Miracle-dz/UniFAR-FTUs huggingface dataset:
Financial Accountability Mechanisms in Local Governments in Uganda.A Case of Kabale District Local Government

The paper presents financial accountability mechanisms in local governments, with reference to Kabale district local government. A cross-sectional research design, which used both quantitative and qualitative approaches to collect and analyze data, was adopted. Both simple random and purposive sampling techniques were used to select 117 respondents from 174 subjects. Questionnaires and personal interviews were used to collect data from respondents. Frequencies and percentages were used to analyze quantitative data while direct quotes from interviews conducted among key informants formed the basis for qualitative analysis. Quantitative analysis was aided by software for document analysis (SPSS V 20.0). In descending order of perception, service delivery was found to be the most commonly used financial accountability mechanism, followed by financial reporting, expenditure control and budget. The paper therefore, concluded that service delivery is the most used mechanism of financial accountability, though the district’s local budget seemed unclear on reflecting the priorities of the local people. This paper recommended that the local government should ensure that the district’s budget demonstrates community preference; salaries and wages should be paid in accordance with the district’s approved budget; expenditures on development should always be as per the approved budget, and the mode of financial reporting, particularly on liabilities should be standardized. Due to the scope of this paper, which could not quantitatively relate financial accountability and service delivery, there is need for a comprehensive study on the effect of financial accountability and service delivery in Kabale district local government
Blue‐Emitting Cationic Iridium Complexes with 2‐(1H‐Pyrazol‐1‐yl)pyridine as the Ancillary Ligand for Efficient Light‐Emitting Electrochemical Cells

Two blue‐emitting cationic iridium complexes with 2‐(1H‐pyrazol‐1‐yl)pyridine (pzpy) as the ancillary ligands, namely, [Ir(ppy)2(pzpy)]PF6 and [Ir(dfppy)2(pzpy)]PF6 (ppy is 2‐phenylpyridine, dfppy is 2‐(2,4‐difluorophenyl) pyridine, and PF6− is hexafluorophosphate), have been prepared, and their photophysical and electrochemical properties have been investigated. In CH3CN solutions, [Ir(ppy)2(pzpy)]PF6 emits blue‐green light (475 nm), which is blue‐shifted by more than 100 nm with respect to the typical cationic iridium complex [Ir(ppy)2(dtb‐bpy)]PF6 (dtb‐bpy is 4,4′‐di‐tert‐butyl‐2,2′‐bipyridine); [Ir(dfppy)2(pzpy)]PF6 with fluorine‐substituted cyclometalated ligands shows further blue‐shifted light emission (451 nm). Quantum chemical calculations reveal that the emissions are mainly from the ligand‐centered 3π–π* states of the cyclometalated ligands (ppy or dfppy). Light‐emitting electrochemical cells (LECs) based on [Ir(ppy)2(pzpy)]PF6 gave green‐blue electroluminescence (486 nm) and had a relatively high efficiency of 4.3 cd A−1 when an ionic liquid 1‐butyl‐3‐methylimidazolium hexafluorophosphate was added into the light‐emitting layer. LECs based on [Ir(dfppy)2(pzpy)]PF6 gave blue electroluminescence (460 nm) with CIE (Commission Internationale de L'Eclairage) coordinates of (0.20, 0.28), which is the bluest light emission for iTMCs‐based LECs reported so far. Our work suggests that using diimine ancillary ligands involving electron‐donating nitrogen atoms (like pzpy) is an efficient strategy to turn the light emission of cationic iridium complexes to the blue region.
The Popular Origins of Legislative Jurisdictions: Petitions and Standing Committee Formation in Colonial Virginia and the Early US House

