Miracle-dz/UniFAR-FTUs huggingface dataset:
Verification of dose calculation algorithms in a multi-layer heterogeneous phantom using films.

UNLABELLED The development of advanced radiation treatment techniques such as intensity modulated radiation therapy (IMRT) and volumetric intensity modulated arc therapy (VMAT) require more accurate dose calculation algorithms within the treatment planning systems (TPS) considering that the human body is composed of tissues of widely differing radiological properties and the characteristics of a therapeutic radiation treatment beam along a heterogeneous path will be different. The objective of this study was to evaluate the accuracy of 2D-isodose distributions predicted by pencil beam convolution algorithm (PBC) and anisotropic analytic algorithm (AAA) in a heterogeneous slab phantom composed of media equivalent to air, water and bone density. The measurements were done by using the films at multiple depths in the phantom for open field sizes 5 × 5 cm2 and 10 × 10 cm2. The results from this study indicated that the AAA had better agreement with the measurement compared to PBC for both the test field sizes at all selected depths; however, the limitation of AAA in predicting doses within and beyond low-density medium was observed, especially for a smaller field size (up to 32.7% and 34.0% for AAA and PBC, respectively). Furthermore, discrepancies up to -4.8% for AAA and -14.6% for PBC was seen in the high-density medium as well. Dose prediction errors by the AAA and PBC were more pronounced for a smaller test field size, especially in the low-density medium. KEYWORDS IMRT, dose calculation algorithms, Anisotropic Analytical Algorithm (AAA), Pencil Beam Convolution (PBC), Varian's Eclipse treatment planning system.
The original colours of fossil beetles

Structural colours, the most intense, reflective and pure colours in nature, are generated when light is scattered by complex nanostructures. Metallic structural colours are widespread among modern insects and can be preserved in their fossil counterparts, but it is unclear whether the colours have been altered during fossilization, and whether the absence of colours is always real. To resolve these issues, we investigated fossil beetles from five Cenozoic biotas. Metallic colours in these specimens are generated by an epicuticular multi-layer reflector; the fidelity of its preservation correlates with that of other key cuticular ultrastructures. Where these other ultrastructures are well preserved in non-metallic fossil specimens, we can infer that the original cuticle lacked a multi-layer reflector; its absence in the fossil is not a preservational artefact. Reconstructions of the original colours of the fossils based on the structure of the multi-layer reflector show that the preserved colours are offset systematically to longer wavelengths; this probably reflects alteration of the refractive index of the epicuticle during fossilization. These findings will allow the former presence, and original hue, of metallic structural colours to be identified in diverse fossil insects, thus providing critical evidence of the evolution of structural colour in this group.
Conditions for Facelike Expertise With Objects

Compared with other objects, faces are processed more holistically and with a larger reliance on configural information. Such hallmarks efface processing can also be found for nonface objects as people develop expertise with them. Is this specifically a result of expertise individuating objects, or would any type of prolonged intensive experience with objects be sufficient? Two groups of participants were trained with artificial objects (Ziggerins). One group learned to rapidly individuate Ziggerins (i.e., subordinate-level training). The other group learned rapid, sequential categorizations at the basic level. Individuation experts showed a selective improvement at the subordinate level and an increase in holistic processing. Categorization experts improved only at the basic level, showing no changes in holistic processing. Attentive exposure to objects in a difficult training regimen is not sufficient to produce facelike expertise. Rather, qualitatively different types of expertise with objects of a given geometry can arise depending on the type of training.
NetDEO: Automating network design, evolution, and optimization

With the ever-increasing number and complexity of applications deployed in data centers, the underlying network infrastructure can no longer sustain such a trend and exhibits several problems, such as resource fragmentation and low bisection bandwidth. In pursuit of a real-world applicable data center network (DCN) optimization approach that continuously maintains balanced network performance with high cost effectiveness, we design a topology independent resource allocation and optimization approach, NetDEO. Based on a swarm intelligence optimization model, NetDEO improves the scalability of the DCN by relocating virtual machines (VMs) and matching resource demand and availability. NetDEO is capable of (1) incrementally optimizing an existing VM placement in a data center; (2) deriving optimal deployment plans for newly added VMs; and (3) providing hardware upgrade suggestions and allowing the DCN to evolve as the workload changes over time. We evaluate the performance of NetDEO using realistic workload traces and simulated large-scale DCN under various topologies.
Double Configuration Checking in Stochastic Local Search for Satisfiability

Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on extensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.
Effect of Stacking Sequence on the Notched Strength of Laminated Composites

Abstract : The effect of laminate stacking sequence on the notched and unnotched tensile strength of quasi-isotropic graphite/epoxy laminates is investigated. Notch geometry includes circular holes and center cracks. Experimental data is presented for two different stacking sequences of quasi-isotropic graphite/epoxy laminates. One stacking sequence produces large interlaminar tensile stresses along straight free-edges of a tensile coupon, while the second yields compression at the same free-edges. Three different notch sizes are used for the circular hole and center crack in conjunction with both stacking sequences. Each data point is represented by a minimum of fifteen replicates so that statistical distributions of notched and unnotched strengths can be obtained. The experimental data indicates that unnotched tensile strength is reduced by the presence of interlaminar tensile stresses at the straight free-edge, while notched strength appears to be independent of stacking sequence for notch sizes which produce tensile failure prior to delamination at the straight free-edge. Experimental results are also compared against theoretical notch strengths obtained from the average stress criterion and the point stress criterion, two recently developed criteria for predicting notch strength of laminated composites.
Towards integration of web data into a coherent educational data graph

Personalisation, adaptation and recommendation are central aims of Technology Enhanced Learning (TEL) environments. In this context, information retrieval and clustering techniques are more and more often applied to filter and deliver learning resources according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the available data, such as metadata about learning resources as well as users. However, quantity and quality of both is still limited. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. In this paper, we propose a large-scale educational dataset which has been generated by exploiting Linked Data methods together with clustering and interlinking techniques to extract import and interlink a wide range of educationally relevant data. We also introduce a set of reusable techniques which were developed to realise scalable integration and alignment of Web data in educational settings.
A new CBIR approach based on relevance feedback and optimum-path forest classification

Recently some CBIR approaches have shown the use of relevance feedback to train a pattern classiﬁer to select relevant images for retrieval. This paper revisits this strategy by using an optimum-path forest (OPF) classiﬁer. During relevance feedback iterations, the proposed method uses the OPF classiﬁer to decide which database images are relevant or not. Images classiﬁed as relevant are sorted and presented to the user for a new iteration. Such images are ordered according to the normalized distance using relevant and irrelevant representative images, computed previously by the OPF classiﬁer. Our experiments show that the proposed approach requires fewer iterations, being faster and more effective than methods based on SVM.
Reasoning about Provenance with OWL and SWRL Rules

Science and engineering are information intensive collaborative enterprises which have their own complex and rapidly evolving processes for creating, discovering, and analyzing digital artifacts. To support multiple contexts (Virtual Organizations), infrastructure must provide general purpose mechanisms for reasoning about processes and data. This paper considers one important aspect of this problem, recording and reasoning about provenance. The Open Provenance Model (OPM) provides a case study for how to use Semantic Web technology and rules to implement semantic metadata. This paper discusses a binding of the OPM written in OWL, with rules written in SWRL. These standards are useful, but cannot implement the entire OPM. However, the use of RDF enables the development of “hybrid” systems that use OWL, SWRL, and other semantic software, interoperating through a shared space of RDF triples.
B+-tree Index Optimization by Exploiting Internal Parallelism of Flash-based Solid State Drives

Previous research addressed the potential problems of the hard-disk oriented design of DBMSs of flashSSDs. In this paper, we focus on exploiting potential benefits of flashSSDs. First, we examine the internal parallelism issues of flashSSDs by conducting benchmarks to various flashSSDs. Then, we suggest algorithm-design principles in order to best benefit from the internal parallelism. We present a new I/O request concept, called psync I/O that can exploit the internal parallelism of flashSSDs in a single process. Based on these ideas, we introduce B+-tree optimization methods in order to utilize internal parallelism. By integrating the results of these methods, we present a B+-tree variant, PIO B-tree. We confirmed that each optimization method substantially enhances the index performance. Consequently, PIO B-tree enhanced B+-tree's insert performance by a factor of up to 16.3, while improving point-search performance by a factor of 1.2. The range search of PIO B-tree was up to 5 times faster than that of the B+-tree. Moreover, PIO B-tree outperformed other flash-aware indexes in various synthetic workloads. We also confirmed that PIO B-tree outperforms B+-tree in index traces collected inside the Postgresql DBMS with TPC-C benchmark.
Measurement and characteristic analysis of refractive index of biological medium adsorption on two-dimensional photonic crystal surface

