nils-herrmann/clean_scirepeval huggingface dataset:
Causality in thought.

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.
The effects of social media on emotions , brand relationship quality , and word of mouth : An empirical study of music festival attendees

This article focuses on two under-researched areas of tourism management e the management of music festivals, and the influence of social media on customer relationships. In the new digital era of marketing communications little is known about how social media interactions with tourism brands affect how consumers think and feel about those brands, and consequently how those interactions affect desired marketing outcomes such as word mouth. Based on the literature, a conceptual model was developed and was tested using structural equation modeling. The results show that social media does indeed have a significant influence on emotions and attachments to festival brands, and that social media-based relationships lead to desired outcomes such as positive word of mouth.
Neural Semantic Encoders

We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.
Project Management Process Maturity „ PM ... 2 Model

This paper presents the project management process maturity (PM) 2 model that determines and positions an organizatio relative project management level with other organizations. The comprehensive model follows a systematic approach to es organization’s current project management level. Each maturity level consists of major project management characteristics, fa processes. The model evolves from functionally driven organizational practices to project driven organization that incorporates co project learning. The (PM) 2 model provides an orderly, disciplined process to achieve higher levels of project management mat DOI: 10.1061/ ~ASCE!0742-597X~2002!18:3~150! CE Database keywords: Project management; Models; Organizations. ve lan ani uiteron’s r th at ctic
A new world malaria map: Plasmodium falciparum endemicity in 2010

Transmission intensity affects almost all aspects of malaria epidemiology and the impact of malaria on human populations. Maps of transmission intensity are necessary to identify populations at different levels of risk and to evaluate objectively options for disease control. To remain relevant operationally, such maps must be updated frequently. Following the first global effort to map Plasmodium falciparum malaria endemicity in 2007, this paper describes the generation of a new world map for the year 2010. This analysis is extended to provide the first global estimates of two other metrics of transmission intensity for P. falciparum that underpin contemporary questions in malaria control: the entomological inoculation rate (Pf EIR) and the basic reproductive number (PfR). Annual parasite incidence data for 13,449 administrative units in 43 endemic countries were sourced to define the spatial limits of P. falciparum transmission in 2010 and 22,212 P. falciparum parasite rate (Pf PR) surveys were used in a model-based geostatistical (MBG) prediction to create a continuous contemporary surface of malaria endemicity within these limits. A suite of transmission models were developed that link Pf PR to Pf EIR and PfR and these were fitted to field data. These models were combined with the Pf PR map to create new global predictions of Pf EIR and PfR. All output maps included measured uncertainty. An estimated 1.13 and 1.44 billion people worldwide were at risk of unstable and stable P. falciparum malaria, respectively. The majority of the endemic world was predicted with a median Pf EIR of less than one and a median PfRc of less than two. Values of either metric exceeding 10 were almost exclusive to Africa. The uncertainty described in both Pf EIR and PfR was substantial in regions of intense transmission. The year 2010 has a particular significance as an evaluation milestone for malaria global health policy. The maps presented here contribute to a rational basis for control and elimination decisions and can serve as a baseline assessment as the global health community looks ahead to the next series of milestones targeted at 2015.
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.

Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples.



To examine intratumor heterogeneity, we performed exome sequencing, chromosome aberration analysis, and ploidy profiling on multiple spatially separated samples obtained from primary renal carcinomas and associated metastatic sites. We characterized the consequences of intratumor heterogeneity using immunohistochemical analysis, mutation functional analysis, and profiling of messenger RNA expression.



Phylogenetic reconstruction revealed branched evolutionary tumor growth, with 63 to 69% of all somatic mutations not detectable across every tumor region. Intratumor heterogeneity was observed for a mutation within an autoinhibitory domain of the mammalian target of rapamycin (mTOR) kinase, correlating with S6 and 4EBP phosphorylation in vivo and constitutive activation of mTOR kinase activity in vitro. Mutational intratumor heterogeneity was seen for multiple tumor-suppressor genes converging on loss of function; SETD2, PTEN, and KDM5C underwent multiple distinct and spatially separated inactivating mutations within a single tumor, suggesting convergent phenotypic evolution. Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Allelic composition and ploidy profiling analysis revealed extensive intratumor heterogeneity, with 26 of 30 tumor samples from four tumors harboring divergent allelic-imbalance profiles and with ploidy heterogeneity in two of four tumors.



Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection. (Funded by the Medical Research Council and others.).
Population genetic structure and ecological niche modelling of the leafhopper Hishimonus phycitis

Witches’ broom disease of lime, caused by ‘Candidatus Phytoplasma aurantifolia’, is responsible for major losses of Mexican lime trees in Southern Iran, Oman and the United Arab Emirates. The causative phytoplasma is transmitted by the leafhopper, Hishimonus phycitis. We combined ecological niche modelling with environmental and genetic data for six populations of H. phycitis from Iran and one from Oman. The mitochondrial cytochrome c oxidase I (COI) gene and nine microsatellite DNA markers were used for the genetic analyses. Although the Oman population had specific haplotypes, the COI sequences were highly conserved among all populations studied. In contrast, the microsatellite data divided the populations from Iran and Oman into two separate clades. An analysis of molecular variance indicated a high level of variation within populations. The Mantel test showed no correlation between genetic and geographical distances. Gene flow values were small between the populations from Iran and north of Oman but significantly higher among the Iranian populations supporting the differentiation between Iran and Oman. In addition, we found that patterns of genetic divergence within Iranian populations were associated strongly with divergence in terms of their ecological niches. Data on six climatic variables, including elevation, were used to create ecological niche models. Our results suggest that the genetic differentiation of H. phycitis may be attributable to climatic conditions and/or geographical barriers.
Document Page Segmentation and Layout Analysis Using Soft Ordering

A document image is composed of a variety of physical entities or regions such as text blocks, lines, words, figures, tables, and background. We could also assign functional or logical labels such as sentences, titles, captions, author names, and addresses to some of these regions. The process of document structure and layout analysis tries to decompose a given document image into its component regions and understand their functional roles and relationships. The processing is carried out in multiple steps, such as preprocessing, page decomposition, structure understanding, etc. We will look into each of these steps in detail in the following sections. Document images are often generated from physical documents by digitization using scanners or digital cameras. Many documents, such as newspapers, magazines and brochures, contain very complex layout due to the placement of figures, titles, and captions, complex backgrounds, artistic text formatting, etc. . A human reader uses a variety of additional cues such as context, conventions and information about language/script, along with a complex reasoning process to decipher the contents of a document. Automatic analysis of an arbitrary document with complex layout is an extremely difficult task and is beyond the capabilities of the state-of-the-art document structure and layout analysis systems. This is interesting since documents are designed to be effective and clear to human interpretation unlike natural images.
Emerging non-volatile memories: Opportunities and challenges

In recent years, non-volatile memory (NVM) technologies have emerged as candidates for future universal memory. NVMs generally have advantages such as low leakage power, high density, and fast read spead. At the same time, NVMs also have disadvantages. For example, NVMs often have asymetric read and write speed and energy cost, which poses new challenges when applying NVMs. This paper contains a collection of four contributions, presenting basic introduction on three emerging NVM technologies, their unique characteristics, potential challenges, and new opportunities that they may bring forward in memory systems.
Automatic Classification for Vietnamese News

This paper proposes an automatic framework to classify Vietnamese news from news sites on the Internet. In this proposed framework, the extracted main content of Vietnamese news is performed automatically by applying the improved performance extraction method from . This information will be classified by using two machine learning methods: Support vector machine and naïve bayesian method. Our experiments implemented with Vietnamese news extracted from some sites showed that the proposed classification framework give acceptable results with a rather high accuracy, leading to applying it to real information systems.
R2O, an extensible and semantically based database-to-ontology mapping language

We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models.
Event Detection in Crowded Videos

Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter. Our approach is based on three key ideas: (1) we efficiently match the volumetric representation of an event against oversegmented spatio-temporal video volumes; (2) we augment our shape-based features using flow; (3) rather than treating an event template as an atomic entity, we separately match by parts (both in space and time), enabling robustness against occlusions and actor variability. Our experiments on human actions, such as picking up a dropped object or waving in a crowd show reliable detection with few false positives.
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control

The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, the lack of key-value separation makes the address distribution resulting from content-based look-up noisy and flat, since the value influences the score calculation, although only the key should. Second, DNC’s deallocation of memory results in aliasing, which is a problem for content-based look-up. Thirdly, chaining memory reads with the temporal linkage matrix exponentially degrades the quality of the address distribution. Our proposed fixes of these problems yield improved performance on arithmetic tasks, and also improve the mean error rate on the bAbI question answering dataset by 43%.
Real-time large-scale dense RGB-D SLAM with volumetric fusion

We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.
Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver

The automation of the overtaking maneuver is considered to be one of the toughest challenges in the development of autonomous vehicles. This operation involves two vehicles (the overtaking and the overtaken) cooperatively driving, as well as the surveillance of any other vehicles that are involved in the maneuver. This operation consists of two lane changes-one from the right to the left lane of the road, and the other is to return to the right lane after passing. Lane-change maneuvers have been used to move into or out of a circulation lane or platoon; however, overtaking operations have not received much coverage in the literature. In this paper, we present an overtaking system for autonomous vehicles equipped with path-tracking and lane-change capabilities. The system uses fuzzy controllers that mimic human behavior and reactions during overtaking maneuvers. The system is based on the information that is supplied by a high-precision Global Positioning System and a wireless network environment. It is able to drive an automated vehicle and overtake a second vehicle that is driving in the same lane of the road.
Design and prototyping of 3-phase BLDC motor

The development of electric vehicle is now growing rapidly. Demands to deliver a reliable and easy to drive in motor control causes Brushless Direct Current (BLDC) motor becomes a potential candidate. A BLDC motor drive is a potential option for an electric vehicle since it has a high reliability, simple design, and ability to work at high rotation per minute (RPM). This paper discussed the Permanent Magnet BLDC Motor design method. The structure of an interior rotor permanent magnet type is selected to be used in the design of Permanent Magnet BLDC motor so that it can be applied in a drive that requires a large torque and capable of acceleration and deceleration with good response. Selection of 12 slots and 8 poles configuration aims for improving the motor performance. The motor is designed and simulated using a software-based Motor Solve FEA (Finite Element Analysis). Based on this design and simulation results, a prototype of BLDC motor is built. Parameters testing as stator resistance, inductances (the d-axis and q-axis inductance), and the back emf constant (Ke) were used to evaluate the result of the design and prototype motor. Measuring the prototype motor's parameters was carried out by several different methods depending on the parameters tested. Stator resistance testing is performed with the measurement of current in the coil which is then obtained by calculating the magnitude of stator resistance as 0.14710296 Ω. Measurements of d-axis stator inductance, q-axis stator inductance, and back emf constant of prototype permanent magnet BLDC motor is obtained as results of 0.35304710 mH, 0.38246769 mH and 0.09690626 Vs/rad respectively. The test results between design and prototype testing were quite good. The difference between the test results and the design of the prototype test results was caused by incompatibility of material composition although using the same type of material. The evaluation shows the electromagnetic parameters is influenced by its constituent materials.
Generating abstract paintings in Kandinsky style

This paper presents a recent project on automatic generation of Kandinsky style of abstract paintings using the programming language Processing. It first offers an analysis of Kandinsky's paintings based on his art theories and the author's own understanding and observation. The generation process is described in details and sample generated images styled on four of Kandinsky's paintings are also demonstrated and discussed. Our approach is highly scalable, limited only by the memory space set in Processing. Using random generation, every styled image generated can be unique. A selection of the images generated in the required resolution is also submitted and 70 images are made into a video companion.
A Large-Scale Database of Images and Captions for Automatic Face Naming

We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision. ee.ethz.ch/~calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches.
Picturing classical and quantum Bayesian inference

We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect to an appropriate compact structure. We characterize classical Bayesian inference in terms of a graphical property and demonstrate that our approach eliminates some purely conventional elements that appear in common representations thereof, such as whether degrees of belief are represented by probabilities or entropic quantities. We also introduce a quantum-like calculus wherein the Frobenius structure is noncommutative and show that it can accommodate Leifer’s calculus of ‘conditional density operators’. The notion of conditional independence is also generalized to our graphical setting and we make some preliminary connections to the theory of Bayesian networks. Finally, we demonstrate how to construct a graphical Bayesian calculus within any dagger compact category.
6-DOF Model Based Tracking via Object Coordinate Regression

This work investigates the problem of 6-Degrees-Of-Freedom (6-DOF) object tracking from RGB-D images, where the object is rigid and a 3D model of the object is known. As in many previous works, we utilize a Particle Filter (PF) framework. In order to have a fast tracker, the key aspect is to design a clever proposal distribution which works reliably even with a small number of particles. To achieve this we build on a recently developed state-of-the-art system for single image 6D pose estimation of known 3D objects, using the concept of so-called 3D object coordinates. The idea is to train a random forest that regresses the 3D object coordinates from the RGB-D image. Our key technical contribution is a two-way procedure to integrate the random forest predictions in the proposal distribution generation. This has many practical advantages, in particular better generalization ability with respect to occlusions, changes in lighting and fast-moving objects. We demonstrate experimentally that we exceed state-of-the-art on a given, public dataset. To raise the bar in terms of fast-moving objects and object occlusions, we also create a new dataset, which will be made publicly available.