Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Nurse Practitioner Students’ Perceptions of a Sleep e-Learning Program

Abstract AIM The aim of this study was to explore nurse practitioner (NP) students’ perceptions of a sleep e-learning program. BACKGROUND Sleep assessment is uncommon as nursing curricula lack sleep education. By preparing NPs to conduct sleep assessment and screening and understand basic sleep diagnostics, sleep health is more likely to be part of the differential diagnosis. METHOD The study is a qualitative descriptive study utilizing two focus groups. A directed content analysis, guided by the Kirkpatrick model, was used for analysis. RESULTS Twenty-four students participated in focus groups. Two overarching themes emerged: perceptions of course design and content. Asynchronous, case-based scenarios and quizzes were favorable. Students spoke of content relevance to themselves and patients and intentions to adopt sleep assessment practices. CONCLUSION NP students embraced sleep education and declared intention to apply learned skills in practice. This study highlights the feasibility of increasing curricular exposure to sleep education and ensuring NPs have skills to recognize implications of poor and disordered sleep in patients.
Simultaneous sensing of fluid velocity and temperature using particle tracers embedding nitrobenzofurazan functionalized thermosensitive hydrogels

Particle image velocimetry is an experimental technique for measuring the velocity field of a moving fluid by tracking small particles dispersed within the flow. To extend this technique to the study of fluid temperature, we propose a novel tracer particle which enables direct measurement of the velocity, while acting as a temperature sensor by increasing its fluorescence intensity when the local fluid temperature rises above 32°C. Thermoresponsive tracers are prepared by incorporating nitrobenzofurazan functionalized hydrogels within optically transparent polydimethylsiloxane microspheres. We demonstrate the application of the tracers in the study of forced thermal convection in water around a heated cylinder in an open channel.
Computer Self Efficacy: A Replication After Thirty Years

This work replicates Compeau and Higgins (1995) study of computer self-efficacy (CSE). Nearly 30 years have passed since those data were collected, and the CSE concept and measurement instrument have been widely used with very limited change. This, despite extensive changes in both the technological and user environment. The original study was conducted using a mail survey of professional workers who learned to use computers for business related tasks in organizational settings. We conduct a conceptual replication with digital natives (undergraduate business students) who were learning to use computers for business related tasks in a university lab setting. We test the original model, with the measures adapted as needed to match the context. Our results confirm some but not all the initial study’s hypotheses (9 replication study vs. 16 original study). These findings suggest the need for additional investigation into the utility of the original CSE conceptualization and the implications of computer self-efficacy in computer use for contemporary IS contexts.
E2E near-standard and practical authenticated transciphering

Homomorphic encryption (HE) enables computation delegation to untrusted third parties while maintaining data confidentiality. Hybrid encryption (a.k.a transciphering) allows a reduction in the number of ciphertexts and storage size, which makes FHE solutions practical for a variety of modern applications. Still, modern tran-sciphering has three main drawbacks: 1) lack of standardization or bad performance of symmetric decryption under FHE; 2) post-HE-evaluation is limited to small-size applications; 3) lack of input data integrity. Interestingly, modern-size secure inference applications were demonstrated using approximated FHE schemes such as CKKS. However, implementing transciphering using standard Authenticated Encryption (AE) over CKKS is challenging due to its approximated nature. In this paper, we aim to close these gaps. First, we report and demonstrate the first end-to-end process that uses transciphering for real-world applications i.e., running deep neural network (DNN) inference under encryption. For that, we discuss the concept of Au-thenticated Transciphering (AT), which like AE, provides some integrity guarantees for the transciphered data. Finally, to demonstrate the AT concept, we report on the first implementation of Ascon decryption under CKKS, and complete the picture with a detailed technical description of our AES-GCM implementation under CKKS.
Global Tides Global Tides

This study examined the relationship between music preference and extraversion on complex task performance in a sample of 34 college students from a small, Christian, liberal arts university. Separated into two groups of high and low extraversion, these 34 participants were invited to participate in the experimental phase of the study. For the experimental phase, each participant experienced three different music conditions (Preferred, Preset, and Silence) while performing a complex reading comprehension task. The results revealed a significant interaction effect between level of extraversion and music condition. Individuals with higher levels of extraversion performed marginally significantly better listening to preferred music during the complex task compared to silence and a preset music selection; all other sound conditions were not significant.
EEG-based functional networks evoked by acupuncture at ST 36: A data-driven thresholding study

