Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a radially inhomogeneous medium and construction of the aperture antennas on its basis

A method of constructing and calculating of radially inhomogeneous dielectric is presented. A cylindrical Mikaelianlens (ML) antenna composed from stacked flower-like dielectric layers is proposed. The plane-wave expansion method (PEM) for calculating the photonic crystals (PC) is applied to investigate frequency and polarization properties of the inhomogeneous dielectric. The proposed methods were verified by numerical simulations.
2D image classification for 3D anatomy localization: employing deep convolutional neural networks

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs — heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was ≥0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.
Research on Elman feedback Process Neural Network and its applications

To address the drawbacks of slow convergenceand low accuracy of Double Parallel Feed-forward ProcessNeural Networks with output as function, an Elman-stylefeedback process neural network model with output as function is proposed. The learning algorithm of this model is given and the effectiveness of this model is proved by a non-linear system identification problem.
Pictorial structure based people detection and pose estimation in videos

An open vision problem is to detect the people and automatically estimate the body configuration. Finding people and localizing their body parts is hard because people can appear in variety of clothes, pose and they can move unpredictable. Numerous solution and methods have been developed over the years and often this address some special cases. One proposed direction to solve the non-rigid object detection problem is the Pictorial Structure based methods.
Integrating Planning and a Rule-Based System for Controlling a Robot Soccer Team

There are many potential field techniques to control multiple robots. Among them are Arkin's Potential Field, Harmonic Potential Field and other ones based on the solution of the Boundary Value Problem (BVP). Most of them have been applied to control just one robot. In this paper, a modification in a technique of Potential Field based on the solution of BVP is proposed for controlling multiple robots. This modification is called Locally Oriented Potential Field (LOPF) and it overcomes the drawback of BVP based techniques when they are applied for controlling multi robots. Besides, a rule based system has been proposed to characterize the functionality of each robot for realizing an specific task. This is necessary for that the behavior of the equip members not be competitive but collaborative. It will be shown that LOPF is very appropriated to control robot soccer teams, in which each robot has the necessity of adapting its behavior during a game. Furthermore, some experiment results are presented to show the efficiency of the proposed technique.
A Rating Scale for Drug-Induced Akathisia

A rating scale for drug-induced akathisia has been derived that incorporates diagnostic criteria for pseudoakathisia, and mild, moderate, and severe akathisia. It comprises items for rating the observable, restless movements which characterise the condition, the subjective awareness of restlessness, and any distress associated with the akathisia. In addition, there is an item for rating global severity. A standard examination procedure is recommended. The inter-rater reliability for the scale items (Cohen's x) ranged from 0.738 to 0.955. Akathisia was found in eight of 42 schizophrenic inpatients, and nine had pseudoakathisia, where the typical sense of inner restlessness was not reported.
Output Changes and Inflationary Bias in Transition

Mismeasurement of inflation is likely to be more severe in a transition economy than in a more stable environment. Reasonable estimates of the size of the inflationary bias in the Czech Republic suggest that conventionally reported declines in real output and living standards during the transition may be a statistical artifact rather than a real phenomenon.
Export-Led Growth: A Survey of the Empirical Literature and Some Noncausality Results

This paper continues the investigation of Giles and Williams (2000) on export-led growth (ELG). In the first part, we surveyed the empirical export-led growth literature; it was evident that Granger non-causality tests are commonly applied as a test for ELG. In this paper, we explore the sensitivity of the test for exclusions restrictions often used as the Granger non-causality test for ELG by reconsidering two applications: Oxley's (1993) study for Portugal and Henriques and Sadorsky's (1996) analysis for Canada. We focus on the robustness of the method adopted to deal with non-stationarity, including the choice of deterministic trend degree. We show that different noncausality outcomes are easy to obtain, and consequently we recommend that readers interpret the empirical ELG literature with care. Our analysis also highlights the importance of examining the robustness of Granger non-causality test results to avoid spurious outcomes in applications.
Forensic Readiness for SCADA/ICS Incident Response

