Cold mix asphalt- an alternative method to pavement rehabilitation

Failure or distress on pavement surface such as pothole, alligator cracking and shear cracking that commonly occurred and required pavement rehabilitation for maintenance proposes. Regarding the cost of reconstruction and the cost of transport and storing removed pavement materials, paying attention to recycling strategies for asphalt pavements has been considered extremely by transportation agencies [1] Pavement rehabilitation is considered as cost efficient instead of reconstruction for new pavement. Pavement rehabilitation is a structural or functional enhancement of a pavement which produces a substantial extension in service life, by substantially improving pavement condition and ride quality. The characteristic of existing pavement conditions is an important component of any rehabilitation design methodology

Investment Strategy Analysis using Support Vector Machines

Investment strategy is the key point of investors who can make profits or otherwise. Investors always focus on their viewpoints subjectively, which may make them fall into the logic puzzle. The purpose of this paper is to integrate the technical analysis of financial markets with an emerging neural network model, Support Vector Machine (SVM), to solve the problem of investment strategy in Taiwan Futures Market (TAIFEX). The evaluation of investment strategy is the most essential task of investment analysis. However, the evaluation is usually time-consuming and laborious for investment experts. An effective and efficient decision support tool could significantly alleviate his/her burden and improve decision quality. The experimental results from a real-case study demonstrate its salient features of generalization and usability compared with original technical analysis

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives

Implicit neural representation has emerged as a powerful method for reconstructing 3D scenes from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. In order to expand the use cases for implicit neural representations, we need to incorporate camera pose estimation capabilities as part of the representation learning, as this is necessary for reconstructing scenes from real-world video sequences where cameras are generally not being tracked. Existing approaches like COLMAP and, most recently, bundle-adjusting neural radiance field methods often suffer from lengthy processing times. These delays ranging from hours to days, arise from laborious feature matching, hardware limitations, dense point sampling, and long training times required by a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP). Our approach leverages accelerated sampling and hash encoding to expedite both pose refinement/estimation and 3D scene reconstruction. Experimental results demonstrate that our method achieves a more than 10 to 20 $\times$ speed improvement in novel view synthesis compared to other bundle-adjusting neural radiance field methods without sacrificing the quality of pose estimation. The github repository can be found here https://github.com/IntelLabs/baa-ngp

Large Language Models for Mathematicians

Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. For many professions, LLMs represent an invaluable tool that can speed up and improve the quality of work. In this note, we discuss to what extent they can aid professional mathematicians. We first provide a mathematical description of the transformer model used in all modern language models. Based on recent studies, we then outline best practices and potential issues and report on the mathematical abilities of language models. Finally, we shed light on the potential of LLMs to change how mathematicians work

Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs

Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semisupervised node classification on graphs, by training a student MLP by knowledge distillation from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during distillation, it has not been systematically studied how to inject the structural information in an explicit and interpretable manner. Inspired by GNNs that separate feature transformation $T$ and propagation $\Pi$, we re-frame the distillation process as making the student MLP learn both $T$ and $\Pi$. Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher, it still comes with a high computational cost from large matrix multiplications during training. To solve this problem, we propose Propagate & Distill (P&D), which propagates the output of the teacher before distillation, which can be interpreted as an approximate process of the inverse propagation. We demonstrate that P&D can readily improve the performance of the student MLP.Comment: 17 pages, 2 figures, 8 tables; 2nd Learning on Graphs Conference (LoG 2023) (Please cite our conference version.). arXiv admin note: substantial text overlap with arXiv:2311.1175

Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation

Instance-based concept learning from multiclass DNA microarray data

BACKGROUND: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance. RESULTS: We investigated the performance of instance-based classifiers, including a NN classifier able to assign a degree of class membership to each sample. This model alleviates a major problem of conventional instance-based learners, namely the lack of confidence values for predictions. The model translates the distances to the nearest neighbors into 'confidence scores'; the higher the confidence score, the closer is the considered instance to a pre-defined class. We applied the models to three real gene expression data sets and compared them with state-of-the-art methods for classifying microarray data of multiple classes, assessing performance using a statistical significance test that took into account the data resampling strategy. Simple NN classifiers performed as well as, or significantly better than, their more intricate competitors. CONCLUSION: Given its highly intuitive underlying principles – simplicity, ease-of-use, and robustness – the k-NN classifier complemented by a suitable distance-weighting regime constitutes an excellent alternative to more complex models for multiclass microarray data sets. Instance-based classifiers using weighted distances are not limited to microarray data sets, but are likely to perform competitively in classifications of high-dimensional biological data sets such as those generated by high-throughput mass spectrometry

The Convergence Envelope For Iterative Estimation In SS-Parse

A novel and recently developed fast magnetic resonance imaging (MRI) technique, Single-Shot Parameter Assessment by Retrieval from Signal Encoding (SSPARSE), promises significant improvements in robustness and accuracy of local signal parameter estimates compared to the conventional MRI methods. In using a more accurate signal model, the reconstruction for SS-PARSE differs from the traditional Fourier transform method. An iterative estimation algorithm, progressive length conjugate gradient (PLCG), is currently employed by SS-PARSE to reconstruct independent parameter maps of local magnetization, transverse relaxation rate and frequency. In practice, the large number of degrees of freedom and partial poor conditioning of the problem itself brings tremendous difficulties for PLCG convergence. In this thesis, investigations of the PLCG convergence were executed using simulations and experiments. Simulation studies were performed on a numerical clock shaped phantom to explore the convergence envelope, which contains the successfully “converged” parameter values but not the partially or “non-converged” parameter values. Also, various PLCG control parameter settings are tested, and the best setting in terms of the convergence envelope is a steadily increasing data length with a small regularization operator. Experimental four-tube phantom results confirmed the convergence characteristics predicted by simulation studies. Further, a signal-area based clustering iii method is presented which segments and eliminates background noise and unreliable estimates where local convergence failed