Miracle-dz/UniFAR-FTUs huggingface dataset:
Camera PPG waveforms at the forehead

In order to obtain insights into the feasibility of replacing ECG-guided triggering in magnetic resonance imaging (MRI) by a system based on video photoplethysmography (PPG), PPG and ECG data were collected from volunteers in an MRI scanner. PPG waveforms obtained using remote camera PPG directed at the forehead are studied in qualitative and quantitative sense over a number of volunteers. The data analysis considers variations in PPG waveforms across volunteers, modelling of the waveforms in Fourier series, dependencies of waveforms and features on the interbeat interval (IBI) and breath-holding, and models for ECG-blind estimation of R-peak position. The main findings are that the PPG waveform depends on the volunteer and that its shape changes with IBI and does not depend on breath-holding in the given scenario. Low-order harmonic models provide accurate approximations to the PPG waveform, where for higher IBI the waveform shows more temporal details. Accurate predictions (20 ms std) of the delays between markers in ECG and PPG appear feasible from a single PPG feature.
Winsorized Robust Credibility Models

The B\"uhlmann model, a branch of classical credibility theory, has been successively applied to the premium estimation for group insurance contracts and other insurance specifications. In this paper, we develop a robust B\"uhlmann credibility via the censored version of loss data, or the censored mean (a robust alternative to traditional individual mean). This framework yields explicit formulas of structural parameters in credibility estimation for both scale-shape distribution families, location-scale distribution families, and their variants, which are commonly used to model insurance risks. The asymptotic properties of the proposed method are provided and corroborated through simulations, and their performance is compared to that of credibility based on the trimmed mean. By varying the censoring/trimming threshold level in several parametric models, we find all structural parameters via censoring are less volatile compared to the corresponding quantities via trimming, and using censored mean as a robust risk measure will reduce the influence of parametric loss assumptions on credibility estimation. Besides, the non-parametric estimations in credibility are discussed using the theory of $L-$estimators. And a numerical illustration from Wisconsin Local Government Property Insurance Fund indicates that the proposed robust credibility can prevent the effect caused by model mis-specification and capture the risk behavior of loss data in a broader viewpoint.
Gamma-ray blazars and active galactic nuclei seen by the Fermi-LAT

Abstract The third catalog of active galactic nuclei (AGNs) detected by the Fermi-LAT (3LAC) is presented. It is based on the third Fermi-LAT catalog (3FGL) of sources detected with a test statistic (TS) greater than 25 using the first 4 years of data. The 3LAC includes 1591 AGNs located at high Galactic latitudes, |b| > 10 (with 28 duplicate associations, thus corresponding to 1563 gamma-ray sources among 2192 sources in the 3FGL catalog), a 71% increase over the second catalog based on 2 years of data. A very large majority of these AGNs (98%) are blazars. About half of the newly detected blazars are of unknown type, i.e., they lack spectroscopic information of sufficient quality to determine the strength of their emission lines. The general properties of the 3LAC sample confirm previous findings from earlier catalogs, but some new subclasses (e.g., intermediate- and high-synchrotron-peaked FSRQs) have now been significantly detected.
Korevaar-Schoen spaces on Sierpi\'nski carpets

We prove that certain $L^p$-regularity functional inequality holds on \emph{generalized Sierpi\'nski carpets}. This gives an affirmative answer to an open question raised by Fabrice Baudoin. Our technique originates from an old idea of Alf Jonsson in 1996.
Listener Model for the PhotoBook Referential Game with CLIPScores as Implicit Reference Chain

PhotoBook is a collaborative dialogue game where two players receive private, partially-overlapping sets of images and resolve which images they have in common.It presents machines with a great challenge to learn how people build common ground around multimodal context to communicate effectively.Methods developed in the literature, however, cannot be deployed to real gameplaysince they only tackle some subtasks of the game,and they require additional reference chains inputs, whose extraction process is imperfect.Therefore, we propose a reference chain-free listener modelthat directly addresses the game’s predictive task, i.e., deciding whether an image is shared with partner.Our DeBERTa-based listener model reads the full dialogue, and utilizesCLIPScore features to assess utterance-image relevance.We achieve >77% accuracy on unseen sets of images/game themes, outperforming baseline by >17 points.
Testing Regular Black Holes with X-ray data of GX~339--4

