Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimal Streaming Algorithm for Submodular Maximization with a Cardinality Constraint

We study the problem of maximizing a nonmonotone submodular function subject to a cardinality constraint in the streaming model. Our main contribution is a single-pass (semi) streaming algorithm that uses roughly [Formula: see text] memory, where k is the size constraint. At the end of the stream, our algorithm postprocesses its data structure using any off-line algorithm for submodular maximization and obtains a solution whose approximation guarantee is [Formula: see text], where α is the approximation of the off-line algorithm. If we use an exact (exponential time) postprocessing algorithm, this leads to [Formula: see text] approximation (which is nearly optimal). If we postprocess with the state-of-the-art offline approximation algorithm, whose guarantee is [Formula: see text], we obtain a 0.2779-approximation in polynomial time, improving over the previously best polynomial-time approximation of 0.1715. It is also worth mentioning that our algorithm is combinatorial and deterministic, which is rare for an algorithm for nonmonotone submodular maximization, and enjoys a fast update time of [Formula: see text] per element.
Risk Factors and a Prognostic Scoring System for Severe Disease in Patients with COVID-19: A Retrospective Cohort Study

Background: The corona virus disease 2019 (COVID-19) has made a big threat on the global public health. The aim of the study is to comprehensively explore the risk factor for severe disease events (intensive care, invasive ventilation, or death) in patients with COVID-19, and to establish a prognostic scoring system. Methods: Patients with laboratory-confirmed COVID-19 admitted to the Wuhan Leishenshan Hospital from February 13 to March 14, 2020, was retrospectively analyzed. Demographic data, symptoms, laboratory values at baseline, comorbidities, treatments and clinical outcomes were extracted. The LASSO and multivariate logistic regression models were developed to explore the risk factors for critical-ill events. A risk model was established via nomogram. Results: 463 COVID-19 patients were included, of whom 397 were non-critically ill and 66 were critically ill. The LASSO identified four risk factors (hypersensitive cardiac troponin I [cTnI], Blood Urea Nitrogen [BUN], haemoglobin, and Interleukin-6 [IL-6]) contributing to the critical-ill events. Multivariable regression showed increasing odds of in-hospital critical-ill events associated with hypersensitive cTnI greater than 0.04 ng/mL (OR, 95% CI: 20.98, 3.51-125.31), BUN greater than 7.6 mmol/L (OR, 95% CI: 5.22, 1.52-17.81), decreased haemoglobin (OR, 95% CI: 1.06, 1.04-1.10), and higher IL-6 (OR, 95% CI: 1.05, 1.02-1.08) on admission. The risk model constructed by the selected four risk factors showed high calibration (Hosmer-Lemeshow, p=1.00). Conclusion: Elevated hypersensitive cTnI, BUN, IL-6, and decreased hemoglobin were risk factors of critical-ill events. The risk model could help clinicians with early identification of patients with COVID-19 who will progress to severe disease.
collpcm: Bayesian model selection in latent position cluster models for networks

The latent position cluster model is a popular model for the statistical analysis of network data. This model attaches a latent position to vertices in the network graph, with the motivation that the distance between two latent positions is connected to the probability of there being an edge between the corresponding vertices in the graph. Bayesian methods are used to estimate the likely conﬁgurations of the latent positions based on a prior assumption on their layout. This prior assumption can reﬂect a clustering or grouping in the latent positions which can be used to say something about community formation between vertices in the network graph. This package provides convenient computational facilities to ﬁt latent position cluster models, while exploring probable community patterns in the network i.e. exploring the likely groupings or clustering present. The methods do not require specifying a number of groupings or communities in the network at the outset.
Intelligent Recognition Method of Decorative Openwork Windows with Sustainable Application for Suzhou Traditional Private Gardens in China