Committee formation in early American legislatures happened when those assemblies were inundated with petitions, a relationship unexamined in institutional political science. We develop a model where a floor creates committees to respond to topic-specific petitions, predicting committee creation when petitions (1) are topically specific, (2) are spread across constituencies, and (3) have complex subject matter, and predicting committee appointments from petition-heavy constituencies. Analysis of case studies and with two original data sets—petitions sent to the Virginia House of Burgesses from 1766 to 1769, and over 100,000 petitions sent to Congress and recorded in the House Journal (1789–1875)—shows that petitions, their complexity, and their geographic dispersion predict committee creation. Our theoretical argument helps reinterpret the entropy of political agendas and the origins of standing committees in American legislatures.
Uroguanylin increases Ca2+ concentration in astrocytes via guanylate cyclase C-independent signaling pathway

Aim To investigate the cyclic guanosine monophosphate (cGMP)/guanylate cyclase C (GC-C) -independent signaling pathway in astrocytes, which are a suitable model due to their lack of GC-C expression. Methods Patch clamp was performed and intracellular Ca2+ concentrations and pH were measured in primary astrocyte cultures and brain slices of wild type (WT) and GC-C knockout (KO) mice. The function of GC-C-independent signaling pathway in the cerebellum was determined by behavior tests in uroguanylin (UGN) KO and GC-C KO mice. Results We showed for the first time that UGN changed intracellular Ca2+ levels in different brain regions of the mouse. In addition to the midbrain and hypothalamus, GC-C was expressed in the cerebral and cerebellar cortex. The presence of two signaling pathways in the cerebellum (UGN hyperpolarized Purkinje cells via GC-C and increased intracellular Ca2+ concentration in astrocytes) led to a different motoric function in GC-C KO and UGN KO mice, probably via different regulation of intracellular pH in astrocytes. Conclusion The UGN effects on astrocytes via a Ca2+-dependent signaling pathway could be involved in the modulation of neuronal activity.
PLANT-PARASITIC NEMATODES ASSOCIATED WITH PLANTAIN (MUSA PARADISIACA) IN TALAMANCA, LIMÓN, COSTA RICA

Peraza-Padilla, W. , R. Artavia-Carmona, E. Arboleda-Julio, R. Rodríguez-Porras, and S. Orozco-Cayasso. 2020. Plant-parasitic nematodes associated with plantain (Musa paradisiaca) in Talamanca, Limón, Costa Rica. Nematropica 50:151-159. Plantain is one of the most important food and commercial crops in the Huetar Atlantica region of Costa Rica. Plant-parasitic nematodes (PPN) are one of the major constraints of plantain production and are therefore a threat to food security. The present study was part of an investigation that was carried out in Talamanca, Limón, Costa Rica with the objective to quantify and identify the main PPN genera associated with plantain cultivation. Nematodes were extracted from a total of 76 samples; 63 from soil and 13 from roots of plantain collected from 10 smallholder farms. In terms of absolute frequencies of distribution, the most abundant PPN genus in soil was Pratylenchus, which was detected in over 76.9% samples, followed by Helicotylenchus (69.2%), Meloidogyne and Radopholus (61.5%). In the roots, Helicotylenchus (100%), Meloidogyne (90.5%), Pratylenchus (74.6%), and Radopholus (66.7%) were the most frequently detected genera. In general, frequency and population densities of PPN were high in comparison to other similar surveys. Overall, the data suggest that PPNs could pose a serious threat to plantain production in Costa Rica and that control measures should be further developed and implemented.
Risk factors for severe COVID-19 disease and death in patients aged 70 and over: a retrospective observational cohort study

