Miracle-dz/UniFAR-FTUs huggingface dataset:
FonMTL: Towards Multitask Learning for the Fon Language

The Fon language, spoken by an average 2 million of people, is a truly low-resourced African language, with a limited online presence, and existing datasets (just to name but a few). Multitask learning is a learning paradigm that aims to improve the generalization capacity of a model by sharing knowledge across different but related tasks: this could be prevalent in very data-scarce scenarios. In this paper, we present the first explorative approach to multitask learning, for model capabilities enhancement in Natural Language Processing for the Fon language. Specifically, we explore the tasks of Named Entity Recognition (NER) and Part of Speech Tagging (POS) for Fon. We leverage two language model heads as encoders to build shared representations for the inputs, and we use linear layers blocks for classification relative to each task. Our results on the NER and POS tasks for Fon, show competitive (or better) performances compared to several multilingual pretrained language models finetuned on single tasks. Additionally, we perform a few ablation studies to leverage the efficiency of two different loss combination strategies and find out that the equal loss weighting approach works best in our case. Our code is open-sourced at https://github.com/bonaventuredossou/multitask_fon.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. We make MedAlign available under a research data use agreement to enable LLM evaluations on tasks aligned with clinician needs and preferences.
Quark-hadron pasta phase in neutron stars: The role of medium-dependent surface and curvature tensions

We investigate the properties of the hadron-quark mixed phase, often termed the \textit{pasta} phase, expected to exist in the cores of massive neutron stars. To construct the equations of state (EoS), we combine an analytical representation based on the APR EoS for hadronic matter with the MIT bag model featuring vector interactions for quark matter. For modeling the mixed phase, we utilize the compressible liquid drop model that consistently accounts for finite-size and Coulomb effects. Unlike most previous analyses that treated surface tension as a constant free parameter and neglected curvature tension, we employ microphysical calculations using the multiple reflection expansion formalism to determine these parameters, while also ensuring their self-consistency with the EoS. We construct an extensive set of mixed hybrid EoSs by varying model parameters, solve the stellar structure equations to obtain neutron star mass-radius relationships, and select the models that satisfy current astrophysical constraints. Our findings closely align with calculations using a constant surface tension in terms of EoS stiffness and resulting stellar structure. However, they reveal significant differences in the types of geometric structures and their prevalence ranges within the mixed phase. Specifically, curvature effects enhance the emergence of tubes and bubbles at high densities despite the large value of surface tension, while suppressing the existence of drops and rods at low densities.
Self-Enforced Job Matching

The classic two-sided many-to-one job matching model assumes that firms treat workers as substitutes and workers ignore colleagues when choosing where to work. Relaxing these assumptions may lead to nonexistence of stable matchings. However, matching is often not a static allocation, but an ongoing process with long-lived firms and short-lived workers. We show that stability is always guaranteed dynamically when firms are patient, even with complementarities in firm technologies and peer effects in worker preferences. While no-poaching agreements are anti-competitive, they can maintain dynamic stability in markets that are otherwise unstable, which may contribute to their prevalence in labor markets.
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy

Generative models have emerged as a promising technique for producing high-quality images that are indistinguishable from real images. Generative adversarial networks (GANs) and variational autoencoders (VAEs) are two of the most prominent and widely studied generative models. GANs have demonstrated excellent performance in generating sharp realistic images and VAEs have shown strong abilities to generate diverse images. However, GANs suffer from ignoring a large portion of the possible output space which does not represent the full diversity of the target distribution, and VAEs tend to produce blurry images. To fully capitalize on the strengths of both models while mitigating their weaknesses, we employ a Bayesian non-parametric (BNP) approach to merge GANs and VAEs. Our procedure incorporates both Wasserstein and maximum mean discrepancy (MMD) measures in the loss function to enable effective learning of the latent space and generate diverse and high-quality samples. By fusing the discriminative power of GANs with the reconstruction capabilities of VAEs, our novel model achieves superior performance in various generative tasks, such as anomaly detection and data augmentation. Furthermore, we enhance the model's capability by employing an extra generator in the code space, which enables us to explore areas of the code space that the VAE might have overlooked. With a BNP perspective, we can model the data distribution using an infinite-dimensional space, which provides greater flexibility in the model and reduces the risk of overfitting. By utilizing this framework, we can enhance the performance of both GANs and VAEs to create a more robust generative model suitable for various applications.
Learning to Read Analog Gauges from Synthetic Data

