Offline and Online Models for Learning Pairwise Relations in Data

Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting

Inference of Effective Pairwise Relations for Data Processing

In various data science and artificial intelligence areas, representation learning is a performance-critical step. While different representation learning methods can detect different descriptive and latent features, many representation learning methods reflect on pairwise relations. The thesis consists of two parts, studying pairwise relations from two points of view: i) Pairwise relations between the states of a Markov chain. ii) Pairwise relations between objects in a dataset based on a desired (dis)similarity measure. In the first part of the thesis, we consider Markov chains, noting that pairwise relations between its states are naturally modeled by the state-transition matrix. We propose a method for modeling the performance of a synchronization method for a multi-processor architecture. Our model introduces and builds upon a cache line bouncing process that models the interaction of threads accessing the shared cache lines. In the second part of the thesis, we consider representation learning using the transitive-aware Minimax distance, which enables the extraction of elongated manifolds and structures in the data. While recent work has made Minimax distances computationally feasible, little attention has been put to its memory footprint, which is naturally O(N^2), the cost of storing all pairwise distances. We do, however, compute a novel hierarchical representation of the data, requiring O(N) memory, from which pairwise Minimax distances can then be efficiently inferred, in total requiring O(N) memory, at the cost of higher computational cost. An alternative sampling-based approach is also derived, which computes approximate Minimax distances, also in O(N) memory but with a significantly reduced computational cost, while still yielding a good approximation, as verified by impressive results on clustering benchmarks. Finally, we develop an unsupervised learning framework for clustering vehicle trajectories based on Minimax distances. The performance of the framework is validated on real-world datasets collected from real driving scenarios, on which satisfactory performance is demonstrated

Korištenje policijskog sustava upozoravanja za otklanjanje pogrešaka u inteligentnim sustavima otkrivanja prekršaja

A broad range of diverse technologies under the generic topic of intelligent transportation systems holds the answer to many transportation problems. Tailgating and the failure to maintain sufficient distance from the next vehicle are among the biggest causes of motorway accidents. There are intelligent systems to detect this violation. Their insufficient accuracy is, however, the reason they are used with reluctance. In this research, a police warning system is proposed to note the detected violating drivers by a short message service. If the violation is repeated, the driver will be fined. Using robability logic, it has been shown that by the proposed system, we can be sure that the error in traffic ticketing will be very low. A survey of police officers found that most of them welcome the proposed system.Širok raspon raznih tehnologija obuhvaćenih pod generičkom temom inteligentnih prometnih sustava nudi odgovor na mnoge probleme u prometu. Vožnja na repu i neodržavanje dovoljnog razmaka iza drugog vozila među najvećim su uzrocima nesreća na autocestama. Iako postoje inteligentni sustavi za otkrivanje ovakvih prekršaja, nevoljko se koriste zbog njihove nedostatne preciznosti. Ovim se istraživanjem predlaže policijski sustav upozoravanja koji bi uslugom kratkih poruka bilježio vozače zatečene u kršenju pravila. U slučaju ponovljenog kršenja, vozaču se izriče novčana kazna. Koristeći se logikom vjerojatnosti, razvidno je da se predloženim sustavom mogu znatno smanjiti pogreške pri izricanju prometnih kazni. Anketa provedena među policijskim službenicima pokazuje da većina njih pozdravlja predloženi sustav

The effect of six weeks of voluntary wheel running exercise on hepatic superoxide dismutase levels and apoptosis-inducing factor after doxorubicin administration in aging model rats

Background: In the last decade, cancer incidence increases progressively with age and older patients cannot participate in treatment programs due to the special physical condition and side effects of anti-tumor drugs. Therefore, the aim of this study was to examine the effect of six-week voluntary wheel running exercise on doxorubicin (DOX)-induced hepatotoxicity in aging model rats. Materials and Methods: Thirty-two Wistar male rats were randomly assigned to exercise and control groups with 4 subgroups. All subjects received a daily injection of D-galactose for nine weeks (100 mg/kg/day, intraperitoneally). The training protocol included six weeks of voluntary wheel running exercise. Doxorubicin was administrated intraperitoneally (1mg/kg/day) for 15 days. Superoxide dismutase (SOD) and apoptosis-inducing factor (AIF) levels were measured after liver tissue homogenization by enzyme-linked immunosorbent assay (ELISA) method. Results: Administration of cumulative dose of DOX caused no significant decrease in SOD and AIF levels (P=0.797, and P=0.869, respectively). Six weeks of voluntary wheel running exercise led to insignificant decrease in SOD and insignificant increase in AIF in comparison to the control group (P=0.377, and P=0.948, respectively). Conclusion: According to the results of the present study, the six-week voluntary wheel running exercise has no significant effect on DOX-induced hepatotoxicity in aging model rats