701 research outputs found
Indian Power Metal
For my CE, I intend to produce, compose, record, mix and perform a collection of 5 power-metal songs, with a professional level music video to accompany one song.https://remix.berklee.edu/graduate-studies-production-technology/1038/thumbnail.jp
Infrastructure Development and the National Rural Employment Guarantee Scheme: Evidence from Bankura, West Bengal
Infrastructure Development and the National Rural Employment Guarantee Scheme: Evidence from Bankura, West Bengal
Infrastructure Development and the National Rural Employment Guarantee Scheme: Evidence from Bankura, West Bengal
Infrastructure Development and the National Rural Employment Guarantee Scheme: Evidence from Bankura, West Bengal
Introduction. The immediate goal of the National Rural Employment Guarantee Scheme (NREGS) is to ensure a social safety net for vulnerable groups by providing a fall-back source of employment when other alternatives are scarce. However, its long-term goals are to create durable rural assets and infrastructure which meet local needs and help address chronic poverty and to foster a model of governance based on the principles of grass-root democracy and transparency (Ministry of Rural Development, 2008). While several papers have examined pertinent aspects of the functioning of the programme, such as targeting (Jha et al., 2009), impact on consumption (Ravi and Engler, 2009) and various implementation issues including corruption (Rai, 2008; Institute of Applied Manpower Research, 2008) there is limited work on the quality and upkeep of the infrastructure built through the programme and indeed whether the constructed projects meet local needs. ..
3-Way Test Suite Prioritization and Fault Detection: A Case Study
GUI and web based applications are becoming universal. Functional accuracy of those applications is vital. Software defects caused by poor software testing can cost billions of dollars. Further, web application defects can be costly due to the fact that most web applications handle regular user interaction. By improving the time efficiency of software testing, many of the costs associated with defects can be saved. Web application users generate large numbers of possible test-cases and out of all those test-cases only some of them are vital for functional testing. Therefore testing correctness of these applications is expensive and time consuming and hence challenging at times. However, software testing is often under time and budget constraints. Earlier studies came up with different abstract models to face this kind of challenges where a tester can select and execute a subset of all the possible test-cases (test-°©‐case prioritization) based on some criterion to assure performance goal. In the context of test suite prioritization, earlier studies showed that 2-way inter-window interaction coverage/criteria are effective at finding faults quickly in the test execution cycle. However, since faults may be caused by interactions between more than 2 parameters, in this project we exercise test suite prioritization by t-way combinatorial coverage of inter-window interactions on an existing web application Music-Store. Our results show that the rates of fault detection for 2-way and 3-way prioritization are very close to each other
FairBranch: Fairness Conflict Correction on Task-group Branches for Fair Multi-Task Learning
The generalization capacity of Multi-Task Learning (MTL) becomes limited when
unrelated tasks negatively impact each other by updating shared parameters with
conflicting gradients, resulting in negative transfer and a reduction in MTL
accuracy compared to single-task learning (STL). Recently, there has been an
increasing focus on the fairness of MTL models, necessitating the optimization
of both accuracy and fairness for individual tasks. Similarly to how negative
transfer affects accuracy, task-specific fairness considerations can adversely
influence the fairness of other tasks when there is a conflict of fairness loss
gradients among jointly learned tasks, termed bias transfer. To address both
negative and bias transfer in MTL, we introduce a novel method called
FairBranch. FairBranch branches the MTL model by assessing the similarity of
learned parameters, grouping related tasks to mitigate negative transfer.
Additionally, it incorporates fairness loss gradient conflict correction
between adjoining task-group branches to address bias transfer within these
task groups. Our experiments in tabular and visual MTL problems demonstrate
that FairBranch surpasses state-of-the-art MTL methods in terms of both
fairness and accuracy
Performance of buried channel n-type MOSFETs in 0.18-μm CMOS image sensor process
Buried channel (BC) MOSFETs are known to have better noise performance than surface channel (SC) MOSFETs when used as source followers in modern Charge Coupled Devices (CCD). CMOS image sensors find increasing range of applications and compete with CCDs in high performance imaging, however BC transistors are rarely used in CMOS. As a part of the development of charge storage using BC CCDs in CMOS, we designed and manufactured deep depletion BC n-type MOSFETs in 0.18 μm CMOS image sensor process. The transistors are designed in a way similar to the source followers in a typical BC CCD. In this paper we report the results from their characterization and compare with enhancement mode and “zero-threshold” SC devices. In addition to the detailed current-voltage and noise measurements, semiconductor device simulation results are presented to illustrate and understand the different conditions affecting the channel conduction and the noise performance of the BC transistors at low operating voltages. We show that the biasing of the BC transistors has to be carefully adjusted for optimal operation, and that their noise performance at the right operating conditions can be superior to SC devices, despite their lower gain as in-pixel source followers
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