Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

We postulate and analyze a nonlinear subsampling accuracy loss (SSAL) model based on the root mean square error (RMSE) and two SSAL models based on the mean square error (MSE), suggested by extensive preliminary simulations. The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped (FUD) and the fraction of items dropped (FID). We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering (CF) algorithm. The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items. Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics. The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user (or an item) vs. not dropping it. Moreover, one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient. Most importantly, the models are constant in the sense that they are written in closed-form using the considered data characteristics (densities and numbers of users and items). The models are validated through extensive simulations based on 850 synthetically generated primary (pre-subsampling) matrices derived from the 25M MovieLens dataset. Nearly 460 000 subsampled rating matrices were then simulated and subjected to the singular value decomposition (SVD) CF algorithm. Further validation was conducted using the 1M MovieLens and the Yahoo! Music Rating datasets. The models were constant and significant across all 3 datasets

Motion Planning of a Snake-Like Robot Using an Optimized Harmonic Potential Field

Snake-like robots have gained popularity in the last three decades for their ability to utilize several gaits in order to navigate through different terrains. They are analogous in morphology to snakes, tentacles, and elephant trunks. We propose a novel method of navigating a snake-like robot based on the Harmonic Field with Optimized Boundary Conditions (HFOBC) and a boundary following algorithm. We apply the HFOBC navigation function using a number of fictitious charges equally spaced on each link. These charges actively follow the potential field towards the target. Futhermore, a generalized mathematical model for an n-link snake-like robot based on Lagrange formulation has also been proposed in this paper

Parallel-SymD: A Parallel Approach to Detect Internal Symmetry in Protein Domains

Internally symmetric proteins are proteins that have a symmetrical structure in their monomeric single-chain form. Around 10–15% of the protein domains can be regarded as having some sort of internal symmetry. In this regard, we previously published SymD (symmetry detection), an algorithm that determines whether a given protein structure has internal symmetry by attempting to align the protein to its own copy after the copy is circularly permuted by all possible numbers of residues. SymD has proven to be a useful algorithm to detect symmetry. In this paper, we present a new parallelized algorithm called Parallel-SymD for detecting symmetry of proteins on clusters of computers. The achieved speedup of the new Parallel-SymD algorithm scales well with the number of computing processors. Scaling is better for proteins with a larger number of residues. For a protein of 509 residues, a speedup of 63 was achieved on a parallel system with 100 processors

Traffic Estimation of Various Connected Vehicle Penetration Rates: Temporal Convolutional Network Approach

Traffic estimation using probe vehicle data is a crucial aspect of traffic management as it provides real-time information about traffic conditions. This study introduced a novel framework for traffic density estimation using Temporal Convolutional Network (TCN) for time series data. The study used two datasets collected from a three-leg intersection in Greece and a four-leg intersection in Germany. The model was built to predict the density in an approach of the signalized intersection using features extracted from the other approaches. The results showed that the highest accuracy was achieved when only probe vehicle data was used. This implies that relying solely on probe vehicle data from two approaches can effectively predict traffic density in the third approach, even when the Market Penetration Rate (MPR) is low. The results also indicated that having Signal Phase and Timing (SPaT) information may not be necessary for high accuracy in traffic estimation and that as the MPR increases, the model becomes more predictable.</p

Evaluating a signalized intersection performance using unmanned aerial Data

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.</p