Miracle-dz/UniFAR-FTUs huggingface dataset:
Citizen participation in times of crisis: Understanding participatory budget during the COVID-19 pandemic in Nepal

This research assumes that various forms and scales of lockdowns and social distancing measures have limited local decision-makers’ ability to reach out to communities as part of their mandatory annual participatory budgeting processes. Building upon this proposition, this article assesses Nepal’s local budgeting process of 2020 to understand the degree to which it succeeded (or failed) in incorporating citizen’s voices in the annual handbook of local public policies and budgets. The research followed a qualitative case study research methodology. It generated interviews with participants including ordinary people, local politicians, and bureaucrats from 20 different municipalities and a federal ministry in Nepal. Other textual data (official publications, field notes and observational scripts) provided complementary evidence to answer the research questions. Findings suggest that the coronavirus pandemic has played significant roles in limiting ordinary people’s democratic right to participate in local decisionmaking yet flaws in institutional design and unpredictable mayoral power contributed to delaying the annual budget approval process. The research contributes to the literature on participatory governance in developing countries, with some practical suggestions to increase citizen engagement chances in times of crisis.
ScenarioNet: An Interpretable Data-Driven Model for Scene Understanding

The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at complex scene understanding lack representational power, efﬁciency, and the ability to create robust meta-knowledge about scenes. We introduce scenarios as a new way of representing scenes. The scenario is an interpretable, low-dimensional, data-driven representation consisting of sets of frequently co-occurring objects that is useful for a wide range of scene understanding tasks. Scenarios are learned from data using a novel matrix factorization method which is integrated into a new neural network architecture, the Scenari-oNet . Using ScenarioNet, we can recover semantic information about real world scene images at three levels of granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a single ScenarioNet model enables us to perform scene classiﬁcation, scenario recognition, multi-object recognition, content-based scene image retrieval, and content-based image comparison. Sce-narioNet is efﬁcient because it requires signiﬁcantly fewer parameters than other CNNs while achieving similar performance on benchmark tasks, and it is interpretable because it produces evidence in an un-derstandable format for every decision it makes. We validate the utility of scenarios and ScenarioNet on a diverse set of scene understanding tasks on several benchmark datasets.
Band Structure And Optical Functions Of SnS Compound

In the present work, on the basis of calculations from the first principles within the framework of the pseudopotential theory, the band structure of the SnS binary compound was calculated. It is revealed that the band structure can be divided into three subgroups: the lowest group, which is distant from the main group of the valence band by a wide energy gap of about 6 eV, is derived from the s-states of the anion. The next group, consisting of four bands and located about 7 eV, is associated with the s-states of Sn. The calculated values of the band gap with the available theoretical data for the monoclinic structure of the crystal lattice were compared. The calculation of the band structure of the compound shows a satisfactory agreement with the experimental data. In the paper, based on the electronic structure of the compound, the calculation results from first principles, using the LAPW Linearized Augmented Plane Wave method by WIEN2k software package, atomic coordinates in the unit cell and the parameters of the Murnaghan and BirchMurnaghan equation of state are presented. The fluorescence spectra of SnS were studied in the wavelength range 200-600 nm, and it was found that this material can be widely used in multifunctional electronic devices and used as new types of composites with unique properties. Keywords—Compound SnS ; Pseudopotential; Band structure; Optical functions.
Frequency Dependency of Binaural Masking Level Differences in Normal-Hearing and Hearing-Impaired Listeners

In binaural tone-in-noise detection experiments humans are able to achieve substantially lower thresholds if either the tone or the noise has interaural differences in time (ITD) or level (ILD). This effect is named binaural masking level difference (BMLD) and was mainly investigated for 500 Hz (e.g. [1]). An effective model, which is able to describe the binaural unmasking, is the equalization-cancellation (EC) mechanism [2]. In the EC model, first, left and right ear signal are equalized in level and time based on the interfering signal, which is assumed to be the dominant source. In a second step, the left ear signal is subtracted from the right ear signal leading to an attenuation of the noise due to destructive interferences and an amplification of the target signal due to constructive interferences as long as target and interfering signal differ in their ITD and/or ILD. In the EC model, binaural processing inaccuracies are incorporated, which have been derived from binaural tone-in-noise detection experiments [3], in order to explain perceptual data and to prevent the model from yielding an infinite SNR improvement. The EC model has been combined with the speech intelligibility index (SII) [4] in order to predict binaural speech intelligibility (e.g. [5]). Moreover, individual hearing abilities were accounted for by incorporating information about the audiogram in the model [6]. Even though the audiogram is considered in the model, individual differences in supra-threshold deficits are not incorporated.
Full Direction Local Neighbors Pattern (FDLNP)

