Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mesh Morphing Based Method To Estimate Cruciate Ligament Attachments Based On CT-Data

The consideration of the individual knee ligament attachments is crucial for the application of patient specific musculoskeletal models in the clinical routine, e.g. in knee arthroplasty. Commonly, the pre-operative planning is based on CT images, where no soft tissue information is available. The goal of this study was to evaluate the accuracy of a full automatic and robust mesh morphing method that estimates locations of cruciate ligament attachments on the basis of training data. The cruciate ligament attachments from 6 (n=6) different healthy male subjects (BH 184±6cm, BW 90±10kg) were identified in MRI-datasets by a clinical expert. The insertion areas were exported as point clouds and the centres of gravitation served as approximations of the attachments. These insertion points were used to annotate mean shapes of femur and tibia. The mean shapes were built up from 332 training data sets each. The surface data were obtained from CT scans by performing an automatic segmentation followed by manual cleaning steps. The mean shapes were computed by selecting a data set randomly and aligning this reference rigidly to each of the remaining data sets. The data were fitted using the non-rigid ICP variant (N-ICP-A). Due to this morphing step, point correspondences were established. By morphing a mean shape to the target geometries, including the cruciate ligament attachments, the distribution of the insertions on the original mean shape was obtained. Subsequently, a statistical mean was computed (annotated mean). The annotated mean shape was again morphed to the target data sets and the deviations of the respective predicted insertion points from the measured insertion points were computed. The training data was successfully morphed to all 6 subjects in an automatic manner with virtually no distance error (10-5 mm). The mean distance between the measured and morphed ligament attachments was highest for the ACL in the femur (4.26±1.48 mm) and lowest for PCL in the tibia (1.63±0.36 mm). The highest deviation was observed for femoral ACL (6.93 mm). In this study, a morphing based approach was presented to predict origins and insertions of the knee ligaments on the basis of CT-data, exemplarily shown for the cruciate ligaments. It has been demonstrated, that the N-ICP-A is applicable to predict the attachments automatic and robust with a high accuracy. This might help to improve patient-specific biomechanical models and their integration in the clinical routine.
Architecture for automatic poetry generation through pattern recognition

Document representation and topic modelling are important problems for artificial intelligence researchers, with applications ranging from education technology to bioinformatics. Many approaches have been proposed, the majority falling broadly into categories of Statistical Analysis and Natural Language Processing (NLP). This thesis proposes an architecture that optimizes a combination of statistical and linguistic analysis in an unsupervised machine learning environment. The proposed architecture is a design for agile, stable, document modelling. By clustering within the statistical inference algorithm, it reduces the computational cost of time and space associated with conventional classifying algorithms such as K-means, increasing the threshold for size and frequency of aggregate data analysis. This translates to an increased stability for evolution of learning. The architecture builds on the concept of socio-linguistic connections as an inherent combination of statistics and linguistics, and employs well-researched concepts of statistical and linguistic analysis, including embedded sub-manifold analysis. It optimizes both linguistic connections and computational cost. Trials are run with three sets of parameters, and results distributed for volunteer evaluation. Feedback from the evaluation indicates that the proposed architecture produces groups of sentences (poems) with a high degree of social acceptance and response.
An empirical study on evolutionary feature selection in intelligent tutors for learning concept detection

Concept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content, specifying relations among them, and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed‐forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition, the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state‐of‐the‐art algorithms have been made from the viewpoint of a typical classification problem.
The Complementary Nature Of Fundamental And Technical Analysis Evidence From Indonesia

