Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the automated alignment of 3D human body scans

The statistical analysis of large corpora of human body scans requires that these scans be in alignment, either for a small set of key landmarks or densely for all the vertices in the scan. Existing techniques tend to rely on hand-placed landmarks or algorithms that extract landmarks from scans. The former is time consuming and subjective while the latter is error prone. Here we show that a model-based approach can align meshes automatically, producing alignment accuracy similar to that of previous methods that rely on many landmarks. Specifically, we align a low-resolution, artist- created template body mesh to many high-resolution laser scans. Our alignment procedure employs a robust iterative closest point method with a regularization that promotes smooth and locally rigid deformation of the template mesh. We evaluate our approach on 50 female body models from the CAESAR dataset that vary significantly in body shape. To make the method fully automatic, we define simple feature detectors for the head and ankles, which provide initial landmark locations. We find that, if body poses are fairly similar, as in CAESAR, the fully automated method provides dense alignments that enable statistical analysis and anthropometric measurement.
Requirements Engineering as Innovation Journalism: A Research Preview

The successful launch of an innovation project depends on an attractive vision and how well the vision is supported by the complementary capabilities of the consortium partners that want to cooperate to realize the vision. Alas, for an innovator the search for the right partners and visions can be lengthy and difficult. Even though the analysis of context and problem, the identification of stakeholders, the analysis of stakeholder goals and techno-logical capabilities, and the definition of requirements belong to the cornerstones of requirements engineering, this requirements engineering problem has hardly been explored so far. In an attempt to avoid re-invention of well-tried solutions, we discovered journalism as a discipline that, as a rich body of methodical knowledge, may act as a source for the guidance of how a requirements engineer may support the launch of innovation projects. In the role of a journalist, the requirements engineer makes partners and their capabilities visible and thereby allows the parties to identify each other and meet. The transparency that develops between the parties becomes an instrument for consortia to emerge and to eventually answer calls for innovation with appropriate visions for innovation projects. This paper introduces the problem of initiating innovation projects and describes the potential role of journalism as a metaphor for addressing the problem. The paper also describes our plans in designing an innovation journalism approach that we hope will enable requirements engineers to increase innovation potential and ease the launch of projects that bring about these innovations.
Locally induced predictive models

Most predictive modeling techniques utilize all available data to build global models. This is despite the wellknown fact that for many problems, the targeted relationship varies greatly over the input space, thus suggesting that localized models may improve predictive performance. In this paper, we suggest and evaluate a technique inducing one predictive model for each test instance, using only neighboring instances. In the experimentation, several different variations of the suggested algorithm producing localized decision trees and neural network models are evaluated on 30 UCI data sets. The main result is that the suggested approach generally yields better predictive performance than global models built using all available training data. As a matter of fact, all techniques producing J48 trees obtained significantly higher accuracy and AUC, compared to the global J48 model. For RBF network models, with their inherent ability to use localized information, the suggested approach was only successful with regard to accuracy, while global RBF models had a better ranking ability, as seen by their generally higher AUCs.
Nanoconnectomic upper bound on the variability of synaptic plasticity

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes. DOI: http://dx.doi. org/10.7554/eLife.10778. 001
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce the beta-risk as a generalized formulation of the standard empirical risk based on surrogate margin-based loss functions. This risk allows us to express the reliability on the labels and to derive different kinds of learning algorithms. We specifically focus on SVMs and propose a soft margin beta-svm algorithm which behaves better that the state of the art.
Effect of Reward Function Choices in MDPs with Value-at-Risk

This paper studies Value-at-Risk (VaR) problems in short- and long-horizon Markov decision processes (MDPs) with finite state space and two different reward functions. Firstly we examine the effects of two reward functions under two criteria in a short-horizon MDP. We show that under the VaR criterion, when the original reward function is on both current and next states, the reward simplification will change the VaR. Secondly, for long-horizon MDPs, we estimate the Pareto front of the total reward distribution set with the aid of spectral theory and the central limit theorem. Since the estimation is for a Markov process with the simplified reward function only, we present a transformation algorithm for the Markov process with the original reward function, in order to estimate the Pareto front with an intact total reward distribution.
KM as a Facilitator for Project Performance Through Team Process: Does Information Technology Make a Difference?

