Miracle-dz/UniFAR-FTUs huggingface dataset:
From Student to Employee: Group Compatibility Predicts Group Identification and Intergroup Attitudes

ABSTRACT Transitions go along with the need to integrate a previous outgroup into the self-concept. This study examines whether this integration follows cognitive consistency principles. In line with recent theorizing, we test whether the perceived compatibility of a previous and a current ingroup determines identification with these groups and intergroup attitudes. We primed employees who were former students (N = 269) to perceive the groups of students and employees as either more compatible or less compatible, and measured the strength of identification and group attitudes with regard to both groups. The findings largely confirmed predictions. Greater compatibility between social groups increased identity integration and reduced intergroup bias. Identity integration mediated the effect of compatibility on intergroup bias. In line with predictions, the voluntariness of group change explained which of the groups people identified more strongly with when groups were perceived low in compatibility. The results of the study provide evidence that integrating identities into the self-concept follows cognitive consistency principles and suggests that emphasizing similarities between previous and current ingroups fosters identity integration and reduces intergroup bias.
Dynamics of Mud Blister Worm Infestation and Shell Repair by Oysters

Mud blister worms bore into oyster shells; and oysters respond to shell penetration by secreting new layers of shell, resulting in mud blisters on inner surfaces of oyster shells. We conducted two experiments in off-bottom oyster farms along Alabama’s coast in summer 2017 to explore the dynamics of worm infestation, blister formation, and shell repair. Results support our hypothesis that only a small proportion of worms that bore into oysters cause blisters. Triploid oysters had fewer blisters than diploids, likely because of faster growth and shell repair. We treated oysters to remove mud blister worms, redeployed them at intertidal and subtidal sites for nine weeks, and found that reinfestation by worms occurred only in subtidal oysters. Intertidally deployed oysters showed no visible blister coverage, indicating recovery, whereas blister coverage increased in subtidal oysters. Reinfestation of subtidal oysters was correlated with previous burrow damage, visualized with X-ray images, thus supporting our hypothesis that worms preferentially settle in previously infested shells. Forces required to break blisters, measured with a custom-built shucking knife with an integrated force sensor, were low relative to forces required to shuck oysters, possibly because our experiment was conducted when worm infestation was increasing. Higher forces were required to break smaller, lighter-colored blisters, consistent with blister recovery; but results were highly variable and not consistent across sites and sampling times, suggesting that size and color of blisters alone did not explain shell strength. Our results indicate that oysters repair shells slowly relative to more dynamic patterns of worm infestation.
Influence of Maternal Size on Offspring Traits in a Marine Gastropod with Direct Development and without Sibling Interaction

In most animal taxa, large mothers (or those with high nutritional status) produce large offspring, leading to a maternal size-offspring size correlation, that is, a positive correlation between maternal size and offspring size. Here, we used the natural variation in maternal size between three natural populations of Buccinanops deformis (a marine snail with direct development, nurse egg feeding, and a single embryo per egg capsule) to study maternal investment and offspring size. The main objectives were to compare offspring size and maternal investment traits within and between populations and to evaluate the relationship between maternal size and offspring size. Although not supported in every population, our results show that maternal size was positively correlated with offspring size, thus representing an example of the maternal size-offspring size correlation in a species in which there is no competition for food between capsule mates because only one embryo develops per capsule. These findings also suggest that in B. deformis larger mothers produce more offspring and provide their offspring with more resources, and that this between-population variation in offspring size is related to differences in the number of nurse eggs allocated per egg capsule and in egg capsule size. The ubiquity of the maternal size-offspring size correlation in B. deformis needs to be tested further across populations, because factors other than maternal size could influence offspring size variation in this marine gastropod.
Scoping review on automatic color equalization algorithm

Abstract. Digital image processing is at the base of everyday applications aiding humans in several fields, such as underwater monitoring, analysis of cultural heritage drawings, and medical imaging for computer-aided diagnosis. The starting point of all such application regards the image enhancement step. A desirable image enhancement step should simultaneously standardize the illumination in the image set, possibly removing bad or not-uniform illumination effects, and reveal all hidden details. In 2002, a successful perceptual image enhancement model, the automatic color equalization (ACE) algorithm, was proposed, which mimics the color and contrast adjustment of the human visual system (HVS). Given its widespread usage, its correlation with the HVS, and since it is easily implementable, we propose a scoping review to identify and classify the available evidence on ACE, starting from the papers citing the two funding papers on the algorithm. The aim of this work is the identification of what extent and in which ways ACE may have influenced the research in the color imaging field. Thanks to an accurate process of papers tagging, classification, and validation, we provide an overview of the main application domains in which ACE was successfully used and of the different ways in which this algorithm was implemented, modified, used, or compared.
C-shaped split ring resonator type metamaterial antenna design using neural network

