Miracle-dz/UniFAR-FTUs huggingface dataset:
Gaussian Sphere sampling based Surface Approximation

Sampling of 3D meshes is at the foundation of any surface simplification technique. In this paper, we use the recent results on quantization and surface approximation theory to propose a simple, robust, linear time, output sensitive algorithm for sampling meshes with the purpose of surface approximation. Our algorithm is based on the mapping of regular sampling and triangulation of the Gaussian sphere onto a manifold surface. An interesting aspect of our algorithm is that we do not explicitly measure, minimize, or prioritize any error to simplify and do not explicitly cluster the faces to find proxies, but still achieve bounded error approximation of the shape.
Mind your probes: De-anonymization of large crowds through smartphone WiFi probe requests

Whenever our smartphones have their WiFi radio interface on, they periodically try to connect to known wireless APs (networks the user has connected to in the past). This is done through WiFi Probe requests - special wireless frames that contain the MAC address of the sending device and, in most of the cases, the human-readable name-string (SSID) of the known AP. This semantic information, inherent to the network protocol, is sent in the clear and, if sniffed, can help discover important information and phenomena of people and human nature that have nothing to do with technology. In this paper we present the idea of exploiting WiFi probe requests to de-anonymize the origin of participants in large events. We make use of several, publicly available datasets containing more than 11M of probe requests collected in scenarios that are of citywide, national (two political meetings), and international religion-related relevance. We show how, by exploiting the semantic information brought by the relative WiFi probes, we are able to discover with high accuracy the provenance of the crowds in each event. In particular, the de-anonymization outcome of the two political meetings held few days before the election days in Italy match surprisingly well the official voting results reported for the two respective parties.
Exocortical Cognition: Heads in the cloud

From a neurocentric perspective, H. sapiens-generated technology can be seen as our neocortex - primarily the prefrontal and sensorimotor cortices - extending and augmenting our physiological perceptual and motor structures by projecting itself into the external environment. Moreover, given our brain-based creation and subjective ranking of ideational importance, the emerging capability to directly enhance neurocognitive functionality presents a singularly foundational scientific and technological undertaking. Here I propose Exocortical Cognition (ECC) - a transdisciplinary augmented cognition framework based on a review of the literature, accelerating progress in and convergence of a range of independent areas of science and technology, and the application of technology forecasting techniques. If realized, Exocortical Cognition would significantly enhance human cognitive function and memory capacity while bridging the often-presumed gap between human and future machine intelligence.
RELATIVE WHO AND WHOM: PRESCRIPTIVE RULES AND LINGUISTIC REALITY

RULES FOR THE "CORRECT" USE of relative who and whom are to be found in almost every book on English usage. Their origin can be traced back to the prescriptive grammars of the eighteenth century. Since then, who has gained a great deal of ground at the expense of whom, particularly in spoken and informal English. Although some grammarians claim that whom is virtually dead, prescriptivists continue to defend this form as the only 'correct' one in functions other than the subject. Prescriptivist teaching has ensured that most educated speakers of English are aware of the existence of rules for the use of relative who and whom in specific syntactic contexts. The trouble is that they are not always sure what these contexts are. This leads to insecurity, which, in turn, leads to sentences which violate the prescriptive rules. This paper shows that who/whom usage has been strongly influenced by prescriptive precepts. It provides evidence from spoken English to show that there is obviously a clash in this area of English grammar between what people actually say and what prescriptivists believe they ought to say. Finally, it compares data from spoken English with data from two corpora of written English to demonstrate that the use of relative whom is primarily restricted to formal style. The "incorrect" use of relative who and whom has a long history in
Transperineal Sonography Evaluation of Muscles and Vascularity in the Male Pelvic Floor

Idiopathic chronic male pelvic pain is difficult to diagnose and treat. Currently, diagnosis relies on subjective symptoms; objective measures of neuromuscular mechanisms have not been investigated. Sonographic imaging has been used to investigate these neuromuscular mechanisms in the female pelvic floor, but neither research nor books describe sonography evaluation of the male pelvic floor. The purpose of this study was to develop and evaluate a perineal sonographic technique for the examination of the male pelvic floor muscles. Anatomic landmarks were identified with images collected from two subjects, one with intermittent reports of pelvic pain and one with no history of pain in the pelvic region. A description of the equipment settings, the examination protocol, and the resulting comparative image analysis is included. A validated protocol such as this may be useful in documenting differences in the soft tissue structures between asymptomatic individuals and patients with chronic pelvic pain to aid in diagnosis and treatment. This is the first known study to report sonographic findings of the individual muscles in the male pelvic floor, and additional research is needed to validate the techniques that have been deemed feasible.
The Assessment and Treatment of Sensory Defensiveness in Adult Mental Health: A Literature Review

