Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Moderate Sodium Restriction and Hydrochlorothiazide on Iodine Excretion in Diabetic Kidney Disease: Data from a Randomized Cross-Over Trial

Sodium restriction may potentially reduce iodine intake. This study aimed to determine the effect of sodium restriction (dietary counseling) on 24-h urinary iodine excretion. Diuretics provide an alternative to sodium restriction and are frequently added to sodium restriction, so the effects of hydrochlorothiazide (50 mg daily) and combined therapy were also studied. We performed a post-hoc analysis of a Dutch multi-center, randomized cross-over trial in 45 patients with diabetic kidney disease with a mean age of 65 ± 9 years, mean eGFR of 65 ± 27 mL/min/1.73 m2, median albuminuria of 648 [230–2008] mg/24 h and 84% were male. During regular sodium intake with placebo, mean 24 h urinary sodium and iodine excretion were 224 ± 76 mmol/24 h and 252 ± 94 ug/24 h, respectively (r = 0.52, p < 0.001). Mean iodine excretion did not change significantly if sodium restriction and hydrochlorothiazide were applied separately; mean difference −8 ug/day (95% CI −38, 22; p = 0.6) and 14 ug/day (95% CI −24, 52; p = 0.5), respectively. Combined therapy induced a significant decrease in mean iodine excretion (−37 ug/day; 95% CI −67, −7; p = 0.02), yet this was not seen to a clinically meaningful level. The number of patients with an estimated intake below recommended daily allowances did not differ significantly between the four treatment periods (p = 0.3). These findings show that sodium restriction is not a risk factor for iodine deficiency.
Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen’s kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., “active aging”, biofeedback-based rehabilitation for fall prevention, and the management of Parkinson’s disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.
The importance of job satisfaction in hospital quality processes

Purpose. The purpose of the study was to evaluate the level of job satisfaction among the staff of Parnu County Hospital and to describe variables related to their job satisfaction.Methods. The study was carried out in December 1998. The opinions of staff regarding satisfaction with their jobs were studied using an Estonian translation of the Norwegian Medical Association Job Satisfaction questionnaire, which was distributed to all 673 staff members of the hospital. Three levels of analyses were carried out. Stepwise regression analysis was used to determine which single item factors were most important in job satisfaction for the various groups of hospital staff. Further, global job satisfaction item and a four items construct called nature of job satisfaction (α=60) were selected as dependent variables. The independent variables consisted of constructs measuring: knowledge in planning (3 items α=70), relationship with superior (5 items α=89), strategic knowledge of workplace (5 items α=84), work stress (6 items α=77), unrealistic expectations (5 items α=72), collegial relationships (2 items α=75), discussions with colleagues (3 items α=80), recognition (3 items α=67) and sense of belonging (single item). Pearson correlations were used to determine whether the independent variables were significantly correlated with job satisfaction. Scheffe’s F-test was used to conduct post-hoc analyses of variance for each independent variable between categories of hospital personnel. Results. The response rate was 77%. The key results were that overall job satisfaction was moderately high (3.86 on a scale of 5); however there are differences in satisfaction models between categories of personnel. In addition, there are significant differences in the correlations between job satisfaction and the independent variables among the groups of personnel.Conclusions. The findings of this study suggest that executives of a health care institution that are informed about the expectations and daily work-related problems of their employees are better able to understand the needs of their employees. Executives should: 1) build up effective relationships between managers and staff; 2) identifynegative working conditionswhich affect staff; 3) appropriately delegate authority to personnel, and hold them accountable for the work done in their organizational units; 4) keep staff informed about changes that will affect them; and 5) express appreciation and recognition for the efforts of staff members in accomplishing the work of the health care organization. In doing this, executives will create a favorable working environment for the hospital staff. The value of the study is that the results can be used as a set of reference levels and indicators for the human resources development component of the quality management system of Parnu County Hospital
QCD corrections in Standard Model EFT fits to 
WZ
 and 
WW
 production

