Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation and standardization of Kveim test suspensions prepared from two human sarcoid spleens.

Single lots of a Chase-Siltzbach type I Kveim test material from each of two sarcoid spleens and designated lot 5 of spleen K12 and lot 1 of spleen K13 have been validated alongside a single lot (lot 10) of a 'standard' suspension provided by Dr L. E. Siltzbach and prepared identically from the spleen of patient J (SPLEEN J) in New York. Additionally, a half-dilution of lot 5, K12, was included in this comparison. The reactivity of each suspension was assessed among patients with active and inactive sarcoidosis. The selectivity of each suspension for sarcoidosis was assessed similarly by comparison with results in patients with active and quiescent pulmonary parenchymal tuberculosis and in healthy subjects. All patients were closely matched and two Kveim tests were made in each subject according to a prearranged statistical design. The reactivity of the K12, K12 1/2 dilution, and K13 suspensions among patients with active and inactive sarcoidosis was closely similar to that with the 'standard' S10 suspension and in accordance with the expected proportions of reactions in patients at different stages of sarcoidosis. The K12, K13, and 'standard' S10 suspensions yielded a negligible proportion of positive reactions among patients with active and quiescent pulmonary tuberculosis and among healthy subjects: thus, as judged by these tests each suspension showed a high degree of selectivity for sarcoidosis. The results of this validation study are discussed in relation to the results of other studies in which lots 5 and 14 of K12 and early and late batches of a suspension prepared from another sarcoid spleen at the Commonwealth Serum Laboratories designated CSL and provided by Dr T.H. Hurley in Melbourne were employed. Using lot 5 of K12 positive reactions were found in an appreciable proportion of patients with Crohn's disease, ulcerative colitis, and tuberculous lymphadenitis. A closely similar rate of positive reactions was encountered among patients with Crohn's disease following tests with batch 0025 of CSL suspension and with another lot (lot 14) derived from spleen K12. A close concordance of results was obtained with lot 5(K12) and with batch 0042 CSL among patients with ulcerative colitis, but at a lower rate of reactivity. We conclude that positive reactions also occur in some diseases other than sarcoidosis and consider that the difficulties in determining the criteria for an acceptable test suspension become increasingly apparent as additional Kveim tests are made with one particular lot and with seqential lots of material from a 'validated' tissue source.
FIRST VISUAL ORBIT FOR THE PROTOTYPICAL COLLIDING-WIND BINARY WR 140

Wolf–Rayet (WR) stars represent one of the final stages of massive stellar evolution. Relatively little is known about this short-lived phase and we currently lack reliable mass, distance, and binarity determinations for a representative sample. Here we report the first visual orbit for WR 140 (= HD193793), a WC7+O5 binary system known for its periodic dust production episodes triggered by intense colliding winds near periastron passage. The Infrared-Optical Telescope Array and Center for High Angular Resolution Astronomy interferometers resolved the pair of stars in each year from 2003 to 2009, covering most of the highly eccentric, 7.9 year orbit. Combining our results with the recently improved double-line spectroscopic orbit of Fahed et al., we find the WR 140 system is located at a distance of 1.67 ± 0.03 kpc, composed of a WR star with MWR = 14.9 ± 0.5 M☉ and an O star with MO = 35.9 ± 1.3 M☉. Our precision orbit yields key parameters with uncertainties ∼6× smaller than previous work and paves the way for detailed modeling of the system. Our newly measured flux ratios at the near-infrared H and Ks bands allow a spectral energy distribution decomposition and analysis of the component evolutionary states.
Optical flow for self-supervised learning of obstacle appearance

We introduce a novel setup of self-supervised learning (SSL), in which optical flow provides the supervised outputs. Optical flow requires significant movement for obstacle detection. The main advantage of the introduced method is that after learning, a robot can detect obstacles without moving - reducing the risk of collisions in narrow spaces. We investigate this novel setup of SSL in the context of a Micro Air Vehicle (MAV) that needs to select a suitable landing place. Initially, when the MAV flies over a potential landing area, the optical flow processing estimates a `surface roughness' measure, capturing whether there are obstacles sticking out of the landing surface. This measure allows the MAV to select a safe landing place and then land with other optical flow measures such as the divergence. During flight, SSL takes place. For each image a texton distribution is extracted (capturing the visual appearance of the landing surface in sight), and mapped to the current roughness value by a linear regression function. We first demonstrate this principle to work with offline tests involving images captured on board an MAV, and then demonstrate the principle in flight. The experiments show that the MAV can land safely on the basis of optical flow. After learning it can also successfully select safe landing spots in hover. It is even shown that the appearance learning allows the pixel-wise segmentation of obstacles.
Stream Processors and GPUs : Architectures for High Performance Computing

