Miracle-dz/UniFAR-FTUs huggingface dataset:
Calcium dysregulation in Parkinson's disease.

The major motor features by which Parkinson’s disease is diagnosed are mainly the consequence of degeneration of dopaminergic neurons in the substantia nigra pars compacta. This observation was the basis upon which dopamine replacement therapy became the cornerstone of symptomatic treatment. But as our understanding of the clinical phenotype, pathology and pathogenesis of Parkinson’s disease has improved, it has become clear that non-dopaminergic neuronal loss and synuclein-related pathology extend far outside the substantia nigra pars compacta, and even involve the autonomic plexuses of the gut and endocrine system. Non-dopaminergic neuronal loss in Parkinson’s disease is associated with a considerable clinical burden that increases with disease progression and becomes the predominant determinant of quality of life, and the requirements and costs for long-term care in these patients (Chaudhuri et al. , 2006). The features that do not depend on loss of dopaminergic neurons are often referred to as non-motor, although dopamine replacement can improve more than the motor symptoms of Parkinson’s disease (Chaudhuri and Schapira, 2009). Therefore a major unmet need in Parkinson’s disease is the development of treatments that can delay or prevent progression of the neurodegenerative process that affects both dopaminergic and non-dopaminergic neurons. The pathogenesis of Parkinson’s disease has been informed by a number of related and interdependent fields of research: pathology, biochemistry, physiology and genetics. Each provides separate insights into the molecular pathogenesis of the disease, but when the individual evidence is convergent on common pathways to neurodegeneration, the relevance of these data becomes particularly compelling. Mitochondrial dysfunction, oxidative stress, protein aggregation and inflammation each play a role in the pathogenesis of Parkinson’s disease. Additional data support the interaction of these systems not only between each other but also …
Performance comparison for lane detection and tracking with two different techniques

This paper presents the performance comparison for lane detection and tracking using two techniques; Hough transform and color thresholding. Hough transform includes the following steps: gray scale conversion, edge detection, Hough space accumulation and un-Hough step to draw line. Color thresholding algorithm comprises color thresholding, morphology filtering and drawing tracking label. The results showed that Hough transform technique outperforms color technique in terms of reliability, efficiency and speed of code implementation. Both techniques are coded using MATLAB R2010b.
Jackknife Bias Reduction in the Presence of a Unit Root

This paper analyses the properties of jackknife estimators of the first-order autoregressive coefficient when the time series of interest contains a unit root. It is shown that, when the sub-samples do not overlap, the sub-sample estimators have different limiting distributions from the full-sample estimator and, hence, the jackknife estimator in its usual form does not eliminate fully the first-order bias as intended. The joint moment generating function of the numerator and denominator of these limiting distributions is derived and used to calculate the expectations that determine the optimal jackknife weights. Two methods of avoiding this procedure are proposed and investigated, one based on inclusion of an intercept in the regressions, the other based on adjusting the observations in the sub-samples. Extensions to more general augmented Dickey-Fuller (ADF) regressions are also considered. In addition to the theoretical results extensive simulations reveal the impressive bias reductions that can be obtained with these computationally simple jackknife estimators and they also highlight the importance of correct lag-length selection in ADF regressions.
Avoiding debt traps: Fiscal consolidation, financial backstops and structural reforms

In this article we develop a simple and stylised analytical framework, which is both tractable and feasible to estimate, capturing several key dimensions of the sovereign debt crisis in Europe. We use it to examine if and how a combination of fiscal consolidation, structural reform and financial backstops can help countries, notably the southern euro-area countries, to escape from the debt trap. Our analysis confirms that the loss of fiscal policy space in countries trapped in bad dynamics inevitably requires that fiscal action be directed towards consolidation despite some output loss in the short run. In particular, reducing debt levels breeds stronger growth and results in lower sovereign risk premia. We identify also a very important role for structural reform to help countries escape from bad dynamics. Last but not least, we find that financial backstops are helpful, but only to “buy time”. This additional time must be used productively, for fiscal consolidation and structural reforms to bear fruit as well as to make progress with institutional reforms of the European monetary union.
Recognition of building features in high-resolution SAR and optical imagery

In data of modern high-resolution SAR sensors like TerraSAR-X (TSX) man-made objects become visible in high detail. However, layover, occlusions, and interfering backscatter of different objects within the same resolution cell complicate automatic analysis particularly in urban areas. Two possibilities to facilitate interpretability are the use of additional data sources and time series of SAR images. We will present the current status of two research projects concerning those possibilities. First, we show building detection results achieved with a combination of SAR and optical aerial images. Features are extracted and analysed in a Conditional Random Field (CRF) framework exploiting context-knowledge. Second, the persistent scatterer (PS) interferometric SAR (InSAR) technique is applied to discover building deformations in a TSX time series. Those PS are grouped and interpreted using a 3D city-model.
UvA-DARE (Digital Academic Repository) Logico-Computational Aspects of Rationality

