Domain Adaptive Neural Networks for Object Recognition

We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks

Interferometric Mapping of Magnetic fields: NGC2071IR

We present polarization maps of NGC2071IR from thermal dust emission at 1.3 mm and from CO J=$2 \to 1$ line emission. The observations were obtained using the Berkeley-Illinois-Maryland Association array in the period 2002-2004. We detected dust and line polarized emission from NGC2071IR that we used to constrain the morphology of the magnetic field. From CO J=$2 \to 1$ polarized emission we found evidence for a magnetic field in the powerful bipolar outflow present in this region. We calculated a visual extinction $A_{\rm{v}} \approx 26$ mag from our dust observations. This result, when compared with early single dish work, seems to show that dust grains emit polarized radiation efficiently at higher densities than previously thought. Mechanical alignment by the outflow is proposed to explain the polarization pattern observed in NGC2071IR, which is consistent with the observed flattening in this source.Comment: 17 pages, 4 Figures, Accepted for publication in Ap

Submillimetre dust polarisation and opacity in the HD163296 protoplanetary ring system

We present ALMA images of the sub-mm continuum polarisation and spectral index of the protoplanetary ringed disk HD163296. The polarisation fraction at 870{\mu}m is measured to be ~0.9% in the central core and generally increases with radius along the disk major axis. It peaks in the gaps between the dust rings, and the largest value (~4%) is found between rings 1 and 2. The polarisation vectors are aligned with the disk minor axis in the central core, but become more azimuthal in the gaps, twisting by up to +/-9degrees in the gap between rings 1 and 2. These general characteristics are consistent with a model of self-scattered radiation in the ringed structure, without requiring an additional dust alignment mechanism. The 870/1300{\mu}m dust spectral index exhibits minima in the centre and the inner rings, suggesting these regions have high optical depths. However, further refinement of the dust or the disk model at higher resolution is needed to reproduce simultaneously the observed degree of polarisation and the low spectral index.Comment: 5 pages +2 pages supplemental data. v2 - revised figures and final values; conclusions unchange

Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity, namely, short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning, and may serve as substrates for neural systems manipulating temporal information on relevant time scales. The present study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks (CANNs) and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors-the network that is initially being stimulated to an active state decays to a silent state very slowly on the time scale of STD rather than on the time scale of neural signaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.Comment: 40 pages, 17 figure

Algorithms for identification and categorization

The main features of a family of efficient algorithms for recognition and classification of complex patterns are briefly reviewed. They are inspired in the observation that fast synaptic noise is essential for some of the processing of information in the brain.Comment: 6 pages, 5 figure

KINEMATICS ANALYSIS OF POLE VAULT DURING NATIONAL INDOOR ATHLETICS CHAMPIONSHIPS

There should be a minimal level of individual variation presented by athletes in high level competitions, reflecting a high degree of consistency in the form of execution. By registering and subsequently analysing kinematic and kinetic data, obtained during athletic exercises, it is possible to verify such differences. The objective of this work was to collect kinematic data in order to quantify and verify these differences. Parameters such as amplitude, frequency, velocity, inter-segmental angles and kinetic energy were quantified, in order to understand the variations found in the different parameters. One should assume that an athlete that presents major variations from the above-mentioned parameters is not at his or her best form. We analysed 16 exercises of 3 athletes in the Portugal Indoor Championship in the year of the Sydney Olympic Games. This analysis enabled trainers to gain access to information on stability of technique in the exercise of each jump

Kinematics of a globular cluster with an extended profile: NGC5694

We present a study of the kinematics of the remote globular cluster NGC5694 based on GIRAFFE@VLT medium resolution spectra. A sample of 165 individual stars selected to lie on the Red Giant Branch in the cluster Color Magnitude Diagram was considered. Using radial velocity and metallicity from Calcium triplet, we were able to select 83 bona-fide cluster members. The addition of six previously known members leads to a total sample of 89 cluster giants with typical uncertainties <1.0 km/s in their radial velocity estimates. The sample covers a wide range of projected distances from the cluster center, from ~0.2 arcmin to 6.5 arcmin = 23 half-light radii (r_h). We find only very weak rotation, as typical of metal-poor globular clusters. The velocity dispersion gently declines from a central value of sigma=6.1 km/s to sigma = 2.5 km/s at ~2 arcmin = 7.1= r_h, then it remainins flat out to the next (and last) measured point of the dispersion profile, at ~4 arcmin = 14.0 r_h, at odds with the predictions of isotropic King models. We show that both isotropic single-mass non-collisional models and multi-mass anisotropic models can reproduce the observed surface brightness and velocity dispersion profiles.Comment: Accepted for publication by MNRAS. Pdflatex, 10 pages, 10 figure

The effect of neural adaptation of population coding accuracy

Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.Comment: 35 pages, 8 figure

ALMA Science Verification Data: Millimeter Continuum Polarimetry of the Bright Radio Quasar 3C 286

We present full-polarization observations of the compact, steep-spectrum radio quasar 3C~286 made with the ALMA at 1.3~mm. These are the first full-polarization ALMA observations, which were obtained in the framework of Science Verification. A bright core and a south-west component are detected in the total intensity image, similar to previous centimeter images. Polarized emission is also detected toward both components. The fractional polarization of the core is about 17\%, this is higher than the fractional polarization at centimeter wavelengths, suggesting that the magnetic field is even more ordered in the millimeter radio core than it is further downstream in the jet. The observed polarization position angle (or EVPA) in the core is $\sim$\,$39^{\circ}$, which confirms the trend that the EVPA slowly increases from centimeter to millimeter wavelengths. With the aid of multi-frequency VLBI observations, we argue that this EVPA change is associated with the frequency-dependent core position. We also report a serendipitous detection of a sub-mJy source in the field of view, which is likely to be a submillimeter galaxy.Comment: 10 pages, 9 figures, Accepted for publication in the Ap

Collaborative Layer-wise Discriminative Learning in Deep Neural Networks

Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore such cross-layer heterogeneities when classifying samples of different complexities. For example, if a training sample has already been correctly classified at a specific layer with high confidence, we argue that it is unnecessary to enforce rest layers to classify this sample correctly and a better strategy is to encourage those layers to focus on other samples. In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification. Towards this target, we introduce multiple classifiers on top of multiple layers. Each classifier not only tries to correctly classify the features from its input layer, but also coordinates with other classifiers to jointly maximize the final classification performance. Guided by the other companion classifiers, each classifier learns to concentrate on certain training examples and boosts the overall performance. Allowing for end-to-end training, our method can be conveniently embedded into state-of-the-art deep networks. Experiments with multiple popular deep networks, including Network in Network, GoogLeNet and VGGNet, on scale-various object classification benchmarks, including CIFAR100, MNIST and ImageNet, and scene classification benchmarks, including MIT67, SUN397 and Places205, demonstrate the effectiveness of our method. In addition, we also analyze the relationship between the proposed method and classical conditional random fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before camera-ready versio