Miracle-dz/UniFAR-FTUs huggingface dataset:
Signing the Unsigned: Robust Surface Reconstruction from Raw Pointsets

We propose a modular framework for robust 3D reconstruction from unorganized, unoriented, noisy, and outlierridden geometric data. We gain robustness and scalability over previous methods through an unsigned distance approximation to the input data followed by a global stochastic signing of the function. An isosurface reconstruction is finally deduced via a sparse linear solve. We show with experiments on large, raw, geometric datasets that this approach is scalable while robust to noise, outliers, and holes. The modularity of our approach facilitates customization of the pipeline components to exploit specific idiosyncracies of datasets, while the simplicity of each component leads to a straightforward implementation.
A Signal-to-noise Framework for Quantifying Search Diiculties in Genetic Algorithms

This paper presents a signal-to-noise perspective of the search process in genetic algorithms. First we pose a decision problem in terms of multiple 2-armed, mutually dependent bandits. This presents a theoretical framework for understanding stochastic search techniques in general. The deterministic and nondeterministic e ects on the convolution kernel associated with the bandits are identi ed as signal and noise respectively. We discuss what these quantities mean in a GA and how they can be computed in GA. Next, we present a signal-to-noise perspective of GA hardness. It is noted that there are two fundamental modes of introducing di culty into the decision making process: 1) sending a wrong signal and 2) increasing or decreasing noise depending on the direction of signal. We also note that the di culty due to crosstalk[21] can be quanti ed by the higher order components of the noise kernel. Our experiments on Royal Road functions R1 & R2 [13] clearly demonstrate that increasing signal in the right direction alone does not necessarily make a problem easy for GA, unless the noise is also reduced.
Anaesthetic drugs and survival: a Bayesian network meta-analysis of randomized trials in cardiac surgery.

BACKGROUND Many studies have compared desflurane, isoflurane, sevoflurane, total i.v. anaesthesia (TIVA), or all in cardiac surgery to assess their effects on patient survival. METHODS We performed standard pairwise and Bayesian network meta-analyses; the latter allows indirect assessments if any of the anaesthetic agents were not compared in head-to-head trials. Pertinent studies were identified using BioMedCentral, MEDLINE/PubMed, Embase, and the Cochrane Library (last updated in June 2012). RESULTS We identified 38 randomized trials with survival data published between 1991 and 2012, with most studies (63%) done in coronary artery bypass grafting (CABG) patients with standard cardiopulmonary bypass. Standard meta-analysis showed that the use of a volatile agent was associated with a reduction in mortality when compared with TIVA at the longest follow-up available [25/1994 (1.3%) in the volatile group vs 43/1648 (2.6%) in the TIVA arm, odds ratio (OR)=0.51, 95% confidence interval (CI) 0.33-0.81, P-value for effect=0.004, number needed to treat 74, I(2)=0%] with results confirmed in trials with low risk of bias, in large trials, and when including only CABG studies. Bayesian network meta-analysis showed that sevoflurane (OR=0.31, 95% credible interval 0.14-0.64) and desflurane (OR=0.43, 95% credible interval 0.21-0.82) were individually associated with a reduction in mortality when compared with TIVA. CONCLUSIONS Anaesthesia with volatile agents appears to reduce mortality after cardiac surgery when compared with TIVA, especially when sevoflurane or desflurane is used. A large, multicentre trial is warranted to confirm that long-term survival is significantly affected by the choice of anaesthetic.
Word distributed representation based text clustering

The fast growth of Internet web documents has posed new challenges on how to efficiently and accurately manage and retrieve the textual collections, text clustering plays a significant role. Traditional document clustering is an unsupervised categorization of a given document collection based on vector space model, which is a high sparse vector. In this paper, we propose a means to fight the existing shortcomings with a word vector in distributed representation which is obtained from a neural probabilistic language model. To improve the representation of document vector and enhance the accuracy of text clustering, we first computing semantic similarities between words using word embedded vector, and then expanding the keywords of each document. The experiment results show the method can improve the accuracy of clustering.
Trajectories of Peer Victimization: The Role of Multiple Relationships

This study examined early elementary school children's trajectories of peer victimization with a sample of 218 boys and girls. Peer victimization was assessed (via peer report) in kindergarten and first, second, and fifth grades. Hierarchical linear modeling (HLM) was used to examine multiple types of relationships (mother-child, student-teacher, friendship) as predictors of kindergarten levels of peer victimization and changes in peer victimization across time. Results indicated that the mother-child relationship predicted kindergarten levels of peer victimization, and that the student-teacher relationship did not provide additional information, once the mother-child relationship was accounted for in the analyses. Friendship predicted changes in peer victimization during the elementary school years. Results are discussed in a developmental psychopathology framework with special emphasis on the implication for understanding the etiology of peer victimization.
Extracting information ASAP!

