A new frontier in novelty detection: Pattern recognition of stochastically episodic events

A particularly challenging class of PR problems in which the, generally required, representative set of data drawn from the second class is unavailable, has recently received much consideration under the guise of One-Class (OC) classification. In this paper, we extend the frontiers of OC classification by the introduction of a new field of problems open for analysis. In particular, we note that this new realm deviates from the standard set of OC problems based on the following characteristics: The data contains a temporal nature, the instances of the classes are “interwoven”, and the labelling procedure is not merely impractical - it is almost, by definition, impossible, which results in a poorly defined training set. As a first attempt to tackle these problems, we present two specialized classification strategies denoted by Scenarios S 1 and S 2 respectively. In Scenarios S 1, the data is such that standard binary and one-class classifiers can be applied. Alternatively, in Scenarios S 2, the labelling challenge prevents the application of binary classifiers, and instead, dictates a novel application of OC classifiers. The validity of these scenarios has been demonstrated for the exemplary domain involving the Comprehensive Nuclear Test-Ban-Treaty (CTBT), for which our research endeavour has also developed a simulation model. As far as we know, our research in this field is of a pioneering sort, and the results presented here are novel

Probabilistic Memory Model for Visual Images Categorization

During the past decades, numerous memory models have been proposed, which focused mainly on how spoken words are studied, whereas models on how visual images are studied are still limited. In this study, we propose a probabilistic memory model (PMM) for visual images categorization which is able to mimic the workings of the human brain during the image storage and retrieval. First, in the learning phase, the visual images are represented by the feature vectors extracted with convolutional neural network (CNN) and each feature component is assumed to conform to a Gaussian distribution and may be incompletely copied with a certain probability or randomly produced in accordance to an exponential distribution. Then, in the test phase, the likelihood ratio between the test image and each studied image is calculated based on the probabilistic inference theory, and an odd value in favor of an old item over a new one is obtained based on all likelihood values. Finally, if the odd value is above a certain threshold, the Bayesian decision rule is applied for image classification. Experimental results on two benchmark image datasets demonstrate that the proposed PMM can perform well on categorization tasks for both studied and non-studied images

Varieties of recollective experience.

Four variants on Tulving's "Remember/Know" paradigm supported a tripartite classification of recollective experience in recognition memory into Remembering (as in conscious recollection of a past episode), Knowing (similar to retrieval from semantic memory), and Feeling (a priming-based judgment of familiarity). Recognition-by-knowing and recognition-by-feeling are differentiated by level of processing at the time of encoding (Experiments 1-3), shifts in the criterion for item recognition (Experiment 2), response latencies (Experiments 1-3), and changes in the response window (Experiment 3). False recognition is often accompanied by "feeling", but rarely by "knowing"; d' is higher for knowing than for feeling (Experiments 1-4). Recognition-by-knowing increases with additional study trials, while recognition-by-feeling falls to zero (Experiment 4). In these ways, recognition-by-knowing is distinguished from recognition-by-feeling in much the same way as, in the traditional Remember/Know paradigm, recognition-by-remembering can be distinguished from recognition-without-remembering. Implications are discussed for dual-process theories of memory, and the search for the neural substrates of memory retrieval

Encoding and the durability of episodic memory: a functional magnetic resonance imaging study.

Memories vary in their durability even when encoding conditions apparently remain constant. We investigated whether, under these circumstances, memory durability is nonetheless associated with variation in the neural activity elicited during encoding. Event-related functional magnetic resonance imaging data were acquired while volunteers semantically classified visually presented words. Using the “remember/know” procedure, memory for one-half of the words was tested after 30 min and for the remaining half after 48 h. In several regions, including left hippocampus and left dorsal inferior frontal gyrus (IFG), activity at encoding differed depending on whether items were later recollected regardless of study–test delay. Delay-selective effects were also evident, however. Recollection after 48 h was associated with enhanced activity in bilateral ventral IFG, whereas recollection after 30 min was associated with greater fusiform activity. Thus, there is a relationship between the neural activity elicited by an event as it is encoded and the durability of the resulting memory representation

Statistical Computations Underlying the Dynamics of Memory Updating

Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

Meta-Learning Probabilistic Inference For Prediction

This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task

Pulsar State Switching from Markov Transitions and Stochastic Resonance

Markov processes are shown to be consistent with metastable states seen in pulsar phenomena, including intensity nulling, pulse-shape mode changes, subpulse drift rates, spindown rates, and X-ray emission, based on the typically broad and monotonic distributions of state lifetimes. Markovianity implies a nonlinear magnetospheric system in which state changes occur stochastically, corresponding to transitions between local minima in an effective potential. State durations (though not transition times) are thus largely decoupled from the characteristic time scales of various magnetospheric processes. Dyadic states are common but some objects show at least four states with some transitions forbidden. Another case is the long-term intermittent pulsar B1931+24 that has binary radio-emission and torque states with wide, but non-monotonic duration distributions. It also shows a quasi-period of $38\pm5$ days in a 13-yr time sequence, suggesting stochastic resonance in a Markov system with a forcing function that could be strictly periodic or quasi-periodic. Nonlinear phenomena are associated with time-dependent activity in the acceleration region near each magnetic polar cap. The polar-cap diode is altered by feedback from the outer magnetosphere and by return currents from an equatorial disk that may also cause the neutron star to episodically charge and discharge. Orbital perturbations in the disk provide a natural periodicity for the forcing function in the stochastic resonance interpretation of B1931+24. Disk dynamics may introduce additional time scales in observed phenomena. Future work can test the Markov interpretation, identify which pulsar types have a propensity for state changes, and clarify the role of selection effects.Comment: 25 pages, 6 figures, submitted to the Astrophysical Journa

Memory for elements of a complex scene : binding and the influence of attention

Memory of a complex event includes a multitude of features (e.g., objects, people, and actions) as well as the overall context (e.g., going to a picnic). To recall a complex event you must bind together these features and context into an episodic memory representation. This process of binding creates the subjective experience that certain details belong together. In two experiments, I examined whether particular types of information are bound together (object-to-object, object-to-context) within a memory representation of a scene and how attention may influence this process. Participants viewed a series of scenes and their attention was drawn to some objects (focus of attention), but not others. At test, they attempted to identify previously seen objects that were cued by objects-only, context-only, or a blurred context. Exp. 1 provided evidence of object-to-object binding when the objects used as cues and targets had been in the focus of attention at encoding. Exp. 2 revealed evidence of object-to-context binding, in that context cues enhanced memory for target objects whether or not the objects had been in the focus of attention at encoding. Altogether, these studies demonstrate the importance of attentional deployment in determining which components of an episodic memory will bind together