We study the impact of anomalous gauge boson and fermion couplings on the production of $WZ$ and $W^+W^-$ pairs at the LHC to NLO QCD in the Standard Model effective field theory, including dimension-6 operators. Our results are implemented in a publicly available version of the POWHEG-BOX. We combine our $WZ$ results in the leptonic final state $e\nu \mu^+\mu^-$ with previous $W^+W^-$ results to demonstrate the numerical effects of NLO QCD corrections on the limits on effective couplings derived from ATLAS and CMS $8$ and $13\,$TeV differential measurements. Our study demonstrates the importance of including NLO QCD SMEFT corrections in the $WZ$ analysis, while the effects on $WW$ production are smaller. Finally, we examine the extreme sensitivity of the analysis to the expansion in $1/\Lambda^2$ or $1/\Lambda^4$, where $\Lambda$ is the high energy scale associated with the SMEFT.
Local Hadamard well-posedness results for the Navier-Stokes equations.

In this paper we consider classes of initial data that ensure local-in-time Hadamard well-posedness of the associated weak Leray-Hopf solutions of the three-dimensional Navier-Stokes equations. In particular, for any solenodial $L_{2}$ initial data $u_{0}$ belonging to certain subsets of $VMO^{-1}(\mathbb{R}^3)$, we show that weak Leray-Hopf solutions depend continuously with respect to small divergence-free $L_{2}$ perturbations of the initial data $u_{0}$ (on some finite-time interval). Our main result is inspired and improves upon previous work of the author \cite{barker2018} and work of Jean-Yves Chemin \cite{chemin}. Our method builds upon \cite{barker2018} and \cite{chemin}. In particular our method hinges on decomposition results for the initial data inspired by Calder\'{o}n \cite{Calderon90} together with use of persistence of regularity results. The persistence of regularity statement presented may be of independent interest, since it does not rely upon the solution or the initial data being in the perturbative regime.
Kinetic Plasma Turbulence: Recent Insights and Open Questions From 3D3V Simulations

Turbulence and kinetic processes in magnetized space plasmas have been extensively investigated over the past decades via \emph{in-situ} spacecraft measurements, theoretical models and numerical simulations. In particular, multi-point high-resolution measurements from the \emph{Cluster} and \emph{MMS} space missions brought to light an entire new world of processes, taking place at the plasma kinetic scales, and exposed new challenges for their theoretical interpretation. A long-lasting debate concerns the nature of ion and electron scale fluctuations in solar-wind turbulence and their dissipation via collisionless plasma mechanisms. Alongside observations, numerical simulations have always played a central role in providing a test ground for existing theories and models. In this Perspective, we discuss the advances achieved with our 3D3V (reduced and fully) kinetic simulations, as well as the main questions left open (or raised) by these studies. To this end, we combine data from our recent kinetic simulations of both freely decaying and continuously driven fluctuations to assess the similarities and/or differences in the properties of plasma turbulence in the sub-ion range. Finally, we discuss possible future directions in the field and highlight the need to combine different types of numerical and observational approaches to improve the understanding of turbulent space plasmas.
Gated Channel Transformation for Visual Recognition

In this work, we propose a generally applicable transformation unit for visual recognition with deep convolutional neural networks. This transformation explicitly models channel relationships with explainable control variables. These variables determine the neuron behaviors of competition or cooperation, and they are jointly optimized with the convolutional weight towards more accurate recognition. In Squeeze-and-Excitation (SE) Networks, the channel relationships are implicitly learned by fully connected layers, and the SE block is integrated at the block-level. We instead introduce a channel normalization layer to reduce the number of parameters and computational complexity. This lightweight layer incorporates a simple l2 normalization, enabling our transformation unit applicable to operator-level without much increase of additional parameters. Extensive experiments demonstrate the effectiveness of our unit with clear margins on many vision tasks, i.e., image classification on ImageNet, object detection and instance segmentation on COCO, video classification on Kinetics.
Focus Your Attention: A Bidirectional Focal Attention Network for Image-Text Matching