ABSTRACT Objectives The COVID-19 pandemic resulted in rapid reorganisations of hospital care. In our hospital, the geriatrics team introduced the Clinical Frailty Scale (CFS) on the non-ICU COVID-19 units during these reorganisations. A retrospective analysis was performed to investigate the CFS as a risk factor for severe COVID-19 disease and in-hospital death in older patients with COVID-19. Methods In patients aged ≥70 years, an online geriatric assessment questionnaire was launched, from which the CFS was scored by the geriatrics team. Additional clinical data were collected from the electronic medical records. Risk factors related to ageing, such as the CFS, age-adjusted Charlson Comorbidity Index, living situation and cognitive decline, were examined alongside frequently reported risk factors in the general population. Outcomes were in-hospital death (primary outcome) and oxygen need of ≥6 litres and early warning score ≥7, as parameters for severe disease (secondary outcomes). Baseline characteristics were described with descriptive statistics. Associations were analysed with uni- and multivariable analyses. Results One hundred and five patients were included, median age 82 years. CFS scores were 1–4 in 43, 5–6 in 45, and 7–9 in 17 patients. In multivariable analysis, CFS and cognitive decline were the only risk factors that were independently associated with in-hospital mortality. Chronic obstructive pulmonary disease, presence of respiratory symptoms on admission and male gender showed and independent association with severe disease. Conclusion A retrospective analysis shows that CFS and cognitive decline have added value for predicting in-hospital mortality in older patients with COVID-19 disease.
XAI for Analyzing and Unlearning Spurious Correlations in ImageNet

Contemporary learning models for computer vision are typically trained on very large data sets with millions of samples. There may, however, be biases, artifacts, or errors in the data that have gone unnoticed and are exploitable by the model, which in turn becomes a biased ‘Clever-Hans‘ predictor. In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora, here ImageNet. Based on Spectral Relevance Analysis we propose the following technical contributions and resulting ﬁndings: (a) a scalable quantiﬁcation of artifactual classes where the ML models under study exhibit Clever-Hans behavior, (b) an approach denoted as Class-Artifact Compensation (ClArC) that allows to ﬁne-tune an existing model to effectively eliminate its focus on artifacts and biases yielding signiﬁcantly reduced Clever-Hans behavior.
Leveraging benchmarking data for informed one-shot dynamic algorithm selection

A key challenge in the application of evolutionary algorithms in practice is the selection of an algorithm instance that best suits the problem at hand. What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process. Dynamic algorithm selection and configuration are therefore well-researched topics in evolutionary computation. Two different settings are classically considered: hyper-heuristics and parameter control studies typically assume a setting in which the algorithm needs to be chosen and adjusted during the run, without prior information, other approaches such as hyper-parameter tuning and automated algorithm configuration assume the possibility of evaluating different configurations before making a final recommendation. In practical applications of evolutionary algorithms we are often in a middle-ground between these two settings, where one needs to decide upon the algorithm instance before the run ("oneshot" setting), but where we have (possibly lots of) data available on which we can base an informed decision. We analyze in this work how such prior performance data can be used to infer informed dynamic algorithm selection schemes for the solution of pseudo-Boolean optimization problems. Our specific use-case considers a family of genetic algorithms.
Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model

Earth observation offers new insight into anthropogenic changes to nature, and how these changes are effecting (and are effected by) the built environment and the real economy. With the global availability of medium-resolution (10-30m) synthetic aperture radar (SAR) Sentinel-1 and multispectral Sentinel-2 imagery, machine learning can be employed to offer these insights at scale, unbiased to the reporting of companies and countries. In this paper, I introduce DeepSentinel, a data pipeline and experimentation framework for producing general-purpose semantic embeddings of paired Sentinel-1 and Sentinel-2 imagery. I document the development of an extensible corpus of labelled and unlabelled imagery for the purposes of sensor fusion research. With this new dataset I develop a set of experiments applying popular self-supervision methods and encoder architectures to a land cover classification problem. Tile2vec spatial encoding with a self-attention enabled ResNet model outperforms deeper ResNet variants as well as pretraining with variational autoencoding and contrastive loss. All supporting and derived data and code are made publicly available.
Exploring HNC and HCN line emission as probes of the protoplanetary disk temperature

