Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Breathy Vocal Quality on Speech Intelligibility and Listening Effort in Background Noise

Speech perception is challenging under adverse conditions. However, there is limited evidence regarding how multiple adverse conditions affect speech perception. The present study investigated two conditions that are frequently encountered in real-life communication: background noise and breathy vocal quality. The study first examined the effects of background noise and breathiness on speech perception as measured by intelligibility. Secondly, the study tested the hypothesis that both noise and breathiness affect listening effort, as indicated by linear and nonlinear changes in pupil dilation. Low-context sentences were resynthesized to create three levels of breathiness (original, mild-moderate, and severe). The sentences were presented in a fluctuating nonspeech noise with two signal-to-noise ratios (SNRs) of −5 dB (favorable) and −9 dB (adverse) SNR. Speech intelligibility and pupil dilation data were collected from young listeners with normal hearing thresholds. The results demonstrated that a breathy vocal quality presented in noise negatively affected speech intelligibility, with the degree of breathiness playing a critical role. Listening effort, as measured by the magnitude of pupil dilation, showed significant effects with both severe and mild-moderate breathy voices that were independent of noise level. The findings contributed to the literature by demonstrating the impact of vocal quality on the perception of speech in noise. They also highlighted the complex dynamics between overall task demand and processing resources in understanding the combined impact of multiple adverse conditions.
What is the most effective and safest Non-steroidal anti-inflammatory drug for treating osteoarthritis in patients with comorbidities?

ABSTRACT Introduction Understanding what the most effective and safe non-steroidal anti-inflammatory drug (NSAID) is for managing osteoarthritis (OA) is complicated. OA is prevalent worldwide and people living with OA commonly have multiple comorbidities. The efficacy and safety of NSAIDs in a patient are influenced by their intrinsic and extrinsic factors. Current guidelines recommend the lowest dose for the shortest duration, monitoring patients for risk factors and comorbidities but generally do not specify, which NSAID is most suitable for a patient with specific comorbidities. Areas Covered This paper looks at the mechanism of action of all NSAIDs and reviews the current literature concerning their safety in patients with and without comorbidities. Relevant publications were identified by searching PubMed and Cochrane Library using key terms. The search was conducted from inception to 18 July 2023 and included results published before 18 July 2023. The search results and their references were then manually reviewed. Expert Opinion In the paper, we determine whether the current practice of ‘lowest dose for shortest duration’ is in fact the best approach for prescribing NSAIDs and identify which NSAIDs are most suitable given a patient’s risk factors and comorbidities. Our aim is to help guide health professionals in recommending the most suitable NSAID for each patient.
Heterogeneous cardiac sympathetic innervation gradients promote arrhythmogenesis in murine dilated cardiomyopathy

Ventricular arrhythmias (VAs) in heart failure are enhanced by sympathoexcitation. However, radiotracer studies of catecholamine uptake in failing human hearts demonstrate a proclivity for VAs in patients with reduced cardiac sympathetic innervation. We hypothesized that this counterintuitive finding is explained by heterogeneous loss of sympathetic nerves in the failing heart. In a murine model of dilated cardiomyopathy (DCM), delayed PET imaging of sympathetic nerve density using the catecholamine analog [11C]meta-Hydroxyephedrine demonstrated global hypoinnervation in ventricular myocardium. Although reduced, sympathetic innervation in 2 distinct DCM models invariably exhibited transmural (epicardial to endocardial) gradients, with the endocardium being devoid of sympathetic nerve fibers versus controls. Further, the severity of transmural innervation gradients was correlated with VAs. Transmural innervation gradients were also identified in human left ventricular free wall samples from DCM versus controls. We investigated mechanisms underlying this relationship by in silico studies in 1D, 2D, and 3D models of failing and normal human hearts, finding that arrhythmogenesis increased as heterogeneity in sympathetic innervation worsened. Specifically, both DCM-induced myocyte electrical remodeling and spatially inhomogeneous innervation gradients synergistically worsened arrhythmogenesis. Thus, heterogeneous innervation gradients in DCM promoted arrhythmogenesis. Restoration of homogeneous sympathetic innervation in the failing heart may reduce VAs.
SBDSR126T rescues survival of sbds−/− zebrafish in a dose-dependent manner independently of Tp53

