Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Hyper-parameters of Proximal Policy Optimization Robust to Time Discretization

A small action cycle time can help reinforcement learning agents by granting them fast reaction and a more temporally detailed perception of the environment. The learning performance of both policy gradient and action value methods, however, deteriorates as the cycle time duration is reduced, which necessitates the tuning of the cycle time as a hyper-parameter. Since tuning an additional hyper-parameter is time-consuming when learning on real-world robots, existing algorithms can beneﬁt from having hyper-parameters that are more robust to the choice of cycle time. In this work, we show the inefﬁcacy of the default hyper-parameters of a policy gradient algorithm across different cycle times and propose a novel set of hyper-parameters. We investigate the effectiveness of the new hyper-parameters on a simulated task and validate them on simulated and real-world robotic tasks. Our experiments highlight the superiority of small cycle times over large ones and show that these novel hyper-parameters, unlike the default ones, are robust to different cycle times.
The Assessment of the Sewage and Sludge Contamination by Phthalate Acid Esters (PAEs) in Eastern Europe Countries

Phthalate acid esters (PAEs) are widely used as raw materials for industries that are well known for their environmental contamination and toxicological effects as “endocrine disruptors”. The determining of PAE contamination was based on analysis of dimethyl phthalate (DMP), diethyl phthalate (DEP), dipropyl phthalate (DPP), dibutyl phthalate (DBP), diisobutyl phthalate (DiBP), dicyclohexyl phthalate (DCHP) and di(2-ethylhexyl) phthalate (DEHP) in wastewater and sediment samples collected from city sewer systems of Lithuania and Poland, and Denmark for comparison. The potential PAE sources as well as their concentrations in the wastewater were analyzed and discussed. The intention of the study was to determine the level and key sources of pollution by phthalates in some Eastern European countries and to reveal the successful managerial actions to minimize PAEs taken by Denmark. Water and sludge samples were collected in 2019–2020 and analyzed by gas chromatography-mass spectrometry. The highest contamination with phthalates in Lithuania can be attributed to DEHP: up to 63% of total PAEs in water samples and up to 94% of total PAEs in sludge samples, which are primarily used as additive compounds to plastics but do not react with them and are gradually released into the environment. However, in water samples in Poland, the highest concentration belonged to DMP—up to 210 μg/L, while the share of DEHP reached 15 μg/L. The concentrations of priority phthalate esters in the water samples reached up to 159 μg/L (DEHP) in Lithuania and up to 1.2 μg/L (DEHP) in Denmark. The biggest DEHP concentrations obtained in the sediment samples were 95 mg/kg in Lithuania and up to 6.6 mg/kg in Denmark. The dominant compounds of PAEs in water samples of Lithuania were DEHP > DEP > DiBP > DBP > DMP. DPP and DCHP concentrations were less than 0.05 μg/L. However, the distribution of PAEs in the water samples from Poland was as follows: DMP > DEHP > DEP > DBP, and DiBP, as well as DPP and DCHP, concentrations were less than 0.05 μg/L. Further studies are recommended for adequate monitoring of phthalates in wastewater and sludge in order to reduce or/and predict phthalates’ potential risk to hydrobiots and human health.
Destination Attractiveness Impact Novelty-Seeking Motivation and Satisfaction of Tourists with Eco-Tourism in an Binh Islet, Vinh Long Province

Applying structural equation modeling, the study has shown that the attractiveness of An Binh islet has a positive influence on the novelty-seeking motivation, thereby positively impacts tourists' satisfaction with eco-tourism in An Binh islet, Vinh Long Province. Some policy suggestions have been proposed to motivate the noveltyseeking of visitors for eco-tourism in An Binh islet, Vinh Long Province
SYNTHESIS, CHARACTERIZATION AND ANTIBACTERIAL ACTIVITY OF NEW Ni(II), Co(II) AND Cu(II) COMPLEXES WITH N,N’-DIACYLHYDRAZINES

