Miracle-dz/UniFAR-FTUs huggingface dataset:
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively access and use stereo information for the super-resolution (SR) purpose is often a challenge. In this paper, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to simultaneously compute a super-resolved image in a stereo image pair. In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules. Experiment results on laparoscopic images demonstrate that the proposed framework outperforms current SR methods on both quantitative and qualitative evaluations. Our DCSSRnet provides a promising solution on enhancing spatial resolution of stereo image pairs, which will be extremely beneficial for the endoscopic surgery.
SAPIEN: A SimulAted Part-Based Interactive ENvironment

Building home assistant robots has long been a goal for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable. Existing environments achieve these requirements for robotics simulation with different levels of simplification and focus. We take one step further in constructing an environment that supports household tasks for training robot learning algorithm. Our work, SAPIEN, is a realistic and physics-rich simulated environment that hosts a large-scale set of articulated objects. SAPIEN enables various robotic vision and interaction tasks that require detailed part-level understanding.We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks using heuristic approaches and reinforcement learning algorithms. We hope that SAPIEN will open research directions yet to be explored, including learning cognition through interaction, part motion discovery, and construction of robotics-ready simulated game environment.
A multi-functional intelligent metasurface: Electromagnetic design accounting for fabrication aspects

In this paper we present the theoretical considerations and the design evolution of a proof-of-concept intelligent metasurface, which can be used as a tunable microwave absorber, as well as a wavefront manipulation and polarization conversion device in reflection. We outline the design evolution and all considerations taken into account, from the selection of patch shape, unit cell size, and substrate, to the topology of the structure that realizes the desired tunability. The presented design conforms to fabrication restrictions and is co-designed to work with an integrated circuit chip for providing tunable complex loads to the metasurface, using a commercially available semiconductor process. The proposed structure can perform multiple tunable functionalities by appropriately biasing the integrated circuit chip: Perfect absorption for a wide range of incidence angles of both linear polarization states, accommodating a spectral range in the vicinity of 5 GHz, as well as wavefront control demonstrated exemplified via anomalous reflection and polarization conversion. The end vision is for this unit-cell design to be scalable and deployable as a practical HyperSurface, i.e., an intelligent multi-functional metasurface capable of concurrent reconfigurable functionalities, such as absorption, beam steering, polarization conversion, wavefront shaping, holography, sensing etc.
CLASH-VLT: a full dynamical reconstruction of the mass profile of Abell S1063 from 1 kpc out to the virial radius

Context. The shape of the mass density profiles of cosmological halos informs us of the nature of dark matter (DM) and DM-baryons interactions. Previous estimates of the inner slope of the mass density profiles of clusters of galaxies are in opposition to predictions derived from numerical simulations of cold dark matter (CDM). Aims. We determine the inner slope of the DM density profile of a massive cluster of galaxies, Abell S1063 (RXC J2248.7−4431) at z = 0.35, with a dynamical analysis based on an extensive spectroscopic campaign carried out with the VIMOS and MUSE spectrographs at the ESO VLT. This new data set provides an unprecedented sample of 1234 spectroscopic members, 104 of which are located in the cluster core (R ≲ 200 kpc), extracted from the MUSE integral field spectroscopy. The latter also allows the stellar velocity dispersion profile of the brightest cluster galaxy (BCG) to be measured out to 40 kpc. Methods. We used an upgraded version of the MAMPOSSt technique to perform a joint maximum likelihood fit to the velocity dispersion profile of the BCG and to the velocity distribution of cluster member galaxies over a radial range from 1 kpc to the virial radius (r200 ≈ 2.7 Mpc). Results. We find a value of γDM = 0.99 ± 0.04 for the inner logarithmic slope of the DM density profile after marginalizing over all the other parameters of the mass and velocity anisotropy models. Moreover, the newly determined dynamical mass profile is found to be in excellent agreement with the mass density profiles obtained from the independent X-ray hydrostatic analysis based on deep Chandra data, as well as the strong and weak lensing analyses. Conclusions. Our value of the inner logarithmic slope of the DM density profile γDM is in very good agreement with predictions from cosmological CDM simulations. We will extend our analysis to more clusters in future works. If confirmed on a larger cluster sample, our result makes this DM model more appealing than alternative models.
The Generalized Uncertainty Principle

