Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Nonparametric Ordinary differential Equations: Application to Sparse and Noisy Data

Learning nonparametric systems of Ordinary Diﬀerential Equations (ODEs) ˙ x = f ( t,x ) from noisy and sparse data is an emerging machine learning topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces (RKHS) to deﬁne candidates for f for which the solution of the ODE exists and is unique. Learning f consists of solving a constrained optimization problem in an RKHS. We propose a penalty method that iteratively uses the Representer theorem and Euler approximations to provide a numerical solution. We prove a generalization bound for the L 2 distance between x and its estimator. Experiments are provided for the FitzHugh–Nagumo oscillator and for the prediction of the Amyloid level in the cortex of aging subjects. In both cases, we show competitive results when compared with the state of the art.
Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission

Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning-based near-orthogonal superposition (NOS) coding scheme is proposed to transmit short messages in multiple-input multiple-output (MIMO) channels for mMTC applications. In the proposed MIMO-NOS scheme, a neural network-based encoder is optimized via end-to-end learning with a corresponding neural network-based detector/decoder in a superposition-based auto-encoder framework including a MIMO channel. The proposed MIMO-NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector and reshaped for the space-time transmission. For the receiver, we propose a novel looped $K$ -best tree-search algorithm with cyclic redundancy check (CRC) assistance to enhance the error correcting ability in the block-fading MIMO channel. For a comprehensive understanding of the proposed MIMO-NOS scheme, we further quantify the gain from individual components/modules in the framework, and analyze the decoding complexity measured by the floating point operations (FLOPs). Simulation results show the proposed MIMO-NOS scheme outperforms maximum likelihood (ML) MIMO detection combined with a polar code with CRC-assisted list decoding by 1 – 2 dB in various MIMO systems for short (32 – 64 bit) message transmission.
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR

This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3 kB to represent a 1.4 MB laser scan. We verify the efficacy of our system on the SemanticKITTI dataset, where we achieve a new state-of-the-art in place recognition, with an average of 88.4 % recall at 100 % precision where the next closest competitor follows with 64.9 %. We also show accurate metric pose estimation performance - estimating 6-DoF pose with median errors of 10cm and 0.33 deg.
On Bayesian Dirichlet Scores for Staged Trees and Chain Event Graphs

Chain event graphs (CEGs) are a recent family of probabilistic graphical models that have emerged as a suitable alternative to Bayesian networks (BNs) for asymmetric processes. These asymmetries include context-speciﬁc independencies and structural asymmetries (i.e. structural zeros and structural missing values). Model selection in CEGs is done through an intermediate model called staged trees, and similar to BNs, this can be done through a score-based approach. Moreover, a CEG is uniquely deﬁned by its staged tree. In BNs, the Bayesian Dirichlet equivalent uniform (BDeu) score – obtained through a speciﬁc hyperparameter setting in the Bayesian Dirichlet score function – is popular for score-based model selection for its desirable theoretical properties such as ease of hyperparameter setting, preservation of e ﬀ ective sample size, and score equivalence. It has been shown that, under standard assumptions, the BD score function can anal-ogously be deﬁned for staged trees and thereby, for CEGs. However, unlike in BNs, there has been little research into the e ﬀ ects of hyperparameter setting in the BD score function on both these models. In this paper, we derive a BDeu score for staged trees and CEGs. Further, we explore the relationship between the BD sparse (BDs) score, proposed for BNs that contain unobserved conﬁgurations of its variables within a dataset, and the BDeu for staged trees and CEGs. Through this relationship, we demonstrate the favourable properties of CEGs in modelling processes with sparsity or asymmetry.
LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables

Memory access patterns have been demonstrated to leak critical information such as security keys and a program's spatial and temporal information. This information leak poses a significant privacy challenge in machine learning models with embedding tables. Embedding tables are used to learn categorical features from training data. The address of an embedding table entry carries privacy sensitive information since the address of an entry discloses features associated with a user. Oblivious RAM (ORAM), and its enhanced variants, such as PathORAM, have emerged as viable solutions to hide leakage from memory access streams. PathORAM fetches an entire path of memory blocks for every memory fetch request, thereby leading to substantial bandwidth and performance overheads. In this work, we present Look Ahead ORAM (LAORAM), an ORAM framework designed to protect user privacy during embedding table training. LAORAM exploits the unique property of ML training, namely the training samples that are going to be used in the future are known beforehand. LAORAM preprocesses the training samples to identify the memory blocks which are accessed together in the near future. LAORAM combines multiple blocks accessed together as superblocks and tries to assign all blocks in a superblock to few paths. Thus, future accesses to a collection of blocks can be satisfied from a few paths, effectively reducing the number of reads and writes to the ORAM. To further increase performance, LAORAM uses a fat-tree structure for PathORAM, i.e. a tree with variable bucket size, effectively reducing the number of background evictions required, which improves the stash usage. We have evaluated LAORAM using both a recommendation model (DLRM) and an NLP model (XLM-R) embedding table configurations. LAORAM performs 5 times faster than PathORAM on a recommendation dataset (Kaggle) and 5.4 times faster on an NLP dataset (XNLI) while guaranteeing the same security guarantees as the original PathORAM.
Relationships Between Sports Club Participation and Health Determinants in Adolescents and Young Adults

Physical fitness is a powerful marker of health in adolescents and young adults. The purpose of this study was to measure the relationships between age, sex, body mass index, and sports club participation on physical fitness. The population included 49,988 participants (23,721 girls and 26,267 boys) who were divided into five age groups (11–12, 13–14, 15–16, 17–18, and 19–21 years). Body mass index was calculated. Physical fitness was assessed with the Diagnoform® Tonic battery. Sports club participation was also documented. The practiced sport was collected. The effects of age, sex, body mass index class, and sports club participation were tested. Boys' PF increased with age at a faster rate and was better than that of girls, except for flexibility (p < 0.001). For girls, a decrease was observed in endurance, speed and flexibility at 17-18 years. Sports club participation was greater for boys at every age. Obese participants had the lowest physical fitness and sports club participation. Sports club participation increased physical fitness. Team sports seemed best for improving physical fitness, except flexibility. The study shows that sports club participation may be a key element for building health in adolescents. Preventive healthcare projects that promote sports club are needed to target sports club dropouts (obese adolescents and girls). Bridges should be built between physical education classes and sports clubs in adolescence to improve the health status of young people.
Changes in brain activity with tominersen in early-manifest Huntington’s disease

Abstract It is unknown whether alterations in EEG brain activity caused by Huntington’s disease may be responsive to huntingtin-lowering treatment. We analysed EEG recordings of 46 patients (mean age = 47.02 years; standard deviation = 10.19 years; 18 female) with early-manifest Stage 1 Huntington’s disease receiving the huntingtin-lowering antisense oligonucleotide tominersen for 4 months or receiving placebo as well as 39 healthy volunteers (mean age = 44.48 years; standard deviation = 12.94; 22 female) not receiving treatment. Patients on tominersen showed increased resting-state activity within a 4–8 Hz frequency range compared with patients receiving placebo (cluster-based permutation test, P < 0.05). The responsive frequency range overlapped with EEG activity that was strongly reduced in Huntington’s disease compared with healthy controls (cluster-based permutation test, P < 0.05). The underlying mechanisms of the observed treatment-related increase are unknown and may reflect neural plasticity as a consequence of the molecular pathways impacted by tominersen treatment. Hawellek et al. report that patients with Huntington’s disease treated with the huntingtin-lowering antisense oligonucleotide tominersen exhibited increased EEG power in the theta/alpha frequency range. The underlying mechanisms of the observed changes are unknown and may reflect neural plasticity as a consequence of the molecular pathways impacted by tominersen treatment.
Comparison of learning curves for laparoendoscopic single-site myomectomy performed by 2 surgeons

