An Empirical Investigation of Typicality and Uniqueness Effects on Article Choice in Attributive-Possession NPs

In lieu of an abstract, here is a brief excerpt:Previous analyses of the use of English definite descriptions have generally taken one of two approaches, loosely characterizable as familiarity and uniqueness. That is, felicitous use of the definite article has been argued to require that the referent of the NP be either known to the hearer within the current context of utterance (e.g. Heim 1983, Prince 1992) or uniquely identifiable to the hearer (e.g. Gundel et al. 1993, Birner & Ward 1994, inter alia). What is common to all previous analyses is that the explanatory principle is presented as categorical; i.e. a referent is familiar or not, unique or not. There is generally no provision for gradience within the theory. In what follows we will investigate how familiarity- and uniqueness-based accounts of definiteness fare in accounting for the class of EMBEDDED DEFINITES known as ATTRIBUTIVE-POSSESSION constructions (McKercher 2001) and how the gradient notion of typicality impacts article choice in these constructions.

The Iowa Homemaker vol.26, no.5

Poem, Rosemary Moody, page 2 The Homemaker Celebrates, Kathleen Boland, page 4 From the Man’s Angle, Tom Swearingen, page 5 Success Formula, Maryann Jones, page 6 Radio Workshop Broadcasts, Luanne Madsen, page 7 Home for Christmas, Shirliann Fortman, page 8 Christmas Lighting, Ann Rozeboom, page 9 What’s New in Home Economics, Marjorie Clampitt, page 10 Vicky’s Christmas Fashion Cue, Shirliann Fortman, page 12 I Moved to Texas, Jo Ahern, page 14 Hawaiian Interlude, June Welch, page 16 ’46 Speaks, Goldie Rouse, page 18 Across Alumnae Desks, Mary Neff, page 2

Reinforced Self-Training (ReST) for Language Modeling

Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner.Comment: 23 pages, 16 figure

Pennsylvania Folklife Vol. 27, Folk Festival Supplement

• Hex Signs: A Living Tradition • Decoys and How to Make Them • Kutztown\u27s Plain People • The Old Country Kitchen: Where Food Preparation was an Art • Wooden Toys, Games and Puzzles: The Delight of All Children • A Sketch of the Seminar Stage Programs • Festival Focus • Folk Festival Programs • The Furniture-Makers at the Kutztown Festival • The Muzzle-Loading Gunsmith • Those Rare Things Called Antiques! • Mouth-Watering Baked Goods, Fresh From the Ovens! • The Art of the Potterhttps://digitalcommons.ursinus.edu/pafolklifemag/1079/thumbnail.jp

Effect of a Perioperative, Cardiac Output-Guided Hemodynamic Therapy Algorithm on Outcomes Following Major Gastrointestinal Surgery A Randomized Clinical Trial and Systematic Review

Importance: small trials suggest that postoperative outcomes may be improved by the use of cardiac output monitoring to guide administration of intravenous fluid and inotropic drugs as part of a hemodynamic therapy algorithm.Objective: to evaluate the clinical effectiveness of a perioperative, cardiac output–guided hemodynamic therapy algorithm.Design, setting, and participants: OPTIMISE was a pragmatic, multicenter, randomized, observer-blinded trial of 734 high-risk patients aged 50 years or older undergoing major gastrointestinal surgery at 17 acute care hospitals in the United Kingdom. An updated systematic review and meta-analysis were also conducted including randomized trials published from 1966 to February 2014.Interventions: patients were randomly assigned to a cardiac output–guided hemodynamic therapy algorithm for intravenous fluid and inotrope (dopexamine) infusion during and 6 hours following surgery (n=368) or to usual care (n=366).Main outcomes and measures: the primary outcome was a composite of predefined 30-day moderate or major complications and mortality. Secondary outcomes were morbidity on day 7; infection, critical care–free days, and all-cause mortality at 30 days; all-cause mortality at 180 days; and length of hospital stay.Results: baseline patient characteristics, clinical care, and volumes of intravenous fluid were similar between groups. Care was nonadherent to the allocated treatment for less than 10% of patients in each group. The primary outcome occurred in 36.6% of intervention and 43.4% of usual care participants (relative risk [RR], 0.84 [95% CI, 0.71-1.01]; absolute risk reduction, 6.8% [95% CI, ?0.3% to 13.9%]; P?=?.07). There was no significant difference between groups for any secondary outcomes. Five intervention patients (1.4%) experienced cardiovascular serious adverse events within 24 hours compared with none in the usual care group. Findings of the meta-analysis of 38 trials, including data from this study, suggest that the intervention is associated with fewer complications (intervention, 488/1548 [31.5%] vs control, 614/1476 [41.6%]; RR, 0.77 [95% CI, 0.71-0.83]) and a nonsignificant reduction in hospital, 28-day, or 30-day mortality (intervention, 159/3215 deaths [4.9%] vs control, 206/3160 deaths [6.5%]; RR, 0.82 [95% CI, 0.67-1.01]) and mortality at longest follow-up (intervention, 267/3215 deaths [8.3%] vs control, 327/3160 deaths [10.3%]; RR, 0.86 [95% CI, 0.74-1.00]).Conclusions and relevance: in a randomized trial of high-risk patients undergoing major gastrointestinal surgery, use of a cardiac output–guided hemodynamic therapy algorithm compared with usual care did not reduce a composite outcome of complications and 30-day mortality. However, inclusion of these data in an updated meta-analysis indicates that the intervention was associated with a reduction in complication rate

Occam's Razor and Petascale Visual Data Analysis

One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today?s largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena

The Modified Oral Palatopharyngoplasty or Modified Ahern Procedure

An oral palatopharyngoplasty, was a surgical procedure developed in the 1980’s and first published in 1992. The purpose of the procedure was to reduce the incidence of palatal instability (PI) and subsequent dorsal displacement of the soft palate (DDSP). In the years following the first publication, the procedure underwent numerous modifications to improve the likelihood of obtaining useful increases in tension in the tissues supporting the palatine aponeurosis and at the same time reducing the incidence of wound breakdown. These changes have led to the present technique now referred to as a Modified Oral Palatopharyngoplasty or Modified Ahern Procedure