Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of pre-existing eye donation awareness of the next of kin on donation rate after grief counseling - A cross-sectional study.

This cross-sectional study was conducted to assess the impact of pre-existing eye donation awareness of the next of kin on the donation rate after grief counseling with due ethical approval. The N=164 most stable next-of-kin, mostly the Brother (20.7%) or Father (20.1%) of the deceased, were approached by the Eye Donation Counselor (EDC). After assessing their awareness about eye donation through the Awareness and Perception on Eye Donation questionnaire (Ronanki, V.R, et. al), the EDC grief counseled the next of kin. 84.8% of all participants were aware of eye donation. The mean awareness, knowledge, and perception scores of the study population were 2.46 +/- 1.34 out of 4, 2.73 +/- 2.37 out of 6, and 1.79 +/- 1.72 out of 4 respectively. Eye care professionals (N=105 (64%)) and mass media (N=61 (37.2%)) were identified as the most common sources of information. While 52.4% expressed willingness to donate, only 7.3% donated and there was 1 voluntary donation. Counseling and the belief is eye donation being a noble deed were identified as the major motivators, and objections by other family members, and religious beliefs were the major barriers. There is no significant association between eye donation and the pre-existing awareness of the next of kin regarding eye donation. Although awareness is associated with the increased willingness to donate eyes.
AfriMTE and AfriCOMET: Empowering COMET to Embrace Under-resourced African Languages

Despite the progress we have recorded in scaling multilingual machine translation (MT) models and evaluation data to several under-resourced African languages, it is difficult to measure accurately the progress we have made on these languages because evaluation is often performed on n-gram matching metrics like BLEU that often have worse correlation with human judgments. Embedding-based metrics such as COMET correlate better; however, lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with a simplified MQM guideline for error-span annotation and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET, a COMET evaluation metric for African languages by leveraging DA training data from high-resource languages and African-centric multilingual encoder (AfroXLM-Roberta) to create the state-of-the-art evaluation metric for African languages MT with respect to Spearman-rank correlation with human judgments (+0.406).
The efficacy of the sugar-free labels is reduced by the health-sweetness tradeoff

In the present study, we use an experimental setting to explore the effects of sugar-free labels on the willingness to pay for food products. In our experiment, participants placed bids for sugar-containing and analogous sugar-free products in a Becker-deGroot-Marschak auction to determine the willingness to pay. Additionally, they rated each product on the level of perceived healthiness, sweetness, tastiness and familiarity with the product. We then used structural equation modelling to estimate the direct, indirect and total effect of the label on the willingness to pay. The results suggest that sugar-free labels significantly increase the willingness to pay due to the perception of sugar-free products as healthier than sugar-containing ones. However, this positive effect is overridden by a significant decrease in perceived sweetness (and hence, tastiness) of products labelled as sugar-free compared to sugar-containing products. As in our sample, healthiness and tastiness are positively related, while healthiness and sweetness are related negatively, these results suggest that it is health-sweetness rather than health-tastiness tradeoff that decreases the efficiency of the sugar-free labelling in nudging consumers towards healthier options.
Reconstruction of inhomogeneous turbulence based on stochastic Fourier-type integrals. Part I: Modeling and analysis

In this article, we develop and analyze a Gaussian random field model for the reconstruction of inhomogeneous turbulence from characteristic flow quantities provided by $k$-$\varepsilon$ simulations. The model is based on stochastic integrals that combine moving average and Fourier-type representations in time and space, respectively, where both the time integration kernel and the spatial energy spectrum depend on the macroscopically varying characteristic quantities. The structure of the model is derived from standard spectral representations of homogeneous fields by means of a two scale approach in combination with specific stochastic integral transformations. Our approach allows for a rigorous analytical verification of the desired statistical properties and is accessible to numerical simulation. The constructed inhomogeneous field is shown to satisfy the characteristic $k$-$\varepsilon$ flow properties and the condition of incompressibility in an asymptotic sense. Moreover, a novel inhomogeneous ergodicity result establishes the approximation of local characteristic values by means of local averages in time and space.
Efficient End-to-End Visual Document Understanding with Rationale Distillation

