Miracle-dz/UniFAR-FTUs huggingface dataset:
A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort

Abstract Objective To assess whether an individual’s degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and Methods Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5–7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70. Discussion In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct. Conclusions These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.
Helicobacter pylori Infection Promotes Gastric Premalignancies and Malignancies Lesions and Demotes Hyperplastic Polyps: A 5 Year Multicentric Study among Cameroonian Dyspeptic Patients

Background: Helicobacter pylori infection is the most well-known risk factor for gastric mucosa abnormalities. However, some geographic regions with persistent high H. pylori infection rates do not suffer from high gastric mucosa lesions incidence. The aim of the study was to establish the relationship between H. pylori infection and gastric pathological features in Cameroon. Methods: We performed a retrospective study, collecting data from the University Teaching Hospital and the Cameroon Pasteur institute on 1290 patients (mean age 46.31 ± 16.45 years, sex ratio 1.19:1) for whom histological features of the gastric mucosa and H. pylori infection were investigated from 2014 to 2019. Data were extracted from the medical records; hospital computerized databases; or clinical charts of these patients and reviewed according to gender and age of participants. The study was approved by the Ethical Committee of Medical Sciences. Result: Approximately 3% (2.56%) of the sample population were with normal gastric mucosa whereas chronic gastritis, atrophic gastritis, intestinal metaplasia, dysplasia, carcinoma, hyperplastic polyps and MALT lymphoma was found in 75.35, 8.2, 7.7, 2.8, 9.3, 1.55 and 0.8% of cases respectively. Unlike hyperplasia (OR= 0.3838), infected participants were in a high risk to develop gastric lesions with an odds ratio of 1.1775, 1.4866, 1.4415, 1.2088, 0.9408 and 0.9075 for gastritis, atrophic gastric, dysplasia, carcinoma, intestinal metaplasia and MALT lymphoma respectively. Conclusion: our finding showed that chronic gastritis, gastric premalignancies and malignancies are positively link to Helicobacter pylori infection and that hyperplastic polyp is inversely associated with H. pylori infection in our milieu.
Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Twitter Messages Related to Autism Spectrum Disorders

Background: The aim of our study was to conduct an emotional analysis of Turkish Twitter messages related to autism spectrum disorders (ASD). Methods: An emotion analysis was performed using quantitative and qualitative analysis methods on Turkish Twitter messages shared between November 2021 and January 2022 that contained the words "autism" and "autistic. " Results: It was found that 81.5% of the 13,042 messages that constituted the sample of this study contained neutral emotions. The most frequently used words in Twitter messages were autism, a, universe, strong, patience, warriors, and happy. The qualitative analysis revealed three main themes. These themes were: "experiences," "informing society and awareness," and "humiliation. " Conclusion: In this study, it was found that Turkish Twitter messages related to autism, which were analyzed using artificial intelligence-based emotion analysis, often contained neutral emotions. While the content of these messages, often shared by parents, was related to experiences, and the messages shared by pediatric psychiatrists and rehabilitation center employees were informative in nature, it was determined that the word "autism" was used to insult, which is outside of its medical meaning.
On the Convergence of SARSA with Linear Function Approximation

SARSA, a classical on-policy control algorithm for reinforcement learning, is known to chatter when combined with linear function approximation: SARSA does not diverge but oscillates in a bounded region. However, little is known about how fast SARSA converges to that region and how large the region is. In this paper, we make progress towards this open problem by showing the convergence rate of projected SARSA to a bounded region. Importantly, the region is much smaller than the region that we project into, provided that the magnitude of the reward is not too large. Existing works regarding the convergence of linear SARSA to a fixed point all require the Lipschitz constant of SARSA's policy improvement operator to be sufficiently small; our analysis instead applies to arbitrary Lipschitz constants and thus characterizes the behavior of linear SARSA for a new regime.
Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts

We investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of models fine-tuned on LLM-generated and human-generated data. Data generated with generative LLMs can be used to augment training data, especially in domains with smaller amounts of labeled data. We build ChatGPT-RetrievalQA based on an existing dataset, human ChatGPT Comparison Corpus (HC3), consisting of public question collections with human responses and answers from ChatGPT. We fine-tune a range of cross-encoder re-rankers on either human-generated or ChatGPT-generated data. Our evaluation on MS MARCO DEV, TREC DL'19, and TREC DL'20 demonstrates that cross-encoder re-ranking models trained on ChatGPT responses are statistically significantly more effective zero-shot re-rankers than those trained on human responses. In a supervised setting, the human-trained re-rankers outperform the LLM-trained re-rankers. Our novel findings suggest that generative LLMs have high potential in generating training data for neural retrieval models. Further work is needed to determine the effect of factually wrong information in the generated responses and test our findings' generalizability with open-source LLMs. We release our data, code, and cross-encoders checkpoints for future work.
Baler -- Machine Learning Based Compression of Scientific Data

Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry. Here, we present Baler's performance for the compression of High Energy Physics (HEP) data, as well as its application to Computational Fluid Dynamics (CFD) toy data as a proof-of-principle. We also present suggestions for cross-disciplinary guidelines to enable feasibility studies for machine learning based compression for scientific data.
A Curriculum View of Robust Loss Functions

Robust loss functions are designed to combat the adverse impacts of label noise, whose robustness is typically supported by theoretical bounds agnostic to the training dynamics. However, these bounds may fail to characterize the empirical performance as it remains unclear why robust loss functions can underfit. We show that most loss functions can be rewritten into a form with the same class-score margin and different sample-weighting functions. The resulting curriculum view provides a straightforward analysis of the training dynamics, which helps attribute underfitting to diminished average sample weights and noise robustness to larger weights for clean samples. We show that simple fixes to the curriculums can make underfitting robust loss functions competitive with the state-of-the-art, and training schedules can substantially affect the noise robustness even with robust loss functions. Code is available at \url{github}.
Probing the History of the Galaxy Assembly of the Counterrotating Disk Galaxy PGC 66551

Stellar counterrotation in disk galaxies directly relates to the complex phenomenon of the disk mass assembly believed to be driven by external processes, such as accretion and mergers. The study of such systems makes it possible to reveal the source of external accretion and establish the details of this process. In this paper, we investigate the galaxy PGC 66551 (MaNGA ID 1-179561), which hosts two large-scale counterrotating (CR) stellar disks identified in the Sloan Digital Sky Survey MaNGA data and then confirmed using deep follow-up spectroscopy with the 10 m Southern African Large Telescope. We measured the properties of ionized gas and stellar populations of both CR disks in PGC 66551. We found that the CR disk is compact, contains young stars with subsolar metallicity, and has a stellar mass of 5 × 109 M ⊙, which amounts to ≈20% of the galaxy’s total. Surprisingly, the main 8 Gyr old disk has a significantly lower metallicity of −0.8 dex than other CR galaxies. We developed a simple analytic model of the history of the metal enrichment, which we applied to PGC 66551 and constrained the parameters of the galactic outflow wind, and estimated the metallicity of the infalling gas that formed the CR disk to be −0.9. −0.5 dex. Our interpretation prefers a merger with a gas-rich satellite over cold accretion from a cosmic filament as a source of gas, which then formed the CR disk in PGC 66551.
Tensor Network Message Passing

When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the exponential growth of the state space as the system size increases. Many standard methods have significant weaknesses. For instance, message-passing algorithms can be inaccurate and even fail to converge due to short loops. At the same time, tensor network methods can have exponential computational complexity in large graphs due to long loops. This work proposes a new method called ``tensor network message passing. '' This approach allows us to compute local observables like marginal probabilities and correlations by combining the strengths of tensor networks in contracting small sub-graphs with many short loops and the strengths of message-passing methods in globally sparse graphs, thus addressing the crucial weaknesses of both approaches. Our algorithm is exact for systems that are globally tree-like and locally dense-connected when the dense local graphs have limited treewidth. We have conducted numerical experiments on synthetic and real-world graphs to compute magnetizations of Ising models and spin glasses, to demonstrate the superiority of our approach over standard belief propagation and the recently proposed loopy message-passing algorithm. In addition, we discuss the potential applications of our method in inference problems in networks, combinatorial optimization problems, and decoding problems in quantum error correction.
Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms

OBJECTIVE High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity). MATERIALS AND METHODS ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputations are then used for ROC parameter estimation to yield more precise estimates of PA performance relative to classical supervised ROC analysis (supROC) using only labeled data. We evaluated ssROC with synthetic, semi-synthetic, and EHR data from Mass General Brigham (MGB). RESULTS ssROC produced ROC parameter estimates with minimal bias and significantly lower variance than supROC in the simulated and semi-synthetic data. For the 5 PAs from MGB, the estimates from ssROC are 30% to 60% less variable than supROC on average. DISCUSSION ssROC enables precise evaluation of PA performance without demanding large volumes of labeled data. ssROC is also easily implementable in open-source R software. CONCLUSION When used in conjunction with weakly-supervised PAs, ssROC facilitates the reliable and streamlined phenotyping necessary for EHR-based research.
A deep optical survey of young stars in the Carina Nebula. I. -- UBVRI photometric data and fundamental parameters

We present the deep homogeneous $UBVRI$ photometric data of 135,071 stars down to $V\sim23$ mag and I ~ 22 mag toward the Carina Nebula. These stars are cross-matched with those from the previous surveys in the X-ray, near-infrared, and mid-infrared wavelengths as well as the Gaia Early Data Release 3 (EDR3). This master catalog allows us to select reliable members and determine the fundamental parameters distance, size, stellar density of stellar clusters in this star-forming region. We revisit the reddening toward the nebula using the optical and the near-infrared colors of early-type stars. The foreground reddening [E(B-V)_fg] is determined to be 0.35+/-0.02, and it seems to follow the standard reddening law. On the other hand, the total-to-selective extinction ratio of the intracluster medium (R_V,cl) decreases from the central region (Trumpler 14 and 16, R_V,cl ~ 4.5) to the northern region (Trumpler 15, R_V,cl ~ 3.4). It implies that the central region is more dusty than the northern region. We find that the distance modulus of the Carina Nebula to be 11.9+/-0.3 mag (d = 2.4+/-0.35 kpc) using a zero-age main-sequence fitting method, which is in good agreement with that derived from the Gaia EDR3 parallaxes. We also present the catalog of 3,331 pre-main-sequence (PMS) members and 14,974 PMS candidates down to V ~ 22 mag based on spectrophotometric properties of young stars at infrared, optical, and X-ray wavelengths. From the spatial distribution of PMS members and PMS candidates, we confirm that the member selection is very reliable down to faint stars. Our data will have a legacy value for follow-up studies with different scientific purposes.
Short definitions in constraint languages

A first-order formula is called primitive positive (pp) if it only admits the use of existential quantifiers and conjunction. Pp-formulas are a central concept in (fixed-template) constraint satisfaction since CSP($\Gamma$) can be viewed as the problem of deciding the primitive positive theory of $\Gamma$, and pp-definability captures gadget reductions between CSPs. An important class of tractable constraint languages $\Gamma$ is characterized by having few subpowers, that is, the number of $n$-ary relations pp-definable from $\Gamma$ is bounded by $2^{p(n)}$ for some polynomial $p(n)$. In this paper we study a restriction of this property, stating that every pp-definable relation is definable by a pp-formula of polynomial length. We conjecture that the existence of such short definitions is actually equivalent to $\Gamma$ having few subpowers, and verify this conjecture for a large subclass that, in particular, includes all constraint languages on three-element domains. We furthermore discuss how our conjecture imposes an upper complexity bound of co-NP on the subpower membership problem of algebras with few subpowers.
Grassmann-odd three-point functions of conserved supercurrents in 3D 
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 SCFT

