Miracle-dz/UniFAR-FTUs huggingface dataset:
ADAPTATION OF SCENEDESMUS OBLIQUUS (CHLOROPHYCEAE) TO COPPER‐DEFICIENCY: TRANSCRIPTIONAL REGULATION OF PCY1 BUT NOT CPX1

In contrast to the situation in Chlamydomonas reinhardtii, plastocyanin expression in the green alga Scenedesmus obliquus (Turp.) Kütz. (Chlorophyceae) is regulated by copper ion availability at the level of both protein and mRNA accumulation. To study the mechanism of copper‐dependent mRNA accumulation, plastocyanin‐encoding genomic and cDNA clones have been isolated and sequenced. The single nuclear S. obliquus Pcy1 gene contains two introns and a bipartite transit peptide sequence typical of plastid lumen‐localized proteins. Despite the similarity of the S. obliquus and Chlamydomonas reinhardtii Pcy1 genes, the S. obliquus Pcy1 gene was not expressed from its own promoter, nor could the S. obliquus Pcy1 promoter drive expression of a C. reinhardtii gene when it was introduced into the genome of C. reinhardtii cells. Using the cDNA clone as a probe, copper‐responsive regulation of Pcy1 expression was further characterized in S. obliquus. Experiments examining the time course of Pcy1 degradation on depletion of copper argue against a specific mechanism for Pcy1 mRNA decay in S. obliquus, suggesting that the copper‐responsive change in abundance occurs through transcriptional regulation. The Cpx1 gene (encoding coproporphyrinogen III oxidase) also responds differently to changes in cellular copper status in S. obliquus compared to C. reinhardtii. Whereas Cpx1 is highly induced in copper‐deficient cultures of C. reinhardtii, in S. obliquus the Cpx1 gene is not copper responsive.
Site and refuge use by giant river otters (Pteronura brasiliensis) in the Western Brazilian Amazonia

Giant river otters are endangered and knowledge about this species is incomplete, especially in the Brazilian Amazonia, which holds large populations of the species. In order to improve our knowledge for species conservation, we present data on site and refuge use by giant river otters in the Amanã Reserve, Brazilian Amazonia. During 40 sampling trips (October 2004 to September 2008) we recorded information and the physical characteristics of 193 campsites, 182 dens and 62 resting sites. Animals were site-selective, preferentially building their dens, campsites and resting sites at < 2 m from the water, on slopes < 45°, and avoiding using water bodies < 1 m deep. The information presented here stresses the importance of river margins for giant river otters, indicating that these areas are key habitats for their conservation.
Dynamic Merge Point Prediction

Despite decades of research, conditional branch mispredictions still pose a significant problem for performance. Moreover, limit studies on infinite size predictors show that many of the remaining branches are impossible to predict by current strategies. Our work focuses on mitigating performance loss in the face of impossible to predict branches. This paper presents a dynamic merge point predictor, which uses instructions fetched on the wrong path of the branch to dynamically detect the merge point. Our predictor locates the merge point with an accuracy of 95%, even when faced with branches whose direction is impossible to predict. Furthermore, we introduce a novel confidence-cost system, which identifies costly hard-to-predict branches. Our complete system replaces 58% of all branch mispredictions with a correct merge point prediction, effectively reducing MPKI by 43%. This result demonstrates the potential for dynamic merge point prediction to significantly improve performance.
Controlling Fairness and Bias in Dynamic Learning-to-Rank

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users -- as done by virtually all learning-to-rank algorithms -- can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.
Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware

As the COVID-19 pandemic emerges in early 2020, a number of campaigns have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware, and there is no publicly accessible dataset that could be utilized to boost the related research. In this paper, we present the first systematic study of coronavirus-themed mobile malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains $2,016$ COVID-19 themed apps and $277$ malware samples by the time of May 26, 2020. We then present an analysis of these apps from multiple perspectives including popularity and trends, installation methods, malicious behaviors and malicious campaigns. We observe that the growth of the number of COVID-19 themed apps is highly related to the number of confirmed cases of COVID-19 in the world. Most of them were released through distribution channels beyond app markets. A majority of the malicious apps (over 53%) are camouflaged as official apps using the same app identifiers and some of them use confusing similar app icons with the official ones to mislead users. Their main purposes are either stealing users' private information or making profit by using the tricks like phishing and extortion. Furthermore, we find that only 40\% of the COVID-19 malware creators are habitual developers who are active for a long time, while 60\% of them are new emerging ones in this pandemic and only released COVID-19 themed malware. The malicious developers are mainly located in US, mostly targeting countries including English countries, Arabic countries, Europe and China. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community.
Assessment of Parkinson's Disease Medication State Through Automatic Speech Analysis

