Miracle-dz/UniFAR-FTUs huggingface dataset:
Lack of knowledge of hormone receptor status and use of endocrine therapy in invasive breast cancer.

OBJECTIVE The aim of this study was to investigate the level of understanding in women with newly diagnosed invasive breast cancer of the key clinical features of their disease that are important determinants in treatment decision making. METHODS The 1684 women aged between 26 and 88 years at diagnosis enrolled in a 5-year cohort study were asked by questionnaire about their estrogen receptor (ER) and progesterone receptor (PR) status and about their past or current treatment with adjuvant endocrine therapy. Information was linked with their ER and PR status determined from the histopathology report. Logistic regression analysis was used to explore the relationship between age and education status and the likelihood of being able to correctly report hormone receptor status, as well as the relationship between the likelihood of receiving adjuvant endocrine therapy and knowledge of hormone receptor status in women who were ER or PR positive. RESULTS Not being able to correctly report hormone receptor status was associated with being older and having a lower level of education. Of women who were ER positive or PR positive or both and were at least 40 weeks from diagnosis, having received some form of endocrine therapy was significantly associated with self-identification as being ER or PR positive (OR=1.82, 95% CI 1.24-2.68, p=0.002), even when age was taken into account. CONCLUSIONS That self-knowledge of hormone receptor status was independently associated with likelihood of receiving endocrine therapy suggests that the methods of helping women understand the nature of their breast cancer are worthy of review.
Growth and optical properties of III-V semiconductor nanowires: : Studies relevant for solar cells

This thesis deals with epitaxial growth and optical properties of semiconductor nanowires with implications and insights about materials for solar cells. The chapters leading up to the papers give a broader background and an introduction to the field and include nanowires and their synthesis, semiconductor properties, solar cell operational principles, light interaction with a nanowire array, and optical characterization of such arrays.The nanowires were grown from gold seed particles using metal organic vapor phase epitaxy. Growth of ternary GaInP nanowires has been developed with a triethylgallium precursor that has not been commonly used before for this material structure in nanowires. We have achieved high yield and wide composition range nanowires with high control, which will be a crucial element for development of tandem solar cells where a high band-gap GaInP nanowire array could be the top cell.In terms of optical properties, lifetimes and carrier dynamics are important parameters for optoelectronic devices, including solar cells. We have investigated surface passivation of nanowires by capping GaAs nanowires with in situ grown shells, at the same time evaluating the possibility of measuring time-resolved photoluminescence signal of as-grown nanowires, even when the substrate is made of the same material. We have identified that depending on doping levels in the substrate and the nanowires, excitation wavelength can be chosen to separate nanowire signal from the substrate signal. Moreover, we have preliminary proposed a simple way to extract doping, which needs to be tested more extensively in the future. Such measurements of as-grown arrays could provide a fast and completely non-destructive characterization method for solar cell materials and allow further processing of the devices.Further, we have investigated reflectance and transmittance of flexible nanowire arrays embedded in a transparent polymer. Such flexible membranes could be interesting as flexible solar cells on their own, or could be incorporated on top of a lower band-gap material, for example silicon, to create a tandem solar cell. We have identified two potential issues with such structures. First of all, gold can absorb a significant fraction of the incoming light. The gold particles can be etched away, which recovers transmittance for the long wavelengths. However, a resonant reflectance peak is then observed. Through our work, we have identified that in-plane array modes arise in nanowire arrays embedded in a polymer that lead to resonant reflectance or absorptance in weakly absorbing materials. Such effect would be detrimental for transmitting long wavelengths to the bottom cell. Thus, we have investigated how these resonances depend on geometry in order to give guidelines for controlling this effect. (Less)
An hybrid method for the Arabic queries disambiguation to improve the relevance calculation in the IRS

In the information systems, the query’s expansion brings more benefices in the relevant documents extraction. However, the current expansion types are focused on the retrieve of the maximum of documents (reduce the silence). In Arabic, the queries are derived in many morphosemantical variants. Hence the diversity of the semantic interpretations that often creates a problem of ambiguity. Our objective is to prepare the Arabic request before its introduction to the document retrieval system. This type of preparation is based on pretreatment which makes morphological changes to the query by separating affixes of the words. Then, present all of morphosemantical derivatives as a first step to the lexical audit agent, and check the consistency between the words by the context parser. Finally we present a new method of semantic similarity based on the equivalence probability calculation between two words.
Learning personal + social latent factor model for social recommendation

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship. In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.
Chiral surface states in the bulk quantum Hall effect.