We aimed to compare the learning curves of 2 surgeons with different endoscopic bases when performing laparoendoscopic single-site myomectomy (LESS-M). We retrospectively analyzed and compared 2 groups of patients who underwent LESS-M performed by 2 surgeons with different bases in multi-port laparoscopic surgery (MLS) from October 2019 to December 2020 at West China Second Hospital of Sichuan University. Patients’ characteristics and related surgical indicators were compared, and surgeons’ learning curves were analyzed using a cumulative sum analysis. All of the patients completed LESS-M without converting to MLS or laparotomy, despite Surgeon A being MLS-unqualified and Surgeon B being MLS-qualified. There were no significant differences in patients’ characteristics or surgical indicators between the 2 groups (P > 0.05 for all). Surgeons A and B crossed the learning curve after 21 and 18 cases, respectively. LESS-M is safe and feasible. Approximately 20 cases are required for surgeons to achieve LESS-M proficiency, and surgeons without MLS experience can still master LESS-M.
A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set

Background The C-Score, which is an individual health score, is based on a predictive model validated in the UK and US populations. It was designed to serve as an individualized point-in-time health assessment tool that could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications that reduce the risk of premature death. Objective Our study aimed to conduct an external validation of the C-Score in the US population and expand the original score to improve its predictive capabilities in the US population. The C-Score is intended for mobile health apps on wearable devices. Methods We conducted a literature review to identify relevant variables that were missing in the original C-Score. Subsequently, we used data from the 2005 to 2014 US National Health and Nutrition Examination Survey (NHANES; N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III data from 1988 to 1994 (N=1440) to conduct an external validation of the test. Only participants with complete data were included in this study. Discrimination and calibration tests were conducted to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test. Results Higher C-Scores were associated with reduced odds of all-cause mortality (odds ratio 0.96, P<.001). We found a good fit of the C-Score for all-cause mortality with an area under the curve (AUC) of 0.72. Among participants aged between 40 and 69 years, C-Score models had a good fit for all-cause mortality and an AUC >0.72. A sensitivity analysis using NHANES III data (1988-1994) was performed, yielding similar results. The inclusion of sociodemographic and clinical variables in the basic C-Score increased the AUCs from 0.72 (95% CI 0.71-0.73) to 0.87 (95% CI 0.85-0.88). Conclusions Our study shows that this digital biomarker, the C-Score, has good capabilities to predict all-cause mortality in the general US population. An expanded health score can predict 87% of the mortality in the US population. This model can be used as an instrument to assess individual mortality risk and as a counseling tool to motivate behavior changes and lifestyle modifications.
A New Ring Oscillator with Dual Voltage/Current Control

A new Temperature compensated Ring Oscillator is presented. This CMOS ring oscillator consists of five current starved stage which is fed by a voltage and a current reference. In this paper a new method based on previous works has been proposed, in which required Temperature characteristic is imposed to the oscillator. The oscillation frequency of the proposed oscillator is 14.5 MHz and the simulation results in CMOS 0. 18µm technology with a 1.8- V power supply showed that the temperature coefficient is 31.1 ppm/oC in the Temperature range of -15 oC to 65 oC. The total power consumption of circuit is 587 µW.
Accessibility to rabies centers and human rabies post-exposure prophylaxis rates in Cambodia: A Bayesian spatio-temporal analysis to identify optimal locations for future centers