The two-dimensional (2D) SiO2 photonic crystal (PC) is constructed with the substrate of polyester film. The PC period is 800nm, and the duty cycle is 0.5. The high refractive index coating is deposited on the surface of PC. Rigorous coupled-wave (RCWA) theory is used to analyze 2D PC narrowband reflection spectrum characteristic. A relationship model between reflection peak wavelength and medium refractive index adsorption on surface of 2D PC is established. The conclusion shows that there is a linear relationship between reflection wavelength of the PC and the refractive index of adsorption medium, with the refractive index of adsorption medium in the range of 1.3-1.8. The effects of the refractive index of deposited coating on the sensitivity of the PC biosensor are analyzed. With the increase of the refractive index of the deposited coating, the sensitivity of the sensor is increasing.
Computing Dual Time-scale Dynamic User Equilibria

This chapter presents a dual time-scale model of dynamic network traffic flows that integrates a day-to-day demand growth model with a differential variational inequality formulation of within-day Dynamic User Equilibrium (DUE); this model is compatible with a variety of network loading models. The differential variational inequality formulation are given for within-day DUE advantages: (1) the differential variational inequality may be very easily analyzed using the minimum principle from optimal control theory; (2) the fixed point algorithm is able to consider non-differential and non-analytic path delay operators; and (3) the rapidly growing literature on different variational inequalities will likely yield additional computational tools in the years ahead that may be exploited to find DUE flows.
A way to account for models in image analysis illustrated by motion extraction

Image analysis requires adequate models, i.e. efficacious symbolic representations of a priori knowledge or hypotheses. These are expressed in terms of basic structural entities (grey-values, derivatives, etc.), defined by a conventional preprocessing of image data. That such entities are in turn subject to models is, however, much less in focus. This article argues in favour of a manifest segregation of task-based models versus image-based models, and of maintaining a transparent relation between the latter and the raw data. A possible approach is suggested based on topological duality (to model the data) and gauge invariance (to model the task). The procedure is applied to motion extraction. By virtue of model transparency it has indeed proven possible to obtain results for a (2+1)D benchmark sequence outperforming all existing algorithms that have been reported in a comparative study. The extraction of cardiac wall motion from an MR sequence of a canine heart is illustrated using the same method.
Named Entity Recognition for Astronomy Literature

We present a system for named entity recognition (ner) in astronomy journal articles. We have developed this system on a ne corpus comprising approximately 200,000 words of text from astronomy articles. These have been manually annotated with ∼40 entity types of interest to astronomers. We report on the challenges involved in extracting the corpus, defining entity classes and annotating scientific text. We investigate which features of an existing state-of-the-art Maximum Entropy approach perform well on astronomy text. Our system achieves an F-score of 87.8%.
Uninformative Parameters and Model Selection Using Akaike's Information Criterion

Abstract As use of Akaike's Information Criterion (AIC) for model selection has become increasingly common, so has a mistake involving interpretation of models that are within 2 AIC units (ΔAIC ≤ 2) of the top-supported model. Such models are <2 ΔAIC units because the penalty for one additional parameter is +2 AIC units, but model deviance is not reduced by an amount sufficient to overcome the 2-unit penalty and, hence, the additional parameter provides no net reduction in AIC. Simply put, the uninformative parameter does not explain enough variation to justify its inclusion in the model and it should not be interpreted as having any ecological effect. Models with uninformative parameters are frequently presented as being competitive in the Journal of Wildlife Management, including 72% of all AIC-based papers in 2008, and authors and readers need to be more aware of this problem and take appropriate steps to eliminate misinterpretation. I reviewed 5 potential solutions to this problem: 1) report all models but ignore or dismiss those with uninformative parameters, 2) use model averaging to ameliorate the effect of uninformative parameters, 3) use 95% confidence intervals to identify uninformative parameters, 4) perform all-possible subsets regression and use weight-of-evidence approaches to discriminate useful from uninformative parameters, or 5) adopt a methodological approach that allows models containing uninformative parameters to be culled from reported model sets. The first approach is preferable for small sets of a priori models, whereas the last 2 approaches should be used for large model sets or exploratory modeling.
Cavity pressure behaviour in micro-injection moulding

