Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived burden and need for support among caregivers of cancer patients

Abstract Background The aims of this study were to examine (1) the perceived burden among caregivers and identify those in risk of high burden and (2) the need for support among caregivers and identify associated factors. Materials and methods Cancer patients who participated in an educational program at the Montebello Center (MBC) in Norway between May 2021 and February 2022 were asked to invite a caregiver to answer a questionnaire. The caregiving burden was assessed with the Caregivers Reaction Assessment (CRA) that consists of 24 questions scored from 1 (strongly disagree) to 5 (strongly agree), covering five domains. A mean sum score was calculated for each domain. Higher subscale scores indicate higher levels of burden, except for caregiver esteem where a high score indicates a low burden. Need for support was assessed with 13 questions. Results Of 464 invitations, 185 caregivers responded (response 40%), median age was 58.0 years and 58% were male. Caregiver burden mean scores were: 2.6 (SD 1.03) for Impact on schedule, 2.1 (SD 0.79) for Lack of family support, 2.1 (SD 0.76) for Impact on health, 2.0 (SD 0.86) for Impact on finances, and 4.2 (SD 0.47) for Caregiver esteem. Female caregivers, younger, higher education, having comorbidities, caring for patients having recurrence of cancer, and shorter time since diagnosis were associated with higher burden measured on individual subscales of the CRA. Most reported needs were information about: cancer, late effects and rehabilitation services and support from peers and professionals to cope with the new situation. Younger, caregiver comorbidity and recurrence of cancer of the patient were associated with more needs. Conclusions The results indicate that caregivers of cancer patients participating at the MBC report moderate caregiver burden, however, numerous caregivers reported need for support within several areas. Our findings need to be confirmed in a larger unselected group.
Cumulative exposure to extreme heat and trajectories of cognitive decline among older adults in the USA

Background The projected increase in extreme heat days is a growing public health concern. While exposure to extreme heat has been shown to negatively affect mortality and physical health, very little is known about its long-term consequences for late-life cognitive function. We examined whether extreme heat exposure is associated with cognitive decline among older adults and whether this association differs by race/ethnicity and neighbourhood socioeconomic status. Methods Data were drawn from seven waves of the Health and Retirement Study (2006–2018) merged with historical temperature data. We used growth curve models to assess the role of extreme heat exposure on trajectories of cognitive function among US adults aged 52 years and older. Results We found that high exposure to extreme heat was associated with faster cognitive decline for blacks and residents of poor neighbourhoods, but not for whites, Hispanics or residents of wealthier neighbourhoods. Conclusion Extreme heat exposure can disproportionately undermine cognitive health in later life for socially vulnerable populations. Our findings underscore the need for policy actions to identify and support high-risk communities for increasingly warming temperatures.
Bayesian Feature Learning for Seismic Compressive Sensing and Denoising

ABSTRACTExtracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations. Compressive sensing (CS) is a framework that allows reconstruction of sparse signals from fewer measurements than conventional sampling rates. In seismic CS, the use of sparse transforms has some success; however, defining fixed basis functions is not trivial given the plethora of possibilities. Furthermore, the assumption that every instance of a seismic signal is sparse in any acquisition domain under the same transformation is limiting. We use beta process factor analysis (BPFA) to learn sparse transforms for seismic signals in the time slice and shot record domains from available data, and we use them as dictionaries for CS and denoising. Algorithms that use predefined basis functions are compared against BPFA, with BPFA obtaining state-of-the-art reconstructions, illustrating the importance of decomposing seismic signals into.
Efficient Manifold and Subspace Approximations with Spherelets

Data lying in a high-dimensional ambient space are commonly thought to have a much lower intrinsic dimension. In particular, the data may be concentrated near a lower-dimensional subspace or manifold. There is an immense literature focused on approximating the unknown subspace, and in exploiting such approximations in clustering, data compression, and building of predictive models. Most of the literature relies on approximating subspaces using a locally linear, and potentially multiscale, dictionary. In this article, we propose a simple and general alternative, which instead uses pieces of spheres, or spherelets, to locally approximate the unknown subspace. Building on this idea, we develop a simple and computationally efficient algorithm for subspace learning and clustering. Results relative to state-of-the-art competitors show dramatic gains in ability to accurately approximate the subspace with orders of magnitude fewer components. This leads to substantial gains in data compressibility, few clusters and hence better interpretability, and much lower MSE based on small to moderate sample sizes. Basic theory on approximation accuracy is presented, and the methods are applied to multiple examples.
TightCap: 3D Human Shape Capture with Clothing Tightness

