Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer-Based Analysis of News Values: A Case Study on National Day Reporting

ABSTRACT This article uses Discursive News Values Analysis (DNVA) in a case study of national day reporting, focussing on the discursive dimension of news values. The study crosses disciplinary boundaries by bringing together linguistics and journalism studies. More specifically, it examines reporting about “Australia Day”—the official national day of Australia, which is set on a date that is becoming increasingly controversial, as it marks the beginning of colonisation. Importantly, the article utilises digital technologies (computer-based text analysis and visualisation of word co-occurrence) to apply and test these methods for the analysis of news values in English-language reporting. The study findings will thus have broader implications for journalism research. These methods (e.g., word frequency, collocation, concordancing) have been used in corpus linguistics and other areas of the digital humanities, including in the analysis of journalistic texts, but most often with a focus on language practices or discourses. Our study demonstrates that journalism research can profit from the use of free and user-friendly tools such as the ones that are applied in this study. Beyond this methodological innovation, the study will contribute new insights to research on news values in national day reporting.
On the relationship between rules of origin and global value chains

This paper uses a modified gravity model to investigate the effects of rules of origin (RoO) on 61 countries’ participation in global value chains (GVCs) during the period 2005–2015. We define GVC participation as the value added contained in exports, looking both backward and forward from a reference nation. RoO are heterogeneous in the degrees of restrictiveness that govern the origin of products to obtain preferential tariff treatment. The empirical results show negative relationships between RoO’s restrictiveness and both backward and forward participation, and these adverse effects become more prominent when we control the endogeneity bias. However, the implementation of regime-wide RoO, including diagonal cumulation and de minimis, can reduce these negative effects. The mitigating roles of both regime-wide rules are quite similar in terms of magnitude, which is significant as the current literature on the liberalization of RoO only focuses on the role of cumulation. Our findings suggest that in order to upgrade participation in international production networks, a country should adopt a less restrictive and regime-wide RoO in free trade agreement (FTA) negotiations.
The Effect of Molecular Structure on Prebreakdown Phenomena in Dielectric Liquids under Nonuniform Field

In the present work, prebreakdown phenomena in dielectric liquids which have various molecular structure, especially turning our attention to chlorine atoms or fluorine atoms and some double bonds included in moleculer formula, are observed in detail for positive and negative point polarities, under the application of an impulse voltage (1.1/225 us) to the point electrode. As test liquids, cyclic hydrocarbon liquids such as cyclohexane, benzene, chlorocyclohexane, monochloro benzene, hexafluorobenzene and cyclohexene, are used. Results obtained in these liquids are compared with ones obtained in straight-chain hydrocarbon liquids.
Compound eye image scanner optimally arranged for image combining and suppressing axial chromatic aberration by wavefront coding

Abstract. A compound eye optical system design allows us to construct a compact image scanner with a large field of view and a large depth of field, both of which are beneficial for use in a copier’s flatbed scanner. Our prototypes are constructed of 32 or 28 imaging units, which are ommatidia, and the representations from the imaging units are electrically combined. Since the arrangement of the imaging units is a key factor in combining the images correctly without any loss at the boundaries, we discuss two types of arrangements along with results of their prototypes: a one-line arrangement of varying magnification imaging units and a two-line staggered arrangement of telecentric imaging units. We also discuss the effectiveness of wavefront coding (WFC) in suppressing chromatic aberration, which is caused using cheap refractive imaging units, along with experimental results of applying both cubic- and quartic-phase-modulated WFC.
Autoweighted multi-view smooth representation preserve projection for dimensionality reduction

Abstract. Over the past few decades, we have witnessed a large family of algorithms that has been designed to provide different solutions to the problem of dimensionality reduction (DR). DR is an essential tool to excavate important information from the high-dimensional data by mapping the data to a low-dimensional subspace. Even though most DR algorithms can achieve satisfactory performance for many practical applications, they fail to fully consider the information from multiple views. Therefore, how to learn the subspace for high-dimensional features by utilizing the consistency and complementary properties of multi-view features is important and challenging in the present. To tackle this problem, we propose an effective multi-view DR algorithm named multi-view smooth representation preserve projection (MvSMRP2). MvSMRP2 seeks to find a set of linear transformations to project each view features into one common low-dimensional subspace where the multi-view smooth reconstructive weights are preserved as much as possible. A pair-wise-consistent scheme is designed by fully exploiting the Hilbert–Schmidt independence criterion to maximize the dependence among different views, which can obtain one common subspace and force multiple views to learn from each other jointly. Moreover, MvSMRP2 can allocate ideal weight for each view automatically according to view importance without explicit weight definition. Finally, an iterative alternating strategy is proposed to obtain the optimal solution of MvSMRP2. Plenty of experiments on various datasets show the excellent performances of the proposed method.
Joint blind image deblurring and super-resolution via double-branch projection feedback network

