Political Islam’s relation to capital and class

The last three decades have witnessed a relentless growth of Islamic movements, so that, today political Islam is an undeniable reality on the world scene. The events of September 11, 2001 and since have given it further prominence. From the Middle East to North Africa and South Asia, it has, in its various manifestations, become a major player that needs to be analysed both politically and theoretically. The contradictory nature of political Islam means that such analyses must deal with it not only in relation to the interests of capital, but also in relation to the challenge it poses to socialist ideas

Capacity Region of the Symmetric Injective K-User Deterministic Interference Channel

We characterize the capacity region of the symmetric injective K-user Deterministic Interference Channel (DIC) for all channel parameters. The achievable rate region is derived by first projecting the achievable rate region of Han-Kobayashi (HK) scheme, which is in terms of common and private rates for each user, along the direction of aggregate rates for each user (i.e., the sum of common and private rates). We then show that the projected region is characterized by only the projection of those facets in the HK region for which the coefficient of common rate and private rate are the same for all users, hence simplifying the region. Furthermore, we derive a tight converse for each facet of the simplified achievable rate region.Comment: A shorter version of this paper to appear in International Symposium on Information Theory (ISIT) 201

Decoding the activity of neuronal populations in macaque primary visual cortex

Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for sensory decoding. These results show how neuronal responses can best be used to inform perceptual decision-making

FROB:Few-shot ROBust Model for Classification and Out-of-Distribution Detection

Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and because of adversarial attacks. Accomplishing these aims is important for critical systems in safety, security, and defence. In parallel, OoD detection is challenging since deep neural network classifiers set high confidence to OoD samples away from the training data. To address such limitations, we propose the Few-shot ROBust (FROB) model for classification and few-shot OoD detection. We devise FROB for improved robustness and reliable confidence prediction for few-shot OoD detection. We generate the support boundary of the normal class distribution and combine it with few-shot Outlier Exposure (OE). We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models. The contribution of FROB is the combination of the generated boundary in a self-supervised learning manner and the imposition of low confidence at this learned boundary. FROB implicitly generates strong adversarial samples on the boundary and forces samples from OoD, including our boundary, to be less confident by the classifier. FROB achieves generalization to unseen OoD with applicability to unknown, in the wild, test sets that do not correlate to the training datasets. To improve robustness, FROB redesigns OE to work even for zero-shots. By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots. The few-shot robustness analysis evaluation of FROB on different sets and on One-Class Classification (OCC) data shows that FROB achieves competitive performance and outperforms benchmarks in terms of robustness to the outlier few-shot sample population and variability.Comment: Paper, 22 pages, Figures, Table

Boundary Of Distribution Support Generator (BDSG): Sample Generation On The Boundary

Generative models, such as Generative Adversarial Networks (GANs), have been used for unsupervised anomaly detection. While performance keeps improving, several limitations exist particularly attributed to difficulties at capturing multimodal supports and to the ability to approximate the underlying distribution closer to the tails, i.e. the boundary of the distribution's support. This paper proposes an approach that attempts to alleviate such shortcomings. We propose an invertible-residual-network-based model, the Boundary of Distribution Support Generator (BDSG). GANs generally do not guarantee the existence of a probability distribution and here, we use the recently developed Invertible Residual Network (IResNet) and Residual Flow (ResFlow), for density estimation. These models have not yet been used for anomaly detection. We leverage IResNet and ResFlow for Out-of-Distribution (OoD) sample detection and for sample generation on the boundary using a compound loss function that forces the samples to lie on the boundary. The BDSG addresses non-convex support, disjoint components, and multimodal distributions. Results on synthetic data and data from multimodal distributions, such as MNIST and CIFAR-10, demonstrate competitive performance compared to methods from the literature.Comment: 5 pages, 2020 IEEE International Conference on Image Processing (ICIP

The astrocytes number in different subfield of rat's hippocampus in reference memory learning method

In this study with usage of morris water maze and reference memory technique, we used 10 male albino wistar rats. Five rats in control group and 5 rats in Reference memory group. After histological preparation, the slides were stained widi PTAH staining for showing the Astrocytes. Present results showed significant difference in astrocytes number in CA1, CA2 and CA3 area of hippocampus between control and reference memory group. The number of astrocytes is increased in reference memory group. Then we divided the hippocampus to three parts: Anterior, middle and posterior and with compare of different area (CA1, CA2 and CA3) of hippocampus, we found that the increase of astrocytes number in posterior two-third of CA2 and CA3 is more man of it's number in the anterior one-third. © 2007 Asian Network for Scientific Information