Rabies is endemic in Cambodia. For exposed humans, post-exposure prophylaxis (PEP) is very effective in preventing this otherwise fatal disease. The Institut Pasteur du Cambodge (IPC) in Phnom Penh was the primary distributor of PEP in Cambodia until 2018. Since then, and to increase distribution of PEP, two new centers have been opened by IPC in the provinces of Battambang and Kampong Cham. Data on bitten patients, who sometimes bring the head of the biting animal for rabies analyses, have been recorded by IPC since 2000. However, human cases are not routinely recorded in Cambodia, making it difficult to establish a human burden of disease and generate a risk map of dog bites to inform the selection of future PEP center locations in high-risk areas. Our aim was to assess the impact of accessibility to rabies centers on the yearly rate of PEP patients in the population and generate a risk map to identify the locations where new centers would be the most beneficial to the Cambodian population. To accomplish this, we used spatio-temporal Bayesian regression models with the number of PEP patients as the outcome. The primary exposure variable considered was travel time to the nearest IPC center. Secondary exposure variables consisted of travel time to a provincial capital and urban proportion of the population. Between 2000 and 2016, a total of 293,955 PEP patient records were identified. Our results showed a significant negative association between travel time to IPC and the rate of PEP patients: an increase in one hour travel time from the living location to IPC PEP centers leads to a reduction in PEP rate of 70% to 80%. Five provinces were identified as the most efficient locations for future centers to maximize PEP accessibility: Banteay Meanchey, Siem Reap, Takeo, Kampot and Svay Rieng. Adding a PEP center in every provincial capital would increase the proportion of Cambodians living within 60 minutes of a PEP center from 26.6% to 64.9%, and living within 120 minutes from 52.8% to 93.3%, which could save hundreds of lives annually.
Mixed-methods findings from the Ngutulu Kagwero (agents of change) participatory comic pilot study on post-rape clinical care and sexual violence prevention with refugee youth in a humanitarian setting in Uganda

ABSTRACT There is a dearth of evidence-based post-rape clinical care interventions tailored for refugee adolescents and youth in low-income humanitarian settings. Comics, a low-cost, low-literacy and youth-friendly method, integrate visual images with text to spark emotion and share health-promoting information. We evaluated a participatory comic intervention to increase post-exposure prophylaxis (PEP) knowledge and acceptance, and prevent sexual and gender-based violence, in Bidi Bidi refugee settlement, Uganda. Following a formative qualitative phase, we conducted a pre-test post-test pilot study with refugee youth (aged 16–24 years) (n = 120). Surveys were conducted before (t0), after (t1), and two-months following (t2) workshops. Among participants (mean age: 19.7 years, standard deviation: 2.4; n = 60 men, n = 60 women), we found significant increases from t0 to t1, and from t0 to t2 in: (a) PEP knowledge and acceptance, (b) bystander efficacy, and (c) resilient coping. We also found significant decreases from t0 to t1, and from t0 to t2 in sexual violence stigma and depression. Qualitative feedback revealed knowledge and skills acquisition to engage with post-rape care and violence prevention, and increased empathy to support survivors. Survivor-informed participatory comic books are a promising approach to advance HIV prevention through increased PEP acceptance and reduced sexual violence stigma with refugee youth. Trial registration: ClinicalTrials.gov identifier: NCT04656522.
Downregulation of DNMT3A Attenuates the Warburg Effect, Proliferation, and Invasion via Promoting the Inhibition of miR-603 on HK2 in Ovarian Cancer

