Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Life Impact of Cochlear Implantation for Single-Sided Deafness: Assessing the Interrelationship of Objective and Subjective Measures

Objective To determine the effect on quality of life (QOL) of cochlear implantation (CI) for single-sided deafness (SSD) and asymmetric hearing loss (AHL) using the first psychometrically developed CI-specific QOL tool for English-speaking patients and to assess its relationship to objective perceptual measures. Study Design Retrospective cohort study. Setting Tertiary-care medical center. Patients English-speaking adults with SSD or AHL. Interventions Unilateral CI. Main Outcome Measures Cochlear Implant Quality of Life (CIQOL) score, CI-alone speech-in-quiet (SIQ) score (CNC and AzBio), binaural speech-in-noise (SIN) threshold, binaural azimuthal sound localization (SL) error. Results At the most recent postoperative evaluation (median, 9.3 months postimplantation), 25 of 28 subjects (89%) had a CIQOL improvement, with the improvement considered clinically beneficial (>3 points) for 18 of 28 subjects (64%). Group-mean CIQOL improvement was observed at the first postoperative visit and did not change significantly thereafter. Objective perceptual measures (SL, SIQ, SIN) continued to improve over 12 months after implantation. Linear mixed-model regression analyses showed a moderate positive correlation between SIN and SIQ improvements (r = 0.50 to 0.59, p < 0.0001) and a strong positive correlation between the improvement in the two SIQ measures (r = 0.89, p < 0.0001). No significant relationships were observed (p > 0.05) among QOL or the objective perceptual measures. Conclusions QOL improved for the majority of subjects implanted for SSD and AHL. Different time courses for improvement in QOL and audiologic tests, combined with the lack of significant relationships among them, suggest that QOL outcomes reflect different aspects of the CI experience than those captured by speech-understanding and localization measures. SIQ may substitute for SIN when clinical constraints exist.
BioDecoder: A miRNA Bio-interpretable Neural Network Model for Noninvasive Diagnosis of Breast Cancer

Early diagnosis of breast cancer remains a major clinical challenge. Liquid biopsy has become a powerful tool for cancer diagnosis by the aid of various the state-of-the-art detection technologies and artificial intelligence (AI) methods. Although the prediction performance is superior, the clinical application of existing AI models is greatly limited due to their poor interpretability. Here, we designed a miRNA-Gene-Module-Pathway-Disease biological decoding path, and developed BioDecoder thereof, a miRNA bio-interpretable neural network model for breast cancer early diagnosis. We demonstrated that BioDecoder could achieve early non-invasive diagnosis of breast cancer with a remarkable performance (AUC = 0.989) and showed strong generalizability in an external cohort (AUC = 0.890). Meanwhile, the biologically interpretable results of BioDecoder revealed that significant changes in metabolic pathway and oxidative phosphorylation were the main action pathways of circulating miRNA in breast cancer. Our study indicate that BioDecoder offers the promise of non-invasive early diagnosis of breast cancer and can be generalized to other cancers and corresponding biomarkers.
Global stabilisation of Lur'e network

A new complex dynamical network model consisting of Lur'e systems are proposed and studied in this paper. Some new global stabilisation conditions are derived based on the Lyapunov stability theory, which guarantee that the whole network can be pinned to its equilibrium by placing some feedback controllers on a small fraction of nodes. In some particular cases, even a single controller can achieve the control objective. It is found that global stabilisation of such a network is completely determined by the dynamics of each individual node, the coupling strength, the inner-coupling matrix, and the eigenvalues of the coupling matrix as well as the feedback-gain matrix of the network.
Piloting a pragmatic clinical audit tool for quality improvement in rural paediatric care in northern Sierra Leone

Hospital admissions and their clinical outcome can reflect the disease burden in a population and can be utilised as effective surveillance and impact monitoring tools. Inadequate documentation of admissions and their outcomes have contributed to the poor quality of paediatric care in many health care settings in sub-Saharan Africa. We have developed and piloted a simple tool for documentation of basic, standardised patient-level information on causes of admissions, diagnoses, treatments and outcomes in patients admitted to the paediatric ward of a district hospital in a rural community in Sierra Leone. From 1 August 2019 to 31 July 2021, we used this tool to document the admissions, treatments and clinical outcomes of 1,663 children admitted to this hospital. The majority of the children (1015, 62%) were aged between 12-59 months, were boys (942, 57%), were wasted (516, 31%), stunted (238 14%) or underweight (537, 32%). More than half of the children lived more than 1 km distance from the hospital (876/1410, 62%). Most were admitted before 4pm (1171/1626, 72%) and during weekdays (1231/1662, 74%). The highest number of paediatric admissions occurred in November 2019 and the lowest in April 2020. Severe malaria was the leading cause of admission. More than 80% of the children were successfully treated and discharged home (1356/1663, 81.5%) while 122/1663 (7.3%) died. Children aged under-five years who were underweight, and those who presented with danger signs (e.g. signs of breathing difficulty, dehydration, head injury or severe infections) had a higher risk of death than children without these features (p<0.01; p=0.03; p=0.011 and p= 0.009, respectively). Lack of systematic documentation of medical histories and poor record keeping of hospital admissions and outcomes can be overcome by using a simple tool. Continuous use of the tool with regular audits could improve delivery of paediatric care in resource-limited settings.
Development and Validation of the Overall Fidelity Enactment Scale for Complex Interventions (OFES-CI)

