Miracle-dz/UniFAR-FTUs huggingface dataset:
Project Education and Adams’ Theory of Equity

This work-in-progress paper in research category is concerned with students’ perceptions of justice in software engineering group projects. Taking a directed content analysis approach, the aim is to analyze the relevance of Adams’ theory of Equity for small-group project education. The analysis was applied to the data collected from a third-year project course in which students encounter an open-ended group assignment. The results indicate that the theory can serve as a useful framework that helps teachers to identify justice-related issues in project courses. On the other hand, it was concluded that special carefulness is advisable considering the simplicity of the theory.
Root exudates a key factor for soil and plant: An overview

Root exudate is one of the ways for plant communication to the neighboring plant and adjoining of microorganisms present in the rhizosphere of the root. The chemicals ingredients of the root exudates are specific to a particular plant species and also depend on the nearby biotic and abiotic environment. The chemical ingredient exuded by plant roots include amino acids, sugars, organic acids, vitamins, nucleotides, various other secondary metabolites and many other high molecular weight substances as primarily mucilage and some unidentified substances. Through the exudation of a wide variety of compounds, roots may regulate the soil microbial community in their immediate vicinity, cope with herbivores, encourage beneficial symbioses, change the chemical and physical properties of the soil and inhibit the growth of competing plant species. Root exudates mediate various positive and negative interactions like plant-plant and plant-microbe interactions. The present review has been undertaken to examine the possible role of root exudates on nourishing the neighboring microorganisms present in the rhizosphere of the root. Plants secrete both high-and low-molecular weight compounds from their roots, and these root exudates function not only as nutrients for soil microbes but as signal molecules in plant– microbe interactions. All plants establish symbiotic interactions with rhizobia and arbuscular mycorrhizal fungi to obtain several nutrients such as nitrogen and phosphate. In these interactions, flavonoids and strigolactones in root exudates serve as signal molecules to establish the symbiotic interactions. Root exudates from some plants also function to acidify surrounding soils to acquire phosphate. Here, we provide an overview of the functions of root exudates with emphasis on the interaction between plants and soil microbes and also on the acquisition of nutrients from surrounding soil.
Permeability computation of high resolution µCTscan with an unfitted boundary method to improve accuracy

Flow simulations on porous media, reconstructed from Micro-Computerised Tomography (μCT)-scans, is becoming a common tool to compute the permeability of rocks. In order for the value of this homogenised hydraulic property to be representative of the rock at a continuum scale, the sample considered needs to be at least as large as the Representative Elementary Volume. More- over, the numerical discretisation of the digital rock needs to be fine enough to reach numerical convergence. In the particular case of Finite Elements (FE), studies have shown that simulations should use structured meshes at least two times finer than the original image resolution in order to reach the mesh convergence. These two conditions and the increased resolution of μCT-scans to observe finer details of the microstructure, can lead to extremely computationally expensive numerical simulations. In order to reduce this cost, we couple a FE numerical model for Stokes flow in porous media with an unfitted boundary method, which allows to improve results precision for coarse meshes. Indeed, this method enables to obtain a definition of the pore-grain interface as precise as for a conformal mesh, without a computationally expensive and complex mesh generation for μCT-scans of rocks. From the benchmark of three different rock samples, we observe a clear improvement of the mesh convergence for the permeability value using the unfitted boundary method. An accurate permeability value is obtained for a mesh coarser than the initial image resolution. The method is then applied to a large sample of a high resolution μCT-scan to showcase its advantage.
StyleUV: Diverse and High-fidelity UV Map Generative Model

Reconstructing 3D human faces in the wild with the 3D Morphable Model (3DMM) has become popular in recent years. While most prior work focuses on estimating more robust and accurate geometry, relatively little attention has been paid to improving the quality of the texture model. Meanwhile, with the advent of Generative Adversarial Networks (GANs), there has been great progress in reconstructing realistic 2D images. Recent work demonstrates that GANs trained with abundant high-quality UV maps can produce high-fidelity textures superior to those produced by existing methods. However, acquiring such high-quality UV maps is difficult because they are expensive to acquire, requiring laborious processes to refine. In this work, we present a novel UV map generative model that learns to generate diverse and realistic synthetic UV maps without requiring high-quality UV maps for training. Our proposed framework can be trained solely with in-the-wild images (i.e., UV maps are not required) by leveraging a combination of GANs and a differentiable renderer. Both quantitative and qualitative evaluations demonstrate that our proposed texture model produces more diverse and higher fidelity textures compared to existing methods.
Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE).
Prognostic Factors After Hepatectomy for Gastric Adenocarcinoma Liver Metastases: Desmoplastic Growth Pattern as the Key to Improved Overall Survival

