Miracle-dz/UniFAR-FTUs huggingface dataset:
Revising traditional theory on the link between plant body size and fitness under competition: evidence from old-field vegetation

The selection consequences of competition in plants have been traditionally interpreted based on a “size-advantage” hypothesis – that is, under intense crowding/competition from neighbors, natural selection generally favors capacity for a relatively large plant body size. However, this conflicts with abundant data, showing that resident species body size distributions are usually strongly right-skewed at virtually all scales within vegetation. Using surveys within sample plots and a neighbor-removal experiment, we tested: (1) whether resident species that have a larger maximum potential body size (MAX) generally have more successful local individual recruitment, and thus greater local abundance/density (as predicted by the traditional size-advantage hypothesis); and (2) whether there is a general between-species trade-off relationship between MAX and capacity to produce offspring when body size is severely suppressed by crowding/competition – that is, whether resident species with a larger MAX generally also need to reach a larger minimum reproductive threshold size (MIN) before they can reproduce at all. The results showed that MIN had a positive relationship with MAX across resident species, and local density – as well as local density of just reproductive individuals – was generally greater for species with smaller MIN (and hence smaller MAX). In addition, the cleared neighborhoods of larger target species (which had relatively large MIN) generally had – in the following growing season – a lower ratio of conspecific recruitment within these neighborhoods relative to recruitment of other (i.e., smaller) species (which had generally smaller MIN). These data are consistent with an alternative hypothesis based on a ‘reproductive-economy-advantage’ – that is, superior fitness under competition in plants generally requires not larger potential body size, but rather superior capacity to recruit offspring that are in turn capable of producing grand-offspring – and hence transmitting genes to future generations – despite intense and persistent (cross-generational) crowding/competition from near neighbors. Selection for the latter is expected to favor relatively small minimum reproductive threshold size and hence – as a tradeoff – relatively small (not large) potential body size.
A taxonomy of attacks on secure devices

Evaluating the security of hardware devices requires an organised assessment of which attacks the device might be exposed to. This in turn requires a structured body of knowledge about such attacks, classified in such a way that an evaluator can easily determine which attacks are applicable to a particular device. This paper presents such a collection, organised as a taxonomy of attacks on secure devices. The taxonomy covers many attacks applicable to hardware which are frequently overlooked in a software or protocol-centric evaluation.
Tracking the small object through clutter with adaptive particle filter

Cluttered background and occlusion cause large ambiguity in the tracking of video objects. When the object is small (like a soccer ball in broadcast game video signals), the ambiguity gets even more severe. In this paper, we propose an adaptive particle filter with effective proposal distribution to handle these situations. In the proposed tracking approach, motion estimation is embedded into the state transition to tackle abrupt motion changes and generate good proposal distributions. We also propose a mixture model to account for multiple hypotheses in the template correlation surface when estimating the appearance likelihood. In addition, motion continuity and trajectory smoothness are combined with template correlation in the observation likelihood to further filter out visual distracters. As an example of small object tracking, promising results of the ball tracking (as small as 30 pixels) in soccer game videos are presented to illustrate that the proposed scheme handles the cluttered background and occlusion effectively.
On the relationship between novelty and popularity of user-generated content

This work deals with the task of predicting the popularity of user-generated content. We demonstrate how the novelty of newly published content plays an important role in affecting its popularity. We study three dimensions of novelty: contemporaneous novelty, self novelty, and discussion novelty. We demonstrate the contribution of the new novelty measures to estimating blog-post popularity by predicting the number of comments expected for a fresh post. We further demonstrate how novelty based measures can be utilized for predicting the citation volume of academic papers.
aximizing the Benefits of arallel Sear-c

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of certain aspects of the search process. In this paper we describe the EUREKA system, which combines the benefits of many different approaches to parallel heuristic search. EUREKA uses a machine learning system to decide upon the optimal parallel search strategy for a given problem space. When a new search task is input to the system, EUREKA gathers information about the search space and automatically selects the appropriate search strategy. EUREKA includes diverse approaches to task distribution, load balancing, and tree ordering, and has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results in the fifteen puzzle domain, robot arm path planning domain, and an artificial domain indicate that EUREKA outperforms any existing strategy used exclusively for all problem instances.
Localization of a Team of Heterogeneous Robots for a Distributed Sensing Task