Background. Assessing fidelity in complex interventions is important. Ignoring it increases the risk of discarding potentially effective interventions that failed to work because they were not implemented as intended or, accepting ineffective interventions whose effects were brought about by factors other than the intervention. Unlike simple interventions, fidelity assessment in complex interventions is underexplored and not straightforward. Adapting the evaluative approach used in Objective Structured Clinical Exams (OSCEs), we describe the development and validation of an easy to use, objective approach to fidelity assessment (the Overall Fidelity Enactment Scale for use in complex interventions: OFES-CI). Methods. We developed the OFES-CI and assessed its accuracy using secondary data collected as part of the process evaluation of the SCOPE intervention. Specifically, we compared the OFES-CI ratings obtained from trained study observers during SCOPE intervention workshops to more detailed and comprehensive qualitative fidelity enactment data collected between and during the same workshops (our gold standard). Results. The OFES-CI demonstrates acceptable reliability against our gold standard assessment approach and addresses the practicality problem in fidelity assessment by virtue of its suitable implementation qualities (acceptability, ease of completion, low burden, low training requirements). It can be easily adapted to assess enactment of a variety of complex interventions. Conclusions. Global ratings approaches validated in the context of assessing clinician competence in OSCEs provide a novel, practical and underexplored opportunity to assess fidelity enactment in complex trials. Given important questions that are increasingly being asked regarding evaluation of complex pragmatic interventions, the proposed approach to fidelity assessment, if incorporated into trial evaluation, stands to provide valuable insights not only into why an intervention may succeed or fail but also how we might carry out adaptations of complex interventions when they are needed.
Potentially probiotic Limosilactobacillus reuteri from human milk strengthens the gut barrier in T84 cells and a murine enteroid model.

AIMS At conception, the infant gut barrier is immature, gradually developing with regular intake of maternal milk. This study addressed whether the barrier-strengthening effect of breast feeding might be attributable, at least in part, to autochthonous beneficial human milk bacteria. METHODS AND RESULTS Twelve bacterial strains from the breast milk of Pakistani mothers who underwent cesarean delivery (NPL-88, NPL-157, NPL-179, NPL-181, NPL-388 (Limosilactobacillus reuteri), NPL-76, NPL-495, NPL-504 (Limosilactobacillus fermentum), NPL-415 (Lactobacillus pentosus), NPL-412, NPL-416 (Lactiplantibacilllus plantarum) and NPL-374 (Bifidobacterium longum) were shortlisted based on their tolerance to acidic pH (2.8-4.2) and bile (0.1-0.3%). The effect of these bacteria on gut barrier function in the presence and absence of pathogens was assessed as changes in transepithelial electrical resistance (TEER) in the human T84 colonic epithelial cell line and in murine enteroid-derived monolayers (EDMs). The TEER of T84 cells monolayers rose in the presence of most of the human milk strains, being most pronounced in case of L. reuteri NPL-88 (34% within five h), exceeding the effect of the well-known probiotic L. acidophilus (20%). qRT-PCR, western blot and immunofluorescent staining associated the increase in TEER with enhanced expression of tight junction proteins. Pretreatment of murine EDMs with NPL-88 also largely prevented the ability of the pathogen, Salmonella, to decrease TEER (87 ± 1.50%; P < 0.0001, n = 4). CONCLUSIONS Human milk lactic acid bacteria are potential probiotics that can strengthen gut barrier function and protect breastfed neonates against enteric infections.
Maxwell Demon and Einstein-Podolsky-Rosen Steering

The study of Maxwell demon and quantum entanglement is important because of its foundational significance in physics and its potential applications in quantum information. Previous research on the Maxwell demon has primarily focused on thermodynamics, taking into account quantum correlations. Here we consider from another perspective and ask whether quantum non-locality correlations can be simulated by performing work. The Maxwell demon-assisted Einstein-Podolsky-Rosen (EPR) steering is thus proposed, which implies a new type of loophole. The application of Landauer's erasure principle suggests that the only way to close this loophole during a steering task is by continuously monitoring the heat fluctuation of the local environment by the participant. We construct a quantum circuit model of Maxwell demon-assisted EPR steering, which can be demonstrated by current programmable quantum processors, such as superconducting quantum computers. Based on this quantum circuit model, we obtain a quantitative formula describing the relationship between energy dissipation due to the work of the demon and quantum non-locality correlation. The result is of great physical interest because it provides a new way to explore and understand the relationship between quantum non-locality, information, and thermodynamics.
Low Frequency Quasi-periodic Oscillation in MAXI J1820+070: Revealing distinct Compton and Reflection Contributions

