Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved delay-probability-dependent results for stochastic neural networks with randomly occurring uncertainties and multiple delays

Abstract This study seeks to address the delay-probability-dependent stability problem for a new class of stochastic neural networks with randomly occurring uncertainties, neutral type delay, distributed delay and probability-distribution delay. The system not only includes the randomly occurring uncertainties of parameters (ROUPs) but also contains stochastic disturbances, which is not yet investigated in existing papers. First, several stochastic variables which obey Bernoulli distribution are introduced to describe the ROUPs, based on which a new model is built. Second, through fully considering the information on kinds of delays and utilising general delay-partitioning method, an improved Lyapunov–Krasovskii function (LKF) is constructed. Combining Itô's differential formula, general bounding, free-weighting matrix and stochastic methods, a new delay-probability-dependent robustly mean square stable criterion is formulated in terms of linear matrix inequality. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed results.
Using voluntary agreements to exclude stock from waterways: An evaluation of project success and persistence

Agriculture is one of the major drivers of ecological degradation in river basins. Excluding stock (cows and sheep) from grazing riverbanks and accessing rivers is one of the most common river restoration activities. To be effective, stock exclusion must be maintained indefinitely. In Australia, and elsewhere, stock exclusion projects are most commonly implemented by establishing voluntary agreements between landholders and government agencies. This study examined the extent to which landholders in 3 catchment management authority (CMA) regions in southeast Australia maintain stock exclusion from waterways, whether vegetation on riverbanks recovered, and the effectiveness of assessment methods. It was found that nearly half of landholders continue to graze stock on the riverbank. There has been some success with improving the condition of riparian vegetation. Sites with full stock exclusion contain the pre‐European abundance of juvenile trees, and sites with continued grazing contain significantly lower abundance of juvenile trees. Establishing the effectiveness of management was made more difficult by the inconsistent methods used by the different CMAs. Stock exclusion projects implemented with voluntary agreements have the potential to succeed if oversight is improved between government agencies and CMAs and between CMAs and landholders. Projects will be easier to assess if regional authorities use consistent methods of assessment. Voluntary agreements are only suitable for environmental management if projects are monitored, maintained, and assessed appropriately. Integr Environ Assess Manag 2019;15:237–247. © 2018 SETAC
Early Growth Response Gene-1 Suppresses Foot-and-Mouth Disease Virus Replication by Enhancing Type I Interferon Pathway Signal Transduction

Early growth response gene-1 (EGR1) is a multifunctional transcription factor that is implicated in viral infection. In this study, we observed that foot-and-mouth disease virus (FMDV) infection significantly triggered EGR1 expression. Overexpression of EGR1 suppressed FMDV replication in porcine cells, and knockdown of EGR1 considerably promoted FMDV replication. A previously reported FMDV mutant virus (with two amino acids mutations in SAP domain) that displays a strong type I interferon (IFN) induction activity was used in this study. We found that SAP mutant FMDV infection induced a higher expression of EGR1 than wildtype FMDV infection, and also triggered higher IFN-β and IFN-stimulated genes (ISGs) expression than wildtype FMDV infection. This implied a link between EGR1 and type I IFN signaling. Further study showed that overexpression of EGR1 resulted in Sendai virus (SeV)-induced IFN-stimulated response element (ISRE) and NF-κB promoter activation. In addition, the SeV-induced ISGs expression was impaired in EGR1 knockdown cells. EGR1 upregulation promoted type I IFN signaling activation and suppressed FMDV and Seneca Valley virus replication. Suppression of the transcriptional activity of EGR1 did not affect its antiviral effect against FMDV. This study reveals a new mechanism evolved by EGR1 to enhance type I IFN signaling and suppress FMDV replication.
Bounded Rationality and the Emergence of Simplicity Amidst Complexity

The purpose of this essay is to explore the relationship between the simple and the complex in economics by anchoring our analysis on bounded rationality. Much of the conventional literature focuses on ‘un‐bounded rationality’ of the rationality‐as‐consistency variety. Theorizing of bounded rationality tends to assume that the problem to be solved is independent of the nature of bounded rationality. Following the insights from the works of Herbert Simon and contributions from outside economics, both bounded rationality and the environment are inextricably linked. The boundaries between bounded rationality and its environment can shift. The form in which bounded rationality is found depends on the complexity of the environment. Furthermore, if local interactions between bounded rational agents result in the formation of hierarchies – the complexity of the collective system will change. Whether this will occur depends on the nature of bounded rationality at the individual level.
Screening of medicinal plants for antibacterial activities on Staphylococcus aureus strains isolated from bovine mastitis

