Schnyder woods for higher genus triangulated surfaces, with applications to encoding

Schnyder woods are a well-known combinatorial structure for plane triangulations, which yields a decomposition into 3 spanning trees. We extend here definitions and algorithms for Schnyder woods to closed orientable surfaces of arbitrary genus. In particular, we describe a method to traverse a triangulation of genus $g$ and compute a so-called $g$-Schnyder wood on the way. As an application, we give a procedure to encode a triangulation of genus $g$ and $n$ vertices in $4n+O(g \log(n))$ bits. This matches the worst-case encoding rate of Edgebreaker in positive genus. All the algorithms presented here have execution time $O((n+g)g)$, hence are linear when the genus is fixed.Comment: 27 pages, to appear in a special issue of Discrete and Computational Geometr

Algorithms and Bounds for Drawing Non-planar Graphs with Crossing-free Subgraphs

We initiate the study of the following problem: Given a non-planar graph G and a planar subgraph S of G, does there exist a straight-line drawing {\Gamma} of G in the plane such that the edges of S are not crossed in {\Gamma} by any edge of G? We give positive and negative results for different kinds of connected spanning subgraphs S of G. Moreover, in order to enlarge the subset of instances that admit a solution, we consider the possibility of bending the edges of G not in S; in this setting we discuss different trade-offs between the number of bends and the required drawing area.Comment: 21 pages, 9 figures, extended version of 'Drawing Non-planar Graphs with Crossing-free Subgraphs' (21st International Symposium on Graph Drawing, 2013

Treewidth distance on phylogenetic trees

In this article we study the treewidth of the display graph, an auxiliary graph structure obtained from the fusion of phylogenetic (i.e., evolutionary) trees at their leaves. Earlier work has shown that the treewidth of the display graph is bounded if the trees are in some formal sense topologically similar. Here we further expand upon this relationship. We analyse a number of reduction rules, commonly used in the phylogenetics literature to obtain fixed parameter tractable algorithms. In some cases (the subtree reduction) the reduction rules behave similarly with respect to treewidth, while others (the cluster reduction) behave very differently, and the behaviour of the chain reduction is particularly intriguing because of its link with graph separators and forbidden minors. We also show that the gap between treewidth and Tree Bisection and Reconnect (TBR) distance can be infinitely large, and that unlike, for example, planar graphs the treewidth of the display graph can be as much as linear in its number of vertices. A number of other auxiliary results are given. We conclude with a discussion and list a number of open problems

