Miracle-dz/UniFAR-FTUs huggingface dataset:
Is Historical Data an Appropriate Benchmark for Reviewer Recommendation Systems? : A Case Study of the Gerrit Community

Reviewer recommendation systems are used to suggest community members to review change requests. Like several other recommendation systems, it is customary to evaluate recommendations using held out historical data. While history-based evaluation makes pragmatic use of available data, historical records may be: (1) overly optimistic, since past assignees may have been suboptimal choices for the task at hand; or (2) overly pessimistic, since "incorrect" recommendations may have been equal (or even better) choices.In this paper, we empirically evaluate the extent to which historical data is an appropriate benchmark for reviewer recommendation systems. We replicate the CHREV and WLRREC approaches and apply them to 9,679 reviews from the GERRIT open source community. We then assess the recommendations with members of the GERRIT reviewing community using quantitative methods (personalized questionnaires about their comfort level with tasks) and qualitative methods (semi-structured interviews). We find that history-based evaluation is far more pessimistic than optimistic in the context of GERRIT review recommendations. Indeed, while 86% of those who had been assigned to a review in the past felt comfortable handling the review, 74% of those labelled as incorrect recommendations also felt that they would have been comfortable reviewing the changes. This indicates that, on the one hand, when reviewer recommendation systems recommend the past assignee, they should indeed be considered correct. Yet, on the other hand, recommendations labelled as incorrect because they do not match the past assignee may have been correct as well.Our results suggest that current reviewer recommendation evaluations do not always model the reality of software development. Future studies may benefit from looking beyond repository data to gain a clearer understanding of the practical value of proposed recommendations.
Mountain building, mantle convection, and supercontinents: Holmes (1931) revisited

a Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, USA b Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, USA c Lab. Experimental Tectonics, Department of Science, Università Roma TRE, Italy d Rosenstiel School of Marine and Atmospheric Science, University of Miami, FL, USA e Geoscience Rennes, Universitè of Rennes I, France
Online Appendix for “Notching R&D Investment with Corporate Income Tax Cuts in China” Not For Publication

This appendix contains multiple additional analyses. Appendix A includes additional details on the Chinese corporate income tax system. Appendix B describes in more detail the data that we use in our analysis. Appendix C discusses the estimation of our measure of log TFP. Appendix D discusses details of the implementation of the bunching estimator. Appendix E discusses additional robustness checks of our bunching estimates. Appendix F describes details of the implementation of the estimator of Diamond and Persson (2016). Appendix G shows that firms do not respond to the InnoCom program by manipulating sales expenses. Appendix H provides a detailed derivation of the model. Appendix I links the model to more traditional bunching estimates used in the public finance literature. Appendix J provides additional details behind the structural estimation. Appendix K explores the robustness of our structural estimation. Finally, Appendix L provides details on the welfare implications of the program.
Solipsistic Reinforcement Learning

We present a model-based reinforcement learning framework that aims to tackle environments with high dimensional state spaces. In contrast to traditional approaches, agents under our framework learn a low dimensional internal representation of the environment while avoiding the need to learn a generative model of the environment itself. This solipsistic representation is trained to encode a belief that is consistent with the dynamics of the environment and is then exploited for eﬀective planning. We present speciﬁc cases of our framework with choices of model and corresponding planning algorithms that can deal with both discrete and continuous state environments. We demonstrate empirically gains in eﬃciency over existing model-free methods when learning directly from pixels and analyze the properties of our learned representations.
Smallest possible electromagnetic mode volume in a dielectric cavity : Photonic crystals and microstructures

In photonic crystals, electromagnetic waves can be confined in all three dimensions - leading to very small mode volumes. A computational search has been made for the smallest electromagnetic mode volume which occurs in two-dimensional, dielectric photonic crystals slabs. The smallest mode volume found was V 2(λ/2n) 3 , where n is the refractive index. This small mode volume can lead to significant enhancement of spontaneous emission rates in semiconductor nano-cavities due to the Purcell effect.
Lignin Extraction from Lignocellulosic Biomass Using Sub- and Supercritical Fluid Technology as Precursor for Carbon Fiber Production

Recent technologies in the production of chemicals and bio-materials products are focusing on lignocellulosic resources since it is the world’s most abundant material, low cost, as well as sustainable. Lignocellulosic biomass consists of three main compounds: cellulose, hemicellulose, and lignin. Productions of carbon fiber from lignin as its precursor are proposed to reduce the usage of fossil fuel based materials. However, the difficulties on recovering lignin from biomass are widely known. Therefore, several studies were conducted to explore possible technologies to isolate lignin from the complex lignocellulosic biomass in simple and minimal cost. One of the potential technologies is by using suband supercritical fluids. The polymer made of from phenylpropane units (p-coumaryl alcohol, coniferyl alcohol and sinapyl alcohol), the building block of lignin in plants can be converted to low molecular weight compounds using suband supercritical fluid technology with diverse applications including carbon fiber production. Hence, this paper aims to review on the lignin extraction from lignocellulosic biomass using suband supercritical fluid technology as precursor for carbon fiber production.
Time-Dependent Alternative Route Planning

