Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimization Method for Numerically Solving Three-point BVPs of Linear Second-order ODEs with Variable Coefﬁcients

: It is known that most numerical methods for solving differential equations are based on iterative methods or Taylor expansion methods. This paper tries to study a numerical method from a new perspective—optimization method. By means of the idea of kernel ε -SVR, the paper constructs an optimization model for a class of three-point boundary value problems (BVPs) of linear second-order ordinary differential equations (ODEs) with variable coefﬁcients and proposes a novel numerical method for solving them. The proposed method has a certain versatility and can be used to solve some other kinds of differential equations and integral equations. In order to verify the effectiveness of the proposed method, comparative experiments with six speciﬁc linear second-order ODEs are performed. Experimental results show that the proposed method has a good approximation property.
Dashboard of Sentiment in Austrian Social Media During COVID-19

To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we built a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This allows decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We used web scraping and API access to retrieve data from the news platform derstandard.at, Twitter, and a chat platform for students. We documented the technical details of our workflow to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allowed us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We used special word clouds to visualize that overall difference. Longitudinally, our time series showed spikes in anxiety that can be linked to several events and media reporting. Additionally, we found a marked decrease in anger. The changes lasted for remarkably long periods of time (up to 12 weeks). We have also discussed these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at http://www.mpellert. at/covid19_monitor_austria/. Our work is part of a web archive of resources on COVID-19 collected by the Austrian National Library.
MARS: Masked Automatic Ranks Selection in Tensor Decompositions

Tensor decomposition methods have recently proven to be efficient for compressing and accelerating neural networks. However, the problem of optimal decomposition structure determination is still not well studied while being quite important. Specifically, decomposition ranks present the crucial parameter controlling the compression-accuracy trade-off. In this paper, we introduce MARS -- a new efficient method for the automatic selection of ranks in general tensor decompositions. During training, the procedure learns binary masks over decomposition cores that "select" the optimal tensor structure. The learning is performed via relaxed maximum a posteriori (MAP) estimation in a specific Bayesian model. The proposed method achieves better results compared to previous works in various tasks.
N=1 Modelling of Lifestyle Impact on SleepPerformance

Sleep is critical to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night's rest is going to be. For an activity that humans spend around a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is important to create a personalized sleep model. This research proposes a sleep model that can identify causal relationships between daily activities and sleep quality and present the user with specific feedback about how their lifestyle affects their sleep. Our method uses N-of-1 experiments on longitudinal user data and event mining to generate understanding between lifestyle choices (exercise, eating, circadian rhythm) and their impact on sleep quality. Our experimental results identified and quantified relationships while extracting confounding variables through a causal framework. These insights can be used by the user or a personal health navigator to provide guidance in improving sleep.
A revisit of Chen-Teboulle's proximal-based decomposition method

In this paper, we show that Chen-Teboulle's proximal-based decomposition method can be interpreted as a proximal augmented Lagrangian method. More precisely, it coincides with a linearized augmented Lagrangian method. We then proposed three generalized methods based on this interpretation. By invoking recent work (He et al., IMA J. Numer. Anal., 32 (2020), pp. 227--245), we show that the step size condition of Chen-Teboulle's method can be relaxed without adding any further assumptions. Our analysis offers a new insight into this proximal-based decomposition method.
Poverty in the time of epidemic: A modelling perspective

We create a network model to study the spread of an epidemic through physically proximate and accidental daily human contacts in a city, and simulate outcomes for two kinds of agents—poor and non-poor. Under non-intervention, peak caseload is maximised, but no differences are observed in infection rates across poor and non-poor. Introducing interventions to control spread, peak caseloads are reduced, but both cumulative infection rates and current infection rates are systematically higher for the poor than for non-poor, across all scenarios. Larger populations, higher fractions of poor, and longer durations of intervention are found to progressively worsen outcomes for the poor; and these are of particular concern for economically vulnerable populations in cities of the developing world. Addressing these challenges requires a deeper, more rigorous understanding of the relationships between structural poverty and epidemy, as well as effective utilization of extant community level infrastructure for primary care in developing cities. Finally, improving iniquitous outcomes for the poor creates better outcomes for the whole population, including the non-poor.
From Defensive Registration to Subdomain Protection: Evaluation of Email Anti-Spoofing Schemes for High-Profile Domains

