Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Evaluation of the Quantitative Gingival Displacement with Cordless and Conventional Cord Gingival Retraction Techniques in Thin and Thick Gingival Biotype

Original Research Article Background: Gingival retraction is an inevitable step in fabrication of fixed partial denture. With the evolution in the cordless retraction materials like Magic foam and Expasyl, the selection of among these materials has still been a dilemma as there are no specific criteria on selection among this material in literature. The purpose of this study was to compare and evaluate the quantitative gingival displacement among cordless and conventional cord gingival retraction techniques in thin and thick gingival biotype. Methods: A total of 20 heathy participants with 10 participants each in thin and thick gingival biotype category on the right central incisor were selected. Under standard protocol and manufactures instruction gingival retraction followed by impression were carried out using 5% aluminium chloride impregnated retraction cord, Magic foam and Expasyl. Through the cast obtained a 3mm samples were prepared and observed under digital binocular compound microscope at 40x magnification which was affixed to a computer with the Image Analyser Multimedia software-Motic Image Plus 2.0ML which would detect the edges and generate the results of quantitative displacement in vertical, horizontal and area of displacement. Results: In relation to the thin gingival biotype the maximum vertical displacement was observed in magic foam followed by Retraction Cord and Expasyl, whereas the maximum lateral displacement was observed by Retraction cord followed by Magic Foam and Expasyl. In thick gingival biotype the maximum vertical displacement was observed in Retraction Cord followed by Magic Foam and Expasyl, whereas the maximum lateral displacement was observed by Retraction cord followed by Expasyl and Magic Foam. The overall total area of displacement for both the thin and thick gingival biotype was observed by Retraction Cord followed by Magic Foam and Expasyl. Conclusion: The conventional retraction cord still stands to deliver the overall maximum amount of displacement in general for both the gingival biotype. Whereas among the cordless gingival retraction technique Magic Foam has proven to be more productive in thin gingival biotype than thick compared to Expasyl and vice versa in thick gingival biotype Expasyl has been more productive than Magic Foam. The study can be conclusive to state that the choice on cordless retraction material is also biotype dependent.
Towards a Human-Centric Design Framework for AI Assisted Music Production

In this paper, we contribute to the discussion on how to best design human-centric MIR tools for live audio mixing by bridging the gap between research on complex systems, the psychology of automation and the design of tools that support creativity in music production. We present the design of the Channel-AI, an embedded AI system which performs instrument recognition and generates parameter settings suggestions for gain levels, gating, compression and equalization which are specific to the input signal and the instrument type. We discuss what we believe to be the key design principles and perspectives on the making of intelligent tools for creativity and for experts in the loop. We demonstrate how these principles have been applied to inform the design of the interaction between expert live audio mixing engineers with the Channel-AI (i.e. a corpus of AI features embedded in the Midas HD Console. We report the findings from a preliminary evaluation we conducted with three professional mixing engineers and reflect on mixing engineers’ comments about the Channel-AI on social media.
University of Birmingham Glutamate Receptor 2D is a novel angiogenic tumour endothelial marker in colorectal cancer

Current vascular-targeted therapies in colorectal cancer (CRC) have shown limited benefit. The lack of novel, specific treatment in CRC has been hampered by a dearth of specific endothelial markers. Microarray comparison of endothelial gene expression in patient-matched CRC and normal colon identified a panel of putative colorectal tumour endothelial markers. Of these the glutamate dependent NMDA receptor GRIN2D emerged as the most interesting target. GRIN2D expression was shown to be specific to colorectal cancer vessels by RTqPCR and IHC analysis. Its expression was additionally shown be predictive of improved survival in CRC. Targeted knockdown studies in vitro demonstrated a role for GRIN2D in endothelial function and angiogenesis. This effect was also shown in vivo as vaccination against the extracellular region of GRIN2D resulted in reduced vascularisation in the subcutaneous sponge angiogenesis assay. The utility of immunologically targeting GRIN2D in CRC was demonstrated by the vaccination approach inhibiting murine CRC tumour growth and vascularisation. GRIN2D represents a promising target for the future treatment of CRC.
Unsupervised and Semi-supervised Novelty Detection using Variational Autoencoders in Opportunistic Science Missions

