Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Intelligence Scale for Male Nursing Students and Its Latent Regression on Gender and Background Variables

This study aimed to develop an emotional intelligence (EI) scale for male nursing students and investigate its associations with gender, age, religious beliefs, and father’s and mother’s education level. We recruited 384 male nursing students in Taiwan to construct an EI scale comprising 16 items with four factors: recognizing the emotions of others, emotional self-awareness, self-emotional expression, and self-emotional management. The scale had factor loadings of 0.64−0.80. The reliability coefficients for the subscales ranged from 0.80 to 0.84, while that for the total scale was 0.93. We also recruited 402 female nursing students for comparison. Latent multiple regression of the EI factors showed that male students had higher self-emotional expression but lower self-emotional management than females. Age was negatively associated with self-emotional management for both genders. Religious beliefs were negatively associated with emotional self-awareness in male students, and with recognizing the emotions of others in females. Father’s and mother’s education had no association with EI in male students; however, father’s education was positively associated with all EI factors in females, and mother’s education was negatively associated with recognizing the emotions of others and self-emotional expression. These results provide insight into male nursing students’ EI and the background variables influencing EI.
Video Denoising with Local Linear Denoising and Non-Local Means

—Denoising of images is a well-studied subject, and some very sophisticated methods to denoise images already exist. The denoising methods used for images could be used to denoise videos frame by frame, but we wanted to investigate how the similarities between different frames could be taken advantage of to improve the denoising of videos. In this study, we implemented four different denoising methods for videos, namely an averaging of successive frames, an averaging of successive frames with Gaussian distributed weights, a non-local means algorithm that only searches for similar patches in the same frame, and a non-local means algorithm which searches for similar patches in the same frame as well as in other frames in the video. Each of the methods was tested for different parameters to find the best parameters. The results from each method were then compared with the state-of-the-art Video BM3D method. The results show that the two averaging methods perform very poorly. The two non-local means methods perform much better, but none of our methods can come close, regarding PSNR performance, to Video BM3D.
Semi-automatic Annotation Proposal for Increasing a Fake News Dataset in Spanish

The digital era has become an ally of fake news, since it has increased the spread and amount of false information. Fake news is a global problem that causes disorder and generates fear. This phenomenon must be attacked in the same environment in which it is generated: in the digital environment. This paper presents the current state of my doctoral thesis which focuses on the linguistic modelling applied to the automatic detection of fake news through Natural Language Processing (NLP). In order to study the linguistic characteristics of fake news and to create computational models that automate its detection, labelled datasets are needed, but this is a costly task that requires time and expertise. A fake news dataset and an annotation guide were created ad hoc in a previous work to analyse all the parts and elements of a news item. However, after creating and training our system, we realised that the time spent was not proportional to the low annotated data obtained. The need of creating a larger corpus to train and test our hypothesis has led us to think about a way of increasing our corpus without spending so much time. For that purpose, a semi-automatic annotation is proposed for reducing time while increasing speed and quantity of the examples annotated. This proposal, besides allowing us to make progress in our research, may facilitate the creation of datasets, which are essential in NLP research.
Continual / Lifelong Learning III: SupSup - Supermasks in Superposition

The paper proposes a three-letter taxonomy to describe different scenarios of continual learning, which is summarized in Figure 1. The first and second letters specify if the task identity is provided during training and inference respectively (G if provided, N if not). The third letter specifies whether labels are shared (s) or not (u). GG is the simplest case where the task identity is given at both training and inference time, and the model performs the corresponding task directly. For example, for a model which predicts the breeds of dogs, cats or birds, if the model is told that the input picture is a dog, then it switches to the ’dog mode’, looks at the distribution over the breeds of dogs and picks the one with the highest probability. In the GG case, whether the labels are shared or not does not make any difference since the model can take the corresponding outputs based on the task identity. When the task identity is unknown during inference, the model has to infer the task identity, switch to the corresponding mode, and then pick the answer from that task. If the labels of the tasks are not shared, the model needs to look at the distribution over all the classes across all tasks to predict the task identity, which is harder than the shared case. If we take the previous example but assume that the species of the input is unknown, the model has to look at the distribution of all breeds of dogs, cats and birds to guess which species it belongs to before making a prediction. In another example, if the tasks are predicting the breed of adult dogs, puppies and older dogs, but the labels are shared, the model only needs to look at the distribution of these shared labels, which is much easier. The NN case is the hardest because the model has to predict the task identity during both training and inference time. New tasks may be added during training when necessary, and how many tasks in total are unknown. For the NN case, only shared labels are considered, since the output domain is unknown if the labels are different for each task, which is unrealistic. The NNu scenario is invalid because unseen labels signal the presence of a new task, making the scenario actually GNu.
Photosynthetic Responses of Two Woody Halophyte Species to Saline Groundwater Irrigation in the Taklimakan Desert