Decorative openwork windows (DO-Ws) in Suzhou traditional private gardens play a vital role in Chinese traditional garden art. Due to the delicate and elegant patterns, as well as their rich cultural meaning, DO-Ws have quite high protection and utilization value. In this study, we firstly visited 15 extant traditional gardens in Suzhou and took almost 3000 photos to establish the DO-W datasets. Then, we present an effective visual recognition method named CSV-Net to classify different DO-Ws’ patterns in Suzhou traditional gardens. On the basis of the backbone module of the cross stage partial network optimized with the Soft-VLAD architecture, the proposed CSV-Net achieves a preferable representation ability for distinguishing different DO-Ws in practical scenes. The comparative experimental results show that the CSV-Net model achieves a good balance between its performance, robustness and complexity for identifying DO-Ws, also having further potential for sustainable application in traditional gardens. Moreover, the Canglang Pavilion and the Humble Administrator’s Garden were selected as the cases to analyze the relation between identifying DO-W types and their locations in intelligent approaches, which further reveals the design rules of the sustainable culture contained in Chinese traditional gardens. This work ultimately promotes the sustainable application of artificial intelligence technology in the field of garden design and inheritance of the garden art.
Experimental study of multiquantum, multiphoton resonances and Bloch-Siegert shifts in low-frequency EPR spectra

Annotation. The EPR from the DPPG was investigated on a spectrometer with different angles between a constant polarization field and an adjustable RF pump field at a frequency of 20 MHz in a continuous mode and a receiver tuned to the second harmonic frequency of the EPR signal. A technique for processing and more accurate measurement of the parameters of the EPR lines and the Bloch – Siegert shift is proposed. The conditions for maximizing the signal of even EPR harmonics are obtained depending on the angle of inclination of the HF field to the constant field. On the basis of experimental data and numerical solutions of the Bloch system of differential equations, the effect of asymmetry and a change in the sign of the second harmonic of the EPR line when the direction of the constant field is reversed is discovered and explained. An additional effect of displacement of the resonance line was discovered due to the inhomogeneous polarization of this line during scanning of the polarizing field. The effect of the appearance of the observed and possible resonant EPR harmonics is explained by the frequency modulation of the resonance conditions by both the perpendicular and parallel components of the RF pump field. It is concluded that the Bloch equations describe well in quality and satisfactory in accuracy all the observed parameters of EPR lines, such as intensity, width, shape, and their shifts. It is also shown that these equations describe the experiment on Bloch-Siegert shifts at least five times more accurately than the data of competing theories. A detailed description of the spectrometer is given and a computer program for the numerical calculation of the EPR spectra is presented. E-mail: ilmas-im-sadykov@yandex.ru
Detecting Opportunistic Behavior in Public Short Campaigns

The high hurdles in proving short campaigns to be illegal manipulation, as well as the possibility of the transgressor claiming “honest errors,” leave room for opportunistic short campaign behavior. Motivated by the theory of Benabou and Laroque (1992), this paper proposes an empirical approach to detect such behavior. Using comprehensive data on short campaigns and linking them to daily short-sale metrics and post-campaign stock market performance, I find a greater presence of opportunistic behavior when campaigns are clustered, that is, when multiple campaigns are published on the same target at the same time. I further find that the evidence suggesting clustered campaigns are more opportunistic concentrates in the subsamples where target firms are more prone to price manipulation, campaign authors are better connected, and authors’ reputation concerns are lower.
Green and efficient synthesis of dispersible cellulose nanocrystals in biobased polyesters for engineering applications

Despite attractive properties of cellulose nanocrystals (CNCs) such as high natural abundance, inherent biodegradability and high modulus, CNCs tend to degrade and aggregate when exposed to high temperatures during melt processing. In the present work, the surface of CNCs was modified with PMMA to take advantage of the miscibility with various biobased polymers including PLLA when melt-blended. Particular attention was paid to grafting techniques in water medium using two different redox initiators: Fe2+/H2O2 (Fenton’s reagent) and ceric ammonium nitrate (CAN). The successful synthesis of CNC-g-PMMA was verified by gravimetric analysis, FTIR, CP-MAS 13C NMR and suspension tests. A high grafting efficiency of 77% was achieved using CAN as the redox initiator. Increasing the PMMA content on CNC surfaces led to higher CNC thermal stability. As a consequence of PMMA grafting in water, modified CNCs were found to be predispersed in a PMMA network. PLLA/CNC nanocomposites were then prepared by melt-blending, i.
Substrate-free Determination of the Radical Scavenging Activity of Phenolic Compounds by Photochemical Generation of Hydroxyl Radicals and HPLC-UV Detection