We created a transgenic line expressing the disease-associated mutation SBDSR126T in the sbds KO zebrafish background. We demonstrated that amount of SBDSR126T is important for development and tp53 pathway activation. Tp53M214K did not rescue neutropenia or survival in the sbds-null zebrafish. Defects in ribosomal biogenesis profoundly affect organismal development and cellular function, and these ribosomopathies produce a variety of phenotypes. One ribosomopathy, Shwachman–Diamond syndrome (SDS) is characterized by neutropenia, pancreatic exocrine insufficiency, and skeletal anomalies. SDS results from biallelic mutations in SBDS, which encodes a ribosome assembly factor. Some individuals express a missense mutation, SBDSR126T, along with the common K62X mutation. We reported that the sbds-null zebrafish phenocopies much of SDS. We further showed activation of Tp53-dependent pathways before the fish died during the larval stage. Here, we expressed SBDSR126T as a transgene in the sbds−/− background. We showed that one copy of the SBDSR126T transgene permitted the establishment of maternal zygotic sbds-null fish which produced defective embryos with cdkn1a up-regulation, a Tp53 target involved in cell cycle arrest. None survived beyond 3 dpf. However, two copies of the transgene resulted in normal development and lifespan. Surprisingly, neutropenia persisted. The surviving fish displayed suppression of female sex differentiation, a stress response in zebrafish. To evaluate the role of Tp53 in the pathogenesis of sbds−/− fish phenotype, we bred the fish with a DNA binding deficient allele, tp53M214K. Expression of the loss-of-function tp53M214K did not rescue neutropenia or survival in sbds-null zebrafish. Increased expression of cdkn1a was abrogated in the tp53M214K/M214K;sbds−/− fish. We conclude that the amount of SBDSR126T protein is important for development, inactivation of Tp53 fails to rescue neutropenia or survival in the sbds-null background, and cdkn1a up-regulation was dependent on WT tp53. We hypothesize that additional pathways are involved in the pathophysiology of SDS.
Geometric distortions in clinical MRI sequences for radiotherapy: insights gained from a multicenter investigation

Abstract Background As magnetic resonance imaging (MRI) becomes increasingly integrated into radiotherapy (RT) for enhanced treatment planning and adaptation, the inherent geometric distortion in acquired MR images pose a potential challenge to treatment accuracy. This study aimed to evaluate the geometric distortion levels in the clinical MRI protocols used across Danish RT centers and discuss influence of specific sequence parameters. Based on the variety in geometric performance across centers, we assess if harmonization of MRI sequences is a relevant measure. Materials and methods Nine centers participated with 12 MRI scanners and MRI-Linacs (MRL). Using a travelling phantom approach, a reference MRI sequence was used to assess variation in baseline distortion level between scanners. The phantom was also scanned with local clinical MRI sequences for brain, head/neck (H/N), abdomen, and pelvis. The influence of echo time, receiver bandwidth, image weighting, and 2D/3D acquisition was investigated. Results We found a large variation in geometric accuracy across 93 clinical sequences examined, exceeding the baseline variation found between MRI scanners (σ = 0.22 mm), except for abdominal sequences where the variation was lower. Brain and abdominal sequences showed lowest distortion levels ([0.22, 2.26] mm), and a large variation in performance was found for H/N and pelvic sequences ([0.19, 4.07] mm). Post hoc analyses revealed that distortion levels decreased with increasing bandwidth and a less clear increase in distortion levels with increasing echo time. 3D MRI sequences had lower distortion levels than 2D (median of 1.10 and 2.10 mm, respectively), and in DWI sequences, the echo-planar imaging read-out resulted in highest distortion levels. Conclusion There is a large variation in the geometric distortion levels of clinical MRI sequences across Danish RT centers, and between anatomical sites. The large variation observed makes harmonization of MRI sequences across institutions and adoption of practices from well-performing anatomical sites, a relevant measure within RT.
SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA

Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data. Some of the existing algorithms require ancillary data such as topographic map, multiple laser returns or intensity data which may not be available, and some deal only with the single isolated outliers. This is an attempt to present an algorithm to remove both the single and cluster types of outliers, by exclusively use of the last return data. The outliers will be removed by spatial analyzing of LiDAR point clouds in a hierarchical scheme that is uses a cross-validation technique. The algorithm is tested on a dataset including many single and cluster outliers. Our algorithm can deal with both the irregular LiDAR point clouds and the regular grid data. Experimental results show that the presented algorithm almost completely detects both the single and cluster outliers, but some inlier points are wrongly removed as outlier. An accuracy assessment indicated 0.018% Error α and, 0.352% Error β that are very satisfactory.
Depth dependent seismic scattering attenuation in the Nuevo Cuyo region (southern central Andes)

In the present work we separated intrinsic from scattering attenuation coefficients both for the crust and the upper mantle in the tectonically highly active areas of the Southern‐Central Andes ‐ Nuevo Cuyo region, analyzing two groups of earthquakes, well separated in depth. This region is characterized by the presence of flat subduction. We apply MLTWA (Multiple Lapse Time Window Analysis), coda normalization and Q‐coda techniques to measure the scattering and intrinsic attenuation coefficient and the total Q for S waves. We find that intrinsic attenuation does not decrease with depth whereas scattering attenuation is higher in the crust than in the upper mantle, and that intrinsic attenuation predominates over scattering attenuation. We interpret this observation in terms of the release of water and other fluids into the overlying lithosphere due to the dynamics of the subduction process, in agreement with most of the prevalent geodynamic models.
Nurses' experiences when conducting the mental state examination (MSE): A scoping review.

