Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Influence Evaluation of Additional Training on Deep Neural Networks

—During operation of a system including a deep neural network (DNN), new input values to the DNN that were not included in the initial dataset retained during development may be given. In such a case, the DNN may be additionally trained with the new input values; however, that additional training may reduce the accuracy of the DNN in regard to the initial dataset. It is therefore necessary to evaluate the inﬂuence of the additional training on the accuracy for the initial dataset. However, evaluation by testing all the input values included in the initial dataset takes time. In this paper, we newly propose a method for quickly evaluating the inﬂuence on the accuracy for the initial dataset. As for the proposed method, the gradient of the parameter values (such as weight and bias) for the initial dataset is extracted by running the DNN before the additional training. Then, after the additional training, its inﬂuence on the accuracy with respect to the initial dataset is calculated from the gradient and update differences of the parameter values. To show the feasibility of the proposed method, results of experiments with the MNIST dataset are presented, Accordingly, it is conﬁrmed that the calculation amount of the proposed method after the additional training depends on the number of parameters; therefore, even if the number of input values included in the initial dataset is enormous, the inﬂuence of additional training can be evaluated quickly.
Soluble organic nitrogen in Venezuelan rains

We present data which show that soluble organic nitrogen (SON) is an important component of tropical continentalrains, especially in remote unpolluted sites where it represents up to 90% of the total soluble nitrogen. The inclusionof SON wet deposition in the N budget significantly increases the N nutrient input to the soil vegetation reservoir ofVenezuelan ecosystems.
A Study of the pH Dependence of the Two-Step Oximation of Acetone by in situ Fourier Transform Infrared Spectroscopy

In situ Fourier transform IR spectroscopy has been found to be an appropriate tool for the evaluation, monitoring and optimization of the pH dependent and two-step oximation of acetone in water. When carrying out the reaction at 5 °C, the intermediate formation of the acetone-hydroxylamine adduct (2) was observed at pH 8.5, while at pH 10, the oxime (3) was formed almost instantly. Identification of the intermediate (2) was promoted by the IR spectrum obtained by B3LYP/6-31++G(d,p)//PCM calculations. At pH ∼ 2.5, the protonated form of the oxime (3') developed gradually, but the intermediate (2) could not be observed. The pH dependence of the stability of the intermediate (2) and the (3' → 3) and (3 → 3') interconversions were studied in separate experiments. An equilibrium was observed in the latter case.
Viscoelastic properties and pasting characteristics of fermented maize: influence of the addition of malted cereals.

Summary Investigations were conducted on the viscoelastic properties and pasting characteristics of fermented maize with malted cereals and their suitability for infant feeding. A 3 × 3 × 2 factorial experimental design with malting time, cereal malt concentration and cereal type was used. Maize, millet, and sorghum malts were added to fermented maize to reducing its bulk density. Samples were analysed for their viscoelastic properties and pasting characteristics using Brabender Viscoamylograph. Sorghum malt in comparison to millet and maize malts was not effective in terms of lowering the hot and cold paste viscosities of the fermented maize. Maize and millet malts liquefied the dough considerably during both the hot and cold paste viscosities. The effect of 4-day malted millet was most pronounced whilst the highest activity of maize malt was observed with the 3-day malted flours. The addition of maize and millet malts to fermented maize were most effective in lowering the viscoelastic properties of the resulting porridges.
Remediation of Heavy Metals by Biomolecules: A Review

Some biomolecules are well known for the complexation of heavy metals from wastewater: cellulose, alginate, pectins, and starches for example. However, less used natural molecules could also be efficient to chelate heavy metals. Indeed, some cells contain polypeptides that can help detoxifying living organisms containing heavy metals. This natural detoxification process is of great interest, and particularly the molecules taking part in it. The amino acids composing these polypeptides, especially cysteine, tyrosine, and histidine, can be used for water purification. The efficiency of biomolecules containing aromatic rings on heavy metals complexation is also investigated. All these biomolecules are able to chelate heavy metals thanks to some chemical groups and atoms: the most well-known and efficient chemical groups are listed in the article. Finally, some potential environmental applications of biomolecules are suggested at the end of the article.
Incorporating 9GAG Memes to Develop EFL Learners’ Speaking Ability and Willingness to Communicate

