Miracle-dz/UniFAR-FTUs huggingface dataset:
Eurographics/ Ieee-vgtc Symposium on Visualization 2008 Interaction-dependent Semantics for Illustrative Volume Rendering

In traditional illustration the choice of appropriate styles and rendering techniques is guided by the intention of the artist. For illustrative volume visualizations it is difficult to specify the mapping between the 3D data and the visual representation that preserves the intention of the user. The semantic layers concept establishes this mapping with a linguistic formulation of rules that directly map data features to rendering styles. With semantic layers fuzzy logic is used to evaluate the user defined illustration rules in a preprocessing step. In this paper we introduce interaction-dependent rules that are evaluated for each frame and are therefore com-putationally more expensive. Enabling interaction-dependent rules, however, allows the use of a new class of semantics, resulting in more expressive interactive illustrations. We show that the evaluation of the fuzzy logic can be done on the graphics hardware enabling the efficient use of interaction-dependent semantics. Further we introduce the flat rendering mode and discuss how different rendering parameters are influenced by the rule base. Our approach provides high quality illustrative volume renderings at interactive frame rates, guided by the specification of illustration rules.
Transgenic GDNF Positively Influences Proliferation, Differentiation, Maturation and Survival of Motor Neurons Produced from Mouse Embryonic Stem Cells

Embryonic stem cells (ESC) are pluripotent and thus can differentiate into every cell type present in the body. Directed differentiation into motor neurons (MNs) has been described for pluripotent cells. Although neurotrophic factors promote neuronal survival, their role in neuronal commitment is elusive. Here, we developed double-transgenic lines of mouse ESC (mESC) that constitutively produce glial cell line-derived neurotrophic factor (GDNF) and also contain a GFP reporter, driven by HB9, which is expressed only by postmitotic MNs. After lentiviral transduction, ESC lines integrated and expressed the human GDNF (hGDNF) gene without altering pluripotency markers before differentiation. Further, GDNF-ESC showed significantly higher spontaneous release of this neurotrophin to the medium, when compared to controls. To study MN induction, control and GDNF cell lines were grown as embryoid bodies and stimulated with retinoic acid and Sonic Hedgehog. In GDNF-overexpressing cells, a significant increase of proliferative Olig2+ precursors, which are specified as spinal MNs, was found. Accordingly, GDNF increases the yield of cells with the pan motor neuronal markers HB9, monitored by GFP expression, and Isl1. At terminal differentiation, almost all differentiated neurons express phenotypic markers of MNs in GDNF cultures, with lower proportions in control cells. To test if the effects of GDNF were present at early differentiation stages, exogenous recombinant hGDNF was added to control ESC, also resulting in enhanced MN differentiation. This effect was abolished by the co-addition of neutralizing anti-GDNF antibodies, strongly suggesting that differentiating ESC are responsive to GDNF. Using the HB9::GFP reporter, MNs were selected for electrophysiological recordings. MNs differentiated from GDNF-ESC, compared to control MNs, showed greater electrophysiological maturation, characterized by increased numbers of evoked action potentials (APs), as well as by the appearance of rebound APs, sag inward rectification, spike frequency adaptation and spontaneous synaptic potentials. Upon challenge with kainate, GDNF-overexpressing cells are more resistant to excitotoxicity than control MNs. Together these data indicate that GDNF promotes proliferation of MN-committed precursors, promotes neuronal differentiation, enhances maturation, and confers neuroprotection. GDNF-expressing ESC can be useful in studies of development and disease.
Summarizing Complex Development Artifacts by Mining Heterogeneous Data

Summarization is hailed as a promising approach to reduce the amount of information that must be taken in by the person who wants to understand development artifacts, such as pieces of code, bug reports, emails, etc. However, existing approaches treat artifacts as pure textual entities, disregarding the heterogeneous and partially structured nature of most artifacts, which contain intertwined pieces of distinct type, such as source code, diffs, stack traces, human language, etc. We present a novel approach to augment existing summarization techniques (such as LexRank) to deal with the heterogeneous and multidimensional nature of complex artifacts. Our preliminary results on heterogeneous artifacts suggest our approach outperforms the current text-based approaches.
Cache replacement based on reuse-distance prediction

