USE OF LONG TERM WEATHER DATA AND SPATIALLY DELINEATED FIELD ATTRIBUTES TO PREDICT WATER AND ENERGY CONSERVATION FROM VARIABLE RATE IRRIGATION

The declining levels of the Ogallala aquifer calls for more judicious use of water. Studies have shown that VRI has the potential for water savings. But adoption of VRI is still very low. The major reason is lack of information on the returns from the VRI systems and its feasibility in different fields. Also, a quantification of the required reduction in prices of VRI is necessary. So, an economic return analysis of VRI strategies was done to compare it to uniform irrigation management (UIM) using a water balance model based on long term weather data and field properties for a field near Elgin, Nebraska, containing four soil textures, for a period of 1988-2016. Five strategies were established to work on the study, namely, Field Capacity-VRI, Driest Soil Trigger-VRI, Water Mining-VRI, CUIM and Total IUM. Thirteen field distributions were developed to study the variation in the field requirements of VRI and results were quantified based on three cost factors (100%, 75%, and 50%). The water balance model predicted irrigation amounts and frequencies for the five strategies which were used to determine the total water applied, total cost of application as well as an input for the AquaCrop model to simulate the yields. Irrigation costs were calculated based on three prices of VRI ($21,379,$16,034 and $10,690) and profits were calculated for each strategy and distribution and savings were established by comparing profits with those of CUIM. Results indicate that VRI is not feasible for the field near Elgin, NE, at present costs because an average yearly application reduction of 2.4$2907 yearly savings on CUIM. It was also observed that VRI worked best in fields where water mining is justified, that is, the fields with higher variation in water holding capacities soils with the more wet soils covering at least 60% of area. Also, a reduction of at least 25% in the initial costs was considered essential for VRI to be beneficial. Adviser: William Kran

Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching

Current techniques and systems for distributed model training mostly assume that clusters are comprised of homogeneous servers with a constant resource availability. However, cluster heterogeneity is pervasive in computing infrastructure, and is a fundamental characteristic of low-cost transient resources (such as EC2 spot instances). In this paper, we develop a dynamic batching technique for distributed data-parallel training that adjusts the mini-batch sizes on each worker based on its resource availability and throughput. Our mini-batch controller seeks to equalize iteration times on all workers, and facilitates training on clusters comprised of servers with different amounts of CPU and GPU resources. This variable mini-batch technique uses proportional control and ideas from PID controllers to find stable mini-batch sizes. Our empirical evaluation shows that dynamic batching can reduce model training times by more than 4x on heterogeneous clusters

Scavenger: A Cloud Service for Optimizing Cost and Performance of ML Training

While the pay-as-you-go nature of cloud virtual machines (VMs) makes it easy to spin-up large clusters for training ML models, it can also lead to ballooning costs. The 100s of virtual machine sizes provided by cloud platforms also makes it extremely challenging to select the ``right'' cloud cluster configuration for training. Furthermore, the training time and cost of distributed model training is highly sensitive to the cluster configurations, and presents a large and complex tradeoff-space. In this paper, we develop principled and practical techniques for optimizing the training time and cost of distributed ML model training on the cloud. Our key insight is that both parallel and statistical efficiency must be considered when selecting the optimum job configuration parameters such as the number of workers and the batch size. By combining conventional parallel scaling concepts and new insights into SGD noise, our models accurately estimate the time and cost on different cluster configurations with < 5% error. Using the repetitive nature of training and our models, we can search for optimum cloud configurations in a black-box, online manner. Our approach reduces training times by 2 times and costs more more than 50%. Compared to an oracle-based approach, our performance models are accurate to within 2% such that the search imposes an overhead of just 10%

On Characterization of δ-Topological Vector Space

The main objective of this paper is to present the study of δ-topological vector space, δ-topological vector space are defined by using δ-open sets and δ-continuous mapping which was introduced by J.H.H. Bayati[3] in 2019. In this paper, along with basic inherent properties of the space, δ-closure and δ-interior operators are discussed in detail. We characterize some important properties like translation, dilation of the δ-topological vector space and an example of δ-topological vector space is also established

Vitamin D Intoxication in An Elderly: A Case Report

Vitamin D is a very common prescribed drug for numerous indications. Due to scarce knowledge and poor awareness of the various formulations, preparations and dosages of Vitamin D, there are many chances of prescription errors, medication errors, product use issue and undesirable adverse drug reactions. We hereby detail case of 70-year-old ex-army gentleman reported to us with a history of lethargy, confusion, reduced appetite and gait imbalance since few days with a history of knee replacement surgery 2 years back. Medical history was not of much relevance before it was revealed that he was getting cholecalciferol injection with a strength of 600000 IU once a week for few months. He was detected to have very high serum vitamin D level and hypercalcemia. He was started on intravenous fluids, diuretics and glucocorticoids. In a few days, after effective treatment, the patient was discharged in a recovering stage and advised to stop intake calcium and vitamin D in any form. At his last follow up, after a few months of discharge, he had totally recovered

The NTT and residues of a polynomial modulo factors of X2d+1X^{2^d} + 1

The Number Theoretic Transform (NTT) plays a central role in efficient implementations of cryptographic primitives selected for Post Quantum Cryptography. Although it certainly exists, academic papers that cite the NTT omit the connection between the NTT and residues of a polynomial modulo factors of $X^{2^d} + 1$ and mention only the final expressions of what the NTT computes. This short paper establishes that connection and, in doing so, elucidates key aspects of computing the NTT. Based on this, the specific instantiations of the NTT function used in CRYSTALS-Kyber and CRYSTALS-Dilithium are derived