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

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

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

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

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

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

In-situ Oxide, GaN interlayer based vertical trench MOSFET (OG-FET)

The surge in world-wide energy consumption places a growing need for highly efficient power electronics for generation, transportation, and utilization of electricity. With the advent of new markets such as electric vehicles, PV solar inverters, the market for these power electronics components is predicted to reach $15 billion by 2020. Silicon-based devices are most commonly used in traditional power electronics applications, however, wide bandgap semiconductors such as gallium nitride (GaN) are more efficient and thus, useful for future energy applications.Consequently, Gallium Nitride (GaN) based power devices have gained increased attention in recent years. For 600 V class power devices, lateral GaN high electron mobility transistors are available today. However, it is generally considered that for high voltage/high current applications (>900V/100 A), vertical device structures might be more suitable owing to their capability of achieving lower specific on-resistance and high breakdown voltage simultaneously.Amongst numerous vertical device structures, the trench MOSFET is an attractive device structure to reduce on-resistance due to the capability of high cell density and the absence of a JFET region. However, high channel resistance in trench MOSFETs due to poor electron mobility in the channel creates reliability issues as a higher gate bias needs to be applied to reduce the channel resistance.In this dissertation work, we developed a novel device design (called OG-FET) to enhance the channel mobility and therefore, lower the channel-resistance for the trench MOSFET structure while maintaining normally-off operation and same breakdown voltage. In OG-FET, a GaN interlayer is regrown followed by in-situ dielectric deposition via MOCVD on the n-p-n trenched structure to enhance the channel mobility. In addition, the in-situ gate-dielectric growth onto the GaN interlayer allows this device to achieve lower interface trap density compared to devices with ex-situ dielectrics deposited onto the trenched structure. This thesis discusses the OG-FET device design, growth and fabrication process alongside device results and analysis. With sustained efforts, OG-FETs with high DC performance were achieved. The OG-FETs demonstrated threshold voltage between 1-4 V, breakdown voltage beyond 1 kV with a low on-resistance between 1.5-3 mΩ.cm2. The on-resistance values were achieved at a relatively low gate bias (~12 V-15 V) and low gate-dielectric field (~2-3 MV/cm) compared to conventional GaN trench MOSFETs. These results are promising for the future application of OG-FETs for high voltage and high-power electronics