Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i.e., the data generating mechanism can be recovered from the joint distribution. However, no such studies have been done to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effect from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and important research question, i.e., the effect of oxygen therapy intervention in the intensive care unit (ICU); the result of this project is useful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC III database, a standard database in the machine learning community that contains 58,976 admissions from an ICU in Boston, MA, for estimating the oxygen therapy effect on morality. We also identified the covariate-specific effect to oxygen therapy from the model for more personalized intervention.
3 Quantifying Generalization 3 . 1 The CoinRun Environment

In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent’s ability to generalize. We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.
BBB-Voting: Self-Tallying 1-out-of-k Blockchain-Based Boardroom Voting

—Voting is a means to agree on a collective decision based on available choices (e.g., candidates), where participants (voters) agree to abide by their outcome. To improve some features of e-voting, decentralized solutions based on a blockchain can be employed, where the blockchain represents a public bulletin board that in contrast to a centralized bulletin board provides 100% availability and censorship resistance. A blockchain ensures that all entities in the voting system have the same view of the actions made by others due to its immutable and append-only log. The existing blockchain-based boardroom voting solution called Open Voting Network (OVN) provides the privacy of votes and perfect ballot secrecy, but it supports only two choices. We present BBB-Voting, an equivalent blockchain-based approach for decentralized voting such as OVN, but in contrast to it, BBB-Voting supports 1-out-of-k choices and provides a fault tolerance mechanism that enables recovery from stalling participants. We provide a cost-optimized implementation using Ethereum, which we compare with OVN and show that our work decreases the costs for voters by 13 . 5% in terms of gas consumption.
PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image Supplementary Material

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Department of Economics Discussion Paper Series the Value of Innovation: the Interaction of Competition, R&d and Ip the Value of Innovation: the Interaction of Competition, R&d and Ip*

This paper analyses market valuations of UK companies using a new data set of their R&D and IP activities (1989-1999). In contrast to previous studies, the analysis is conducted at the sectoral level, where the sectors are based on the technological classification in Pavitt (1984). The first main result is that the valuation of R&D and IP varies substantially across these sectors. To explore these variations the paper links competitive conditions with the market valuation of innovation. Using profit persistence as a measure of competitive pressure, we find that the sectors that are the most competitive have the lowest market valuation of R&D. Furthermore, within the most competitive sector ('science based'), firms with larger market shares (an inverse indicator of competitive pressure) also have higher R&D valuations. Another important result is that, on average, firms that receive only UK patents tend to have no market premium. In direct contrast, patenting through the European Patent Office does raise market value, as does the registration of trade marks in the UK. Trust and the UK Patent Office. The current development of the database to include more service sector firms, which are so far only partly covered in the database, is taking place with the support of ESRC award RES-334-25-0002 under The Evolution of Business Knowledge Programme. We are grateful to St Peter's College for accommodation and administrative support for this research.
SOLUTION OF NON-LINEAR PROBLEMS WITH FULL STIFFNESS MATRICES

In this contribution the non-incremental system of FEM equations containing the full non-linear tangent stiffness matrix is derived from the principle of virtual work. This formulation is applied to the derivation of basic FEM formulae for 1D-bar element. Two iterative solution methods of non-linear equations are presented and their effectiveness is verified on several numerical experiments.
A Degenerated Plane Truss Model of Base Force Element Method on Complementary Energy Principle

In order to study the performance of base force element method (BFEM), a new 2-node degenerated plane element model based on the BFEM of complementary energy principle is presented for linear elasticity problem and geometrically nonlinear problem respectively in this paper. The plane truss element model can easily be obtained by degenerating from a four-mid-node plane element of the BFEM based on complementary energy principle for linear elasticity problem or geometrically nonlinear problem. The compliance matrix of the rod element model is same as the compliance matrix of the four-mid-node plane element of the BFEM. According to the characteristics of analysis for truss structure, the nodal equilibrium conditions and the displacement coordination conditions of each rod at the nodes have been considered when the compliance matrix of structure is integrated. In order to verify the feasibility of the degenerated plane element model, several truss problems are analyzed and their solutions are compared to the analytical solutions and the numerical results which are calculated with the traditional displacement FEM. It is found that the model has shown high precision and good performance.
Adaptive Multirate Method for Coupled Parabolic and Elliptic Equations

