Clarifying the Business Trust in Bankruptcy: A Proposed Restatement Test

When bankruptcy courts attempt to define the business trust, the “decisions are sharply, and perhaps hopelessly, divided.” The Bankruptcy Code, which guides the determinations of bankruptcy courts, specifically lists business trusts as eligible for protection. However, the Code does not define what a business trust is and does not list any criteria for determining when a trust is a business trust. The lack of a concrete definition has led many courts to formulate their own definitions of business trusts. While the courts hoped that they would eventually settle on a uniform test to tackle this issue, it has yet to occur. Presently, courts apply varying tests, some of which propose twenty-four individual factors to consider while others adopt tax and state laws. This confusion regarding the appropriate test leads to uncertainty on behalf of debtors, who are unsure if they will be eligible for bankruptcy protection. This Comment proposes a restatement test that incorporates the history of the business trust as well as courts’ various previous approaches into a single uniform test. Part II of this Comment will address the history of the business trust from its roots in feudal England to its use as a tool to circumvent strict corporate statutes. Part II will outline the current state of the law regarding business trusts, both within and outside the bankruptcy context. Part IV proposes a four-factor test to remedy the courts’ current confusion regarding business trusts

A Typology of Virtual Organizations:An Empirical Study

This paper reports on a survey of 55 organizations employing the virtual model. Based on survey responses and additional project and background information supplied by the organizations, this descriptive study develops a typology of virtual organizations including four distinct types: virtual teams, virtual projects, temporary virtual organizations and permanent virtual organizations. These four forms differed on the range of involvement of members, the membership of the group, organizational mission, and length of the project(s) undertaken. The four forms also differ in their use of information technology : fully virtual organizations using the Internet actively for connections, virtual teams and virtual projects relying more on the mature applications such as EDI, e-mail and fax and temporary virtual organizations relying more on groupware and WANs

Inhomogeneity of donor doping in SrTiO3 substrates studied by fluorescence-lifetime imaging microscopy

Fluorescence-lifetime imaging microscopy (FLIM) was applied to investigate the donor distribution in SrTiO3 single crystals. On the surfaces of Nb- and La-doped SrTiO3, structures with different fluorescence intensities and lifetimes were found that could be related to different concentrations of Ti3+. Furthermore, the inhomogeneous distribution of donors caused a non-uniform conductivity of the surface, which complicates the production of potential electronic devices by the deposition of oxide thin films on top of doped single crystals. Hence, we propose FLIM as a convenient technique (length scale: 1 $\mu$m) for characterizing the quality of doped oxide surfaces, which could help to identify appropriate substrate materials

Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression

Advancements in machine learning algorithms have had a beneficial impact on representation learning, classification, and prediction models built using electronic health record (EHR) data. Effort has been put both on increasing models' overall performance as well as improving their interpretability, particularly regarding the decision-making process. In this study, we present a temporal deep learning model to perform bidirectional representation learning on EHR sequences with a transformer architecture to predict future diagnosis of depression. This model is able to aggregate five heterogenous and high-dimensional data sources from the EHR and process them in a temporal manner for chronic disease prediction at various prediction windows. We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence. The model generated the highest increases of precision-recall area under the curve (PRAUC) from 0.70 to 0.76 in depression prediction compared to the best baseline model. Furthermore, the self-attention weights in each sequence quantitatively demonstrated the inner relationship between various codes, which improved the model's interpretability. These results demonstrate the model's ability to utilize heterogeneous EHR data to predict depression while achieving high accuracy and interpretability, which may facilitate constructing clinical decision support systems in the future for chronic disease screening and early detection.Comment: in IEEE Journal of Biomedical and Health Informatics (2021

A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning

Histopathological images provide rich information for disease diagnosis. Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group >= 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.Comment: 9 pages, 6 figure

Investigating child participation in the everyday talk of a teacher and children in a preparatory year