Scientiﬁc opportunities are missed in planetary explorations due to the lack of communication and/or long-time communication delays between rovers and ground stations. By enabling rovers to autonomously detect and explore targets the overall scientiﬁc outcome of extraterrestrial missions can be increased. In this paper, we have designed, developed, and evaluated unsupervised as well as semi-supervised approaches to novelty detection based on Variational Autoencoders (VAE). Our VAE model was trained on typical data from previous missions and tested to infer the novelty of scientiﬁc targets. In an ablation study, we investigate the effectiveness of different types of loss functions. We compare losses based on reconstruction errors, losses obtained from the VAE’s latent space as well as a combination of both. In our experiments, we have evaluated both unsupervised and semi-supervised approaches on datasets obtained from NASA’s Mars Curiosity rover. Results show that our VAE-based approaches are not only robust but also comparable, or better, than the state-of-the-art.
Performance of a 9-kW Magnetically-Shielded Hall Thruster with Krypton

The performance of a 9-kW magnetically-shielded Hall thruster operating on xenon and krypton is characterized. The anode efficiencies were measured with a thrust stand at several conditions from 300 V to 600 V and 15 A to 20 A, and a probe suite was used to estimate the specific efficiencymodes at the 300V, 4.5 kWcondition. It was found that the difference between krypton and xenon efficiency increased from the typical, historical value of approximately 515% on unshielded thrusters to a gap of 9-19% on the shielded thruster. This increase is primarily attributable to the differences in mass utilization and current utilization efficiencies between krypton and xenon, possibly caused by lower electron temperatures at the wall and higher electronmobility. The results of the study are discussed in the context how the operation of krypton on a magnetically-shielded Hall thruster may be fundamentally different from operation on xenon and potential pathways into the improvement of performance on krypton.
Supervised Learning of Swarm Behaviour using Learning Classifier Systems

In Swarm Robotics, complex behaviour can emerge from the local interactions of multiple simple agents. This behaviour is generally robust, scalable and flexible. In this thesis, I investigate how the simple agents have to act locally for a given collective behaviour to emerge. For this task, I introduce Global-Local Swarm Learning (GLSL). Unlike previous approaches, GLSL uses supervised learning to recreate an existing behaviour that was created with information that the swarm does not have. As a classifier, I use a Michigan-Style Learning Classifier System (LCS). The research question is how the success of the learning depends on the difficulty of the task. I use a leader-follower task with different levels of difficulty. The results show no clear connection between the difficulty and the success. In its current configuration including the LCS, GLSL does not produce the desired behaviours reliably.
Meta-RetinaNet for Few-shot Object Detection

Few shot object detection (FSD) is gaining popularity, enhanced by the deep learning methods in recent years. Meanwhile, meta-learning has achieved great success in few-shot image classiﬁcation beneﬁtting from its adaptive capability corresponding to a suite of tasks. Yet, most object detection models are based on deep neural networks (DNNs), and they are prone to the overﬁtting problem due to limited samples available during training. To adapt the learned prior knowledge more effectively to new tasks, this paper proposes a novel Meta-RetinaNet for FSD, which avoids a biased meta-learner and improves its generalization ability. It employs a Meta Coefﬁcient Learner (MCL) trained by the Balanced Loss (BL) to augment the DNNs. Speciﬁcally, the MCL adapts to tasks by the product of pre-trained convolution weights and coefﬁcient vectors densely for all the convolutional layers, such that it could adequately transfer the learned knowledge to new tasks (while overcoming the overﬁtting problem) by training fewer parameters. In addition, the BL expedites the training of a Meta-RetinaNet by balancing the performance of a host of tasks, and it also retains stable performance for new tasks. Our experiments showcase the effectiveness of our method, which achieves the state-of-the-art performance on the multiple settings of Pascal VOC and COCO datasets.
On the Challenges of Economic Development in Post- Conflict Sudan

