Understanding Double Descent Using VC-Theoretical Framework.

In spite of many successful applications of deep learning (DL) networks, theoretical understanding of their generalization capabilities and limitations remains limited. We present analysis of generalization performance of DL networks for classification under VC-theoretical framework. In particular, we analyze the so-called "double descent" phenomenon, when large overparameterized networks can generalize well, even when they perfectly memorize all available training data.

To understand double descent, we need to understand VC theory.

We analyze generalization performance of over-parameterized learning methods for classification, under VC-theoretical framework. Recently, practitioners in Deep Learning discovered 'double descent' phenomenon, when large networks can fit perfectly available training data, and at the same time, achieve good generalization for future (test) data. The current consensus view is that VC-theoretical results cannot account for good generalization performance of Deep Learning networks.

Online Prediction of Lead Seizures from iEEG Data.

We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system.

Performance metrics for online seizure prediction.

Many recent studies on online seizure prediction from iEEG signal describe various prediction algorithms and their prediction performance. In contrast, this paper focuses on proper specification of system parameters, such as prediction period, prediction horizon and data-driven characterization of lead seizures. Whereas prediction performance clearly depends on these system parameters many researchers simply set the values of these parameters in an ad hoc manner.

Machine Learning Approach to Predicting Stem Cell Donor Availability.

The success of unrelated donor stem cell transplants depends on not only finding genetically matched donors, but also donor availability. On average 50% of potential donors in the National Marrow Donor Program database are unavailable for a variety of reasons, after initially matching a patient, with significant variations in availability among subgroups (eg, by race or age). Several studies have established univariate donor characteristics associated with availability.

Quasi-Talbot effect with vortex beams and formation of vortex beamlet arrays.

We study diffraction of Bessel vortex beams with topological charges of ±1 and ±2 and a wavelength of 130 µm on two-dimensional amplitude periodic gratings. Results of simulations and experiments at the Novosibirsk Free Electron Laser facility show that there appear periodic patterns in the planes corresponding to the classical main and fractional Talbot planes, but instead of self-images of the holes, there are observed periodic lattices of annular vortex microbeams with topological charges corresponding to the charge of the beam illuminating the grating. The ring diameters are the same for beams with different topological charges, but they are proportional to the grating period and inversely proportional to the diameter of the beam illuminating the grating.

Commonalities and differences in the neural representations of English, Portuguese, and Mandarin sentences: When knowledge of the brain-language mappings for two languages is better than one.

This study extended cross-language semantic decoding (based on a concept's fMRI signature) to the decoding of sentences across three different languages (English, Portuguese and Mandarin). A classifier was trained on either the mapping between words and activation patterns in one language or the mappings in two languages (using an equivalent amount of training data), and then tested on its ability to decode the semantic content of a third language. The model trained on two languages was reliably more accurate than a classifier trained on one language for all three pairs of languages.

Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states.

Even though much has recently been learned about the neural representation of individual concepts and categories, neuroimaging research is only beginning to reveal how more complex thoughts, such as event and state descriptions, are neurally represented. We present a predictive computational theory of the neural representations of individual events and states as they are described in 240 sentences. Regression models were trained to determine the mapping between 42 neurally plausible semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activation patterns of various cortical regions that process different types of information.

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy.

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.

The aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang et al., submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations.

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

Objective: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling.

Methods: Our work emphasizes the understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions.

Identifying thematic roles from neural representations measured by functional magnetic resonance imaging.

The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions.

Correction: Forecasting Seizures Using Bivariate Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

[This corrects the article DOI: 10.1371/journal.pone.

Modality-independent representations of small quantities based on brain activation patterns.

Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa.

Aberrant functioning of the theory-of-mind network in children and adolescents with autism.

Background: Theory-of-mind (ToM), the ability to infer people's thoughts and feelings, is a pivotal skill in effective social interactions. Individuals with autism spectrum disorders (ASD) have been found to have altered ToM skills, which significantly impacts the quality of their social interactions. Neuroimaging studies have reported altered activation of the ToM cortical network, especially in adults with autism, yet little is known about the brain responses underlying ToM in younger individuals with ASD.

Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm.

Identifying autism from neural representations of social interactions: neurocognitive markers of autism.

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism.

Development and evaluation of cost-sensitive universum-SVM.

Many machine learning applications involve analysis of high-dimensional data, where the number of input features is larger than/comparable to the number of data samples. Standard classification methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. One such new learning methodology is called learning through contradiction or Universum-support vector machine (U-SVM).

Identifying Emotions on the Basis of Neural Activation.

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction.

Brain function differences in language processing in children and adults with autism.

Comparison of brain function between children and adults with autism provides an understanding of the effects of the disorder and associated maturational differences on language processing. Functional imaging (functional magnetic resonance imaging) was used to examine brain activation and cortical synchronization during the processing of literal and ironic texts in 15 children with autism, 14 children with typical development, 13 adults with autism, and 12 adult controls. Both the children and adults with autism had lower functional connectivity (synchronization of brain activity among activated areas) than their age and ability comparison group in the left hemisphere language network during irony processing, and neither autism group had an increase in functional connectivity in response to increased task demands.