ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Definitions Matter: Dynamic Policy Framing of the Arts in Boston's Sustainable Cultural Development

Cultural sustainability has become a fourth pillar in sustainable development studies. Different from the research approach to embedding culture into conventional sustainable discourse, this article argues that the sustainability and resilience issues within the arts and cultural sector should be paid more attention to. Putting the arts and cultural sector in urban settings, sustainable cultural development entails dynamic policy framing and changing policy justifications in response to an evolving socioeconomic and political environment. Taking the policy framing of the arts as an analytical lens, this paper aims to investigate this dynamic change and key driving factors through an in-depth case study of Boston's urban cultural development. This article finds that different definitions of the arts are associated with different arts-based urban development strategies across four stages of cultural development in Boston spanning a period of over 75 years. The working definition moved from art to the arts, then to the creative arts industry, and eventually to cultural assets and creative capital. The policy framing of the arts keeps evolving and layering in pursuit of more legitimacy and resources regarding groups of stakeholders, field industry components, types of industrial structure, and multiple policy goals. This dynamic policy framing has been driven by arts advocacy groups, policy learning process, urban leadership change, and cultural institutional change, allowing Boston to draw on a growing and diversifying set of cultural resources in pursuit of sustainable cultural development.
Out of sight, out of mind: a distance-aware forgetting strategy for adaptive random testing

Adaptive random testing (ART) achieves better failure-detection effectiveness than random testing by increasing the diversity of test cases. However, the intention of ensuring even spread of test cases inevitably causes an overhead problem. Although two basic forgetting strategies (i.e. random forgetting and consecutive retention) were proposed to reduce the computation cost of ART, they only considered the temporal distribution of test cases. In the paper, we presented a distance-aware forgetting strategy for the fixed size candidate set version of ART (DF-FSCS), in which the spatial distribution of test cases is taken into consideration. For a given candidate, the test cases out of its sight are ignored to reduce the distance computation cost. At the same time, the dynamic adjustment for partitioning and the second-round forgetting are adopted to ensure the linear complexity of DF-FSCS algorithm. Both simulation analysis and empirical study are employed to investigate the efficiency and effectiveness of DF-FSCS. The experimental results show that DF-FSCS significantly outperforms the classical ART algorithm FSCS-ART in efficiency, and has comparable failure-detection effectiveness. Com-pared with two basic forgetting methods, DF-FSCS is better in both efficiency and effectiveness. In contrast with a typical linear-time ART algorithm RBCVT-Fast, our algorithm requires less computational overhead and exhibits the similar failure-detection capability. In addition, DF-FSCS has more reliable performance than RBCVT-Fast in detecting failures for the programs with high-dimensional input domain.
Decorated boulders and other neglected features of the Central Saharan rock art

Although the Central Saharan rock art has been studied for more than five decades, attention has been given almost exclusively to figurative paintings and engravings. Images were divided into stylistic groups, which functioned as a sequence of relative chronology. Non-figurative engravings, such as grooves, ovals, kettles and cupules, have been considered only marginally or were completely ignored. Next to the impressive images of animals and human beings, these small engravings were not even considered a true form of rock art and were never included into the stylistic rock art table. Even more surprising is the exclusion of Kel Essuf. These particular anthropomorphic petroglyphs were already known in the 1960s, however, because of their marked stylistic and thematic difference from other engravings, they were treated as anomalous and, like the non-figurative art, they were not included among the official styles. This paper focuses on previously neglected or unpublished rock art, and suggests that the simplest engravings are signs bearing their own meaning. When studied as a whole instead of being treated as isolated units without any relation to other forms of rock art, it emerges that grooves, ovals, cupules and Kel Essuf engravings were probably created following the same pattern.
Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging Using a Dictionary Approach

Background signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals. Reconstruction is performed frame-by-frame with minimal assumptions on the temporal evolution of background signals. Thus, even non-linear temporal evolution of the latter can be captured. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.
Symmetry-aware graph matching

Visual symmetry encodes middle- to high-level geometric information and plays an important role in many computer vision applications. Not much effort, however, has been devoted to utilize symmetry information for graph matching. In this paper, we propose a new framework for symmetry-aware graph matching in the context of image matching. In the framework, we first define symmetry matrices to characterize the geometric symmetry relation among image features, and then develop two methods to discover such symmetry. After that we design general strategies to boost graph matching algorithms with symmetry constraints, and apply these strategies to several state-of-the-art algorithms. For evaluation, the proposed symmetry-aware graph matching algorithms are first applied to two datasets involving clear symmetry patterns: a recently proposed architecture image set and a dental panoramic radiograph collection. The results clearly demonstrate the benefits of using symmetry information. An experiment is also conducted on a dataset where symmetry is less obvious, the results show that our strategies still achieve better of similar performance compared with state-of-the-arts. (C) 2016 Elsevier Ltd. All rights reserved.
Archer: A History-Based Global Routing Algorithm