This paper empirically investigates a sample of research and development (R&D) projects in Taiwanese high-tech industry with a three-fold purpose. The first objective is to assess the associations among information technology (IT) application, knowledge management (KM), team process (TP), and R&D project performance. The second objective is to determine whether TP may mediate the effect of KM on R&D project performance. The third objective is to examine the moderating role of project characteristics in the relationship between TP and R&D project performance. The structural equation modeling (SEM) approach was used to validate the research model. These analyses suggest that TP may fully mediate the effects of KM on R&D project performance in terms of quality and development success and schedule and cost performance. The results also show that industry sector (IS) and team size (TS) have a moderating effect on the relationship between TP and R&D project performance.
A Bayesian approach to estimate the biomass of anchovies off the coast of Perú

The Northern Humboldt Current System (NHCS) is the world's most productive ecosystem in terms of fish. In particular, the Peruvian anchovy (Engraulis ringens) is the major prey of the main top predators, like seabirds, fish, humans, and other mammals. In this context, it is important to understand the dynamics of the anchovy distribution to preserve it as well as to exploit its economic capacities. Using the data collected by the “Instituto del Mar del Perú” (IMARPE) during a scientific survey in 2005, we present a statistical analysis that has as main goals: (i) to adapt to the characteristics of the sampled data, such as spatial dependence, high proportions of zeros and big size of samples; (ii) to provide important insights on the dynamics of the anchovy population; and (iii) to propose a model for estimation and prediction of anchovy biomass in the NHCS offshore from Perú. These data were analyzed in a Bayesian framework using the integrated nested Laplace approximation (INLA) method. Further, to select the best model and to study the predictive power of each model, we performed model comparisons and predictive checks, respectively. Finally, we carried out a Bayesian spatial influence diagnostic for the preferred model.
A Patient Care Program for Adjusting the Autoinjector Needle Depth According to Subcutaneous Tissue Thickness in Patients With Multiple Sclerosis Receiving Subcutaneous Injections of Glatiramer Acetate

ABSTRACT Background: The perceived pain on injection site caused by subcutaneous (SC) self-injection may negatively affect acceptance and adherence to treatment in patients with multiple sclerosis (MS). Pain on injection may be caused by inaccurate injection technique, inadequate needle length adjustment, or repeated use of the same injection body area. However, information is lacking concerning the optimal needle depth to minimize the injection pain. Objective: The purpose of this program was to characterize the perceived injection-site pain associated with the use of various injection depths of the autoinjector of glatiramer acetate (GA) based on SC tissue thickness (SCT) of the injection site. Methods: This was a pilot program performed by MS-specialized nurses in patients with MS new to GA. Patients were trained by MS nurses on the preparation and administration of SC injection and on an eight-site rotation (left and right arms, thighs, abdomen, and upper quadrant of the buttock). The needle length setting was selected based on SCT measures as follows: 4 or 6 mm for SCT < 25 mm, 6 or 8 mm for SCT between 25 and 50 mm, and 8 or 10 mm for SCT > 50 mm. Injection pain was rated using a visual analog scale (VAS) at 5- and 40-minute postinjection and during two 24-day treatment periods. Results: Thirty-eight patients with MS were evaluated. The mean SCT ranged from 15.5 mm in the upper outer quadrant of the buttocks to 29.2 mm in the thighs. The mean perceived pain on injection was below 3 for all the injection sites, at both time points (5 and 40 minutes) and during both 24-day evaluation periods. The mean VAS scores were significantly greater after 5 minutes of injection compared with that reported 40-minute postinjection during both 24-day treatment periods and for all the injection areas. Mean VAS measures at 5- and 40-minute postinjection significantly decreased during the second 24-day treatment period with respect to that reported during the first 24 SC injections for all injection sites. Conclusions: Our findings suggest that the adjustment of injection depth of SC GA autoinjector according to SCT of body injection areas is suitable to maintain a low degree of postinjection pain. Moreover, our results also may indicate that the use of needle lengths of 6 mm or shorter is appropriate with regard to injection pain for adult patients with MS with SCT < 50 mm.
Ultra-structural characterization of enamel–resin interface using FIB-TEM technology

