Miracle-dz/UniFAR-FTUs huggingface dataset:
Environment-mediated drug resistance in Bcr/Abl-positive acute lymphoblastic leukemia

Although cure rates for acute lymphoblastic leukemia (ALL) have increased, development of resistance to drugs and patient relapse are common. The environment in which the leukemia cells are present during the drug treatment is known to provide significant survival benefit. Here, we have modeled this process by culturing murine Bcr/Abl-positive acute lymphoblastic leukemia cells in the presence of stroma while treating them with a moderate dose of two unrelated drugs, the farnesyltransferase inhibitor lonafarnib and the tyrosine kinase inhibitor nilotinib. This results in an initial large reduction in cell viability of the culture and inhibition of cell proliferation. However, after a number of days, cell death ceases and the culture becomes drug-tolerant, enabling cell division to resume. Using gene expression profiling, we found that the development of drug resistance was accompanied by massive transcriptional upregulation of genes that are associated with general inflammatory responses such as the metalloproteinase MMP9. MMP9 protein levels and enzymatic activity were also increased in ALL cells that had become nilotinib-tolerant. Activation of p38, Akt and Erk correlated with the development of environment-mediated drug resistance (EMDR), and inhibitors of Akt and Erk in combination with nilotinib reduced the ability of the cells to develop resistance. However, inhibition of p38 promoted increased resistance to nilotinib. We conclude that development of EMDR by ALL cells involves changes in numerous intracellular pathways. Development of tolerance to drugs such as nilotinib may therefore be circumvented by simultaneous treatment with other drugs having divergent targets.
International Conference on Green Buildings and Sustainable Cities Research on the sustainability of China ' s real estate market

Although China's real estate market (CREM) has made great development since the Chinese government adopted the reform and opening-up policy in 1978, it has been confronted with many problems during this period. This paper describes the background of CREM and its basic situation, outlines its main problems, and presents suggestions for its sustainable development. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of APAAS
The Psychological Experience of Middle-Power in Social Hierarchies: A Theoretical and Empirical Investigation

The Psychological Experience of Middle-Power in Social Hierarchies: A Theoretical and Empirical Investigation Eric M. Anicich In this dissertation, I theoretically and empirically examine the psychological experience of middle-power, which occurs when someone frequently alternates between adopting behavioral strategies targeting higher-power and lower-power interaction partners. In Chapter 1, I update and extend the approach/inhibition theory of power (Keltner, Gruenfeld, & Anderson, 2003) by developing a novel theoretical framework related to the psychological experience of middlepower. This new theoretical perspective draws from and integrates insights from role-based identity (Ashforth & Johnson, 2001; Stryker, 1980) and role transition theories (Ashforth, Kreiner, & Fugate, 2000). In Chapter 2, I conduct a systematic review of the social hierarchy literature over the past 10 years and demonstrate that scholars have considered the middle of the distribution with respect to stratifying variables in only 5.4% of past empirical investigations. This conscious absence of the middle forces us to reconsider existing findings in the social hierarchy literature. In Chapters 3 and 4, I examine the relationship between power and unethical behavior and present evidence of a curvilinear relationship: middle-power individuals consistently behave more ethically than both their higher and lower-power counterparts. Taken together, these insights highlight the importance of considering the antecedents and consequences of middle-power states.
Detection of defects in stripe-patterned mask aerial images

In the semiconductor industry, detecting defects in mask patterns is very important, because even a single defect may lead to failure. Thus, controlling and detecting defects are necessary steps in the manufacturing process. Since there are numerous types of mask patterns, a universal automatic method for defect recognition has not been developed. In this paper, we deal with regular stripe pattern image, which is one of the most frequently occurring patterns in the dynamic random-access memory industry. This paper suggests an image processing method that manipulates the given images to reveal the ambiguous defects and determine the exact position of the defects. The experiments have revealed that this method finds defects efficiently and quickly.
On the Detectability of Lyman-alpha Emission in Star-forming Galaxies: The Role of Dust

