Miracle-dz/UniFAR-FTUs huggingface dataset:
CONSUMER THEORY FOR CHEAP INFORMATION

In many settings, a decision-maker facing uncertainty must decide not only how much information to purchase, but also from which sources. It is already known that when uncertainty is about a binary variable a decision-maker with a sufficiently large budget will spend their entire budget on signals from a single source: the most precise one. When there are more than two possible states of the world, however, the story is different: different information sources may differ in the pair of states they are worst at distinguishing, and the decision-maker may therefore purchase information from different sources which cover for each others’ weaknesses. I characterize tradeoffs between samples from different sources in a setting with low costs (or large budgets), where the probability of a mistake is small and well-described by large deviations theory. I show that, for large samples, the marginal rate of substitution between samples from distinct information sources is approximately given by the ratio of precision-like indices for each source, and that the asymptotically optimal bundle satisfies a maxi-min rule: it maximizes the precision per dollar for the worst case pair of states. Since the precision of each signal does not depend on the characteristics of the decision-maker (their prior beliefs or the payoffs of the decision problem they face), all decision-makers agree on the fraction of signals from each source in an asymptotically optimal bundle. Different information sources with the same worst-case pair of states—the two states the signal is least precisely able to differentiate—are redundant and are never demanded together. The asymptotically optimal bundle occurs either at a corner, or at one of a finite number of interior “kink points” where the worst-case pair of states switches. To illustrate these results, I consider a number of basic consumer theory exercises and discuss implications for information demand.
PROJECT TRAILER: TANDEM OF ROVER AND ASSOCIATED WAIN FOR LUNAR EXTENDED ROAMING

TRAILER is a two-year project contracted by the European Space Agency ESA to test a novel architecture of robotic cooperation based on a tandem of two rovers for lunar surface exploration missions. The future lunar GATEWAY can be considered as a “Base Camp” that will serve to perform human and robotic expeditions on the surface of the Moon. Astronauts and robots will work together to prepare the coming era of space exploration with missions to the lunar South Pole and other locations: robotic explorers will search for ISRU elements that could support a human presence on the surface. These scouts will be operated either from Earth or from GATEWAY; and they will need to perform certain tasks in autonomy. The main objective of the TRAILER activity is to develop a rover system consisting of i) An agile and powerful rover (TRACTOR) equipped with high performance locomotion and navigation, local wireless transceiver, short-term energy storage, sample acquisition system, and ii) An active trailer (WAIN) equipped with limited locomotion and navigation, high power generation or storage and local wireless network that could assure lunar-earth communication and a scientific laboratory for sample collection and analysis. The TRAILER activity is under a contract by ESA and led by the Marseille-based company COMEX that develops the “WAIN". The German Research Center for Artificial Intelligence Robotics Innovation Center (DFKI RIC) in Germany is responsible for simulations and development of the second robot, the “TRACTOR". LIQUIFER Systems Group in Austria defines the mission and operation scenarios of the robotic tandem and planning. The TRAILER system will be tested in facilities at the partner organizations in France, Germany and Austria.
From Knowledge-based to Competency-based Computing Education: Future Directions

Competency is often associated with proficiency, expertise, capability, and performance. Professional schools such as those in dentistry and medicine generally require the practice of skills and knowledge within human environments for someone to become a competent professional in their respective fields. In computing, competency is a relatively new concept. For decades, computing curricula focused on knowledge. However, competency is much more than knowledge. The ACM/IEEE Computing Curricula 2020 (CC2020) project identifies competency as a combination of knowledge, skills, and human dispositions, in context or task. That is, knowledge alone is not enough to produce a competent graduate from a computing program. Industry expects graduates to perform competently from the first day of employment. While computing educators have been sharing computing knowledge for decades, can they do it better under the rubric of competency? The authors leverage on the current activities of the CC2020 project, their personal experiences, and outcomes from current research on computing competency and related topics. Future directions and strategies for transforming the current paradigm of knowledge-based learning toward a broad acceptance of competency-based learning are an important part of this work. The authors recommend several steps needed to achieve such a transformation over time. While universities are not training grounds for industry needs, it would be a mistake to ignore such influence within the rubric of competency-based learning. This work will be of interest to all computing and engineering educators.
The use of gamification to support the teaching-learning of software exploratory testing: an experience report based on the application of a framework