Architectures for parallel computing are becoming all the more essential with the increasing demands of multimedia, scientific, and engineering applications. These applications require architectures which can scale to meet their real-time constraints, with all current technology limitations in mind such as the memory gap, and power wall. While current many-core CPU architectures show an increase in performance, they still cannot achieve the necessary real-time performance required by today`s applications as they fail to efficiently utilize a large number of ALUs. New programming models, computer architecture innovations, coupled with advancements in process technology have set the foundations for the development of the next generation of supercomputers for high-performance computing (HPC). At the center of these emerging architectures are Stream Processors and Graphics Processing Units (GPUs). Over the years GPUs exhibited increased programmability that has made it possible to harvest their computational power for non graphics applications, while stream processors because of their programming model and novel design have managed to utilize a large number of ALUs to provide increased performance. The objective of this survey paper is to provide an overview of the architecture, organization, and fundamental concepts of stream processors and Graphics Processing Units.
The Dynamical Inverse Problem for Axisymmetric Stellar Systems

The standard method of modelling axisymmetric stellar systems begins from the assumption that mass follows light. The gravitational potential is then derived from the luminosity distribution, and a unique two-integral distribution function f(E,Lz) that generates the stellar density in this potential is found. We show that the gravitational potential can instead be generated directly from the velocity data in a two-integral galaxy, thus allowing one to drop the assumption that mass follows light. The rotational velocity field can also be recovered in a model-independent way. We present regularized algorithms for carrying out the inversions and test them by application to pseudo-data from a family of oblate models.
It's Not Just About the Money: Motivations for Youth Migration in Rural China

This study investigates the incentives for labor migration of youth in rural China using panel data from the Gansu Survey of Children and Families, a longitudinal study of youth in rural Gansu Province of China. We investigate the individual and altruistic economic motivations featured prominently in demographic and economic research on migration. However, we propose that the non-economic goal of personal development, a motivation suggested in numerous qualitative studies of women migrants in China and elsewhere, is also important, especially for young migrants. Analyzes indicate that, while young men and young women hold different motivations for migration, the desire for personal development is a common motivator for young migrants. Results suggest that non-economic incentives may play an important role in youth migration in rural China and that positioning in family structures shapes the susceptibility of individuals to migrate due to altruistic economic motivations.
Extracting position relations from the web

In this paper, we present a new algorithm to extract people's position in a corporation from the Web. People's position in a corporation, which the term position relation refers to, is a kind of significant competitive intelligence for enterprises. Our algorithm is based on the structural feature of position relation in Web contents, i.e., position relation is usually described in Web pages as a table or a list. In order to define the structural feature of Web contents, we first introduce a structural coefficient for each Web page. This structural coefficient is then used to generate structural file segments from Web pages. A structural file segment consists of all the candidates of position relation with a similar structure. We then employ a pattern-matching method to extract position relations from the structural file segments. Finally, we conduct experiments on a real data set containing 6028 Chinese Web pages gathered through the Baidu search engine, and evaluate the precision and recall of our approach. The experimental results show that our algorithm has a high precision over 96% as well as a recall over 87%.
Towards an autonomous pilot system for a tunnel boring machine

Modern Tunnel Boring Machines (TBMs) used in mines or for road construction often consist of several modules for the drill head and further equipment as well as wagons for carrying the mined material (dump). These machines can have a total length of up to 70 meters. In the mines, the machine has to be navigated over long stretches through the mine adits to reach the borehole. The problem is to get there without getting stuck in the galleries. Usually, these long trains are navigated manually and theodolites deal as sensory input for the teleoperator. In this paper we outline a robot system that can deal as a pilot system for such long machines for semi-autonomously driving the TBMs right to their destination. For this, the pilot robot needs to fulfil the following tasks: (1) it has to acquire an accurate 3D map of the tunnel environment, (2) plan a path from a start location to the destination and (3) guide the TBM through the tunnel. We outline the hardware and software system that we envision for our pilot system in this paper. For the hardware, we plan to deploy a Husky A200 platform which is particularly suitable for rugged terrain. For the control software we want to make use of the robot operating system ROS with its many well-implemented modules for map building, navigation and path-planning. As the system is in the planning stage at the moment, we are very interested to learn more about and share experiences and ideas of operating robots in rugged mining environments at the conference.
User Modeling Of Online Consumers: Between-Gender Differences In Click Path Data

