Coherent oscillations and giant edge magnetoresistance in singly connected topological insulators

A topological insulator has a pair of extended states at the edge in the bulk insulating regime. We study a geometry in which such edge states will manifest themselves in a qualitative manner through periodic oscillations in the magnetoconductance of a singly connected sample coupled to leads through narrow point contacts. Detailed calculations identify the parameters for which these oscillations are expected to be the strongest, and also show their robustness to disorder. Such oscillations can be used as a spectroscopic tool of the edge states. A large change in the device resistance at small B, termed giant edge magnetoresistance, can have potential for application. © 2009 The American Physical Society.published_or_final_versio

Recognizing Scoring in Basketball Game from AER Sequence by Spiking Neural Networks

The automatic score detection and recognition in basketball game has important application potentials, for examples, basketball technique analysis and 24 second control in the game. Although existing studies have been conducted on broadcast videos, most of them usually learned a machine learning algorithm on long videos recorded by traditional cameras. Address Event Representation (AER) sensor provides a possibility to deal with the problem by a human sensing manner. It represents the visual information as a series of spike-based events and records event sequences. Compared to traditional videos, AER events can fully utilize their addresses and timestamp information, forming precise spatio-temporal features with significantly less storage cost. More importantly, it issues spikes which can be naturally processed by human-style spiking neural networks (SNNs). In this paper, we propose to recognize scoring in basketball game from AER sequences. A new model is designed to extract dynamic features and discriminate different event streams using SNN. To handle the imbalance problem between positive and negative samples, we use an imbalanced Tempotron algorithm in our SNN model. Meanwhile, an AER sequence dataset of basketball games is collected. The experimental results demonstrate that our method achieves better performance compared with existing models

HybridSNN: Combining Bio-Machine Strengths by Boosting Adaptive Spiking Neural Networks

Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as closely as possible, for example, the temporally local learning rule of spike-timing-dependent plasticity (STDP), or apply the gradient descent rule to optimize a multilayer SNN with fixed structure. However, the learning rule used in the former is local and how the real brain might do the global-scale credit assignment is still not clear, which means that those shallow SNNs are robust but deep SNNs are difficult to be trained globally and could not work so well. For the latter, the nondifferentiable problem caused by the discrete spike trains leads to inaccuracy in gradient computing and difficulties in effective deep SNNs. Hence, a hybrid solution is interesting to combine shallow SNNs with an appropriate machine learning (ML) technique not requiring the gradient computing, which is able to provide both energy-saving and high-performance advantages. In this article, we propose a HybridSNN, a deep and strong SNN composed of multiple simple SNNs, in which data-driven greedy optimization is used to build powerful classifiers, avoiding the derivative problem in gradient descent. During the training process, the output features (spikes) of selected weak classifiers are fed back to the pool for the subsequent weak SNN training and selection. This guarantees HybridSNN not only represents the linear combination of simple SNNs, as what regular AdaBoost algorithm generates, but also contains neuron connection information, thus closely resembling the neural networks of a brain. HybridSNN has the benefits of both low power consumption in weak units and overall data-driven optimizing strength. The network structure in HybridSNN is learned from training samples, which is more flexible and effective compared with existing fixed multilayer SNNs. Moreover, the topological tree of HybridSNN resembles the neural system in the brain, where pyramidal neurons receive thousands of synaptic input signals through their dendrites. Experimental results show that the proposed HybridSNN is highly competitive among the state-of-the-art SNNs

One-Dimensional Nanostructures and Devices of II–V Group Semiconductors

The II–V group semiconductors, with narrow band gaps, are important materials with many applications in infrared detectors, lasers, solar cells, ultrasonic multipliers, and Hall generators. Since the first report on trumpet-like Zn3P2nanowires, one-dimensional (1-D) nanostructures of II–V group semiconductors have attracted great research attention recently because these special 1-D nanostructures may find applications in fabricating new electronic and optoelectronic nanoscale devices. This article covers the 1-D II–V semiconducting nanostructures that have been synthesized till now, focusing on nanotubes, nanowires, nanobelts, and special nanostructures like heterostructured nanowires. Novel electronic and optoelectronic devices built on 1-D II–V semiconducting nanostructures will also be discussed, which include metal–insulator-semiconductor field-effect transistors, metal-semiconductor field-effect transistors, andp–nheterojunction photodiode. We intent to provide the readers a brief account of these exciting research activities

