Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of a multi-pronged cholera intervention in an endemic setting

Cholera is a bacterial water-borne diarrheal disease that causes high morbidity in sub-Saharan Africa and Asia. It is transmitted via the fecal-oral route and can be prevented with vaccination and Water, Sanitation, and Hygiene (WASH) improvements. WASH improvements are resource and time intensive but provide long lasting benefits. Vaccination campaigns can be implemented more rapidly than WASH but provide only temporary immunity; cholera vaccines are effective in epidemic settings but their utility in endemic settings is unclear. The city of Kalemie in the Democratic Republic of Congo, on the shore of Lake Tanganyika, is a cholera endemic area with annual outbreaks. Both seasonal mobility and the lake, which is a bacterial reservoir, promote transmission, though their relative contributions are unknown. In 2013-2016 Kalemie received a targeted vaccination campaign and WASH improvements. We assessed the short-term impact of this intervention to guide future control strategies in endemic settings. We fit a Susceptible-Infected-Recovered-Susceptible model with a compartment for the aquatic bacterial population. We estimated the number of cases avoided by each arm of the intervention, explored alternative vaccination strategies, and investigated the relative contributions of mobility and environmentally-based transmission in local cholera dynamics. We estimated that vaccination and WASH improvements prevented 3,484 cases (95% Credible Interval: 2,412-4,833) over a 118-week period. We showed that vaccination could prevent more cases by altering the timing and increasing the target population size. We found that transmission was primarily environmentally-driven and that removing environmental exposure or reducing environmental contamination could decrease local transmission. Together, the targeted nature of the vaccination campaign, the modest scale of WASH improvements, and the high background immunity of the population limited the impact of the intervention. Constant environmentally-driven force of infection maintains high levels of cholera immunity in the population and decreases the impact of vaccination in this endemic area.
South African Nursery Practice—The State of the Art

ABSTRACT The state of South African nursery practice is briefly sketched from its beginnings in the late 19th century to the present day. Systems used and numbers of plants raised by species are summarised for 1984/85; cultural practices and their effect on plant quality, survival and early growth on planting are examined. It is concluded that while satisfactory plants are being grown with current practices, substantial improvements in efficiency are possible, indeed essential, if the industry is to meet the challenge of rising costs.
Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored.
Exploring the links between types 2 diabetes and liver‐related complications: A comprehensive review

Abstract In recent decades, the prevalence of type 2 diabetes has been steadily increasing, presenting a significant global public health challenge. These epidemiological trends can be attributed to significant lifestyle changes in modern societies, characterized by sedentary behavior and the consumption of hypercaloric, highly processed foods, along with the aging of the human population. As a result, it has become crucial for both public healthcare systems and healthcare providers to prioritize the management of diabetes and identify its systemic consequences. Emerging research has shed light on the links and risks between diabetes and liver events. This comprehensive review aims to explore the complex interplay between type 2 diabetes mellitus and liver‐related outcomes, especially hepatocellular carcinoma and cirrhosis, offering insights into effective methods for detecting liver risk in individuals with diabetes. Additionally, the review will assess the various treatments that could hold the potential for positive outcomes in managing both diabetes and metabolic dysfunction‐associated steatotic liver disease and liver fibrosis.
Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Tasks

Despite being a unique source of information on patients' status and disease progression, clinical notes are characterized by high levels of duplication and information redundancy. In general domain text, it has been shown that deduplication does not harm language model (LM) pretraining, thus helping reduce the training cost. Although large LMs have proven to learn medical knowledge, they still require specialized domain adaptation for improved downstream clinical tasks. By leveraging large real-world clinical corpora, we first provided a fine-grained characterization of duplicates stemming from common writing practices and clinical relevancy. Second, we demonstrated that deduplicating clinical text can help clinical LMs encode less redundant information in a more efficient manner and do not harm classification tasks via prompt-based learning.
A merging interaction model explains human drivers' behaviour from input signals to decisions

