Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.
Characterization of the ordered phase formed by sphingomyelin analogues and cholesterol binary mixtures

The influences of structural alterations of sphingomyelin (SM) on its interactions with cholesterol (chol) and on ordered phase formation were examined by density measurements and surface pressure vs. molecular area isotherm measurements. In addition, we quantitatively characterized the ordered phase formed in each SM and chol binary mixture on the basis of the molecular compressional modulus of SM ( Cmol−1). Density measurements demonstrated that the ordered phase formation in threo-SM (tSM)/chol and dihydrosphingomyelin (DHSM)/chol binary bilayers shows similar chol concentration-dependency to that of natural erythro-SM (eSM)/chol bilayers; the ordered phase formation was completed in the presence of 25 mol% chol. In contrast, SM bearing a triple bond in the place of a double bond (tripleSM) required a greater concentration of chol to completely transform the bilayer into the ordered phase (at 40 mol% chol). Surface pressure vs. molecular area isotherms showed that the DHSM molecule ( Cmol−1 = 290 mN/m) is more rigid than eSM ( Cmol−1 = 240 mN/m) above 30 mol% chol (in the ordered phase), although these values are similar (140–150 mN/m) in the absence of chol (liquid condensed phase). Most likely, the DHSM/chol mixture forms a more ordered membrane than the eSM/chol mixture does. Moreover, in the absence of chol, the rigidity of the tripleSM molecule ( Cmol−1 = 250 mN/m) is significantly higher as compared with that of the eSM molecule ( Cmol−1 = 150 mN/m), which is probably due to the presence of a triple bond.
Detecting irregularity in videos using kernel estimation and KD trees

Automatic event understanding is the ultimate goal for many visual surveillance systems. In this paper, we propose a novel approach for on-line detecting unusual human activities in videos without the need to explicitly define all valid configurations. Within the framework of Bayesian inference, the detection process is formulated as an MAP estimation where we attempt to find whether activities in new video segments have similar activities in a video database. Our approach has three contributions: firstly, we build the statistical representation of normal behaviors in the database using nonparametric kernel density estimation; secondly, local feature descriptors are highly compressed using PCA and stored in a K-D tree structure, making the search for behavior-based similarity fast and effective; thirdly, the K-D trees are used to generate multiple hypotheses which compete for the optimal classification. The approach requires no tracking, no explicit motion estimation, and no predefined class-based templates. Experimental results have validated our approach in many real-world video sequences.
A Case Study on the RCMD Method and Fuzzy C-Regression Models for Mining Regression Classes

It is well known that the classical regression analysis, especially parametric regression analysis, is one of the important methods of extracting information from data sets. A family of regression functions, called fuzzy c-regression models (FCRM), has been presented which can be used to characterize the linear relationship to certain types of mixed data. More generally, an effective and robust method, coined regression class mixture decomposition (RCMD), has also been proposed for the mining of regression classes in large data sets. In this paper, we focus on a special case of regression class mixture models, switching regression models, and adopt the RCMD method and the FCRM method in a real example and a simulation experiment. It is shown that the RCMD method has some special advantages over some traditional methods of data analysis and these two methods give almost consistent estimation results in switching regression models.
License Plate Detection and Recognition Using a Dual-Camera Module in a Large Space

In this study, a calibrated dual-camera device, a fixed camera and a pan-tilt-zoom camera, is setup to monitor moving vehicles in an open space. This device not only tracks multiple targets but also gets the license plate images with high quality. Next, a convolutional neural network (CNN) is designed to be a detector and a character classifier for efficiently locating the regions of license plates and recognizing the alphabets on them. Two working environments were setup at the entrance of a university and at a pedestrian-only region in campus. Some experimental results are given to show the validity of the proposed approach.
Phase I Metabolic Genes and Risk of Lung Cancer: Multiple Polymorphisms and mRNA Expression