The actions carried out following any cyber-attack are vital in limiting damage, regaining control and determining the cause and those responsible. Within SCADA and ICS environments there is certainly no exception. Critical National Infrastructure (CNI) relies heavily on SCADA systems to monitor and control critical processes. Many of these systems span huge geographical areas and contain thousands of individual devices, across an array of asset types. When an incident occurs, those assets contain forensic artefacts, which can be thought of as any data that provides explanation to the current state of the SCADA system. Knowing what devices exist within the network and the tools and methods to retrieve data from them are some of the biggest challenges for incident response within CNI. This paper aims to identify those assets and their forensic value whilst providing the tools needed to perform data acquisition in a forensically sound manner. It will also discuss the key stages in which the incident response process can be managed.
Light Generation and Harvesting in a van der Waals Heterostructure

Two-dimensional (2D) materials are a new type of materials under intense study because of their interesting physical properties and wide range of potential applications from nanoelectronics to sensing and photonics. Monolayers of semiconducting transition metal dichalcogenides MoS2 or WSe2 have been proposed as promising channel materials for field-effect transistors. Their high mechanical flexibility, stability, and quality coupled with potentially inexpensive production methods offer potential advantages compared to organic and crystalline bulk semiconductors. Due to quantum mechanical confinement, the band gap in monolayer MoS2 is direct in nature, leading to a strong interaction with light that can be exploited for building phototransistors and ultrasensitive photodetectors. Here, we report on the realization of light-emitting diodes based on vertical heterojunctions composed of n-type monolayer MoS2 and p-type silicon. Careful interface engineering allows us to realize diodes showing rectification and light emission from the entire surface of the heterojunction. Electroluminescence spectra show clear signs of direct excitons related to the optical transitions between the conduction and valence bands. Our p–n diodes can also operate as solar cells, with typical external quantum efficiency exceeding 4%. Our work opens up the way to more sophisticated optoelectronic devices such as lasers and heterostructure solar cells based on hybrids of 2D semiconductors and silicon.
Alignment of three-dimensional point clouds using combined descriptors

This paper presents a new methodology for aligning three-dimensional (3D) models of objects, based on point correspondences. In this case, objects are modelled as 3D point clouds. The proposed methodology considers pairs of such point clouds and firstly down-samples them in order to further improve processing time. Then, corresponding points are allocated between the processed point clouds, by using a novel combinational descriptor scheme. Finally, a global transformation is estimated from the inliers of the obtained correspondences. This transformation is used to align the two point clouds. The proposed methodology was applied to five pairs of large scale 3D point clouds. Results indicate that the proposed scheme achieved satisfactory alignment accuracy for all tested data pairs.
Situating multimodal learning analytics

Author(s): Worsley, M; Abrahamson, D; Blikstein, P; Grover, S; Schneider, B; Tissenbaum, M | Abstract: © ISLS. The digital age has introduced a host of new challenges and opportunities for the learning sciences community. These challenges and opportunities are particularly abundant in multimodal learning analytics (MMLA), a research methodology that aims to extend work from Educational Data Mining (EDM) and Learning Analytics (LA) to multimodal learning environments by treating multimodal data. Recognizing the short-term opportunities and longterm challenges will help develop proof cases and identify grand challenges that will help propel the field forward. To support the field's growth, we use this paper to describe several ways that MMLA can potentially advance learning sciences research and touch upon key challenges that researchers who utilize MMLA have encountered over the past few years.
Contextually-Mediated Semantic Similarity Graphs for Topic Segmentation

We present a representation of documents as directed, weighted graphs, modeling the range of influence of terms within the document as well as contextually determined semantic relatedness among terms. We then show the usefulness of this kind of representation in topic segmentation. Our boundary detection algorithm uses this graph to determine topical coherence and potential topic shifts, and does not require labeled data or training of parameters. We show that this method yields improved results on both concatenated pseudo-documents and on closed-captions for television programs.
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.
PolyMerge: a fast approach for hex-dominant mesh generation

classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. SIGGRAPH 2014, August 10 – 14, 2014, Vancouver, British Columbia, Canada. 2014 Copyright held by the Owner/Author. ACM 978-1-4503-2958-3/14/08 PolyMerge: a fast approach for hex-dominant mesh generation
Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last