Lucrarea prezintă un nou ligand, N’-[4-(4-clorofenilsulfonil)benzoil]-2-oxoimidazolidin-1-carbohidrazida (L) care a fost sintetizat prin reacţia dintre hidrazida acidului 4-(4-clorofenilsulfonil)benzoic şi clorura acidului 2-oxoimidazolidin-1carboxilic. De asemenea, au fost obţinuţi şi caracterizaţi prin analiză elementală, măsurători de susceptibilitate magnetică, spectre electronice, RES şi IR şi complecşii [Cu(L-H)2], [Co(L-H)2(H2O)2] şi [Ni(L-H)2(H2O)2]. Aceşti complecşi au fost testaţi pentru acţiunea antibacterială pe culturi de Staphylococcus epidermidis ATCC 14990, Bacillus subtilis ATCC 6633, Bacillus cereus ATCC 14579, Pseudomonas aeruginosa ATCC 9027 şi Escherichia coli ATCC 11775, comparativ cu ligandul necomplexat.
Breaking down barriers: Can LC-MS revolutionize the quantitation of drug product impurities?

In this article, the authors assess the precision, linearity, selectivity, accuracy, and sensitivity of three mass analyzers for liquid chromatography‐mass spectrometry (LC‐MS). They evaluated time-of-flight (TOF MS), single quadrupole, and triple quadrupole (MS‐MS) mass analyzers for quantitation of drug product impurities. When compared with LC UV methods typically applied in these applications, the modes of LC‐MS detection described within this article offer substantial improvements in selectivity and enable more-rapid method development and shorter method run times. The authors compare quantitative results obtained by MS‐MS analyses with those obtained using a previously validated LC UV method. The data obtained by MS‐MS in the multiple reaction monitoring mode displayed acceptable precision, accuracy, and linearity throughout 2‐2.5 orders of magnitude.
Ultra-Wideband radar cross section reduction by thin AMC metasurface

An ultra-wideband thin metasurface is designed and fabricated to reduce the radar cross-section (RCS) from 9.72 GHz to 26.77 GHz (93 % bandwidth) more than 10 dB. The proposed metasurface is composed of two different artificial magnetic conductor (AMC) tiles with about 180° reflection phase differences to destruct the reflected wave over ultra-wideband frequency. Although the designed tiles have similar unit cell configuration, their reflected phase responses are properly tuned by changing the dimensions. The comparison between the proposed AMC metasurface and some sate of the art references clearly proves the high RCS reduction bandwidth with very low thickness of the designed AMC metasurface rather than the references. The measured results are in good agreement with the simulation ones which prove the idea.
Smell and Sustainability: Can Odour Shorten the Life Span of Clothing?

Under Sustainable Development Goal 12, the UN Economic Programme noted a rapid increase in both worldwide material consumption and per capita material footprint, with the latter jumping from 7.3 tonnes per person in 1990 to 10.9 tonnes in 2015 (UN Secretary General 2019). Consequently, landfilled clothing and textile waste pose significant environmental problems, as textile waste has increased considerably over the last few decades due to the increased consumption of clothing (Morgan and Birtwistle 2009; Tokatli et al. 2008). Incineration and landfilling have accounted for about 84% of textile disposal in the US for more than a decade; both the volume of this waste and the cost to manage it on a per tonne basis are growing across the country (Johnson and Adler 2017). Clothing made from petroleum sources does not biodegrade, which adds to the growing problem of non-biodegradable waste (Geyer et al. 2017). Carbon dioxide is also produced during decomposition of organic materials, and methane, a potent greenhouse gas, is produced due to its incomplete biodegradation within landfills in the absence of oxygen (European Commission 2010). Despite methane capture technology and utilisation of landfill gas as a fuel source, it is still estimated that methane emissions from Canadian landfills make up 20% of the total national methane emissions (Government of Canada 2017). Incinerating textiles as an alternative to landfilling can also pose detrimental environmental and health impacts. If appropriate capture technology is not used, persistent organic pollutants such as dioxins can be released during incineration, and the ash from the incineration process still needs to be landfilled (Rabl et al. 2008; European Commission 2010). In the USA, it is estimated that 21 billion pounds (9.5 million tonnes) of textile waste ends up in the landfill each year (Council for Textile Recycling 2017). In the UK, government initiatives aimed at improving the sustainability of the fashion and textiles industry, including focusing on consumer use, have resulted in reductions of 50,000 tonnes of textiles ending up in household waste since 2012 (WRAP 2017). While this is an encouraging start, 300,000 tonnes of clothing still enter household waste that is destined for landfilling or waste-to-energy initiatives (WRAP 2017). According to Value Village’s State of Reuse Report (Value Village 2018), the average
A Psychology of Visualization or (External) Representation?

