Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Language Models for Argumentative Reasoning

Large transformer-based causal language models are capable of strong performance on many natural language processing tasks. Here, we systematically evaluate the performance of the 2.7 billion parameter GPT Neo pre-trained language model on 6 argumentative reasoning tasks under 5 different optimization strategies, including prompt programming, soft prompts, and parameter tuning. We report both intrinsic evaluation metrics (perplexity), and extrinsic measures of the coherence of model outputs, as judged by an expert human rater. With a few exceptions, the rate at which models produced coherent responses ranged from 15-50%. In contrast, human performance (users of the Kialo argument mapping platform) ranged from 65-82% coherent, depending on the task. These results suggest that larger, suitably optimized language models may be capable of supporting authors and auditors of natural language argument maps in human-in-the-loop settings. We share our finetuned models and code.
Fine-Tuning BERT Models to Extract Named Entities from Archival Finding Aids

In recent works, several NER models were developed to extract named entities from Portuguese Archival Finding Aids. In this paper, we are complementing the work already done by presenting a different NER model with a new architecture, Bidirectional Encoding Representation from Transformers (BERT). In order to do so, we used a BERT model that was pre-trained in Portuguese vocabulary and fine-tuned it to our concrete classification problem, NER. In the end, we compared the results obtained with previous architectures. In addition to this model we also developed an annotation tool that uses ML models to speed up the corpora annotation process.
CHEMISTRY IN A FORMING PROTOPLANETARY DISK: MAIN ACCRETION PHASE

We investigate the chemistry in a radiation-hydrodynamics model of a star-forming core that evolves from a cold (∼10 K) prestellar core to the main accretion phase in ∼105 years. A rotationally supported gravitationally unstable disk is formed around a protostar. We extract the temporal variation of physical parameters in ∼1.5 × 103 SPH particles that end up in the disk, and perform post-processing calculations of the gas-grain chemistry adopting a three-phase model. Inside the disk, the SPH particles migrate both inward and outward. Since a significant fraction of volatiles such as CO can be trapped in the water-dominant ice in the three-phase model, the ice mantle composition depends not only on the current position in the disk, but also on whether the dust grain has ever experienced higher temperatures than the water sublimation temperature. Stable molecules such as H2O, CH4, NH3, and CH3OH are already abundant at the onset of gravitational collapse and are simply sublimated as the fluid parcels migrate inside the water snow line. On the other hand, various molecules such as carbon chains and complex organic molecules (COMs) are formed in the disk. The COMs abundance sensitively depends on the outcomes of photodissociation and diffusion rates of photofragments in bulk ice mantle. As for S-bearing species, H2S ice is abundant in the collapse phase. In the warm regions in the disk, H2S is sublimated to be destroyed, while SO, H2CS, OCS, and SO2 become abundant.
A report of Bilharziella polonica cercariae in Knowsley Safari, Prescot, United Kingdom, with notes on other trematodes implicated in human cercarial dermatitis

Abstract As part of surveillance of snail-borne trematodiasis in Knowsley Safari (KS), Prescot, United Kingdom, a collection was made in July 2021 of various planorbid (n = 173) and lymnaeid (n = 218) snails. These were taken from 15 purposely selected freshwater habitats. In the laboratory emergent trematode cercariae, often from single snails, were identified by morphology with a sub-set, of those most accessible, later characterized by cytochrome oxidase subunit 1 (cox1) DNA barcoding. Two schistosomatid cercariae were of special note in the context of human cercarial dermatitis (HCD), Bilharziella polonica emergent from Planorbarius corneus and Trichobilharzia spp. emergent from Ampullacaena balthica. The former schistosomatid was last reported in the United Kingdom over 50 years ago. From cox1 analyses, the latter likely consisted of two taxa, Trichobilharzia anseri, a first report in the United Kingdom, and a hitherto unnamed genetic lineage having some affiliation with Trichobilharzia longicauda. The chronobiology of emergent cercariae from P. corneus was assessed, with the vertical swimming rate of B. polonica measured. We provide a brief risk appraisal of HCD for public activities typically undertaken within KS educational and recreational programmes.
Outcasting the Aggressor: The Deployment of the Sanction of “Non-participation”

Abstract This Essay explores the sanction of “non-participation,” which has been used against Russia following the start of the war in Ukraine. After mapping out the multifaceted instances of Russia's exclusion from international organizations, the analysis considers the legality of measures adopted that do not have an explicit basis in institutional rules. The Essay concludes with broad reflections on the use of international organizations as platforms to stigmatize and isolate the violator and outlines some consequences and functions that the sanction of “non-participation” has today.
Improved TransUNet retinal vessel segmentation method

