Miracle-dz/UniFAR-FTUs huggingface dataset:
The Network Architecture of the Connection Machine CM-5

The Connection Machine Model CM-5 Supercomputer is a massively parallel computer system designed to offer performance in the range of 1 teraflops (1012floating-point operations per second). The CM-5 obtains its high performance while offering ease of programming, flexibility, and reliability. The machine contains three communication networks: a data network, a control network, and a diagnostic network. This paper describes the organization of these three networks and how they contribute to the design goals of the CM-5.
Embedding Bounded Bandwidth Graphs into ‘ p

We prove that graphs can be embedded randomly into ‘ p (for any p ≥ 1 ) with expected distortion polynomial in k , where k is the bandwidth of the graph. This implies the existence of an inﬁnite family of graphs which embed well into ‘ p , which is surprising since trees do not form such a class, having a lower bound of Ω(log log n ) for ‘ 2 embedding. Our results extend to graphs of bounded tree-bandwidth when the target metric is ‘ 1 , or when we allow an additional term of size O (log log n ) in our distortion.
Motion Planning for Unexplored Underwater Environments using Autonomous Underwater Vehicles

We present an approach to endow an autonomous underwater vehicle (AUV) with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to incrementally build a map of the surroundings, while simultaneously (re)planning a feasible path to a specified goal. To accomplish this, the framework considers motion constraints to plan feasible 3D paths, i.e., those that meet the vehicle’s motion capabilities. It also incorporates a risk function to avoid navigating close to nearby obstacles. Furthermore, the framework makes use of two strategies to ensure meeting online computation limitations. The first one is to reuse the last best known solution to eliminate time-consuming pruning routines. The second one is to opportunistically check the states’ risk of collision. ∗J.D. Hernández is currently a Postdoctoral Research Associate in the Department of Computer Science at Rice University, Houston, TX, USA. juandhv@rice.edu Accepted for publication in Journal of Field Robotics, 2018
Multiscale Investigation on Biofilm Distribution and Its Impact on Macroscopic Biogeochemical Reaction Rates

Biofilms are critical locations for biogeochemical reactions in the subsurface environment. The occurrence and distribution of biofilms at microscale as well as their impacts on macroscopic biogeochemical reaction rates are still poorly understood. This paper investigated the formation and distributions of biofilms in heterogeneous sediments using multiscale models and evaluated the effects of biofilm heterogeneity on local and macroscopic biogeochemical reaction rates. Sediment pore structures derived from X‐ray computed tomography were used to simulate the microscale flow dynamics and biofilm distribution in the sediment column. The response of biofilm formation and distribution to the variations in hydraulic and chemical properties was first examined. One representative biofilm distribution was then utilized to evaluate its effects on macroscopic reaction rates using nitrate reduction as an example. The results revealed that microorganisms primarily grew on the surfaces of grains and aggregates near preferential flow paths where both electron donor and acceptor were readily accessible, leading to the heterogeneous distribution of biofilms in the sediments. The heterogeneous biofilm distribution decreased the macroscopic rate of biogeochemical reactions as compared with those in homogeneous cases. Operationally considering the heterogeneous biofilm distribution in macroscopic reactive transport models such as using dual porosity domain concept can significantly improve the prediction of biogeochemical reaction rates. Overall, this study provided important insights into the biofilm formation and distribution in soils and sediments as well as their impacts on the macroscopic manifestation of reaction rates.
A Mechanism for Efficient Management of Changes in BPEL based Business Processes: An Algebraic Methodology

Business process execution language (BPEL) is an XML-based Web service composition language that is commonly used for the orchestration of business processes. Orchestration refers to the sequencing of services, usually defined in a separate Web services definition language (WSDL) document, in order to provide additional logic to process data, which essentially creates the application logic. In certain situations, when business goals or policies change, these services (external to the orchestrated process) may change in a way that it affects the orchestrated process. For example, some services may be removed and new ones introduced. In situations like this, BPEL models need to be changed accordingly. In large and complex business processes, due to the verbose nature of BPEL, making alterations to BPEL descriptions is an arduous task, and can often lead to inconsistencies and errors. This paper demonstrates a way to map BPEL processes and related WSDL service descriptions into algebraic expressions, which encapsulate all process elements into a single phrase. The purpose of these algebraic expressions is to easily identify the effect of service changes in the orchestrated process, which in turn enables the required changes to be carried out efficiently in-situ within a given context, without disturbing the consistency of the overall BPEL document. The proposed algebraic methodology is based on Kleene algebra with tests (KAT), which is a new branch of algebra that lends itself for practical modeling purposes
DETERMINING PRIORITY OF THE CRITERIA FOR STRUCTURAL OPTIMIZATION OF TECHNICAL SYSTEMS

