Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-Scale Numerical Optimization Instructor : Michael Saunders Spring

The primary application of LUSOL has been for square basis factorizations B = LU (and column replacement) as part of the optimization packages MINOS [14, 15, 16], SQOPT and SNOPT [7, 8], and the nonlinear complementarity packages MILES [20], PATH [3, 5], and PATHNLP [17]. In the optimization packages it is also applied to rectangular matrices ( B S ) (transposed) to find a column ordering that makes B better conditioned [7]. The original LUSOL procedures are described by Gill, Murray, Saunders and Wright [9]. The main factorization uses a traditional Markowitz strategy with Threshold Partial Pivoting similar to other early sparse LU packages—notably Y12M [24], LA05 [18], LA15 [19], MA28 [4] and MOPS [22]. The Bartels-Golub update for column-replacement follows the sparse implementation of Reid [18]. This involves a “forward sweep” of eliminations. The other updates are implemented similarly (some of them requiring a backward sweep). LUSOL has continued to evolve with the addition of rank-revealing LU factorizations for sparse matrices, using either Threshold Rook Pivoting or Threshold Complete Pivoting. A further option is intended for symmetric quasi-definite matrices [23]. The online source code [21] contains two versions (f77 and f90). The Fortran 77 version is suitable for f77, f90, and later compilers and convertible to C by the f2c translator. A Matlab interface is provided [13]. In 2004, a Fortran 77 → Pascal → C translation was created by Kjell Eikland for use within the open source LP/MILP system lp solve [12]. Since 2005 it has been lp solve’s default basis factorization package (BFP). Since 2012, an f90 version of LUSOL has been running within a new f90 version of SQOPT, and it has a new Matlab interface [10]. In both f77 and f90 versions, the efficiency of rook pivoting was significantly improved in 2013.
Diverse early Paleocene megaflora from the Ojo Alamo Sandstone, San Juan Basin, New Mexico, USA

Earliest Paleocene megafloras from North America are hypothesized to be low diversity and dominated by long-lived cosmopolitan species following the Cretaceous-Paleogene (K/Pg) mass extinction. However, megafloras used to develop this hypothesis are from the Northern Great Plains of North America, and relatively little is known about floras from southern basins. Here, we present a quantitative analysis of a diverse earliest Paleocene megaflora (<350 kyr after K/Pg boundary) from the Ojo Alamo Sandstone in the San Juan Basin (SJB), New Mexico. The megaflora, comprised of 55 morphotypes, was dominated by dicotyledonous angiosperms, with accessory taxa composed of ferns, monocotyledonous angiosperms, and conifers. The majority of morphotypes were restricted to the SJB indicating the presence of an endemic flora in the earliest Paleocene. Diversity analyses indicate that the flora was species rich, highly uneven, and laterally heterogeneous. Paleoclimate estimates using multivariate and univariate methods indicate warm temperatures and high precipitation. When compared to contemporaneous floras from the Denver Basin (DB) of Colorado and the Williston Basin (WB) of North Dakota, the SJB flora had significantly higher species richness, site rarefied richness, and basin rarefied richness, but was significantly less even. Paleoclimate estimates from the SJB were 7-14 °C warmer than the DB and WB, indicating a shift from a temperate deciduous forest biome in the Northern Great Plains to a tropical seasonal forest biome in the SJB. These results demonstrate that the SJB flora is markedly different from floras in the Northern Great Plains, that there were latitudinal differences in species composition and diversity, and that there was a variable floral response to the K/Pg boundary in North America during the earliest Paleocene. We hypothesize that the warm, wet conditions in the earliest Paleocene SJB drove rapid rates of speciation following the K/Pg boundary resulting in a heterogenous, endemic flora.
Plays as Effective Multiagent Plans Enabling Opponent-Adaptive Play Selection

Coordinated action for a team of robots is a challenging problem, especially in dynamic, unpredictable environments. Robot soccer is an instance of a domain where well defined goals need to be achieved by multiple executors in an adversarial setting. Such domains offer challenging multiagent planning problems that need to coordinate multiagent execution in response to other agents that are not part of our team plans. In this work, we introduce the concept of a play as a multiagent plan that combines both reactive principles, which are the focus of traditional approaches for coordinating robot actions, and deliberative principles. We further introduce the concept of a playbook as a method for seamlessly combining multiple team plans. The playbook provides a set of alternative team behaviors which form the basis for our third contribution of play adaptation. We describe how these concepts were concretely implemented in the CMDragons robot soccer team. We also show empirical results indicating the importance of adaptation in adversarial or other unpredictable environments.
Safety and performance in the SPIN operating system