The aim of this literature review was to explore the nature of the relationship between sensory defensiveness and mental health problems. The bibliographical database Dialog DataStar and various online resources were searched. Six papers were selected, based on the inclusion and exclusion criteria. They were analysed by identifying and discussing the common themes that emerged. The literature indicated that sensory defensiveness can have a detrimental effect on quality of life experiences. It can have an impact on functional abilities, behaviour, emotions and mental health. The coping strategies that people develop to deal with sensory defensiveness add to the negative impact on their quality of life. From the limited literature available, it was found that the treatment of sensory defensiveness delivered positive results. This review highlights the importance of recognising and treating sensory defensiveness. It also highlights the lack of literature available in this area. The implications for occupational therapy practice include further extensive research to improve knowledge and experience in the assessment and treatment of sensory defensiveness in mental health settings.
Two-Stage Interpolation Algorithm Based on Fuzzy Logics and Edges Features for Image Zooming

This work presents an innovative two-stage interpolation algorithm for image resolution enhancement and zooming applications. The desired high-resolution images are obtained via two interpolative stages. In the first stage, aligned pixels are first estimated using a fuzzy inference system, whose critical parameters are optimized by particle swarm intelligence. In the second stage, interior pixels are then restored by utilizing the edge properties of nearby pixels. From experimental results, numerical comparison confirms the superiority of the proposed interpolation algorithm over other existing methods. Furthermore, visual illustrations including zoomed parts and error maps demonstrate the significant improvement of the proposed method, particularly in the regions that contain many local edges and sharp details.
Opportunistic Subchannel Scheduling for OFDMA Networks with Network Coding at Relay Stations

In this paper, we study an opportunistic subchannel scheduling problem for the relay-based OFDMA cellular network where relay stations (RSs) can perform opportunistic network coding with downlink and uplink sessions of a mobile station (MS). In wireless networks, network coding is one of promising techniques to improve network performance in various aspects. However, previous studies did not provide optimal opportunistic resource allocation solutions for the OFDMA networks with network coding-aware RSs as well as they did not consider practical duplexing schemes for the transmission of network coded data. In this paper, to allow network coding with downlink and uplink sessions at RSs, we consider a time-division duplexing (TDD) based time-slot structure. We formulate a stochastic optimization problem to maximize the average weighted-sum rate for both downlink and uplink sessions. Then, by solving the problem, we provide an optimal opportunistic subchannel scheduling algorithm that can opportunistically use network coding with considering time-varying channel states.
Business Model Transformation Patterns of Data-Driven Innovations

Driven by advances in information and communication technologies (ICT), manifold business opportunities emerge in diverse industries. In this development, data plays an important role as an essential element of change and economic growth. As a consequence of this development, the analysis of service business models and the role of data in these models has become a focus of interest in practice and research. This paper focuses on international companies that implemented successfully data-driven innovations and aims, in particular, at the analysis of the effects of data-driven innovation on companies’ business models. Based on a multiple case study method, this paper proposes four different transformation patterns: (1) cooperative value innovation, (2) customer-centric value innovation, (3) cooperative productivity improvement, and (4) company-centric productivity improvement. These patterns show that data-driven innovations enable a transformation from product to service-oriented offerings. Furthermore, an optimization of internal and external processes and thus, of the productivity is possible.
The SATIN Component System-A Metamodel for Engineering Adaptable Mobile Systems

Mobile computing devices, such as personal digital assistants and mobile phones, are becoming increasingly popular, smaller, and more capable. We argue that mobile systems should be able to adapt to changing requirements and execution environments. Adaptation requires the ability-to reconfigure the deployed code base on a mobile device. Such reconfiguration is considerably simplified if mobile applications are component-oriented rather than monolithic blocks of code. We present the SATIN (system adaptation targeting integrated networks) component metamodel, a lightweight local component metamodel that offers the flexible use of logical mobility primitives to reconfigure the software system by dynamically transferring code. The metamodel is implemented in the SATIN middleware system, a component-based mobile computing middleware that uses the mobility primitives defined in the metamodel to reconfigure both itself and applications that it hosts. We demonstrate the suitability of SATIN in terms of lightweightedness, flexibility, and reusability for the creation of adaptable mobile systems by using it to implement, port, and evaluate a number of existing and new applications, including an active network platform developed for satellite communication at the European space agency. These applications exhibit different aspects of adaptation and demonstrate the flexibility of the approach and the advantages gained
Enzyme immunoassay for rabies antibody in hybridoma culture fluids and its application to differentiation of street and laboratory strains of rabies virus

