Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space

In this paper we consider the problem of computing spectral approximations to graphs in the single pass dynamic streaming model. We provide a linear sketching based solution that given a stream of edge insertions and deletions to a $n$-node undirected graph, uses $\tilde O(n)$ space, processes each update in $\tilde O(1)$ time, and with high probability recovers a spectral sparsifier in $\tilde O(n)$ time. Prior to our work, state of the art results either used near optimal $\tilde O(n)$ space complexity, but brute-force $\Omega(n^2)$ recovery time [Kapralov et al.'14], or with subquadratic runtime, but polynomially suboptimal space complexity [Ahn et al.'14, Kapralov et al.'19]. Our main technical contribution is a novel method for `bucketing' vertices of the input graph into clusters that allows fast recovery of edges of sufficiently large effective resistance. Our algorithm first buckets vertices of the graph by performing ball-carving using (an approximation to) its effective resistance metric, and then recovers the high effective resistance edges from a sketched version of an electrical flow between vertices in a bucket, taking nearly linear time in the number of vertices overall. This process is performed at different geometric scales to recover a sample of edges with probabilities proportional to effective resistances and obtain an actual sparsifier of the input graph. This work provides both the first efficient $\ell_2$-sparse recovery algorithm for graphs and new primitives for manipulating the effective resistance embedding of a graph, both of which we hope have further applications.
Long Noncoding RNA EZR-AS1 Regulates the Proliferation, Migration, and Apoptosis of Human Venous Endothelial Cells via SMYD3

Numerous studies have shown that long noncoding RNAs (lncRNAs) play essential roles in the development and progression of human cardiovascular diseases. However, whether lncRNA ezrin antisense RNA 1 (EZR-AS1) is associated with the progression of coronary heart disease (CHD) remains unclear. Accordingly, the aim of the present study was to evaluate the role of lncRNA EZR-AS1 in patients with CHD and in human venous endothelial cells (HUVECs). The findings revealed that lncRNA EZR-AS1 was highly expressed in the peripheral blood of patients with CHD. In vitro experiments showed that the overexpression of EZR-AS1 could enhance proliferation, migration, and apoptosis by upregulating the expression of EZR in HUVECs; downregulation of lncRNA EZR-AS1 resulted in the opposite effect. lncRNA EZR-AS1 was also found to regulate SET and MYND domain-containing protein 3 (SMYD3), a histone H3 lysine 4-specific methyltransferase, which subsequently mediated EZR transcription. Collectively, these results demonstrate that lncRNA EZR-AS1 plays an important role in HUVECs function via SMYD3 signaling.
Korean Music Therapy Students’ Experience of Group Music Therapy: A Qualitative Case Study

The purpose of this qualitative case study is to describe the in-depth experiences of Korean students undergoing group music therapy. Seven students participated in eight consecutive weeks of group music therapy. The researcher collected and triangulated three data resources: individual interview transcripts, participant journals, and audiotaped sessions. The data were analyzed using the case study method and peer debriefing was conducted for trustworthiness. The four emergent themes and six categories were as follows: (1) Discovering one’s self (categories: what it means to be “me,” learning about self), (2) Inside group music therapy (categories: various responses to experiencing non-directive group, two different sides of experiencing silence, feeling a sense of safety and acceptance, sharing difficult experience with one’s own family), (3) Musical experience as a way to explore emotions, and (4) What learned through books became real. The results indicated that the student therapists’ experiences were linked to their personal development, unique experience of group music therapy dynamics, musical experience as an emotional container, and clinical development. Finally, it is recommended that cultural adaptations are made by providing both structure and space for Korean students to express themselves.
Phytolacca yunnanensis (Phytolaccaceae), a new species from China with distinctive inflorescence characteristics

Phytolacca yunnanensis is described and illustrated as a new species from Yunnan Province, China. This new species is characterized by the distinctive dense translucent multicellular trichomes along the peduncles, raches, and pedicels of both racemose inflorescences and infructescences. These translucent multicellular trichomes, which were revealed by using stereomicroscope and SEM, represents the first discovery in the genus Phytolacca . P. yunnanensis resembles P. acinosa in sharing the free carpels, but differs by the 4-5-carpellate gynoecia. P. yunnanensis is also similar to P. exiensis by the number of carpels, but can be easily distinguished from the latter in having free carpels.
Automatic building detection from very high-resolution images using multiscale morphological attribute profiles

