Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk factors for dysfunctional grief and functional impairment for all causes of death during the COVID-19 pandemic: The mediating role of meaning

Abstract The COVID-19 pandemic, coupled with significant social changes due to legislative and public health requirements, has changed the way in which people experience grief. We examined whether dysfunctional grief symptoms, disrupted meaning, risk factors, and functional impairment differed between people bereaved from COVID-19 and from other natural or violent causes in this same period. A sample of 409 participants (67.73% male; M = 37.54 years) completed an online survey in June 2021. There were no statistically significant differences between the three groups on any of the outcome variables; all three groups manifested clinical levels of functional impairment equal to or greater than bereaved groups diagnosed with complicated or prolonged grief disorder prior to the pandemic. Disrupted meaning partially mediated the relationship between risk factors on the one hand and functional impairment and dysfunctional grief symptoms on the other. Findings indicate that deaths during COVID-19, rather than deaths from COVID-19, may precipitate symptoms of significant clinical concern.
Kernel PCA with the Nystr\"om method

The Nystr\"om method is one of the most popular techniques for improving the scalability of kernel methods. However, it has not yet been derived for kernel PCA in line with classical PCA. In this paper we derive kernel PCA with the Nystr\"om method, thereby providing one of the few available options to make kernel PCA scalable. We further study its statistical accuracy through a finite-sample confidence bound on the empirical reconstruction error compared to the full method. The behaviours of the method and bound are illustrated through computer experiments on multiple real-world datasets. As an application of the method we present kernel principal component regression with the Nystr\"om method, as an alternative to Nystr\"om kernel ridge regression for efficient regularized regression with kernels.
Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation

Deep neural network based methods have been successfully applied to music source separation. They typically learn a mapping from a mixture spectrogram to a set of source spectrograms, all with magnitudes only. This approach has several limitations: 1) its incorrect phase reconstruction degrades the performance, 2) it limits the magnitude of masks between 0 and 1 while we observe that 22% of time-frequency bins have ideal ratio mask values of over~1 in a popular dataset, MUSDB18, 3) its potential on very deep architectures is under-explored. Our proposed system is designed to overcome these. First, we propose to estimate phases by estimating complex ideal ratio masks (cIRMs) where we decouple the estimation of cIRMs into magnitude and phase estimations. Second, we extend the separation method to effectively allow the magnitude of the mask to be larger than 1. Finally, we propose a residual UNet architecture with up to 143 layers. Our proposed system achieves a state-of-the-art MSS result on the MUSDB18 dataset, especially, a SDR of 8.98~dB on vocals, outperforming the previous best performance of 7.24~dB. The source code is available at: this https URL
Gaps in Screening Recommendations Must Be Addressed to Protect Youth and Adults from Substance Use-Related Harm

ABSTRACT Background: In June 2020, the U.S. Preventive Services Task Force (USPSTF) issued a final recommendation on screening for unhealthy drug use in adults and adolescents. It assigned a “B” rating on a recommendation for screening in primary care for adults when services for accurate diagnosis, effective treatment, and appropriate care can be provided or referred, but declined to recommend the same for adolescents, concluding that current evidence is insufficient to assess the balance of benefits and harms. Objectives: To examine and provide recommendations to address gaps in the USP ST recommendation on screening for unhealthy drug use. Results: The lack of recommendation to screen adolescents represents a critical gap and highlights the need for research on adolescent substance use screening. While research is limited, available evidence shows short-term benefits from youth screening and early intervention and no evidence of harm. The lack of recommendation for youth leaves practitioners without guidance and incentive to intervene with youth who use substances, discourages expansion/support of youth screening, and reinforces treatment barriers. The statement also lacks guidance to address barriers to implementing screening in adults. Despite promotion of the practice, implementation in primary care is sporadic. Providers should be made aware of implementation barriers and the clinical guides and training available to facilitate implementation. Conclusions/Importance: Screening and intervention are vital for addressing the addiction crisis, and USPSTF recommendations are critical for making preventive health care services accessible and affordable. These gaps must be addressed for the recommendation statement to reach its full life-saving potential.
Propensity matched analysis of minimally invasive versus conventional isolated aortic valve replacement