In layered samples which exhibit a bulk quantum Hall effect (QHE), a two-dimensional (2d) surface ``sheath" of gapless excitations is expected. These excitations comprise a novel 2d chiral quantum liquid which should dominate the low temperature transport along the field (z-axis). For the integer QHE, we show that localization effects are completely absent in the ``sheath", giving a metallic z-axis conductivity. For fractional filling $\nu =1/3$, the ``sheath" is a 2d non-Fermi liquid, with incoherent z-axis transport and $\sigma_{zz} \sim T^3$. Experimental implications for the Bechgaard salts are discussed.
1000fps human segmentation with deep convolutional neural networks

Efficiency and effectiveness are two key factors to evaluate a human segmentation algorithm for real vision applications. However, most existing algorithms only focus on one of them. That is, fast and accurate human segmentation is not yet well addressed. In this paper, we propose a super-fast and highly accurate human segmentation method with very deep convolutional neural networks. We also provide a comprehensive study on the proposed approach, including different net structures, various techniques of alleviating over-fitting, and performance enhancement with different extra data. Experimental results on the database of Baidu people segmentation competition [1] demonstrate that the proposed model outperforms traditional segmentation algorithms in accuracy and speed. Although it is slightly worse than the very complex champion algorithm, it is encouraging that our method can obtain more than 10,000 times acceleration, showing that it has great potential for practical applications.
Mind the gap: smart cities and entrepreneurship policies

The concept of the smart city gained increasing expression and has become an intensely discussed field in the recent past among, either the research community, either the political stakeholders. Yet, despite its semantic attractiveness, the concept still lacks definitional rigor and precision. In particular, if a special focus has been given to the role ICT interventions can fulfill, emphasising its high-tech characteristics, little attention has been paid to complementary dimensions that are decisive to upgrade urban dynamics competitiveness and sustainability, namely the contribution brought by entrepreneurship. This paper aims, first, to contribute to clarify the meaning of the word "smart" in the context of urban development through an approach based on an in-depth literature review of relevant studies and then, to establish the links with the determinants of entrepreneurship that can help guide more effective urban development policies. Furthermore the paper aims to inform and improve policy-making on the subject of promoting entrepreneurial mindsets and contexts, speculating on some principles and guidelines that may help fertilize urban dynamics and build smarter cities.
Gesture-Enabled Remote Control for Healthcare

In recent years, wearable sensor-based gesture recognition is proliferating in the field of healthcare. It could be used to enable remote control of medical devices, contactless navigation of X-ray display and Magnetic Resonance Imaging (MRI), and largely enhance patients' daily living capabilities. However, even though a few commercial or prototype devices are available for wearable gesture recognition, none of them provides a combination of (1) fully open API for various healthcare application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. In addition, the existing gesture recognition algorithms are mainly designed for discrete gestures. Accurate recognition of continuous gestures is still a significant challenge, which prevents the wide usage of existing wearable gesture recognition technology. In this paper, we present Gemote, a smart wristband-based hardware/software platform for gesture recognition and remote control. Due to its affordability, small size, and comfortable profile, Gemote is an attractive option for mass consumption. Gemote provides full open API access for third party research and application development. In addition, it employs a novel continuous gesture segmentation and recognition algorithm, which accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously.
Glutamatergic Signaling at the Vestibular Hair Cell Calyx Synapse

In the vestibular periphery a unique postsynaptic terminal, the calyx, completely covers the basolateral walls of type I hair cells and receives input from multiple ribbon synapses. To date, the functional role of this specialized synapse remains elusive. There is limited data supporting glutamatergic transmission, K+ or H+ accumulation in the synaptic cleft as mechanisms of transmission. Here the role of glutamatergic transmission at the calyx synapse is investigated. Whole-cell patch-clamp recordings from calyx endings were performed in an in vitro whole-tissue preparation of the rat vestibular crista, the sensory organ of the semicircular canals that sense head rotation. AMPA-mediated EPSCs showed an unusually wide range of decay time constants, from <5 to >500 ms. Decay time constants of EPSCs increased (or decreased) in the presence of a glutamate transporter blocker (or a competitive glutamate receptor blocker), suggesting a role for glutamate accumulation and spillover in synaptic transmission. Glutamate accumulation caused slow depolarizations of the postsynaptic membrane potentials, and thereby substantially increased calyx firing rates. Finally, antibody labelings showed that a high percentage of presynaptic ribbon release sites and postsynaptic glutamate receptors were not juxtaposed, favoring a role for spillover. These findings suggest a prominent role for glutamate spillover in integration of inputs and synaptic transmission in the vestibular periphery. We propose that similar to other brain areas, such as the cerebellum and hippocampus, glutamate spillover may play a role in gain control of calyx afferents and contribute to their high-pass properties.
Relating phonetic and phonological categories