We consider the problem of computing a set of meaningful alternative origin-to-destination routes, in real-world road network instances whose arcs are accompanied by travel-time functions rather than fixed costs. In this time-dependent alternative route scenario, we present a novel query algorithm, called Time-Dependent Alternative Graph (TDAG), that exploits the outcome of a time-consuming preprocessing phase to create a manageable amount of travel-time metadata, in order to provide answers for arbitrary alternative-routes queries, in only a few milliseconds for continental-size instances. The resulting set of alternative routes is aggregated in the form of a time-dependent alternative graph, which is characterized by the minimum route overlap, small stretch factor, small size, and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. The preprocessed metadata prescribe the minimum travel-time informations between a small set of “landmark” nodes and all other nodes in the graph. The TDAG query algorithm carries out the work in two distinct phases: initially, a collection phase constructs candidate alternative routes; consequently, a pruning phase cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real-world, time-dependent road networks demonstrates that TDAG performed much better (by one or two orders of magnitude) than the existing baseline approaches.
Fuzzy Rules Extraction from Deep Neural Networks *

This article presents the basic methods of machine learning and explanational artificial intelligence that can help in the issue of extracting rules and other models of knowledge representation not only from data, but from the artificial neural networks themselves. The paper discusses classification meth-ods for rule-based learning methods for neural networks and the current state of technologies for extracting rules from neural networks. Next, we formulate the main problems that arise when extracting rules from neural networks, as well as the main methods for solving them.
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images

Digital pathology is an emerging topic in the analysis of pathologic tissue samples. It includes providing the tools towards more automated workﬂows to derive clinically rele-vant information. Digitization and storage of whole slide images have become commonplace and allow modern image analysis methods to be used. In recent years, computer-based segmentation of cell nuclei has gathered considerable attention in the development of highly specialized algorithms. Currently, most of these algorithms are based on performing semantic segmentation of all cell nuclei and separating overlapping instances in a post-processing step. Recently, instance-aware segmentation methods such as Mask R-CNN have been proposed to enable uniﬁed instance detection and segmentation, even in overlapping cases. In this work, we propose a modiﬁed Mask R-CNN-based approach by incorporating distance maps of instances and hematoxylin-stain intensities as extra input channels to the model. Moreover, we explore the impact of three well-known inference strategies, namely test-time augmentation, ensembling, and knowledge transfer through pre-training on the segmentation performance. We perform extensive ablation experiments across multiple runs to quantitatively deﬁne the most optimal inference strategy in the proposed Mask R-CNN algorithm. Our results show that average instance segmentation improvements of up to 3.5% and 4.1% based on Aggregate Jaccard Index and Panoptic Quality score can be obtained, respectively, using the proposed techniques in comparison to a standard Mask R-CNN model. Our ﬁndings conﬁrm the eﬀectiveness of aggregating information at the network input stage and optimizing inference workﬂows using minimal eﬀort. Imple-mented modiﬁcations and codes are publicly available through a GitHub repository under: https://github.com/bbanc/Improved-Mask-R-CNN-for-nuclei-segmentation .
Aspiration Adaptation: A Test and Comparison of the Cyert and March Model

This study compares the original Cyert and March (1963) aspiration adaptation model with three other frequently-used aspiration adaptation models. Using actual aspirations and performance data gathered from student teams in simulated organizations, our research setting enables direct observation of self and social aspiration comparison reference points that are theorized to impact aspiration adaptation. Our results support the separate and weighted average aspiration models over the original Cyert and March (1963) aspiration adaptation formulation. Though both the Cyert and March (1963) model and the separate model include similar explanatory variables, the separate aspirations model allows for varying influences of performance above and below aspirations, as does the weighted average aspirations model. We conclude with implications for future research concerning both aspiration adaptation and influences of aspirations on firm behavior.
Tidal Response and Shape of Hot Jupiters