This Research to Practice Full Paper presents exploratory testing, an approach that has become quite relevant in the testing software, growing its application in the industrial scenario. The main reason for that is the emerging utilization of agile practices in the software development process to satisfy the needs of the market (Time to Market), which is essential for a company to remain active in the market. However, the systematic mapping study performed found few studies about the application of exploratory testing, a subject little discussed in the academic context. For this reason, this work uses gamification as a systematic strategy in exploratory test teaching and learning in the form of an experiment with two classes. One class has undergraduate students in Computer Science, and the other has students who graduated as Computer Technicians. The aim is to engage students to obtain a better performance, preparing them to use that test approach in the industrial and academic context. As a result, students achieved good overall performance; with reports from students that gamification facilitated and significantly collaborated to achieve a better performance converging with the quantitative data obtained. This can be evidenced mainly by the fact that both runs of the experiment (classes) reached a percentage higher than 70% of achievement, a great overall performance analyzed by the ratio of medals obtained by participation.
Course Satisfaction in Engineering Education Through the Lens of Student Agency Analytics

This Research Full Paper presents an examination of the relationships between course satisfaction and student agency resources in engineering education. Satisfaction experienced in learning is known to benefit the students in many ways. However, the varying significance of the different factors of course satisfaction is not entirely clear. We used a validated questionnaire instrument, exploratory statistics, and supervised machine learning to examine how the different factors of student agency affect course satisfaction among engineering students (N = 293). Teacher’s support and trust for the teacher were identified as both important and critical factors concerning experienced course satisfaction. Participatory resources of agency and gender proved to be less important factors. The results provide convincing evidence about the possibility to identify the most important factors affecting course satisfaction.
In vitro effects of cyanobacteria (Oscillatoria tenuis) extracellular products on date palm (Phoenix dactylifera L. cv. ‘Barhee’) propagation

E-mail: majid.abdulhameedl@uobasrah.edu. iq Received: 04/09/2020 Acceptance: 09/10/2020 Available Online: 14/10/2020 Published: 01/01/2021 This study was conducted to investigate the effect of the medium type (halfstrength MS medium and MS free medium) and the extracellular products of cyanobacteria (Oscillatoria tenuis) that were grown under photoperiods of 8 or 16 h day-1 on the shoot proliferation and rooting of in vitro ‘Barhee’ date palm ciltures. MS free medium failed to establish half-strength MS medium results, which indicates that the medium could not sustain cultures requirments during shoot proliferation and rooting phase. On the other hand, half-strength MS medium supplemented with extracellular products of O. tenuis grown at 16 h day-1 photoperiod recorded the best results in regards of response to direct organogenesis and rooting.
Does Health Insurance Make You Fat

The prevalence of obesity has been rising dramatically in the U.S., leading to poor health and rising health care expenditures. The role of policy in addressing rising rates of obesity, however, is controversial. Policy recommendations for interventions intended to influence body weight decisions often assume the obesity creates negative externalities for the non-obese. We build on earlier work demonstrating that this argument depends on two important assumptions: 1) that the obese do not pay for their higher medical expenditures through differential payments for health care and health insurance, and 2) that body weight decisions are responsive to the incidence of medical care costs associated with obesity. In this paper, we test the latter proposition - that body weight is influenced by insurance coverage - using two approaches. First, we use data from the Rand Health Insurance Experiment, in which people were randomly assigned to varying levels of health insurance, to examine the effect of generosity of insurance coverage on body weight along the intensive coverage margin. Second, we use instrumental variables methods to estimate the effect of type of insurance coverage (private, public and none) on body weight along the extensive margin. We explicitly address the discrete nature of the endogenous indicator of health insurance coverage by estimating a nonlinear instrumental variables model. We find weak evidence that more generous insurance coverage increases body mass index. We find stronger evidence that being insured increases body mass index and obesity.
P1.14 ERROR GROWTH AND DATA ASSIMILATION IN A PARAMETERIZED PBL

Surface-layer in-situ observations comprise a rich, accurate, and often dense data source, but they are generally under-utilized in current operational data assimilation (DA) systems because of the complex interactions between the surface and the atmosphere aloft. Representing the structure of the PBL accurately in a mesoscale NWP model initialization could lead to improved shortrange forecasts of local thermally-driven flows such as the sea breeze and slope flows. Forecasts of larger-scale phenomena could also be improved. When a mesoscale NWP model is used as a tool to generate 3-D datasets for process studies, an accurate representation of the PBL would increase confidence in the results. The EnKF (Burgers et al. 1995; Anderson and Anderson 1999) is attractive for assimilation of surface observations to specify the state of a parameterized PBL (hereafter PPBL) because it is easy to implement and is free from many of the limitations of the variational approach. An adjoint model is unnecessary, explicit balance constraints are not applied, and anisotropic estimates of the background error covariance structure are readily available. More importantly, the background error covariances contain information about the PPBL and the appropriate vertical extent of influence of a surface-layer observation. Accounting for uncertainty in model parameters, which are ubiquitous in PPBL schemes, can also be easily accomplished with the EnKF. While model error in a dynamical model may have unknown characteristics, it is reasonable to expect that in a physical parameterization scheme a large part of the error is related to poorly chosen (empirically or otherwise) parameters that are static. With the EnKF, stochastic distributions of PPBL parameters (“constants” either physical or not) can be estimated through correlation with observations. Each parameter is treated stochastically, and carries a distribution in the
Contempory thought of researcher on online shopping – a literature review

