Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovering Facial Shape Using a Statistical Model of Surface Normal Direction

In this paper, we show how a statistical model of facial shape can be embedded within a shape-from-shading algorithm. We describe how facial shape can be captured using a statistical model of variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to Cartesian points on a local tangent plane. The distribution of surface normal directions is captured using the covariance matrix for the projected point positions. The eigenvectors of the covariance matrix define the modes of shape-variation in the fields of transformed surface normals. We show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by Lambert's law. We demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details. We assess the accuracy of the technique on a variety of images with ground truth and real-world images
An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users' diversified interest and its categories by analysing their implicit user profile. Depending on users' interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users' diversified interest. The performance of the proposed approach for predicting overall users' interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users' interest categories.
Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics

We present the design of a low-cost wheeled mobile robot, and an analytical model for predicting its motion under the influence of motor torques and friction forces. Using our proposed model, we show how to analytically compute the gradient of an appropriate loss function, that measures the deviation between predicted motion trajectories and real-world trajectories, which are estimated using Apriltags and an overhead camera. These analytical gradients allow us to automatically infer the unknown friction coefficients, by minimizing the loss function using gradient descent. Motion trajectories that are predicted by the optimized model are in excellent agreement with their real-world counterparts. Experiments show that our proposed approach is computationally superior to existing black-box system identification methods and other data-driven techniques, and also requires very few real-world samples for accurate trajectory prediction. The proposed approach combines the data efficiency of analytical models based on first principles, with the flexibility of data-driven methods, which makes it appropriate for low-cost robots. Using the learned model and our gradient-based optimization approach, we show how to automatically compute motor control signals for driving the robot along pre-specified curves.
Performance Evaluation of Sales Representatives Based on the European Customer Satisfaction Index Model (Case Study: Saipa Automotive Company)

The aim of this article is Performance evaluation of sales representatives in Saipa automotive company. The current research has a descriptive-correlative method. The matters are chosen among customers of Saipa automotive company in Shiraz. Data collection is done through random sampling. The validity of its content was ensured by using the expert viewpoints and consensus and analyzed using SPSS software. The validity of its structure was measured through using the Cronbach’s alpha. Overall results of research indicated that the offered product, price, distribution and expectation by Saipa automotive company is not desirable from their customer perspective and the offered service and image by Saipa automotive company is desirable from their customer perspective.
Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle

MLflow is a popular open source platform for managing ML development, including experiment tracking, reproducibility, and deployment. In this paper, we discuss user feedback collected since MLflow was launched in 2018, as well as three major features we have introduced in response to this feedback: a Model Registry for collaborative model management and review, tools for simplifying ML code instrumentation, and experiment analytics functions for extracting insights from millions of ML experiments.
MicroMon: A Monitoring Framework for Tackling Distributed Heterogeneity

We present MicroMon, a multi-dimensional monitoring framework for geo-distributed applications using heterogeneous hardware. In MicroMon, we introduce micrometrics, which is a set of fine-grained hardware and software metrics required to study the combined impact of heterogeneous resources on application performance. Besides collecting micrometrics, in MicroMon, we propose anomaly reports and concerted effort between the programmable switches and host OSes to reduce the overhead of collecting and disseminating thousands of micrometrics in WAN. We evaluate the MicroMon prototype on Cassandra deployed across multiple data centers and show 10–50% throughput gains in a geodistributed setting with storage and network heterogeneity.
FOSTERING OLD-AGE SAVING UNDER INCOMPLETE RATIONALITY

Most developed countries provide dedicated old-age savings programs with tax incentives. These programs are typically motivated by the argument that the fiscal incentives absent, people would not save enough for the old-age and thus face the risk of poverty in the last decades of their life. We verify this conjecture. We develop a model in which individuals’ age and the subsequent generations of individuals overlap. In this model economy, individuals exhibit many types of bounded (incomplete) rationality, which prevents them from taking the old-age savings decisions such as a fully rational homo oeconomicus would take. We study these savings patterns. We then introduce a government-subsidized old-age saving instrument and study participation by incompletely rational individuals, as well as the changes in their savings behavior. Our simulations of this policy instrument are calibrated to the case of Poland, but our results may easily be adapted to any other economy. Key finding of this report is that the government-subsidized old-age saving instruments may raise consumption by the incompletely rational individuals, but will not actually raise wealth accumulated prior to the retirement. We also document who benefits and who loses from fiscal incentives typically featured in old-age voluntary savings programs.
Advanced Control Method for DC Motor Drives