Context: Lyman-alpha (Lyα) radiation is now widely used to investigate the galaxy formation and evolution in the high redshift universe. However, without a rigorous understanding of the processes regulating the Lyα escape fraction, physical interpretations of high-z observations remain questionable. Aims: We examine six nearby star forming galaxies to disentangle the role of the dust from other parameters such as gas kinematics, geometry, and ISM morphology in the obscuration of Lyα. Thereby, we attempt to understand the Lyα escape physics and infer the implications for high-redshift studies. Methods: We use HST/ACS imaging to produce continuum-subtracted Lyα maps, and ground-based observations (ESO/NTT and NOT) to map the Hα emission and the extinction E(B-V) in the gas phase derived from the Balmer decrement Hα/Hβ. Results: When large outflows are present, the Lyα emission does not appear to be correlated with the dust content, confirming the role of the Hi kinematics in the escape of Lyα photons. In the case of a dense, static Hi covering, we observe a damped absorption with a negative correlation between Lyα and E(B-V). We found that the Lyα escape fraction does not exceed 10% in all our galaxies and is mostly about 3% or below. Finally, because of the radiative transfer complexity of the Lyα line, star formation rate based on Lyα luminosity is underestimated with respect to that derived from UV luminosity. Simple reddening correction does not reconcile SFR(Lyα) with the total star formation rate. Conclusions: The dust is not necessarily the main Lyα escape regulatory factor. ISM kinematics and geometry may play a more significant role. The failure of simple dust correction to recover the intrinsic Lyα/Hα ratio or the total star formation rate should prompt us to be more cautious when interpreting high-z observations and related properties, such as SFRs based on Lyα alone. To this end, we propose a more realistic calibration for SFR(Lyα), which accounts for dust attenuation and resonant scattering effects via the Lyα escape fraction.
CARD: a decision-guidance framework and application for recommending composite alternatives

This paper proposes a framework for Composite Alternative Recommendation Development (CARD), which supports composite product and service definitions, top-k decision optimization, and dynamic preference learning. Composite services are characterized by a set of sub-services, which, in turn, can be composite or atomic. Each atomic and composite service is associated with metrics, such as cost, duration, and enjoyment ranking. The framework is based on the Composite Recommender Knowledge Base, which is composed of views, including Service Metric Views that specify services and their metrics; Recommendation Views that specify the ranking definition to balance optimality and diversity; parametric Transformers that specify how service metrics are defined in terms of metrics of its subservices; and learning sets from which the unknown parameters in the transformers are iteratively learned. Also introduced in the paper is the top-k selection criterion that, based on a vector of utility metrics, provides the balance between the optimality of individual metrics and the diversity of recommendations. To exemplify the framework, specific views are developed for a travel package recommender system.
A Multi-class SVM Classifier Utilizing Binary Decision Tree

In this paper a novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass problems is presented. The hierarchy of binary decision subtasks using SVMs is designed with a clustering algorithm. For consistency between the clustering model and SVM, the clustering model utilizes distance measures at the kernel space, rather than at the input space. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The SVMBDT architecture was designed to provide superior multi-class classification performance. Its performance was measured on samples from MNIST, Pendigit, Optdigit and Statlog databases of handwritten digits and letters. The results of the experiments indicate that while maintaining comparable or offering better accuracy with other SVM based approaches, ensembles of trees (Bagging and Random Forest) and neural network, the training phase of SVM-BDT is faster. During recognition phase, due to its logarithmic complexity, SVM-BDT is much faster than the widely used multi-class SVM methods like “one-against-one” and “one-against-all”, for multiclass problems. Furthermore, the experiments showed that the proposed method becomes more favourable as the number of classes in the recognition problem increases. Povzetek: Predstavljena je metoda gradnje binarnih dreves z uporabo SVM za vecrazredne probleme.
Medical Image Compression Based on Ordered Binary Decision Diagrams

A new approach for lossless compression of medical images has been proposed. After preprocessing, the image is split into bit planes to which the compressed technique is applied. These bit planes can be expressed as ordered binary decision diagrams (OBDD) and coding. This approach compared well with WINZIP in terms of compression ratio.
Reduced-rank multiuser detectors based on vector and matrix conjugate gradient Wiener filters

Using the notion of expanding subspace and the framework of reduced-rank signal processing, we present our latest discovery on applying the vector and matrix conjugate gradient (CG) methods to design reduced-rank linear MMSE multiuser detectors (MUD) for code division multiple access (CDMA) systems. We show that for a synchronous CDMA system with K users, each using a distinct length N spreading code, the vector CG method converges to the full-rank linear MUD in at most K steps (K/spl les/N). The matrix CG method converges to the full-rank linear MUD in one step. Furthermore, when the Gold codes are used as spreading codes in combination with a groupwise power control scheme, early convergence in the vector CG Wiener filter can be reached in just L steps (L/spl Lt/K/spl les/N), typically L=2/spl sim/4 independent of the user number K and the spreading length N.
Sfx: An Architecture For Action-oriented Sensor Fusion