Observing the fundus vessels in a noninvasive manner can play an auxiliary role in the clinical diagnosis of a variety of diseases. It is inevitable that misdiagnosis will occur by means of manual analysis. Aiming at the problems that the accuracy of computer-aided segmentation of fundus vessels is not high and the use of convolution layer sampling will cause the loss of global context information, an improved fundus vessel segmentation network based on TransUNet is proposed. Firstly, the transformer layer of TransUNet can effectively obtain the connection of global context information. Secondly, the convolution unit sampled under TransUNet is replaced by residual space attention block (RSAB) to improve the attention to the vascular region and prevent the information from disappearing and over fitting in the process of passing through the network. Finally, mini-UNet and TransUNet are cascaded to make the model correct the segmentation results and improve the accuracy of vascular segmentation. The experimental results show that the improved algorithm improves the accuracy of drive dataset by 0.23% compared with UNet network. Each evaluation index is better than most of the compared segmentation models.
SCERL: A Benchmark for intersecting language and Safe Reinforcement Learning

The issue of safety and robustness is a critical focus for AI research. Two lines 1 of research are so far distinct, namely (i) safe reinforcement learning , where an 2 agent needs to interact with the world under safety constraints, and (ii) textual 3 reinforcement learning , where agents need to perform robust reasoning and model- 4 ing of the state of the environment. In this paper, we propose Safety-Constrained 5 Environments for Reinforcement Learning (SCERL), a benchmark to bridge the 6 gap between these two research directions. The contribution of this benchmark is 7 safety-relevant environments with i) a sample set of 20 games built on new logical 8 rules to represent physical safety issues; ii) added monitoring of safety violations 9 and iii) a mechanism to further generate a more diverse set of games with safety 10 constraints and their corresponding metrics of safety types and difﬁculties. This 11 paper shows selected baseline results on the benchmark. Our aim is for the SCERL 12 benchmark and its ﬂexible framework to provide a set of tasks to demonstrate 13 language-based safety challenges to inspire the research community to further 14 explore safety applications in a text-based domain. 15
Automatic graph generation method used in intelligent tutoring system

In the intelligent tutoring system, it becomes unrealistic to manually draw the corresponding auxiliary graphics for various plane geometry problems proposed by the system for learners, which is not only time-consuming and laborintensive but also inefficient. Therefore, We design and develop a schematic diagram automatic generation module based on semantic web for the intelligent tutoring system. Firstly, we analyze geometry learning problems proposed by the tutoring system, and develop an interface which is able to converte geometric semantic network to geometric graph command. The geometry command then calls the geometry drawing method library to draw the relevant geometry. This module helps the intelligent tutoring system to realize the function of dynamically generating geometric figures related to the current learning problem when system proposes geometric learning problems for learners, Thus it can make learners learn and understand geometry knowledge effectively.
Interpretability study of pretrained models via transfer learning on ImageNet for lung cancer prediction

Transfer learning is an effective strategy to obtain models for lung cancer analysis due to the limited number of labeled datasets. But few studies have made an interpretable explanation that why networks pretrained on ImageNet without medical images can be transferred to identify lung nodule images. In this paper, three convolutional neural networks with different depths, VGG16, MobileNet, and ResNet50, are selected for experiments to compare the performance and training difficulty between models with and without transfer learning, and to study the impact of transfer learning on networks of different depths. All the models are retrained on the same set of lung CT images with the same parameters, with and without the initial weights pretrained on the ImageNet dataset. The weights of the pretrained model are frozen, and pooling layers and classification output layers are created and concatenated to get the models required for experiments. The experimental results indicate that the initial network weights and feature extraction capabilities derived from ImageNet are applicable after being transferred to the lung nodule image set, making retraining easier and significantly improving prediction performance. In addition, transfer learning has a better effect on models with deeper networks, which contributes to alleviating model overfitting and poor generalization.
A new stroke prediction model combined algorithm based on artificial neural networks and logistic regression

Stroke is an important factor contributed to cause of death worldwide. It is particularly important to build a model to accurately predict the incidence of stroke. Traditional models typically select only one common binary classification algorithm for prediction. This paper proposes a method that uses artificial neural networks (ANNs) algorithm to complete feature extraction and learning of the data, and then selects a suitable binary classification algorithm and combines them to build the model. Data preprocessing as well as data visualization analysis was first performed on a stroke dataset using Python. The models were then trained separately using different algorithms, among which logistic regression performed the best. Finally, a combined algorithm using it and artificial neural networks was used to construct the prediction model. The AUC values were employed in this case to evaluate the performance of the model and the experimental results indicated that the AUC values of the new model were approximately 0.39% higher than those of the logistic regression-only model. This indicates that the new model outperforms the traditional prediction model to some extent.
Analysis of prostate cancer using serum surface-enhanced Raman spectroscopy and multivariate statistical algorithm

