Showing papers by "University of Macau published in 2018"

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

Abstract: Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

Techniques for extraction and isolation of natural products: a comprehensive review

Abstract: Natural medicines were the only option for the prevention and treatment of human diseases for thousands of years. Natural products are important sources for drug development. The amounts of bioactive natural products in natural medicines are always fairly low. Today, it is very crucial to develop effective and selective methods for the extraction and isolation of those bioactive natural products. This paper intends to provide a comprehensive view of a variety of methods used in the extraction and isolation of natural products. This paper also presents the advantage, disadvantage and practical examples of conventional and modern techniques involved in natural products research.

ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

Abstract: Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .

The role of TGF-β/SMAD4 signaling in cancer.

Abstract: Transforming growth factor β (TGF-β) signaling pathway plays important roles in many biological processes, including cell growth, differentiation, apoptosis, migration, as well as cancer initiation and progression. SMAD4, which serves as the central mediator of TGF-β signaling, is specifically inactivated in over half of pancreatic duct adenocarcinoma, and varying degrees in many other types of cancers. In the past two decades, multiple studies have revealed that SMAD4 loss on its own does not initiate tumor formation, but can promote tumor progression initiated by other genes, such as KRAS activation in pancreatic duct adenocarcinoma and APC inactivation in colorectal cancer. In other cases, such as skin cancer, loss of SMAD4 plays an important initiating role by disrupting DNA damage response and repair mechanisms and enhance genomic instability, suggesting its distinct roles in different types of tumors. This review lists SMAD4 mutations in various types of cancer and summarizes recent advances on SMAD4 with focuses on the function, signaling pathway, and the possibility of SMAD4 as a prognostic indicator.

Spin control in reduced-dimensional chiral perovskites

Abstract: Hybrid organic–inorganic perovskites exhibit strong spin–orbit coupling1, spin-dependent optical selection rules2,3 and large Rashba splitting4–8. These characteristics make them promising candidates for spintronic devices9 with photonic interfaces. Here we report that spin polarization in perovskites can be controlled through chemical design as well as by a magnetic field. We obtain both spin-polarized photon absorption and spin-polarized photoluminescence in reduced-dimensional chiral perovskites through combined strategies of chirality transfer and energy funnelling. A 3% spin-polarized photoluminescence is observed even in the absence of an applied external magnetic field owing to the different emission rates of σ+ and σ− polarized photoluminescence. Three-dimensional perovskites achieve a comparable degree of photoluminescence polarization only under an external magnetic field of 5 T. Our findings pave the way for chiral perovskites as powerful spintronic materials.

Multiresponsive Graphene-Aerogel-Directed Phase-Change Smart Fibers.

Abstract: Wearable devices and systems demand multifunctional units with intelligent and integrative functions. Smart fibers with response to external stimuli, such as electrical, thermal, and photonic signals, etc., as well as offering energy storage/conversion are essential units for wearable electronics, but still remain great challenges. Herein, flexible, strong, and self-cleaning graphene-aerogel composite fibers, with tunable functions of thermal conversion and storage under multistimuli, are fabricated. The fibers made from porous graphene aerogel/organic phase-change materials coated with hydrophobic fluorocarbon resin render a wide range of phase transition temperature and enthalpy (0-186 J g-1 ). The strong and compliant fibers are twisted into yarn and woven into fabrics, showing a self-clean superhydrophobic surface and excellent multiple responsive properties to external stimuli (electron/photon/thermal) together with reversible energy storage and conversion. Such aerogel-directed smart fibers promise for broad applications in the next-generation of wearable systems.

Around the world, adolescence is a time of heightened sensation seeking and immature self-regulation.

Abstract: The dual systems model of adolescent risk-taking portrays the period as one characterized by a combination of heightened sensation seeking and still-maturing self-regulation, but most tests of this model have been conducted in the United States or Western Europe. In the present study, these propositions are tested in an international sample of more than 5000 individuals between ages 10 and 30 years from 11 countries in Africa, Asia, Europe and the Americas, using a multi-method test battery that includes both self-report and performance-based measures of both constructs. Consistent with the dual systems model, sensation seeking increased between preadolescence and late adolescence, peaked at age 19, and declined thereafter, whereas self-regulation increased steadily from preadolescence into young adulthood, reaching a plateau between ages 23 and 26. Although there were some variations in the magnitude of the observed age trends, the developmental patterns were largely similar across countries.

