集群网络评测模型的新探索

传统集群网络(cluster area network,简称cLAN)的评测模型主要考虑了延迟、带宽、路由、拥塞、网络拓扑结构等因素.但这些因素是否足以描述实际应用程序在集群上的通信行为,或者对其在集群系统上的性能给出一个很好的预测呢?当对NAS Parallel Benchmark(2.4版本)在集群系统深腾1800(DeepComp 1800)上进行大量测试时发现,集群网络的通信性能可以被一种特殊的通信模式(LU模式)所严重影响.更深入的研究表明,这个影响LU模式的因素是独立于前面所述的如延迟、带宽、路由、拥塞、网络拓扑结构等因素的.因此有必要对集群网络的评测模型重新进行审视,并增加一个新的性能评测因子以反映这个新发现的现象.从研究结果来看,这个重新审视也将对集群系统上的并行算法设计以及实际大规模科学计算的应用程序性能的优化提供一些新的思路.

国产万亿次机群系统NPB性能测试分析

对3个国产万亿次机群系统进行了NPB性能测试分析,重点研究大规模并行处理时(处理器数目达到上千个)的性能特点和趋势.分析了不同的处理器、互连网络等系统配置对NPB性能的影响,发现NPB的8个程序在3个万亿次机器上的性能特点和表现并不一致,表明国产高性能机群在设计上正在逐渐走出同质化的趋势,向多样化发展.进一步分析表明,目前NPB程序的可扩展性可以达到几百个处理器,但尚不能达到上千个处理器,NPB程序能发挥出的系统峰值的百分比仍然徘徊在10%左右,机群系统的并行可扩展性和应用程序对机器运算潜能的利用还需要进一步提高.对于处理器数目达到上千个的万亿次机群系统来说,对集合通信和细粒度通信能力的支持亟需提高.

约束求解与优化技术的结合

提出了将混合约束问题转化为混合整数规划问题的方法.用约束求解方法及混合整数规划方法共同求解混合约束问题可以令二者相互借鉴,从而促进二者求解技术的进一步发展.同时,由混合约束问题转化而来的混合整数规划问题也可作为求解混合整数规划问题的测试问题（benchmarks）.

污染土壤修复基准建立的方法体系、案例研究与评价

为了今后我国在国家层面上建立污染土壤修复基准系统，促进我国国家生态安全体系的建立，本研究对国外发达国家建立污染土壤修复基准的情况进行了系统详细的文献检索。结合一些发达国家土壤修复标准以及我国土壤污染实际情况，提出建立污染土壤修复标准应从多方面综合考虑。并且以铅和乙草胺两种在我国东北地区普遍存在的污染物作为研究对象，首次开展区域水平上建立污染土壤修复基准方法和修复效果评判的尝试性研究。 通过农作物（小麦、大豆和白菜）发芽毒理实验，以食品卫生标准为反推基础的农作物毒物吸收实验，土壤动物毒理实验，生化水平毒理实验，土壤化学毒理实验和土壤酶学水平效应实验得出对土壤中主要组分和功能不产生影响，棕壤中乙草胺和铅浓度阈值。其基准不是所谓的不产生不良或有害影响的最大单一浓度或单一的无作用剂量，而是一个基于不同保护对象的多目标函数或一个范围值，所以对于不同的修复要求和保护对象确定乙草胺的修复阈值为0.4～12mg·kg-1，铅的修复阈值为3.98～793 mg·kg-1。 以沈阳某冶炼厂废弃厂区重金属污染监测为依据，采用美国环保局（US EPA）最新的人类健康风险评价标准方法对冶炼厂废弃地块污染土壤进行评价的结果显示：冶炼厂厂区内土壤污染非常严重；无论是工业用地假设还是休闲用地假设，由无机Cu 造成的人类健康风险在整个风险中所占的比例最大；单纯依靠US EPA 的健康风险评价并不能正确指示出土壤的潜在风险。运用土壤酶、暴露在土壤环境中的陆生植物以及与土壤环境直接接触的无脊椎动物等可靠的生态毒理指标，来判定、评价污染土壤的修复效果是可行的。