Regular black holes are singularity-free black hole spacetimes proposed to solve the problem of the presence of spacetime singularities that plagues the black holes of general relativity and most theories of gravity. In this work, we consider the regular black holes recently proposed by Mazza, Franzin \&Liberati and we extend previous studies to get a more stringent observational constraint on the regularization parameter $l$. We study simultaneous observations of \textit{NuSTAR} and \textit{Swift} of the Galactic black hole in GX~339--4 during its outburst in 2015. The quality of the \textit{NuSTAR} data is exceptionally good and the spectrum of the source presents both a strong thermal component and prominent relativistically blurred reflection features. This permits us to measure the regularization parameter $l$ from the simultaneous analysis of the thermal spectrum and the reflection features. From our analysis, we find the constraint $l/M<0.39$ (90\% CL), which is stronger than previous constraints inferred with X-ray and gravitational wave data.
On the Sensitivity of Tropical Cyclone Intensification under Upper-Level Trough Forcing

This study is part of the efforts undertaken to resolve the “bad trough/good trough” issue for tropical cyclone (TC) intensity changes and to improve the prediction of these challenging events. Sensitivity experiments are run at 8-km resolution with vortex bogusing to extend the previous analysis of a real case of TC–trough interaction (Dora in 2007). The initial position and intensity of the TC are modified, leaving the trough unchanged to describe a realistic environment. Simulations are designed to analyze the sensitivity of TC prediction to both the variety of TC–trough configurations and the current uncertainty in model analysis of TC intensity and position. Results show that TC intensification under upper-level forcing is greater for stronger vortices. The timing and geometry of the interaction between the two cyclonic potential vorticity anomalies associated with the cutoff low and the TC also play a major role in storm intensification. The intensification rate increases when the TC (initially located 12° northwest of the trough) is displaced 1° closer. By allowing a gradual deformation and equatorward tilting of the trough, both scenarios foster an extended “inflow channel” of cyclonic vorticity at midlevels toward the vortex inner core. Conversely, unfavorable interaction is found for vortices displaced 3° or 4° east or northeast. Variations in environmental forcing relative to the reference simulation illustrate that the relationship between intensity change and the 850–200-hPa wind shear is not systematic and that the 200-hPa divergence, 335–350-K mean potential vorticity, or 200-hPa relative eddy momentum fluxes may be better predictors of TC intensification during TC–trough interactions.
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation

Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL) techniques, they do not usually design evaluations with high scaling factors, capping it at 2x or 4x. Moreover, the datasets are generally benchmarks which do not truly encompass significant diversity of domains to proper evaluate the techniques. It is also interesting to remark that blind SR is attractive for real-world scenarios since it is based on the idea that the degradation process is unknown, and hence techniques in this context rely basically on low-resolution (LR) images. In this article, we present a high-scale (8x) controlled experiment which evaluates five recent DL techniques tailored for blind image SR: Adaptive Pseudo Augmentation (APA), Blind Image SR with Spatially Variant Degradations (BlindSR), Deep Alternating Network (DAN), FastGAN, and Mixture of Experts Super-Resolution (MoESR). We consider 14 small datasets from five different broader domains which are: aerial, fauna, flora, medical, and satellite. Another distinctive characteristic of our evaluation is that some of the DL approaches were designed for single-image SR but others not. Two no-reference metrics were selected, being the classical natural image quality evaluator (NIQE) and the recent transformer-based multi-dimension attention network for no-reference image quality assessment (MANIQA) score, to assess the techniques. Overall, MoESR can be regarded as the best solution although the perceptual quality of the created HR images of all the techniques still needs to improve. Supporting code: https://github.com/vsantjr/DL_BlindSR. Datasets: https://www.kaggle. com/datasets/valdivinosantiago/dl-blindsr-datasets.
Studying Generalization on Memory-Based Methods in Continual Learning

One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting. To mitigate complete knowledge overwriting, memory-based methods store a percentage of previous data distributions to be used during training. Although these methods produce good results, few studies have tested their out-of-distribution generalization properties, as well as whether these methods overfit the replay memory. In this work, we show that although these methods can help in traditional in-distribution generalization, they can strongly impair out-of-distribution generalization by learning spurious features and correlations. Using a controlled environment, the Synbol benchmark generator (Lacoste et al., 2020), we demonstrate that this lack of out-of-distribution generalization mainly occurs in the linear classifier.
Barzilai-Borwein Proximal Gradient Methods for Multiobjective Composite Optimization Problems with Improved Linear Convergence