ABSTRACT Morphological building indexes (MBI) have proven to be effective tools for automated building spatial-feature-extraction tasks in images from urban areas. However, owing to the intrinsic shortcomings of MBI, commission and omission errors occur in regions with spectral properties similar to those of buildings and dark heterogeneous roofs, respectively. Some targets (such as bright bare land or roads) can cause substantial interference, which poses an even greater challenge in performing accurate building detection from images of complex environments. In this study, a new automated building detection approach based on a morphological attribute profile is presented with the goal of reducing commission and omission errors. As the first step, corners are detected in very high-resolution (VHR) remote sensing images through an automatic optimization procedure, and weighted spatial voting is performed to predict the presence of built-up areas. Then, by investigating the properties between the attribute filters and buildings, a novel morphological attribute building index is constructed by considering the extracted built-up area as an input image. To validate the detection performance, the approach was tested using VHR images with 1-m spatial resolution. The quantitative assessment indicates that the proposed approach improves the building detection accuracy in images of complex environments.
Metagenomic Analyses of Microbial and Carbohydrate-Active Enzymes in the Rumen of Holstein Cows Fed Different Forage-to-Concentrate Ratios

The objectives of this study were to investigate the effects of different forage-to-concentrate ratios and sampling times on the genetic diversity of carbohydrate-active enzymes (CAZymes) and the taxonomic profile of rumen microbial communities in dairy cows. Six ruminally cannulated Holstein cows were arbitrarily divided into groups fed high-forage (HF) or low-forage (LF) diets. The results showed that, for glycoside hydrolase (GH) families, there were greater differences based on dietary forage-to-concentrate ratio than sampling time. The HF treatment group at 4 h after feeding (AF4h) had the most microbial diversity. Genes that encode GHs had the highest number of CAZymes, and accounted for 57.33% and 56.48% of all CAZymes in the HF and LF treatments, respectively. The majority of GH family genes encode oligosaccharide-degrading enzymes, and GH2, GH3, and GH43 were synthesized by a variety of different genera. Notably, we found that GH3 was higher in HF than LF diet samples, and mainly produced by Prevotella, Bacteroides, and unclassified reads. Most predicted cellulase enzymes were encoded by GH5 (the BF0h group under HF treatment was highest) and GH95 (the BF0h group under LF treatment was highest), and were primarily derived from Bacteroides, Butyrivibrio, and Fibrobacter. Approximately 67.5% (GH28) and 65.5% (GH53) of the putative hemicellulases in LF and HF treatments, respectively. GH28 under LF treatment was more abundant than under HF treatment, and was mainly produced by Ruminococcus, Prevotella, and Bacteroides. This study revealed that HF-fed cows had increased microbial diversity of CAZyme producers, which encode enzymes that efficiently degrade plant cell wall polysaccharides in the cow rumen.
Content and network analysis of tweets tagged with #aphasia: an emergent community of practice

ABSTRACT Background: A reduced quality of life for people with aphasia is in part associated with their loss of friendships and social networks. Twitter offers people with communication disability a way to exchange information, develop and maintain their social networks and connections, and participate in society online. However, little is known about how Twitter is used to exchange information about aphasia or to build communities online that might support people with aphasia in their communication goals. Aims: To examine the network data and content of tweets tagged with #aphasia to understand more about how Twitter is used by people with aphasia, aphasia organisations, aphasiologists, clinicians, and the public. Method: A Twitter hashtag study was conducted to locate tweets highly relevant to aphasia. A daily tweet capture using the Twitter search bar was conducted for one month, November 2018, searching for publicly available tweets tagged with aphasia-related hashtags. The tweets collected were analysed using both qualitative and quantitative methods. Results: A sample of 2,519 tweets were included in the analysis. The sample comprised 865 original tweets and 1,654 retweets which included “quote tweets”, sent by 839 unique Twitter @Users. Tweet content reflected users discussing aphasia research, exchanging health information, advice and inspiration, providing personal stories, raising awareness about aphasia, and an emergent aphasia community online. Conclusion: The aphasia community in Twitter is relatively small compared to other communities focused on communication disability and is emergent in terms of its size and the strength of connections. The majority of people or organisations tweeting about aphasia, and hence influencing the network, were health professionals working in the field of aphasia. There was minimal conversational interaction evident. These results demonstrate that Twitter is underutilised as a platform for interaction and engagement in relation to aphasia, and further research is needed.
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

Modern approaches for semantic segmentation usually employ dilated convolutions in the backbone to extract high-resolution feature maps, which brings heavy computation complexity and memory footprint. To replace the time and memory consuming dilated convolutions, we propose a novel joint upsampling module named Joint Pyramid Upsampling (JPU) by formulating the task of extracting high-resolution feature maps into a joint upsampling problem. With the proposed JPU, our method reduces the computation complexity by more than three times without performance loss. Experiments show that JPU is superior to other upsampling modules, which can be plugged into many existing approaches to reduce computation complexity and improve performance. By replacing dilated convolutions with the proposed JPU module, our method achieves the state-of-the-art performance in Pascal Context dataset (mIoU of 53.13%) and ADE20K dataset (final score of 0.5584) while running 3 times faster.
Intelligent Processing in Vehicular Ad hoc Networks: a Survey

