Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech and language therapy/pathology: perspectives on a gendered profession.

BACKGROUND The speech and language therapy/pathology (SLT/SLP) profession is characterized by extreme 'occupational sex segregation', a term used to refer to persistently male- or female-dominated professions. Men make up only 2.5% of all SLTs in the UK, and a similar imbalance is found in other countries. Despite calls to increase diversity in the allied health professions more generally, research into the reasons for occupational sex segregation and gender as a potential key factor remains scarce. AIMS This study aims to explore the potential role of gender/gendered discourses in people's decision to pursue a career in SLT/SLP. It seeks to illustrate how gendered assumptions/expectations/discourses continue to construct SLT as a 'gendered' profession, and to make some recommendations in this area for SLT recruitment and practice. METHODS & PROCEDURES The study adopted a qualitative design which elicited research participants' views, knowledge and experiences (in their own words) in relation to the research questions. Data collection involved two iterative phases: a preliminary data phase--which involved semi-structured interviews with newly qualified SLT graduates and practising SLTs, and the completion of questionnaires by undergraduate SLTs--and a main/focus group phase. In the focus group phase reported in this paper, six focus groups in total were held with SLTs, teachers of SLT, and careers advisors in London, UK. The data were analysed qualitatively using grounded theory principles, thematic analysis and discourse analysis. OUTCOMES & RESULTS The findings extend our knowledge and understanding of gender as a parameter of people's motivations and perceptions, which can influence their choice of career (e.g. as regards pay and flexibility). The findings also show that discourses around women as carers, nurturers and communicators constitute key ways through which the SLT profession continues to be constructed as 'women's work'. The topic of structural gender inequalities in the profession was also discussed in the data. Some recommendations for change, with implications for SLT recruitment and practice, were made by the participants themselves. CONCLUSIONS & IMPLICATIONS Gender imbalance in SLT needs to be researched further in order to help address inequalities, re-evaluate professional practices and develop service delivery in the profession. This area also needs to be researched via analysis that goes beyond gender distribution in numerical terms to consider the complex perceptions or discourses around gender and work. Cross-disciplinary and comparative perspectives in future research would also be fruitful.
iPSC technology-Powerful hand for disease modeling and therapeutic screen

Cardiovascular and neurodegenerative diseases are major health threats in many developed countries. Recently, target tissues derived from human embryonic stem (hES) cells and induced pluripotent stem cells (iPSCs), such as cardiomyocytes (CMs) or neurons, have been actively mobilized for drug screening. Knowledge of drug toxicity and efficacy obtained using stem cell-derived tissues could parallel that obtained from human trials. Furthermore, iPSC disease models could be advantageous in the development of personalized medicine in various parts of disease sectors. To obtain the maximum benefit from iPSCs in disease modeling, researchers are now focusing on aging, maturation, and metabolism to recapitulate the pathological features seen in patients. Compared to pediatric disease modeling, adult-onset disease modeling with iPSCs requires proper maturation for full manifestation of pathological features. Herein, the success of iPSC technology, focusing on patient-specific drug treatment, maturation-based disease modeling, and alternative approaches to compensate for the current limitations of patient iPSC modeling, will be further discussed. [BMB Reports 2015; 48(5): 256-265]
Novel Marker for the Onset of Frontotemporal Dementia: Early Increase in Activity-Dependent Neuroprotective Protein (ADNP) in the Face of Tau Mutation

Tauopathy, a major pathology in Alzheimer's disease, is also found in ∼50% of frontotemporal dementias (FTDs). Tau transcript, a product of a single gene, undergoes alternative splicing to yield 6 protein species, each with either 3 or 4 microtubule binding repeat domains (tau 3R or 4R, associated with dynamic and stable microtubules, respectively). While the healthy human brain shows a 1/1 ratio of tau 3R/4R, this ratio may be dramatically changed in the FTD brain. We have previously discovered that activity-dependent neuroprotective protein (ADNP) is essential for brain formation in the mouse, with ADNP+/− mice exhibiting tauopathy, age-driven neurodegeneration and behavioral deficits. Here, in transgenic mice overexpressing a mutated tau 4R species, in the cerebral cortex but not in the cerebellum, we showed significantly increased ADNP expression (∼3-fold transcripts) in the cerebral cortex of young transgenic mice (∼disease onset), but not in the cerebellum, as compared to control littermates. The transgene-age-related increased ADNP expression paralleled augmented dynamic tau 3R transcript level compared to control littermates. Blocking mutated tau 4R transgene expression resulted in normalization of ADNP and tau 3R expression. ADNP was previously shown to be a member of the SWItch/Sucrose NonFermentable (SWI/SNF) chromatin remodeling complex. Here, Brahma (Brm), a component of the SWI/SNF complex regulating alternative splicing, showed a similar developmental expression pattern to ADNP. Immunoprecipitations further suggested Brm-ADNP interaction coupled to ADNP - polypyrimidine tract-binding protein (PTB)-associated splicing factor (PSF)-binding, with PSF being a direct regulator of tau transcript splicing. It should be noted that although we have shown a correlation between levels of ADNP and tau isoform expression three months of age, we are not presenting evidence of a direct link between the two. Future research into ADNP/tau relations is warranted.
THE ENIGMATIC PAIR OF DWARF GALAXIES LEO IV AND LEO V: COINCIDENCE OR COMMON ORIGIN?