Parkinson's disease (PD) is a progressive degenerative disorder of the central nervous system characterized by motor and non-motor symptoms. As the disease progresses, patients alternate periods in which motor symptoms are mitigated due to medication intake (ON state) and periods with motor complications (OFF state). The time that patients spend in the OFF condition is currently the main parameter employed to assess pharmacological interventions and to evaluate the efficacy of different active principles. In this work, we present a system that combines automatic speech processing and deep learning techniques to classify the medication state of PD patients by leveraging personal speech-based bio-markers. We devise a speaker-dependent approach and investigate the relevance of different acoustic-prosodic feature sets. Results show an accuracy of 90.54% in a test task with mixed speech and an accuracy of 95.27% in a semi-spontaneous speech task. Overall, the experimental assessment shows the potentials of this approach towards the development of reliable, remote daily monitoring and scheduling of medication intake of PD patients.
Implementing accelerated particle beams in a 3D simulation of the quiet Sun

Context. The magnetic field in the solar atmosphere continually reconnects and accelerates charged particles to high energies. Simulations of the atmosphere in three dimensions that include the effects of accelerated particles can aid our understanding of the interplay between energetic particle beams and the environment where they emerge and propagate. We presented the first attempt at such a simulation in a previous paper, emphasising the physical model of particle beams. However, the numerical implementation of this model is not straightforward due to the diverse conditions in the atmosphere and the way we must distribute computation between multiple CPU cores. Aims. Here, we describe and verify our numerical implementation of energy transport by electron beams in a 3D magnetohydrodynamics code parallelised by domain decomposition. Methods. We trace beam trajectories using a Runge-Kutta scheme with adaptive step length control and integrate deposited beam energy along the trajectories with a hybrid analytical and numerical approach. To parallelise this, we coordinate beam transport across subdomains owned by separate processes using a buffering system designed to optimise data flow. Results. Using an ad hoc magnetic field with analytical field lines as a test scenario, we show that our parallel implementation of adaptive tracing efficiently follows a challenging trajectory with high precision. By timing executions of electron beam transport with different numbers of processes, we found that the processes communicate with minimal overhead but that the parallel scalability is still sublinear due to workload imbalance caused by the uneven spatial distribution of beams.
The effect of dietary inulin on aerobic bacteria associated with hindgut of Arctic charr (Salvelinus alpinus L.)

The primary aim of the present study was to evaluate the population level of adherent (autochthonous) aerobic and facultative anaerobic bacteria in the hindgut of healthy Arctic charr (Salvelinus alpinus L.) fed dextrin or inulin. This was assessed by the dilution plate technique, and visualized using both transmission and scanning electron microscopy. A population level of 4.8 × 105 adherent bacteria per gram wet mass was found in the hindgut of fish fed a casein-based diet supplemented with 15% dextrin. However, substituting dextrin with 15% inulin reduced the bacterial population level in the hindgut (3.56 × 104). A total of 217 bacterial isolates were identified by key phenotypical and biochemical characteristics. In addition, 22 strains were also identified by partial sequencing of the 16S rRNA gene. The composition of bacteria colonizing the hindgut of Arctic charr fed dextrin was dominated by the genera Staphylococcus, Pseudomonas, Micrococcus, Psychrobacter glacincola and Streptococcus. However, bacteria colonizing the hindgut of fish fed inulin were dominated by Gram-positive bacteria of the genera Staphylococcus, Streptococcus, Carnobacterium and Bacillus. While Carnobacterium divergens-like strains were isolated from charr fed dextrin, Carnobacterium maltaromicus-like strains were isolated from the hindgut of fish fed inulin. Electron microscopical analysis of hindgut regions confirmed traditional culture-based microbial analysis as fewer bacterial cells were observed between microvilli and associated with the surfaces of enterocytes of fish fed inulin rather than dextrin.
Energy Trading and Time Scheduling for Heterogeneous Wireless-Powered and Backscattering-based IoT Networks