In this paper, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single 3D human scan, which enables numerous applications such as virtual try-on, biometrics and body evaluation. To break the severe variations of the human poses and garments, we propose to model the clothing tightness - the displacements from the garments to the human shape implicitly in the global UV texturing domain. To this end, we utilize an enhanced statistical human template and an effective multi-stage alignment scheme to map the 3D scan into a hybrid 2D geometry image. Based on this 2D representation, we propose a novel framework to predicted clothing tightness via a novel tightness formulation, as well as an effective optimization scheme to further reconstruct multi-layer human shape and garments under various clothing categories and human postures. We further propose a new clothing tightness dataset (CTD) of human scans with a large variety of clothing styles, poses and corresponding ground-truth human shapes to stimulate further research. Extensive experiments demonstrate the effectiveness of our TightCap to achieve high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting and animation.
Hybrid method for solving nonlinear Volterra-Fredholm integro differential equations

The modified Laplace Adomian decomposition method (LADM) has been developed to find the analytic approximation solution of the nonlinear Volterra-Fredholm integro differential equations under the initial or boundary conditions. We prove the convergence of LADM applied to the Volterra-Fredholm integro differential equations. In this paper, some examples will be examined to support the proposed analysis.
Yang-Mills Families

The Yang-Mills theory structure is based on group theory. It rules the symmetry relationship where the number of potential fields transforming under a same group must be equal to the number of group generators. It defines the group valued expression from where the corresponding non-abelian symmetry properties are derived. Nevertheless based on different origins as Kaluza-Klein, fibre bundles, supersymmetry, s-model , BRST and anti-BRST algorithm, counting degrees of freedom leads to a Yang-Mills extension under the existence of different potential fields rotating under a same single group. They establish for SU(N) the relationship where and is a flavor index, . Physically, it says that different Yang-Mills families can share a common symmetry group. They develop a whole non-abelian gauge theory. The effort in this work is to explore such non-abelian extension. First, to build up it on the so-called constructor basis where gauge symmetry is more available for expressing the corresponding fields strengths, Lagrangian and classical equations. After that, given that the physical fields are those associated to the poles of two-point Green functions, one derives the physical Lagrangian L written in the physical basis . A new physical Lagrangian associated to symmetry is generated. The meaning of Yang-Mills families appears. A symmetry of difference is realized. Where every quanta is distinguished from each other. It yields a quanta diversity associated to corresponding Yang-Mills families. There are N-spin 1 and N-spin 0 quanta separated by different quantum numbers through a whole N-dynamics. An extension to QCD becomes possible.
On the robustness of the toroidal velocity theorem

Abstract In dynamo theory the toroidal velocity theorem in its classical version (Elsasser, Phys. Rev. 1946, vol. 69, pp. 106–116, Bullard and Gellman, Phil. Trans. R. Soc. Lond. 1954, vol. A247, pp. 213–278) rules out dynamo action in a spherical conducting volume provided that the fluid is incompressible, the conductivity is uniform, and the velocity field is purely toroidal. We prove in this note that this result is robust in the sense that slight compressibility of the fluid, small non-radial variations and even large radial variations in conductivity, and the presence of a small non-toroidal velocity component do not invalidate the theorem. Moreover, by proper choice of the conductivity distribution modelling the conducting volume, small deviations from spherical symmetry of the conductor can also be taken into account.
Multi-layer Random Walks Synchronization for Multi-attributed Multiple Graph Matching

Many applications in computer vision can be formulated as a multiple graph matching problem that finds global correspondences across a bunch of data. To solve this problem, matching consistency should be carefully considered with matching accuracy to prevent conflicts between graph pairs. In this paper, we aim to solve a multiple graph matching problem in complicated environments by using multiple attributes that are represented in a set of multi-layer structures. The main contribution of this paper is twofold. First, we formulate the global correspondence problem of multi-attributed graphs using multiple layered structures. The formulation is derived by aggregating the multi-layer structures that describe individual pairwise matching problems respectively. Second, we solve the global correspondence problem by using a novel multi-attributed multiple graph matching method that is based on the multi-layer random walks framework. The proposed framework contains additional synchronization steps to lead random walkers to consistent matching candidates. In our extensive experiments, the proposed method exhibited robust and accurate performance over the state-of-the-art algorithms.
Predicting passenger loading level on a train car: A Bayesian approach

