Miracle-dz/UniFAR-FTUs huggingface dataset:
Adherence patterns to medications and their association with subsequent hospitalization in adult Egyptian patients with systemic lupus erythematosus

Objective The aim of this study was to evaluate the adherence pattern to medications and the influence of non-adherence on hospitalization rate among Egyptian patients who were diagnosed with systemic lupus erythematosus (SLE). Methods This cross-sectional interview-based study was conducted in an Egyptian rheumatology outpatient clinic. Individuals with SLE who were prescribed oral medications were included in the study. The 19-item Compliance Questionnaire of Rheumatology (CQR) was employed to evaluate drug adherence. Patients were classified as non-adherent and adherent group based on their CQR score Sociodemographic variables, clinical and therapeutic data, and the frequency of outpatient visits and hospitalization were collected and compared between the two groups. Results In total, there were 161 SLE patients who participated in the study with mean age 30.70 ± 9.62 years, 129 females and 32 males (F:M 5.4:1). Of them, 93 patients were assigned to the non-adherent group, whereas 68 patients were assigned to the adherent group. There was a statistically significant difference in terms of marital status (p = 0.009), employment (p = 0.032), and the frequency of physical activity (p = 0.045) between the two groups. The adherents had a higher rate of administration of antimalarials (88.2% vs 75.3%, p = 0.039). The frequency of recurrent fever was lower in adherents (23.5% vs 38.7%, p = 0.042), lymphadenopathy (11.8% vs 31.2%, p = 0.004), recurring oral ulcers (25% vs 50.5%, p = 0.001), and substantial hair loss (87.1% vs 98.5%, p = 0.009) in comparison to non-adherents. The number of yearly outpatient visits was considerably greater in adherents (p = 0.007), while the number of prior hospitalizations was higher in non-adherents (p < 0.001). Non-adherents believed that taking medicines disrupt patients’ daily routine (p < 0.001) and were more afraid of the occurrence of an adverse drug reaction (p < 0.001). Conclusions Egyptian SLE patients have an alarmingly high percentage of medication non-adherence. There is a strong association between medication non-adherence and frequency of hospitalization and cumulative clinical manifestations.
External validity of the PRECISE-DAPT score in patients undergoing PCI: a systematic review and meta-analysis.

AIMS To summarise the totality of evidence validating the Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy (PRECISE-DAPT) score, ascertaining its aggregate discrimination and validation power in multiple population subsets. METHODS AND RESULTS We searched electronic databases from 2017 (PRECISE-DAPT proposal) up to March 2023 for studies that reported the occurrence of out-of-hospital bleedings according to the PRECISE-DAPT score in patients receiving DAPT following percutaneous coronary intervention (PCI). Pooled odds ratio (OR) with 95% confidence interval (CI) were used as summary statistics and were calculated using a random-effects model. Primary and secondary endpoints were the occurrence of any and major bleeding, respectively. A total of twenty-one studies and 67 283 patients were included; 24.7% of patients (N = 16 603) were at high-bleeding risk (PRECISE-DAPT ≥ 25) and when compared to those at low-bleeding risk, they experienced a significantly higher rate of any out-of-hospital bleeding (OR: 2.71; 95% CI: 2.24-3.29; p-value < 0.001) and major bleedings (OR: 3.51; 95% CI: 2.71-4.55; p-value < 0.001). Pooling data on c-stat whenever available, the PRECISE-DAPT score showed a moderate discriminative power in predicting major bleeding events at 1-year (pooled c-stat: 0.71; 95% CI: 0.64-0.77). CONCLUSION This systematic review and meta-analysis confirms the external validity of the PRECISE-DAPT score in predicting out-of-hospital bleeding outcomes in patients on DAPT following PCI. The moderate discriminative ability highlights the need for future improved risk prediction tools in the field.
Mesh-Wise Prediction of Demographic Composition from Satellite Images Using Multi-Head Convolutional Neural Network