Objective: To analyse the early and mid-term outcome of patients undergoing conventional aortic valve replacement (AVR) versus minimally invasive via hemi-sternotomy aortic valve replacement (MIAVR). Methods: A single centre retrospective study involving 653 patients who underwent isolated aortic valve replacement (AVR) either via conventional AVR (n = 516) or MIAVR (n = 137) between August 2015 and March 2020. Using pre-operative characteristics, patients were propensity matched (PM) to produce 114 matched pairs. Assessment of peri-operative outcomes, early and mid-term survival and echocardiographic parameters was performed. Results: The mean age of the PM conventional AVR group was 71.5 (±8.9) years and the number of male (n = 57) and female (n = 57) patients were equal. PM MIAVR group mean age was 71.1 (±9.5) years, and 47% of patients were female (n = 54) and 53% male (n = 60). Median follow-up for PM conventional AVR and MIAVR patients was 3.4 years (minimum 0, maximum 4.8 years) and 3.4 years (minimum 0, maximum 4.8 years), respectively. Larger sized aortic valve prostheses were inserted in the MIAVR group (median 23, IQR = 4) versus conventional AVR group (median 21, IQR = 2; p = 0.02, SMD = 0.34). Cardiopulmonary bypass (CPB) time was longer with MIAVR (94.4 ± 19.5 minutes) compared to conventional AVR (83.1 ± 33.3; p = 0.0001, SMD = 0.41). Aortic cross-clamp (AoX) time was also longer in MIAVR (71.6 ± 16.5 minutes) compared to conventional AVR (65.0 ± 52.8; p = 0.0001, SMD = 0.17). There were no differences in the early post-operative complications and mortality between the two groups. Follow-up echocardiographic data showed significant difference in mean aortic valve gradients between conventional AVR and MIAVR groups (17.3 ± 8.2 mmHg vs 13.0 ± 5.1 mmHg, respectively; p = 0.01, SMD = −0.65). There was no significant difference between conventional AVR and MIAVR in mid-term survival at 3 years (88.6% vs 92.1%; log-rank test p = 0.31). Conclusion: Despite the longer CPB and AoX times in the MIAVR group, there was no significant difference in early complications, mortality and mid-term survival between MIAVR and conventional AVR.
Explaining Deep Learning Representations by Tracing the Training Process

We propose a novel explanation method that explains the decisions of a deep neural network by investigating how the intermediate representations at each layer of the deep network were refined during the training process. This way we can a) find the most influential training examples during training and b) analyze which classes attributed most to the final representation. Our method is general: it can be wrapped around any iterative optimization procedure and covers a variety of neural network architectures, including feed-forward networks and convolutional neural networks. We first propose a method for stochastic training with single training instances, but continue to also derive a variant for the common mini-batch training. In experimental evaluations, we show that our method identifies highly representative training instances that can be used as an explanation. Additionally, we propose a visualization that provides explanations in the form of aggregated statistics over the whole training process.
How is Time Frequency Space Modulation Related to Short Time Fourier Signaling?

We investigate the relationship between Orthogonal Time Frequency Space (OTFS) modulation and Orthogonal Short Time Fourier (OSTF) signaling. OTFS was recently proposed as a new scheme for high Doppler scenarios and builds on OSTF. We first show that the two schemes are unitarily equivalent in the digital domain. However, OSTF defines the analog-digital interface with the waveform domain. We then develop a critically sampled matrix-vector model for the two systems and consider linear minimum mean-squared error (MMSE) filtering at the receiver to suppress inter-symbol interference. Initial comparison of capacity and (uncoded) probability of error reveals a surprising observation: OTFS under-performs OSTF in capacity but over-performs in probability of error. This result can be attributed to characteristics of the channel matrices induced by the two systems. In particular, the diagonal entries of OTFS matrix exhibit nearly identical magnitude, whereas those of the OSTF matrix exhibit wild fluctuations induced by multipath randomness. It is observed that by simply replacing the unitary matrix, relating OTFS to OSTF, by an arbitrary unitary matrix results in performance nearly identical to OTFS. We then extend our analysis to orthogonal frequency division multiplexing (OFDM) and also consider a more extreme scenario of relatively large delay and Doppler spreads. Our results demonstrate the significance of using OSTF basis waveforms rather than sinusoidal ones in OFDM in highly dynamic environments, and also highlight the impact of the level of channel state information used at the receiver.
An End-to-end Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.
Stochastic Adversarial Koopman Model for Dynamical Systems

