High current density tris(8-hydroxyquinoline) aluminum-based hybrid transistor in vertical architecture

We report the construction of hybrid permeable-base transistors, in vertical architecture, using tris(8-hydroxyquinoline) aluminum as emitter, a thin gold layer as base, and n-type silicon as collector. These transistors present high common-base current gain, can be operated at low driving voltages, and allow high current density.

Response of chlorophyll fluorescence to dynamic light in three alpine species differing in plant architecture

A study was carried out to examine the effect of dynamic photosynthetically active photon flux density (PPFD) on photoinhibition and energy use in three herbaceous species, prostrate Saussurea superba, erect-leaved S. katochaete, and half-erect-leaved Gentiana straminea, from the Qinghai-Tibet Plateau. Chlorophyll fluorescence response was measured under each of three sets of high-low PPFD combinations: 1700-0, 1400-300, and 1200-500 mu mol m(-2) s(-1), illuminating in four dynamic frequencies: 1, 5, 15, and 60 cycles per 2 h. The total light exposure time was 2h and the integrated PPFD was the same in all treatments. The highest frequency of PPFD fluctuation resulted in the lowest photochemical activity, the highest level of non-photochemical quenching, and the greatest decrease of F-v/F-m (maximal photochemical efficiency of PSII). The 5 and 15 cycles per 2h treatments resulted in higher photochemical activity than the 1 cycle per 2h treatment. The 1700-0 PPFD combination led to the lowest photochemical activity and more serious photoinhibition in all species. S. superba usually exhibited the highest photochemical activity and CO2 uptake rate, the lowest reduction of F-v/F-m,F- and the smallest fraction of energy in thermal dissipation. With similar fractions of thermal dissipation, S. katochaete had relatively less photoinhibition than G. straminea owing to effective F-o quenching. The results suggest that high frequency of fluctuating PPFD generally results in photoinhibition, which is more serious under periods of irradiation with high light intensity. (c) 2005 Elsevier B.V. All rights reserved.

Photosynthetic depression in relation to plant architecture in two alpine herbaceous species

The Qinghai-Tibet Plateau is characterized by extremely high radiation, which may induce down-regulation of photosynthesis in plants living in this alpine ecosystem. To clarify whether photoinhibition occurs in the alpine environment and to discern its underlying mechanisms, we examined photosynthetic gas exchange and fluorescence emission in response to the changes in photosynthetic photon flux density (PPFD) and leaf temperature under natural regimes for two herbaceous species: prostrate Saussurea superba and erect-leaved Saussurea katochaete from altitude 3250 m on the Qinghai-Tibet Plateau. S. superba intercepted a higher maximum PPFD and experienced much higher leaf temperature than the erect-leaved S. katochaete. S. superba exhibited a much higher light saturation point for photosynthesis than S. katochaete. Under controlled conditions, the former species had higher CO2 uptake rates and neither species showed obvious photosynthetic down-regulation at high PPFD. Under natural environmental conditions, however, apparent photoinhibition, indicated by reduced electron transport rate (ETR), was evident at high PPFD for both species. After a night frost, the photochemistry of S. katochaete was depressed markedly in the early morning and recovered by mid-day. After a frost-free night, it was high in the morning and low at noon due to high radiation. S. superba did not respond to the night frost in terms of daytime photochemical pattern. In both species, photochemical depression was aggravated by high leaf temperature and the erect species was more sensitive to high temperature. This study suggests that the high radiation on the Qinghai-Tibet Plateau is likely to induce rapidly reversible photoinhibition, which is related closely to plant architecture. Photochemistry in the prostrate species seems able to tolerate higher PPFD, without obvious suppression, than the erect species. (C) 2003 Elsevier Science B.V. All rights reserved.

Message-Driven Processor Architecture: Verson 11

The Message-Driven Processor is a node of a large-scale multiprocessor being developed by the Concurrent VLSI Architecture Group. It is intended to support fine-grained, message passing, parallel computation. It contains several novel architectural features, such as a low-latency network interface, extensive type-checking hardware, and on-chip memory that can be used as an associative lookup table. This document is a programmer's guide to the MDP. It describes the processor's register architecture, instruction set, and the data types supported by the processor. It also details the MDP's message sending and exception handling facilities.

Towards an Example-Based Image Compression Architecture for Video-Conferencing

This paper consists of two major parts. First, we present the outline of a simple approach to very-low bandwidth video-conferencing system relying on an example-based hierarchical image compression scheme. In particular, we discuss the use of example images as a model, the number of required examples, faces as a class of semi-rigid objects, a hierarchical model based on decomposition into different time-scales, and the decomposition of face images into patches of interest. In the second part, we present several algorithms for image processing and animation as well as experimental evaluations. Among the original contributions of this paper is an automatic algorithm for pose estimation and normalization. We also review and compare different algorithms for finding the nearest neighbors in a database for a new input as well as a generalized algorithm for blending patches of interest in order to synthesize new images. Finally, we outline the possible integration of several algorithms to illustrate a simple model-based video-conference system.