Understanding visually situated language requires recognizing text and visual elements, and interpreting complex layouts. State-of-the-art methods commonly use specialized pre-processing tools, such as optical character recognition (OCR) systems, that map document image inputs to extracted information in the space of textual tokens, and sometimes also employ large language models (LLMs) to reason in text token space. However, the gains from external tools and LLMs come at the cost of increased computational and engineering complexity. In this paper, we ask whether small pretrained image-to-text models can learn selective text or layout recognition and reasoning as an intermediate inference step in an end-to-end model for pixel-level visual language understanding. We incorporate the outputs of such OCR tools, LLMs, and larger multimodal models as intermediate ``rationales'' on training data, and train a small student model to predict both rationales and answers for input questions based on those training examples. A student model based on Pix2Struct (282M parameters) achieves consistent improvements on three visual document understanding benchmarks representing infographics, scanned documents, and figures, with improvements of more than 4\% absolute over a comparable Pix2Struct model that predicts answers directly.
A Survey on Online User Aggression: Content Detection and Behavioural Analysis on Social Media Platforms

The rise of social media platforms has led to an increase in cyber-aggressive behavior, encompassing a broad spectrum of hostile behavior, including cyberbullying, online harassment, and the dissemination of offensive and hate speech. These behaviors have been associated with significant societal consequences, ranging from online anonymity to real-world outcomes such as depression, suicidal tendencies, and, in some instances, offline violence. Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies. In this paper, we analyzed the diversity of definitions and proposed a unified cyber-aggression definition. We examine the comprehensive process of Aggression Content Detection, spanning from dataset creation, feature selection and extraction, and detection algorithm development. Further, we review studies on Behavioral Analysis of Aggression that explore the influencing factors, consequences, and patterns associated with cyber-aggressive behavior. This systematic literature review is a cross-examination of content detection and behavioral analysis in the realm of cyber-aggression. The integrated investigation reveals the effectiveness of incorporating sociological insights into computational techniques for preventing cyber-aggressive behavior. Finally, the paper concludes by identifying research gaps and encouraging further progress in the unified domain of socio-computational aggressive behavior analysis.
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains

Medical imaging data is often siloed within hospitals, limiting the amount of data available for specialized model development. With limited in-domain data, one might hope to leverage larger datasets from related domains. In this paper, we analyze the benefit of transferring self-supervised contrastive representations from moment contrast (MoCo) pretraining on out-of-distribution data to settings with limited data. We consider two X-ray datasets which image different parts of the body, and compare transferring from each other to transferring from ImageNet. We find that depending on quantity of labeled and unlabeled data, contrastive pretraining on larger out-of-distribution datasets can perform nearly as well or better than MoCo pretraining in-domain, and pretraining on related domains leads to higher performance than if one were to use the ImageNet pretrained weights. Finally, we provide a preliminary way of quantifying similarity between datasets.
Full range spectral correlations and their spectral form factors in chaotic and integrable models

Quantum chaotic systems are characterized by energy correlations in their spectral statistics, usually probed by the distribution of nearest-neighbor level spacings. Some signatures of chaos, like the spectral form factor (SFF), take all the correlations into account, while others sample only short-range or long-range correlations. Here, we characterize correlations between eigenenergies at all possible spectral distances. Specifically, we study the distribution of $k$-th neighbor level spacings ($k$nLS) and compute its associated $k$-th neighbor spectral form factor ($k$nSFF). This leads to two new full-range signatures of quantum chaos, the variance of the $k$nLS distribution and the minimum value of the $k$nSFF, which quantitatively characterize correlations between pairs of eigenenergies with any number of levels $k$ between them. We find exact and approximate expressions for these signatures in the three Gaussian ensembles of random matrix theory (GOE, GUE and GSE) and in integrable systems with completely uncorrelated spectra (the Poisson ensemble). We illustrate our findings in a XXZ spin chain with disorder, which interpolates between chaotic and integrable behavior. Our refined measures of chaos allow us to probe deviations from Poissonian and Random Matrix behavior in realistic systems. This illustrates how the measures we introduce bring a new light into studying many-body quantum systems, which lie in-between the fully chaotic or fully integrable models.
A Proposed Artificial Neural Network based Approach for Molecules Bitter Prediction

