Miracle-dz/UniFAR-FTUs huggingface dataset:
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining

Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold and distribution. However, training samples are often distributed in a non-uniform fashion on the manifold, due to costs or convenience of collection. For example, the CelebA dataset contains a large fraction of smiling faces. These inconsistencies will be reproduced when sampling from the trained DGN, which is not always preferred, e.g., for fairness or data augmentation. In response, we develop MaGNET, a novel and theoretically motivated latent space sampler for any pre-trained DGN, that produces samples uniformly distributed on the learned manifold. We perform a range of experiments on various datasets and DGNs, e.g., for the state-of-the-art StyleGAN2 trained on FFHQ dataset, uniform sampling via MaGNET increases distribution precision and recall by 4.1\% \&3.0\% and decreases gender bias by 41.2\%, without requiring labels or retraining. As uniform distribution does not imply uniform semantic distribution, we also explore separately how semantic attributes of generated samples vary under MaGNET sampling.
Advances and Challenges in Deep Lip Reading

Driven by deep learning techniques and large-scale datasets, recent years have witnessed a paradigm shift in automatic lip reading. While the main thrust of Visual Speech Recognition (VSR) was improving accuracy of Audio Speech Recognition systems, other potential applications, such as biometric identification, and the promised gains of VSR systems, have motivated extensive efforts on developing the lip reading technology. This paper provides a comprehensive survey of the state-of-the-art deep learning based VSR research with a focus on data challenges, task-specific complications, and the corresponding solutions. Advancements in these directions will expedite the transformation of silent speech interface from theory to practice. We also discuss the main modules of a VSR pipeline and the influential datasets. Finally, we introduce some typical VSR application concerns and impediments to real-world scenarios as well as future research directions.
Modeling Proficiency with Implicit User Representations

We introduce the problem of proficiency modeling: Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency. This enables the filtering and ranking of social media posts on a given topic as per user proficiency. Unlike experts on a given topic, proficient users may not have received formal training and possess years of practical experience, but may be autodidacts, hobbyists, and people with sustained interest, enabling them to make genuine and original contributions to discourse. While predicting whether a user is an expert on a given topic imposes strong constraints on who is a true positive, proficiency modeling implies a graded scoring, relaxing these constraints. Put another way, many active social media users can be assumed to possess, or eventually acquire, some level of proficiency on topics relevant to their community. We tackle proficiency modeling in an unsupervised manner by utilizing user embeddings to model engagement with a given topic, as indicated by a user's preference for authoring related content. We investigate five alternative approaches to model proficiency, ranging from basic ones to an advanced, tailored user modeling approach, applied within two real-world benchmarks for evaluation.
A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection

Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, providing information that can be interpreted clinically to support the prediction. A subset of the kaggle EyePACS and the Messidor-2 datasets, labeled with ocular lesions, is made publicly available. The kaggle EyePACS subset is used as training set and the Messidor-2 as a test set for lesions and DR classification models. For DR diagnosis, our model has an area-under-the-curve, sensitivity, and specificity of 0.948, 0.886, and 0.875, respectively, which competes with state-of-the-art approaches.
Dimerization tendencies of the pyrochlore Heisenberg antiferromagnet: A functional renormalization group perspective

We investigate the ground state properties of the spin-$1/2$ pyrochlore Heisenberg antiferromagnet using pseudofermion functional renormalization group techniques. The first part of our analysis is based on an enhanced parton mean-field approach which takes into account fluctuation effects from renormalized vertex functions. Our implementation of this technique extends earlier approaches and resolves technical difficulties associated with a diagrammatic overcounting. Using various parton ans\"atze for quantum spin liquids, dimerized and nematic states our results indicate a tendency for lattice symmetry breaking in the ground state. While overall quantum spin liquids seem unfavorable in this system, the recently proposed monopole state still shows the strongest support among all spin liquid ans\"atze that we have tested, which is further confirmed by our complementary variational Monte Carlo calculations. In the second part of our investigation, we probe lattice symmetry breaking more directly by applying the pseudofermion functional renormalization group to perturbed systems. Our results from this technique confirm that the system's ground state either exhibits broken $C_3$ rotation symmetry, or a combination of inversion and $C_3$ symmetry breaking.
Receptive Field Broadening and Boosting for Salient Object Detection

Salient object detection requires a comprehensive and scalable receptive field to locate the visually significant objects in the image. Recently, the emergence of visual transformers and multi-branch modules has significantly enhanced the ability of neural networks to perceive objects at different scales. However, compared to the traditional backbone, the calculation process of transformers is time-consuming. Moreover, different branches of the multi-branch modules could cause the same error back propagation in each training iteration, which is not conducive to extracting discriminative features. To solve these problems, we propose a bilateral network based on transformer and CNN to efficiently broaden local details and global semantic information simultaneously. Besides, a Multi-Head Boosting (MHB) strategy is proposed to enhance the specificity of different network branches. By calculating the errors of different prediction heads, each branch can separately pay more attention to the pixels that other branches predict incorrectly. Moreover, Unlike multi-path parallel training, MHB randomly selects one branch each time for gradient back propagation in a boosting way. Additionally, an Attention Feature Fusion Module (AF) is proposed to fuse two types of features according to respective characteristics. Comprehensive experiments on five benchmark datasets demonstrate that the proposed method can achieve a significant performance improvement compared with the state-of-the-art methods.
Dissecting the labdane‐related diterpenoid biosynthetic gene clusters in rice reveals directional cross‐cluster phytotoxicity

Summary Rice (Oryza sativa) is a staple food crop and serves as a model cereal plant. It contains two biosynthetic gene clusters (BGCs) for the production of labdane‐related diterpenoids (LRDs), which serve important roles in combating biotic and abiotic stress. While plant BGCs have been subject to genetic analyses, these analyses have been largely confined to the investigation of single genes. CRISPR/Cas9‐mediated genome editing was used to precisely remove each of these BGCs, as well as simultaneously knock out both BGCs. Deletion of the BGC from chromosome 2 (c2BGC), which is associated with phytocassane biosynthesis, but not that from chromosome 4 (c4BGC), which is associated with momilactone biosynthesis, led to a lesion mimic phenotype. This phenotype is dependent on two closely related genes encoding cytochrome P450 (CYP) mono‐oxygenases, CYP76M7 and CYP76M8, from the c2BGC. However, rather than being redundant, CYP76M7 has been associated with the production of phytocassanes, whereas CYP76M8 is associated with momilactone biosynthesis. Intriguingly, the lesion mimic phenotype is not present in a line with both BGCs deleted. These results reveal directional cross‐cluster phytotoxicity, presumably arising from the accumulation of LRD intermediates dependent on the c4BGC in the absence of CYP76M7 and CYP76M8, further highlighting their interdependent evolution and the selective pressures driving BGC assembly.
Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach

ABSTRACT Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses.
Development and Evaluation of a Novel Investment Decision System in Cryptocurrency Market

ABSTRACT More and more people are entering the cryptocurrency market after Bitcoin (BTC) soared to nearly USD 20,000 in 2017. To promote the development of information technology and cryptocurrency marketing, various computerized systems integrating information technology with investment and financing are innovated continuously. In this study, the daily cryptocurrency prices were input to Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM); and the developing trend plots were drawn to predict and analyze the future cryptocurrency prices through deep learning. Finally, the business practices of cryptocurrency investment were modularized based on High-Level Fuzzy Petri Nets (HLFPNs) to make a better investment decision so that all investors can use this decision system to quickly understand the future cryptocurrency trend. The experimental results have shown that this decision system can provide effective investment information to achieve investors’ personal financial goals with the expectation of improving financial situations.
Hyperspectral image classification with multi-scale graph convolution network

ABSTRACT Hyperspectral images with high intra-class variability and dimensionality, which greatly weakens the performance of supervised classification methods, especially with the limited number of samples. The graph convolution networks (GCNs) have attracted much attention in the field of hyperspectral classification as it is able to formulate robust spectral-spatial features simultaneously. To this end, we propose a multiscale graph convolutional network based on the spectral-spatial features called MSGCN for hyperspectral image classification. The framework allows us to fuse spectral-spatial features within the graph for node and edge feature generation. Based on that, we further aggregate information from multi-order adjacent nodes to obtain multiscale spectral-spatial features, which proves to be beneficial for hyperspectral image classification. To demonstrate the robustness of the proposed method, three widely used hyperspectral data sets were selected, with the overall accuracy of 99.52 , 99.34 and 98.31 , respectively.
Multi-resolution satellite images bathymetry inversion of Bangda Co in the western Tibetan Plateau