In-memory database management systems (DBMSs) are a key component of modern on-line analytic processing (OLAP) applications, since they provide low-latency access to large volumes of data. Because disk accesses are no longer the principle bottleneck in such systems, the focus in designing query execution engines has shifted to optimizing CPU performance. Recent systems have revived an older technique of using just-in-time (JIT) compilation to execute queries as native code instead of interpreting a plan. The state-of-the-art in query compilation is to fuse operators together in a query plan to minimize materialization overhead by passing tuples efficiently between operators. Our empirical analysis shows, however, that more tactful materialization yields better performance. We present a query processing model called "relaxed operator fusion" that allows the DBMS to introduce staging points in the query plan where intermediate results are temporarily materialized. This allows the DBMS to take advantage of inter-tuple parallelism inherent in the plan using a combination of prefetching and SIMD vectorization to support faster query execution on data sets that exceed the size of CPU-level caches. Our evaluation shows that our approach reduces the execution time of OLAP queries by up to 2.2× and achieves up to 1.8× better performance compared to other in-memory DBMSs.
Neither Snow nor Rain, but Maybe Anthrax: Bioterrorism and Absentee Ballots in New Jersey

415 MANY READERS OF THIS JOURNAL probably know that one consequence of the September 11, 2001 attack on the World Trade Center was the postponement of the New York City primary election. Fewer may be aware that the anthrax-laden letters sent by bioterrorists the next month also had electoral consequences, in this case on municipal elections in New Jersey. When a postal facility in Hamilton, New Jersey, was closed on October 18, 2001, because of the anthrax crisis, delivery of mail in the area was slower than usual for many weeks, including the period leading up to the November 6 state and local elections. New Jersey law defines the deadline for absentee ballots by the time they are received, not by when they are mailed or postmarked. Because of the slow delivery of mail, several absentee ballots were late that in normal circumstances would have been delivered by the deadline. As it happened, the township council election in Lawrence Township was extremely close—close enough that counting absentee ballots that were late by reason of the anthraxrelated delay could well change the outcome.1 And, as it turned out, did change the outcome. Two seats on the council were up for election. Each voter could vote for two candidates, and the two highest vote-getters would be elected. Two Democrats—Michael Powers and Mark Sebastian—ran against two Republicans, Richard Miller and Mark Holmes. When the votes cast on election day and the absentee ballots received on or before election day—the statutory deadline—were counted, the results were as follows:
Moderators of the relationship between person-job fit and subjective well-being among asian employees.

The moderating effects of core self-evaluation and person-organization (P-O) fit on the relationship between person-job (P-J) fit and subjective well-being were examined using cross-sectional surveys. Most of the participants of the studies were Asian employees in the United States. In Study 1 (N 90), the interaction between core self-evaluation and needs-supplies (N-S) fit significantly predicted happiness but not depression. Workers with high levels of core self-evaluation were less affected by N-S fit, whereas employees who had low core selfevaluation displayed a reduced level of happiness when there was a lack of N-S fit. In Study 2 (N 90), P-O fit moderated the relationship between P-J fit and happiness. Employees with high P-O fit displayed more dramatic increases in happiness than employees with low P-O fit as P-J fit increased. These studies suggest that core self-evaluation and P-O fit may moderate the relationship between P-J fit and psychological well-being. In addition, facets of P-J fit showed differential relationships with mental health in a pattern consistent with previous research. Implications for research and occupational health interventions were discussed.
Teacher and Student Language Practices and Ideologies in a Third-Grade Two-Way Dual Language Program Implementation