We study the response of hot Jupiters to a static tidal perturbation using the concentric MacLaurin spheroid method. For strongly irradiated planets, we first performed radiative transfer calculations to relate the planet’s equilibrium temperature, T eq, to its interior entropy. We then determined the gravity harmonics, shape, moment of inertia, and static Love numbers for a range of two-layer interior models that assume a rocky core plus a homogeneous and isentropic envelope composed of hydrogen, helium, and heavier elements. We identify general trends and then study HAT-P-13b, the WASP planets 4b, 12b, 18b, 103b, and 121b, and Kepler-75b and CoRot-3b. We compute the Love numbers, k nm , and transit radius correction, ΔR, which we compare with predictions in the literature. We find that the Love number, k 22, of tidally locked giant planets cannot exceed a value of 0.6, and that the high T eq consistent with strongly irradiated hot Jupiters tends to further lower k 22. While most tidally locked planets are well described by a linear regime response of k 22 = 3J 2/q 0 (where q 0 is the rotation parameter of the gravitational potential), for extreme cases such as WASP-12b, WASP-103b, and WASP-121b, nonlinear effects can account for over 10% of the predicted k 22. The k 22 values larger than 0.6, as they have been reported for planets WASP-4b and HAT-P13B, cannot result from a static tidal response without extremely rapid rotation and thus are inconsistent with their expected tidally locked state.
Vietnamese Short Text Classification via Distributed Computation

Social networking has been growing rapidly in Vietnam. The sharing information is diverse and circulates in many forms. It requires user-friendly solutions such as topic sorting and perspectives analysis in analyzing community trends, advertisements or anticipating and monitoring the spread of bad news. Unfortunately, Vietnamese is highly different from other languages and little research has been conducted in the literature on messages classification. The implementation of machine learning models on Vietnamese has not been thoroughly investigated and these models’ performance is unknown when applying in a different language. Vietnamese text is a serialization of syllables, hence, word boundary identification is not trivial. This research portrays our endeavor to construct an effective distributed framework for addressing the task of classification of short Vietnamese texts on social networks using the idea of probability categorization. The authors argue that addressing the task sharps the successful combination of machine learning, natural language processing, and ambient intelligence. The proposed framework is effective and enables fast calculation, suitable for implementation in Apache Spark, meeting the demand for dealing with large amounts of textual data on the current social networks. Our data has been collected from several online text sources of 12412 short messages classified into five different topics. The evaluation shows that our approach has achieved an average of 82.73% classification accuracy. Thoughtfully learning the literature, we could state that this is the first attempt to classify short Vietnamese messages under a distributed computation framework. Keywords—Short text classification; Naı̈ve Bayes; Apache Spark; Vietnamese; distributed computation
Improved ice content, radiation, precipitation and low-level circulation over the tropical pacific from ECMWF ERA-interim to ERA5

This study evaluates changes in simulated Pacific climate between two ECMWF re-analyses; the ERA Interim (ERAI) and the newest ERA5. Changes in the Integrated Forecasting System (IFS) and possibly sea surface temperature result in greatly reduced discrepancies in ERA5’s ice water path (IWP), radiative fluxes and precipitation relative to satellite-based observational products. IWP shows the largest percentage change, increasing by over 300% from ERAI to ERA5, due to inclusion of falling ice (snow) that impacts radiative calculation. ERAI to ERA5 changes in high-cloud fraction are generally anticorrelated as expected with outgoing longwave radiation, with ERA5 having smaller longwave discrepancies versus CERES observations compared with ERAI. Reflected shortwave discrepancies are similarly reduced from ERAI to ERA5, which appears to be due to changes in both cloud fraction and optical depth. Finally, ERA5 also reduces a longstanding precipitation excess relative to the GPCP observational product in the southern trade winds region between the Southern Pacific and intertropical convergence zones. This appears to be related to cooler prescribed sea surface temperatures, thereby reducing local moisture supply via suppressing net latent heat flux and stronger surface trade-winds. Compared with GPCP and CERES, ERA5 shows similar geographic patterns of discrepancies to ERAI in terms of precipitation and top-of-atmosphere radiation, but their magnitudes are greatly reduced in ERA5.
Micro groove cutting on the surfaces of Cu-B4C nanocomposites by fiber laser

Micromachining has evolved into a key facilitating technology and is widely employed today for the creation of complex micro/nano surface features for the parts used in various industries like nano/micro electro-mechanical systems, medical and telecommunications. At present fiber lasers have made an increasingly significant contribution to micro-fabrications to generate intricate shapes. The increased usage of copper in micro-domains is attributed to the advancements in personal computing and telecom industry. Against this backdrop, this work focuses upon the production of micro-grooves on the surfaces of copper/nano-boron carbide (Cu-B4C) metal matrix nanocomposites (MMNCs) by 10 μm fiber laser spot beam. To begin with, Cu-B4C MMNCs were synthesized through the powder metallurgical route. Response Surface Methodology-I optimal design was then attempted to optimize the laser machining factors like laser power, number of passes, scanning speed and frequency with the intention to obtain the least surface roughness, minimum kerf width, lower heat affected zone, higher depth and higher material removal. An atomic force microscope is used to inspect the surface characteristics by measuring the roughness values of the surfaces adjacent to the top of micro-grooves. A 3D surface profiler investigated the depth of the groove produced by the fiber laser. Results indicate that a minimum surface roughness of 23.06 nm and a minimum kerf width of 35.49 μm are obtained in the run order 2. Similarly, the groove with a maximum depth of cut of 245.87 μm is achieved in the run order 3. Scanning electron microscopy images helped to examine the surface morphologies and heat-affected zone of the micro-grooved samples.
Teaching Machine Learning in the Context of Critical Quantitative Information Literacy

