An experimental tailor-made ESP course: experience of teaching English to students of Economics

[EN] The purpose of the study was to find out how enhanced learner autonomy techniques can influence students' professional communication skills, subject-specific knowledge, levels of motivation in studying the language and general satisfaction from the studies. The problem under investigation is if students should be allowed to choose materials for language input and if the teacher will be able to work out an appropriate didactic approach in developing students' grammar accuracy, vocabulary range, speaking, listening and writing skills. the expermental course was designed for students of economics. Students' responsibility, the use of online resources and students' freedom in selection of teaching materials are viewed as key elements of the approach. the methodology of the course is worked out on the basis of close teacher-student interactin in and out of class. The results indicate that despite the fact that the course was time-consuming for both teachers and students, there are some positive results in respect of increased proffessional vocabulary range, levels of motivation and cognition.http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Shirokikh, A. (2018). An experimental tailor-made ESP course: experience of teaching English to students of Economics. Editorial Universitat Politècnica de València. 277-285. https://doi.org/10.4995/HEAD18.2018.7977OCS27728

Creating a Diversity Audit & Web Audit to Address the Current Fundraising Issues of the Veteran Sports Association

The Veteran Sports Association was founded in June 2003 in Brooklyn, NY by current Executive Director Eduard Luchin.The mission of the Veteran Sports Association is“To provide helpful and useful resources to any and all former professional athletes from overseas who have recently immigrated to the United States.” Veteran Sports Association is interested in restoring its current fundraising efforts by increasing the number of attendees at their events.This project will look at why the Veteran Sports Association feels the need to restore its fundraising efforts and what can be done to push them in thehttps://orb.binghamton.edu/mpa_capstone/1032/thumbnail.jp

Bryophyte Diversity in the Forests of the Southern Urals

Mountain forest monitoring is closely related to the survey of bryophyte species since it is there that these organisms are common and show very specialized ecological niches. This work is aimed to show how bryophyte richness and taxonomic and ecological categories differ in the various types of indigenous forests in the Southern Ural Mountains. The distribution of bryophytes in mountain forests of the Southern Urals was examined at about 1700 sample plots. Frequency and abundance patterns suggested that species richness, taxonomic distribution, and substrate group distribution are mostly determined by the forest type. According of bryological data, the forest associations characterized by high diversity and concentration of rare species were identified. This is mainly tall herb spruce-fir and mixed forests. The proportion of rare species in these forests is about 9%, including a significant number relicts both of European and Asian origins. The sites of these forests are most valuable for nature conservation and should be protected

Machine Learning for SAT: Restricted Heuristics and New Graph Representations

Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling. To solve large instances, SAT solvers have to rely on heuristics, e.g., choosing a branching variable in DPLL and CDCL solvers. Such heuristics can be improved with machine learning (ML) models; they can reduce the number of steps but usually hinder the running time because useful models are relatively large and slow. We suggest the strategy of making a few initial steps with a trained ML model and then releasing control to classical heuristics; this simplifies cold start for SAT solving and can decrease both the number of steps and overall runtime, but requires a separate decision of when to release control to the solver. Moreover, we introduce a modification of Graph-Q-SAT tailored to SAT problems converted from other domains, e.g., open shop scheduling problems. We validate the feasibility of our approach with random and industrial SAT problems

Selective and flexible depletion of problematic sequences from RNA-seq libraries at the cDNA stage