A rapid and sensitive enzyme immunoassay is described for detecting rabies antibody in hybridoma culture fluids. Glass fiber filter disks were used to immobilize gamma-irradiated mouse neuroblastoma cells infected with street or laboratory strains of rabies virus. Bound rabies-specific antibody was detected by reaction with horseradish peroxidase-labeled goat anti-mouse immunoglobulin G. The assay was performed in a 96-well filtration device developed by Cleveland et al. (J. Clin. Microbiol. 15:402-407, 1982) for the typing of herpes simplex viruses. When partially disrupted cells were used, both internal and external viral antigens were available for reaction. The procedure is rapid (less than 4 h for completion) and requires only small amounts of fluid, and the gamma-irradiated antigen is noninfectious. When the procedure was used to screen 145 fluids from rabies-immune spleen-myeloma cell fusions, 132 were positive for rabies antibody. Other commonly used assays for the detection of rabies-specific antibody were less sensitive. Simultaneous analyses of many hybridoma fluids against a battery of street and laboratory strains of rabies virus are possible and allow rapid selection of useful monoclones.
Synthesis and antimicrobial studies of some Mannich bases carrying imidazole moiety

3 Starting from 2-methyl-4-nitro-imidazole, new 5-(2-methyl- 4-nitro-1-imidazomethyl)-1,3,4-oxadiazole-2-thione () was synthesized and was subjected to Mannich reaction with appropriate amines to yield a new series of 3-substituted aminomethyl-5-(2-methyl-4-nitro-1-imidazomethyl)- 1,3,4-oxadiazole-2-thiones (4a-j). The structure of the title compounds was elucidated by elemental analysis and spectral data. The newly synthesized Mannich bases were screened for their antibacterial and antifungal activity. Many of these compounds exhibited potent antifungal activity.
Training in the 7 Languages of Infant Mental Health: The Graduate Certificate Program at Wayne State University's Merrill‐Palmer Institute

Training for infant mental health specialists involves unusual requirements for depth and breadth as it seeks to address 7 languages: the languages of the infant, the parent, the parent-infant relationship, the family, infant mental health specialists, collaboration, and self-reflection. The training approach in the Graduate Certificate Program in Infant Mental Health at Merrill-Palmer Institute, Wayne State University, built upon the training and intervention legacy of Selma Fraiberg (1980), includes graduate coursework, direct observational and clinical experience, reflective supervision, and a yearlong clinical seminar. The program's history, orientation, structure, resources, and outcomes are described. Essential aspects of the program include its focus on observational skills, knowledge of normative development in infancy, understanding of early relationship development, understanding of the family and cultural context of development, and clinical assessment and intervention skills. Since 1988, more than 75 graduate trainees have completed the program's special blend of interdisciplinary study and supervised clinical practice, earning their graduate certificates, and making important contributions to the field of infant mental health.
Linearly Interpolated Hierarchical N-gram Language Models for Speech Recognition Engines

Language modeling is a crucial component in natural language continuous speech recognition, due to the difficulty involved by continuous speech [1], [2]. Language modeling attempts to capture regularities in natural language for the purpose of improving the recognition performance. Many studies have shown that the word error rate of automatic speech recognition (ASR) systems decreases significantly when using statistical language models [3], [4], [5]. The purpose of language models (LMs) is to compute the probability of a sequence of words . The probability can be expressed as: , where is the history or the context of word . The probability becomes difficult to estimate as the number of words in increases. To overcome this problem, we can introduce equivalent classes on the histories in order to reduce their cardinality. The n-gram language models approximate the dependence of each word (regardless of i) to the n — 1 words preceding it: . The probability can then be expressed as:
Understanding, leveraging and improving human navigation on the web

Navigating websites represents a fundamental activity of users on the Web. Modeling this activity, i.e., understanding how predictable human navigation is and whether regularities can be detected has been of interest to researchers for nearly two decades. This is crucial for improving the Web experience of users by e.g., enhancing interfaces or information network structures. This thesis envisions to shedding light on human navigational patterns by trying to understand, leverage and improve human navigation on the Web. One main goal of this thesis is the construction of a versatile framework for modeling human navigational data with the use of Markov chains and for detecting the appropriate Markov chain order by using several advanced inference methods. It allows us to investigate memory and structure in human navigation patterns. Furthermore, we are interested in detecting whether pragmatic human navigational data can be leveraged by e.g., being useful for the task of calculating semantic relatedness between concepts. Finally, we want to find ways of enhancing human navigation models. Concretely, we plan on incorporating prior knowledge about the semantic relatedness between concepts to our Markov chain models as it is known that humans navigate the Web intuitively instead of randomly. Our experiments should be conducted on a variety of distinct navigational data including both goal oriented and free form navigation scenarios. We not only look at navigational paths over websites, but also abstract away to navigational paths over topics in order to get insights into cognitive patterns.
Racial and Ethnic Differences in an Estimated Measure of Insulin Resistance Among Individuals with Type 1 Diabetes Running Title : Racial / Ethnic Differences in Insulin Resistance

