New Deformation Model for Realistic Real-Time Simulation of Soft Bodies in the Context of Virtual Surgery

OSSimTech develops and sells virtual reality (VR) surgical simulators. This proposal aims to improve the efficiency of the simulation of cutting and deforming organs. The current simulator used by the company is appropriate for a range of surgery scenarios, but for several scenarios, it is too long to compute. The company has plans to expand its portfolio of simulators to less powerful devices, potentially including portable devices such as tablets. In this context, the development of a low-cost simulation approach is of great interest for OSSimTech.

LAFORGE: Log Analytics For Operational Intelligence

The goal of this project is to explore the use of log analytics and machine/deep learning techniques to improve Ubisoft operational intelligence. Logs contain a wealth of information, but often hindered by the lack of best practices, tools, and processes. Despite the importance of logging, the area has not evolved much over the years. At Ubisoft, logs are used extensively for various system diagnosis tasks. The analysis of logs, however, is usually performed manually, limiting the full potential of the information contained in logs.

Efficacy of a perceptive-cognitive training to improve ice-hockey performance

The Neurotracker training requires participants to follow and identify holographic tennis balls moving randomly in a 3D environment. The company developing this training, CogniSens, inc. claims that it can improve sports performance by enhancing, among others, selective attention and working memory. This research project tests the efficacy of the NeuroTracker training to improve sports performance in ice-hockey.

Kali: Optimizing Resource Utilization in Distributed Clusters

Decreasing operational costs is a key criterion for organizations that manage compute clusters, such as Amazon, Microsoft, Google, Alibaba, etc. One way to decrease costs it to improve resource utilization in the cluster [13, 14]. Yet, high resource utilization can negatively affect workload performance and thus user satisfaction. Performance degradation happens when workloads running on the same machine “compete” for shared resources, e.g., a workload that consumes a large portion of memory delays execution of other, memory-intensive workloads.

Signal recognition with machine learning using wavelet features

The emerging techniques of machine learning and artificial intelligence are making revolutionary changes in all kinds of the industrial world. As a high-tech business solution company, Quartic.ai uses these modern techniques to help industrial manufactory companies work more efficiently. One of the challenging problems is to make the computer automatically recognize the status and behavior of the machine from the data collected by different sensors, so that people can record the history of the machine and conduct further analysis.

Anomaly Detection in Land Vehicle Traffic Activity

This project’s objective is to develop a capability to detect and describe anomalous situations in ground vehicle traffic. Anomalous situations are described as substantial/important changes from the traffic frequently observed for a particular route and/or time. In this sense, anomaly can be quantitatively measured by the degree of predictability of current traffic given historical observations. In the use case of interest, information from traffic will be captured from a GMTI sensor performing recurrent surveillances (1-3 hours per day, multiple days per week) over the same area.

Named Entities Recognition for Customer Service Automated System

This project aims at creating a robust, efficient and reliable tool for Named Entities Recognition (NER) from vast amounts of textual data related to the customer service.
Named entities recognition, a subtask of information extraction, seeks to locate and classify elements in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
Moreover, those extracted named entities will be mapped to existing concepts of an ontology.
The development of such tool will enable easier and quicker decision-ma

Development of transparent near-eye display using a sparse microlens array and lightfield principles

Head-mounted displays (HMDs) allow a convenient delivery of visualized data to the user. HMDs in the form of glasses and goggles (otherwise known as smart glasses and goggles), such as Vuzix Blade and Epson Moverio [1-3], have been introduced but the public acceptance of these devices have been rather lackluster. Part of the sluggish acceptance may be attributed to the still-high device costs (>$1000) and a large form-factor, owning largely to the fact that these devices utilize unique and sophisticated optics on dedicated and non-retrofittable platforms.

Automated Extraction of Chemical Synthesis Procedures Using Machine Learning

The project involves the development of a system to automate the extraction of synthesis procedures from the texts of organic chemistry journal articles that describe explicit, experimental syntheses of organic compounds and their corresponding properties.

The Development of an Improved Model for Start-up Accelerators

The goal of this study will be to develop a new and improved model for start-up accelerators. These accelerators can be defined as programs that help start-ups develop their business model and acquire capital such that they can grow and thrive in today’s business environment. To create the new model, start-ups that are part of the Masters in Technology Management and Entrepreneurship (MTME) program will be examined. This way, it will be possible to find which program characteristics have the greatest effect on start-ups and how they can be improved.

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