Learning semantic correspondence between image and text is significant as it bridges the semantic gap between vision and language. The key challenge is to accurately find and correlate shared semantics in image and text. Most existing methods achieve this goal by representing the shared semantic as a weighted combination of all the fragments (image regions or text words), where fragments relevant to the shared semantic obtain more attention, otherwise less. However, despite relevant ones contribute more to the shared semantic, irrelevant ones will more or less disturb it, and thus will lead to semantic misalignment in the correlation phase. To address this issue, we present a novel Bidirectional Focal Attention Network (BFAN), which not only allows to attend to relevant fragments but also diverts all the attention into these relevant fragments to concentrate on them. The main difference with existing works is they mostly focus on learning attention weight while our BFAN focus on eliminating irrelevant fragments from the shared semantic. The focal attention is achieved by preassigning attention based on inter-modality relation, identifying relevant fragments based on intra-modality relation and reassigning attention. Furthermore, the focal attention is jointly applied in both image-to-text and text-to-image directions, which enables to avoid preference to long text or complex image. Experiments show our simple but effective framework significantly outperforms state-of-the-art, with relative Recall@1 gains of 2.2% on both Flicr30K and MSCOCO benchmarks.
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans

In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e.g., as part of regular daily use). We propose an algorithm that analyzes these ``rescans'' to infer a temporal model of a scene with semantic instance information. Our algorithm operates inductively by using the temporal model resulting from past observations to infer an instance segmentation of a new scan, which is then used to update the temporal model. The model contains object instance associations across time and thus can be used to track individual objects, even though there are only sparse observations. During experiments with a new benchmark for the new task, our algorithm outperforms alternate approaches based on state-of-the-art networks for semantic instance segmentation.
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training

In a pipeline speech translation system, automatic speech recognition (ASR) system will transmit errors in recognition to the downstream machine translation (MT) system. A standard machine translation system is usually trained on parallel corpus composed of clean text and will perform poorly on text with recognition noise, a gap well known in speech translation community. In this paper, we propose a training architecture which aims at making a neural machine translation model more robust against speech recognition errors. Our approach addresses the encoder and the decoder simultaneously using adversarial learning and data augmentation, respectively. Experimental results on IWSLT2018 speech translation task show that our approach can bridge the gap between the ASR output and the MT input, outperforms the baseline by up to 2.83 BLEU on noisy ASR output, while maintaining close performance on clean text.
Deep Learning for Deepfakes Creation and Detection

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
Bell’s Theorem Versus Local Realism in a Quaternionic Model of Physical Space

In the context of EPR-Bohm type experiments and spin detections confined to spacelike hypersurfaces, a local, deterministic and realistic model within a Friedmann-Robertson-Walker spacetime with a constant spatial curvature (<inline-formula> <tex-math notation="LaTeX">${S^{3}}$ </tex-math></inline-formula>) is presented that describes simultaneous measurements of the spins of two fermions emerging in a singlet state from the decay of a spinless boson. Exact agreement with the probabilistic predictions of quantum theory is achieved in the model without data rejection, remote contextuality, superdeterminism or backward causation. A singularity-free Clifford-algebraic representation of <inline-formula> <tex-math notation="LaTeX">${S^{3}}$ </tex-math></inline-formula> with vanishing spatial curvature and non-vanishing torsion is then employed to transform the model in a more elegant form. Several event-by-event numerical simulations of the model are presented, which confirm our analytical results with the accuracy of 4 parts in <inline-formula> <tex-math notation="LaTeX">${10^{4}}$ </tex-math></inline-formula>. Possible implications of our results for practical applications such as quantum security protocols and quantum computing are briefly discussed.
Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilities

Modern sequence to sequence neural TTS systems provide close to natural speech quality. Such systems usually comprise a network converting linguistic/phonetic features sequence to an acoustic features sequence, cascaded with a neural vocoder. The generated speech prosody (i.e. phoneme durations, pitch and loudness) is implicitly present in the acoustic features, being mixed with spectral information. Although the speech sounds natural, its prosody realization is randomly chosen and cannot be easily altered. The prosody control becomes an even more difficult task if no prosodic labeling is present in the training data. Recently, much progress has been achieved in unsupervised speaking style learning and generation, however human inspection is still required after the training for discovery and interpretation of the speaking styles learned by the system. In this work we introduce a fully automatic method that makes the system aware of the prosody and enables sentence-wise speaking pace and expressiveness control on a continuous scale. While being useful by itself in many applications, the proposed prosody control can also improve the overall quality and expressiveness of the synthesized speech, as demonstrated by subjective listening evaluations. We also propose a novel augmented attention mechanism, that facilitates better pace control sensitivity and faster attention convergence.
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models