The intelligent Processing technique is more and more attractive to researchers due to its ability to deal with key problems in Vehicular Ad hoc networks. However, several problems in applying intelligent processing technologies in VANETs remain open. The existing applications are comprehensively reviewed and discussed, and classified into different categories in this paper. Their strategies, advantages/disadvantages, and performances are elaborated. By generalizing different tactics in various applications related to different scenarios of VANETs and evaluating their performances, several promising directions for future research have been suggested.
Minimization of Fraction Function Penalty in Compressed Sensing

In this paper, we study the minimization problem of a non-convex sparsity-promoting penalty function, i.e., fraction function, in compressed sensing. First, we discuss the equivalence of <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> minimization and fraction function minimization. It is proved that the optimal solution to fraction function minimization solves <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> minimization and the optimal solution to the regularization problem also solves fraction function minimization if the certain conditions are satisfied, which is similar to the regularization problem in a convex optimization theory. Second, we study the properties of the optimal solution to the regularization problem, including the first-order and second-order optimality conditions and the lower and upper bounds of the absolute value for its nonzero entries. Finally, we derive the closed-form representation of the optimal solution to the regularization problem and propose an iterative <inline-formula> <tex-math notation="LaTeX">$FP$ </tex-math></inline-formula> thresholding algorithm to solve the regularization problem. We also provide a series of experiments to assess the performance of the <inline-formula> <tex-math notation="LaTeX">$FP$ </tex-math></inline-formula> algorithm, and the experimental results show that the <inline-formula> <tex-math notation="LaTeX">$FP$ </tex-math></inline-formula> algorithm performs well in sparse signal recovery with and without measurement noise.
High Fidelity Face Manipulation with Extreme Pose and Expression

Face manipulation has shown remarkable advances with the flourish of Generative Adversarial Networks. However, due to the difficulties of controlling the structure and texture in high-resolution, it is challenging to simultaneously model pose and expression during manipulation. In this paper, we propose a novel framework that simplifies face manipulation with extreme pose and expression into two correlated stages: a boundary prediction stage and a disentangled face synthesis stage. In the first stage, we propose to use a boundary image for joint pose and expression modeling. An encoder-decoder network is employed to predict the boundary image of the target face in a semi-supervised way. Pose and expression estimators are used to improve the prediction accuracy. In the second stage, the predicted boundary image and the original face are encoded into the structure and texture latent space by two encoder networks respectively. A proxy network and a feature threshold loss are further imposed as constraints to disentangle the latent space. In addition, we build up a new high quality Multi-View Face (MVF-HQ) database that contains 120K high-resolution face images of 479 identities with pose and expression variations, which will be released soon. Qualitative and quantitative experiments on four databases show that our method pushes forward the advance of extreme face manipulation from 128 $\times$ 128 resolution to 1024 $\times$ 1024 resolution, and significantly improves the face recognition performance under large poses.
Tree Search vs Optimization Approaches for Map Generation

Search-based procedural content generation uses stochastic global optimization algorithms to search for game content. However, standard tree search algorithms can be competitive with evolution on some optimization problems. We investigate the applicability of several tree search methods to level generation and compare them systematically with several optimization algorithms, including evolutionary algorithms. We compare them on three different game level generation problems: Binary, Zelda, and Sokoban. We introduce two new representations that can help tree search algorithms deal with the large branching factor of the generation problem. We find that in general, optimization algorithms clearly outperform tree search algorithms, but given the right problem representation certain tree search algorithms perform similarly to optimization algorithms, and in one particular problem, we see surprisingly strong results from MCTS.
Production of Marker-Free Apple Plants Expressing the Supersweet Protein Gene Driven by Plant Promoter

Laboratory of Expression Systems and Modification of the Plant Genome “Biotron”, Branch of the Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Pushchino, Russia, 2 Laboratory of Plant Bioengineering, Nikita Botanical Gardens – National Scientific Center, Russian Academy of Sciences, Yalta, Russia, 3 Laboratory of Plant Genetic Engineering, All-Russia Research Institute of Agricultural Biotechnology, Russian Academy of Sciences, Moscow, Russia
Supported Decision Making in the Prevention of Compulsory Interventions in Mental Health Care

