Miracle-dz/UniFAR-FTUs huggingface dataset:
Pregnancy-related complications and associated factors among women attending antenatal care at a specialised maternal and child health national referral hospital, in Uganda

Background Although pregnancy and child birth-related complications remain a global public health concern, little is known about their prevalence and associated factors. Our study assessed pregnancy-related complications and associated factors among women attending antenatal care at a specialized maternal and child health national referral hospital in Uganda. Methodology A cross-sectional study was conducted among 285 pregnant women. Data were collected using the KoboCollect mobile application, and analysed using STATA 14. A modified poisson regression model was used for inferential statistics. Findings Out of the 285 women, 27.4% (78/285) had pregnancy-related complications. The most reported complications were anaemia, 10.9% (31/285); eclampsia, 8.1% (23/285); and still births, 4.9% (14/285). Having a higher gravidity of 4-6 (APR= 0.32, 95% CI: 0.17-0.57) and of more than 7 (APR= 0.32, 95% CI: 0.14-0.73) was negatively associated with pregnancy-related complications. Factors positively associated with pregnancy-related complications included; late first ANC (APR=1.85, 95% CI: 1.17-2.92), parity of [≥] 3 (APR= 3.69, 95% CI: 1.50-9.08) and induced abortion prior to current pregnancy (APR= 1.64, 95% CI: (1.08-2.47). Conclusion The prevalence of pregnancy-related complications was very high. Anaemia, eclampsia and still births were the most prevalent. A late first ANC, gravidity higher than 4, parity higher than 3, and history of an induced abortion prior to the current pregnancy were associated with having pregnancy-related complications. Interventions aimed at reducing maternal morbidity and mortality should aim at promoting early ANC attendance, and increasing access to safe abortion and family planning services.
Between mind and mathematics. Different kinds of computational representations of music

Dans cet article nous analysons differents types de representations de la musique, aussi bien d’un point de vue cognitif que computationnel. Si les representations mentales de la musique sont l’objet de l’esprit musical, et donc par definition si elles constituent une question philosophique et cognitive, on peut faire l'hypothese que les representations mathematiques aussi ont des correlats cognitifs permettant la comprehension de la musique non-tonale. Parmi les nombreuses typologies de representations mathematiques de la musique, nous analyserons en detail quelques exemples relevant du paradigme transformationnel, un sous-domaine formalise de la musicologie computationnelle provenant de la tradition ensembliste americaine. La demarche transformationnelle en musique ouvre aussi de nouvelles questions sur les ramifications cognitives et philosophiques des approches algebriques et categorielles en theorie de la musique, analyse et composition.
GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation

Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. However, vanilla Transformer mainly exploits the top-layer representation, assuming the lower layers provide trivial or redundant information and thus ignoring the bottom-layer feature that is potentially valuable. In this work, we propose the Group-Transformer model (GTrans) that flexibly divides multi-layer representations of both encoder and decoder into different groups and then fuses these group features to generate target words. To corroborate the effectiveness of the proposed method, extensive experiments and analytic experiments are conducted on three bilingual translation benchmarks and three multilingual translation tasks, including the IWLST-14, IWLST-17, LDC, WMT-14, WMT-21 and OPUS-100 benchmark. Experimental and analytical results demonstrate that our model outperforms its Transformer counterparts by a consistent gain. Furthermore, it can be successfully scaled up to 60 encoder layers and 36 decoder layers.
Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks

Freely available and easy-to-use audio editing tools make it straightforward to perform audio splicing. Convincing forgeries can be created by combining various speech samples from the same person. Detection of such splices is important both in the public sector when considering misinformation, and in a legal context to verify the integrity of evidence. Unfortunately, most existing detection algorithms for audio splicing use handcrafted features and make specific assumptions. However, criminal investigators are often faced with audio samples from unconstrained sources with unknown characteristics, which raises the need for more generally applicable methods. With this work, we aim to take a first step towards unconstrained audio splicing detection to address this need. We simulate various attack scenarios in the form of post-processing operations that may disguise splicing. We propose a Transformer sequence-to-sequence (seq2seq) network for splicing detection and localization. Our extensive evaluation shows that the proposed method outperforms existing dedicated approaches for splicing detection [3, 10] as well as the general-purpose networks EfficientNet [28] and RegNet [25].
Dynamics of several point vortices for the lake equations