The study of plant photosynthesis under different degrees of drought stress can provide a deeper understanding of the mechanism of plant drought resistance. In the Taklimakan Desert, saline groundwater is the only local water source with regard to shelterbelt construction and determines plant growth and photosynthetic changes. In this study, daily dynamics of gas exchange parameters and their responses to photosynthetic photon flux density at three irrigation levels (W1 = 17.5, W2 = 25, W3 = 35 mm) were measured for two main species, i.e., Calligonum mongolicum (C) and Haloxylon ammodendron (H). H was better adapted to drought stress than C. Net photosynthetic rate (PN) was mainly related to soil water status in the main root system activity layer. In July, the daily variations of PN and transpiration (Tr) for C were higher than H. C increased water use efficiency (WUE) with increases in PN, while H decreased Tr to obtain a higher WUE. Either C or H, drought reduced the low light and metabolic capacity, and thus decreased the light adaptability and photosynthesis potential. We suggest a prerequisite understanding of physiological mechanisms and possible plant morphological adjustments required to adapt plant species to desert drought conditions.
Effect of Different Carbon Supports on the Activity of PtNi Bimetallic Catalysts toward the Oxygen Reduction

: To evaluate supports’ effects on catalytic activity toward the oxygen reduction reaction (ORR), a simple and controlled chemical synthesis, involving the hot injection of metal precursors, was developed to produce bimetallic PtNi nanoparticles (75 wt.% Pt and 25 wt.% Ni), supported on carbon nanotubes (CNTs) and carbon nanoﬁbers (CNFs). The synthesized electrocatalyst was characterized using X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDS), and scanning transmission electron microscopy (STEM). To determine the catalytic activity, an electrochemical evaluation of the synthesized catalysts in an acidic medium was performed using cyclic voltammetry (CV), CO stripping, and rotating disk electrode (RDE) tests. The presence of Pt and Ni in the nanoparticles was conﬁrmed by EDS and XRD. Based on the STEM micrographs, the average particle size was 30 nm. Compared to the commercial Pt/C catalyst, the PtNi/CNT catalyst exhibited higher speciﬁc activity and slightly lower mass activity toward ORR in a 0.1 M HClO 4 electrolyte solution. the strong 25 ◦ in an O 2 0.1 M HClO 4 sigmoidal polarization curves and for obtained at in a potential range of vs. RHE at mV s − 1 . Three zones are distinguished for the tested materials: (1) the kinetic control (1.05–0.99 V vs. RHE), (2) the mixed kinetic-diffusion (0.99–0.65 V vs. RHE), and (3) mass transport (0.65–0.20 V vs. RHE). The PtNi/CNT, PtNi/CNF, and Pt/C catalysts had half-wave potentials (E 1/2 ) of 0.89, 0.87, and 0.89 V vs. RHE, respectively. These results agree with those reported in the literature, and they indicate that our synthesized PtNi/CNT material could be used as a cathodic catalyst in PEMFCs. An increase in the limiting current density of the PtNi/CNT and PtNi/CNF catalysts was observed at high overpotentials between 0.65 and 0.20 V vs. RHE. The increase in the limiting current of the synthesized catalyst could be due to an increase in oxygen diffusion across the surface of the ink-type working electrode. These results agree with those reported in the literature, and they indicate that our synthesized PtNi/CNT material could be used as a cathodic catalyst in PEMFCs. An increase in the limiting current density of the PtNi/CNT and PtNi/CNF catalysts was observed at high overpotentials between 0.65
X-ray Photon Fluctuation Spectroscopy Analysis using Convolutional Neural Networks

In this contribution, we apply Convolutional Neural Networks (CNNs) to study X-ray Photon Fluctuation Spectroscopy (XPFS) data. We use a DnCNN based architecture for background noise removal and show that it significantly outperforms the standard experimental baseline of manual thresholding on simulation data and appears to be qualitatively better on experimental data. We also find that this relative improvement increases with decreasing SNR. Furthermore, we show that the capacity to better denoise the background leads to a better estimate of the unknown photon ADU for the experiment. Finally, we also propose and develop a U-net with a novel photon conserving loss term which, according to preliminary results, appears outperform a standard U-net for the photon assignment task
Low-light Images Haze Removal