Particulate material is dispensed from a portable supply container by application of positive pressure in the open space of the container above the material to fluidize the same, and simultaneously, applying a vacuum through a venturi nozzle. The pressurized fluid for feeding the venturi nozzle and for fluidizing the particulate material preferably originates from a single source, a self-contained propellant tank positioned inside the supply container. An expansion chamber is provided at one end of the venturi nozzle and a dispensing orifice is positioned at the other end. Interconnecting transfer means between the expansion chamber and the open space may be a restricted passage or an open interface; in the latter embodiment a stream splitting lip being formed by said one end of said nozzle. A modified embodiment may be operated in either the upright or inverted position by providing alternative dispensing apertures in the top of the supply container for cooperation with a coupling stopper; sealing and transfer stoppers being positioned to automatically close off the unused aperture and supply pressure to the open space of the container.
Performance of Sirohi Kids Under Hot And Humid Climate of Sindhudurg District

The present study entitled ―“Performance of Sirohi kids under hot and humid climate of Sindhudurg district” was undertaken to study the effect of hot and humid climate on growth performance, behavior, physiological parameters and stress response in Sirohi kids. Total Twenty four Sirohi kids around 0-12 months age were selected irrespective of sex from Kalifornia 36 farm, A/PTalawade, Tehsil: Sawantwadi, District: Sindhudurg. These kids were randomly selected and divided in 4 groups viz, T1 (0-3 m), T2 (4-6 m), T3 (7-9 m) and T4 (1012) according to different age comprising of 6 kids in each Group. The feeding and other management practices for all the four groups were similar. The average birth weight of T1, T2, T3 & T4 group was 2.88±0.09 kg, 2.49±0.03 kg, 2.63±0.12 kg & 2.72±0.12 kg respectively which differed significantly (p≤0.05). The average initial body weight of kids was 6.49, 12.10, 14.75 and 17.83 kg respectively for Group T1, T2, T3 and T4. The average daily gain and average weekly gain in body weight were 97.30 ± 2.11, 63.09 ± 0.82, 45.33 ± 0.81, 44.77 ± 0.97 gm/day and 680.89 ± 14.79, 441.15 ± 5.74, 317.31 ± 5.67, 313.33 ± 6.73 gm/wk for Group T1, T2, T3 and T4 respectively. The body weight, average daily gain and average weekly gain of kids in all age groups was statistically significant (P<0.01). The body measurements with respect to body length, height at wither and heart girth was also statistically significant (P<0.01) between all age groups. The total average feed intake per day recorded at weekly interval in T1, T2, T3 & T4 groups were 1.77±2.11 kg/day, 2.43±0.08 kg/day, 2.76±0.08 kg/day and 3.35±0.07 kg/day respectively and statistically significant. The average cortisol concentration in plasma was found to be 23.33, 23.72, 25.44 & 27.06 nm/l for group T1, T2, T3, & T4 respectively. The cortisol level changed significantly in different age groups. The average hemoglobin concentration (gm%) in Sirohi kids was found to be 11.00±0.14, 11.73±0.19, 11.37±0.16 and 10.41±0.13 gm% for group T1, T2, T3, & T4 respectively and were statistically significant (p≤0.01). The average RBCs count in Sirohi kids was found to be 14.90±0.28, 18.36±0.44, 18.05±0.43 and 16.06±0.22 (millions/mm) for groupT1, T2, T3, & T4 respectively and were statistically significant (p≤0.01). The average WBC (10000/μl) in Sirohi kids was found to be 10130.56±311.39, 12622.22±199.77, 10425±262.15 and 8995.83±229.63/μl in groupT1, T2, T3, & T4 respectively and were statistically significant (p≤0.01). The average PCV (%) in Sirohi kids was found to be 32.33±0.47, 34.36±0.6, 34.22±0.53 and 31.64±0.37 % for groupT1, T2, T3, & T4
International Journal of Geographical Information Science

This paper presents a new method to discover knowledge for geographical cellular automata (CA) by using a data-mining technique. CA have the ability to simulate complex geographical phenomena. Very few studies have been carried out on how to determine and validate the transition rules of CA from observed data. The transition rules of traditional CA are usually expressed by mathematical equations. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The explicit transition rules are more intuitive to decision-makers. The transition rules are obtained by applying data-mining techniques to spatial data. The proposed method can reduce the uncertainties in deﬁning transition rules and help to generate more reliable simulation results.
An Investigation of Temperate Region Lake Ice in Central Ontario