This paper investigates how acupuncture at ST 36 modulates the brain functional network. 20 channel EEG signals from 15 healthy subjects are respectively recorded before, during and after acupuncture. The correlation between two EEG channels is calculated by using Pearson’s coefficient. A data-driven approach is applied to determine the threshold, which is performed by considering the connected set, connected edge and network connectivity. Based on such thresholding approach, the functional network in each acupuncture period is built with graph theory, and the associated functional connectivity is determined. We show that acupuncturing at ST 36 increases the connectivity of the EEG-based functional network, especially for the long distance ones between two hemispheres. The properties of the functional network in five EEG sub-bands are also characterized. It is found that the delta and gamma bands are affected more obviously by acupuncture than the other sub-bands. These findings highlight the modulatory effects of acupuncture on the EEG-based functional connectivity, which is helpful for us to understand how it participates in the cortical or subcortical activities. Further, the data-driven threshold provides an alternative approach to infer the functional connectivity under other physiological conditions.
RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent

With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field generation from sparse view inputs. Existing methods can be classified into offline techniques, which can generate high-quality novel views but at the cost of long inference/training time, and online methods, which either lack generalizability or produce unsatisfactory results. However, we have observed that the intrinsic sparse manifold of Multi-plane Images (MPI) enables a significant acceleration of light field generation while maintaining rendering quality. Based on this insight, we introduce EffLiFe, a novel light field optimization method, which leverages the proposed Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light fields from sparse view images in real time. Technically, the coarse MPI of a scene is first generated using a 3D CNN, and it is further sparsely optimized by focusing only on important MPI gradients in a few iterations. Nevertheless, relying solely on optimization can lead to artifacts at occlusion boundaries. Therefore, we propose an occlusion-aware iterative refinement module that removes visual artifacts in occluded regions by iteratively filtering the input. Extensive experiments demonstrate that our method achieves comparable visual quality while being 100x faster on average than state-of-the-art offline methods and delivering better performance (about 2 dB higher in PSNR) compared to other online approaches.
RELAXATION OF THE CONTACT LINE IN THE WILHELMY-PLATE GEOMETRY: ASYMPTOTIC SOLUTIONS OF ? COMBINED CONTACT LINE DISSIPATION MODEL

The quasi-static motion is considered of straight contact lines when vertical homogeneous solid plate is withdrawn from a bath of liquid in the partial wetting regime. We apply the contact line dissipation approach to describe the relaxation of the contact line taking into account the friction dissipation at the moving contact line and the viscous flow dissipation in the wedge. Asymptotic solutions are derived of the differential equations describing the capillary rise height and the contact angle relaxation. We find that the time relaxation of the height and the cosine of the contact angle are given by sums of exponential functions up to a second order in the expansion of the small parameter. We point out the implications which follow when only one dissipation channel is taken into account and compare them to the case when both dissipation channels are included.
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

We tackle the problems of latent variables identification and ``out-of-support'' image generation in representation learning. We show that both are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.
Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of"pseudo label"during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate"pseudo label"from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.
Efficient learning of the structure and parameters of local Pauli noise channels

The unavoidable presence of noise is a crucial roadblock for the development of large-scale quantum computers and the ability to characterize quantum noise reliably and efficiently with high precision is essential to scale quantum technologies further. Although estimating an arbitrary quantum channel requires exponential resources, it is expected that physically relevant noise has some underlying local structure, for instance that errors across different qubits have a conditional independence structure. Previous works showed how it is possible to estimate Pauli noise channels with an efficient number of samples in a way that is robust to state preparation and measurement errors, albeit departing from a known conditional independence structure. We present a novel approach for learning Pauli noise channels over n qubits that addresses this shortcoming. Unlike previous works that focused on learning coefficients with a known conditional independence structure, our method learns both the coefficients and the underlying structure. We achieve our results by leveraging a groundbreaking result by Bresler for efficiently learning Gibbs measures and obtain an optimal sample complexity of O(log(n)) to learn the unknown structure of the noise acting on n qubits. This information can then be leveraged to obtain a description of the channel that is close in diamond distance from O(poly(n)) samples. Furthermore, our method is efficient both in the number of samples and postprocessing without giving up on other desirable features such as SPAM-robustness, and only requires the implementation of single qubit Cliffords. In light of this, our novel approach enables the large-scale characterization of Pauli noise in quantum devices under minimal experimental requirements and assumptions.
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference

Autoregressive large language models (LLMs) have made remarkable progress in various natural language generation tasks. However, they incur high computation cost and latency resulting from the autoregressive token-by-token generation. To address this issue, several approaches have been proposed to reduce computational cost using early-exit strategies. These strategies enable faster text generation using reduced computation without applying the full computation graph to each token. While existing token-level early exit methods show promising results for online inference, they cannot be readily applied for batch inferencing and Key-Value caching. This is because they have to wait until the last token in a batch exits before they can stop computing. This severely limits the practical application of such techniques. In this paper, we propose a simple and effective token-level early exit method, SkipDecode, designed to work seamlessly with batch inferencing and KV caching. It overcomes prior constraints by setting up a singular exit point for every token in a batch at each sequence position. It also guarantees a monotonic decrease in exit points, thereby eliminating the need to recompute KV Caches for preceding tokens. Rather than terminating computation prematurely as in prior works, our approach bypasses lower to middle layers, devoting most of the computational resources to upper layers, allowing later tokens to benefit from the compute expenditure by earlier tokens. Our experimental results show that SkipDecode can obtain 2x to 5x inference speedups with negligible regression across a variety of tasks. This is achieved using OPT models of 1.3 billion and 6.7 billion parameters, all the while being directly compatible with batching and KV caching optimization techniques.
MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection

Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forgery face images. In response, various deepfake detection methods have been proposed to assess image authenticity. Sequential deepfake detection, which is an extension of deepfake detection, aims to identify forged facial regions with the correct sequence for recovery. Nonetheless, due to the different combinations of spatial and sequential manipulations, forgery face images exhibit substantial discrepancies that severely impact detection performance. Additionally, the recovery of forged images requires knowledge of the manipulation model to implement inverse transformations, which is difficult to ascertain as relevant techniques are often concealed by attackers. To address these issues, we propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forgery face images and achieve recovery without requiring knowledge of the corresponding manipulation method. Furthermore, existing evaluation metrics only consider detection accuracy at a single inferring step, without accounting for the matching degree with ground-truth under continuous multiple steps. To overcome this limitation, we propose a novel evaluation metric called Complete Sequence Matching (CSM), which considers the detection accuracy at multiple inferring steps, reflecting the ability to detect integrally forged sequences. Extensive experiments on several typical datasets demonstrate that MMNet achieves state-of-the-art detection performance and independent recovery performance. Code will be available at https://github.com/xarryon/MMNet
Reconstructing the boundary of AdS from an infrared defect

We argue that the boundary of an asymptotically anti-de Sitter (AdS) space of dimension $d+1$, say $M^{d+1}$, can be locally reconstructed from a codimension-two defect located in the deep interior of a negatively curved Einstein manifold $X^{d+2}$ of one higher dimension. This means that there exist two different ways of thinking about the same $d$-submanifold, $\Sigma^d$: either as a defect embedded in the interior of $X^{d+2}$, or as the boundary of $M^{d+1}$ in a certain zero radius limit. Based on this idea and other geometric and symmetry arguments, we propose the existence of an infrared field theory on a bulk $\mathbb Z_n$-orbifold defect, located in the deepest point of the interior of AdS$^{d+2}$. We further conjecture that such a theory gives rise to the holographic theory at the asymptotic boundary of AdS$^{d+1}$, in the limit where the orbifold parameter $n\to\infty$. As an example, we compute a defect central charge when $\Sigma$ is a 2-manifold of fixed positive curvature, and show that its $n\to\infty$ limit reproduces the central charge of Brown and Henneaux.
Generalized Lotka-Volterra Systems with Time Correlated Stochastic Interactions

The dynamics of species communities are typically modelled considering fixed parameters for species interactions. The problem of over-parameterization that ensues when considering large communities has been overcome by sampling species interactions from a probability distribution. However, species interactions are not fixed in time, but they can change on a time scale comparable to population dynamics. Here we investigate the impact of time-dependent species interactions using the generalized Lotka-Volterra model, which serves as a paradigmatic theoretical framework in several scientific fields. In this work we model species interactions as stochastic colored noise. Assuming a large number of species and a steady state, we obtain analytical predictions for the species abundance distribution, which matches well empirical observations. In particular, our results suggest the absence of extinctions, unlike scenarios with fixed species interactions.
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition

Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli.
Particle current, noise, and counting statistics of quantum transport in the presence of a single-particle loss

How dissipation affects transport is an important theme in quantum science. Here we theoretically investigate an impact of a single-particle loss in mesoscopic transport, which has been an issue in experiments of ultracold atomic gases. By explicitly analyzing quantum point contact and quantum dot systems, we obtain a cumulant generating function on the particle current whose formal expression turns out to be common to two systems. In terms of this generating function, behaviors of average current, particle loss rate, and noises in presences of losses introduced in conduction channels are exemplified for free fermions. It is shown that the current noise contains the component proportional to the particle loss rate, which may be measurable in experiments.
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight

This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case. Motivated by real-world settings such as loading in game playing, we propose an enhanced feedback model called ``multiple observations in hindsight'', where after each episode of interaction with the POMDP, the learner may collect multiple additional observations emitted from the encountered latent states, but may not observe the latent states themselves. We show that sample-efficient learning under this feedback model is possible for two new subclasses of POMDPs: \emph{multi-observation revealing POMDPs} and \emph{distinguishable POMDPs}. Both subclasses generalize and substantially relax \emph{revealing POMDPs} -- a widely studied subclass for which sample-efficient learning is possible under standard trajectory feedback. Notably, distinguishable POMDPs only require the emission distributions from different latent states to be \emph{different} instead of \emph{linearly independent} as required in revealing POMDPs.