Miracle-dz/UniFAR-FTUs huggingface dataset:
Isodose feature‐preserving voxelization (IFPV) for radiation therapy treatment planning

Purpose Inverse planning involves iterative optimization of a large number of parameters and is known to be a labor‐intensive procedure. To reduce the scale of computation and improve characterization of isodose plan, this paper presents an isodose feature‐preserving voxelization (IFPV) framework for radiation therapy applications and demonstrates an implementation of inverse planning in the IFPV domain. Methods A dose distribution in IFPV scheme is characterized by partitioning the voxels into subgroups according to their geometric and dosimetric values. Computationally, the isodose feature‐preserving (IFP) clustering combines the conventional voxels that are spatially and dosimetrically close into physically meaningful clusters. A K‐means algorithm and support vector machine (SVM) runs sequentially to group the voxels into IFP clusters. The former generates initial clusters according to the geometric and dosimetric information of the voxels and SVM is invoked to improve the connectivity of the IFP clusters. To illustrate the utility of the formalism, an inverse planning framework in the IFPV domain is implemented, and the resultant plans of three prostate IMRT and one head‐and‐neck cases are compared quantitatively with that obtained using conventional inverse planning technique. Results The IFPV generates models with significant dimensionality reduction without compromising the spatial resolution seen in traditional downsampling schemes. The implementation of inverse planning in IFPV domain is demonstrated. In addition to the improved computational efficiency, it is found that, for the cases studied here, the IFPV‐domain inverse planning yields better treatment plans than that of DVH‐based planning, primarily because of more effective use of both geometric and dose information of the system during plan optimization. Conclusions The proposed IFPV provides a low parametric representation of isodose plan without compromising the essential characteristics of the plan, thus providing a practically valuable framework for various applications in radiation therapy.
A novel cell zooming strategy towards energy efficient based on load balancing in random heterogeneous networks

This paper presents an energy efficient aware continuous cell zooming strategy based on load balancing in green network. Firstly, we derive a two-layer Poisson Voronoi Tessellation (PVT) random heterogeneous network (HetNets) model. Secondly, we periodically adjust the user equipment (UE) among cells to realize load balancing of the HetNets. According to base station cooperation, transferring or switching UE which in light-load or heavy-load at the same time. Finally, the radius of cells can be adjusted adaptively to achieve cell zooming. The performance of this work is examined under different user densities and ratios compared with some reference algorithms. Numerical evaluations show that our proposed algorithm can optimize system performance, ensure UE's quality of service (QoS) and improve energy efficient.
Towards Continuous Dialogue Corpus Creation: writing to corpus and generating from it

This paper describes a method to create dialogue corpora annotated with interoperable semantic information. The corpus development is performed following the ISO linguistic annotation framework and primary data encoding initiatives. The Continuous Dialogue Corpus Creation (D3C) methodology is proposed, where a corpus is used as a shared repository for analysis and modelling of interactive dialogue behaviour, and for implementation, integration and evaluation of dialogue system components. All these activities are supported by the use of ISO standard data models including annotation schemes, encoding formats, tools, and architectures. Standards also facilitate practical work in dialogue system implementation, deployment, evaluation and re-training, and enabling automatic generation of adequate system behaviour from the data. The proposed methodology is applied to the data-driven design of two multimodal interactive applications - the Virtual Negotiation Coach, used for the training of metacognitive skills in a multi-issue bargaining setting, and the Virtual Debate Coach, used for the training of debate skills in political contexts.
Unravelling migratory connections: the next level

Migration is widespread among birds, and the strength of the link between the breeding and wintering grounds, migratory connectivity, influences many ecological and evolutionary processes. Despite its importance, migratory connectivity is poorly estimated for most species. Traditionally, visual observations and bird ringing have been used to monitor migration, but these methods require more effort for relatively little return. Genetic markers and stable isotope signatures have increasingly been used to study connectivity. Each approach has its distinct strengths and weaknesses, and as is often the case, a combination may yield the most insight. In this issue of Molecular Ecology, Rundel and colleagues (2013) present a novel Bayesian statistical framework in which genetics and stable isotope data can be combined to improve the assignment of individuals to different winter or breeding regions. The development of such new statistical methods combined with the increasing number and ease of access of isotopic and genetic data sets will greatly enhance our understanding of migratory connectivity. Add to this the developments of miniature devices to track movements of individuals, and the field is destined to make major progression in the decades to come.
Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