Enhance the optical absorptivity of nanocrystalline TiO2 film with high molar extinction coefficient ruthenium sensitizers for high performance dye-sensitized solar cells

We report two new heteroleptic polypyridyl ruthenium complexes, coded C101 and C102, with high molar extinction coefficients by extending the pi-conjugation of spectator ligands, with a motivation to enhance the optical absorptivity of mesoporous titania film and charge collection yield in a dye-sensitized solar cell. On the basis of this C101 sensitizer, several DSC benchmarks measured under the air mass 1.5 global sunlight have been reached.

Three Cuts for Accelerated Interval Propagation

This paper addresses the problem of nonlinear multivariate root finding. In an earlier paper we described a system called Newton which finds roots of systems of nonlinear equations using refinements of interval methods. The refinements are inspired by AI constraint propagation techniques. Newton is competative with continuation methods on most benchmarks and can handle a variety of cases that are infeasible for continuation methods. This paper presents three "cuts" which we believe capture the essential theoretical ideas behind the success of Newton. This paper describes the cuts in a concise and abstract manner which, we believe, makes the theoretical content of our work more apparent. Any implementation will need to adopt some heuristic control mechanism. Heuristic control of the cuts is only briefly discussed here.

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.

Generating Representative Web Workloads for Network and Server Performance Evaluation

One role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web workload generation tool which mimics a set of real users accessing a server. The tool, called Surge (Scalable URL Reference Generator) generates references matching empirical measurements of 1) server file size distribution; 2) request size distribution; 3) relative file popularity; 4) embedded file references; 5) temporal locality of reference; and 6) idle periods of individual users. This paper reviews the essential elements required in the generation of a representative Web workload. It also addresses the technical challenges to satisfying this large set of simultaneous constraints on the properties of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner significantly different from other Web server benchmarks.

Default ARTMAP 2

Default ARTMAP combines winner-take-all category node activation during training , distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network system for supervised learning and recognition. Winner-take-all ARTMAP learning is designed so that each input would make a correct prediction if re-presented immediately after its training presentation, passing the "next-input test." Distributed activation has been shown to improve test set prediction on many examples, but an input that made a correct winner-take-all prediction during training could make a different prediction with distributed activation. Default ARTMAP 2 introduces a distributed next-input test during training. On a number of benchmarks, this additional feature of the default system increases accuracy without significantly decreasing code compression. This paper includes a self-contained default ARTMAP 2 algorithm for implementation.

Self-Supervised ARTMAP

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semisupervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative lowdimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.bu.edu/SSART/.

Specification of optimum holistic building environmental and energy performance information to support informed decision making

