Miracle-dz/UniFAR-FTUs huggingface dataset:
The Nature of Core-competencies required for Effective Strategic Project Leadership

The unique sets of technical and interpersonal skills that were developed for project leadership predicated on the traditional definition of project success, have been found to be inadequate to effectively lead a project team as current dynamics makes project increasingly complex, uncertain and competitive. Strategic project leadership regards a project to be successful not only when it meets its objectives with the time, quality and budget constraints, but if it adds value to the organisation competitiveness. The aim of this study is to explore the nature and relationship of the core competencies needed for effective leadership and management as apply to strategic project. A questionnaire containing project success and leadership dimension questions was use to obtain statistically useful data from 35 individuals consisting of 5 project managers, 26 project team members, 3 operations staffs and 1 project sponsor drawn from government and private-owned companies including the manufacturing, construction, transportation and communication industry. The respondents cut across different age and gender groups and level of involvements in projects. The questionnaires were administered using the face-to-face and paperand-pencil approach. Analyses of the responses revealed 15 competencies necessary for effective strategic project leadership. Out of these general leadership competencies, the core competencies were found to be 10. It was also found that the position held and the years of experience of the respondents influenced the choice (and the rationale) of the requisite core competencies required for effective strategic project management. Keywords—Core competencies; strategic project leadership; effective project management;
1 Tobacco Taxes , Smoking Restrictions , and Tobacco Use

Volume Title: The Economic Analysis of Substance Use and Abuse: An Integration of Econometrics and Behavioral Economic Research Volume Author/Editor: Frank J. Chaloupka, Michael Grossman, Warren K. Bickel and Henry Saffer, editors Volume Publisher: University of Chicago Press Volume ISBN: 0-262-10047-2 Volume URL: http://www.nber. org/books/chal99-1 Conference Date: March 27-28, 1997 Publication Date: January 1999
The Financial Development And Economic Growth Nexus: The Role Of Saudi Arabian Financial Institutions

The purpose of this paper is to assess the effects of the financial development and the financial institutions quality on the economic growth for the Saudi Arabia. Using generalized Method of Moments (GMM) with a dynamic panel framework, this paper employs different measures of financial development namely the Liquid liabilities (LIQ), Private credit by deposit money banks and other financial institutions (CRE) and Central bank assets (ASS), and for financial institutions quality including socioeconomic conditions, investment profile, law and order, corruption, external conflicts and democratic accountability. For the period (1990-2017), our findings strongly support the hypothesis that financial development leads to growth in the Saudi Arabia. Moreover, empirical results support a positive and significant relationship observed between financial institutions quality and growth. The findings of this paper suggest the need to give more support to the financial development for Saudi Arabia banking that have been launched in the country since the last three decades and to improve the role played by the financial institutions to stimulate saving/investment and, consequently, long-term economic growth.
Self Diagnostics and Isolation Mechanisms for Mixed Criticality Systems

Virtualization is a technology that is frequently employed in computers and servers to provide isolation for execution environments, and to support the execution of multiple Operating Systems (OS) on the same hardware platform. In the embedded systems´ world, virtualization has been a rising trend, essentially because it offers an isolation mechanism that provides hardware manufacturer´ independence and it avoids obsolescence issues. The isolation mechanism supports safety and security measures, and assists in the certification of safety-critical systems. Virtualization offers improved performances, better transparency, portability and interoperability by integrating hardware and software resources, and also networking services into one computing entity. It makes the integration process of Mixed Criticality Systems (MCS) easier. For industries, FieldProgrammable Gate Arrays (FPGAs) hardware solutions provide the needed level of flexibility and performance. In this paper, a Self-test application is integrated in the hardware and also in the software level. The importance of self-test applications for Instrumentation and Control (I&C) systems will be discussed in the context of virtualization. For this implementation a type 1 hypervisor called Xtratum is used. An analysis of inter-partition communication channels´ performance will be provided including the implications multicore approaches will have on communication. The novelty of this work is to study the isolation impact multicore approaches can have on inter-partitions communications in Xtratum. Another novel aspect is the implementation of a self-test application in the hypervisor and the board as well.
Learning Multi-instrument Classification with Partial Labels

