Miracle-dz/UniFAR-FTUs huggingface dataset:
S2045796020001122jra 1..10

Aims. Late-life depression has substantial impacts on individuals, families and society. Knowledge gaps remain in estimating the economic impacts associated with late-life depression by symptom severity, which has implications for resource prioritisation and research design (such as in modelling). This study examined the incremental health and social care expenditure of depressive symptoms by severity. Methods. We analysed data collected from 2707 older adults aged 60 years and over in Hong Kong. The Patient Health Questionnaire-9 (PHQ-9) and the Client Service Receipt Inventory were used, respectively, to measure depressive symptoms and service utilisation as a basis for calculating care expenditure. Two-part models were used to estimate the incremental expenditure associated with symptom severity over 1 year. Results. The average PHQ-9 score was 6.3 (standard deviation, S.D. = 4.0). The percentages of respondents with mild, moderate and moderately severe symptoms and non-depressed were 51.8%, 13.5%, 3.7% and 31.0%, respectively. Overall, the moderately severe group generated the largest average incremental expenditure (US$5886; 95% CI 1126–10 647 or a 272% increase), followed by the mild group (US$3849; 95% CI 2520–5177 or a 176% increase) and the moderate group (US$1843; 95% CI 854–2831, or 85% increase). Non-psychiatric healthcare was the main cost component in a mild symptom group, after controlling for other chronic conditions and covariates. The average incremental association between PHQ-9 score and overall care expenditure peaked at PHQ-9 score of 4 (US$691; 95% CI 444–939), then gradually fell to negative between scores of 12 (US$ 35; 95% CI 530 to 460) and 19 (US$ -171; 95% CI 417 to 76) and soared to positive and rebounded at the score of 23 (US$601; 95% CI -1652 to 2854). Conclusions. The association between depressive symptoms and care expenditure is stronger among older adults with mild and moderately severe symptoms. Older adults with the same symptom severity have different care utilisation and expenditure patterns. Non-psychiatric healthcare is the major cost element. These findings inform ways to optimise policy efforts to improve the financial sustainability of health and long-term care systems, including the involvement of primary care physicians and other geriatric healthcare providers in preventing and treating depression among older adults and related budgeting and accounting issues across services.
Spectral Gamut Mapping using LabPQR

Spectral color management requires inversion of printer spectral characterizations and necessarily involves the concept of spectral gamut mapping. A printer was spectrally characterized and the spectra were transformed to an interim connection space (ICS), a spectral description space with low dimensionality useful for building lookup tables (LUTs) of feasible sizes. LabPQR is the ICS used. It has separate dimensions describing colorimetry (CIELAB) and a spectrum's metameric black difference from a standard metamer (PQR). The relationship between digital value and LabPQR was inverted using a single stage objective function combining colorimetric and spectral criteria. The objective function's colorimetric criterion minimized CIEDE2000 under chosen conditions and its spectral criterion minimized Euclidian distance in PQR coordinates. A weight series was performed to find the optimal trade-off between colorimetric and spectral error. A 1:50 weighting ratio, CIEDE2000 to PQR difference, was deemed best. For the GretagMacbeth ColorChecker, the proposed single stage objective function showed equivalent levels of the performance to a full 31-dimensional unmodified spectra approach, resulting in an average RMS error of 4.18% and an average CIEDE2000 of 0.03. The single stage objective function for spectral gamut mapping using LabPQR proved to be promising for spectral reproduction.
XAFS Study on the Specific Deoxidation Behavior of Iron Titanate Catalyst for the Selective Catalytic Reduction of NOx with NH3