Global routing is an important step in the physical design process. In this paper, we propose a new global routing algorithm Archer, which resolves some of the most common problems with the state-of-the-art global routers. It is known that concurrent global routing algorithms are typically too expensive to be applied on today's large designs, which may contain up to a million nets. On the other hand, iterative rip-up and reroute (RNR)-based algorithms are susceptible to getting stuck in local optimal solutions. In this paper, we propose an RNR-based global routing algorithm that guides the routing iterations out of local optima through effective usage of congestion histories. We also focus on the problem of how to enable a smooth tradeoff between seemingly conflicting objectives of overflow and wirelength minimization. Furthermore, we propose a Lagrangian relaxation-based bounded-length min-cost topology improvement algorithm that enables Steiner trees to change dynamically for the purpose of congestion optimization. Our experiments on public benchmarks show the effectiveness of Archer compared to other state-of-the-art global routers.
Adaptation planning in France: Inputs from narratives of change in support of a community-led foresight process

In France, integrating adaptation to climate change into planning policies is a prerogative that has recently been delegated to municipalities. There are also various injunctions to engage the local population in this decision-making process. How can municipalities co-construct an adaptive future with their citizens? This article critically describes a community-led foresight process, based on the mapping, analysis and interpretation of narratives of change. Based on empirical results, we explore and discuss the role past, present and future narratives may play in the process of outlining incremental scenarios and how these might enable the identification of pathways and hinge points. The role of design in supporting the process by proposing an innovative foresight workshop is also discussed. We then highlight how these narratives stimulated reflections through an art, design and science foresight experiment.
Participatory arts and affective engagement with climate change: The missing link in achieving climate compatible behaviour change?

Despite a growing number of arts based climate change interventions and the importance emphasised in the social psychology literature of achieving affective (emotional) engagement with climate change before climate compatible behaviour change is likely, to date there has been no systematic application of interpretive social science techniques to understand the ways in which these arts based interventions do or do not achieve affective public engagement with climate change and hence might hold the key to unlocking broader climate compatible behaviour change. This article makes two key contributions.. First, it analyses the literature across social psychology and participatory arts to demonstrate why participatory, climate change based arts interventions could hold the key to more effective approaches to engaging multiple publics in climate compatible behaviour change. Second, using a small sample of participants in an arts based climate change intervention, in the Inner Hebrides, Scotland, it demonstrates the potential value of combining social science techniques (in this case Q Methodology) with participatory arts interventions to better understand and learn from the ways in which climate based arts interventions achieve affective public engagement with climate change. This raises the potential for, a significant new research and policy agenda looking forward.
Clinical Characteristics and Prognosis of Multiple Myeloma With Myelomatous Pleural Effusion: A Retrospective Single-Center Study

Objectives: Myelomatous pleural effusion is a rare presentation of extramedullary disease in multiple myeloma, which has been reported with dismal prognosis. We aimed to explore whether it has distinctive clinical characteristics and outcomes compared to other anatomic locations of extramedullary involvements. Methods: Multiple myeloma patients diagnosed at our institution from 2010 to 2020 were retrieved retrospectively. In total, 42 pairs of patients with and without extramedullary disease were enrolled, including 13 with myelomatous pleural effusion. The clinical and laboratory parameters were collected and compared between different groups. Prognostic effect of myelomatous pleural effusion was assessed in cox regression model and Kaplan-Meier curves. Results: Myelomatous pleural effusion patients presented a higher level of β2-microglobulin (P = .041), greater prevalence of multisites extramedullary lesions (69.2% vs 38.0%, P = .036) and International Staging System stage III (76.9% vs 44.8%, P = .016). Median overall survival was 60.6 months in patients without extramedullary disease versus 35.0 months in patients with extramedullary disease (P = .045). Notably, median overall survival was 13.0 months in myelomatous pleural effusion patients versus 37.0 months in other extramedullary disease patients with a significant difference (P = .029). Furtherly, multivariate analysis recognized myelomatous pleural effusion as an independent prognostic indicator (Hazard ratio: 2.669, 95% CI [1.132-6.293], P = .025). Conclusion: Myelomatous pleural effusion patients presented heavier tumor burden and worse outcomes than other extramedullary diseases.
New generation material for oil spill cleanup

