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Questions: What was the tree species composition of forests prior to European settlement at the northern hardwood range limit in eastern Québec, Canada? What role did human activities play in the changes in forest composition in this region? Location: Northern range limit of northern hardwoods in the Lower St. Lawrence region of eastern Québec, Canada. Methods: We used early land survey records (1846—1949) of public lands to reconstruct pre-settlement forest composition. The data consist of ranked tree species enumerations at points or for segments along surveyed lines, with enumerations of forest cover types and notes concerning disturbances. An original procedure was developed to weigh and combine these differing data types (line versus point observations; taxa versus cover enumerations). Change to present-day forest composition was evaluated by comparing survey records with forest decadal surveys conducted by the government of Québec over the last 30 years (1980—2009). Results: Pre-settlement dominance of conifers was strong and uniform across the study area, whereas dominance of maple and birches was patchy. Cedar and spruce were less likely to dominate with increasing altitude, whereas maple displayed the reverse trend. Frequency of disturbances, especially logging and fire, increased greatly after 1900. Comparison of survey records and modern plots showed general increases for maple (mentioned frequency increased by 39%), poplar (36%) and paper birch (31%). Considering only taxa ranked first by surveyors, cedar displayed the largest decrease (19%), whereas poplar (15%) and maple (9%) increased significantly. Conclusions: These changes in forest composition can be principally attributed to clear-cutting and colonization fire disturbances throughout the 20th century, and mostly reflected the propensity of taxa to expand (maples/aspen) or decline (cedar/spruce) with increased disturbance frequency. Québec's land survey archives provide an additional data source to reconstruct and validate our knowledge of North America's pre-settlement temperate and sub-boreal forests.

QUESTIONS: Do early land survey records of the ‘line description’ type allow accurate reconstructions of pre‐settlement forest composition? Did surveyors record all tree taxa in forest stands encountered along the surveyed lines? Were taxa ranked according to their relative importance in forest stands? What criteria did surveyors used to rank taxa in stands? LOCATION: Northern range limit of northern hardwoods, Lower St. Lawrence region, eastern Québec, Canada. METHODS: Validation of 1695 taxon lists recorded by surveyors in the 19th century through comparison of the number of stems by tree species and stem diameter classes recorded in 2790 old‐growth plots over the same two regions during a 1930 forest inventory. RESULTS: Taxon prevalence and dominance (i.e. proportion of observations for which each taxon is dominant) are highly correlated between the pre‐settlement surveys and the 1930 forest inventory data sets. Surveyors ranked taxa in decreasing order of relative importance, using criteria directly equivalent to basal area of stems in modern forest inventory plots. Taxon prevalence is more accurately reconstructed using relative metrics (i.e. ranks of taxon prevalence in a region), whereas taxon dominance is more accurately reconstructed using absolute metrics (percentage of dominant stands across landscapes). The early land surveys allow spatial patterns of forest composition to be reconstructed by computing relative taxon prevalence in cells of 3 km × 3 km. Prevalence of balsam fir (Abies balsamea) and white birch (Betula papyrifera) are underestimated in survey data, probably reflecting their low economic value in the 19th century. CONCLUSIONS: Taxon lists of early surveyors can accurately reconstruct pre‐settlement forest composition and spatial patterns using metrics of taxon prevalence and dominance across landscapes. Relative prevalence is a more comprehensive description of forest composition than dominance, but tends to underestimate some taxa. Absolute taxon dominance is a more robust metric than prevalence, but only reports on the abundance of the most dominant taxa.

Purpose When manufacturing an arc-shaped tube product using push bending process, the transition zone and outfeed zone will inevitably occur. Transition zone and outfeed zone are caused by the kinematical motion of mobile tools. The existence of transition zone and outfeed zone will lead to a big deviation between the forming product and desired shape. To improve the forming quality of arc-shaped products in push bending, the transition zone and outfeed zone are investigated in this paper. Design/methodology/approach A piecewise function is used to describe the bending characteristics along bending line, in which a series of vibration parameters are extracted and considered as control values. Findings The new strategy is helpful for finding the relationship between tools motion and curvature distribution and improving the bending lines design procedure in flexible push bending. Originality/value The new strategy is helpful for finding the relationship between tools motion and curvature distribution and improving the bending lines design procedure in flexible push bending.

Line feature description is important for image matching. However, its development is much slow compared to point description, and is still in the stage of manual design, which suffers from the problem of weak distinguish ability and poor robustness under complex conditions. To improve on this situation, this paper proposes to learn the line feature description based on convolutional neural network. First, a large-scale dataset consisting of about 229,000 labeled pairs of matched lines is built for training and testing. Then, a paradigm for learning the line descriptors based on the constructed line dataset is proposed. Specifically, the line is represented uniquely by the stacked mean and standard deviation patches of the support regions of those points lying on the line, which is subsequently fed into the L2Net to output the required line descriptors directly. Based on the line matching principals, the network is also trained with the triplet loss that is widely used for learning point descriptors. Experimental results for line matching and curve matching both demonstrate the superiority and effectiveness of the proposed learning-based descriptor, especially, averaged increases of 4.66 ~ 5.7% mAPs, 10.59 ~ 12.10% mAPs, 0.96 ~ 3.75% mAPs and 3.73% mAP on testing subset, Oxford dataset, line dataset and curve dataset are obtained compared to handcrafted descriptors. As an application, we apply the learned line descriptor to image stitching and also obtain good results.