—This paper proposes a new post-processing pipeline focusing on the haze removal of images captured under low-light condition. Most of the existing haze removal methods are either developed on statistical observation of the daytime hazy images or trained on daytime hazy image datasets, and therefore perform poorly on the low-light hazy images. We introduce an end-to-end system to perform both the low-light image enhancement and haze removal using the input image to obtain a visually brighter and clearer image. This dehazing pipeline can also serve as a pre-processing for object detection and classification algorithms in advanced driver assistant systems and surveillance systems.
Structure Analysis of Deep Image Prior

—While deep convolutional neural network’s power of solving inverse image problem is mostly imputed to the ability to ﬁnd patterns within large datasets, the Deep Image Prior paper [1] introduces a fresh new idea. They simply ”train” a randomly initialized network on a corrupted image before overﬁtting and get satisfactory restoration results. In this project I analyze the impact of network structure on the output by modifying some components in the original network and ﬁnally build a new model with slightly better performance
Comparison of Rectenna Array Configurations for Enhanced Efficiency

A rectenna is a rectifying antenna, that is used for converting microwave energy to DC. This can be used for the transmission of electric power to a long distance, without physical cabling, i.e., by using microwaves (2). DC power generated by multiple rectennas can be used for reliable device operation. Two recent methods adopted for rectenna array configuration are RF Combiner and DC Combiner5. In this proposal we are planning to make more detailed investigations on some novel methods for improving the efficiency of rectenna in various coupling combinations so that we can attain a more reliable RF power harvesting method.
Application of data mining technology in fields of geological information system

Geological information is of spatial and multi-purpose service objects. The mining technology and its model as well as its familiar types are illustrated in the text.The necessity and feasibility of data mining technology in the fields of geological information system are focused . The author concludes the application prospect of data mining technology in the fields of geological information system.
HDR image reconstruction from a single short-exposure image

—Cameras on mobile devices, such as cell phones, have small sensor pixels which limit the number of photos capture, leading to limited dynamic range images. Conventionally, in order to create high dynamic range (HDR) images, a set of images with different exposures is captured and then combined in post-processing. This approach faces a challenge in image fusion where different parts of the fused image represent the scene at different times. Another challenge faced by mobile devices is power-constraint. Capturing a burst of images results in cleaner and less noisy images but it consumes power, time and memory space. In this project, we propose a new approach to reconstruct a HDR image from a single low exposure image using a deep neural network. We will see and compare the results from the networks trained using different image patch sizes. We will also discuss what we have learned from this project and the potential improvement to the algorithm.
Late Pleistocene Glacial History of Whidbey Island, WA

Introduction During the last glaciation, the margin of the Cordilleran Ice Sheet (CIS) expanded from the Coast Mountains and Fraser Lowland of southwestern British Columbia into northwestern Washington. As the CIS reached the Olympic Mountains, it divided into two lobes, one (Juan de Fuca Lobe) flowed west through the Strait of Juan de Fuca out onto the now-submerged continental shelf, while the second (Puget Lobe) flowed south into the Puget Lowland between the Olympic Mountains and the Cascade Range (Fig. 1).
C-type Lectin

: 25 Aspergillus fumigatus is a ubiquitous environmental mold that causes significant mortality 26 particularly amongst immunocompromised patients. The detection of the Aspergillus- derived 27 carbohydrate galactomannan in patient sera and bronchoalveolar lavage fluid is the major 28 biomarker used to detect A. fumigatus infection in clinical medicine. Despite the clinical relevance 29 of this carbohydrate, we lack a fundamental understanding of how galactomannan is recognized 30 by the innate immune system. Galactomannan is composed of a linear mannan backbone with 31 galactofuranose sidechains and is found both attached to the cell surface of Aspergillus and as a 32 soluble carbohydrate in the extracellular milieu. In this study, we utilized fungal-like particles 33 composed of purified Aspergillus galactomannan to identify a C-type lectin host receptor 34 responsible for binding this fungal carbohydrate. We identified a novel interaction between 35 Aspergillus galactomannan and the C-type lectin receptor Dectin-2. We demonstrate that 36 galactomannan bound to Dectin-2 and induced Dectin-2 dependent signaling including activation 37 of spleen tyrosine kinase and potent TNFa production. Deficiency of Dectin-2 increased neutrophil 38 recruitment to the lungs but was dispensable for survival in a mouse model of pulmonary 39 aspergillosis. Our results identify a novel interaction between galactomannan and Dectin-2 and 40 demonstrate that Dectin-2 is a receptor for Aspergillus galactomannan which leads to a pro- 41 inflammatory response. 42 45 46 47 lectin receptor Dectin-2. demonstrate galactomannan induces Dectin-2 proinflammatory signaling in macrophages. mannan a-1,2-linked mannotetraose b-1,5-linked lifecycle. b-1,3- morphology kinetics cells (PBMCs) soluble galactomannan LPS-induced cytokine an galactomannan Th2/Th17 immunity novel important role for Dectin-2 in innate immune recognition of Aspergillus galactomannan. A recent case a found a in Dectin-2 that led to decreased responsiveness to Aspergillus Previous also demonstrated a role for Dectin-2 in mediating plasmacytoid dendritic (pDC) A.
Fractional Josephson effect versus fractional charge in superconducting–normal metal hybrid circuits