Prostate cancer (PCa) is an epithelial malignant tumor occurring in the prostate gland and is the most common malignancy of the male genitourinary system. Prostate cancer is often asymptomatic in its early stage, and the best treatment time is usually missed when it is found. Therefore, early diagnosis and treatment is the key to reduce the mortality of prostate cancer patients. In this work, we developed a method to identify patients with prostate cancer and healthy volunteers. We collected serum samples from patients with prostate cancer and healthy volunteers, detected the SERS spectra of these serum samples, and analyzed the SERS spectrum of the obtained serum samples with a preliminary spectral peak assignment. Then principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose the SERS spectra of serum from patients with prostate cancer and healthy volunteers. Difference spectrum analysis showed that there are obvious differences in several characteristic peaks between the serum of patients with prostate cancer and the serum of healthy volunteers, which may be related to the special changes of nucleic acids, proteins, lipids and other biological molecules in the process of carcinogenesis. Using principal component analysis (PCA) combined with linear discriminant analysis (LDA) multivariate statistical method, the diagnostic sensitivity and specificity were 85% and 95%, respectively. The receiver operating characteristic (ROC) curve further proves the effectiveness of diagnosis algorithm based on PCA-LDA technology, and the area under the curve (AUC) is 0.998. These results show that the combination of SERS technology and PCA-LDA algorithm has great potential in screening prostate cancer patients.
Deep learning-based research on a long-term time-series prediction algorithm for urban air quality

To overcome the limitations of deep learning models that can only capture single temporal feature: First. Concentrating solely on the abstract time series features discovered over the entire dataset, rather than the local periodic segments that contribute more to prediction accuracy; Second, only the time-series variation pattern is fitted, while other non-time series periodic patterns in the PM2.5 data set are omitted, making it impossible to increase the forecast accuracy further. A Bi-directional long and short-term memory-convolutional neural network model including the Attention Mechanism is proposed as BiLSTM-Attention-CNN. The temporal characteristics acquired by BiLSTM filtered by attention mechanism are fused with the cross-periodic features retrieved by 1DCNN to generate an ordered complementary but non-interfering feature. The final experiments revealed that, when compared to other mainstream models, the model reduces error and improves prediction accuracy, and performs well in the long-period time series prediction problem.
The Luminous X-Ray Halos of Two Compact Elliptical Galaxies

There is mounting evidence that compact elliptical galaxies (CEGs) are local analogs of the high-redshift “red nuggets” that are thought to represent progenitors of today’s early-type galaxies (ETGs). We report the discovery of extended X-ray emission from a hot interstellar/intragroup medium in two CEGs, Mrk 1216 and PGC 032873, using shallow Chandra observations. We find that PGC 032873 has an average gas temperature of kBT = 0.67 ± 0.06 keV within a radius of 15 kpc and a luminosity Lx = (1.8 ± 0.2) × 1041 erg s−1 within a radius of 100 kpc. For Mrk 1216, which is closer and more luminous (Lx(<100 kpc) = (12.1 ± 1.9) × 1041 erg s−1), we used an entropy-based hydrostatic equilibrium (HE) procedure and obtained a good constraint on the H-band stellar mass-to-light ratio, Mstars/LH = 1.33 ± 0.21 solar, that is in good agreement with stellar dynamical (SD) studies, which supports the HE approximation. We obtain a density slope of 2.22 ± 0.08 within Re that is consistent with other CEGs and normal local ETGs, while the dark matter fraction within Re, fDM = 0.20 ± 0.07 is similar to local ETGs. We constrain the supermasssive black hole mass, MBH = (5 ± 4) × 109 M⊙, with MBH > 1.4 × 1010 M⊙ (90% confidence), which is consistent with a recent SD measurement. We obtain a halo concentration (c200 = 17.5 ± 6.7) and mass (M200 = (9.6 ± 3.7) × 1012 M⊙), where c200 exceeds the mean ΛCDM value (≈7), which is consistent with a system that formed earlier than the general halo population. We suggest that these galaxies should be classified as fossil groups.
A lightweight and efficient CNN based on VGG-16 variant for emotion recognition

Emotion recognition plays an important role in medicine, criminal investigation, human-computer interaction and other fields. Emotion recognition through machine learning has become a promising research direction. VGG network is a kind of classic convolutional neural networks (CNN) which can be used for emotion recognition, and VGG-16 is one of the classic architectures of VGG. However, VGG-16 has too many parameters and it seems that VGG-16 doesn't perform well on FER2013 because of overfitting. To solve these problems, this paper proposes a CNN network based on VGG-16 variant called VGG-LIGHT. This network inherits characters of small kernel size and similar architectures between different convolutional layers of VGG. Compared with traditional VGG architecture, VGG-LIGHT has fewer parameters. Compared with other lightweight network, VGG-LIGHT is more efficient and needs less training time. The paper also built a medical data set which is composed by faces of different patients and verified the proposed network by comparing its results with Deepface, a famous framework for face analysis. The experimental results show that the recognition results of VGG-LIGHT are in good agreement with Deepface.
Visible and infrared images fusion target tracking based on deep learning, progress, and prospects

