Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder

By sampling from the latent space of an autoencoder and decoding the latent space samples to the original data space, any autoencoder can simply be turned into a generative model. For this to work, it is necessary to model the autoencoder's latent space with a distribution from which samples can be obtained. Several simple possibilities (kernel density estimates, Gaussian distribution) and more sophisticated ones (Gaussian mixture models, copula models, normalization flows) can be thought of and have been tried recently. This study aims to discuss, assess, and compare various techniques that can be used to capture the latent space so that an autoencoder can become a generative model while striving for simplicity. Among them, a new copula-based method, the Empirical Beta Copula Autoencoder, is considered. Furthermore, we provide insights into further aspects of these methods, such as targeted sampling or synthesizing new data with specific features.
Platelet and Erythrocyte Parameters in Type 2 Diabetic Moroccan Patients: A Cross-Sectional Study

Abstract Introduction Complete blood count parameters have gained renewed interest as predictors of endothelial dysfunction in type 2 diabetes mellitus (T2DM). Thus, the aim of this study was to observe the changes in platelet and erythrocyte parameters between diabetics and nondiabetics and to evaluate these changes in relation to glycemic control. Materials and Methods This cross-sectional study was performed in Mohammed VI University Hospital, Marrakech, Morocco, from January 2020 to July 2021. A total of 307 Moroccan patients were enrolled in this study, which included 222 diabetic patients and 85 nondiabetics. The diabetic patients were divided into two groups A (hemoglobin A1c [HbA1c] ≥ 6.5%) and B (HbA1c < 6.5%) according to their glycated hemoglobin levels (HbA1c). Biological parameters were processed as per the standard technique. Data analysis was performed using SPSS statistical software. Results A significant difference was mentioned regarding mean corpuscular volume (MCV) ( p < 0.001), red blood cell distribution width (RDW) ( p < 0.001), and volume/platelet count ( p = 0.023) between diabetics and nondiabetics. MCV and RDW were lower in group B than group A ( p = 0.004; p = 0.04), while MPV was higher ( p = 0.342). RDW significantly and negatively correlated with HbA1c in diabetic population ( r = –0.182; p = 0.006). On the other hand, a significant and positive correlation between MPV and HbA1c was mentioned (r = +0.184; p = 0.006). Conclusion Our study showed that RDW and MPV correlate with HbA1c in T2DM and can be used as a reproducible and inexpensive means to ensure proper control of glycemic status. Further studies are warranted to define specific RDW and MPV values predictive of complication risk in the diabetic population.
HiT: Building Mapping With Hierarchical Transformers

Deep-learning-based methods have been extensively explored for automatic building mapping from high-resolution remote sensing images over recent years. While most building mapping models produce vector polygons of buildings for geographic and mapping systems, dominant methods typically decompose polygonal building extraction in some subproblems, including segmentation, polygonization, and regularization, leading to complex inference procedures, low accuracy, and poor generalization. In this article, we propose a simple and novel building mapping method with hierarchical transformers, called HiT, improving polygonal building mapping quality from high-resolution remote sensing images. HiT builds on a two-stage detection architecture by adding a polygon head parallel to classification and bounding box regression heads. HiT simultaneously outputs building bounding boxes and vector polygons, which is fully end-to-end trainable. The polygon head formulates a building polygon as serialized vertices with the bidirectional characteristic, a simple and elegant polygon representation avoiding the start or end vertex hypothesis. Under this new perspective, the polygon head adopts a transformer encoder–decoder architecture to predict serialized vertices supervised by the designed bidirectional polygon loss. Furthermore, a hierarchical attention mechanism combined with convolution operation is introduced in the encoder of the polygon head, providing more geometric structures of building polygons at vertex and edge levels. Comprehensive experiments on two benchmarks (the CrowdAI and Inria datasets) demonstrate that our method achieves a new state-of-the-art (SOTA) in terms of instance segmentation and polygonal metrics compared with SOTA methods. Moreover, qualitative results verify the superiority and effectiveness of our model under complex scenes.
On adaptive kernel intensity estimation on linear networks