This study evaluated the enamel–resin interface of three adhesive resins (ARs) by a transmission electron microscope (TEM) after milling with focused ion beam (FIB). Simple Class-I cavities (0.5 mm within enamel depth, 4 mm length, and 2 mm width) were prepared on human caries-free molars. The enamel of two groups was conditioned by one-step self-etch bonding system (Scotchbond™/Single Bond Universal [SBU], and Xeno V®+ [XV+]), while the enamel of the third group was etched by phosphoric acid, and then treated by two-step self-priming etch-rinse system (Prime&Bond® NT [PBNT]). The application of the adhesive systems was carried out according to their respective manufacturer’s instructions. The cavities were restored by a nanofill resin composite (Filtek™ Z350 XT Universal; 3 M ESPE) in one bulk-fill, and cured for 40 s at 550 mW/cm2 by a halogen light (Optilux 501, Demetron/Kerr, Danbury, USA). The specimens were milled by FIB into 100 nm thickness slices, and then observed under TEM. The transmission electron micrographs showed an adequate adhesion of both two-step etch-rinse (PBNT) and one-step self-etch (SBU) to enamel surface. The deeply etched enamel prisms were impregnated by the etch-rinse PBNT adhesive. A relatively inadequate adhesion associated with some areas of bond degradation underneath the hybrid layer and within the adhesive was noted for the XV+ AR. Apart from the mild acidic adhesives of one-step self-etch and two-step etch-rinse investigated, the highly hydrophilic and acidic water-based one-step self-etch adhesive (XV+) proved to be less effective enamel bond by ultra-structural characterization technique using FIB-TEM.
Quality enhancement in information extraction from scanned documents

When constructing a large document archive, an important element is the digitizing of printed documents. Although various techniques for document image analysis such as Optical Character Recognition (OCR) have been developed, error handling is required in constructing real document archive systems. This paper discusses the problem from the quality enhancement perspective and proposes a robust reference extraction method for academic articles scanned with OCR mark-up. We applied the proposed method to articles appearing in various journals, and these experiments showed that the proposed method achieved a recognition accuracy of more than 94%. This paper also discusses manual correction and investigates experimentally the relationship between extraction accuracy and cost reduction.
Waveform base clock tree delay analysis using parallel processing

A Novel method is proposed for VLSI H-tree type clock tree delay analysis. Proposed method propagates clock waveform from VLSI primary clock pin to F/F clock pin along the clock path instead of manipulating large differential equation like Spice®. Random Walk method is employed to fix the interconnect output waveform, and GPGPU parallel processing is used to accelerate the clock waveform propagation. 60(nm) process benchmarks demonstrate proposed method can yield acceleration by 101-102 × with less than 3(%) analysis error for 50% delay at F/F clock pin compared with Spice®.
Controlled Radiation Pattern of Circular Antenna Array

This paper presents a seven-element dipole circular array with different lengths and separation between the elements that was used to produce maximum gain in certain directions. The method of moments was used to formulate the problem. The radiation characteristics of the array were calculated, considering that one element acts as an active element while the others to act as parasitic elements. The genetic algorithm was used to optimize the dimensions and the separation between the elements
Reconstruct Dynamic Systems from Large-Scale Open Data

With the prosperity of e-commerce, on-line vendors use recommendation systems in different fields. Classic recommendation algorithms are designed assuming that data is stationary and will not change over time. However, since the scale and variability of data are growing gradually, these methods will encounter the issues of the memory deficient and the out-of-date model, which degrade the recommendation accuracy intensively. In addition, retraining the whole model for every new arrival record results in high complexity. In this paper we propose a light-weight adaptive updating method to overcome these issues. Comparing with the explicit feedback recommendation, which asks the customers to express their opinions on the recommended items, the implicit feedback recommendation is easier to collect and non- intrusive way. However, the dynamic time-variant system with implicit feedback has not been seen in the literature. In this paper, we propose a real- time incremental updating algorithm (RI-SGD) to deal with time-variant systems based on the implicit feedback. We compare our method with methods that retraining the whole model and show that our method costs less than 1% of the retraining time with a competitive accuracy.
Mesoporous Silica-Coated Hollow Manganese Oxide Nanoparticles as Positive T1 Contrast Agents for Labeling and MRI Tracking of Adipose-Derived Mesenchymal Stem Cells

Mesoporous silica-coated hollow manganese oxide (HMnO@mSiO2) nanoparticles were developed as a novel T1 magnetic resonance imaging (MRI) contrast agent. We hypothesized that the mesoporous structure of the nanoparticle shell enables optimal access of water molecules to the magnetic core, and consequently, an effective longitudinal (R1) relaxation enhancement of water protons, which value was measured to be 0.99 (mM−1s−1) at 11.7 T. Adipose-derived mesenchymal stem cells (MSCs) were efficiently labeled using electroporation, with much shorter T1 values as compared to direct incubation without electroporation, which was also evidenced by signal enhancement on T1-weighted MR images in vitro. Intracranial grafting of HMnO@mSiO2-labeled MSCs enabled serial MR monitoring of cell transplants over 14 days. These novel nanoparticles may extend the arsenal of currently available nanoparticle MR contrast agents by providing positive contrast on T1-weighted images at high magnetic field strengths.
Visual Image-Induced Craving for Ethanol (VICE): Development, Validation, and a Pilot fMRI Study

