Microwave-Assisted Synthesis and Evaluation of Antimicrobial Activity of 3-{3-(s-Aryl and s-Heteroaromatic)acryloyl}-2Hchromen-2-one Derivatives

The exploration of potential utilization of microwaves as an energy source for heterocyclic synthesis was herein investigated using condensation of 3-acetylcoumarin (1) with aromatic and heteroaromatic aldehydes to afford the corresponding aromatic chalcones (2a–j) and heteroaromatic chalcones (3a–e and 4a–e), respectively, in good to excellent yield within 1–3 min. The chemical structures were confirmed by analytical and spectral data. All the synthesized compounds were screened for their antibacterial activity and 3-{3-(4-dimethylaminophenyl)acryloyl}-2H-chromen-2-one (2i) was discovered to be the most active at minimum inhibitory concentration (MIC) value of 7.8 µg/m

Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1

An unprecedented 2D covalent organic framework with an htb net topology.

A 2D imine-linked COF with a hitherto unreported htb type topology was synthesized from a linear diamine linker and a judiciously designed tetra-aldehyde building block. This work opens the door to the development of COFs with unprecedented topologies and may broaden the scope of COF functional materials by pore size and pore surface engineering

Workplace Accommodations for Individuals with Arthritis

This brochure on individuals with arthritis and the Americans with Disabilities Act (ADA) is one of a series on human resources practices and workplace accommodations for persons with disabilities edited by Susanne M. Bruyère, Ph.D., CRC, SPHR, Director, Program on Employment and Disability, School of Industrial and Labor Relations – Extension Division, Cornell University. Cornell University was funded in the early 1990’s by the U.S. Department of Education National Institute on Disability and Rehabilitation Research as a National Materials Development Project on the employment provisions (Title I) of the ADA (Grant #H133D10155). These updates, and the development of new brochures, have been funded by Cornell’s Program on Employment and Disability, the Pacific Disability and Business Technical Assistance Center, and other supporters

SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets

The paper describes the best performing system for the SemEval-2018 Affect in Tweets (English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal classification valence is classified into 7 different classes ranging from -3 to 3 whereas emotion is classified into 4 different classes 0 to 3 separately for each emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate the intensity of valence and each emotion. The system performs domain adaptation of 4 different models and creates an ensemble to give the final prediction. The proposed system achieved 1st position out of 75 teams which participated in the fore-mentioned sub-tasks. We outperform the baseline model by margins ranging from 49.2% to 76.4%, thus, pushing the state-of-the-art significantly.Comment: SemEval-2018 Task 1: Affect in Tweet

The Accelerator Complex from the International Design Study of the Neutrino Factory

The Neutrino Factory produces high-energy neutrino beams with a well-defined flavour content and energy spectrum from the decay of intense, high-energy, stored muon beams to establish CP violation in the neutrino sector. The International Design Study for the Neutrino Factory (the IDSNF) will provide a Reference Design Report (RDR) for the facility. The present baseline design has been re-evaluated to take into account the recent measurements of θ13. This talk describes the status of the accelerator facility and the accelerator subsystems of which it is comprised. This is a modification of the facility described in the Interim Design Report (IDR) completed in 2011. The accelerator facility will deliver 1021 muon decays per year from 10 GeV stored muon beams. The straight sections of the storage ring point to a 100 kton Magnetised Iron Neutrino Detector (MIND) at a distance of 2000-2500 km from the source. The accelerator-physics challenges, and the R&D underway to meet them, will be described together with alternative designs that are being developed to mitigate the technical risks that some of the subsystems present

Hyperspectral imaging applied to end-of-life (EOL) concrete recycling

The recovery of materials from DW is an important target of the recycling industry and it is important to know which materials are presents in order to set up efficient sorting and/or quality control actions. The implementation of an automatic recognition system of recovered products from End-Of-Life (EOL) concrete materials can be an useful way to maximize DW conversion into secondary raw materials. In this paper a new approach, based on HyperSpectral Imaging (HSI) sensors, is investigated in order to develop suitable and low cost strategies finalized to the preliminary detection and characterization of materials constituting Demolition Waste (DW) flow stream. The described HSI quality control approach is based on the utilization of a device working in the near infrared range (1000-1700 nm). Acquired hyperspectral images were analyzed. Different chemometric methods were applied. Results showed that it is possible to recognize DW materials and to distinguish the recycled aggregates from the investigated contaminants (brick, gypsum, plastic, wood and foam)

Improving Distributed Representations of Tweets - Present and Future

Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function which aids the understanding of the literature. We also provide interesting future directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models.Comment: To be presented in Student Research Workshop (SRW) at ACL 201