iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes

We describe and implement iMapD, a computer-assisted approach for accelerating the exploration of uncharted effective Free Energy Surfaces (FES), and more generally for the extraction of coarse-grained, macroscopic information from atomistic or stochastic (here Molecular Dynamics, MD) simulations. The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator towards new, unexplored phase space regions by exploiting the smoothness of the (gradually, as the exploration progresses) revealed intrinsic low-dimensional geometry of the FES

Conversation therapy for agrammatism: exploring the therapeutic process of engagement and learning by a person with aphasia.

A recent systematic review of conversation training for communication partners of people with aphasia has shown that it is effective, and improves participation in conversation for people with chronic aphasia. Other research suggests that people with aphasia are better able to learn communication strategies in an environment which closely mirrors that of expected use, and that cognitive flexibility may be a better predictor of response to therapy than severity of language impairment. This study reports results for a single case, one of a case series evaluation of a programme of conversation training for agrammatism that directly involves a person with aphasia (PWA) as well as their communication partner. It explores how a PWA is able to engage with and learn from the therapy, and whether this leads to qualitative change in post-therapy conversation behaviours

High-resolution 3D direct-write prototyping for healthcare applications

The healthcare sector has much to benefit from the vast array of novelties erupting from the manufacturing world. 3D printing (additive manufacturing) is amongst the most promising recent inventions with much research concentrated around the various approaches of 3D printing and applying this effectively in the health sector. Amongst these methods, the direct-write assembly approach is a promising candidate for rapid prototyping and manufacturing of miniaturised medical devices/sensors and in particular, miniaturised flexible capacitive pressure sensors. Microstructuring the dielectric medium of capacitive pressure sensors enhances the sensitivity of the capacitive pressure sensor. The structuring has been predominantly achieved with photolithography and similar subtractive approaches. In this project high-resolution 3D direct write printing was used to fabricate structured dielectric mediums for capacitive pressure sensors. This involved the development and rheological characterisation of printability-tuned water soluble polyvinyl pyrrolidone (PVP) based inks (10%-30% polymer content) for stable high-resolution 3D printing. These inks were used to print water soluble micromoulds that were filled and cured with otherwise difficult to structure low G’ materials like PDMS. Our approach essentially decouples ink synthesis from printability at the micrometre scale. The developed micro moulding approach was employed for printing pyramidal micro moulds, that were used as templates for fabricating pyramid structured dielectric mediums for capacitive pressure sensing. The power of the approach was used to alter the microstructures and reap enhanced pressure sensing characteristics for effective miniaturised capacitive pressure sensors. A pressure sensing ring – that could be worn by doctors and surgeons – was prototyped with our approach and employed successfully to monitor in real-time the radial pulse signal of a 29 year old male volunteer. The print resolution of the inks was enhanced by formulating and rheologically characterising a PVP/PVDF polymer blend ink that would wet the printing nozzle less due to the hydrophobicity of the PVDF

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 43)

Abstracts are provided for 128 patents and patent applications entered into the NASA scientific and technical information system during the period Jan. 1993 through Jun. 1993. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

Lexically specific verb information: understanding sentence processing in the aphasic population

Transitivity is the frequency with which verbs are used with a direct object (transitively) or without one (intransitively), and it has been shown that unimpaired adults use transitivity information as they read (Clifton, Connie & Frazier 1984; Trueswell, Tanenhaus & Kello, 1993; Garnsey, 1997; Staub, 2007) or listen to sentences (Arai & Keller, 2012) to predict upcoming words. The current study tested persons with aphasia and age-matched, unimpaired adults as they read sentences containing verbs which varied in their transitivity. Gahl (2000) reported that both people with aphasia and unimpaired controls show sensitivity to verb frequency information under the Lexical Bias Hypothesis. Results from the unimpaired group indicated no use of transitivity in their initial parsing of sentences. Results from people with aphasia showed a significant use of transitivity during sentence processing. The data suggests that in the wake of language impairment, an individual may rely on transitivity to glean information from a sentence

ClinQC: quality control of an X-ray imaging system using clinical images

The work presented in this thesis is part of a research project of Leiden University Medical Center (LUMC) in The Netherlands. It belongs to the field of Diagnostic Radiology analysed from a Medical Physics point of view. After a short overview of the weekly quality controls of an X-ray imaging device, performed using simple phantoms, the thesis focuses on a novel approach called ClinQC (Clinical images-based Quality Control): it has the purpose to monitor the stability of imaging devices, aiming at the early detection of changes in image quality or radiation dose, by deriving quality parameters from chest images of routine patient examinations. The ClinQC algorithm extracts the noise from clinical images and derives the main dose quantities. The noise study presented in this thesis comprehends a validation of the algorithm, performed in several ways: image deteriorations, simulations, phantom studies and real clinical examples. For dose and homogeneity studies only some preliminary results are presented. The thesis collects also some ideas of improvement that can be considered for the future versions of the algorithm and to extend the ClinQC project to other X-ray anatomies and imaging modalities. The obtained similar results for the two compared methods prove that ClinQC is able to give immediate feedbacks of the quality of the imaging devices using patient images. It provides reliable, on-the-fly and sensitive parameters of the quality of the X-ray imaging system, that have the same physical meaning and similar relative variation as the quality indicators of the gold standard QClight method. It can be concluded that the ClinQC algorithm could be already applied in clinical practice, with the initial support of the QClight weekly quality control. In this way, a comparison between the two methods in a real test period will be a guide to find the necessary adjustments of the algorithm until the final version is being installed and stably used in clinical practice

One-Stage Cascade Refinement Networks for Infrared Small Target Detection

Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.Comment: Submitted to TGR