Fractionally charged excitations play a central role in condensed matter physics, and can be probed in diﬀerent ways. If transport occurs via dissipation-less supercurrents, they manifest as a fractional Josephson eﬀect, whereas in dissipative transport they can be revealed by the transport statistics. However, in a regime where supercurrents and lossy currents coincide, a full understanding of the relationship between these two transport phenomena is still missing. Moreover, especially for superconducting circuits, the question of how noninteger quasicharges can be reconciled with charge quantization is still not fully resolved, and plays an important role for the circuit dynamics. Here, we aim to unify the above concepts by studying the system-detector dynamics in terms of a Lindbladian capturing both coherent and dissipative transport. Charge quantization is here a conserved property of the detector basis of the Lindbladian, while charge fractionalization is a topological property of its complex-valued eigenspectrum. We show that already conventional superconductor-normal metal hybrid circuits exhibit a variety of topological phases, including an open quantum system version of a fractional Josephson eﬀect. Surprisingly, quasiparticles, usually considered a detrimental side eﬀect, are here a necessary ingredient to observe nontrivial transport behaviour. ( work.
Semi‐MoreGAN: Semi‐supervised Generative Adversarial Network for Mixture of Rain Removal

Real‐world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMI‐supervised Mixture Of rain REmoval Generative Adversarial Network (Semi‐MoreGAN). Unlike most of existing methods, Semi‐MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation; and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real‐world rainy and clean images to bridge the gap between synthetic and real‐world rain. Extensive experiments show clear improvements of our approach over twenty representative state‐of‐the‐arts on both synthetic and real‐world rainy images. Source code is available at https://github.com/syy-whu/Semi-MoreGAN.
A Soft-Bodied Aerial Robot for Collision Resilience and Contact-Reactive Perching

Current aerial robots demonstrate limited interaction capabilities in unstructured environments when compared with their biological counterparts. Some examples include their inability to tolerate collisions and to successfully land or perch on objects of unknown shapes, sizes, and texture. Efforts to include compliance have introduced designs that incorporate external mechanical impact protection at the cost of reduced agility and flight time due to the added weight. In this work, we propose and develop a lightweight, inflatable, soft-bodied aerial robot (SoBAR) that can pneumatically vary its body stiffness to achieve intrinsic collision resilience. Unlike the conventional rigid aerial robots, SoBAR successfully demonstrates its ability to repeatedly endure and recover from collisions in various directions, not only limited to in-plane ones. Furthermore, we exploit its capabilities to demonstrate perching where the three-dimensional collision resilience helps in improving the perching success rates. We also augment SoBAR with a novel hybrid fabric-based bistable (HFB) grasper that can utilize impact energies to perform contact-reactive grasping through rapid shape conforming abilities. We exhaustively study and offer insights into the collision resilience, impact absorption, and manipulation capabilities of SoBAR with the HFB grasper. Finally, we compare the performance of conventional aerial robots with the SoBAR through collision characterizations, grasping identifications, and experimental validations of collision resilience and perching in various scenarios and on differently shaped objects.
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets

This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures’ learning limitations. However, in reality, the orders are discrete. For example, the orders have minimum order price unit, or order types. Thus, we change the generation method to place the generated fake orders into discrete spaces in this study. Because this change disabled the ordinary GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. In our experiments, we processed significant volumes of order data over half a year and evaluated our model. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN’s learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed.
Post-Training Dialogue Summarization using Pseudo-Paraphrasing

Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models. These features either require additional knowledge to recognize or make the resulting models harder to tune. To bridge the format gap between dialogues and narrative summaries in dialogue summarization tasks, we propose to post-train pretrained language models (PLMs) to rephrase from dialogue to narratives. After that, the model is fine-tuned for dialogue summarization as usual. Comprehensive experiments show that our approach significantly improves vanilla PLMs on dialogue summarization and outperforms other SOTA models by the summary quality and implementation costs.