ABSTRACT Water depth is important information for lake research. However, it is difficult to obtain the depth distribution of a whole lake. The method of remote-sensing inversion by combining bathymetric data has been proved to be feasible. In order to improve the inversion accuracy, it is very crucial to research the optimal inversion model and the most suitable depth for modelling, and the most appropriate satellite images for the study area. In this study, we used the three kinds of multispectral image data (Sentinel-2, GF-1, and Landsat-8) to establish models by combining bathymetric data in Bangda Co, which is located in the northwestern Tibetan Plateau. We verified the two empirical models (Stumpf model and Lyzenga model) with the three bands of blue, green, and red in 0–30 m water depth. We created a multi-factor combination model (S-L model) to compare with a BP neural network model using three images in five different water depth ranges (0–10, 0–15, 0–20, 0–25, and 0–30 m). The results indicated that the S-L model with mean absolute error (MAE) of 3.09–4.70 m, which the accuracy was slightly better than that of the two empirical models (3.35–5.03 and 3.23–4.94 m). The MAE of the BP model over the five depth ranges was 0.71–2.41 m for Landsat-8, 1.02–3.53 m for GF-1, and 1.15–3.68 m for Sentinel-2, which was higher than the S-L model. For the five ranges of three images by using the BP model, both the accuracy of Sentinel-2 and GF-1 in the range of 0–15 m was higher, but Landsat-8 was the highest at 0–20 m among five ranges; the mean relative errors (MRE) were 19.5%, 18.7%, 13.1%, and the root-mean-square error (RMSE) were 1.82, 1.72, and 1.86 m, respectively. The best applicability of the three images was Landsat-8, followed by GF-1 and Sentinel-2 in this study. The results suggested that the freely available Sentinel-2, GF-1 and Landsat-8 images could be used to estimate water storage for a shallow lake by combining bathymetric data on the Tibetan Plateau.
Bringing society back into our understanding of European cross-border care

We are pleased to discuss our study on the European Health Insurance Card (EHIC) and the redistributive effects of EHIC-related east–west patient and payment flows across regions and social classes. Our critics confirm our key finding: EHIC patient outflows from Eastern European (EE) to Western European (WE) countries result in a much higher relative burden for the budgets of EE states than outflows from WE to EE do for WE countries. Starting from what they see as the true mission of social security coordination, however, they also tell us that we should never have studied the redistributive impact of EHIC patient and payment flows in the first place. In this response, we therefore explicate the differences between our empirical sociological perspective and our critics’ normative legal approach. This is important, especially when social facts contradict normative legal assumptions as in our case. The EU laws that govern EHIC patient and payment flows are indeed based on the free movement provisions of the EU’s internal market project, but our empirical findings show that the promise of ‘economic, social and territorial cohesion, and solidarity among Member States’ contained in Article 3.3 of the Treaty of the European Union is not realized in practice in the case of east–west EHIC payment flows and patient mobility.
The impact of COVID-19 on child welfare-involved families: Implications for parent–child reunification and child welfare professionals

The COVID-19 pandemic has impacted children and parents involved in the child welfare system and the professionals working with these families. Using survey data collected August–September of 2020, this mixed-methods study examined the perspectives of 196 child welfare-involved professionals (77 attorneys, 99 caseworkers, and 20 therapists) in the United States about the impact of COVID-19 on parents of origin, children, foster parents, and child welfare professionals. Particular attention was paid to the implications of COVID-19 and associated challenges for parent–child contact and reunification. With respect to professional stresses, more than half of participants worried about their own personal safety and health amidst COVID-19, and more than three-quarters expressed concerns about the safety and well-being of child welfare-involved families. Participants, especially attorneys, expressed concerns about parent–child contact and disruptions to reunification. In-person parent–child visits had all but ceased during the early part of the pandemic, and participants identified barriers to effective virtual visits, including lack of foster parent oversight, technology issues, and children’s developmental stage and/or lack of engagement. Attorneys were especially critical of the cessation of in-person visits and viewed this as a serious threat to child-parent bonds and reunification. Participants, especially child welfare workers, voiced concerns about children’s mental health and educational outcomes amidst the pandemic. Findings have implications for attorneys, child welfare workers, and other practitioners who directly and indirectly interface with child welfare-involved families.
A key parameter extraction method for scaled RCS measurement of coated targets in the THz band