Abstract. For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.
A Large Resource of Patterns for Verbal Paraphrases

Paraphrases play an important role in natural language understanding, especially because there are ﬂuent jumps between hidden paraphrases in a text. For example, even to get to the meaning of a simple dialog like I bought a computer. How much did the computer cost? involves quite a few steps. A computational system may actually have a huge problem in linking the two sentences as their connection is not overtly present in the text. However, it becomes easier if it has access to the following paraphrases: [ HUMAN ] buy [ ARTIFACT ] ⇐⇒ [ HUMAN ] pay [ PRICE ] for [ ARTIFACT ] ⇐⇒ [ ARTIFACT ] cost [ HUMAN ] [ PRICE ], and also to the information that I IsA [ HUMAN ] and computer IsA [ ARTIFACT ]. In this paper we introduce a resource of such paraphrases that was extracted by investigating large corpora in an unsupervised manner. The resource contains tens of thousands of such pairs and it is available for academic purposes.
echanisms underlying the neurotoxicity induced by lyphosate-based herbicide in immature rat hippocampus : nvolvement of glutamate excitotoxicity

Previous studies demonstrate that glyphosate exposure is associated with oxidative damage and neurotoxicity. Therefore, the mechanism of glyphosate-induced neurotoxic effects needs to be determined. The aim of this study was to investigate whether Roundup® (a glyphosate-based herbicide) leads to neurotoxicity in hippocampus of immature rats following acute (30 min) and chronic (pregnancy and lactation) pesticide exposure. Maternal exposure to pesticide was undertaken by treating dams orally with 1% Roundup® (0.38% glyphosate) during pregnancy and lactation (till 15-day-old). Hippocampal slices from 15 day old rats were acutely exposed to Roundup® (0.00005–0.1%) during 30 min and experiments were carried out to determine whether glyphosate affects 45Ca2+ influx and cell viability. Moreover, we investigated the pesticide effects on oxidative stress parameters, 14C-methyl-amino-isobutyric acid (14C-MeAIB) accumulation, as well as glutamate uptake, release and metabolism. Results showed that acute exposure to Roundup® (30 min) increases 45Ca2+ influx by activating NMDA receptors and voltagedependent Ca2+ channels, leading to oxidative stress and neural cell death. The mechanisms underlying Roundup®-induced neurotoxicity also involve the activation of CaMKII and ERK. Moreover, acute exposure to Roundup® increased 3H-glutamate released into the synaptic cleft, decreased GSH content and increased the lipoperoxidation, characterizing excitotoxicity and oxidative damage. We also observed that both acute and chronic exposure to Roundup® decreased 3H-glutamate uptake and metabolism, while induced 45Ca2+ uptake and 14C-MeAIB accumulation in immature rat hippocampus. Taken together, these results demonstrated that Roundup® might lead to excessive extracellular glutamate levels and consequently to glutamate excitotoxicity and oxidative stress in rat hippocampus. © 2014 Elsevier Ireland Ltd. All rights reserved.
Assembly Systems with Sequential Supplier Decisions and Uncertain Demand

We consider multitier push assembly systems with sequential supplier decisions and a wholesale price contract. We show that both an Original Equipment Manufacturer (OEM)–Contract Manufacturer (CM) assembly and a modular assembly with sequential supplier decisions are mathematically equivalent to the corresponding traditional assembly. We determine that, in most cases, the first mover supplier realizes a higher profit than the second mover supplier but we also identify the sufficient conditions for the reverse to occur. We provide conditions under which the order quantity, the second mover profit, total supplier profits, and the assembler profit are either higher or lower for a multitier system with sequential suppliers compared to simultaneous suppliers. We conclude that the first mover is always better off in a three‐tier sequential system while she can be either better off or worse off in a four‐tier sequential system compared to the corresponding simultaneous systems. We also analyze the impact of information asymmetry on the supplier and assembler profits in a three‐tier sequential system. Finally, we determine the profit threshold for an independent manufacturer in a three‐tier system to become a CM in a four‐tier system and vice versa.
Lesion margin analysis for automated classification of cervical cancer lesions