Staphylococcus aureus is the main causative agent of bovine mastitis. The activity of several extracts from ten medicinal plants traditionally used in Brazil as antiseptic was investigated against fifteen strains of Staphylococcus aureus isolated from animals with mastitis manifestation by the disc diffusion method and broth microdilution assay. The interference of the extracts on cell in the form of adherent colonies was also evaluated. MIC values ranged from 0.5 mg/mL to 1.0 mg/mL and biofilm inhibitory concentration (BIC) were between 0.25 mg/mL and 0.8 mg/mL. Results revealed the potential of extracts of Senna macranthera, Artemisia absinthium, Cymbopogon nardus and Baccharis dracunculifolia as antibacterial agents against S. aureus strains isolated from bovine mastitis and support the possible use of these phytotherapic agents in the clinical management of the disease.
Consensus Maximization for Semantic Region Correspondences

We propose a novel method for the geometric registration of semantically labeled regions. We approximate semantic regions by ellipsoids, and leverage their convexity to formulate the correspondence search effectively as a constrained optimization problem that maximizes the number of matched regions, and which we solve globally optimal in a Branch-and-Bound fashion. To this end, we derive suitable linear matrix inequality constraints which describe ellipsoid-to-ellipsoid assignment conditions. Our approach is robust to large percentages of outliers and thus applicable to difficult correspondence search problems. In multiple experiments we demonstrate the flexibility and robustness of our approach on a number of challenging vision problems.
The therapeutic effects of olive leaf extract on Leishmania major infection in BALB/c mice

At present, no effective vaccine has been found for CL; thus, the upgrading and improvement of the present drugs and development of the novel agents have been demonstrated to be the merely control choice for this disease. The present study aims to evaluate the effects of olive leaf extract (Olea europaea L. OLE) on Leishmania major infection in BALB/c mice. OLE tested on cutaneous leishmaniasis (CL) in male BALB/c mice with L. major promastigotes (MRHO/IR/75/ER) at the doses of 10, 20, and 30 mg/kg. Results demonstrated that after treatment of the groups with the concentrations of 10, 20, and 40 mg/ kg of OLE, mean diameter of lesions was reduced by 2.16, 5.87, and 7.61 cm, respectively. Moreover, after 4 weeks of treatment, 83.3%, 66.64%, and 33.32% recovery was observed in the infected mice treated with 40, 20, and 10 mg/kg of OLE, respectively. In conclusion, the present study showed OLE has potent therapeutic effects on cutaneous leishmaniasis.
Comprehensive Model of Oxygen Steelmaking Part 3: Decarburization in Impact Zone

The development of a global model for oxygen steelmaking and its validation against industrial data has been reported in Part 1 of this paper. Part 2 of this paper explained the model development of decarburization reaction of emulsified droplets and discussed the effects of bloating behaviour of metal droplets on the overall kinetics of the process. Part 3 of this paper focussed on the development of one sub-model on the decarburization reaction in the impact zone and critically evaluates the important process variables affecting the decarburization kinetics. Decarburization rates in the impact zone were calculated using the semi-empirical relationships developed from experimental results. Based on previous experimental studies, both diffusion through the gas phase and surface control were considered in the rate calculations. The model was validated against experimental data from Belton and Sain, and against plant data from Cicutti et al. The model developed was consistent with the experimental and plant data, and provided a reasonable basis for predicting the decarburization of iron in the impact region of oxygen steelmaking. The model predicted that the decarburization rate is sensitive to the partial pressure of carbon dioxide and oxygen in the impact zone. As the partial pressure of oxygen decreased from 26.4 to 16.2 kPa, the model predicted that that the reaction rate of decarburization via oxygen decreased from 202 to 134 kg/min. The model predicted that approx. 40% of decarburization takes place in the impact zone during the main blow.
Salience Guided Depth Calibration for Perceptually Optimized Compressive Light Field 3D Display

Multi-layer light field displays are a type of computational three-dimensional (3D) display which has recently gained increasing interest for its holographic-like effect and natural compatibility with 2D displays. However, the major shortcoming, depth limitation, still cannot be overcome in the traditional light field modeling and reconstruction based on multi-layer liquid crystal displays (LCDs). Considering this disadvantage, our paper incorporates a salience guided depth optimization over a limited display range to calibrate the displayed depth and present the maximum area of salience region for multi-layer light field display. Different from previously reported cascaded light field displays that use the fixed initialization plane as the depth center of display content, our method automatically calibrates the depth initialization based on the salience results derived from the proposed contrast enhanced salience detection method. Experiments demonstrate that the proposed method provides a promising advantage in visual perception for the compressive light field displays from both software simulation and prototype demonstration.
Physical layer of wireless ad-hoc control networks

