Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian estimation of two‐part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation

Two‐part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two‐part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.
Cognitive reflection test and the polarizing force-identification questions in the FCI

The set of polarizing force-identification (PFI) questions in the force concept inventory (FCI) consists of six items all basically asking only one question: the set of forces acting on a given body. Although it may sound trivial, these questions are among the most challenging in the FCI. In this work involving 163 students, we investigate the correlation between student performance on the set of PFI questions and the cognitive reflection test. We find that for both scores in the FCI as a whole and in the PFI questions, the range of values of the Pearson coefficient at 95% confidence interval suggests that cognitive reflection may be one of the contributing factors in student performance in the FCI. This is consistent with the idea that a high level of cognitive reflection may help in eliminating seemingly valid choices (misconceptions) in the FCI that are intuitive from everyday experience or ‘common sense’ but otherwise misleading. The ability to activate system 2 in dual process theory, whether from system 1 or right after reading a physics problem, may contribute in narrowing down the set of prospective valid answers in a given physics problem. Complementary to cognitive reflection are other factors associated with deep understanding of physics, the effects of which are expected to become more evident with the level of difficulty of a set of physics problems. Given two students with the same level of cognitive reflection, the one with deeper understanding of physics is more likely to get the correct answer. In our analysis, the range of correlation coefficients for the set of PFI questions is downshifted with respect to that for the FCI as a whole. This may be attributed to the more challenging nature of the latter compared to a significant fraction of the remaining questions in the former.
Gaussian Asymptotics of Jack Measures on Partitions from Weighted Enumeration of Ribbon Paths

In this paper we determine two asymptotic results for Jack measures on partitions, a model defined by two specializations of Jack polynomials proposed by Borodin-Olshanski in [European J. Combin. 26.6 (2005): 795-834]. Assuming these two specializations are the same, we derive limit shapes and Gaussian fluctuations for the anisotropic profiles of these random partitions in three asymptotic regimes associated to diverging, fixed, and vanishing values of the Jack parameter. To do so, we introduce a generalization of Motzkin paths we call "ribbon paths", show for general Jack measures that certain joint cumulants are weighted sums of connected ribbon paths on $n$ sites with $n-1+g$ pairings, and derive our two results from the contributions of $(n,g)=(1,0)$ and $(2,0)$, respectively. Our analysis makes use of Nazarov-Sklyanin's spectral theory for Jack polynomials. As a consequence, we give new proofs of several results for Schur measures, Plancherel measures, and Jack-Plancherel measures. In addition, we relate our weighted sums of ribbon paths to the weighted sums of ribbon graphs of maps on non-oriented real surfaces recently introduced by Chapuy-Dol\k{e}ga.
Shifting Concepts of Value: Designing Algorithmic Decision-Support Systems for Public Services

Calls for responsible design in algorithmic decision-support systems, especially those used in public services, are increasingly common. While an algorithmic system might promise greater precision and efficiency in domains such as critical care, the same efficiency is difficult to replicate in the public service domain, where caseworkers must exercise discretion in applying complex legal frameworks that directly affect individual lives. In this paper we examine the challenges in responsibly designing such an algorithmic decision-support system. We report findings from a large cross-disciplinary research project, aiming to develop an algorithmic component for municipal decision-support systems for job placement in Denmark. Our data showcases insights from how a team of data scientists, caseworkers, and system developers negotiated notions of value metrics and usefulness in a participatory design set-up. Whereas data scientists expected to focus on profiling individuals, the caseworkers instead pushed for systems that could help mitigate organizational contradictions and obscured processes in casework. We close with a discussion of challenges and future directions for participatory algorithmic systems design in municipal contexts.
ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research

Machine learning (ML) techniques have successfully been applied to many complex domains. Yet, applying it to UX research (UXR) received little academic attention so far. To better understand how UX practitioners envision the synergies between empathy-focused UX work and data-driven ML techniques, we surveyed 49 practitioners experienced in UX, ML, or both and conducted 13 semi-structured interviews with UX experts. We derived an inventory of ML’s impact on current UXR activities and practitioners’ predictions about its potentials. We learned that ML methods may help to automate mundane tasks, complement decisions with data-driven insights, and enrich UXR with insights from users’ emotional worlds. Challenges may arise from a potential obligation to utilize data and a more restrictive access to user data. We embed our insights into recent academic work on ML for UXR and discuss automated UX evaluation as a promising use case for future research.
Mirage Net: Low Calculation Cost Network for Image Denoising

