Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirical investigation of critical success factors in agile sotware development projects

While software is so important for all facets of the modern world, software development itself is not a perfect process. Agile software engineering methods have recently emerged as a new and better way of developing software as compared to the traditional methodologies. However, their success has mostly been anecdotal, and research in this subject is still scant in the academic circles. This research study was carried out to explore and determine the critical success factors of Agile software development projects using quantitative approach. The research compiled success and failure factors of Agile projects identified in the existing literature into a preliminary list of potential critical success factors. Subsequently, reliability analysis and factor analysis were conducted in order to consolidate the list into a final set of 12 possible critical success factors for each of the four project success categories---Quality, Scope, Time, and Cost. A survey was conducted among Agile professionals, gathering empirical data from 109 Agile projects from 25 countries across the world. Multiple regression techniques were used, both at the full regression model and at the optimized regression model via the stepwise screening procedure. The results revealed that only 10 out of 48 hypotheses were supported, identifying three critical success factors for Agile software development projects: (a) Delivery Strategy, (b) Agile Software Engineering Techniques, and (c) Team Capability. The results provided researchers and practitioners with insight on the comparative values of success factors, and might help managers to quickly determine if their organizations are ready to jump onto the Agile bandwagon.
Perceptual Coding for Compressed Video Understanding: A New Framework and Benchmark

—Most video understanding methods are learned on high-quality videos. However, in most real-world scenarios, the videos are ﬁrst compressed before the transportation and then decompressed for understanding. The decompressed videos are degraded in terms of perceptual quality, which may degenerate the downstream tasks. To address this issue, we propose the ﬁrst coding framework for compressed video understanding, where another learnable perceptual bitstream is introduced and simultaneously transported with the video bitstream. With the sophisticatedly designed optimization target and network architectures, this new stream largely boosts the perceptual quality of the decoded videos yet with a small bit cost. Our framework can enjoy the best of both two worlds, (1) highly efﬁcient content-coding of industrial video codec and (2) ﬂexible perceptual-coding of neural networks (NNs). Finally, we build a rigorous benchmark for compressed video understanding over four different compression levels, six large-scale datasets, and two popular tasks. The proposed Dual-bitstream Perceptual Video Coding framework Dual-PVC consistently demonstrates signiﬁcantly stronger performances than the baseline codec under the same bitrate level.
Abelian subalgebras and ideals of maximal dimension in Zinbiel algebras

Abstract In this paper, we compare the abelian subalgebras and ideals of maximal dimension for finite-dimensional Zinbiel algebras. We study Zinbiel algebras containing maximal abelian subalgebras of codimension 1 and supersolvable Zinbiel algebras in which such subalgebras have codimension 2, and we also analyze the case of filiform Zinbiel algebras. We give examples to clarify some results, including listing the values for α and β for the low dimensional Zinbiel algebras over the complex field that have been classified. Communicated by Alberto Elduque
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning

In reinforcement learning (RL), offline learning decoupled learning from data collection and is useful in dealing with exploration-exploitation tradeoff and enables data reuse in many applications. In this work, we study two offline learning tasks: policy evaluation and policy learning. For policy evaluation, we formulate it as a stochastic optimization problem and show that it can be solved using approximate stochastic gradient descent (aSGD) with time-dependent data. We show aSGD achieves $\tilde O(1/t)$ convergence when the loss function is strongly convex and the rate is independent of the discount factor $\gamma$. This result can be extended to include algorithms making approximately contractive iterations such as TD(0). The policy evaluation algorithm is then combined with the policy iteration algorithm to learn the optimal policy. To achieve an $\epsilon$ accuracy, the complexity of the algorithm is $\tilde O(\epsilon^{-2}(1-\gamma)^{-5})$, which matches the complexity bound for classic online RL algorithms such as Q-learning.
Global convergence and optimality of the heavy ball method for non-convex optimization

In this letter we revisit the famous heavy ball method and study its global convergence for a class of nonconvex problems. We characterize the parameters that render the method globally convergent and yield the best Rconvergence factor. We show that for a family of functions, this convergence factor is superior to the factor obtained from the triple momentum method.
Estimating the Euclidean Quantum Propagator with Deep Generative Modelling of Feynman paths