Summary Taking a broad historical line, we discuss major aspects of rationality that can be analyzed in a logical and computational perspective. Topics include classical notions from the foundations of computability, insights from the development of computer science and AI, and the richer picture of rationality emerging in current logical studies of agency. We also discuss two challenges. Distributed computing and evolutionary games replace 'high rationality' by 'low rationality' in behavior, machine learning defies classical views of representation and inference. Both trigger new logical themes in the study of rationality.
Towards unsupervised semantic segmentation of street scenes from motion cues

Motion provides a rich source of information about the world. It can be used as an important cue to analyse the behaviour of objects in a scene and consequently identify interesting locations within it. In this paper, given an unannotated video sequence of a dynamic scene from fixed viewpoint, we first present a set of useful motion features that can be efficiently extracted at each pixel by optical flow. Using these features, we then develop an algorithm that can extract motion topic models and identify semantically significant regions and landmarks in a complex scene from a short video sequence. For example, by watching a street scene our algorithm can extract meaningful regions such as roads and important landmarks such as parking spots. Our method is robust to complicating factors such as shadows and occlusions.
Attitudes of local residents towards the development of tourism in Slovenia : The case of the Primorska , Dolenjska , Gorenjska and Ljubljana regions

This study focuses on the attitudes of local residents toward tourism development in some tourist areas in Slovenia. Based on the place attachment model, this paper seeks to propose an integral approach to management of the local residents’ attitudes towards tourism development. Research data were collected in the Primorska, Dolenjska, Gorenjska and Ljubljana regions. A PCA factor analysis and a hierarchical regression analysis were used to analyze the research data. The findings confirm the proposed hypothesis that attitudes of local residents toward a tourism development are influenced by place attachment, the perception of tourism impacts and by the perceived and desired type of tourism. One important contribution of this study to the understanding of the obstacles to tourism development is the finding that length of residency strongly influences the development process. It can be concluded that long-term residents are generally less favourable to tourism development. In contrast, perceived positive tourism impacts, cultural tourism, quality of tourism and leisure structure and quality of natural environment positively influence local residents’ perceptions of tourism development. The economic reasons for tourism development revealed by the attitudes of local residents are evident. In spite of that, local residents are deeply concerned about the quality of their life and for the quality of their environment. Managers and planners should be aware that tourism development activities could evoke strong emotional responses of some groups of local residents. To avoid conflicts, local residents should be actively involved in the decision making process of tourism development.
Advances in Treatment Options for High-grade Glioma - Current Status and Future Perspectives

High-grade gliomas, including glioblastoma, anaplastic astrocytoma, anaplatic oligodendroglioma and anaplastic oligoastrocytoma, account for the majority of malignant primary brain tumours diagnosed in adults. The prognosis for these tumours is poor despite multimodality therapy with surgery, radiation and/or chemotherapy. This review summarises treatment options for high-grade glioma, including standard regimens, targeted agents and novel therapies.
A Formal Framework for Provenance Security

Provenance, or information about the origin, derivation, or history of data, is becoming an important topic especially for shared scientific or public data on the Web. It clearly has implications on security (and vice versa) yet these implications are not well-understood. A great deal of work has focused on mechanisms for recording, managing or using some kind of provenance information, but relatively little progress has been made on foundational models that define provenance and relate it to security goals such as availability, confidentiality or privacy. We argue that such foundations are essential to making meaningful progress on these problems and should be developed. In this paper, we outline a formal model of provenance, propose formalizations of security properties for provenance such as disclosure and obfuscation, and explore their implications in domains based on automata, database queries and workflow provenance graphs.
Quantitative hidden wall-thinning detection of Lamb waves in aluminium plate with wavenumber analysis