In this research work, trajectory tracking of DC motor is established. A hierarchical controller with the dc motor based on differential flatness and the dc/dc Buck converter is designed. In dc motor, angular velocity tracked from a desired velocity trajectory from the desired voltage profile. The inner current loop and the outer voltage loop of cascade control is modelled. The desired angular velocity trajectory is well tracked and the controller is robust in such dynamic operating conditions. The actual value follows the trajectory with time delay and reduced magnitude in simulation. So controller gains are introduced to overcome these issues. Along with that, hardware is to be implemented for the simulation model of overall system. It is important that these types of abrupt variations do not happen in practice at the same time, or with such large variations regarding their nominal values.
Transformational Leadership: Examining Subordinates’ Perception of Corporate Social Responsibility and Organizational Outcomes in Nigeria

Today, leadership requires more than skills and the ability to lead as ethical lapses and erosion of trust are on the increase. Therefore, for a healthy implementation of corporate social responsibility (CSR) requires carrying all stakeholders along. Thus, higher cognition and creativeinnovative abilities has been evidenced as essential but mostly underresearched. This paper is an attempt to contribute in filling this existing gap in literature by exploring how transformational leadership style and subordinates’ perception of corporate social responsibility (CSR) can enhance and propel followers creative-innovation in an organization. Quality leadership, employees are very crucial and vital assets in an organization. Conceptually therefore, transformational leadership style have been evidenced as an important and supportive weapon in the formulation and implementation of strategies geared towards attainment of common and mutual obligation. Literature evidenced that it strengthens followers perception of CSR, job satisfaction, and organizational outcomes which has so far attracted less or little attention in research. Additionally, we introduced and investigated CSR, trust and job satisfaction as potential mediators on the linkages between transformational leadership and followers creative-nnovations. Data was collected from 176 respondents in 96 organizations. Results shows a positive and significant linkage between transformational leadership and subordinates’ creative-innovation. A sufficient direct and indirect mediating effects were established between the variables using SEM-AMOS24, a significant direct and indirect linkages between the variables was reported. Thus, using both symmetrical and asymmetrical methodology, we concluded with some suggestions based on literature while the implication and limitations of the study were highlighted and future research directions were given.
Character Mapping and Ad-hoc Adaptation: Edinburgh’s IWSLT 2020 Open Domain Translation System

This paper describes the University of Edinburgh’s neural machine translation systems submitted to the IWSLT 2020 open domain Japanese\leftrightarrowChinese translation task. On top of commonplace techniques like tokenisation and corpus cleaning, we explore character mapping and unsupervised decoding-time adaptation. Our techniques focus on leveraging the provided data, and we show the positive impact of each technique through the gradual improvement of BLEU.
Research Express@NCKU-Articles Digest

Quantum entanglement is a marvelous phenomenon that plays an essential task in quantum informatics [1, 2, 3, 4, 5, 6]. The miracle demonstrates how pairs or groups of particles generate, interact, or share properties so that the variation of one particle will instantly transform the characteristics of another particle. In other words, we could imagine that there is a mysterious communication channel which forwards information even faster than the speed of light. To the study of entanglement, we suitably represent quantum states in terms of some properly selected basis over the complex field. Then, we express the rule of entanglement in quantum mechanics systems as Kronecker products between states of density matrices of the subsystems. Subsequently, we can investigate the property of entanglement as the following low rank approximation problem:
Text Summarization Using Named Entity Recognition

Text summarization is a task in the ﬁeld of natural language processing that consists of summarizing a text with a shorter and concise text. Summarization systems usually work just with the input text converted to a list of vectors. Our work explores the eﬀect of enriching the input with named entity features. We used a translation neural network architecture, sequence to sequence, to achieve state-of-the-art results on the largest dataset for Czech text summarization, SumeCzech and we found that adding named entities helpes the model achieve better results on out of domain texts. We explored different named entity recognition datasets, methods and frameworks, compared them in terms of speed, memory requirements and F-score and chose the most suitable for text summarization.
Low-cost 3D scanning systems for cultural heritage documentation