—Sending forged emails by taking advantage of do- main spooﬁng is a common technique used by attackers. The lack of appropriate email anti-spooﬁng schemes or their misconﬁgu- ration lead to successful phishing attacks or spam dissemination. In this paper, we evaluate the adoption of the SPF and DMARC security extensions by high-proﬁle domains and analyze spooﬁng possibilities enabled by the absence or misconﬁgurations of their rules. The results show that for top 500 domains of 139 countries, the adoption rate of SPF and DMARC rules are 65.9% and 34.3%, respectively. For banking websites, we obtain almost the same results (64.9% and 35.9%) as for the TOP500 list. However, for defensively registered domains, the results are signiﬁcantly higher especially in terms of published SPF records with 95.37% adoption and 40.1% for DMARC. We also, for the ﬁrst time, investigate the problem of subdomains in the anti-spooﬁng techniques and their possible abuse to send forged emails. We show that even major companies such as Microsoft or ESET Security do not correctly conﬁgure the SPF rules, which leads to the possibility of mail spooﬁng. Based on the emulation of the SPF check function, we show that syntactically wrong SPF rules may break the trust-based authentication system of email service providers by allowing forged emails to land in the user inbox. Finally, to help in remediation, we have issued notiﬁcations to CSIRTs responsible for domains with misconﬁgured SPF records to engage them in the mitigation action.
Heart Rate Variability(HRV) Analysis: A Comprehensive Review

Heart Rate Variability (HRV) analysis a comprehensive review Abstract HRV is an indirect indicator of functioning of Autonomic nervous system(ANS). Many diseases can be identified at an early stage and their prognosis can be monitored by analysing HRV. The common diseases correlated with HRV are Myocardial-Infarction, Hypertension, Arrhythmias and Diabetes. HRV contains voluminous data and require much time to analyse. If it is interpreted into numerical values, which is easy to understand. The literature review goes in detail about the different techniques and various biosignals used to analyse HRV. The signals used are Electrocardiogram(ECG), Photoplethysmography(PPG) and Ballistocardiogram(BCG) signals. Among ECG signals analysis, common technique used for QRS detection is Pan and Thompkins. How-ever much more accuracy and removal of ripples was achieved by Hamilton technique by using search back threshold values. The Phasor transform algorithm is the latest technique which converts HRV data into complex numbers. The advantage of this technique is easy to compute, require less data storage and its robustness. BCG is another non-invasive technique, produces graphical representation of vibrations produced by pumping action of heart, by placing sensors at bed or chairs it can be recorded. But it has difficulty in detecting individual heart beats. Among ECG, PPG and BCG signals, ECG is the standard technique. PPG analysis gives 98.8% accuracy by adopting different motion artefact removal techniques and algorithms. PPG signals analysis has attracted the researchers as it can be recorded easily at the fingertip. Most of the soft wares are available for HRV analysis. Among them KUBIOS software is found to be best and reliable. The latest version is 3.2. The review gives an insight of the usefulness, applications of various techniques used to extract HRV parameters by different soft wares and algorithms. It also suggests the physiological signals to extract HRV.
A FIRST LOOK AT SITE EFFECTS AT ICELANDIC STRONG- MOTION STATIONS USING MICROSEISMIC DATA