Several cache management techniques have been proposed that indirectly try to base their decisions on cacheline reuse-distance, like Cache Decay which is a postdiction of reuse-distances: if a cacheline has not been accessed for some ldquodecay intervalrdquo we know that its reuse-distance is at least as large as this decay interval. In this work, we propose to directly predict reuse-distances via instruction-based (PC) prediction and use this information for cache level optimizations. In this paper, we choose as our target for optimization the replacement policy of the L2 cache, because the gap between the LRU and the theoretical optimal replacement algorithm is comparatively large for L2 caches. This indicates that, in many situations, there is ample room for improvement. We evaluate our reusedistance based replacement policy using a subset of the most memory intensive SPEC2000 and our results show significant benefits across the board.
CHANDRA OBSERVATION OF QUIESCENT LOW-MASS X-RAY BINARIES IN THE GLOBULAR CLUSTER NGC 6304

This paper presents the analysis of candidate low-mass X-ray binaries in quiescence (qLMXBs) observed during a short Chandra/ACIS observation of the globular cluster (GC) NGC 6304. Two out of the three candidate qLMXBs of this cluster, XMMU 171433−292747 and XMMU 171421−292917, lie within the field of view. This permits comparison with the discovery observation of these sources. The one in the GC core—XMMU 171433−292747—is spatially resolved into two separate X-ray sources, one of which is consistent with a pure H-atmosphere qLMXB, and the other is an X-ray power-law spectrum source. These two spectral components separately account for those observed from XMMU 171433−292747 in its discovery observation. We find that the observed flux and spectral parameters of the H-atmosphere spectral components are consistent with the previous observation, as expected from a qLMXB powered by deep crustal heating. XMMU 171421−292917 also has neutron star atmosphere spectral parameters consistent with those in the XMM-Newton observation and the observed flux has decreased by a factor 0.54+0.30-0.24.
SNAPper: gene order predicts gene function

UNLABELLED SNAPper is a network service for predicting gene function based on the conservation of gene order. AVAILABILITY The SNAPper server is available at http://pedant.gsf. de/snapper. SNAPper-based functional predictions will soon be offered as part of the PEDANT genome analysis server http://pedant.gsf. de.
Video scene extraction by force competition

In this paper, we present a novel scheme for automatic video scene extraction. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and an force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experiment on MPEG-7 test videos achieves promising results by the proposed algorithm.
PBMAN: a Policy-based Management Framework for Ambient Networks

Ambient networks aim at providing new networking solutions to highly mobile users considering a mix of current and future wireless technologies. In the last years, policy-based approaches have been proposed as an effective way of managing large networks, e.g. the IETF policy framework. In this paper, we present the design and implementation of PBMAN, a Policy-based Management Framework for Ambient Networks. PBMAN is based on a P2P infrastructure, aimed at providing scalability and self-configuration features
3D velocity from 3D Doppler radial velocity

We present local least squares and regularization frameworks for computing 3D velocity (3D optical flow) from 3D radial velocity measured by a Doppler radar. We demonstrate the performance of our algorithms quantitatively on synthetic radial velocity data and qualitatively on real radial velocity data, obtained from the Doppler radar at Kurnell Radar station, Botany Bay, New South Wales, Australia. Radial velocity can be used to predict the future positions of storms in sequences of Doppler radar datasets. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 189–198, 2005
Textline information extraction from grayscale camera-captured document images

Cameras offer flexible document imaging, but with uneven shading and non-planar page shape. Therefore cameracaptured documents need to go through dewarping before being processed by traditional text recognition methods. Curled textline detection is an important step of dewarping. Previous approaches of curled textline detection use binarization as a pre-processing step, which can negatively affect the detection results under uneven shading. Furthermore, these approaches are sensitive to high degrees of curl and estimate x-line1 and baseline pairs using regression which may result in inaccurate estimation. We introduce a novel curled textline detection approach for grayscale document images. First, the textline structure is enhanced by using match filter bank smoothing and then central lines of textlines are detected using ridges. Then, x-line and baseline pairs are estimated by adapting active contours (snakes) over ridges. Unlike other approaches, our approach does not use binarization and applies directly on grayscale images. We achieved 91% of detection accuracy with good estimation of x-line and baseline pairs on the dataset of CBDAR 2007 document image dewarping contest.
Housing and Personal Wealth in a Global Context

