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Deals with the decision of the Computer Society to increase its funding for the Intel International Science and Engineering Fair. Student innovation encourage by the science fair; Total awards offered at the fair; Prizes offered in the fair.

Features the winners of the 1998 International Science and Fair (ISEF), a science competition for high school students sponsored by Intel and administered by Science Service of Washington, D.C. Volunteer judges; Web site of Science Service and ISEF.

In November 2018, the General Conference on Weights and Measures (CGPM) gave its unanimous approval to Resolution 1, a major revision of the SI to take effect on May 20, 2019 [1]. The intentions of the CGPM had already been set out in a resolution adopted in 2011. All of the work needed to meet the requirements of the 2011 resolution was monitored by the International Committee for Weights and Measures (CIPM), which drafted both resolutions. The CIPM relied heavily on its technical Consultative Committees, composed primarily of experts from National Metrology Institutes and international science bodies with a focus on metrology. [ABSTRACT FROM AUTHOR]

The gender gap in digital skills and computing persists globally despite initiatives to include women and girls. This work discusses recent initiatives in Latin America, with a special focus on Brazil, that aim to increase women's access, skills, and leadership in technology. The featured programs have reached thousands of girls with coding workshops, supported hundreds of female computing students, and engaged girls through hands-on technology-related activities. Early evaluation suggests increased interest and confidence in computing careers among participants. Initiatives in Latin America have increased technical skills and engagement among women, but continued cultural change and inclusive opportunities are needed to close the tenacious gender gap in computing. [ABSTRACT FROM AUTHOR]

Emotion-cause pair extraction (ECPE) aims to extract emotions and their corresponding cause from a document. The previous works have made great progress. However, there exist two major issues in existing works. First, most existing works mainly focus on the semantic relation between the emotion clause and cause clause, ignoring their inner statistical relation in representation space. Second, the existing works are sensitive to the relative position between the emotion clause and cause clause, which damages the model's robustness. To address the two issues, we propose a hierarchical contrastive learning framework (HCL-ECPE), which hierarchically performs contrastive learning on representation from two levels. The first level is inter-clause contrastive learning (ICCL), which performs between emotion clause and cause clause through mutual information maximization. The second level is intra-pair contrastive learning (IPCL), which performs between clause representation and pair representation through contrastive predictive coding (CPC). HCL-ECPE integrates ICCL and IPCL modules to explore the statistical relations between the emotion clause, cause clause, and their constructed emotion-cause pair from the perspective of mutual information, thereby improving the model performance and robustness. Experimental results on two public datasets, ECPED and RECCON, demonstrate that HCL-ECPE outperforms the most competitive baselines. Furthermore, ICCL and IPCL are orthogonal to the existing model, and introducing them into the current models updates state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The Society for Science and the Public hosts an international science and engineering fair that encourages today's teenagers to create tomorrow's future. [ABSTRACT FROM PUBLISHER]

Science, technology, and innovation are critical to national competitiveness, security, domestic and international policy, governance, politics, economic growth, culture, and human well-being. One course of action is to only collaborate with known, established partners. But this approach comes with the risk of missing out on the next “up and coming” player, losing the beneficial products that could arise if collaboration had been established earlier. Newcomers are nations that are developing competency and breakthroughs in specific disciplines but have not yet attained global recognition for their expertise and capacity. This article explores the practical policy and governance challenges associated with identifying and partnering with lesser-known countries, with the goal of providing an advantage to both newcomer nations and more established partners. [ABSTRACT FROM AUTHOR]

Emotional arousal, an essential dimension of game users’ experience, plays a crucial role in determining whether a game is successful. Game users’ emotion arousal assessment (GUEA) is of great importance. However, GUEA often faces challenges, such as selecting emotion-inducing games, labeling emotional arousal, and improving accuracy. In this study, the scheme for verifying the effectiveness of emotion-induced games is proposed so that the selected games can induce the target emotions. In addition, the personalized arousal label generation method is developed to reduce the errors caused by individual differences among subjects. Furthermore, to improve the accuracy of GUEA, the Breath Rate Variability (BRV) signal is used as a GUEA indicator along with commonly used physiological signals. Comparative experiments on GUEA based on multimodal physiological signals are conducted. The experimental result shows that the accuracy of GUEA is improved by adding the BRV signal, up to 92%. [ABSTRACT FROM AUTHOR]

Laser chaos is promising for constructing high range-resolution and anti-jamming radars, whereas the range resolution and anti-jamming capability are unfortunately limited by laser relaxation oscillation and external-cavity resonance, respectively. Here, we theoretically propose using a broadband white chaos generated by optical heterodyne of two chaotic external-cavity semiconductor lasers as source signal to construct a radar system with enhanced range resolution and anti-jamming capability. The white chaos not only has a white-noise-like wide spectrum without relaxation oscillation, but also has no signature of external-cavity resonance. Benefitting from these merits, the proposed white-chaos radar shows a marvelous unambiguous detection performance with subcentimeter range resolution. Moreover, the elimination of external-cavity resonance enables the randomness improvement of source signal, and resultantly, the anti-jamming capability is enhanced as well. [ABSTRACT FROM PUBLISHER]

