Machine Learning to Elucidate Mechanisms of Human Cognition and Epilepsy

Machine learning approaches, a branch of computer science based on the study of complex statistical algorithms, help researchers predict and discover facts about the outside world that may otherwise be too latent and sophisticated for more commonplace approaches. Machine learning techniques are able to explore large amounts of data in a multivariate fashion, so that multiple factors comprising a phenomenon are analyzed simultaneously; a technique that human intelligence is not fully optimized for. Accordingly, machine learning is becoming a widely used assistive tool in many fields of science and technology. In the same vein, the current thesis aims to methodize two main scientific questions within the realm of the neurosciences using machine learning frameworks. Here, machine learning is used to give a viable solution for decoding ongoing brain activities in cognitive studies using data obtained via intracranial electroencephalography (iEEG). IEEG data, represented as a 3D model is proposed, allowing the data to be broken down into distinct bins of information, and in addition, to be able to identify and discard non-informative components. Combining this data modeling approach with suitable machine learning algorithms, facilitates the procedure of interpreting brain activity and enables a traceable and plausible pattern classification solution. Regarding the second scientific question, machine learning is implemented to aid epilepsy patients in tracking and recording their seizures. In order for patients with epilepsy to receive adequate counseling and treatment, accurate documentation of seizure activity is required, however research has shown that self-reporting of seizure activity is often fundamentally unreliable. In this thesis, extensive studies aiming to investigate this question were carried out and subsequently machine learning approaches are proposed to track and register the seizure activity of individuals with epilepsy based on bio-feedback signals. Additionally, an introduction to the state-of-the-art deep artificial neural networks is given; in addition to discussing the applicability of deep learning on natural neural data

Antimagic Labelings of Weighted and Oriented Graphs

A graph $G$ is $k$-$weighted-list-antimagic$ if for any vertex weighting $\omega\colon V(G)\to\mathbb{R}$ and any list assignment $L\colon E(G)\to2^{\mathbb{R}}$ with $|L(e)|\geq |E(G)|+k$ there exists an edge labeling $f$ such that $f(e)\in L(e)$ for all $e\in E(G)$, labels of edges are pairwise distinct, and the sum of the labels on edges incident to a vertex plus the weight of that vertex is distinct from the sum at every other vertex. In this paper we prove that every graph on $n$ vertices having no $K_1$ or $K_2$ component is $\lfloor{\frac{4n}{3}}\rfloor$-weighted-list-antimagic. An oriented graph $G$ is $k$-$oriented-antimagic$ if there exists an injective edge labeling from $E(G)$ into $\{1,\dotsc,|E(G)|+k\}$ such that the sum of the labels on edges incident to and oriented toward a vertex minus the sum of the labels on edges incident to and oriented away from that vertex is distinct from the difference of sums at every other vertex. We prove that every graph on $n$ vertices with no $K_1$ component admits an orientation that is $\lfloor{\frac{2n}{3}}\rfloor$-oriented-antimagic.Comment: 10 pages, 1 figur

Русская религиозная гносеология XIX - начала XX вв.: актуальность и методы изучения

Проводится исследование гносеологических идей русских религиозных мыслителей в контексте проблемы понимания. Автор полагает, что сравнительное изучение русской и западной философии открывает перспективы не только для истолкования самой русской мысли, но также и для углубленного осмысления проблем западной философии, развития всей мировой цивилизации в целом, исходя именно из контекста русской культуры

Triangle-degree and triangle-distinct graphs

Let $G$ be a simple graph and $v$ be a vertex of $G$. The triangle-degree of $v$ in $G$ is the number of triangles that contain $v$. While every graph has at least two vertices with the same degree, there are graphs in which every vertex has a distinct triangle-degree. In this paper, we construct an infinite family of graphs with this property. We also study the vertex degrees and size of graphs with this property

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application

Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions

Objective To identify non-EEG-based signals and algorithms for detection of motor and non-motor seizures in people lying in bed during video-EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings. Methods Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one-lead ECG. All seizure types were analyzed. Feature extraction and machine-learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F$_{1}$ score, and false positives per 24 hours. Results The algorithms were developed in group 1 (35 PwE, 33 seizures) and achieved best results (F$_{1}$ score 56%, sensitivity 67%, precision 45%, false positives 0.7/24 hours) when ECG features alone were used, with no improvement by including accelerometry and electrodermal activity. In group 2 (97 PwE, 255 seizures), this ECG-based algorithm largely achieved the same performance (F$_{1}$ score 51%, sensitivity 39%, precision 73%, false positives 0.4/24 hours). In group 3 (30 PwE, 51 seizures), the same ECG-based algorithm failed to meet up with the performance in groups 1 and 2 (F$_{1}$ score 27%, sensitivity 31%, precision 23%, false positives 1.2/24 hours). ECG-based algorithms were also separately trained on data of groups 2 and 3 and tested on the data of the other groups, yielding maximal F1 scores between 8% and 26%. Significance Our results suggest that algorithms based on ECG features alone can provide clinically meaningful performance for automatic detection of all seizure types. Our study also underscores that the circumstances under which such algorithms were developed, and the selection of the training and test data sets need to be considered and limit the application of such systems to unseen patient groups behaving in different conditions