In this thesis, motivated by the simulation of fuel cells and batteries, we develop an adaptive discretization algorithm to reduce the computational cost for solving the coupled parabolic/elliptic system. This system is the model for the electrochemical processes within the cathode of a solid oxide fuel cell (SOFC). First, the coupled system is discretized in time and in space by the Finite Element Method. Then, it is split into parabolic and elliptic sub-problems through an operator splitting method. These two equations are solved sequentially by the multirate iterative solving method that allows for different time step sizes for the temporal discretizations. The main focus of this work is to derive goal-oriented, a posteriori error estimators based on the Dual Weighted Residual method that are computable and separately assess the temporal discretization error, the spatial discretization error and the splitting error for each sub-problem. Instead of natural norms, the errors are measured in an arbitrary quantity of interest, as is often used in practical applications. The sub-problems are solved in temporal discretizations with different step lengths. If the ratio between the two step lengths is too large, this can result in the divergence of the coupling iteration within the multirate scheme. In this case, the algorithm uses the information from the splitting error estimator to control the convergence behavior. The error contributions of both discretizations and splitting method are balanced at the end of the refinement cycle that halts when the error estimators reach a desired accuracy. The described methods are validated on a simplified model that simulates the cathode of a SOFC. In this application, the parabolic part consists of a reaction-diffusion equation describing the concentration distribution of ions, and the elliptic part describes electrical potential. For a given accuracy, the adaptive algorithm finds the least required number of degrees of freedom of the parabolic and the elliptic parts of the system. Since the electrical potential equation has the faster time scale, we use the multirate method and see that the elliptic problem requires a smaller number of degrees of freedom to attain the same desired accuracy within the system. This significantly saves the total computational cost, since the elliptic equation in the coupled system is more expensive to solve. Therefore, this combination of the degrees of freedoms is optimal, in that it gives the least computational cost and the convergence within the algorithm.
CoKe: Localized Contrastive Learning for Robust Keypoint Detection

Today's most popular approaches to keypoint detection learn a holistic representation of all keypoints. This enables them to implicitly leverage the relative spatial geometry between keypoints and thus to prevent false-positive detections due to local ambiguities. However, our experiments show that such holistic representations do not generalize well when the 3D pose of objects varies strongly, or when objects are partially occluded. In this paper, we propose CoKe, a framework for the supervised contrastive learning of distinct local feature representations for robust keypoint detection. In particular, we introduce a feature bank mechanism and update rules for keypoint and non-keypoint features which make possible to learn local keypoint detectors that are accurate and robust to local ambiguities. Our experiments show that CoKe achieves state-of-the-art results compared to approaches that jointly represent all keypoints holistically (Stacked Hourglass Networks, MSS-Net) as well as to approaches that are supervised with the detailed 3D object geometry (StarMap). Notably, CoKe performs exceptionally well when objects are partially occluded and outperforms related work on a range of diverse datasets (PASCAL3D+,ObjectNet, MPII).
Distributed Mirror Descent With Integral Feedback: Asymptotic Convergence Analysis of Continuous-Time Dynamics

This letter addresses distributed optimization, where a network of agents wants to minimize a global strongly convex objective function. The global function can be written as a sum of local convex functions, each of which is associated with an agent. We propose a continuous-time distributed mirror descent algorithm that uses purely local information to converge to the global optimum. Unlike previous work on distributed mirror descent, we incorporate an integral feedback in the update, allowing the algorithm to converge with a constant step-size when discretized. We establish the asymptotic convergence of the algorithm using Lyapunov stability analysis. We further illustrate numerical experiments that verify the advantage of adopting integral feedback for improving the convergence rate of distributed mirror descent.
Spectral unmixing

Satellite imagery is formed by finite digital numbers representing a specific location of ground surface in which each matrix element is denominated as a picture element or pixel. The pixels represent the sensor measurements of spectral radiance. The radiance recorded in the satellite images is then an integrated sum of the radiances of all targets within the instantaneous field of view (IFOV) of the sensors. Therefore, the radiation detected is caused by a mixture of several different materials within the image pixels. For this reason, spectral unmixing has been used as a technique for analysing the mixture of components in remotely sensed images for almost 30 years. Different spectral unmixing approaches have been described in the literature. In recent years, many authors have proposed more complex models that permit obtaining a higher accuracy and use less computing time. Although the most widely used method consists of employing a single set of endmembers (typically three or four) on the whole image and using a constrained least squares method to perform the unmixing linearly, every different algorithm has its own merits and no single approach is optimal and applicable to all cases. Additionally, the number of applications using unmixing techniques is increasing. Spectral unmixing techniques are used mainly for providing information to monitor different natural resources (agricultural, forest, geological, etc.) and environmental problems (erosion, deforestation, plagues and disease, forest fires, etc.). This article is a comprehensive exploration of all of the major unmixing approaches and their applications.
SUPERCONVERGENCE OF C 0 − Q K FINITE ELEMENT METHOD 1 FOR ELLIPTIC EQUATIONS WITH APPROXIMATED 2 COEFFICIENTS 3

Abstract. We prove that the superconvergence of C0-Qk finite element method at the Gauss 5 Lobatto quadrature points still holds if variable coefficients in an elliptic problem are replaced by 6 their piecewise Qk Lagrange interpolants at the Gauss Lobatto points in each rectangular cell. In 7 particular, a fourth order finite difference type scheme can be constructed using C0-Q2 finite element 8 method with Q2 approximated coefficients. 9
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities

—Quantiﬁcation of cerebral white matter hyperinten-sities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientiﬁc challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). SixtyT1+FLAIRimages from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from ﬁve MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefﬁcient, (2) modiﬁed Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank signiﬁcantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.
Tale of two studies: challenges in field research with low-literacy adult learners in a developed country