Digital colposcopy is an emerging technology, replacing the traditional colposcope for diagnosis of cervical lesions. Incorporating automated algorithms within a digital colposcopy system can improve the reliability and the diagnostic accuracy of cervical precancer and cancer. An automated computer-aided diagnosis (CAD) system can assess the three important cervical diagnostic cues: the color, the vascular patterns and the lesion margins with quantitative measures, similar to the way colposcopists use the Reid's index in traditional colposcopy. In this work we present a novel way to analyze and classify the global and the local features of one of the three major components in colposcopy diagnosis - the lesion margins. The margins of cervical lesion can be described as 'feathered,' 'geographic,' 'satellite,' 'regular or smooth' and 'margin-in-margin,' or they can be of mixed type. As margin characterization is a complex task, we use irregularity descriptors such as compactness indices and curvature descriptors. To address the complexity of the problem, the dependency of scale and the position of the lesion on the cervical image, our method use novel Fourier energy descriptors. The conceptually complex analysis of describing lesions as 'satellite' lesions or lesions with multiple margins is performed using descriptors, where the distance, the position and the local statistical estimates of image intensity play important role. We trained this new algorithm to classify and diagnose the cervix, evaluating only the lesions. The accuracy of the results is assessed against a 'ground truth' scheme, using colposcopists' annotations and pathology results. We report the resulted accuracy of the classification method assessed against this scheme.
Experiments with the Graph Traverser program

An automatic method is described for the solution of a certain family of problems. To belong to this family a problem must be expressible in the language of graph theory as that of finding a path between two specified nodes of a specified graph. The method depends upon the evaluation of intermediate states of the problem according to the extent to which they have features in common with the goal state. We define evaluation functions each of which assigns to any state of the problem a value which is in some way related to its ‘distance’ from the goal state. Equivalently we assign to nodes of the corresponding graph values which are related to the distance over the graph from the goal node. Distance is reckoned as the smallest number of arcs needed to connect two nodes. An Algol program, the Graph Traverser, has been written to operate in this context. It is designed in a completely general way, and has two ‘empty’ procedures one of which must be written to specify the structure of the graph, that is the constraints of the problem, and the other to define an evaluation function. Results obtained by supplying the program with definitions of various sliding block puzzles and also a simple problem of algebraic manipulation are reported for a range of evaluation functions.
Are the Healthier Wealtier or the Wealthier Healthier?The European Evidence

Are the healthier wealthier or the wealthier healthier? Evidence from Europe presents an extensive and updated survey of the research findings on socio-economic inequalities in health in six European countries (Denmark, Finland, France, Greece, Netherlands and United Kingdom). Three main questions are the focal points of this book : First, how does individual socio-economic and/or occupational status affect the physical and mental health and sense of well-being of older individuals of working age? Second, how does individual socio-economic and/or occupational status affect the ability of older workers to participate in the labour market? And third, how might policy initiatives be developed to enhance the effectiveness of welfare services for the older workforce? The primary focus of this work is to cover all the research done so far, including unpublished papers and those written in the national language of respective country. Each chapter reviews the empirical research carried out for that country, describes the available data, comments on the adopted estimation strategies and discusses methodological limitations of the reviewed studies, hence critically assessing their reliability. The results are surveyed and interpreted within their specific institutional context in order to facilitate comparison with the international evidence published in mainstream journals and surveyed in one of the chapters. This way, the reader is able to identify the knowledge gaps in each country and to also assess what could be learned from that particular countrys experience.
Legislative-Executive Relations And Legislative Oversight

As a focus of scholarship, the subject of legislative-executive relations is both elusively broad and remarkably diverse. This essay attempts to make both the breadth and diversity of the field more comprehensible by examining and identifying: (1) attributes and characteristics of key legislative and executive "players," (2) various points of intersection between legislative and executive "players" and their institutions, and, especially, (3) forms, conditions, and impacts of legislative supervision of the executive (oversight). Analysis of the research and scholarly literature on these topics is situated in a context of different normative perspectives toward governance.
Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT

In this study, we investigated a pattern-classification technique which can be trained with a small number of cases with a massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network (ANN), which is capable of operating on image data directly. The MTANN is trained by use of a large number of sub-regions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule. " The output image is obtained by scanning of an input image with the MTANN. In the MTANN, the distinction between nodules and non-nodules is treated as an image-processing task, in other words, as a highly nonlinear filter that performs both nodule enhancement and non-nodule suppression. This allows us to train the MTANN not on a case basis, but on a sub-region basis. Therefore, the MTANN can be trained with a very small number of cases. Our database consisted of 101 LDCT scans acquired from 71 patients in a lung cancer screening program. The scans consisted of 2,822 sections, and contained 121 nodules including 104 nodules representing confirmed primary cancers. With our current CAD scheme, a sensitivity of 81.0% (98/121 nodules) with 0.99 false positives per section (2,804/2,822) was achieved. By use of the MTANN trained with a small number of training cases (n=10), i.e., five pairs of nodules and non-nodules, we were able to remove 55.8% of false positives without a reduction in the number of true positives, i.e., a classification sensitivity of 100%. Thus, the false-positive rate of our current CAD scheme was reduced from 0.99 to 0.44 false positive per section, while the current sensitivity (81.0%) was maintained.
Airway and systemic inflammatory responses to ultrafine carbon black particles and ozone in older healthy subjects

ABSTRACT Increased adverse health effects in older subjects due to exposure to ambient air pollutants may be related to the inflammatory response induced by these contaminants. The aim of this study was to assess airway and systemic inflammatory responses in older healthy subjects to a controlled experimental exposure with spark-generated elemental carbon black ultrafine particles (cbUFPs) and ozone (O3). Twenty healthy subjects, age 52–75 years, were exposed on three occasions separated by at least 8 weeks. The exposures to filtered air (FA), to cbUFP (50 μg/m3), or to cbUFP in combination with 250 ppb ozone (cbUFP + O3) for 3 h with intermittent exercise were performed double blind, and in random order. Sputum and blood samples were collected 3.5 h after each exposure. Exposure to cbUFP + O3 significantly increased plasma club cell protein 16 (CC16), the number of sputum cells, the number and percent of sputum neutrophils, and sputum interleukin 6 and matrix metalloproteinase 9. Exposure to cbUFP alone exerted no marked effect, except for an elevation in sputum neutrophils in a subgroup of 13 subjects that displayed less than 65% sputum neutrophils after FA exposure. None of the inflammatory markers was correlated with age, and serum cardiovascular risk markers were not markedly affected by cbUFP or cbUFP + O3. Exposure to cbUFP+O3 induced a significant airway and systemic inflammatory response in older healthy volunteer subjects. The effects induced by cbUFP alone suggest that the inflammation was predominantly mediated by O3, although one cannot rule out that the interaction of cbUFP and O3 played a role.
Max-SNR Opportunistic Routing for Large-Scale Energy Harvesting Sensor Networks

Providing sensors with adequate energy in large-scale wireless sensor networks (WSNs) over long periods is a major bottleneck in their implementation. In this regard, energy harvesting (EH), i.e., capturing energy from ambient renewable energy sources, is a promising solution for low-power and low data-rate WSNs. The randomness in the energy available forces the redesign of WSN protocols. Our specific interest here is to enable the delivery of sensed data to a fusion center (FC) in a large-scale EH-WSN. We propose a novel, energy-aware, opportunistic routing protocol in a large-scale EH-WSN requiring multi-hop communication. In choosing the best forwarding partner, our scheme considers the energy available at sensor nodes, their distances from the FC and also the amount of data to be transmitted. Our protocol requires no prior knowledge of the network topology. We provide a mathematical analysis of our routing protocol to confirm the achieved numerical results. As our results show, the proposed protocol significantly increases data delivery as compared to the state-of-the-art technologies. We also introduce an EH-aware manner for distributing sensors in the environment such that all nodes have approximately equal transmission load independent of their locations, which significantly increases the data delivery ratio.
A real-time traffic congestion detection system using on-line images

The heavily-loaded traffic system in Macao is characterized by narrow and complex street networks, along with many traffic bottlenecks. In this paper, we propose an economical real-time traffic congestion detection system using on-line images provided by the local government. The proposed system mainly consists of the detection of vehicles using the on-line images and the estimation of traffic congestion based on the estimated number of vehicles. For the detection of vehicles, we study a method of using the signs on the road and experiment the technique of using the Haar-like features. We find that Haar-like features can be used for the detection of vehicles using the on-line images from different camera locations. For the traffic congestion estimation, a threshold for the image correlation coefficient of the consecutive images is used, along with a threshold for the number of vehicles detected. Two different levels of congestion are considered, namely NORMAL and CONGESTED, although the number of congestion level can be easily extended. Experimental results show that the proposed system can estimate the traffic congestion correctly and in real-time at low cost. Compared with traditional traffic congestion estimation systems, this system provides a more economical solution with potential commercial applications for the local residents and for the tourists in Macao.