X-ray low frequency quasi-periodic oscillations (LFQPOs) of black hole X-ray binaries, especially those type-C LFQPOs, are representative timing signal of black hole low/hard state and intermediate state, which has been suspected as to originate due to Lense-Thirring precession of the accretion flow. Here we report an analysis of one of the Insight-HXMT observations of the black hole transient MAXI J1820+070 taken near the flux peak of its hard spectral state during which strong type-C LFQPOs were detected in all three instruments up to photon energies above 150 keV. We obtained and analyzed the short-timescale X-ray spectra corresponding to high- and low-intensity phases of the observed LFQPO waveform with a spectral model composed of Comptonization and disk reflection components. We found that the normalization of the spectral model is the primary parameter that varied between the low and high-intensity phases. The variation in the LFQPO flux at the hard X-ray band (≳ 100 keV) is from the Compton component alone, while the energy dependent variation in the LFQPO flux at lower energies (≲ 30 keV) is mainly caused by the reflection component with a large reflection fraction in response to the incident Compton component. The observed X-ray LFQPOs thus should be understood as manifesting the original timing signals or beats in the hard Compton component, which gives rise to additional variability in softer energies due to disk reflection.
Delay-bound determination for path constraint satisfaction

This paper addresses the problem of path constraint satisfaction form delay bound determination. Based on a path delay profiling tool a method is developed to determine the feasibility of delay constraint imposed on circuit path. From the evolution of the path delay profile with transistor sizing conditions, upper and lower bounds of delay are defined and characterized in terms of loading factors. Using these bounds as a reference, a method is developed to define average loading factors and equivalently transistor size allowing to satisfy timing constraint on critical path. Examples of application are given on different ISCAS circuits.
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary civilians (silent) on the twitter’s social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing Graph Neural Networks (GNNs) assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KTGNN), which models distribution-shifts during message passing and learns representation by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open-sourced1.
Reversible and DRAD difference sets in

The existence of Hadamard difference sets has been a central question in design theory. Reversible difference sets have been studied extensively. Dillon gave a method for finding reversible difference sets in groups of the form (C 2 r )2. DRAD difference sets are a newer concept. Davis and Polhill showed the existence of DRAD difference sets in the same groups as Dillon. This article determines the existence of reversible and DRAD difference sets in groups of the form (C 2 2r )3. These are the only abelian 2‐groups outside of direct products of C4 and (C 2 r )2 known to contain reversible and DRAD difference sets. © 2011 Wiley Periodicals, Inc. J Combin Designs 20:58–67, 2012
Cooperative Saliency-based Obstacle Detection and AR Rendering for Increased Situational Awareness

Autonomous vehicles are expected to operate safely in real-life road conditions in the next years. Nevertheless, unanticipated events such as the existence of unexpected objects in the range of the road, can put safety at risk. The advancement of sensing and communication technologies and Internet of Things may facilitate the recognition of hazardous situations and information exchange in a cooperative driving scheme, providing new opportunities for the increase of collaborative situational awareness. Safe and unobtrusive visualization of the obtained information may nowadays be enabled through the adoption of novel Augmented Reality (AR) interfaces in the form of windshields. Motivated by these technological opportunities, we propose in this work a saliency-based distributed, cooperative obstacle detection and rendering scheme for increasing the driver's situational awareness through (i) automated obstacle detection, (ii) AR visualization and (iii) information sharing (upcoming potential dangers) with other connected vehicles or road infrastructure. An extensive evaluation study using a variety of real datasets for pothole detection showed that the proposed method provides favorable results and features compared to other recent and relevant approaches.
Surface-phonon spectroscopy of LiF(001) by inelastic scattering of He atoms: Theory and interpretation of time-of-flight spectra

A distorted-wave theory based on a hard-corrugated-wall potential with a time-dependent modulation (dynamic corrugation) and accurately calculated surface-projected phonon densities is used to calculate the relative amplitudes of time-of-flight spectra. The calculated spectra are compared with measured spectra over a wide range of angles. In general, good agreement is obtained under conditions where final-state resonances with bound states of the atom-surface potential are not expected. When these resonance processes occur, anomalies are found in the measured spectra. The results indicate that single-phonon processes dominate the measured spectra and that under certain conditions bulk modes can affect the spectra. Resonance processes may be useful in enhancing otherwise weak interactions.
Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors

Fast generation of high-quality 3D digital humans is important to a vast number of applications ranging from entertainment to professional concerns. Recent advances in differentiable rendering have enabled the training of 3D generative models without requiring 3D ground truths. However, the quality of the generated 3D humans still has much room to improve in terms of both fidelity and diversity. In this paper, we present Get3DHuman, a novel 3D human framework that can significantly boost the realism and diversity of the generated outcomes by only using a limited budget of 3D ground-truth data. Our key observation is that the 3D generator can profit from human-related priors learned through 2D human generators and 3D reconstructors. Specifically, we bridge the latent space of Get3DHuman with that of StyleGAN-Human [13] via a specially-designed prior network, where the input latent code is mapped to the shape and texture feature volumes spanned by the pixel-aligned 3D reconstructor [50]. The outcomes of the prior network are then leveraged as the supervisory signals for the main generator network. To ensure effective training, we further propose three tailored losses applied to the generated feature volumes and the intermediate feature maps. Extensive experiments demonstrate that Get3DHuman greatly outperforms the other state-of-the-art approaches and can support a wide range of applications including shape interpolation, shape re-texturing, and single-view reconstruction through latent inversion.
Propositional Logics for the Lawvere Quantale

Lawvere showed that generalised metric spaces are categories enriched over $[0, \infty]$, the quantale of the positive extended reals. The statement of enrichment is a quantitative analogue of being a preorder. Towards seeking a logic for quantitative metric reasoning, we investigate three $[0,\infty]$-valued propositional logics over the Lawvere quantale. The basic logical connectives shared by all three logics are those that can be interpreted in any quantale, viz finite conjunctions and disjunctions, tensor (addition for the Lawvere quantale) and linear implication (here a truncated subtraction); to these we add, in turn, the constant $1$ to express integer values, and scalar multiplication by a non-negative real to express general affine combinations. Quantitative equational logic can be interpreted in the third logic if we allow inference systems instead of axiomatic systems. For each of these logics we develop a natural deduction system which we prove to be decidably complete w.r. t. the quantale-valued semantics. The heart of the completeness proof makes use of the Motzkin transposition theorem. Consistency is also decidable; the proof makes use of Fourier-Motzkin elimination of linear inequalities. Strong completeness does not hold in general, even (as is known) for theories over finitely-many propositional variables; indeed even an approximate form of strong completeness in the sense of Pavelka or Ben Yaacov -- provability up to arbitrary precision -- does not hold. However, we can show it for theories axiomatized by a (not necessarily finite) set of judgements in normal form over a finite set of propositional variables when we restrict to models that do not map variables to $\infty$; the proof uses Hurwicz's general form of the Farkas' Lemma.
On the Efficacy of Differentially Private Few-shot Image Classification

There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models. These DP models are typically pretrained on large public datasets and then fine-tuned on private downstream datasets that are relatively large and similar in distribution to the pretraining data. However, in many applications including personalization and federated learning, it is crucial to perform well (i) in the few-shot setting, as obtaining large amounts of labeled data may be problematic; and (ii) on datasets from a wide variety of domains for use in various specialist settings. To understand under which conditions few-shot DP can be effective, we perform an exhaustive set of experiments that reveals how the accuracy and vulnerability to attack of few-shot DP image classification models are affected as the number of shots per class, privacy level, model architecture, downstream dataset, and subset of learnable parameters in the model vary. We show that to achieve DP accuracy on par with non-private models, the shots per class must be increased as the privacy level increases. We also show that learning parameter-efficient FiLM adapters under DP is competitive with learning just the final classifier layer or learning all of the network parameters. Finally, we evaluate DP federated learning systems and establish state-of-the-art performance on the challenging FLAIR benchmark.
Constant RMR Recoverable Mutex under System-wide Crashes

We design two Recoverable Mutual Exclusion (RME) locks for the system-wide crash model. Our first algorithm requires only $O(1)$ space per process, and achieves $O(1)$ worst-case RMR complexity in the CC model. Our second algorithm enhances the first algorithm to achieve (the same) $O(1)$ space per process and $O(1)$ worst-case RMR complexity in both the CC and DSM models. Furthermore, both algorithms allow dynamically created threads of arbitrary names to join the protocol and access the locks. To our knowledge, these are the only RME locks to achieve worst-case $O(1)$ RMR complexity assuming nothing more than standard hardware support. In light of Chan and Woelfel's $\Omega(\log n / \log\log n)$ worst-case RMR lower bound for RME in the individual crash model, our results show a separation between the system-wide crash and individual crash models in worst-case RMR complexity in both the CC and DSM models.