Efforts to address the problems of literacy are often focused on developing countries. However, functional illiteracy is a challenge encountered by up to 50% of adults in developed countries. In this paper we reflect on the challenges we faced in trying to design and study the use of a mobile application to support adult literacy with two user groups: adults enrolled in literacy classes and carpenters without a high school education enrolled in an essential skills program. We also elaborate on aspects of the evaluations that are specific to a marginalized, functionally illiterate, group in a developed country - aspects that are less frequently present in similar studies of mobile literacy support technologies in developing countries. We conclude with presenting the lessons learnt from our evaluations and the impact of the studies' specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training.
Numerical assessment of the mixed convection and volumetric radiation in a vertical channel with MRT-LBM

Purpose The paper aims to focus on modeling of combined mixed convection and volumetric radiation within a vertical channel using a hybrid thermal lattice Boltzmann method (LBM). The multiple relaxation time LBM (MRT-LBM) is used to compute the dynamical field. The thermal field is determined by a finite difference method (FDM), and the simple relaxation time-LBM (SRT-LBM) serves to calculate the radiative part. The geometry considered concerns a vertical channel defined by two diffuse and isothermal walls. The active fluid represents a gray gas participating in absorption, emission and isotropically scattering. The parametrical study conducted aims to highlight the effect of Richardson number (Ri), Planck number (Pl) and the optical thickness (τ) on dynamical and thermal fields. It is found that radiation affects greatly heat transfer. Design/methodology/approach MRT-LBM is used to compute the dynamical field. The thermal field is determined by FDM, and SRT-LBM serves to calculate the radiative part. Findings This study has shown the strong capability of this approach to simulate similar problems. The Planck number largely affects the streamlines and isotherms distribution. Also, it causes disappearance of reversal flow, undesirable in most industrial applications, for low Planck numbers. The optical thickness causes the disappearance of reversal flow, in the case in which it appears, for lower opacity. However, for higher opacity it leads to a recurrence of reversed flow. Originality/value The use of a new original method composed of MRT-LBM to solve the fluid velocity, FDM to handle the temperature equation and extended SRT-LBM to compute the radiative part of the energy equation.
INTERNAÇÕES E COMPLICAÇÕES APRESENTADAS POR IDOSOS EM HOSPITAL DE REFERÊNCIA EM DOENÇAS INFECCIOSAS

Justificativa e Objetivos: A mortalidade por doencas infecciosas vem declinando, entretanto, permanece como um desafio para a Saude Publica Nacional, devido a persistencia de niveis elevados de doencas associadas a pobreza e exclusao social. Deste modo, o objetivo do estudo e identificar as causas de internacao dos idosos e as complicacoes apresentadas em hospital de infectologia do Rio Grande do Norte, Brasil e avaliar o custo financeiro das internacoes. Metodos: Estudo seccional realizado com idosos internados no Hospital Giselda Trigueiro do ano 2005 a 2010, por meio da analise de prontuarios. As variaveis dependentes foram: “consequencia da internacao” e “custo financeiro”. Foram realizados teste qui-quadrado determinar a associacao entre as variaveis e a analise do efeito independente das variaveis intervenientes sobre o desfecho foi realizada atraves da regressao logistica multipla. Resultados: A idade media foi 72,4 anos e predominaram homens. Diagnosticos prevalentes: Infeccao pulmonar nao tuberculosa; Infeccoes de pele e partes moles e Tuberculose pulmonar e extrapulmonar. Na analise multipla com a variavel “consequencia da internacao”, foram significativas: ventilacao mecânica, sonda nasogastrica e enterica, hemorragia digestiva alta e insuficiencia renal aguda. Para “custo financeiro”, as variaveis infeccao pulmonar nao tuberculosa, tuberculose pulmonar e extrapulmonar, sonda vesical de demora e ventilacao mecânica permaneceram significativas. Conclusoes: O predominio de infeccoes respiratorias aponta para a necessidade de medidas preventivas que devem ocorrer no nivel da atencao primaria a saude e consequentemente reduzindo a demanda pela atencao especializada. DESCRITORES: Idoso; Hospitalizacao; Saude do Idoso; Custos hospitalares; Doencas transmissiveis.
Interpolation-Based Decoding of Interleaved Gabidulin Codes

Interleaved) Gabidulin codes are a special class of codes in rank metric and can be applied in random linear network coding and space-time coding. In this contribution, a new interpolation-based approach for decoding interleaved Gabidulin codes of interleaving order s and length n is considered. The decoding algorithm relies on constructing a multi-variate linearized polynomial, which interpolates the points {(αi, r (1) i , . , r (s) i )} n−1 i=0 , where r (1) i , . , r (s) i are the coefficients of the received words. We prove that the evaluation polynomials (of qdegree less than k) of any interleaved Gabidulin codeword in rank distance less than s/(s+1)(n − k + 1) are roots of the multi-variate polynomial. Moreover, we show how determining these roots can be done by simply solving a linear system of equations. Our decoder can be interpreted as either a list decoding algorithm or a unique decoding algorithm with a certain failure probability. To our knowledge, it is the first list decoding algorithm for interleaved Gabidulin codes. For the unique decoder, we show a connection to known unique decoding approaches, which provides an upper bound on the failure probability.