Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired Orthotopic Glioma Growth and Vascularization in Transgenic Mouse Models of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia among the aging population and is characterized pathologically by the progressive intracerebral accumulation of β-amyloid (Aβ) peptides and neurofibrillary tangles. The level of proangiogenic growth factors and inflammatory mediators with proangiogenic activity is known to be elevated in AD brains which has led to the supposition that the cerebrovasculature of AD patients is in a proangiogenic state. However, angiogenesis depends on the balance between proangiogenic and antiangiogenic factors and the brains of AD patients also show an accumulation of endostatin and Aβ peptides which have been shown to be antiangiogenic. To determine whether angiogenesis is compromised in the brains of two transgenic mouse models of AD overproducing Aβ peptides (Tg APPsw and Tg PS1/APPsw mice), we assessed the growth and vascularization of orthotopically implanted murine gliomas since they require a high degree of angiogenesis to sustain their growth. Our data reveal that intracranial tumor growth and angiogenesis is significantly reduced in Tg APPsw and Tg PS1/APPsw mice compared with their wild-type littermates. In addition, we show that Aβ inhibits the angiogenesis stimulated by glioma cells when cocultured with human brain microvascular cells on a Matrigel layer. Altogether our data suggest that the brain of transgenic mouse models of AD does not constitute a favorable environment to support neoangiogenesis and may explain why vascular insults synergistically precipitate the cognitive presentation of AD.
MUSE: a MUlti-Source Entity recognition system

This paper describes a robust and easily adaptable system for named entity recog- nition from a variety of different text types. Most information extraction systems need to be customised according to the domain, either by collecting a large set of training data or by rewriting grammar rules, gazetteer lists etc., both of which methods can be costly and time- consuming. The MUSE system incorporates a modular set of resources from which different subsets can be mixed and matched as required. The process of selecting the correct resources depending on the text type is fully automatic. This method could be easily extended to deal with different languages in the same way. Results show figures in the 90th percentile for news texts, and slightly lower for other text types.
Personality, Organizational Culture, and Cooperation: Evidence from a Business Simulation.

We thank the Center for Creative Leadership, the Kellogg Graduate School of Management, and the University of California's Haas School of Business and Institute of Personality and Social Research for financial support; Dave Caldwell, Ben Hermalin, Chris McCusker, Barry O'Neill, Charles O'Reilly, Jeff Polzer, Brent Roberts, and Tom Tyler for comments on earlier drafts of this paper; and Rebecca Brown, Mary Cusack, Karen Etty Jehn, Kathy Mozier, Margaret Neale, Jeff Polzer, Barry Staw, and Jonathan Whitney for help with data collection or coding. We are especially grateful to Bob Sutton, three anonymous ASO reviewers, and Linda Pike for their editorial guidance. Deriving predictions from congruence theory, we explored the personal and situational sources of cooperation by contrasting behavior under conditions of personality fit and misfit with culture in an organizational simulation. We assessed MBA students' disposition to cooperate and randomly assigned them to simulated organizations that either emphasized collectivistic or individualistic cultural values. We found that cooperative subjects in collectivistic cultures were rated by coworkers as the most cooperative; they reported working with the greatest number of people, and they had the strongest preferences for evaluating work performance on the basis of contributions to teams rather than individual achievement. Results also showed that cooperative people were more responsive to the individualistic or collectivistic norms characterizing their organization's culture: They exhibited greater differences in their level of cooperative behavior across the two cultures than did individualistic people. We discuss the organizational implications of the conditions influencing behavioral expressions of personal cooperativeness.
Outlier Mining for Multivariate Time Series Based on Local Sparsity Coefficient

Multivariate time series (MTS) datasets are common in various financial, multimedia and medical applications. MTS samples which differ significantly from the remaining MTS samples are referred to as outliers. In this paper, the outlier mining method based on local sparsity coefficient for MTS is proposed. An extended Frobenius norm is used to compare the similarity between MTS samples, K-NN searches are performed by using two-phase sequential scan. MTS samples that are not possible outlier candidates are pruned, which reduces the number of computations and comparisons. The datasets of stock market and Australian sign language are used for outlier mining, the results show the effectiveness of the algorithm
Association of circulating osteocalcin with cardiovascular disease and intermediate cardiovascular phenotypes: systematic review and meta-analysis