Iceland is located on the Mid-Atlantic Ridge, the diverging plate boundary of the North American and Eurasian plates in the North Atlantic Ocean. Crossing the island from southwest to north, the onshore part of the plate boundary is shifted eastward, resulting in two transform zones, the South Iceland Seismic Zone, SISZ, and the Tjörnes Fracture Zone, TFZ, both of which are regions where earthquake hazard is the highest in the country, and have the greatest potential for the occurrence of large earthquakes. Geologically, Iceland is characterized by basaltic lavas, as well as tuff layers, often with intermediate layers of sediments or alluvium. In a first attempt to assess relative differences in local site conditions at recording sites of the Icelandic Strong-motion Network (IceSMN), Nakamura's 1989 H/V Spectral Ratio, HVSR, method was applied to ambient noise data collected at IceSMN strong motion stations in and near the South Iceland Seismic Zone. The stability of the estimated predominant frequency and consistency shown by the shapes of the HVSRs for each measurement site gives credence for using Nakamura’s HVSR method for estimating local site effects and site characterization in Iceland.
Small RNA Sequencing Reveals Transfer RNA-derived Small RNAs

Expression Profiles in Retinal Neovascularization 2 3 Yingqian Peng, Jingling Zou, Jiang-Hui Wang, Huilan Zeng, Wei Tan, 4 Shigeo Yoshida, Liwei Zhang, Yun Li, Yedi Zhou* 5 6 1. Department of Ophthalmology, The Second Xiangya Hospital, Central South 7 University, Changsha, Hunan 410011, China; 8 2. Hunan Clinical Research Center of Ophthalmic Disease, Changsha, Hunan 410011, 9 China; 10 3. Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East 11 Melbourne, Victoria, Australia; 12 4. Ophthalmology, Department of Surgery, University of Melbourne, East Melbourne, 13 Victoria, Australia; 14 5. Department of Ophthalmology, Kurume University School of Medicine, Kurume, 15 Fukuoka 830-0011, Japan 16 17 Corresponding author: Yedi Zhou, MD, PhD 18 Department of Ophthalmology, The Second Xiangya Hospital, Central South 19 University, Changsha, Hunan 410011, China 20 Telephone: +86-731-85292175 21 E-mail: zhouyedi@csu.edu. cn 22 Running title: tsRNAs in OIR. 23
Agent-Aware State Estimation: Effective Trafﬁc Light Classiﬁcation for Autonomous Vehicles

—Autonomous systems often operate in environments where the behavior of all agents is mostly governed by the perception of a speciﬁc feature of the environment. When an autonomous system cannot recover this feature, there can be disastrous consequences. We introduce a novel framework for agent-aware state estimation that exploits the dependency of all agents’ behavior on a feature to better indirectly observe the feature. To allow for fast and accurate inference, we provide a mapping of our framework to a dynamic Bayesian network and show that speed of inference scales favorably with the number of agents in the environment. We then apply our approach to trafﬁc light classiﬁcation, focusing on instances where direct vision of the light may be obstructed by glare, heavy rain, vehicles, or other environmental factors. Finally, we show that agent-aware state estimation outperforms prevailing methods that only use direct image data of the trafﬁc light on a real-world autonomous vehicle data set of challenging scenarios.
Modelling of the Behaviour of Salmonella enterica serovar Reading on Commercial Fresh-Cut Iceberg Lettuce Stored at Different Temperatures

The aim of this study was to model the growth and survival behaviour of Salmonella Reading and endogenous lactic acid bacteria on fresh pre-cut iceberg lettuce stored under modified atmosphere packaging for 10 days at different temperatures (4, 8 and 15 °C). The Baranyi and Weibull models were satisfactorily fitted to describe microbial growth and survival behaviour, respectively. Results indicated that lactic acid bacteria (LAB) could grow at all storage temperatures, while S. Reading grew only at 15 °C. Specific growth rate values (μmax) for LAB ranged between 0.080 and 0.168 h−1 corresponding to the temperatures 4 and 15 °C while for S. Reading at 15 °C, μmax = 0.056 h−1. This result was compared with published predictive microbiology models for other Salmonella serovars in leafy greens, revealing that predictions from specific models could be valid for such a temperature, provided they were developed specifically in lettuce regardless of the type of serovars inoculated. The parameter delta obtained from the Weibull model for the pathogen was found to be 16.03 and 18.81 for 4 and 8 °C, respectively, indicating that the pathogen underwent larger reduction levels at lower temperatures (2.8 log10 decrease at 4 °C). These data suggest that this Salmonella serovar is especially sensitive to low temperatures, under the assayed conditions, while showcasing that a correct refrigeration could be an effective measure to control microbial risk in commercial packaged lettuce. Finally, the microbiological data and models from this study will be useful to consider more specifically the behaviour of S. Reading during transport and storage of fresh-cut lettuce, elucidating the contribution of this serovar to the risk by Salmonella in leafy green products.
A nuclear GFP/β‐galactosidase fusion protein as a marker for morphogenesis in living Drosophila

