Using ultra-short pulses to determine particle size and density distributions

We analyze the time dependent response of strongly scattering media (SSM) to ultra-short pulses of light. A random walk technique is used to model the optical scattering of ultra-short pulses of light propagating through media with random shapes and various packing densities. The pulse spreading was found to be strongly dependent on the average particle size, particle size distribution, and the packing fraction. We also show that the intensity as a function of time-delay can be used to analyze the particle size distribution and packing fraction of an optically thick sample independently of the presence of absorption features. Finally, we propose an all new way to measure the shape of ultra-short pulses that have propagated through a SSM.Comment: 15 pages, 29 figures, accepted for publication in Optics Express will update with full reference when it is availabl

Preregistering NLP Research

Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study. This practice is increasingly common in medicine and psychology, but is rarely discussed in NLP. This paper discusses preregistration in more detail, explores how NLP researchers could preregister their work, and presents several preregistration questions for different kinds of studies. Finally, we argue in favour of registered reports, which could provide firmer grounds for slow science in NLP research. The goal of this paper is to elicit a discussion in the NLP community, which we hope to synthesise into a general NLP preregistration form in future research.Comment: Accepted at NAACL2021; pre-final draft, comments welcom

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.Comment: 22 pages (excluding bibliography and appendix

A gain-coefficient switched Alexandrite laser

We report on a gain-coefficient switched Alexandrite laser. An electro-optic modulator is used to switch between high and low gain states by making use of the polarization dependent gain of Alexandrite. In gain-coefficient switched mode, the laser produces 85 ns pulses with a pulse energy of 240 mJ at a repetition rate of 5 Hz.Comment: 8 pages, 5 figure

One-Watt level mid-IR output, singly resonant, continuous-wave optical parametric oscillator pumped by a monolithic diode laser

We report more than 1.1 Watt of idler power at 3373 nm in a singly resonant optical parametric oscillator (SRO), directly pumped by a single-frequency monolithic tapered diode laser. The SRO is based on a periodically poled MgO:LiNbO3 crystal in a four mirror cavity and is excited by 8.05 W of 1062 nm radiation. The SRO pump power at threshold is 4 W. The internal slope-efficiency and conversion efficiency reach 89% and 44% respectively. The signal and idler waves are temperature tuned in the range of 1541 to 1600 nm and 3154 to 3415 nm respectively. To the best of our knowledge, this is the highest output obtained for a diode pumped optical parametric oscillator (OPO), and the first time a SRO is directly pumped by a monolithic tapered diode laser

Hypertrophic cardiomyopathy in a large community-based population: clinical outcome and identification of risk factors for sudden cardiac death and clinical deterioration

AbstractObjectivesThis study evaluates the clinical course and identifies risk factors for sudden cardiac death (SCD) and clinical deterioration in hypertrophic cardiomyopathy (HCM) in a large community-based population. Comparison was made with data from six tertiary referral and six nonreferral institutions.BackgroundHypertrophic cardiomyopathy is a disease with marked heterogeneity in clinical presentation and prognosis. Risk factors for SCD are not well defined in patients free of referral bias.MethodsBetween 1970 and 1999, 225 consecutive patients (mean age [±SD] 41±16 years) were examined and followed at yearly intervals.ResultsForty-four deaths were recorded of which 27 cases were cardiovascular. Fourteen patients died suddenly, six were successfully resuscitated, and seven patients died of congestive heart failure. The annual mortality, annual cardiac mortality, and annual mortality due to sudden death were 1.3%, 0.8%, and 0.6%, respectively. At least one New York Heart Association (NYHA) functional class deterioration was reported in 33% of the patients with a significant (≥50 mm Hg) left ventricular outflow tract (LVOT) gradient in contrast to 7% without obstruction. The presence of syncope was related to SCD (p < 0.05). Younger age and more severe functional limitation distinguishes patients from in hospital-based centers from the ones in community-based centers.ConclusionsHypertrophic cardiomyopathy is a benign disease in an unselected population with a low incidence of cardiac death. Syncope was associated with a higher incidence of SCD and patients with a significant LVOT obstruction were more susceptible to clinical deterioration

Template-based multilingual football reports generation using Wikidata as a knowledge base

This paper presents a new version of a football reports generation system called PASS. The original version generated Dutch text and relied on a limited handcrafted knowledge base. We describe how, in a short amount of time, we extended PASS to produce English texts, exploiting machine translation and Wikidata as a large-scale source of multilingual knowledge

Effects of Relationship Goal on Linguistic Behavior in Online Dating Profiles: A Multi-Method Approach

This study uses two methods to examine whether online daters looking for a long-term relationship behave linguistically different in their profile texts compared to daters seeking casual relationships. To investigate these linguistic differences, 12,310 existing Dutch dating profiles were analyzed using the Linguistic Inquiry and Word Count (LIWC) program and a word-based classifier. Results of both methods suggest there are reliable differences in the linguistic behavior long-term and casual relationship seekers employ in their dating profiles: long-term relationship seekers mention more topics that are relevant when looking for a long- term relationship, such as internal personality traits and qualities. Additionally, long-term relationship seekers seem to self-disclose more in their profile texts by providing more personal information and using more I-references. Profile texts of casual relationship seekers are more diffuse and harder to classify. Moreover, the study demonstrates that using a multi-method approach, with LIWC and a data-driven word-based classifier, provides a deeper understanding of linguistic differences between the two relationship seeking groups

Neural data-to-text generation: A comparison between pipeline and end-to-end architectures

Traditionally, most data-to-text applications have been designed using a modular pipeline architecture, in which non-linguistic input data is converted into natural language through several intermediate transformations. In contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in-between. This study introduces a systematic comparison between neural pipeline and end-to-end data-to-text approaches for the generation of text from RDF triples. Both architectures were implemented making use of state-of-the art deep learning methods as the encoder-decoder Gated-Recurrent Units (GRU) and Transformer. Automatic and human evaluations together with a qualitative analysis suggest that having explicit intermediate steps in the generation process results in better texts than the ones generated by end-to-end approaches. Moreover, the pipeline models generalize better to unseen inputs. Data and code are publicly available.Comment: Preprint version of the EMNLP 2019 articl

High confinement, high yield Si3N4 waveguides for nonlinear optical application

In this paper we present a novel fabrication technique for silicon nitride (Si3N4) waveguides with a thickness of up to 900 nm, which are suitable for nonlinear optical applications. The fabrication method is based on etching trenches in thermally oxidized silicon and filling the trenches with Si3N4. Using this technique no stress-induced cracks in the Si3N4 layer were observed resulting in a high yield of devices on the wafer. The propagation losses of the obtained waveguides were measured to be as low as 0.4 dB/cm at a wavelength of around 1550 nm.Comment: 10 pages, 4 figure