Abstract. A C-shaped split ring resonator-based metamaterial (MTM) structure placed on a patch antenna is proposed. An artificial neural network (ANN) is applied to design and determine dimensions of an MTM structure that is similar to those used in wireless communication systems, such as Wi-Fi and WiMax, as well as in military vehicles and space stations. The proposed microstrip antenna interacts with the MTM-based structures to improve the reflection coefficient (S11) and quality factor. The design of the proposed structure is optimized for this purpose in accordance with the ANN studies in the related literature. Both simulation and experimental studies show that ANN calculated MTM antennas improved the quality factor, which is calculated as 230 at 5.18 GHz. The simulated S11 is verified by experiment to show the integrity of the proposed work.
Global-locally reinforced feature extraction for person re-identification

Abstract. Recently, the attention mechanism and part-based architectures have greatly boosted the research of person re-identification (Re-ID). However, most attention-based works extract first-order information and lack diversity. Meanwhile, the classic part-based works are not able to make use of cross-part information because of the unified partitions. These two kinds of methods ignore visual relationships in the global scope. Accordingly, we propose multi-subspace non-local attention (MSNA) and reinforced loss (R-Loss) to alleviate the issues above. MSNA is an improved attention module. It can be integrated into existing networks to utilize rich low-level information and extract the global relationships from different subspaces. R-Loss module is motivated to reinforce the capability of extracting fine-grained features by making full use of intra-part and cross-part information. We combine them and provide a global-locally reinforced feature extraction strategy. In addition, we design a feature fusion module to combine features from different branches. Equipped with the modules above, our model can extract important local and fine-grained features by identifying diverse visual relationships in the global scope. The models with our proposed modules achieve significant improvements over the baselines on four public datasets and establish new state-of-the-art results.
Optical homogeneity measurement of parallel plates using nonuniform fast Fourier transform based on low-rank approximation and Taylor expansion

Abstract. When the optical homogeneity of parallel plates is measured using wavelength phase shifting interferometers, the nonlinearity of phase shifts caused by nonlinear wavelength tuning results in spectrum aliasing, and thus the optical homogeneity cannot be obtained accurately. We propose a nonuniform fast Fourier transform algorithm based on low-rank approximation and Taylor expansion (LRTE-NUFFT) to solve the problem. The principle of the LRTE-NUFFT method is introduced and compared with the FFT method and the nonuniform fast Fourier transform algorithm based on fast Gauss gridding (FGG-NUFFT) method proposed earlier through simulations and experiments. The results show that LRTE-NUFFT can improve the accuracy of the optical homogeneity measurement of parallel plates compared with the FFT method, and it can also improve the computational efficiency compared with the FGG-NUFFT method.
Sparse adversarial attack based on ℓq-norm for fooling the face anti-spoofing neural networks

Abstract. Neural networks are vulnerable to various adversarial perturbations added to the input. Highly sparse adversarial perturbations are difficult to identify, which is especially dangerous to network security. Previous research has shown that ℓ0-norm has good sparsity but is challenging to solve. We use ℓq-norm to approach ℓ0-norm and propose a new white-box algorithm to generate adversarial examples aiming at minimizing ℓq distance of the original image. Meanwhile, we extend the adversarial attack to facial anti-spoofing task in the field of face recognition security. This extension enables us to generate sparse and unobservable facial attack perturbation. To increase the diversity of the data set, we make a new data set of real and fake facial images containing images produced by various latest spoofing methods. Extensive experiments show that our proposed method can effectively generate a sparse perturbation and successfully mislead the classifier in multi-classification tasks and facial anti-spoofing tasks.
Deep multiscale divergence hashing for image retrieval

Abstract. Image retrieval based on deep learning of hash has made great progress. The hash method increases retrieval speed greatly while saving storage space. However, some problems exist, such as the distinctiveness of image feature still needs to be improved and some image features are still redundant. We propose a new deep learning to hash method, namely, deep multiscale divergence hashing, which provides a high diversity and compact image feature for image retrieval. The discriminative features from deep neural networks are identified by the importance criterion in network pruning techniques and the feature redundancy is reduced. Then, the selected features across different layers are fused in a certain proportion to increase the diversity of features for image retrieval. We also present a hybrid loss function in hash space, which consists of the weighted pairwise cross-entropy loss function and the KL-divergence. It tends to minimize the hamming distance between similar images and maximize the hamming distance between different images, which helps improve the accuracy. Massive experimental results show that our method achieves better feature distinguishability and more advanced image retrieval accuracy, and surpasses HashNet by 11.46%, 7.58%, and 13.86% on MS COCO, NUS-WIDE, and CIFAR-10 datasets, respectively.
Gradient-domain image reconstruction framework using chroma-preserving intensity-range and base-structure constraints