A general, non‐invasive method to trace morphogenesis in living Drosophila was developed. To label specific cells, green fluorescence protein (GFP) of jellyfish Aequorea victoria was expressed by the Ga14‐UAS system. Green‐fluorescence from GFP fused to the nuclear localization signal was detectable in polytene larval tissue, but not in diploid tissue. Further fusion to bacterial β‐galactosidase produced GFPN‐lacZ, which fluoresced brightly in several diploid larval and embryonic tissues. GFPN‐lacZ was used to trace dynamic cell movement during the formation of the embryonic tracheal system. These results indicate that GFPN‐lacZ can be used to mark specific cells to study cell movement and gene expression in living animals.
Combating the Coronavirus Pandemic: Early Detection, Medical Treatment, and a Concerted Effort by the Global Community

The World Health Organization (WHO) has declared the outbreak of 2019 novel coronavirus, known as 2019-nCoV, a pandemic, as the coronavirus has now infected over 2.6 million people globally and caused more than 185,000 fatalities as of April 23, 2020. Coronavirus disease 2019 (COVID-19) causes a respiratory illness with symptoms such as dry cough, fever, sudden loss of smell, and, in more severe cases, difficulty breathing. To date, there is no specific vaccine or treatment proven effective against this viral disease. Early and accurate diagnosis of COVID-19 is thus critical to curbing its spread and improving health outcomes. Reverse transcription-polymerase chain reaction (RT-PCR) is commonly used to detect the presence of COVID-19. Other techniques, such as recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), clustered regularly interspaced short palindromic repeats (CRISPR), and microfluidics, have allowed better disease diagnosis. Here, as part of the effort to expand screening capacity, we review advances and challenges in the rapid detection of COVID-19 by targeting nucleic acids, antigens, or antibodies. We also summarize potential treatments and vaccines against COVID-19 and discuss ongoing clinical trials of interventions to reduce viral progression.
Particle size effect in the mechanically assisted synthesis of ß-cyclodextrin mesitylene sulfonate

Mechanically assisted synthesis of organic compounds has recently focused considerable attention as it may be unique in features to selectively orient the reaction pathway. In the continuation of our work on the synthesis of modified cyclodextrins (CDs) via mechanochemical activation, we sought to discriminate the contribution of supramolecular effects and grinding during the course of a reaction in the solid state. As such, we recently investigated the influence of the particles size of β-CD in the synthesis of β-CD mesitylene sulfonate (β-CDMts) in the solid state using a vibrating ball-mill. We were particularly interested in the role of the particles size on the kinetics of the reaction. In this study, we show that grinding β-CD reduces the particles size over time down to a limit of 167 nm. The granulometric composition remains rather invariant for grinding times over 1 h. Each type of β-CD particles reacted with mesitylene sulfonic chloride (MtsCl) to produce β-CDMts. Contrary to what could be intuitively anticipated, smaller particles did not lead to the highest conversions. The impact of grinding over the conversions was limited. Interestingly, the proportion of β-CDMts mono-substituted on the primary face significantly increased over time when the reaction was carried out in the presence of KOH as a base. The data series were confronted with kinetics models to get clues on the way the reactions proceeded. The diversity of possible models suggests that multiple mechanochemical processes can account for the syntheses of β-CDMts in the solid state. Throughout this study, we found that the reactivity depends more upon diffusion phenomena in the crystalline parts of the material than upon the increase in the surface area of the CD particles resulting from grinding.
Debogonising 2a10: : /12: Analysis of One Week's Visibility of a New /12

