Miracle-dz/UniFAR-FTUs huggingface dataset:
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach

Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks. Although worker scheduling has been well studied recently, most existing works only focus on the static deployment of workers but ignore their temporal movement continuity. In this paper, we explicitly consider the use of unmanned vehicular workers, e.g., drones and driverless cars, which are more controllable and can be deployed in remote or dangerous areas to carry on long-term and hash tasks as a vehicular crowdsourcing (VC) campaign. We propose a novel deep reinforcement learning (DRL) approach for curiosity-driven energy-efficient worker scheduling, called "DRL-CEWS", to achieve an optimal trade-off between maximizing the collected amount of data and coverage fairness, and minimizing the overall energy consumption of workers. Specifically, we first utilize a chief-employee distributed computational architecture to stabilize and facilitate the training process. Then, we propose a spatial curiosity model with a sparse reward mechanism to help derive the optimal policy in large crowdsensing space with unevenly distributed data. Extensive simulation results show that DRL-CEWS outperforms the state-of-the-art methods and baselines, and we also visualize the benefits curiosity model brings and show the impact of two hyperparameters.
Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar neural network for scalable on-chip learning

On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully connected neural network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or even on a crossbar FCNN based on other non-volatile memory devices. However there are issues with respect to scalability of the on-chip learning scheme in the domain wall synapse based FCNN. Unless the scheme is scalable, it will not be competitive with respect to training a neural network on a conventional computing unit for real applications. In this paper, we have proposed a modification in the standard gradient descent algorithm, used for training such FCNN, by including appropriate thresholding units. This leads to optimization of the synapse cell at each intersection of the crossbars and makes the system scalable. In order for the system to approximate a wide range of functions for data classification, hidden layers must be present and the backpropagation algorithm (extension of gradient descent algorithm for multi-layered FCNN) for training must be implemented on hardware. We have carried this out in this paper by employing an extra crossbar. Through a combination of micromagnetic simulations and SPICE circuit simulations, we hence show highly improved accuracy for domain wall syanpse based FCNN with a hidden layer compared to that without a hidden layer for different machine learning datasets.
Vaccination of Rainbow Trout against Infectious Hematopoietic Necrosis (IHN) by Using Attenuated Mutants Selected by Neutralizing Monoclonal Antibodies

Abstract A neutralizing monoclonal antibody against infectious hematopoietic necrosis virus (IHNV) was used to select neutralization-resistant mutants from isolates of virus obtained from adult steelhead Oncorhynchus mykiss returning to the Round Butte Hatchery (RB mutants) on the Deschutes River in Oregon, USA, and from rainbow trout (nonanadromous O. mykiss) at a commercial hatchery in the Hagerman Valley of Idaho, USA (193-110 mutants). Two of the mutants, RB-1 and 193-110-4, were significantly (P < 0.001) attenuated compared with parental strains. Vaccination of rainbow trout by waterborne exposure to the mutants conferred solid protection against challenge with wild-type virus. In some trials, fish vaccinated with the RB-1 mutant at 50% tissue culture infectious doses (TCID50) of 1 × 104–1 × 105 TCID50/mL or with the 193-110-4 mutant at 1 × 102–1 × 103 TCID50/mL, held for 14 d, then challenged with the homologous wild-type strain at 1 × 105 TCID50/mL showed relative percent survival of 95–100% (P < 0.
MusX: Online Exploring and Visualizing Graph-Based Musical Adaptations

This paper introduces MusX, a visualization-based system that helps to search and explore a large multivariate bipartite graph of artists and songs. An additional tree structure for the song nodes is also inherited from the musical adaptation relations. In tight collaboration with a public national library and archives institution, we propose a novel artist-centered interactive set of representations, focusing on several identified user tasks. This online system is targeted towards laypersons, willing to quickly navigate artists’ body of songs and explore their relationships to other artists through their implication in song creation. In this paper, we present a detailed description of MusX along with design and technical considerations, and the demonstration scenarios we intend to present to the audience.
Locally Designed Campus Smart Bike Sharing System: Lessons Learned and Design Optimization for Thailand