In the present paper is proposed a classification of the problems and the mathematical models for choosing optimal structural variant while designing a technical system. Algorithms and software models were developed based on known methods for determining the priority of criteria functions. The algorithms and models are implemented in a dialog system for structural optimization. Results are shown from using the software for solving a particular problem.
Extracting Personal Names from Emails : Applying Named Entity Recognition to Informal Text

The problem of named entity recognition (NER) has been well-studied, but there has been little prior work on NER for “informal” documents—i.e., documents like email messages and bulletin board postings that are prepared quickly, and intended for a narrow audience. In this paper, we investigate NER for informal text, via an experimental study of recognizing personal names in email. We study the performance on four new annotated corpora of two machine-learning based methods: conditional random fields, and a perceptron-based method for learning HMMs. Experiments show that baseline F1 performance can be improved substantially by introduction of email-specific learning features. F1 performance is also improved by a novel recall-enhancing method, which exploits the fact that in email corpora, names are typically repeated many times across the corpus.
AI 2 : Safety and Robustness Certification of Neural Networks with Abstract Interpretation

transformers for each of the types of layers. a) Fully-connected Layer, Convolutional Layer: ReLU applied to the i-th component of the abstract element a ∈ A can be represented as gi(au (xi < 0))t (au (xi ≥ 0)), where gi : R → R is an affine transformation that assigns 0 to the i-th component of the input. Both the fully-connected and the convolutional layer are particular affine transformations followed by componentwise ReLU, therefore we can obtain abstract transformers for them by composing ReLU abstract transformers for all components with an affine transformer. b) Max Pooling Layer: The abstract transformer for the max pooling layer operates on one subrectangle at a time. For one such subrectangle, it uses meets with linear constraints to express a case distinction with one case for each possible location of the maximal element within the subrectangle. For each case, it then uses the affine transformer to extract that particular element. The results from all cases are joined, and the abstract transformers for all subrectangles are composed together.
DISCUSSION PAPERS IN STATISTICS AND ECONOMETRICS

We introduce a measure of diversification for portfolios comprising d risky assets. This measure relates the smallest possible return variance among these d assets to the overall portfolio return variance, yielding the portion of non-diversifiable risk. In the context of normally distributed asset returns, its estimator and finite-sample properties are explored when being applied to the trivial asset allocation strategy. An overview of different previous approaches towards the measurement of diversification is provided, and the shortcomings of some of these approaches are illustrated. A categorization of tests regarding the portfolio return variance is given, especially for comparing naively allocated with minimum-variance portfolios. The empirical part of this work is carried out on monthly return data for the S&P500 constituents, with a return history spanning the last five decades. When measuring the diversification of naively allocated 40-asset portfolios, the average degree of diversification barely exceeds 60%. This result indicates that for the mutual fund manager as well as for the private investor well-founded selection of assets indeed leads to better portfolio diversification than naive allocation does. JEL Classification: C13, C16, C58, G11.
TR-CS-96-06 A parallel algorithm for the reduction to tridiagonal form for eigendecomposition

A list of technical reports, including some abstracts and copies of some full reports may be found at: A parallel architecture for query processing over a terabyte of text. June 1996. TR-CS-96-03 Brendan D. McKay. autoson – a distributed batch system for unix workstation networks (version 1.3). March 1996. Abstract. A new algorithm for the orthogonal reduction of a symmetric matrix to tridiagonal form is developed and analysed. It uses a Cholesky factorization of the original matrix and the rotations are applied to the factors. The idea is similar to the one used for the one-sided Jacobi algorithms B. The algorithm uses little communication , accesses data with stride one and is to a large extent independent of data distribution. It has been implemented on the Fujitsu VPP 500. The algorithm is designed to be the rst step of an eigensolver so the procedure for accumulating transforms for eventual calculation of eigenvectors is given.
AIS Electronic Library (AISeL)