Benefit from feature presentation with huge parameters and high GPU computing resources, deep convolution neural network has been widely studied in image denoising due to its considerable denoising performance. However, these parameters will consume quantities of memory and computing resources, meanwhile, lots of them are correlated and redundant. We propose a low calculation cost and fast denoising convolution neural network, namely Mirage Net, inspired by the natural phenomenon of mirage. Based on our refraction convolution, which is the combination of depth-wise and point-wise convolution, Mirage Net can reduce parameter redundancy and learn effective presentations from one-layer deeper feature maps by cheap cost linear transformations which will be concatenated with previous feature maps as input of the next convolution layer. We also use alternating training strategy with multi-loss which accelerate the training processing and convergence rate. Our experiments on public datasets show that Mirage Net can achieve higher quality denoised images than DnCNN, and furthermore, the calculation cost is only half of them.
Papyrus for gamers, let's play modeling

Gamification refers to the exploitation of gaming mechanisms for serious purposes, like learning hard-to-train skills such as modeling. We present a gamified version of Papyrus, the well-known open source modeling tool. Instructors can use it to easily create new modeling games (including the tasks, solutions, levels, rewards.) to help students learning any specific modeling aspect. The evaluation of the game components is delegated to the GDF gamification framework that bidirectionally communicates with the Papyrus core via API calls. Our gamified Papyrus includes as well a game dashboard component implemented with HTML/CSS/Javascript and displayed thanks to the integration of a web browser embedded in an Eclipse view.
In vitro Compatibility between Botanicals and Pesticides against Xanthomonas axonopodis pv. punicae

Bacterial blight caused by Xanthomonas axonopodis pv. punicae is a major concern. Since then several workers have reported the occurrence of blight and resultant losses from different states of the country viz., Tamil Nadu (Rangaswami, 1962), Himachal Pradesh (Sohi et al., 1964), Haryana (Kanwar, 1976), Karnataka (Chand and Kishun, 1991), Maharashtra (Kamble, 1990) and Punjab (Rani and Verma, 2001). Stem infection is also observed in severely infected orchards with girdling and cracking symptoms. Spots on fruits are variably dark brown in colour, irregular, slightly raised with oily appearance, which splits open with L or Y-shaped cracks under severe stages of disease (Bora and Kataki, 2014). Use of chemicals is not only the solution for management of any disease, while its use leads to pollution and hazard to the ecosystem.
Testing Evolutionary Hypotheses Regarding Individual Differences in Human Mating Strategies

Finding and attracting a mate is an enormous component of human life, and indeed the life of all sexually reproducing organisms. In this search, we share many common goals in attaining a partner. For instance, most people typically seek a partner who is kind, attractive, and funny. Yet, we also differ remarkably in the traits that we desire in our partners and what drives us towards choosing some mates over others. This variation represents somewhat of a Darwinian paradox, as selection typically reduces variation in traits under strong selection, such as those relating to mating strategy. To resolve this paradox, evolutionary psychologists have proposed adaptive mechanisms that cause variation in mating strategy in response to environmental contingencies. In this thesis, I present tests of a number of these hypotheses, and integrate approaches from evolutionary behavioural genetics to provide alternative explanations as to how variation in mating strategy arises. Firstly, I review broad evolutionary approaches to explaining variation in psychological traits. Specifically, I introduce evolutionary literature regarding a proposed trade-off between parenting traits and signals of genetic quality in males that permeates approaches to various aspects of mate choice. Evidence for and against this trade-off is reviewed in the context of the maintenance of variation in facial masculinity in males and inter-partner variation in female orgasm frequency during penetrative sex. I then introduce behavioural genetics as an approach to understanding both genetic and environmental causes of variation in human mating strategy. In my first empirical study, I present a paper testing evolutionary hypotheses regarding female variation in orgasm frequency in response to male characteristics. Where previous research has focused on women reporting on a single partner and observing male characteristics across the sample, this paper had females report on partners with whom orgasm was easy and with whom orgasm was difficult. This controls for the possibility of confounds relating to between-subject studies and increases our power to detect male traits associated with orgasm over and above between women differences. Furthermore, the study also included measures of male sexual behaviour, as these are highly likely to be related to female orgasmability. We showed mixed support for evolutionary theories regarding variation in the female orgasm, but were unable to distinguish the two leading hypotheses. Additionally, we found important contributions of male sexual behaviour to female orgasm. Consequently, little can be inferred about the role of variation in the evolution of the female orgasm. However, we demonstrate that within-subjects designs are able to detect between-partner variance with high resolution. We also demonstrate that future studies need account for male sexual behaviour when attempting to explain adaptive variation in female orgasm between different male partners.
Using Revealed Preference Methods to Estimate the Value of Reduced Mortality Risk: Best Practice Recommendations for the Hedonic Wage Model