ABSTRACTThis research aimed at examining whether employing 9GAG humorous memes as ELT materials is effective in developing EFL learners’ speaking ability and their willingness to speak English. A group of Indonesian intermediate EFL learners were selected as respondents for this study and a series of pre-test was conducted to indicate their initial speaking ability and willingness to communicate before the incorporation of 9GAG humorous memes. The group then learned and practiced speaking skills using their routine topics but complemented with humorous materials adopted from the 9GAG application. Ten teaching and learning sessions were held and were followed by post-test to measure participants’ learning achievements as to speaking ability and willingness to speak. A SPSS software was used to calculate samples t-test to make comparisons between the pre-test and post-test of group’s test scores. The results indicated that using 9GAG humorous memes resulting in laughter is impactful in creating a relaxed atmosphere for EFL learners during learning sessions. Students' attention, retention, creativity, and critical thinking are also maintained and increased. It is concluded that humorous materials in language classes are effectively applicable in increasing learners’ speaking ability and willingness to communicate.ABSTRAKPenelitian ini bertujuan untuk menguji apakah penggunaan meme humor 9GAG sebagai materi ELT efektif dalam mengembangkan kemampuan berbicara dan kemauan siswa EFL untuk berbicara bahasa Inggris. Sekelompok pelajar EFL menengah Indonesia dipilih sebagai responden untuk penelitian ini dan serangkaian tes awal dilakukan untuk menunjukkan kemampuan berbicara awal mereka dan kemauan untuk berkomunikasi sebelum penggabungan meme lucu 9GAG. Kelompok tersebut kemudian belajar dan mempraktekkan keterampilan berbicara menggunakan topik rutin mereka tetapi dilengkapi dengan materi humor yang diadopsi dari aplikasi 9GAG. Sepuluh sesi belajar mengajar diadakan dan dilanjutkan dengan post test untuk mengukur prestasi belajar peserta dalam hal kemampuan berbicara dan kemauan berbicara. Software SPSS digunakan untuk menghitung sample t-test untuk membuat perbandingan antara nilai tes awal dan tes akhir kelompok. Hasil penelitian menunjukkan bahwa penggunaan meme humor 9GAG yang menghasilkan tawa berdampak dalam menciptakan suasana santai bagi peserta didik EFL selama sesi pembelajaran. Perhatian, retensi, kreativitas, dan pemikiran kritis siswa juga dijaga dan ditingkatkan. Oleh karena itu, dapat disimpulkan bahwa materi humor di kelas bahasa dapat diterapkan secara efektif dalam meningkatkan kemampuan berbicara dan kemauan siswa untuk berkomunikasi.
The Influence of Traditional and Modern Learning Spaces on Pedagogical Affect, Classroom Community, and Learning Outcomes for Marketing Students

*Theresa B. Clarke, Ph.D., Academic Unit Head and Professor of Marketing, James Madison University, College of Business, MSC 0205, Harrisonburg, VA 22807, Tel: 540-568-3238, Email: clarketb@jmu.edu. ORCID: 00000002-4582-7533. C. Leigh Nelson, Ph.D., Professor of Communications Studies, James Madison University, MSC 2106, Harrisonburg, VA 22807, Tel: 540-568-3387, Email: nelsoncl@jmu.edu. ORCID: 0000-0003-13212638. Scott R. Gallagher, Ph.D., Associate Dean of Academic Affairs and Professor of Management, James Madison University, College of Business, MSC 0207, Harrisonburg, VA 22807, Tel: 540-568-8792, Email: gallagsr@jmu.edu. *Corresponding Author
Investigating Objectives for Oﬀ-policy Value Estimation in Reinforcement Learning

This paper investigates the problem of online prediction learning, where prediction, action, and learning proceed continuously as the agent interacts with an unknown environment. The predictions made by the agent are contingent on a particular way of behaving—specifying what would happen if the agent behaved in a particular way—represented as a value function. However, the behavior used to select actions and generate the behavior data might be diﬀerent from the behavior used to deﬁne the predictions, and thus the samples are generated oﬀ-policy. The ability to learn behavior-contingent predictions online and oﬀ-policy has long been advocated as a key capability of predictive-knowledge learning systems, but has remained an open algorithmic challenge for decades. The fundamental issue lies with the temporal diﬀerence learning update at the heart of most value-function learning algorithms: combining bootstrapping, oﬀ-policy sampling, and ﬁxed-basis function approximation may cause the value estimate to diverge to inﬁnity (e.g., Q-learning with linear function approximation). A major breakthrough came with the development of a new objective function, called the projected Bellman error, that admitted light-weight stochastic gradient descent variants of temporal diﬀerence learning. Since then, many sound online oﬀ-policy prediction algorithms have been developed, but largely for the linear setting. With this development has come several modiﬁcations on the objective itself, and has exposed a fundamental open question in oﬀ-policy value estimation: what objective should we use? In this work, we ﬁrst summarize the large body of literature on oﬀ-policy learning, (1) highlighting the similarities in the underlying objectives for algorithms and (2) extracting the key strategies behind many of the algorithms, that can then be used across objectives. We then describe a generalized projected Bellman error, that naturally extends to the nonlinear value estimation setting. We show how this generalized objective uniﬁes previous work, including previous theory. We use this simpliﬁed view to derive easy-to-use, but sound, algorithms that we show perform well in both prediction and control
University of Birmingham Generalization performance of multi-pass stochastic gradient descent with convex loss functions

