Miracle-dz/UniFAR-FTUs huggingface dataset:
CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information

As a platform, Twitter has been a significant public space for discussion related to the COVID-19 pandemic. Public social media platforms such as Twitter represent important sites of engagement regarding the pandemic and these data can be used by research teams for social, health, and other research. Understanding public opinion about COVID-19 and how information diffuses in social media is important for governments and research institutions. Twitter is a ubiquitous public platform and, as such, has tremendous utility for understanding public perceptions, behavior, and attitudes related to COVID-19. In this research, we present CML-COVID, a COVID-19 Twitter data set of 19,298,967 million tweets from 5,977,653 unique individuals and summarize some of the attributes of these data. These tweets were collected between March 2020 and July 2020 using the query terms coronavirus, covid and mask related to COVID-19. We use topic modeling, sentiment analysis, and descriptive statistics to describe the tweets related to COVID-19 we collected and the geographical location of tweets, where available. We provide information on how to access our tweet dataset (archived using twarc).
Quantum Speed Limit and Divisibility of the Dynamical Map

The quantum speed limit (QSL) is the theoretical lower limit of the time for a quantum system to evolve from a given state to another one. Interestingly, it has been shown that non-Markovianity can be used to speed-up the dynamics and to lower the QSL time, although this behaviour is not universal. In this paper, we further carry on the investigation on the connection between QSL and non-Markovianity by looking at the effects of P- and CP-divisibility of the dynamical map to the quantum speed limit. We show that the speed-up can also be observed under P- and CP-divisible dynamics, and that the speed-up is not necessarily tied to the transition from P-divisible to non-P-divisible dynamics.
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators

In this paper, we propose a mixed-precision convolution unit architecture which supports different integer and floating point (FP) precisions. The proposed architecture is based on low-bit inner product units and realizes higher precision based on temporal decomposition. We illustrate how to integrate FP computations on integer-based architecture and evaluate overheads incurred by FP arithmetic support. We argue that alignment and addition overhead for FP inner product can be significant since the maximum exponent difference could be up to 58 bits, which results into a large alignment logic. To address this issue, we illustrate empirically that no more than 26-bitproduct bits are required and up to 8-bit of alignment is sufficient in most inference cases. We present novel optimizations based on the above observations to reduce the FP arithmetic hardware overheads. Our empirical results, based on simulation and hardware implementation, show significant reduction in FP16 overhead. Over typical mixed precision implementation, the proposed architecture achieves area improvements of up to 25% in TFLOPS/mm2and up to 46% in TOPS/mm2with power efficiency improvements of up to 40% in TFLOPS/Wand up to 63% in TOPS/W.
Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization

Making the gradients small is a fundamental optimization problem that has eluded unifying and simple convergence arguments in first-order optimization, so far primarily reserved for other convergence criteria, such as reducing the optimality gap. We introduce a novel potential function-based framework to study the convergence of standard methods for making the gradients small in smooth convex optimization and convex-concave min-max optimization. Our framework is intuitive and it provides a lens for viewing algorithms that make the gradients small as being driven by a trade-off between reducing either the gradient norm or a certain notion of an optimality gap. On the lower bounds side, we discuss tightness of the obtained convergence results for the convex setup and provide a new lower bound for minimizing norm of cocoercive operators that allows us to argue about optimality of methods in the min-max setup.
Multi-Modal Aesthetic Assessment for Mobile Gaming Image

With the proliferation of various gaming technology, services, game styles, and platforms, multi-dimensional aesthetic assessment of the gaming contents is becoming more and more important for the gaming industry. Depending on the diverse needs of diversified game players, game designers, graphical developers, etc. in particular conditions, multi-modal aesthetic assessment is required to consider different aesthetic dimensions/perspectives. Since there are different underlying relationships between different aesthetic dimensions, e.g., between the ‘Colorfulness’ and ‘Color Harmony’, it could be advantageous to leverage effective information attached in multiple relevant dimensions. To this end, we solve this problem via multi-task learning. Our inclination is to seek and learn the correlations between different aesthetic relevant dimensions to further boost the generalization performance in predicting all the aesthetic dimensions. Therefore, the ‘bottleneck’ of obtaining good predictions with limited labeled data for one individual dimension could be unplugged by harnessing complementary sources of other dimensions, i.e., augment the training data indirectly by sharing training information across dimensions. According to experimental results, the proposed model outperforms state-of-the-art aesthetic metrics significantly in predicting four gaming aesthetic dimensions.
Problems of Hydrate Formation in Oil and Gas Pipes Deals