Given people’s differences in preferences, personalities, or demographic factors, building useradaptive systems can aid in providing each individual user with the interface that helps to best achieve the desired outcome. For example, online retailers now have the possibility to offer customized Web sites to individual customers, so as to meet each customer’s specific needs. However, online retailers typically only have little data available to segment customers in real-time. In this study, we propose that observable usage data (such as click path data) can be used to distinguish female and male Web site visitors. Drawing on research in psychology and consumer behavior, we hypothesize that observable differences in Web surfing behavior can be indicative of a user’s gender. Our results show that women tend to need less time and have shorter click paths than men in online product configuration tasks, and thus confirm the utility of using observable usage data to distinguish Web site visitors based on their gender.
Computational optoelectronics as analysis and design tool

This paper gives an overview of physics-based modeling of semiconductor optoelectronic devices, which are key components in fields such as photovoltaics, solid-state lighting, communications and sensing. Despite this broad application range, some common underlying theory can be identified: accurate modeling of light-matter interaction, semi-coherent carrier transport in nanostructures, accurate calculation of electromagnetic characteristics. In this contribution, a selection of models and devices are discussed and their applicability for analysis purposes is demonstrated
CNG Method with Weighted Voting

CNG Method for Authorship Attribution. The Common N-Grams (CNG) classification method for authorship attribution (AATT) was described in [2]. The method is based on extracting the most frequent byte n-grams of size n from the training data. The n-grams are sorted by their normalized frequency, and the first L most-frequent n-grams define an author profile. Given a test document, the test profile in produced in the same way, and then the distances between the test profile and the author profiles are calculated. The test document is classified using k-nearest neighbours method with k = 1, i.e., the test document is attributed to the author whose profile is closest to the test profile. Given two profiles f1 and f2, which map n-grams from sets D1 and D2 to their respective frequencies, the distance measure between them is defined by the formula: X g2D1[D2 f1(g) f2(g) f1(g)+f2(g) 2 ! 2 = X g2D1[D2 2 · (f1(g) f2(g)) f1(g) + f2(g) 2
RF Self-Interference Reduction Techniques for Compact Full Duplex Radios

This paper describes three RF self-interference reduction techniques for full-duplex wireless links, which specifically target integration in compact radios. Concretely, a self-interference cancelling front-end, a dual-polarized antenna, and an electrical balance duplexer are proposed. Each technique offers specific benefits in terms of implementation density, self-interference rejection, bandwidth and flexibility. Depending on their characteristics, they can be adopted in different next-generation full-duplex applications and standards. All concepts are prototyped, and achieve at least 45dB of self-interference reduction over more than 10MHz bandwidth.
WEIGHTED PRINCIPAL COMPONENT ANALYSIS BASED ON FUZZY CLUSTERING

In this paper, we propose a weighted principal component analysis (WPCA) using the result of fuzzy clustering (4). The principal component analysis (PCA) (1), (7) is one widely used and well-known data analysis method. However there is a prob- lem, when the data does not have a structure that the PCA can capture we cannot obtain any satisfactory results. For the most part, this is due to the uniformity of the data structure, which means we cannot find any significant proportion or accumulated proportion for the obtained principal components. In order to solve this problem, we use the cluster structure and degree of belong- ingness of objects to clusters, which is obtained as the fuzzy clustering result. By the introduction of the pre-classification and the degree of belongingness to the data, we can transform the data into a clearer structured data, so avoiding the noise in the data. 1 Introduction Recently, PCA is the method of multivariate analysis which is the most widely used. Due to the particular feature of PCA which can represent the main tendency of an observed data compactly, PCA has been used as a method to follow up a clue when we can not see any significant structure in the data. Beside the importance of PCA as an exploratory method, several extended methods of PCA have been proposed with the viewpoint that PCA can show the ability to the full, by bringing additional considerations of the observed data into the analysis. For example, constrained PCA (CPCA) (11), (12), nonlinear PCA (10), and the time dependent principal component analysis (2) are typical examples of this. The proposed method in this paper also takes the position that the method is based on the idea that we try to bring PCA's ability into full play by introducing pre-information of the cluster structure of an observed data. The pre-information of the data is represented as weights based on a fuzzy clustering result. That is, the main difference between conven- tional PCA and WPCA based on fuzzy clustering is the introduction of cluster degree as weights on clusters of the observation space. The weights are represented by the product of the degree of belongingness of objects to fuzzy clusters with respect to the fuzzy clusters when an object is fixed. Due to a property of the algebraic product and the condition of the degree of belongingness, these weights can show how much the data has the clustering structure. According to weighted linear combination, we can show that the estimates of principal components for WPCA based on fuzzy clustering are obtained in a similar way as in conventional PCA. The time dependent principal component analysis (2) also intro- duces weights to PCA, however, the analysis dose not use the cluster structure of the data. Moreover, a possibilistic regression model (13), the switching regression model (6), and the weighted regression analysis based on fuzzy clustering (9) are among several examples of research that introduced the concept of fuzziness to the regression model.
Improving performance of semantic web services discovery with a hybrid matchmaker