This paper describes the study of the radical scavenging activity of the natural antioxidants rutin, quercetin, caffeic acid, ferulic acid, and resveratrol by using a substrate-free chromatographic method based on the generation of •OH radicals by the photolysis of H2O2 in a photochemical reactor. The comparative studies permitted the classification of some phenolic compounds regarding their in vitro radical scavenging properties (rutin > caffeic acid ≈ quercetin>ferulic acid > resveratrol). The results obtained by the proposed HPLC-UV/H2O2 photolysis method could be well corroborated by the other methods, which employ the generation of free radicals with different reactivity (ROO•, , and DPPH•). Furthermore, the reverse-phase chromatographic separation of the antioxidants from their sub-products generated after attacking with HO• radicals improved advantageously the selectivity of the measured signal used for calculating the radical scavenging activity.
Optimized proportion of chitosan complex biological fresh-keeping agents for the preservation of Trichiurus haumela under the cold storage

On the base of the research of single biological fresh-keeping agent,the orthogonal experiment was made to investigate the fresh-keeping effects of complex biological fresh-keeping agents(a mixture of chitosan,lysozyme and tea polyphenol) in the preservation of Trichiurus haumela under the cold storage(4±1 ℃). The sensory scores,total volatile basic nitrogen(TVB-N),total bacterial count,2-thiobarbituric acid(TBA) and pH values were used to evaluate its quality.According to the results of orthogonal experiment L9(33),the order influenced was: chitosanlysozymetea polyphenol,and the optimum composition of chitosan 10.0 g·L-1,lysozyme 0.3 g·L-1 and tea polyphenol 3.0 g·L-1 was determined.It showed that total bacterial count,TVB-N,TBA and pH value of T.haumela processed by complex biological fresh-keeping agents were much lower than those of the controlled,and had the higher sensory evaluation values.The shelf-life of T.haumela treated by the complex biological fresh-keeping agents could be extended 11-13 days.The cost of processing one kilogram T.haumela was only 0.24 yuan,which have great application prospects.
Recursive Mixture Factor Analyzer for Monitoring Multimode Time-Variant Industrial Processes

A critical issue for real-time monitoring of industrial processes with data-based methods is how to adjust the constructed model during monitoring procedure so as to adapt to the change of the process. Traditional latent variable models like factor analyzers are static models and are simply based on the single Gaussian assumption. Therefore, when one comes to multimode and time-variant process conditions, conventional strategies become cumbersome. In this work, a recursive mixture factor analyzer is proposed for multimode time-variant process modeling and monitoring. The developed model with a Bayesian mechanism can automatically select and update the Gaussian components during modeling. Furthermore, a corresponding monitoring mechanism is proposed so that the recursive model can also elegantly adjust parameters according to the newly incoming data and effectively employ more components for new operating modes. Feasibility and efficiency of the proposed method are illustrated through two case studies.
What drives variation in the marginal benefits of renewable energy and demand-side efficiency Preliminary and incomplete

Greenhouse gas mitigation efforts in the electricity sector emphasize accelerated deployment of energy efficiency measures and renewable energy resources. Short-run benefits associated with incremental investments in energy efficiency or renewables manifest indirectly as reductions in the economic operating costs and emissions of marginal electricity generating units. We evaluate different renewable energy (RE) and energy efficiency (EE) technologies across regional power systems. Using standard social cost of carbon assumptions, our estimates of emissions-related benefits comprise a significant share of estimated returns on investment in some regions. On a per-MWh basis, regional variation in emissions displaced and costs avoided is more significant than variation across technologies within individual regions. This implies that the choice of location, more than the technology choice, determines the value generated by these investments. We also find that regional variation in avoided carbon benefits generates significant regional variation in the implied abatement costs associated with each technology. These results underscore the importance of designing policy incentives that accurately capture regional differences in emissions-related returns on RE and EE
A New Decavanadate with Organic Cation: Synthesis, Crystal Structure, and Hirshfeld Surface Analysis