Consumers are progressively adopting electronic channels for buying. Explaining on-line client behavior continues to be a significant issue as studies accessible target a multiple set of variables and relied on totally different approaches and theoretical foundations. Based on previous analysis 2 main drivers of on-line behavior are identified: perceived edges of on-line searching associated with utilitarian and hedonistic characteristics and perceived risk. In addition factors affecting choice of online shopping are given as alleviative variables of the connection between perceived blessings and downsides of web searching and on-line client behavior.
Initial results from using ATMS data at ECMWF

This paper reports on the first experiences with ATMS data at ECMWF, both in terms of the contribution to the calibration/validation exercise, and in terms of preliminary assimilation trials. Comparisons in brightness temperature space against short-term forecasts are used to further establish the fidelity of the data. Monitoring of ATMS data against short-term forecasts show that the data are generally of good quality, with a noise performance that is well within specification and, after appropriate averaging, comparable to or better than that of AMSU-A. Biases vary smoothly with scan-positions, even before an appropriate antenna pattern correction has been established, and ATMS looks better than AMSU-A in this regard. Outer scan positions can be assimilated without restrictions due to biases, and together with the wider swath this leads to a much improved coverage from ATMS compared to one AMSU-A. The analysis and forecast impact in preliminary assimilation trials is significantly positive over the Southern Hemisphere, whereas forecast impact over the Northern Hemisphere is more neutral.
Artificial Intelligence Governance For Businesses

ABSTRACT While artificial intelligence (AI) governance is thoroughly discussed on a philosophical, societal, and regulatory level, few works target companies. We address this gap by deriving a conceptual framework from literature. We decompose AI governance into governance of data, machine learning models, and AI systems along the dimensions of who, what, and how “is governed. ” This decomposition enables the evolution of existing governance structures. Novel, business-specific aspects include measuring data value and novel AI governance roles.
Optical forces and torques on asymmetric nanoscale core-shell particles

The optical trapping and manipulation of small particles is an important tool for probing fluid properties at the microscale. In particular, microrheology exploits the manipulation and rotation of micron-scale particles to probe local viscosity, especially where these properties may be perturbed as a function of their local environment, for example in the vicinity of cells. To this end, birefringent particles are useful as they can be readily controlled using optically induced forces and torques, and thereby used to probe their local environment. However the magnitude of optical torques that can be induced in birefringent particles is small, and a function of the particle diameter, meaning that rotational flow cannot readily be probed on length scales much small than the micron level. Here we show modelling that demonstrates that asymmetric, spherical core-shell nanoparticles can be used to generate considerable optical torques. The asymmetry is a result of the displacement of the centre of the core from the shell. Our results show that, for particles ranging from 90 nm to 180 nm in diameter, we may achieve rotation rates exceeding 800 Hz. This fills a missing size gap in the rotation of microparticles with optical forces. The diameter of particle we may rotate is almost an order of magnitude smaller than the smallest birefringent particles that have been successfully rotated to date. The rotation of asymmetric core-shell nanoparticles therefore offers a pathway to nanorheology.
Concurrent anatomical, physiological and network changes in cognitively impaired multiple sclerosis patients

Cognitive impairment in multiple sclerosis is associated with functional connectivity abnormalities, but the pathological substrates of these abnormalities are not well understood. It has been proposed that resting-state network nodes that integrate information from disparate regions are susceptible to metabolic stress, which may impact functional connectivity. In multiple sclerosis, pathology could increase metabolic stress within axons, damaging the anatomical connections of network regions, and leading to functional connectivity changes. We tested this hypothesis by assessing whether resting state network regions that show functional connectivity abnormalities in people with cognitive impairment also show anatomical connectivity abnormalities. Multimodal MRI and neuropsychological assessments were performed in 102 relapsing remitting multiple sclerosis patients and 27 healthy controls. Patients were considered cognitively impaired if they obtained a z-score of [≤]1.5 on [≥]2 tests of the Brief Repeatable Battery of Neuropsychological Tests (n=55). Functional connectivity was assessed with Independent Component Analysis of resting state fMRI images, and anatomical connectivity with Anatomical Connectivity Mapping of diffusion-weighted MRI. Exploratory analyses of fractional anisotropy and cerebral blood flow changes were conducted to assess local tissue characteristics. We found significantly decreased functional connectivity in the anterior and posterior default mode networks and significant increases in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients. Networks showing functional abnormalities also showed reduced anatomical connectivity and white matter microstructure integrity as well as reduced local tissue cerebral blood flow. Our results identify key pathological correlates of functional connectivity abnormalities associated with impaired cognitive function in multiple sclerosis, consistent with metabolic dysfunction in functional network regions.
Making mean-estimation more efficient using an MCMC trace variance approach: DynaMITE