The number of available Web services, nowadays, is growing rapidly due their potential in many fields. As a result, the discovery process becomes a challenging issue. Traditional syntactic keywords based discovery techniques are inefficient as they fail to recognize similarities between Web services capabilities. Thus, the most promising approach seems to be the introduction of semantic technologies. Existing Web services semantic-based discovery techniques rely only on subsumption relations between ontological concepts. On this basis, we present in this paper a novel discovery framework which performs a hybrid matchmaking that combines several similarity techniques to efficiently discover the most appropriate services to user requirements. Our approach involves formatting and extracting useful semantic information from user query expressed in natural language. We implemented our proposed approach in a prototype system called OWLS-LLCR. We also present some evaluation results of our system compared to the OWLS-MX matchmaker. Our experimental results validate the efficiency and the scalability of our approach.
The Cyclohedron Test for Finding Periodic Genes in Time Course Expression Studies

The problem of finding periodically expressed genes from time course microarray experiments is at the center of numerous efforts to identify the molecular components of biological clocks. We present a new approach to this problem based on the cyclohedron test, which is a rank test inspired by recent advances in algebraic combinatorics. The test has the advantage of being robust to measurement errors, and can be used to ascertain the significance of top-ranked genes. We apply the test to recently published measurements of gene expression during mouse somitogenesis and find 32 genes that collectively are significant. Among these are previously identified periodic genes involved in the Notch/FGF and Wnt signaling pathways, as well as novel candidate genes that may play a role in regulating the segmentation clock. These results confirm that there are an abundance of exceptionally periodic genes expressed during somitogenesis. The emphasis of this paper is on the statistics and combinatorics that underlie the cyclohedron test and its implementation within a multiple testing framework.
Coaching at-risk youth in a school within a socially challenging environment

The purpose of this study was to implement group coaching in a school setting and examine the participants’ experiences. Participants were all males (age 12–16 years), primarily with a Middle Eastern family background and from a socioeconomically deprived area. A 2-year intervention with regular coaching counselling during school hours was delivered. Qualitative longitudinal interviews (n = 6) and long-term fieldwork found that group coaching enhanced social cohesion and social resilience. The study concludes that group coaching can be a valid tool for addressing at-risk youth in schools. Even though this study was limited to one school in a certain context, the implications can be important knowledge in other settings. An important practical finding was that bodily experience incorporated as part of the coaching sessions was highlighted as beneficial, as well as the use of a group approach. This study is the first to include at-risk youth in a coaching intervention structured through an experiential and existentially oriented approach (third-generation coaching).
Development of Small Footprint Korean Large Vocabulary Speech Recognition for Commanding a Standalone Robot

The work in this paper concerns a small footprint Acoustic Model (AM) and its use in the implementation of a Large Vocabulary Isolated Speech Recognition (LVISR) system for commanding a robot in the Korean language, which requires about 500KB of memory. Tree-based state clustering was applied to reduce the number of total unique states, while preserving its original performance. A decision tree induction method was developed for the tree-based state clustering. For this method, a binary question set, measurement function and stopping criterion were devised. A phoneme set consisting of 38 phonemes was defined for the implementation of small footprint Korean LVISR. Further reduction in memory requirement was achieved through integer arithmetic operation. The best multiplication factor was determined for this operation. As a result, we successfully developed a small footprint Korean LVISR that requires memory space about 500KB.
Markerless Augmented Reality on Mobile Devices with Integrated Sensors

In this paper we investigate the use of integrated sensors present in many modern mobile devices to aid the approximation of pose of a smart-phone device relative to a previously unknown object on a flat surface. Pose estimation is necessary to provide a believable Augmented Reality (AR) environment. We avoid full 3-D visual tracking in favour of sensor-aided pose estimation to minimise computational complexity since mobile devices have relatively low processing capabilities. We show that by making a number of reasonable assumptions based on the typical range of motions a mobile AR device may experience, it is possible to create a believable AR environment by combining data from an integrated accelerometer with 2-D visual tracking. For each two consecutive image frames, we determine the velocity of interest points using FAST detection and sparse optical flow tracking. Relative translation and scale are determined from the point velocities and orientation is determined from accelerometer data. We present a number of experiments that evaluate the system’s speed and accuracy for a certain set of movements and show that our system is capable of real-time performance with up to 12 frames per second.