Iran among countries which have rich reserves of natural gas is one of the issues related to natural gas; gas hydrate formation that can cause problems in oil and gas industries to make, though the recent formation of hydrate as an advantage special use is named. But the hydrate formation in natural gas pipelines increased flow pressure drop flow path blockage and sometimes explosive pipeline will flow every year and ultimately lead to injury very much for the cost of oil and gas industry in Iran and around the world is. Hence the ability to predict hydrate formation conditions from the standpoint of production process, transfer and utilization is important and can prevent unexpected problems. In terms of structure, the solid hydrate material is a physical mixture of water with some hydrocarbons in the natural gas comes despite apparent or like ice, its properties are different. This article reviews the problems of hydrate formation in oil and gas pipes deals.
Federated Multi-Armed Bandits

Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL. This paper proposes a general framework of FMAB and then studies two specific federated bandit models. We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution. This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite local models are perfectly known. Furthermore, this uncertainty cannot be quantified a priori without knowledge of the suboptimality gap. We solve the approximate model by proposing Federated Double UCB (Fed2-UCB), which constructs a novel “double UCB” principle accounting for uncertainties from both arm and client sampling. We show that gradually admitting new clients is critical in achieving an O(log(T)) regret while explicitly considering the communication loss. The exact model, where the global bandit model is the exact average of heterogeneous local models, is then studied as a special case. We show that, somewhat surprisingly, the order-optimal regret can be achieved independent of the number of clients with a careful choice of the update periodicity. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed algorithms.
Model-Based Policy Search Using Monte Carlo Gradient Estimation With Real Systems Application

In this article, we present a model-based reinforcement learning (MBRL) algorithm named Monte Carlo probabilistic inference for learning control (MC-PILCO). This algorithm relies on Gaussian processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient. This defines a framework in which we ablate the choice of the components, which are the selection of the cost function, the optimization of policies using dropout, and an improved data efficiency through the use of structured kernels in the GP models. The combination of the aforementioned aspects affects dramatically the performance of MC-PILCO. Numerical comparisons in a simulated cart–pole environment show that MC-PILCO exhibits better data efficiency and control performance w.r. t. state-of-the-art GP-based MBRL algorithms. Finally, we apply MC-PILCO to real systems, considering, in particular, systems with partially measurable states. We discuss the importance of modeling both the measurement system and the state estimators during policy optimization. The effectiveness of the proposed solutions has been tested in simulation and on two real systems, which are the Furuta pendulum and the ball-and-plate rig.
Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges---1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate---on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.
How the Design of YouTube Influences User Sense of Agency

In the attention economy, video apps employ design mechanisms like autoplay that exploit psychological vulnerabilities to maximize watch time. Consequently, many people feel a lack of agency over their app use, which is linked to negative life effects such as loss of sleep. Prior design research has innovated external mechanisms that police multiple apps, such as lockout timers. In this work, we shift the focus to how the internal mechanisms of an app can support user agency, taking the popular YouTube mobile app as a test case. From a survey of 120 U.S. users, we find that autoplay and recommendations primarily undermine sense of agency, while playlists and search support it. From 13 co-design sessions, we find that when users have a specific intention for how they want to use YouTube they prefer interfaces that support greater agency. We discuss implications for how designers can help users reclaim a sense of agency over their media use.
An extensive empirical study of inconsistent labels in multi-version-project defect data sets