Multi-instrument recognition is the task of predicting the presence or absence of different instruments within an audio clip. A considerable challenge in applying deep learning to multi-instrument recognition is the scarcity of labeled data. OpenMIC is a recent dataset containing 20K polyphonic audio clips. The dataset is weakly labeled, in that only the presence or absence of instruments is known for each clip, while the onset and offset times are unknown. The dataset is also partially labeled, in that only a subset of instruments are labeled for each clip. In this work, we investigate the use of attention-based recurrent neural networks to address the weakly-labeled problem. We also use different data augmentation methods to mitigate the partially-labeled problem. Our experiments show that our approach achieves state-of-the-art results on the OpenMIC multi-instrument recognition task.
ItLnc-BXE: a Bagging-XGBoost-ensemble method with multiple features for identification of plant lncRNAs

Motivation: Since long non-coding RNAs (lncRNAs) have involved in a wide range of functions in cellular and developmental processes, an increasing number of methods have been proposed for distinguishing lncRNAs from coding RNAs. However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identification. Different from lncRNAs in animal systems, plant lncRNAs have distinct characteristics. It is desirable to develop a computational method for accurate and robust identification of plant lncRNAs. Results: Herein, we present a plant lncRNA identification method ItLnc-BXE, which utilizes multiple features and the ensemble learning strategy. First, a diversity of lncRNA features is collected and filtered by feature selection to represent RNA transcripts. Then, several base learners are trained and further combined into a single meta-learner by ensemble learning, and thus an ItLnc-BXE model is constructed. ItLnc-BXE models are evaluated on datasets of six plant species, the results show that ItLnc-BXE outperforms other state-of-the-art plant lncRNA identification methods, achieving better and robust performances (AUC>95.91%). We also perform some experiments about cross-species lncRNA identification, and the results indicate that dicots-based and monocots-based models can be used to accurately identify lncRNAs in lower plant species, such as mosses and algae. Availability: source codes are available at https://github.com/BioMedicalBigDataMiningLab/ItLnc-BXE. Contact: zhangwen@mail.hzau. edu.cn (or) zhangwen@whu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
PKS 2250–351: A giant radio galaxy in Abell 3936

Abstract We present a detailed analysis of the radio galaxy PKS $2250{-}351$ , a giant of 1.2 Mpc projected size, its host galaxy, and its environment. We use radio data from the Murchison Widefield Array, the upgraded Giant Metre-wavelength Radio Telescope, the Australian Square Kilometre Array Pathfinder, and the Australia Telescope Compact Array to model the jet power and age. Optical and IR data come from the Galaxy And Mass Assembly (GAMA) survey and provide information on the host galaxy and environment. GAMA spectroscopy confirms that PKS $2250{-}351$ lies at $z=0.2115$ in the irregular, and likely unrelaxed, cluster Abell 3936. We find its host is a massive, ‘red and dead’ elliptical galaxy with negligible star formation but with a highly obscured active galactic nucleus dominating the mid-IR emission. Assuming it lies on the local M– $\sigma$ relation, it has an Eddington accretion rate of $\lambda_{\rm EDD}\sim 0.014$ . We find that the lobe-derived jet power (a time-averaged measure) is an order of magnitude greater than the hotspot-derived jet power (an instantaneous measure). We propose that over the lifetime of the observed radio emission ( ${\sim} 300\,$ Myr), the accretion has switched from an inefficient advection-dominated mode to a thin disc efficient mode, consistent with the decrease in jet power. We also suggest that the asymmetric radio morphology is due to its environment, with the host of PKS $2250{-}351$ lying to the west of the densest concentration of galaxies in Abell 3936.
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Social Networks

Influence maximization (IM) is one of the most important problems in social network analysis. Its objective is to find a given number of seed nodes that maximize the spread of information through a social network. Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeat Monte Carlo (MC) simulations over and over to reliably estimate the influence (i.e., the number of infected nodes) of a seed set. In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training. To the best of our knowledge, MONSTOR is the first inductive method for this purpose. MONSTOR can greatly accelerate existing IM algorithms by replacing repeated MC simulations. In our experiments, MONSTOR provided highly accurate estimates, achieving 0.998 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Moreover, IM algorithms equipped with MONSTOR are more accurate than state-of-the-art competitors in 63% of IM use cases.
Reasoning About Generalization via Conditional Mutual Information