In this paper, we proposed the Full Direction Local Neighbor Pattern (FDLNP) algorithm, which is a novel method for Content-Based Image Retrieval. FDLNP consists of many steps, starting from generating Max and Min Quantizers followed by building two matrix types (the Eight Neighbors Euclidean Decimal Coding matrix, and Full Direction Matrixes). After that, we extracted Gray-Level Co-occurrence Matrix (GLCM) from those matrixes to derive the important features from each GLCM matrixes and finishing with merging the output of previous steps with Local Neighbor Patterns (LNP) histogram. For decreasing the feature vector length, we proposed five extension methods from FDLNP by choosing the specific direction matrixes. Our results demonstrate the effectiveness of our proposed algorithm on color and texture databases, comparing with recent works, with regard to the Precision, Recall, mean Average Precision (mAP), and Average Retrieval Rate (ARR). For enhancing the image retrieval accuracy, we proposed a novel framework that combined the image retrieval system with clustering and classification algorithms. Moreover, we proposed a distributed model that used our FDLNP method with Hadoop to get the ability to process a huge number of images in a reasonable time.
Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments

Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accurately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inversion algorithms are available, there has been limited assessment of performance and no work has been published comparing their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational efficiencies of one empirical and five radiative‐transfer‐based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative‐transfer—based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moderately accurate retrievals of bathymetry, water column inherent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benthic/ substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re‐analysis of these same or additional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches.
Building Indoor Point Cloud Datasets with Object Annotation for Public Safety

An accurate model of building interiors with detailed annotations is critical to protecting the first responders’ safety and building occupants during emergency operations. In collaboration with the City of Memphis, we collected extensive LiDAR and image data for the city’s buildings. We apply machine learning techniques to detect and classify objects of interest for first responders and create a comprehensive 3D indoor space database with annotated safety-related objects. This paper documents the challenges we encountered in data collection and processing, and it presents a complete 3D mapping and labeling system for the environments inside and adjacent to buildings. Moreover, we use a case study to illustrate our process and show preliminary evaluation
South-south collaboration on HIV/AIDS prevention and treatment: when birds of a feather rarely flock together

South-south collaboration on health and development is a critical mechanism for social and economic progress, allowing sharing and replicating experiences to find a “southern solution” to meet specific needs. In this paper, bibliometric and social network analysis methods were used to assess the 10-year (2006-2016) scientific contribution of lowand middle-income countries (LMIC) to HIV/AIDS prevention and/or treatment, one of their shared health challenges. Four dimensions oriented the analysis: knowledge production, collaboration, research themes/types, and funding sources. We have shown a substantial increase in LMIC contribution, but a small expression of south-south collaboration, stressing the need for strategies to foster these partnerships. Research interests differed according to author income profile and funding source, suggesting a tendency of LMIC-based researchers and funders to embrace a “linear model” of innovation. It is expected that the evidence presented here can be used to strengthen a knowledge platform to inform future policy, planning and funding decisions, contributing to the development of enhanced collaboration and a common research agenda between LMICs. Fonseca, Bruna (1); Albuquerque, Priscila (1); Noyons, Ed (2); Zicker, Fabio (1) 1: Centro de Desenvolvimento Tecnológico em Saúde (CDTS), Fundação Oswaldo Cruz, Brazil; 2: Centre for Science and Technology Studies (CWTS), Leiden University, Netherlands
Modeling of Dengue Fever Death Counts Using Hidden Markov Model

We explore the use of Poisson-hidden Markov model to describe an overdispersed data on monthly death counts due to Dengue fever. Independent Poisson mixture models of various components and stationary Poisson hidden Markov models of different states are fitted and the performance of each model is judged using model selection criteria. The sequence of hidden states are estimated based on the best fitted model. The method can be applied in identifying environmental factors affecting a stochastic process.
Thompson Sampling for Contextual Combinatorial Bandits

Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications, where the exploration-exploitation trade-oﬀ can be naturally taken care of. However, many complex real-world applications that involve multiple content recommendations cannot ﬁt into the traditional MAB setting. To address this issue, we consider a combinatorial bandit problem where the learner selects S actions from a base set of K actions, and displays the results in S (out of M ) diﬀerent positions. The aim is to maximize the cumulative reward or equivalently minimize the regret with respect to the best possible subset and positions in hind-sight. By the adaptation of network ﬂow formulations, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability. The network ﬂow formulations can also be incorporated in (cid:15) -greedy and pure exploitation algorithms to provide an exact single-step solution without enumerating on a gigantic number of possible combinatorial subsets. With its potential to work with any probabilistic models, to illustrate the eﬀectiveness of our method, we focus on context-free Gaussian process optimization and a contextual setting where click-through-rate is predicted by logistic regression. We demonstrate the algorithms’ performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.
Multiagent Driving Policy for Congestion Reduction in a Large Scale Scenario

Trafﬁc congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed trafﬁc scenarios with both AVs and human-driven vehicles, a small fraction of AVs executing a controlled multiagent driving policy can mitigate congestion. In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We start by showing that a congestion metric used by past research is manipulable in open road network scenarios where vehicles dynamically join and leave the road. We then propose using a different metric that is robust to manipulation and reﬂects open network trafﬁc efﬁciency. Next, we propose a modular transfer learning approach and use it to scale up the multiagent driving policy to a realistic simulated scenario that is an order of magnitude larger than past scenarios (hundreds rather than tens of vehicles). Our experimental study shows that the resulting policy improves trafﬁc efﬁciency over human-driven trafﬁc in a large open network, where existing approaches fail to do so. Another key advantage of our modular transfer learning approach is that it avoids collecting samples from entire network, which saves up to 80% of training and data collection time in our experiments.
CubicleOS: a library OS with software componentisation for practical isolation

Library OSs have been proposed to deploy applications isolated inside containers, VMs, or trusted execution environments. They often follow a highly modular design in which third-party components are combined to offer the OS functionality needed by an application, and they are customised at compilation and deployment time to fit application requirements. Yet their monolithic design lacks isolation across components: when applications and OS components contain security-sensitive data (e.g., cryptographic keys or user data), the lack of isolation renders library OSs open to security breaches via malicious or vulnerable third-party components. We describe CubicleOS, a library OS that isolates components in the system while maintaining the simple, monolithic development approach of library composition. CubicleOS allows isolated components, called cubicles , to share data dynamically with other components. It provides spatial memory isolation at the granularity of function calls by using Intel MPK at user-level to isolate components. At the same time, it supports zero-copy data access across cubicles with feature-rich OS functionality. Our evaluation shows that CubicleOS introduces moderate end-to-end performance overheads in complex applications: 2× for the I/O-intensive NGINX web server with 8 partitions, and 1.7–8× for the SQLite database engine with 7 partitions.
Post-Keynesian Macroeconomic Foundations for Comparative Political Economy

The global financial crisis and ensuing weak growth have increased interest in macroeconomic issues within comparative political economy (CPE). CPE, particularly the dominant Varieties of Capitalism approach, has based its analyses on mainstream economics, which limits analysis of the relation between distribution and growth and neglects the role finance plays in modern economies. It overstates the stability of the capitalist growth process and understates the potential effectiveness of government interventions. Baccaro and Pontusson have suggested a post-Keynesian (PK) theory of distribution and growth as an alternative. This article generalizes their point. PK theory highlights the instability of the growth process and lends itself to an analysis of income distribution and power relations. The article identifies the analysis of financialization and financial cycles, the understanding of neoliberal growth models, and the political economy of central banks as areas where PK economics provides specific insights for CPE. It also highlights that these arguments have important implications for government policy in an era of secular stagnation with ongoing social, distributional, and economic crises.
Brain structure and function show distinct relations with genetic predispositions to mental health and cognition

Mental health and cognitive achievement are partly heritable. To identify the underlying neural mechanisms, we associated genetic predispositions to various mental health and cognitive traits with a large set of structural and functional brain measures from the UK Biobank (N=36,799). We show that genetic predispositions to attention deficit hyperactivity disorder, smoking initiation, and cognitive traits have stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders have stronger associations with brain function than with brain structure. These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.
Tocilizumab efficacy in COVID-19 patients is associated with respiratory severity-based stages