We consider the analytic construction of three-point functions of conserved higher-spin supercurrents in three-dimensional $\mathcal{N}=1$ superconformal field theory which are Grassmann-odd in superspace. In particular, these include the three-point functions of the supercurrent and flavour currents, which contain the three-point functions of the energy-momentum tensor and conserved vector currents at the component level. We present an analytic proof for arbitrary superspins that these correlators do not possess a parity-violating contribution. We also prove that the parity-even contribution is unique, and exists (under an assumption that is well supported by the computational approach of arXiv:2302.00593) for arbitrary superspins. The construction of the parity-even sector is shown to reduce to solving a system of linear homogeneous equations with a tri-diagonal matrix of co-rank one, which we solve explicitly for arbitrary superspins.
Scalable Data Point Valuation in Decentralized Learning

Existing research on data valuation in federated and swarm learning focuses on valuing client contributions and works best when data across clients is independent and identically distributed (IID). In practice, data is rarely distributed IID. We develop an approach called DDVal for decentralized data valuation, capable of valuing individual data points in federated and swarm learning. DDVal is based on sharing deep features and approximating Shapley values through a k-nearest neighbor approximation method. This allows for novel applications, for example, to simultaneously reward institutions and individuals for providing data to a decentralized machine learning task. The valuation of data points through DDVal allows to also draw hierarchical conclusions on the contribution of institutions, and we empirically show that the accuracy of DDVal in estimating institutional contributions is higher than existing Shapley value approximation methods for federated learning. Specifically, it reaches a cosine similarity in approximating Shapley values of 99.969 % in both, IID and non-IID data distributions across institutions, compared with 99.301 % and 97.250 % for the best state of the art methods. DDVal scales with the number of data points instead of the number of clients, and has a loglinear complexity. This scales more favorably than existing approaches with an exponential complexity. We show that DDVal is especially efficient in data distribution scenarios with many clients that have few data points - for example, more than 16 clients with 8,000 data points each. By integrating DDVal into a decentralized system, we show that it is not only suitable for centralized federated learning, but also decentralized swarm learning, which aligns well with the research on emerging internet technologies such as web3 to reward users for providing data to algorithms.
Pay More Attention to Relation Exploration for Knowledge Base Question Answering

Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning, which results in limited supervision for this task. Moreover, the relations, which empirically determine the reasoning path selection, are not fully considered in recent advancements. In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision. We explore guidance from relations in three aspects, including (1) distinguishing similar entities by employing a variational graph auto-encoder to learn relation importance; (2) exploring extra supervision by predicting relation distributions as soft labels with a multi-task scheme; (3) designing a relation-guided re-ranking algorithm for post-processing. Experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our framework, improving the F1 score by 5.7% from 40.5 to 46.3 on CWQ and 5.8% from 62.8 to 68.5 on WebQSP, better or on par with state-of-the-art methods.
Uncovering ChatGPT’s Capabilities in Recommender Systems

The debut of ChatGPT has recently attracted significant attention from the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows significant improvement in a range of downstream NLP tasks, but the capabilities and limitations of ChatGPT in terms of recommendations remain unclear. In this study, we aim to enhance ChatGPT’s recommendation capabilities by aligning it with traditional information retrieval (IR) ranking capabilities, including point-wise, pair-wise, and list-wise ranking. To achieve this goal, we re-formulate the aforementioned three recommendation policies into prompt formats tailored specifically to the domain at hand. Through extensive experiments on four datasets from different domains, we analyze the distinctions among the three recommendation policies. Our findings indicate that ChatGPT achieves an optimal balance between cost and performance when equipped with list-wise ranking. This research sheds light on a promising direction for aligning ChatGPT with recommendation tasks. To facilitate further explorations in this area, the full code and detailed original results are open-sourced at https://github.com/rainym00d/LLM4RS.