Abstract. Unexpected situations, such as object movement and camera shaking, cause interference and produce blurry, degraded images. These degraded images can adversely affect visual processing tasks, such as target detection and facial recognition. In addition, the super-resolution (SR) of a single image is a basic task to improve the image quality in visual applications. However, due to the existence of artifacts, it is difficult to generate clear SR images from rough and degraded images. In this work, we propose a double-branch projection feedback network that can generate clear high-resolution (HR) images from severely blurred low-resolution (LR) images. One is a deep feature projection feedback branch that can obtain texture-rich SR feature maps and reduce the accumulative errors with depth, and a channel adjustment and multi-scale feature fusion mechanism are proposed to better obtain the mapping relation between LR and HR images. The other is an autoencoder deblurring module, which is used to get deblurred features of an input image. Moreover, a mapping associated feature block is applied to match non-local feature information in the feature fusion module. Extensive experiments show that the network is effective and performs better than the state-of-the-art methods in single image deblurring and SR.
Semi-supervised face aging and rejuvenating

Abstract. Face aging and rejuvenating work effectively in public security criminal investigation, cross-age recognition, and entertainment. However, three main problems still exist: the lack of accurate and sufficient dataset, low aging effect, and poor preservation of personal information. We propose a semi-supervised face aging and rejuvenating method for face aging and rejuvenating. In particular, a conditional encoder is utilized to map an input face into a latent vector, which is used by the generator network with age conditions to produce a new face. The latent vector preserves identity information, whereas the age label controls face aging or rejuvenating. To make generated features closer to prior features, the discriminator network is designed to assist the generator network. In addition, a cycle optimized method is utilized to preserve the personal information of the generated face. Experimental results demonstrate that our network can generate more realistic faces, both in personal identity and age consistency.
Programmable spatially variant single-pixel imaging based on compressive sensing

Abstract. Single-pixel camera is developed to mitigate the constraints faced by the conventional cameras especially in invisible wavelengths and low light conditions. Nyquist–Shannon theorem requires as many measurements as the image pixels to reconstruct images flawlessly. In practice, obtaining more measurements increases the cost and acquisition time, which are the major drawbacks of single-pixel imaging (SPI). Therefore, compressive sensing was proposed to enable image reconstruction with fewer measurements. We present a design of sensing patterns to obtain image information by utilizing spatially variant resolution (SVR) technique in SPI. The proposed method reduces the measurements by prioritizing the resolution in the region of interest (ROI). It successfully achieves the programmable imaging concept where multiresolution adaptively optimizes the balance between the image quality and the measurements number. Results show that SVR images can be reconstructed from significantly fewer measurements yet able to achieve better image quality than uniform resolution images. In addition, the SVR images can be further enhanced by integrating the dynamic supersampling technique. Consequently, the concerns of image quality, long acquisition, and processing time can be addressed. The proposed method potentially benefits imaging applications where the target ROI is prioritized over the background and most importantly it requires fewer measurements.
Precisely tunable heterodyne-injection-locked dual-loop optoelectronic oscillator

Abstract. A precisely tunable heterodyne-injection-locked dual-loop optoelectronic oscillator (HIL-DL OEO) is analyzed and experimentally demonstrated. The primary loop, utilizing a narrow bandpass filter, generates a frequency-fixed signal, and the secondary loop, incorporating a yttrium iron garnet filter, outputs the tunable signal. The oscillating signal in each loop experiences frequency translation at an external frequency translation module (FTM) and is then fed to the other loop. The frequency translation at the FTM, which is composed of two mixers and a power splitter, is realized with the help of a tunable low-frequency signal. Thanks to the low-frequency signal, the oscillation frequency in the secondary loop would vary accordingly under injection locking.
Local feature descriptor using discrete first and second fundamental forms