Feynman path integrals provide an elegant, classically inspired representation for the quantum propagator and the quantum dynamics, through summing over a huge manifold of all possible paths. From computational and simulational perspectives, the ergodic tracking of the whole path manifold is a hard problem. Machine learning can help, in an efficient manner, to identify the relevant subspace and the intrinsic structure residing at a small fraction of the vast path manifold. In this work, we propose the Feynman path generator for quantum mechanical systems, which efficiently generates Feynman paths with fixed endpoints, from a (low-dimensional) latent space and by targeting a desired density of paths in the Euclidean space-time. With such path generators, the Euclidean propagator as well as the ground-state wave function can be estimated efficiently for a generic potential energy. Our work provides an alternative approach for calculating the quantum propagator and the ground-state wave function, paves the way toward generative modeling of quantum mechanical Feynman paths, and offers a different perspective to understand the quantum-classical correspondence through deep learning.
Boundary actions of CAT(0) spaces and their $C^*$-algebras

In this paper, we study boundary actions of CAT(0) spaces from a point of view of topological dynamics and C∗-algebras. First, we investigate the actions of right-angled Coexter groups and right-angled Artin groups with finite defining graphs on the visual boundaries and the Nevo-Sageev boundaries of their natural assigned CAT(0) cube complexes. In particular, we establish (strongly) pure infiniteness results for reduced crossed product C∗-algebras of these actions through investigating the corresponding CAT(0) cube complexes and establishing necessary dynamical properties such as minimality, topological freeness and pure infiniteness of the actions. In addition, we study actions of fundamental groups of graphs of groups on the visual boundaries of their Bass-Serre trees. We show that the existence of repeatable paths essentially implies that the action is 2-filling, from which, we also obtain a large class of unital Kirchberg algebras. Furthermore, our result also provides a new method in identifying C∗-simple generalized Baumslag-Solitar groups. The examples of groups obtained from our method have n-paradoxical towers in the sense of [26]. This class particularly contains non-degenerated free products, Baumslag-Solitar groups and fundamental groups of n-circles or wedge sums of n-circles.
How fuel treatment types, locations, and amounts impact landscape-scale fire behavior and carbon dynamics

When managing for fire across a large landscape, the types of fuel treatments, the locations of treatments, and the percentage of the landscape being treated should all interact to impact not only potential fire size, but also carbon dynamics across that landscape. To investigate these interactions, we utilized a forest growth model (FVS-FFE) and fire simulation software (FlamMap, Randig), integrated through GIS software (ArcMap9.3), to quantify the impacts that varied landscape-scale fuel treatments have on burn probability, fire size, short-term carbon loss, and long-term carbon storage. Thirteen fuel treatment scenarios were simulated on a 42,000 hectare landscape in the Klamath Mountains of northern California including one untreated, three proposed by the U.S. Forest Service, and nine that were spatially optimized and developed within the Treatment Optimization Model in FlamMap. The nine spatially optimized scenarios varied by treatment type (prescribed fire, mastication, and thin + burn) and landscape intensity (10 percent, 20 percent, and 30 percent of the landscape treated). Each treatment scenario was subjected to 10,000 simulated wildfires with random ignition locations in order to develop burn probability and mean fire size for each scenario, which subsequently impacted long-term carbon storage projections.
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, a Task-Agnostic and Modality-Agnostic framework that supports Task Comprehensiveness. OFA unifies a diverse set of cross-modal and unimodal tasks, including image generation, visual grounding, image captioning, image classification, language modeling, etc., in a simple sequence-to-sequence learning framework. OFA follows the instruction-based learning in both pretraining and finetuning stages, requiring no extra task-specific layers for downstream tasks. In comparison with the recent state-of-the-art vision&language models that rely on extremely large cross-modal datasets, OFA is pretrained on only 20M publicly available image-text pairs. Despite its simplicity and relatively small-scale training data, OFA achieves new SOTAs in a series of cross-modal tasks while attaining highly competitive performances on uni-modal tasks. Our further analysis indicates that OFA can also effectively transfer to unseen tasks and unseen domains. Our code and models are publicly available at https://github.com/OFA-Sys/OFA.
Neural Models for Output-Space Invariance in Combinatorial Problems