Designing I/O systems capable of scaling up to deal with the next generation of extreme scale scientific environments is a significant challenge. Scientific applications already strain the capabilities of current filesystems and storage systems. This work presents a middleware-based approach that generalizes from previous work on staging areas to focus more generally on staging resources. Exploiting the steady increase of the ratio of compute capability to I/O bandwidth, the EnStage middleware system allows for metadata characterization and I/O processing to occur when as where appropriate. This includes in reserved staging areas, buffered memory, and even in the writing processes' execution context. Using the EnStage extension to previous work, we find a 1.4% increase in runtime due to additional functionality resulted in I/O time in the staging area dropping to only 16% of the non-reduced output.
Systematic Review of Rehabilitation Versus Operative Stabilization for the Treatment of First-Time Anterior Shoulder Dislocations

Context: Primary anterior shoulder dislocation is a frequent injury in young active patients. Traditionally, conservative nonoperative primary treatment has been advocated for a majority of first-time dislocators, whereas operative stabilization has been reserved for recurrent dislocators or those involved in strenuous physical activity. Currently, no consensus exists on whether to treat a first-time anterior shoulder dislocation surgically. Objective: (1) To provide clinical recommendations regarding the therapeutic intervention for first-time anterior shoulder dislocators and (2) to determine if there is sufficient Level 1 or 2 evidence available for the establishment of a uniform, optimal treatment protocol. Data Sources: A systematic review of prospective randomized controlled trials with human participants was performed. PubMed, the Cochrane Database of Systematic Reviews, and secondary references were appraised for studies published between 1994 and 2009. Study Selection: Inclusion criteria were English-language Level 1 or 2 studies involving the treatment of primary anterior shoulder dislocation. Exclusion criteria included non-English-language articles; Level 3, 4, or 5 studies; and studies examining treatment of recurrent/posterior shoulder dislocation or diagnoses other than primary anterior shoulder dislocations. Data Extraction: Each author conducted an independent quality appraisal of the included studies, identifying strengths, weaknesses, and biases, then reached consensus regarding their values. Results: Five randomized controlled trials were included, and they supported the use of operative management in a focused population. No long-term follow-up data were available describing the effects of surgical intervention or the development of osteoarthritis. Each study design had weaknesses that decreased the validity of the findings. Conclusions: While limited, the available evidence from randomized controlled trials supports operative stabilization as a reasonable alternative to nonoperative treatment for primary acute shoulder dislocation in young, active adults participating in highly demanding physical activities. Recommendations on the optimal surgical intervention cannot be provided. There is no conclusive evidence available to determine whether operative stabilization or conservative rehabilitation is superior for other patient or injury types.
Reliability‐aware server consolidation for balancing energy‐lifetime tradeoff in virtualized cloud datacenters

Server consolidation using virtualization technologies allow large‐scale datacenters to improve resource utilization and energy efficiency. However, most existing consolidation strategies solely focused on balancing the tradeoff between performance service‐level‐agreements (SLAs) desired by cloud applications and energy costs consumed by hosting servers. With the presence of fluctuating workloads in datacenters, the lifetime and reliability of servers under dynamic power‐aware consolidation could be adversely impacted by repeated on–off thermal cycles, ware‐and‐tear and temperature rise. In this paper, we propose a Reliability‐Aware server Consolidation stratEgy, named RACE, to address when and how to perform energy‐efficient server consolidation in a reliability‐friendly and profitable way. The focus is on the characterization and analysis of this problem as a multi‐objective optimization, by developing a utility model that unifies multiple constraints on performance SLAs, reliability factors and energy costs in a holistic manner. An improved grouping genetic algorithm is proposed to search the global optimal solution, which takes advantage of a collection of reliability‐aware resource buffering and virtual machines‐to‐servers re‐mapping heuristics for generating good initial solutions and improving the convergence rate. Extensive simulations are conducted to validate the effectiveness, scalability and overhead of RACE—in improving the overall utility of datacenters while avoiding unprofitable consolidation in the long term—compared with pMapper and PADD strategies for server consolidation. Copyright © 2013 John Wiley & Sons, Ltd.
Strategic management of uncertainty