Abstract Objectives. Circulating osteocalcin (OC), a marker which is central in bone mineralization, may be involved in the atherosclerotic process and influence the risk of developing cardiovascular disease (CVD). We conducted a systematic review and meta-analysis of published observational evidence, to assess and quantify the associations of circulating OC (total, undercarboxylated, and carboxylated OC) with cardiovascular outcomes (clinical CVD endpoints and intermediate cardiovascular phenotypes). Design. Relevant studies were identified in a literature search of MEDLINE, EMBASE, and reference lists of relevant studies to March 2019. Mean differences and risk ratios with 95% CIs were aggregated using random-effects models. Results. Thirty-three observational studies (prospective and retrospective cohort, case-control, and cross-sectional) with data on 21,021 unique participants were eligible. The pooled risk ratio in a comparison of extreme fourths of total OC levels was 0.98 (95% CI 0.89, 1.08) for composite CVD. Circulating total OC levels were significantly lower in patients with cardiovascular conditions compared with those without these conditions −2.58 ng/ml (95% CI −3.85, −1.32; p < .001). Prospective and cross-sectional data showed significant inverse associations between total OC and traits such as aortic or coronary calcification, coronary atherosclerosis or calcification, carotid intima-media thickness, and plaque score. There was limited data on carboxylated and undercarboxylated OC, with no evidence of associations. Conclusion. Observational evidence generally supports inverse associations of circulating total OC with risk of atherosclerotic outcomes and CVD endpoints; however, the data were mostly based on cross-sectional evaluations. Large-scale prospective data are needed.
Real-time motion capture system using one video camera based on color and edge distribution

This paper proposes a real-time, video based motion capture system using only one video camera. Since conventional video based motion capture systems need many cameras and take a long time to deal with many video images, they cannot generate motion data in real time. Therefore they cannot be used as a real-time input device for a standard PC. On the other hand, the prototype system proposed in this paper uses only one video camera, it takes video images of the upper body of the person, e.g., x, y, z position of the hands, a face rotation, a body rotation, etc., and it employs a very simple motion-tracking method to generate such upper body motion data in real time. This paper mainly describes its aspects as a hand and face motion-capturing device for a standard PC showing its application examples. Key-Words: -Image understanding, Motion capture, Motion recognition, Interface, Virtual reality
Channel aware scheduling for broadcast MIMO systems with orthogonal linear precoding and fairness constraints

In the downlink of a broadcast fading channel, the base station can capitalize on multiuser diversity through channel aware scheduling. In MIMO systems, the design of the scheduler has to take into account the processing performed at the transmitter and the receivers. In this work, we consider channel aware scheduling for orthogonal linear precoding at the base station that guarantees interference free reception for each scheduled users. The problem is set in a novel mathematical framework and a scheduling algorithm is proposed that is shown by simulation to guarantee superior performance as compared to know techniques. Moreover, fairness constraints inspired by the proportional fair criterion are introduced in the scheduling process in order to guarantee the desired long term fairness properties.
Ultrasonic method for nuclear fuel rods pressure and gas composition released measurement

The objective of this work was to develop a non-destructive acoustic method giving an easy access to two important pieces of information in the irradiated fuel rods: the pressure and the composition of the internal gas mixture in the upper plenum of a standard LWR fuel rod. A first attempt started in 1993 made possible the development of a focused sensor, able to inject acoustic power from a piezoelectric transducer, through the fuel rod cladding and excite the internal gas mixture. This step was achieved in 1999 and covered by a first patent [1]. However, the initial problem was not totally solved because of the presence of the upper spring in the LWR fuel rod. This spring mainly induced a large decrease in the acoustic amplitude response and this solution was not applicable. The method has been revised recently for several aspects: the design of the sensor to optimize the acousmtic power injection through the cladding rod, and the entire experimental protocol, including the signal processing in the time and frequency spaces. A single λ/2 layer (instead of the standard solution in λ/4) of low impedance material (compared to the transducer and tube wall impedance) as water, was found to be a better matching layer.
Globality-Locality Preserving Projections for Biometric Data Dimensionality Reduction

In a biometric recognition task, the manifold of data is the result of the interactions between the sub-manifold of dynamic factors of subjects and the sub-manifold of static factors of subjects. Therefore, instead of directly constructing the graph Laplacian of samples, we firstly divide each subject data into a static part (subject-invariant part) and a dynamic part (intra-subject variations) and then jointly learn their graph Laplacians to yield a new graph Laplcian. We use this new graph Laplacian to replace the original graph Laplacian of Locality Preserving Projections (LPP) to present a new supervised dimensionality reduction algorithm. We name this algorithm Globality-Locality Preserving Projections (GLPP). Moreover, we also extend GLPP into a 2D version for dimensionality reduction of 2D data. Compared to LPP, the subspace learned by GLPP more precisely preserves the manifold structures of the data and is more robust to the noisy samples. We apply it to face recognition and gait recognition. Extensive results demonstrate the superiority of GLPP in comparison with the state-of-the-art algorithms.
Improving Ranking Using Quantum Probability