Manually reading and logging gauge data is time inefficient, and the effort increases according to the number of gauges available. We present a computer vision pipeline that automates the reading of analog gauges. We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading. To facilitate the training of our approach, a synthetic dataset is generated thus obtaining a set of realistic analog gauges with their corresponding annotation. To validate our proposal, an additional real-world dataset was collected with 4.813 manually curated images. When compared against state-of-the-art methodologies, our method shows a significant improvement of 4.55 in the average error, which is a 52% relative improvement. The resources for this project will be made available at: https://github.com/fuankarion/automatic-gauge-reading.
Spectral Theory of Isogeny Graphs

We consider finite graphs whose vertexes are supersingular elliptic curves, possibly with level structure, and edges are isogenies. They can be applied to the study of modular forms and to isogeny based cryptography. The main result of this paper says that these graphs have the Ramanujan property, which means that the eigenvalues of their adjacency matrices are as small as possible. We also study the asymptotic distribution of the eigenvalues of the adjacency matrices, the number of connected components, the automorphisms of the graphs, and the connection between the graphs and modular forms.
Bone Metastases: An Overview

Bone is a frequent site of metastases and typically indicates a short-term prognosis in cancer patients. Once cancer has spread to the bones it can rarely be cured, but often it can still be treated to slow its growth. The majority of skeletal metastases are due to breast and prostate cancer. Bone metastasis is actually much more common than primary bone cancers, especially in adults. The diagnosis is based on signs, symptoms and imaging. New classes of drugs and new interventions are given a better quality of life to these patients and improved the expectancy of life. It is necessary a multidisciplinary approach to treat patients with bone metastasis. In this paper we review the types, clinical approach and treatment of bone metastases.
Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco

Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a <italic>pipeline</italic> of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an <italic>exploratory study</italic> to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco—a framework to model visualization knowledge—to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to <xref ref-type="disp-formula" rid="deqn1">(1)</xref> identify gaps in existing graphical perception literature informing recommendation algorithms, <xref ref-type="disp-formula" rid="deqn2">(2)</xref> cluster papers by their preferred design rules and constraints, and <xref ref-type="disp-formula" rid="deqn3">(3)</xref> investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.
Online GentleAdaBoost - Technical Report

We study the online variant of GentleAdaboost, where we combine a weak learner to a strong learner in an online fashion. We provide an approach to extend the batch approach to an online approach with theoretical justifications through application of line search. Finally we compare our online boosting approach with other online approaches across a variety of benchmark datasets.
The anticancer activity of genistein is increased in estrogen receptor beta 1-positive breast cancer cells

Background: Most breast cancers are estrogen dependent and were sensitive to endocrine therapy, and genistein (GEN) shows strong affinity with human oestrogen receptor beta (ER β ). Purpose: The present study aimed to investigate the anticancer activity of GEN in breast cancer cell lines that constitutively expressing ER β 1 in vitro and in vivo. Methods: MCF-7/ER β 1 and MDA-MB-231/ER β 1 cell sub-lines were established through lentiviral infection. Then, cells were treated with increasing concentrations of GEN (10 - 6 mol/l, 10 - 5 mol/l and 10 - 4 mol/l) for 48 h, and cell proliferation, cell cycle analyses were performed to investigate different biological characteristics of ER β 1-overexpressing cell lines. Studies in vivo were also performed to investigate the effects of dietary GEN on MCF-7/ER β 1 and MDA-MB-231/ER β 1 cells implanted mice. Results: Results showed that compared to parental cells, GEN inhibited the proliferation ability of MCF-7/ER β 1 cells to a greater extent, especially at high concentrations. MDA-MB-231 cells were also inhibited by high doses of GEN, but the overexpressed ER β 1 did not enhance the anti-proliferative effect on MDA-MB-231 cells. ER β 1 arrested cells in G2/M phase, and GEN arrested cells in G0/G1, which led to a combinatorial effect on cell cycle blockade. Furthermore, ER β 1 increased the anti-tumour activity of dietary GEN in MCF-7/ER β 1 subcutaneous tumour models. Our data indicated that ER β 1 increased the anticancer efficacy of GEN in MCF-7 cells by affecting cell cycle transition. Conclusion: As a result, GEN could be a potential therapeutic agent for ER β 1-positive cancer.
A VMiPG method for composite optimization with nonsmooth term having no closed-form proximal mapping

