First results of systematic studies done with different types of Silicon Photomultipliers

The presented results are obtained during the first steps taken in order to develop a setup and measurement procedures which allow to compare properties of diverse kinds of silicon photomultipliers. The response to low-intensity light was studied for silicon photomultipliers produced by CPTA (Russia), Hamamatsu (Japan), ITC-irst (Italy) and SensL (Ireland).Comment: 3 pages, 3 figures, proceedings of the Internationa Linear Collider Workshop LCWS2007, Hamburg, German

Intra- and inter-examiner Reliability of Direct Facial Soft Tissue Measurements Using Digital Calipers

Background: The objective of this study is to determine if facial soft tissue measurements using digital calipers can be reliably taken by the same examiner and by a large group of examiners. Materials and Methods: Ten examiners performed a set of 18 in-clinic measurements on 10 female and 10 male dental students using a digital caliper twice over a 3-week period. The intra-class correlation coefficient and the Shrout-Fleiss method were used for the statistical analysis. Results: Anthropometric intra-examiner reliability was high for all measurements (none fell below R = 0.934). However, inter-examiner reliability exhibited a wide range of values, some reliable (nasal width at widest nostrils [R = 0.922] and subnasale to upper lip [R = 0.926]), and others unreliable [base of nose (R = 0.590), mouth height (R = 0.585), and soft tissue B point to gnathion (R = 0.623)]. Conclusions: Soft tissue measurements of clearly identifiable points measured by the same examiner produced highly consistent, accurate and reliable measurements. Soft tissue points with poor definition resulted in average-to-poor reliabilities measurements

Quality control facilities for large optical reflectors at ENEA-Casaccia for physics application

The paper describes the quality control facilities for large optical reflectors available at ENEA-Casaccia. Commercial and custom spectrophotometers allow to measure the reflectance; specular and diffused for flat samples, and specular for the full-size reflector. In the case of spherical shape, the 2f and the pin-hole optical tests give a quick evaluation of the focusing effectiveness and the curvature uniformity, respectively. An optical profilometer allows to accurately measure the reflector profile and its deviations from the project specifications

First results of comparative studies of silicon photomultipliers

The presented results are obtained during the first steps taken in order to develop a set-up and measurement procedures which allow to compare properties of diverse samples of silicon photomultipliers. The response to low-intensity light was studied for silicon photomultipliers produced by CPTA (Russia), Hamamatsu(Japan), ITC-irst (Italy) and SensL (Ireland)

Efficient Neural Network Approximation via Bayesian Reasoning

Approximate Computing (AxC) trades off between the accuracy required by the user and the precision provided by the computing system to achieve several optimizations such as performance improvement, energy, and area reduction. Several AxC techniques have been proposed so far in the literature. They work at different abstraction levels and propose both hardware and software implementations. The standard issue of all existing approaches is the lack of a methodology to estimate the impact of a given AxC technique on the application-level accuracy. This paper proposes a probabilistic approach based on Bayesian networks to quickly estimate the impact of a given approximation technique on application-level accuracy. Moreover, we have also shown how Bayesian networks allow a backtrack analysis that automatically identifies the most sensitive components. That influence analysis dramatically reduces the space exploration for approximation techniques. Preliminary results on a simple artificial neural network shown the efficiency of the proposed approach

A Genetic-algorithm-based Approach to the Design of DCT Hardware Accelerators

As modern applications demand an unprecedented level of computational resources, traditional computing system design paradigms are no longer adequate to guarantee significant performance enhancement at an affordable cost. Approximate Computing (AxC) has been introduced as a potential candidate to achieve better computational performances by relaxing non-critical functional system specifications. In this article, we propose a systematic and high-abstraction-level approach allowing the automatic generation of near Pareto-optimal approximate configurations for a Discrete Cosine Transform (DCT) hardware accelerator. We obtain the approximate variants by using approximate operations, having configurable approximation degree, rather than full-precise ones. We use a genetic searching algorithm to find the appropriate tuning of the approximation degree, leading to optimal tradeoffs between accuracy and gains. Finally, to evaluate the actual HW gains, we synthesize non-dominated approximate DCT variants for two different target technologies, namely, Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs). Experimental results show that the proposed approach allows performing a meaningful exploration of the design space to find the best tradeoffs in a reasonable time. Indeed, compared to the state-of-the-art work on approximate DCT, the proposed approach allows an 18% average energy improvement while providing at the same time image quality improvement