Bike-sharing systems have been widely implemented in around 700 cities worldwide since the 2000s. The quick expansion is due to the growing concerns over environmental impacts and climate change problems. Bicycles are deemed to be a promising mode of transportation for achieving urban sustainability and sustainability in higher education. Sustainable transportation is an important factor in achieving the UN's Sustainable Development Goals. In Thailand, a bike-sharing service was first launched in Bangkok in 2012. However, bike-sharing in Thailand heavily relied on systems designed and operated by international companies. Many systems have not been very successful and some were discontinued. We discuss lessons learned from a locally designed bike-sharing system and its optimization for a Thai university. A pilot-scale of public bicycles was launched and over 24,000 trips were observed in six months. Most trips were 0–10 minutes and peak hours were in the morning, which means most students picked up the public bike on the last-mile based on study timetable. From heat maps of bicycle usage, nearly educational buildings and connecting transit points had the highest departure and arrival rates.
Helical Cell Wall Thickenings in Root Cortical Cells of Polypodiaceae Species from Northwestern Argentina

Abstract The occurrence of helical cell wall thickenings in fern roots is not well investigated and there are few records about it in the literature. To assess the presence of thickenings and their chemical composition, we studied all species of Polypodiaceae, which grow in northwestern Argentina, using light microscopy, scanning electron microscopy, and fluorescence microscopy. Twenty of the twenty-one species studied showed the thickening in the roots. Only in Melpomene peruviana are helical cell wall thickenings absent. All thickenings have cellulose as the main compound. The structure of the thickening may be classified as simple, furcate, or anastomosing. All data presented in this paper corroborate the same structure and chemical composition of thickenings previously reported for Aspleniaceae.
A two-stage method for constructing real-time high-precision temperature map

Temperature monitoring has many real-life applications in the fields of electricity, meteorology, manufacturing, agriculture, security, etc. Therefore, it is important to efficiently construct a high-precision temperature map based on a number of sampling temperature data collected by temperature sensors. For this problem, the traditional methods include the average value method, the finite element method, and the Kriging method, among which the Kriging method has the highest accuracy, but requires the highest complexity, preventing its applications in large-scale scenes. To overcome this drawback, a two-stage constructing method is developed, which divides all the pixels into two classes, called 1-level pixels and 2-level pixels respectively. For the 1-level pixels, it still uses the Kriging method to estimate the corresponding temperatures (to ensure accuracy). Based on estimated temperatures of the 1-level pixels, for the 2-level pixels it uses a local weighted mean method with low complexity to estimate the corresponding temperatures (to improve efficiency). Experimental results show that, the new method could greatly improve the efficiency of calculation, only with very few loss of accuracy, thus satisfying the real-time requirement in large-scale cases.
FlashSchema: Achieving High Quality XML Schemas with Powerful Inference Algorithms and Large-scale Schema Data

Getting high quality XML schemas to avoid or reduce application risks is an important problem in practice, for which some important aspects have yet to be addressed satisfactorily in existing work. In this paper, we propose a tool FlashSchema for high quality XML schema design, which supports both one-pass and interactive schema design and schema recommendation. To the best of our knowledge, no other existing tools support interactive schema design and schema recommendation. One salient feature of our work is the design of algorithms to infer k-occurrence interleaving regular expressions, which are not only more powerful in model capacity, but also more efficient. Additionally, such algorithms form the basis of our interactive schema design. The other feature is that, starting from large-scale schema data that we have harvested from the Web, we devise a new solution for type inference, as well as propose schema recommendation for schema design. Finally, we conduct a series of experiments on two XML datasets, comparing with 9 state-of-the-art algorithms and open-source tools in terms of running time, preciseness, and conciseness. Experimental results show that our work achieves the highest level of preciseness and conciseness within only a few seconds. Experimental results and examples also demonstrate the effectiveness of our type inference and schema recommendation methods.
Operation of compact cryocooled 2V programmable josephson voltage standard at NIM