Stochastic gradient descent (SGD) has become the method of choice to tackle large-scale datasets due to its low computational cost and good practical performance. Learning rate analysis, either capacity-independent or capacity-dependent, provides a unifying viewpoint to study the computational and statistical properties of SGD, as well as the implicit regularization by tuning the number of passes. Existing capacity-independent learning rates require a nontrivial bounded subgradient assumption and a smoothness assumption to be optimal. Furthermore, existing capacity-dependent learning rates are only established for the speciﬁc least squares loss with a special structure. In this paper, we provide both optimal capacity-independent and capacity-dependent learning rates for SGD with general convex loss functions. Our results require neither bounded subgradient assumptions nor smoothness assumptions, and are stated with high probability. We achieve this improve-ment by a reﬁned estimate on the norm of SGD iterates based on a careful martingale analysis and concentration inequalities on empirical processes.
Testing Legislator Responsiveness to Citizens and Firms in Single-Party Regimes: A Field Experiment in the Vietnamese National Assembly

We investigate whether communicating constituents’ preferences to legislators increases the responsiveness of delegates to the Vietnamese National Assembly (VNA). Using a randomized control trial, we assign legislators to three groups: (1) those briefed on the opinions of their provincial citizenry, (2) those presented with the preferences of local firms, and (3) those receiving only information on the Communist Party’s objectives. Because voting data are not public, we collect data on a range of other potentially responsive behaviors during the 2018 session. These include answers to a VNA Library survey about debate readiness; whether delegates spoke in group caucuses, query sessions, and floor debates; and the content of those speeches. We find consistent evidence that citizen-treated delegates were more responsive, via debate preparation and the decision to speak, than control delegates; evidence from speech content is mixed.
Income Inequality and Incentives in Economies with Other-Regarding Preferences

We analyze model economies populated by individuals who care about their own income and wealth but also regard their position relative to the economy’s average values of these variables. Furthermore, these individuals differ in their initial wealth. We consider inequality averse and competitive populations. Poor individuals are inferiority averse in both cases while rich individuals are superiority averse in the former but superiority seeking in the latter case. We investigate the impact of such preferences and wealth inequality on incentive contracts, output, and welfare. Unlike former agency models with inequality aversion within firms, we find that increasing inferiority aversion at the societal level tends to raise equilibrium effort and reduce wage costs. The same holds also for superiority seeking workers. By contrast, raising superiority aversion lowers effort and increases wage costs. A parameterized version of the model which roughly mimics some key features of the industrialized world shows that, even under inequality aversion, increased initial wealth differences lead to higher average output, entail distributional utility losses, and result in a more uneven income distribution. JEL Classifications: D31, D50, D63, D82, M52, M54
Childcare and depression during the coronavirus pandemic in South Africa: A gendered analysis

Background The 2019 coronavirus disease (COVID-19) pandemic resulted in the closure of businesses and schools, the remote provision of services and the disruption of the services of professional childminders. These disruptions resulted in a significant increase in parental responsibility for childcare. Such a substantial increase in time requirements for childcare domestically has potential mental health consequences. We therefore ascertained the relationship between childcare and depression in South Africa during the pandemic. Methods Data came from the National Income Dynamics Study-Coronavirus Rapid Mobile Survey, a longitudinal telephonic survey conducted during the COVID-19 pandemic in South Africa. The outcome was a depression index obtained from the two-item Patient Health Questionnaire while the main covariate was the average number of hours spent in taking care of children per weekday. We employed the ordered logit model. Findings We found a positive relationship between spending more hours on childcare and worse depressive health for caregivers in both periods analyzed. Childcare responsibilities preventing/mitigating the ability of caregivers to work as well as preventing caregivers from searching for jobs moderated the depression-childcare relationship. Conclusion These findings highlight the need to carefully consider policy responses aimed at containing the pandemic. We advocate a multi-stakeholder approach to mitigating the mental health impact of COVID-19 by encouraging more collaboration between government, school authorities, employers and parents/guardians.
Health and safety implementation in Indonesia and risk of COVID-19