In the analysis of spatial point patterns on linear networks, a critical statistical objective is estimating the first-order intensity function, representing the expected number of points within specific subsets of the network. Typically, non-parametric approaches employing heating kernels are used for this estimation. However, a significant challenge arises in selecting appropriate bandwidths before conducting the estimation. We study an intensity estimation mechanism that overcomes this limitation using adaptive estimators, where bandwidths adapt to the data points in the pattern. While adaptive estimators have been explored in other contexts, their application in linear networks remains underexplored. We investigate the adaptive intensity estimator within the linear network context and extend a partitioning technique based on bandwidth quantiles to expedite the estimation process significantly. Through simulations, we demonstrate the efficacy of this technique, showing that the partition estimator closely approximates the direct estimator while drastically reducing computation time. As a practical application, we employ our method to estimate the intensity of traffic accidents in a neighbourhood in Medellin, Colombia, showcasing its real-world relevance and efficiency.
CNS: Correspondence Encoded Neural Image Servo Policy

Image servo is an indispensable technique in robotic applications that helps to achieve high precision positioning. The intermediate representation of image servo policy is important to sensor input abstraction and policy output guidance. Classical approaches achieve high precision but require clean keypoint correspondence, and suffer from limited convergence basin or weak feature error robustness. Recent learning-based methods achieve moderate precision and large convergence basin on specific scenes but face issues when generalizing to novel environments. In this paper, we encode keypoints and correspondence into a graph and use graph neural network as architecture of controller. This design utilizes both advantages: generalizable intermediate representation from keypoint correspondence and strong modeling ability from neural network. Other techniques including realistic data generation, feature clustering and distance decoupling are proposed to further improve efficiency, precision and generalization. Experiments in simulation and real-world verify the effectiveness of our method in speed (maximum 40fps along with observer), precision (<0.3{\deg} and sub-millimeter accuracy) and generalization (sim-to-real without fine-tuning). Project homepage (full paper with supplementary text, video and code): https://hhcaz.github. io/CNS-home
RECAP: Retrieval-Augmented Audio Captioning

We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and effective audio captioning system that generates captions conditioned on an input audio and other captions similar to the audio retrieved from a datastore. Additionally, our proposed method can transfer to any domain without the need for any additional fine-tuning. To generate a caption for an audio sample, we leverage an audio-text model CLAP to retrieve captions similar to it from a replaceable datastore, which are then used to construct a prompt. Next, we feed this prompt to a GPT-2 decoder and introduce cross-attention layers between the CLAP encoder and GPT-2 to condition the audio for caption generation. Experiments on two benchmark datasets, Clotho and AudioCaps, show that RECAP achieves competitive performance in in-domain settings and significant improvements in out-of-domain settings. Additionally, due to its capability to exploit a large text-captions-only datastore in a \textit{training-free} fashion, RECAP shows unique capabilities of captioning novel audio events never seen during training and compositional audios with multiple events. To promote research in this space, we also release 150,000+ new weakly labeled captions for AudioSet, AudioCaps, and Clotho.
Holistic Geometric Feature Learning for Structured Reconstruction

The inference of topological principles is a key problem in structured reconstruction. We observe that wrongly predicted topological relationships are often incurred by the lack of holistic geometry clues in low-level features. Inspired by the fact that massive signals can be compactly described with frequency analysis, we experimentally explore the efficiency and tendency of learning structure geometry in the frequency domain. Accordingly, we propose a frequency-domain feature learning strategy (F-Learn) to fuse scattered geometric fragments holistically for topology-intact structure reasoning. Benefiting from the parsimonious design, the F-Learn strategy can be easily deployed into a deep reconstructor with a lightweight model modification. Experiments demonstrate that the F-Learn strategy can effectively introduce structure awareness into geometric primitive detection and topology inference, bringing significant performance improvement to final structured reconstruction. Code and pre-trained models are available at https://github.com/Geo-Tell/F-Learn.
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy

Unlike in natural images, in endoscopy there is no clear notion of an up-right camera orientation. Endoscopic videos therefore often contain large rotational motions, which require keypoint detection and description algorithms to be robust to these conditions. While most classical methods achieve rotation-equivariant detection and invariant description by design, many learning-based approaches learn to be robust only up to a certain degree. At the same time learning-based methods under moderate rotations often outperform classical approaches. In order to address this shortcoming, in this paper we propose RIDE, a learning-based method for rotation-equivariant detection and invariant description. Following recent advancements in group-equivariant learning, RIDE models rotation-equivariance implicitly within its architecture. Trained in a self-supervised manner on a large curation of endoscopic images, RIDE requires no manual labeling of training data. We test RIDE in the context of surgical tissue tracking on the SuPeR dataset as well as in the context of relative pose estimation on a repurposed version of the SCARED dataset. In addition we perform explicit studies showing its robustness to large rotations. Our comparison against recent learning-based and classical approaches shows that RIDE sets a new state-of-the-art performance on matching and relative pose estimation tasks and scores competitively on surgical tissue tracking.
An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset

Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficient neural network for retinal vessel segmentation in OCTA images. The proposed network achieves accuracy comparable to other SOTA methods, while having fewer parameters and faster inference speed (e.g. 110x lighter and 1.3x faster than U-Net), which is very friendly for industrial applications. This is achieved by applying the modified Recurrent ConvNeXt Block to a full resolution convolutional network. In addition, we create a new dataset containing 918 OCTA images and their corresponding vessel annotations. The data set is semi-automatically annotated with the help of Segment Anything Model (SAM), which greatly improves the annotation speed. For the benefit of the community, our code and dataset can be obtained from https://github.com/nhjydywd/OCTA-FRNet.
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing Flows

Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equation. Bayesian inference for these models has been extensively studied and applied in tasks such as time series prediction. However, the use of standard GPs with basic kernels like squared exponential kernels has been common in GPODE research, limiting the model's ability to represent complex scenarios. To address this limitation, we introduce normalizing flows to reparameterize the ODE vector field, resulting in a data-driven prior distribution, thereby increasing flexibility and expressive power. We develop a data-driven variational learning algorithm that utilizes analytically tractable probability density functions of normalizing flows, enabling simultaneous learning and inference of unknown continuous dynamics. Additionally, we also apply normalizing flows to the posterior inference of GP ODEs to resolve the issue of strong mean-field assumptions in posterior inference. By applying normalizing flows in both these ways, our model improves accuracy and uncertainty estimates for Bayesian Gaussian Process ODEs. We validate the effectiveness of our approach on simulated dynamical systems and real-world human motion data, including time series prediction and missing data recovery tasks. Experimental results show that our proposed method effectively captures model uncertainty while improving accuracy.
PwR: Exploring the Role of Representations in Conversational Programming

Large Language Models (LLMs) have revolutionized programming and software engineering. AI programming assistants such as GitHub Copilot X enable conversational programming, narrowing the gap between human intent and code generation. However, prior literature has identified a key challenge--there is a gap between user's mental model of the system's understanding after a sequence of natural language utterances, and the AI system's actual understanding. To address this, we introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language. We conducted an in-lab task-centered study with 14 users of varying programming proficiency and found that representations significantly improve understandability, and instilled a sense of agency among our participants. Expert programmers use them for verification, while intermediate programmers benefit from confirmation. Natural language-based development with LLMs, coupled with representations, promises to transform software development, making it more accessible and efficient.
On Model Explanations with Transferable Neural Pathways

