593 research outputs found
Upsurge of the Bharatiya Janata Party in India
This research paper examines the development of the Bharatiya Janata Party (BJP) in India since its establishment and its governance inside the country. The BJP is influenced by the ideals of Hindu nationalism, and such ideals can be visible through the party’s responses to critical issues, such as the ongoing Indo-Pakistani conflict over Kashmir and Jammu. This research paper reviews three issues that seem to be prominent in India and correlated to the influences of the BJP in the government: The Indo-Pakistani conflict, transformations of India’s economy, and religious discriminations
Metabolic scaling theory and remote sensing to model large-scale patterns of forest biophysical properties
Advanced understanding of the global carbon budget requires large-scale and long-term information on forest carbon pools and fluxes. In situ and remote sensing measurements have greatly enhanced monitoring of forest carbon dynamics, but incomplete data coverage in space and time results in significant uncertainties in carbon accounting. Although theoretical and mechanistic models have enabled continental-scale and global mapping, robust predictions of forest carbon dynamics are difficult without initialization, adjustment, and parameterization using observations. Therefore, this dissertation is focused on a synergistic combination of lidar measurements and modeling that incorporates biophysical principles underlying forest growth.
First, spaceborne lidar data from the Geoscience Laser Altimeter System (GLAS) were analyzed for monitoring and modeling of forest heights over the U.S. Mainland. Results showed the best GLAS metric representing the within-footprint heights to be dependent on topography. Insufficient data sampling by the GLAS sensor was problematic for spatially-complete carbon quantification. A modeling approach, called Allometric Scaling and Resource Limitations (ASRL), successfully alleviated this problem. The metabolic scaling theory and water-energy balance equations embedded within the model also provided a generalized mechanistic understanding of valid relationships between forest structure and geo-predictors including topographic and climatic variables.
Second, the ASRL model was refined and applied to predict large-scale patterns of forest structure. This research successfully expanded model applicability by including eco-regional and forest-type variations, and disturbance history. Baseline maps (circa 2005; 1-km2 grids) of forest heights and aboveground biomass were generated over the U.S. Mainland. The Pacific Northwest/California forests were simulated as the most favorable region for hosting large trees, consistent with observations. Through sensitivity and uncertainty analyses, this research found that the refined ASRL model showed promise for prognostic applications, in contrast to conventional black-box approaches. The model predicted temporal evolution of forest carbon stocks during the 21st century. The results demonstrate the effects of CO2 fertilization and climate feedbacks across water- and energy-limited environments.
This dissertation documents the complex mechanisms determining forest structure, given availability of local resources. These mechanisms can be used to monitor and forecast forest carbon pools in combination with satellite observations to advance our understanding of the global carbon cycle
Implications of Youth Education on Intrastate Conflict: The Relevance of Postmaterialism
The concept of postmaterialism posits that individuals who are born in an economically and socially secure environment tend to be more open to changes in their societies and accepting of different values among individuals compared to those who are materialists (i.e., individuals who tend to value security, affluence, and strong law and order more in comparison to postmaterialists). Postmaterialism is associated with individuals who are more educated and have access to different educational opportunities, given the existence of economic stability in postmaterialist societies. Focusing on the role of postmaterialist values, I analyze the relationship between educational attainment among youths and the number of internal armed conflicts a country experiences. I find that there is a statistically significant relationship between educational attainment among youths and the number of internal armed conflicts a country experiences annually
Predicting Whole Forest Structure, Primary Productivity, and Biomass Density From Maximum Tree Size and Resource Limitations
In the face of uncertain biological response to climate change and the many
critiques concerning model complexity it is increasingly important to develop
predictive mechanistic frameworks that capture the dominant features of
ecological communities and their dependencies on environmental factors. This is
particularly important for critical global processes such as biomass changes,
carbon export, and biogenic climate feedback. Past efforts have successfully
understood a broad spectrum of plant and community traits across a range of
biological diversity and body size, including tree size distributions and
maximum tree height, from mechanical, hydrodynamic, and resource constraints.