Epidemiology and management of uterine fibroids

Uterine leiomyomas are one of the most common and yet understudied diseases in women. These tumors, commonly known as fibroids, affect women mainly during their reproductive years and are diagnosed in up to 70% of white women and more than 80% of women of African ancestry during their lifetime. This disease has a profound impact on health care delivery and costs worldwide. Though most women with fibroids are asymptomatic, approximately 30% of them will present with severe symptoms which can include abnormal uterine bleeding, anemia, pelvic pain and pressure, back pain, urinary frequency, constipation, or infertility, and will require intervention. Furthermore, fibroids have been associated with poor obstetrical outcomes. The current options for symptomatic fibroid treatment include expectant, medical, and surgical management, and interventional radiology procedures. This article reviews the recent progress and available management strategies for uterine fibroids and highlights areas where further research is needed to find new therapeutic targets and better personalize treatments.We provide a review of the management options for uterine fibroids.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154526/1/ijgo13102.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154526/2/ijgo13102_am.pd

Robust Transcoding Sensory Information With Neural Spikes

Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies only aim at dealing with the continuous signal of neural systems, while lacking a unique feature of biological neurons, termed spike, which is the fundamental information unit for neural computation as well as a building block for brain-machine interface. Aiming at these limitations, we propose a transcoding framework to encode multi-modal sensory information into neural spikes and then reconstruct stimuli from spikes. Sensory information can be compressed into 10% in terms of neural spikes, yet re-extract 100% of information by reconstruction. Our framework can not only feasibly and accurately reconstruct dynamical visual and auditory scenes, but also rebuild the stimulus patterns from functional magnetic resonance imaging (fMRI) brain activities. More importantly, it has a superb ability of noise immunity for various types of artificial noises and background signals. The proposed framework provides efficient ways to perform multimodal feature representation and reconstruction in a high-throughput fashion, with potential usage for efficient neuromorphic computing in a noisy environment

Hierarchical Spiking-Based Model for Efficient Image Classification With Enhanced Feature Extraction and Encoding

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be great computation-efficient models. The spiking neurons encode beneficial temporal facts and possess excessive anti-noise properties. However, the high-quality encoding of spatio-temporal complexity and also its training optimization of SNNs are restricted by means of the contemporary problem, this article proposes a novel hierarchical event-driven visual device to explore how information transmits and signifies in the retina the usage of biologically manageable mechanisms. This cognitive model is an augmented spiking-based framework consisting of the function learning capacity of convolutional neural networks (CNNs) with the cognition capability of SNNs. Furthermore, this visual device is modeled in a biological realism way with unsupervised learning rules and advanced spike firing rate encoding methods. We train and test them on some image datasets (Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research (CIFAR)10, and its noisy versions) to show that our mannequin can process greater vital data than present cognitive models. This article also proposes a novel quantization approach to make the proposed spiking-based model more efficient for neuromorphic hardware implementation. The outcomes show this joint CNN-SNN model can reap excessive focus accuracy and get more effective generalization ability

Four small puzzles that Rosetta doesn't solve

A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular "puzzles" should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special Collectio

Functional divergence in the role of N-linked glycosylation in smoothened signaling

The G protein-coupled receptor (GPCR) Smoothened (Smo) is the requisite signal transducer of the evolutionarily conserved Hedgehog (Hh) pathway. Although aspects of Smo signaling are conserved from Drosophila to vertebrates, significant differences have evolved. These include changes in its active sub-cellular localization, and the ability of vertebrate Smo to induce distinct G protein-dependent and independent signals in response to ligand. Whereas the canonical Smo signal to Gli transcriptional effectors occurs in a G protein-independent manner, its non-canonical signal employs Gαi. Whether vertebrate Smo can selectively bias its signal between these routes is not yet known. N-linked glycosylation is a post-translational modification that can influence GPCR trafficking, ligand responsiveness and signal output. Smo proteins in Drosophila and vertebrate systems harbor N-linked glycans, but their role in Smo signaling has not been established. Herein, we present a comprehensive analysis of Drosophila and murine Smo glycosylation that supports a functional divergence in the contribution of N-linked glycans to signaling. Of the seven predicted glycan acceptor sites in Drosophila Smo, one is essential. Loss of N-glycosylation at this site disrupted Smo trafficking and attenuated its signaling capability. In stark contrast, we found that all four predicted N-glycosylation sites on murine Smo were dispensable for proper trafficking, agonist binding and canonical signal induction. However, the under-glycosylated protein was compromised in its ability to induce a non-canonical signal through Gαi, providing for the first time evidence that Smo can bias its signal and that a post-translational modification can impact this process. As such, we postulate a profound shift in N-glycan function from affecting Smo ER exit in flies to influencing its signal output in mice