Miracle-dz/UniFAR-FTUs huggingface dataset:
Attribute Enhanced Face Aging with Wavelet-based Generative Adversarial Networks

Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings for rendering aging effects on a given young face image. However, the matching ambiguity between young and aged face images inherent to unpaired training data may lead to inconsistent facial attributes in generated images, which could not be solved by only enforcing identity consistency. In this paper, we propose a novel attribute enhanced face aging model with wavelet-based Generative Adversarial Networks (GANs) to address the above issues. To be specific, we embed facial attribute vectors into both generator and discriminator of the model to encourage each synthesized elderly face image to be faithful to the attribute of its corresponding input. In addition, a wavelet packet transform (WPT) module is incorporated to improve the visual fidelity of generated images by capturing age-related texture details at multiple scales in the frequency space. Qualitative results demonstrate the ability of our model to synthesize visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of-the-art performance on existing databases.
Geology prediction based on operation data of TBM: comparison between deep neural network and soft computing methods

Tunnel boring machine (TBM) is a complex engineering system widely used for tunnel construction. In view of the complicated construction environments, it is necessary to predict geology conditions prior to excavation. In recent years, massive operation data of TBM has been recorded, and mining these data can provide important references and useful information for designers and operators of TBM. Therefore, a deep neural network-based geology prediction method is proposed in this work, which can provide relatively accurate geology prediction results according to the TBM operation data. The application case study on a tunnel in China shows that the proposed method can accurately estimate the geological conditions prior to excavation compared with the previous prediction methods based on soft computing methods. This work can be regarded as a good complement to the geophysical prospecting method during the construction of tunnels, and also highlights the applicability and potential of deep neural networks for other data mining tasks of TBMs.
An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

Based on a pay‐as‐you‐go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains. However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy‐aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real‐time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real‐time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy‐aware resource provisioning mechanism for cloud data centers, which are capable of serving real‐time periodic tasks following the Software as a Service model. The proposed method is compared against an energy‐aware version of the RT‐OpenStack. RT‐OpenStack is a recently proposed approach to provide a time‐predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy‐aware version of the RT‐OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large‐scale data centers is more considerable.
On the Computation of the Dispersion Diagram of Symmetric One-Dimensionally Periodic Structures

A critical discussion on the computation of the dispersion diagram of electromagnetic guiding/radiating structures with one-dimensional periodicity using general-purpose electromagnetic simulation software is presented in this work. In these methods, full-wave simulations of finite sections of the periodic structure are combined with appropriate simplifying network models. In particular, we analyze the advantages and limitations of two different combined methods, with emphasis on the determination of their range of validity. Our discussion is complemented with several selected numerical examples in order to show the most relevant aspects that a potential user of these methods should be aware of. Special attention is paid to the relevant role played by the high-order coupling between adjacent unit cells and between the two halves of unit cells exhibiting reflection, inversion, and glide symmetries.
Standards for Deriving Nonhuman Primate-Induced Pluripotent Stem Cells, Neural Stem Cells and Dopaminergic Lineage

Humans and nonhuman primates (NHP) are similar in behavior and in physiology, specifically the structure, function, and complexity of the immune system. Thus, NHP models are desirable for pathophysiology and pharmacology/toxicology studies. Furthermore, NHP-derived induced pluripotent stem cells (iPSCs) may enable transformative developmental, translational, or evolutionary studies in a field of inquiry currently hampered by the limited availability of research specimens. NHP-iPSCs may address specific questions that can be studied back and forth between in vitro cellular assays and in vivo experimentations, an investigational process that in most cases cannot be performed on humans because of safety and ethical issues. The use of NHP model systems and cell specific in vitro models is evolving with iPSC-based three-dimensional (3D) cell culture systems and organoids, which may offer reliable in vitro models and reduce the number of animals used in experimental research. IPSCs have the potential to give rise to defined cell types of any organ of the body. However, standards for deriving defined and validated NHP iPSCs are missing. Standards for deriving high-quality iPSC cell lines promote rigorous and replicable scientific research and likewise, validated cell lines reduce variability and discrepancies in results between laboratories. We have derived and validated NHP iPSC lines by confirming their pluripotency and propensity to differentiate into all three germ layers (ectoderm, mesoderm, and endoderm) according to standards and measurable limits for a set of marker genes. The iPSC lines were characterized for their potential to generate neural stem cells and to differentiate into dopaminergic neurons. These iPSC lines are available to the scientific community. NHP-iPSCs fulfill a unique niche in comparative genomics to understand gene regulatory principles underlying emergence of human traits, in infectious disease pathogenesis, in vaccine development, and in immunological barriers in regenerative medicine.
Sequence‐based searching of custom proteome and transcriptome databases

A long‐term goal in renal physiology is to understand the mechanisms involved in collecting duct function and regulation at a cellular and molecular level. The first step in modeling of these mechanisms, which can provide a guide to experimentation, is the generation of a list of model components. We have curated a list of proteins expressed in the rat renal inner medullary collecting duct (IMCD) from proteomic data from 18 different publications. The database has been posted as a public resource at https://hpcwebapps.cit. nih.gov/ESBL/Database/IMCD_Proteome_Database/. It includes 8956 different proteins. To search the IMCD Proteomic Database efficiently, we have created a Java‐based program called curated database Basic Local Alignment Search Tool (cdbBLAST), which uses the NCBI BLAST kernel to search for specific amino acid sequences corresponding to proteins in the database. cdbBLAST reports information on the matched protein and identifies proteins in the database that have similar sequences. We have also adapted cdbBLAST to interrogate our previously published IMCD Transcriptome Database. We have made the cdbBLAST program available for use either as a web application or a downloadable . jar file at https://hpcwebapps.cit. nih.gov/ESBL/Database/cdbBLAST/. Database searching based on protein sequence removes ambiguities arising from the standard search method based on official gene symbols and allows the user efficient identification of related proteins that may fulfill the same functional roles.
Mask Editor : an Image Annotation Tool for Image Segmentation Tasks

Deep convolutional neural network (DCNN) is the state-of-the-art method for image segmentation, which is one of key challenging computer vision tasks. However, DCNN requires a lot of training images with corresponding image masks to get a good segmentation result. Image annotation software which is easy to use and allows fast image mask generation is in great demand. To the best of our knowledge, all existing image annotation software support only drawing bounding polygons, bounding boxes, or bounding ellipses to mark target objects. These existing software are inefficient when targeting objects that have irregular shapes (e.g., defects in fabric images or tire images). In this paper we design an easy-to-use image annotation software called Mask Editor for image mask generation. Mask Editor allows drawing any bounding curve to mark objects and improves efficiency to mark objects with irregular shapes. Mask Editor also supports drawing bounding polygons, drawing bounding boxes, drawing bounding ellipses, painting, erasing, super-pixel-marking, image cropping, multi-class masks, mask loading, and mask modifying.
“I Would Like to Be Heard.” Communicating With Singaporean Chinese Patients With Dementia: A Focused Ethnography Study

Introduction: Although first-generation Singaporean Chinese were mainly immigrants from Mainland China, the communication patterns of Singaporean Chinese in the present-day context differs from Mainland Chinese. This difference could be attributed to the subculture but is not conclusive. This study aimed to explore the experiences of nursing staff communicating with Singaporean Chinese with dementia in Singapore. Method: A focused ethnographic study was conducted in a long-term care setting for people with dementia, where data were obtained using nonparticipant observations and semistructured interviews. Data analyses were conducted using Braun and Clarke’s thematic analysis. Results: Nineteen nursing staff were interviewed. The central theme of “embracing diversity and accepting differences” was supported by three subthemes: acknowledging cultural differences, understanding the patients, and overcoming communicative challenges. Conclusions: This study highlighted the need for nurses to understand cultural characteristics among Singaporean Chinese with dementia especially on emotional expressions when they are not understood by others.
Essential Domain-Dependent Roles Within Soluble IgG for in vivo Superantigen Properties of Staphylococcal Protein A: Resolving the B-Cell Superantigen Paradox

Staphylococcus aureus is a common commensal and frequent opportunistic pathogen that causes invasive infections that often recur. Co-evolution with the host has led to the development of toxins that affect diverse immune cell types. Recent reports have highlighted the contributions of staphylococcal protein A (SpA). This small oligomeric secreted protein contains 4–5 homologous domains with two distinct immunoglobulin-binding sites; one for IgG Fc domains, while a separate site binds an evolutionarily conserved surface on Fab encoded by VHIII clan related genes. The Fab-binding site has been implicated in in vivo supraclonal VHIII-BCR targeted B-cell depletion by an activation induced death pathway. Yet the concept of a superantigen for B lymphocytes poses a seeming paradox. Unlike TCR that are expressed only in a membrane-associated form, BCR are expressed in both a membrane BCR form and in secreted Ig forms, which permeate virtually every part of the body at high levels. We therefore asked, why circulating immunoglobulin do not block the superantigen properties of SpA? Herein, we show that soluble IgG molecules are not in vivo inhibitors of these B-cell superantigen effects but are instead essential for potentiating these properties. We also show that the Fc subclass of circulating IgG is an indirect critical determinant of the B-cell superantigen effect. In contrast, host FcγR and complement are not required for SpA mediated in vivo B-cell depletion. Unexpectedly, after VHIII-IgG2a pretreatment SpA challenge resulted in fatal anaphylactic reactions, which we speculate may have involved FcγR interactions with mast cells and basophils. Cumulatively, our findings illuminate a cunning and potent molecular strategy by which a bacterial toxin effectively confounds the contributions of host B-lymphocytes to immune defenses.
When to Cheat: Modeling Dynamics of Paternity and Promiscuity in Socially Monogamous Prairie Voles (Microtus ochrogaster)

In many socially monogamous species, individuals form long-term pair bonds and males mate guard females. Such behavior is thought to help secure intra-pair fertilizations, the result of intra-pair copulations (IPCs), and ensure paternity. However, socially monogamous males are also often opportunistic and seek additional mating opportunities with other females, leaving their partner unguarded. The success associated with a male’s decision to seek more mates over guarding his partner might be impacted by the activity of other males, specifically the proportion of other males leaving their territories to seek extra-pair copulations (EPCs). The amount of EPC-seeking males can impact the likelihood of a given male encountering an unguarded paired female, but also of being cuckolded (losing IPCs). It remains unclear under which conditions it is optimal to stay and guard or seek EPCs. Using field data from socially monogamous prairie voles (Microtus ochrogaster) to generate parameters, we used optimal performance modeling (Monte Carlo simulations) to ask when is it most reproductively advantageous for a bonded male to seek EPCs, despite the risk of losing IPCs. We defined three types of males: exclusive mating bonded males (true residents), non-exclusive mating bonded residents (roving residents), and unpaired males (wanderers). We first modeled the success of an individual male living in a context that incorporated only true and roving residents. We next added wandering males to this model. Finally, we considered the effects of including wandering males and unpaired females in our model. For all contexts, we found that as EPC-seeking in the population increases, the potential reproductive benefit for seeking EPCs increasingly outpaces the rate of cuckolding. In other words, we observe a shift in optimal strategy from true residents to rovers among paired males. Our models also demonstrate that reproductive fitness is likely to remain constant, despite the shift toward obtaining success via EPCs over IPCs. Our results show the dynamic nature of reproductive decision-making, and demonstrate that alternative reproductive decisions yield subtle but important differences despite appearing as balanced strategies.
Media and games: an intermedial framework

This paper addresses the relationship between games and media and discusses to which extend games can be conceived as media, and how they should be studied as such. The paper proposes a revised version of Elleström's intermedial framework that has been harnessed to better describe games. The framework acknowledges the heterogeneous nature of not only games but all media. Elleström's framework allows us to address games as particular tangible objects in the world in a way that avoids normative assumptions about games in general as well as specific types of games such as digital or analog games. This framework has several usages. It can be applied in an analysis the relationship between different modalities in a single game; it can be used to compare different games; and finally, it can be used to discuss the similarities and differences between games and other media objects.
Executive functions and health behaviors associated with the leading causes of death in the United States: A systematic review

Research indicates that executive functioning may predict health behavior. This systematic review provides an overview of the relationship between domains of executive functioning and health behaviors associated with the leading causes of death in the United States. A total of 114 articles met the inclusion criteria (adult sample, published in English between 1990 and November 2016) and were reviewed and synthesized. Results indicated that although many studies had mixed findings, at least one executive function component was associated with every health behavior. Based on these results, health professionals should consider the role of executive functions in behavior change interventions.
Random problems with R

R (Version 3.5.1 patched) has an issue with its random sampling functionality. R generates random integers between $1$ and $m$ by multiplying random floats by $m$, taking the floor, and adding $1$ to the result. Well-known quantization effects in this approach result in a non-uniform distribution on $\{ 1, \ldots, m\}$. The difference, which depends on $m$, can be substantial. Because the sample function in R relies on generating random integers, random sampling in R is biased. There is an easy fix: construct random integers directly from random bits, rather than multiplying a random float by $m$. That is the strategy taken in Python's numpy.random. randint() function, among others. Example source code in Python is available at this https URL (see functions getrandbits() and randbelow_from_randbits()).
Fast and Stable Ionic Electroactive Polymer Actuators with PEDOT:PSS/(Graphene–Ag-Nanowires) Nanocomposite Electrodes

Ionic electroactive polymer (IEAP) actuators that are driven by electrical stimuli have been widely investigated for use in practical applications. However, conventional electrodes in IEAP actuators have a serious drawback of poor durability under long-term actuation in open air, mainly because of leakage of the inner electrolyte and hydrated cations through surface cracks on the metallic electrodes. To overcome this problem, a top priority is developing new high-performance ionic polymer actuators with graphene electrodes that have superior mechanical, electrical conductivity, and electromechanical properties. However, the task is made difficultby issues such as the low electrical conductivity of graphene (G). The percolation network of silver nanowires (Ag-NWs) is believed to enhance the conductivity of graphene, while poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), which exhibits excellent stability under ambient conditions, is expected to improve the actuation performance of IEAP actuators. In this study, we developed a very fast, stable, and durable IEAP actuator by employing electrodes made of a nanocomposite comprising PEDOT:PSS and graphene–Ag-NWs (P/(G–Ag)). The cost-effective P/(G–Ag) electrodes with high electrical conductivity displayed a smooth surface resulting from the PEDOT:PSS coating, which prevented oxidation of the surface upon exposure to air, and showedstrong bonding between the ionic polymer and the electrode surface. More interestingly, the proposed IEAP actuator based on the P/G–Ag electrode can be used in active biomedical devices, biomimetic robots, wearable electronics, and flexible soft electronics.
The Impact of Yoga Nidra and Seated Meditation on the Mental Health of College Professors

Background: World statistics for the prevalence of anxiety and mood disorders shows that a great number of individuals will experience some type of anxiety or mood disorder at some point in their lifetime. Mind–body interventions such as Hatha Yoga and seated meditation have been used as a form of self-help therapy and it is especially useful for challenging occupations such as teachers and professors. Aims: In this investigation, we aimed at observing the impact of Yoga Nidra and seated meditation on the anxiety and depression levels of college professors. Materials and Methods: Sixty college professors, men and women, aged between 30 and 55 years were randomly allocated in one of the three experimental groups: Yoga Nidra, seated meditation, and control group. Professors were evaluated two times throughout the 3-month study period. Psychological variables included anxiety, stress, and depression. Results: Data analysis showed that the relaxation group presented better intragroup results in the anxiety levels. Meditation group presented better intragroup results only in the anxiety variable (physical component). Intergroup analysis showed that, except for the depression levels, both intervention groups presented better results than the control group in all other variables. Conclusions: Prepost results indicate that both interventions represent an effective therapeutic approach in reducing anxiety and stress levels. However, there was a tendency toward a greater effectiveness of the Yoga Nidra intervention regarding anxiety, which might represent an effective tool in reducing both cognitive and physiological symptoms of anxiety.
A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance.
Modular acceleration: tricky cases of functional high-performance computing

This case study examines the data-parallel functional implementation of three algorithms: generation of quasi-random Sobol numbers, breadth-first search, and calibration of Heston market parameters via a least-squares procedure. We show that while all these problems permit elegant functional implementations, good performance depends on subtle issues that must be confronted in both the implementations of the algorithms themselves, as well as the compiler that is responsible for ultimately generating high-performance code. In particular, we demonstrate a modular technique for generating quasi-random Sobol numbers in an efficient manner, study the efficient implementation of an irregular graph algorithm without sacrificing parallelism, and argue for the utility of nested regular data parallelism in the context of nonlinear parameter calibration.