Context. The distributions and abundances of molecules in protoplanetary disks are powerful tracers of the physical and chemical disk structures. The abundance ratios of HCN and its isomer HNC are known to be sensitive to gas temperature. Their line ratios might therefore offer a unique opportunity to probe the properties of the emitting gas. Aims. We investigate the HNC and HCN line emission in disks at (sub-)millimeter wavelengths and explore their potential utility for probing disk temperature and other disk properties. Methods. Using the 2D thermochemical code DALI, we ran a set of disk models accounting for different stellar properties and radial and vertical disk structures, with an updated chemical network for the nitrogen chemistry. These modeling results were then compared with observations, including new observations obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) of HNC J = 3−2 for the TW Hya disk and HNC J = 1−0 for 29 disks in Lupus. Results. Similar to CN, HCN and HNC have brighter line emission in models with larger disk flaring angles and higher UV fluxes. HNC and HCN are predicted to be abundant in the warm surface layer and outer midplane region, which results in ring-shaped emission patterns. However, the precise emitting regions and emission morphology depend on the probed transition, as well as on other parameters such as C and O abundances. The modeled HNC-to-HCN line intensity ratio increases from <0.1 in the inner disk to up to 0.8 in the outer disk regions, which can be explained by efficient HNC destruction at high temperatures. Disk-integrated HNC line fluxes from current scarce observations and its radial distribution in the TW Hya disk are broadly consistent with our model predictions. Conclusions. The HNC-to-HCN flux ratio robustly increases with radius (decreasing temperature), but its use as a chemical thermometer in disks is affected by other factors, including UV flux and C and O abundances. High-spatial resolution ALMA disk observations of HNC and HCN that can locate the emitting layers would have the great potential to constrain both the disk thermal and UV radiation structures, and also to verify our understanding of the nitrogen chemistry.
On Automatic Parsing of Log Records

Software log analysis helps to maintain the health of software solutions and ensure compliance and security. Existing software systems consist of heterogeneous components emitting logs in various formats. A typical solution is to unify the logs using manually built parsers, which is laborious. Instead, we explore the possibility of automating the parsing task by employing machine translation (MT). We create a tool that generates synthetic Apache log records which we used to train recurrent-neural-network-based MT models. Models' evaluation on real-world logs shows that the models can learn Apache log format and parse individual log records. The median relative edit distance between an actual real-world log record and the MT prediction is less than or equal to 28%. Thus, we show that log parsing using an MT approach is promising.
Radio Spectral Imaging of an M8.4 Eruptive Solar Flare: Possible Evidence of a Termination Shock

Solar flare termination shocks have been suggested as one of the viable mechanisms for accelerating electrons and ions to high energies. Observational evidence of such shocks, however, remains rare. Using radio dynamic spectroscopic imaging of a long-duration C1.9 flare obtained by the Karl G. Jansky Very Large Array (VLA), Chen et al. suggested that a type of coherent radio bursts, referred to as “stochastic spike bursts,” were radio signatures of nonthermal electrons interacting with myriad density fluctuations at the front of a flare termination shock. Here we report another stochastic spike burst event recorded during the extended energy release phase of a long-duration M8.4-class eruptive flare on 2012 March 10. VLA radio spectroscopic imaging of the spikes in 1.0–1.6 GHz shows that, similar to the case of Chen et al., the burst centroids form an extended, ∼10″-long structure in the corona. By combining extreme-ultraviolet imaging observations of the flare from two vantage points with hard X-ray and ultraviolet observations of the flare ribbon brightenings, we reconstruct the flare arcade in three dimensions. The results show that the spike source is located at ∼60 Mm above the flare arcade, where a diffuse supra-arcade fan and multitudes of plasma downflows are present. Although the flare arcade and ribbons seen during the impulsive phase do not allow us to clearly understand how the observed spike source location is connected to the flare geometry, the cooling flare arcade observed 2 hr later suggests that the spikes are located in the above-the-loop-top region, where a termination shock presumably forms.
To Reuse or Not To Reuse?

