Review of Lectures d\u27une amvre: George Eliot, \u27The Mill on the Floss\u27

George Eliot\u27s novels have never been received in France with great enthusiasm. Translatiom have been relatively sparse, and it is still difficult to find more than one or two titles in Paris. even in the best academic bookshops, whereas Jane Austen\u27s novels are widely read. Only a detailed reception history could reveal the reasons for this response, although it no doubt has something to do with the high Victorian tone and sentiments of George Eliot\u27s writing, which would have fallen on deaf ears in late nineteenth-century France and a fortiori in the successive phases of twentieth-century modernism. The renewed interest brought about first by Leavis and later by feminist critics have had no equivalent in France, where feminism has in the main been more theoretically oriented and where the doxa of the \u27death of the (realist) novel\u27 has remained persistent. However, The Mill on the Floss (together with Si/as Mamer) has been something of an exception, perhaps because of its sophisticated probing of the themes of childhood and memory, which drew applause from Proust; the blurb calls it \u27[le] roman le plus connu de George Eliot.\u27 It appears regularly on the syllabus of higher education examinations in France, and this collection of fourteen essays appears to be designed primarily for French students of English literature. Six (plus the Introduction) are in English, the remainder in French; the authors include some very well-known George Eliot specialists (Beryl Gray, Barbara Hardy, John Rignall), two senior French anglicistes (Fran~oise Dupeyron-Lafay, Annie Escuret), and a number of younger colleagues, most of them in the postdoctoral phase of their careers. The essays are loosely grouped under the headings \u27Entre nature et culture\u27, \u27Temps et tragedie\u27, \u27Du visuel au visionnaire: images et imagination\u27, \u27Realisme, verite, interpretation\u27, and \u27Epistemologies\u27. However, there are many themes which cut across the sectional divisions: images of the natural world, the question of metaphor itself, the relation of language to truth, gender issues and the position of the woman writer, the scientific (especially pre-Darwinian) frame of reference, the tragic trajectory of the novel, and of course the vexed question of its final episode. These overlaps helpfully allow the reader to compare different interpretations of particular passages and sometimes conflicting perspectives on key critical problems. The volume would have gained considerably in coherence and focus, however, if the authors had been encouraged to exchange drafts and insert some mutual cross-references. As it is, there is at times a disconcerting sense of dija vu when, for example, the putative mole on the waggoner\u27s face or Mr. Stelling\u27s educational principles reappear for the third or fourth time. The brief bibliographies supplied by each author could also have been usefully supplemented by the addition of a more comprehensive and balanced bibliography at the end of the volume

GPU-based Parallelization of a Sub-pixel Highresolution Stereo Matching Algorithm for Highthroughput Biomass Sorghum Phenotyping

To automate high-throughput phenotyping for infield biomass sorghum morphological traits characterization, a capable 3D vision system that can overcome challenges imposed by field conditions including variable lighting, strong wind and extreme plant height is needed. Among all available 3D sensors, traditional stereo cameras offer a viable solution to obtaining high-resolution 3D point-cloud data with the use of high-accuracy (sub-pixel) stereo matching algorithms, which, however, are inevitably highly computational. This paper reports a GPU-based parallelized implementation of the PatchMatch Stereo algorithm which reconstructs highly slanted leaf and stalk surfaces of sorghum at high speed from high-resolution stereo image pairs. Our algorithm enhanced accuracy and smoothness by using L2 norm for color distance calculation instead of L1 norm and speeded up convergence by testing the plane of the lowest cost within a local window in addition to the original spatial propagation. To better handle textureless regions, after left-right consistency check, the disparity of an occluded pixel is assigned to that of a nearby non-occluded pixel with the most similar pattern. Some of these occluded pixels in textureless region would survive a following left-right consistency check. Therefore more valid pixels would exist in textureless regions for occlusion filling. Accuracy and performance were evaluated on Middlebury datasets as well as our sorghum datasets. It achieved a high ranking in Middlebury table of subpixel precision and revealed subtle details on leaf and stalk surfaces. The output disparity maps were used to estimate stalk diameters of different varieties and growth stages. The results showed high correlation to hand measurement

A high-throughput, field-based phenotyping technology for tall biomass crops

Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum (Sorghum bicolor L. Moench). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included: i) User-interactive Individual Plant Height Extraction based on dense stereo 3D reconstruction (UsIn-PHe); ii) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; iii) User-interactive Dense Stereo Matching Stem Diameter Extraction (DenS-Di); and iv) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species

Infield Biomass Sorghum Yield Component Traits Extraction Pipeline Using Stereo Vision

Yield component traits such as plant height and stem diameter are dominant phenotypic data for biomass sorghum yield prediction. Extraction of these traits by machine vision during the growing season significantly reduces labor and time cost for large breeding programs. An automated 3D point cloud processing pipeline was developed to quantify different phenotypic variations in plant architecture of infield biomass sorghum. The input point cloud was generated by three side-view stereo camera heads placed vertically to capture extremely high plants. The features were extracted on a row plot basis instead of individual due to severe occlusion caused by densely populated leaves. Available features include plant height, plant width, vegetation volume index, and vegetation area index. Our strategy was to slice the point cloud along row direction into several equal volume slices and sum up the feature values with weights based on the point population and distribution in each volume slice. Therefore, the results were robust against empty space and abnormal individuals in the row plot. In addition, a semi-automated user interface was developed for users to measure stem diameters from the stereo images according to their specific sampling strategies. Users only need to zoom in on a stem segment and pick four corners of the rectangular segment. Metric measurement is then computed automatically based on image patch stereo matching using normalized cross correlation. The extracted stem diameters were compared to manual measurements in the field and a high correlation was obtained. The extracted features revealed great potential for automated field-based high-throughput phenotyping for plant architecture

Housing Subsidies: A Closer Look at the Issues

To improve poor household’s access to housing, the government has provided subsidies to lower production costs and make housing units more affordable to low-income groups. However, mechanisms should be implemented to ensure that the intended targets are the ones who receive it. This issue reviews the beneficiaries of subsidies, its transfer mechanisms and its budgetary implications. This hopes to eliminate the mismatch between what the government should provide and the estimated housing targets.housing finance, housing subsidy, subsidy

Field-based Robot Phenotyping of Sorghum Plant Architecture using Stereo Vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops

The design and investigation of nanocomposites containing dimeric nematogens and liquid crystal gold nanoparticles with plasmonic properties showing a nematic-nematic phase transition (Nᵤ-Nₓ/Ntb)

The construction of liquid crystal compositions consisting of the dimeric liquid crystal, CB_C9_CB (cyanobiphenyl dimer = 1",9"-bis(4-cyanobiphenyl-4'-yl)nonane), and the range of nematic systems is explored. The materials include a laterally functionalized monomer, which was used to construct a phase diagram with CB_C9_CB, as well as one laterally linked dimer liquid crystal material and two liquid crystal gold nanoparticle (LC-Au-NPs) systems. For the Au-NP-LCs, the NP diameters were varied between ~3.3 nm and 10 nm. Stable mixtures that exhibit a nematic-nematic phase transition are reported and were investigated by POM (polarizing optical microscopy), DSC (differential scanning calorimetry) and X-ray diffraction studies