On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge

A novel Markov logic rule induction strategy for characterizing sports video footage

The grounding of high-level semantic concepts is a key requirement of video annotation systems. Rule induction can thus constitute an invaluable intermediate step in characterizing protocol-governed domains, such as broadcast sports footage. We here set out a novel “clause grammar template” approach to the problem of rule-induction in video footage of court games that employs a second-order meta grammar for Markov Logic Network construction. The aim is to build an adaptive system for sports video annotation capable, in principle, both of learning ab initio and also adaptively transferring learning between distinct rule domains. The method is tested with respect to both a simulated game predicate generator and also real data derived from tennis footage via computer-vision based approaches including HOG3D based player-action classification, Hough-transform based court detection, and graph-theoretic ball-tracking. Experiments demonstrate that the method exhibits both error resilience and learning transfer in the court domain context. Moreover the clause template approach naturally generalizes to any suitably-constrained, protocol-governed video domain characterized by feature noise or detector error

Domain anomaly detection in machine perception: a system architecture and taxonomy

We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifacetted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature

Composite Kernel Optimization in Semi-Supervised Metric

Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the topic of metric learning, especially using kernel functions, which map data to feature spaces with enhanced class separability, and implicitly define a new metric in the original feature space. The formulation of the problem of metric learning depends on the supervisory information available for the task. In this paper, we focus on semi-supervised kernel based distance metric learning where the training data set is unlabelled, with the exception of a small subset of pairs of points labelled as belonging to the same class (cluster) or different classes (clusters). The proposed method involves creating a pool of kernel functions. The corresponding kernels matrices are first clustered to remove redundancy in representation. A composite kernel constructed from the kernel clustering result is then expanded into an orthogonal set of basis functions. The mixing parameters of this expansion are then optimised using point similarity and dissimilarity information conveyed by the labels. The proposed method is evaluated on synthetic and real data sets. The results show the merit of using similarity and dissimilarity information jointly as compared to using just the similarity information, and the superiority of the proposed method over all the recently introduced metric learning approaches

Revealing the molecular signatures of host-pathogen interactions.

Advances in sequencing technology and genome-wide association studies are now revealing the complex interactions between hosts and pathogen through genomic variation signatures, which arise from evolutionary co-existence

Portland Daily Press: July 28,1882

https://digitalmaine.com/pdp_1882/1116/thumbnail.jp

Stabilization of GABAA Receptors at Endocytic Zones Is Mediated by an AP2 Binding Motif within the GABAA Receptor β3 Subunit

The strength of synaptic inhibition can be controlled by the stability and endocytosis of surface and synaptic GABAA receptors (GABAARs), but the surface receptor dynamics that underpin GABAAR recruitment to dendritic endocytic zones (EZs) have not been investigated. Stabilization of GABAARs at EZs is likely to be regulated by receptor interactions with the clathrin-adaptor AP2, but the molecular determinants of these associations remain poorly understood. Moreover, although surface GABAAR downmodulation plays a key role in pathological disinhibition in conditions such as ischemia and epilepsy, whether this occurs in an AP2-dependent manner also remains unclear. Here we report the characterization of a novel motif containing three arginine residues (405RRR407) within the GABAAR β3-subunit intracellular domain (ICD), responsible for the interaction with AP2 and GABAAR internalization. When this motif is disrupted, binding to AP2 is abolished in vitro and in rat brain. Using single-particle tracking, we reveal that surface β3-subunit-containing GABAARs exhibit highly confined behavior at EZs, which is dependent on AP2 interactions via this motif. Reduced stabilization of mutant GABAARs at EZs correlates with their reduced endocytosis and increased steady-state levels at synapses. By imaging wild-type or mutant super-ecliptic pHluorin-tagged GABAARs in neurons, we also show that, under conditions of oxygen–glucose deprivation to mimic cerebral ischemia, GABAARs are depleted from synapses in dendrites, depending on the 405RRR407 motif. Thus, AP2 binding to an RRR motif in the GABAAR β3-subunit ICD regulates GABAAR residency time at EZs, steady- state synaptic receptor levels, and pathological loss of GABAARs from synapses during simulated ischemia

Mitochondrial Ca²⁺ Uniporter haploinsufficiency enhances long-term potentiation at hippocampal mossy fibre synapses

Long-term changes in synaptic strength form the basis of learning and memory. These changes rely upon energy demanding mechanisms which are regulated by local Ca2+ signaling. Mitochondria are optimised for providing energy and buffering Ca2+. However, our understanding of the role of mitochondria in regulating synaptic plasticity is incomplete. Here we have used optical and electrophysiological techniques in cultured hippocampal neurons and ex vivo hippocampal slices from mice with haploinsufficiency of the mitochondrial Ca2+ uniporter (MCU+/-) to address whether reducing mitochondrial Ca2+ uptake alters synaptic transmission and plasticity. We found that cultured MCU+/- hippocampal neurons have impaired Ca2+ clearance, and consequently enhanced synaptic vesicle fusion at presynapses occupied by mitochondria. Furthermore, long-term potentiation (LTP) at mossy fibre (MF) synapses, a process which is dependent on presynaptic Ca2+ accumulation, is enhanced in MCU+/- slices. Our results reveal a previously unrecognized role for mitochondria in regulating presynaptic plasticity of a major excitatory pathway involved in learning and memory

Neuronal activity mediated regulation of glutamate transporter GLT-1 surface diffusion in rat astrocytes in dissociated and slice cultures.

The astrocytic GLT-1 (or EAAT2) is the major glutamate transporter for clearing synaptic glutamate. While the diffusion dynamics of neurotransmitter receptors at the neuronal surface are well understood, far less is known regarding the surface trafficking of transporters in subcellular domains of the astrocyte membrane. Here, we have used live-cell imaging to study the mechanisms regulating GLT-1 surface diffusion in astrocytes in dissociated and brain slice cultures. Using GFP-time lapse imaging, we show that GLT-1 forms stable clusters that are dispersed rapidly and reversibly upon glutamate treatment in a transporter activity-dependent manner. Fluorescence recovery after photobleaching and single particle tracking using quantum dots revealed that clustered GLT-1 is more stable than diffuse GLT-1 and that glutamate increases GLT-1 surface diffusion in the astrocyte membrane. Interestingly, the two main GLT-1 isoforms expressed in the brain, GLT-1a and GLT-1b, are both found to be stabilized opposed to synapses under basal conditions, with GLT-1b more so. GLT-1 surface mobility is increased in proximity to activated synapses and alterations of neuronal activity can bidirectionally modulate the dynamics of both GLT-1 isoforms. Altogether, these data reveal that astrocytic GLT-1 surface mobility, via its transport activity, is modulated during neuronal firing, which may be a key process for shaping glutamate clearance and glutamatergic synaptic transmission