Modeling sub-threshold slope and DIBL mismatch of sub-22nm FinFet

A great challenge for future information technologies is building reliable systems on top of unreliable components. Parameters of modern and future technology devices are affected by severe levels of process variability and devices will degrade and even fail during the normal lifeDme of the chip due to aging mechanisms. These extreme levels of variability are caused by the high device miniaturizaDon and the random placement of individual atoms. Variability is considered a "red brick" by the InternaDonal Technology Roadmap for Semiconductors. The session is devoted to this topic presenDng research experiences from the Spanish Network on Variability called VARIABLES. In this session a talk entlited "Modeling sub-threshold slope and DIBL mismatch of sub-22nm FinFet" was presented.

Regression models with MoPs Bayesian networks

We present a model of Bayesian network for continuous variables, where densities and conditional densities are estimated with B-spline MoPs. We use a novel approach to directly obtain conditional densities estimation using B-spline properties. In particular we implement naive Bayes and wrapper variables selection. Finally we apply our techniques to the problem of predicting neurons morphological variables from electrophysiological ones.

Variable threshold algorithm for division of labor analyzed as a dynamical system

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.

Enhancing regression models for complex systems using evolutionary techniques for feature engineering

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

System failure prediction through rare-events elastic-net logistic regression

Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

Written on the threshold

One of the most productive discoveries of art and architectural criticism is the comparative reading between different situations. It is an approach that does away with all conventions and destroys any attempt to codifing the future. It also reveals the futility of established styles or movements. In the early 20th century, the Russian Formalists spoke of the dissimilarity of the similar in literature and more than a few studies on the visual arts have used the comparative method, from Heinrich Wolfflin to Robert Venturi. But something more has to be added, personal biography. The involvement of the individual, their name and their life, in the history of art bursts in with all the force of what is real, with no need for credibility, because it merely is what it is. The histories of architecture and art tell things in a believable, perfectly connected manner, without the fits and starts of personal biography - when this appears we find ourselves in a different sort of situation.

Conditional switching of vascular endothelial growth factor (VEGF) expression in tumors: Induction of endothelial cell shedding and regression of hemangioblastoma-like vessels by VEGF withdrawal

We have recently shown that VEGF functions as a survival factor for newly formed vessels during developmental neovascularization, but is not required for maintenance of mature vessels. Reasoning that expanding tumors contain a significant fraction of newly formed and remodeling vessels, we examined whether abrupt withdrawal of VEGF will result in regression of preformed tumor vessels. Using a tetracycline-regulated VEGF expression system in xenografted C6 glioma cells, we showed that shutting off VEGF production leads to detachment of endothelial cells from the walls of preformed vessels and their subsequent death by apoptosis. Vascular collapse then leads to hemorrhages and extensive tumor necrosis. These results suggest that enforced withdrawal of vascular survival factors can be applied to target preformed tumor vasculature in established tumors. The system was also used to examine phenotypes resulting from over-expression of VEGF. When expression of the transfected VEGF cDNA was continuously “on,” tumors became hyper-vascularized with abnormally large vessels, presumably arising from excessive fusions. Tumors were significantly less necrotic, suggesting that necrosis in these tumors is the result of insufficient angiogenesis.

Stability threshold approach for complex dynamical systems

Acknowledgments This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP, and supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS). The first author thanks Dr Roman Ovsyannikov for valuable discussions regarding estimation of the mistake probability.

Interleukin 12 and B7-1 costimulatory molecule expressed by an adenovirus vector act synergistically to facilitate tumor regression