The paper shows that ranking information units by quantum probability differs from ranking them by classical probability provided the same data used for parameter estimation. As probability of detection (also known as recall or power) and probability of false alarm (also known as fallout or size) measure the quality of ranking, we point out and show that ranking by quantum probability yields higher probability of detection than ranking by classical probability provided a given probability of false alarm and the same parameter estimation data. As quantum probability provided more effective detectors than classical probability within other domains that data management, we conjecture that, the system that can implement subspace-based detectors shall be more effective than a system which implements a set-based detectors, the effectiveness being calculated as expected recall estimated over the probability of detection and expected fallout estimated over the probability of false alarm.
CSCW: the convergence of two development contexts

CSCW research and groupware development represent converging interests from two contexts of interactive systems development. Issues of group dynamics and organizational impact have primarily been explored in the in-house development of systems for organizations — systems that support organizational goals. Similar issues are now being encountered by researchers and developers with a product development orientation who are seeking to support small groups. We have not integrated effectively the interests, experiences and approaches arising in these two development contexts. To do so, we have to go beyond what is shared and explore the differences.
X-ray properties of UV-selected star-forming galaxies at z ∼ 1 in the Hubble Deep Field North

We present an analysis of the X-ray emission from a large sample of ultraviolet (UV) selected, star-forming galaxies with 0.74 < z < 1.32 in the Hubble Deep Field North (HDF-N) region. By excluding all sources with significant detected X-ray emission in the 2-Ms Chandra observation we are able to examine the properties of galaxies for which the dominant emission in both UV and X-ray is expected to be predominantly the result of star formation. Stacking the X-ray flux from 216 galaxies in the soft and hard bands produces significant detections (14.9σ and 3.2σ, respectively). The derived mean 2-10 keV rest-frame luminosity is 2.97 ± 0.26 x 10 40 erg s -1 , corresponding to an X-ray derived star formation rate (SFR) of 6.0 ± 0.6 M ○. yr -1 . Comparing the X-ray value with the mean UV derived SFR, uncorrected for attenuation, we find that the average UV attenuation correction factor is ∼3. By binning the galaxy sample according to UV magnitude and colour, and stacking the observed frame soft band X-ray flux in each bin, correlations between UV and X-ray emission are examined. We find a strong positive correlation between X-ray emission and rest-frame UV emission, consistent with a strict linear relationship, L X L UV , at the 90 per cent level. A correlation between the ratio of X-ray-to-UV emission and UV colour is also seen, such that L X /L UV increases for redder galaxies. We find no direct relation between X-ray flux and UV colour. Given that X-ray emission offers a view of star formation regions that is relatively unaffected by extinction, results such as these can be used to evaluate the effects of dust on the UV emission from high-z galaxies. For instance, using the observed correlation between UV colour excess and the ratio of X-ray-to-UV emission (a measure of UV obscuration), we derive a relationship for estimating UV attenuation corrections as a function of colour excess. The observed relation is inconsistent with the Calzetti et al. reddening law, which overpredicts the range in UV attenuation corrections by a factor of ∼100 for the UV-selected z ∼ 1 galaxies in this sample.
TLB Improvements for Chip Multiprocessors: Inter-Core Cooperative Prefetchers and Shared Last-Level TLBs

Translation Lookaside Buffers (TLBs) are critical to overall system performance. Much past research has addressed uniprocessor TLBs, lowering access times and miss rates. However, as Chip MultiProcessors (CMPs) become ubiquitous, TLB design and performance must be reevaluated. Our article begins by performing a thorough TLB performance evaluation of sequential and parallel benchmarks running on a real-world, modern CMP system using hardware performance counters. This analysis demonstrates the need for further improvement of TLB hit rates for both classes of application, and it also points out that the data TLB has a significantly higher miss rate than the instruction TLB in both cases. In response to the characterization data, we propose and evaluate both Inter-Core Cooperative (ICC) TLB prefetchers and Shared Last-Level (SLL) TLBs as alternatives to the commercial norm of private, per-core L2 TLBs. ICC prefetchers eliminate 19% to 90% of Data TLB (D-TLB) misses across parallel workloads while requiring only modest changes in hardware. SLL TLBs eliminate 7% to 79% of D-TLB misses for parallel workloads and 35% to 95% of D-TLB misses for multiprogrammed sequential workloads. This corresponds to 27% and 21% increases in hit rates as compared to private, per-core L2 TLBs, respectively, and is achieved this using even more modest hardware requirements. Because of their benefits for parallel applications, their applicability to sequential workloads, and their readily implementable hardware, SLL TLBs and ICC TLB prefetchers hold great promise for CMPs.
Interactive query expansion with automatically generated category-specific thesauri

