Synthesis of High-Performance Packet Processing Pipelines

Packet editing is a fundamental building block of data communication systems such as switches and routers. Circuits that implement this function are critical and define the features of the system. We propose a high-level synthesis technique for a new model for representing packet editing functions. Experiments show our circuits achieve a throughput of up to 40Gb/s on a commercially available FPGA device, equal to state-of-the-art implementations

Synthesis and Optimization of Pipelined Packet Processors

We consider pipelined architectures of packet processors consisting of a sequence of simple packet-processing modules interconnected by first-in first-out buffers. We propose a new model for describing their function, an automated synthesis technique that generates efficient hardware for them, and an algorithm for computing minimum buffer sizes that allow such pipelines to achieve their maximum throughput. Our functional model provides a level of abstraction familiar to a network protocol designer; in particular, it does not require knowledge of register-transfer-level hardware design. Our synthesis tool implements the specified function in a sequential circuit that processes packet data a word at a time. Finally, our analysis technique computes the maximum throughput possible from the modules and then determines the smallest buffers that can achieve it. Experimental results conducted on industrial-strength examples suggest that our techniques are practical. Our synthesis algorithm can generate circuits that achieve 40 Gb/s on field-programmable gate arrays, equal to state-of-the-art manual implementations, and our buffer-sizing algorithm has a practically short runtime. Together, our techniques make it easier to quickly develop and deploy high-speed network switches

Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data – Towards a Data Basis for Predictive AI Models

Background Early fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations. Objective The aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies. Methods To this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization. Results A workflow with a high degree of automation is established, that links large distortion-corrected microstructure data with damage localization and evolution kinetics. The workflow enables cycling up to the VHCF regime in comparatively short time spans, while maintaining unprecedented time resolution of damage evolution. Resulting data sets capture the interaction of damage with microstructural features and hold the potential to unravel a mechanistic understanding. Conclusions The proposed workflow lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials

Exploiting the Enumeration of All Feature Model Configurations

.Feature models are widely used to encode the configurations of a software product line in terms of mandatory, optional and exclusive features as well as propositional constraints over the features. Numerous computationally expensive procedures have been developed to model check, test, configure, debug, or compute relevant information of feature models. In this paper we explore the possible improvement of relying on the enumeration of all configurations when performing automated analysis operations. We tackle the challenge of how to scale the existing enumeration techniques by relying on distributed computing. We show that the use of distributed computing techniques might offer practical solutions to previously unsolvable problems and opens new perspectives for the automated analysis of software product lines.Junta de Andalucía P12-TIC-1867Ministerio de Economía y Competitividad TIN2015- 70560-

Continuously-variable survival exponent for random walks with movable partial reflectors

We study a one-dimensional lattice random walk with an absorbing boundary at the origin and a movable partial reflector. On encountering the reflector, at site x, the walker is reflected (with probability r) to x-1 and the reflector is simultaneously pushed to x+1. Iteration of the transition matrix, and asymptotic analysis of the probability generating function show that the critical exponent delta governing the survival probability varies continuously between 1/2 and 1 as r varies between 0 and 1. Our study suggests a mechanism for nonuniversal kinetic critical behavior, observed in models with an infinite number of absorbing configurations.Comment: 5 pages, 3 figure

Quantitative Kinetic Study of the Actin-Bundling Protein L-Plastin and of Its Impact on Actin Turn-Over

BACKGROUND: Initially detected in leukocytes and cancer cells derived from solid tissues, L-plastin/fimbrin belongs to a large family of actin crosslinkers and is considered as a marker for many cancers. Phosphorylation of L-plastin on residue Ser5 increases its F-actin binding activity and is required for L-plastin-mediated cell invasion. METHODOLOGY/PRINCIPAL FINDINGS: To study the kinetics of L-plastin and the impact of L-plastin Ser5 phosphorylation on L-plastin dynamics and actin turn-over in live cells, simian Vero cells were transfected with GFP-coupled WT-L-plastin, Ser5 substitution variants (S5/A, S5/E) or actin and analyzed by fluorescence recovery after photobleaching (FRAP). FRAP data were explored by mathematical modeling to estimate steady-state reaction parameters. We demonstrate that in Vero cell focal adhesions L-plastin undergoes rapid cycles of association/dissociation following a two-binding-state model. Phosphorylation of L-plastin increased its association rates by two-fold, whereas dissociation rates were unaffected. Importantly, L-plastin affected actin turn-over by decreasing the actin dissociation rate by four-fold, increasing thereby the amount of F-actin in the focal adhesions, all these effects being promoted by Ser5 phosphorylation. In MCF-7 breast carcinoma cells, phorbol 12-myristate 13-acetate (PMA) treatment induced L-plastin translocation to de novo actin polymerization sites in ruffling membranes and spike-like structures and highly increased its Ser5 phosphorylation. Both inhibition studies and siRNA knock-down of PKC isozymes pointed to the involvement of the novel PKC-delta isozyme in the PMA-elicited signaling pathway leading to L-plastin Ser5 phosphorylation. Furthermore, the L-plastin contribution to actin dynamics regulation was substantiated by its association with a protein complex comprising cortactin, which is known to be involved in this process. CONCLUSIONS/SIGNIFICANCE: Altogether these findings quantitatively demonstrate for the first time that L-plastin contributes to the fine-tuning of actin turn-over, an activity which is regulated by Ser5 phosphorylation promoting its high affinity binding to the cytoskeleton. In carcinoma cells, PKC-delta signaling pathways appear to link L-plastin phosphorylation to actin polymerization and invasion