In this paper, an economic model is proposed for joint time resource allocation and energy trading between two service providers, i.e., IoT service provider (ISP) and energy service provider (ESP), in a heterogeneous IoT wireless-powered communication network. In particular, IoT devices (with various communication types and energy constraints) are assumed to belong the ISP who collects sensing data from IoT devices for its services. Meanwhile, the ESP utilizes a power beacon to provide energy services for the ISP. A Stackelberg game model is formulated to jointly maximize the revenue of both the ISP and ESP (i.e., network throughput and energy efficiency) through investigating the energy interaction between them. Specially, the ISP leads the game by requesting an optimal energy price and service time that maximize its revenue to the ESP. Following the requested deal from the ISP, the ESP supplies an optimized transmission power which satisfies the energy demand of the ISP while maximizes its utility. To obtain the Stackelberg Equilibrium, we first derive a closed-form solution for the ESP. Then two relaxed schemes (i.e., partial or joint energy price and service time adjustments) based on block coordinate descent (BCD) and convex-concave procedure (CCCP) techniques are proposed to solve the non-convex optimization problem for the ISP. Due to the selfish behavior of both players, we investigate the inefficiency of the proposed approach by proposing two baseline scenarios, i.e., non-negotiated energy trading and social welfare scenarios, and the Price of Anarchy (PoA). Finally, numerical results reveal that our approach can achieve significant improvements in revenues of both providers compared with conventional transmission methods, e.g., bistatic backscatter, and harvest-then-transmit communication methods.
A Survey on Blockchain Interoperability: Past, Present, and Future Trends

Blockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to achieve interoperability among blockchains seamlessly. Given this new domain’s novelty and potential, we conduct a literature review on blockchain interoperability by collecting 284 papers and 120 grey literature documents, constituting a corpus of 404 documents. From those 404 documents, we systematically analyzed and discussed 102 documents, including peer-reviewed papers and grey literature. Our review classifies studies in three categories: Public Connectors, Blockchain of Blockchains, and Hybrid Connectors. Each category is further divided into sub-categories based on defined criteria. We classify 67 existing solutions in one sub-category using the Blockchain Interoperability Framework, providing a holistic overview of blockchain interoperability. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies and cross-chain asset transfers. Finally, this article discusses supporting technologies, standards, use cases, open challenges, and future research directions, paving the way for research in the area.
Deep convolutional tensor network

Tensor networks are linear algebraic representations of quantum many-body states based on their entanglement structure. People are exploring their applications to machine learning. Deep convolutional neural networks achieve state of the art results in computer vision and other areas. Supposedly this happens because of parameter sharing, locality, and deepness. We devise a novel tensor network based model called Deep convolutional tensor network (DCTN) for image classification, which has parameter sharing, locality, and deepness. It is based on the Entangled plaquette states (EPS) tensor network. We show how Entangled plaquette states can be implemented as a backpropagatable layer which can be used in neural networks. We test our model on FashionMNIST dataset and find that deepness increases overfitting and decreases test accuracy. Also, we find that the shallow version performs well considering its low parameter count. We discuss how hyperparameters of DCTN affect its training and overfitting.
Extracting low-dimensional psychological representations from convolutional neural networks

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations.
The MeerKAT telescope as a pulsar facility: System verification and early science results from MeerTime