Purpose Hepatectomy (Hp) is an alternative approach for the treatment of gastric carcinoma liver metastases (GCLM). However, prognostic factors that may assist patient selection are still controversial. Several pathologic features, such as the growth pattern (GP), associated with prognosis in colorectal cancer liver metastases, were never investigated in GCLM. Our principal aim was to assess if the GP has prognostic impact on GCLM. Patients and Methods Review of the clinical and pathological characteristics of 19 consecutive patients submitted to surgical resection of GCLM with curative intent at our department. Major potential prognostic factors considered were patients’ gender, age, timing and extent of Hp, postoperative course, as well as histopathological characteristics of primary and secondary tumors. Results Major morbidity occurred in four patients, mortality in one. Median and 5-year overall survival were 17 months and 26.7%, respectively. Ten patients developed recurrent disease and two patients survived more than 10 years. Factors independently associated with overall survival were the absence of major morbidity, distal location of the primary tumor, and desmoplastic GP (p<0.05). Conclusion The selection of patients is crucial for the improvement of survival rates of GCLM. Consequently, we demonstrate for the first time that the desmoplastic GP of GCLM is associated with improved outcomes, prompting further research on tumor–host interactions.
Analyzing the Machine Learning Conference Review Process

Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias. In this work, we critically analyze the review process through a comprehensive study of papers submitted to ICLR between 2017 and 2020. We quantify reproducibility/randomness in review scores and acceptance decisions, and examine whether scores correlate with paper impact. Our findings suggest strong institutional bias in accept/reject decisions, even after controlling for paper quality. Furthermore, we find evidence for a gender gap, with female authors receiving lower scores, lower acceptance rates, and fewer citations per paper than their male counterparts. We conclude our work with recommendations for future conference organizers.
Redundancy Resolution and Disturbance Rejection via Torque Optimization in Hybrid Cable-Driven Robots

This article presents redundancy resolution and disturbance rejection via torque optimization in hybrid cable-driven robots (HCDRs). To begin with, we present a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: 1) joint-space torque optimization for actuated joints (TOAJ) and 2) joint-space torque optimization for actuated and unactuated joints (TOAUJ), and they can be extended to other HCDRs. Compared to the existing approaches, this article provides the first solution (TOAUJ-based method) for HCDRs that can solve the redundancy resolution problem as well as disturbance rejection. Additionally, this article develops detailed algorithms targeting TOAJ and TOAUJ implementation. A simple yet effective controller is designed for analysis and validation. Case studies are conducted to evaluate the performance of TOAJ and TOAUJ. The results show the effectiveness of the aforementioned approaches.
Use of an excess power method and a convolutional neural network in an all-sky search for continuous gravitational waves

The signal of continuous gravitational waves has a longer duration than the observation period. Even if the waveform in the source frame is monochromatic, we will observe the waveform with modulated frequencies due to the motion of the detector. If the source location is unknown, a lot of templates having different sky positions are required to demodulate the frequency and the required large computational cost restricts the applicable parameter region of coherent search. In this work, we propose and examine a new method to select candidates, which reduces the cost of coherent search by following-up only the selected candidates. As a first step, we consider a slightly idealized situation in which only a single-detector having 100% duty cycle is available and its detector noise is approximated by the stationary Gaussian noise. We combine several methods: 1) the short-time Fourier transform with the re-sampled data such that the Earth motion for the source is canceled in some reference direction, 2) the excess power search in the Fourier transform of the time series obtained by picking up the amplitude in a particular frequency bin from the short-time Fourier transform data, and 3) the deep learning method to further constrain the source sky position. We compare the computational cost and the minimum amplitude of the detectable signal with the coherent matched filtering analysis. With a reasonable computational cost, we find that our method can detect the signal having only 32% larger amplitude than that of the coherent search with 95% detection efficiency.
Deep Learning-based Resource Allocation For Device-to-Device Communication

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives.
Humble Teacher and Eager Student: Dual Network Learning for Semi-supervised 2D Human Pose Estimation

Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, the methods degenerate and predict every pixel in unlabeled images as background. This is because contradictory predictions are gradually pushed to the background class due to highly imbalanced class distribution. But this is not an issue in supervised learning because it has accurate labels. This inspires us to stabilize the training by obtaining reliable pseudo labels. Specifically, we learn two networks to mutually teach each other. In particular, for each image, we compose an easy-hard pair by applying different augmentations and feed them to both networks. The more reliable predictions on easy images in each network are used to teach the other network to learn about the corresponding hard images. The approach successfully avoids degeneration and achieves promising results on public datasets. The source code will be released.
Quantization of Blackjack: Quantum Basic Strategy and Advantage

Quantum computers that process information by harnessing the remarkable power of quantum mechanics are increasingly being put to practical use. In the future, their impact will be felt in numerous fields, including in online casino games. This is one of the reasons why quantum gambling theory has garnered considerable attention. Studies have shown that the quantum gambling theory often yields nontrivial consequences that classical theory cannot interpret. We formulated blackjack game, which is one of the most famous card games, as a quantum game and found possible quantum entanglement between strategies. We also devised a quantum circuit reproducing classical blackjack. This circuit can be realized in the near future when quantum computers are commonplace. Furthermore, we showed that the player’s expectation increases compared to the classical game using quantum basic strategy, which is a quantum version of the popular basic strategy of blackjack.
Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning

U-Nets have been tremendously successful in many imaging inverse problems. In an effort to understand the source of this success, we show that one can reduce a U-Net to a tractable, well-understood sparsity-driven dictionary model while retaining its strong empirical performance. We achieve this by extracting a certain multiscale convolutional dictionary from the standard U-Net. This dictionary imitates the structure of the U-Net in its convolution, scale-separation, and skip connection aspects, while doing away with the nonlinear parts. We show that this model can be trained in a task-driven dictionary learning framework and yield comparable results to standard U-Nets on a number of relevant tasks, including CT and MRI reconstruction. These results suggest that the success of the U-Net may be explained mainly by its multiscale architecture and the induced sparse representation.
Fast Region Proposal Learning for Object Detection for Robotics

Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable performance. Unfortunately, training such systems requires several hours of GPU time. For robots, to successfully adapt to changes in the environment or learning new objects, it is also important that object detectors can be re-trained in a short amount of time. A recent method [1] proposes an architecture that leverages on the powerful representation of deep learning descriptors, while permitting fast adaptation time. Leveraging on the natural decomposition of the task in (i) regions candidate generation, (ii) feature extraction and (iii) regions classification, this method performs fast adaptation of the detector, by only re-training the classification layer. This shortens training time while maintaining state-of-the-art performance. In this paper, we firstly demonstrate that a further boost in accuracy can be obtained by adapting, in addition, the regions candidate generation on the task at hand. Secondly, we extend the object detection system presented in [1] with the proposed fast learning approach, showing experimental evidence on the improvement provided in terms of speed and accuracy on two different robotics datasets. The code to reproduce the experiments is publicly available on GitHub.
Mental health, coping, and social support among people living with HIV in the Americas: A comparative study between Argentina and the USA during the SARS-CoV-2 pandemic

Abstract Background: The COVID-19 pandemic pose significant risk to mental health and may disproportionately affect people living with HIV (PLWH). This study examined the interaction of social support and resilient coping in predicting depressive symptoms among PLWH. Methods : PLWH residing in Buenos Aires, Argentina and in Miami, Florida (US) were asked to complete an anonymous survey on the impact of COVID-19. Statistical analysis included ordinary least squares regression. Results: A total of 1,554 participants were included. Mean age was 47.30 years; 63.7 % were men. A test of three-way interaction of social support resilient coping study site indicated differences by site (b = -0.63.862, p = .043010, 95% CI [-1.24, -0.02.205, 1.52]). In Argentina, at higher social support and resilient coping, depressive symptoms were lowest. At lower social support and resilient coping, depressive symptoms were highest. Discussion: The impact of COVID-19 on mental health illustrates the need to develop innovative strategies to support resilience and to enhance coping with stress and adversity among PLWH.
Social support, adherence to Mediterranean diet and physical activity in adults: results from a community-based cross-sectional study

There is a growing recognition that social support can potentially exert consistent or opposing effects in influencing health behaviours. The present paper presents a cross-sectional study, including 2,064 adults from Italy, Spain and Greece, who were participants in a multi-centre randomised controlled trial (C4H study), aiming to examine whether social support is correlated with adherence to a healthy Mediterranean diet and physical activity. Social support data were available for 1,572 participants. The majority of the sample reported emotional support availability (84·5 %), financial support availability (72·6 %) and having one or more close friends (78·2 %). Mediterranean diet adherence was significantly associated with emotional support (P = 0·009) and social network support (P = 0·021). No statistically significant associations were found between participant physical activity and the social support aspects studied. In conclusion, emotional and social network support may be associated with increased adherence to the Mediterranean diet. However, further research is needed to evaluate the role of social support in adherence to healthy Mediterranean diet.
Phylogenetic Analysis Revealed the Dissemination of Closely Related Epidemic Vibrio cholerae O1 Isolates in Laos, Thailand, and Vietnam

Abstract We performed whole-genome sequencing of Vibrio cholerae O1 isolates from Laos, Thailand, and Vietnam, where cholera outbreaks occurred, to determine their genetic lineages. Core genome phylogenetic analysis revealed that the isolates located in same lineage without regional clusters, which suggests that closely related strains circulated in Southeast Asia.
Modern multiple imputation with functional data

This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state‐of‐the‐art methods, which face major challenges in the fitting of more complex non‐linear models. Currently, many of these models cannot be consistently estimated unless the number of observed points per curve grows sufficiently quickly with the sample size, whereas we show numerically that a modified approach with more modern multiple imputation methods can produce better estimates in general. We also propose a new imputation approach that combines the ideas of MissForest with Local Linear Forest and compare their performance with PACE and several other multivariate multiple imputation methods. This work is motivated by a longitudinal study on smoking cessation, in which the electronic health records (EHR) from Penn State PaTH to Health allow for the collection of a great deal of data, with highly variable sampling. To illustrate our approach, we explore the relation between relapse and diastolic blood pressure. We also consider a variety of simulation schemes with varying levels of sparsity to validate our methods.