Miracle-dz/UniFAR-FTUs huggingface dataset:
Capsular Mechanics After Periacetabular Osteotomy for Hip Dysplasia

Background: Hip dysplasia is characterized by insufficient acetabular coverage around the femoral head, which leads to instability, pain, and injury. Periacetabular osteotomy (PAO) aims to restore acetabular coverage and function, but its effects on capsular mechanics and joint stability are still unclear. The purpose of this study was to examine the effects of PAO on capsular mechanics and joint range of motion in dysplastic hips. Methods: Twelve cadaveric dysplastic hips (denuded to bone and capsule) were mounted onto a robotic tester and tested in multiple positions: (1) full extension, (2) neutral 0°, (3) flexion of 30°, (4) flexion of 60°, and (5) flexion of 90°. In each position, the hips underwent internal and external rotation, abduction, and adduction using 5 Nm of torque. Each hip then underwent PAO to reorient the acetabular fragment, preserving the capsular ligaments, and was retested. Results: The PAO reduced internal rotation in flexion of 90° (∆IR = –5°; p = 0.003), and increased external rotation in flexion of 60° (∆ER = +7°; p = 0.001) and flexion of 90° (∆ER = +11°; p = 0.001). The PAO also reduced abduction in extension (∆ABD = –10°; p = 0.002), neutral 0° (∆ABD = –7°; p = 0.001), and flexion of 30° (∆ABD = –8°; p = 0.001), but increased adduction in neutral 0° (∆ADD = +9°; p = 0.001), flexion of 30° (∆ADD = +11°; p = 0.002), and flexion of 60° (∆ADD = +11°; p = 0.003). Conclusions: PAO caused reductions in hip abduction and internal rotation but greater increases in hip adduction and external rotation. The osseous acetabular structure and capsule both play a role in the balance between joint mobility and stability after PAO.
Behavioural and emotional comorbidities in school-aged children with neurological conditions in Kilifi, Kenya, and their long-term consequences

ABSTRACT Background Neurological conditions and mental health problems are common in children in low- and middle-income countries, but the risk factors and downstream impact of these problems on children with neurological conditions are not reported. Objective To determine the association of neurological conditions with behavioural and emotional problems in children, the prevalence and risk factors of behavioural and emotional problems, and long-term impact of these conditions. Methods Data on multiple neurological conditions and mental health problems were available for 1,616 children (aged 6–9 years) from Kilifi, Kenya. Neurological conditions were diagnosed using standardised tools and clinical examination. Behavioural and emotional problems assessed using Child Behaviour Questionnaire for Parents. Long-term outcomes were obtained from census data of the Kilifi Health and Demographic Surveillance System. Logistic and linear regression were used to measure associations. Results Mental health problems were higher in those with any neurological condition compared to those without (24% vs. 12%, p < 0.001). Cognitive (odds ratio (OR) = 2.39; 95% CI: 1.59–3.59), motor (OR = 3.17; 95% CI: 1.72–5.82), hearing (OR = 2.07; 95% CI:1.12–3.83) impairments, and epilepsy (OR = 4.18; 95% CI: 2.69–6.48), were associated with mental health problems. Prevalence of any mental health problem was 15%, with externalizing problems more common than internalizing problems (21% vs. 17%, p = 0.004). Longitudinal follow-up indicated that the disorders affected an individual’s future schooling (e.g. OR = 1.25; 95% CI: 0.14–1.46 following cognitive impairments), occupation (OR = 2.44; 95% CI: 1.09–5.44 following mental health problems), and access to household assets (OR = 2.78; 95% CI: 0.99–7.85 following epilepsy). Conclusions Neurological conditions in school-aged children in Kilifi are associated with mental health problems, and both disorders have long-term consequences. Preventive and therapeutic measures for these conditions are needed to improve outcomes of these children.
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro

For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state of the art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased towards synergistic agents and these results do not necessarily generalise out of distribution. We employ a sequential model optimization search utilising a deep learning model to quickly discover synergistic drug combinations active against a cancer cell line, requiring substantially less screening than an exhaustive evaluation. Our small scale wet lab experiments only account for evaluation of ~5% of the total search space. After only 3 rounds of ML-guided in vitro experimentation (including a calibration round), we find that the set of drug pairs queried is enriched for highly synergistic combinations; two additional rounds of ML-guided experiments were performed to ensure reproducibility of trends. Remarkably, we rediscover drug combinations later confirmed to be under study within clinical trials. Moreover, we find that drug embeddings generated using only structural information begin to reflect mechanisms of action. Prior in silico benchmarking suggests we can enrich search queries by a factor of ~5-10x for highly synergistic drug combinations by using sequential rounds of evaluation when compared to random selection, or by a factor of>3x when using a pretrained model selecting all drug combinations at a single time point.
Memristor compact model with oxygen vacancy concentrations as state variables

We present a unique compact model for oxide memristors based upon the concentration of oxygen vacancies as state variables. In this model, the increase (decrease) in oxygen vacancy concentration is similar in effect to the reduction (expansion) of the tunnel gap used as a state variable in existing compact models, providing a mechanism for the electronic current to increase (decrease) based upon the polarity of the applied voltage. Rate equations defining the dynamics of state variables are obtained from simplifications of a recent paper in which electronic processes (i.e., electron capture/emission) were combined with atomic processes (i.e., Frenkel-pair generation/recombination, diffusion) stemming from the thermochemical model of dielectric breakdown. Central to the proposed model is the effect of the electron occupancy of oxygen vacancy traps on resistive switching dynamics. The electronic current is calculated considering Ohmic, band-to-band, and bound-to-band contributions. The model includes uniform self-heating with Joule heating and conductive loss terms. The model is calibrated using experimental current–voltage characteristics for [Formula: see text] memristors with different electrode materials. Though a general model is presented, a delta-shaped density of states profile for oxygen vacancies is found capable of accurately representing experimental data while providing a minimal description of bound-to-band transitions. The model is implemented in Verilog-A and tested using read/write operations in a [Formula: see text] 1T1R nonvolatile memory array to evaluate its ability to perform circuit simulations of practical interest. A particular benefit is that the model does not make strong assumptions regarding filament geometry of which scant experimental-evidence exists to support.
Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]

Enhanced sampling methods for molecular dynamics simulations [Article v1.0] Jérôme Hénin1,2*, Tony Lelièvre3*, Michael R. Shirts4*, Omar Valsson5,6*, Lucie Delemotte7* 1Laboratoire de Biochimie Théorique UPR 9080, CNRS, Paris, France; 2Institut de Biologie Physico-Chimique–Fondation Edmond de Rothschild, Paris, France; 3CERMICS, Ecole des Ponts, INRIA, Marne-la-Vallée, France; 4Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA, 80309; 5University of North Texas, Department of Chemistry, Denton, TX, USA; 6Max Planck Institute for Polymer Research, Mainz, Germany; 7KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models

Pre-trained language models (LMs) are shown to easily generate toxic language. In this work, we systematically explore domain-adaptive training to reduce the toxicity of language models. We conduct this study on three dimensions: training corpus, model size, and parameter efficiency. For the training corpus, we propose to leverage the generative power of LMs and generate nontoxic datasets for domain-adaptive training, which mitigates the exposure bias and is shown to be more data-efficient than using a curated pre-training corpus. We demonstrate that the self-generation method consistently outperforms the existing baselines across various model sizes on both automatic and human evaluations, even when it uses a 1/3 smaller training corpus. We then comprehensively study detoxifying LMs with parameter sizes ranging from 126M up to 530B (3x larger than GPT-3), a scale that has never been studied before. We find that i) large LMs have similar toxicity levels as smaller ones given the same pre-training corpus, and ii) large LMs require more endeavor to detoxify. We also explore parameter-efficient training methods for detoxification. We demonstrate that adding and training adapter-only layers in LMs not only saves a lot of parameters but also achieves a better trade-off between toxicity and perplexity than whole model adaptation for the large-scale models.
Improving greedy core-set configurations for active learning with uncertainty-scaled distances

We scale perceived distances of the core-set algorithm by a factor of uncertainty and search for low-confidence configurations, finding significant improvements in sample efficiency across CIFAR10/100 and SVHN image classification, especially in larger acquisition sizes. We show the necessity of our modifications and explain how the improvement is due to a probabilistic quadratic speed-up in the convergence of core-set loss, under assumptions about the relationship of model uncertainty and misclassification.
A Multimodal Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement

This paper proposes a novel multimodal self-supervised architecture for energy-eﬃcient AV speech enhancement by integrating graph neural networks with canonical correlation analysis (CCA-GNN). This builds on a state-of-the-art CCA-GNN that aims to learn representative embeddings by maximizing the correlation between pairs of augmented views of the same input while decorrelating disconnected features. The key idea of the conventional CCA-GNN involves discarding augmentation-variant information and preserving augmentation-invariant information whilst preventing capturing of redundant information. Our proposed AV CCA-GNN model is designed to deal with the challenging multimodal representation learning context. Speciﬁcally, our model improves contextual AV speech processing by maximizing canonical correlation from augmented views of the same channel, as well as canonical correlation from audio and visual embeddings. In addition, we propose a positional encoding of the nodes that considers a prior-frame sequence distance instead of a feature-space representation while computing the node’s nearest neighbors. This serves to introduce temporal information in the embeddings through the neigh-borhood’s connectivity. Experiments conducted with the benchmark ChiME3 dataset show that our proposed prior frame-based AV CCA-GNN reinforces better feature learning in the temporal context, leading to more energy-eﬃcient
Abelian pole systems and Riemann-Schottky type systems

In this survey of works on a characterization of Jacobians and Prym varieties among indecomposable principally polarized abelian varieties via the soliton theory we focus on a certain circle of ideas and methods which show that the characterization of Jacobians as ppav whose Kummer variety admits a trisecant line and the Pryms as ppav whose Kummer variety admits a pair of symmetric quadrisecants can be seen as an abelian version of pole systems arising in the theory of elliptic solutions to the basic soliton hierarchies. We present also recent results in this direction on the characterization of Jacobians of curves with involution, which were motivated by the theory of two-dimensional integrable hierarchies with symmetries.
Time-varying harmonic models for voice signal analysis

Assessment of voice signals has long been performed with the assumption of periodicity as this facilitates analysis. Near periodicity of normal voice signals makes short-time harmonic modeling an appealing choice to extract vocal feature parameters. For dysphonic voice, however, a fixed harmonic structure could be too constrained as it strictly enforces periodicity in the model. Slight variation in amplitude or frequency in the signal may cause the model to misrepresent the observed signal. To address these issues, this paper presents a time-varying harmonic model, which allows its fundamental frequency and harmonic amplitudes to be polynomial functions of time. The model decouples the slow deviations of frequency and amplitude from fast irregular vocal fold vibratory behaviors such as subharmonics and diplophonia. The time-varying model is shown to track the frequency and amplitude modulations present in voice with severe tremor. This reduces the sensitivity of the model-based harmonics-to-noise ratio measures to slow frequency and amplitude variations while maintaining its sensitivity to increase in turbulent noise or the presence of irregular vibration. Other uses of the model include the vocal tract filter estimation and the rates of frequency and intensity changes. These use cases are experimentally demonstrated along with the modeling accuracy.
Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces

Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurably. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

We consider the regret minimization task in a dueling bandits problem with context information. In every round of the sequential decision problem, the learner makes a context-dependent selection of two choice alternatives (arms) to be compared with each other and receives feedback in the form of noisy preference information. We assume that the feedback process is determined by a linear stochastic transitivity model with contextualized utilities (CoLST), and the learner's task is to include the best arm (with highest latent context-dependent utility) in the duel. We propose a computationally efficient algorithm, $\texttt{CoLSTIM}$, which makes its choice based on imitating the feedback process using perturbed context-dependent utility estimates of the underlying CoLST model. If each arm is associated with a $d$-dimensional feature vector, we show that $\texttt{CoLSTIM}$ achieves a regret of order $\tilde O( \sqrt{dT})$ after $T$ learning rounds. Additionally, we also establish the optimality of $\texttt{CoLSTIM}$ by showing a lower bound for the weak regret that refines the existing average regret analysis. Our experiments demonstrate its superiority over state-of-art algorithms for special cases of CoLST models.
A modular implementation of an effective interaction approach for harmonically trapped fermions in 1D