Macroeconomic impact of stranded fossil fuel assets

Abstract: Several major economies rely heavily on fossil fuel production and exports, yet current low-carbon technology diffusion, energy efficiency and climate policy may be substantially reducing global demand for fossil fuels1–4. This trend is inconsistent with observed investment in new fossil fuel ventures1,2, which could become stranded as a result. Here, we use an integrated global economy–environment simulation model to study the macroeconomic impact of stranded fossil fuel assets (SFFA). Our analysis suggests that part of the SFFA would occur as a result of an already ongoing technological trajectory, irrespective of whether or not new climate policies are adopted; the loss would be amplified if new climate policies to reach the 2 °C target of the Paris Agreement are adopted and/or if low-cost producers (some OPEC countries) maintain their level of production (‘sell out’) despite declining demand; the magnitude of the loss from SFFA may amount to a discounted global wealth loss of US$1–4 trillion; and there are clear distributional impacts, with winners (for example, net importers such as China or the EU) and losers (for example, Russia, the United States or Canada, which could see their fossil fuel industries nearly shut down), although the two effects would largely offset each other at the level of aggregate global GDP.

Effect of cation substitution on the pseudocapacitive performance of spinel cobaltite MCo2O4 (M = Mn, Ni, Cu, and Co)

Abstract: Cation substitution is a promising strategy for modulating the structural properties and optimizing the electrochemical performance of spinel cobalt oxide (Co3O4); however, the underlying mechanism of this action induced by different cation substitutions has not yet been clearly addressed. Herein, a systematic investigation is performed to elucidate the effect of cation substitution on the pseudocapacitive performance of spinel cobaltite (MCo2O4; M = Mn, Ni, Cu, and Co) mesoporous nanowires grown on nickel foam (NF). Theoretical and experimental analyses reveal that the substitution of Co by transition metals (i.e., Mn, Ni, and Cu) in the lattice of Co3O4 can simultaneously improve charge transfer and ion diffusion, thereby exhibiting enhanced electrochemical properties. Herein, as a representative example, MnCo2O4 achieves a high specific capacitance of 2146 F g−1 at a current density of 1 A g−1, while 92.1% of its initial capacitance is retained after 5000 cycles. An asymmetric supercapacitor with MnCo2O4 as the positive material and activated carbon (AC) as the negative material delivers a high energy density of 56.1 W h kg−1 at a power density of 800 W kg−1, and a favorable energy density of 29.3 W h kg−1 at a power density as high as 8000 W kg−1.

Super-adsorbent hydrogel for removal of methylene blue dye from aqueous solution

Abstract: Adsorptive removal of dyes from industrial effluent has attracted intensive interest in the treatment of water pollution. Although we have witnessed great development in adsorbents for treating dyeing waste water, super-adsorbent hydrogels still cannot meet the high requirement for huge adsorption capacities towards various dyes. Traditional uncross-linked poly(acrylic acid) (PAA) adsorbents possess a low adsorption capacity towards dyes due to the poor three-dimensional structure in the bulk, which has a negative effect on the exposure of adsorptive sites and the penetration of water within the internal channels of the adsorbent. Thus, other monomers or chemical cross-linkers are introduced to design a well-cross-linked PAA-based super-adsorbent hydrogel with the intention of improving its adsorptive behavior. However, the above-mentioned additives may negatively affect the adsorptive properties of composite adsorbents based on the PAA hydrogel. To the best of our knowledge, adsorption capacities of PAA-based super-adsorbent hydrogels for organic dyes reaching more than 2000 mg g−1 have rarely been reported as yet. Therefore, it is urgent to explore super-adsorbent hydrogels with high adsorptive properties to alleviate dye-based water pollution. In this work, a novel poly(acrylic acid) (PAA)-based super-adsorbent nanocomposite hydrogel (NC gel) acting as an effective dye adsorbent is prepared via free radical in situ polymerization of acrylic acid by employing non-aggregated calcium hydroxide (Ca(OH)2) nano-spherulites (CNSs) with a diameter less than 5 nm as cross-linkers. The high water penetration of our super-adsorbent NC gel with a high swelling ratio (500 times) allows the internal adsorption sites to be fully exposed to methylene blue (MB). Thus, the adsorption capacity is as high as 2100 mg g−1 for MB under near neutral pH conditions due to the exposure of a large amount of active sites. This is the first time that it has been reported that a maximum adsorption of more than 2000 mg g−1 for MB has been obtained using a PAA-based super-adsorbent NC gel. In addition, the adsorption behavior of our NC gel matches well with the pseudo-second-order model and Langmuir adsorption isotherm model. This work confirms that CNSs can create a well-cross-linked structure of a PAA hydrogel without the aid of any other cross-linker and further confirms that the PAA-based super-adsorbent NC gel has a potential application in the removal of organic dyes from dyeing waste water.