Dynamical systems are ubiquitous and are often modeled using a non-linear system of governing equations. Numerical solution procedures for many dynamical systems have existed for several decades, but can be slow due to high-dimensional state space of the dynamical system. Thus, deep learning-based reduced order models (ROMs) are of interest and one such family of algorithms along these lines are based on the Koopman theory. This paper extends a recently developed adversarial Koopman model (Balakrishnan \&Upadhyay, arXiv:2006.05547) to stochastic space, where the Koopman operator applies on the probability distribution of the latent encoding of an encoder. Specifically, the latent encoding of the system is modeled as a Gaussian, and is advanced in time by using an auxiliary neural network that outputs two Koopman matrices $K_{\mu}$ and $K_{\sigma}$. Adversarial and gradient losses are used and this is found to lower the prediction errors. A reduced Koopman formulation is also undertaken where the Koopman matrices are assumed to have a tridiagonal structure, and this yields predictions comparable to the baseline model with full Koopman matrices. The efficacy of the stochastic Koopman model is demonstrated on different test problems in chaos, fluid dynamics, combustion, and reaction-diffusion models. The proposed model is also applied in a setting where the Koopman matrices are conditioned on other input parameters for generalization and this is applied to simulate the state of a Lithium-ion battery in time. The Koopman models discussed in this study are very promising for the wide range of problems considered.
SHAPE: Shifted Absolute Position Embedding for Transformers

Position representation is crucial for building position-aware representations in Transformers. Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost. We investigate shifted absolute position embedding (SHAPE) to address both issues. The basic idea of SHAPE is to achieve shift invariance, which is a key property of recent successful position representations, by randomly shifting absolute positions during training. We demonstrate that SHAPE is empirically comparable to its counterpart while being simpler and faster.
Chronic Kidney Disease-Associated Pruritus: A Review

Background: Chronic kidney disease-associated pruritus (CKD-aP), also known as uraemic pruritus, is a disabling symptom for patients and a challenging condition for clinicians. Despite being common amongst end-stage kidney disease (ESKD) patients, it remains underestimated and underdiagnosed. The exact pathogenesis remains largely elusive, which hampers the synthesis of a definite treatment approach. Summary: Chronic pruritus (lasting 6 weeks or more in duration) is a common and potentially disabling symptom in patients with advanced CKD. A unified hypothesis of pathogenesis has not yet been concluded. Studies have shown changes in the immunochemical milieu of the skin in patients with CKD-aP with several inciting stimuli identified. However, other unrecognized factors are likely to be involved. This article will review the current observations and understanding of the postulated pathogenesis of CKD-aP, as well as the evidence for current management strategies. Key Messages: CKD-aP is a common and troubling symptom amongst ESKD patients that is associated with decreased quality of life and poor prognosis. Its exact pathogenesis, at the time of writing, is not well-understood. A stepwise approach is recommended for management. Systematic reviews show the largest body of evidence was found for the effectiveness of gabapentin. Comparison is needed between newly emerging pharmacological agents such as kappa-opioid receptor agonists and more established agents, such as the gabapentinoids. Finally, renal transplantation should be considered in severe and refractory cases who are suitable transplant candidates as it has shown an excellent outcome in most cases.
Principal-axis Analysis of the Eddington Tensor for the Early Post-bounce Phase of Rotational Core-collapse Supernovae

Using full Boltzmann neutrino transport, we performed 2D core-collapse supernova simulations in axisymmetry for two progenitor models with 11.2 and 15.0 M ⊙, both rotational and nonrotational. We employed the results obtained in the early post-bounce phase (t ≲ 20 ms) to assess performance under rapid rotation of some closure relations commonly employed in the truncated moment method. We first made a comparison in 1D under spherical symmetry, though, of the Eddington factor p defined in the fluid rest frame (FR). We confirmed that the maximum entropy closure for the Fermionic distribution (MEFD) performs better than others near the proto–neutron star surface, where p < 1/3 occurs, but does not work well even in 1D when the phase-space occupancy satisfies e < 0.5 together with p < 1/3, the condition known to be not represented by MEFD. For the 2D models with the rapid rotation, we employed the principal-axis analysis of the Eddington tensor. We paid particular attention to the direction of the longest principal axis. We observed in FR that it is aligned neither with the radial direction nor with the neutrino flux in 2D, particularly so in convective and/or rapidly rotating regions, the fact not accommodated in the moment method. We repeated the same analysis in the laboratory frame and found again that the direction of the longest principal axis is not well reproduced by MEFD because the interpolation between the optically thick and thin limits is not very accurate in this frame.
Flexible-bandwidth needlets

We investigate here a generalized construction of spherical wavelets/needlets which admits extra-flexibility in the harmonic domain, i.e., it allows the corresponding support in multipole (frequency) space to vary in more general forms than in the standard constructions. We study the analytic properties of this system and we investigate its behaviour when applied to isotropic random fields: more precisely, we establish asymptotic localization and uncorrelation properties (in the high-frequency sense) under broader assumptions than typically considered in the literature.
On Tilted Losses in Machine Learning: Theory and Applications