We provide an information-theoretic framework for studying the generalization properties of machine learning algorithms. Our framework ties together existing approaches, including uniform convergence bounds and recent methods for adaptive data analysis. Specifically, we use Conditional Mutual Information (CMI) to quantify how well the input (i.e., the training data) can be recognized given the output (i.e., the trained model) of the learning algorithm. We show that bounds on CMI can be obtained from VC dimension, compression schemes, differential privacy, and other methods. We then show that bounded CMI implies various forms of generalization.
SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.
Limits on Gradient Compression for Stochastic Optimization

We consider stochastic optimization overℓp spaces using access to a first-order oracle. We ask: What is the minimum precision required for oracle outputs to retain the unrestricted convergence ratesƒ We characterize this precision for every p≥ 1 by deriving information theoretic lower bounds and by providing quantizers that (almost) achieve these lower bounds. Our quantizers are new and easy to implement. In particular, our results are exact for p = 2 and p = ∞, showing the minimum precision needed in these settings are Θ(d) and Θ(log d), respectively. The latter result is surprising since recovering the gradient vector will require Ω(d) bits.
Black Hole Evaporation: A Perspective from Loop Quantum Gravity

A personal perspective on the black hole evaporation process is presented using, as guidelines, inputs from: (i) loop quantum gravity, (ii) simplified models where concrete results have been obtained, and, (iii) semi-classical quantum general relativity. On the one hand, the final picture is conservative in that there are concrete results that support each stage of the argument, and there are no large departures from general relativity or semi-classical gravity in tame regions outside macroscopic black holes. On the other hand, it argues against certain views that are commonly held in many quarters, such as persistence of a piece of singularity that constitutes a part of the final boundary of space–time; presence of an event horizon serving as an absolute barrier between the interior and the exterior, and the (often implicit) requirement that purification must be completed by the time the ‘last rays’ representing the extension of this event horizon reach I + .
Wibson Protocol for Secure Data Exchange and Batch Payments

Wibson is a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell information in a trusted environment. The combination of the Wibson token and blockchain-enabled smart contracts hopes to allow Data Sellers and Data Buyers to transact with each other directly while providing individuals the ability to maintain anonymity as desired. The Wibson marketplace will provide infrastructure and financial incentives for individuals to securely sell personal information without sacrificing personal privacy. Data Buyers receive information from willing and actively participating individuals with the benefit of knowing that the personal information should be accurate and current. We present here two different components working together to achieve an efficient decentralized marketplace. The first is a smart contract called Data Exchange, which stores references to Data Orders that different Buyers open in order to show to the market that they are interested in buying certain types of data, and provides secure mechanisms to perform the transactions. The second is used to process payments from Buyers to Sellers and intermediaries, and is called Batch Payments.
From Nesterov's Estimate Sequence to Riemannian Acceleration

We propose the first global accelerated gradient method for Riemannian manifolds. Toward establishing our result we revisit Nesterov's estimate sequence technique and develop an alternative analysis for it that may also be of independent interest. Then, we extend this analysis to the Riemannian setting, localizing the key difficulty due to non-Euclidean structure into a certain ``metric distortion. '' We control this distortion by developing a novel geometric inequality, which permits us to propose and analyze a Riemannian counterpart to Nesterov's accelerated gradient method.
Priority-based Fair Scheduling in Edge Computing

Scheduling is important in Edge computing. In contrast to the Cloud, Edge resources are hardware limited and cannot support workload-driven infrastructure scaling. Hence, resource allocation and scheduling for the Edge requires a fresh perspective. Existing Edge scheduling research assumes availability of all needed resources whenever a job request is made. This paper challenges that assumption, since not all job requests from a Cloud server can be scheduled on an Edge node. Thus, guaranteeing fairness among the clients (Cloud servers offloading jobs) while accounting for priorities of the jobs becomes a critical task. This paper presents four scheduling techniques, the first is a naive first come first serve strategy and further proposes three strategies, namely a client fair, priority fair, and hybrid that accounts for the fairness of both clients and job priorities. An evaluation on a target platform under three different scenarios, namely equal, random, and Gaussian job distributions is presented. The experimental studies highlight the low overheads and the distribution of scheduled jobs on the Edge node when compared to the naive strategy. The results confirm the superior performance of the hybrid strategy and showcase the feasibility of fair schedulers for Edge computing.
The model structure for chain complexes

