalizability

One-Shot Federated Learning For LEO Constellations That Reduces Convergence Time From Days To 90 Minutes

A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy friendly. However, FL requires many communication rounds between clients (satellites) and the parameter server (PS), leading to substantial delays of up to several days in LEO constellations. In this paper, we propose a novel one-shot FL approach for LEO satellites, called LEOShot, that needs only a single communication round to complete the entire learning process. LEOShot comprises three processes: (i) synthetic data generation, (ii) knowledge distillation, and (iii) virtual model retraining. We evaluate and benchmark LEOShot against the state of the art and the results show that it drastically expedites FL convergence by more than an order of magnitude. Also surprisingly, despite the one-shot nature, its model accuracy is on par with or even outperforms regular iterative FL schemes by a large margin

Bioluminescence in vivo imaging of autoimmune encephalomyelitis predicts disease

<p>Abstract</p> <p>Background</p> <p>Experimental autoimmune encephalomyelitis is a widely used animal model to understand not only multiple sclerosis but also basic principles of immunity. The disease is scored typically by observing signs of paralysis, which do not always correspond with pathological changes.</p> <p>Methods</p> <p>Experimental autoimmune encephalomyelitis was induced in transgenic mice expressing an injury responsive luciferase reporter in astrocytes (GFAP-luc). Bioluminescence in the brain and spinal cord was measured non-invasively in living mice. Mice were sacrificed at different time points to evaluate clinical and pathological changes. The correlation between bioluminescence and clinical and pathological EAE was statistically analyzed by Pearson correlation analysis.</p> <p>Results</p> <p>Bioluminescence from the brain and spinal cord correlates strongly with severity of clinical disease and a number of pathological changes in the brain in EAE. Bioluminescence at early time points also predicts severity of disease.</p> <p>Conclusion</p> <p>These results highlight the potential use of bioluminescence imaging to monitor neuroinflammation for rapid drug screening and immunological studies in EAE and suggest that similar approaches could be applied to other animal models of autoimmune and inflammatory disorders.</p

Heat shock protein 90 in neurodegenerative diseases

Hsp90 is a molecular chaperone with important roles in regulating pathogenic transformation. In addition to its well-characterized functions in malignancy, recent evidence from several laboratories suggests a role for Hsp90 in maintaining the functional stability of neuronal proteins of aberrant capacity, whether mutated or over-activated, allowing and sustaining the accumulation of toxic aggregates. In addition, Hsp90 regulates the activity of the transcription factor heat shock factor-1 (HSF-1), the master regulator of the heat shock response, mechanism that cells use for protection when exposed to conditions of stress. These biological functions therefore propose Hsp90 inhibition as a dual therapeutic modality in neurodegenerative diseases. First, by suppressing aberrant neuronal activity, Hsp90 inhibitors may ameliorate protein aggregation and its associated toxicity. Second, by activation of HSF-1 and the subsequent induction of heat shock proteins, such as Hsp70, Hsp90 inhibitors may redirect neuronal aggregate formation, and protect against protein toxicity. This mini-review will summarize our current knowledge on Hsp90 in neurodegeneration and will focus on the potential beneficial application of Hsp90 inhibitors in neurodegenerative diseases