The uncertainty principle lies at the heart of quantum physics, and is widely thought of as a fundamental limit of the measurement precision of incompatible observables. Here it is shown that the traditional uncertainty relation in fact belongs to the leading order approximation of a generalized uncertainty relation. That is, the leading order linear dependence of observables gives the Heisenberg type of uncertainty relations, while higher order nonlinear dependence may reveal more different and interesting correlation properties. Applications of the generalized uncertainty relation and the high order nonlinear dependence between observables in quantum information science are also discussed.
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking

Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods. To solve this problem we develop C-FAR, a novel method for Fast, Automated and Reproducible assessment of multiple hierarchical clustering algorithms simultaneously. Our algorithm takes any number of hierarchical clustering trees as input, then strategically queries pairs for human feedback, and outputs an optimal clustering among those nominated by these trees. While it is applicable to large dataset in any domain that utilizes pairwise comparisons for assessment, our flagship application is the cluster aggregation step in spike-sorting, the task of assigning waveforms (spikes) in recordings to neurons. On simulated data of 96 neurons under adverse conditions, including drifting and 25\% blackout, our algorithm produces near-perfect tracking relative to the ground truth. Our runtime scales linearly in the number of input trees, making it a competitive computational tool. These results indicate that C-FAR is highly suitable as a model selection and assessment tool in clustering tasks.
Volumetric parcellation of the right ventricle for regional geometric and functional assessment

3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks. We developed a technique for regionally assessing the 3 relevant RV regions: apical, inlet and outflow. The method's inputs are end-diastolic (ED) and end-systolic (ES) segmented 3D surface models. The method first defines a partition of the ED endocardium using the geodesic distances from each surface point to apex, tricuspid valve and pulmonary valve: the landmarks that define the 3 regions. The ED surface mesh is then tetrahedralised, and the endocardial-defined partition is interpolated in the blood cavity via the Laplace equation. For obtaining an ES partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking, and then interpolated towards the endocardium, similarly to ED, for computing volumes and ejection fraction (EF). We present a full assessment of the method's validity and reproducibility. First, we assess reproducibility under segmentation variability, obtaining intra- and inter- observer errors (4-10% and 10-23% resp.). Finally, we use a synthetic remodelling dataset to identify the situations in which our method is able to correctly determine the region that has remodelled. This dataset is generated by a novel mesh reconstruction method that deforms a reference mesh, locally imposing a given strain, expressed in anatomical coordinates. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodelling.
Prevalent uniqueness in ergodic optimisation

One of the fundamental results of ergodic optimisation asserts that for any dynamical system on a compact metric space X X and for any Banach space of continuous real-valued functions on X X which embeds densely in C ( X ) C(X) there exists a residual set of functions in that Banach space for which the maximising measure is unique. We extend this result by showing that this residual set is additionally prevalent, answering a question of J. Bochi and Y. Zhang.
Bipartite Link Prediction based on Topological Features via 2-hop Path

A variety of real-world systems can be modeled as bipartite networks. One of the most powerful and simple link prediction methods is Linear-Graph Autoencoder(LGAE) which has promising performance on challenging tasks such as link prediction and node clustering. LGAE relies on simple linear model w.r. t. the adjacency matrix of the graph to learn vector space representations of nodes. In this paper, we consider the case of bipartite link predictions where node attributes are unavailable. When using LGAE, we propose to multiply the reconstructed adjacency matrix with a symmetrically normalized training adjacency matrix. As a result, 2-hop paths are formed which we use as the predicted adjacency matrix to evaluate the performance of our model. Experimental results on both synthetic and real-world dataset show our approach consistently outperforms Graph Autoencoder and Linear Graph Autoencoder model in 10 out of 12 bipartite dataset and reaches competitive performances in 2 other bipartite dataset.
Adjust Planning Strategies to Accommodate Reinforcement Learning Agents

In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such a boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis of the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of the reaction capability of specific agents.
Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images

Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression. However, automated mitotic nuclei detection poses several challenges because of the unavailability of pixel-level annotations, different morphological configurations of mitotic nuclei, their sparse representation, and close resemblance with non-mitotic nuclei. These challenges undermine the precision of the automated detection model and thus make detection difficult in a single phase. This work proposes an end-to-end detection system for mitotic nuclei identification in breast cancer histopathological images. Deep object detection-based Mask R-CNN is adapted for mitotic nuclei detection that initially selects the candidate mitotic region with maximum recall. However, in the second phase, these candidate regions are refined by multi-object loss function to improve the precision. The performance of the proposed detection model shows improved discrimination ability (F-score of 0.86) for mitotic nuclei with significant precision (0.86) as compared to the two-stage detection models (F-score of 0.701) on TUPAC16 dataset. Promising results suggest that the deep object detection-based model has the potential to learn the characteristic features of mitotic nuclei from weakly annotated data and suggests that it can be adapted for the identification of other nuclear bodies in histopathological images.
Learning to Fly via Deep Model-Based Reinforcement Learning

Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions. In this work, by leveraging a learnt probabilistic model of drone dynamics, we learn a thrust-attitude controller for a quadrotor through model-based reinforcement learning. No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input. The controller and value function are optimised entirely by propagating stochastic analytic gradients through generated latent trajectories. We show that "learning to fly" can be achieved with less than 30 minutes of experience with a single drone, and can be deployed solely using onboard computational resources and sensors, on a self-built drone.
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow

We propose an end‐to‐end trained neural network architecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single‐phase smoke simulations in 2D and 3D based on the incompressible Navier‐Stokes (NS) equations, which are relevant for a wide range of practical problems. To achieve stable predictions for long‐term flow sequences with linear execution times, a convolutional neural network (CNN) is trained for spatial compression in combination with a temporal prediction network that consists of stacked Long Short‐Term Memory (LSTM) layers. Our core contribution is a novel latent space subdivision (LSS) to separate the respective input quantities into individual parts of the encoded latent space domain. As a result, this allows to distinctively alter the encoded quantities without interfering with the remaining latent space values and hence maximizes external control. By selectively overwriting parts of the predicted latent space points, our proposed method is capable to robustly predict long‐term sequences of complex physics problems, like the flow of fluids. In addition, we highlight the benefits of a recurrent training on the latent space creation, which is performed by the spatial compression network. Furthermore, we thoroughly evaluate and discuss several different components of our method.
Superconducting quantum many-body circuits for quantum simulation and computing

Quantum simulators are attractive as a means to study many-body quantum systems that are not amenable to classical numerical treatment. A versatile framework for quantum simulation is offered by superconducting circuits. In this perspective, we discuss how superconducting circuits allow the engineering of a wide variety of interactions, which in turn allows the simulation of a wide variety of model Hamiltonians. In particular we focus on strong photon-photon interactions mediated by nonlinear elements. This includes on-site, nearest-neighbour and four-body interactions in lattice models, allowing the implementation of extended Bose-Hubbard models and the toric code. We discuss not only the present state in analogue quantum simulation, but also future perspectives of superconducting quantum simulation that open up when concatenating quantum gates in emerging quantum computing platforms.
Anxiety Levels and Needs of Fathers of Children Hospitalized in Pediatric Surgery Intensive Care Units

Ad dress for Cor res pon den ce Nurdan Akçay MD, Department of Pediatric Nursing, Ege University Nursing Faculty, İzmir, Turkey Phone: +90 506 810 76 97 E-mail: n_akcayyy@hotmail.com ORCID: orcid.org/0000-0002-5444-530X Re cei ved: 06.04. 2019 Ac cep ted: 31.05. 2019 1Department of Pediatric Nursing, Ege University Nursing Faculty, İzmir, Turkey 2Department of Pediatric Nursing, Katip Çelebi University, İzmir, Turkey 3Pediatric Surgery Intensive Care Unit, İzmir, Turkey 4Department of Pediatric Surgery, Ege University, İzmir, Turkey Nurdan Akçay Didişen1, Atiye KARAKUL2, Sevinç Ertürk3, Zafer Dökümcü4
Endocrine complications of beta-thalassemia major patients—Cross-sectional study

