Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing models of articulating objects: range data partitioning

In this paper we consider one aspect of the problem of automatically building shape models of articulating objects from example range images. Central to the model construction problem is the registration of range data, taken from different vantage points, into a common coordinate frame. This involves determining a transformation for each set of range data which aligns overlapping surface points in the common frame. Current registration algorithms have been developed specifically for rigid objects, but it is not obvious how these can be extended to articulated or more generally deformable objects. Here, we propose that range images of articulated objects are first segmented into their rigid subcomponents. Each subcomponent can then be registered in isolation using the existing algorithms designed specifically for rigid parts and the final model formed by reassembling all of the submodels. This has motivated the development of a rigid part segmentation algorithm which is described and demonstrated here. The algorithm is currently limited to non-umbilic surfaces, but in this more restricted domain is shown to work well.
Distribution free evolvability of polynomial functions over all convex loss functions

We formulate a notion of evolvability for functions with domain and range that are real-valued vectors, a compelling way of expressing many natural biological processes. We show that linear and fixed-degree polynomial functions are evolvable in the following dually robust sense: There is a single evolution algorithm that for all convex loss functions converges for all distributions. It is possible that such dually robust results can be achieved by simpler and more natural evolution algorithms. In the second part of the paper we introduce a simple and natural algorithm that we call "wide-scale random noise" and prove a corresponding result for the L2 metric. We conjecture that the algorithm works for more general classes of metrics.
REFA3D: Robust Spatio-temporal Analysis of Video Sequences

This article proposes a generalization of our approach REFA (Grand-brochier et al., 2011) to spatio-temporal domain. Our new method REFA3D, is based mainly on hes-STIP detector and E-HOG3D. SIFT3D and HOG/HOF are the two must used methods for space-time analysis and give good results. So their studies allow us to understand their construction and to extract some components to improve our approach. The mask of analysis used by REFA is modified and therefore relies on the use of ellipsoids. The validation tests are based on video clips from synthetic transformations as well as real sequences from a simulator or an onboard camera. Our system (detection, description and matching) must be as invariant as possible for the image transformation (rotations, scales, time-scaling). We also study the performance obtained for registration of subsequence, a process often used for the location, for example. All the parameters (analysis shape, thresholds) and changes to the space-time generalization will be detailed in this article.
Machine Learning for Classifying Learning Objects

Building an ontology for learning objects can be useful for translating such objects between learning contexts. Such translations are important because they afford learners and educators with the opportunity to a survey a wide selection of learning and teaching material. For instance, university instructors are sometimes required to assess curriculum from courses delivered from other programs or universities, even internationally. Often, the only learning object available to do so is the course outline made available in HTML format on a Web page. Generally there is an abundance of metadata available from such learning objects and this information can be used to generate useful components of the ontology. Other useful information can be derived from first establishing the domain of the object, electricity and computing for instance, or possibly history. Once extracted, the information representing learning objects can be stored as elements in an XML template. The purpose of this work was to develop and implement a machine learning strategy for classifying course outlines into pre-defined domains and sub-domains in order to provide this information to an ontology repository designed to aid in the translation of such objects. First some typical domains were identified. Then, 20-30 course outlines were chosen to represent each sub-domain. Next, frequency tables of words common to the course outlines for a given sub-domain were generated in order to compile an ordered list of synonyms used to represent the sub-domains. Finally, a new set of course outlines were randomly selected for classification based on an analysis of the synonym content of each. Establishing the frequency tables and completing the synonym analysis was automated completely thereby constituting the machine learning strategy
Reliability Pyramid Based Dense Corresponding between Facial Texture Images

Dense corresponding between facial texture images is the core step of the morphable model based 3D face reconstruction. In this paper, we propose an original dense corresponding algorithm by means of the reliability pyramid, which utilizes the output of highly accurate sparse matching algorithm as the initial estimation of the iterative Lucas-Kanade optical flow. Furthermore, two constraints for facial texture corresponding and a rapid 3D face reconstruction strategy are also briefly introduced. We conduct experiments on the BJUT-3D Face Database, proving the above-mentioned theories to be powerful.
Susceptibility-Weighted Imaging in Heat Stroke