Recent advances in Lamb wave-based nondestructive testing technology have demonstrated the feasibility of detecting and locating damages in structural components. However, there remains many challenges to realize defect detection quantitatively due to the uncertainty of the relationship between the features of Lamb waves and defect size. Therefore, it is important to discover an intrinsical parameter of Lamb waves that can be used as a damage sensitive feature. Local wall-thinning is a common defect in aluminium plate-like components that are in widespread use in aerospace, and other industries. The aim of this paper is to present a quantitative defect detection method for achieving damage visualization in plate-like structures based on wave propagation phenomenon. Full wavefield data of propagating Lamb waves in plates contains a wealth of information about the wave interaction with damage such as the change of wavenumber components. The wavenumber of excited Lamb wave mode is a fixed value for a given frequency, the thickness and material properties of the plate. When Lamb waves propagate to the structural discontinuity, new wavenumber components are created by abrupt wave changes. To estimate spatially dependent wavenumber values, a short space three-dimensional Fourier transform (FT) method is used for processing wavefield data. The method utilizes extracting and processing of full wavefield transceived by PZT-laser detection system consisted by PZT element and two-wave mixing interferometer. A local wavenumber analysis method is presented for further processing full wavefield data at a certain frequency to estimate spatially dependent wavenumber values, which can be used to determine thin-walled thickness and shape. Simulation wavefield data are obtained by ABAQUS simulation. Experimental wavefield data are obtained by PZT-laser for confirming the effectiveness of the method. Tests performed on an aluminium plate with artificially introduced notch to confirm the effectiveness of the method and its potential for application for the inspection of a variety of structural components.
Combining Entity Matching Techniques for Detecting Extremist Behavior on Discussion Boards

Many extremist groups and terrorists use the Web for various purposes such as exchanging and reinforcing their beliefs, making monitoring and analysis of discussion boards an important task for intelligence analysts in order to detect individuals that might pose a threat towards society. In this work we focus on how to automatically analyze discussion boards in an effective manner. More specifically, we propose a method for fusing several alias (entity) matching techniques that can be used to identify authors with multiple aliases. This is one part of a larger system, where the aim is to provide the analyst with a list of potential extremist worth investigating further.
Interaction for style-constrained OCR

The error rate can be considerably reduced on a style-consistent document if its style is identified and the right style-specific classifier is used. Since in some applications both machines and humans have difficulty in identifying the style, we propose a strategy to improve the accuracy of style-constrained classification by enlisting the human operator to identify the labels of some characters selected by the machine. We present an algorithm to select the set of characters that is likely to reduce the error rate on unlabeled characters by utilizing the labels to reclassify the remaining characters. We demonstrate the efficacy of our algorithm on simulated data.
Towards approximate spatial queries for large-scale vehicle networks

With advances in vehicle-to-vehicle communication, future vehicles will have access to a communication channel through which messages can be sent and received when two get close to each other. This enabling technology makes it possible for authenticated users to send queries to those vehicles of interest, such as those that are located within a geographic region, over multiple hops for various application goals. However, a naive method that requires flooding the queries to each active vehicle in a region will incur a total communication overhead that is proportional to the size of the area and the density of vehicles. In this paper, we study the problem of spatial queries for vehicle networks by investigating probabilistic methods, where we only try to obtain approximate estimates within desired confidence intervals using only sublinear overheads. We consider this to be particularly useful when spatial query results can be made approximate or not precise, as is the case with many potential applications. The proposed method has been tested on snapshots from real world vehicle network traces.
Musical-noise-free blind speech extraction using ICA-based noise estimation and iterative spectral subtraction

In this paper, we propose a new iterative signal extraction method using a microphone array that can be applied to nonstationary noise. In our previous study, it was found that optimized iterative spectral subtraction (SS) results in speech enhancement with almost no musical noise generation, but this method is valid only for stationary noise. The proposed method consists of iterative blind dynamic noise estimation by independent component analysis (ICA) and musical-noise-free speech extraction by modified iterative SS, where multiple iterative SS is applied to each channel while maintaining the multi-channel property reused for ICA. Also, related to the proposed method, we discuss the justification of applying ICA to such signals nonlinearly distorted by SS. From objective and subjective evaluations, we reveal that the proposed method outperforms the conventional method.
Source of Innovation File Systems

7 Scalability is one of the primary forces driving popularity and adoption of the Apache Hadoop project. A typical use case for Hadoop is an emerging Web site starting to run a five-node Hadoop cluster and then gradually increasing it to hundreds of nodes as business grows. Last year ;login: published my article [12] summarizing one aspect of Hadoop scalability, namely, the limits of scalability of the Hadoop Distributed File System [13]. There are many other dimensions to Hadoop scalability. Here I would like to address some of them. This has been an eventful year for Apache Hadoop [1]. The project has emerged as a data mining platform, becoming an industry standard for Big Data. Apache Hadoop is successfully used in science and a variety of industries. Scientific applications include mathematics, high energy physics, astronomy, genetics, and oceanography. The platform adoption has scaled far beyond information technology—its original target area—into most industries, excluding only hunting and fishing, but probably not for long. Apache Hadoop was used to compute the record two quadrillionth (10 15) digit of π [15], which turned out to be 0, and helped IBM's Watson to win on Jeopardy in the " Man versus Machine race, " as media presented it. Recognized for its influence on technological innovation, the Apache Hadoop project has won the 2011 MediaGuardian Innovation Award over nominees such as iPad and WikiLeaks. While Hadoop was the driving force of technological innovation, its internal innovation has been on a rather slow rise. Not to imply that it's inert. On the contrary, a lot of development is going on in the field. Hard to underestimate the value of implementing security for Hadoop, or building analytical tools with Hadoop, or stabilizing internal company releases, which are essential for businesses running Hadoop. However, due to the lack of dominating gravitational force, these highly dedicated activities did not materialize in common production-veteran Hadoop developer. He is a principal Hadoop architect at eBay. Konstantin specializes in efficient data structures and algorithms for large-scale distributed storage systems. He discovered a new type of balanced trees, S-trees, for optimal indexing of unstructured data, and he was a primary developer of an S-tree-based Linux file system, treeFS, a prototype of reiserFS. Konstantin holds a PhD in computer science from Moscow State University, Russia. He is also a member of the Project Management Committee for Apache Hadoop.
Public deliberation in municipal planning: supporting action and reflection with mobile technology