Aims: To evaluate the prevalence of endocrine complications in beta thalassaemia major patients in one of thalassaemia treatment units in Sri Lanka. Methods: The patients (n = 40) aged >2 years admitted to the Teaching Hospital Peradeniya during the period of December 2013 to December 2014, under the treatment of iron chelation drugs were recruited for the study. The patients were interviewed for the sociodemographic variables and the data regarding endocrine complications were gathered from medical records. Anthropometric (height and weight) measurers and pubertal status were assessed by a qualified medical officer. The data were statistically analyzed by SPSS version 21. Results: The mean age of the patients was 10.97±5.9 years (range 2–20). The most common endocrine complication was a pubertal delay (53%). The prevalence of short stature, hypothyroidism, diabetes mellitus, and hypoparathyroidism were 50%, 10%, 5%, and 2.5%, respectively. All the patients with endocrine complications were older children (>10-year-olds). Conclusion: Endocrine complications were common among the studied group of betathalassaemia major patients and it is related to progression of age. Regular assessment of endocrine function is imperative in the proper management of beta-thalassemia major patients.
Solanumnigrum, Amaranthaceaeand Cucurbitacucurbitacea using chemically modified millet tassels, beans husks and maize cobs

Iodine in Solanumnigrum, Amaranthaceaeand Cucurbitacucurbitaceawas pre-concentrated using new adsorbent of a powdered mixture of millet tassels (MT), maize cobs (MC) and beans husks (BH). Prior to its use, powdered MT, MC and BH were separately treated with thionylchloride (SOCl2) in dimethylformaldehyde (DMF), filtered then dried. The resulting solid was refluxed in ethylenediamine for 4 hours. The surface characteristics of the adsorbents were determined using fourier transform infrared. Batch sorption experiments were carried out to determine the optimum conditions of pH, concentration, dosage and time for the adsorption of iodine. Correlation coefficient obtains by modelling data to Langmuir and Freundlich isotherms were compared to determine the best model for adsorption. Sorption kinetics was studied using pseudo first order and pseudo second order. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 29-04-2019 Date of acceptance: 13-05-2019 -------------------------------------------------------------------------------------------------------------------------------------------------
Measuring the Quality of Workplace Relations and Organizational Performance with Alternative Balanced Scorecards from Strategic HRM and Employment-Industrial Relations

This paper uses the popular balanced scorecard from strategic management, and a new survey data set, to empirically measure and evaluate the state of workplace employment capabilities, relations, practices, and performance for shareholders and stakeholders. An innovative feature is that two alternative scorecards are constructed based, respectively, on the high-performance work system (HPWS) model and model of an employment relations system (ERS). The two models are depicted and compared in diagrams, used as theoretical frameworks to build alternative scorecards, and filled in with nationally-representative data on more than fifty workplace attributes provided by separate panels of managers and employees from over 2,000 U.S. workplaces. The workplace performance scores are transformed into frequency distributions showing, first, the mean and dispersion of U.S. workplaces as ordered from lowest to highest performance and, second, evidence that the HPWS and ERS models yield different evaluation assessments – indicating "models matter" for scorecard analysis.
International Journal of Social Science and Economic Research

The mobile numbers portability (MNP) regulation exists for two decade. His regulation has been object of multiple studies in economy. These studies are mainly focused on his costs. Whereas these costs are function too the technical solutions utilized. This article focuses on the regulation of PNM in terms of the choice of ONS or IN technical solutions in mobile markets that do not practice PNM but have reached maturity in terms of density as in some countries of West African Economic and Monetary Union (WAEMU). In order to reach our results we use Bertrand’s model in duopoly context where each operator formulates that the conjectural variations of his rival are null in a static game with one round. Our results show that in such markets the implementation of MNP by sectoral regulation authorities must pass by the choice of IN technical solution to the detriment of ONS technical solution. And more, they must opt for symmetric regulation in terms of technical solutions choice.