What is a visualization? There is limited utility in trifling with definitions, except insofar as one serves as a tool for communicating and conceptualizing our subject matter; a statement of identity for a community. To establish Visualization Psychology as a viable interdisciplinary research programme, we must first define the object(s) of our collective inquiry. I propose that while we might refer to the study of “visualization” for the term’s colloquial accessibility and pragmatic alignment with other fields, we should consider for exploration a class of artifacts and corresponding processes more expansive and profound: external representations. What follows is an argument for the study of external representation as the foundation for a new interdisciplinary endeavor, and approach to mapping the corresponding problem space.
Comparison of the Plantar Pressure and Foot Function and Range of Motion in Distance Runners With the Normal, Pronated, and Supinated Feet During Static Standing

Introduction: This study aimed to investigate the foot function, range of motion, plantar pressure, and plantar contact area in the distance runners with normal, pronated, highly-pronated, supinated, and highly-supinated foot posture groups during static standing.Materials and Methods: In this comparative cross-sectional study, a total of 75 distance runners were divided into 5 groups using the foot posture index. The foot function and knee and foot range of motion were assessed using the Foot And Ankle Ability Measure questionnaire (FAAM) and the goniometer, respectively. The mean of the plantar pressure percentage and the mean of the contact area on the forefoot and rearfoot were investigated during static standing. One-way ANOVA was used to compare the outcomes between the groups.Results: Among the groups, the normal foot group showed the highest scores in the activities of daily living subscale and sport subscale. Compared with the other groups, the highly-pronated foot group had a significantly greater range of motion in the ankle plantar flexion (P<0.002), and the normal foot group showed more range of motion in the first metatarsophalangeal extension (P<0.0001). In all groups, the mean plantar pressure percentage on the rearfoot was greater than the mean plantar pressure percentage on the forefoot. Of the groups, the highly-supinated foot group showed the highest plantar pressure percentage on the rearfoot (P<0.0001). However, the highly-pronated foot group showed the highest plantar pressure percentage and the largest contact area on the forefoot (P<0.0001) and the rearfoot (P>0.0001), respectively.Conclusion: Based on the results of this study, the foot posture is an important option that could affect function and range of motion of foot and ankle and distribution of the plantar pressure and plantar contact area.
Recognition of arthropod species names using bigram-based classification

BackgroundThe task of recognizing species names in scientific articles is a quintessential step for a large number of applications in high-throughput text mining and data analytics, such as species-specific information collection, construction of species food networks and trophic relationship extraction. These tasks become even more important in fast-paced species-discovery areas such as entomology, where an impressive number of new arthropod species are discovered each year. This article explores the use of twocharacter n-grams (bigrams) in machine learning models for arthropod species name recognition. This particular method has been previously applied successfully to the task of language identification [1] but the application to species name identification had yet to be explored.ResultsArthropod species names, regular English words used in scientific publications and person names were collected from the public domain and bigrams were extracted and used as classifier features. A number of learning classifiers spanning 7 algorithmic categories (tree-based, rule-based, artificial neural network, Bayesian, boosting, lazy and kernel-based) were tested and the highest accuracies were consistently obtained with LIBLINEAR [2], Bayesian Logistic Regression [3], the Multilayer Perceptron [4], Random Forest [5], and the LIBSVM [6] classifiers. When compared with dictionary-based external software tools such as GNRD [7] and TaxonFinder [8], our top-3 classifiers were insensitive to words capitalization and were able to correctly classify novel species names that are absent in dictionary-based approaches with accuracies between 88.6% and 91.6%. ConclusionsOur results suggest that character bigram-based classification is a suitable method for distinguishing arthropod species names from regular English words and person names commonly found in scientific literature. Moreover, our method can also be used to reduce the number of false positives produced by dictionary-based methods.
Agricultural Product Price Prediction Based on Improved Temporal Convolutional Network

