Approximate Range Counting Revisited

We study range-searching for colored objects, where one has to count (approximately) the number of colors present in a query range. The problems studied mostly involve orthogonal range-searching in two and three dimensions, and the dual setting of rectangle stabbing by points. We present optimal and near-optimal solutions for these problems. Most of the results are obtained via reductions to the approximate uncolored version, and improved data-structures for them. An additional contribution of this work is the introduction of nested shallow cuttings

Potential Value-added Utilization of Wood Ash in Construction Materials

Wood ash is a byproduct from biomass power plants. Most of the wood ash is currently being disposed of as landfilling material that causes severe economic and environmental concerns. This project focuses on the feasibility of using this wood ash in construction materials. Wood ash was found to contain varieties of mineral phases including calcium carbonate, calcium aluminate, and quartz. Based on the chemical composition, the efficacy of wood ash (i) as supplementary cementitious materials (SCM), (ii) in the controlled low strength materials (CLSM) production and (iii) Portland cement production was evaluated. Wood ash with adequate pozzolanic properties can be used as supplementary cementitious materials (SCM) in concrete production. In practice, coal fly ash, slag, and silica fume are commonly used SCM in concrete and these materials positively contribute to the concrete strength and durability at later ages via pozzolanic reaction. Controlled low strength materials (CLSM) are typically produced with high coal fly ash content, low cement content, water and aggregates, and the strength are attained via pozzolanic reaction. Because of the probable pozzolanic properties, wood ash can partially or fully replace fly ash in the production of controlled low strength materials (CLSM). Production of Ordinary Portland Cement (OPC) requires 60 to 70% of CaO phases and generally, it is supplied by using limestone (CaCO3) phases. The significant amount of calcium carbonate phase present in wood ash makes it a potential material to be used as a raw material for cement production. For SCM, the test results illustrated that the workability of wood ash blended samples are found to reduce as the replacement level is increased, this is because of the presence of metallic alumina. The replacement of wood ash in both ground and sieved form is studied because of the presence of less fine particles. The ground samples are noted to give better strength than that of the sieved ones. The samples with ground wood ash are found to have hydraulic properties. The ground wood ash can replace cement up to 30% and sieved wood ash can replace cement up to 20% in mortar samples without any significant effect on compressive strength. In the CLSM production, the wood ash can replace fly ash by 100 percent without any decrease in the target strength. The cement clinker produced using wood ash as a raw material is found to have a higher reaction rate than that of Ordinary Portland Cement (OPC). The wood ash cement clinker is found to have a very similar chemical composition as that of an ordinary Portland cement clinker

Object-oriented shipboard electric power system library

The objective of this thesis is to explore the powerful capabilities of using an object-oriented modeling language to model and simulate an all electric Naval Shipboard Power System. Modelica has been used to model and simulate the shipboard power system which acts as an alternative simulation tool. The shipboard system is developed using the concept of packages. Different components like the buck converter, inverter, and AC machines have been modeled as a part of the library to develop the power system. The shipboard system has been simulated as two decoupled systems, the AC and DC systems. This research further focuses on developing a networked protection system to detect and clear faults and protect the shipboard power system from complete breakdown. A discrete supervisory controller has been designed using Petri nets as part of the protection system to control the converters and clear faults. A communication network has also been modeled for communication. Two different case studies, the open circuit test, and short circuit test were performed to test the effectiveness of the protection system and the simulation results are presented. This thesis also gives an overview of different properties of Modelica along with its advantages over other simulation tools, a detailed survey of different types of object-oriented simulation tools available, a comparison of different power electronics simulation tools, and some of the previous work done in Modelica

4D Range Reporting in the Pointer Machine Model in Almost-Optimal Time

In the orthogonal range reporting problem we must pre-process a set $P$ of multi-dimensional points, so that for any axis-parallel query rectangle $q$ all points from $q\cap P$ can be reported efficiently. In this paper we study the query complexity of multi-dimensional orthogonal range reporting in the pointer machine model. We present a data structure that answers four-dimensional orthogonal range reporting queries in almost-optimal time $O(\log n\log\log n + k)$ and uses $O(n\log^4 n)$ space, where $n$ is the number of points in $P$ and $k$ is the number of points in $q\cap P$ . This is the first data structure with nearly-linear space usage that achieves almost-optimal query time in 4d. This result can be immediately generalized to $d\ge 4$ dimensions: we show that there is a data structure supporting $d$-dimensional range reporting queries in time $O(\log^{d-3} n\log\log n+k)$ for any constant $d\ge 4$.Comment: Accepted for publication in SODA'2

Simple Multi-Pass Streaming Algorithms for Skyline Points and Extreme Points

In this paper, we present simple randomized multi-pass streaming algorithms for fundamental computational geometry problems of finding the skyline (maximal) points and the extreme points of the convex hull. For the skyline problem, one of our algorithm occupies O(h) space and performs O(log n) passes, where h is the number of skyline points. This improves the space bound of the currently best known result by Das Sarma, Lall, Nanongkai, and Xu [VLDB\u2709] by a logarithmic factor. For the extreme points problem, we present the first non-trivial result for any constant dimension greater than two: an O(h log^{O(1)}n) space and O(log^dn) pass algorithm, where h is the number of extreme points. Finally, we argue why randomization seems unavoidable for these problems, by proving lower bounds on the performance of deterministic algorithms for a related problem of finding maximal elements in a poset

Recommending Related Products Using Graph Neural Networks in Directed Graphs

Related product recommendation (RPR) is pivotal to the success of any e-commerce service. In this paper, we deal with the problem of recommending related products i.e., given a query product, we would like to suggest top-k products that have high likelihood to be bought together with it. Our problem implicitly assumes asymmetry i.e., for a phone, we would like to recommend a suitable phone case, but for a phone case, it may not be apt to recommend a phone because customers typically would purchase a phone case only while owning a phone. We also do not limit ourselves to complementary or substitute product recommendation. For example, for a specific night wear t-shirt, we can suggest similar t-shirts as well as track pants. So, the notion of relatedness is subjective to the query product and dependent on customer preferences. Further, various factors such as product price, availability lead to presence of selection bias in the historical purchase data, that needs to be controlled for while training related product recommendations model. These challenges are orthogonal to each other deeming our problem nontrivial. To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph. In order to capture product asymmetry, we employ an asymmetric loss function and learn dual embeddings for each product, by appropriately aggregating features from its neighborhood. DAEMON leverages multi-modal data sources such as catalog metadata, browse behavioral logs to mitigate selection bias and generate recommendations for cold-start products. Extensive offline experiments show that DAEMON outperforms state-of-the-art baselines by 30-160% in terms of HitRate and MRR for the node recommendation task.Comment: This work was accepted in ECML PKDD 202