Chacarita Project: Conformation and analysis of a modern and documented human osteological collection from Buenos Aires City - Theoretical, methodological and ethical aspects

Osteological reference collections play a key role in bioanthropological research; they allow the development and testing of methods for sexing and aging individuals using different bone and dental attributes. This paper presents the first stage results of the ongoing Chacarita Research Project, which aims to generate and study a reference collection of adult skeletons representative of the contemporary population of Buenos Aires city. The Chacarita Collection is being conformed of unclaimed human remains of individuals of known nationality, sex, age, cause and date of death from the Chacarita Public Cemetery. Unlike other similar endeavors, this sample has been completely exhumed using archaeological techniques. So far, a total of 146 adult skeletons have been recovered (60 females - 41.1% - and 86 males - 58.90% -), the majority of which have ages-at-death in the range of 71-90 years. They were born primarily in Argentina (n = 133; 91.1%), although other nationalities are also represented. Dates of death go between 1987 and 2000. In the short term, the osteological study of this collection will allow assessment of the performance of classical methods of sex determination and age-at-death estimation in a local setting. A special priority will be given to the study of osteological changes in individuals over 50 years. As the sample is being retrieved by exhumation, the impact of taphonomic agents on the most diagnostic bones structures is also being assessed. In the long term, this osteological collection will be available to generate new population-specific techniques, and to develop comparative biological studies.Fil: Bosio, L. A.. Universidad de Buenos Aires. Facultad de Medicina. Cátedra de Medicina Legal y Deontología Médica. Servicio de Antropología Forense; ArgentinaFil: García Guraieb, S.. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; ArgentinaFil: Luna, Leandro Hernan. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Museo Etnográfico "Juan B. Ambrosetti"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aranda, C.. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Museo Etnográfico "Juan B. Ambrosetti"; Argentin

Cross-layer soft-error resilience analysis of computing systems

In a world with computation at the epicenter of every activity, computing systems must be highly resilient to errors even if miniaturization makes the underlying hardware unreliable. Techniques able to guarantee high reliability are associated to high costs. Early resilience analysis has the potential to support informed design decisions to maximize system-level reliability while minimizing the associated costs. This tutorial focuses on early cross-layer (hardware and software) resilience analysis considering the full computing continuum (from IoT/CPS to HPC applications) with emphasis on soft errors

A Functional Verification based Fault Injection Environment

Fault injection is needed for different purposes such as analyzing the reaction of a system in a faulty environment or validating fault-detection and/or fault-correction techniques. In this paper we propose a simulation-based fault injection tool able to work at different abstraction levels and with user-defined fault models. By exploiting the facilities provided by a functional verification environment it allows to speed up the entire fault injection process: from the creation of the workload to the analysis of the results of injection campaigns. Moreover, the adoption of techniques to optimize the fault list significantly reduces the simulation time. Being the tool targeted to the validation of dependable systems, it includes a way to extract information from the Failure Mode and Effect Analysis and to correlate fault injection results with estimates

Resilience-Performance Tradeoff Analysis of a Deep Neural Network Accelerator

Nowadays, Deep Neural Networks (DNNs) are one of the most computationally-intensive algorithms because of the (i) huge amount of data to be transferred from/to the memory, and (ii) the huge amount of matrix multiplications to compute. These issues motivate the design of custom DNN hardware accelerators. These accelerators are widely used for low-latency safety-critical applications such as object detection in autonomous cars. Safety-critical applications have to be resilient with respect to hardware faults and Deep Learning (DL) accelerators are subjected to hardware faults that can cause functional failures, potentially leading to catastrophic consequences. Although DNNs possess a certain level of intrinsic resilience, it varies depending on the hardware on which they are run. The intent of the paper is to assess the resilience of a systolic-array-based DNN accelerator in the presence of hardware faults, in order to identify the architectural parameters that may mainly impact the DNN resilience