Abstract. We present a gradient-domain image reconstruction framework with the chroma-preserving pixel-wise intensity-range constraint and the base-structure constraint. The existing methods for manipulating base structures and detailed textures are classifiable into two major streams: gradient-domain and layer-decomposition. To generate detail-preserving and artifact-free output images, we combine the two approaches’ benefits into the proposed framework by introducing the chroma-preserving intensity-range constraint and the base-structure constraint. To preserve details of the input image, the proposed method takes advantage of reconstructing the output image in the gradient domain, whereas the output intensity is guaranteed to lie within the specified intensity range by the intensity-range constraint. The reconstructed image lies close to the base structure by the base-structure constraint, which is effective for restraining artifacts. Using this chroma-preserving pixel-wise luminance constraint, the proposed algorithm does not require post-processing such as intensity clipping or rescaling. The proposed framework directly generates the output luminance, which guarantees that the output RGB intensities are within the target intensity range, preserving the chromatic component. Experiments demonstrated that (1) the proposed framework is effective for various applications such as tone mapping, seamless image cloning, detail enhancement, and image restoration, and (2) the proposed framework can preserve chroma components compared with the existing methods.
Underwater image restoration based on modified color-line model

Abstract. Underwater images suffer from haze, color distortion, and low contrast due to the absorption and scattering of transmitted light. A systematic approach including accurate background light (BL) estimation and transmission (TM) estimation is proposed based on color-line model for underwater images restoration. The estimation of BL candidate region is improved by combining quadtree subdivision and minimum unary gray entropy evaluation, to realize a high-quality restoration and improve accuracy of TM estimation with color-line model. Given incomplete intersection of color lines with BL, a convex optimization function is proposed to improve accuracy of TM estimation. On the basis of restorations with BL and TM estimations, pixel distribution stretching and Unsharp Masking are employed to correct color and enhance edge information for better performance of vision. Comprehensive evaluations illustrate that the proposed method could efficiently restore underwater images with visibility improvement, natural color correction, and contrast enhancement.
ADCI-Net: an adaptive discriminative clip identification strategy for fast video action recognition

Abstract. The most common method for video-level classification is to operate models over a whole series of fixed-temporal-length clips in a long video. However, a video usually consists of several meaningless sections, and similar clips that are fed into models could lead to a suboptimal recognition accuracy and waste of computing resources. To address this issue, we introduced an adaptive discriminative clip identification network to evaluate every video clip with respect to its relevance. We adaptively choose top-ranked clips as inputs for prediction and filter out irrelevant clips. Specifically, for a given trained cumbersome convolutional neural network action recognition model, we use a lightweight hallucination network (H-Net) to study its generalization ability based on distillation. Then, the evaluation of relevant clips is performed by considering the imitated features of H-Net. Thus, heavy calculations of overlapping content or meaningless clips can be avoided during inferences. We validate our approach by examining its performance on two datasets: UCF101 and HMDB51. Our strategy can be applied to any clip-based action recognition model. Experimental results demonstrate that on UCF101, we reduced the computational cost by 70% and increased the accuracy.
Moving object properties-based video saliency detection

Abstract. Video saliency detection, which suffers from the interference of complicated motion and complex background, aims at discovering the motion-related and the most noticeable object in a video sequence while maintaining the spatiotemporal consistency of saliency maps. We propose a video saliency detection approach by constructing the moving object properties, meaning that motion is in concurrence with a general object for detected regions. With the assistance of different key frame strategies, the consistency propagation, which is respectively conducted to refine the obtained results of motion saliency and object proposals to improve low-quality detections and avoid the excessive accumulation of errors, is orderly implemented through sparse reconstruction based on the constructed adjacent relationships. Meanwhile, by integrating the refined object proposals with the refined salient motion detection, a pixel-based fusion strategy is applied to locate the most trustworthy motion object regions and suppress detection noises, such as dynamic background and stationary objects. Moreover, a Bayesian fusion framework that incorporates the global features obtained using low-rank constraints is employed to further enhance the accuracy and global temporal consistency for the obtained initial motion object regions. Then based on the obtained priors of motion objects and background regions, the spatial saliency map is estimated using the geodesic distances transform to discover more complete and spatially salient motion objects. Finally, an energy optimization function is proposed to intuitively integrate multiple saliency clues (i.e., spatial saliency, global features, and motion objects) and generate the global spatiotemporal consistency saliency map. Experimental results on three different benchmark datasets demonstrate that the proposed method successfully infers video moving objects and extracts the most salient regions, outperforming the state-of-the-art methods.
Influence of installation error of grating interferometer on high-precision displacement measurement