When minimizing a multiobjective optimization problem (MOP) using multiobjective gradient descent methods, the imbalances among objective functions often decelerate the convergence. In response to this challenge, we propose two types of the Barzilai-Borwein proximal gradient method for multi-objective composite optimization problems (BBPGMO). We establish convergence rates for BBPGMO, demonstrating that it achieves rates of $O(\frac{1}{\sqrt{k}})$, $O(\frac{1}{k})$, and $O(r^{k})(0<r<1)$ for non-convex, convex, and strongly convex problems, respectively. Furthermore, we show that BBPGMO exhibits linear convergence for MOPs with several linear objective functions. Interestingly, the linear convergence rate of BBPGMO surpasses the existing convergence rates of first-order methods for MOPs, which indicates its enhanced performance and its ability to effectively address imbalances from theoretical perspective. Finally, we provide numerical examples to illustrate the efficiency of the proposed method and verify the theoretical results.
Physico-chemical characteristics and sensory quality of pineapple vermouth

Pineapple fruits are highly perishable and have a limited shelf-life. In an attempt to add value to the seasonal fruits, vermouth was developed using pineapple wine as a base material. The pineapple wine had 7.9% of total soluble solids, 1.23% of total sugars and 0.63% of acidity. The effect of alcohol (15 and 17%), sugar (8 and 10%) and spice extract levels (2.5 and 5%) on the physico-chemical characteristics and sensory quality on pineapple vermouth was evaluated. Vermouth with 17% alcohol level showed a pronounced increase in TSS, pH, alcohol and esters than corresponding vermouth with 15% alcohol level. Increasing the sugar level notably increased the TSS and sugar content. Addition of spice extract increased the level of esters, phenolic content, titratable acidity and colour units. Pineapple vermouth with 17% alcohol, 8% sugar and 2.5% spice extract had the highest sensory score (16.69) and considered the most acceptable product.
Induced gravitational waves via warm natural inflation

We analyze the spectrum of gravitational waves generated by the induced spectrum of tensor fluctuation during warm natural inflation. In our previous work it has been demonstrated that an epoch of warm natural inflation can lead to cosmologically relevant dark matter production in the form of primordial black holes. Here we show that models which solve the dark-matter production also produce a contribution to the cosmic gravitational wave background that satisfies current constraints from pulsar timing and big bang nucleosynthesis. More importantly, this gravitational wave background may be observable in the next generation of space-based and ground-based gravitational wave interferometers.
Linear convergence of Nesterov-1983 with the strong convexity

For modern gradient-based optimization, a developmental landmark is Nesterov's accelerated gradient descent method, which is proposed in [Nesterov, 1983], so shorten as Nesterov-1983. Afterward, one of the important progresses is its proximal generalization, named the fast iterative shrinkage-thresholding algorithm (FISTA), which is widely used in image science and engineering. However, it is unknown whether both Nesterov-1983 and FISTA converge linearly on the strongly convex function, which has been listed as the open problem in the comprehensive review [Chambolle and Pock, 2016, Appendix B]. In this paper, we answer this question by the use of the high-resolution differential equation framework. Along with the phase-space representation previously adopted, the key difference here in constructing the Lyapunov function is that the coefficient of the kinetic energy varies with the iteration. Furthermore, we point out that the linear convergence of both the two algorithms above has no dependence on the parameter $r$ on the strongly convex function. Meanwhile, it is also obtained that the proximal subgradient norm converges linearly.
Uniform bounds of families of analytic semigroups and Lyapunov Linear Stability of planar fronts

We study families of analytic semigroups, acting on a Banach space, and depending on a parameter, and give sufficient conditions for existence of uniform with respect to the parameter norm bounds using spectral properties of the respective semigroup generators. In particular, we use estimates of the resolvent operators of the generators along vertical segments to estimate the growth/decay rate of the norm for the family of analytic semigroups. These results are applied to prove the Lyapunov linear stability of planar traveling waves of systems of reaction–diffusion equations, and the bidomain equation, important in electrophysiology.
Unique information from common diffusion MRI models about white-matter differences across the human adult lifespan

