An Analysis of the Prevalence of Over-the-Counter Painkiller Abuse

Pain, aches, fever, and discomfort are a part of the lives of many people. For most people, this pain can be managed using over the counter painkillers. OTC medications have a multibillion-dollar market. In the United States, patients can purchase as much of this medication as they desire to treat their pain. This medication can be easily abused and misused unintentionally. Every year, there are thousands of hospitalizations from misuse of these medications, and several cases of death. When taken incorrectly, these helpful medications can cause detrimental effects such as ulcers, kidney failure, and loss of liver function. The max daily dose is not much higher for these medications than for a prescription medication. The lack of regulations increases the prevalence of abuse. By analyzing restrictions in foreign nations, examining the avenues to abuse, and understanding how the medications work, the abuse can be limited

Detection of major ASL sign types in continuous signing for ASL recognition

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker

Incorporated nominals as antecedents for anaphora, or How to save the thematic arguments theory

The paper discusses the incorporation facts, mainly from Hungarian and Hindi, and the extended version of DRT developed by Farkas and de Swart (2003) to capture the properties of incorporated nominals across languages. It is shown that though the framework as defined by Farkas and de Swart does not derive all predictions it was aimed to derive by the authors, it can be easily extended into a version which does predict the relevant data. However, after examining more closely the data from Hungarian-type incorporation languages, I put forward a preliminary hypothesis alternative to Farkas and de Swart’s proposal, namely, that the possibility of anaphora to singular incorporated nominals is not a parameter of a language, but arises in languages like Hungarian if and only if the context makes it clear that the maximal entity denoted by the incorporated nominal is at most atomic. It is left for future empirical research to find out whether this hypothesis can actually account for the whole range of relevant data

A new framework for sign language recognition based on 3D handshape identification and linguistic modeling

Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical. Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens