Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial expression recognition based on mixture of basic expressions and intensities

Facial expression recognition can provide rich emotional information for human-robot interaction. This paper presents a facial expression recognition design that recognizes facial expressions as well as intensity and mixture ratio of six basic facial expressions. In this system, Active Appearance Model (AAM) and Lucas-Kanade image alignment algorithms are adopted to align the input facial images to obtain texture features. A novel method is proposed to recognize mixture ratio of basic facial expressions and the intensity of the expression. Three kinds of texture features are used in this method: 1. texture features of the whole face, which are used as inputs of facial expression intensity recognition; 2. texture features of the upside face, which are used as inputs of upper face action units recognition; 3. texture features of the downside face, which are used as the inputs of lower face action units recognition. Back propagation neural networks are used to obtain the recognition scores, which are then exploited to classify the facial expression results. Experimental results verified that the proposed method can effectively recognize mixture ratio of six basic expressions and the expression intensity.
Generating vascular conduits: from tissue engineering to three-dimensional bioprinting

Abstract Vascular disease – including coronary artery disease, carotid artery disease, and peripheral vascular disease – is a leading cause of morbidity and mortality worldwide. The standard of care for restoring patency or bypassing occluded vessels involves using autologous grafts, typically the saphenous veins or internal mammary arteries. Yet, many patients who need life- or limb-saving procedures have poor outcomes, and a third of patients who need vascular intervention have multivessel disease and therefore lack appropriate vasculature to harvest autologous grafts from. Given the steady increase in the prevalence of vascular disease, there is great need for grafts with the biological and mechanical properties of native vessels that can be used as vascular conduits. In this review, we present an overview of methods that have been employed to generate suitable vascular conduits, focusing on the advances in tissue engineering methods and current three-dimensional (3D) bioprinting methods. Tissue-engineered vascular grafts have been fabricated using a variety of approaches such as using preexisting scaffolds and acellular organic compounds. We also give an extensive overview of the novel use of 3D bioprinting as means of generating new vascular conduits. Different strategies have been employed in bioprinting, and the use of cell-based inks to create de novo structures offers a promising solution to bridge the gap of paucity of optimal donor grafts. Lastly, we provide a glimpse of our work to create scaffold-free, bioreactor-free, 3D bioprinted vessels from a combination of rat vascular smooth muscle cells and fibroblasts that remain patent and retain the tensile and mechanical strength of native vessels.
Diabetes and Cognitive Decline: Investigating the Potential Influence of Factors Related to Health Disparities

Objectives: The authors investigated whether factors related to health disparities—race, rural residence, education, perceived racial discrimination, vascular disease, and health care access and utilization—may moderate the association between diabetes and cognitive decline. Method: Participants were 624 community-dwelling older adults (49% African American and 49% rural) who completed in-home mini-mental state examination at baseline and 4-year follow-up. Results: Diabetes at baseline predicted four-year cognitive decline in regression models adjusted for a number of possible confounds. Only perceived discrimination and health utilization showed significant interaction effects with diabetes. Among African Americans who reported experiencing racial discrimination, there was a stronger relationship between diabetes and cognitive decline. Among participants who reported absence of visiting a physician within the past 6 months, the association between diabetes and cognitive decline was substantially larger. Discussion: Findings suggest that factors related to health disparities may influence cognitive outcomes among older adults with diabetes.
Criteria for R-boundedness of operator families

The main results state that large classes of convolution and Fourier multiplier operators are R-bounded. R-boundedness is of importance in connection with maximal regularity and functional calculus. Also shown is that smooth operator-valued functions have a R-bounded range, where the degree of smoothness depends on the geometry of the Banach space.
NOSIX: a lightweight portability layer for the SDN OS

In spite of the standardization of the OpenFlow API, it is very difficult to write an SDN controller application that is portable (i.e., guarantees correct packet processing over a wide range of switches) and achieves good performance (i.e., fully leverages switch capabilities). This is because the switch landscape is fundamentally diverse in performance, feature set and supported APIs. We propose to address this challenge via a lightweight portability layer that acts as a rendezvous point between the requirements of controller application and the vendor knowledge of switch implementations. Above, applications specify rules in virtual flow tables annotated with semantic intents and expectations. Below, vendor specific drivers map them to optimized switch-specific rule sets. NOSIX represents a first step towards achieving both portability and good performance across a diverse set of switches.
Shortened Telomere Length Is Associated with Increased Risk of Cancer: A Meta-Analysis

Background Telomeres play a key role in the maintenance of chromosome integrity and stability, and telomere shortening is involved in initiation and progression of malignancies. A series of epidemiological studies have examined the association between shortened telomeres and risk of cancers, but the findings remain conflicting. Methods A dataset composed of 11,255 cases and 13,101 controls from 21 publications was included in a meta-analysis to evaluate the association between overall cancer risk or cancer-specific risk and the relative telomere length. Heterogeneity among studies and their publication bias were further assessed by the χ2-based Q statistic test and Egger's test, respectively. Results The results showed that shorter telomeres were significantly associated with cancer risk (OR = 1.35, 95% CI = 1.14–1.60), compared with longer telomeres. In the stratified analysis by tumor type, the association remained significant in subgroups of bladder cancer (OR = 1.84, 95% CI = 1.38–2.44), lung cancer (OR = 2.39, 95% CI = 1.18–4.88), smoking-related cancers (OR = 2.25, 95% CI = 1.83–2.78), cancers in the digestive system (OR = 1.69, 95% CI = 1.53–1.87) and the urogenital system (OR = 1.73, 95% CI = 1.12–2.67). Furthermore, the results also indicated that the association between the relative telomere length and overall cancer risk was statistically significant in studies of Caucasian subjects, Asian subjects, retrospective designs, hospital-based controls and smaller sample sizes. Funnel plot and Egger's test suggested that there was no publication bias in the current meta-analysis (P = 0.532). Conclusions The results of this meta-analysis suggest that the presence of shortened telomeres may be a marker for susceptibility to human cancer, but single larger, well-design prospective studies are warranted to confirm these findings.
Model Checking of Linear-Time Properties Based on Possibility Measure

Using possibility measure, we study model checking of linear-time properties in possibilistic Kripke structures. First, the notion of possibilistic Kripke structures and the related possibility measure are introduced, and then, model checking of reachability and repeated reachability linear-time properties in finite possibilistic Kripke structures are studied. Standard safety properties and ω-regular properties in possibilistic Kripke structures are introduced; the verification of regular safety properties and ω-regular properties using finite automata are thoroughly studied. It has been shown that the verification of regular safety properties and ω-regular properties in a finite possibilistic Kripke structure can be transformed into the verification of reachability properties and repeated reachability properties in the product possibilistic Kripke structure that is introduced in this paper. Several examples are given to illustrate the methods that are presented in this paper.
3D Morphable Model fitting from multiple views

This paper presents a method to fit a 3D Morphable Model to a sequence of extracted facial features. The approach is a direct extension of a single view method. The novelty is presented as a new mathematical derivation of the same method but for multiple views where identity and pose are known. The new fitting method exploits point-to-model correspondences and can deal with occlusion since features are not required to correspond across views. The mathematics is explained in detail and the methods single tunable parameter is empirically determined using a database of scanned heads.
Application of Non-Orthogonal Multiplexing to mmWave Multi-User Systems

Limited wireless spectrum in future networks is an issue that millimeter-Wave (mmWave) frequencies can resolve. Beamforming is a challenging operation for mmWave systems. Both analogue and hybrid solutions have been proposed. Apart from Orthogonal Multiple Access (OMA) systems, the Non-Orthogonal Multiple Access (NOMA) scheme has been recently considered as an alternative and can outperform OMA in many cases. Our contribution is the application of NOMA to a hybrid precoder design in mmWave indoor scenarios with multiple users. Introducing to our system appropriate user grouping and scheduling schemes, we show that the NOMA outperforms, in terms of spectral efficiency, OMA by providing a 17.1% improvement for high SNR values. Finally, applying NOMA with fair power allocation improves the rate of users with weak channel conditions by around 1 bps/Hz for SNR>5dB, without degrading the overall spectral efficiency in the cell.
INTERNAL DAMAGE EVALUATION OF CFRTP CUT BY A CIRCULAR SAW

In recent years, due to the serious environmental issues, the development of the gasoline mileage improvement technology has become essential in the automotive industry. Weight-saving of the car body can contribute to improving the gasoline mileage. CFRP (Carbon Fiber Reinforced Plastics) are widely used as structural material for airplanes, various kinds of sports gear and premium automobiles because of their low density and high performance in mechanical properties. In CFRP, CFRTP (Carbon Fiber Reinforced Thermoplastics) are expected to be used for lightweight component parts because of their high recycling efficiency and high productivity. Accordingly, a high-efficiency machining method of CFRTP has been needed; however, CFRTP are known as difficult-to-machine materials. Delamination and huge burrs are major problems in CFRTP cutting. Although a circular saw that can be used at high speed is usually used as a cutting tool, the internal damage of CFRTP resulting from machining has not been clarified yet. In this study, in order to evaluate the influence of cutting speed on the internal damage of CFRTP cut by circular saw, the internal damage depth and the temperature of specimens during cutting process were measured. The internal damage occurred by circular saw processing and the internal damage depth were decreased in the case of fast cutting speed. The temperature of the specimen was increased in the case of slow cutting speed because of the longer contact time of the circular saw with the specimen.
Stimulated Deep Neural Network for Speech Recognition

Copyright © 2016 ISCA. Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of tasks, including speech recognition. However, the parameters of the network are hard to analyze, making network regularization and robust adaptation challenging. Stimulated training has recently been proposed to address this problem by encouraging the node activation outputs in regions of the network to be related. This kind of information AIDS visualization of the network, but also has the potential to improve regularization and adaptation. This paper investigates stimulated training of DNNs for both of these options. These schemes take advantage of the smoothness constraints that stimulated training offers. The approaches are evaluated on two large vocabulary speech recognition tasks: a U.S. English broadcast news (BN) task and a Javanese conversational telephone speech task from the IARPA Babel program. Stimulated DNN training acquires consistent performance gains on both tasks over unstimulated baselines. On the BN task, the proposed smoothing approach is also applied to rapid adaptation, again outperforming the standard adaptation scheme.
Model-based, memory-centric performance and power optimization on NUMA multiprocessors

Non-Uniform Memory Access (NUMA) architectures are ubiquitous in HPC systems. NUMA along with other factors including socket layout, data placement, and memory contention significantly increase the search space to find an optimal mapping of applications to NUMA systems. This search space may be intractable for online optimization and challenging for efficient offline search. This paper presents DyNUMA, a framework for dynamic optimization of programs on NUMA architectures. DyNUMA uses simple, memory-centric, performance and energy models with non-linear terms to capture the complex and interacting effects of system layout, program concurrency, data placement, and memory controller contention. DyNUMA leverages an artificial neural network (ANN) with input, output, and intermediate layers that emulate program threads, memory controllers, processor cores, and their interactions. Using an ANN in conjunction with critical path analysis, DyNUMA autonomously optimizes programs for performance or energy-efficiency metrics. We used DyNUMA on a variety of benchmarks from the NPB and ASC Sequoia suites on three different architectures (a 16-core AMD Barcelona system, a 32-core AMD Magny-Cours system, and a 64-core Tilera TilePro64 system). Our results show that DyNUMA achieves on average 8.7% improvement in performance (12.9% in the best case), 16% improvement in Energy-Delay (30.6% in the best case) and 9.1% improvement in MFLOPS/Watt (10.7% in the best case) compared to the default Linux scheduling.
On Combining Unsupervised Classification and Ontology Knowledge

This paper presents a way to combine knowledge obtained from a clustering algorithm and from an ontology. Using the both sources of information allows to improve the results of the knowledge discovery process. The basic property of clustering algorithms, which is to group similar objects, is the key of this approach. We use it to extend the knowledge given by an ontology. Indeed, this knowledge can be partial or not enough accurate, and clustering can then be used to fill this lack of information. We also present results and validation in the field of remote sensing image interpretation.
Orthographic Influences When Processing Spoken Pseudowords: Theoretical Implications

When we hear an utterance, is the orthographic representation of that utterance activated when it is being processed? Orthographic influences have been previously examined in relation to spoken pseudoword processing in three different paradigms. Unlike real word processing, no orthographic effects with pseudowords have been observed in a phoneme goodness ratings task, and there is a mixed outcome in studies looking for spelling–sound consistency effects. In contrast, the orthography of spoken pseudohomographs has been shown to be activated, given that they prime their homographic base word. Explanations are sought for the findings in these three paradigms, leading to an exploration of theoretical models of spoken word recognition.
Performance analysis of GSM networks with intelligent underlay-overlay

The paper presents an analytical model for a GSM-based cellular mobile network that applies an intelligent under-overlay (IUO) scheme to increase capacity by increasing frequency reuse while maintaining service quality. The IUO is a multi-layer cell structure that is based on dividing the frequency band into super layer and regular layer frequency groups. The super frequencies (channels) can be used by mobile stations with good C/I (carrier/interferer) ratio, while the regular frequencies can be used over the whole cell. The use of IUO is expected to provide up to 40% gain of capacity (see Nokia Telecommunications, www.nokia.com). We study the effect of various parameters on the performance of networks using IUO and provide practical planning support based on the analytical results. The considered parameters include network parameters, like super area coverage, and mobile user mobility parameters, like moving mobile ratio and average mobile speed.
Comparison and Evaluation of a Class of IDA* Algorithms

In this paper, we study the performance of various IDA*-style searches and investigate methods to improve their performance by predicting in each stage the threshold to be used for pruning. Without loss of generality, we consider minimization problems in this paper. We first present three models to approximate the distribution of the number of search nodes by lower bounds: exponential, geometric, and linear, and illustrate these distributions based on some well-known combinatorial search problems. Based on these distributions, we show the performance of an ideal IDA* algorithm and identify reasons why existing IDA*-style algorithms perform well. In practice, we will be able to know from experience the type of distribution for a given problem instance, but will not be able to know the parameters of this distribution until the instance is solved. Hence, we develop RIDA*, a method that estimates dynamically the parameters of the distribution, and predicts the best threshold to be used in each stage. Finally, we compare the performance of several IDA*-style algorithms—Korf’s IDA* and RBFS, RIDA*, IDA*_CR and DFS*—on several application problems, and identify conditions under which each of these algorithms will perform well.
Putting the collaborator back into collaborative filtering

