A Statistical STT-RAM Design View and Robust Designs at Scaled Technologies

Rapidly increased demands for memory in electronic industry and the significant technical scaling challenges of all conventional memory technologies motivated the researches on the next generation memory technology. As one promising candidate, spin-transfer torque random access memory (STT-RAM) features fast access time, high density, non-volatility, and good CMOS process compatibility. In recent years, many researches have been conducted to improve the storage density and enhance the scalability of STT-RAM, such as reducing the write current and switching time of magnetic tunneling junction (MTJ) devices. In parallel with these efforts, the continuous increasing of tunnel magneto-resistance(TMR) ratio of the MTJ inspires the development of multi-level cell (MLC) STT-RAM, which allows multiple data bits be stored in a single memory cell. Two types of MLC STT-RAM cells, namely, parallel MLC and series MLC, were also proposed. However, like all other nanoscale devices, the performance and reliability of STT-RAM cells are severely affected by process variations, intrinsic device operating uncertainties and environmental fluctuations. The storage margin of a MLC STT-RAM cell, i.e., the distinction between the lowest and highest resistance states, is partitioned into multiple segments for multi-level data representation. As a result, the performance and reliability of MLC STT-RAM cells become more sensitive to the MOS and MTJ device variations and the thermal-induced randomness of MTJ switching. In this work, we systematically analyze the impacts of CMOS and MTJ process variations, MTJ resistance switching randomness that induced by intrinsic thermal fluctuations, and working temperature changes on STT-RAM cell designs. The STT-RAM cell reliability issues in both read and write operations are first investigated. A combined circuit and magnetic simulation platform is then established to quantitatively study the persistent and non-persistent errors in STT-RAM cell operations. Then, we analyzed the extension of STT-RAM cell behaviors from SLC (single-level- cell) to MLC (multi-level- cell). On top of that, we also discuss the optimal device parameters of the MLC MTJ for the minimization of the operation error rate of the MLC STT-RAM cells from statistical design perspective. Our simulation results show that under the current available technology, series MLC STT-RAM demonstrates overwhelming benefits in the read and write reliability compared to parallel MLC STT-RAM and could potentially satisfy the requirement of commercial practices. Finally, with the detail analysis study of STT-RAM cells, we proposed several error reduction design, such as ADAMS structure, and FA-STT structure

The Study on Design and Research Complex of Buildings in the City

In this paper, domestic new commercial complex in the city as the object, focus on city complex building design, as well as to commercial buildings city complex carrier for various uses functions. Structure professional needs in considering structural safety and reasonable at the same time, through structural transformation, increased column and beam combining section steel structure design, to meet the security under the premise of realization of these functions and the need for mutual transformation. Because commercial buildings there is a change in nature, often in the sales process after the completion of the main construction be modified according to the requirements of owners of commercial use, operation of the process and there will be some variability, which adds to the difficulty of the professional work structure

Regulation of T cell expansion by antigen presentation dynamics

An essential feature of the adaptive immune system is the proliferation of antigen-specific lymphocytes during an immune reaction to form a large pool of effector cells. This proliferation must be regulated to ensure an effective response to infection while avoiding immunopathology. Recent experiments in mice have demonstrated that the expansion of a specific clone of T cells in response to cognate antigen obeys a striking inverse power law with respect to the initial number of T cells. Here, we show that such a relationship arises naturally from a model in which T cell expansion is limited by decaying levels of presented antigen. The same model also accounts for the observed dependence of T cell expansion on affinity for antigen and on the kinetics of antigen administration. Extending the model to address expansion of multiple T cell clones competing for antigen, we find that higher affinity clones can suppress the proliferation of lower affinity clones, thereby promoting the specificity of the response. Employing the model to derive optimal vaccination protocols, we find that exponentially increasing antigen doses can achieve a nearly optimized response. We thus conclude that the dynamics of presented antigen is a key regulator of both the size and specificity of the adaptive immune response

The Ramsey Numbers of Fans Versus a Complete Graph of Order Five

For two given graphs $F$ and $H$, the Ramsey number $R(F,H)$ is the smallest integer $N$ such that for any graph $G$ of order $N$, either $G$ contains $F$ or the complement of $G$ contains $H$. Let $F_l$ denote a fan of order $2l+1$, which is $l$ triangles sharing exactly one vertex, and $K_n$ a complete graph of order $n$. Surahmat et al. conjectured that $R(F_l,K_n)=2l(n-1)+1$ for $l\geq n\geq 5$. In this paper, we show that the conjecture is true for n=5

Three results on cycle-wheel Ramsey numbers

Given two graphs G1 and G2, the Ramsey number R(G1,G2) is the smallest integer N such that, for any graph G of order N, either G1 is a subgraph of G, or G2 is a subgraph of the complement of G. We consider the case that G1 is a cycle and G2 is a (generalized) wheel. We expand the knowledge on exact values of Ramsey numbers in three directions: large cycles versus wheels of odd order; large wheels versus cycles of even order; and large cycles versus generalized odd wheels

ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity

<p>Abstract</p> <p>Background</p> <p>The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error.</p> <p>Results</p> <p>In this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak's existence on the primary peak's intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving <it>de novo </it>identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra).</p> <p>Conclusions</p> <p>We have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.</p

Bayesian CART models for insurance claims frequency

Accuracy and interpretability of a (non-life) insurance pricing model are essential qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In recent years, the classification and regression trees (CARTs) and their ensembles have gained popularity in the actuarial literature, since they offer good prediction performance and are relatively easily interpretable. In this paper, we introduce Bayesian CART models for insurance pricing, with a particular focus on claims frequency modelling. Additionally to the common Poisson and negative binomial (NB) distributions used for claims frequency, we implement Bayesian CART for the zero-inflated Poisson (ZIP) distribution to address the difficulty arising from the imbalanced insurance claims data. To this end, we introduce a general MCMC algorithm using data augmentation methods for posterior tree exploration. We also introduce the deviance information criterion (DIC) for the tree model selection. The proposed models are able to identify trees which can better classify the policy-holders into risk groups. Some simulations and real insurance data will be discussed to illustrate the applicability of these models.Comment: 46 page

Stoichiometry controls the dynamics of liquid condensates of associative proteins

Multivalent associative proteins with strong complementary interactions play a crucial role in phase separation of intracellular liquid condensates. We study the internal dynamics of such "bond-network" condensates comprised of two complementary proteins via scaling analysis and molecular dynamics. We find that when stoichiometry is balanced, relaxation slows down dramatically due to a scarcity of alternative partners following a bond break. This microscopic slow-down strongly affects the bulk diffusivity, viscosity and mixing, which provides a means to experimentally test our predictions