This paper presents a strategic view of environmental uncertainty for profit-oriented organizations. It argues that managers make decisions that sometimes result in the aggressive creation of environmental uncertainty. A proposed model suggests that although strategy, structure, and performance constitute environmental enactment processes, the environment also directly influences organization performance. Further, the performance of others influences the environment through individual and collective actions.
Identifying Security Relevant Warnings from Static Code Analysis Tools through Code Tainting

Static code analysis tools are often used by developers as early vulnerability detectors. Due to their automation they are less time-consuming and error-prone then manual reviews. However, they produce large quantities of warnings that developers have to manually examine and understand.In this paper, we look at a solution that makes static code analysis tools more useful as an early vulnerability detector. We use flow-sensitive, interprocedural and context-sensitive data flow analysis to determine the point of user input and its migration through the source code to the actual exploit. By determining a vulnerabilities point of entry we lower the number of warnings a tool produces and we provide the developer with more information why this warning could be a real security threat. We use our approach in three different ways depending on what tool we examined. First, With the commercial static code analysis tool, Coverity, we reanalyze its results and create a set of warnings that are specifically relevant from a security perspective. Secondly, we altered the open source analysis tool Findbugs to only analyze code that has been tainted by user input. Third, we created an own analysis tool that focuses on XSS vulnerabilities in Java code.
TR 05-009 Simplifying Texture Classification

In texture classification modeling the full joint probability distribution of features is of questionable value. This paper demonstrates that marginal distributions of filter responses and marginal conditional distributions of intensity values over small neighborhoods are adequate to classify textures and can outperform methods using the joint distribution. The use of the Earth Mover’s Distance for marginal distributions is extended using PCA to build a Gaussian probability model for each class that captures the dependence between feature histograms. This framework is then generalized to include marginal conditional distributions for MRF models. These methods are demonstrated on the ColumbiaUtrecht database by classifying over 2800 images in all 61 texture classes. Results surpass those of Varma & Zisserman (CVPR ‘03) and Hayman (ECCV ’04).
Economic geography and growth: recent advances and new results

Two fields of economic theory have developed recently in parallel: on the one hand, economic geography aims to explain the formation of a heterogeneous space, i.e.how the agglomeration of households and firms determines differentiated regions, urban structures or urban systems; on the other hand, theories of endogenous growth propose new views on growth mechanisms which invalidate the traditional convergence rule. These theories are based on common foundations such as increasing returns, spatial externalities and monopolistic competition. We examine here in some detail how the spatial dimension is implicit in endogenous growth theories. We emphasize the whys and wherefores of the combination of these two theories, showing what new results can be derived and what the limits of this new approach are.
Workflow-Based Testing Process Management of Software Project

Testing is a natural and primary step for obtaining high-quality software. However, the relevant process management about testing activity has usually been neglected. In this paper, we introduce workflow to describe the activities in the course of software testing. The process definition is viewed as workflow engine to describe the control relation of each activity, and business form (i.e. B_form) has also been detailedly discussed as the information flow. The whole testing process has been divided into testing planning, testing design, testing execution and faults correction, and each sub-process is modeled through the typical workflow net (WF_net). Finally, these four steps are all integrated into a complete testing process management framework, which can direct the construction of testing workflow management system.
Causal Phenotype Discovery via Deep Networks

The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the work in this area has focused on traditional statistical learning paradigms, such as classification, prediction, clustering, pattern mining. In this paper, we propose a related but different paradigm called causal phenotype discovery, which aims to discover latent representations of illness that are causally predictive. We illustrate this idea with a two-stage framework that combines the latent representation learning power of deep neural networks with state-of-the-art tools from causal inference. We apply this framework to two large ICU time series data sets and show that it can learn features that are predictively useful, that capture complex physiologic patterns associated with critical illnesses, and that are potentially more clinically meaningful than manually designed features.
The changing organizational basis of African agricultural research

National agricultural research systems in Africa increased markedly in size throughout the past three decades, but from an especially small base. In 1961, public systems in 33 of 48 African countries employed fewer than 25 full-time equivalent (fte) researchers, by 1991 there were only 8 such systems (and 23 countries employed more than 100 fte researchers, compared with only 4 countries in 1961). Despite this overall growth, and the efforts that began in the late 1980s to consolidate the conduct of agricultural research, most African agencies are still very small and fragmented by international standards, making it difficult to realize the scale and scope economies that seem increasingly evident in agricultural RD they employed 87 percent of the total number of researchers in 1991. University research has grown the most rapidly, but still accounted for only 10 percent of the total number of African researchers in 1991. Partly in response to the small, fragmented, and comparatively isolated structure of agricultural R&D agencies, but partly from local political and, especially, donor pressure too, there has been a proliferation of research networks in recent years. We identified 86 networks, of which 72 involved Africans linked to Africans, a rather parochial strategy in an increasingly interdependent world. Regional approaches to the conduct and funding of agricultural R&D have been revived in more recent years, a feature of much of the regions's research in earlier, colonial times, as we describe here. However, the political and economic realities of today bear little relationship to those of colonial times, and it remains unclear how these regional approaches will prosper and effect meaningful research given the organizational uncertainties that still abound.
Simplexidine, a 4-Alkylpyridinium Alkaloid from the Caribbean Sponge Plakortis simplex

