Non-linear Convolution Filters for CNN-based Learning

During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.Comment: 9 pages, 5 figures, code link, ICCV 201

Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training

Objective: In this work, we study the problem of cross-subject motor imagery (MI) decoding from electroenchephalography (EEG) data. Multi-subject EEG datasets present several kinds of domain shifts due to various inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and also impede robust cross-subject generalization. Method: We propose a two-stage model ensemble architecture, built with multiple feature extractors (first stage) and a shared classifier (second stage), which we train end-to-end with two loss terms. The first loss applies curriculum learning, forcing each feature extractor to specialize to a subset of the training subjects and promoting feature diversity. The second loss is an intra-ensemble distillation objective that allows collaborative exchange of knowledge between the models of the ensemble. Results: We compare our method against several state-of-the-art techniques, conducting subject-independent experiments on two large MI datasets, namely Physionet and OpenBMI. Our algorithm outperforms all of the methods in both 5-fold cross-validation and leave-one-subject-out evaluation settings, using a substantially lower number of trainable parameters. Conclusion: We demonstrate that our model ensembling approach combining the powers of curriculum learning and collaborative training, leads to high learning capacity and robust performance. Significance: Our work addresses the issue of domain shifts in multi-subject EEG datasets, paving the way for calibration-free BCI systems.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Code: https://github.com/gzoumpourlis/Ensemble-M

Regulation of components of AP-1 transcription factor by early and late Ras signals

Experiments utilising either tumour cells or over-expression of oncogenes like Ras and its potential downstream mediators have yielded a wealth of information over the last decade. Qualitative and quantitative analysis of AP-1 transcription factor has been extensively analysed in response to various oncogenic signals. One basic criticism is that the continued presence of an activated component of cellular signaling renders the study of early Ras-mediated signaling impossible. Inducible systems for oncogene expression offer a valuable alternative for detailed analysis of signal transduction pathways. Here, we report the comparative analysis of components of oncogenic pathways between tumour cells and cells that carry inducible oncogenes

Deep Learning Techniques for Electroencephalography Analysis

In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

Pairwise Ranking Network for Affect Recognition

In this work we study the problem of emotion recognition under the prism of preference learning. Affective datasets are typically annotated by assigning a single absolute label, i.e. a numerical value that describes the intensity of an emotional attribute, to each sample. Then, the majority of existing works on affect recognition employ sample-wise classification/regression methods to predict affective states, using those annotations. We take a different approach and use a deep network architecture that performs joint training on the tasks of classification/regression of samples and ordinal ranking between pairs of samples. By treating input samples in a pairwise manner, we leverage the auxiliary task of inferring the ordinal relation between their corresponding affective states. Incorporating the ranking objective allows capturing the inherently ordinal structure of emotions and learning the inter-sample relations, resulting in better generalization. Our method is incorporated into existing affect recognition architectures and evaluated on datasets of electroencephalograms (EEG) and images. We show that the approach proposed in this work leads to consistent performance gains when incorporated in classification/regression networks

How Do Cytokines Trigger Genomic Instability?

Inflammation is a double-edged sword presenting a dual effect on cancer development, from one hand promoting tumor initiation and progression and from the other hand protecting against cancer through immunosurveillance mechanisms. Cytokines are crucial components of inflammation, participating in the interaction between the cells of tumor microenvironment. A comprehensive study of the role of cytokines in the context of the inflammation-tumorigenesis interplay helps us to shed light in the pathogenesis of cancer. In this paper we focus on the role of cytokines in the development of genomic instability, an evolving hallmark of cancer

Progression of mouse skin carcinogenesis is associated with increased ERα levels and is repressed by a dominant negative form of ERα.

Estrogen receptors (ER), namely ERα and ERβ, are hormone-activated transcription factors with an important role in carcinogenesis. In the present study, we aimed at elucidating the implication of ERα in skin cancer, using chemically-induced mouse skin tumours, as well as cell lines representing distinct stages of mouse skin oncogenesis. First, using immunohistochemical staining we showed that ERα is markedly increased in aggressive mouse skin tumours in vivo as compared to the papilloma tumours, whereas ERβ levels are low and become even lower in the aggressive spindle tumours of carcinogen-treated mice. Then, using the multistage mouse skin carcinogenesis model, we showed that ERα gradually increases during promotion and progression stages of mouse skin carcinogenesis, peaking at the most aggressive stage, whereas ERβ levels only slightly change throughout skin carcinogenesis. Stable transfection of the aggressive, spindle CarB cells with a dominant negative form of ERα (dnERα) resulted in reduced ERα levels and reduced binding to estrogen responsive elements (ERE)-containing sequences. We characterized two highly conserved EREs on the mouse ERα promoter through which dnERα decreased endogenous ERα levels. The dnERα-transfected CarB cells presented altered protein levels of cytoskeletal and cell adhesion molecules, slower growth rate and impaired anchorage-independent growth in vitro, whereas they gave smaller tumours with extended latency period of tumour onset in vivo. Our findings suggest an implication of ERα in the aggressiveness of spindle mouse skin cancer cells, possibly through regulation of genes affecting cell shape and adhesion, and they also provide hints for the effective targeting of spindle cancer cells by dnERα