Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Leader-follower System for Unmanned Ground Vehicle

Mehitamata sõidukeid saab kasutada efektiivselt, kui neil on autonoomse sõitmise võimekus.Autonoomse sõitmise üks funktsionaalsustest on juht-järgijasüsteem, mis tähendab, et robot peab järgi minema etteantud objektile. Tavaliselt on defineeritud objektiks inimene, mis võimaldab hoida inimeste käed vabana, võrreldes situatsiooniga, kus masina liigutamiseks tuleb kasutada pulti, mis muudab sõdurid militaarolukorras haavatavaks sihtmärgiks. Olemasolevad juht-järgija süsteemid kasutavad funktsioneerimiseks kombinatsiooni erinevatest sensoritest, kasutades nii LIDAReid, GPS-i, infrapunamarkereid kui ka kaameraid. Selle töö eesmärgiks on arendada ja testida juht-järgijasüsteem, mis kasutab ainult kaamerasisendit ning mis oma kontseptsioonilt peaks olema võimeline järgnema igale objektile, st objekti tüüp ja välimus ei pea olema eeldefineeritud.Täpsemalt kasutab süsteem käitumuslikku kloonimist sügavate konvolutsiooniliste tehisnärvivõrkude treenimiseks, et ennustada kaamera sisendi põhjal roboti liigutamiseks vajalikke kiiruse ja pööramise käske. Tulemused näitavad, et pakutud süsteem töötab, kuid vajab edasiarendust, et teha see reaalse olukorra jaoks piisavalt robustseks ning turvaliseks.Unmanned ground vehicles can be utilized effectively, if they have autonomous capabilities. One of those capabilities is leader-follower functionality, which means that the robot has to follow a predefined object. Usually the predefined object is a person, which is still more effective than controlling the robot with teleoperation using a remote control. It is worthwhile, because teleoperation requires the full attention of the operator while leader-follower system allows the person to keep their situational awareness, which is essential in a military environment. Leader-follower systems often exploit multipletechnologies: LIDARs, GPS, infrared markers, radar transponder tags and cameras. The aim of this thesis is to develop and test the proof of concept leader-follower system, which relies on camera vision only and is able to follow any object which means that the class of the object does not have to be predefined. Specifically, the approach uses behavioral cloning to train deep siamese network with convolutional layers to determine the velocity and turning commands of the vehicle based on input from camera. The results show that the proof of concept system works, but requires further development in order to make it robust and safe

Metabotropic Glutamate Receptor Activation in Cerebelar Purkinje Cells as Substrate for Adaptive Timing of the Classicaly Conditioned Eye Blink Response

To understand how the cerebellum adaptively times the classically conditioned nictitating membrane response (NMR), a model of the metabotropic glutamate receptor (mGluR) second messenger system in cerebellar Purkinje cells is constructed. In the model slow responses, generated postsynaptically by mGluR-mediated phosphoinositide hydrolysis, and calcium release from intracellular stores, bridge the interstimulus interval (ISI) between the onset of parallel fiber activity associated with the conditioned stimulus (CS) and climbing fiber activity associated with unconditioned stimulus (US) onset. Temporal correlation of metabotropic responses and climbing fiber signals produces persistent phosphorylation of both AMPA receptors and Ca2+-dependent K+ channels. This is responsible for long-term depression (LTD) of AMPA receptors. The phosphorylation of Ca2+-dependent K+ channels leads to a reduction in baseline membrane potential and a reduction of Purkinje cell population firing during the CS-US interval. The Purkinje cell firing decrease disinhibits cerebellar nuclear cells which then produce an excitatory response corresponding to the learned movement. Purkinje cell learning times the response, while nuclear cell learning can calibrate it. The model reproduces key features of the conditioned rabbit NMR: Purkinje cell population response is properly timed, delay conditioning occurs for ISIs of up to four seconds while trace conditioning occurs only at shorter ISIs, mixed training at two different ISis produces a double-peaked response, and ISIs of 200-400ms produce maximal responding. Biochemical similarities between timed cerebellar learning and photoreceptor transduction, and circuit similarities between the timed cerebellar circuit and a timed dentate-CA3 hippocampal circuit, are noted.Office of Naval Research (N00014- 92-J-4015, N00014-92-J-1309, N00014-95-1-0409); Air Force Office of Scientific Research (F49620-92-J-0225);National Science Foundation (IRI-90-24877

Exploring the structure of a real-time, arbitrary neural artistic stylization network

In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference (BMVC) 201