Objective To assess the role of susceptibility-weighted imaging in the detection of intracranial hemorrhage after heat stroke and in the prognosis. Materials and Methods The study group consisted of eight patients after heat stroke, with a score of 3 to 9 in Glasgow Coma Scale. The MR studies were performed with a 1.5 T scanner. Susceptibility-weighted imaging data were collected within 2–5 days after heat stroke. The study was approved by ethics committee, and written informed consents were obtained from family members of the patients. Results Punctate hemorrhages were detected in brain stem, corona radiata and frontal lobe by susceptibility-weighted imaging for three patients. Among the three cases, two patients came to death in the 5th day and the 25th day after heat stroke respectively. Another patient became a persistent vegetative state and died about 3 months later. Five patients with no hemorrhage detected gradually recovered and cerebellar dysfunction remained to various degrees. Conclusions Heat stroke is a life-threatening condition characterized by hyperthermia and accompanied by various complications such as disseminated intravascular coagulation. Susceptibility-weighted imaging is a very useful tool for detection of intracranial hemorrhage and may probably evaluate the prognosis after heat stroke.
Topology Preserving Maps—Extracting Layout Maps of Wireless Sensor Networks From Virtual Coordinates

A method for obtaining topology-preserving maps (TPMs) from virtual coordinates (VCs) of wireless sensor networks is presented. In a virtual coordinate system (VCS), a node is identified by a vector containing its distances, in hops, to a small subset of nodes called anchors. Layout information such as physical voids, shape, and even relative physical positions of sensor nodes with respect to x- y directions are absent in a VCS description. The proposed technique uses Singular Value Decomposition to isolate dominant radial information and to extract topological information from the VCS for networks deployed on 2-D/3-D surfaces and in 3-D volumes. The transformation required for TPM extraction can be generated using the coordinates of a subset of nodes, resulting in sensor-network-friendly implementation alternatives. TPMs of networks representing a variety of topologies are extracted. Topology preservation error ( ETP), a metric that accounts for both the number and degree of node flips, is defined and used to evaluate 2-D TPMs. The techniques extract TPMs with ( ETP) less than 2%. Topology coordinates provide an economical alternative to physical coordinates for many sensor networking algorithms.
Exploring the Martian subsurface of Athabasca using MARSIS radar data: Testing the volcanic and fluvial hypotheses for the origin of the morphology

This work aims to test the formation hypotheses of the rafted-plate morphology observed in the Martian area of Athabasca Valles (5°N, 150°E,) using the subsurface radar echo from the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) 5 MHz-band data over this area. The backscattered signal losses are compared to those arising from two geoelectrical subsurface models (differing in their assumed ice content) generated by finite-difference time-domain (FDTD) simulations. Within this region, the MARSIS signal experience average losses of 0.09dB/m in the first 160 m beneath the surface. FDTD simulations suggests that, if the near-surface environment is ice-rich (80% by volume), it will result in average losses of 0.048dB/m, whereas the losses associated with an ice-poor model (20% of ice by volume) increase to 0.10dB/m. Comparing the observed MARSIS losses with the simulated ones suggests that the propagation characteristics of Athabasca's subsurface are more consistent with a volcanic rather than a fluvial origin for the rafted-plate terrain.
Improving Knowledge Acquisition in Capstone Projects Using Learning Spaces for Experiential Learning

Students have to cope with new technologies, changing environments, and conflicting changes in capstone projects. They often lack practical experience, which might lead to failing to achieve a projectpsilas learning goals. Reusing experiences from other students provides a first step towards building up practical knowledge and implementing experiential learning in higher education. In order to further improve knowledge acquisition during experience reuse, we present an approach that generates so-called learning spaces, which automatically enrich experiences with additional learning content and contextual information. To evaluate our approach, we conducted a controlled experiment that showed a statistically significant improvement for knowledge acquisition by 204% compared to conventional experience descriptions. From a technical perspective, the approach provides a good basis for future applications that support learning at the workplace in academia and industry.
A Flexible Context Aware Reasoning Approach for IoT Applications

The Internet of Things (IoT) paradigm, extending the computing and communicating capabilities to virtually every object, raises many new challenges to IoT-enabling systems. At the middleware layer, a context aware adaptive component has to fit the requirements and constraints for different applications, deal with heterogeneous data, be scalable to large scenarios and to resource constrained devices, and manage interoperability. This paper describes Concept Reply context awareness solution, based on a hybrid framework that integrates a semantic reasoning module and multiple processing agents for specialized / optimized processing tasks. Combining different reasoning approaches and adopting proper design choices we developed an extensible and configurable module. We show the advantages of this flexible approach in two case studies that benefit from two different tailorings of the reasoning framework.
Improving search in folksonomies: a task based comparison of WordNet and ontologies