Sensor fusion has an important role in the navigation of autonomous mobile robots. Our research has generated a generic and robust pro- cess model based on the action-oriented percep- tion paradigm. The autonomous execution and ex- ception handling abilities of this model have been implemented as the Sensor Fusion Effects (SFX) architecture. The key aspects of this implemen- tation are the sensing plan, the uncertainty man- agement mechanism, the application of feedback from the sensing process to individual sensors, the detection of exceptions to the sensing plan, and handling of those exceptions. This paper gives an overview of the SFX architecture, concentrating on the sensing plan as the central control struc- ture guiding autonomous execution. This paper also reports on experiments using sensor data col- lected from our mobile robot which demonstrate the use of the sensing plan representation, the ex- ecution sequence, the application of feedback, and how feedback improves the overall sensing capa- bilities of the robot.
Statistical Performance Monitoring

Traditional performance monitoring methods have an overhead that is proportional in both space and time to the spill rate. They are not scalable in a distributed computing environment of hundreds or even thousands of managed nodes; the large quantity of data collected cannot be pushed or pulled efficiently across the network for displaying/reporting. In this paper we propose a few scalable alternatives, such as only sending and displaying (at a lower frequency) essential data/information (mean, standard deviation, exceptional values, etc.) for each metric and show that the restricted data can actually help highlight important performance information about system status.
Real-time live broadcast news subtitling system for Spanish

Subtitling of live broadcast news is a very important application to meet the needs of deaf and hard of hearing people. However, live subtitling is a high cost operation in terms of qualiﬁcation human resources and therefore, money if high precision is de-sired. Automatic Speech Recognition researchers can help to perform this task saving both time and money developing systems that deliver subtitles fully synchronized with speech without human assistance. In this paper we present a real-time system for automatic subtitling of live broadcast news in Spanish based on the News Redaction Computer texts and an Automatic Speech Recognition engine to provide precise temporal alignment of speech to text scripts with negligible latency. The presented system is working satisfactory on the Aragonese Public Television from June 2008 without human assistance.
The generation of prograde P – T – t points and paths ; a textural , compositional , and chronological study of metamorphic monazite

Analogue and computational models principally provide our present understanding of the mechanisms of prograde metamorphism and orogenesis, yet, due to the difficulty of linking prograde age information with the pressure–temperature (P–T) evolution of a rock, these models for the most part remain untested. Here, we describe an approach that allows multiple prograde pressure–temperature–time (P–T–t) points to be generated on single samples providing a means to construct detailed P–T–t loops, enabling the duration of metamorphic events, the timing of burial and the rate of heating experienced by individual samples to be determined. We use this combination approach to generate multiple prograde P–T– t points for three samples from the Himalayan and Canadian Cordillera and show for the first time that the duration of the metamorphic events are similar in each area and prograde heating rates vary from 2.4F1. 2 8C/Ma to 5.0F2. 0 8C/Ma. The P–T–t paths generated this way agree well with the established geological history of the studied areas and allow additional constraints to be placed on the mechanisms of orogenesis. D 2004 Elsevier B.V. All rights reserved.
Multiplet features and magnetic properties of Fe on Cu(111): From single atoms to small clusters

The observation of sharp atomiclike multiplet features is unexpected for individual 3d atoms adsorbed on transition-metal surfaces. However, we show by means of x-ray absorption spectroscopy and x-ray magnetic circular dichroism that individual Fe atoms on Cu(111) exhibit such features. They are reminiscent of a low degree of hybridization, similar to 3d atoms adsorbed on alkali-metal surfaces. We determine the spin, orbital, and total magnetic moments, as well as magnetic anisotropy energy for the individual Fe atoms and for small Fe clusters that we form by increasing the coverage. The multiplet features are smoothened and the orbital moment rapidly decreases with increasing cluster size. For Fe monomers, comparison with density functional theory and multiplet calculations reveals a d 7 electronic configuration, owing to the transfer of one electron from the 4s to
A visibility algorithm for converting 3D meshes into editable 2D vector graphics

Artists often need to import and embellish 3D models coming from CAD-CAM into 2D vector graphics software to produce, e.g., brochures or manuals. Current automatic solutions tend to result, at best, in a 2D triangle soup and artists often have to trace over 3D renderings. We describe a method to convert 3D models into 2D layered vector illustrations that respect visibility and facilitate further editing. Our core contribution is a visibility method that can partition a mesh into large components that can be layered according to visibility. Because self-occluding objects and objects forming occlusion cycles cannot be represented by layers without being cut, we introduce a new cut algorithm that uses a graph representation of the mesh and curvature-aware geodesic distances.
Automatization of the Analysis and Diagnosis in Power Transformers using Artificial Intelligence