This study investigates ice formation for three lakes in central Ontario, detailing the climatological influences on the ice phenology for two seasons. This study also presents an investigation of the use of the Canadian Lake Ice Model (CLIMo) for simulating ice and snow cover for two of the study lakes during the 2015-2016 and 2016-2017 ice season. Field-based measurements combined with data collected from an on-shore automatic weather station, camera imagery during both seasons, as well as the addition of ice thickness from a Shallow Water Ice Profiler (SWIP) for the 2016-2017 ice year, form the basis of the results presented in this thesis. Snow cover, lake ice and climate interactions at lower latitudes have rarely been studied and the results of this thesis form the basis for further exploration of these interactions as lakes ice impacts climate, regional energy and water budgets, aquatic productivity, and socioeconomic
Introduction to Parallel Algorithms

Written by an authority in the field, this book provides an introduction to the design and analysis of parallel algorithms. The emphasis is on the application of the PRAM (parallel random access machine) model of parallel computation, with all its variants, to algorithm analysis. Special attention is given to the selection of relevant data structures and to algorithm design principles that have proved to be useful. Features *Uses PRAM (parallel random access machine) as the model for parallel computation. *Covers all essential classes of parallel algorithms. *Rich exercise sets. *Written by a highly respected author within the field. 0201548569B04062001
POLITECNICO DI TORINO Repository ISTITUZIONALE

NAND ﬂash memories are becoming the predominant technology in the implementation of mass storage systems for both embedded and high-performance applications. However, when considering data and code storage in non-volatile memories (NVMs), such as NAND ﬂash memories, reliability and performance become a serious concern for systems’ designer. Designing NAND ﬂash based systems based on worst-case scenarios leads to waste of resources in terms of performance, power consumption, and storage capacity. This is clearly in contrast with the request for run-time reconﬁgurability, adaptivity, and resource optimization in nowadays computing systems. There is a clear trend toward supporting differentiated access modes in ﬂash memory controllers, each one setting a differentiated trade-off point in the performance-reliability optimization space. This is supported by the possibility of tuning the NAND ﬂash memory performance, reliability and power consumption acting on several tuning knobs such as the ﬂash programming algorithm and the ﬂash error correcting code. However, to successfully exploit these degrees of freedom, it is mandatory to clearly understand the effect the combined tuning of these parameters have on the full NVM sub-system. This paper performs a comprehensive quantitative analysis of the beneﬁts provided by the run-time reconﬁgurability of an MLC NAND ﬂash controller through the combined effect of an adaptable memory programming circuitry coupled with run-time adaptation of the ECC correction capability. The full non-volatile memory (NVM) sub-system is taken into account, starting from the characterization of the low level circuitry to the effect of the adaptation on a wide set of realistic benchmarks in order to provide the readers a clear ﬁgure of the beneﬁt this combined adaptation would provide at the system level.
S²E: A Platform for In-Vivo Multi-Path Analysis of Software Systems

System developers routinely need to analyze the behavior of what they build. One basic analysis is to understand observed behavior, such as why a file system crashes or corrupts a given document, or why a web server is slow on a given benchmark. More sophisticated analyses aim to characterize future behavior in previously unseen circumstances, such as what will a web server’s maximum latency and minimum throughput be, once deployed at a customer site, or whether there will be ways for attackers to exploit the not-yet-known bugs in the file system implementation. This thesis introduces SE, a scalable platform for analyzing the properties and behavior of software systems. SE’s strength is the ability to scale to large systems, such as a full Windows stack, using two new ideas: selective symbolic execution and execution consistency models. Selectivity limits multi-path exploration to the module of interest (e.g., a library) to minimize the amount of symbolically-executed code, which avoids path explosion outside of that module. Execution consistency models allow to make principled performance/accuracy trade-offs during analysis. For example, a relaxed consistency model allows exploring all paths through a module of interest that would be followed by some concrete execution, without actually incurring the cost of finding such paths through the entire system. This thesis also shows how SE enables two novel analysis techniques: an in-vivo multi-path performance profiler (PROFS) and a system for semi-automatically reverse engineering binary device drivers (RevNIC). PROFS allows predicting the performance for certain classes of inputs, using metrics such as instruction count or cache misses. RevNIC analyzes closed-source device drivers to synthesize new, safer, and portable drivers for different OSes and architectures. This thesis shows how one can build such a platform. SE is a virtual machine augmented with symbolic execution and modular path analyzers. SE’s engine runs guest binaries natively in a VM and switches to a symbolic interpreter whenever an instruction accesses symbolic data. In case of conditional branches, the interpreter splits the execution path by efficiently cloning the entire VM state. Execution splits recursively for every branch, forming a tree that covers code otherwise hard to exercise manually. Under the hood, SE extends QEMU with an LLVM backend that translates machine code to bitcode suitable for symbolic interpretation, implements copy-on-write for path splitting, and instruments the MMU to synchronize symbolic and native machine states.
Feature clustering and ranking for selecting stable features from high dimensional remotely sensed data