Several strands of thought, international law and clinical practice shaped the emergence of supported decision making in mental health care: the UN Convention on the Rights of Persons with Disabilities, in particular Article 12 on Equal Recognition before the Law (1), the General Comment No.1 of the UN Committee on the Rights of Persons with Disabilities (2) emphasizing “support in the exercise of legal capacity” and obliging states “to replace regimes of substitute decision-making by supported decision-making, which respects the person’s autonomy, will and preferences,” the introduction of shared decision making in medicine (3), and the users’ movement challenging traditional paternalistic approaches in psychiatry (4). Within psychiatry the uptake of the convention with supported decision making was rather hesitant and perceived as challenging (5). Some commentators went as far as suggesting “an urgent consideration (of the General Comment No.1) with the full participation of practitioners” (6). Rather than extending this discussion, I will look at how supported decision making could work in the treatment of severe depression and psychosis, with the aim to prevent coercive interventions. Arguably, the widespread use of detention, coercion and isolation is a major obstacle for users of mental health services to perceive their service as trustworthy and helpful. Particularly coercive medication may have negative consequences on subsequent service use, as it is strongly linked to disapproval of treatment (7) or associated with lower acceptance of any form of containment (8). With an emphasis on will and preferences, supported decision making should have some potential in reducing coercive interventions in mental health settings.
GANs-NQM: A Generative Adversarial Networks based No Reference Quality Assessment Metric for RGB-D Synthesized Views

In this paper, we proposed a no-reference (NR) quality metric for RGB plus image-depth (RGB-D) synthesis images based on Generative Adversarial Networks (GANs), namely GANs-NQM. Due to the failure of the inpainting on dis-occluded regions in RGB-D synthesis process, to capture the non-uniformly distributed local distortions and to learn their impact on perceptual quality are challenging tasks for objective quality metrics. In our study, based on the characteristics of GANs, we proposed i) a novel training strategy of GANs for RGB-D synthesis images using existing large-scale computer vision datasets rather than RGB-D dataset; ii) a referenceless quality metric based on the trained discriminator by learning a `Bag of Distortion Word' (BDW) codebook and a local distortion regions selector; iii) a hole filling inpainter, i.e., the generator of the trained GANs, for RGB-D dis-occluded regions as a side outcome. According to the experimental results on IRCCyN/IVC DIBR database, the proposed model outperforms the state-of-the-art quality metrics, in addition, is more applicable in real scenarios. The corresponding context inpainter also shows appealing results over other inpainting algorithms.
A Perspective on Recent Advances in Phosphorene Functionalization and Its Applications in Devices

Phosphorene, the 2D material derived from black phosphorus, has recently attracted a lot of interest for its properties, suitable for applications in materials science. The physical features and the prominent chemical reactivity on its surface render this nanolayered substrate particularly promising for electrical and optoelectronic applications. In addition, being a new potential ligand for metals, it opens the way for a new role of the inorganic chemistry in the 2D world, with special reference to the field of catalysis. The aim of this review is to summarize the state of the art in this subject and to present our most recent results in the preparation, functionalization, and use of phosphorene and its decorated derivatives. We discuss several key points, which are currently under investigation: the synthesis, the characterization by theoretical calculations, the high pressure behavior of black phosphorus, as well as its decoration with nanoparticles and encapsulation in polymers. Finally, device fabrication and electrical transport measurements are overviewed on the basis of recent literature and the new results collected in our laboratories.
From closed to open access: A case study of flipped journals

In recent years, increased stakeholder pressure to transition research to Open Access has led to many journals "flipping" from a toll access to an open access publishing model. Changing the publishing model can influence the decision of authors to submit their papers to a journal, and increased article accessibility may influence citation behaviour. The aim of this paper is to show changes in the number of published articles and citations after the flipping of a journal. We analysed a set of 171 journals in the Web of Science (WoS) which flipped to open access. In addition to comparing the number of articles, average relative citation (ARC) and normalized impact factor (IF) are applied, respectively, as bibliometric indicators at the article and journal level, to trace the transformation of flipped journals covered. Our results show that flipping mostly has had positive effects on journal's IF. But it has had no obvious citation advantage for the articles. We also see a decline in the number of published articles after flipping. We can conclude that flipping to open access can improve the performance of journals, despite decreasing the tendency of authors to submit their articles and no better citation advantages for articles.
Pyramid Mask Text Detector

Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically. Some recent attempts benefiting from Mask R-CNN formulate scene text detection task as an instance segmentation problem and achieve remarkable performance. In this paper, we present a new Mask R-CNN based framework named Pyramid Mask Text Detector (PMTD) to handle the scene text detection. Instead of binary text mask generated by the existing Mask R-CNN based methods, our PMTD performs pixel-level regression under the guidance of location-aware supervision, yielding a more informative soft text mask for each text instance. As for the generation of text boxes, PMTD reinterprets the obtained 2D soft mask into 3D space and introduces a novel plane clustering algorithm to derive the optimal text box on the basis of 3D shape. Experiments on standard datasets demonstrate that the proposed PMTD brings consistent and noticeable gain and clearly outperforms state-of-the-art methods. Specifically, it achieves an F-measure of 80.13% on ICDAR 2017 MLT dataset.