In the past, most of worldwide Josephson voltage standard systems use liquid helium to cool down the Josephson junction chips. Due to the liquid helium shortage, the cost of operating liquid helium based Josephson voltage standards rises year by year. Therefore there is an increasing need for a compact cryocoooled Josephson voltage standard which has no need of liquid helium. This paper presents a domestic liquid-helium-free dc programmable Josephson voltage standard (PJVS) operating on a cryocooler at 4K or 10 K. In this work, the performance of a NIST-fabricated two volt Josephson junction array (JJA) and a NMIJ-fabricated two volt JJA operating in a cryopackage around the Gifford-McMahon (GM) cryocooler is presented. The JJA chip has been shown to operate successfully with the domestic cryopakage. Stable quantum steps with operating margin greater than 1.4 mA was obtained. Differences in behavior of the NIST-fabricated JJA in liquid helium and on the cryocooler are also discussed.
Roles of the Site 2 Protease Eep in Staphylococcus aureus

The emergence of multidrug-resistant Staphylococcus aureus makes the treatment of staphylococcal infections much more difficult. S. aureus can acquire a drug resistance gene from other bacteria, such as Enterococcus faecalis. Intriguingly, S. aureus produces a sex pheromone for the E. faecalis plasmid pAM373, raising the possibility that S. aureus actively promotes plasmid conjugation from E. faecalis. In this study, we found that the staphylococcal Eep protein is responsible for sex pheromone processing and contributes to the survival of the bacteria in the host. These results will enhance future research on the drug resistance acquisition of S. aureus and can lead to the development of novel antivirulence drugs. ABSTRACT In Enterococcus faecalis, the site 2 protease Eep generates sex pheromones, including cAM373. Intriguingly, in Staphylococcus aureus, a peptide similar to cAM373, named cAM373_SA, is produced from the camS gene. Here, we report that the staphylococcal Eep homolog is not only responsible for the production of cAM373_SA but also critical for staphylococcal virulence. As with other Eep proteins, the staphylococcal Eep protein has four transmembrane (TM) domains, with the predicted zinc metalloprotease active site (HEXXH) in the first TM domain. eep deletion reduced the cAM373_SA activity in the culture supernatant to the level of the camS deletion mutant. It also markedly decreased the cAM373 peptide peak in a high-performance liquid chromatography (HPLC) analysis. Proteomics analysis showed that Eep affects the production and/or the release of diverse proteins, including the signal peptidase subunit SpsB and the surface proteins SpA, SasG, and FnbA. eep deletion decreased the adherence of S. aureus to host epithelial cells; however, the adherence of the eep mutant was increased by overexpression of the surface proteins SpA, SasG, and FnbA. eep deletion reduced staphylococcal resistance to killing by human neutrophils as well as survival in a murine model of blood infection. The overexpression of the surface protein SpA in the eep mutant increased bacterial survival in the liver. Our study illustrates that in S. aureus, Eep not only generates cAM373_SA but also contributes to the survival of the bacterial pathogen in the host. IMPORTANCE The emergence of multidrug-resistant Staphylococcus aureus makes the treatment of staphylococcal infections much more difficult. S. aureus can acquire a drug resistance gene from other bacteria, such as Enterococcus faecalis. Intriguingly, S. aureus produces a sex pheromone for the E. faecalis plasmid pAM373, raising the possibility that S. aureus actively promotes plasmid conjugation from E. faecalis. In this study, we found that the staphylococcal Eep protein is responsible for sex pheromone processing and contributes to the survival of the bacteria in the host. These results will enhance future research on the drug resistance acquisition of S. aureus and can lead to the development of novel antivirulence drugs.
Natural Language Processing for Literary Text Analysis: Word-Embeddings-Based Analysis of Zofka Kveder's Work