As the price of agricultural product indirectly reﬂects the relationship between supply and demand of the agricultural market, so predicted price could be a reference of farmers and the country to modify their strategy to smooth the impact from the market. The Agricultural product price prediction has been widely studied in recent years, but it generally stays in the stage of traditional linear regression analysis or deep learning model such as LSTM neural network. Therefore, combined with the latest research on time series prediction in deep learning, we apply the temporal convolutional network TCN model to agricultural product price prediction. We also propose an improved TCN neural network model, which introduces LSTM neural network on the basis of TCN model. Thanks to the gate mechanism of LSTM cell, the model’s control to long-term memory is strengthened. Compared with TCN, LSTM neural network and GRU neural network, all single models, we evaluate the TCN stacked LSTM model and results shows that it gets better performance in some cases.
Cost-Efficiency Comparison of an ARM Cluster & Intel Server

- Data processing requirements can be met by using a small group of complex fast processors or a large number of extremely low-cost and low-power processors. This paper empirically compares the cost-effectiveness of these two computing paradigms and evaluates the hypothesis that the low-cost processor solution is more cost-effective. The control group consists of data obtained from a traditional Intel server (2 Intel Xeon CPUs) and the experimental group consists of data obtained from an ARM cluster—6 low-cost Orange Pi One single-board-computers using ARM processors. The ARM cluster was found to be significantly more cost-efficient than the traditional Intel server.
Replication of Key Findings in “Labor-Technology Substitution: Implications for Asset Pricing” using Publicly Available Industry-Level Data

In this mimeo, I construct the share of routine-task labor at detailed industry level (iRShare) to proxy for firms’ RShare in Zhang (2019). I then replicate two key findings in Zhang (2019). First, betas decrease monotonically for both equal-weighted and value-weighted portfolios sorted by iRShare within broad industry sectors. Second, when GDP growth is low, firms with a high iRShare invest more in machines (but not in other capital) compared to other firms in the same sector with a low iRShare. The full iRShare series can be downloaded at https://www.MiaoBenZhang. com/.
DETECTION APPROACH BASED ON MULTI-HEAD STRUCTURE AND ENHANCED FEATURES IN DRIVING ENVIRONMENTS

Most recent state-of-the-art detection approaches based on deep convolutional neural networks are manually designed. These approaches include two-stage frameworks and one-stage frameworks. While one-stage frameworks provide real-time performance in most recent systems, two-stage frameworks usually show better detection accuracy. Most recent two-stage object detection frameworks share a head for both classification and bounding box regression in detection stage. Inspired by recent improvement in double-head structures, this paper proposes a detection framework based on multi-head structure for localizing objects in driving environments. First, the extracted feature maps generated by feature extraction network are enhanced by the enhancement module, which effectively enlarges the receptive field and refines the representation ability of thin feature maps by leveraging both local and global context. The enhanced feature map is then fed to a detection network. Next, the detection network is designed based on double-head structure, where a fully connected head is adopted for classification and a convolution head is used for bounding box regression. In addition, this paper proposes to improve RoI pooling algorithm based on deformable RoI pooling. With the improved RoI pooling process, the harsh quantization of RoI pooling is removed, and the extracted features are properly aligning with the input, thus leading to large improvements. Experiments on public datasets show the effectiveness of the proposed method for localizing objects in driving environments.
Nanoporous Membranes of Hydrogen‐bridged Smectic Networks with Nanometer Transverse Pore Dimensions

