Robust invariant sets for constrained storage systems

Several items are produced and stored into n buffers in order to supply an external demand without interruptions. We consider the classical problem of determining control laws and smallest buffer levels guaranteeing that an unknown bounded demand is always satisfied. A simple model with n decoupled integrators and n additive bounded disturbances is employed. The coupling arises from bounds on the total production capacity and on the total demand. Invariant set theory for linear and switched linear systems is exploited to compute robust positive invariant sets and controlled robust invariant sets for two commonly adopted scheduling policies. This paper provides the explicit expression of the invariant sets for any arbitrary n.     

NSCLC as the Paradigm of Precision Medicine at Its Finest: The Rise of New Druggable Molecular Targets for Advanced Disease

Standard treatment for advanced non-small cell lung cancer (NSCLC) historically consisted of systemic cytotoxic chemotherapy until the early 2000s, when precision medicine led to a revolutionary change in the therapeutic scenario. The identification of oncogenic driver mutations in EGFR, ALK and ROS1 rearrangements identified a subset of patients who largely benefit from targeted agents. However, since the proportion of patients with druggable alterations represents a minority, the discovery of new potential driver mutations is still an urgent clinical need. We provide a comprehensive review of the emerging molecular targets in NSCLC and their applications in the advanced setting.     

Water governance and scalar politics across multiple-boundary river basins: states,catchments and regional powers in the Iberian Peninsula

Karezes (qanats), historically vital sources of water for multiple uses in the driest parts of the world, have been deteriorating over the past five decades. This paper provides a review of the global literature on the status and potential of karezes, and offers country case studies for Afghanistan, Iran, Pakistan, and Kingdom of Saudi Arabia. It offers syntheses of key issues, lessons, options and proposed interventions for re-vitalizing the karez systems. The paper identifies criteria for prioritizing karez systems for rehabilitation and outlines key conditions for their effective functioning and sustainability. The paper suggests that the benefits of restoring karez systems to local communities are large and some systems, such as the Ain Zubaida in the Makkah Al-Mukaramah Region, are of global importance.     

Parent–offspring conflict theory: An evolutionary framework for understanding conflict within human families.

Decades of research demonstrate that conflict shapes and permeates a broad range of family processes. In the current article, we argue that greater insight, integration of knowledge, and empirical achievement in the study of family conflict can be realized by utilizing a powerful theory from evolutionary biology that is barely known within psychology: parent–offspring conflict theory (POCT). In the current article, we articulate POCT for psychological scientists, extend its scope by connecting it to the broader framework of life history theory, and draw out its implications for understanding conflict within human families. We specifically apply POCT to 2 instances of early mother–offspring interaction (prenatal conflict and weaning conflict); discuss the effects of genetic relatedness on behavioral conflict between parents, children, and their siblings; review the emerging literature on parent–offspring conflict over the choice of mates and spouses; and examine parent–offspring conflict from the perspective of imprinted genes. This review demonstrates the utility of POCT, not only for explaining what is known about conflict within families but also for generating novel hypotheses, suggesting new lines of research, and moving us toward the “big picture” by integrating across biological and psychological domains of knowledge. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Use of Neural Networks for Automatic Classification From High-Resolution Images

The effectiveness of multilayer perceptron (MLP) networks as a tool for the classification of remotely sensed images has been already proven in past years. However, most of the studies consider images characterized by high spatial resolution (around 15-30 m) while a detailed analysis of the performance of this type of classifier on very high resolution images (around 1-2 m) such as those provided by the Quickbird satellite is still lacking. Moreover, the classification problem is normally understood as the classification of a single image while the capabilities of a single network of performing automatic classification and feature extraction over a collection of archived images has not been explored so far. In this paper, besides assessing the performance of MLP for the classification of very high resolution images, we investigate on the generalization capabilities of this type of algorithms with the purpose of using them as a tool for fully automatic classification of collections of satellite images, either at very high or at high-resolution. In particular, applications to urban area monitoring have been addressed     

Audio decoding with frequency and complexity scalability

The paper describes a technique to design a complexity-scalable audio codec based on the partial reconstruction of the spectrum of a signal. The technique can be applied to any subband coding system based on cosine-modulated filterbanks. In the decoder, only a part of the subband is reconstructed, achieving an output signal with a lower sampling rate. The new synthesis filterbank, with a reduced number of channels and low computational cost, must be suitably designed. Hence, a computational saving is traded for lower quality in the reconstructed signal. The technique can be used to design low-complexity decoders compatible with the MPEG audio standard, i.e. able to decode only a part of an MPEG bit stream. In this way, a single MPEG-coded audio can be decoded by different receivers, i.e. standard and low-complexity ones. The problem of having fixed bit rate streams for all the decoders is discussed.     

Emulation-based transient thermal modeling of 2D/3D systems-on-chip with active cooling

State-of-the-art devices in the consumer electronics market are relying more and more on Multi-Processor Systems-On-Chip (MPSoCs) as an efficient solution to meet their multiple design constrains, such as low cost, low power consumption, high performance and short time-to-market. In fact, as technology scales down, logic density and power density increase, generating hot spots that seriously affect the MPSoC performance and can physically damage the final system behavior. Moreover, forthcoming three-dimensional (3D) MPSoCs can achieve higher system integration density, but the aforementioned thermal problems are seriously aggravated. Thus, new thermal exploration tools are needed to study the temperature variation effects inside 3D MPSoCs. In this paper, we present a novel approach for fast transient thermal modeling and analysis of 3D MPSoCs with active (liquid) cooling solutions, while capturing the hardware-software interaction. In order to preserve both accuracy and speed, we propose a close-loop framework that combines the use of Field Programmable Gate Arrays (FPGAs) to emulate the hardware components of 2D/3D MPSoC platforms with a highly optimized thermal simulator, which uses an RC-based linear thermal model to analyze the liquid flow. The proposed framework offers speed-ups of more than three orders of magnitude when compared to cycle-accurate 3D MPSoC thermal simulators. Thus, this approach enables MPSoC designers to validate different hardware- and software-based 3D thermal management policies in real-time, and while running real-life applications, including liquid cooling injection control.     

A Multichip Pulse-Based Neuromorphic Infrastructure and Its Application to a Model of Orientation Selectivity

The growing interest in pulse-mode processing by neural networks is encouraging the development of hardware implementations of massively parallel networks of integrate-and-fire neurons distributed over multiple chips. Address-event representation (AER) has long been considered a convenient transmission protocol for spike based neuromorphic devices. One missing, long-needed feature of AER-based systems is the ability to acquire data from complex neuromorphic systems and to stimulate them using suitable data. We have implemented a general-purpose solution in the form of a peripheral component interconnect (PCI) board (the PCI-AER board) supported by software. We describe the main characteristics of the PCI-AER board, and of the related supporting software. To show the functionality of the PCI-AER infrastructure we demonstrate a reconfigurable multichip neuromorphic system for feature selectivity which models orientation tuning properties of cortical neurons