Fundamental analysis and technical analysis has been used independently to predict the stock price movement. Both type of analysis usually used without interacting each other. This study was intended to test the complementary nature of fundamental and technical analysis as to whether it will increase the explanatory power to explain the stock price movement in Indonesia LQ45 market. The result shows that fundamental or technical analysis alone in isolation have the ability to predict future prices. But, by integrating both factors together in a single model will give the superiorexplanatory power to the prediction. However, in Indonesia stock market, technical analysis plays the biggest role in determining future price movements, while technical data was widely available in the market rather than the fundamental data such as analyst’s forecasted EPS. These fndings prove that fundamental analysis can be used in determining which stocks or portfolio is prosperous in the future, and technical analysis can be usd in determining the right time to buy or sell the stocks. By integrating both type of analysis, investors could have superior proft than the buy and hold strategy. The result should also enrich the knowledge of stock traders in gaining more proft.
Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Planetary gearboxes are widely used in the drivetrain of wind turbines. Planetary gearbox fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and life span of the wind turbines. The wind energy industry is currently using condition monitoring systems to collect massive real-time data and conventional vibratory analysis as a standard method for planetary gearbox condition monitoring. As an attractive option to process big data for fault diagnosis, deep learning can automatically learn features that otherwise require much skill, time, and experience. This article presents a new deep-learning-based method for wind turbine planetary gearbox fault diagnosis developed by a large memory storage and retrieval neural network with dictionary learning. The developed approach can automatically extract self-learned fault features from raw vibration monitoring data and perform planetary gearbox fault diagnosis without supervised fine-tuning process. From the raw vibration monitoring data, a dictionary is first learned by a large memory storage and retrieval with dictionary learning network. Based on the learned dictionary, a sparse representation of the raw vibration signals is generated by shift-invariant sparse coding and input to a large memory storage and retrieval network classifier to obtain fault diagnosis results. The structure of the large memory storage and retrieval with dictionary learning is determined by optimal selection of the sliding box size to generate sub-patterns from the vibration data. The effectiveness of the presented method is tested and validated with a set of seeded fault vibration data collected at a planetary gearbox test rig in laboratory. The validation results have shown a promising planetary gearbox fault diagnosis performance with the presented method.
Tools and Methods for Processing and Visualizing Large Corpora

We present several approaches and methods which we develop or use to create workflows from data to evidence. They start with looking for specific items in large corpora, exploring overuse of particular items, and using off-the-shelf visualization such as GoogleViz. Second, we present the advanced visualization tools and pipelines which the Visualization Group at University of Konstanz is developing. After an overview, we apply statistical visualizations, Lexical Episode Plots and Interactive Hierarchical Modeling to the vast historical linguistics data offered by the Corpus of Historical American English (COHA), which ranges from 1800 to 2000. We investigate on the one hand the increase of noun compounds and visually illustrate correlations in the data over time. On the other hand we compute and visualize trends and topics in society from 1800 to 2000. We apply an incremental topic modeling algorithm to the extracted compound nouns to detect thematic changes throughout the investigated time period of 200 years. In this paper, we utilize various tailored analysis and visualization approaches to gain insight into the data from different perspectives.
Representation and Immersion. The Embodied Meaning of Literature

Summary This article explores the relations among three forms of representations (artistic, mental, and neural) and immersion, considered as an altered state of consciousness, in the context of literary reading. We first define immersive reading as an intensification of our embodied experience of literary representation, in accordance to neuropsychological studies about embodied cognition. We further consider the style of interpretation demanded by such immersive reading and its ethical and ecological underpinnings.
Development of Predictive Signatures for Treatment Selection in Precision Medicine

Advances in molecular technology have shifted the development of new drugs towards precision medicine, identifying patient subgroups likely to benefit from a targeted treatment. Today, many cancer treatments are being developed for targeted therapies [1-8], in which only a subpopulation of patients is expected to benefit from the therapy. The term “personalized (precision) medicine”, which is commonly referred to the right treatment for the right patient at the right time, has been used to convey the concept of customizing medical therapies to select best treatments tailored to the individual patients. In conventional drug development, it is based on the concept of “one-size-fits all”, and assumed that the drug effect is similar for all patients with the particular disease. However, if a drug is only effective for a small proportion of patients, this drug may not be available for those needed patients since drug’s approval is based on mean difference between treated and untreated patients based on the entire patient population.
Children's Relative Influence in Purchase Decision-Making: A Multi-Theoretical Approach.

Sociological changes in the American family have positioned children to wield greater power in family purchase decisions. Although past descriptive research has confirmed that children are an important source of power in the household, this research has not explored the conceptual justification for the observed patterns of children’s relative influence. To fill this gap in the family decision-making literature, this research develops a multi-theoretical conceptual model to explain children’s relative influence in a purchase context. Social power theory, resource theory, social exchange theory, and social comparison theory were the conceptual frameworks for this research. Six studies were conducted, including a pretest and a final study with 1211 mother-child pairs. Results of these studies demonstrate that children’s relative influence is affected by four factors: children’s active influence, children’s passive influence, decision history, and preference intensity. In addition, this research examined the determinants of children’s direct influence attempts and found that children were capable of assessing their personal resources and determining the appropriate direct influence attempt which yielded the greatest return. Decision history and preference intensity did not have a direct effect on children’s direct influence attempts. This research also examined the impact of communication on a child’s preference intensity for a product or service and found that interpersonal communication, not impersonal communication, was the most important factor in determining a child’s desire for a toy purchase. Finally, children’s relative
Lattice Boltzmann liquid simulations on graphics hardware