We have obtained deep photometry in two 1° × 1° fields covering the close pair of dwarf spheroidal galaxies Leo IV and Leo V and part of the area in between. From the distribution of likely red giant branch (RGB) and horizontal-branch (HB) stars in the data set, we find that both Leo IV and Leo V are significantly larger than indicated by previous measurements based on shallower data. With a half-light radius of rh = 4. ′6 ± 0. ′8 (206 ± 36 pc) and rh = 2. ′6 ± 0. ′6 (133 ± 31 pc), respectively, both systems are now well within the physical size bracket of typical dwarf spheroidal Milky Way satellites. Both are also found to be significantly elongated with an ellipticity of ϵ ≃ 0.5, a characteristic shared by many of the fainter (MV> − 8) Milky Way dwarf spheroidals. The large spatial extent of our survey allows us to search for extra-tidal features in the area between the two dwarf galaxies with unprecedented sensitivity. The spatial distribution of candidate RGB and HB stars in this region is found to be non-uniform at the ∼3σ level. Interestingly, this substructure is aligned along the direction connecting the two systems, indicative of a possible “bridge” of extra-tidal material. Fitting the stellar distribution with a linear Gaussian model yields a significance of 4σ for this overdensity, a most likely FWHM of ∼16 arcmin, and a central surface brightness of ≃32 mag arcsec−2. We investigate different scenarios to explain the close proximity of Leo IV and Leo V, and the possible tidal bridge between them. Orbit calculations demonstrate that the two systems cannot share the exact same orbit, while a compromise orbit does not approach the Galactic center more than ∼160 kpc, rendering it unlikely that they are remnants of a single disrupted progenitor. A comparison with cosmological simulations shows that a chance collision between unrelated subhalos is negligibly small. Given their relative distance and velocity, Leo IV and Leo V could be a bound “tumbling pair,” if their combined mass exceeds (8 ± 4) × 109 M☉. The scenario of an internally interacting pair that fell into the Milky Way together appears to be the most viable explanation for this close celestial companionship.
Semantic Decomposition and Reconstruction of Compound Buildings with Symmetric Roofs from LiDAR Data and Aerial Imagery

3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify this problem, this article proposes a novel approach to automatically decompose the compound buildings with symmetric roofs into semantic primitives by exploiting local symmetry contained in the building structure. In this approach, the proposed decomposition allows the overlapping of neighbor primitives and each decomposed primitive can be represented as a parametric form, which simplify the complexity of the building reconstruction and facilitate the integration of LiDAR data and aerial imagery into a parameters optimization process. The proposed method starts by extracting isolated building regions from the LiDAR point clouds. Next, point clouds belonging to each compound building are segmented into planar patches to construct an attributed graph, and then the local symmetries contained in the attributed graph are exploited to automatically decompose the compound buildings into different semantic primitives. In the final step, 2D image features are extracted depending on the initial 3D primitives generated from LiDAR data, and then the compound building is reconstructed using constraints from LiDAR data and aerial imagery by a nonlinear least squares optimization. The proposed method is applied to two datasets with different point densities to show that the complexity of building reconstruction can be reduced considerably by decomposing the compound buildings into semantic primitives. The experimental results also demonstrate that the traditional model driven methods can be further extended to the automated reconstruction of compound buildings by using the proposed semantic decomposition method.
Suppliers and Environmental Innovation: The Automotive Paint Process

Automobile assembly plants worldwide face increasing pressures in the environmental arena. How a plant responds to these issues has significant implications for the cost and quality of plant operations. This paper uses three case studies of US assembly plants to examine the role of partnerships between original equipment manufacturers (OEMs) and their suppliers in improving the environmental performance of manufacturing operations. We find that strong partnerships with suppliers, supported by appropriate incentive systems, were a significant element of the successful application of innovative environmental technologies. Supplier staff members were an important part of achieving environmental performance improvements while maintaining production quality and cost goals. The management factors influencing the extent and nature of supplier involvement are identified. The results of this work point to the importance of suppliers in addressing the manufacturing challenges of the future.
Surface Registration via Foliation

