INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

On-line BIST for performance failure prediction under aging effects in automotive safety-critical applications

Abstract: Electronic design of high-performance digital systems in nano-scale CMOS technologies under Process, power supply Voltage, Temperature and Aging (PVTA) variations is a challenging process Such variations induce abnormal timing delays leading to systems errors, harmful in safety-critical applications Performance Failure Prediction (PFP), instead of error detection, becomes necessary, particularly in the presence of aging effects In this paper, an on-line BIST methodology for PFP in a standard cell design flow is proposed The methodology is based on abnormal delay detection associated with critical signal paths PVTA-aware aging sensors are designed Multilevel simulation is used Functional and structural test pattern generation is performed, targeting the detection of critical path delay faults A sensor insertion technique is proposed, together with an up-graded version of a proprietary software tool, DyDA Finally, a novel strategy for gate-level Aging fault injection is proposed, using the concept of an Aging de-rating factor Results are presented for a Serial Parallel Interface (SPI) controller, designed with commercial UMC 130nm CMOS technology and Faraday™ cell library Only seven sensors are required to monitor unsafe performance operation, due to Negative Bias Thermal Instability (NBTI)-induced aging

Comparison of contention aware list scheduling heuristics for cluster computing

Abstract: In the area of static scheduling, list scheduling is one of the most common heuristics for the temporal and spatial assignment of a Directed Acyclic Graph (DAG) to a target machine. As most heuristics, list scheduling assumes fully connected homogeneous processors and ignores contention on the inter communication links. This article extends the list scheduling heuristic for contention aware scheduling on heterogenous arbitrary machines. The extension is based on the idea of scheduling edges to links, likewise the scheduling of nodes to processors. Based on this extension, we compare eight priority schemes for the node order determination of the first phase of list scheduling. Random graphs are generated and scheduled with the different schemes to homogenous and heterogenous parallel systems from the area of cluster computing. The experiments demonstrate the appropriateness of our extended list scheduling for homogeneous and heterogenous cluster architectures.

Managing Authorship in Plot Conduction

Abstract: The increasing complexity of interactive applications and crescent demand for educational systems aggravates the urge for a balance between interactivity and scripted content. Approaches to this problem have ranged from a focus on interactivity and emotional engagement to linear narratives, with limited interactive capacities. The introduction of non-linearity frequently imposes a heavy burden to the authoring process. We argue that the definition of a domain ontology is the foundation that bonds all intervenients in the creative process. Furthermore, we propose a framework with which to cope the different issues involved in the development of interactive narrative applications. Going through a concrete example, we argue that these concepts and tools can contribute to a work methodology with which to attain good results within a feasible time frame.

TFRank: network-based prioritization of regulatory associations underlying transcriptional responses.

Abstract: Motivation: Uncovering mechanisms underlying gene expression control is crucial to understand complex cellular responses. Studies in gene regulation often aim to identify regulatory players involved in a biological process of interest, either transcription factors coregulating a set of target genes or genes eventually controlled by a set of regulators. These are frequently prioritized with respect to a context-specific relevance score. Current approaches rely on relevance measures accounting exclusively for direct transcription factor–target interactions, namely overrepresentation of binding sites or target ratios. Gene regulation has, however, intricate behavior with overlapping, indirect effect that should not be neglected. In addition, the rapid accumulation of regulatory data already enables the prediction of large-scale networks suitable for higher level exploration by methods based on graph theory. A paradigm shift is thus emerging, where isolated and constrained analyses will likely be replaced by whole-network, systemic-aware strategies. Results: We present TFRank, a graph-based framework to prioritize regulatory players involved in transcriptional responses within the regulatory network of an organism, whereby every regulatory path containing genes of interest is explored and incorporated into the analysis. TFRank selected important regulators of yeast adaptation to stress induced by quinine and acetic acid, which were missed by a direct effect approach. Notably, they reportedly confer resistance toward the chemicals. In a preliminary study in human, TFRank unveiled regulators involved in breast tumor growth and metastasis when applied to genes whose expression signatures correlated with short interval to metastasis. Availability: Prototype at http://kdbio.inesc-id.pt/software/tfrank/. Contact: jpg@kdbio.inesc-id.pt; sara.madeira@ist.utl.pt Supplementary Information: Supplementary data are available at Bioinformatics online.

Influence of peak selection methods on onset detection

Abstract: Finding the starting time of musical notes in an audio signal, that is, to perform onset detection, is an important task as this information can be used as the basis for high-level musical processing tasks. Many different methods exist to perform onset detection. However their results depend on a Peak Selection step that makes the decision whether an onset is present at some point in time. In this paper we review a number of different Peak Selection methods and compare their influence in the performance of different onset detection methods and on 4 distinct onset classes. Our results show that the post-processing method used deeply influences both positively and negatively the results obtained.