In the terahertz (THz) band, the scaling relation between scaled models and prototypes with coatings needs to be reconsidered due to the dispersive nature of the coating materials. Besides, it is difficult to obtain the material parameters, such as the permittivity. Based on the comparison of RCS between a metal plate and a coated plate, a key parameter extraction method is proposed. Combining with the scaled method which is based on the dimensional analysis theory, an improved scaled method is proposed for predicting the RCS of a coated prototype. The parameter extraction method can provide the key parameter corresponding to the coated material at certain frequency when the intrinsic parameters are unknown. The validity of the proposed method is proved by simulation. The scope of RCS scaled measurements on coated targets will be expanded in the THz band.
A new method to reduce the false detection rate of ceramic tile surface defects online inspection system

The application of intelligent inspection to the online inspection of ceramic tile surface defects not only ensures the stability of product quality but also reduces the cost of labor for enterprises. Existing detection methods often achieve good results for the detection of defects in known background textures, however, its false detection rate increases dramatically, and its practicality is greatly reduced when there are unknown background textures or defects with similar characteristics to known background textures. This paper proposes a non-defective and defective samples synchronous comparison detection(N-DSCD) algorithm which combines traditional detection with deep convolutional neural network(DCNN). Firstly, a reference image library of non-defective ceramic tiles which contains all random backgrounds of the same batch of tiles is constructed based on DCNN extracting image features in real-time. After that, a reference image with the most similar features to the image to be detected is searched out from the image library relying on the feature comparison method. Next, we feed the image to be detected and the reference image to the defect detector at the same time, the difference in results is used as the basis of judging whether the ceramic tile has defects or not. Tested on a total of 12,000 images of ceramic tiles with 20 different textures found that: the computational time of a single ceramic tile increased by only 120 ms, and the average false detection rate of 20 unknown background texture tiles was reduced from 23.1% to 5.8%. It greatly improves the practicality of the ceramic tile surface defects online inspection system and has wider economic benefits.
A Model‐Based Workflow to Benchmark the Clinical Cholestasis Risk of Drugs

We present a generic workflow combining physiology‐based computational modeling and in vitro data to assess the clinical cholestatic risk of different drugs systematically. Changes in expression levels of genes involved in the enterohepatic circulation of bile acids were obtained from an in vitro assay mimicking 14 days of repeated drug administration for 10 marketed drugs. These changes in gene expression over time were contextualized in a physiology‐based bile acid model of glycochenodeoxycholic acid. The simulated drug‐induced response in bile acid concentrations was then scaled with the applied drug doses to calculate the cholestatic potential for each compound. A ranking of the cholestatic potential correlated very well with the clinical cholestasis risk obtained from medical literature. The proposed workflow allows benchmarking the cholestatic risk of novel drug candidates. We expect the application of our workflow to significantly contribute to the stratification of the cholestatic potential of new drugs and to support animal‐free testing in future drug development.
MageAdd: Real-Time Interaction Simulation for Scene Synthesis

While recent researches on computational 3D scene synthesis have achieved impressive results, automatically synthesized scenes do not guarantee satisfaction of end users. On the other hand, manual scene modelling can always ensure high quality, but requires a cumbersome trial-and-error process. In this paper, we bridge the above gap by presenting a data-driven 3D scene synthesis framework that can intelligently infer objects to the scene by incorporating and simulating user preferences with minimum input. While the cursor is moved and clicked in the scene, our framework automatically selects and transforms suitable objects into scenes in real time. This is based on priors learnt from the dataset for placing different types of objects, and updated according to the current scene context. Through extensive experiments we demonstrate that our framework outperforms the state-of-the-art on result aesthetics, and enables effective and efficient user interactions.
Enabling meaningful use of AI-infused educational technologies for children with blindness: Learnings from the development and piloting of the PeopleLens curriculum

Novel AI-infused educational technologies can give children with blindness the opportunity to explore concepts learned incidentally through vision by using alternative perceptual modalities. However, more effort is needed to support the meaningful use of such technological innovations for evaluations at scale and later wide-spread adoption. This paper presents the development and pilot evaluation of a curriculum to enable educators to support blind learners’ self-exploration of social attention using the PeopleLens technology. We reflect on these learnings to present four design guidelines for creating curricula aimed to enable meaningful use. We then consider how formulations of “success” by our participants can help us think about ways of assessing efficacy in low-incidence disability groups. We conclude by arguing for our community to widen the scope of discourse around assistive technologies from design and engineering to include supporting their meaningful use.