One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that provide accurate predictions of joint and individual driver behaviour of high-level decisions, safety margins, and low-level control inputs are required to improve the interactive capabilities of automated driving. Existing driver models typically focus on one of these aspects. Unified models capturing all aspects are missing which hinders understanding of the principles that govern human traffic interactions. This in turn limits the ability of automated vehicles to resolve merging interactions. Here, we present a communication-enabled interaction model based on risk perception with the potential to capture merging interactions on all three levels. Our model accurately describes human behaviour in a simplified merging scenario, addressing both individual actions (such as velocity adjustments) and joint actions (such as the order of merging). Contrary to other interaction models, our model does not assume humans are rational and explicitly accounts for communication between drivers. Our results demonstrate that communication and risk-based decision-making explain observed human interactions on multiple levels. This explanation improves our understanding of the underlying mechanisms of human traffic interactions and poses a step towards interaction-aware automated driving.
Weyl formula and thermodynamics of geometric flow

We study the Weyl formula for the asymptotic number of eigenvalues of the Laplace-Beltrami operator with Dirichlet boundary condition on a Riemannian manifold in the context of geometric flows. Assuming the eigenvalues to be the energies of some associated statistical system, we show that geometric flows are directly related with the direction of increasing entropy chosen. For a closed Riemannian manifold we obtain a volume preserving flow of geometry being equivalent to the increment of Gibbs entropy function derived from the spectrum of Laplace-Beltrami operator. Resemblance with Arnowitt, Deser, and Misner (ADM) formalism of gravity is also noted by considering open Riemannian manifolds, directly equating the geometric flow parameter and the direction of increasing entropy as time direction.
SELM: Speech Enhancement using Discrete Tokens and Language Models

Language models (LMs) have shown superior performances in various speech generation tasks recently, demonstrating their powerful ability for semantic context modeling. Given the intrinsic similarity between speech generation and speech enhancement, harnessing semantic information holds potential advantages for speech enhancement tasks. In light of this, we propose SELM, a novel paradigm for speech enhancement, which integrates discrete tokens and leverages language models. SELM comprises three stages: encoding, modeling, and decoding. We transform continuous waveform signals into discrete tokens using pre-trained self-supervised learning (SSL) models and a k-means tokenizer. Language models then capture comprehensive contextual information within these tokens. Finally, a detokenizer and HiFi-GAN restore them into enhanced speech. Experimental results demonstrate that SELM achieves comparable performance in objective metrics alongside superior results in subjective perception. Our demos are available https://honee-w.github. io/SELM/.
Neural Time-Reversed Generalized Riccati Equation

Optimal control deals with optimization problems in which variables steer a dynamical system, and its outcome contributes to the objective function. Two classical approaches to solving these problems are Dynamic Programming and the Pontryagin Maximum Principle. In both approaches, Hamiltonian equations offer an interpretation of optimality through auxiliary variables known as costates. However, Hamiltonian equations are rarely used due to their reliance on forward-backward algorithms across the entire temporal domain. This paper introduces a novel neural-based approach to optimal control. Neural networks are employed not only for implementing state dynamics but also for estimating costate variables. The parameters of the latter network are determined at each time step using a newly introduced local policy referred to as the time-reversed generalized Riccati equation. This policy is inspired by a result discussed in the Linear Quadratic (LQ) problem, which we conjecture stabilizes state dynamics. We support this conjecture by discussing experimental results from a range of optimal control case studies.
Laser Thomson scattering measurements around magnetized model in rarefied argon arcjet plume

To elucidate the role of the Hall effect in magnetohydrodynamic (MHD) aerobraking in rarefied flows,we measured the radial distributions of electron temperature and density in front of a magnetized model in a rarefied argon arcjet wind tunnel using the laser Thomson scattering method. We also developed a water-cooled magnetized model to prevent thermal demagnetization during the measurement. The measured electron density distributions were in excellent agreement with computational fluid dynamics (CFD) predictions. It was also found that the magnetic field had little effect on the electron density distribution around the model. In the case without the magnetic field, the measured electron temperature almost agreed with the CFD prediction. However, the measured electron temperature increase caused by applying the magnetic field was about 1,000 K less than that of the CFD prediction. This discrepancy indicates that the location of an insulating boundary in the plasma is far from the model.
Neurosymbolic Value-Inspired AI (Why, What, and How)