In this paper we use word embeddings to analyse the corpus of work of a Slovenian modernist writer Zofka Kveder. For a selection of central concepts of her work, we compute the closest FastText embed-dings. The interpretation shows that many of the word relations can be interpreted in line with the ﬁndings in literary studies (e.g., woman is discussed in relation to men and Catholicism), or open novel paths for interpretation (e.g., woman in relation to European). On the other hand we also point at some problems resulting from the lemmatization and from using FastText embeddings.
Adaptive Top-k Overlap Set Similarity Joins

The set similarity join (SSJ) is core functionality in a range of applications, including data cleaning, near-duplicate object detection, and data integration. Threshold-based SSJ queries return all pairs of sets with similarity no smaller than a given threshold. As results, and their utility, are very sensitive to the choice of threshold value, it is a problem that it is difficult to choose such an appropriate value. Doing so requires prior knowledge of the data, which users often do not have. To avoid this problem, we propose a solution to the top-k overlap set similarity join (TkOSSJ) that returns k pairs of sets with the highest overlap similarities. The state-of-the-art solution disregards the effect of the so-called step size, which is the number of elements accessed in each iteration of the algorithm. This affects its performance negatively. To address this issue, we first propose an algorithm that uses a fixed step size, thus taking advantage of the benefits of a large step size, and then we present an adaptive step size algorithm that is capable of automatically adjusting the step size, thus reducing redundant computations. An extensive empirical study offers insight into the new algorithms and indicates that they are capable of outperforming the state-of-the-art method on real, large-scale data sets.
Development of an Indicator Model for Anticipation of Strategy Implementation Failures

Strategy is the fundamental means to plan and obtain a competitive advantage. Studies suggest that an extensive gap exists between strategy intent and strategy implementation outcomes, indicated by a low rate of achieved objectives. Especially manufacturing companies face a situation of shorter strategy-to-implementation timespans. Frontloading and the customer-induced demand for continuous innovation lead to shorter product life cycles and force manufacturing companies to adapt quickly. Whereas the number of tools and models for strategy design and good implementation practices is vast, attempts to quantify and anticipate strategy implementation failure risk are almost nonexistent. This paper introduces a new model based on quantifiable and easily available indicators for a topic that remains otherwise solely qualitatively addressed in literature. Following a use-case adapted selection of indicators, a methodology for analysis and prediction is proposed using logistic regression analysis. The model's performance is tested and validated with publicly available data from 14 car manufacturers over 20 years. The results show a statistically significant predictive power for strategy implementation failures and suggest the future use of the model as a tool in strategy implementation processes.
Characterization of PfiT/PfiA toxin-antitoxin system of Pseudomonas aeruginosa that affects cell elongation and prophage induction.

Toxin-antitoxin (TA) systems are small genetic modules usually consisting of two elements - a toxin and an antitoxin. The abundance of TA systems among various bacterial strains may indicate an important evolutionary role. Pseudomonas aeruginosa, which can be found in a variety of niches in nature, is an opportunistic pathogen for various hosts. While P. aeruginosa strains are very versatile and diverse, only a few TA systems were characterized in this species. Here, we describe a newly characterized TA system in P. aeruginosa that is encoded within the filamentous Pf4 prophage. This system, named PfiT/PfiA, is a homolog of the ParE/YefM TA system. It is a type II TA system, in which the antitoxin is a protein that binds the toxic protein and eliminates the toxic effect. PfiT/PfiA carries several typical type II characteristics. Specifically, it constitutes two small genes expressed in a single operon, PfiT inhibits growth and PfiA eliminates this effect, PfiA binds PfiT, and PfiT expression results in elongated cells. Finally, we assigned a novel function to this TA system, where an imbalance between PfiT and PfiA, favoring the toxin, resulted in an increase in virion production. This article is protected by copyright. All rights reserved.
Adaptive digital calibration of timing mismatch for TIADCs using correlation