In today’s e-commerce era, more than ever, we need to equip our students to be independent on-line learners. In this article, we will look into the use of online education as a means of understanding the emerging educational paradigm (from a teacher to a student-focus paradigm). Specifically, the focus is on the secondary school system in which the aim is to comprehend and explore the reasons behind the emerging trend, the flaws in the existing schooling system, the characteristics and effects of our proposed paradigm. Finally, we conclude that the proposed educational paradigm whereby online education is provided to supplement the current traditional classroom based teaching could become a reality sooner than expected.
Bulletin of Electrical Engineering and Informatics

Received Jun 11, 2022 Revised Aug 13, 2022 Accepted Aug 23, 2022 Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.
Motion Simulation and Mechanism Design with SolidWorks Motion 2009

Motion Simulation and Mechanism Design with SolidWorks Motion 2009 is written to help you become familiar with SolidWorks Motion, an add-on module of the SolidWorks software family. This book covers the basic concepts and frequently used commands required to advance readers from a novice to intermediate level in using SolidWorks Motion. SolidWorks Motion allows you to use solid models created in SolidWorks to simulate and visualize mechanism motion and performance. Using SolidWorks Motion early in the product development stage could prevent costly redesign due to design defects found in the physical testing phase. Therefore, using SolidWorks Motion contributes to a more cost effective, reliable, and efficient product design process. Basic concepts discussed in this book include model generation, such as creating assembly mates for proper motion; carrying out simulation and animation; and visualizing simulation results, such as graphs and spreadsheet data. These concepts are introduced using simple, yet realistic examples. Verifying the results obtained from the computer simulation is extremely important. One of the unique features of this book is the incorporation of theoretical discussions for kinematic and dynamic analyses in conjunction with the simulation results obtained using SolidWorks Motion. Verifying the simulation results will increase your confidence in using the software and prevent you from being fooled by erroneous simulations. Table of Contents 1. Introduction to SolidWorks Motion 2. Animation and Basic Motion - A Single Piston Engine Example 3. A Ball Throwing Example 4. A Simple Pendulum 5. A Spring Mass System 6. A Slider-Crank Mechanism 7. A Rail-Carriage Example 8. A Compound Spur Gear Train 9. Cam and Follower Appendix A: Defining Joints Appendix B: The Unit Systems Appendix C: Importing Pro/ENGINEER Parts and Assemblies
Evaluation of a subnet management mechanism for InfiniBand networks

The InfiniBand architecture is a high-performance network technology for the interconnection of processor nodes and I/O devices using a point-to-point switch-based fabric. The InfiniBand specification defines a basic management infrastructure that is responsible for subnet configuration, activation, and fault tolerance. Subnet management entities and functions are described, but the specifications do not impose any particular implementation. We present and analyze a complete subnet management mechanism for this architecture. We allow to anticipate future directions to obtain efficient management protocols
Intermodeling, queries and Kleisli categories

Speciﬁcation and maintenance of relationships between models are vital for MDE. We show that a wide class of such relationships can be speciﬁed in a compact and precise manner if intermodel mappings involve derived model elements computed by corresponding queries. Composition of such mappings is not straightforward and requires specialized algebraic machinery. We present a formal framework, in which such machinery can be generically deﬁned for a wide class of metamodel deﬁni-tions, and thus important intermodeling scenarios can be algebraically speciﬁed.
A Biologically Inspired Algorithm for the Recovery of Shading and Reflectance Images

We present an algorithm for separating the shading and reflectance images of photographed natural scenes. The algorithm exploits the constraint that in natural scenes chromatic and luminance variations that are co-aligned mainly arise from changes in surface reflectance, whereas near-pure luminance variations mainly arise from shading and shadows. The novel aspect of the algorithm is the initial separation of the image into luminance and chromatic image planes that correspond to the luminance, red–green, and blue–yellow channels of the primate visual system. The red–green and blue–yellow image planes are analysed to provide a map of the changes in surface reflectance, which is then used to separate the reflectance from shading changes in both the luminance and chromatic image planes. The final reflectance image is obtained by reconstructing the chromatic and luminance-reflectance-change maps, while the shading image is obtained by subtracting the reconstructed luminance-reflectance image from the original luminance image. A number of image examples are included to illustrate the successes and limitations of the algorithm.
LCA application in urban design

Why expanding the scale ? - large influence of decisions at the urban scale on environmental performance of buildings, e.g. spatial organization (compactness, solar exposure, .) has a strong influence on energy and material flows - possibility to exchange energy between buildings - possibility to study collective equipment like district heating, wasterwater treatment etc. - transport related impacts are important
NeuroImage: Clinical