As the amount of information online continues to grow, a correspondingly important opportunity is for individuals to reuse knowledge which has been summarized by others rather than starting from scratch. However, appropriate reuse requires judging the relevance, trustworthiness, and thoroughness of others' knowledge in relation to an individual's goals and context. In this work, we explore augmenting judgements of the appropriateness of reusing knowledge in the domain of programming, specifically of reusing artifacts that result from other developers' searching and decision making. Through an analysis of prior research on sensemaking and trust, along with new interviews with developers, we synthesized a framework for reuse judgements. The interviews also validated that developers express a desire for help with judging whether to reuse an existing decision. From this framework, we developed a set of techniques for capturing the initial decision maker's behavior and visualizing signals calculated based on the behavior, to facilitate subsequent consumers' reuse decisions, instantiated in a prototype system called Strata. Results of a user study suggest that the system significantly improves the accuracy, depth, and speed of reusing decisions. These results have implications for systems involving user-generated content in which other users need to evaluate the relevance and trustworthiness of that content.
Uncertainty-of-Information Scheduling: A Restless Multiarmed Bandit Framework

This paper proposes using the uncertainty of information (UoI), measured by Shannon’s entropy, as a metric for information freshness. We consider a system in which a central monitor observes M binary Markov processes through m communication channels (m<M). The UoI of a Markov process corresponds to the monitor’s uncertainty about its state. At each time step, only m Markov processes can be selected to update its state to the monitor; hence there is a tradeoff among the UoIs of the processes that depend on the scheduling policy used to select the processes to be updated. The age of information (AoI) of a process corresponds to the time since its last update. In general, the associated UoI can be a non-increasing function, or even an oscillating function, of its AoI, making the scheduling problem particularly challenging. This paper investigates scheduling policies that aim to minimize the average sum-UoI of the processes over the infinite time horizon. We formulate the problem as a restless multi-armed bandit (RMAB) problem, and develop a Whittle index policy that is near-optimal for the RMAB after proving its indexability. We further provide an iterative algorithm to compute the Whittle index for the practical deployment of the policy. Although this paper focuses on UoI scheduling, our results apply to a general class of RMABs for which the UoI scheduling problem is a special case. Specifically, this paper’s Whittle index policy is valid for any RMAB in which the bandits are binary Markov processes and the penalty is a concave function of the belief state of the Markov process. Numerical results demonstrate the excellent performance of the Whittle index policy for this class of RMABs.
Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services

This doctoral dissertation proposes a novel approach to enhance the development of smart services for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). The proposed approach offers abstraction and automation to the software engineering processes, as well as the Data Analytics (DA) and Machine Learning (ML) practices. This is realized in an integrated and seamless manner. We implement and validate the proposed approach by extending an open source modeling tool, called ThingML. ThingML is a domain-specific language and modeling tool with code generation for the IoT/CPS domain. Neither ThingML nor any other IoT/CPS modeling tool supports DA/ML at the modeling level. Therefore, as the primary contribution of the doctoral dissertation, we add the necessary syntax and semantics concerning DA/ML methods and techniques to the modeling language of ThingML. Moreover, we support the APIs of several ML libraries and frameworks for the automated generation of the source code of the target software in Python and Java. Our approach enables platform-independent, as well as platform-specific models. Further, we assist in carrying out semiautomated DA/ML tasks by offering Automated ML (AutoML), in the background (in expert mode), and through model-checking constraints and hints at design-time. Finally, we consider three use case scenarios from the domains of network security, smart energy systems and energy exchange markets.
Guided Variational Autoencoder for Speech Enhancement with a Supervised Classifier

Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variational autoencoders are trained on clean speech only, which results in a limited ability of extracting the speech signal from noisy speech compared to supervised approaches. In this paper, we propose to guide the variational autoencoder with a supervised classifier separately trained on noisy speech. The estimated label is a high-level categorical variable describing the speech signal (e.g. speech activity) allowing for a more informed latent distribution compared to the standard variational autoencoder. We evaluate our method with different types of labels on real recordings of different noisy environments. Provided that the label better informs the latent distribution and that the classifier achieves good performance, the proposed approach outperforms the standard variational autoencoder and a conventional neural network- based supervised approach.