Recently many neural models have been proposed to solve combinatorial puzzles by implicitly learning underlying constraints using their solved instances, such as sudoku or graph coloring (GCP). One drawback of the proposed architectures, which are often based on Graph Neural Networks (GNN), is that they cannot generalize across the size of the output space from which variables are assigned a value, for example, set of colors in a GCP, or board-size in sudoku. We call the output space for the variables as 'value-set'. While many works have demonstrated generalization of GNNs across graph size, there has been no study on how to design a GNN for achieving value-set invariance for problems that come from the same domain. For example, learning to solve 16 x 16 sudoku after being trained on only 9 x 9 sudokus. In this work, we propose novel methods to extend GNN based architectures to achieve value-set invariance. Specifically, our model builds on recently proposed Recurrent Relational Networks. Our first approach exploits the graph-size invariance of GNNs by converting a multi-class node classification problem into a binary node classification problem. Our second approach works directly with multiple classes by adding multiple nodes corresponding to the values in the value-set, and then connecting variable nodes to value nodes depending on the problem initialization. Our experimental evaluation on three different combinatorial problems demonstrates that both our models perform well on our novel problem, compared to a generic neural reasoner. Between two of our models, we observe an inherent trade-off: while the binarized model gives better performance when trained on smaller value-sets, multi-valued model is much more memory efficient, resulting in improved performance when trained on larger value-sets, where binarized model fails to train.
What Makes a Good Commit Message?

A key issue in collaborative software development is communication among developers. One modality of communication is a commit message, in which developers describe the changes they make in a repository. As such, commit messages serve as an “audit trail” by which developers can understand how the source code of a project has changed-and why. Hence, the quality of commit messages affects the effectiveness of communication among developers. Commit messages are often of poor quality as developers lack time and motivation to craft a good message. Several automatic approaches have been proposed to generate commit messages. However, these are based on uncurated datasets including considerable proportions of poorly phrased commit messages. In this multi-method study, we first define what constitutes a “good” commit message, and then establish what proportion of commit messages lack information using a sample of almost 1,600 messages from five highly active open source projects. We find that an average of circa 44% of messages could be improved, suggesting the use of uncurated datasets may be a major threat when commit message generators are trained with such data. We also observe that prior work has not considered semantics of commit messages, and there is surprisingly little guidance available for writing good commit messages. To that end, we develop a taxonomy based on recurring patterns in commit messages' expressions. Finally, we investigate whether “good” commit messages can be automatically identified; such automation could prompt developers to write better commit messages.
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation

Autonomous driving (AD) datasets have progressively grown in size in the past few years to enable better deep representation learning. Active learning (AL) has re-gained attention recently to address reduction of annotation costs and dataset size. AL has remained relatively unexplored for AD datasets, especially on point cloud data from LiDARs. This paper performs a principled evaluation of AL based dataset distillation on (1/4th) of the large Semantic-KITTI dataset. Further on, the gains in model performance due to data augmentation (DA) are demonstrated across different subsets of the AL loop. We also demonstrate how DA improves the selection of informative samples to annotate. We observe that data augmentation achieves full dataset accuracy using only 60\% of samples from the selected dataset configuration. This provides faster training time and subsequent gains in annotation costs.
Image-based eeg classification of brain responses to song recordings

Classifying EEG responses to naturalistic acoustic stimuli is of theoretical and practical importance, but standard approaches are limited by processing individual channels separately on very short sound segments (a few seconds or less). Recent developments have shown classification for music stimuli (~2 mins) by extracting spectral components from EEG and using convolutional neural networks (CNNs). This paper proposes an efficient method to map raw EEG signals to individual songs listened for end-to-end classification. EEG channels are treated as a dimension of a [Channel x Sample] image tile, and images are classified using CNNs. Our experimental results (88.7%) compete with state-of-the-art methods (85.0%), yet our classification task is more challenging by processing longer stimuli that were similar to each other in perceptual quality, and were unfamiliar to participants. We also adopt a transfer learning scheme using a pre-trained ResNet-50, confirming the effectiveness of transfer learning despite image domains unrelated from each other.
Using Partial Monotonicity in Submodular Maximization

Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications. Traditionally, the study of submodular functions was based on binary function properties. However, such properties have an inherit weakness, namely, if an algorithm assumes functions that have a particular property, then it provides no guarantee for functions that violate this property, even when the violation is very slight. Therefore, recent works began to consider continuous versions of function properties. Probably the most significant among these (so far) are the submodularity ratio and the curvature, which were studied extensively together and separately. The monotonicity property of set functions plays a central role in submodular maximization. Nevertheless, and despite all the above works, no continuous version of this property has been suggested to date (as far as we know). This is unfortunate since submoduar functions that are almost monotone often arise in machine learning applications. In this work we fill this gap by defining the monotonicity ratio, which is a continues version of the monotonicity property. We then show that for many standard submodular maximization algorithms one can prove new approximation guarantees that depend on the monotonicity ratio; leading to improved approximation ratios for the common machine learning applications of movie recommendation, quadratic programming and image summarization.
Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Modern deep learning holds a great potential to transform clinical studies of human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep-staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to provide the shared view of the authors on the most recent state-of-the-art developments in automatic sleep staging, the challenges that still need to be addressed, and the future directions needed for automatic sleep scoring to achieve clinical value.
Estimating Growing Season Length Using Vegetation Indices Based on Remote Sensing: A Case Study for Vineyards in Washington State

Knowledge of phenological events in grapevines is essential for successful vineyard management. Conventional ground-observed phenological measurements are limited mainly due to their spatial coverage. However, satellite data provide access to global spatial coverage with the potential for high temporal resolution. The goal of this study was to evaluate the ability of vegetation indices based on remote sensing to estimate the growing season length of grapes in central Washington State. Several phenological metrics for vineyards located in the Columbia Valley region were derived from the satellite time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) using the normalized difference vegetation index (NDVI). The methodology included exponential smoothing and a moving average to compute both the onset of greenness and the end of greenness. The MODIS NDVI values were evaluated with aerial NDVI images of the same vineyard from August 2011. The average bias was -0.08, the average root mean squared error (RMSE) was 0.16, and the coefficient of determination (R 2 ) was 0.5 (p = 0.06). The results revealed an average growing season duration of 216 days for grapevines in this region for a period of five years. The average starting date of the growing season was April 2, and the computed end of the growing season was November 4. The highest NDVI value was 0.55, which coincided with July 12. On average, the lowest NDVI value was 0.3, and the average range of NDVI was 0.25. The preliminary results of this study showed that MODIS NDVI can be used to monitor vineyard vegetation dynamics in the Columbia Valley and has potential to be applied to other grape-growing regions in the U.S. and across the globe.
Subgraph Permutation Equivariant Networks

In this work we develop a new method, named Sub-graph Permutation Equivariant Networks (SPEN), which provides a framework for building graph neural networks that operate on sub-graphs, while using a base update function that is permutation equivariant, that are equivariant to a novel choice of automorphism group. Message passing neural networks have been shown to be limited in their expressive power and recent approaches to over come this either lack scalability or require structural information to be encoded into the feature space. The general framework presented here overcomes the scalability issues associated with global permutation equivariance by operating more locally on sub-graphs. In addition, through operating on sub-graphs the expressive power of higher-dimensional global permutation equivariant networks is improved; this is due to fact that two non-distinguishable graphs often contain distinguishable sub-graphs. Furthermore, the proposed framework only requires a choice of $k$-hops for creating ego-network sub-graphs and a choice of representation space to be used for each layer, which makes the method easily applicable across a range of graph based domains. We experimentally validate the method on a range of graph benchmark classification tasks, demonstrating statistically indistinguishable results from the state-of-the-art on six out of seven benchmarks. Further, we demonstrate that the use of local update functions offers a significant improvement in GPU memory over global methods.
Meet the Oligarchs: Business Legitimacy and Taxation at the Top

We study the causal impact of trust in business elites and trust in government on preferences for taxation at the top. Using new survey data, we find that distrust causes an increase in desired taxes on the top 1 percent. For example, our distrust-in-business-elites treatment leads to an increase in desired taxes on the top 1 percent of 2.4 percentage points (it closes 27 percent of the Democrat-Republican gap in tax preferences) when trust in government is low; a similar result is obtained for our distrust-in-government treatment.