This paper presents a methodology for localizing the members of a heterogeneous team of mobile robots in an unstructured and unknown environment. In this approach robots are being divided into two groups: (1) localizers, and (2) missioners. The localizers are equipped with precise non-contact sensors such as laser range finders and/or vision. Therefore, they can find the distance, bearing and orientation of the other robots with high accuracy, as long as they are in localizer's field of view. The missioners would be equipped with simple odometry and/or local range sensors (i.e., SONAR, Infrared etc.) for collision avoidance. All robots are capable of cross communication by passing messages via a network. When a missioner is being detected by a localizer, it receives a message from the localizer which contains missioner's precise position information. Hence it is able to update its belief about its position on fly. The effect of this methodology in a distributed sensing task in the context of solving a multi-depot traveling salesman problem (MDTSP) has been briefly addressed where missioners go through sub-optimal paths to their target positions being assigned through an auction process
Improving Click Fraud Detection by Real Time Data Fusion

Click fraud is a type of Internet crime that occurs in pay per click online advertising when a person, automated script, or computer program imitates a legitimate user of a Web browser clicking on an ad, for the purpose of generating a charge per click without having actual interest in the target of the ad's link. Most of the available commercial solutions are just click fraud reporting systems, not real-time click fraud detection and prevention systems. A new solution is proposed in this paper that will analyze the detailed user click activities based on data collected form different sources. More information about each click enables better evaluation of the quality of click traffic. We utilize the multi source data fusion to merge client side and server side activities. Proposed solution is integrated in our CCFDP V1.0 system for a real-time detection and prevention of click fraud. We have tested the system with real world data from an actual ad campaign where the results show that additional real-time information about clicks improve the quality of click fraud analysis.
The Schrödinger formulation of the Feynman path centroid density

We present an analysis of the Feynman path centroid density that provides new insight into the correspondence between the path integral and the Schrodinger formulations of statistical mechanics. The path centroid density is a central concept for several approximations (centroid molecular dynamics, quantum transition-state theory, and pure quantum self-consistent harmonic approximation) that are used in path integral studies of thermodynamic and dynamical properties of quantum particles. The centroid density is related to the quasistatic response of the equilibrium system to an external force. The path centroid dispersion is the canonical correlation of the position operator, which measures the linear change in the mean position of a quantum particle upon the application of a constant external force. At low temperatures, this quantity provides an approximation to the excitation energy of the quantum system. In the zero temperature limit, the particle’s probability density obtained by fixed centroid path in.
Acute otitis media due to penicillin-nonsusceptible Streptococcus pneumoniae before and after the introduction of the pneumococcal conjugate vaccine.

BACKGROUND The impact of the 7-valent pneumococcal conjugate vaccine (PCV7 [Prevnar]) on penicillin-nonsusceptible Streptococcus pneumoniae (PNSP) recovered from children with acute otitis media (AOM) is unclear. METHODS At 5 hospitals, 505 pneumococcal isolates were collected from children with AOM between 1 January 1999 and 31 December 2002. Molecular subtyping was performed on 158 isolates. RESULTS Overall, the percentage of AOM cases due to non-PCV7 serogroups (including serotype 3) increased over time (from 12% in 1999 to 32% in 2002; P < .01) and according to the number of PCV7 doses received (18% [< or = 1 dose] vs. 35% [2-4 doses]; P < .01). The percentage of cases due to vaccine-related serotypes (including serotype 19A) increased according to the number of PCV7 doses received (10% [< or = 1 dose] vs. 19% [2-4 doses]; P = .05) but not over time, whereas the percentage of cases due to serotype 19F remained unchanged both over time and according to the number of PCV7 doses received. The frequency of penicillin nonsusceptibility among PCV7 serotypes (range, 65%-75%) and non-PCV7 serogroups (range, 11%-27%) did not significantly change overall. Although no change was detected among isolates collected from children with spontaneous drainage, the percentage of pneumococci recovered at the time of myringotomy and/or tympanostomy tube placement that were nonresistant to penicillin decreased over time (from 73% in 1999 to 53% in 2002; P = .03). All of the serotype 3 strains were genetically related, whereas 88% of the isolates that were either serotype 19F or serotype 23F were related to 1 of 3 international clones. CONCLUSIONS Among children with AOM, the proportion of cases due to non-PCV7 serogroups increased, vaccine-related serotypes increased, and serotype 19F remained unchanged. Although a decrease in the proportion of cases due to PNSP occurred among children who required myringotomy and/or tympanostomy tube placement, the proportion of PNSP remained unchanged overall and among children with spontaneous drainage. Because future trends in the susceptibility patterns of pneumococcal isolates recovered from children with AOM are not easy to predict, continued surveillance is essential.
High-eccentricity planets from the Anglo-Australian Planet Search