Abstract. 3D object recognition from point clouds is a fast-growing field of research in computer vision. 3D object recognition methods can be classified into two categories: global feature-based and local feature-based methods. The local feature-based methods are more popular than global ones. Because the global feature-based methods need a prior segmentation of the scene, they are not suitable for real-world scenes. Many previous local descriptor methods limit their performance by introducing a local reference frame or axis (LRF/A). Estimating the LRF/A for each keypoint leads to extra computational time and error. We use the fundamental theorem of surface theory to introduce a simple and efficient local feature descriptor based on the coefficients of discrete first and second fundamental forms. The proposed method overrides the necessity of an LRF/A, and it required a small feature dimension of seven, which means it is a low-complexity and fast procedure. To assess the proposed method, we have compared it with eight state-of-the-art descriptors and applied it to the three popular datasets to extract features and recognize the correspondences. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of pairwise registration measurements, recall versus 1-precision curve, and the computational time.
Weighted feature fusion and attention mechanism for object detection

Abstract. Recently, anchor-free methods have brought new ideas to the field of object detection that eliminate the need for anchor boxes in object detection and provide a simpler detection structure. CenterNet is the representative anchor-free method. However, this method still has the problem of obtaining high-resolution representation from low-resolution representation using upsampling, and the predicted heatmap is not accurate enough in space and does not make full use of the shallow low-level features of the network. We introduce CenterNet-HRA to solve this problem. An attention module is proposed to calibrate the high-level semantic features of the network output using the shallow low-level features from different receptive fields; HRNet is used as the backbone to maintain high-resolution feature representation through the whole process rather than using upsampling to generate high-resolution feature representation as HourglassNet. Considering that the feature representations with different resolutions have different contributions to the network but HRNet fuses them without distinction, a novel weighted feature fusion HRNet is designed to achieve higher detection precision. Our method achieves an average precision (AP) of 42.3% at 13.5 frames-per-second (FPS) (40.3% AP at 13.3 FPS for CenterNet-HG) on the MS-COCO benchmark.
Multiclass triplet metric-learning network combined with feature mixing block for few shot learning

Abstract. Few-shot learning aims to learn classification with only a few training examples per class. The metric-based approaches aim to learn a set of embedding functions, so that when represented in this embedding, images are easy to recognize. For metric-based few-shot learning, how to get the class feature representation under a few support samples and what metric to use are important. We propose a multiclass triplet metric-learning network combined with a simple foreground–background feature mixing block. With the foreground–background feature mixing block, we “hallucinate” the information from few support examples to get conceptual representation of classes, which is effective to promote few-shot learning. Furthermore, using the multiclass triplet loss, it learns a feature embedding function that could bring similar samples close to each other and keep samples of different classes far apart. Our proposed network is trained in an end-to-end manner from scratch, so as to learn a good embedding function, conceptual representation of classes, and a nonlinear metric simultaneously. Experimental results on the challenging datasets show that our method with Conv-64F feature extracting block is competitive and effective compared to the metric-based baselines with Conv-64F.
High-precision and robust visible-light dynamic imaging positioning system based on improved kernelized correlation filters and fast-weighted Levenberg–Marquardt algorithm

Abstract. This paper proposes a high-precision and robust visible-light dynamic imaging positioning system based on improved kernelized correlation filters (IKCF) and fast-weighted Levenberg–Marquardt (FWLM) algorithm. First, using IKCF, we propose a novel LED-ID dynamic recognition algorithm, which can achieve fast LED-region of interest detection, and an adaptive model update mechanism is used to reduce the interference of occlusion. Second, an FWLM positioning algorithm is designed. The direct linear transformation is used to obtain the initial estimate of the pose quickly, and LM is employed to optimize the pose iteratively by minimizing the reprojection error. An adaptive weighting matrix is used to reduce the impact of LEDs with low detection reliability on positioning accuracy. Finally, an experimental platform is set up to verify the effectiveness of the proposed dynamic positioning algorithm. The experimental results show that the average positioning time is 4.08 ms, the average positioning accuracy is 1.9830 cm without occlusion, and the average positioning accuracy is 4.4024 cm with occlusion.
Toward semantic image inpainting: where global context meets local geometry

Abstract. Image inpainting attempts to fill the missing areas of an image with plausible content that is visually coherent with the image context. Semantic image inpainting has remained a challenging task even with the emergence of deep learning-based approaches. We propose a deep semantic inpainting model built upon a generative adversarial network and a dense U-Net network. Such a design helps achieve feature reuse while avoiding feature explosion along the upsampling path of the U-Net. The model also uses a composite loss function for the generator network to enforce a joint global and local content consistency constraint. More specifically, our new loss function combines the global reconstruction loss characterizing the semantic similarity between the missing and known image regions with the local total variation loss characterizing the natural transitions among adjacent regions. Experimental results on CelebA-HQ and Paris StreetView datasets have demonstrated encouraging performance when compared with other state-of-the-art methods in terms of both quantitative and qualitative metrics. For the CelebA-HQ dataset, the proposed method can more faithfully infer the semantics of human faces; for the StreetView dataset, our method achieves improved inpainting results in terms of more natural texture transitions, better structural consistency, and enriched textural details.
Video stabilization algorithm based on virtual sphere model