Polymorphisms in genes coding for enzymes that activate tobacco lung carcinogens may generate inter-individual differences in lung cancer risk. Previous studies had limited sample sizes, poor exposure characterization, and a few single nucleotide polymorphisms (SNPs) tested in candidate genes. We analyzed 25 SNPs (some previously untested) in 2101 primary lung cancer cases and 2120 population controls from the Environment And Genetics in Lung cancer Etiology (EAGLE) study from six phase I metabolic genes, including cytochrome P450s, microsomal epoxide hydrolase, and myeloperoxidase. We evaluated the main genotype effects and genotype-smoking interactions in lung cancer risk overall and in the major histology subtypes. We tested the combined effect of multiple SNPs on lung cancer risk and on gene expression. Findings were prioritized based on significance thresholds and consistency across different analyses, and accounted for multiple testing and prior knowledge. Two haplotypes in EPHX1 were significantly associated with lung cancer risk in the overall population. In addition, CYP1B1 and CYP2A6 polymorphisms were inversely associated with adenocarcinoma and squamous cell carcinoma risk, respectively. Moreover, the association between CYP1A1 rs2606345 genotype and lung cancer was significantly modified by intensity of cigarette smoking, suggesting an underling dose-response mechanism. Finally, increasing number of variants at CYP1A1/A2 genes revealed significant protection in never smokers and risk in ever smokers. Results were supported by differential gene expression in non-tumor lung tissue samples with down-regulation of CYP1A1 in never smokers and up-regulation in smokers from CYP1A1/A2 SNPs. The significant haplotype associations emphasize that the effect of multiple SNPs may be important despite null single SNP-associations, and warrants consideration in genome-wide association studies (GWAS). Our findings emphasize the necessity of post-GWAS fine mapping and SNP functional assessment to further elucidate cancer risk associations.
A Genetic-Based Extensible Stemmer for Arabic Verbs

Firstly we covered the problem of rule definition for x-fixing Arabic roots. Instead of the traditional approach that relies on the semantics conveyed by x-fixing, we based our approach on the lexica. Presented in this paper an extensible schema for rules definition, which we used to partially cover the cases of verbs produced by preand suffixing triliteral roots. We then present a stemming system that processes and extract the rules. The rules are used later for stemming inputted words.
Improving engagement in an early career academic setting: can existing models guide early career academic support strategies?

ABSTRACT Early career academics (ECAs) represent the future of the academic workforce, but competition and career uncertainty is resulting in disengagement and burnout. In professions outside academia, increased engagement is associated with perceived organisational support and fair recognition and rewards, as well as opportunities to meet basic psychological needs of autonomy, competence and relatedness. In contrast, decreased engagement is linked with increasing demands on effort and over-commitment to work. The current study used multiple linear regression to test whether comparable relationships were observed in a sample of 151 ECAs in an Australian university. Opportunities to build and demonstrate competence at work, the presence of meaningful relationships and perceived organisational support were independent and statistically significant predictors of engagement. The need for autonomy and fair rewards and recognition appeared to be correlated but not statistically significant predictors of engagement. Contrary to prediction, increasing effort and over-commitment to work did not predict decreases in engagement. These results are discussed in light of implications for programs designed to support the development of ECAs into various career pathways.
Financial Institutions, Financial Contagion, and Financial Crises

This paper endogenizes financial contagion and financial crises from financial institutions. We show that financial crises can emanate from financial institutions which generate soft-budget constraints (SBC). The prevailing SBC in an economy distort in-formation such that the interbank lending market faces a "lemon" problem. The lemon problem in the lending market may contribute to bank-run contagions and can lead to the collapse of the lending market while inducing a run on the economy. Moreover, due to the lemon problem in the financial system, a rational government policy in this economy will lead to a SBC trap that all the illiquid banks are to be bailed out. In comparison, we show that an economy with a predominance of diversified financial institutions will be featured by hard-budget constraints. From this point, we show mechanisms that in this economy firms disclose timely information to the banks and to the financial market as a whole. Thus bank runs can be stopped, contagious risks contained and financial crisis prevented.
Fast intra-collection audio matching