Abstract Diffusion Magnetic Resonance Imaging (dMRI) is sensitive to white matter microstructural changes across the human lifespan. Several models have been proposed to provide more sensitive and specific metrics than those provided by the conventional Diffusion Tensor Imaging (DTI) analysis. However, previous results using different metrics have led to contradictory conclusions regarding the effect of age on fibre demyelination and axonal loss in adults. Moreover, it remains unclear whether these metrics provide distinct information about the effects of age, for example, on different white-matter tracts. To address this, we analysed dMRI data from 651 adults approximately uniformly aged from 18 to 88 years in the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort, using six dMRI metrics: Fractional Anisotropy (FA) from standard DTI; Mean Signal Diffusion (MSD) and Mean Signal Kurtosis (MSK) from Diffusional Kurtosis Imaging (DKI) applied to directional averaged diffusion-weighted signals; and Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and isotropic Free water volume fraction (Fiso) estimated from Neurite Orientation Dispersion and Density Imaging (NODDI). Averaging across white-matter regions-of-interest (ROIs), second-order polynomial fits revealed that MSD, MSK, and Fiso showed the strongest effects of age, with significant quadratic components suggesting more rapid and sometimes inverted effects in old age. Analysing the data in different age subgroups revealed that some apparent discrepancies in previous studies may be explained by the use of cohorts with different age ranges. Factor analysis of the six metrics across all ROIs revealed three independent factors that can be associated to 1) tissue microscopic properties (e.g., differences in fibre density/myelin), 2) free-water contamination, and 3) tissue configuration complexity (e.g., crossing, dispersing, fanning fibres). While FA captures a combination of different factors, other dMRI metrics are strongly aligned to specific factors (NDI and MSK with Factor 1, Fiso with Factor 2, and ODI with Factor 3). To assess whether directional diffusion and kurtosis quantities provide additional information about the effects of age, further factor analyses were also performed, which showed that additional information about the effects of age may be present in radial and axial kurtosis estimates (but not standard axial and radial diffusivity). In summary, our study offers an explanation for previous discrepancies reported in dMRI ageing studies and provides further insights on the interpretation of different dMRI metrics in the context of white-matter microstructural properties.
Vacant Holes for Unsupervised Detection of the Outliers in Compact Latent Representation

Detection of the outliers is pivotal for any machine learning model deployed and operated in real-world. It is essential for the Deep Neural Networks that were shown to be overconfident with such inputs. Moreover, even deep generative models that allow estimation of the probability density of the input fail in achieving this task. In this work, we concentrate on the specific type of these models: Variational Autoencoders (VAEs). First, we unveil a significant theoretical flaw in the assumption of the classical VAE model. Second, we enforce an accommodating topological property to the image of the deep neural mapping to the latent space: compactness to alleviate the flaw and obtain the means to provably bound the image within the determined limits by squeezing both inliers and outliers together. We enforce compactness using two approaches: (i) Alexandroff extension and (ii) fixed Lipschitz continuity constant on the mapping of the encoder of the VAEs. Finally and most importantly, we discover that the anomalous inputs predominantly tend to land on the vacant latent holes within the compact space, enabling their successful identification. For that reason, we introduce a specifically devised score for hole detection and evaluate the solution against several baseline benchmarks achieving promising results.
WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition

Though research has shown the complementarity of camera- and inertial-based data, datasets which offer both egocentric video and inertial-based sensor data remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). The dataset comprises data from 18 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and camera (egocentric video) data recorded at 10 different outside locations. Unlike previous egocentric datasets, WEAR provides a challenging prediction scenario marked by purposely introduced activity variations as well as an overall small information overlap across modalities. Benchmark results obtained using each modality separately show that each modality interestingly offers complementary strengths and weaknesses in their prediction performance. Further, in light of the recent success of temporal action localization models following the architecture design of the ActionFormer, we demonstrate their versatility by applying them in a plain fashion using vision, inertial and combined (vision + inertial) features as input. Results demonstrate both the applicability of vision-based temporal action localization models for inertial data and fusing both modalities by means of simple concatenation, with the combined approach (vision + inertial features) being able to produce the highest mean average precision and close-to-best F1-score. The dataset and code to reproduce experiments is publicly available via: https://mariusbock.github. io/wear/
MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing

Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to produce desirable outcomes in practice. To address this issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/), the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises over 10K manually annotated triplets (source image, instruction, target image), which supports trainining large-scale text-guided image editing models. We fine-tune InstructPix2Pix on MagicBrush and show that the new model can produce much better images according to human evaluation. We further conduct extensive experiments to evaluate current image editing baselines from multiple dimensions including quantitative, qualitative, and human evaluations. The results reveal the challenging nature of our dataset and the gap between current baselines and real-world editing needs.
SAFER: Situation Aware Facial Emotion Recognition

In this paper, we present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features from facial images and incorporates contextual information, such as background and location type, to enhance its performance. The system has been designed to operate in an open-world setting, meaning it can adapt to unseen and varied facial expressions, making it suitable for real-world applications. An extensive evaluation of SAFER against existing works in the field demonstrates improved performance, achieving an accuracy of 91.4% on the CAER-S dataset. Additionally, the study investigates the effect of novelty such as face masks during the Covid-19 pandemic on facial emotion recognition and critically examines the limitations of mainstream facial expressions datasets. To address these limitations, a novel dataset for facial emotion recognition is proposed. The proposed dataset and the system are expected to be useful for various applications such as human-computer interaction, security, and surveillance.