Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoxyl Acetate as a Substrate for Analysis of Lipase Activity

Lipases play a crucial role in metabolism of microbes, fungi, plants, and animals, and in analytical chemistry, they are often used in detection of fats and triglycerides. Determination of lipase activity is also important in toxicology, when lipase activity can be both increased and decreased by organophosphates and other pesticides and in medicine for diagnosis of heart diseases. The standard method for lipase activity determination is based on cleaving ester bonds in lipase buffer containing Tween. Our aim was to find a method with faster and more sensitive response. It is known that acetylcholinesterase belongs to the same group of hydrolases enzymes as lipases and it cleaves indoxyl acetate, so we assume indoxyl acetate could report a similar reaction with lipase. Our method is based on indoxyl acetate as a substrate for lipase, where indoxyl acetate is cleaved by lipase to indoxyl and acetate moiety and blue indigo is created. The method was optimized for different times and amount of enzyme and compared with the standard Tween assay. The calibration curve measured in reaction time 20 minutes with 10 μl of lipase exhibited the best analytical parameters, and it showed Michaelis–Menten response with the Michaelis–Menten constant equal to 8.72 mmol/l. The indoxyl acetate-based method showed faster and more sensitive response than the standard method for lipase activity determination, so it has great potential in biosensor construction and it could be used in industry, medicine, toxicology, and common practice where the activity of lipases is need to be measured.
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning

Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text. In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set. Empirical results show that the state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models.
Is the Written Component of Tootling Effective? A Comparison of the Group Contingency to a Comparison Writing Procedure

ii ACKNOWLEDGMENTS . iii DEDICATION . iv LIST OF TABLES . viii LIST OF ILLUSTRATIONS . ix CHAPTER IINTRODUCTION . 1 Group Contingencies . 3 Tootling . 8 Purpose . 25 CHAPTER IIMETHODS . 27 Participants and Setting . 27 Materials . 29 Problem Identification Interview (PII). 29 User Rating ProfileIntervention Revised (URPIR) . 30 Children’s Usage Rating Profile (CURP) . 30 Dependent Measures . 31 Data Collection . 33 Design . 35 Procedures . 36 vi Screening. 36 Baseline . 37 Training . 37 Intervention . 38 Traditional Tootling . 39 Comparison Writing Procedure . 40 No-treatment Control . 40 Interobserver Agreement (IOA) . 41 Procedural Integrity . 42 Treatment Integrity . 43 Data Analysis . 44 CHAPTER IIIRESULTS . 46 Classroom A. 46 Classroom B . 49 Classroom C . 52 Effect Sizes . 59 Social Validity . 64 URP-IR . 64 CURP . 65 CHAPTER IVDISCUSSION . 66 vii Research Questions . 66 Question 1 . 66 Question 2 . 67 Question 3 . 69 Question 4 . 70 Question 5 . 72 Limitations . 72 Future Directions . 75 Conclusion . 75 APPENDIX AInstitutional Review Board Approval Letter. 77 REFERENCES . 78
The Effects of Perspective Taking Implementing Intentions on Employee Evaluations and Hostile Sexism

The current research examined whether gender bias in the workplace could be reduced through perspective taking implementation intentions, which are if–then statements that specify how to accomplish goals (Gollwitzer, 1999). Amazon MTurk participants (N = 180, 53% male) learned they would complete a two-step performance review for a consulting company. Prior to receiving a male or female employee’s record, all participants were given a goal strategy to be fair in their review, with half also receiving an if–then strategy that encouraged perspective taking. Participants rated the employee on three work related dimensions (skillset, performance, and traits), provided an overall promotion recommendation, and completed the Ambivalent Sexism Inventory (Glick & Fiske, 1996). Although we did not find evidence of gender bias on the work measures, we found that the implementation intention strategy resulted in more positive employee evaluations overall and less hostile sexism than a simple goal strategy. We discuss the potential organizational benefits of employing perspective taking implementation intentions.
Residual Blocks Up Sampling MLP AdaIN ... Down Sampling Residual Blocks ... Down Sampling 1 * 1 conv 1 * 1 conv

An object can be described as the combination of primary visual attributes. Disentangling such underlying primitives is the long-term objective of representation learning. It is observed that categories have the natural multi-granularity or hierarchical characteristics, i.e. any two objects can share some common primitives in a particular category granularity while they may possess their unique ones in another granularity. However, previous works usually operate in a flat manner (i.e. in a particular granularity) to disentangle the representations of objects. Though they may obtain the primitives to constitute objects as the categories in that granularity, their results are obviously not efficient and complete. In this paper, we propose the hierarchical disentangle network (HDN) to exploit the rich hierarchical characteristics among categories to divide the disentangling process in a coarse-to-fine manner, such that each level only focuses on learning the specific representations in its granularity and finally the common and unique representations in all granularities jointly constitute the raw object. Specifically, HDN is designed based on an encoder-decoder architecture. To simultaneously ensure the disentanglement and interpretability of the encoded representations, a novel hierarchical generative adversarial network (GAN) is elaborately designed. Quantitative and qualitative evaluations on four object datasets validate the effectiveness of our method.
A Demonstration of the OpenInterface Interaction Development Environment

Multimodal interaction is hard to develop, especially in mobile/ubiquitous settings with novel interaction devices. This is true, at least in part, because prototypes in this domain are difficult to build, to change and to monitor in order to analyse, interpret and evaluate interaction data. In this demonstration we present the OpenInterface Interaction Development Environment (OIDE) that addresses these challenges via its component repository and its construction, debugging and logging tools. ACM Classification: D.2. 2 [Software Engineering]: Design Tools and Techniques. – User interfaces. General terms: Design, Experimentation, Human Factors
Diving into Optimization of Topology in Neural Networks

Seeking effective networks has become one of the most crucial and practical areas in deep learning. The architecture of a neural network can be represented as a directed acyclic graph, whose nodes denote the transformation of layers and edges represent information flow. Despite the selection of micro node operations, macro connections among the whole network, noted as topology, largely affect the optimization process. We first rethink the residual connections via a new topological view and observe the benefits provided by dense connections to the optimization. Motivated by this, we propose an novel method to optimize the topology of a neural network. The optimization space is defined as a complete graph, and through assigning learnable weights which reflect the importance of connections, the optimization of topology is transformed into learning a set of continuous variables of edges. To extend the optimization to larger search spaces, a new series of networks, called TopoNet, are designed. To further focus on critical edges and promote generalization ability in dense topologies, an auxiliary sparsity constraint is adopted to constrain the distribution of edges. Experiments on classical networks prove the effectiveness of the optimization of topology. Experiments with TopoNets further verify both availability and transferability of the proposed method in different tasks e.g. image classification, object detection, and face recognition.
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective

This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations. We use tropical geometry, a new development in the area of algebraic geometry, to characterize the decision boundaries of a simple neural network of the form (Affine, ReLU, Affine). Our main finding is that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes. The generators of these zonotopes are functions of the neural network parameters. This geometric characterization provides new perspective to three tasks. Specifically, we propose a new tropical perspective to the lottery ticket hypothesis, where we see the effect of different initializations on the tropical geometric representation of a network's decision boundaries. Moreover, we use this characterization to propose a new set of tropical regularizers, which directly deal with the decision boundaries of a network. We investigate the use of these regularizers in neural network pruning (by removing network parameters that do not contribute to the tropical geometric representation of the decision boundaries) and in generating adversarial input attacks (by producing input perturbations that explicitly perturb the decision boundaries' geometry and ultimately change the network's prediction).
Prediction of instantaneous driving safety in emergency scenarios based on connected vehicle basic safety messages

Purpose Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency. Design/methodology/approach To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term. Findings The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages. Originality/value The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.
Low-Thrust Trajectory Design Using Finite Fourier Series Approximation of Pseudoequinoctial Elements

A new low-thrust trajectory design method is proposed that is based on the finite Fourier series method with pseudoequinoctial elements rather than the more common cylindrical coordinate components. The bijection relation between the elements and control variables is ensured by introducing an additional equality constraint derived from the angular momentum conservation. The guidance law and on-line control variables are obtained by applying inverse dynamics and the framework of inverse simulation technology, respectively. The pseudoequinoctial finite Fourier series method has the advantages of both the Fourier series and the perturbation analysis methods. For two-body problems, three cases were studied: the Earth to Mars, 1989ML, and Tempel-1 missions. Regarding the design of a rendezvous trajectory with a large inclination angle and a high eccentricity rate, this method yields a broader range of feasible results than the traditional Fourier series method. The circular restricted three-body problem was solved for the first time using the pseudoequinoctial finite Fourier series method combined with the patched conics method. The low-thrust Earth-Moon transfer was analyzed, and the results show that this method improves window analysis efficiency and guarantees precision of the initial geocentric trajectory for the low-thrust transfer.
Patriarchy and Intimate Partner Violence in Region of Nigeria

The fabrics of society are defined by its cultural v lues and socio is considered right, acceptable and justified s mostly hinged on its shared values and norms. One of such values is reflected in patriarchy and the preference of the m ale over the female in most communities in various parts of Nigeria. This as contributed to the incessancy and justification of violence against women aimed at th e control and subjugati sex. The relationship between patriarchy (male child) pre fe nce and intimate partner violence in Nigeria, particularly the Niger Delta r egion empirical investigation, data wa s generated from 354 respondents using the str questionnaire and analysis utilized both descriptiv e and inferential hypotheses were tested with results indicating that m le dominance, as well as the low level of education in the male gender, both contributed to i ntim findings, it was concluded that patriarchy and the emphasis on the m ale gender over the female contributes to intimate partner violence in the recommended that there should be aware ness creation in churches and in communities on the implication of these obnoxious cultural practices t hat
Sensitivity of Dynamical Downscaling Seasonal Precipitation Forecasts to Convection and Land Surface Parameterization in a High-Resolution Regional Climate Model

Both convection and land surface parameterization influence seasonal precipitation forecasts. In this study, the sensitivity of dynamical downscaling seasonal precipitation forecasts to convection and land surface parameterization was investigated by nesting the Weather Research and Forecasting (WRF) model into the NCEP’s Climate Forecast System version 2 (CFSv2) retrospective forecasts with four convective schemes: Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ), Grell–Freitas (GF), and new simplified Arakawa–Schubert (NSAS) schemes, and two land surface schemes: Noah and simplified Simple Biosphere (SSiB) schemes over the Han River basin. The CFSv2 model biases are reduced when the KF convective scheme is used in the wet summer season. However, negative biases still exist especially when the combination of BMJ and SSiB schemes is used. Compared with CFSv2 reforecasts and other combinations of schemes, the forecast skills of spatial patterns of precipitation anomalies are highest when the combination of KF and Noah schemes is used in summer. In contrast, the combination of BMJ and SSiB schemes shows lowest forecast skills in summer. To understand the causes of the differences in precipitation forecasts using different parameterization schemes, the simulated moisture flux convergence, thermodynamic parameters at different pressure levels, convective available potential energy (CAPE), convective inhibition (CIN), and heat fluxes are compared with the data in the ERA-5 reanalysis dataset. The WRF model-simulated moisture flux convergence is closer to that of the ERA-5 reanalysis compared with that of the CFSv2 reforecasts in summer. The vertical thermodynamic profiles also suggest that the combination of the KF and Noah schemes has caused a more unstable atmosphere, which is crucial for precipitation. In contrast, the combination of BMJ and SSiB schemes shows a less unstable atmospheric environment in summer, which explains the lower forecast skills compared with other schemes. The spatial patterns of CAPE are also improved when using the WRF model, which further enhances the precipitation forecast skills over the Han River basin.
Exploring the Efficacy of Baited Remote Underwater Video Systems When Assessing Fish Biodiversity, Composition, and Abundance in Coral Reefs

3 Introduction . 4 Materials and Methods . 10 I. BRUV design and construction . 10 II. Study location . 12 III. BRUV deployments . 13 IV. Processing of video footage by student teams . 14 V. Statistical analysis . 15 Results . 19 I. Species richness . 19 II. Species abundance . 20 III. Species composition . 20 IV. Comparison between student groups (with author data) . 21 Discussion . 23 I. Quantifying fish richness, abundance, and composition in the Cozumel reefs . 24 II. Comparison of recorded species richness, cumulative abundance, and inter-rater reliability between observer teams . 25 III. Guidelines for future studies involving BRUVs and citizen scientists . 29 Conclusion . 33 Acknowledgements . 34 Figures . 35 References . 46
Mining and Poverty in Papua New Guinea: Case Studies at Ok Tedi and Porgera