The label quality of defect data sets has a direct influence on the reliability of defect prediction models. In this study, for multi-version-project defect data sets, we propose an approach to automatically detecting instances with “inconsistent labels” (i.e. the phenomena of instances having the same source code but different labels over multiple versions of a software project) and understand their influence on the evaluation and interpretation of defect prediction models. Based on five multi-version-project defect data sets (either widely used or the most up-to-date in the literature) collected by diverse approaches, we find that: (1) most versions (>90%) in the investigated defect data sets contain inconsistent labels with varying degrees; (2) the existence of inconsistent labels in a training data set may considerably change the prediction performance of a defect prediction model as well as can lead to the identification of substantially different true defective modules; and (3) the importance ranking of independent variables in a defect prediction model can be substantially shifted due to the existence of inconsistent labels. The above findings reveal that inconsistent labels in defect data sets can profoundly change the prediction ability and interpretation of a defect prediction model. Therefore, we strongly suggest that practitioners should detect and exclude inconsistent labels in defect data sets to avoid their potential negative influence on defect prediction models. What is more, it is necessary for researchers to improve existing defect label collection approaches to reduce inconsistent labels. Furthermore, there is a need to re-examine the experimental conclusions of previous studies using multi-version-project defect data sets with a high ratio of inconsistent labels. Keywords—Inconsistent label, multi-version, defect prediction models, detection
Traffic Refinery: Cost-Aware Traffic Representation for Machine Learning in Networks

Ever more frequently network management tasks apply machine learning on network traffic. Both the accuracy of a machine learning model and its effectiveness in practice ultimately depend on the representation of raw network traffic as features. Often, the representation of the traffic is as important as the choice of the model itself; furthermore, the features that the model relies on will ultimately determine where (and even whether) the model can be deployed in practice. This paper develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance ( e.g. , model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for a practical network management task, video streaming quality inference, and show that the appropriate operating point for these two dimensions depends on the deployment scenario. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models. Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance.
Sensitized P-type delayed fluorescence

Two general mechanisms of sensitized delayed fluorescence in solution are proposed. Both mechanisms require, as an initial stage, the transfer of triplet energy from the sensitizer to the acceptor. In one mechanism the excited singlet of the acceptor is then formed by selfquenching of acceptor triplets; in the other it is formed by mixed triplet quenching between donor and acceptor triplets. Evidence is adduced for the occurrence of the mixed triplet mechanism in the delayed fluorescence of naphthacene sensitized by anthracene. The naphthacene is found to undergo photosensitized decomposition. The possibility of stable mixed dimers formed by mixed triplet quenching is discussed.
One new species and one new record of Zasmidium in China

Abstract Background Two hyphomycetous species were collected from leaves of Smilax china (Liliales, Smilacaceae) and Cremastra appendiculata (Asparagales, Orchidaceae). ITS barcoding indicated that they belong to the genus Zasmidium. New information Morphological data in combination with molecular phylogenetic analyses based on ITS, LSU and rpb2 confirmed that our Chinese strains represented a new species, Zasmidium liboense and a new record of Z. citri-griseum.
Comprehensive Analysis of ceRNA Regulation Network Involved in the Development of Coronary Artery Disease

Background Coronary artery disease (CAD) is one of the most common causes of sudden death with high morbidity in recent years. This paper is aimed at exploring the early peripheral blood biomarkers of sudden death and providing a new perspective for clinical diagnosis and forensic pathology identification by integrated bioinformatics analysis. Methods Two microarray expression profiling datasets (GSE113079 and GSE31568) were downloaded from the Gene Expression Omnibus (GEO) database, and we identified differentially expressed lncRNAs, miRNAs, and mRNAs in CAD. Gene Ontology (GO) and KEGG pathway analyses of DEmRNAs were executed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified. Finally, we constructed a competitive endogenous RNA (ceRNA) regulation network among lncRNAs, miRNAs, and mRNAs. Also, the 5 miRNAs of the ceRNA network were verified by RT-PCR. Results In total, 86 DElncRNAs, 148 DEmiRNAs, and 294 DEmRNAs were dysregulated in CAD. We received 12 GO terms and 5 pathways of DEmRNAs. 31 nodes and 78 edges were revealed in the PPI network. The top 10 genes calculated by degree method were identified as hub genes. Moreover, there were a total of 26 DElncRNAs, 5 DEmiRNAs, and 13 DEmRNAs in the ceRNA regulation network. We validated the 5 miRNAs of the ceRNA network by RT-PCR, which were consistent with the results of the microarray. Conclusions In this paper, a CAD-specific ceRNA network was successfully constructed, contributing to the understanding of the relationship among lncRNAs, miRNAs, and mRNAs. We identified potential peripheral blood biomarkers in CAD and provided novel insights into the development and progress of CAD.
Preliminary Results: The Impact of Smartphone Use and Short-Wavelength Light during the Evening on Circadian Rhythm, Sleep and Alertness