Let $\text{Ch}$ be the category of (possibly unbounded) chain complexes of abelian groups. In this note we construct the standard Quillen model structure on $\text{Ch}$, by a method that is somewhat different from the standard one. Essentially, we use a functorial two-stage projective resolution for abelian groups, and build everything directly from that. This has the advantage of being very concrete, explicit and functorial. It does not rely on the small object argument, or make any explicit use of transfinite induction. On the other hand, it is not so conceptual, and it does use the fact that subgroups of free abelian groups are free, so it does not generalise to many rings other than $\mathbb{Z}$. We do not claim any great technical benefit for this approach, but it seems like an interesting alternative, and may be pedagogically useful.
6D Object Pose Regression via Supervised Learning on Point Clouds

This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud. Deep features learned by convolutional neural networks from color information have been the dominant features to be used for inferring object poses, while depth information receives much less attention. However, depth information contains rich geometric information of the object shape, which is important for inferring the object pose. We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression. We argue that the axis-angle representation is a suitable rotation representation for deep learning, and use a geodesic loss function for rotation regression. Ablation studies show that these design choices outperform alternatives such as the quaternion representation and L2 loss, or regressing translation and rotation with the same network. Our simple yet effective approach clearly outperforms state-of-the-art methods on the YCB-video dataset.
Towards Graph Representation Learning in Emergent Communication

Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolutional networks to support the evolution of language and cooperation in multi-agent systems. Motivated by an image-based referential game, we propose a graph referential game with varying degrees of complexity, and we provide strong baseline models that exhibit desirable properties in terms of language emergence and cooperation. We show that the emerged communication protocol is robust, that the agents uncover the true factors of variation in the game, and that they learn to generalize beyond the samples encountered during training.
Perception as prediction using general value functions in autonomous driving applications

We propose and demonstrate a framework called perception as prediction for autonomous driving that uses general value functions (GVFs) to learn predictions. Perception as prediction learns data-driven predictions relating to the impact of actions on the agent’s perception of the world. It also provides a data-driven approach to predict the impact of the anticipated behavior of other agents on the world without explicitly learning their policy or intentions. We demonstrate perception as prediction by learning to predict an agent’s front safety and rear safety with GVFs, which encapsulate anticipation of the behavior of the vehicle in front and in the rear, respectively. The safety predictions are learned through random interactions in a simulated environment containing other agents. We show that these predictions can be used to produce similar control behavior to an LQR-based controller in an adaptive cruise control problem as well as provide advanced warning when the vehicle behind is approaching dangerously. The predictions are compact policy-based predictions that support prediction of the long term impact on safety when following a given policy. We analyze two controllers that use the learned predictions in a racing simulator to understand the value of the predictions and demonstrate their use in the real-world on a Clearpath Jackal robot and an autonomous vehicle platform.
Localization of Critical Findings in Chest X-Ray Without Local Annotations Using Multi-Instance Learning

The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings. While deep learning (DL) models has become a promising predictive technology with near-human accuracy, they commonly suffer from a lack of explainability, which is an important aspect for clinical deployment of DL models in the highly regulated healthcare industry. For example, localizing critical findings in an image is useful for explaining the predictions of DL classification algorithms. While there have been a host of joint classification and localization methods for computer vision, the state-of-the-art DL models require locally annotated training data in the form of pixel level labels or bounding box coordinates. In the medical domain, this requires an expensive amount of manual annotation by medical experts for each critical finding. This requirement becomes a major barrier for training models that can rapidly scale to various findings. In this work, we address these shortcomings with an interpretable DL algorithm based on multi-instance learning that jointly classifies and localizes critical findings in CXR without the need for local annotations. We show competitive classification results on three different critical findings (pneumothorax, pneumonia, and pulmonary edema) from three different CXR datasets.