Population aging is one of the most serious problems in certain countries. In order to implement its countermeasures, understanding its rapid progress is of urgency with a granular resolution. However, a detailed and rigorous survey with high frequency is not feasible due to the constraints of financial and human resources. Nowadays, Deep Learning is prevalent for pattern recognition with significant accuracy, with its application to remote sensing. This paper proposes a multi-head Convolutional Neural Network model with transfer learning from pre-trained ResNet50 for estimating mesh-wise demographics of Japan as one of the most aged countries in the world, with satellite images from Landsat-8/OLI and Suomi NPP/VIIRS-DNS as inputs and census demographics as labels. The trained model was performed on a testing dataset with a test score of at least 0.8914 in $\text{R}^2$ for all the demographic composition groups, and the estimated demographic composition was generated and visualised for 2022 as a non-census year.
Phantom fluid cosmology: Impact of a phantom hidden sector on cosmological observables

Phantom scalar theories are widely considered in cosmology, but rarely at the quantum level, where they give rise to negative-energy ghost particles. These cause decay of the vacuum into gravitons and photons, violating observational gamma-ray limits unless the ghosts are effective degrees of freedom with a cutoff $\Lambda$ at the few-MeV scale. We update the constraints on this scale, finding that $\Lambda \lesssim 19$ MeV. We further explore the possible coupling of ghosts to a light, possibly massless, hidden sector particle, such as a sterile neutrino. Vacuum decays can then cause the dark matter density of the universe to grow at late times. The combined phantom plus dark matter fluid has an effective equation of state $w<-1$, and functions as a new source of dark energy. We derive constraints from cosmological observables on the rate of vacuum decay into such a phantom fluid. We find a mild preference for the ghost model over the standard cosmological one, and a modest amelioration of the Hubble and $S_8$ tensions.
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance

We present accumulator-aware quantization (A2Q), a novel weight quantization method designed to train quantized neural networks (QNNs) to avoid overflow when using low-precision accumulators during inference. A2Q introduces a unique formulation inspired by weight normalization that constrains the ℓ1-norm of model weights according to accumulator bit width bounds that we derive. Thus, in training QNNs for low-precision accumulation, A2Q also inherently promotes unstructured weight sparsity to guarantee overflow avoidance. We apply our method to deep learning-based computer vision tasks to show that A2Q can train QNNs for low-precision accumulators while maintaining model accuracy competitive with a floating-point baseline. In our evaluations, we consider the impact of A2Q on both general-purpose platforms and programmable hardware. However, we primarily target model deployment on FPGAs because they can be programmed to fully exploit custom accumulator bit widths. Our experimentation shows accumulator bit width significantly impacts the resource efficiency of FPGA-based accelerators. On average across our benchmarks, A2Q offers up to a 2.3x reduction in resource utilization over 32-bit accumulator counterparts with 99.2% of the floating-point model accuracy.
Gotta match 'em all: Solution diversification in graph matching matched filters

We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter technique proposed in Sussman et al., with the discovery of multiple diverse matchings being achieved by iteratively penalizing a suitable node-pair similarity matrix in the matched filter algorithm. In addition, we propose algorithmic speed-ups that greatly enhance the scalability of our matched-filter approach. We present theoretical justification of our methodology in the setting of correlated Erdos-Renyi graphs, showing its ability to sequentially discover multiple templates under mild model conditions. We additionally demonstrate our method's utility via extensive experiments both using simulated models and real-world dataset, include human brain connectomes and a large transactional knowledge base.
Deciphering the Slow-rise Precursor of a Major Coronal Mass Ejection

Coronal mass ejections are explosive plasma phenomena prevalently occurring on the Sun and probably on other magnetically active stars. However, how their pre-eruptive configuration evolves toward the main explosion remains elusive. Here, based on comprehensive observations of a long-duration precursor in an event on 2012 March 13, we determine that the heating and slow rise of the pre-eruptive hot magnetic flux rope (MFR) are achieved through a precursor reconnection located above cusp-shaped high-temperature precursor loops. It is observed that the hot MFR threads are built up continually, with their middle initially showing an “M” shape and then being separated from the cusp of precursor loops, causing the slow rise of the entire MFR. The slow rise, in combination with the thermal-dominated hard X-ray source concentrated at the top of the precursor loops, shows that the precursor reconnection is much weaker than the flare reconnection of the main eruption. We also perform a 3D magnetohydrodynamics simulation that reproduces the early evolution of the MFR transiting from the slow to fast rise. It is revealed that the magnetic tension force pertinent to “M”-shaped threads drives the slow rise, which, however, evolves into a magnetic pressure gradient-dominated regime responsible for the rapid acceleration eruption.
3D Face Alignment Through Fusion of Head Pose Information and Features