In recent years, the development of Artificial Intelligence (AI) has offered the possibility to tackle many interdisciplinary problems, and the field of chemistry is not an exception. Drug analysis is crucial in drug discovery, playing an important role in human life. However, this task encounters many difficulties due to the wide range of computational chemistry methods. Drug analysis also involves a massive amount of work, including determining taste. Thus, applying deep learning to predict a molecule's bitterness is inevitable to accelerate innovation in drug analysis by reducing the time spent. This paper proposes an artificial neural network (ANN) based approach (EC-ANN) for the molecule's bitter prediction. Our approach took the SMILE (Simplified molecular-input line-entry system) string of a molecule as the input data for the prediction, and the 256-bit ECFP descriptor is the input vector for our network. It showed impressive results compared to state-of-the-art, with a higher performance on two out of three test sets according to the experiences on three popular test sets: Phyto-Dictionary, Unimi, and Bitter-new set [1]. For the Phyto-Dictionary test set, our model recorded 0.95 and 0.983 in F1-score and AUPR, respectively, depicted as the highest score in F1-score. For the Unimi test set, our model achieved 0.88 in F1-score and 0.88 in AUPR, which is roughly 12.3% higher than the peak of previous models [1, 2, 3, 4, 5].
Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation

This study examines the effect of prompt engineering on the performance of Large Language Models (LLMs) in clinical note generation. We introduce an Automatic Prompt Optimization (APO) framework to refine initial prompts and compare the outputs of medical experts, non-medical experts, and APO-enhanced GPT3.5 and GPT4. Results highlight GPT4 APO's superior performance in standardizing prompt quality across clinical note sections. A human-in-the-loop approach shows that experts maintain content quality post-APO, with a preference for their own modifications, suggesting the value of expert customization. We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.
Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization

Despite the remarkable performance of generative large language models (LLMs) on abstractive summarization, they face two significant challenges: their considerable size and tendency to hallucinate. Hallucinations are concerning because they erode reliability and raise safety issues. Pruning is a technique that reduces model size by removing redundant weights, enabling more efficient sparse inference. Pruned models yield downstream task performance comparable to the original, making them ideal alternatives when operating on a limited budget. However, the effect that pruning has upon hallucinations in abstractive summarization with LLMs has yet to be explored. In this paper, we provide an extensive empirical study across five summarization datasets, two state-of-the-art pruning methods, and five instruction-tuned LLMs. Surprisingly, we find that hallucinations from pruned LLMs are less prevalent than the original models. Our analysis suggests that pruned models tend to depend more on the source document for summary generation. This leads to a higher lexical overlap between the generated summary and the source document, which could be a reason for the reduction in hallucination risk.
JaxMARL: Multi-Agent RL Environments in JAX

Benchmarks play an important role in the development of machine learning algorithms. For example, research in reinforcement learning (RL) has been heavily influenced by available environments and benchmarks. However, RL environments are traditionally run on the CPU, limiting their scalability with typical academic compute. Recent advancements in JAX have enabled the wider use of hardware acceleration to overcome these computational hurdles, enabling massively parallel RL training pipelines and environments. This is particularly useful for multi-agent reinforcement learning (MARL) research. First of all, multiple agents must be considered at each environment step, adding computational burden, and secondly, the sample complexity is increased due to non-stationarity, decentralised partial observability, or other MARL challenges. In this paper, we present JaxMARL, the first open-source code base that combines ease-of-use with GPU enabled efficiency, and supports a large number of commonly used MARL environments as well as popular baseline algorithms. When considering wall clock time, our experiments show that per-run our JAX-based training pipeline is up to 12500x faster than existing approaches. This enables efficient and thorough evaluations, with the potential to alleviate the evaluation crisis of the field. We also introduce and benchmark SMAX, a vectorised, simplified version of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL. We provide code at https://github.com/flairox/jaxmarl.
Delayed X-Ray Brightening Accompanied by Variable Ionized Absorption Following a Tidal Disruption Event