A new concept called Wireless ad-hoc control networks (WACNets), designed for distributed and remote monitoring and control is explored in this work. It is suggested that ad-hoc control networks are the next stage in the development of distributed control and monitoring. WACNet explores a framework for organic, evolutionary and scalable method of integrating a large number of nodes with sensing and/or actuation, local intelligence and control, data processing and communication capabilities. A combination of IEEE 1451 compliant Smart Sensor and Bluetooth standard is employed to develop the first generation test-bed for the study of WACNets. The concept is introduced and the design of the nodes is presented. The progress up to date and the results obtained are reported.
Internationalisation Process: Revisiting the Uppsala Model in the Asian Context

The Uppsala model of the intemationalisation process in terms of gradual incremental steps had been widely adopted in empirical research, particularly in the context of western multinational firms. We revisited this model in the context of Asian multinational firms. Examining case studies of Asian MNEs from Taiwan and Singapore in the textile and electronics industries, we found variations in the process postulated by the Uppsala model, and seek to explain them in terms of proactive actions and absorptive capacity perspective. Future research and limitations are also indicated.
Efficient Module Based Single Image Super Resolution for Multiple Problems

Example based single image super resolution (SR) is a fundamental task in computer vision. It is challenging, but recently, there have been significant performance improvements using deep learning approaches. In this article, we propose efficient module based single image SR networks (EMBSR) and tackle multiple SR problems in NTIRE 2018 SR challenge by recycling trained networks. Our proposed EMBSR allowed us to reduce training time with effectively deeper networks, to use modular ensemble for improved performance, and to separate subproblems for better performance. We also proposed EDSR-PP, an improved version of previous ESDR by incorporating pyramid pooling so that global as well as local context information can be utilized. Lastly, we proposed a novel denoising / deblurring residual convolutional network (DnResNet) using residual block and batch normalization. Our proposed EMBSR with DnResNet and EDSR-PP demonstrated that multiple SR problems can be tackled efficiently and effectively by winning the 2nd place for Track 2 (× 4 SR with mild adverse condition) and the 3rd place for Track 3 (×4 SR with difficult adverse condition). Our proposed method with EDSR-PP also achieved the ninth place for Track 1 (×8 SR) with the fastest run time among top nine teams.
Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.
The Perception-Distortion Tradeoff Supplementary Material

In this supplemental, we first provide a proof for Theorem 1, the derivation of Example 1, and the derivation of the MMSE and MAP estimators which appear in Sections 3.1 and 3.2. We then briefly discuss the real-vs.-fake study setting and its relation to Bayesian hypothesis testing. In the following sections, we specify all training and architecture details for the WGAN experiment which was presented in Section 5, and also include details regarding the super-resolution algorithms comparison in Section 6. Finally, we include additional comparisons between super-resolution algorithms using two extra no-reference methods, and perform a comparison on RGB images as well.
Transparent Object Shape Measurement Based on Deflectometry

This paper proposes a method for obtaining surface normal orientation and 3-D shape of plano-convex lens using refraction stereo. We show that two viewpoints are sufficient to solve this problem under the condition that the refractive index of the object is known. What we need to know is that (1) an accurate function that maps each pixel to the refraction point caused by the refraction of the object. (2) light is refracted only once. In the simulation, the actual measurement process is simplified: light is refracted only once; and the accurate one-to-one correspondence between incident ray and refractive ray is realized by known object points. The deformed grating caused by refraction point is also constructed in the process of simulation. A plano-convex lens with a focal length of 242.8571 mm is used for stereo data acquisition, normal direction acquisition, and the judgment of normal direction consistency. Finally, restoring the three-dimensional information of the plano-convex lens by computer simulation. Simulation results suggest that our method is feasibility. In the actual experiment, considering the case of light is refracted more than once, combining the calibration data acquisition based on phase measurement, phase-shifting and temporal phase-unwrapping techniques to complete (1) calibrating the corresponding position relationship between the monitor and the camera (2) matching incident ray and refractive ray.
Me, Myself, and Future Generations: The Role of Affinity and Effectiveness in the Creation of Consumer Environmental Stewardship (CENS)