Housing is the single most important component of personal wealth in most countries. The special characteristics of housing markets are therefore key to understanding personal portfolios, saving, the household distribution of wealth, and the monetary transmission mechanism. This paper discusses how housing markets and institutions differ across countries, paying particular attention to the UK and the EU, but extending its reach as well to other OECD countries such as Japan and emerging market countries such as South Africa. It analyzes how those differences help to create contrasts in the impact on consumption of housing as a component of personal wealth. The impacts of rates of home ownership, credit market characteristics, interest rates, and macroeconomic conditions are studied. Implications for monetary, fiscal, and other policies are discussed.
A power efficient MAC protocol for implant device communication in Wireless Body Area Networks

Applications of sensor networks in healthcare have undergone major changes in recent times. Implanted wireless sensor devices inside the human body to monitor the activities are a reality now. A new field called the Wireless Body Area Networks (WBAN or BAN) has emerged as a hot research area. To fulfill the needs for a common standard and solve the issues in this emerging field, IEEE has proposed a new task group, the IEEE 802.15. 6 TG6. To control the communications from implanted devices in both PHY and MAC point of view is still a major challenge. An efficient MAC protocol can help manage and control the communication. In this paper, we propose one such MAC protocol to control the communication in implant devices. Our method of using wakeup table for normal communication and radio based wakeup for emergency communication is found to be efficient in terms of energy consumption, and delay.
Semi-Supervised Recursive Autoencoders for Social Review Spam Detection

As spam hampers the productivity and performance of social media and causes erosion in the user base and thus associated financial loss, a semi-supervised recursive autoencoders model is applied to social review spams detection problem in this paper. The model is based on semi supervised recursive autoencoders, which learns vector representations of phrases and full sentences as well as their hierarchical structure from the text. This model exploits hierarchical structure and uses compositional semantics to understand meanings, without requiring any language-specific lexica, parsers or knowledge base. Experiments conducted on real dataset show that the approach can effectively detect the social review spams.
Sequential Experiment Designs for Screening and Tuning Parameters of Stochastic Heuristics

This paper describes a sequential experimentation approach for efficiently screening and tuning the parameters of a stochastic heuristic. Stochastic heuristics such as ant colony algorithms often use a large number of tuning parameters. Testing all combinations of these factors is prohibitive and inefficient. The sequential procedure recommended by this paper uses resolution IV fractional factorial designs with fold-over and centre points as an efficient way to screen the most important tuning parameters. The effects of the most important parameters are then modelled using a central composite design and optimised with standard numerical methods. All designs, their analyses and interpretation are illustrated using the Ant Colony System algorithm. The use of standard designs and methods has the benefit that the presented procedure can easily be followed with commercial software rather that relying on custom methodologies and tools that have only been developed in an academic context. Such a procedure has not been applied to ant colony algorithms before.
Data Mining for Management and Rehabilitation of Water Systems: The Evolutionary Polynomial Regression Approach

Vorgeschlagene Zitierweise/Suggested citation: Giustolisi, Orazio; Savic, Dragan A. ; Laucelli, Daniele (2004): Data Mining for Management and Rehabilitation of Water Systems: The Evolutionary Polynomial Regression Approach. In: Technische Universität Dresden, Institut für Wasserbau und technische Hydromechanik (Hg.): Risiken bei der Bemessung und Bewirtschaftung von Fließgewässern und Stauanlagen. Dresdner Wasserbauliche Mitteilungen 27. Dresden: Technische Universität Dresden, Institut für Wasserbau und technische Hydromechanik. S. 285-296.
Seismic-Reliability-Based Optimal Layout of a Water Distribution Network

We proposed an economic, cost-constrained optimal design of a water distribution system (WDS) that maximizes seismic reliability while satisfying pressure constraints. The model quantifies the seismic reliability of a WDS through a series of procedures: stochastic earthquake generation, seismic intensity attenuation, determination of the pipe failure status (normal, leakage, and breakage), pipe failure modeling in hydraulic simulation, and negative pressure treatment. The network’s seismic reliability is defined as the ratio of the available quantity of water to the required water demand under stochastic earthquakes. The proposed model allows no pipe option in decisions, making it possible to identify seismic-reliability-based optimal layout for a WDS. The model takes into account the physical impact of earthquake events on the WDS, which ultimately affects the network’s boundary conditions (e.g., failure level of pipes). A well-known benchmark network, the Anytown network, is used to demonstrate the proposed model. The network’s optimal topology and pipe layouts are determined from a series of optimizations. The results show that installing large redundant pipes degrades the system’s seismic reliability because the pipes will cause a large rupture opening under failure. Our model is a useful tool to find the optimal pipe layout that maximizes system reliability under earthquakes.
Robust and Secret Data Transmission Over the Noisy Channel