Recently it was shown that global scaling relationships for net primary
productivity are correlated with local meteorology and the overall biomass
density within a forest. Along with previous efforts, this highlights the
connection between widely observed allometric relationships and predictive
ecology. An emerging goal of ecological theory is to gain maximum predictive
power with the least number of parameters. Here we show that the explicit
dependence of such critical quantities can be systematically predicted knowing
just the size of the largest tree. This is supported by data showing that
forests converge to our predictions as they mature. Since maximum tree size can
be calculated from local meteorology this provides a general framework for
predicting the generic structure of forests from local environmental parameters
thereby addressing a range of critical Earth-system questions.Comment: 26 pages, 4 figures, 1 Tabl
From Obama to Trump to Biden: U.S. Involvement and Policy Tactics in the Yemeni Civil War
The analysis intends to overview the history of U.S. involvement in the Yemeni Civil War, starting from the presidency of Barack Obama to Joe Biden. Given that the Biden administration recently has decided to end the U.S. support for Saudi-led military intervention in Yemen, this article hopes to explore what events got the United States up to that point and the U.S. foreign policy tactics and strategies that factored into causing a long-term devastation in Yemen. This analysis will also provide a brief, condensed history regarding what led the war in Yemen to begin
Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Deep neural networks (DNNs) have emerged as successful solutions for variety
of artificial intelligence applications, but their very large and deep models
impose high computational requirements during training. Multi-GPU
parallelization is a popular option to accelerate demanding computations in DNN
training, but most state-of-the-art multi-GPU deep learning frameworks not only
require users to have an in-depth understanding of the implementation of the
frameworks themselves, but also apply parallelization in a straight-forward way
without optimizing GPU utilization. In this work, we propose a workload-aware
auto-parallelization framework (WAP) for DNN training, where the work is
automatically distributed to multiple GPUs based on the workload
characteristics. We evaluate WAP using TensorFlow with popular DNN benchmarks
(AlexNet and VGG-16), and show competitive training throughput compared with
the state-of-the-art frameworks, and also demonstrate that WAP automatically
optimizes GPU assignment based on the workload's compute requirements, thereby
improving energy efficiency.Comment: This paper is accepted in ICASSP201
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
In this paper, a neural network based real-time speech recognition (SR)
system is developed using an FPGA for very low-power operation. The implemented
system employs two recurrent neural networks (RNNs); one is a
speech-to-character RNN for acoustic modeling (AM) and the other is for
character-level language modeling (LM). The system also employs a statistical
word-level LM to improve the recognition accuracy. The results of the AM, the
character-level LM, and the word-level LM are combined using a fairly simple
N-best search algorithm instead of the hidden Markov model (HMM) based network.
The RNNs are implemented using massively parallel processing elements (PEs) for
low latency and high throughput. The weights are quantized to 6 bits to store
all of them in the on-chip memory of an FPGA. The proposed algorithm is
implemented on a Xilinx XC7Z045, and the system can operate much faster than
real-time.Comment: Accepted to SiPS 201
Differentiable Artificial Reverberation
Artificial reverberation (AR) models play a central role in various audio
applications. Therefore, estimating the AR model parameters (ARPs) of a target
reverberation is a crucial task. Although a few recent deep-learning-based
approaches have shown promising performance, their non-end-to-end training
scheme prevents them from fully exploiting the potential of deep neural
networks. This motivates to introduce differentiable artificial reverberation
(DAR) models which allows loss gradients to be back-propagated end-to-end.
However, implementing the AR models with their difference equations "as is" in
the deep-learning framework severely bottlenecks the training speed when
executed with a parallel processor like GPU due to their infinite impulse
response (IIR) components. We tackle this problem by replacing the IIR filters
with finite impulse response (FIR) approximations with the frequency-sampling
method (FSM). Using the FSM, we implement three DAR models -- differentiable
Filtered Velvet Noise (FVN), Advanced Filtered Velvet Noise (AFVN), and
Feedback Delay Network (FDN). For each AR model, we train its ARP estimation
networks for analysis-synthesis (RIR-to-ARP) and blind estimation
(reverberant-speech-to-ARP) task in an end-to-end manner with its DAR model
counterpart. Experiment results show that the proposed method achieves
consistent performance improvement over the non-end-to-end approaches in both
objective metrics and subjective listening test results.Comment: Manuscript submitted to TASL
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