Bates College, is a small liberal arts postsecondary institution in the northeast United States. An information literacy course, Calling Bull, serves as an introductory data science class as well as a prerequisite-free quantitative literacy class. In this context, we spend a week discussing machine learning, with an emphasis on facial recognition algorithms. The emphasis is on the general algorithmic approach, critical inquiry of the process and careful interpretation of results presented in research or decision-making. This module relies on the use of open educational materials, discussion, and careful attention to issues of marginalization and algorithmic justice.
Episodic Memory for Subjective-Timescale Models

Planning in complex environments requires reasoning over multi-step timescales. However, in model-based learning, an agent’s model is more commonly deﬁned over transitions between consecutive states. This leads to plans using intermediate states that are either unnecessary, or worse, introduce cumulative prediction errors. Inspired by the recent works on human time perception, we devise a novel approach for learning a transition dynamics model based on the sequences of episodic memories that deﬁne an agent’s subjective timescale – over which it learns world dynamics and over which future planning is performed. We analyse the emergent beneﬁts of the subjective-timescale model (STM) by incorporating it into two disparate model-based methods – Dreamer and deep active inference. Using 3D visual foraging tasks, we demonstrate that STM can systematically vary the temporal extent of its predictions and is more likely to predict future salient events (such as new objects coming into view). In comparison to the agents trained using objective timescales, STM agents also collect more rewards due to their ability to perform ﬂexible planning and a more pronounced exploratory behaviour.
Maximal ancestral graph structure learning via exact search

Generalizing Bayesian networks, maximal ancestral graphs (MAGs) are a theoretically appealing model class for dealing with unobserved variables. Despite signiﬁcant advances in developing practical exact algorithms for learning score-optimal Bayesian networks, practical exact algorithms for learning score-optimal MAGs have not been developed to-date. We develop here methodology for score-based structure learning of directed maximal ancestral graphs. In particular, we develop local score computation employing a linear Gaussian BIC score, as well as score pruning techniques, which are essential for exact structure learning approaches. Furthermore, employing dynamic programming and branch and bound, we present a ﬁrst exact search algorithm that is guaranteed to ﬁnd a globally optimal MAG for given local scores. The experiments show that our approach is able to ﬁnd considerably higher scoring MAGs than previously proposed in-exact approaches.
The Role of Pulse Voltage Amplitude on Chemical Processes Induced by Streamer Discharge at Water Surface

The paper reports the effects of pulse voltage amplitude on streamer discharge propagation at water surface. The subsequent chemical processes in a reactor following the surface discharges with different voltages are presented. A pulsed power modulator (PPM) system equipped with a control unit was employed to generate 0–25 kV pulses at 500 Hz. A point-plane electrode configuration was used for experiments, with the point electrode placed with 1 mm gap from the water surface in atmospheric air, and plane ground submerged with 30 mm gap in water. The streamer length at water surface was significantly influenced by the pulse voltage amplitude. Colorimetric measurement of hydrogen peroxide (H2O2) and treatment of indigo carmine organic dye solution were carried out to elucidate the chemical processes produced at various pulse voltages. The experimental results reveal that the applied voltage is a factor that greatly affects water surface discharges and their chemical processes.
Beliefs and Attitudes Associated with ERP Adoption Behaviours: Grounded Theory from the IT Managers and End-users Perspective

The Theory of Reasoned Action (TRA) was used to explain the beliefs, attitude and behavioural intention of the adoption of ERP systems by Thai-owned companies and multinational companies operating in Thailand. Interviews were thus conducted with IT managers and end-users in Thailand to identify a number of beliefs towards ERP systems. A grounded theory method was used to collecting and analysing data. The findings of the study suggest that shared beliefs in the benefits of ERP systems to organizations correlate with positive attitudes towards ERP systems while individual beliefs in the perceived problems of adopting ERP systems correlate with negative attitudes, which appear to affect the behavioural intention to adopt an ERP system. This paper concludes with some implications of these findings, and with some possible direction for future research.