The Mental State Examination (MSE) is an assessment framework used to facilitate the collection of subjective and objective data about a person's current mental state. There is a lack of understanding of nurses' experiences when conducting the MSE. The aim of this scoping review is to identify, examine and summarize the available literature relating to nurses' experiences when conducting the MSE. A scoping review was conducted using Arksey and O'Malley (2005) framework to review, examine and synthesize the available literature on nurses' experiences with the MSE. A PRISMA flow diagram was used to describe the systematic literature search. Six databases (APA PsycInfo, CINAHL, MEDLINE, PubMed, ProQuest, and Scopus) were searched including reference lists of eligible sources. Google Scholar, Trove and Proquest Dissertation and Thesis were searched for grey literature. Twelve articles included in this scoping review considered nurses experiences when conducting the MSE. The principles of thematic analysis were used to synthesize the studies. Three distinct themes were identified from the literature: (i) Nurses' role and the MSE, (ii) Nurses' competence and knowledge when conducting the MSE, and (iii) Nurses' confidence when conducting the MSE. The results of this scoping review identified the MSE as a component of the mental health nursing role and a core competency of mental health telephone triage services. The MSE was used by nurses in the Emergency department (ED), acute in-patient and community mental health settings, including mental health telephone triage services. Nurses working in EDs and acute in-patient mental health settings experienced a lack of confidence and competence, including a knowledge deficit in conducting the MSE in comparison to nurses working in community mental health settings. Community mental health nurses identified the importance of conducting an MSE higher than acute in-patient mental health nurses. This review identified the need for evidence-based research related to the MSE and its application in nursing practice. Evidence-based research will inform the development of MSE guidelines and policies, thus, enhance mental health nursing practice related to the MSE, including improving and strengthening consumer-nurse therapeutic alliance in acute in-patient mental health settings.
Maximizing Utilitarian and Egalitarian Welfare of Fractional Hedonic Games on Tree-like Graphs

Fractional hedonic games are coalition formation games where a player's utility is determined by the average value they assign to the members of their coalition. These games are a variation of graph hedonic games, which are a class of coalition formation games that can be succinctly represented. Due to their applicability in network clustering and their relationship to graph hedonic games, fractional hedonic games have been extensively studied from various perspectives. However, finding welfare-maximizing partitions in fractional hedonic games is a challenging task due to the nonlinearity of utilities. In fact, it has been proven to be NP-hard and can be solved in polynomial time only for a limited number of graph classes, such as trees. This paper presents (pseudo)polynomial-time algorithms to compute welfare-maximizing partitions in fractional hedonic games on tree-like graphs. We consider two types of social welfare measures: utilitarian and egalitarian. Tree-like graphs refer to graphs with bounded treewidth and block graphs. A hardness result is provided, demonstrating that the pseudopolynomial-time solvability is the best possible under the assumption P$\neq$NP.
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation

Multimodal learning seeks to utilize data from multiple sources to improve the overall performance of downstream tasks. It is desirable for redundancies in the data to make multimodal systems robust to missing or corrupted observations in some correlated modalities. However, we observe that the performance of several existing multimodal networks significantly deteriorates if one or multiple modalities are absent at test time. To enable robustness to missing modalities, we propose a simple and parameter-efficient adaptation procedure for pretrained multimodal networks. In particular, we exploit modulation of intermediate features to compensate for the missing modalities. We demonstrate that such adaptation can partially bridge performance drop due to missing modalities and outperform independent, dedicated networks trained for the available modality combinations in some cases. The proposed adaptation requires extremely small number of parameters (e.g., fewer than 0.7% of the total parameters) and applicable to a wide range of modality combinations and tasks. We conduct a series of experiments to highlight the missing modality robustness of our proposed method on 5 different datasets for multimodal semantic segmentation, multimodal material segmentation, and multimodal sentiment analysis tasks. Our proposed method demonstrates versatility across various tasks and datasets, and outperforms existing methods for robust multimodal learning with missing modalities.
Combining the D3 dispersion correction with the neuroevolution machine-learned potential