Mining has been a controversial industry in Papua New Guinea (PNG). Its newsworthiness has stemmed from the dramatic environmental and social consequences that have dogged mining development in PNG over the last several decades. Without downplaying the importance of these issues in any way, the focus in the present paper is on the relationship between mining and poverty in two of the country’s largest mining regions – Ok Tedi and Porgera. We use the techniques of logistic regression and propensity score matching to investigate the differences between mining and non-mining villages in these two regions. The results overall suggest that mining does reduce poverty, but when you correct for non-randomness in the data the effects are much smaller than it first appears.
The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests

Abstract. The capacity of Amazon forests to sequester carbon is threatened by climate-change-induced shifts in precipitation patterns. However, the relative importance of plant physiology, ecosystem structure and trait composition responses in determining variation in gross primary productivity (GPP) remain largely unquantified and vary among models. We evaluate the relative importance of key climate constraints to GPP, comparing direct plant physiological responses to water availability and indirect structural and trait responses (via changes to leaf area index (LAI), roots and photosynthetic capacity). To separate these factors we combined the soil–plant–atmosphere model with forcing and observational data from seven intensively studied forest plots along an Amazon drought stress gradient. We also used machine learning to evaluate the relative importance of individual climate factors across sites. Our model experiments showed that variation in LAI was the principal driver of differences in GPP across the gradient, accounting for 33 % of observed variation. Differences in photosynthetic capacity ( Vcmax and Jmax ) accounted for 21 % of variance, and climate (which included physiological responses) accounted for 16 %. Sensitivity to differences in climate was highest where a shallow rooting depth was coupled with a high LAI. On sub-annual timescales, the relative importance of LAI in driving GPP increased with drought stress ( R2=0.72 ), coincident with the decreased importance of solar radiation ( R2=0.90 ). Given the role of LAI in driving GPP across Amazon forests, improved mapping of canopy dynamics is critical, opportunities for which are offered by new satellite-based remote sensing missions such as GEDI, Sentinel and FLEX.
Deciphering the role of multiple compatible solute transporters from Vibrio parahaemolyticus

Vibrio parahaemolyticus is a moderate halophile, capable of causing self-limiting gastroenteritis in humans following the consumption of raw or undercooked shellfish. Severe morbidity and mortality can result if the organism gains entry to an individual through open wounds or upon infection of individuals that are immunocompromised (Yeung and Boor, 2004). V. parahaemolyticus is widely distributed in coastal seawaters, areas where the salt concentrations can rapidly fluctuate, and as such, the organism must have the ability to respond to these osmotic shifts if it is to survive. In such conditions, bacterial persistence relies on mechanisms for rapidly balancing the osmotic pressure between the cell and its external environment. A rapid, short term response to osmotic changes is an influx of ions within the cell, utilizing potassium uptake systems (Sleator and Hill, 2001). This functions initially to stabilize the cell, but over time the increased accumulation of these ions can have a deleterious effect on normal cellular functions and enzymatic activity. In order to cope with changes in salinity over an extended period, mechanisms for the synthesis and/or uptake of compatible solutes or their precursors are essential for organisms inhabiting marine environments. Compatible solutes are low molecular weight organic molecules that are capable of accumulating to extremely high intracellular concentrations, subsequently protecting the cell from osmotic stress, but importantly, do not disrupt normal cellular functions (Empadinhas and da Costa, 2008; Bremer and Kramer, 2000). An array of molecules can function as compatible solutes in bacteria, and the uptake of these solutes or their precursor molecules requires specialized transmembrane proteins that have distinct substrate specificities (Bremer and Kramer, 2000). Here we describe our preliminary studies examining a set of recently identified transporters in V. parahaemolyticus so as to begin to elucidate their importance in allowing for the organism to survive environmental stressors.
Working Together: A Cross-cultural Study Addressing Mathematics Anxiety in K-8 Pre-service Teachers

This study will present data from research on K-8 pre-service teachers’ math anxiety across three universities: one public, one private and one non-U.S. The article discusses background rationale, literature, tools used and results of this study. The results of the study indicated that similar math anxiety levels exist in students in all three types of academic institutions. The paper also incorporates discussion of the importance of including the topic in pre-service teacher training, and possible interventions for alleviating math anxiety.
A longstanding tradition in machine learning opposes rule-based inference to statistical learning

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing these mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.