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as part of human society, sharing human values, especially as these systems are deployed in high-stakes settings (e.g., healthcare, autonomous driving, etc.). Towards this end, neurosymbolic AI systems are attractive due to their potential to enable easy-to-understand and interpretable interfaces for facilitating value-based decision-making, by leveraging explicit representations of shared values. In this paper, we introduce substantial extensions to Khaneman's System one/two framework and propose a neurosymbolic computational framework called Value-Inspired AI (VAI). It outlines the crucial components essential for the robust and practical implementation of VAI systems, aiming to represent and integrate various dimensions of human values. Finally, we further offer insights into the current progress made in this direction and outline potential future directions for the field.
Unsupervised and Supervised learning by Dense Associative Memory under replica symmetry breaking

Statistical mechanics of spin glasses is one of the main strands toward a comprehension of information processing by neural networks and learning machines. Tackling this approach, at the fairly standard replica symmetric level of description, recently Hebbian attractor networks with multi-node interactions (often called Dense Associative Memories) have been shown to outperform their classical pairwise counterparts in a number of tasks, from their robustness against adversarial attacks and their capability to work with prohibitively weak signals to their supra-linear storage capacities. Focusing on mathematical techniques more than computational aspects, in this paper we relax the replica symmetric assumption and we derive the one-step broken-replica-symmetry picture of supervised and unsupervised learning protocols for these Dense Associative Memories: a phase diagram in the space of the control parameters is achieved, independently, both via the Parisi's hierarchy within then replica trick as well as via the Guerra's telescope within the broken-replica interpolation. Further, an explicit analytical investigation is provided to deepen both the big-data and ground state limits of these networks as well as a proof that replica symmetry breaking does not alter the thresholds for learning and slightly increases the maximal storage capacity. Finally the De Almeida and Thouless line, depicting the onset of instability of a replica symmetric description, is also analytically derived highlighting how, crossed this boundary, the broken replica description should be preferred.
Information Extraction from Unstructured and Ungrammatical Data Sources for Semantic Annotation

global e-business, e-learning, knowledge sharing, etc. Due to continuous increase in the volume of web content, it is not practically possible for a user to extract information by browsing and integrating data from a huge amount of web sources retrieved by the existing search engines. The semantic web technology enables advancement in information extraction by providing a suite of tools to integrate data from different sources. To take full advantage of semantic web, it is necessary to annotate existing web pages into semantic web pages. This research develops a tool, named OWIE (Ontology-based Web Information Extraction), for semantic web annotation using domain specific ontologies. The tool automatically extracts information from html pages with the help of pre-defined ontologies and gives them semantic representation. Two case studies have been conducted to analyze the accuracy of OWIE.
Echo chamber formation sharpened by priority users

Priority users (e.g., verified profiles on Twitter) are social media users whose content is promoted by recommendation algorithms. However, the impact of this heterogeneous user influence on opinion dynamics, such as polarization phenomena, is unknown. We conduct a computational mechanistic investigation of such consequences in a stylized setting. First, we allow priority users, whose content has greater reach (similar to algorithmic boosting), into an opinion model on adaptive networks. Then, to exploit this gain in influence, we incorporate stubborn user behavior, i.e., zealot users who remain committed to opinions throughout the dynamics. Using a novel measure of echo chamber formation, we find that prioritizing users can inadvertently reduce polarization if they post according to the same rule but sharpen echo chamber formation if they behave heterogeneously. Moreover, we show that a minority of extremist ideologues (i.e., users who are both stubborn and priority) can push the system into a transition from consensus to polarization with echo chambers. Our findings imply that the implementation of the platform's prioritization policy should be carefully monitored in order to ensure there is no abuse of users with extra influence.
High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D CINE MRI and Unsupervised Neural Networks