Machine-learned potentials (MLPs) have become a popular approach of modeling interatomic interactions in atomistic simulations, but to keep the computational cost under control, a relatively short cutoff must be imposed, which put serious restrictions on the capability of the MLPs for modeling relatively long-ranged dispersion interactions. In this paper, we propose to combine the neuroevolution potential (NEP) with the popular D3 correction to achieve a unified NEP-D3 model that can simultaneously model relatively short-ranged bonded interactions and relatively long-ranged dispersion interactions. We show that improved descriptions of the binding and sliding energies in bilayer graphene can be obtained by the NEP-D3 approach compared to the pure NEP approach. We implement the D3 part into the gpumd package such that it can be used out of the box for many exchange-correlation functionals. As a realistic application, we show that dispersion interactions result in approximately a 10% reduction in thermal conductivity for three typical metal-organic frameworks.
3D Non-Stationary Channel Measurement and Analysis for MaMIMO-UAV Communications

Unmanned aerial vehicles (UAVs) have gained popularity in the communications research community because of their versatility in placement and potential to extend the functions of communication networks. However, there remains still a gap in existing works regarding detailed and measurement-verified air-to-ground (A2G) Massive Multi-Input Multi-Output (MaMIMO) channel characteristics which play an important role in realistic deployment. In this paper, we first design a UAV MaMIMO communication platform for channel acquisition. We then use the testbed to measure uplink Channel State Information (CSI) between a rotary-wing drone and a 64-element MaMIMO base station (BS). For characterization, we focus on multidimensional channel stationarity which is a fundamental metric in communication systems. Afterward, we present measurement results and analyze the channel statistics based on power delay profiles (PDPs) considering space, time, and frequency domains. We propose the stationary angle (SA) as a supplementary metric of stationary distance (SD) in the time domain. We analyze the coherence bandwidth and RMS delay spread for frequency stationarity. Finally, spatial correlations between elements are analyzed to indicate the spatial stationarity of the array. The space-time-frequency channel stationary characterization will benefit the physical layer design of MaMIMO-UAV communications.
A Survey on Conflict Detection in IoT-based Smart Homes

As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user satisfaction. In this survey, we introduce a novel conflict taxonomy, complete with formal definitions of each conflict type that may arise within the smart home environment. We design an advanced conflict model to effectively categorize these conflicts, setting the stage for our in-depth review of recent research in the field. By employing our proposed model, we systematically classify conflicts and present a comprehensive overview of cutting-edge conflict detection approaches. This extensive analysis allows us to highlight similarities, clarify significant differences, and uncover prevailing trends in conflict detection techniques. In conclusion, we shed light on open issues and suggest promising avenues for future research to foster accelerated development and deployment of IoT-based smart homes, ultimately enhancing their overall performance and user experience.
On the dimension of the singular set in optimization problems with measure constraint

In this paper, we prove estimates on the dimension of the singular part of the free boundary for solutions to shape optimization problems with measure constraints. The focus is on the heat conduction problem studied by Aguilera, Caffarelli, and Spruck and the one-phase Bernoulli problem with measure constraint introduced by Aguilera, Alt and Caffarelli. To estimate the Hausdorff dimension of the singular set, we introduce a new formulation of the notion of stability for the one-phase problem along volume-preserving variations, which is preserved under blow-up limits. Finally, the result follows by applying the program developed in [Buttazzo et al. 2022] to this class of domain variation.
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences

Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy optimization a difficult problem. Several works augment this sparse reward with a learned dense reward function, but this can lead to sub-optimal policies if the reward is misaligned. Moreover, recent works have demonstrated that effective shaping rewards for a particular problem can depend on the underlying learning algorithm. This paper introduces a novel way to encourage exploration called $f$-Policy Gradients, or $f$-PG. $f$-PG minimizes the f-divergence between the agent's state visitation distribution and the goal, which we show can lead to an optimal policy. We derive gradients for various f-divergences to optimize this objective. Our learning paradigm provides dense learning signals for exploration in sparse reward settings. We further introduce an entropy-regularized policy optimization objective, that we call $state$-MaxEnt RL (or $s$-MaxEnt RL) as a special case of our objective. We show that several metric-based shaping rewards like L2 can be used with $s$-MaxEnt RL, providing a common ground to study such metric-based shaping rewards with efficient exploration. We find that $f$-PG has better performance compared to standard policy gradient methods on a challenging gridworld as well as the Point Maze and FetchReach environments. More information on our website https://agarwalsiddhant10.github. io/projects/fpg.html.