Fluid simulation is widely used in the visual effects industry. The high level of detail required to produce realistic visual effects requires significant computation. Usually, expensive computer clusters are used in order to reduce the time required. However, generalpurpose Graphics Processing Unit (GPU) computing has potential as a relatively inexpensive way to reduce these simulation times. In recent years, GPUs have been used to achieve enormous speedups via their massively parallel architectures. Within the field of fluid simulation, the Lattice Boltzmann Method (LBM) stands out as a candidate for GPU execution because its grid-based structure is a natural fit for GPU parallelism. This thesis describes the design and implementation of a GPU-based free-surface LBM fluid simulation. Broadly, our approach is to ensure that the steps that perform most of the work in the LBM (the stream and collide steps) make efficient use of GPU resources. We achieve this by removing complexity from the core stream and collide steps and handling interactions with obstacles and tracking of the fluid interface in separate GPU kernels. To determine the efficiency of our design, we perform separate, detailed analyses of the performance of the kernels associated with the stream and collide steps of the LBM. We demonstrate that these kernels make efficient use of GPU resources and achieve speedups of 29.6× and 223.7×, respectively. Our analysis of the overall performance of all kernels shows that significant time is spent performing obstacle adjustment and interface movement as a result of limitations associated with GPU memory accesses. Lastly, we compare our GPU LBM implementation with a single-core CPU LBM implementation. Our results show speedups of up to 81.6× with no significant differences in output from the simulations on both platforms. We conclude that order of magnitude speedups are possible using GPUs to perform free-surface LBM fluid simulations, and that GPUs can, therefore, significantly reduce the cost of performing high-detail fluid simulations for visual effects.
GNU General Public License

The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Lesser General Public License instead.) You can apply it to your programs, too.
Requirements Specification of Context-Aware Systems: A Systematic Mapping

Context-aware computing covers research on computational systems which adapt their behaviour so as to provide services or information to users according to their context of interaction. The literature has reported the complexity of development of such systems in the light of their features such as adaptation and heterogeneity of devices and information. This paper describes the execution of a systematic mapping on the requirements specification activity of contextaware systems, which is one of the early stages of software development. The main result includes the identification of gaps and/or research trends on requirements specification of context-aware systems.
ON THE COMPARATIVE ANALYSIS OF THE COOLEY-TUKEY AND BLUESTEIN NUMERICAL FAST FOURIER TRANSFORMS ALGORITHMS FOR DIGITAL SIGNAL PROCESSING

ABSTRACT This study was designed to compare the computing efficiency of C-TNADSP and the BNADSP to ascertain a more efficient numerical algorithm necessary for the processing of digital signals. The faster numerical algorithm established in this study is abbreviated with RCC-TNADSP (Resultant Compared C-TBNADSP). The methodology adopted in this work was comparative analysis development design. The major technologies used in this work are the C-TNADSP and BNADSP, and the c ++ . The c ++ served as a signal processing language simulator (SPLS). The execution times of the Cooley-Tukey and the Bluestein algorithms were 3.44 seconds and 3.50 seconds respectively. On comparing the speeds of the fast Cooley–Tukey and the fast Bluestein algorithms we observed that the Cooley-Tukey algorithm has 0.06 seconds speed improvement over the Bluestein algorithm. In line with this outcome, we concluded that the Cooley-Tukey algorithm (C-TNADSP) is faster than the Bluestein algorithm (BNADSP). In the same vein the Cooley-Tukey algorithm (C-TNADSP) is therefore the fastest DSP algorithm. This is however faster than the spectrum of FFT algorithms of O(nlogn) computing speed. The algorithms were tested on input block width 1000 units, and above, and can be implemented on input size of 100 000, and 1000 000 000 without the challenge of storage overflow. The input samples tested in this work was the discretized pulse wave form with undulating shape out of which the binary equivalents were extracted. Other forms of signals may also be tested in this fast algorithm provided they are interpreted in the digital wave type. KEYWORDS: Algorithm, FFT, Cooley-Tukey, Bluestein, Comparison, Analysis, FFT
Fetch-and-Phi in Memcached