This is an uncopyedited electronic version of an article accepted for publication in Diabetes Care. The American Diabetes Association, publisher of Diabetes Care, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisherauthenticated version will be available in a future issue of Diabetes Care in print and online at http://care.diabetesjournals. org. Diabetes Care Publish Ahead of Print, published online December 10, 2009
Exploring the familial impact of cold case homicides

ABSTRACT In the aftermath of an unsolved homicide, surviving family members are forced to cope with the murder and how it affects their family unit. This paper examines the experiences of 12 cold case homicide survivors from four families, providing a much needed voice to cold case homicide survivors. Utilizing in-depth interviews to understand the survivors’ stories, five themes emerged including time spent with related survivors, becoming overprotective, impacts on existing relationships, pressure to stay strong and the difficulties of family traditions. The findings provide an initial framework for understanding the impact of a cold case homicide on the family unit for both survivors and related practitioners. The data indicates that throughout the grief process survivors should be encouraged to engage in private and collective grieving, recognize and address their behavioral and emotional changes, openly discuss the perceived impact on their relationships and reshape family traditions.
Transmission of Policy Shocks in a Monetary Asset-Pricing Model

In a framework of a two-country monetary asset-pricing model with production the effects of stochastic and structural fiscal and monetary policy shocks are investigated. The model is kept simple enough to allow the derivation of closed form solutions of the functional equation system for the equilibrium price functions. With money yielding liquidity services in the exchange process some correlation results are derived, especially for the impact of structural and stochastic policy shocks on stock prices, exchange rates etc. Furthermore it is investigated whether shares can provide protection against inflation resulting from monetary shocks.
Transcallosal connection patterns of opposite dorsal premotor regions support a lateralized specialization for action and perception

Lateralization of higher brain functions requires that a dominant hemisphere collects relevant information from both sides. The right dorsal premotor cortex (PMd), particularly implicated in visuomotor transformations, was hypothesized to be optimally located to converge visuospatial information from both hemispheres for goal-directed movement. This was assessed by probabilistic tractography and a novel analysis enabling group comparisons of whole-brain connectivity distributions of the left and right PMd in standard space (16 subjects). The resulting dominance of contralateral PMd connections was characterized by right PMd connections with left visual and parietal areas, indeed supporting a dominant role in visuomotor transformations, while the left PMd showed dominant contralateral connections with the frontal lobe. Ipsilateral right PMd connections were also stronger with posterior parietal regions, relative to the left PMd connections, while ipsilateral connections of the left PMd were stronger with particularly the anterior cingulate, the ventral premotor and anterior parietal cortex. The pattern of dominant right PMd connections thus points at a specific role in guiding perceptual information into the motor system, while the left PMd connections are consistent with action dominance based on a lead in motor intention and fine precision skills.
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models

In this paper, we describe an automatic unsupervised texture segmentation scheme using hidden Markov models (HMMs). First, the feature map of the image is formed using Laws' micromasks and directional macromasks. Each pixel in the feature map is represented by a sequence of 4-D feature vectors. The feature sequences belonging to the same texture are modeled as an HMM. Thus, if there are M different textures present in an image, there are M distinct HMMs to be found and trained. Consequently, the unsupervised texture segmentation problem becomes an HMM-based problem, where the appropriate number of HMMs, the associated model parameters, and the discrimination among the HMMs become the foci of our scheme. A two-stage segmentation procedure is used. First, coarse segmentation is used to obtain the approximate number of HMMs and their associated model parameters. Then, fine segmentation is used to accurately estimate the number of HMMs and the model parameters. In these two stages, the critical task of merging the similar HMMs is accomplished by comparing the discrimination information (DI) between the two HMMs against a threshold computed from the distribution of all DI's. A postprocessing stage of multiscale majority filtering is used to further enhance the segmented result. The proposed scheme is highly suitable for pipeline/parallel implementation. Detailed experimental results are reported. These results indicate that the present scheme compares favorably with respect to other successful schemes reported in the literature.