Abstract. We propose a video stabilization algorithm based on the rotation of a virtual sphere. Unlike traditional video stabilization algorithms relying on two-dimensional motion models or reconstruction of (3D) camera motions, the proposed virtual sphere model stabilizes video by projecting each frame onto the sphere and performing corrective rotations. Specifically, matching feature points between two adjacent frames are first projected onto two virtual spheres to obtain pairs of spherical points. Then, the rotation matrix between the previous and current frame is calculated. The resulting 3D rotation matrix sequence is used to represent the camera motion, and it is smoothed using the geodesic distance on a Riemannian manifold. Finally, the difference between the smoothed and original path allows obtaining the rotation matrix that causes camera jitter, and the virtual spheres are rotated reversely to suppress jitter. Experimental results show that the proposed algorithm can effectively reduce random jitter, outperforming state-of-the-art methods.
Robust image steganography framework based on generative adversarial network

Abstract. Robust steganography enables secret information to be transmitted stealthily and accurately in lossy channels such as social channels and wireless channels. With the development of deep learning, robust steganography can be constructed using the generative model of deep neural networks. Two new robust steganographic frameworks are proposed on the basis of generative models, and two algorithms are proposed on these two frameworks to verify the effectiveness of the proposed framework. Experiments show that the two frameworks proposed are more flexible than existing robust steganographic frameworks. To further verify the validity of the framework, when compared with existing robust steganography based on deep learning, the generative robust steganography algorithm is shown to have a higher secret information embedding capacity and higher steganography image quality.
Failla Memorial Lecture: The Many Facets of Heavy-Ion Science

Heavy ions are riveting in radiation biophysics, particularly in the areas of radiotherapy and space radiation protection. Accelerated charged particles can indeed penetrate deeply in the human body to sterilize tumors, exploiting the favorable depth-dose distribution of ions compared to conventional X rays. Conversely, the high biological effectiveness in inducing late effects presents a hazard for manned space exploration. Even after half a century of accelerator-based experiments, clinical applications and flight research, these two topics remain both fascinating and baffling. Heavy-ion therapy is very expensive, and despite the clinical success it remains controversial. Research on late radiation morbidity in spaceflight led to a reduction in uncertainty, but also pointed to new risks previously underestimated, such as possible damage to the central nervous system. Recently, heavy ions have also been used in other, unanticipated biomedical fields, such as treatment of heart arrhythmia or inactivation of viruses for vaccine development. Heavy-ion science nicely merges physics and biology and remains an extraordinary research field for the 21st century.
Integrating Animal Health and Stress Assessment Tools Using the Green-Lipped Mussel Perna canaliculus as a Case Study

ABSTRACT Marine organisms are constantly exposed to stress, such as changes in seawater temperature, which may elicit biochemical, molecular, and physiological changes. In the present study, an integrative approach to evaluate stress in the green-lipped mussel Perna canaliculus exposed to control, moderate, and severe heat stress was used. Flow cytometry (FCM) was used to measure hemolymph parameters, including hemocyte concentration, viability, and reactive oxygen species (ROS) production. Spectrophotometry and a handheld electrochemical meter were used to determine the total antioxidant capacity (TAC) of hemolymph and gill tissue, and metabolomics was used to contrast specific metabolic responses among treatments. For hemolymph parameters measured using FCM, the number of nonviable hemocytes increased with the severity of heat shock. The proportion of superoxide-positive hemocytes mirrored the increase in nonviability; by contrast, the general ROS production initially increased following moderate heat shock, but then decreased after severe heat shock. Trolox-equivalent antioxidant capacity in the gill tissue and fast-acting antioxidant levels in the haemolymph showed small but significant decreases following heat shock, whereas the reciprocal pattern was seen for slow-acting and TAC in the hemolymph. Metabolomic profiles discriminated between treatments, with energy metabolism pathways appearing the most affected. The approach used in this study provides a deeper mechanistic understanding of the stress responses of organisms exposed to heat stress; however, these analytical techniques may be combined or used independently to measure stress in marine organisms in the laboratory or in the field, which can improve health assessments of wild and farmed aquatic organisms.