Typical crack detection processes in digital images produce a binary-segmented image that constitutes the basis for all of the following analyses. Binary images are, however, an unsatisfactory data format for advanced crack analysis algorithms due to their sparse nature and lack of significant data structuring. Therefore, this work instead proposes a new approach based on Euclidean graphs as functional crack pattern descriptors for all post-detection analyses. Conveying both geometrical and topological information in an integrated representation, Euclidean graphs are an ideal structure for efficient crack path description, as they precisely locate the cracks on the original image and capture salient crack skeleton features. Several Euclidean graph-based algorithms for autonomous crack refining, correlation and analysis are described, with significant advantages in both their capabilities and implementation convenience over the traditional, binary image-based approach. Moreover, Euclidean graphs allow the autonomous selection of specific cracks or crack parts based on objective criteria. Well-known performance metrics, namely precision, recall, intersection over union and F1-score, have been adapted for use with Euclidean graphs. The automated generation of Euclidean graphs from binary-segmented images is also reported, enabling the application of this technique to most existing detection methods (e.g., threshold-based or neural network-based) for cracks and other curvilinear features in digital images.

It is well known that there is a particle-hole symmetry for spin-polarized electrons with two-body interactions in a partially filled Landau level, which becomes exact in the limit where the cyclotron energy is large compared to the interaction strength; thus, one can ignore mixing between Landau levels. This symmetry is explicit in the description of a half-filled Landau level recently introduced by Son, using Dirac fermions, but it was thought to be absent in the older fermion-Chern-Simons approach, developed by Halperin, Lee, and Read (HLR) and subsequent authors. We show here, however, that when properly evaluated, the HLR theory gives results for long-wavelength low-energy physical properties—including the Hall conductance in the presence of impurities and the positions of minima in the magnetoroton spectra for fractional quantized Hall states close to half-filling—that are identical to predictions of the Dirac formulation. In fact, the HLR theory predicts an emergent particle-hole symmetry near half-filling, even when the cyclotron energy is finite.

The journals of William Beawes, Gaylard Roberts, Bartholomew Plaisted, and John Carmichael. This is a new print-on-demand hardback edition of the volume first published in 1929.

The ATLAS experiment at the LHC at CERN uses a large, distributed trigger and data acquisition system composed of many computing nodes, networks, and hardware modules. Its configuration service is used to provide descriptions of control, monitoring, diagnostic, recovery, dataflow and data quality configurations, interconnections, and parameters for modules, chips, and channels of various online systems, detectors, and the whole ATLAS experiment. Those descriptions have historically been stored in more than one thousand interconnected XML files, which are updated by various experts many times per day. Maintaining error-free and consistent sets of such files and providing reliable and fast access to current and historical configurations is a major challenge. This paper gives details of the configuration service upgrade on the modern Git version control system backend for LHC Run 3 and its exploitation experience. It may be interesting for developers using human-readable file formats, where consistency of the files, performance, access control, traceability of modifications, and effective archiving are key requirements.

Humans can categorize complex natural scenes quickly and accurately. Which scene properties enable people to do this with such apparent ease? We extracted structural properties of contours (orientation, length, curvature) and contour junctions (types and angles) from line drawings of natural scenes. All of these properties contain information about scene categories that can be exploited computationally. However, when we compared error patterns from computational scene categorization with those from a six-alternative forced-choice scene-categorization experiment, we found that only junctions and curvature made significant contributions to human behavior. To further test the critical role of these properties, we perturbed junctions in line drawings by randomly shifting contours and found a significant decrease in human categorization accuracy. We conclude that scene categorization by humans relies on curvature as well as the same nonaccidental junction properties used for object recognition. These properties correspond to the visual features represented in area V2.

The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, towards the problems of object occlusion and category imbalance in the field images, Soft-NMS and Focal Loss are applied in RTLCap to promote defect description performance. After that, to improve the image processing speed of RTLCap and reduce the complexity of the model, a reconstructed RTLCap model named Faster RTLCap is presented with the help of YOLOv3. In the encoder part, a multi-level regional feature localization, mapping, and fusion module (MFLMF) are proposed to extract regional features, and an SPP (Spatial Pyramid Pooling) layer is employed after MFLMF to reduce model parameters. As for the decoder part, a stacked LSTM is adopted as the language model for better language representation learning. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed methods.