Political drivers such as the Kyoto protocol, the EU Energy Performance of Buildings Directive and the Energy end use and Services Directive have been implemented in response to an identified need for a reduction in human related CO2 emissions. Buildings account for a significant portion of global CO2 emissions, approximately 25-30%, and it is widely acknowledged by industry and research organisations that they operate inefficiently. In parallel, unsatisfactory indoor environmental conditions have proven to negatively impact occupant productivity. Legislative drivers and client education are seen as the key motivating factors for an improvement in the holistic environmental and energy performance of a building. A symbiotic relationship exists between building indoor environmental conditions and building energy consumption. However traditional Building Management Systems and Energy Management Systems treat these separately. Conventional performance analysis compares building energy consumption with a previously recorded value or with the consumption of a similar building and does not recognise the fact that all buildings are unique. Therefore what is required is a new framework which incorporates performance comparison against a theoretical building specific ideal benchmark. Traditionally Energy Managers, who work at the operational level of organisations with respect to building performance, do not have access to ideal performance benchmark information and as a result cannot optimally operate buildings. This thesis systematically defines Holistic Environmental and Energy Management and specifies the Scenario Modelling Technique which in turn uses an ideal performance benchmark. The holistic technique uses quantified expressions of building performance and by doing so enables the profiled Energy Manager to visualise his actions and the downstream consequences of his actions in the context of overall building operation. The Ideal Building Framework facilitates the use of this technique by acting as a Building Life Cycle (BLC) data repository through which ideal building performance benchmarks are systematically structured and stored in parallel with actual performance data. The Ideal Building Framework utilises transformed data in the form of the Ideal Set of Performance Objectives and Metrics which are capable of defining the performance of any building at any stage of the BLC. It is proposed that the union of Scenario Models for an individual building would result in a building specific Combination of Performance Metrics which would in turn be stored in the BLC data repository. The Ideal Data Set underpins the Ideal Set of Performance Objectives and Metrics and is the set of measurements required to monitor the performance of the Ideal Building. A Model View describes the unique building specific data relevant to a particular project stakeholder. The energy management data and information exchange requirements that underlie a Model View implementation are detailed and incorporate traditional and proposed energy management. This thesis also specifies the Model View Methodology which complements the Ideal Building Framework. The developed Model View and Rule Set methodology process utilises stakeholder specific rule sets to define stakeholder pertinent environmental and energy performance data. This generic process further enables each stakeholder to define the resolution of data desired. For example, basic, intermediate or detailed. The Model View methodology is applicable for all project stakeholders, each requiring its own customised rule set. Two rule sets are defined in detail, the Energy Manager rule set and the LEED Accreditor rule set. This particular measurement generation process accompanied by defined View would filter and expedite data access for all stakeholders involved in building performance. Information presentation is critical for effective use of the data provided by the Ideal Building Framework and the Energy Management View definition. The specifications for a customised Information Delivery Tool account for the established profile of Energy Managers and best practice user interface design. Components of the developed tool could also be used by Facility Managers working at the tactical and strategic levels of organisations. Informed decision making is made possible through specified decision assistance processes which incorporate the Scenario Modelling and Benchmarking techniques, the Ideal Building Framework, the Energy Manager Model View, the Information Delivery Tool and the established profile of Energy Managers. The Model View and Rule Set Methodology is effectively demonstrated on an appropriate mixed use existing ‘green’ building, the Environmental Research Institute at University College Cork, using the Energy Management and LEED rule sets. Informed Decision Making is also demonstrated using a prototype scenario for the demonstration building.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.

Reforming the legal framework for clinical trials with neonates in Ireland: a children’s rights approach

This thesis assesses the current regulatory framework regarding clinical trials with neonates in Ireland from a children’s rights perspective, as derived from the UN Convention on the Rights of the Child 1989 (UN CRC) and its supporting instruments. The focus on neonates in the thesis is due to the particular need for clinical research with this group of children, their dependency on others for their protection and the lack of attention which has been given to them in the regulatory framework. The importance of children’s rights in this area is linked to the role of human rights in the regulation of clinical research in general. A rights-based approach is of great practical relevance in reforming law, policy and practice. For example, the CRC contains a set of commonly agreed legal benchmarks which can be used to assess the current framework and shape recommendations for reform. In this way, it provides a set of binding norms under international law, which must be complied with by states and state actors in all law, policy and practice affecting children. However, the contribution which a children’s rights approach could make to the regulation of research with children has not, to date, been explored in detail. This thesis aims to address this gap by developing a set of children’s rights-based benchmarks, which are used to assess the Irish regulatory framework for clinical trials with neonates and to develop recommendations for reform. The purpose of the analysis and recommendations is to assess Ireland’s compliance with international children’s rights law in the area and to analyse the potential of children’s rights to effectively address inadequacies in the Irish framework. The recommendations ultimately aim to develop a framework which will enhance the protection of neonates’ rights in this important area of children’s lives.

Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Quantifying data quality for clinical trials using electronic data capture.

BACKGROUND: Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes. METHODS AND PRINCIPAL FINDINGS: The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions. CONCLUSIONS: Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.