Search in folksonomies is hampered by the fact that the meaning of tags and their relations are not made explicit in the system. This is typically addressed by using knowledge sources (KS) to semantically enrich tagspaces, most notably WordNet and (online) ontologies. However, there is no insight of how the different characteristics of these KS contribute to search improvement in folksonomies. In this work we compare these two KS in the context of folksonomy search. We show that while WordNet leads to richer tag structures than online ontologies do, its fine-grained sense hierarchy renders these structures less effective in search compared to the ones generated from ontologies.
Utilizing Artificial Neural Networks to Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages in Melanoma

The identification of proteomic patterns from biomarkers in diseases such as cancer could lead to the determination of novel prognostic and diagnostic markers fundamental to the treatment of patients. We apply a recently developed approach utilizing artificial neural networks as a data mining tool to identify and characterize the best subset of biomarkers associated with melanoma. These were capable of predicting whether a sample is from a patient diagnosed with stage I or stage IV melanoma to median accuracies of 98 % on an independent subset of data used for validation. Furthermore, individual response curves have been generated allowing the investigation of whether these markers are up or down regulated with regards to tumor progression.
A new tool to investigate differences between human SP-A1 and SP-A2.

surfactant protein a (SP-A), a ∼35,000-molecular weight glycoprotein characterized by collagen and lectin domains, is the most abundant surfactant-associated protein in the lung ([25][1]). SP-A is a member of the collectin family of innate immune proteins, i.e., a calcium-dependent, pattern
The Diffusion of Development

This paper studies the barriers to the diffusion of development across countries over the very long-run. We find that genetic distance, a measure associated with the amount of time elapsed since two populations%u2019 last common ancestors, bears a statistically and economically significant correlation with pairwise income differences, even when controlling for various measures of geographical isolation, and other cultural, climatic and historical difference measures. These results hold not only for contemporary income differences, but also for income differences measured since 1500 and for income differences within Europe. We uncover similar patterns of coefficients for the proximate determinants of income differences, particularly for differences in human capital and institutions. The paper discusses the economic mechanisms that are consistent with these facts. We present a framework in which differences in human characteristics transmitted across generations - including culturally transmitted characteristics - can affect income differences by creating barriers to the diffusion of innovations, even when they have no direct effect on productivity. The empirical evidence over time and space is consistent with this "barriers" interpretation.
The Relationship between Organizational Commitment and Organizational Learning (Boroojerd Telecommunication Company as Case Study)

The purpose of this study is to study the relationship between organizational commitment and organizational learning among employees of Borojerd Communication Company. The statistical population of the study is employees of Borojerd Communication Company. The sample size was determined 162 based on Morgan table of sampling for population with 280 members. The sample members selected by random sampling method. In order to gather data, the standard questionnaire of Myer and Allen with 18 items to measuring organizational commitment and also the standard questionnaire of Johtibabo et al. for organizational learning with 66 items were used. In order to examine validity of the questionnaire, content validity was used and then the questionnaire correct and modified by some management professors and final version of the questionnaire designed. Also Crocbach’s Alpha was used to examine reliability of the questionnaire. Pearson correlation coefficient was the most important statistical method that used to analyzing hypotheses and concluding results in SPSS. The results of the study indicate that there are significant relationships between organizational commitment and organizational learning. Also significant relationships were found among different levels of organizational learning with organizational commitment and learning in individual, group, and organizational levels.
EPIC: A Testbed for Scientifically Rigorous Cyber-Physical Security Experimentation

Recent malware, like Stuxnet and Flame, constitute a major threat to networked critical infrastructures (NCIs), e.g., power plants. They revealed several vulnerabilities in today's NCIs, but most importantly they highlighted the lack of an efficient scientific approach to conduct experiments that measure the impact of cyber threats on both the physical and the cyber parts of NCIs. In this paper, we present EPIC, a novel cyber-physical testbed, and a modern scientific instrument that can provide accurate assessments of the effects that cyber-attacks may have on the cyber and physical dimensions of NCIs. To meet the complexity of today's NCIs, EPIC employs an Emulab-based testbed to recreate the cyber part of NCIs and multiple software simulators for the physical part. Its main advantage is that it can support very accurate, real-time, repeatable, and realistic experiments with heterogeneous infrastructures. We show through several case studies how EPIC can be applied to explore the impact that cyber-attacks and Information and Communications Technology system disruptions have on critical infrastructures.
Perspectives on gesture from music informatics, performance and aesthetics