Abstract. We investigated a high-precision grating interferometer displacement measurement system that can be applied to high-end immersion lithography machines and explained its composition and measurement algorithm. By analyzing the optical path variation caused by the installation error of the grating interferometer, we calculated the influence of the installation error on the displacement measurement. A displacement measurement model of a grating interferometer using three read heads was established, and the influence of the model coefficients on the displacement measurement model was analyzed. The simulation results show that, under the condition that the error of the measurement model in the X direction and the Y direction is <0.1 nm, the translation error of the read head should be within ±100 μm, and the relative rotation deviation between the two read heads or two gratings placed along the diagonal should be within ±50 μrad. The methods and results of studying the influence of grating interferometer installation error on the displacement measurement provide a theoretical basis for the application of a grating interferometer displacement measurement system in immersive high-end lithography scanners.
Climate Indices Predict Black Gill Prevalence in White Shrimp Penaeus setiferus (Linnaeus, 1767) in South Carolina and Georgia, USA

ABSTRACT The white shrimp fisheries in South Carolina and Georgia, USA, have undergone substantial reductions in commercial landings over the past few decades. These declines are associated with reduced commercial effort, shifting climate patterns, and disease outbreaks. For example, recent increases in the occurrence of gill melanization, also known as black gill, have led to the suggestion that this epidemic is a major contributor to reduced shrimp abundance in the region. The goal of this study was to assess long-term patterns and drivers of black gill, and its effects on white shrimp abundance, particularly during the fall, when most of the commercial harvest of white shrimp in this region has historically occurred. Data were compiled for fishery-independent (2002–2017) metrics of white shrimp abundance and black gill prevalence from seven estuaries along the South Carolina and Georgia coasts. Phenology of black gill was variable among years, but mean black gill prevalence was significantly related to the Pacific Decadal Oscillation and El Niño–Southern Oscillation climate indices. These analyses show that the prevalence of black gill is sensitive to large-scale fluctuations in climate patterns.
Historical Reconstruction of Biomass of Panopea globosa from the Southwestern Coast of Baja California Peninsula, Mexico: A New Perspective for Geoduck Fishery Management

ABSTRACT In this study, fishery-independent data were used to analyze changes in average density and biomass of Panopea globosa between 2007 and 2018. Different statistical estimators based on the probabilistic density function, such as Gaussian and generalized distributions, were used, all of them associated with the spatial patchy distribution of the geoduck clam. Both average density and biomass showed a dramatic decline throughout the study period, decreasing from 31 geoducks/50 m2 (2007) to 10 geoducks/50 m2 (2018) and from 126,000 to 35,000 t, respectively. In addition, a high harvest of individuals greater than 175 mm of shell length was observed, increasing the possibility of recruitment overfishing. Finally, a Kobe phase chart applied to P. globosa fishery in Bahía Magdalena showed that the stock status was identified as overfished.
Responses of the Green Abalone Haliotis fulgens (Philippi, 1845) to Sudden and Recurring Extreme Environmental Variations

ABSTRACT Temperature and hypoxia anomalies are observed in many coastal regions, and climate change is likely to cause a more frequent and intense occurrence of environmental extremes. Understanding their impacts on benthic populations is critical, especially on those with little or no mobility. In this contribution, the effects of hyperthermia (to 26°C) and hypoxia (to 1.0 mg O2 L–1) were analyzed and compared with their combined occurrence through the evaluation of the physiological condition of juveniles of the green abalone Haliotis fulgens. Sudden, single-time exposure of 8 h and the recurrent exposure to 8-h weekly episodes for 6 wk were tested to identify changes in the physiological condition by estimating the respiration rate (RR), the biochemical contents (soluble protein [SP] and glucose [Glc]), and physicochemical variables (osmolality [Osm] and pH) in hemolymph, and energy status (adenylate energy charge [AEC]) was measured in the muscle. The strongest responses under sudden hyperthermia were an increase in RR and Osm as well as a reduction in SP, pH, and AEC, and under hypoxia and combined effects, reductions in RR, Osm, and pH were observed. Abalone's responses to the combined stressors under the recurrent regime were different, showing increases in SP and Glc contents and reductions in pH and AEC. Results suggest that under recurrent stress of hyperthermia or hypoxia alone, the juveniles of H. fulgens can adapt, whereas under recurrent events of the combined stressors, the observed response is similar to that of sudden stimuli (without evidence of adaptation).