Neural pathways as model explanations consist of a sparse set of neurons that provide the same level of prediction performance as the whole model. Existing methods primarily focus on accuracy and sparsity but the generated pathways may offer limited interpretability thus fall short in explaining the model behavior. In this paper, we suggest two interpretability criteria of neural pathways: (i) same-class neural pathways should primarily consist of class-relevant neurons; (ii) each instance's neural pathway sparsity should be optimally determined. To this end, we propose a Generative Class-relevant Neural Pathway (GEN-CNP) model that learns to predict the neural pathways from the target model's feature maps. We propose to learn class-relevant information from features of deep and shallow layers such that same-class neural pathways exhibit high similarity. We further impose a faithfulness criterion for GEN-CNP to generate pathways with instance-specific sparsity. We propose to transfer the class-relevant neural pathways to explain samples of the same class and show experimentally and qualitatively their faithfulness and interpretability.
Genetic architecture of telomere length in 462,675 UK Biobank whole-genome sequences

Telomeres protect the ends of chromosomes from damage, and genetic regulation of their length is associated with human disease and ageing. We developed a joint telomere length (TL) metric, combining both qPCR and whole genome sequencing (WGS) measurements across 462,675 UK Biobank participants that increased our ability to capture TL heritability by 36% (h2mean=0.058 to h2combined=0.079) and improved predictions of age. Exome-wide rare variant (minor allele frequency<0.001) and gene-level collapsing association studies identified 53 variants and 22 genes significantly associated with TL that included allelic series in ACD and RTEL1. Five of the 31 rare-variant TL associated genes (16%) were also known drivers of clonal haematopoiesis (CH), prompting somatic variant analyses. Stratifying by CH clone size, we uncovered novel gene-specific associations with TL, including lengthened telomeres in individuals with large SRSF2-mutant clones, in contrast to the progressive telomere shortening observed with increasing clonal expansions driven by other CH genes. Our findings demonstrate the impact of rare variants on TL with larger effects in genes associated with CH, a precursor of myeloid cancers and several other non-malignant human diseases. Telomere biology is likely to be an important focus for the prevention and treatment of these conditions.
Living alone predicts poor prognosis among patients with acute myocardial infarction

Background Living alone as a proxy for social isolation has been considered to increase the risk of cardiovascular disease. We thus investigated the impact of living alone on mortality in acute myocardial infarction (AMI) patients. Methods Subjects comprised 277 AMI patients who underwent percutaneous coronary intervention (PCI). Associations between all-cause and cardiac deaths after PCI and baseline characteristics including living alone and Global Registry of Acute Coronary Events (GRACE) risk score were assessed. Results Eighty-three patients (30%) were living alone. Thirty patients died after PCI, including 20 cardiac deaths. Patients living alone showed higher incidences of both all-cause and cardiac deaths compared with patients not living alone (18% vs. 8%, P = 0.019 and 14% vs. 4%, P = 0.004). Multivariate Cox proportional hazards regression analysis models showed living alone [hazard ratio (HR), 2.60; 95% confidence interval (CI), 1.20–5.62; P = 0.016 and HR, 4.17; 95% CI, 1.60–10.84; P = 0.003] and GRACE risk score (HR, 1.02; 95% CI, 1.01–1.03; P = 0.003 and HR, 1.03; 95% CI, 1.01–1.04; P < 0.001) correlated significantly with all-cause and cardiac deaths. Cox proportional hazards modeling revealed that patients living alone with GRACE risk score ≥162 derived from the receiver-operating characteristic curve showed a significantly greater risk of all-cause death than patients not living alone with GRACE risk score <162 (HR 16.57; 95% CI 6.67–41.21; P < 0.001). Conclusion Among AMI patients, living alone represents an independent risk factor for all-cause and cardiac deaths after PCI, separate from GRACE risk score. Furthermore, AMI patients living alone with high GRACE risk scores may experience an additively increased risk of mortality after PCI.
On logarithmic double phase problems