This article chronicles the research of the Nordic Network of Music Informatics, Performance and Aesthetics (NNIMIPA), and shows how the milieux bridge the gap between the disciplines involved. As examples, three projects within NNIMIPA involving performance interaction examine the role of audio and gestures in emotional musical expression using motion capture, the visual and auditive cues musicians provide each other in an ensemble when rehearsing, and the decision processes involved when a musician coordinates with other musicians. These projects seek to combine and compare intuitions derived from low-tech instructional music workshops that rely heavily on the use of whole-body gestures with the insights provided by high-tech studies and formal logic models of the performing musician, not only with respect to the sound, but also with regard to the movements of the performer and the mechanisms of group coordination.
Neural Correlates of Timbre Change in Harmonic Sounds

Timbre is a major structuring force in music and one of the most important and ecologically relevant features of auditory events. We used sound stimuli selected on the basis of previous psychophysiological studies to investigate the neural correlates of timbre perception. Our results indicate that both the left and right hemispheres are involved in timbre processing, challenging the conventional notion that the elementary attributes of musical perception are predominantly lateralized to the right hemisphere. Significant timbre-related brain activation was found in well-defined regions of posterior Heschl's gyrus and superior temporal sulcus, extending into the circular insular sulcus. Although the extent of activation was not significantly different between left and right hemispheres, temporal lobe activations were significantly posterior in the left, compared to the right, hemisphere, suggesting a functional asymmetry in their respective contributions to timbre processing. The implications of our findings for music processing in particular and auditory processing in general are discussed.
Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources. Contact: pengh@janelia.hhmi. org Supplementary information: Supplementary data are available at Bioinformatics online.
Laurus nobilis Extract Preserves Testicular Functions in Cryptorchid Rat

The third and fourth group was rendered cryptorchid with the fourth group subsequently treated orally with 60 mg/kg body weight of BLE daily for fifty six days. The animals were sacrificed and testis weight/volume and sperm parameters were determined. Animals with untreated cryptorchidism showed significantly reduction in testis weight/volume ( p<0.05), testis weight/body weight ratio, sperm parameters ( p<0.005) compared to the control and group treated with BLE-alone. Treatment of the cryptorchid rats with BLE significantly improved the sperm parameters ( p<0.05) and testicular SOD and CAT activity levels when compared to cryptorchid rats that were not treated. This showed that deleterious and degenerative changes associated with cryptorchidism were mildly averted by simultaneous treatment with BLE.
Spontaneous navigational strategies and performance in the virtual town

The 4‐on‐8 virtual maze provides evidence for variability in spontaneous strategy use during navigation. Functional magnetic resonance imaging (MRI) confirmed that these spatial and response strategies rely on the hippocampus and caudate nucleus memory systems, respectively. We asked whether the spontaneous use of a particular navigational strategy was associated with a particular ability to navigate in one's environment. We tested 30 young participants on the 4‐on‐8 virtual maze and we assessed their way finding ability in a virtual town. As expected, spatial learners performed well in the virtual town and the response learners, who never used external landmarks and relied purely on an egocentric strategy, performed poorly. Interestingly, a group who used the most efficient response strategy based on external landmarks in the 4‐on‐8 virtual maze, switched to the most efficient spatial strategy in the virtual town. Our data suggest that the best navigators are those who appropriately use spatial or response strategies depending on the demands of the task. © 2007 Wiley‐Liss, Inc.
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate

Adaptive k-means clustering algorithms have been used in several artificial neural network architectures, such as radial basis function networks or feature-map classifiers, for a competitive partitioning of the input domain. The authors present a modification of the traditional k-means algorithm. This approach approximates an optimal clustering solution with an adaptive learning rate, which renders it usable even in situations where the statistics of the problem task slowly vary with time. Simulations comparing this improved adaptive k-means algorithm with other k-means variants are presented. <<ETX>>