The paper reviews the economic and development content of the various protocols signed by the Government of Sudan and the Sudan Peoples Liberation Movement over the period 20 July 2002 to 26 May 2004. It is proposed that these agreements should be understood in the context of the modern economic theory of civil wars. Under this theory economic factors are held to be the most important explanatory variables for the occurance of civil wars; however, resolution of such conflicts have to address the grievances expressed in the political discourse of armed movements. The paper argues that the most important economic and development challenges in post-conflict Sudan include increasing the investment rate; management of the expected surge in government expenditure; the role of the central bank in the context of a dual monetary system; and , establishing social safety nets.
Probatio 1.0: collaborative development of a toolkit for functional DMI prototypes

Probatio is an open-source toolkit for prototyping new digital musical instruments created in 2016. Based on a morphological chart of postures and controls of musical instruments, it comprises a set of blocks, bases, hubs, and supports that, when combined, allows designers, artists, and musicians to experiment with diﬀerent input devices for musical interaction in diﬀerent positions and postures. Several musicians have used the system and based on these past experiences, we assembled a list of improvements to implement version 1.0 of the toolkit through a unique international partnership between two laboratories in Brazil and Canada. In this paper, we present the original toolkit and its use so far, summarize the main lessons learned from musicians using it, and present the requirements behind, and the ﬁnal design of, v1.0 of the project. We also detail the work developed in digital fabrication using two diﬀer-ent techniques: laser cutting and 3D printing, comparing their pros and cons. We ﬁnally discuss the opportunities and challenges of fully sharing the project online and replicating its parts in both countries.
Characterization of the Dissolution of Water Microdroplets in Oil

Water in oil emulsions have a wide range of applications from chemical technology to microfluidics, where the stability of water droplets is of paramount importance. Here, using an accessible and easily reproducible experimental setup we describe and characterize the dissolution of water in oil, which renders nanoliter-sized droplets unstable, resulting in their shrinkage and disappearance in a time scale of hours. This process has applicability in creating miniature reactors for crystallization. We test multiple oils and their combinations with surfactants exhibiting widely different rates of dissolution. We derived simple analytical equations to determine the product of the diffusion coefficient and the relative saturation density of water in oil from the measured dissolution data. By measuring the moisture content of mineral and silicone oils with Karl Fischer titration before and after saturating them with water, we calculated the diffusion coefficient of water in these two oils.
Analytic vs. holistic approaches for the live search of sound presets using graphical interpolation

The comparative study presented in this paper focuses on two approaches for the search of sound presets using a specific geometric touch app. The first approach is based on independent sliders on screen and is called analytic. The second is based on interpolation between presets represented by polygons on screen and is called holistic. Participants had to listen to, memorize, and search for sound presets characterized by four parameters. Ten different configurations of sound synthesis and processing were presented to each participant, once for each approach. The performance scores of 28 participants (not including early testers) were computed using two measurements: the search duration, and the parametric distance between the reference and answered presets. Compared to the analytic sliders-based interface, the holistic interpolation-based interface demonstrated a significant performance improvement for 60% of sound synthesizers. The other 40% led to equivalent results for the analytic and holistic interfaces. Using sliders, expert users performed nearly as well as they did with interpolation. Beginners and intermediate users struggled more with sliders, while the interpolation allowed them to get quite close to experts’ results.
A Comparison of Randomization-based Multivariate Tests for Ecological Difference