BACKGROUND A major hurdle to transcriptome profiling by deep-sequencing technologies is that abundant transcripts, such as rRNAs, can overwhelm the libraries, severely reducing transcriptome-wide coverage. Methods for depletion of such unwanted sequences typically require treatment of RNA samples prior to library preparation, are costly and not suited to unusual species and applications. Here we describe Probe-Directed Degradation (PDD), an approach that employs hybridisation to DNA oligonucleotides at the single-stranded cDNA library stage and digestion with Duplex-Specific Nuclease (DSN). RESULTS Targeting Saccharomyces cerevisiae rRNA sequences in Illumina HiSeq libraries generated by the split adapter method we show that PDD results in efficient removal of rRNA. The probes generate extended zones of depletion as a function of library insert size and the requirements for DSN cleavage. Using intact total RNA as starting material, probes can be spaced at the minimum anticipated library size minus 20 nucleotides to achieve continuous depletion. No off-target bias is detectable when comparing PDD-treated with untreated libraries. We further provide a bioinformatics tool to design suitable PDD probe sets. CONCLUSION We find that PDD is a rapid procedure that results in effective and specific depletion of unwanted sequences from deep-sequencing libraries. Because PDD acts at the cDNA stage, handling of fragile RNA samples can be minimised and it should further be feasible to remediate existing libraries. Importantly, PDD preserves the original RNA fragment boundaries as is required for nucleotide-resolution footprinting or base-cleavage studies. Finally, as PDD utilises unmodified DNA oligonucleotides it can provide a low-cost option for large-scale projects, or be flexibly customised to suit different depletion targets, sample types and organisms.This work was supported by an Australian Research Council Discovery Grant (DP130101928) and a NHMRC Senior Research Fellowship (514904) awarded to TP. NES was supported by a Go8 European Fellowship. We acknowledge technical support from the Australian Cancer Research Foundation Biomolecular Resource Facility

Redesigning Out-of-Distribution Detection on 3D Medical Images

Detecting out-of-distribution (OOD) samples for trusted medical image segmentation remains a significant challenge. The critical issue here is the lack of a strict definition of abnormal data, which often results in artificial problem settings without measurable clinical impact. In this paper, we redesign the OOD detection problem according to the specifics of volumetric medical imaging and related downstream tasks (e.g., segmentation). We propose using the downstream model's performance as a pseudometric between images to define abnormal samples. This approach enables us to weigh different samples based on their performance impact without an explicit ID/OOD distinction. We incorporate this weighting in a new metric called Expected Performance Drop (EPD). EPD is our core contribution to the new problem design, allowing us to rank methods based on their clinical impact. We demonstrate the effectiveness of EPD-based evaluation in 11 CT and MRI OOD detection challenges

Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images

Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection helps to identify such data samples, increasing the model's reliability. Recent works have developed DL-based OOD detection that achieves promising results on 2D medical images. However, scaling most of these approaches on 3D images is computationally intractable. Furthermore, the current 3D solutions struggle to achieve acceptable results in detecting even synthetic OOD samples. Such limited performance might indicate that DL often inefficiently embeds large volumetric images. We argue that using the intensity histogram of the original CT or MRI scan as embedding is descriptive enough to run OOD detection. Therefore, we propose a histogram-based method that requires no DL and achieves almost perfect results in this domain. Our proposal is supported two-fold. We evaluate the performance on the publicly available datasets, where our method scores 1.0 AUROC in most setups. And we score second in the Medical Out-of-Distribution challenge without fine-tuning and exploiting task-specific knowledge. Carefully discussing the limitations, we conclude that our method solves the sample-level OOD detection on 3D medical images in the current setting.Comment: 20 pages, 3 figures, submitted to Computerized Medical Imaging and Graphic

Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation

Deep Learning models perform unreliably when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection methods help to identify such data samples, preventing erroneous predictions. In this paper, we further investigate the OOD detection effectiveness when applied to 3D medical image segmentation. We design several OOD challenges representing clinically occurring cases and show that none of these methods achieve acceptable performance. Methods not dedicated to segmentation severely fail to perform in the designed setups; their best mean false positive rate at 95% true positive rate (FPR) is 0.59. Segmentation-dedicated ones still achieve suboptimal performance, with the best mean FPR of 0.31 (lower is better). To indicate this suboptimality, we develop a simple method called Intensity Histogram Features (IHF), which performs comparable or better in the same challenges, with a mean FPR of 0.25. Our findings highlight the limitations of the existing OOD detection methods on 3D medical images and present a promising avenue for improving them. To facilitate research in this area, we release the designed challenges as a publicly available benchmark and formulate practical criteria to test the OOD detection generalization beyond the suggested benchmark. We also propose IHF as a solid baseline to contest the emerging methods.Comment: This work has been submitted to the IEEE for possible publication. 10 pages, 5 figures, 5 table