Background: The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for precise diagnosis. We investigated whether P300 features in both sensor-level and source-level could be effectively used to classify post-traumatic stress disorder (PTSD) and major depressive disorder (MDD). Method: EEG signals were recorded from fifty-one PTSD patients, 67 MDD patients, and 39 healthy controls (HCs) while performing an auditory oddball task. Amplitude and latency of P300 were evaluated, and the current source analysis of P300 components was conducted using sLORETA. Finally, we classified two groups using machine-learning methods with both sensor-and source-level features. Moreover, we checked the co-morbidity effects using the same approaches (PTSD-mono diagnosis (PTSDm, n =28) and PTSD-comorbid diagnosis (PTSDc, n =23)). Results: PTSD showed significantly reduced P300 amplitudes and prolonged latency compared to HCs and MDD. Moreover, PTSD showed significantly reduced source activities, and the source activities were significantly correlated with symptoms of depression and anxiety. Also, the best classification accuracy at each pair was as follows: 80.00% (PTSD-HCs), 67.92% (MDD-HCs), 70.34% (PTSD-MDD), 82.09% (PTSDm-HCs), 71.58% (PTSDm-MDD), 82.56% (PTSDc-HCs), and 76.67% (PTSDc-MDD). Conclusion: Since abnormal P300 reflects pathophysiological characteristics of PTSD, PTSD patients were well-discriminated from MDD and HCs when using P300 features. Thus, altered P300 characteristics in both sensor-and source-level may be useful biomarkers to diagnosis PTSD.
Deep Sparse-coded Network (DSN) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters

We introduce Deep Sparse-coded Network (DSN), a deep architecture based on sparse coding and dictionary learning. Key advantage of our approach is two-fold. By interlacing max pooling with sparse coding layer, we achieve nonlinear activation analogous to neural networks, but suﬀering less from diminished gradients. We use a novel backpropagation algorithm to ﬁnetune our DSN beyond the pretraining by layer-by-layer sparse coding and dictionary learning. We build an experimental 4-layer DSN with the (cid:96) 1 -regularized LARS and greedy- (cid:96) 0 OMP and demonstrate superior performance over deep stacked autoencoder on CIFAR-10.
Characterization of an unexpected snowfall event in Iqaluit, Nunavut, and surrounding area during the Storm Studies in the Arctic field project

Small accumulation precipitation events are critical for the high‐latitude hydrological cycle. They contribute to more than 50% of total accumulation in the area and occur at a greater frequency than high‐accumulation events. Despite their importance, the processes controlling them have not been investigated in sufficient detail. This study characterizes an unexpected high‐latitude snowfall event at Iqaluit, Nunavut, and surrounding area during the Storm Studies in the Arctic field project. High‐resolution data collected, from both ground based and airborne Doppler radar, along with upper air and surface observations, provided the basis for analysis of the conditions that led to the event and offer some insight as to why it was not well forecast by the Canadian operational model. Several factors worked in concert to produce this event. Low‐level convection and upslope processes were important in cloud and precipitation generation over the orography upstream. When combined with additional lift from the passing of a weak trough, cloud and precipitation production were enhanced, allowing these features to penetrate over the terrain and resulted in precipitation at Iqaluit. Analysis of the global environmental multiscale limited area model (2.5 km resolution) suggests that upstream convection and upslope processes were affected by model errors. As a consequence, precipitation onset was delayed, and the total accumulation was 50% lower than the observations. Results indicate that the complexity of precipitation events in the region represents a significant challenge for predicting and modeling and understanding their role in the region's hydrological cycle.
Enhanced Traffic Simulation for Improved Realism in Driving Simulators

The ability to monitor and detect potentially dangerous behaviour in surrounding traffic is vital for the development of intelligent vehicles. However, data collection for these kinds of scenarios is difficult in real-life, and a driving simulator therefore becomes an important substitute. In this paper we present an approach to enhance driving simulators. We experiment on an open source development platform, which is used to test real-life use cases within a simulated vehicle environment. We propose replacing pre-programmed traffic dynamics with real driving data recorded from human drivers in the same environment. This enhances the engagement of the host driver in the more realistically simulated traffic scenario. Signal lights and indicator sounds are also integrated to enrich the driver's sensation. Our preliminary quantitative and qualitative evaluation shows that our enhanced traffic simulation results in an improvement to the driver's perception of the realism of the driving simulator.