The COVID-19 pandemic impacted the socio-economic sectors since the end of 2019. Indonesian’s Government issued the large-scale social restrictions policy to limit the industrial activities. This study aimed to investigate the difference of the health, safety, and environment (HSE) implementations among Indonesian companies, before and during the disruption risk of COVID-19 pandemic according to the use of personal protective equipment (PPE). It used a random sampling, involved 1,027 workers from various sectors of Indonesian companies. Hypothesis tests used are ANOVA and t-test method. The level of HSE compliance changed quite significantly when the COVID-19 outbreak emerged. The results explained, there is no significant difference in HSE compliance based on company location, company risk level, and position of a respondent in the company. However, there is a significant difference between companies that have a HSE division and a HSE Management System certificate with the company who did not have any. The recommendations of HSE improvement formulated using a Safety Model Canvas and a Focus Group Discussion conducted to convey the jazz scenario in the next normal. This study suggests the priority order for HSE improvement strategy in a company is commitment, responsibility, engagement and involvement, leadership, competence, information and communication, to organizational learning.
Deadline-aware Dynamic Resource Management in Serverless Computing Environments

Serverless computing enables rapid application development and deployment by composing loosely coupled microservices at a scale. This emerging paradigm greatly unburdens the users of cloud environments, from the need to provision and manage the underlying cloud resources. With this shift in responsibility, the cloud provider faces the challenge of providing acceptable performance to the user without compromising on reliability, while having minimal knowledge of the application requirements. Sub-optimal resource allocations, specifically the CPU resources, could result in the violation of performance requirements of applications. Further, the fine-grained serverless billing model only charges for resource usage in terms of function execution time. At the same time, the provider has to maintain the underlying infrastructure in always-on mode to facilitate asynchronous function calls. Thus, achieving optimum utilization of cloud resources without compromising on application requirements is of high importance to the provider. Most of the current works only focus on minimizing function execution times caused by delays in infrastructure set up and reducing resource costs for the end-user. However, in this paper, we focus on both the provider and user’s perspective and propose a function placement policy and a dynamic resource management policy for applications deployed in serverless computing environments. The policies minimize the resource consumption cost for the service provider while meeting the user’s application requirement, i.e., deadline. The proposed solutions are sensitive to deadline and efficiently increase the resource utilization for the provider, while dynamically managing resources to improve function response times. We implement and evaluate our approach through simulation using ContainerCloudSim toolkit. The proposed function placement policy when compared with baseline scheduling techniques can reduce resource consumption by up to three times. The dynamic resource allocation policy when evaluated with a fixed resource allocation policy and a proportional CPU-shares policy shows improvements of up to 25% in meeting the required function deadlines.
Learning Neural Generative Dynamics for Molecular Conformation Generation

We study how to generate molecule conformations (\textit{i.e.}, 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling.
Interaction Between Fluid-Dynamic and Acoustic Instabilities in Combusting Flows Within Ducts

Abstract Measurements of pressure fluctuations in sudden-step expansion flows of round cross-section are examined to determine the effect of matching the fluid-dynamic frequency associated with large scale coherent structures in the near field of the jet to the acoustic or bulk-mode frequency of the confining duct. In one case, the fuel and air were premixed with an equivalence ratio of 1.0 and in the other the fuel was injected radially through discrete holes a short distance upstream of the expansion with an overall equivalence ratio of 0.52. The results show substantial increases in the amplitude of the instability when the two frequencies are matched and the linear relationship between the velocity and frequency implied by a constant Strouhal number of around 0.3 leads to frequencies outside the range previously shown to be associated with the much higher amplitudes of oscillations of rough combustion.
Stable gold nanocolloids with controllable surface modification and functionalization

This paper demonstrates a new method for fabrication of stable gold nanoparticle-poly(ethylene glycol) (PEG) conjugates with a defined number of PEG molecules. The PEG molecules are directly bound to the surface of gold nanoparticles with almost 100% conjugation efficiency and the PEG surface coverage is tunable between values of 0 and 100%. Gold nanoparticles for the nanoparticle PEG conjugates are prepared by femtosecond laser ablation in liquid of a gold bulk target in deionized water. This method for fabrication of nanoparticles creates gold surfaces which are negatively charged and chemically clean. This facilitates uniform and controlled binding of thiolated PEG molecules to the surface of the gold nanoparticles. The method used to bind PEG to gold can be used with other biomolecules to prepare gold nanoparticles with single or mixed monolayers of biologically important molecules.