The ability of humans to infer head poses from face shapes, and vice versa, indicates a strong correlation between the two. Accordingly, recent studies on face alignment have employed head pose information to predict facial landmarks in computer vision tasks. In this study, we propose a novel method that employs head pose information to improve face alignment performance by fusing said information with the feature maps of a face alignment network, rather than simply using it to initialize facial landmarks. Furthermore, the proposed network structure performs robust face alignment through a dual-dimensional network using multidimensional features represented by 2D feature maps and a 3D heatmap. For effective dense face alignment, we also propose a prediction method for facial geometric landmarks through training based on knowledge distillation using predicted keypoints. We experimentally assessed the correlation between the predicted facial landmarks and head pose information, as well as variations in the accuracy of facial landmarks with respect to the quality of head pose information. In addition, we demonstrated the effectiveness of the proposed method through a competitive performance comparison with state-of-the-art methods on the AFLW2000-3D, AFLW, and BIWI datasets.
The relative importance of eutrophication and connectivity in structuring biological communities of the Upper Lough Erne system, Northern Ireland

This study investigates the relative importance of eutrophication and connectivity (dispersal) in structuring macrophyte and invertebrate lake assemblages across spatial and temporal scales in the Upper Lough Erne (ULE) system, Northern Ireland. Riverine systems and their associated flood-plains and lakes comprise dynamic diverse landscapes in which water flow plays a key role in affecting connectivity. However, as for many other freshwater systems, their ecological integrity is threatened by eutrophication and hydrological alteration. Eutrophication results in a shift from primarily benthic to primarily pelagic primary production and reductions in species diversity, while flow regulation often reduces water level fluctuation and hydrological connectivity in linked riverine systems. Low water levels promote isolation between areas and increases the importance of local driving forces (e.g. eutrophication). Conversely, enhanced water flow and flooding events promote connectivity in systems thus potentially increasing local diversity and homogenising habitats through the exchange of species. Therefore, connectivity may help to override the local effects of eutrophication. Attempts at testing the above ideas are rare and typically involve the examination of current community patterns using space for time substitution. However, biological community responses to eutrophication and changes in hydrological connectivity may involve lags, historical contingency, and may be manifested over intergenerational timescales (10s -100s of years), rendering modern studies less than satisfactory for building an understanding of processes that drive community structure and effect change. By combining contemporary and palaeolimnological data this study demonstrates that the ULE system is far from its pre-disturbance state as an oligotrophic-mesotrophic system. Furthermore, contemporary and palaeo-data suggest there has been a strong interaction between eutrophication and hydrological change, which influences the distributions and abundances of representative taxa in the ULE system. Thus, while eutrophication has promoted a decrease in compositional heterogeneity of organisms and has exerted a homogenising effect over time, connectivity has buffered the effects of eutrophication helping to maintain local diversity via re-introductions.
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity

Quality-Diversity is a branch of stochastic optimization that is often applied to problems from the Reinforcement Learning and control domains in order to construct repertoires of well-performing policies/skills that exhibit diversity with respect to a behavior space. Such archives are usually composed of a finite number of reactive agents which are each associated to a unique behavior descriptor, and instantiating behavior descriptors outside of that coarsely discretized space is not straight-forward. While a few recent works suggest solutions to that issue, the trajectory that is generated is not easily customizable beyond the specification of a target behavior descriptor. We propose to jointly solve those problems in environments where semantic information about static scene elements is available by leveraging a Large Language Model to augment the repertoire with natural language descriptions of trajectories, and training a policy conditioned on those descriptions. Thus, our method allows a user to not only specify an arbitrary target behavior descriptor, but also provide the model with a high-level textual prompt to shape the generated trajectory. We also propose an LLM-based approach to evaluating the performance of such generative agents. Furthermore, we develop a benchmark based on simulated robot navigation in a 2d maze that we use for experimental validation.
Investigating the Impact of Vocabulary Difficulty and Code Naturalness on Program Comprehension