Community ecologists seek to determine whether the assemblage of species that occur in one environmental situation differs from that in another, by comparing two data matrices, that contain abundance values for a set of sampled species at each of some number of replicate sites within each environment. There is a need for multivariate tests for difference that can deal with properties that often characterize ecological assemblage data sets, such as data matrix sparseness, non-normality (Clarke, 1993), heterogeneous group dispersion and associations (Biondini et al., 1988). Multivariate, randomization tests for difference present a possible solution to these problems (Legendre and Anderson, 1999). However, little research has been done on the comparative performance of such techniques. This paper compares the performance of four randomization-based, multivariate techniques in detecting differences among ecological assemblages: (1) Multi-response permutation procedure (MRPP; Mielke et al., 1976); (2) Nonparametric Multivariate Analysis of Variance (NP-MANOVA; Anderson, 2001); (3) Analysis of Similarities (ANOSIM; Clarke, 1993) and (4) distance-based Redundancy Analysis (db-RDA; Legendre and Anderson, 1999). The aspect of performance considered is the power of tests in detecting differences in species turnover (the proportion of species shared among two assemblages).
Utilizing Available Data to Warm Start Online Reinforcement Learning

Reinforcement learning (RL) algorithms have shown to achieve significant successes on a large set of simulated environments. However, the application of RL in real-world problems has proven to be more challenging. In most real-world problems, there is no access to an accurate model of the environment. Therefore, a policy has to be learned by interacting with the real system where every action has real consequences. This is very impractical since reinforcement learning algorithms often start with many steps of poor performance before finding a better policy. It is therefore crucial to find a way to minimize poor actions and learn as efficiently as possible. In this thesis, we propose a setup that utilizes an available dataset with logged environment interactions to warm start reinforcement learning (WS-RL). This proposed setup combines both offline and online reinforcement learning techniques. We show WS-RL is able to improve performance for more complex tasks. It reduces the time needed to find a decent policy and therefore improves both initial and overall performance. It however does not seem to improve the convergence of policies in the long run. Additionally, we try to get a better understanding of the effect of exploration when learning on the real system. To do this we compare multiple exploration methods and settings and show which one performs best across different environments.
Forecasting with Serially Corrected Regression Models

Since Cochrane and Orcutt (1949) and Durbin and Watson (1950) developed an approximate transformation to deal with and test for autoregressive disturbances of order 1 we have witnessed a plethora of studies dealing with the problems of serial correlation in regression models. Choudbhury et al. (1999), among others, have documented the evolution of approximate/exact transformations to deal with more complex serial correlation structures, as well as, the development of new, more powerful estimation methods that have occurred in the last fifty years. When disturbances exhibit serial correlation, least squares will yield unbiased, but inefficient estimators of parameters of the model, thus invalidating all tests of significance. In addition these estimated regression coefficients will have larger sampling variances than other estimators such as generalized least squares (GLS) that deal explicitly with the autocorrelation in the residuals. Furthermore, forecasts generated from such models can be seriously inefficient, not only because of the inefficiency of the parameter estimators, but also because the error between the fitted and actual value in the last observation is apt to persist into the actual forecast interval. With few exceptions most of the studies that have been conducted on this topic assume that the error covariance matrix Ω from the regression model, Y = Xβ + 2, where Y is a (T × 1) vector of observations on a dependent variable, X is a (T × k) design matrix and 2 is a random vector, which follows an ARMA process, with E(2) = 0 and E(22′) = σΩ, is either known or could be estimated consistently from data. Until recently, of the very few studies that considered the properties of estimators, or of the forecasts when the structure of Ω was incorrectly identified, or when its parameters were inefficiently estimated, most dealt with or depended on asymptotic results (Goldberger, 1962; Amemiya, 1973; Engle, 1974). Koreisha and Fang (2000), on the other hand, compared the finite-sample efficiencies of ordinary least squares (OLS) and GLS vis-à-vis incorrect GLS (IGLS) estimators, i.e., estimators based on incorrectly identified Ω, and established theoretical efficiency bounds for IGLS relative to GLS and OLS. They found that GLS estimation based on autoregressive representations of autoregressive-moving average (ARMA) disturbances yielded more efficient estimates than OLS particularly when the order of the autoregression was set near [ √ T/2], and that the differences in estimation efficiency between estimated IGLS and GLS were small. In this article we augment the work of Koreisha and Fang (2000) by investigating the impact
ECONOPHYSICS APPROACH TO THE DYNAMICS OF THE ROMANIAN EXCHANGE RATE ROL-USD