Memcached and other in-memory distributed key-value stores play a critical role in large-scale web applications, by reducing traffic to persistent storage and providing an easy-to-access look-aside cache in which programmers can store arbitrary data. These caches typically have a narrow interface, consisting only of gets, sets, and compare-and-set. In the worst case, this interface can cause significant inefficiencies as clients get large data items, perform small changes, and then set the updated items back into the cache. We present extensions to memcached that allow the system administrator to dynamically load custom code modules into memcached, so that clients may execute code directly on the memcached server. Our system permits both fetchand-phi operations, which update the cache, and filtering operations, which compute a function over the data in the cache, and return the result to the client without making an update to the cache. We evaluate our extensions on benchmarks based on workload traces from a Cable/Internet service provider, and find that our new functionality provides a means of dramatically reducing network overheads and increasing responsiveness.
Current Demographic Conditions and Future Scenarios in Mexico´s Coastal Zone

Knowing the behavior of population dynamics in coastal areas is an essential element for its proper management. Using historical information, in this paper, estimates of the total population in the coastal states of Mexico are made up to the year 2050 with an analysis of the population dynamics in the coastal municipalities for the years 2000, 2005, 2010 and 2015. According to the latest census, Mexican total population reached 102 million people in 2015 with 55,3 million (46 %) living in coastal states. In the 17 coastal states of Mexico, a total of 158 municipalities with direct access to the sea were identified in 2015, in which 18,9 million inhabitants lived. During the period 2010 to 2015 the highest relative population growth rate at state level was observed in Quintana Roo (13.3%). In 2015, the coastal municipality of Tijuana in Baja California State was the most populated in the coastal zone with 1,6 million inhabitants. In 15 years (2000-2015), 4 municipalities have increased their population by 100% or more, prevailing in these localities activities related with international tourism. The coastal population growth rate for the period 2010-2015 showed a deceleration compared to 2005-2010. By the year 2050, a population of 65 million people living in the coastal states of Mexico was estimated. The results presented in this study give elements to the decision makers to strengthen the planning and management processes of the Mexican coastal zone in the medium term.
Evapotranspiration cover performance in a high desert environment, north waste rock disposal facility, Rain Mine, USA

Newmont Mining Corporation conducted a performance audit of an evapotranspiration (ET) cover constructed in 2002 at an acid generating waste rock disposal facility located at 2,000 m elevation. Based on technology available at that time, the monolithic ET cover design included approximately 0.9 m of local alluvium with native sagebrush steppe vegetation. The site receives an average of 540 mm of precipitation annually, 84% of which falls during October to April. The cover did not significantly reduce infiltration into the waste rock, as measured by a seepage collection system constructed beneath the waste rock facility. Average annual seepage following reclamation has ranged from 38,000 to 193,000 cu. m, or 13 to 44 % of annual precipitation for the 76 ha facility. Peak monthly average rates during the spring have been as much as 10 litres per second (L/s). Seepage chemistry is poor, with pH typically below 3, total dissolved solids concentration ranging from 5 to 30 g/L, and elevated metals concentrations. Newmont proactively conducted studies of the reclamation cover including installation and monitoring of moisture sensor nests, laboratory testing, surface geophysics, monthly snow surveys, geochemical characterisation of the waste rock, and groundwater monitoring. A detailed assessment of the available design and construction data, site climate, and cover monitoring data was conducted in 2010 to assess cover performance. The study included precipitation gauge catch corrections, modelling of potential ET (PET) by aspect and slope, a direct method for computing infiltration from the moisture content sensor data, an analysis of seepage chemistry, and calibrated numerical modelling. The assessment concluded that the compromised cover performance was due to the compounded effects from winter precipitation, snow drifting, slope and aspect influence on PET, available water holding capacity of the cover material, and cover construction unconformities. While the ET cover is able to fully reset the available water holding capacity of the cover each year, the cover profile is fully wetted each spring from snowmelt, resulting in significant infiltration into the waste rock. Newmont is using the results of this study together with an engineering analysis of alternative reclamation designs to identify a final reclamation strategy for the facility.