University of Tunis El Manar, Faculty of Sciences of Tunis, Laboratory of Materials, Crystal Chemistry and Applied ermodynamics, 2092 El Manar II, Tunis, Tunisia University of Carthage, High Institute of Preparatory Studies in Biology and Geology (ISEP-BG) of Soukra, Soukra, Tunisia Faculty of Sciences of Sfax, Solid State Laboratory, Faculty of Sciences, University of Sfax, 1171 Sfax 3000, Tunisia
Impacts of the 1900–74 Increase in Anthropogenic Aerosol Emissions from North America and Europe on Eurasian Summer Climate

The impact of North American and European (NAEU) anthropogenic aerosol emissions on Eurasian summer climate during the twentieth century is studied using historical single- and all-forcing (including anthropogenic aerosols, greenhouse gases, and natural forcings) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Intermodel agreement on significant linear trends during a period of increasing NAEU sulfate emissions (1900–74) reveals robust features of NAEU aerosol impact, supported by opposite changes during the subsequent period of decreasing emissions. Regionally, these include a large-scale cooling and associated anticyclonic circulation, as well as a narrowing of the diurnal temperature range (DTR) over Eurasian midlatitudes. Remotely, NAEU aerosols induce a drying over the western African and northern Indian monsoon regions and a strengthening and southward shift of the subtropical jet consistent with the pattern of temperature change. Over Europe, the temporal variations of observed temperature, pressure, and DTR tend to agree better with simulations that include aerosols. Throughout the twentieth century, aerosols are estimated to explain more than a third of the simulated interdecadal forced variability of European near-surface temperature and more than half between 1940 and 1970. These results highlight the substantial aerosol impact on Eurasian climate, already identifiable in the first half of the twentieth century. This may be relevant for understanding future patterns of change related to further emission reductions.
Mannan-Modified PLGANanoparticles for Targeted Gene Delivery

The studies of targeted gene delivery nanocarriers have gained increasing attention during the past decades. In this study, mannan modified DNA loaded bioadhesive PLGA nanoparticles (MAN-DNA-NPs) were investigated for targeted gene delivery to the Kupffer cells (KCs). Bioadhesive PLGA nanoparticles were prepared and subsequently bound with pEGFP. Following the coupling of the mannan-based PE-grafted ligands (MAN-PE) with the DNA-NPs, the MAN-DNA-NPs were delivered intravenously to rats. The transfection efficiency was determined from the isolated KCs and flow cytometry was applied for the quantitation of gene expression after 48 h post transfection. The size of the MAN-DNA-NPs was found to be around 190 nm and the Zeta potential was determined to be −15.46mV. The pEGFP binding capacity of MAN-DNA-NPs was (88.9 ± 5.8)% and the in vitro release profiles of the MAN-DNA-NPs follow the Higuchi model. When compared with non-modified DNA-NPs and Lipofectamine 2000-DNA, MAN-DNA-NPs produced the highest gene expressions, especially in vivo. The in vivo data from flow cytometry analysis showed that MAN-DNA-NPs displayed a remarkably higher transfection efficiency (39%) than non-modified DNA-NPs (25%) and Lipofectamine 2000-DNA (23%) in KCs. The results illustrate that MAN-DNA-NPs have the ability to target liver KCs and could function as promising active targeting drug delivery vectors.
The use of online collaborative writing tools by Ph.D. students