In this paper, a normally off Al2O3/AlN/GaN MISFET on Si substrate for achieving high threshold voltage stability and uniformity is obtained based on selective area growth. A thin AlN space layer (SL, ~1 nm) is adopted to GaN-based template, and the AlGaN/GaN het-erostructureis selectivelygrownon the template to naturally form a recessed structure. By insertion of the AlN SL, the Al2O3/AlN/GaN interface trapping and scattering effects are effectively suppressed. As a result, a low threshold voltage hysteresis ( \Delta V\mathrm{ th} , 80 mV at maximum gate sweep voltage of 10 V by dc I – V measurement and smaller than 200 mV in linear region by pulse I – V measurement) and a high peak field-effect mobility ( \mu \sf FE) of 192 cm2/ \textV\cdot \texts are obtained in Al2O3/AlN/GaN MISFET. It is a significant improvement compared with Al2O3/GaN MISFET without AlN SL. The Al2O3/AlN/GaN MISFET also exhibits a maximum drain current ( Id,max) of 620 mA/mm, a low on-resistance ( Rds, \mathrm\scriptscriptstyle ON) of $9.6~\Omega \cdot $ mm, and a high uniformity of {V}_{\mathrm{ th}} (2.5 ± 0.1 V). [ABSTRACT FROM PUBLISHER]

We focus on addressing the problem of shadow removal for an image, and attempt to make a weakly supervised learning model that does not depend on the pixelwise-paired training samples, but only uses the samples with image-level labels that indicate whether an image contains shadow or not. To this end, we propose a deep reciprocal learning model that interactively optimizes the shadow remover and the shadow detector to improve the overall capability of the model. On the one hand, shadow removal is modeled as an optimization problem with a latent variable of the detected shadow mask. On the other hand, a shadow detector can be trained using the prior from the shadow remover. A self-paced learning strategy is employed to avoid fitting to intermediate noisy annotation during the interactive optimization. Furthermore, a color-maintenance loss and a shadow-attention discriminator are both designed to facilitate model optimization. Extensive experiments on the pairwise ISTD dataset, SRD dataset, and unpaired USR dataset demonstrate the superiority of the proposed deep reciprocal model. [ABSTRACT FROM AUTHOR]

Detecting moiré patterns in digital photographs is meaningful as it provides priors towards image quality evaluation and demoiréing tasks. In this paper, we present a simple yet efficient framework to extract moiré edge maps from images with moiré patterns. The framework includes a strategy for training triplet (natural image, moiré layer, and their synthetic mixture) generation, and a Moiré Pattern Detection Neural Network (MoireDet) for moiré edge map estimation. This strategy ensures consistent pixel-level alignments during training, accommodating characteristics of a diverse set of camera-captured screen images and real-world moiré patterns from natural images. The design of three encoders in MoireDet exploits both high-level contextual and low-level structural features of various moiré patterns. Through comprehensive experiments, we demonstrate the advantages of MoireDet: better identification precision of moiré images on two datasets, and a marked improvement over state-of-the-art demoiréing methods. [ABSTRACT FROM AUTHOR]

Facial action units (AUs) refer to a comprehensive set of atomic facial muscle movements. Recent works have focused on exploring complementary information by learning the relationships among AUs. Most existing approaches process AU co-occurrence and enhance AU recognition by learning the dependencies among AUs from labels, however, the complementary information among features of different AUs are ignored. Moreover, ground truth annotations suffer from a large intra-class variance and their associated intensity levels may vary depending on the annotators’ experience. In this paper, we propose the Region Attentive AU intensity estimation method with Uncertainty Weighted Multi-task Learning (RA-UWML). A RoI-Net is first used to extract features from the pre-defined facial patches where the AUs locate. Then, we use the co-occurrence of AUs using both within patch and between patches representation learning. Within a given patch, we propose sharing representation learning in a multi-task manner. To achieve complementarity and avoid redundancy between different image patches, we propose to use a multi-head self-attention mechanism to adaptively and attentively encode each patch specific representation. Moreover, the AU intensity is represented as a Gaussian distribution, instead of a single value, where the mean value indicates the most likely AU intensity and the variance indicates the uncertainty of the estimated AU intensity. The estimated variances are leveraged to automatically weight the loss of each AU in the multitask learning model. In extensive experiments on the Disfa, Fera2015 and Feafa benchmarks, it is shown that the proposed AU intensity estimation model achieves better results compared to the state-of-the-art models. [ABSTRACT FROM AUTHOR]