—In mid 2019, IANA allocated 2a10::/12 to the RIPE NCC, the ﬁrst IPv6 allocation to a Regional Internet Registry (RIR) in over twelve years. By early 2020, we were preparing to issue space from this /12 to our members. Without measurement, it is unclear whether this /12 is safe to use: routing policies, long-forgotten router conﬁgurations, malicious trafﬁc, or networks making private use of unallocated address space may reduce the usability of some or all of the space. In order to provide an opportunity for operators to test the space prior to operational use, we announced the full /12 via BGP for one week followed by eight smaller allocations, each with one target responsive to ICMP echo requests. We captured all packets sent to this /12 while the BGP announcements were active. In addition, a RIPE Atlas measurement campaign was conducted, and route propagation was inspected via the RIPE Routing Information Service (RIS). Notable trafﬁc captured includes low-volume TCP traceroute scans into the address space and DNS trafﬁc, including some from misconﬁgured DNS servers. No trafﬁc type is pervasive, and disregarding trafﬁc generated by RIPE Atlas, only 6.2M packets carrying various transport payloads were captured during this study. Analysis of RIS data indicates that route visibility is good, and RIPE Atlas data shows that reachability to this space is also generally good. From RIPE Atlas measurements we identify a small number of networks where reachability is reduced, in line with reasonable route ﬁltering practices. This work forms the ﬁrst signiﬁcant IPv6 darknet study since 2013. We provide fresh insight into the activity of unsolicited IPv6 trafﬁc today. We show that while there is more unsolicited trafﬁc in the IPv6 space than in the past, this trafﬁc is not as pervasive as in the IPv4 space. Further, this work assists our understanding of the feasibility of operating previously-unused address space from future IANA allocations.
A strong neutron burst in jet-like supernovae of spinstars

Context. Some metal-poor stars have abundance patterns, which are midway between the slow (s) and rapid (r) neutron capture processes. Aims. We show that the helium shell of a fast rotating massive star experiencing a jet-like explosion undergoes two efficient neutron capture processes: one during stellar evolution and one during the explosion. It eventually provides a material whose chemical composition is midway between the s- and r-process. Methods. A low metallicity 40 M⊙ model with an initial rotational velocity of ∼700 km s−1 was computed from birth to pre-supernova with an extended nuclear network following the slow neutron capture process. A two-dimensional hydrodynamic relativistic code was used to model a E = 1052 erg relativistic jet-like explosion hitting the stellar mantle. The jet-induced nucleosynthesis was calculated in post-processing with an optimised network of 1812 nuclei. Results. During the star’s life, heavy elements from 30 ≲ Z ≲ 82 are produced thanks to an efficient s-process, which is boosted by rotation. At the end of evolution, the helium shell is largely enriched in trans-iron elements and in (unburnt) 22Ne, whose abundance is ∼20 times higher than in a non-rotating model. During the explosion, the jet heats the helium shell up to ∼1.5 GK. It efficiently activates (α, n) reactions, such as 22Ne(α, n), and leads to a strong n-process with neutron densities of ∼1019 − 1020 cm−3 during 0.1 s. This has the effect of shifting the s-process pattern, which was built during stellar evolution, towards heavier elements (e.g. Eu). The resulting chemical pattern is consistent with the abundances of the carbon-enhanced metal-poor r/s star CS29528-028, provided the ejecta of the jet model is not homogeneously mixed. Conclusions. The helium burning zones of rotating massive stars experience an efficient s-process during the evolution followed by an efficient n-process during a jet-like explosion. This is a new astrophysical site which can explain at least some of the metal-poor stars showing abundance patterns midway between the s- and r-process.