The global asymptotic dynamics of point vortices for the lake equations is rigorously derived. Vorticity that is initially sharply concentrated around N distinct vortex centers is proven to remain concentrated for all times. Speciﬁcally, we prove weak concentration of the vorticity and in addition strong concentration in the direction of the steepest ascent of the depth function. As a consequence, we obtain the motion law of point vortices following at leading order the level lines of the depth function. The lack of strong localization in the second direction is linked to the vortex ﬁlamentation phenomena. The main result allows for any ﬁxed number of vortices and general assumptions on the concentration property of the initial data to be considered. No further properties such as a speciﬁc proﬁle or symmetry of the data are required. Vanishing topographies on the boundary are included in our analysis. Our method is inspired by recent results on the evolution of vortex rings in 3D axisymmetric incompressible ﬂuids.
Sensitivity of spin-aligned searches for neutron star-black hole systems using future detectors

Current searches for gravitational waves from compact-binary objects are primarily designed to detect the dominant gravitational-wave mode and assume that the binary components have spins which are aligned with the orbital angular momentum. These choices lead to observational biases in the observed distribution of sources. Sources with significant spin-orbit precession or unequal-mass-ratios, which have non-negligible contributions from sub-dominant gravitational-wave modes, may be missed; in particular, this may significantly suppress or bias the observed neutron star -- black hole (NSBH) population. We simulate a fiducial population of NSBH mergers and determine the impact of using searches that only account for the dominant-mode and aligned spin. We compare the impact for the Advanced LIGO design, A+, LIGO Voyager, and Cosmic Explorer observatories. We find that for a fiducial population where the spin distribution is isotropic in orientation and uniform in magnitude, we will miss $\sim 25\%$ of sources with mass-ratio $q>6$ and up to $\sim 60 \%$ of highly precessing sources $(\chi_p>0.5)$, after accounting for the approximate increase in background. In practice, the true observational bias can be even larger due to strict signal-consistency tests applied in searches. The observation of low spin, unequal-mass-ratio sources by Advanced LIGO design and Advanced Virgo may in part be due to these selection effects. The development of a search sensitive to high mass-ratio, precessing sources may allow the detection of new binaries whose spin properties would provide key insights into the formation and astrophysics of compact objects.
Streamwise inclination angle of wall-attached eddies in turbulent channel flows

Abstract We develop a new methodology to assess the streamwise inclination angles (SIAs) of the wall-attached eddies populating the logarithmic region with a given wall-normal height. To remove the influences originating from other scales on the SIA estimated via two-point correlation, the footprints of the targeted eddies in the vicinity of the wall and the corresponding streamwise velocity fluctuations carried by them are isolated simultaneously, by coupling the spectral stochastic estimation with the attached-eddy hypothesis. Datasets produced with direct numerical simulations spanning $Re_{\tau } \sim O(10^2)\unicode{x2013}O(10^3)$ are dissected to study the Reynolds number effect. The present results show, for the first time, that the SIAs of attached eddies are Reynolds-number-dependent in low and medium Reynolds numbers, and tend to saturate at $45^{\circ }$ as the Reynolds number increases. The mean SIA reported by vast previous experimental studies are demonstrated to be the outcomes of the additive effect contributed by multi-scale attached eddies. These findings clarify the long-term debate and perfect the picture of the attached-eddy model.
Inverse Reinforcement Learning from Diverse Third-Person Videos via Graph Abstraction

: Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks. However, most prior works are still limited by training from a relatively restricted domain of videos. In this paper, we argue that the true potential of third-person IRL lies in increasing the diversity of videos for better scaling. To learn a reward function from diverse videos, we propose to perform graph abstraction on the videos followed by temporal matching in the graph space to measure the task progress. Our insight is that a task can be described by entity interactions that form a graph, and this graph abstraction can help remove irrelevant information such as textures, resulting in more robust reward functions. We evaluate our approach, GraphIRL , on cross-embodiment learning in X-MAGICAL and learning from human demonstrations for real-robot manipulation. We show signiﬁcant improvements in robustness to diverse video demonstrations over previous approaches, and even achieve better results than manual reward design on a real robot pushing task. Videos are available at https://sateeshkumar21.github. io/GraphIRL .
Connection dynamics of reduced five-dimensional Kaluza-Klein theory and its deparametrization

The connection dynamics of the 5-dimensional Kaluza-Klein theory reduced on 4-dimensional spacetime is obtained by performing the Hamiltonian analysis and canonical transformations. Deparametrization is achieved in the spherically symmetric model of the theory without introducing additional matter fields beyond the 5-dimensional gravity. Thus the physical time evolution and the physical spatial coordinate can be provided by the geometrical degrees of freedom in the higher dimensional spacetime.
Bayesian nonparametric mixture inconsistency for the number of components: How worried should we be in practice?

We consider the Bayesian mixture of ﬁnite mixtures (MFMs) and Dirichlet process mixture (DPM) models for clustering. Recent asymptotic theory has established that DPMs overestimate the number of clusters for large samples and that estimators from both classes of models are inconsistent for the number of clusters under misspeciﬁcation, but the implica-tions for ﬁnite sample analyses are unclear. The ﬁnal reported estimate after ﬁtting these models is often a single representative clustering obtained using an MCMC summarisation technique, but it is unknown how well such a summary estimates the number of clusters. Here we investigate these practical considerations through simulations and an application to gene expression data, and ﬁnd that (i) DPMs overestimate the number of clusters even in ﬁnite samples, but only to a limited degree that may be correctable using appropriate summaries, and (ii) misspeciﬁcation can lead to considerable overestimation of the number of clusters in both DPMs and MFMs, but results are nevertheless often still interpretable. We provide recommendations on MCMC summarisation and suggest that although the more appealing asymptotic properties of MFMs provide strong motivation to prefer them, results obtained using MFMs and DPMs are often very similar in practice.
Replacing the Framingham-based equation for prediction of cardiovascular disease risk and adverse outcome by using artificial intelligence and retinal imaging

Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk score based on Framingham equations was calculated. The real CVD event rate was also determined for the individuals and overall population. Finally, ORAiCLE was trained using only age, ethnicity, sex plus retinal images. Results: Compared to Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting cardiovascular event in he next 5-years, especially for the highest risk group of people. The reliability and accuracy of each of the restrictive models was suboptimal to ORAiCLE performance ,indicating that it was using data from both sets of data to derive its final results. Conclusion: Retinal photography is inexpensive and only minimal training is required to acquire them as fully automated, inexpensive camera systems are now widely available. As such, AI-based CVD risk algorithms such as ORAiCLE promise to make CV health screening more accurate, more afforadable and more accessible for all. Furthermore, ORAiCLE unique ability to assess the relative contribution of the components that comprise an individuals overall risk would inform treatment decisions based on the specific needs of an individual, thereby increasing the likelihood of positive health outcomes.
Open World Learning Graph Convolution for Latency Estimation in Routing Networks

Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domain-knowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.
Topological structure of complex predictions

Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in the BRCA1 gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure of BRCA1 in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.
Adaptive Gradient Methods at the Edge of Stability

Very little is known about the training dynamics of adaptive gradient methods like Adam in deep learning. In this paper, we shed light on the behavior of these algorithms in the full-batch and sufficiently large batch settings. Specifically, we empirically demonstrate that during full-batch training, the maximum eigenvalue of the preconditioned Hessian typically equilibrates at a certain numerical value -- the stability threshold of a gradient descent algorithm. For Adam with step size $\eta$ and $\beta_1 = 0.9$, this stability threshold is $38/\eta$. Similar effects occur during minibatch training, especially as the batch size grows. Yet, even though adaptive methods train at the ``Adaptive Edge of Stability'' (AEoS), their behavior in this regime differs in a significant way from that of non-adaptive methods at the EoS. Whereas non-adaptive algorithms at the EoS are blocked from entering high-curvature regions of the loss landscape, adaptive gradient methods at the AEoS can keep advancing into high-curvature regions, while adapting the preconditioner to compensate. Our findings can serve as a foundation for the community's future understanding of adaptive gradient methods in deep learning.
Early results from GLASS-JWST. XIII. A faint, distant, and cold brown dwarf