This work introduces a novel surface registration method based on foliation. A foliation decomposes the surface into a family of closed loops, such that the decomposition has local tensor product structure. By projecting each loop to a point, the surface is collapsed into a graph. Two homeomorphic surfaces with consistent foliations can be registered by first matching their foliation graphs, then matching the corresponding leaves. ,,This foliation based method is capable of handling surfaces with complicated topologies and large non-isometric deformations, rigorous with solid theoretic foundation, easy to implement, robust to compute. The result mapping is diffeomorphic. Our experimental results show the efficiency and efficacy of the proposed method.
Code-Partitioning Gossip

Code-Partitioning Gossip (CPG) is a novel technique to facilitate implementation and analysis of gossip protocols. A gossip exchange is a pair-wise transaction between two nodes; a gossip system executes an endless sequence of exchanges between nodes chosen by a randomized procedure. Using CPG, the effects of a gossip exchange are succinctly defined by a single function that atomically updates a pair of node states based on their previous values. This function is automatically partitioned via program slicing into executable code for the roles of gossip-initiator and gossip-recipient, and networking code is added automatically. CPG may have concrete benefits for protocol analysis and authoring composite gossip protocols.
Mechanism of the antiproliferative action of limonene on a lymphoma cell line: participation of nitric oxide. antiproliferative action of limonene on a lymphoma cell line

Previously limonene demonstrated antiproliferative action on a lymphoma cell line (BW5147). The objective of this study was to investigate the mechanism of the antiproliferative action of limonene on BW 5147 cells. The induction of apoptosis and arrest in cell cycle phases, both phenomena related to nitric oxide (NO) production, was analyzed, as well as NO production. Results showed that limonene exhibited antiproliferative action on tumoral lymphocytes exerting a decrease in cell viability that was related to apoptosis induction and to the increase in NO levels at long incubation times. At short times and depending on its concentration, limonene arrested cells in different phases of the cell cycle, related to NO production. Copyright © 2009 John Wiley & Sons, Ltd.
Correlation-based Botnet Detection in Enterprise Networks

Most of the attacks and fraudulent activities on the Internet are carried out by malware. In particular, botnets, as state-of-the-art malware, are now considered as the largest threat to Internet security. In this thesis, we focus on addressing the botnet detection problem in an enterprise-like network environment. We present a comprehensive correlation-based framework for multi-perspective botnet detection consisting of detection technologies demonstrated in four complementary systems: BotHunter, BotSniffer, BotMiner, and BotProbe. The common thread of these systems is correlation analysis, i.e., vertical correlation (dialog correlation), horizontal correlation, and cause-effect correlation. All these Bot* systems have been evaluated in live networks and/or real-world network traces. The evaluation results show that they can accurately detect real-world botnets for their desired detection purposes with a very low false positive rate. We find that correlation analysis techniques are of particular value for detecting advanced malware such as botnets. Dialog correlation can be effective as long as malware infections need multiple stages. Horizontal correlation can be effective as long as malware tends to be distributed and coordinated. In addition, active techniques can greatly complement passive approaches, if carefully used. We believe our experience and lessons are of great benefit to future malware detection.
Workshop on Parallel and Distributed Processing for Computational Social Systems ( ParSocial 2017 )

The information diffusion and dissemination define critical dynamics observed in large complex networks. The underlying information propagation topology, however, is often hidden or incomplete because of the lack of explicit citations of the sources. We proposed a scalable parallel algorithm to derive the node embeddings to better understand the information dissemination patterns and predict emergent cascades of viral events in online media. Unlike previous works which concentrate on modeling the links of information propagation, our algorithm infers the topic-specific output influence and the input selectivity of nodes. The parallel algorithm iteratively merges local node embeddings in particular communities to obtain the global optimal results so that the processing of cascades can be significantly accelerated. Based on the obtained latent representation of nodes, the emergent cascades of viral news events in online media can be successfully predicted with an 80% accuracy at its early stage. Experimental results show that our parallel inference algorithm achieves a 50-fold speedup and requires a low communication overhead, while the accuracy of the cascade size prediction is preserved. Keywords-cascade; information propagation; network; viral news events; prediction; online media
A multi-stage fuzzy classifier for off-line handwritten Chinese character recognition

The decision boundaries in pattern recognition of large scale (PRLS) are usually complex and sometimes it is difficult to get ideal results with only a single classifier. In the paper, based on system integration idea, the method of fuzzy coarse classifying is given and a multi-stage fuzzy classifier is built. The recognition experiments on one level class of 3755 Chinese characters show that this method can largely improve the recognition accuracy. So it is an efficient method for solving PRLS.
Esc: Towards an Elastic Stream Computing Platform for the Cloud