New Timber Architecture in Scotland.

New Timber Architecture in Scotland illustrates 90 exemplar projects and demonstrates clearly that there is no single building type unsuited to the use of this adaptable, variable and infinitely renewable material. Too long out of fashion, timber is now widely specified and has become an important design element in some of the most innovative projects being built today. The projects selected for inclusion are not the work of a few superstar architects: they represent the output of a significant percentage of architectural practices in Scotland and illustrate a burgeoning confidence in timber as an exciting, contemporary construction material. New Timber Architecture in Scotland aims to stimulate others to follow their lead.

Indian Architecture: Social and Political Challenges

The objective of this paper is to reassess the central factors which have shaped the Indian architecture. The author puts forward the concept of plurality introduced by Western art historians and argues that the diversity of the Indian architecture should not be explained in terms of religious differences, but in terms of the socio-economical situation in South Asia. He also elaborates on the Hindu caste system and its impact on the Indian architecture.

The Specialized Mappings Architecture

A probabilistic, nonlinear supervised learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA employs a set of several forward mapping functions that are estimated automatically from training data. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). The SMA can model ambiguous, one-to-many mappings that may yield multiple valid output hypotheses. Once learned, the mapping functions generate a set of output hypotheses for a given input via a statistical inference procedure. The SMA inference procedure incorporates an inverse mapping or feedback function in evaluating the likelihood of each of the hypothesis. Possible feedback functions include computer graphics rendering routines that can generate images for given hypotheses. The SMA employs a variant of the Expectation-Maximization algorithm for simultaneous learning of the specialized domains along with the mapping functions, and approximate strategies for inference. The framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human’s body or hands, given silhouettes from a single image. The accuracy and stability of the SMA are also tested using synthetic images of human bodies and hands, where ground truth is known.

Efficient End-Host Architecture for High Performance Communication Using User-level Sandboxing

Current low-level networking abstractions on modern operating systems are commonly implemented in the kernel to provide sufficient performance for general purpose applications. However, it is desirable for high performance applications to have more control over the networking subsystem to support optimizations for their specific needs. One approach is to allow networking services to be implemented at user-level. Unfortunately, this typically incurs costs due to scheduling overheads and unnecessary data copying via the kernel. In this paper, we describe a method to implement efficient application-specific network service extensions at user-level, that removes the cost of scheduling and provides protected access to lower-level system abstractions. We present a networking implementation that, with minor modifications to the Linux kernel, passes data between "sandboxed" extensions and the Ethernet device without copying or processing in the kernel. Using this mechanism, we put a customizable networking stack into a user-level sandbox and show how it can be used to efficiently process and forward data via proxies, or intermediate hosts, in the communication path of high performance data streams. Unlike other user-level networking implementations, our method makes no special hardware requirements to avoid unnecessary data copies. Results show that we achieve a substantial increase in throughput over comparable user-space methods using our networking stack implementation.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

Assessing the Security of a Clean-Slate Internet Architecture

The TCP/IP architecture was originally designed without taking security measures into consideration. Over the years, it has been subjected to many attacks, which has led to many patches to counter them. Our investigations into the fundamental principles of networking have shown that carefully following an abstract model of Interprocess Communication (IPC) addresses many problems [1]. Guided by this IPC principle, we designed a clean-slate Recursive INternet Architecture (RINA) [2]. In this paper, we show how, without the aid of cryptographic techniques, the bare-bones architecture of RINA can resist most of the security attacks faced by TCP/IP. We also show how hard it is for an intruder to compromise RINA. Then, we show how RINA inherently supports security policies in a more manageable, on-demand basis, in contrast to the rigid, piecemeal approach of TCP/IP.

Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.

The Cyclone Server Architecture: Streamlining Delivery of Popular Content

We propose a new technique for efficiently delivering popular content from information repositories with bounded file caches. Our strategy relies on the use of fast erasure codes (a.k.a. forward error correcting codes) to generate encodings of popular files, of which only a small sliding window is cached at any time instant, even to satisfy an unbounded number of asynchronous requests for the file. Our approach capitalizes on concurrency to maximize sharing of state across different request threads while minimizing cache memory utilization. Additional reduction in resource requirements arises from providing for a lightweight version of the network stack. In this paper, we describe the design and implementation of our Cyclone server as a Linux kernel subsystem.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.