With the success of Vision Transformer (ViT), Transformer is being increasingly used for hyperspectral image (HSI) classification given its ability to extract global context dependencies. However, existing methods based on transformers tend to classify HSI in the traditional patch-wise manner. Thus, these methods cannot obtain true global features because the inputs of the model are local patches. To solve these problems, a hybrid convolution and ViT network (HCVN) is proposed for HSI classification. HCVN realizes the classification task from the perspective of semantic segmentation, and its input is the entire HSI, which makes it possible to obtain truly meaningful global features. By improving the original ViT, an HCV module is proposed, which enhances the ability of local structure characterization while extracting global features. The HCVN hybrid convolution layer and HCV module realize the extraction and fusion of local and global features. Finally, the dual branch network architecture is used to integrate the spatial and spectral features. Extensive experiments on two datasets verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We present a spatial-spectral decoupling framework (SDF) to improve the performance of hyperspectral image classification, it mainly contains three modules, including data preprocessing, feature representation, and collaborative decision-making. Specifically, the data preprocessing module based on band selection (BS) network can effectively emphasize useful spectral bands while suppressing redundant ones. Besides, the feature representation module is based on spatial-spectral decoupling (SD) network to avoid information confusion between the spatial and the spectral domains. In addition, the collaborative decision-making mechanism based on joint optimization can maintain the discriminative properties of different branches and enhance mutual facilitation among them. Finally, the experimental results validate the effectiveness and superiority of our SDF. [ABSTRACT FROM AUTHOR]

BeiDou Satellite-Based Augmentation System (BDSBAS) can notably improve the Global Navigation Satellite System (GNSS) L1 positioning performance by transmitting the orbit, clock, and ionosphere corrections. Aside from terrestrial geodetic receivers, mass-market smartphones also benefit from the augmentation service. In this letter, the smartphone-based GPS-L1 standard point positioning (SPP) augmented by BDSBAS corrections is analyzed using the Huawei-P40 smartphone. The quality of BDSBAS-B1C ionospheric information is evaluated by ionospheric observables retrieved from the selected 20 stations. Compared to the BDS-3 global ionosphere model (BDGIM) and the Global Ionosphere Maps (GIM) of the International GNSS Service (IGS), the accuracy of BDSBAS ionospheric correction is about 28% better than BDGIM and 11% better than the IGS-GIM. Compared to the GPS-L1 SPP, the accuracy of BDSBAS augmented smartphone positioning increases by 27% in the horizontal component and 41% in a vertical component in the open-sky environment, and 50% improvement in both directions in the second experiment where the smartphone connects with an external geodetic GNSS antenna to reduce the negative effects of multipath errors. [ABSTRACT FROM AUTHOR]

In this article, a half-tangent phase-locked loop (HTan-PLL) is proposed for variable-frequency grids of more electric aircrafts. First, the pros and cons of the synchronous reference frame PLL (SRF-PLL) are discussed in a nonlinear framework. To address the disadvantages, a half-tangent phase detector (PD) is developed and a novel PLL is then proposed. Subsequently, a phase portrait-based analysis and a Lyapunov function-based argument are provided. It is proven that the HTan-PLL has a good performance in terms of steady-state accuracy and fast dynamics, even under large frequency changes. Compared with the conventional SRF-PLL, the proposed HTan-PLL outperforms with the following features: faster dynamics than the SRF-PLL when the frequency varies largely; no unstable equilibrium points, while the SRF-PLL has infinite unstable equilibrium points; and when the grid frequency jumps widely, the HTan-PLL converges to the unique equilibrium point (0,0). By contrast, the SRF-PLL goes to different equilibrium points. Experimental tests are provided, which verify the superior performance of the HTan-PLL. [ABSTRACT FROM AUTHOR]

In this article, a new efficient soft-switching synchronous rectification (SR) buck converter is proposed, which is without voltage stress increment for all switches and diodes. By introducing an auxiliary circuit to the SR buck converter,the soft-switching condition is achieved within the wide range of load power for the power semiconductor devices. The auxiliary network, which consists of capacitor, inductor, switch, and three diodes, provides a zero-voltage condition for main switch and SR switch at both the turn-on and the turn-off instant. Besides, the auxiliary inductor creates a zero-current turn-on and a zero-voltage turn-off condition for the auxiliary switch. Considering the resonance between the parasitic capacitors of the semiconductor components and the auxiliary inductor, two diodes are designed to avoid the additional voltage stress on power devices. The operating principle of this proposed converter is analyzed. A 600 W prototype is implemented to test the soft-switching operation. The advantage of the proposed converter is demonstrated by the efficiency curve over a wide range of load power against the conventional SR buck converter. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) is an approach to minimizing the domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images, which improves the performance of the downstream tasks, for example, cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this letter, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL) and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks. Source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG. [ABSTRACT FROM AUTHOR]