We present the serendipitous discovery of a late T-type brown dwarf candidate in JWST NIRCam observations of the Early Release Science Abell 2744 parallel ﬁeld. The discovery was enabled by the sensitivity of JWST at 4 µ m wavelengths and the panchromatic 0.9–4.5 µ m coverage of the spectral energy distribution. The unresolved point source has magnitudes F115W = 27.95 ± 0.15 and F444W = 25.84 ± 0.01 (AB), and its F115W − F444W and F356W − F444W colors match those expected for other known T dwarfs. We can exclude it as a reddened background star, high redshift quasar, or a very high redshift galaxy. Comparison with stellar atmospheric models indicates a temperature of T eff ≈ 650 K and surface gravity log g ≈ 5.25, implying a mass of 0.03 M (cid:12) and age of 5 Gyr. We estimate the distance of this candidate to be 570–720 pc in a direction perpendicular to the Galactic plane, making it a likely thick disk or halo brown dwarf. These observations underscore the power of JWST to probe the very low-mass end of the substellar mass function in the Galactic thick disk and halo.
The brightest galaxies at cosmic dawn

Recent JWST observations suggest an excess of z ≳ 10 galaxy candidates above most theoretical models. Here, we explore how the interplay between halo formation timescales, star formation efficiency and dust attenuation affects the properties and number densities of galaxies observed in the early universe. To guide intuition, we calculate the theoretical upper limit on the UV luminosity function, assuming star formation is 100 % efficient and all gas in halos is converted into stars, and that galaxies are at the peak age for UV emission (∼10 Myr). This upper limit is ∼4 orders of magnitude greater than current observations, implying no formal tension with star formation in ΛCDM cosmology. In a more realistic model, we use the distribution of halo formation timescales derived from extended Press-Schechter theory as a proxy for star formation rate (SFR). We predict that the galaxies observed so far at z ≳ 10 are dominated by those with the fastest formation timescales, and thus most extreme SFRs and young ages. These galaxies can be upscattered by ∼1.5 mag compared to the median UV magnitude vs halo mass relation. This likely introduces a selection effect at high redshift whereby only the youngest (≲ 10 Myr), most highly star forming galaxies (specific SFR$\, \rlap{\lower3.5 pt \hbox{$\sim $}} \raise1pt \hbox{$>$}\, 30\, \mathrm{Gyr}^{-1}$) have been detected so far. Furthermore, our modelling suggests that redshift evolution at the bright end of the UV luminosity function is substantially affected by the build-up of dust attenuation. We predict that deeper JWST observations (reaching m ∼ 30) will reveal more typical galaxies with relatively older ages (∼100 Myr) and less extreme specific SFRs (∼10 Gyr−1 for a MUV ∼ −20 galaxy at z ∼ 10).
Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing

A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom.
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we present a new dataset as well as a new self-supervised learning method for ImageNet pre-training to improve anomaly detection and segmentation in 1-class and 2-class 5/10/high-shot training setups. We release the Visual Anomaly (VisA) Dataset consisting of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 objects in 3 domains, making it the largest industrial anomaly detection dataset to date. Both image and pixel-level labels are provided. We also propose a new self-supervised framework - SPot-the-difference (SPD) - which can regularize contrastive self-supervised pre-training, such as SimSiam, MoCo and SimCLR, to be more suitable for anomaly detection tasks. Our experiments on VisA and MVTec-AD dataset show that SPD consistently improves these contrastive pre-training baselines and even the supervised pre-training. For example, SPD improves Area Under the Precision-Recall curve (AU-PR) for anomaly segmentation by 5.9% and 6.8% over SimSiam and supervised pre-training respectively in the 2-class high-shot regime. We open-source the project at http://github.com/amazon-research/spot-diff .