One of the characteristics of personal knowledge management is sharing this knowledge with peers, which can also be accomplished through tools. That said, this work communicates an empirical investigation that explored the use of online collaborative writing tools (OCW), by individuals as students of two programs in the field of Information Sciences in Portugal, intending to know, the point of view about OCWs throughout this cycle of studies. These objectives were achieved through a mixed methodology, using an exploratory and research approach. As research techniques, a bibliography and a questionnaire were used. It was observed the majority use of three OCWs, considered important for the academic path. On the other hand, it seems that the aforementioned doctoral programs could contribute more actively to the use of these OCW. This work intends to inform other investigations about the students' relationship with the OCW, collaborate so that the students themselves become aware of these tools, and contribute with the educational programs to rethink the practices that support this use.
Methodological and Ecological Caveats in Deciphering Plant Volatile Emissions: the Case Study of Tomato Exposed to Herbivory and Resource Limitation

Over recent years, major progress in experimental approaches have bring insights about the ecological functions of volatile organic compounds (VOCs) released by plants. However, deciphering volatile emissions in a methodologically robust and ecologically relevant manner remains a challenging issue. A surge in interest is required to characterize potential blind spots in volatile sampling that could result in dramatic bias in our understanding of VOCs. In parallel, ecologists need to account for various environmental factors in order to address appropriately the sources of variations of VOCs. Here we use two common porous polymers, polydimethylsiloxane (PDMS) and Porapak Q, to collect VOCs released by tomato exposed to herbivory in combination with nitrogen shortage. We dissect two key features of volatile blends, i.e., their composition and their diversity. Upon nitrogen limitation, Porapak Q stresses the up-regulation of a common defensive compound (methyl salicylate), while herbivory induces three terpenes involved in the recruitment of natural enemies of Tuta absoluta (2-carene, ɑ-pinene and β-phellandrene). This study suggests that the combination of resource availability and herbivory governs the differential production of generalist and specific VOCs that are active against a broad spectrum or particular herbivore species, respectively. But PDMS was found unsuitable to observe such patterns in the composition of VOC emissions. Additionally, Porapak Q was found more sensitive than PDMS to track the increase in the diversity of stress-related VOC emissions upon nitrogen limitation. This suggests that plants growing with poor resources release more information in surroundings. We discuss particular implications for tri-trophic-mediated plant defences.
Current Status in the Study of the Humidified Combustion

Firstly,the authors described in detail the research findings in the humidified combustion of fuel from the following two aspects: one is the influence of the humidification on the combustion characteristics of gas turbine combustors and another is the influence of the humidification on the ignition characteristics and combustible limits of fuel. Secondly,they also analyzed the conclusions currently obtained from the study of the humidified combustion. The air humidification combustion can lower the combustion temperature of the flame,change the chemical reactions during the combustion and heat and mass transfer process and influence the flow field and configuration of the flame,making the flame tend to be instable and at the same time,to lower the temperature in the combustion zone is favorable to reducing the NOx emissions and of a major significance for reducing the NOx emissions from the HAT and IGCC etc. thermal cycles. Finally,the authors pointed out that there do exist some shortcomings in terms of the humidified combustion and gave suggestions for the future studies in this regard on the basis of the foregoing research results.
The shallow of your smile: the ethics of expressive vocal deep-fakes

Rapid technological advances in artificial intelligence are creating opportunities for real-time algorithmic modulations of a person’s facial and vocal expressions, or ‘deep-fakes’. These developments raise unprecedented societal and ethical questions which, despite much recent public awareness, are still poorly understood from the point of view of moral psychology. We report here on an experimental ethics study conducted on a sample of N = 303 participants (predominantly young, western and educated), who evaluated the acceptability of vignettes describing potential applications of expressive voice transformation technology. We found that vocal deep-fakes were generally well accepted in the population, notably in a therapeutic context and for emotions judged otherwise difficult to control, and surprisingly, even if the user lies to their interlocutors about using them. Unlike other emerging technologies like autonomous vehicles, there was no evidence of social dilemma in which one would, for example, accept for others what they resent for themselves. The only real obstacle to the massive deployment of vocal deep-fakes appears to be situations where they are applied to a speaker without their knowing, but even the acceptability of such situations was modulated by individual differences in moral values and attitude towards science fiction. This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part II)’.