Recent proposals to treat phonetic representations as the semantic interpretation of phonological representations are technically problematic to implement. The main di culty is that phonological representations are discrete while phonetic representations are continuous which makes the standard method of describing semantic interpretation as a homomorphism between sortally equivalent algebras hard to generalize. The paper solves the technical problem by introducing a notion of homomorphisms. First, the operation of concatenation is de ned in the usual way for strings and as `continuation' for continuous scalar-vector functions with nite support, and the set of phonetic representations is equipped with a measure. Next a.e. homomorphisms are rigorously de ned and the semantic relationship between phonological and phonetic categories is made explicit in terms of these homomorphisms. Finally constant target (triphone) models, which play a central role in speech recognition, are reconstructed in this semantic framework.
Three‐Dimensional Visualization of Developing Neurovascular Architecture in the Craniofacial Region of Embryonic Mice

Recent studies have highlighted the mechanism of vascular and axonal guidance to ensure proper morphogenesis and organogenesis. We aimed to perform global mapping of developing neurovascular networks during craniofacial development of embryonic mice. To this end, we developed histology‐based three‐dimensional (3D) reconstructions using paraffin‐embedded serial sections obtained from mouse embryos. All serial sections were dual‐immunolabeled with Pecam1 and Pgp9.5/Gap43 cocktail antibodies. All immunolabeled serial sections were digitized with virtual microscopy to acquire high spatial resolution images. The 3D reconstructs warranted superior positional accuracy to trace the long‐range connectivity of blood vessels and individual cranial nerve axons. It was feasible to depict simultaneously the details of angiogenic sprouting and axon terminal arborization and to assess quantitatively the locoregional proximity between blood vessels and cranial nerve axons. Notably, 3D views of the craniofacial region revealed the following: Branchial arch arteries and blood capillary plexi were formed without accompanying nerves at embryonic day (E) 9.5. Cranial nerve axons began to grow into the branchial arches, developing a labyrinth of small blood vessels at E10.5. Vascular remodeling occurred, and axon terminals of the maxillary, mandibular, chorda tympani, and hypoglossal nerve axons had arborized around the lateral lingual swellings at E11.5. The diverged patterning of trigeminal nerves and the arterial branches from the carotid artery became congruent at E11.5. The overall results support the advantage of dual‐immunolabeling and 3D reconstruction technology to document the architecture and wiring of the developing neurovascular networks in mouse embryos. Anat Rec, 298:1824–1835, 2015. © 2015 Wiley Periodicals, Inc.
Machine Learning-Assisted Least Loaded Routing to Improve Performance of Circuit-Switched Networks

The Least Loaded (LL) routing algorithm has been in recent decades the routing method of choice in circuit switched networks and therefore it provides a benchmark against which new methods can be compared. This paper improves the performance of the LL algorithm by additionally incorporating a machine learning approach, using a conceptually simple supervised na\"ive Bayes (NB) classifier. Based on a sequence of historical network snapshots, this predicts the potential future circuit blocking probability between each node pair. These snapshots are taken for each service request arriving to the network and record the number of busy capacity units on each link at that instant. The candidate route for serving a current service request is based on both the link loads and the potential future blocking probability of the entire network in case this route is indeed used. The performance of this proposed approach is studied via simulations and compared with both the conventional LL algorithm and the Shortest Path (SP) based approach. Results indicate that the proposed supervised na\"ive Bayes classifier-assisted LL routing algorithm significantly reduces blocking probability of service connection requests and outperforms both the conventional LL and SP routing algorithms. To enable the learning process based on a large number of network snapshots, we also develop a parallel computing framework to implement parallel learning and performance evaluation. Also, a network control system supporting na\"ive Bayes classifier-assisted LL routing algorithm is addressed.
HER-2 overexpression and survival in colorectal cancer: a meta-analysis