Stimulation of antitumor immune mechanisms is the primary goal of cancer immunotherapy, and accumulating evidence suggests that effective alteration of the host–tumor relationship involves immunomodulating cytokines and also the presence of costimulatory molecules. To examine the antitumor effect of direct in vivo gene transfer of murine interleukin 12 (IL-12) and B7-1 into tumors, we developed an adenovirus (Ad) vector, AdIL12–B7-1, that encodes the two IL-12 subunits in early region 1 (E1) and the B7-1 gene in E3 under control of the murine cytomegalovirus promoter. This vector expressed high levels of IL-12 and B7-1 in infected murine and human cell lines and in primary murine tumor cells. In mice bearing tumors derived from a transgenic mouse mammary adenocarcinoma, a single intratumoral injection with a low dose (2.5 × 107 pfu/mouse) of AdIL12–B7-1 mediated complete regression in 70% of treated animals. By contrast, administration of a similar dose of recombinant virus encoding IL-12 or B7-1 alone resulted in only a delay in tumor growth. Interestingly, coinjection of two different viruses expressing either IL-12 or B7-1 induced complete tumor regression in only 30% of animals treated at this dose. Significantly, cured animals remained tumor free after rechallenge with fresh tumor cells, suggesting that protective immunity had been induced by treatment with AdIL12–B7-1. These results support the use of Ad vectors as a highly efficient delivery system for synergistically acting molecules and show that the combination of IL-12 and B7-1 within a single Ad vector might be a promising approach for in vivo cancer therapy.

Interferon-β gene therapy inhibits tumor formation and causes regression of established tumors in immune-deficient mice

Despite the potential of type 1 interferons (IFNs) for the treatment of cancer, clinical experience with IFN protein therapy of solid tumors has been disappointing. IFN-β has potent antiproliferative activity against most human tumor cells in vitro in addition to its known immunomodulatory activities. The antiproliferative effect, however, relies on IFN-β concentrations that cannot be achieved by parenteral protein administration because of rapid protein clearance and systemic toxicities. We demonstrate here that ex vivo IFN-β gene transduction by a replication-defective adenovirus in as few as 1% of implanted cells blocked tumor formation. Direct in vivo IFN-β gene delivery into established tumors generated high local concentrations of IFN-β, inhibited tumor growth, and in many cases caused complete tumor regression. Because the mice were immune-deficient, it is likely that the anti-tumor effect was primarily through direct inhibition of tumor cell proliferation and survival. Based on these studies, we argue that local IFN-β gene therapy with replication-defective adenoviral vectors might be an effective treatment for some solid tumors.

Time-dependent vascular regression and permeability changes in established human tumor xenografts induced by an anti-vascular endothelial growth factor/vascular permeability factor antibody

The hyperpermeability of tumor vessels to macromolecules, compared with normal vessels, is presumably due to vascular endothelial growth factor/vascular permeability factor (VEGF/VPF) released by neoplastic and/or host cells. In addition, VEGF/VPF is a potent angiogenic factor. Removal of this growth factor may reduce the permeability and inhibit tumor angiogenesis. To test these hypotheses, we transplanted a human glioblastoma (U87), a human colon adenocarcinoma (LS174T), and a human melanoma (P-MEL) into two locations in immunodeficient mice: the cranial window and the dorsal skinfold chamber. The mice bearing vascularized tumors were treated with a bolus (0.2 ml) of either a neutralizing antibody (A4.6.1) (492 μg/ml) against VEGF/VPF or PBS (control). We found that tumor vascular permeability to albumin in antibody-treated groups was lower than in the matched controls and that the effect of the antibody was time-dependent and influenced by the mode of injection. Tumor vascular permeability did not respond to i.p. injection of the antibody until 4 days posttreatment. However, the permeability was reduced within 6 h after i.v. injection of the same amount of antibody. In addition to the reduction in vascular permeability, the tumor vessels became smaller in diameter and less tortuous after antibody injections and eventually disappeared from the surface after four consecutive treatments in U87 tumors. These results demonstrate that tumor vascular permeability can be reduced by neutralization of endogenous VEGF/VPF and suggest that angiogenesis and the maintenance of integrity of tumor vessels require the presence of VEGF/VPF in the tissue microenvironment. The latter finding reveals a new mechanism of tumor vessel regression—i.e., blocking the interactions between VEGF/VPF and endothelial cells or inhibiting VEGF/VPF synthesis in solid tumors causes dramatic reduction in vessel diameter, which may block the passage of blood elements and thus lead to vascular regression.