Crowding in train cars is increasingly a major concern for transit agencies. From the perspective of the passengers and the transit agencies, overcrowding of the train cars has several negative consequences such as: (i) extended duration of passengers boarding and alighting which leads to longer dwell times, (ii) subsequent disruption of the headway and the schedule, and (iii) passenger dissatisfaction (e.g. increased stress and lack of privacy). Moreover, overcrowding during peak service hours also indicates inadequate infrastructure to meet the passenger demands. Realizing the importance of the crowding issue, transit agencies have developed measures to assess the crowding levels. The Transit Capacity and Quality of Service Manual provides guidelines on thresholds for crowding in transit systems in the United States.
Geometrical effect characterization of femtosecond-laser manufactured glass microfluidic chips based on optical manipulation of submicroparticles

Abstract. Microfluidic devices provide a platform with wide ranging applications from environmental monitoring to disease diagnosis. They offer substantive advantages but are often not optimized or designed to be used by nonexpert researchers. Microchannels of a microanalysis platform and their geometrical characterization are of eminent importance when designing such devices. We present a method that is used to optimize each microchannel within a device using high-throughput particle manipulation. For this purpose, glass-based microfluidic devices, with three-dimensional channel networks of several geometrical sizes, were fabricated by employing laser fabrication techniques. The effect of channel geometry was investigated by employing an optical tweezer. The optical trapping force depends on the flow velocity that is associated with the dimensions of the microchannel. We observe a linear dependence of the trapping efficiency and of the fluid flow velocity, with the channel dimensions. We determined that the highest trapping efficiency was achieved for microchannels with aspect ratio equal to one. Numerical simulation validated the impact of the device design dimensions on the trapping efficiency. This investigation indicates that the geometrical characteristics, the flow velocity, and trapping efficiency are crucial and should be considered when fabricating microfluidic devices for cell studies.
Views about perceived training needs of health care professionals in relation to socially assistive robots: an international online survey

Background As Artificial Intelligence and social robots are increasingly used in health and social care, it is imperative to explore the training needs of the workforce, factoring in their cultural background. Objectives Explore views on perceived training needs among professionals around the world and how these related to country cultures. Design Cross-sectional, descriptive, mixed-methods international online survey. Methods Descriptive statistical analysis explored the ranking across countries and relationships with three Hofstede cultural dimensions. Thematic analysis was conducted on the open-ended text responses. Results A sample of N = 1284 participants from eighteen countries. Knowing the capabilities of the robots was ranked as the top training need across all participating countries and this was also reflected in the thematic analysis. Participants' culture, expressed through three Hofstede's dimensions, revealed statistically significant ranking differences. Conclusions Future research should further explore other factors such as the level of digital maturity of the workplace. Impact Statement Training needs of health and social care staff to use robotics are fast growing and preparation should factor in patient safety and be based on the principles of person- and culture-centred care.
HyperGCN : Hypergraph Convolutional Networks for Semi-Supervised Learning and Combinatorial Optimisation

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obvious existence of such complex relationships in many real-world networks naturally motivates the problem of learning with hypergraphs. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabelled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, we propose HyperGCN, a novel GCN for SSL on attributed hypergraphs. Additionally, we show how HyperGCN can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems. We demonstrate HyperGCN’s effectiveness through detailed experimentation on real-world hypergraphs. We have made HyperGCN’s source code available to foster reproducible research.
Machine Learning Based Prediction and Classification for Uplift Modeling

The desire tomodel the true gain from targeting an individual inmarketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investigated. The statistical machine learning methods applied are Random Forests and Neural Networks along with the standard method Logistic Regression. The data is collected from a well established retail company and the purpose of the project is thus to investigate which uplift modeling approach and statistical machine learning method that yields in the best performance given the data used in this project. The variable selection step was shown to be a crucial component in the modeling processes as sowas the amount of control data in each data set. For the uplift to be successful, the method of choice should be either the Modeling Uplift Directly using Random Forests, or the Class Variable Transformation using Logistic Regression. Neural network based approaches are sensitive to uneven class distributions and is hence not able to obtain stable models given the data used in this project. Furthermore, the Subtraction of Two Models did not perform well due to the fact that each model tended to focus too much on modeling the class in both data sets separately instead of modeling the difference between the class probabilities. The conclusion is hence to use an approach that models the uplift directly, and also to use a great amount of control data in the data sets.
Robust filtering for Markovian jump neutral systems with distributed delays