Chemical analysis of the secondary metabolites of the Caribbean sponge Plakortis simplex, a source of many bioactive compounds, showed the presence of the new metabolite simplexidine (4), belonging to the extremely rare class of 4-alkyl-pyridinium alkaloids. The structural characterization of this molecule, based on spectroscopic methods, is reported.
Genetically evolved macro-actions in AI planning problems

Despite recent progress in planning, many complex domains and even simple domains with large problems remain hard and challenging for current planners. A macro-action, defined as a group of actions applied at one time, can make jumps to reach a goal at less depth in the search tree and thus problems, not solvable within a given time limit, might become solvable. FF Style planners like Macro-FF and MARVIN showed some improvement with macro-actions. But both of them somehow need knowledge about the domains and the search algorithms as Macro-FF uses static facts and MARVIN uses plateau escaping sequences to generate macroactions. There is no known method capable of learning good macros without any significant structural knowledge about the domains or the planning algorithms. Genetic algorithms are automatic learning methods that require just a method to seed the initial population, definitions of the genetic operators on the populations, and a method to evaluate individuals across the populations but no structural knowledge about the problem domains and the search algorithms. Genetic algorithms have promising results in learning control knowledge for a domain and some success in generating plans but have not yet been tried to evolve macro-actions. This paper presents initial results of applying a genetic algorithm to learn macro-actions in planning problems.
Marine Ecosystem Response to the Atlantic Multidecadal Oscillation

Against the backdrop of warming of the Northern Hemisphere it has recently been acknowledged that North Atlantic temperature changes undergo considerable variability over multidecadal periods. The leading component of natural low-frequency temperature variability has been termed the Atlantic Multidecadal Oscillation (AMO). Presently, correlative studies on the biological impact of the AMO on marine ecosystems over the duration of a whole AMO cycle (∼60 years) is largely unknown due to the rarity of continuously sustained biological observations at the same time period. To test whether there is multidecadal cyclic behaviour in biological time-series in the North Atlantic we used one of the world's longest continuously sustained marine biological time-series in oceanic waters, long-term fisheries data and historical records over the last century and beyond. Our findings suggest that the AMO is far from a trivial presence against the backdrop of continued temperature warming in the North Atlantic and accounts for the second most important macro-trend in North Atlantic plankton records; responsible for habitat switching (abrupt ecosystem/regime shifts) over multidecadal scales and influences the fortunes of various fisheries over many centuries.
The Sensitivity of the Optimal Hedge Ratio to Model Specification

This paper investigates the effect of the choice of the model used to estimate the hedge ratio on the effectiveness of futures and cross-currency hedging using data from the stock and foreign exchange markets. Four different models are used for this purpose to estimate the hedge ratio. The results show that model specification has little effect on the hedging effectiveness. It seems that what matters most is the correlation between the prices of the unhedged position and the hedging instrument.
The Optimal International Trade Agreement and its Enforcement Under Asymmetric Information

This paper analyzes the role of the DSPs in collecting and disseminating information under uncertainty. The General Agreement on Tariffs and Trade (GATT) was designed to enhance global free trade. Over time, the GATT dispute settlement procedures (DSPs) were codified over broader issues and in more detail. The GATT rule of free trade -- assumed in most theoretical literature to be sustained bythe use of multilateral retaliation -- can increase the welfare of the countries in a perfect information world. In the asymmetric and imperfect information world, however, it can decrease the welfare level because it gives rise to the frequent use of nontariff barriers and periodic trade wars. With perfect monitoring, the GATT DSPs can sustain a free trade regime in which defection never occurs. With imperfect monitoring, however, a free trade regime is not likely to be feasible. Moreover, the defecting countries themselves may have incentives to investigate other countries, which would never occur in the perfect information world. The optimal DSP depends on the required monitoring cost. While multilateralism is one way of sustaining a more cooperative trade regime by aggregating sanctioning power, monitoring stipulated in the DSPs can avoid the misuse of multilateral sanction in the asymmetric information world as well as provide efficiency gains when multilateralism is not feasible.