Anomalous f-electron Hall Effect in the Heavy-Fermion System CeTIn5_{5} (T = Co, Ir, or Rh)

The in-plane Hall coefficient $R_{H}(T)$ of CeRhIn$_{5}$, CeIrIn$_{5}$, and CeCoIn$_{5}$ and their respective non-magnetic lanthanum analogs are reported in fields to 90 kOe and at temperatures from 2 K to 325 K. $R_{H}(T)$ is negative, field-independent, and dominated by skew-scattering above $\sim$ 50 K in the Ce compounds. $R_{H}(H \to 0)$ becomes increasingly negative below 50 K and varies with temperature in a manner that is inconsistent with skew scattering. Field-dependent measurements show that the low-T anomaly is strongly suppressed when the applied field is increased to 90 kOe. Measurements on LaRhIn$_{5}$, LaIrIn$_{5}$, and LaCoIn$_{5}$ indicate that the same anomalous temperature dependence is present in the Hall coefficient of these non-magnetic analogs, albeit with a reduced amplitude and no field dependence. Hall angle ($\theta_{H}$) measurements find that the ratio $\rho_{xx}/\rho_{xy}=\cot(\theta_{H})$ varies as $T^{2}$ below 20 K for all three Ce-115 compounds. The Hall angle of the La-115 compounds follow this T-dependence as well. These data suggest that the electronic-structure contribution dominates the Hall effect in the 115 compounds, with $f$-electron and Kondo interactions acting to magnify the influence of the underlying complex band structure. This is in stark contrast to the situation in most $4f$ and $5f$ heavy-fermion compounds where the normal carrier contribution to the Hall effect provides only a small, T-independent background to $R_{H}.$Comment: 23 pages and 8 figure

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Bilateral infraorbital nerve blocks decrease postoperative pain but do not reduce time to discharge following outpatient nasal surgery

While infraorbital nerve blocks have demonstrated analgesic benefits for pediatric nasal and facial plastic surgery, no studies to date have explored the effect of this regional anesthetic technique on adult postoperative recovery. We designed this study to test the hypothesis that infraorbital nerve blocks combined with a standardized general anesthetic decrease the duration of recovery following outpatient nasal surgery. At a tertiary care university hospital, healthy adult subjects scheduled for outpatient nasal surgery were randomly assigned to receive bilateral infraorbital injections with either 0.5% bupivacaine (Group IOB) or normal saline (Group NS) using an intraoral technique immediately following induction of general anesthesia. All subjects underwent a standardized general anesthetic regimen and were transported to the recovery room following tracheal extubation. The primary outcome was the duration of recovery (minutes) from recovery room admission until actual discharge to home. Secondary outcomes included average and worst pain scores, nausea and vomiting, and supplemental opioid requirements. Forty patients were enrolled. A statistically significant difference in mean [SD] recovery room duration was not observed between Groups IOB and NS (131 [61] min vs 133 [58] min, respectively; P = 0.77). Subjects in Group IOB did experience a reduction in average pain on a 0–100 mm scale (mean [95% confidence interval]) compared to Group NS (−11 [−21 to 0], P = 0.047), but no other comparison of secondary outcomes was statistically significant. When added to a standardized general anesthetic, bilateral IOB do not decrease actual time to discharge following outpatient nasal surgery despite a beneficial effect on postoperative pain

Tree model guided candidate generation for mining frequent subtrees from XML

Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly coneerned with mining frequent induced and embedded ordered subtrees. Our main contributions arc as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation ofour Tree Model Guided (TMG) candidate generation. TMG is an optimal, non-redundant enumeration strategy which enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that TMG has better complexity compared to the commonly used join approach. In this paper, we propose two algorithms, MB3Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint. Our experiments with both synthetic and real datasets against two well known algorithms for mining induced and embedded subtrees, demonstrate the effeetiveness and the efficiency of the proposed techniques