Research Notes: University of Illinois

A soybean germplasm data bank has been set up by members of the Department of Agronomy at the University of Illinois, Urbana-Champaign . Information on named soybean varieties, Plant Introductions, Genetic Type Collection lines, Forage Collection varieties, and species collections, has been compiled and computerized so that it is readily available as a reference source. An information retrieval system enables queries concerning various aspects of the germplasm bank to be answered with a minimum of human effort

Enhancing Personalised Recommendations with the Use of Multimodal Information

Whenever we watch a TV show or movie, we process a substantial amount of information that is conveyed to us via various multimedia mediums, in particular: visual, textual, and audio. These data signify distinctive properties that aid in creating a unique motion picture experience. In effort to not only produce a more personalised recommender system, but also tackle the problem of popularity bias, we develop a system that incorporates the use of multimodal information. Specifically, we investigate the correlation between features that are extracted using state of the art techniques and deep learning models from visual characteristics, audio patterns and subtitles. The framework is evaluated on a dataset comprising of 145 BBC TV programmes against genre and user baselines. We demonstrate that personalised recommendations can not only be improved with the use of multimodal information, but also outperform genre and user-based models in terms of diversity, whilst maintaining matching levels of accuracy

Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency can provide effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.Comment: ECCV 201

The Effect of Nitrogen Rates and Plant Density on Grain Yield Components and Persistence in Intermediate Wheatgrass (\u3ci\u3eThinopyrum intermedium\u3c/i\u3e) and Mountain Rye (\u3ci\u3eSecale strictum\u3c/i\u3e)

Intermediate wheatgrass (IWG; Thinopyrum intermedium) and Mountain Rye (Mtn Rye; Secale strictum) have potential for release as dual-purpose (grazing and grain production) perennial grains in Australia due to their superior longevity compared to hybrid perennial wheats. Initially developed as perennial forage grasses, few management guidelines exist to inform agronomic practices to maximise grain yields and profitability in Australian environments. An experiment was established in 2020 to examine the effect of plant density and nitrogen rates on grain yield components. The experiment compared the two species (IWG, Mtn Rye) sown at three plant densities (50, 100 and 200 plants/m2) with three nitrogen rates (0, 100, 200 kg/ha N). Overall, in the first year of production, Mtn Rye had higher grain yields than Kernza although yield decreased with increasing N rates. With further selection for floret fertility and seed size, Mtn Rye could prove a successful candidate for a perennial grain crop in Australia

Audiovisual, Genre, Neural and Topical Textual Embeddings for TV Programme Content Representation

TV programmes have their contents described by multiple means: textual subtitles, audiovisual files, and metadata such as genres. In order to represent these contents, we develop vectorial representations for their low-level multimodal features, group them with simple clustering techniques, and combine them using middle and late fusion. For textual features, we use LSI and Doc2Vec neural embeddings; for audio, MFCC's and Bags of Audio Words; for visual, SIFT, and Bags of Visual Words. We apply our model to a dataset of BBC TV programmes and use a standard recommender and pairwise similarity matrices of content vectors to estimate viewers' behaviours. The late fusion of genre, audio and video vectors with both of the textual embeddings significantly increase the precision and diversity of the results

Square Patterns and Quasi-patterns in Weakly Damped Faraday Waves

Pattern formation in parametric surface waves is studied in the limit of weak viscous dissipation. A set of quasi-potential equations (QPEs) is introduced that admits a closed representation in terms of surface variables alone. A multiscale expansion of the QPEs reveals the importance of triad resonant interactions, and the saturating effect of the driving force leading to a gradient amplitude equation. Minimization of the associated Lyapunov function yields standing wave patterns of square symmetry for capillary waves, and hexagonal patterns and a sequence of quasi-patterns for mixed capillary-gravity waves. Numerical integration of the QPEs reveals a quasi-pattern of eight-fold symmetry in the range of parameters predicted by the multiscale expansion.Comment: RevTeX, 11 pages, 8 figure

Integral Human Pose Regression

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time

Renormalization Group Method and Reductive Perturbation Method

It is shown that the renormalization group method does not necessarily eliminate all secular terms in perturbation series to partial differential equations and a functional subspace of renormalizable secular solutions corresponds to a choice of scales of independent variables in the reductive perturbation method.Comment: 5 pages, late