Activity recognition from videos with parallel hypergraph matching on GPUs

In this paper, we propose a method for activity recognition from videos based on sparse local features and hypergraph matching. We benefit from special properties of the temporal domain in the data to derive a sequential and fast graph matching algorithm for GPUs. Traditionally, graphs and hypergraphs are frequently used to recognize complex and often non-rigid patterns in computer vision, either through graph matching or point-set matching with graphs. Most formulations resort to the minimization of a difficult discrete energy function mixing geometric or structural terms with data attached terms involving appearance features. Traditional methods solve this minimization problem approximately, for instance with spectral techniques. In this work, instead of solving the problem approximatively, the exact solution for the optimal assignment is calculated in parallel on GPUs. The graphical structure is simplified and regularized, which allows to derive an efficient recursive minimization algorithm. The algorithm distributes subproblems over the calculation units of a GPU, which solves them in parallel, allowing the system to run faster than real-time on medium-end GPUs

A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset

This paper aims to determine which is the best human action recognition method based on features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the papers that make reference to MSR Action3D, the most used dataset that includes depth information acquired from a RGB-D device, has been performed. We found that the validation method used by each work differs from the others. So, a direct comparison among works cannot be made. However, almost all the works present their results comparing them without taking into account this issue. Therefore, we present different rankings according to the methodology used for the validation in orden to clarify the existing confusion.Comment: 16 pages and 7 table

Sparse Approximate Inference for Spatio-Temporal Point Process Models

Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretised log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both non-linear filtering-smoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with both Gaussian and point-process observations and use it to reconstruct the conflict intensity and dynamics in Afghanistan from the WikiLeaks Afghan War Diary

Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Model of models -- Part 1

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.Comment: arXiv admin note: text overlap with arXiv:2301.1355

A Survey on Array Storage, Query Languages, and Systems

Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Navigation with uncertain spatio-temporal resources

Supporting people with intelligent navigation instructions enables users to efficiently achieve trip-related objectives (e.g., minimum travel time or fuel consumption) and preserves them from making unnecessary detours. This, in turn, enables them to save time, money and, additionally, minimize $CO_2$ emissions. For these reasons, manufacturers integrate navigation systems into almost all modern automobiles. Nevertheless, most of them support only simple routing instructions, i.e., how to drive from location A to B. Albeit, people are regularly faced with more complex decisions, e.g. navigating to a cheap gas station on the route while incorporating dynamic gas price changes. Another example-scenario is after reaching the destination, an available facility to park needs to be found. So far, people cruise almost randomly around the goal area in the search for a parking space. As a consequence, persons valuable time is consumed and unnecessary traffic arises. Besides private persons, transportation companies have to make complex mobility decisions. For instance, taxi drivers have to find out where to move next whenever the taxi is idle. There are plenty possibilities for where the taxi driver could go. In case the last drop-off was in a sparsely populated region, waiting for a call from the taxi office will likely result in a longer drive to the next customer. In turn, customer satisfaction decreases with a longer waiting time and implies a potential loss of customers. Recently, the number of data sources that potentially improve these mobility decisions increased. For instance, on-street parking sensors track the current state of the spaces (e.g. Melbourne), mobile applications collect taxi requests from customers and gas stations publish the current prices all in real-time. This thesis investigates the question of how to design algorithms such that they exploit this volatile data. Standard routing algorithms assume a static world. But the availability of passengers, gas prices and the availability of parking spots change over time in a non-deterministic manner. Hence, we model multiple real-world applications as Markov decision processes (MDP), i.e., a framework for sequential decision making under uncertainty. Depending on the task, we propose to solve the MDP with dynamic programming, replanning and hindsight planning or reinforcement learning. Ultimately, we combine all applications in a single problem domain. Subsequently, we propose a reinforcement learning approach that solves all applications in this domain without modification. Furthermore, it decouples the routing task from solving the application itself. Hence, it is transferable to previously unseen street networks without further training.Durch intelligente Navigationssysteme werden Verkehrsteilnehmer davor bewahrt, Umwege zu fahren. Dadurch sparen sie Zeit, Geld und verringern den $CO_2$-Ausstoß. Aus diesem Grund verbauen Hersteller Navigationssysteme in fast allen Neuwägen. Bis heute unterstützen die meisten Systeme nur einfache Routenplanung, die den kürzesten oder schnellsten Pfad von A nach B berechnen. Dennoch müssen Fahrer regelmäßig Entscheidungen darüber hinaus treffen. Beispielsweise soll eine möglichst günstige Tankstelle auf dem Weg zum eigentlichen Ziel besucht werden. Allerdings kann diese ihre Preise, während der Fahrer oder die Fahrerin auf dem Weg dort hin ist, dynamisch ändern. Anschließend muss, sobald das eigentliche Ziel erreicht ist, ein Parkplatz gefunden werden. Bisher fahren Parkplatzsuchende zufällig durch das Zielgebiet in der Hoffnung möglichst schnell einen freien Parkplatz zu finden. Die Suche verursacht zusätzlichen Verkehr und der Fahrer oder die Fahrerin verbringt mehr Zeit auf der Straße. Neben Privatpersonen müssen auch Transportunternehmen komplexe Entscheidungen über Bewegungen treffen. Zum Beispiel muss ein Taxifahrer, wenn er gerade keinen Fahrgast hat, entscheiden, wo er sich als nächstes positioniert. Zwar könnte er am letzten Zielort warten, bis er einen Anruf der Taxizentrale bekommt. Falls jedoch der letzte Zielort in einem entlegenen Gebiet ist, muss der nächste Fahrgast wahrscheinlich lange warten, bis der Fahrer oder die Fahrerin bei ihm ankommt. Damit sinkt die Kundenzufriedenheit, was wiederum einen potentiellen Verlust der Kunden bedeutet. Seit Kurzem gibt es immer mehr Datenquellen, die Entscheidungen für diese Probleme verbessern. Beispielsweise wird durch Parkplatzsensoren die Verfügbarkeit der Parkplätze verfolgt, mobile Anwendungen sammeln Anfragen über Fahrgäste und Tankstellen veröffentlichen ihren aktuellen Preis in Echtzeit. In dieser Arbeit wird der Forschungsfrage nachgegangen, wie Algorithmen gestaltet werden können, sodass diese veränderlichen Informationen verwendet werden können. Standard-Routing-Algorithmen gehen von einer statischen Welt aus. Aber die Verfügbarkeit von Fahrgästen, die Tankstellenpreise und die Parkplatzzustände ändern sich nicht deterministisch. Aus diesem Grund modellieren wir eine Reihe von Anwendungen als Markov-Entscheidungsproblem (MDP). Applikationsabhängig schlagen wir vor, das MDP mit dynamischer Programmierung, Replanning bzw. Hindsight Planning oder Reinforcement Learning zu lösen. Abschließend fassen wir alle Anwendungen in einer Domäne zusammen. Dadurch können wir einen Reinforcement Learning Ansatz definieren, der alle Anwendungen in dieser Domäne ohne Änderung lösen kann. Dieser Ansatz ermöglicht es, die Routenplanung von der eigentlichen Problemstellung zu lösen. Dadurch ist die gelernte Funktionsapproximation auch auf bisher unbekannte Straßennetze ohne weiteres Training anwendbar