Three-dimensional graphene-based materials promise oil spill cleanup in water at throughputs much higher than state-of-the-art oil-water separation materials.
Multi-Attribute Crowdsourcing Task Assignment With Stability and Satisfactory

Recently, crowdsourcing applications for smart cities have become more and more popular due to its higher work efficiency and lower work costs. However, the reasonable task assignment is still one of the important challenges for crowdsourcing. The existing researches on crowdsourcing task assignment focus on the tradeoff between maximizing the utility of platforms and minimizing the cost of requesters, but they lack of the considerations of stability and satisfactory. In this paper, we propose an intelligent multi attributes crowdsourcing task assignment with stability and satisfactory, called TASS. TASS can exploit the multi attributes to solve the stability of the transaction, and adopt the game theory to maximize the satisfaction of both sides during the task assignment. Next, we theoretically prove that the task assignment mechanism is truthfulness, individual rationality, stable and satisfactory assignment, and budget-balanced. Finally, we evaluate the performances of TASS with the state-of-the-art task assignment works. The experimental results show that TASS is better than the state-of-the-art task assignment works in terms of truthfulness, individually rationality, stable and satisfactory assignment, and balanced budget.
A novel fluorescence biosensor based on double-stranded DNA branch migration-induced HCR and DNAzyme feedback circuit for sensitive detection of Pseudomonas aeruginosa (clean version)

Pseudomonas aeruginosa (P. aeruginosa) is one of the most common bacteria in nosocomial infection. Here, a novel fluorescence biosensor based on double-stranded DNA branch migration-induced hybridization chain reaction (HCR) and DNAzyme feedback circuit was constructed for sensitive detection of P. aeruginosa. The binding of P. aeruginosa with its aptamer on a DNA three-way junction structure initiated the double-stranded DNA branch migration to form two DNA "Y" junction structures. One DNA "Y" junction structure opened the fluorescence-labelled DNA hairpins and triggered the HCR. The other DNA "Y" junction structure formed a double-stranded DNAzyme and cleaved the specific ribonucleotide site, producing new triggering probes to start the next cycle of the double-stranded DNA branch migration. Ultimately, a large number of DNA "Y" junction structures were produced, which greatly promoted signal amplification. Under optimized conditions, the proposed biosensor detected a wide linearity range of 102-107 CFU mL-1, and the limit of detection was 37 CFU mL-1 (S/N = 3). The recovery test results indicated that the biosensor has promising clinical application potential. Because of the simultaneous initiation of the HCR and the DNAzyme feedback circuit through the double-stranded DNA branch migration, the constructed biosensor provided an ideal platform for pathogenic bacteria detection without protein enzymes and complex signal amplification procedures.
Exploring variation in quality of care and clinical outcomes between neonatal units: a novel use for the UK National Neonatal Audit Programme (NNAP)

Neonatology is a relatively new specialty, in which much of the practice remains non-evidence based. Variation in the quality of care delivered is recognised but measuring this is challenging. One possible indicator of this is variation in practice. For more than a decade, the National Neonatal Audit Project (NNAP) has described variation in practice between UK neonatal units in relation to its annually reviewed audit measures. These are based on evidence based national standards or developed by a consensus method and have become de facto measures defining good quality of neonatal healthcare within the UK. In this article we explore the practicality of using the NNAP to look for associations between quality of care and outcomes. This would not be to validate the measures but could help towards a better understanding of the reasons underlying recognised variation in outcomes, even between neonatal units of the same designation.
A Rare Case of a Vocal Cord Foreign Body in an Infant: A Case Report

A foreign body (FB) is an object or item that is foreign to the area in which it is found. FB in the airway, accompanied by the esophagus, is a common overnight emergency in pediatric otolaryngology. Here we report a case of a healthy 11-month-old girl who presented in the emergency room with stridor and a weak cry. The patient was admitted as a case of croup (laryngotracheobronchitis) and treated with multiple antibiotics for more than five days but showed no improvement, then consulted the ear, nose, and throat team (ENT).
Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration

High-quality 3D content generation requires high-quality depth maps. In practice, depth maps generated by stereo-matching, depth sensing cameras, or decoders, have low resolution and suffer from unreliable estimates and noise. Therefore, depth enhancement is necessary. Depth enhancement comprises two stages: depth upsampling and temporal post-processing. In this paper, we extend our previous work on depth upsampling in two ways. First we propose PWAS-MCM, a new depth upsampling method, and we show that it achieves on average the highest depth accuracy compared to other efficient state-of-the-art depth upsampling methods. Then, we benchmark all relevant state-of-the-art filter-based temporal post-processing methods on depth accuracy by conducting a parameter space search to find the optimum set of parameters for various upscale factors and noise levels. Then we analyze the temporal post-processing methods qualitatively. Finally, we analyze the computational complexity of each depth upsampling and temporal post-processing method by measuring the throughput and hardware utilization of the GPU implementation that we built for each method.
Novel metal chelating molecules with anticancer activity. Striking effect of the imidazole substitution of the histidine-pyridine-histidine system

Previously we have reported a metal chelating histidine-pyridine-histidine system possessing a trityl group on the histidine imidazole, namely HPH-2Trt, which induces apoptosis in human pancreatic adenocarcinoma AsPC-1 cells. Herein the influence of the imidazole substitution of HPH-2Trt was examined. Five related compounds, HPH-1Trt, HPH-2Bzl, HPH-1Bzl, HPH-2Me, and HPH-1Me were newly synthesized and screened for their activity against AsPC-1 and brain tumor cells U87 and U251. HPH-1Trt and HPH-2Trt were highly active among the tested HPH compounds. In vitro DNA cleavage assay showed both HPH-1Trt and HPH-2Trt completely disintegrate pUC19 DNA. The introduction of trityl group decisively potentiated the activity.
A local search method based on edge age strategy for minimum vertex cover problem in massive graphs

Minimum vertex cover problem (MVC) is a classic combinatorial optimization problem, which has many critical real-life applications in scheduling, VLSI design, artificial intelligence, and network security. For MVC, researchers have proposed many heuristic algorithms, especially local search algorithms. And recently, researchers have increased their interest in solving large real-world graphs which require algorithms with faster searching performance. In this work, we propose a new edge weighting method called EABMS. EABMS has a time complexity of O(1). Based on EABMS, we propose our MVC solver framework called EAVC in solving MVC for massive graphs. We conducted experiments and compared the results of EAVC solvers with state of the art solvers. The results show that EABMS is effective in weighing edges for large sparse graphs and EAVC solvers outperform state of the art solvers.
Generalizing state-of-the-art object detectors for autonomous vehicles in unseen environments

In scene understanding for autonomous vehicles (AVs), models trained on the available datasets fail to generalize well to the complex, real-world scenarios with higher dynamics. In this work, we attempt to handle the distribution mismatch by employing the generative adversarial network (GAN) and weather modeling to strengthen the intra-domain data. We also alleviate the fragility of our trained models against natural distortions with stateof-the-art augmentation approaches. Finally, we assess our method for cross-domain object detection through CARLA simulation. Our experiments demonstrate that: (1) Augmenting training class with even limited intradomain data captured from the adverse weather conditions boosts the generalization of the two kinds of object detectors; (2) Exploiting GANs and weather modeling to elaborately simulate the adverse, intra-domain weather conditions manages to surmount the adverse data scarcity issue for intra-domain object detection; (3) A combination of Augmix and style augmentations not only can promote the robustness of our trained models against different natural distortions but also can boost their performance in the cross-domain object detection; (4) Training GANs for unsupervised image-to-image translation by means of the existing, large-scale datasets outside of our training domain is found beneficial to alleviate image-based and instance-based domain shifts.
Efficient Minimum Flow Decomposition via Integer Linear Programming

Minimum flow decomposition (MFD) is an NP-hard problem asking to decompose a network flow into a minimum set of paths (together with associated weights). Variants of it are powerful models in multiassembly problems in Bioinformatics, such as RNA assembly. Owing to its hardness, practical multiassembly tools either use heuristics or solve simpler, polynomial time-solvable versions of the problem, which may yield solutions that are not minimal or do not perfectly decompose the flow. Here, we provide the first fast and exact solver for MFD on acyclic flow networks, based on Integer Linear Programming (ILP). Key to our approach is an encoding of all the exponentially many solution paths using only a quadratic number of variables. We also extend our ILP formulation to many practical variants, such as incorporating longer or paired-end reads, or minimizing flow errors. On both simulated and real-flow splicing graphs, our approach solves any instance in <13 seconds. We hope that our formulations can lie at the core of future practical RNA assembly tools. Our implementations are freely available on Github.