Today, most tools for processing big data are batch-oriented. However, many scenarios require continuous, online processing of data streams and events. We present ESC, a new stream computing engine. It is designed for computations with real-time demands, such as online data mining. It offers a simple programming model in which programs are specified by directed acyclic graphs (DAGs). The DAG defines the data flow of a program, vertices represent operations applied to the data. The data which are streaming through the graph are expressed as key/value pairs. ESC allows programmers to focus on the problem at hand and deals with distribution and fault tolerance. Furthermore, it is able to adapt to changing computational demands. In the cloud, ESC can dynamically attach and release machines to adjust the computational capacities to the current needs. This is crucial for stream computing since the amount of data fed into the system is not under the platform's control. We substantiate the concepts we propose in this paper with an evaluation based on a high-frequency trading scenario.
A Butler Matrix Fed Two-Element Microstrip Antenna Array over EBG Substrate

This paper presents a simulation of two-element microstrip antenna array fed by a Butler matrix over electromagnetic bandgap (EBG) substrate. The proposed structure represents a simple smart antenna system working in the ISM band at 5.8 GHz. Small size antenna arrays over high dielectric substrates place the study of mutual coupling problem in an important priority, as it affects the performance of the system. To compensate for mutual coupling effect, mushroom-like EBG structure is used due its winning features. EBG cells are inserted between the two antennas to reduce the mutual coupling effect and to increase the array gain. The performance of the structure with conventional substrate of relative permittivity εr = 10.2 is compared to that with EBG substrate. The simulation results show that changing the number of EBG columns and rows affects the value of mutual coupling reduction and gain enhancement. Results also indicate that the optimum dimensions of the EBG structure depend on the number of EBG columns and rows. A 26 dB reduction of mutual coupling is achieved by using 3 columns and 5 rows of EBG structure. Also, a 1.5 dB improvement of array gain is obtained for the case of 3 columns and 7 rows.
Statistical Input Method based on a Phrase Class n-gram Model

We propose a method to construct a phrase class n-gram model for Kana-Kanji Conversion by combining phrase and class methods. We use a word-pronunciation pair as the basic prediction unit of the language model. We compared the conversion accuracy and model size of a phrase class bi-gram model constructed by our method to a tri-gram model. The conversion accuracy was measured by F measure and model size was measured by the vocabulary size and the number of non-zero frequency entries. The F measure of our phrase class bi-gram model was 90.41%, while that of a word-pronunciation pair tri-gram model was 90.21%. In addition, the vocabulary size and the number of non-zero frequency entries in the phrase class bi-gram model were 5,550 and 206,978 respectively, while those of the tri-gram model were 22,801 and 645,996 respectively. Thus our method makes a smaller, more accurate language model.
Bursty traffic over bursty links

Accurate estimation of link quality is the key to enable efficient routing in wireless sensor networks. Current link estimators focus mainly on identifying long-term stable links for routing. They leave out a potentially large set of intermediate links offering significant routing progress. Fine-grained analysis of link qualities reveals that such intermediate links are bursty, i.e., stable in the short term. In this paper, we use short-term estimation of wireless links to accurately identify short-term stable periods of transmission on bursty links. Our approach allows a routing protocol to forward packets over bursty links if they offer better routing progress than long-term stable links. We integrate a Short Term Link Estimator and its associated routing strategy with a standard routing protocol for sensor networks. Our evaluation reveals an average of 19% and a maximum of 42% reduction in the overall transmissions when routing over long-range bursty links. Our approach is not tied to any specific routing protocol and integrates seamlessly with existing routing protocols and link estimators.
Iterative Constrained Least Squares for Robust Constant Modulus Beamforming

This paper addresses robust beamforming in the presence of model uncertainty for constant modulus (CM) signals that are frequently encountered in radar and communication systems. Instead of employing the commonly used minimum variance approach, we utilize a CM criterion that is different from the conventional techniques for blind equalization. The proposed robust beamformer minimizes the CM objective function while constraining the magnitude response of any steering vector within a spherical uncertainty region to exceed unity. We develop two algorithms to solve the resultant nonconvex and nonsmooth optimization problem. The first is subgradient projection method, which is simple but converges slowly and is sensitive to initialization. By introducing auxiliary phase variables, our second scheme reformulates the original nonsmooth problem into a differentiable one, that can be solved by an iterative constrained least squares (ICLS) procedure. At each iteration of the ICLS, a least squares (LS) problem with a second-order cone constraint is solved. More important, a closed-form solution for this constrained LS problem, which has a low computational complexity, is derived. The ICLS has fast convergence and is not sensitive to the initial value. Simulation results demonstrate that the proposed CM beamformer achieves a substantial performance gain compared with several representative robust beamformers.