In this paper we introduce a new logarithmic double phase type operator of the form\begin{align*}\mathcal{G}u:=-\operatorname{div}\left(|\nabla u|^{p(x)-2}\nabla u+\mu(x)\left[\log(e+|\nabla u|)+\frac{|\nabla u|}{q(x)(e+|\nabla u|)}\right]|\nabla u|^{q(x)-2} \nabla u \right),\end{align*}where $\Omega\subseteq\mathbb{R}^N$, $N\geq 2$, is a bounded domain with Lipschitz boundary $\partial\Omega$, $p,q\in C(\overline{\Omega})$ with $1<p(x)\leq q(x)$ for all $x\in\overline{\Omega}$ and $\mu\in L^1(\Omega)$. First, we prove that the logarithmic Musielak-Orlicz Sobolev spaces $W^{1,\mathcal{H}_{\log}}(\Omega)$ and $W^{1, \mathcal{H}_{\log}}_0(\Omega)$ with $\mathcal{H}_{\log}(x,t)=t^{p(x)}+\mu(x)t^{q(x)}\log(e+t)$ for $(x,t)\in \overline{\Omega}\times [0,\infty)$ are separable, reflexive Banach spaces and $W^{1,\mathcal{H}_{\log}}_0(\Omega)$ can be equipped with an equivalent norm. We also prove several embedding results for these spaces and the closedness of these spaces under truncations. In addition we show the density of smooth functions in $W^{1,\mathcal{H}_{\log}}(\Omega)$ even in the case of an unbounded domain by supposing Nekvinda's decay condition on $p(\cdot)$. The second part is devoted to the properties of the operator and it turns out that it is bounded, continuous, strictly monotone, of type (S$_+$), coercive and a homeomorphism. As a result of independent interest we also present a new version of Young's inequality for the product of a power-law and a logarithm. In the last part of this work we consider equations driven by our new operator with superlinear right-hand sides. We prove multiplicity results for this type of equation, in particular about sign-changing solutions, by making use of a suitable variation of the corresponding Nehari manifold together with the quantitative deformation lemma and the Poincar\'e-Miranda existence theorem.
RELIABILITY AND VALIDITY OF THE SUPINE-TO-STAND TEST IN PEOPLE WITH STROKE

Objective To investigate the psychometric properties of the supine-to-stand test in people with stroke. Design Cross-sectional design. Subjects Fifty-two people with stroke (mean (standard deviation) age 63.13 (6.09) years; time post-stroke 93.13 (61.36) months) and 49 healthy older adults (61.90 (7.29) months). Methods Subjects with stroke were recruited from the community dwelling in Hong Kong and assessed with the supineto- stand test, Fugl-Meyer Motor Assessment, ankle muscle strength test, Berg Balance Scale, limit of stability test, Timed Up-and-Go Test, Six-Minute Walk Test, Chinese version of Activities-specific Balance Confidence scale, Community Integration Measure (CIM-C), and 12-item Short-Form Health Survey (SF-12) in a university-based rehabilitation laboratory. Results The supine-to-stand test completion time demonstrated excellent intra-rater, inter-rater and test-retest reliability (intraclass correlation coefficient 0.946–1.000) for the people with stroke. The completion time was significantly negatively correlated with Berg Balance Scale, Six-Minute Walk Test, limit of stability – maximal excursion, and limit of stability – endpoint excursion results (r = –0.391 to –0.507), whereas it was positively correlated with the Timed Up-and-Go test results (r = 0.461). The optimal cut-off supine-to-stand test completion time of 5.25 s is feasible for a clinical measure to distinguish the performance of people with stroke from healthy older adults (area under the curve = 0.852, sensitivity = 81.1%, specificity = 84.0%). Conclusion The supine-to-stand test is a reliable, sensitive, specific and easy-to-administer clinical test for assessing the supine-to-stand ability of people with stroke. LAY ABSTRACT Adequate supine-to-stand ability is essential to prevent the secondary injury from fall. However, no previous study investigated the supine-to-stand ability in people with stroke. The objectives of this study were to investigate whether the supine-to-stand test is a reliable and valid outcome measure used in stroke rehabilitation. Our results demonstrate that supine-to-stand test completion time is reliable, and is significantly correlated with clinical and laboratory balance performance, walking endurance and level of functional mobility in people with stroke. Thus, the supine-to-stand test is a reliable, sensitive, specific and easy-to-administer clinical test for assessing the supine-to-stand ability of people with stroke.