PurposeDigital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light Detection and Ranging), are able to create precise three-dimensional models of excavations to complement traditional forms of documentation with millimeter to centimeter accuracy. However, these techniques require either expensive pieces of equipment or a long processing time that can be prohibitive during short field seasons in remote areas. This article aims to determine the effectiveness of various low-cost sensors and real-time algorithms to create digital scans of archaeological excavations.Design/methodology/approachThe authors used a class of algorithms called SLAM (Simultaneous Localization and Mapping) along with depth-sensing cameras. While these algorithms have largely improved over recent years, the accuracy of the results still depends on the scanning conditions. The authors developed a prototype of a scanning device and collected 3D data at a Maya archaeological site and refined the instrument in a system of natural caves. This article presents an analysis of the resulting 3D models to determine the effectiveness of the various sensors and algorithms employed.FindingsWhile not as accurate as commercial LiDAR systems, the prototype presented, employing a time-of-flight depth sensor and using a feature-based SLAM algorithm, is a rapid and effective way to document archaeological contexts at a fraction of the cost.Practical implicationsThe proposed system is easy to deploy, provides real-time results and would be particularly useful in salvage operations as well as in high-risk areas where cultural heritage is threatened.Originality/valueThis article compares many different low-cost scanning solutions for underground excavations, along with presenting a prototype that can be easily replicated for documentation purposes.
Regulatory interactions between two actin nucleators,

Developing Drosophila melanogaster oocytes use both actin and microtubule cytoskeletal systems to construct and maintain internal landmarks that defi ne the dorsal-ventral and anteriorposterior axes (Theurkauf et al., 1992; Clark et al., 1994; Pokrywka and Stephenson, 1995; Polesello et al., 2002). The cappuccino and spire genes encode actin fi lament nucleation factors (Quinlan et al., 2005), and mutation of either gene disrupts localization of the earliest known polarity determinants (Manseau and Schupbach, 1989; Manseau et al., 1996). spire and cappuccino were originally identifi ed in the same genetic screen (Manseau and Schupbach, 1989), and loss of either results in the premature onset of microtubule-dependent fast cytoplasmic streaming during oogenesis, loss of oocyte polarity, and female sterility (Theurkauf, 1994; Emmons et al., 1995). Rosales-Nieves et al. (2006) demonstrated a genetic interaction between spire and cappuccino by showing that premature cytoplasmic streaming occurs in fl ies heterozygous for mutations in both genes. Mutation of Drosophila profi lin (Chickadee) or addition of the actin-depolymerizing toxin cytochalasin D (Emmons et al., 1995; Manseau et al., 1996) also cause premature fast cytoplasmic streaming. Together, these data suggest that actin polymerization driven by Spire (Spir), Cappuccino (Capu), and Chickadee suppresses fast cytoplasmic streaming until the appropriate point in oogenesis (Serbus et al., 2005). Consistent with genetic data, Rosales-Nieves et al. (2006) recently showed that Spir and Capu proteins interact directly. These authors found that the N-terminal region of Spir, which contains the kinase noncatalytic C-lobe domain (KIND) and a cluster of actin-binding WH2 domains (Wiskott-Aldrich syndrome protein homology domain 2), binds to the formin homology 2 (FH2) domain of Capu. Their data suggest that interaction is mediated by direct binding of the WH2 cluster to the FH2 domain. These authors report that the Spir–Capu interaction has no effect on actin nucleation by either protein but that interaction with Spir inhibits FH2-dependent cross-linking of actin fi laments and microtubules. Based on these data, Rosales-Nieves et al. (2006) propose a model in which Spir and Capu inhibit premature cytoplasmic streaming by cross-linking microtubules to actin fi laments in the oocyte cortex. Interactions between Spir and Capu have been studied only in Drosophila, but there is evidence linking the two proteins in other organisms. In sequenced metazoan genomes, Capu family formins appear only in organisms that also contain Spir family genes (Higgs and Peterson, 2005). Mammals have two copies of each gene. Arthropods, including Drosophila, contain at least one spire and one cappuccino gene, whereas nematodes, such as Caenorhabditis elegans, contain neither. Because nematodes diverged from arthropods long after Deuterostomes diverged from Protostomes, it appears that nematodes lost both genes at some point in their evolution. Schumacher et al. (2004) found Regulatory interactions between two actin nucleators, Spire and Cappuccino
Classifications of Breast Cancer Images by Deep Learning

Background: Breast cancer is a leading cause of cancer-related death in women. Classifications of pathological images are important for its diagnosis and prognosis. However, the existing computational methods can sometimes hardly meet the accuracy requirement of clinical applications, due to uneven color distribution and subtle difference in features. Methods: In this study, a novel classification method DeepBC was proposed for classifying the pathological images of breast cancer, based on the deep convolution neural networks. DeepBC integrated Inception, ResNet, and AlexNet, extracted features from images, and classified images of benign and malignant tissues. Results: Additionally, complex tests were performed on the existing benchmark dataset to evaluate the performance of DeepBC. The evaluation results showed that, DeepBC achieved 92% and 96.43% accuracy rates in classifying patients and images, respectively, with the F1-score of 97.38%, which better than the state-of-the-art methods. Conclusions:These findings indicated that, the model had favorable robustness and generalization, and was advantageous in the clinical classifications of breast cancer.
Variations in the Circle of Willis in a large population sample using 3D TOF angiography: The Tromsø Study

The main arteries that supply blood to the brain originate from the Circle of Willis (CoW). The CoW exhibit considerable anatomical variations which may have clinical importance, but the variability is insufficiently characterised in the general population. We assessed the anatomical variability of CoW variants in a community-dwelling sample (N = 1,864, 874 men, mean age = 65.4, range 40 - 87 years), and independent and conditional frequencies of the CoW's artery segments. CoW segments were classified as present or missing/hypoplastic (w/1mm diameter threshold) on 3T time-of-flight magnetic resonance angiography images. We also examined whether age and sex were associated with CoW variants. We identified 47 unique CoW variants, of which five variants constituted 68.5% of the sample. The complete variant was found in 11.9% of the subjects, and the most common variant (27.8%) was missing both posterior communicating arteries. Conditional frequencies showed patterns of interdependence across most missing segments in the CoW. CoW variants were associated with mean-split age (P = .0147), and there was a trend showing more missing segments with increasing age. We found no association with sex (P = .0526). Our population study reinforced age as associated with CoW variants, suggesting reduced collateral supply with older age.
WeScream!: Toward Understanding the Design of Playful Social Gustosonic Experiences with Ice Cream

Prior psychology studies have shown that eating ice cream increases happiness, while human-computer interaction work has shown that interactive technology can enrich the eating experience. We explore the opportunity to combine these two through WeScream! , a playful social gustosonic system we designed-social gustosonic referring to the link between the acts of eating and listening as part of a social multisensory experience. WeScream! consists of two interdependent ice-cream cones that allow users to interact with musical sounds generated through the act of eating ice cream together. We report on an in-the-wild study that highlights how our system facilitated a "hard fun" experience through eating together, increased participants' awareness of relatedness, and drew shared attention to the ice cream's taste via increased face-to-face interaction. Drawing on these study insights, we also present three design tactics to guide designers in designing future social gustosonic experiences. Ultimately, we aim to contribute to a playful future of social eating experiences, supporting people in enjoying eating together.
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robotic Manipulation Skills

—Personal robots assisting humans must perform complex manipulation tasks that are typically difﬁcult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration. In this paper, we introduce a state transition model (STM) that generates joint-space trajectories by imitating motions from expert behavior. Given a few demonstrations, we show in real robot experiments that the learned STM can quickly generalize to unseen tasks and synthesize motions having longer time horizons than the expert trajectories. Compared to conventional motion planners, our approach enables the robot to accomplish complex behaviors from high-level instructions without laborious hand-engineering of planning objectives, while being able to adapt to changing goals during the skill execution. In conjunction with a trajectory optimizer, our STM can construct a high-quality skeleton of a trajectory that can be further improved in smoothness and precision.