A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor

In this paper, a siamese DNN model is proposed to learn the characteristics of the audio dynamic range compressor (DRC). This facilitates an intelligent control system that uses audio examples to configure the DRC, a widely used non-linear audio signal conditioning technique in the areas of music production, speech communication and broadcasting. Several alternative siamese DNN architectures are proposed to learn feature embeddings that can characterise subtle effects due to dynamic range compression. These models are compared with each other as well as handcrafted features proposed in previous work. The evaluation of the relations between the hyperparameters of DNN and DRC parameters are also provided. The best model is able to produce a universal feature embedding that is capable of predicting multiple DRC parameters simultaneously, which is a significant improvement from our previous research. The feature embedding shows better performance than handcrafted audio features when predicting DRC parameters for both mono-instrument audio loops and polyphonic music pieces.Comment: 8 pages, accepted in IJCNN 201

Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization

Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. Our key contribution is significantly improving the prediction hitting rate (or prediction accuracy) for each data point based on its nearby data values along multiple dimensions. We derive a series of multilayer prediction formulas and their unified formula in the context of data compression. One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn. We explore the best layer for the prediction by considering the impact of compression errors on the prediction accuracy. Moreover, we propose an adaptive error-controlled quantization encoder, which can further improve the prediction hitting rate considerably. The data size can be reduced significantly after performing the variable-length encoding because of the uneven distribution produced by our quantization encoder. We evaluate the new compressor on production scientific data sets and compare it with many other state-of-the-art compressors: GZIP, FPZIP, ZFP, SZ-1.1, and ISABELA. Experiments show that our compressor is the best in class, especially with regard to compression factors (or bit-rates) and compression errors (including RMSE, NRMSE, and PSNR). Our solution is better than the second-best solution by more than a 2x increase in the compression factor and 3.8x reduction in the normalized root mean squared error on average, with reasonable error bounds and user-desired bit-rates.Comment: Accepted by IPDPS'17, 11 pages, 10 figures, double colum

CPCP violation in charmed hadron decays into neutral kaons

We find a new $CP$ violating effect in charmed hadron decays into neutral kaons, which is induced by the interference between the Cabibbo-favored and doubly Cabibbo-suppressed amplitudes with the $K^{0}-\overline K^{0}$ mixing. It is estimated to be of order of $\mathcal{O}(10^{-3})$, much larger than the direct $CP$ asymmetry, but missed in the literature. To reveal this new $CP$ violation effect, we propose a new observable, the difference of the $CP$ asymmetries in the $D^{+}\to \pi^{+}K_S^0$ and $D_{s}^{+}\to K^{+} K_S^0$ modes. Once the new effect is determined by experiments, the direct $CP$ asymmetry then can be extracted and used to search for new physics.Comment: 6 pages, 3 figures. Contribution to the proceeding of The 15th International Conference on Flavor Physics & CP Violation, 5-9 June 2017, Prague, Czech Republi

Estimating Water Footprint and Water Economic Values in the Southeastern U.S.

Population growth and climate change have brought water disputes to the southeastern United States. To achieve sustainable water use of the region’s water resources and to alleviate future water stress, it is important to determine 1) current water quantity used to support regional economic activities, and 2) the economic value of water in the southeastern U.S. This thesis has three objectives: 1) build a Multi-Regional Input-Output (MRIO) model to describe multiregional transactions for the following analyses; 2) conduct a water footprint analysis to evaluate how much water use is required for meeting changes in final demand of specific region and economic sectors; 3) set up an MRIO Linear Programming (MRIO-LP) to determine water use demand curves for the southeastern U.S. The water footprint analysis indicates that water requirements embedded in the production of a good varies across study region. The MRIO-LP analysis reveals that economic transactions between regions have a significant impact on the water used to meet regional economic demand. The shadow value of water is higher when multi-regional transactions are introduced into the LP model. In general, the southeastern U.S. economy is less likely to experience water stress until the water availability decrease to 60% of the 2010 USGS level of 82,825,409 acre feet. At this level, the aggregated industry price for water in the southeastern U.S. ranges between 4,041 $/ac.ft. to 5,614$/ac.ft., depending on assumptions pertaining to inter-regional transactions

Three essays on the use of spatial data to inform environmental and resource management

Includes bibliographical references.2022 Fall.This dissertation consists of three essays that use of spatial data to inform trade-offs related to environmental and resource management. The first essay explores how a spatially targeted differentiated payment design can reduce the social cost of achieving a given level of ecosystem service (ES) provisions. Performance comparisons between uniform payments and differentiated payments for ecosystem services help to identify the context under which differentiated payments offer the largest advantage relative to a uniform payment. A mathematical programming model is developed to explore the performance of different payment schemes and to derive generalized lessons from simulations. Then generalized lessons are evaluated with two case studies related to water quality management. It is found that the simulations and case studies align with each other in terms of the total cost reductions, but they diverge in the payment rate choice due to the underlying distributional differences. The findings suggest that a higher payment rate for parcels that systematically provide higher levels of ES can reduce the social cost of providing the ES of interest, particularly for cases where the mean ES provision benefits across land types are different and ES provision targets are relatively low. In the second essay, I examine whether China's pilot carbon emission trading system (ETS) has the co-benefit of reducing local PM2.5 levels. Two ETS pilot provinces are selected to be the treated group, while the control group is constructed with institutional knowledge. Static and dynamic difference-in-differences designs are adopted and compared to reveal the ETS treatment effect. The spatial and temporal variation in the ETS pilot areas allows me to adopt a dynamic two-way fixed effects model to estimate heterogeneous treatment effects on the treated areas. I find that the ETS improves the local air quality in Hubei but not in Guangdong. A further analysis suggests that a sector-standards based allowance allocation mechanism can cause local air quality to deteriorate. The third essay revisits the groundwater resource value question in the Ogallala aquifer through estimation of an econometric model of agricultural land prices that includes fixed effects, with the repeated transactions from the ZTRAX data product. Saturated thickness is used to present the groundwater availability and the study includes irrigated parcels only. Heterogeneous responses in land values to groundwater stock changes are found across Colorado and Nebraska. The marginal value of groundwater stock is highest at low levels of groundwater availability, which implies that additional groundwater depletion in Colorado is more costly than depletion in Nebraska

Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by HPC applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are the two leading lossy compressors available to compress scientific data sets. However, their performance is not consistent across different data sets and across different fields of some data sets: for some fields SZ provides better compression performance, while other fields are better compressed with ZFP. This situation raises the need for an automatic online (during compression) selection between SZ and ZFP, with a minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. We implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70% with the same level of data distortion because of very accurate selection (around 99%) of the best-fit compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio

KS0−KL0K_S^0-K_L^0 Asymmetries and CPCP Violation in Charmed Baryon Decays into Neutral Kaons

We study the $K^0_S-K^0_L$ asymmetries and $CP$ violations in charm-baryon decays with neutral kaons in the final state. The $K^0_S-K^0_L$ asymmetry can be used to search for two-body doubly Cabibbo-suppressed amplitudes of charm-baryon decays, with the one in $\Lambda^+_c\to pK^0_{S,L}$ as a promising observable. Besides, it is studied for a new $CP$-violation effect in these processes, induced by the interference between the Cabibbo-favored and doubly Cabibbo-suppressed amplitudes with the neutral kaon mixing. Once the new CP-violation effect is determined by experiments, the direct $CP$ asymmetry in neutral kaon modes can then be extracted and used to search for new physics. The numerical results based on $SU(3)$ symmetry will be tested by the experiments in the future.Comment: 15 pages, 3 tables. Version published in JHE