The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterisation of LA motion and deformation, but it is lacking appropriate acquisition and analysis tools. In this paper, we present Analysis for Left Atrial Displacements and Deformations using unsupervIsed neural Networks, \textit{Aladdin}, to automatically and reliably characterise regional LA deformations from high-resolution 3D Cine MRI. The tool includes: an online few-shot segmentation network (Aladdin-S), an online unsupervised image registration network (Aladdin-R), and a strain calculations pipeline tailored to the LA. We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD). We additionally create an atlas of these biomarkers using the data from the healthy volunteers. Aladdin is able to accurately track the LA wall across the cardiac cycle and characterize its motion and deformation. The overall DVF magnitude and principal strain values are significantly higher in the healthy group vs CVD patients: $2.85 \pm 1.59~mm$ and $0.09 \pm 0.05$ vs $1.96 \pm 0.74~mm$ and $0.03 \pm 0.04$, respectively. The time course of these metrics is also different in the two groups, with a more marked active contraction phase observed in the healthy cohort. Finally, utilizing the LA atlas allows us to identify regional deviations from the population distribution that may indicate focal tissue abnormalities. The proposed tool for the quantification of novel regional LA deformation biomarkers should have important clinical applications. The source code, anonymized images, generated maps and atlas are publicly available: https://github.com/cgalaz01/aladdin_cmr_la.
Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks

Singing melody extraction essentially involves two tasks: one is detecting the activity of a singing voice in polyphonic music, and the other is estimating the pitch of a singing voice in the detected voiced segments. In this paper, we present a joint detection and classification (JDC) network that conducts the singing voice detection and the pitch estimation simultaneously. The JDC network is composed of the main network that predicts the pitch contours of the singing melody and an auxiliary network that facilitates the detection of the singing voice. The main network is built with a convolutional recurrent neural network with residual connections and predicts pitch labels that cover the vocal range with a high resolution, as well as non-voice status. The auxiliary network is trained to detect the singing voice using multi-level features shared from the main network. The two optimization processes are tied with a joint melody loss function. We evaluate the proposed model on multiple melody extraction and vocal detection datasets, including cross-dataset evaluation. The experiments demonstrate how the auxiliary network and the joint melody loss function improve the melody extraction performance. Furthermore, the results show that our method outperforms state-of-the-art algorithms on the datasets.
Variational Quantum Domain Adaptation

Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, a variational quantum domain adaptation method is proposed by using a quantum convolutional neural network, together with a gradient reversal module, and two quantum fully connected layers, named variational quantum domain adaptation(VQDA). The simulations on the local computer and IBM Quantum Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed method. The results demonstrate that, compared to its classical corresponding domain adaptation method, VQDA achieves an average improvement of 4% on the accuracy for MNIST to USPS domain transfer under the same parameter scales. Similarly, for SYNDigits to SVHN domain transfer, VQDA achieves an average improvement of 2% on the accuracy under the same parameter scales.
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training

Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large number of arithmetic operations and memory accesses. Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware. Inspired by the spiking neuron model, the Delta RNN exploits temporal sparsity during inference by skipping over the update of hidden states from those inactivated neurons whose change of activation across two timesteps is below a defined threshold. This work describes a training algorithm for Delta RNNs that exploits temporal sparsity in the backward propagation phase to reduce computational requirements for training on the edge. Due to the symmetric computation graphs of forward and backward propagation during training, the gradient computation of inactivated neurons can be skipped. Results show a reduction of ∼80% in matrix operations for training a 56k parameter Delta LSTM on the Fluent Speech Commands dataset with negligible accuracy loss. Logic simulations of a hardware accelerator designed for the training algorithm show 2-10X speedup in matrix computations for an activation sparsity range of 50%-90%. Additionally, we show that the proposed Delta RNN training will be useful for online incremental learning on edge devices with limited computing resources.