Nanoporous membranes are made based on photoreactive hydrogen-bridged smectic liquid crystal dimers. The self-assembled layered structure is locked by photopolymerization. The hydrogen bridges are reversibly opened by heating or by changing the pH. This forms periodic pores of which the integrity is conserved by a covalent crosslinker, setting the spacing at ca. 1 nm. The pores are covered with carboxylic anions that bind to cations or to amines.
Probabilistic Prediction of Time To Fly using Quantile Regression Forest

—The air trafﬁc is mainly divided into en-route ﬂight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. In our contribution we will focus on the prediction of arrival procedures, in particular the time to ﬂy from turn onto ﬁnal approach course to threshold, which supports utilizing available capacity more efﬁciently. Most prediction methods developed so far provide a sole point estimate for the time to ﬂy. We see the need to cover the uncertain nature of aircraft movement by the implementation of a probabilistic approach. This becomes very important in cases where the air trafﬁc system is operated at its limits to prevent safety critical incidents, e.g. separation infringements due to very tight separation. Our approach is based on the Quantile Regression Forest technique that is able to provide a measure of uncertainty of the prediction, instead of a single value only. While the data preparation, model training and tuning steps are identical to classic Random Forest methods, in the prediction phase, Quantile Regression Forests provide a quantile function to express the uncertainty of the prediction. After developing the model, we further investigate in the interpretation of the results and provide different ways of deriving a probability function from it. We found, the Skew Normal Distribution provides an afﬁrmative ﬁt to reﬂect the characteristics of uncertainty in prediction. With this contribution, there becomes a tool available that allows to predict time to ﬂy more sophisticated, depending on the speciﬁc needs of the use case.
A Comprehensive Dynamic Approach for Selecting the Optimal Position of Telecommunication Towers

The telecommunication industry in Jordan has become highly competitive which adds more pressure on service providers to operate more effectively in the manner of the quality of service without undermining the cost of the service. Selecting the optimal position of the telecommunication tower is one of the most important factors to ensure providing high-quality service at a minimum cost. However, selecting the right position for the tower is complicated and involves different factors such as population density, topology, and reserved area. Unfortunately, the current methods and techniques are not robust enough to ensure selecting the right position effectively, that makes the developing approaches imperative. This paper presents a comprehensive dynamic approach for selecting the optimal position of telecommunication towers; the approach integrates the most common selection factors, rules and the GIS system in a multi-phase process. The accuracy of the proposed approach is relatively high (82%), it is evaluated by comparing the recommended positions with the experts’ selection.
Semantic segmentation for seismic facies classiﬁcation - Computer Vision

The seismic facies classiﬁcation is an essential procedure in exploration seismology as well as reservoir characterization. In general, the manual facies classiﬁcation is a time-consuming task because it requires visual examination of a large volume of 3D seismic image. I suggest to adopt a convolutional neural network to automate the classiﬁcation task. 3D seismic data and corresponding manual analysis results are used for training the networks. Also, I will introduce a novel data augmentation method. The test result shows that the trained model perform reasonably.
Hedonic Prices in the Iran market for mobile phones

this paper provides a hedonic price analysis of mobile telephones for the Iran market, based on data of 111 different handsets from 5 manufacturers, which was collected in 7/20/2010. The hedonic price function is linear. We choose about 10 main features of mobile phones, but only four of them have impact in mobile phone prices in this model. Our results indicate that mobile phone users in Iran are ready to pay about $161 for HTC brand. They are also ready to pay about $101 for wireless network connectivity. Other features that have significant impact in mobile phone prices are Touch Screen, GPS and every Mega Pixel Quality of camera. Mobile phone users are ready to pay $43 for Touch Screen feature of mobile phone. In Iran, all of mobile phone suppliers import them from other countries and distribute them in the market. This paper helps them to know which features of mobile phones have more value to customers. They can import mobile phones that have those features and reach a higher market share. Keywords-component; Hedonic Pricing Analysis, Mobile phone, Regression analysis