This paper concerns the composite optimization of minimizing the sum of a twice continuously differentiable function $f$ and a nonsmooth convex function $g$ without closed-form proximal mapping. For this class of nonconvex and nonsmooth problems, with uniformly bounded positive definite variable metric linear operators, we propose a line-search based variable metric inexact proximal gradient (VMiPG) method. This method computes in each step an inexact minimizer of a strongly convex model such that the difference between its objective value and the optimal value is controlled by its squared distance from the current iterate, and then seeks an appropriate step-size along the obtained direction with an armijo line-search criterion. We justify that the iterate sequence converges to a stationary point when $f$ and $g$ are the KL functions, and that the convergence has a local R-linear rate if the objective function has the KL property and its subdifferential mapping is metrically subregular at the stationary point. The proposed VMiPG method with the variable metric linear operator from the Hessian of $f$ is applied to the scenario that $f$ and $g$ have common composite structure, and numerical comparison with a state-of-art variable metric line-search algorithm indicates that the Hessian-based VMiPG method has a remarkable advantage in terms of the quality of objective values and the running time for those difficult problems such as high-dimensional fused weighted-lasso regressions.
Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts

Detecting online sexual predatory behaviours and abusive language on social media platforms has become a critical area of research due to the growing concerns about online safety, especially for vulnerable populations such as children and adolescents. Researchers have been exploring various techniques and approaches to develop effective detection systems that can identify and mitigate these risks. Recent development of large language models (LLMs) has opened a new opportunity to address this problem more effectively. This paper proposes an approach to detection of online sexual predatory chats and abusive language using the open-source pretrained Llama 2 7B-parameter model, recently released by Meta GenAI. We fine-tune the LLM using datasets with different sizes, imbalance degrees, and languages (i.e., English, Roman Urdu and Urdu). Based on the power of LLMs, our approach is generic and automated without a manual search for a synergy between feature extraction and classifier design steps like conventional methods in this domain. Experimental results show a strong performance of the proposed approach, which performs proficiently and consistently across three distinct datasets with five sets of experiments. This study's outcomes indicate that the proposed method can be implemented in real-world applications (even with non-English languages) for flagging sexual predators, offensive or toxic content, hate speech, and discriminatory language in online discussions and comments to maintain respectful internet or digital communities. Furthermore, it can be employed for solving text classification problems with other potential applications such as sentiment analysis, spam and phishing detection, sorting legal documents, fake news detection, language identification, user intent recognition, text-based product categorization, medical record analysis, and resume screening.
Diffusion Schr\"{o}dinger Bridges for Bayesian Computation

Denoising diffusion models are a novel class of generative models that have recently become extremely popular in machine learning. In this paper, we describe how such ideas can also be used to sample from posterior distributions and, more generally, any target distribution whose density is known up to a normalizing constant. The key idea is to consider a forward ``noising'' diffusion initialized at the target distribution which ``transports'' this latter to a normal distribution for long diffusion times. The time-reversal of this process, the ``denoising'' diffusion, thus ``transports'' the normal distribution to the target distribution and can be approximated so as to sample from the target. To accelerate simulation, we show how one can introduce and approximate a Schr\"{o}dinger bridge between these two distributions, i.e. a diffusion which transports the normal to the target in finite time.
Temporal clustering of extreme events in sequences of dependent observations separated by heavy-tailed waiting times

The occurrence of extreme events like heavy precipitation or storms at a certain location often shows a clustering behaviour and is thus not described well by a Poisson process. We construct a general model for the inter-exceedance times in between such events which combines different candidate models for such behaviour. This allows us to distinguish data generating mechanisms leading to clusters of dependent events with exponential inter-exceedance times in between clusters from independent events with heavy-tailed inter-exceedance times, and even allows us to combine these two mechanisms for better descriptions of such occurrences. We investigate a modification of the Cram\'er-von Mises distance for the purpose of model fitting. An application to mid-latitude winter cyclones illustrates the usefulness of our work.
JWST Observations of the Extraordinary GRB 221009A Reveal an Ordinary Supernova Without Signs of $r$-Process Enrichment in a Low-Metallicity Galaxy