Due to the complementarity of visible and infrared images in target tracking, the target tracking of the fusion of the two has more advantages than traditional target tracking methods in performance and robustness. Recently, the development of artificial intelligence has promoted the progress of visible and infrared images fusion target tracking technology. This paper summarizes the infrared and visible images fusion algorithms in target tracking, introduces the research progress of image fusion technology in detail, and shows the research results of fusion algorithms. Based on the development history of this field, this paper summarizes the classic methods and emerging technologies in recent years. What’s more, a network is designed based on the transformer network structure combined with the feature fusion module. The experimental results show that the comprehensive performance of the transformer-based network is improved by nearly 1%. It proves that the transformer-based network has great application potential in the target tracking network of visible and infrared images fusion.
Data augmentation method in image retrieval of digital equipment

Digital equipment image retrieval is a typical fine-grained problem, In order to solve the fine-grained problem, necessary data enhancement methods will be essential. Propose a data enhancement method of deep learning in digital device image retrieval. Using the data preprocessing method of artificial optimal data enhancement strategy, the enhanced data set is generated through image enhancement(ChannelShuffle, ColorJitter), geometric transformation, noise interference, and the feature distortion in the process of data augmentation is improved. It can not only effectively solve the problem of large amount of manual labeling samples in deep learning, complete data expansion, but also help to improve the generalization ability of the retrieval model. The experimental results show that the CNN model trained by data augmentation method improves the mAP accuracy of digital equipment image retrieval by an average of 10.4.
Photorealistic image style transfer based on attention mechanism and histogram loss

At present, the image stylization method is commonly based on the artistic style transfer method, so the generated results usually have the following problems. Comparing the result of the generated image with the content image, there are obvious content distortion and deformation, accompanied by loss of details, distorted light-dark relationship, and unbalanced pixel distribution. In solve above problems, this paper suggests a photorealistic image style based on attention mechanism and histogram loss. The method based on the channel attention mechanism proposed in this paper can preserve the semantic information of content image fully. At the same time, the proposed histogram loss module can better measure the high-level perceptual and semantic differences among images and optimize the high-dimensional feature distribution of images. In addition, this paper also proposes an evaluation method based on the color gamut space histogram to analyze the image generation results more effectively. Experimental results show that, compared with other mainstream methods, the proposed network model can achieve more realistic style transfer while maintaining the details of image content.
A comprehensive evaluation system for pixel level image fusion

Establishing an accurate and effective quality evaluation system is very important in the practical application and development of image fusion technology in various fields. In the field of image fusion, there is no systematic evaluation system. In order to solve these problems, this paper uses a number of different objective evaluation indicators to design a set of fused image comprehensive evaluation system. The quality of fused results can not only comprehensively evaluate the advantages of the algorithm but also be used as an information feedback mechanism to optimize and improve the image fused algorithm. Through the evaluation and analysis of six image fusion methods, the rationality of the evaluation system is verified.
Infrared ship detection based on latent low-rank decomposition and salient feature fusion

To improve the accuracy of infrared ship detection and more effective background suppression, we propose an infrared ship detection algorithm based on latent low-rank decomposition and salient feature fusion. First, to avoid the influence of the number of superpixel segmentation on the integrity of the GR saliency detection, multiple salient maps with different numbers of superpixels are taken large, and a single foreground coarse salient map of the ship target is obtained using a high-level central prior and graph regularization. Secondly, to suppress the noise influence and refine the foreground coarse saliency map, sparse salient features of the ship target image are extracted using a method based on latent low-rank decomposition. Then, the sparse and null salient features are fused with the feature values. Finally, the accurate ship target is segmented based on the OTSU algorithm. The subjective comparison experiments and quantitative analysis show that the infrared ship segmentation algorithm proposed in this paper has better performance in terms of accuracy and visual effect.
Convolutional neural networks with constrained shortcut connections

This paper proposes a novel network architecture to integrate features in the shallow and the deep layers. The architecture is composed of three components, including the shortcut connection, global representation operator, and regularizer. The shortcut connection alleviates the vanishing gradient problem by feeding features from the hidden layer to the classifier in the last layer. The global representation operator aims to reduce the features’ dimension to mitigate the over-fitting problem. The regularizer is designed to relieve the degradation of network depth which might be caused by the shortcut connection. The results on Optical Coherence tomography (OCT) show that the proposed architecture is an effective regularization method that can mitigate the over-fitting problem.