This paper reports on an exploratory participatory design process aimed at supporting citizen deliberation in municipal planning. It presents the main outcomes of this process in terms of selected prototypes and an approach to the use setting. We support and discuss different ways for citizens to act and reflect on proposed plans: in-situ, while physically close to the planning object, and ex-situ, when citizens are remote from this. The support of in-situ and ex-situ participation allows citizens to engage in continuous reflection-in and on-action as a collaborative activity with other citizens, hereby inspiring citizens to increase their democratic engagement.
Interannual and Spatial Variability in Maturity of Walleye Pollock Gadus chalcogrammus and Implications for Spawning Stock Biomass Estimates in the Gulf of Alaska

Catch quotas for walleye pollock Gadus chalcogrammus, the dominant species in the groundfish fishery off Alaska, are set by applying harvest control rules to annual estimates of spawning stock biomass (SSB) from age-structured stock assessments. Adult walleye pollock abundance and maturity status have been monitored in early spring in Shelikof Strait in the Gulf of Alaska for almost three decades. The sampling strategy for maturity status is largely characterized as targeted, albeit opportunistic, sampling of trawl tows made during hydroacoustic surveys. Trawl sampling during pre-spawning biomass surveys, which do not adequately account for spatial patterns in the distribution of immature and mature fish, can bias estimated maturity ogives from which SSB is calculated. Utilizing these maturity data, we developed mixed-effects generalized additive models to examine spatial and temporal patterns in walleye pollock maturity and the influence of these patterns on estimates of SSB. Current stock assessment practice is to estimate SSB as the product of annual estimates of numbers at age, weight at age, and mean maturity at age for 1983-present. In practice, we found this strategy to be conservative for a time period from 2003–2013 as, on average, it underestimates SSB by a 4.7 to 11.9% difference when compared to our estimates of SSB that account for spatial structure or both temporal and spatial structure. Inclusion of spatially explicit information for walleye pollock maturity has implications for understanding stock reproductive biology and thus the setting of sustainable harvest rates used to manage this valuable fishery.
Steroid receptor coactivator-3 differentially regulates the inflammatory response in peritoneal macrophages

Steroid receptor coactivator-3 (SRC-3) is a transcriptional coactivator that plays an important role in the regulation of cytokine mRNA translation. In the present study, SCR-3 gene knockout mice were used to study the effects of SCR-3 on the regulation of the inflammatory response in peritoneal macrophages induced by lipopolysaccharides (LPS). Peritoneal macrophages (PMs) of SRC-3−/− mice showed a decrease in the release of TNF-α, IL-1β and IL-6, and an increase in the release of IL-10. Furthermore, results of RT-PCR also showed that levels of TNF-α, IL-1β and IL-6 mRNA expression were significantly lower, while the level of IL-10 mRNA expression was higher in the SRC-3−/− mice, compared to those of wild-type mice, following treatment with LPS (p<0.01). In addition, western blotting revealed that: i) the extent of reduction of the glucocorticoid receptor in PMs from SRC-3−/− mice was significantly lower than that in wild-type mice (p<0.01); ii) the extent of increase of AP-1 in PMS from SRC-3−/− mice was significantly lower than that in wild-type mice (p<0.01); iii) the extent of increase of NF-κB p65 in PMs from SRC-3−/− mice was significantly higher than that in wild-type mice (p<0.01). Collectively, our studies revealed that SRC-3 may play a key role in the maintenance of innate immunity. Furthermore, absence of the SRC-3 protein may result in the partial loss of inflammation and phagocytosis barrier function, including suppression of LPS-induced transcriptional activity, release of TNF-α, IL-1β and IL-6, and obstruction of the function of phagocytes and elimination of bacteria, as well as their production.