The general goal of audio matching is to identify all audio extracts of a music collection that are similar to a given query snippet. Over the last years, several approaches to this task have been presented. However, due to the complexity of audio matching the proposed approaches usually either yield excellent matches but have a poor runtime or provide quick responses albeit calculate less satisfying retrieval results. In this paper, we present a novel procedure that combines the positive aspects and efficiently computes good retrieval results. Our idea is to exploit the fact that in some practical applications queries are not arbitrary audio snippets but are rather given as extracts from the music collection itself (intra-collection query). This allows us to split the audio collection into equal sized overlapping segments and to precompute their retrieval results using dynamic time warping (DTW). Storing these matches in appropriate index structures enables us to efficiently recombine them at runtime. Our experiments indicate a significant speedup compared to classical DTW-based audio retrieval while achieving nearly the same retrieval quality.
Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed after the administration of intravenous contrast medium to noninvasively access tumor vascular characteristics. DCE‐MRI techniques utilizing low‐molecular‐weight contrast media have successfully made the transition from methodological development to preclinical and clinical validation and are now rapidly becoming mainstream clinical tools. DCE‐MRI using macromolecular contrast medium (MMCM) can also assay microvascular characteristics of human tumor xenografts. MMCM approval for human use will occur soon. The success of both techniques depends on their ability to demonstrate quantitative differences of contrast medium behavior in a variety of tissues. Evidence is mounting that kinetic parameters correlate with immunohistochemical surrogates of tumor angiogenesis, including microvessel density, and with pathologic tumor grade. DCE‐MRI is being applied to monitor the clinical effectiveness of a variety of treatments, including antiangiogenic drugs. Kinetic parameter changes following treatment have correlated with histopathological outcome and patient survival. This article reviews the current clinical status of low‐molecular‐weight DCE‐MRI and reviews the potential of MMCM techniques for evaluating human tumors. Ongoing challenges faced by DCE‐MRI as clinical and research tools will be explored. J. Magn. Reson. Imaging 2002;16:407–422. © 2002 Wiley‐Liss, Inc.
Detecting Internet Outages with Precise Active Probing ( extended )

Parts of the Internet are down every day, from the intentional shutdown of the Egyptian Internet in Jan. 2011 and natural disasters such as the Mar. 2011 Japanese earthquake, to the thousands of small outages caused by localized accidents, and human error, maintenance, or choices. Understanding these events requires efficient and accurate detection methods, motivating our new system to detect network outages by active probing. We show that a single computer can track outages across the entire analyzable IPv4 Internet, probing a sample of 20 addresses in all 2.5M responsive /24 address blocks. We show that our approach is significantly more accurate than the best current methods, with 31% fewer false conclusions, while providing 14% greater coverage and requiring about the same probing traffic. We develop new algorithms to identify outages and cluster them to events, providing the first visualization of outages. We carefully validate our approach, showing consistent results over two years and from three different sites. Using public BGP archives and news sources we confirm 83% of large events. For a random sample of 50 observed events, we find 38% in partial control-plane information, reaffirming prior work that small outages are often not caused by BGP. Through controlled emulation we show that our approach detects 100% of fullblock outages that last at least twice our probing interval. Finally, we report on Internet stability as a whole, and the size and duration of typical outages, using core-to-edge observations with much larger coverage than prior mesh-based studies. We find that about 0.3% of the Internet is likely to be unreachable at any time, suggesting the Internet provides only 2.5 “nines” of availability.
Global Software Processes Definition in a Distributed Environment

Global software development projects are passing through the boundaries of a company or a country. In order to manage global teams effectively, it is necessary to define a software process adapted to different organizational cultures. A global software process gives all team members a common language to define tasks and activities allowing the better understanding of the business domain terms and the project milestones in spite of their differences in terms of culture and organizational structures. This paper presents the practices adopted to define a global software process for a distributed environment and the factors that impacted in the process definition
An Exploratory Analysis of Intranet Benefits

An exploratory survey of business professionals from a broad range of organizations indicates that potential intranet benefits are both difficult to achieve and highly interrelated. Although many organizations implement intranets in the hope of generating cost savings or improving knowledge sharing across organizational units, this study indicates that the only likely benefit of implementing an intranet is faster access to information. The study also suggests that the most difficult benefit to achieve is generating a more cooperative work environment, and that developing an intranet that improves the quality and relevance of information is foundational to achieving most other intranet benefits.
Time-Optimal Planning in Temporal Problems

This paper presents TPSYS, a Temporal Planning SYStem, which arises as an attempt to combine the ideas of Graphplan andTGP to solve temporal planning problems more efficiently. TPSYS is based on a three-stage process. The first stage, a preprocessing stage, facilitates the management of constraints on duration of actions. The second stage expands a temporal graph and obtains the set of temporal levels at which propositions and actions appear. The third stage, the plan extraction, obtains the plan of minimal duration by finding a proper flow of actions.
Toy quantum categories