Abstract We describe system verification tests and early science results from the pulsar processor (PTUSE) developed for the newly commissioned 64-dish SARAO MeerKAT radio telescope in South Africa. MeerKAT is a high-gain ( ${\sim}2.8\,\mbox{K Jy}^{-1}$ ) low-system temperature ( ${\sim}18\,\mbox{K at }20\,\mbox{cm}$ ) radio array that currently operates at 580–1 670 MHz and can produce tied-array beams suitable for pulsar observations. This paper presents results from the MeerTime Large Survey Project and commissioning tests with PTUSE. Highlights include observations of the double pulsar $\mbox{J}0737{-}3039\mbox{A}$ , pulse profiles from 34 millisecond pulsars (MSPs) from a single 2.5-h observation of the Globular cluster Terzan 5, the rotation measure of Ter5O, a 420-sigma giant pulse from the Large Magellanic Cloud pulsar PSR $\mbox{J}0540{-}6919$ , and nulling identified in the slow pulsar PSR J0633–2015. One of the key design specifications for MeerKAT was absolute timing errors of less than 5 ns using their novel precise time system. Our timing of two bright MSPs confirm that MeerKAT delivers exceptional timing. PSR $\mbox{J}2241{-}5236$ exhibits a jitter limit of $<4\,\mbox{ns h}^{-1}$ whilst timing of PSR $\mbox{J}1909{-}3744$ over almost 11 months yields an rms residual of 66 ns with only 4 min integrations. Our results confirm that the MeerKAT is an exceptional pulsar telescope. The array can be split into four separate sub-arrays to time over 1 000 pulsars per day and the future deployment of S-band (1 750–3 500 MHz) receivers will further enhance its capabilities.
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content. Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of UGC videos are unpredictable, complicated, and often commingled. Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and objective VQA model design. By employing a feature selection strategy on top of efficient BVQA models, we are able to extract 60 out of 763 statistical features used in existing methods to create a new fusion-based model, which we dub the VIDeo quality EVALuator (VIDEVAL), that effectively balances the trade-off between VQA performance and efficiency. Our experimental results show that VIDEVAL achieves state-of-the-art performance at considerably lower computational cost than other leading models. Our study protocol also defines a reliable benchmark for the UGC-VQA problem, which we believe will facilitate further research on deep learning-based VQA modeling, as well as perceptually-optimized efficient UGC video processing, transcoding, and streaming. To promote reproducible research and public evaluation, an implementation of VIDEVAL has been made available online: https://github.com/vztu/VIDEVAL.
Relation Extraction with Explanation

Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused on improving extraction accuracy but little is known about their explanability. In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. We demonstrate that replacing the entity mentions in the sentences with their fine-grained entity types not only enhances extraction accuracy but also improves explanation. We also propose to automatically generate “distractor” sentences to augment the bags and train the model to ignore the distractors. Evaluations on the widely used FB-NYT dataset show that our methods achieve new state-of-the-art accuracy while improving model explanability.
Redundancy Scheduling with Locally Stable Compatibility Graphs.

Redundancy scheduling is a popular concept to improve performance in parallel-server systems. In the baseline scenario any job can be handled equally well by any server, and is replicated to a fixed number of servers selected uniformly at random. Quite often however, there may be heterogeneity in job characteristics or server capabilities, and jobs can only be replicated to specific servers because of affinity relations or compatibility constraints. In order to capture such situations, we consider a scenario where jobs of various types are replicated to different subsets of servers as prescribed by a general compatibility graph. We exploit a product-form stationary distribution and weak local stability conditions to establish a state space collapse in heavy traffic. In this limiting regime, the parallel-server system with graph-based redundancy scheduling operates as a multi-class single-server system, achieving full resource pooling and exhibiting strong insensitivity to the underlying compatibility constraints.
Chondria viticulosa sp. nov. (Rhodomelaceae, Rhodophyta), a distinctly flattened species from south-eastern Queensland, Australia

Chondria viticulosa Millar & Wynne is a diminutive, prostrate, flattened epiphyte on the green alga Halimeda discoidea Decaisne, which grows on coral reefs at Caloundra, Queensland. Plants at maturity reach lengths of only 13 mm, and all axes have acute, exserted apices. Lateral branches have constricted bases and arise submarginally from both ventral and dorsal surfaces of parental axes. Male and female/ cystocarpic plants are described. The characters used to define the subgenus Platychondria are discussed, and the continued recognition of the subgenus is supported.
Investigating Deep Learning Approaches for Hate Speech Detection in Social Media

The phenomenal growth on the internet has helped in empowering individual's expressions, but the misuse of freedom of expression has also led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media. Detecting hate speech from a large volume of text, especially tweets which contains limited contextual information also poses several practical challenges. Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message. Our experiments on three publicly available datasets of different domains shows a significant improvement in accuracy and F1-score.