Background: Ovarian cancer is a highly malignant gynecological cancer. Aerobic glycolysis is one of the features of cancer cell metabolism. Studying the molecular modulation of the Warburg effect in ovarian cancer is significantly valuable for understanding the progression mechanism of ovarian cancer. Materials and Methods: The expression level and prognostic significance of DNMT3A were analyzed using public databases. DNMT3A was overexpressed by plasmid transfection, and DNMT3A was interfered with specific siRNAs transfection. miR-603 was overexpressed by mimic transfection or inhibited by inhibitor transfection. The expression of the molecules was detected by qPCR or western blotting. CCK-8 and transwell assays were used to determine the cell proliferation, migration, and invasion abilities of ovarian cancer. Results: We found that the DNMT3A protein level was higher in ovarian cancer tissues than in normal ovary tissues, but the mRNA level had no significant difference in ovarian cancer tissues and normal ovary tissues. The higher the RNA level of DNMT3A, the poorer prognosis of patients. DNMT3A knocking down impeded the Warburg effect, cell proliferation, migration, and invasion of ovarian cancer cells. Further investigations discovered that DNMT3A promoted ovarian cancer cell malignancy via silencing miR-603. Conclusion: We found that patients who overexpressed DNMT3A showed a poor prognosis. DNMT3A was found to promote the Warburg effect, cell proliferation, migration, and invasion of ovarian cancer by inhibiting the expression of miR-603. As a result, the research revealed that DNMT3A/miR-603/HK2 axis contributed to the Warburg effect of ovarian cancer and DNMT3A may be a potential therapeutic target for ovarian cancer.
Exploring the demographic and situational characteristics of older British people experiencing loneliness as positive within the BBC loneliness experiment

Abstract Objectives The aim of this study is to increase the understanding of loneliness experienced as positive, by exploring the demographic and situational characteristics of older people who experience loneliness as positive. Method Two binary logistic regressions were conducted using data from those aged 60+ from the BBC Loneliness Experiment (N = 5250). Results The first binary logistic regression compared participants who experience loneliness always as positive (N = 219) to those participants who never experience loneliness as positive (N = 3004). Spending time alone did not emerge as relevant to experiencing loneliness as positive, but enjoying time alone was important (OR = 1.561 (95% CI = 1.313 − 1.856)). The lonelier older people were, the less likely they experienced loneliness as positive (OR = 0.708 (95% CI =0.644 − 0.779)). Men were more likely to experience loneliness as positive compared to women (OR = 1.734 (95% CI = 1.269 − 2.370)). Lastly, the experience of loneliness as positive was likely to decrease when older people had more years of education (OR = 0.887 (95% CI = 0.853 − 0.921)) but increased with age (OR = 1.067 (95% CI = 1.037 − 1.098)). The results of the second binary logistic regression comparing participants who indicated loneliness purely as positive with those participants indicate to experience loneliness sometimes as positive (N = 2027), are in line with the first regression analyses. Conclusion The results are critically discussed by emphasizing the role of norms and cultures, gerotranscendence, and severity of loneliness, which might influence the experiences of loneliness. Further qualitative research is needed to elucidate the meanings of these positive experiences of loneliness.
Mental well-being in young people with psychiatric disorders during the early phase of COVID-19 lockdown

Background Mental health and well-being were seriously impacted by the COVID-19 lockdown especially among young people and people with psychiatric disorders. This study aimed to identify factors associated with well-being in young people with psychiatric disorders, during early phase of COVID-19 lockdown in France. Methods A national cross-sectional online study started on the 8th day of COVID-19 lockdown in France (during March 25–30, 2020). We included young people aged from 16 to 29 who responded to the questionnaire, living and being confined in France, with past or current psychiatric treatment. The questionnaire was accessible online and explored demographics and clinical factors, well-being, stress, situation during lockdown. Well-being was measured by the Warwick-Edinburg Mental Well-Being Scale (WEMWBS). Simple and multiple linear regression analyses were carried out. Results 439 individuals were included with 262 (59.7%) previously treated and 177 (40.3%) currently treated. WEMWBS total score were 42.48 (9.05). Feeling of useful was the most affected dimension. Well-being was positively correlated with: currently working on site, physical activity, abilities to cope with difficulties, family and social supports (p<0.05). It was negatively correlated with: elevated stress level, anxious ruminations, dissatisfaction with information, difficulties to sleep or reorganize daily life, feeling supported by medicines (p<0.05). No individual factor was correlated with well-being. The stepwise linear multivariate model had simple R2 coefficient of determination of 0.535. Conclusion In the specific population of young people with psychiatric disorders, factors associated with well-being at early stage of lockdown were mainly psychosocial and related to brutal disorganisation of daily life.