The categorization of documents into subjectspecific categories is a useful enhancement for large document collections addressed by information retrieval systems, as a user can first browse a category tree in search of the category that best matches her interests, and then issue a query for more specific documents “from within the category”. This approach combines two modalities in information seeking that are most popular in Webbased search engines, i.e. category-based site browsing (as exemplified by e.g. Yahoo) and keyword-based document querying (as exemplified by e.g. AltaVista). Appropriate query expansion tools need to be provided, though, in order to allow the user to incrementally refine her query through further retrieval passes, thus allowing the system to produce a series of subsequent document rankings that hopefully converge to the user’s expected ranking. In this work we propose that automatically generated, categoryspecific “associative” thesauri be used for such purpose. We discuss a method for their generation, and discuss how the thesaurus specific to a given category may usefully be endowed with “gateways” to the thesauri specific to its parent and children categories. ∗This work has been carried out in the context of the project EUROSEARCH LE4-8303, funded by the Commission of the European Communities under the ESPRIT Telematics scheme.
Linear feature extraction using sufficient statistic

The objective in feature extraction is to compress the data while maintaining the same Bayes classification error as on the original data. This objective is achieved by a sufficient statistic with the minimum dimension. This paper derives a non-iterative linear feature extractor that approximates the minimal-dimension linear sufficient statistic operator for the classification of Gaussian distributions. This new framework alleviates the bias of an existing similar formulation towards the parameters of a reference class. Moreover, it is a heteroscedastic extension of linear discriminant analysis and captures the discriminative information in the first and second central moments of the data. The proposed method can improve the performance of the similar feature extractors while imposing equal, or even lower, computational complexity.
University of Huddersfield Repository Intelligent Rule based Phishing Websites Classification

— Phishing is described as the art of emulating a website of a creditable firm intending to grab user’s private information such as usernames, passwords and social security number. Phishing websites comprise a variety of cues within its content-parts as well as browser-based security indicators. Several solutions have been proposed to tackle phishing. Nevertheless, there is no single magic bullet that can solve this threat radically. One of the promising techniques that can be used in predicting phishing attacks is based on data mining. Particularly the “induction of classification rules”, since anti-phishing solutions aim to predict the website type accurately and these exactly fit the classification data mining. In this paper, we shed light on the important features that distinguish phishing websites from legitimate ones and assess how rule-based classification data mining techniques are applicable in predicting phishing websites. We also experimentally show the ideal rule based classification technique for detecting phishing.
Mediating Effect of Academic Self-Efficacy Between Teachers' Attitudes and Achievement of Low-Income High School Students

Mediating Effect of Academic Self-Efficacy Between Teachers’ Attitudes and Achievement of Low-Income High School Students by Nickole M. Cottrill MS, Walden University, 2015 BS, Walden University, 2013 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
A routing vector method (RVM) for routing in Bluetooth scatternets

Bluetooth is a promising new technology for short range wireless connectivity between mobile devices. Initially, Bluetooth will be used as a replacement for point to-(multi)point cables. However, in due course of time, solutions for forming multihop ad hoc networks over Bluetooth (referred to as scatternets) will be needed. In this paper, we explore the design space of routing protocols over Bluetooth scatternets. We argue that ad hoc routing protocols for Bluetooth must make a different set off design compromises compared to those being developed by the MANET working group. These differences result primarily from the specific characteristics of the Bluetooth physical and link layer. Our proposed routing method is based on the concept of route vector, which is an efficient method for encoding source route paths in Bluetooth scatternets. We describe the protocols for route discovery and packet forwarding. Our design illustrates three main design compromises, namely minimization of soft-state, protocol simplicity, and bandwidth conservation, all of are crucial for efficient operation over small size Bluetooth scatternets.
Position-Dependent Importance Sampling of Light Field Luminaires

The possibility to use real world light sources (aka luminaires) for synthesizing images greatly contributes to their physical realism. Among existing models, the ones based on light fields are attractive due to their ability to represent faithfully the near-field and due to their possibility of being directly acquired. In this paper, we introduce a dynamic sampling strategy for complex light field luminaires with the corresponding unbiased estimator. The sampling strategy is adapted, for each 3D scene position and each frame, by restricting the sampling domain dynamically and by balancing the number of samples between the different components of the representation. This is achieved efficiently by simple position-dependent affine transformations and restrictions of Cumulative Distributive Functions that ensure that every generated sample conveys energy and contributes to the final result. Therefore, our approach only requires a low number of samples to achieve almost converged results. We demonstrate the efficiency of our approach on modern hardware by introducing a GPU-based implementation. Combined with a fast shadow algorithm, our solution exhibits interactive frame rates for direct lighting for large measured luminaires.