Context: Developers spend most of their time comprehending source code during software development. Automatically assessing how readable and understandable source code is can provide various benefits in different tasks, such as task triaging and code reviews. While several studies have proposed approaches to predict software readability and understandability, most of them only focus on local characteristics of source code. Besides, the performance of understandability prediction is far from satisfactory. Objective: In this study, we aim to assess readability and understandability from the perspective of language acquisition. More specifically, we would like to investigate whether code readability and understandability are correlated with the naturalness and vocabulary difficulty of source code. Method: To assess code naturalness, we adopted the cross-entropy metric, while we use a manually crafted list of code elements with their assigned advancement levels to assess the vocabulary difficulty. We will conduct a statistical analysis to understand their correlations and analyze whether code naturalness and vocabulary difficulty can be used to improve the performance of code readability and understandability prediction methods. The study will be conducted on existing datasets.
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, leading to low performance, especially in extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (\textbf{OmniQuant}) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family size 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4 (4-bit weight, 4-bit activation), W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes are available at \url{https://github.com/OpenGVLab/OmniQuant}.
Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE

Variational autoencoder (VAE) is a popular method for drug discovery and various architectures and pipelines have been proposed to improve its performance. However, VAE approaches are known to suffer from poor manifold recovery when the data lie on a low-dimensional manifold embedded in a higher dimensional ambient space [Dai and Wipf, 2019]. The consequences of it in drug discovery are somewhat under-explored. In this paper, we explore applying a multi-stage VAE approach, that can improve manifold recovery on a synthetic dataset, to the field of drug discovery. We experimentally evaluate our multi-stage VAE approach using the ChEMBL dataset and demonstrate its ability to improve the property statistics of generated molecules substantially from pre-existing methods without incorporating property predictors into the training pipeline. We further fine-tune our models on two curated and much smaller molecule datasets that target different proteins. Our experiments show an increase in the number of active molecules generated by the multi-stage VAE in comparison to their one-stage equivalent. For each of the two tasks, our baselines include methods that use learned property predictors to incorporate target metrics directly into the training objective and we discuss complications that arise with this methodology.
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction

With the increasing complexity and scale of click-through rate (CTR) prediction tasks in online advertising and recommendation systems, accurately estimating the importance of features has become a critical aspect of developing effective models. In this paper, we propose an attention-based approach that leverages max and mean pooling operations, along with a bit-wise attention mechanism, to enhance feature importance estimation in CTR prediction. Traditionally, pooling operations such as max and mean pooling have been widely used to extract relevant information from features. However, these operations can lead to information loss and hinder the accurate determination of feature importance. To address this challenge, we propose a novel attention architecture that utilizes a bit-based attention structure that emphasizes the relationships between all bits in features, together with maximum and mean pooling. By considering the fine-grained interactions at the bit level, our method aims to capture intricate patterns and dependencies that might be overlooked by traditional pooling operations. To examine the effectiveness of the proposed method, experiments have been conducted on three public datasets. The experiments demonstrated that the proposed method significantly improves the performance of the base models to achieve state-of-the-art results.
EVM: Incorporating Model Checking into Exploratory Visual Analysis

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data—e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools.
On the Practicality of Dynamic Updates in Fast Searchable Encryption

Searchable encrypted (SE) indexing systems are a useful tool for utilizing cloud services to store and manage sensitive information. However, much of the work on SE systems to date has remained theoretical. In order to make them of practical use, more work is needed to develop optimal protocols and working models for them. This includes, in particular, the creation of a working update model in order to maintain an encrypted index of a dynamic document set such as an email inbox. I have created a working, real-world end-to-end SE implementation that satisfies these needs, including the first empirical performance evaluation of the dynamic SE update operation. In doing so, I show a viable path to move from the theoretical concepts described by previous researchers to a future production-worthy implementation and identify issues for follow-on investigation.