We report Doppler measurements of the stars HD 187085 and HD 20782 which indicate two high eccentricity low-mass companions to the stars. We find HD 187085 has a Jupiter-mass companion with a �1000 d orbit. Our formal ‘best fit’ solution suggests an eccentricity of 0.47, however, it does not sample the periastron passage of the companion and we find that orbital solutions with eccentricities between 0.1 and 0.8 give only slightly poorer fits (based on RMS and � 2) and are thus plausible. Observations made during periastron passage in 2007 June should allow for the reliable determination of the orbital eccentricity for the companion to HD 187085. Our dataset for HD 20782 does sample periastron and so the orbit for its companion can be more reliably determined. We find the companion to HD 20782 has M sin i = 1.77±0.22 MJup, an orbital period of 595.86±0.03 d and an orbit with an eccentricity of 0.92±0.03. The detection of such high-eccentricity (and relatively low velocity amplitude) exoplanets appears to be facilitated by the long-term precision of the Anglo-Australian Planet Search. Looking at exoplanet detections as a whole, we find that those with higher eccentricity seem to have relatively higher velocity amplitudes indicating higher mass planets and/or an observational bias against the detection of high eccentricity systems.
The Whole Is the Sum of Its Parts.

I can still recall my reaction upon first seeing a Chuck Close painting. It must have been during one of the semi-regular weekend " culture " trips to New York City that my family would take to see a museum or Broadway show. On this particular visit we had ended up at the Whitney Museum and there it was, on the third floor, a huge portrait (Phil, 1969), measuring 9 feet by 7 feet, and so realistic that I was sure it had to be a photograph. I can remember being transfixed by the image, and tentatively approaching it, almost afraid that this gigantic head might reach out of the canvas and take me up into its mouth. I had to see for myself some evidence that this was in fact a painting. I approached it cautiously and deferentially, feeling almost rude for imposing on the subject. Somehow, this two-dimensional representation of a person's head felt alive. The creation of a great portrait is a complex work of data compression. On a simply physical level a person is three-dimensional, having a thickness, height, and width. But three dimensions only describes us spatially. What of our thoughts, feelings, histories, the way we smell, speak, and laugh-how many dimensions are really needed to describe any one of us? The artist is free to choose the medium, the brush stroke, the coloring, even the style. Physical attributes can be accentuated, distorted or even disregarded. Yet no matter what, the final portrait is a two-, or in the case of sculpture, three-dimensional summary, fixed in time, and it is the great challenge of the portrait artist to accomplish this reduction, this data compression, in 1
How Age Affects the Relation between Personality Facets and Work Values of Business and Private Bankers

Personality traits and work values are important characteristics in personnel selection. Studies on their associations show limited agreement. In order to clarify, this paper investigates their association on a personality facet level. Work values are differentiated in intrinsic and extrinsic factors. This paper adds the role of age to the association. Earlier studies on traits and values about the influence of age on their development and associations are reviewed. Then the moderating influence of age in the association between facets of the Five-Factor Model and work values of the Universal Values Model of 465 Dutch bankers is studied. The results elucidate the association between personality facets and work values and the role of age in their associations. Considering this in personnel ion might contribute to sustainable employability of both the young as well as the older worker. Therewith, the study contributes to the debate of ageing in recruitment and ion.
Do spacing and self-testing predict learning outcomes?

Studies have demonstrated that utilizing spacing (spreading out study sessions at regular intervals) and self-testing strategies are optimal for learning. While some applied work has examined the relationship between these strategies on general academic achievement, there is still a need to explore how both spacing and self-testing are related to course-level learning outcomes. The goal of this study was to examine whether utilizing spacing and self-testing strategies was related to final course grade. Participants were asked to report the study strategies they utilized during the course. We found no relationship between using spacing strategies and final course grade. However, self-testing was significantly related to higher final course grades. The relationship between self-testing and final course grade remained significant even after accounting for demographic differences and prior academic achievement. Our results suggest that self-testing strategies can enhance learning even in a highly structured course.
High-quality point-based rendering on modern GPUs

