163 research outputs found

    Transcriptome-based Gene Networks for Systems-level Analysis of Plant Gene Functions

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    Present day genomic technologies are evolving at an unprecedented rate, allowing interrogation of cellular activities with increasing breadth and depth. However, we know very little about how the genome functions and what the identified genes do. The lack of functional annotations of genes greatly limits the post-analytical interpretation of new high throughput genomic datasets. For plant biologists, the problem is much severe. Less than 50% of all the identified genes in the model plant Arabidopsis thaliana, and only about 20% of all genes in the crop model Oryza sativa have some aspects of their functions assigned. Therefore, there is an urgent need to develop innovative methods to predict and expand on the currently available functional annotations of plant genes. With open-access catching the ‘pulse’ of modern day molecular research, an integration of the copious amount of transcriptome datasets allows rapid prediction of gene functions in specific biological contexts, which provide added evidence over traditional homology-based functional inference. The main goal of this dissertation was to develop data analysis strategies and tools broadly applicable in systems biology research. Two user friendly interactive web applications are presented: The Rice Regulatory Network (RRN) captures an abiotic-stress conditioned gene regulatory network designed to facilitate the identification of transcription factor targets during induction of various environmental stresses. The Arabidopsis Seed Active Network (SANe) is a transcriptional regulatory network that encapsulates various aspects of seed formation, including embryogenesis, endosperm development and seed-coat formation. Further, an edge-set enrichment analysis algorithm is proposed that uses network density as a parameter to estimate the gain or loss in correlation of pathways between two conditionally independent coexpression networks

    Online Platt Scaling with Calibeating

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    We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method.Comment: ICML 2023; 24 pages and 16 figure

    Comprehensive TCAD Simulation Study of High Voltage (>650V) Common Drain Bidirectional AlGaN/GaN HEMTs

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    A broad TCAD simulation analysis of a monolithic common drain bidirectional GaN HEMT was performed. We used gate-to-gate distances of 4 microns and 6 microns for the devices optimized with two field plates. The breakdown voltages were 675V and 915V respectively. Inclusion of field plates near both the gates produced electric field peaks at the opposite ends of the transistor simultaneously. This resulted in better electric field management or higher blocking voltage per unit length. Consequently, the 675V monolithic bidirectional HEMT had an impressive 40% improvement in on-resistance than its 650V typical series/parallel counterpart.Comment: 3 pages, 7 figure

    Study of Future Wireless Technology: Li-Fi

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    Since the day earth exists, human is going develop day by day. New technologies are generated as human beings are developing. Now, internet is a compulsory part of our life. We are using Wi-Fi for internet access. But it has some limitations, so there is a new wireless technology i.e. Li-Fi (Light Fidelity), which overcomes some of the shortcomings of Wi-Fi. This paper consists a study of Li-Fi basics like advantages, limitations, applications and future scope

    Parity Calibration

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    In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-decrease (or "parity") event in a timeseries. Parity probabilities can be extracted from a forecasted distribution for the output, but we show that such a strategy leads to theoretical unpredictability and poor practical performance. We then observe that although the original task was regression, parity calibration can be expressed as binary calibration. Drawing on this connection, we use an online binary calibration method to achieve parity calibration. We demonstrate the effectiveness of our approach on real-world case studies in epidemiology, weather forecasting, and model-based control in nuclear fusion.Comment: To appear at UAI 2023; 19 pages and 10 figure
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