Background: Tocilizumab treatment is investigated, and effectiveness in ICU-admitted COVID-19 patients has been reported. Although controversy exists regarding the efficacy of tocilizumab treatment, it has been suggested that tocilizumab might show positive results depending on patient severity status. We examined an association between tocilizumab and distinct disease severity stages. Methods and Findings: From March 3 to March 23 2020, 494 consecutively admitted COVID-19 patients received tocilizumab or standard treatment alone. Data were obtained retrospectively. Clinical respiratory severity (CRS) stages were defined by patient oxygenation status and were also associated to scores of WHO clinical progression scale. We categorized patients in three stages, mild/moderate CRS1 (FiSpO2<0.35; WHO score 5), moderate/severe CRS2 (FiO2=0.5/high flow mask; WHO score 6) and severe/critical CRS3 (FiO2<80%/high flow/prone position or mechanical ventilation; score>6). The primary outcome was the composite of death or ICU admission in patients of stages CRS1, CRS2, and CRS3, as well as in total patients. We also addressed mortality alone in total patients. Kaplan-Maier curves, Cox proportional regression and inverse probability weighting marginal structural models were used. We conducted the study from March 3 to April 7 2020 with broad-ranged severity patients; 167 tocilizumab-treated and 327 untreated. CRS1 patients showed no apparent benefit after treatment, while the risk of the primary outcome was greatly reduced in CRS2 treated participants ((HR=0.22; 95% CI (0.16-0.44)). Moreover, tocilizumab treatment was associated with significantly decreased CRS2 patient proportion that reached the outcome compared to non-treated controls (27.8.0% vs. 65.4%; p<0.001). Severe/critical CRS3 patients, also showed benefit after treatment (HR=0.38; 95% CI (0.16-90)), although not as robust as was that of CRS2 treated individuals. Tocilizumab was associated with reduced outcome risk in total patients (HR=0.42; 95% CI (0.26-0.66)) after CRS adjustment, but not if CRS classification was not accounted as confounding factor (HR=1.19; 95% CI (0.84-1.69)). The outcome of mortality alone upon tocilizumab treatment was significant (HR=0.58; 95% CI (0.35-0.96)) after accounting for CRS classification. Conclusions: Tocilizumab treatment is associated with reduced COVID-19 escalation in CRS2 patients, suggesting efficacy in moderate/severe non-ICU-admitted patients. CRS classification could represent an essential confounding factor in evaluating tocilizumab in studies of broad-ranged severity patients.
Comparison of two approaches to grafting hydrophilic polymer chains onto polysulfone films

To reduce the surface protein adsorption of polysulfone (PSf) film, we improved the hydrophilicity of this film by photochemical grafting of methoxypoly (ethylene glycol) (MPEG) derivatives on its surface. Grafting was achieved with both the simultaneous method and the sequential method. Surface analysis of the grafted film by X-ray photoelectron spectroscopy (XPS) revealed that the PEG chains had successfully grafted onto the surface of the film. The grafting efficiencies by simultaneous and sequential methods were 20.8% and 10.2%, respectively. With an atomic force microscope (AFM), the surface topography of PEG-grafted films by these two methods was compared. Static water contact angle measurement indicated that the surface hydrophilicity of the film had been improved. Protein adsorption measurement showed that the surface protein adsorption of the modified film was significantly reduced compared with that of the unmodified PSf film. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3818–3826, 2007
S0950268818000298jra 642..647

Toll-like receptor 2 (TLR2) is a key member of TLRs, which is crucial in the initial inflammatory response against bacteria. TLR2, is also the initial barrier against bacterial infection and plays an important role in recognising a variety of bacterial lipoproteins. Several studies have been performed to investigate the TLR2 + 2477G/A polymorphism and bacterial meningitis susceptibility. Unfortunately, the results of previous studies were controversial. Therefore, we performed a meta-analysis to derive a more precise estimation of the association. The association between the TLR2 + 2477G/A polymorphism and bacterial meningitis susceptibility was assessed by odds ratios together with their 95% confidence intervals (CI). Six studies were enrolled in the present meta-analysis. Overall, no significant association between TLR2 + 2477G/A polymorphism and bacterial meningitis risk were found under allele contrast (A vs. G: OR = 1.15, 95% CI = 0.93–1.43, P = 0.202), recessive genetic model (AA vs. AG/GG: OR = 1.12, 95% CI = 0.90–1.41, P = 0.313). The significant association was found between TLR2 + 2477G/A polymorphism and pneumococcal meningitis risk under allele contrast (A vs. G: OR = 1.54, 95% CI = 1.01–2.36, P = 0.046), recessive genetic model (AA vs. AG/ GG: OR = 1.63, 95% CI = 1.03–2.57, P = 0.035). We conclude that TLR2 + 2477G/A polymorphism is not associated with meningococcal meningitis risk but contributes an increased risk of pneumococcal meningitis.