Process monitoring of micro injection moulding (µIM) is of crucial importance to analyse the effect of different parameter settings on the process and to assess its quality. Quality factors related to cavity pressure can provide useful information directly connected with the dynamics of the process as well as with the filling of the cavity by the polymer melt. In this paper, two parameters derived from cavity pressure data collected by a pressure sensor have been defined: pressure increase rate during filling and the integral of pressure over time (i.e. pressure work). The influence of four µIM parameters (melt temperature, mould temperature, injection speed, and packing pressure) on the two pressure-related outputs has been investigated by moulding a micro fluidic component on three different polymers (PP, ABS, PC) using the design of experiment approach. Similar trends such as the effects of a higher injection speed in decreasing the pressure work and of a lower mould temperature in decreasing pressure rate have been found for all three materials.
A phase-field study of ternary multiphase microstructures

A diffuse-interface model for microstructures with an arbitrary number of components and phases was developed from basic thermodynamic and kinetic principles and applied to the study of ternary eutectic phase transformations. Gradients in composition and phase were included in the free energy functional, and a generalized diffusion potential equal to the chemical potential at equilibrium was defined as the driving force for diffusion. Problematic pair-wise treatment of phases at interfaces and triple junctions was avoided, and a cutoff barrier was introduced to constrain phase fractions to physically meaningful values. Parameters in the model were connected to experimentally measurable quantities. Numerical methods for solving the phase-field equations were investigated. Explicit finite difference suffered from stability problems while a semi-implicit spectral method was orders of magnitude more stable but potentially inaccurate. The source of error was found to be the rich temporal dynamics of spinodal decomposition combined with large timesteps and a first-order time integrator. The error was addressed with a second-order semi-implicit Runge-Kutta time integrator and adaptive timestepping, resulting in two orders of magnitude improvement in efficiency. A diffusion-limited growth instability in multiphase thin-film systems was discovered, highlighting how ternary systems differ from binary systems, and intricate asymmetries in the processes of solidification and melting were simulated. A nucleation barrier for solidification was observed and prompted development of a Monte-Carlo-like procedure to trigger nucleation. However when solid was heated from below the melting point, premelting was observed first at phase triple junctions and then at phase boundaries with stable liquid films forming under certain conditions. Premelting was attributed to the shape and position of the metastable liquid curve, which was found to affect microstructure by creating low energy pathways through composition space. Slow diffusivity in solid relative to liquid was shown to produce solutal melting of solid below the melting point. Finally, the multiphase method was used to produce the first reported simulation of the entire transient liquid phase bonding process. The model shows promise for optimizing the bonding process and for simulating non-planar solidification interfaces. Thesis Supervisor: W. Craig Carter Title: MacVicar Fellow Professor of Materials Science and Engineering
Exploring a Differentiated Approach to Total Cost of Ownership

Purpose – The purpose of this paper is to develop a differentiated approach to the cost management system of total cost of ownership (TCO), a system based on cost data. Existing TCO‐related literature has investigated the tool from a focal buyer perspective but has consequently neglected to study TCO from an inter‐organisational perspective.Design/methodology/approach – An explorative single case‐study approach is used, involving a large, industrial Danish manufacturing company. Interviews were conducted with relevant representatives from both the purchasing division and a range of the division's suppliers and observations made.Findings – This paper indicates that a differentiated approach to TCO might be necessary, as suppliers might react negatively to the focal company presenting TCO data in negotiations. A matrix is proposed involving two dimensions: the nature of the relationship and the complexity of cost drivers.Research limitations/implications – This study involves a single case study and is ther.
Effect of Machining Parameters on Cutting Forces and Surface Roughness in Al-(1-2) Fe-1V-1Si Alloys

Two Al-alloys with compositions Al-1Fe-1V-1Si and Al-2Fe-1V-1Si were cast in the form of plates to study machinability. The paper deals with the effect of Fe on machinability of Al-Fe-V-Si alloys subjected to milling operation using high speed steel end milling cutting tool. The results show that cutting force increases with increasing depth of cut and feed rate, and decreases with increasing cutting speed. From the experimental results, it is observed that cutting force is most significantly affected by the depth of cut, followed by feed rate and cutting speed. The studies also underline the fact that surface finish improves with decrease in feed rate and an increase in cutting speed. The optimum cutting speed zone lies in the range 125 to 175 m/min for machining of above Al alloys, where cutting forces are almost independent of cutting speed. High speed, low feed rate, and low depth of cut are recommended for better surface finish. The chips formed were also observed. It is observed that as Fe content increases from 1% to 2%, cutting forces increase appreciably with increase in the depth of cut, while for other parameters cutting forces are relatively stable.