Background: Craving induction in a controlled environment is helpful in the research of craving mechanism and its role in development of alcohol dependence (AD). We describe a novel tool Visual Image-induced Craving for Ethanol (VICE) and its effects on brain activation with pilot functional magnetic resonance imaging (fMRI). Materials and Methods: Alcohol-related visual cues (ARCs) in 5 scenarios were photographed, which included pictures of bars, alcoholic beverage bottles, pouring of alcohol into glasses, glasses filled with alcohol, and scenes of people sipping alcohol, counterbalanced with neutral pictures (involving water, milk etc.,). Craving scores were obtained from 15 hospitalized patients with AD to validate this tool. In the pilot fMRI (3-Tesla) study, 5 patients were examined using VICE in a symptom provocation model. Group level-fixed effect analysis of brain activation differences was done using SPM8. Results: VICE showed a high internal consistency with Cronbach's α coefficient of 0.86, which confirmed its reliability. Concurrent validity of VICE was demonstrated via its convergence with the Penn Alcohol Craving Scale. ARCs had significantly greater mean craving scores than neutral cues in all the 5 scenarios (intentional validity). In the pilot fMRI, patients were found to have greater activation while viewing ARCs compared to the neutral cues in right insular cortex and deficient activation in right orbitofrontal cortex. Conclusions: The VICE is a reliable and valid measure of alcohol craving with promising clinical and translational research implications. Preliminary fMRI findings indicate it can be used as a symptom provocation tool for fMRI experiments.
A Non-Destructive Method for Distinguishing Reindeer Antler (Rangifer tarandus) from Red Deer Antler (Cervus elaphus) Using X-Ray Micro-Tomography Coupled with SVM Classifiers

Over the last decade, biomedical 3D-imaging tools have gained widespread use in the analysis of prehistoric bone artefacts. While initial attempts to characterise the major categories used in osseous industry (i.e. bone, antler, and dentine/ivory) have been successful, the taxonomic determination of prehistoric artefacts remains to be investigated. The distinction between reindeer and red deer antler can be challenging, particularly in cases of anthropic and/or taphonomic modifications. In addition to the range of destructive physicochemical identification methods available (mass spectrometry, isotopic ratio, and DNA analysis), X-ray micro-tomography (micro-CT) provides convincing non-destructive 3D images and analyses. This paper presents the experimental protocol (sample scans, image processing, and statistical analysis) we have developed in order to identify modern and archaeological antler collections (from Isturitz, France). This original method is based on bone microstructure analysis combined with advanced statistical support vector machine (SVM) classifiers. A combination of six microarchitecture biomarkers (bone volume fraction, trabecular number, trabecular separation, trabecular thickness, trabecular bone pattern factor, and structure model index) were screened using micro-CT in order to characterise internal alveolar structure. Overall, reindeer alveoli presented a tighter mesh than red deer alveoli, and statistical analysis allowed us to distinguish archaeological antler by species with an accuracy of 96%, regardless of anatomical location on the antler. In conclusion, micro-CT combined with SVM classifiers proves to be a promising additional non-destructive method for antler identification, suitable for archaeological artefacts whose degree of human modification and cultural heritage or scientific value has previously made it impossible (tools, ornaments, etc.).
Word Recognition from Continuous Articulatory Movement Time-series Data using Symbolic Representations

Although still in experimental stage, articulation-based silent speech interfaces may have significant potential for facilitating oral communication in persons with voice and speech problems. An articulation-based silent speech interface converts articulatory movement information to audible words. The complexity of speech production mechanism (e.g., coarticulation) makes the conversion a formidable problem. In this paper, we reported a novel, real-time algorithm for recognizing words from continuous articulatory movements. This approach differed from prior work in that (1) it focused on word-level, rather than phoneme-level; (2) online segmentation and recognition were conducted at the same time; and (3) a symbolic representation (SAX) was used for data reduction in the original articulatory movement timeseries. A data set of 5,900 isolated word samples of tongue and lip movements was collected using electromagnetic articulograph from eleven English speakers. The average speaker-dependent recognition accuracy was up to 80.00%, with an average latency of 302 miliseconds for each word prediction. The results demonstrated the effectiveness of our approach and its potential for building a real-time articulationbased silent speech interface for clinical applications. The across-speaker variation of the recognition accuracy was discussed.