In natural language processing, it has been observed recently that generalization could be greatly improved by finetuning a large-scale language model pretrained on a large unlabeled corpus. Despite its recent success and wide adoption, finetuning a large pretrained language model on a downstream task is prone to degenerate performance when there are only a small number of training instances available. In this paper, we introduce a new regularization technique, to which we refer as "mixout", motivated by dropout. Mixout stochastically mixes the parameters of two models. We show that our mixout technique regularizes learning to minimize the deviation from one of the two models and that the strength of regularization adapts along the optimization trajectory. We empirically evaluate the proposed mixout and its variants on finetuning a pretrained language model on downstream tasks. More specifically, we demonstrate that the stability of finetuning and the average accuracy greatly increase when we use the proposed approach to regularize finetuning of BERT on downstream tasks in GLUE.
Beam-induced atomic motion in alkali borate glasses

Applying coherent X-rays by the method of atomic-scale X-ray Photon Correlation Spectroscopy results in beam-induced dynamics in a number of oxide glasses. Here these studies are extended to rubidium and caesium borates with varying alkali contents. While no cumulative beam damage is observed, the observed rate of structural rearrangements shows a linear relation to the dose rate. In agreement with the increasing glass transition temperature, the rate of dynamics at given dose rate decreases with increasing alkali content, while the shape of the decay of correlations becomes progressively stretched. This behavior is explained in terms of faster dynamics of the alkali positions compared to the borate network. Finally, the q-dependent behavior of the correlation decay rate implies the observed dynamics to proceed via small-scale atomic displacements subject to de Gennes narrowing.
Prism: Scaling Bitcoin by 10,000 ×

Bitcoin is the first fully-decentralized permissionless blockchain protocol to achieve a high level of security: the ledger it maintains has guaranteed liveness and consistency properties as long as the adversary has less compute power than the honest nodes. However, its throughput is only 7 transactions per second and the confirmation latency can be upto hours. Prismis anewblockchainprotocolthat is designed to achieve a natural scaling of Bitcoin’s performance while maintaining its full security guarantees. We present an implementation of Prism that achieves a throughput of over 70 , 000 transactions per second and confirmation latency of tens of seconds on networks of up to 1000 EC2 Virtual Machines. The code can be found at [5].
Coherent terahertz radiation from a nonlinear oscillator of viscous electrons

Compressible electron flow through a narrow cavity is theoretically unstable, and the oscillations occurring during the instability have been proposed as a method of generating Terahertz radiation. We numerically demonstrate that the endpoint of this instability is a nonlinear hydrodynamic oscillator, consisting of an alternating shock wave and rarefaction-like relaxation flowing back and forth in the device. This qualitative physics is robust to cavity inhomogeneity and changes in the equation of state of the fluid. We discuss the frequency and amplitude dependence of the emitted radiation on physical parameters (viscosity, momentum relaxation rate, and bias current) beyond linear response theory, providing clear predictions for future experiments.
Identities for hyperconvex Anosov representations

In this paper, we establish Basmajian’s identity for certain (1,1,2)-hyperconvex Anosov representations from a free group into [Formula: see text]. We then study our series identities on holomorphic families of Cantor non-conformal repellers associated to complex (1,1,2)-hyperconvex Anosov representations. We show that the series is absolutely summable if the Hausdorff dimension of the Cantor set is strictly less than one. Throughout the domain of convergence, these identities can be analytically continued and they exhibit nontrivial monodromy.
Structural Balance via Gradient Flows Over Signed Graphs

Structural balance is a classic property of signed graphs satisfying Heider's seminal axioms. Mathematical sociologists have studied balance theory since its inception in the 1940s. Recent research has focused on the development of dynamic models explaining the emergence of structural balance. In this article, we introduce a novel class of parsimonious dynamic models for structural balance based on an interpersonal influence process. Our proposed models are gradient flows of an energy function, called the dissonance function, which captures the cognitive dissonance arising from the violations of Heider's axioms. Thus, we build a new connection with the literature on energy landscape minimization. This gradient-flow characterization allows us to study the transient and asymptotic behaviors of our model. We provide mathematical and numerical results describing the critical points of the dissonance function.