The hedonic wage model provides a key input into benefit–cost analyses of public policies that are aimed at reducing mortality risks: an estimate of the value of reduced mortality risk (VRMR), also known as the value of a statistical life. Because a large majority of the benefits associated with policies that improve air quality stem from mortality risk reductions, estimates of the VRMR play an exceptionally important role. The use of VRMR estimates from hedonic wage studies in benefit–cost analyses of environmental policies is not without controversy. This article reviews the use and importance of the VRMR in environmental regulation and policy and summarizes the major shortcomings of existing VRMR estimates derived from hedonic wage models. We propose a set of best practices for estimating and reporting VRMR estimates using the hedonic wage framework
RNA, Genome Output and Input

RNA, the transcriptional output of genomes, not only templates protein synthesis or directly engages in catalytic functions, but can feed back to the genome and serve as regulatory input for gene expression. Transcripts affecting the RNA abundance of other genes act by mechanisms similar to and in concert with protein factors that control transcription. Through recruitment or blocking of activating and silencing complexes to specific genomic loci, RNA and protein factors can favor transcription or lower the local gene expression potential. Most regulatory proteins enter nuclei from all directions to start the search for increased affinity to specific DNA sequences or to other proteins nearby genuine gene targets. In contrast, RNAs emerge from spatial point sources within nuclei, their encoding genes. A transcriptional burst can result in the local appearance of multiple nascent RNA copies at once, in turn increasing local nucleic acid density and RNA motif abundance before diffusion into the nuclear neighborhood. The confined initial localization of regulatory RNAs causing accumulation of protein co-factors raises the intriguing possibility that target specificity of non-coding, and probably coding, RNAs is achieved through gene/RNA positioning and spatial proximity to regulated genomic regions. Here we review examples of positional cis conservation of regulatory RNAs with respect to target genes, spatial proximity of enhancer RNAs to promoters through DNA looping and RNA-mediated formation of membrane-less structures to control chromatin structure and expression. We speculate that linear and spatial proximity between regulatory RNA-encoding genes and gene targets could possibly ease the evolutionary pressure on maintaining regulatory RNA sequence conservation.
Daylist. Exploring the Design of Configuration Settings to Establish a Lean Back On-Demand Radio Experience

Compared to other traditional media such as television and newspapers, radio consumption has remained relatively stable over the past decades. Meanwhile, radio is increasingly exploring the rich possibilities that non-linear consumption patterns offer, such as on-demand or personalised radio. Yet from a human-computer-interaction perspective, radio remains a relatively under-researched topic. Therefore, we study how to offer personalised on-demand radio fragments without losing the highly appreciated ‘ease-of-use’ of traditional radio. We designed and developed a high-fidelity smartphone application that allows listeners to catch up informational items on a daily basis. The study was set up in two conditions; a playlist that deploys a content-based recommender without user configuration; and a playlist based on indicated interests and time-to-spend. The results of our study are based on both a quantitative and qualitative evaluation approach, including 13 in-depth interviews. We discuss the main findings and conclude with design recommendations for on-demand radio applications.
Design of a 3D printed hybrid mechanical structure for a hand exoskeleton

Abstract Stroke survivors often have difficulty performing activities of daily living (ADLs) due to hand impairments. Several assistive devices have been developed for stroke survivors to assist them with ADLs but most of these devices are difficult to don and doff for a stroke survivor due to highly flexed postures of the wrist and digits. This paper presents a hybrid 3D printed mechanical structure for an assistive hand exoskeleton created for stroke survivors. The design facilitates donning and doffing of the assistive exoskeleton by enabling an approach entirely from the dorsal side of the hand, thereby allowing the fingers to stay flexed. The design criteria, resulting design and the prototype development are presented. The initial prototype of the structure, using a hybrid combination of rigid and flexible materials, was lightweight (only 185 g), while maintaining a high range of motion. Future directions for further improvements and user studies are described.
Left Ventricular Assist Device-Related Complications