Analiza cursului de schimb ROL-USD pe o perioadă de zece ani, de la începutul anului 1991 până la apariţia unei anumite stabilităţi (sfârşitul lui 2000) pune în evidenţă o dimensiune fractală clară a seriei temporale. În această perioadă, considerată perioadă de tranziţie, am constatat prezenţa unor celule caracteristice, care reproduc forma generală a semnalului la diferite scări de analiză, adică am pus în evidenţă o auto-similaritate teoretică. Celulele observate au caracteristici fractale apropiate, sugerând existenţa unui tip de dinamică neliniară dominant monofractală, caracteristică deprecierii puternice a monedei naţionale din perioada de tranziţia spre o economie de piaţă deschisă.
SIA-GCN: A Spatial Information Aware Graph Neural Network with 2D Convolutions for Hand Pose Estimation

Graph Neural Networks (GNNs) generalize neural networks from applications on regular structures to applications on arbitrary graphs, and have shown success in many application domains such as computer vision, social networks and chemistry. In this paper, we extend GNNs along two directions: a) allowing features at each node to be represented by 2D spatial confidence maps instead of 1D vectors; and b) proposing an efficient operation to integrate information from neighboring nodes through 2D convolutions with different learnable kernels at each edge. The proposed SIA-GCN can efficiently extract spatial information from 2D maps at each node and propagate them through graph convolution. By associating each edge with a designated convolution kernel, the SIA-GCN could capture different spatial relationships for different pairs of neighboring nodes. We demonstrate the utility of SIA-GCN on the task of estimating hand keypoints from single-frame images, where the nodes represent the 2D coordinate heatmaps of keypoints and the edges denote the kinetic relationships between keypoints. Experiments on multiple datasets show that SIA-GCN provides a flexible and yet powerful framework to account for structural constraints between keypoints, and can achieve state-of-the-art performance on the task of hand pose estimation.
The impact of anthropogenic forcing and natural 1 processes on past , present and future rainfall over 2 Victoria , Australia 3 4 By 5

18 19 Cool season (April to October) rainfall dominates the annual average rainfall over Victoria, 20 Australia, and is important for agriculture and replenishing reservoirs. Rainfall during the cool 21 season has been unusually low since the beginning of the Millennium Drought in 1997 (~12% 22 below the 20 century average). In this study, 24 CMIP5 climate models are used to estimate (i) 23 the extent to which this drying is driven by external forcing, and (ii) future rainfall, taking both 24 external forcing and internal natural climate variability into account. All models have 25 preindustrial, historical, and 21 century (RCP2.6, RCP4.5 and RCP8.5) simulations. It is found 26 that rainfall in the past two decades is below the preindustrial average in two-thirds or more of 27 model simulations. However, the magnitude of the multi-model median externally-forced drying 28 is equivalent to only 20% of the observed drying (inter-quartile range of 40% to -4%), suggesting 29 that the drying is dominated by internally-generated rainfall variability. Underestimation of 30 internal variability of rainfall by the models, however, increases the uncertainties in these 31 estimates. According to models the anthropogenically-forced drying becomes dominant from 32 2010-2029, when drying is evident in over 90% of the model simulations. For 2018 – 2037 33 period, it is found that there is only a ~12% chance that internal rainfall variability could 34 completely offset the anthropogenically-forced drying. By the late 21 century the 35 anthropogenically-forced drying under RCP8.5 is so large that internal variability appears too 36 small to be able to offset it. Confidence in the projections is lowered because models have 37 difficulty in simulating the magnitude of the observed decline in rainfall. 38 39