Identifying the astrophysical sites of the $r$-process, one of the primary mechanisms by which heavy elements are formed, is a key goal of modern astrophysics. The discovery of the brightest gamma-ray burst of all time, GRB 221009A, at a relatively nearby redshift, presented the first opportunity to spectroscopically test the idea that $r$-process elements are produced following the collapse of rapidly rotating massive stars. Here we present spectroscopic and photometric $\textit{James Webb Space Telescope}$ (JWST) observations of GRB 221009A obtained $+168$ and $+170$ rest-frame days after the initial gamma-ray trigger, and demonstrate they are well-described by a supernova (SN) and power-law afterglow, with no evidence for an additional component from $r$-process emission, and that the SN component strongly resembles the near-infrared spectra of previous SNe, including SN 1998bw. We further find that the SN associated with GRB 221009A is slightly fainter than the expected brightness of SN 1998bw at this phase, concluding that the SN is therefore not an unusual GRB-SN. We infer a nickel mass of $\approx0.09$ M$_{\odot}$, consistent with the lack of an obvious SN detection in the early-time data. We find that the host galaxy of GRB 221009A has a very low metallicity of $\approx0.12$ Z$_{\odot}$ and our resolved host spectrum shows that GRB 221009A occurred in a unique environment in its host characterized by strong H$_2$ emission lines consistent with recent star formation, which may hint at environmental factors being responsible for its extreme energetics.
Origin of symptoms in chronic heart failure

Skeletal muscle abnormalities are highly prevalent in chronic heart failure and are associated with an increase in the ergoreflex, a muscle reflex stimulated by work done. Stimulation of the ergoreflex results in increased ventilation and contributes to the increased sympathetic activation of the heart failure syndrome. The origin of the skeletal myopathy is related to a chronic imbalance between catabolic and anabolic processes, presumably as a consequence of chronic haemodynamic stress. Symptoms arise from the skeletal myopathy, causing the sensation of fatigue and contributing to the sensation of breathlessness as the myopathy affects respiratory muscle. Ergoreflex activation causes a greater ventilatory response to exercise than normal, contributing to the sensation of breathlessness.
The efficacy and safety of intravesical gemcitabine vs Bacille calmette-guérin for adjuvant treatment of non-muscle invasive bladder cancer: a meta-analysis

Objective: Several studies have compared the safety and efficacy of intravesical gemcitabine (Gem) with Bacille Calmette-Guérin (BCG) for non-muscle invasive bladder cancer. However, the results are not consistent. We carried out a meta-analysis to provide a more comprehensive analysis of the efficacy and safety of these 2 drugs. Methods: We searched PubMed, EMBASE, Cochrane Library, Clinical Trials.gov, and reference lists. Randomized controlled trials and retrospective controlled trials comparing intravesical Gem and BCG in adjuvant therapy for non-muscle invasive bladder cancer published in English were included in this study. The strength of association was weighed by pooled risk ratio (RR) with 95% CIs. Sensitivity analysis was performed to examine whether the findings of the meta-analysis were robust. Results: We analyzed 386 subjects from 5 pooled trials. Compared with BCG, intravesical Gem had lower incidence of dysuria (overall RR = 0.31, 95% CI: 0.16, 0.61, I 2 = 0%, p = 0.001) and hematuria (overall RR = 0.27, 95% CI: 0.11, 0.71, I 2 = 0%, p = 0.008). There were no statistical differences in risk of recurrence, progression, incidence of fever, and any adverse events between intravesical Gem and BCG therapy ( p . 0.05). No publication bias was found. Conclusion: This meta-analysis suggests that intravesical Gem may have similar efficacy and lower incidence of dysuria and hematuria compared with BCG. Nevertheless, we recommend additional high-quality randomized controlled trials to confirm these results.
Fixating on Attention: Integrating Human Eye Tracking into Vision Transformers

Modern transformer-based models designed for computer vision have outperformed humans across a spectrum of visual tasks. However, critical tasks, such as medical image interpretation or autonomous driving, still require reliance on human judgments. This work demonstrates how human visual input, specifically fixations collected from an eye-tracking device, can be integrated into transformer models to improve accuracy across multiple driving situations and datasets. First, we establish the significance of fixation regions in left-right driving decisions, as observed in both human subjects and a Vision Transformer (ViT). By comparing the similarity between human fixation maps and ViT attention weights, we reveal the dynamics of overlap across individual heads and layers. This overlap is exploited for model pruning without compromising accuracy. Thereafter, we incorporate information from the driving scene with fixation data, employing a"joint space-fixation"(JSF) attention setup. Lastly, we propose a"fixation-attention intersection"(FAX) loss to train the ViT model to attend to the same regions that humans fixated on. We find that the ViT performance is improved in accuracy and number of training epochs when using JSF and FAX. These results hold significant implications for human-guided artificial intelligence.