Mode Normalization

Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced. As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features. We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets

Impact of spinal manipulation on cortical drive to upper and lower limb muscles

This study investigates whether spinal manipulation leads to changes in motor control by measuring the recruitment pattern of motor units in both an upper and lower limb muscle and to see whether such changes may at least in part occur at the cortical level by recording movement related cortical potential (MRCP) amplitudes. In experiment one, transcranial magnetic stimulation input–output (TMS I/O) curves for an upper limb muscle (abductor pollicus brevis; APB) were recorded, along with F waves before and after either spinal manipulation or a control intervention for the same subjects on two different days. During two separate days, lower limb TMS I/O curves and MRCPs were recorded from tibialis anterior muscle (TA) pre and post spinal manipulation. Dependent measures were compared with repeated measures analysis of variance, with p set at 0.05. Spinal manipulation resulted in a 54.5% ± 93.1% increase in maximum motor evoked potential (MEPmax) for APB and a 44.6% ± 69.6% increase in MEPmax for TA. For the MRCP data following spinal manipulation there were significant difference for amplitude of early bereitschafts-potential (EBP), late bereitschafts potential (LBP) and also for peak negativity (PN). The results of this study show that spinal manipulation leads to changes in cortical excitability, as measured by significantly larger MEPmax for TMS induced input–output curves for both an upper and lower limb muscle, and with larger amplitudes of MRCP component post manipulation. No changes in spinal measures (i.e., F wave amplitudes or persistence) were observed, and no changes were shown following the control condition. These results are consistent with previous findings that have suggested increases in strength following spinal manipulation were due to descending cortical drive and could not be explained by changes at the level of the spinal cord. Spinal manipulation may therefore be indicated for the patients who have lost tonus of their muscle and/or are recovering from muscle degrading dysfunctions such as stroke or orthopaedic operations and/or may also be of interest to sports performers. These findings should be followed up in the relevant populations

A Critical Assessment of Psychological Theories of Ecstasy. Towards an Integrative Model for Theorising Ecstasy

In this article a number of approaches toward ecstasy or ecstatic spirit possession are explored that take a decisively non-sociological approach to the subject. They stress the importance of acknowledging ecstasy and related phenomena not as by-products of social struggle but as actual experiences that are events with meaning and importance in the biographies of those who experience them. Some of these are psychological theories (exemplified by Abraham Maslow), some are theological (Teresa of Ávila), and some stand in between (Martin Buber). These psycho-theological theories contribute to understanding ecstasy and have to be taken into account. Emphasised at the end of the article is the need to reconcile these views with the seemingly contradictory theories of ecstasy such as that of Lewis

Image classification over unknown and anomalous domains

A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

The Next Step? Suggestions for an Integrative Model for Theorising Ecstasy

The article emphasises the necessity of an integral approach for theorising ecstasy and makes a suggestion for how this could be achieved. Although at first it seemed that the compelling sociological theory of ecstasy by I.M. Lewis and the psychological theories by proponents such as Abraham Maslow, Martin Buber or Theresa of Avila contradicted each other and could not both be true at the same time, it now turns out that these two sets of theories have different scopes of application that hardly overlap. They are thus not conflicting, but incommensurable and useful in different contexts. A very elegant and simple model for demonstrating this is the quadrant model by the integral theorist Ken Wilber, as it makes the diverging applicability compellingly visual. Adapting it for the academic study of ecstasy, it can thus be understood that, while sociological theories apply mostly to the occurrence of ecstasy in hierarchical societies among individuals who identify strongly with their group bespeaking their socio-material desires, psychological theories are best employed with individuals who do not strongly identify with group norms and whose ecstatic states cannot be connected with upward social mobility or means to acquire material gain