cAMP Response Element-Binding Protein (CREB): A Possible Signaling Molecule Link in the Pathophysiology of Schizophrenia

Abstract: Dopamine is a brain neurotransmitter involved in the pathology of schizophrenia The dopamine hypothesis states that, in schizophrenia, dopaminergic signal transduction is hyperactive The cAMP-response element binding protein (CREB) is an intracellular protein that regulates the expression of genes that are important in dopaminergic neurons Dopamine affects the phosphorylation of CREB via G protein-coupled receptors Neurotrophins, such as brain derived growth factor (BDNF), are critical regulators during neurodevelopment and synaptic plasticity The CREB is one of the major regulators of neurotrophin responses since phosphorylated CREB binds to a specific sequence in the promoter of BDNF and regulates its transcription Moreover, susceptibility genes associated with schizophrenia also target and stimulate the activity of CREB Abnormalities of CREB expression is observed in the brain of individuals suffering from schizophrenia, and two variants (-933T to C and -413G to A) were found only in schizophrenic patients The CREB was also involved in the therapy of animal models of schizophrenia Collectively, these findings suggest a link between CREB and the pathophysiology of schizophrenia This review provides an overview of CREB structure, expression, and biological functions in the brain and its interaction with dopamine signaling, neurotrophins, and susceptibility genes for schizophrenia Animal models in which CREB function is modulated, by either overexpression of the protein or knocked down through gene deletion/mutation, implicating CREB in schizophrenia and antipsychotic drugs efficacy are also discussed Targeting research and drug development on CREB could potentially accelerate the development of novel medications against schizophrenia

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

Abstract: In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Phase Pure 2D Perovskite for High-Performance 2D–3D Heterostructured Perovskite Solar Cells

Abstract: Three-dimensional (3D) metal-halide perovskite solar cells (PSCs) have demonstrated exceptional high efficiency. However, instability of the 3D perovskite is the main challenge for industrialization. Incorporation of some long organic cations into perovskite crystal to terminate the lattice, and function as moisture and oxygen passivation layer and ion migration blocking layer, is proven to be an effective method to enhance the perovskite stability. Unfortunately, this method typically sacrifices charge-carrier extraction efficiency of the perovskites. Even in 2D-3D vertically aligned heterostructures, a spread of bandgaps in the 2D due to varying degrees of quantum confinement also results in charge-carrier localization and carrier mobility reduction. A trade-off between the power conversion efficiency and stability is made. Here, by introducing 2D C6 H18 N2 O2 PbI4 (EDBEPbI4 ) microcrystals into the precursor solution, the grain boundaries of the deposited 3D perovskite film are vertically passivated with phase pure 2D perovskite. The phases pure (inorganic layer number n = 1) 2D perovskite can minimize photogenerated charge-carrier localization in the low-dimensional perovskite. The dominant vertical alignment does not affect charge-carrier extraction. Therefore, high-efficiency (21.06%) and ultrastable (retain 90% of the initial efficiency after 3000 h in air) planar PSCs are demonstrated with these 2D-3D mixtures.

A Mechanistic Overview of Triptolide and Celastrol, Natural Products from Tripterygium wilfordii Hook F.

Abstract: Triptolide and celastrol are predominantly active natural products isolated from the medicinal plant Tripterygium wilfordii Hook F. These compounds exhibit similar pharmacological activities, including anti-cancer, anti-inflammation, anti-obesity, and anti-diabetic activities. Triptolide and celastrol also provide neuroprotection and prevent cardiovascular and metabolic diseases. However, toxicity restricts the further development of triptolide and celastrol. In this review, we comprehensively review therapeutic targets and mechanisms of action, and translational study of triptolide and celastrol. We systemically discuss the structure-activity-relationship of triptolide, celastrol, and their derivatives. Furthermore, we propose the use of structural derivatives, targeted therapy, and combination treatment as possible solutions to reduce toxicity and increase therapeutic window of these potent natural products from T. wilfordii Hook F.