In early years research, policy and education, a democratic perspective that positions children as participants and citizens is increasingly emphasized. These ideas take seriously listening to children’s opinions and respecting children’s influence over their everyday affairs. While much political and social investment has been paid to the inclusion of participatory approaches little has been reported on the practical achievement of such an approach in the day to day of early childhood education within school settings. This paper investigates talk and interaction in the everyday activities of a teacher and children in an Australian preparatory class (for children age 4-6 years) to see how ideas of child participation are experienced. We use an interactional analytic approach to demonstrate how participatory methods are employed in practical ways to manage routine interactions. Analysis shows that whilst the teacher seeks the children’s opinion and involves them in decision-making, child participation is at times constrained by the context and institutional categories of “teacher” and “student” that are jointly produced in their talk. The paper highlights tensions that arise for teachers as they balance a pedagogical intent of “teaching” and the associated institutional expectations, with efforts to engage children in decision-making. Recommendations include adopting a variety of conversational styles when engaging with children; consideration of temporal concerns and the need to acknowledge the culture of the school

Combined Free-running 4D anatomical and flow MRI with native contrast using Synchronization of Neighboring Acquisitions by Physiological Signals (SyNAPS).

BACKGROUND 4D flow MRI often relies on the injection of gadolinium- or iron-oxide-based contrast agents to improve vessel delineation. In this work, a novel technique is developed to acquire and reconstruct 4D flow data with excellent dynamic visualization of blood vessels but without the need for contrast injection. Synchronization of Neighboring Acquisitions by Physiological Signals (SyNAPS) uses Pilot Tone (PT) navigation to retrospectively synchronize the reconstruction of two free-running 3D radial acquisitions, to create co-registered anatomy and flow images. METHODS Thirteen volunteers and two Marfan Syndrome patients were scanned without contrast agent using one free-running fast interrupted steady-state (FISS) sequence and one free-running phase-contrast MRI (PC-MRI) sequence. PT signals spanning the two sequences were recorded for retrospective respiratory motion correction and cardiac binning. The magnitude and phase images reconstructed, respectively, from FISS and PC-MRI, were synchronized to create SyNAPS 4D flow datasets. Conventional 2D flow data were acquired for reference in ascending (AAo) and descending aorta (DAo). The blood-to-myocardium contrast ratio, dynamic vessel area, net volume, and peak flow were used to compare SyNAPS 4D flow with Native 4D flow (without FISS information) and 2D flow. A score of 0-4 was given to each dataset by two blinded experts regarding the feasibility of performing vessel delineation. RESULTS Blood-to-myocardium contrast ratio for SyNAPS 4D flow magnitude images (1.5±0.3) was significantly higher than for Native 4D flow (0.7±0.1, p<0.01), and was comparable to 2D flow (2.3±0.9, p=0.02). Image quality scores of SyNAPS 4D flow from the experts (MP: 1.9±0.3, ET: 2.5±0.5) were overall significantly higher than the scores from Native 4D flow (MP: 1.6±0.6, p=0.03, ET: 0.8±0.4, p<0.01) but still significantly lower than the scores from the reference 2D flow datasets (MP: 2.8±0.4, p<0.01, ET: 3.5±0.7, p<0.01). The Pearson correlation coefficient between the dynamic vessel area measured on SyNAPS 4D flow and that from 2D flow was 0.69±0.24 for the AAo and 0.83±0.10 for the DAo, whereas the Pearson correlation between Native 4D flow and 2D flow measurements was 0.12±0.48 for the AAo and 0.08±0.39 for the DAo. Linear correlations between SyNAPS 4D flow and 2D flow measurements of net volume (r2=0.83) and peak flow (r2=0.87) were larger than the correlations between Native 4D flow and 2D flow measurements of net volume (r2=0.79) and peak flow (r2=0.76). DISCUSSION AND CONCLUSION The feasibility and utility of SyNAPS was demonstrated for joint whole-heart anatomical and flow MRI without requiring ECG gating, respiratory navigators, or contrast agents. Using SyNAPS a high-contrast anatomical imaging sequence can be used to improve 4D flow measurements that often suffer from poor delineation of vessel boundaries in the absence of contrast agents