ABSTRACT Over the past decades, a great deal of attention has been devoted to parameter-invariant neutral systems. However, in practical applications, the jump of parameters frequently occurs to neutral systems on account of sudden environmental changes, which can be described by Markovian jump systems. This paper mainly deals with the problem of robust filtering for uncertain Markovian jump neutral systems with distributed delays. The uncertain parameters are assumed to be time varying and norm bounded. By utilizing the Newton–Leibniz formula and integral inequality technique, the delay-dependent sufficient conditions for the existence of the filter are derived such that the filtering error system is stochastically stable with an performance constraint. Based on the obtained criteria and by employing congruent transformation, the robust filter is designed in terms of linear matrix inequalities. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.
Revisiting Gray Pixel for Statistical Illumination Estimation

We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.
Analysis of Rain Types and Their Z–R Relationships at Different Locations in the High Andes of Southern Ecuador

AbstractInformation on the spatiotemporal rainfall occurrence, its microphysical characteristics, and its reflectivity–rainfall (Z–R) relations required to provide rainfall mapping based on rain radar data is limited for tropical high mountains. Therefore, this study aims to analyze rainfall types in the Andes cordillera to derive different rain-type Z–R relations using disdrometer observations at three study sites representative for different geographic positions and elevations (2610, 3626, and 3773 m MSL). Rain categorization based on mean drop volume diameter (Dm) thresholds [0.1 < Dm (mm) ≤ 0.5; 0.5 < Dm (mm) ≤ 1.0; 1.0 < Dm (mm) ≤ 2.0] was performed using drop size distribution data at a 5-min time step over an approximate 2-yr period at each location. The findings are as follows: (i) Rain observations characterized by higher (lower) Dm and rain rates are more frequent at the lower (higher) site. (ii) Because of its geographic position, very light rain (drizzle) is more common at higher altitudes wit.
Pre-launch instrument characterization results and in-orbit verification plan of GCOM-C/SGLI

The Global Change Observation Mission (GCOM) aims to establish and demonstrate a global, long-term satelliteobserving system to measure essential geophysical parameters to facilitate understanding the global water circulation and climate change, and eventually contribute to improving future climate projection through a collaborative framework with climate model institutions. GCOM consists of two polar orbiting satellite observing systems, GCOM-W (Water) and GCOM-C (Climate). The first satellite, GCOM-W with Advance Microwave Radiometer -2 (AMSR-2), was already launched in 2012 and has been observing continuously. The follower satellite, GCOM-C with Second Generation Global Imager (SGLI), will be launched in Japanese fiscal year 2017. SGLI enables a new generation of operational moderate resolution-imaging capabilities following the legacy of the GLI on ADEOS-II (Advanced Earth Observing Satellite-II) satellite. The SGLI empowers surface and atmospheric measurements related to the carbon cycle and radiation budget, with two radiometers of Visible and Near Infrared Radiometer (VNR) and Infrared Scanning Radiometer (IRS) which perform a wide-band (380nm-12μm) optical observation not only with as wide as 1150-1400km FOV (field of view) but also with as high as 250-500m resolution. Also, polarization and along-track slant view observation are quite characteristic of SGLI, providing the sensor data records for more than 28 standard products and 23 research products including clouds, aerosols, ocean color, vegetation, snow and ice, and other applications. Sensor instrument proto-flight tests including optical characterization tests such as radiometric and geometric were completed, and satellite system proto-flight tests have finished including thermal vacuum, vibration and acoustic test. In this paper, the pre-launch phase instrument characterization of SGLI flight model and status of GCOM-C satellite system flight model along with the overview of them will be described. Especially we focus on the pre-launch geometric and radiometric performance test results, in-orbit calibration activities and methodologies: VNR's on-board calibrator, IRS's on-board calibrator and calibration maneuver, and in-orbit verification plan during a commissioning phase lasting approximately 3 months.