Objective: Numerous studies examining the relationship between human epidermal growth factor receptor 2 (HER-2) overexpression and survival in patients with colorectal cancer (CRC) have yielded controversial results. We therefore performed a meta-analysis more precisely to estimate its prognostic value. Methods: Published studies investigating the effect of HER-2 overexpression on CRC survival were identified; the hazard ratios (HRs) and their corresponding 95% confidence intervals (95% CIs) were pooled in terms of disease-specific or overall survival. Results: Eleven studies were included in the meta-analysis. The pooled data showed that HER-2 overexpression was negatively related to CRC survival (HR=1.10, 95% CI: 0.77–1.44). Subgroup analyses regarding test method and study quality also demonstrated little association between HER-2 overexpression and CRC survival (HR=0.89, 95% CI: 0.50–1.29; HR=0.90, 95% CI: 0.43–1.37, respectively). Conclusions: Regardless of several limitations, our study suggested that HER-2 overexpression probably had little impact on CRC survival.
Management of asymptomatic mitral regurgitation

Nowadays, mitral regurgitation (MR) already represents a major health problem, and has been recognised as the most frequent valvular disease in the USA and the second most common form of valve disease requiring surgery in Europe.1 2 In the future, due to the ageing and growth of the general population, the negative impact of MR on patients' health is expected to increase further.3 As healthcare providers, we will thus be more and more frequently called upon to challenge this disease, which is indeed extremely heterogeneous in terms of prognosis and treatment options.3 This article aims to provide a practical, step-by-step approach to the daily management of asymptomatic MR in the light of the most recent diagnostic and therapeutic advances. ### Essential knowledge required to approach any patient presenting with any amount of MR A diagnosis of MR should be made in the presence of a pathological amount of blood regurgitating from the left ventricle (LV) into the left atrium (LA) because of an incompetent mitral valve (MV). The mere detection of MR is not sufficient for diagnosis, and for correct decision making we also need to assess: (1) the aetiology; (2) the mechanism (ie, how the disease prevents the MV from being competent); and (3) the severity of the regurgitation. An appropriate and comprehensive diagnosis not only enhances communication, but more importantly leads to a different prognostic assessment and therapeutic management. The numerous aetiologies of MR can be roughly grouped into either ischaemic (ie, due to coronary artery disease) or non-ischaemic (all other causes), and the mechanisms into organic or functional. ### Mechanisms of mitral regurgitation
An Alternative Economic Paradigm: How Redistribution would Drive the Economy

In 1943 in Bengal, inequality together with the inelasticity of supply led to an increase of entitlements for some citizens that generated inflation in the subsistence sector and decreased entitlements for others. These latter experienced tragic famine. This shows that distribution matters. A subdivision of the economy between subsistence sector and wealth sector brings such arguments in the whole economy. The analysis specifies aggregates of variables and conditions whereby these dynamics currently remain hidden behind aggregation. For example, a decoupling between demand side, which originates locally, and supply side, which may be delocalised, shuffles rankings hiding dynamics. Increase in inequality would generate an inequality trap inflating the subsistence sector in relation to the wealth sector, generating recession in this latter. This would eventually overtake increasing efficiency. Some policy options are discussed.
An efficient resource-optimized learning prefetcher for solid state drives

In recent years, solid-state drives (SSDs) have been widely deployed in modern storage systems. To increase the performance of SSDs, prefetchers for SSDs have been designed both at operating system (OS) layer and flash translation layer (FTL). Prefetchers in FTL have many advantages like OS-independence, easy-using, and compatibility. However, due to the limitation of computing capabilities and memory resources, existing prefetchers in FTL merely employ simple sequential prefetching which may incur high penalty cost for I/O access stream with complex patterns. In this paper, an efficient learning prefetcher implemented in FTL is proposed. Considering the resource limitation of SSDs, a learning algorithm based on Markov chains is employed and optimized so that high hit ratio and low penalty cost can be achieved even for complex access patterns. To validate our design, a simulator with the prefetcher is designed and implemented based on Flashsim. The TPC-H benchmark and an application launch trace are tested on the simulator. According to experimental results of the TPC-H benchmark, more than 90% of memory cost can be saved in comparison with a previous design at OS layer. The hit ratio can be increased by 24.1% and the number of times of misprefetching can be reduced by 95.8% in comparison with the simple sequential prefetching strategy.