In the last years, point-based rendering has been shown to offer the potential to outperform traditional triangle based rendering both in speed and visual quality when it comes to processing highly complex models. Existing surface splatting techniques achieve superior visual quality by proper filtering but they are still limited in rendering speed. On the other hand the increasing availability and programmability of graphics hardware lead to the development of very efficient hardware-accelerated rendering methods. However, since no filtered splats are used, these approaches trade visual quality for rendering speed. In this paper, we propose a rendering framework for point-based geometry providing high visual quality as well as efficient rendering. Our approach is based on a two-pass splatting technique with Gaussian filtering, resulting in a visual quality comparable to existing software rendering systems. Using programmable graphics hardware we delegate all expensive rendering tasks to the GPU, thereby minimizing data transfer and saving CPU resources. The proposed system renders up to 28M mid-quality or up to 10M high-quality surface splats per second on the latest graphics hardware.
Assessing the Feasibility of Learning Biomedical Phenotypes via Large Scale Omics Profiles

This paper applies the computational learning theory framework to elucidate the differences that distinguish hard bioinformatics learning tasks from easy. While most of the published predictive studies present the empirical error of a model used to learn a specific phenotype pattern given a group of subjects profiled by a recent omics measurement technology, very few explain why learning is feasible in some cases and infeasible in others. Our recent published results show that some tasks (such as predicting (sub)continental ancestral origins of individuals) are quite easy, while others (such as predicting the susceptibility to breast cancer) are extremely difficult. Our analysis suggests that the ancestral origin prediction problem is a case of realizable learning in the presence of many irrelevant features, which suggests that a training dataset with ln |H| + ln samples would suffice for PAC learning this target concept. On the other hand, our analysis suggests that the breast cancer prediction problem appears a case of unrealizable learning from incomplete examples with relevant hidden features, and hidden subclasses, which suggests that at least a training dataset with max  ×  ,  × ln  , ( ) samples is necessary for PAC learning this target concept in the worst case. The paper also discusses the effect of the number of irrelevant features, relevant hidden features, and hidden subclasses on the sample complexity of learning biomedical phenotypes – which is very relevant to our task involving high-throughput omics profiles. This paper can aid future omics researchers interested in predictive studies to estimate the necessary and sufficient number of training examples required for their predictive studies.
Comparison of the expressiveness w . r . t . timed bisimilarity of k-bounded Arc , Place and Transition Time Petri Nets with weak and strong single server semantics

In this paper, we consider bounded Time Petri Nets where time intervals (strict and large) are associated with places (P-TPN), arcs (A-TPN) or transitions (T-TPN). We give the formal strong and weak semantics of these models in terms of Timed Transition Systems. We compare the expressiveness of the six models w.r. t. (weak) timed bisimilarity (behavioral semantics). The main results of the paper are : (i) with strong semantics, A-TPN is strictly more expressive than P-TPN and T-TPN ; (ii) with strong semantics P-TPN and T-TPN are incomparable ; (iii) T-TPN with strong semantics and T-TPN with weak semantics are incomparable. Moreover, we give a classification by a set of 9 relations explained in Fig. 20 (p. 25).
The current developing state

This chapter examines what leads to the growth or decline of state capacity in the contemporary period by developing propositions drawn from a variety of perspectives in the scholarly literature and subjecting them to large-sample empirical tests using the State Capacity Dataset. In the process, the chapter contributes to the de-centering of the study of state formation and state capacity-building in three ways. First, by focusing on states in the contemporary period, it tests the extent to which foundational theories in the literature generalize beyond the historical contexts in which they were developed. Second, the approach is geographically inclusive, moving beyond Eurocentric accounts. Third, the approach does not give war primacy over any other factor that may affect the formation and growth of states. Prepared for inclusion as Chapter 4 in the volume De-centering State-Making: Historical and Geographical Perspectives. Martin Hall, Jens Bartelson, and Jan Teorell, eds. Northampton, MA: Edward Elgar Publishing, Inc. 2018.
Emotion recognition based on texture analysis of facial expression

The work being presented here portrays a novel approach for emotion recognition based on the texture analysis of facial images. The expression of the emotion on facial image manifests in variations in spatial arrangement and intensity of the pixels. The emotions can be recognized by taking note of these variations corresponding to the features being used for emotion detection in human interaction. In human interaction the most discriminating visual features of emotions are captured from lip, nose, eye, eye-brow and forehead segments of face. Hence in present approach the assimilated spatial and intensity variation of pixels are appreciated by the means of texture features extracted from the promising region of interest (ROI) on facial images. We have used MAZDA to extract texture features from the ROIs of the each class of emotions from JAFFE as well as In-House data set the most discriminating ten features were shortlisted using fisher discriminate analysis. These texture features were used for classifying the JAFFE and In-House data set using machine learning approach. A comparison is also being made between the proposed method and popular existing methods such as wavelet transform. The outcome of evaluation and comparison are included in original. The results are inclined to indicate that proposed method outperforms competing methods.