This article provides an in-depth exploration of the language ecologies of two classrooms attempting to implement a two-way dual language (TWDL) program and its mediating conditions. Drawing on ethnographic methods and a sociocultural understanding of language, we examined both teachers’ and students’ language ideologies and language practices, including the use of Spanish, English, and code-switching. The English language arts teacher adhered to strict language separation as dictated by the TWDL model, while the Spanish language arts teacher instructed in both English and Spanish to accommodate standardized test preparation. Students enacted agency to engage in their hybrid language practices. Despite the multiplicity and, at times, contradictory ideologies embodied and articulated by both teachers, the overarching dominant language ideology of English superiority was present and powerful. We discuss implications for dual language implementation, including the role of standardized testing, students as language policy makers, and teacher (mis)alignment between articulated and embodied ideologies.
Node-Disjoint Multipath Routing and Distributed Cross-Layer QoS Guarantees in Mobile Ad hoc Networks

In this paper, a novel distributed cross-layer QoS (DCLQ) architecture based on node disjoint multipath routing is proposed to provide QoS guarantees for realtime traffic and best-effort traffic in mobile ad hoc networks. Without any extra control overhead, DCLQ can schedule packets of real-time flows according to their per-hop QoS requirements. DCLQ implements per-hop delay QoS-aware priority scheduling and QoS consideration of MAC layer to ensure that real-time flows and best-effort flows can achieve their desired service level
Assembling Fragments Into a Lens: A Review, Critique, and Proposed Research Agenda for the Organizational Work Climate Literature

Work climates exert an important influence on organizations and the people who work in them. For more than half a century, scholars have sought to understand their antecedents and consequences. However, in recent years, this literature has become fragmented and somewhat adrift. This article attempts to remedy this by reviewing existing research related to organizational work climates and providing a review and critique of the current state of knowledge. Furthermore, the authors seek to assemble the individual pieces into a unified lens capable of identifying overarching themes and challenges facing researchers. Finally, the authors turn this lens to the future, so as to provide a clearer view of some promising avenues for research opportunities and potential for reintegrating the field.
Older children have a greater chance to be accepted to gifted student programmes

Selection to programmes for gifted students in Israel, performed in the second grade, relies on raw ability and achievement test scores, irrespective of age, thereby ignoring the well-known effect of within-grade age differences on test scores. Employing the entire cohort of third graders of legal age (67,366 students, 1.4% of whom were enrolled in a gifted programme) in 2011, this study examined the relationship between chronological age and the probability of acceptance to programmes for the gifted in Israel. The results reveal an almost perfect correlation between chronological age and the probability of being selected for a gifted programme (r2 = 0.92): older students have approximately 3.5 times greater chance of acceptance than younger students. Reliance on raw ability and achievement test scores, unadjusted for age, in selection to gifted students programmes is thus shown to be biased in favour of older students and to unintentionally discriminate against younger students.
Common logic errors made by novice programmers

Errors in the logic of a program (sometimes referred to as semantic errors) can be very frustrating for novice programmers to locate and resolve. Developing a better understanding of the kinds of logic error that are most common and problematic for students, and finding strategies for targeting them, may help to inform teaching practice and reduce student frustration. In this paper we analyse 15,000 code fragments, created by novice programming students, that contain logic errors, and we classify the errors into algorithmic errors, misinterpretations of the problem, and fundamental misconceptions. We find that misconceptions are the most frequent source of logic errors, and lead to the most difficult errors for students to resolve. We list the most common errors of this type as a starting point for designing specific teaching interventions to address them.
Tourism as an ordering

This article offers an entirely different way of understanding the origins, significance and relational materialism of tourism. Borrowing from the emergent sociology of ordering, which combines aspects of Foucault’s notion of governance with ‘post-ANT’ insistence on relational materialism, it shows how tourism came to be a heterogenous assemblage ‘at large’ in the world, remaking the world anew as a touristic world; a world to be seen, felt, interpellated and travelled. In doing so it underlines the paradoxical significance of nationalism as an ordering with clear implications for the emergence of the tourism ordering. It also, at last, invites research on the relationality of technologies and objects of tourism as well as key individuals whose dreams of tourism were essential to the history of the tourism ordering. Seen as an ordering this conception of tourism offers an alternative to structuralist accounts that have long influenced and inhibited tourist studies. It also explains why tourism was so hard to define, until now.