Time-interleaved analog-to-digital converters (TIADCs) play an important role in high-speed data conversion. However, the timing mismatch in TIADCs structure will affect the performance of acquisition systems, such as the spurious free dynamic range (SFDR). In this study, we propose a new all-digital calibration technique to suppress the timing mismatches’ effect by using an adaptive stochastic gradient descent technique based on the cross-correlation method and combined with the fractional delay (FD) filter. This post-processing compensation could quickly track the varying timing skew at a low complexity. The experimental results verify its effectiveness.
Desloratadine and loratadine stand out among common H1-antihistamines for association with improved breast cancer survival

Abstract Background As tumors maintain an inflammatory microenvironment, anti-inflammatory medication can be useful in cancer therapy. We have previously shown an association with improved survival in melanoma for use of the H1-antihistamines desloratadine and loratadine, and here we examine use of H1-antihistamines and breast cancer mortality. Material and methods We investigated use of the six major H1-antihistamines (cetirizine, clemastine, desloratadine, ebastine, fexofenadine and loratadine) and breast cancer-specific and overall mortality in a nation-wide register-based study of all 61,627 Swedish women diagnosed with breast cancer 2006–2013. Both peri- and post-diagnostic antihistamine use was analyzed using Cox regression models. Analyses were stratified for age and subgroup analyses based on estrogen receptor status and menopausal status were performed. Results We found a consistently improved survival of desloratadine users (HR = 0.67; 95% CI 0.55–0.81, p < .001), as well as of loratadine users (HR = 0.80; 95% CI 0.67–0.95, p = .012), relative to nonusers, regardless of patient age, menopause, estrogen receptor status or stage of the tumor, or whether breast cancer-specific or overall survival was analyzed. The survival of users of other antihistamines varied relative to non-users. Conclusion Based on their safety and current use within the patient population, together with our observations, we suggest the initiation of trials of desloratadine and loratadine as treatment of breast cancer as well as studies of the mechanism behind their possible effect. Further studies on any effects of other H1-antihistamines may also be merited, as well as of H1-antihistamine use and survival in other malignancies.
The genetic basis of oviposition preference differences between sympatric host races of the brown planthopper (Nilaparvata lugens)

We have previously analysed the genetic architecture of host–associated performance differences between stocks of the brown planthopper, Nilaparvata lugens (Homoptera: Delphacidae), derived from two host plants: cultivated rice variety TN1 and the semi–aquatic weed Leersia hexandra. It has been established that performance is influenced by a small effective number of loci (1 to 3), with dominance in the direction of the rice population. The results reported here show that, like performance, oviposition preference has a simple genetic basis, but in this case there is no evidence of directional dominance, sex linkage, non–allelic interaction or genotype–environment interaction. The simple genetic architecture of host–associated performance and oviposition preference are as expected if there was a sympatric host shift, most probably from Leersia to rice. However, contrary to the theoretical expectation from models of sympatric host–race formation, there was no detectable genetic association between the traits. The fact that individual females tend to distribute their eggs between plants may have helped to promote a host shift despite this lack of association.
Unlimited Rulebook: a Reference Architecture for Economy Mechanics in Digital Games

In game development, mechanics are one of the basis of the entertainment experience. However, the cost of implementing, improving, and refactoring economy mechanics is high because solutions cannot be easily reused across products. We argue that a reference architecture reduces this cost by providing knowledge reuse in addition to software reuse. To achieve that, we designed Unlimited Rulebook, a reference architecture for economy subsystems in games. It builds on established techniques such as Predicate Dispatching, the Entity-Component-System pattern, and the Adaptive Object-Model architectural style to facilitate the addition and modification of entity types and mechanics to the game while reducing the cost of changing existing code. We evaluated this cost reduction empirically via quasi-experiments with university students in two game programming courses.