Can we optimize locations of hospitals by minimizing the number of patients at risk?

Background: To reduce risk of death in acute ST-segment elevation myocardial infraction (STEMI), patients must reach a percutaneous coronary intervention (PCI) within 120 min from the start of symptoms. Current hospital locations represent choices made long since and may not provide the best possibilities for optimal care of STEMI patients. Open questions are: (1) how the hospital locations could be better optimized to reduce the number of patients residing over 90 min from PCI capable hospitals, and (2) how this would affect other factors like average travel time.

Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System.

Indoor motion planning challenges researchers because of the high density and unpredictability of moving obstacles. Classical algorithms work well in the case of static obstacles but suffer from collisions in the case of dense and dynamic obstacles. Recent reinforcement learning (RL) algorithms provide safe solutions for multiagent robotic motion planning systems.

A lightweight classification of adaptor proteins using transformer networks.

Background: Adaptor proteins play a key role in intercellular signal transduction, and dysfunctional adaptor proteins result in diseases. Understanding its structure is the first step to tackling the associated conditions, spurring ongoing interest in research into adaptor proteins with bioinformatics and computational biology. Our study aims to introduce a small, new, and superior model for protein classification, pushing the boundaries with new machine learning algorithms.

Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018.

Background: Multiple chronic diseases in patients are a major burden on the health service system. Currently, diseases are mostly treated separately without paying sufficient attention to their relationships, which results in the fragmentation of the care process. The better integration of services can lead to the more effective organization of the overall health care system.

Solving the Large-Scale TSP Problem in 1 h: Santa Claus Challenge 2020.

The scalability of traveling salesperson problem (TSP) algorithms for handling large-scale problem instances has been an open problem for a long time. We arranged a so-called Santa Claus challenge and invited people to submit their algorithms to solve a TSP problem instance that is larger than 1 M nodes given only 1 h of computing time. In this article, we analyze the results and show which design choices are decisive in providing the best solution to the problem with the given constraints.

Planning Your Route: Where to Start?

Tour planning is an important part of location-based applications. A tour planner provides an optimized path through places of interests (targets) by minimizing the tour length or by applying some other constraints. It is usually formulated as a travelling salesman problem (TSP) or vehicle routing problem (VRP).

A fast O(N) multiresolution polygonal approximation algorithm for GPS trajectory simplification.

Recent advances in geopositioning mobile phones have made it possible for users to collect a large number of GPS trajectories by recording their location information. However, these mobile phones with built-in GPS devices usually record far more data than needed, which brings about both heavy data storage and a computationally expensive burden in the rendering process for a Web browser. To address this practical problem, we present a fast polygonal approximation algorithm in 2-D space for the GPS trajectory simplification under the so-called integral square synchronous distance error criterion in a linear time complexity.

Lossless compression of color map images by context tree modeling.

Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploits interlayer dependencies. Even though the use of a binary alphabet simplifies the context tree construction and exploits spatial dependencies efficiently, it is expected that an equivalent or better result would be obtained by operating directly on the color image without layer separation. In this paper, we extend the previous context-tree-based method to operate on color values instead of binary layers.

Fast agglomerative clustering using a k-nearest neighbor graph.

We propose a fast agglomerative clustering method using an approximate nearest neighbor graph for reducing the number of distance calculations. The time complexity of the algorithm is improved from O(tauN2) to O(tauNlogN) at the cost of a slight increase in distortion; here, tau denotes the number of nearest neighbor updates required at each iteration. According to the experiments, a relatively small neighborhood size is sufficient to maintain the quality close to that of the full search.

Context quantization by kernel Fisher discriminant.

Optimal context quantizers for minimum conditional entropy can be constructed by dynamic programming in the probability simplex space. The main difficulty, operationally, is the resulting complex quantizer mapping function in the context space, in which the conditional entropy coding is conducted. To overcome this difficulty, we propose new algorithms for designing context quantizers in the context space based on the multiclass Fisher discriminant and the kernel Fisher discriminant (KFD).

Compression of map images by multilayer context tree modeling.

We propose a method for compressing color map images by context tree modeling and arithmetic coding. We consider multicomponent map images with semantic layer separation and images that are divided into binary layers by color separation. The key issue in the compression method is the utilization of interlayer correlations, and to solve the optimal ordering of the layers.