The environmentally friendly catalyst iron titanate (FeTiOx) was reported to be very active for the selective catalytic reduction of NOx with NH3 (NH3‐SCR), with high N2 selectivity and H2O/SO2 durability in the medium temperature range, and the specific microstructure of iron titanate crystallites as the active phase was determined. In consideration of the probable existence of a redox cycle between Fe3+ and Fe2+ species in the NH3‐SCR reaction, the deoxidation behavior of the FeTiOx catalyst in an H2 temperature‐programmed reduction process was studied extensively by X‐ray absorption near‐edge structure (XANES) and extended X‐ray absorption fine‐structure (EXAFS) methods. Owing to the presence of an electronic inductive effect between Fe and Ti species in the unique edge‐shared Fe3+(O)2Ti4+ structure, the reducibility of Fe3+ species in the FeTiOx catalyst was greatly enhanced compared with that in pristine Fe2O3, leading to the higher oxidation ability of Fe species in FeTiOx. In the H2 temperature‐programmed reduction process, the well‐dispersed Fe3+ species in iron titanate crystallites could be totally converted into Fe2+ in the form of ilmenite FeTiO3 below 500 °C, whereas pristine Fe2O3 could only be reduced to Fe3O4 up to this temperature point. The typical NH3‐SCR reaction is usually conducted below 500 °C, and the enhanced oxidation ability of Fe3+ species in FeTiOx catalyst is responsible for its excellent catalytic NOx reduction performance at low temperatures. Based on XANES linear fitting and EXAFS curve‐fitting results, the specific deoxidation process of the FeTiOx catalyst was proposed, which can provide useful information for the characterization of the microstructure and redox ability of active sites simultaneously in mixed oxide catalysts for certain catalytic reactions.
How the bar properties affect the induced spiral structure

Stellar bars and spiral arms co-exist and co-evolve in most disc galaxies in the local Universe. However, the physical nature of this interaction remains a matter of debate. In this work, we present a set of numerical simulations based on isolated galactic models aimed to explore how the bar properties affect the induced spiral structure. We cover a large combination of bar properties, including the bar length, axial ratio, mass and rotation rate. We use three galactic models describing galaxies with rising, flat and declining rotation curves. We found that the pitch angle best correlates with the bar pattern speed and the spiral amplitude with the bar quadrupole moment. Our results suggest that galaxies with declining rotation curves are the most efficient forming grand design spiral structure, evidenced by spirals with larger amplitude and pitch angle. We also test the effects of the velocity ellipsoid in a subset of simulations. We found that as we increase the radial anisotropy, spirals increase their pitch angle but become less coherent with smaller amplitude.
On Robustness of Neural Semantic Parsers

Semantic parsing maps natural language (NL) utterances into logical forms (LFs), which underpins many advanced NLP problems. Semantic parsers gain performance boosts with deep neural networks, but inherit vulnerabilities against adversarial examples. In this paper, we provide the first empirical study on the robustness of semantic parsers in the presence of adversarial attacks. Formally, adversaries of semantic parsing are considered to be the perturbed utterance-LF pairs, whose utterances have exactly the same meanings as the original ones. A scalable methodology is proposed to construct robustness test sets based on existing benchmark corpora. Our results answered five research questions in measuring the sate-of-the-art parsers’ performance on robustness test sets, and evaluating the effect of data augmentation.
Technology Developments in Touch-Based Accessible Graphics: A Systematic Review of Research 2010-2020

This paper presents a systematic literature review of 292 publications from 97 unique venues on touch-based graphics for people who are blind or have low vision, from 2010 to mid-2020. It is the first review of its kind on touch-based accessible graphics. It is timely because it allows us to assess the impact of new technologies such as commodity 3D printing and low-cost electronics on the production and presentation of accessible graphics. As expected our review shows an increase in publications from 2014 that we can attribute to these developments. It also reveals the need to: broaden application areas, especially to the workplace; broaden end-user participation throughout the full design process; and conduct more in situ evaluation. This work is linked to an online living resource to be shared with the wider community.
Unscented Kalman Inversion