Supermassive black holes can experience super-Eddington peak mass fallback rates following the tidal disruption of a star. The theoretical expectation is that part of the infalling material is expelled by means of an accretion disk wind, whose observational signature includes blueshifted absorption lines of highly ionized species in X-ray spectra. To date, however, only one such ultrafast outflow (UFO) has been reported in the tidal disruption event (TDE) ASASSN–14li. Here we report on the discovery of a transient absorption-like signature in X-ray spectra of the TDE AT2020ksf/Gaia20cjk (at a redshift of z = 0.092), following an X-ray brightening ∼230 days after UV/optical peak. We find that while no statistically significant absorption features are present initially, they appear on a timescale of several days and remain detected up to 770 days after peak. Simple thermal continuum models, combined with a power-law or neutral absorber, do not describe these features well. Adding a partial-covering, low-velocity ionized absorber improves the fit at early times but fails at late times. A high-velocity (v w ∼ 42,000 km s−1), ionized absorber (UFO) provides a good fit to all data. The few-day timescale of variability is consistent with expectations for a clumpy wind. We discuss several scenarios that could explain the X-ray delay, as well as the potential for larger-scale wind feedback. The serendipitous nature of the discovery could suggest a high incidence of UFOs in TDEs, alleviating some of the tension with theoretical expectations.
Anytime Solvers for Variational Inequalities: the (Recursive) Safe Monotone Flows

This paper synthesizes anytime algorithms, in the form of continuous-time dynamical systems, to solve monotone variational inequalities. We introduce three algorithms that solve this problem: the projected monotone flow, the safe monotone flow, and the recursive safe monotone flow. The first two systems admit dual interpretations: either as projected dynamical systems or as dynamical systems controlled with a feedback controller synthesized using techniques from safety-critical control. The third flow bypasses the need to solve quadratic programs along the trajectories by incorporating a dynamics whose equilibria precisely correspond to such solutions, and interconnecting the dynamical systems on different time scales. We perform a thorough analysis of the dynamical properties of all three systems. For the safe monotone flow, we show that equilibria correspond exactly with critical points of the original problem, and the constraint set is forward invariant and asymptotically stable. The additional assumption of convexity and monotonicity allows us to derive global stability guarantees, as well as establish the system is contracting when the constraint set is polyhedral. For the recursive safe monotone flow, we use tools from singular perturbation theory for contracting systems to show KKT points are locally exponentially stable and globally attracting, and obtain practical safety guarantees. We illustrate the performance of the flows on a two-player game example and also demonstrate the versatility for interconnection and regulation of dynamical processes of the safe monotone flow in an example of a receding horizon linear quadratic dynamic game.
EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction on Mobile Devices

Reconstructing real-world 3D objects has numerous applications in computer vision, such as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time. Traditional methods match pixels between images using photo-consistency constraints or learned features, while differentiable rendering methods like Neural Radiance Fields (NeRF) use differentiable volume rendering or surface-based representation to generate high-fidelity scenes. However, these methods require excessive runtime for rendering, making them impractical for daily applications. To address these challenges, we present $\textbf{EvaSurf}$, an $\textbf{E}$fficient $\textbf{V}$iew-$\textbf{A}$ware implicit textured $\textbf{Surf}$ace reconstruction method on mobile devices. In our method, we first employ an efficient surface-based model with a multi-view supervision module to ensure accurate mesh reconstruction. To enable high-fidelity rendering, we learn an implicit texture embedded with a set of Gaussian lobes to capture view-dependent information. Furthermore, with the explicit geometry and the implicit texture, we can employ a lightweight neural shader to reduce the expense of computation and further support real-time rendering on common mobile devices. Extensive experiments demonstrate that our method can reconstruct high-quality appearance and accurate mesh on both synthetic and real-world datasets. Moreover, our method can be trained in just 1-2 hours using a single GPU and run on mobile devices at over 40 FPS (Frames Per Second), with a final package required for rendering taking up only 40-50 MB.
Towards more Practical Threat Models in Artificial Intelligence Security

Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, they form part of larger ML pipelines in practice. Recent works also brought forward that adversarial manipulations introduced by academic attacks are impractical. We take a first step towards describing the full extent of this disparity. To this end, we revisit the threat models of the six most studied attacks in AI security research and match them to AI usage in practice via a survey with 271 industrial practitioners. On the one hand, we find that all existing threat models are indeed applicable. On the other hand, there are significant mismatches: research is often too generous with the attacker, assuming access to information not frequently available in real-world settings. Our paper is thus a call for action to study more practical threat models in artificial intelligence security.