ABSTRACT High dimensional remote sensing data sets typically contain redundancy amongst the features. Traditional approaches to feature selection are prone to instability and selection of sub-optimal features in these circumstances. They can also be computationally expensive, especially when dealing with very large remote sensing data sets. This article presents an efficient, deterministic feature ranking method that is robust to redundancy. Affinity propagation is used to group correlated features into clusters. A relevance criterion is evaluated for each feature. Clusters are then ranked based on the median of the relevance values of their constituent features. The most relevant individual features can then be selected automatically from the best clusters. Other criteria, such as computation time or measurement cost, can optionally be considered interactively when making this selection. The proposed feature selection method is compared to competing filter approach methods on a number of remote sensing data sets containing feature redundancy. Mutual information and naive Bayes relevance criteria were evaluated in conjunction with the feature selection methods. Using the proposed method it was shown that the stability of selected features improved under different data samplings, while similar or better classification accuracies were achieved compared to competing methods.
IMPROVING CONSUMER DECISIONS THROUGH PREFERENCE RELAXATION

In online shopping scenarios, it can be difficult for consumers to process large amounts of information and make a good purchase decision, particularly for multiattributed products selection. Interactive decision aids that support preferential choice using information filtering are a potential solution to this problem. However, current methods that enable filtering based on initial customer preferences may eliminate potentially valuable alternatives early in the decision process, and possibly negatively impact decision quality. Drawing from recent developments in recommender systems and query rewriting, we propose a new method that minimizes the elimination of high quality alternatives early in the decisionmaking process. We develop a model of such a decision aid and provide an evaluation scenario based on 56915 adverts gathered from popular used car advertising website. We discuss the potential impact of our method and describe the experimental methodology designed to evaluate our research propositions. Finally, we give an overview of the implications of our study for practitioners and researchers.
Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge

Research in the area of educational technology has often been critiqued for a lack of theoretical grounding. In this article we propose a conceptual framework for educational technology by building on Shulrnan's formulation of “pedagogical content knowledge” and extend it to the phenomenon of teachers integrating technology into their pedagogy. This framework is the result of 5 years of work on a program of research focused on teacher professional development and faculty development in higher education. It attempts to capture some of the essential qualities of teacher knowledge required for technology integration in teaching, while addressing the complex, multifaceted, and situated nature of this knowledge. We argue, briefly, that thoughtful pedagogical uses of technology require the development of a complex, situated form of knowledge that we call Technological Pedagogical Content Knowledge (TPCK). In doing so, we posit the complex roles of, and interplay among, three main components of learning environments: content, pedagogy, and technology. We argue that this model has much to offer to discussions of technology integration at multiple levels: theoretical, pedagogical, and methodological. In this article, we describe the theory behind our framework, provide examples of our teaching approach based upon the framework, and illustrate the methodological contributions that have resulted from this work.
Information Architecture for the World Wide Web: Designing Large-Scale Web Sites

Information Architecture for the World Wide Web is about applying the principles of architecture and library science to web site design. Each website is like a public building, available for tourists and regulars alike to breeze through at their leisure. The job of the architect is to set up the framework for the site to make it comfortable and inviting for people to visit, relax in, and perhaps even return to someday.
Software Process Improvement in the NASA Software Engineering Laboratory

Abstract : The Software Engineering Laboratory (SEL) was established in 1976 for the purpose of studying and measuring software processes with the intent of identifying improvements that could be applied to the production of ground support software within the Flight Dynamics Division (FDD) at the National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC). The SEL has three member organizations: NASA/GSFC, the University of Maryland, and Computer Sciences Corporation (CSC). The concept of process improvement within the SEL focuses on the continual understanding of both process and product as well as goal-driven experimentation and analysis of process change within a production environment.
Machine learning and big data analytics in bipolar disorder: A Position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

Objectives: The International Society for Bipolar Disorders (ISBD) Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method : A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion: Machine learning based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.
Climatic Changes and Cultural Responses During the African Humid Period Recorded in Multi-Proxy Data

The expansion and intensification of summer monsoon precipitation in North and East Africa during the African Humid Period (AHP; c. 15,000–5,000 years before present) is recorded by a wide range of natural archives, including lake and marine sediments, animal and plant remains, and human archaeological remnants. Collectively this diverse proxy evidence provides a detailed portrait of environmental changes during the AHP, illuminating the mechanisms, temporal and spatial evolution, and cultural impacts of this remarkable period of monsoon expansion across the vast expanse of North and East Africa.