Adaptive Fuzzy Output Feedback Control for a Class of Nonlinear Systems With Full State Constraints

Abstract: In the paper, the adaptive observer and controller designs based fuzzy approximation are studied for a class of uncertain nonlinear systems in strict feedback. The main properties of the considered systems are that all the state variables are not available for measurement and at the same time, they are required to limit in each constraint set. Due to the properties of systems, it will be a difficult task for designing the controller and the stability analysis. Based on the structure of the considered systems, a fuzzy state observer is framed to estimate the unmeasured states. To ensure that all the states do not violate their constraint bounds, the Barrier type of functions will be employed in the controller and the adaptation laws. In the stability analysis, the effect caused by the constraints for all the states can be overcome by using the Barrier Lyapunov functions. Based on the proposed control approach, it is proved that the system output is driven to track the reference signal to a bounded compact set, all the signals in the closed-loop system are guaranteed to be bounded, and all the states do not transgress their constrained sets. The effectiveness of the proposed control approach can be verified by setting a simulation example.

Self-Supplied Tumor Oxygenation through Separated Liposomal Delivery of H2O2 and Catalase for Enhanced Radio-Immunotherapy of Cancer

Abstract: The recent years have witnessed the blooming of cancer immunotherapy, as well as their combinational use together with other existing cancer treatment techniques including radiotherapy. However, hypoxia is one of several causes of the immunosuppressive tumor microenvironment (TME). Herein, we develop an innovative strategy to relieve tumor hypoxia by delivering exogenous H2O2 into tumors and the subsequent catalase-triggered H2O2 decomposition. In our experiment, H2O2 and catalase are separately loaded within stealthy liposomes. After intravenous (iv) preinjection of CAT@liposome, another dose of H2O2@liposome is injected 4 h later. The sustainably released H2O2 could be decomposed by CAT@liposome, resulting in a long lasting effect in tumor oxygenation enhancement. As the result, the combination treatment by CAT@liposome plus H2O2@liposome offers remarkably enhanced therapeutic effects in cancer radiotherapy as observed in a mouse tumor model as well as a more clinically relevant patient-derived xenograf...

Capacitive Pressure Sensor with High Sensitivity and Fast Response to Dynamic Interaction Based on Graphene and Porous Nylon Networks.

Abstract: Flexible pressure sensors are of great importance to be applied in artificial intelligence and wearable electronics. However, assembling a simple structure, high-performance capacitive pressure sensor, especially for monitoring the flow of liquids, is still a big challenge. Here, on the basis of a sandwich-like structure, we propose a facile capacitive pressure sensor optimized by a flexible, low-cost nylon netting, showing many merits including a high response sensitivity (0.33 kPa–1) in a low-pressure regime (<1 kPa), an ultralow detection limit as 3.3 Pa, excellent working stability after more than 1000 cycles, and synchronous monitoring for human pulses and clicks. More important, this sensor exhibits an ultrafast response speed (<20 ms), which enables its detection for the fast variations of a small applied pressure from the morphological changing processes of a droplet falling onto the sensor. Furthermore, a capacitive pressure sensor array is fabricated for demonstrating the ability to spatial pres...

A review on price-driven residential demand response

Abstract: Smart grid enables the two-way communication between the suppliers and consumers. Price-driven demand response (PDDR) is one of the important demand response categories that uses price of the energy as control signals to affect consumers’ electricity consumption. The current PDDR programs include critical peak pricing (CPP), time-of-use (TOU) pricing, and real-time pricing. In this paper, we provide a review of the PDDR studies. Detailed evaluations on advantages and disadvantages of each PDDR are provided. Concerns and future research challenges on PDDR are also addressed. It is believed that with the installation of smart meter infrastructures at residential households, price signal can be an efficient market tool for peak demand shaving, risk and reliability management, carbon emission reduction, and energy cost reduction.

Modifications of dietary flavonoids towards improved bioactivity: An update on structure-activity relationship.

Abstract: Over the past two decades, extensive studies have revealed that inflammation represents a major risk factor for various human diseases. Chronic inflammatory responses predispose to pathological progression of chronic illnesses featured with penetration of inflammatory cells, dysregulation of cellular signaling, excessive generation of cytokines, and loss of barrier function. Hence, the suppression of inflammation has the potential to delay, prevent, and to treat chronic diseases. Flavonoids, which are widely distributed in humans daily diet, such as vegetables, fruits, tea and cocoa, among others, are considered as bioactive compounds with anti-inflammatory potential. Modification of flavonoids including hydroxylation, o-methylation, and glycosylation, can alter their metabolic features and affect mechanisms of inflammation. Structure-activity relationships among naturally occurred flavonoids hence provide us with a preliminary insight into their anti-inflammatory potential, not only attributing to the antioxidant capacity, but also to modulate inflammatory mediators. The present review summarizes current knowledge and underlies mechanisms of anti-inflammatory activities of dietary flavonoids and their influences involved in the development of various inflammatory-related chronic diseases. In addition, the established structure-activity relationships of phenolic compounds in this review may give an insight for the screening of new anti-inflammatory agents from dietary materials.