The maintenance of power transformers is normally performed by specialists who interpret technical rules developed by recognized institutions and has included little automation. The high costs of equipment, combined with the immense economic and social importance of its adequate maintenance, point to the importance of efficient control of such equipment. The Itaipu Hydroelectric Plant, among the largest of the world and generates around one fourth of all the electricity consumed in Brazil and more than ninety-five percent of the electricity consumed in Paraguay. This article studies the development of a computational solution to automate the analysis and diagnosis of the operating condition of power transformers. Our approach adopts the technique of Expert Systems - ES, from the field of AI. The ES we propose is based on tables which store information about relations between five gases extracted from the transformer isolating oil through gas chromatography. We conclude that the application of the ES approach constitutes an efficient proposal for the automation of the process. In order to deal with unforeseen cases in the available tables, provided through diverse criteria, we also propose improvements obtained through the application of "Rough Sets". In order to accomplish this, we suggest a prototype containing techniques from Symbolic AI, through ES, together with concepts from Fuzzy Sets.
Action and predicate safety of hybrid processes

Abstract In this paper, we study two kinds of safety properties for hybrid processes, namely safety for actions and safety for predicates on model variables. We give an algebraic specification of these safety properties using the process algebra HyPA, and show how to reduce the question of safety of a linear process specification to the question of safety of its sub-processes. As an example, we study a variant of Fischer? s protocol, in which there can be a relative error between the clocks that are used.
MODIFICATIONS OF TWO CONVECTIVE SCHEMES USED IN THE NCEP ETA MODEL

Among the challenges facing numerical weather prediction (NWP) and global models are nonlinear interactions between various physical processes parameterized within the modeling systems. The complexity of these interactions and their rate are often accelerated in the presence of active convection, which is so dominant during the warm season and is almost always present along the Gulf Coast throughout the year. Because of the small-scale nature of convection, their effects must be parameterized in current operational forecast models. Progress in improving the representation of convective cloud processes has been slow, causing many investigators to try a myriad of approaches to resolve the problem. Some have decided to focus future development on explicitly resolving these processes in higher resolution models with grid sizes of several km or less. This, however, is not a viable option for the current suite of NCEP operational models, given the limited computational resources that are available and given the constraints for providing rapid and continuous dissemination of forecast model products.
Flotillin-2 Expression in the Human Gut: from a Cell Model to Human Tissue in Health and Inflammatory Bowel Diseases

Background and aims: The etiopathogenesis of inflammatory bowel diseases (IBD) remains largely unexplained. Flotillins (flotillin-1 and flotillin-2) are ubiquitous proteins which have been linked to inflammation and regeneration. We hypothesized that alterations in the expression of flotillin-2 in enterocytes may be related to the pathogenesis of IBD as a classical example of an inflammatory disorder of mostly unknown origin. Methods: Cell and tissue localization of flotillin-2 (and -1) were investigated by immunofluorescent staining in 1. polarized and unpolarized CaCo-2w cells as a model of human enterocytes (native and after TNFα stimulation) and 2. intestinal biopsies from controls, patients with ulcerative colitis (UC) and patients with Crohn's disease (CD). For quantification of flotillin-2, we analyzed its expression in ileal and colonic biopsies from controls, UC patients and CD patients using real-time RT-PCR, Western blot and indirect immunofluorescence. Results: In polarized CaCo-2w cells and human enterocytes in biopsies, flotillins were localized at the basolateral membrane and on subapical vesicles, but not in the apical membrane. Flotillin-2 expression did not differ between UC patients, CD patients and controls. However, it was significantly higher in colonic biopsies compared to ileal biopsies in all groups. Conclusions: By virtue of its abundant expression in enterocytes, flotillin-2 must have an essential function in intestinal physiology, especially in the colon. Yet our data could not link flotillin-2 to the pathogenesis of IBD.
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

The current literature of evolutionary many-objective optimization is merely focused on the scalability to the number of objectives, while little work has considered the scalability to the number of decision variables. Nevertheless, many real-world problems can involve both many objectives and large-scale decision variables. To tackle such large-scale many-objective optimization problems (MaOPs), this paper proposes a specially tailored evolutionary algorithm based on a decision variable clustering method. To begin with, the decision variable clustering method divides the decision variables into two types: 1) convergence-related variables and 2) diversity-related variables. Afterward, to optimize the two types of decision variables, a convergence optimization strategy and a diversity optimization strategy are adopted. In addition, a fast nondominated sorting approach is developed to further improve the computational efficiency of the proposed algorithm. To assess the performance of the proposed algorithm, empirical experiments have been conducted on a variety of large-scale MaOPs with up to ten objectives and 5000 decision variables. Our experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art evolutionary algorithms in terms of the scalability to decision variables on MaOPs.