Most of the published approaches to collaborative filtering and recommender systems concentrate on mathematical approaches for identifying user / item preferences. This paper demonstrates that by considering the psychological decision making processes that are being undertaken by the users of the system it is possible to achieve a significant improvement in results. This approach is applied to the Netflix dataset and it is demonstrated that it is possible to achieve a score better than the Cinematch score set at the beginning of the Netflix competition without even considering individual preferences for individual movies. The result has important implications for both the design and the analysis of the data from collaborative filtering systems.
Trypanosoma cruzi CYP51 Inhibitor Derived from a Mycobacterium tuberculosis Screen Hit

Background The two front-line drugs for chronic Trypanosoma cruzi infections are limited by adverse side-effects and declining efficacy. One potential new target for Chagas' disease chemotherapy is sterol 14α-demethylase (CYP51), a cytochrome P450 enzyme involved in biosynthesis of membrane sterols. Methodology/Principal Finding In a screening effort targeting Mycobacterium tuberculosis CYP51 (CYP51Mt), we previously identified the N-[4-pyridyl]-formamide moiety as a building block capable of delivering a variety of chemotypes into the CYP51 active site. In that work, the binding modes of several second generation compounds carrying this scaffold were determined by high-resolution co-crystal structures with CYP51Mt. Subsequent assays against the CYP51 orthologue in T. cruzi, CYP51Tc, demonstrated that two of the compounds tested in the earlier effort bound tightly to this enzyme. Both were tested in vitro for inhibitory effects against T. cruzi and the related protozoan parasite Trypanosoma brucei, the causative agent of African sleeping sickness. One of the compounds had potent, selective anti–T. cruzi activity in infected mouse macrophages. Cure of treated host cells was confirmed by prolonged incubation in the absence of the inhibiting compound. Discrimination between T. cruzi and T. brucei CYP51 by the inhibitor was largely based on the variability (phenylalanine versus isoleucine) of a single residue at a critical position in the active site. Conclusions/Significance CYP51Mt-based crystal structure analysis revealed that the functional groups of the two tightly bound compounds are likely to occupy different spaces in the CYP51 active site, suggesting the possibility of combining the beneficial features of both inhibitors in a third generation of compounds to achieve more potent and selective inhibition of CYP51Tc.
Molecular Systematics of Genus Atractylodes (Compositae, Cardueae): Evidence from Internal Transcribed Spacer (ITS) and trnL-F Sequences

To determine the evolutionary relationships among all members of the genus Atractylodes (Compositae, Cardueae), we conducted molecular phylogenetic analyses of one nuclear DNA (nrDNA) region (internal transcribed spacer, ITS) and one chloroplast DNA (cpDNA) region (intergenic spacer region of trnL-F). In ITS and ITS + trnL-F trees, all members of Atractylodes form a monophyletic clade. Atractylodes is a sister group of the Carlina and Atractylis branch. Atractylodes species were distributed among three clades: (1) A. carlinoides (located in the lowest base of the Atractylodes phylogenetic tree), (2) A. macrocephala, and (3) the A. lancea complex, including A. japonica, A. coreana, A. lancea, A. lancea subsp. luotianensis, and A. chinensis. The taxonomic controversy over the classification of species of Atractylodes is mainly concentrated in the A. lancea complex. In base on molecular results, the intraspecific division of Atractylodes lancea is not supported, and A. coreana should be treated as a synonym A. chinensis.
Design, Fabrication and Application of Multi-Scale, Multi- Functional Nanostructured Carbon Fibers

To further improve and upgrade the existing functions of carbon fibers, and to endow the carbon fiber with new and desired functions, the most effective and economic way is to create nanostructures on the carbon fiber surface. The carbon fibers with nanostructures grown on the surface, or namely nanostructured carbon fibers, not only maintain the intrinsic high strength, light weight, high thermal conductivity of carbon fiber, but also obtain significant functional enhancements in mechanical properties, interfacial bonding and electrocatalytic property. Different kinds of nanostructures, such as nanoparticles, nanorods, nanotubes, nanosheets, and nanoflowers, are controllably grown on the surface of carbon fibers by using various kinds of techniques, including chemical vapor deposi- tion (CVD), laser ablation, microwave treatment, and hydrothermal process. These multiscale, multifunctional nanostructured carbon fibers not only add new and interesting branches to the carbon fiber family, but also pave the way for the application of carbon fibers in next-generation fiber-reinforced composite, energy storage device and green energy production.