Rotating machinery is ubiquitous, and its failures constitute a major cause of the failures of transportation infrastructures. Most fault-diagnosis methods for rotating machinery are based on vibration-signal analysis because vibrations directly reflect the transient regime of machinery elements. This article proposes a novel multiscale shared-learning network (MSSLN) architecture to extract and classify the fault features inherent to multiscale factors of vibration signals. The architecture fuses layer-wise activations with multiscale flows, to enable the network to fully learn the shared representation with consistency across multiscale factors. This characteristic helps MSSLN provide more faithful diagnoses than existing single- and multiscale methods. Experiments on bearing and gearbox datasets are used to evaluate the fault-diagnosis performance of transportation infrastructures. Extensive experimental results and comprehensive analyses demonstrate the superiority of the proposed MSSLN in fault diagnosis for bearings and gearboxes, the two foundational elements in transportation infrastructures. [ABSTRACT FROM AUTHOR]

Multi-modal classification (MMC) aims to integrate the complementary information from different modalities to improve classification performance. Existing MMC methods can be grouped into two categories: traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space. Besides, they mostly focus on the inter-modal fusion and neglect the intra-modal fusion. Thus, the representation capacity of fused features induced by them is insufficient. The deep learning-based methods implement the fusion in a high-level feature space where the associations among features are considered, while the whole process is implicit and the fused space lacks interpretability. Based on these observations, we propose a novel interpretative association-based fusion method for MMC, named AF. In AF, both the association information and the high-order information extracted from feature space are simultaneously encoded into a new feature space to help to train an MMC model in an explicit manner. Moreover, AF is a general fusion framework, and most existing MMC methods can be embedded into it to improve their performance. Finally, the effectiveness and the generality of AF are validated on 22 datasets, four typically traditional MMC methods adopting best modality, early, late and model fusion strategies and a deep learning-based MMC method. [ABSTRACT FROM AUTHOR]

Innovation is seen today as an indispensable practice if an organization wishes to achieve competitive advantage and sustainable success in the marketplace. The development of new products and services and new technologies that enable those products and services seem to be the crown jewel of innovation. The management of these technologies, more specifically the process through which technologies are transferred, has been the focus of scholarly debates and studies for several decades. Currently, there are no quantitative, comprehensive, and easily applicable models in the literature that researchers and practitioners can use to assess organizations’ technology transfer capabilities. This article has sought to delineate important factors and perspectives applicable to technology transfer, and to use those in order to assess an organization's capabilities, through the application of a hierarchical decision modeling model to create a technology transfer score. The research model is applied to a utility company in the Pacific Northwest of the United States, and the results provide insights on how to measure and interpret technology transfer capabilities as well as a starting point for technology managers to improve those capabilities. [ABSTRACT FROM AUTHOR]

In this article, the fuzzy $\mathcal {H}_{\infty }$ sliding mode control problem for continuous-time switched nonlinear systems is investigated. The switching signal conforms to the persistent dwell-time switching mechanism. The first objective of this article is to construct a switched integral sliding surface that not only accommodates the switched nonlinear model, but ensures that the sliding mode dynamics are globally uniformly exponentially stable and have an $\mathcal {H}_{\infty }$ performance by the equivalent controller derived from the sliding surface. Another aim is to integrate the switched sliding mode control law to force the system trajectories to the sliding surface in a finite amount of time. Then, on the basis of the Lyapunov function technique, the specific form of the sliding mode control law gains is given first, and later the finite-time reachability of the sliding surface is ensured. Finally, the validity of the proposed switched sliding mode control method is validated by a numerical example. [ABSTRACT FROM AUTHOR]

While deep learning models have contributed to the advancement of sensor-based human activity recognition (HAR), it usually requires large amounts of annotated sensor data to extract robust features. To alleviate the limitations of data annotation, contrastive learning has been applied to sensor-based HAR. One of the essential factors of contrastive learning is data augmentation, significantly impacting the performance of pretraining. However, current popular augmentation methods do not achieve competitive performance in contrastive learning for sensor-based HAR. Motivated by this issue, we propose a new sensor data augmentation method by resampling, which introduces variable domain information and simulates realistic activity data by varying the sampling frequency to maximize the coverage of the sampling space. The resampling augmentation method was evaluated in supervised learning and contrastive learning [SimCLR for HAR (SimCLRHAR) and MoCo for HAR (MoCoHAR)]. In the experiment, we use four datasets, UCI-HAR, MotionSense, USC-HAD, and MobiAct, using the mean F1-score as the evaluation metric for downstream tasks. The experimental results show that the resampling data augmentation outperforms all state-of-the-art augmentation methods in supervised learning and contrastive learning with a small amount of labeled data. The results also demonstrate that not all data augmentation methods have positive effects in contrastive learning frameworks. [ABSTRACT FROM AUTHOR]