Policymakers, consumer advocate groups, and researchers agree that consumers need to increase their proenvironmental behaviors if a decent standard of living is to be ensured for future generations. Despite high levels of environmental concern, consumers still refrain from large-scale adoption of proenvironmental behaviors. Social marketers agree that a change in attitudes is not enough to stimulate the necessary behavioral change and are looking for ways to help consumers overcome the costs (e.g., price premiums, inconvenience) that are often associated with proenvironmental behaviors. Currently, consumers often see proenvironmental behavior as a trade-off between short-term personal benefits and longer term collective benefits. The authors contribute to the social marketing literature on proenvironmental behavior by introducing the concept of Consumer Environmental Stewardship (CENS), which centers on the use of intrinsic motivation to stimulate a personal sense of responsibility for the environment. The findings, based on a survey and three experiments, show that the stimulation of consumers’ affinity with future generations (AFGs) and perceived consumer effectiveness (PCE) can help to promote CENS, which in turn raises proenvironmental behaviors. However, this research also shows that increasing levels of AFGs can backfire and result in lower levels of CENS, if consumers experience low levels of PCE.
Antioxidant properties and enzyme inhibitory effects of extracts from Mandragora autumnalis and its fatty acid composition

The present research was undertaken to evaluate antioxidant activities and enzyme inhibitory potentials of two extracts from two parts of Mandragora autumnalis . The antioxidant properties were evaluated by using different methods [ABTS (2,2-azino-bis (3-ethylbenzothiazloine-6-sulphonic acid)), DPPH (1,1 diphenyl-2-picrylhydrazyl radical), CUPRAC (cupric ion reducing antioxidant capacity), and FRAP (ferric reducing antioxidant power assays)]. Enzyme inhibitory potentials were tested against cholinesterase (AChE and BChE), tyrosinase, α-amylase and α-glucosidase. For phytochemical composition, fatty acids, total phenolics and flavonoid content were investigated. Generally F-Met exhibited the strongest antioxidant effect with the highest level of phenolics. Total phenolic and flavonoid content were determined as 26.10-46.92 mgGAE/g abd 3.60-26.11 mgRE/g, respectively. α- linolenic acid was found to be major fatty acids in the studied oils (33.81 % for flowers and 39.15 % for leaves). The study suggested that M. autumnalis could be valuable as a source of natural agents in food and pharmaceutical applications.
Min-Entropy Latent Model for Weakly Supervised Object Detection

Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors. The inconsistency between the weak supervision and learning objectives introduces randomness to object locations and ambiguity to detectors. In this paper, a min-entropy latent model (MELM) is proposed for weakly supervised object detection. Min-entropy is used as a metric to measure the randomness of object localization during learning, as well as serving as a model to learn object locations. It aims to principally reduce the variance of positive instances and alleviate the ambiguity of detectors. MELM is deployed as two sub-models, which respectively discovers and localizes objects by minimizing the global and local entropy. MELM is unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations. Experiments demonstrate that MELM significantly improves the performance of weakly supervised detection, weakly supervised localization, and image classification, against the state-of-the-art approaches.
Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation

Automatic intensity estimation of facial action units (AUs) is challenging in two aspects. First, capturing subtle changes of facial appearance is quite difficult. Second, the annotation of AU intensity is scarce and expensive. Intensity annotation requires strong domain knowledge thus only experts are qualified. The majority of methods directly apply supervised learning techniques to AU intensity estimation while few methods exploit unlabeled samples to improve the performance. In this paper, we propose a novel weakly supervised regression model-Bilateral Ordinal Relevance Multi-instance Regression (BORMIR), which learns a frame-level intensity estimator with weakly labeled sequences. From a new perspective, we introduce relevance to model sequential data and consider two bag labels for each bag. The AU intensity estimation is formulated as a joint regressor and relevance learning problem. Temporal dynamics of both relevance and AU intensity are leveraged to build connections among labeled and unlabeled image frames to provide weak supervision. We also develop an efficient algorithm for optimization based on the alternating minimization framework. Evaluations on three expression databases demonstrate the effectiveness of the proposed method.
Micro-and Nanostructure of Human Teeth : a Synchrotron Radiation-based X-Ray Study

Human teeth are anisotropic composites. Dentin as the core material of the tooth consists of nanometer-sized calcium phosphate crystallites embedded in collagen fiber networks. It shows its anisotropy on the micrometer scale by its welloriented microtubules. The detailed three-dimensional nanostructure of the hard tissues namely dentin and enamel, however, is not understood, although numerous studies on the anisotropic mechanical properties have been performed and evaluated to explain the tooth function including the enamel-dentin junction acting as effective crack barrier. Small angle X-ray scattering (SAXS) with a spatial resolution in the 10 μm range allows determining the size and orientation of the constituents on the nanometer scale with reasonable precision. So far, only some dental materials, i.e. the fiber reinforced posts exhibit anisotropic properties related to the micrometer-size glass fibers. Dental fillings, composed of nanostructures oriented similar to the natural hard tissues of teeth, however, do not exist at all. The current X-ray-based investigations of extracted human teeth provide evidence for oriented microand nanostructures in dentin and enamel. These fundamental quantitative findings result in profound knowledge to develop biologically inspired dental fillings with superior resistance to thermal and mechanical shocks.