Smartphone usage strongly increased in the last decade, especially before bedtime. There is growing evidence that short-wavelength light affects hormonal secretion, thermoregulation, sleep and alertness. Whether blue light filters can attenuate these negative effects is still not clear. Therefore, here, we present preliminary data of 14 male participants (21.93 ± 2.17 years), who spent three nights in the sleep laboratory, reading 90 min either on a smartphone (1) with or (2) without a blue light filter, or (3) on printed material before bedtime. Subjective sleepiness was decreased during reading on a smartphone, but no effects were present on evening objective alertness in a GO/NOGO task. Cortisol was elevated in the morning after reading on the smartphone without a filter, which resulted in a reduced cortisol awakening response. Evening melatonin and nightly vasodilation (i.e., distal-proximal skin temperature gradient) were increased after reading on printed material. Early slow wave sleep/activity and objective alertness in the morning were only reduced after reading without a filter. These results indicate that short-wavelength light affects not only circadian rhythm and evening sleepiness but causes further effects on sleep physiology and alertness in the morning. Using a blue light filter in the evening partially reduces these negative effects.
Microbial community structure in aquifers associated with arsenic: analysis of 16S rRNA and arsenite oxidase genes

The microbiomes of deep and shallow aquifers located in an agricultural area, impacted by an old tin mine, were explored to understand spatial variation in microbial community structures and identify environmental factors influencing microbial distribution patterns through the analysis of 16S rRNA and aioA genes. Although Proteobacteria, Cyanobacteria, Actinobacteria, Patescibacteria, Bacteroidetes, and Epsilonbacteraeota were widespread across the analyzed aquifers, the dominant taxa found in each aquifer were unique. The co-dominance of Burkholderiaceae and Gallionellaceae potentially controlled arsenic immobilization in the aquifers. Analysis of the aioA gene suggested that arsenite-oxidizing bacteria phylogenetically associated with Alpha-, Beta-, and Gamma proteobacteria were present at low abundance (0.85 to 37.13%) and were more prevalent in shallow aquifers and surface water. The concentrations of dissolved oxygen and total phosphorus significantly governed the microbiomes analyzed in this study, while the combination of NO3--N concentration and oxidation-reduction potential significantly influenced the diversity and abundance of arsenite-oxidizing bacteria in the aquifers. The knowledge of microbial community structures and functions in relation to deep and shallow aquifers is required for further development of sustainable aquifer management.
Factors associated with mental health of graduate nursing students in China

Abstract The increasing number of graduate nursing students in China has resulted in the wide concern for their mental health problems. Quantitative studies using validated questionnaires on mental health of graduate nursing students are rare. This study aimed to investigate the factors potentially involved in the level of mental health of graduate nursing students. The sample consisted of 339 graduate nursing students from 5 universities of China. The participants were evaluated using the Symptom Checklist-90 (SCL-90) and Chinese Perceived Stress Scale (CPSS) between March and November 2020. Multivariate stepwise linear regression analysis and spearman correlation test were performed to assess the association between various factors associated with mental health. The SCL-90 total score was 109.00 (96.00 –134.00) and psychological abnormalities (total score > 160) accounted for 14.2%. The highest score in the nine dimensions was compulsion dimension. The positive detection rate of compulsion dimension was 21.5% (dimensions score > 2),and the average of compulsion score was 1.50(1.20 –2.00). Multivariable stepwise linear regression analysis showed that grade, environmental adaptation level, number of good friends were independently associated with lower SCL-90 scores (both P < .05). SCL-90 scores were correlated with Chinese Perceived Stress Scale (r = .159, P = .003). Results indicate that learning about ways to adapt to the environment, strengthening good friends support and alleviating perceived stress can help improve graduate nursing students’ level of mental health. The conclusions of this study can provide a reference to improve the psychological intervention strategies for graduate nursing students.