Exponential tilting is a technique commonly used in fields such as statistics, probability, information theory, and optimization to create parametric distribution shifts. Despite its prevalence in related fields, tilting has not seen widespread use in machine learning. In this work, we aim to bridge this gap by exploring the use of tilting in risk minimization. We study a simple extension to ERM -- tilted empirical risk minimization (TERM) -- which uses exponential tilting to flexibly tune the impact of individual losses. The resulting framework has several useful properties: We show that TERM can increase or decrease the influence of outliers, respectively, to enable fairness or robustness; has variance-reduction properties that can benefit generalization; and can be viewed as a smooth approximation to the tail probability of losses. Our work makes rigorous connections between TERM and related objectives, such as Value-at-Risk, Conditional Value-at-Risk, and distributionally robust optimization (DRO). We develop batch and stochastic first-order optimization methods for solving TERM, provide convergence guarantees for the solvers, and show that the framework can be efficiently solved relative to common alternatives. Finally, we demonstrate that TERM can be used for a multitude of applications in machine learning, such as enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance. Despite the straightforward modification TERM makes to traditional ERM objectives, we find that the framework can consistently outperform ERM and deliver competitive performance with state-of-the-art, problem-specific approaches.
MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

Among the biggest challenges we face in utilizing neural networks trained on waveform data (i.e., seismic, electromagnetic, or ultrasound) is its application to real data. The requirement for accurate labels forces us to develop solutions using synthetic data, where labels are readily available. However, synthetic data often do not capture the reality of the field/real experiment, and we end up with poor performance of the trained neural network (NN) at the inference stage. We describe a novel approach to enhance supervised training on synthetic data with real data features (domain adaptation). Specifically, for tasks in which the absolute values of the vertical axis (time or depth) of the input data are not crucial, like classification, or can be corrected afterward, like velocity model building using a well-log, we suggest a series of linear operations on the input so the training and application data have similar distributions. This is accomplished by applying two operations on the input data to the NN model: 1) The crosscorrelation of the input data (i.e., shot gather, seismic image, etc.) with a fixed reference trace from the same dataset. 2) The convolution of the resulting data with the mean (or a random sample) of the autocorrelated data from another domain. In the training stage, the input data are from the synthetic domain and the auto-correlated data are from the real domain, and random samples from real data are drawn at every training epoch. In the inference/application stage, the input data are from the real subset domain and the mean of the autocorrelated sections are from the synthetic data subset domain. Example applications on passive seismic data for microseismic event source location determination and active seismic data for predicting low frequencies are used to demonstrate the power of this approach in improving the applicability of trained models to real data.
Convenient Method for Enhancing Hydrophobicity and Dispersibility of Starch Nanocrystals by Crosslinking Modification with Citric Acid

Abstract This study investigated the chemical, wettability, thermal and structural properties of starch nanocrystals (SNCs) modified through crosslinking modification using citric acid in aqueous medium. The results of Fourier transform infrared spectroscopy analysis suggested that new interactions occurred between starch and citric acid after modification and an increase of the reaction time increased the degree of substitution. X-ray diffraction analysis showed that the crystalline structure of SNCs was maintained after the crosslinking modification by adjusting the pH to 3.5, and destroyed after the modification by pretreatments without adjusting the pH of the citric acid solution. Crosslinking modification decreased the size and aggregation of SNCs and longer reaction time gave rise to the smaller particle size of SNCs modified by the pretreatments of adjusting the pH of the citric acid solution. The crosslinked SNCs had lower polarity and showed stronger hydrophobicity and dispersion stability which allowed them be used as reinforcements in hydrophobic polymer matrices.
Cosmology of quadratic metric-affine gravity

We investigate the cosmological aspects of the most general parity preserving Metric-Affine Gravity theory quadratic in torsion and non-metricity in the presence of a cosmological hyperfluid. The equations of motion are obtained by varying the action with respect to the metric and the independent affine connection. Subsequently, considering a Friedmann-Lemâıtre-Robertson-Walker background, we derive the most general form of the modified Friedmann equations for the full quadratic theory. We then focus on a characteristic sub-case involving only two quadratic contributions given in terms of torsion and non-metricity vectors. In this setup, studying the modified Friedmann equations along with the conservation laws of the perfect cosmological hyperfluid, we provide exact solutions both for purely dilation and for purely spin hypermomentum sources. We then discuss the physical consequences of our model and the prominent role of torsion and non-metricity in this cosmological setup.