The Markov-Chain Monte-Carlo (MCMC) method has been used widely in the literature for various applications, in particular estimating the expectation $\mathbb{E}_{\pi}[f]$ of a function $f:\Omega\to [a,b]$ over a distribution $\pi$ on $\Omega$ (a.k.a. mean-estimation), to within $\varepsilon$ additive error (w.h.p.). Letting $R \doteq b-a$, standard variance-agnostic MCMC mean-estimators run the chain for $\tilde{\cal O}(\frac{TR^{2}}{\varepsilon^{2}})$ steps, when given as input an (often loose) upper-bound $T$ on the relaxation time $\tau_{\rm rel}$. When an upper-bound $V$ on the stationary variance $v_{\pi} \doteq \mathbb{V}_{\pi}[f]$ is known, $\tilde{\cal O}\bigl(\frac{TR}{\varepsilon}+\frac{TV}{\varepsilon^{2}}\bigr)$ steps suffice. We introduce the DYNAmic {Mcmc} Inter-Trace variance Estimation (DynaMITE) algorithm for mean-estimation. We define the inter-trace variance $v_{T}$ for any trace length $T$, and show that w.h. p., DynaMITE estimates the mean within $\varepsilon$ additive error within $\tilde{\cal O}\bigl(\frac{TR}{\varepsilon} + \frac{\tau_{\rm rel} v_{\tau\rm rel}}{\varepsilon^{2}}\bigr)$ steps, without {a priori} bounds on $v_{\pi}$, the variance of $f$, or the trace variance $v_{T}$. When $\epsilon$ is small, the dominating term is $\tau_{\rm rel} v_{\tau\rm rel}$, thus the complexity of DynaMITE principally depends on the {\it a priori unknown} $\tau_{\rm rel}$ and $v_{\tau\rm rel}$. We believe in many situations $v_{T}=o(v_{\pi})$, and we identify two cases to demonstrate it. Furthermore, it always holds that $v_{\tau\rm rel} \leq 2v_{\pi}$, thus the worst-case complexity of DynaMITE is $\tilde{\cal O}(\frac{TR}{\varepsilon} +\frac{\tau_{\rm rel} v_{\pi}}{\varepsilon^{2}})$, improving the dependence of classical methods on the loose bounds $T$ and $V$.
Measuring ultralarge scale effects in the presence of 21 cm intensity mapping foregrounds

H i intensity mapping will provide maps of the large-scale distribution of neutral hydrogen (H i) in the universe. These are prime candidates to be used to constrain primordial non-Gaussianity using the large-scale structure of the Universe as well as to provide further tests of Einstein’s theory of Gravity (GR). But H i maps are contaminated by foregrounds, which can be several orders of magnitude above the cosmological signal. Here we quantify how degenerated are the large-scale effects (fNL and GR effects) with the residual foregrounds. We conclude that a joint analysis does not provide a catastrophic degradation of constraints and provides a framework to determine the marginal errors of large scale-effects in the presence of foregrounds. Similarly, we conclude that the macroscopical properties of the foregrounds can be measured with high precision. Notwithstanding, such results are highly dependent on accurate forward modelling of the foregrounds, which incorrectly done catastrophically bias the best-fitting values of cosmological parameters, foreground parametrizations, and large-scale effects.
Time series classification for predictive maintenance on event logs

Time series classification (TSC) gained a lot of attention in the past decade and number of methods for representing and classifying time series have been proposed. Nowadays, methods based on convolutional networks and ensemble techniques represent the state of the art for time series classification. Techniques transforming time series to image or text also provide reliable ways to extract meaningful features or representations of time series. We compare the state-of-the-art representation and classification methods on a specific application, that is predictive maintenance from sequences of event logs. The contributions of this paper are twofold: introducing a new data set for predictive maintenance on automated teller machines (ATMs) log data and comparing the performance of different representation methods for predicting the occurrence of a breakdown. The problem is difficult since unlike the classic case of predictive maintenance via signals from sensors, we have sequences of discrete event logs occurring at any time and the lengths of the sequences, corresponding to life cycles, vary a lot.