Abstract Left ventricular assist device (LVAD) has emerged as a safe, durable, and revolutionary therapy for end-stage heart failure patients. Despite the appearance of newer-generation devices that have improved patient outcomes, the burden of adverse events remains significant. Although the survival rate for patients with LVAD is appreciated to be 81% at 1 year and 70% at 2 years, the incidence of adverse events is also high. Over time, both early and late postimplant complications have diminished in terms of prevalence and impact; however, complications, such as infections, bleeding, right heart failure, pump thrombosis, aortic insufficiency, or stroke, continue to represent a challenge for the practitioner. Therefore, the aim of this review is to highlight the most recent data regarding the current use of LVAD in the treatment of end-stage heart failure, with a specific focus on LVAD-related complications, in order to improve device-related outcomes. It will also revise how to mitigate the risk and how to approach specific adverse events. Withal, understanding the predisposing risk factors associated with postimplant complications, early recognition and appropriate treatment help to significantly improve the prognosis for patients with end-stage heart failure.
Deductive reconstruction of MLT* for multi-level modeling

In the last two decades, about a dozen proposals were made to extend object-oriented modeling by multiple abstraction levels. One group of proposals designates explicit levels to objects and classes. The second group uses the powertype pattern to implicitly establish levels. From this group, we consider two proposals, DeepTelos and MLT*. Both have been defined via axioms and both give a central role to the powertype pattern. In this paper, we reconstruct MLT* with the deductive axiomatization style used for DeepTelos. The resulting specification is executed in a deductive database to check MLT* multi-level models for errors and complete them with derived facts that do not have to be explicitly asserted by modelers. This leverages the rich rules of MLT* with the deductive approach underlying DeepTelos. The effort also allows us to clearly establish the relation between DeepTelos and MLT*, in an attempt to clarify the relations between approaches in this research domain. As a byproduct, we supply MLT-Telos as a fully operational deductive implementation of MLT* to the research community.
Being Part of the Swarm: Experiencing Human-Swarm Interaction with VR and Tangible Robots

A swarm is the coherent behavior that emerges ubiquitously from simple interaction rules between self-organized agents. Understanding swarms is of utmost importance in many disciplines and jobs, but hard to teach due to the elusive nature of the phenomenon, which requires to observe events at different scales (i.e., from different perspectives) and to understand the links between them. In this article, we investigate the potential of combining a swarm of tangible, haptic-enabled robots with Virtual Reality, to provide a user with multiple perspectives and interaction modalities on the swarm, ultimately aiming at supporting the learning of emergent behaviours. The framework we developed relies on Cellulo robots and Oculus Quest and was preliminarily evaluated in a user study involving 15 participants. Results suggests that the framework effectively allows users to experience the interaction with the swarm under different perspectives and modalities.
Causality Analysis of Disaggregated FDI Inflows on Sectorial Growth in OECD Area

Abstract This article revisits the link between disaggregated Foreign Direct Investment (FDI) inflows and sectorial growth using the panel dataset of 25 Organisation for Economic Co-operation and Development (OECD) countries for the period 1990 to 2017. It adopted the panel fixed effect and Feasible Generalized Least Squares Approach in its analysis. The findings show that disaggregated FDI inflows have the potential to improve growth in the OECD area with adverse effects on domestic investment and inflationary pressure. Additionally, the results further indicate that disaggregated FDI inflows have a positive and significant relationship on the service and manufacturing sector but with no evidence shown on the agricultural industry. Thus, the study concludes that efficient reallocation of FDI resource(s) among sectors will not only boost output growth but also impact on the real economy. However, the necessary policy strategy to regulate this inflow is vital to mitigate its negative impact on domestic investment and inflation pressure.
DARK CARBON DIOXIDE RELEASE AND DARK GLUCOSE-6-PHOSPHATE METABOLISM BY SPINACH CHLOROPLAST PREPARATIONS

-6-Abstract: Dark carbon metabolism of reconstitllted spinach chloroplast prepara tions was studied using l4C labeled glucose-6-phosphate and 3Zp i. In the dark. chloroplasts synthesized ATP leading to the appearance of 32p in all phosphorylated intermediates of the oxidative pentose cycle with 11igh concentrations of 3Zp in sugar diphosphates and nucleotides. carbon dioxide was released preferentially from the C-l position of glucose suggesting substantial carbon flow through the glucose-6-phosphate, 6-P gluconic acid dehydrogenase system. The release of COZ occurred in measurable quantities in the light and was stimulated by OZ. accumulate in the dark were 6-phospho-gluconic The amount of acid plus dihydroxyacetone formed from Cl -. ,C6-. or U-14C glucose lvas essentially the same suggesting triose- formation through phosphofructokinase.