A useful approach to solve inverse problems is to pair the parameter-to-data map with a stochastic dynamical system for the parameter, and then employ techniques from ﬁltering to estimate the parameter given the data. Three classical approaches to ﬁltering of nonlinear systems are the extended, ensemble and unscented Kalman ﬁlters. The extended Kalman ﬁlter (which we refer to as ExKI in the context of inverse problems) is impractical when the forward map is not readily diﬀerentiable and given as a black box, and also for high dimensional parameter spaces because of the need to propagate large covariance matrices. Ensemble Kalman inversion (EKI) has emerged as a useful tool which overcomes both of these issues: it is derivative free and works with a low-rank covariance approximation formed from the ensemble. In this paper, we demonstrate that unscented Kalman methods also provide an eﬀective tool for derivative-free inversion in the setting of black-box forward models, introducing unscented Kalman inversion – UKI. Theoretical analysis is provided for linear inverse problems, and a smoothing property of the data mis-ﬁt, under the unscented transform used to deﬁne the UKI as a modiﬁcation of the ExKI, is explained. We provide numerical experiments, including proof-of-concept linear examples and various applications: learning of permeability parameters in subsurface ﬂow; learning the damage ﬁeld from structure deformation; learning the Navier-Stokes initial condition from solution data at positive times; and learning subgrid-scale parameters in a general circulation model (GCM) from time-averaged statistics. The theory and experiments show that the UKI outperforms the EKI on parameter learning problems with moderate numbers of parameters and outperforms the ExKI on problems where the forward model is not readily diﬀerentiable, or where the derivative is very sensitive. In particular, UKI based methods are of particular value for parameter estimation problems in which the number of parameters is moderate but the forward model is expensive and provided as a black box which is impractical to diﬀerentiate.
Mobile-end Tone Mapping based on Integral Image and Integral Histogram

Wide dynamic range (WDR) image tone mapping is in high demand in many applications like film production, security monitoring, and photography. It is especially crucial for mobile devices because most of the images taken today are from mobile phones, hence such technology is highly demanded in the consumer market of mobile devices and is essential for a good customer experience. However, high-quality and high-performance WDR image tone mapping implementations are rarely found in the mobile-end. In this paper, we introduce a high performance, mobile-end WDR image tone mapping implementation. It leverages the tone mapping results of multiple receptive fields and calculates a suitable value for each pixel. The utilization of integral image and integral histogram significantly reduce the required computation. Moreover, GPU parallel computation is used to increase the processing speed. The experimental results indicate that our implementation can process a high-resolution WDR image within a second on mobile devices and produce appealing image quality.
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication

We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where M machines work in parallel over the course of R rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute K stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.
FEDZIP: A Compression Framework for Communication-Efficient Federated Learning

Federated Learning marks a turning point in the implementation of decentralized machine learning (especially deep learning) for wireless devices by protecting users' privacy and safeguarding raw data from third-party access. It assigns the learning process independently to each client. First, clients locally train a machine learning model based on local data. Next, clients transfer local updates of model weights and biases (training data) to a server. Then, the server aggregates updates (received from clients) to create a global learning model. However, the continuous transfer between clients and the server increases communication costs and is inefficient from a resource utilization perspective due to the large number of parameters (weights and biases) used by deep learning models. The cost of communication becomes a greater concern when the number of contributing clients and communication rounds increases. In this work, we propose a novel framework, FedZip, that significantly decreases the size of updates while transferring weights from the deep learning model between clients and their servers. FedZip implements Top-z sparsification, uses quantization with clustering, and implements compression with three different encoding methods. FedZip outperforms state-of-the-art compression frameworks and reaches compression rates up to 1085x, and preserves up to 99% of bandwidth and 99% of energy for clients during communication.
The Macroeconomic Impacts of Entitlements

The worries expressed by Alan Greenspan that the long run economic growth of the United States will fade away due to increasing burden of entitlements motivated us to empirically investigate the impact of entitlements of key macroeconomic variables. To examine this contemporary issue, we estimate a vector error-correction model is used to analyze the impact of entitlements on the price level, real output, and the long-term interest rate. The results show that a shock to entitlements leads to decrease in output and lends support to the assertion made by Alan Greenspan. Several robustness checks are conducted and the results of the model qualitatively remains unchanged.
Hardware-efficient Residual Networks for FPGAs

Residual networks (ResNets) employ skip connections in their networks -- reusing activations from previous layers -- to improve training convergence, but these skip connections create challenges for hardware implementations of ResNets. The hardware must either wait for skip connections to be processed before processing more incoming data or buffer them elsewhere. Without skip connections, ResNets would be more hardware-efficient. Thus, we present the teacher-student learning method to gradually prune away all of a ResNet's skip connections, constructing a network we call NonResNet. We show that when implemented for FPGAs, NonResNet decreases ResNet's BRAM utilization by 9% and LUT utilization by 3% and increases throughput by 5%.
Fast Parametric Model Checking through Model Fragmentation

Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into fragments whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle.
Chandra grating spectroscopy of embedded wind shock X-ray emission from O stars shows low plasma temperatures and significant wind absorption

We present a uniform analysis of six examples of embedded wind shock (EWS) O star X-ray sources observed at high resolution with the Chandra grating spectrometers. By modelling both the hot plasma emission and the continuum absorption of the soft X-rays by the cool, partially ionized bulk of the wind we derive the temperature distribution of the shock-heated plasma and the wind mass-loss rate of each star. We find a similar temperature distribution for each star’s hot wind plasma, consistent with a power-law differential emission measure, $\frac{{\rm d}\log EM}{{\rm d}\log T}$, with a slope a little steeper than −2, up to temperatures of only about 107 K. The wind mass-loss rates, which are derived from the broadband X-ray absorption signatures in the spectra, are consistent with those found from other diagnostics. The most notable conclusion of this study is that wind absorption is a very important effect, especially at longer wavelengths. More than 90 per cent of the X-rays between 18 and 25 Å produced by shocks in the wind of ζ Pup are absorbed, for example. It appears that the empirical trend of X-ray hardness with spectral subtype among O stars is primarily an absorption effect.
Survey on Artificial Intelligence Approaches for Visualization Data

—Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artiﬁcial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We deﬁne visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different ﬁelds in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github. io.
Policy Analysis using Synthetic Controls in Continuous-Time

Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference. Despite its popularity, the current description only considers time series aligned across units and synthetic controls expressed as linear combinations of observed control units. We propose a continuous-time alternative that models the latent counterfactual path explicitly using the formalism of controlled differential equations. This model is directly applicable to the general setting of irregularly-aligned multivariate time series and may be optimized in rich function spaces -- thereby improving on some limitations of existing approaches.
Polyimide-Based Flexible Coupled-Coils Design and Load-Shift Keying Analysis

Wireless power transfer using inductive coupling is commonly used for medical implantable device. The design of the secondary coil on the implantable device is important as it will affect the power transfer efficiency, the size of the implant and also the data transmission between the implant and the in-vitro controller. In this paper, we present a design of the secondary coil on polyimide-based flexible substrate to achieve high power transfer efficiency. Load shift keying modulation is used for the data communication between the primary and secondary coils. Thorough analysis is done for the ideal and practical scenario and it shows that a mismatched secondary LC tank will affect the communication range and communication correctnes. A solution to achieve robust data transmission is proposed and then verified by SPICE simulations. Keywords—Implantable Medical Devices; Flexible inductive coil; Load-Shift Keying.
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question Answering Data

In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks. In this paper, we investigate this question. We collect a large-scale multi-subject multiple-choice question-answering dataset, ExamQA, and use incomplete and noisy snippets returned by a web search engine as the relevant context for each question-answering instance to convert it into a weakly-labeled MRC instance. We then propose a self-teaching paradigm to better use the generated weakly-labeled MRC instances to improve a target MRC task. Experimental results show that we can obtain an improvement of 5.1% in accuracy on a multiple-choice MRC dataset, C^3, demonstrating the effectiveness of our framework and the usefulness of large-scale subject-area question-answering data for machine reading comprehension.
Disaggregation for Energy Efficient Fog in Future 6G Networks

We study the benefits of adopting server disaggregation in the fog computing tier by evaluating energy efficient placement of interactive apps in a future (6G) fog network. Using a mixed integer linear programming (MILP) model, we compare the adoption of traditional server (TS) and disaggregated server (DS) architectures in a fog network that comprises selected fog sites. We also propose a heuristic for energy efficient and delay aware placement of interactive fog apps in a fog network which effectively mimics the MILP model formulated in this paper. Compared to a non-federated fog computing layer, federation of selected fog computing sites over the metro-access network enables significant reductions of the total fog computing power consumption (TFPC). Relative to the use of TSs in the fog network, the adoption of DSs improves the energy efficiency of the fog network and enables up to 18% reduction in TFPC. To minimize response time, more instances of interactive fog apps are provisioned in a fog network that is implemented over a network topology with high delay penalty. Our result also shows that the proximity of metro-central offices and radio cell sites to geo-distributed users makes them important fog sites for provisioning delay-sensitive fog applications.