Are You Being Rejected or Excluded? Insights from Neuroimaging Studies Using Different Rejection Paradigms

Rejection sensitivity is the heightened tendency to perceive or anxiously expect disengagement from others during social interaction. There has been a recent wave of neuroimaging studies of rejection. The aim of the current review was to determine key brain regions involved in social rejection by selectively reviewing neuroimaging studies that employed one of three paradigms of social rejection, namely social exclusion during a ball-tossing game, evaluating feedback about preference from peers and viewing scenes depicting rejection during social interaction. A cross the different paradigms of social rejection, there was concordance in regions for experiencing rejection, namely dorsal anterior cingulate cortex (ACC), subgenual ACC and ventral ACC. Functional dissociation between the regions for experiencing rejection and those for emotion regulation, namely medial prefrontal cortex, ventrolateral prefrontal cortex (VLPFC) and ventral striatum, was evident in the positive association between social distress and regions for experiencing rejection and the inverse association between social distress and the emotion regulation regions. The paradigms of social exclusion and scenes depicting rejection in social interaction were more adept at evoking rejection-specific neural responses. These responses were varyingly influenced by the amount of social distress during the task, social support received, self-esteem and social competence. Presenting rejection cues as scenes of people in social interaction showed high rejection sensitive or schizotypal individuals to under-activate the dorsal ACC and VLPFC, suggesting that such individuals who perceive rejection cues in others down-regulate their response to the perceived rejection by distancing themselves from the scene

Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

We consider a small extent sensor network for event detection, in which nodes take samples periodically and then contend over a {\em random access network} to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center to process the measurements. The Bayesian setting is assumed; i.e., the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is \emph{network-oblivious} and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is \emph{network-aware} and processes measurements as they arrive, but in a time causal order. In this case, the decision statistic depends on the network delays as well, whereas in the network-oblivious case, the decision statistic does not depend on the network delays. This yields a Bayesian change detection problem with a tradeoff between the random network delay and the decision delay; a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network--oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network--aware case, the optimal stopping problem is analysed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient statistic for decision is found to be the network-state and the posterior probability of change having occurred given the measurements received and the state of the network. The optimal regimes are studied using simulation.Comment: To appear in ACM Transactions on Sensor Networks. A part of this work was presented in IEEE SECON 2006, and Allerton 201

Linking density functional and mode coupling models for supercooled liquids

We compare predictions from two familiar models of the metastable supercooled liquid respectively constructed with thermodynamic and dynamic approach. In the so called density functional theory (DFT) the free energy $F[\rho]$ of the liquid is a functional of the inhomogeneous density $\rho({\bf r})$. The metastable state is identified as a local minimum of $F[\rho]$. The sharp density profile characterizing $\rho({\bf r})$ is identified as a single particle oscillator, whose frequency is obtained from the parameters of the optimum density function. On the other hand, a dynamic approach to supercooled liquids is taken in the mode coupling theory (MCT) which predict a sharp ergodicity-nonergodicity transition at a critical density. The single particle dynamics in the non-ergodic state, treated approximately, represents a propagating mode whose characteristic frequency is computed from the corresponding memory function of the MCT. The mass localization parameters in the above two models (treated in their simplest forms) are obtained respectively in terms of the corresponding natural frequencies depicted and are shown to have comparable magnitudes.Comment: 24 pages, 10 figure

Bidirectional Conditional Generative Adversarial Networks

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles $z$ and $c$ in the generation process and provides an encoder that learns inverse mappings from $x$ to both $z$ and $c$, trained jointly with the generator and the discriminator. We present crucial techniques for training BiCoGANs, which involve an extrinsic factor loss along with an associated dynamically-tuned importance weight. As compared to other encoder-based cGANs, BiCoGANs encode $c$ more accurately, and utilize $z$ and $c$ more effectively and in a more disentangled way to generate samples.Comment: To appear in Proceedings of ACCV 201

Probabilistic Non-Local Means

In this paper, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian noise; and 3) we employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. The probabilistic nature of the new weight function also provides a theoretical basis to choose thresholds rejecting dissimilar patches for fast computations. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of peak signal noise ratio (PSNR) and structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the probabilistic weights in tested NLM variants.Comment: 11 pages, 3 figure