A Survey of Data Mining and Deep Learning in Bioinformatics

Abstract: The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

Highly Efficient Visible Colloidal Lead-Halide Perovskite Nanocrystal Light-Emitting Diodes

Abstract: Lead-halide perovskites have been attracting attention for potential use in solid-state lighting Following the footsteps of solar cells, the field of perovskite light-emitting diodes (PeLEDs) has been growing rapidly Their application prospects in lighting, however, remain still uncertain due to a variety of shortcomings in device performance including their limited levels of luminous efficiency achievable thus far Here we show high-efficiency PeLEDs based on colloidal perovskite nanocrystals (PeNCs) synthesized at room temperature possessing dominant first-order excitonic radiation (enabling a photoluminescence quantum yield of 71% in solid film), unlike in the case of bulk perovskites with slow electron–hole bimolecular radiative recombination (a second-order process) In these PeLEDs, by reaching charge balance in the recombination zone, we find that the Auger nonradiative recombination, with its significant role in emission quenching, is effectively suppressed in low driving current density range

Effect of Stromal Cells in Tumor Microenvironment on Metastasis Initiation.

Abstract: The cellular environment where tumor cells reside is called the tumor microenvironment (TME), which consists of borders, blood vessels, lymph vessels, extracellular matrix (ECM), stromal cells, immune/inflammatory cells, secreted proteins, RNAs and small organelles. By dynamically interacting with tumor cells, stromal cells participate in all stages of tumor initiation, progression, metastasis, recurrence and drug response, and consequently, affect the fate of patients. During the processes of tumor evolution and metastasis initiation, stromal cells in TME also experience some changes and play roles in both the suppression and promotion of metastasis, while the overall function of stromal cells is beneficial for cancer cell survival and movement. In this review, we examine the effects of stromal cells in TME on metastasis initiation, including angiogenesis, epithelial-mesenchymal transition (EMT) and invasion. We also highlight functions of proteins, RNAs and small organelles secreted by stromal cells in their influences on multiple stages of tumor metastasis.

Age Patterns in Risk Taking Across the World

Abstract: Epidemiological data indicate that risk behaviors are among the leading causes of adolescent morbidity and mortality worldwide. Consistent with this, laboratory-based studies of age differences in risk behavior allude to a peak in adolescence, suggesting that adolescents demonstrate a heightened propensity, or inherent inclination, to take risks. Unlike epidemiological reports, studies of risk taking propensity have been limited to Western samples, leaving questions about the extent to which heightened risk taking propensity is an inherent or culturally constructed aspect of adolescence. In the present study, age patterns in risk-taking propensity (using two laboratory tasks: the Stoplight and the BART) and real-world risk taking (using self-reports of health and antisocial risk taking) were examined in a sample of 5227 individuals (50.7% female) ages 10–30 (M = 17.05 years, SD = 5.91) from 11 Western and non-Western countries (China, Colombia, Cyprus, India, Italy, Jordan, Kenya, the Philippines, Sweden, Thailand, and the US). Two hypotheses were tested: (1) risk taking follows an inverted-U pattern across age groups, peaking earlier on measures of risk taking propensity than on measures of real-world risk taking, and (2) age patterns in risk taking propensity are more consistent across countries than age patterns in real-world risk taking. Overall, risk taking followed the hypothesized inverted-U pattern across age groups, with health risk taking evincing the latest peak. Age patterns in risk taking propensity were more consistent across countries than age patterns in real-world risk taking. Results suggest that although the association between age and risk taking is sensitive to measurement and culture, around the world, risk taking is generally highest among late adolescents.

Modeling Localness for Self-Attention Networks

Abstract: Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.

Finite-Time Filter Decentralized Control for Nonstrict-Feedback Nonlinear Large-Scale Systems

Abstract: This paper solves the finite-time decentralized control problem for uncertain nonlinear large-scale systems in nonstrict-feedback form. The considered controlled plants are different from the previous results of finite-time control systems, which are the nonstrict-feedback large-scale systems with the unknown functions consisting of all states, interactions, and immeasurable states. Fuzzy logic systems and a filter-based state observer are utilized to model uncertain systems and deal with the immeasurable states, respectively. By combining the backstepping recursive design with Lyapunov function theory, a finite-time adaptive fuzzy decentralized control approach is raised. It is testified that the developed control strategy can guarantee that the closed-loop signals are bounded, and the outputs of systems have satisfactory tracking performance in a finite time. A quadruple-tank process system is given to testify the effectiveness and applicability of the proposed approach.