Storage systems for mobile-cloud applications

Mobile devices have become the major computing platform in todays world. However, some apps on mobile devices still suffer from insufficient computing and energy resources. A key solution is to offload resource-demanding computing tasks from mobile devices to the cloud. This leads to a scenario where computing tasks in the same application run concurrently on both the mobile device and the cloud. This dissertation aims to ensure that the tasks in a mobile app that employs offloading can access and share files concurrently on the mobile and the cloud in a manner that is efficient, consistent, and transparent to locations. Existing distributed file systems and network file systems do not satisfy these requirements. Furthermore, current offloading platforms either do not support efficient file access for offloaded tasks or do not offload tasks with file accesses. The first part of the dissertation addresses this issue by designing and implementing an application-level file system named Overlay File System (OFS). OFS assumes a cloud surrogate is paired with each mobile device for task and storage offloading. To achieve high efficiency, OFS maintains and buffers local copies of data sets on both the surrogate and the mobile device. OFS ensures consistency and guarantees that all the reads get the latest data. To effectively reduce the network traffic and the execution delay, OFS uses a delayed-update mechanism, which combines write-invalidate and write-update policies. To guarantee location transparency, OFS creates a unified view of file data. The research tests OFS on Android OS with a real mobile application and real mobile user traces. Extensive experiments show that OFS can effectively support consistent file accesses from computation tasks, no matter where they run. In addition, OFS can effectively reduce both file access latency and network traffic incurred by file accesses. While OFS allows offloaded tasks to access the required files in a consistent and transparent manner, file accesses by offloaded tasks can be further improved. Instead of retrieving the required files from its associated mobile device, a surrogate can discover and retrieve identical or similar file(s) from the surrogates belonging to other users to meet its needs. This is based on two observations: 1) multiple users have the same or similar files, e.g., shared files or images/videos of same object; 2) the need for a certain file content in mobile apps can usually be described by context features of the content, e.g., location, objects in an image, etc.; thus, any file with the required context features can be used to satisfy the need. Since files may be retrieved from surrogates, this solution improves latency and saves wireless bandwidth and power on mobile devices. The second part of the dissertation proposes and develops a Context-Aware File Discovery Service (CAFDS) that implements the idea described above. CAFDS uses a self-organizing map and k-means clustering to classify files into file groups based on file contexts. It then uses an enhanced decision tree to locate and retrieve files based on the file contexts defined by apps. To support diverse file discovery demands from various mobile apps, CAFDS allows apps to add new file contexts and to update existing file contexts dynamically, without affecting the discovery process. To evaluate the effectiveness of CAFDS, the research has implemented a prototype on Android and Linux. The performance of CAFDS was tested against Chord, a DHT based lookup scheme, and SPOON, a P2P file sharing system. The experiments show that CAFDS provides lower end-to-end latency for file search than Chord and SPOON, while providing similar scalability to Chord

Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance

The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance. This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems by exploiting data from numerical simulations, experimental campaigns, and sensor datasets (SCADA). The different Machine Learning approaches adopted include clustering algorithms, regression algorithms and dimensionality reduction techniques: Autoencoder and Principal Component Analysis. A first work shows the potential of unsupervised learning approaches (clustering algorithms) in exploring a Design of Experiment of 76 numerical simulations for turbomachinery design purposes. The second work takes advantage of a nonsequential experimental dataset, measured on a rotating turbine rig characterized by 48 blades divided into 7 sectors that share the same baseline rotor geometry but have different tip designs, to infer and dissect the causal relationship among different tip geometries and unsteady aero-thermodynamic performance via a novel Machine-Learning procedure based on dimensionality reduction techniques. The last application proposes a new anomaly detection framework for gensets in DH networks, based on SCADA data that exploits and compares the performance of regression algorithms such as XGBoost and Multi-layer Perceptron

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Predicting High-Cap Tech Stock Polarity: A Combined Approach using Support Vector Machines and Bidirectional Encoders from Transformers

The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not contain sentiment analysis-related features. The results indicated that sentiment containing datasets were typically better predictors, with improved model accuracy. However, the results did not reflect the improvements shown by similar research and will require further research to determine the nature of the relationship between sentiment and higher model performance

Fast Reconfiguration for Programmable Matter

The concept of programmable matter envisions a very large number of tiny and simple robot particles forming a smart material. Even though the particles are restricted to local communication, local movement, and simple computation, their actions can nevertheless result in the global change of the material's physical properties and geometry. A fundamental algorithmic task for programmable matter is to achieve global shape reconfiguration by specifying local behavior of the particles. In this paper we describe a new approach for shape reconfiguration in the amoebot model. The amoebot model is a distributed model which significantly restricts memory, computing, and communication capacity of the individual particles. Thus the challenge lies in coordinating the actions of particles to produce the desired behavior of the global system. Our reconfiguration algorithm is the first algorithm that does not use a canonical intermediate configuration when transforming between arbitrary shapes. We introduce new geometric primitives for amoebots and show how to reconfigure particle systems, using these primitives, in a linear number of activation rounds in the worst case. In practice, our method exploits the geometry of the symmetric difference between input and output shape: it minimizes unnecessary disassembly and reassembly of the particle system when the symmetric difference between the initial and the target shapes is small. Furthermore, our reconfiguration algorithm moves the particles over as many parallel shortest paths as the problem instance allows