We introduce a generic and accessible implementation of an exact diagonalization method for studying few-fermion models. Our aim is to provide a testbed for the newcomers to the field as well as a stepping stone for trying out novel optimizations and approximations. This userguide consists of a description of the algorithm, and several examples in varying orders of sophistication. In particular, we exemplify our routine using an effective-interaction approach that fixes the low-energy physics. We benchmark this approach against the existing data, and show that it is able to deliver state-of-the-art numerical results at a significantly reduced computational cost.
$\mathbb{Q}$-Fano threefolds and Laurent inversion

We construct families of non-toric Q -factorial terminal Fano ( Q -Fano) threefolds of codimension ≥ 20 corresponding to 54 mutation classes of rigid maximally mutable Laurent polynomials. From the point of view of mirror symmetry, they are the highest codimension (non-toric) Q -Fano varieties for which we can currently establish the Fano/Landau–Ginzburg correspondence. We construct 46 additional Q -Fano threefolds with codimensions of new examples ranging between 19 and 5. Some of these varieties will be presented as toric complete intersections, and others as Pfaﬃan varieties.
OSM-tree: A Sortedness-Aware Index

Indexes facilitate efficient querying when the selection predicate is on an indexed key. As a result, when loading data, if we anticipate future selective (point or range) queries, we typically maintain an index that is gradually populated as new data is ingested. In that respect, indexing can be perceived as the process of adding structure to an incoming, otherwise unsorted, data collection. The process of adding structure comes at a cost, as instead of simply appending incoming data, every new entry is inserted into the index. If the data ingestion order matches the indexed attribute order, the ingestion cost is entirely redundant and can be avoided (e.g., via bulk loading in a B+-tree). However, state-of-the-art index designs do not benefit when data is ingested in an order that is close to being sorted but not fully sorted. In this paper, we study how indexes can benefit from partial data sortedness or near-sortedness, and we propose an ensemble of techniques that combine bulk loading, index appends, variable node fill/split factor, and buffering, to optimize the ingestion cost of a tree index in presence of partial data sortedness. We further augment the proposed design with necessary metadata structures to ensure competitive read performance. We apply the proposed design paradigm on a state-of-the-art B+-tree, and we propose the Ordered Sort-Merge tree (OSM-tree). OSM-tree outperforms the state of the art by up to 8.8x in ingestion performance in the presence of sortedness, while falling back to a B+-tree's ingestion performance when data is scrambled. OSM-tree offers competitive query performance, leading to performance benefits between 28% and 5x for mixed read/write workloads.
Facebook Shadow Profiles

We quantify Facebook’s ability to build shadow proﬁles by tracking individuals across the web, irrespective of whether they are users of the social network. For a representative sample of US Internet users, we ﬁnd that Facebook is able to track about 40% of the browsing time of both users and non-users of Facebook, including on privacy-sensitive domains and across user demographics. We show that the collected browsing data can produce accurate predictions of personal information that is valuable for advertisers, such as age or gender. Because Facebook users reveal their demographic information to the platform, and because the browsing behavior of users and non-users of Facebook overlaps, users impose a data externality on non-users by allowing Facebook to infer their personal information.
Joint user association and power allocation in ultra-dense mmWave networks: a multi-connectivity approach

In ultra-dense millimeter wave (mmWave) networks, mmWave signals suffer from severe path losses and are easily blocked by obstacles. Meanwhile, ultra-dense deployment causes excessive handovers, which reduces the data link reliability. To alleviate the above issues, the novel technology, known as multi-connectivity enabled user association (MCUA) is incorporated in this letter. We aim to jointly optimize MCUAs and downlink (DL) power allocations (PAs) to maximize the DL rate of each user simultaneously, rather than total. This is a non-convex nonlinear 0-1 mixed integer multi-objective optimization problem and quite complicated. To solve it, we first use the weighted sum method to scalarize it as a single-objective optimization problem (SOOP), and then relax the binary association variables to real ones. Considering that the relaxed SOOP is still non-convex, we perform a series of transformations upon it and make it a differential of convex programming. Finally, we develop an iterative algorithm based on the convex-concave procedure to solve the SOOP. Numerical results are presented to demonstrate the effectiveness of the proposed algorithms.