We show that Rob Spekken’s toy quantum theory arises as an instance of our categorical approach to quantum axiomatics, as a (proper) subcategory of the dagger compact category FRel of finite sets and relations with the cartesian product as tensor, where observables correspond to dagger Frobenius algebras. This in particular implies that the quantum-like properties of the toy model are in fact very general categorytheoretic properties. We also show the remarkable fact that we can already interpret complementary quantum observables on the two-element set in FRel.
Handling Exceptions in Petri Net-Based Digital Architecture: From Formalism to Implementation on FPGAs

A component-based approach to the specification and implementation of complex digital systems on field-programmable gate arrays (FPGAs) has been developed, with the behavior and composition of the components specified by Petri nets (PNs). Yet modeling behavior in the case of error becomes intricate if only PNs are used. In this case, the designer often has to address every possible situation when an error occurs, which leads to complex models and human errors. This paper offers a way to model exception handling by adding the concept of macroplace (MP) to the formalism while preserving the conformity and efficiency of the implementation on a programmable logic device (such as FPGAs), as well as the analyzability of the model.
Networks, alliances and loyalty programs in Australia : power not passion drives the affair

Previous research on loyalty programs in Australia has shown the benefits gained by partners in these programs. This paper develops this research and introduces the concept of network development in loyalty programs. It builds on the theory of network development (Hertz and Mattson, 2004) and uses the role of strategic alliances to explain the implications of networks for the success of loyalty programs. The loyalty program utilised in this paper is FlyBuys which is run by Loyalty Pacific, a joint venture formed by National Australia Bank, Shell Australia and the Coles Myer Group. The initiators of Pacific Loyalty recognised the need to increase and improve their individual customer databases. The development, maintenance and usage of a data system that size, requires special skills and a large financial commitment. A sophisticated hardware and software solution is essential in order to track and manage the growing amount of data, transactions and services offered such as statements, service call centre and point collections. As the computer system plays a vital and ongoing role in the survival of this consumer loyalty scheme, a strategic alliance with HP/Compaq services was formed. This paper highlights the importance of HP/Compaq as the provider of the information technology platform which drives the program and shows how their role has developed and been expanded overtime.
A review of methods for hydrodynamic analysis of helical swimming flagella

The recent years it is stressed an increased interest in developing models of swimmers at low-Reynolds number. The interest ranges from biological to nanotechnology applications. This article reviews the relate researches and investigates the accuracy of hydrodynamic analysis methods which are used for swimming flagella. There are several different swimming patterns and structures in microorganisms and we focus on helical shape of flagellum. By reviewing the related researches, we discuss several application examples based on these methods.
Graph Partition Based Decomposition Approach for Large-Scale RailwayLocomotive Assignment

Locomotive assignment is a classic planning problem in railway industry. Different models and algorithms (e.g., network flow model, MIP model, adaptive dynamic programming) are proposed to solve this problem and have got remarkable progress. In practice, the locomotives are usually in the size of hundreds to even more than a thousand. So as- signing individual locomotives to trains on railway network will result in too huge modeling space for the problem to be solved. Hence most existing research work takes a trade off to concern the types (or type combinations) of locomotives, which are usually in the size of tens at the most. This impedes the application value for being unable to consider the avail- ability and maintenance requirement of individual locomotives. In this paper, a novel path-based MIP model is proposed for modeling the locomotive assignment problem. By adopting graph partition method on the space-time network, the original problem is decomposed into inter-connected multiple sub-problems. Then an iterative MIP solving algorithm is devised to find the near optimized solution for the original problem. The approach proposed in this paper has been vali- dated in a railway bureau in China. The experiment shows that the approach has superior advantage in both scalability and performance with reasonable cost of objective value.
Total Variation as a Local Filter

In the Rudin-Osher-Fatemi (ROF) image denoising model, total variation (TV) is used as a global regularization term. However, as we observe, the local interactions induced by TV do not propagate much at long distances in practice, so that the ROF model is not far from being a local filter. In this paper, we propose building a purely local filter by considering the ROF model in a given neighborhood of each pixel. We show that appropriate weights are required to avoid aliasing-like effects, and we provide an explicit convergence criterion for an associated dual minimization algorithm based on Chambolle's work. We study theoretical properties of the obtained local filter and show that this localization of the ROF model brings an interesting optimization of the bias-variance trade-off, and a strong reduction of an ROF drawback called the “staircasing effect. ” Finally, we present a new denoising algorithm, TV-means, that efficiently combines the idea of local TV-filtering with the nonlocal means patch-based method.