We propose the secret and robust data transmission over the noisy channel. The secret data is encrypted and permutated using the permutation function, further encoded the data using the error detection and correction code. Experiments show the good quality of the stego-image and resistant against the various noise attacks (Like Salt and Pepper)
Evolutionary approaches to fuzzy modelling for classification

An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques have been adapted to satisfy the common evaluation criteria of the induced knowledge—classification accuracy, comprehensibility and novelty value—are also considered. The review concludes by highlighting common limitations of the experimental methodology used and indicating ways of resolving them.
Day and night-time drive analysis using stereo vision for naturalistic driving studies

In order to understand dangerous situations in the driving environment, naturalistic driving studies (NDS) are conducted by collecting and analyzing data from sensors looking inside and outside of the car. Manually processing the overwhelming amounts of data that are generated in such studies is very comprehensive. We propose a method for automatic data reduction for NDS based on stereo vision vehicle detection and tracking during day- and nighttime. The developed system can automatically register five NDS events, mainly related to intersections, from an existing NDS dictionary. We propose a new drive event which takes advantage of the extra dimension provided by stereo vision. In total, six drive events are selected on the basis of them being problematic to detect automatically using conventional monocular computer vision approaches. The proposed system is evaluated on day-and nighttime data, resulting in drive analysis report. The proposed system reach an overall precision of 0.78 and an overall recall of 0.72.
Frequency dividers with enhanced locking range

The locking range of the CML divide-by-two divider with the inductive shunt peaking is analyzed. The dividers using the locking-range-enhanced techniques have been realized in 0.13 mum CMOS process. Experimental results show that the locking range of the proposed divider is improved 30.8% and 62.5% by adopting the current-reused and the gm-boosted technique, respectively. When both techniques are adopted, the locking range is 101.67% larger than the conventional one at the same power consumption. The maximum locking range of the proposed divider is from 46.22 to 48.64 GHz while the input power level is -4 dbm.
Effects of age, gender, and retirement on perceived sleep problems: results from the VISAT combined longitudinal and cross-sectional study.

OBJECTIVES This study examined the effects of age, gender, and retirement on the subjective frequency of various sleep problems in individuals on a normal work schedule. DESIGN Data were taken from the VISAT study (Aging, Health, - Work), which allowed both cross-sectional and longitudinal aspects of age-related changes to be examined. SETTING Various sorts of companies in southern France. PARTICIPANTS The cohorts comprised 623 male and female, employed and retired, wage earners who were 32, 42, 52, and 62 years old at the time of the first measurement (t1, 1996), and who were seen again 5 (t2) and 10 (t3) years later. INTERVENTIONS N/A. MEASUREMENTS AND RESULTS Subjective ratings of the frequency of sleep problems and hypnotic usage were recorded on all 3 occasions, as was the employment status of the individuals. After controlling for age and gender, an effect of decade was observed for difficulty falling asleep and difficulty maintaining sleep, indicating that the frequency of these sleep problems was rated higher in 2006 than in 1996 by people of the same age at both measurement occasions. The perceived frequency of difficulty maintaining sleep, difficulty getting back to sleep, and premature awakening was found to increase up to the mid-50s but to then remain relatively constant, or even in the case of premature awakening to reduce, up to the age of 72. There was also a significant improvement in premature awakening among those individuals who changed from being active to being retired during the study period (n = 111). In contrast, the rated frequency of difficulty falling asleep and hypnotic usage increased fairly linearly over the entire age range. CONCLUSIONS Sleep complaints were reported early in the workers' lives, and were more frequent with age, but some of them improved after retirement, especially the complaint of premature awakening.
A novel recommender system fusing the opinions from experts and ordinary people

In this paper, we propose a novel recommendation algorithm fusing the opinions from experts and ordinary people. Instead of regarding one's judgement capability as his/her expertise, we present a new definition which measures the amount of the recommendable items one know in a certain area. When computing the expertise, we consider both the average value and the accumulative value, and introduce a free parameter α to tune between these two values. To evaluate the proposed algorithm, simulations are run on the Moviepilot dataset, and the results demonstrate that our algorithm outperforms the conventional collaborative filtering algorithm.