Miracle-dz/UniFAR-FTUs huggingface dataset:
Steps toward an AI scientist: neuro-symbolic models for concept generalization and theory learning

: In this talk, I will introduce my works that expand machine learning models’ capability for learning novel concepts and theories. Such capability is essential for building an AI Scientist, an agent that has human’s concept generalization capability and can propose scientific theories on the relevant concepts. Firstly, I introduce a paradigm for learning theories for describing a dynamical system [1]. Instead of using one model to learn everything, we propose a novel paradigm centered around the learning and manipulation of theories , which parsimoniously predict both aspects of the future (from past observations) and the domain in which these predictions are accurate. We incorporate four common strategies with a long history in physics: divide-and-conquer, Occam's razor, unification and lifelong learning, for the discovery of theories. Specifically, we propose a novel generalized-mean-loss to encourage each theory to specialize in its comparatively advantageous domain, and a differentiable description length objective to downweight bad data and "snap" learned theories into simple symbolic formulas. Theories are stored in a "theory hub", which continuously unifies learned theories and can propose theories when encountering new environments. We test our implementation, the toy "AI Physicist" learning agent, on a suite of increasingly complex physics environments. From unsupervised observation of trajectories through worlds involving random combinations of gravity, electromagnetism, harmonic motion and elastic bounces, our agent typically learns faster and produces mean-squared prediction errors about a billion times smaller than a standard feedforward neural net of comparable complexity, typically recovering integer and rational theory parameters exactly. Our agent successfully identifies domains with different laws of motion also for a nonlinear chaotic double pendulum in a piecewise constant force field. Secondly, I will introduce Zero-shot Concept Recognition and Acquisition (ZeroC) [2], a neuro-symbolic architecture that can recognize and acquire more complex, hierarchical concepts at inference time in a zero-shot way. ZeroC represents concepts (e.g., rectangles, tables) as graphs of constituent concept models (as nodes) and their relations (as edges). It establishes a one-to-one mapping between a symbolic graph structure of a concept and its corresponding probability model (in terms of
Reinforcement Learning with Reward Shaping for Planning Domains in Jani

Reinforcement Learning tries to obtain optimal policies, according to which an agent can solve given tasks. In our case, the policies will be represented by neural networks. Finding this policy is not always easy, particularly when it comes to large instances of such tasks. In this work, we will try to approach this issue in Planning domains, specified in the framework Jani, by applying Reward Shaping in a setting already specified by Vinzent et al. 2022 [16]. The following work gives a theoretical background on the concepts used, an analysis of the policies obtained, and an outlook on further research topics to follow up on.
CONCEPT LEVEL EXPLORATION OF IMA-BASED NETWORKED PLATFORMS WITH MIXED TIME-SENSITIVE COMMUNICATION REQUIREMENTS

In this paper, we apply a grammar-based approach to generate computation and communication platforms for avionic applications with mixed classes of time-sensitive communication messages. Then, we propose an evolutionary algorithm to schedule communication in the platform considering the interaction between time-triggered and bandwidth-constrained traffic. Together, the platform generation approach and the scheduling algorithm support the exploration of avionic systems at the concept level
Centrality measures in temporal networks with time series analysis

The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.
Drought reconstruction since 1796 CE based on tree-ring widths in the Upper 1 Heilongjiang ( Amur ) River Basin in Northeast Asia , and its linkage to Pacific 2 Ocean climate variability 3

The economic and environmental impacts of persistent droughts in East 18 Asia are of growing concern, and therefore it is important to study the cyclicity and 19 causes of these regional droughts. The self-calibrating Palmer Drought Severity Index 20 (scPDSI) has been extensively employed to describe the severity of regional drought, 21 and several PDSI reconstructions based on tree rings have been produced. We 22 compiled a tree-ring chronology for Hailar pine (Pinus sylvestris var. Mongolica) 23 from two sites in the Hailar region in the Upper Heilongjiang (Amur) River Basin. 24 Analysis of the climate response revealed that scPDSI was the primary factor limiting 25 https://doi.org/10.5194/cp-2023-28 Preprint. Discussion started: 10 May 2023 c © Author(s) 2023. CC BY 4.0 License.
Position: A study protocol for the prevention of fall injuries in french special forces selection courses using a body-centered intervention

Introduction - The Military Physical and Sports Training program was developed by the French Army in order to train, optimize, and maintain individual readiness. Although the health benefits of sport practice do not need to be demonstrated, such activities can cause acute musculoskeletal injuries that need to be addressed. The prevalence of lower limb injury is rather high in the French military population and, in particular, ranges from 15 to 45% during Special Forces selection courses. Thus, this project aims to investigate the efficiency of a body-centered program designed to enhance body awareness. The program seeks to train the mind to actively pay attention to body information, while the latter is viewed as a protective factor against fall injuries. We assume: (i) that postural control can be improved by enhancing the level of body awareness; and (ii) that greater postural awareness could be beneficial in reducing the risk of fall injuries. The body-centered prevention program is based on the Optimization of the Resources of the Armed Forces (ORAF) intervention, which focuses on mental preparation and recovery, and has been deployed in the French Army for many years. Method and analyses - The study focuses on five French Special Forces selection courses (400 soldiers/ participants). It is divided into two stages (year 1, year 2). The first year is dedicated to data collection from the control group (200 participants), while in the second year the ORAF intervention will be deployed. The main objective is to evaluate the effectiveness of the ORAF intervention in reducing the rate of fall injuries during military selection, based on a multidisciplinary method that captures demographic, biological, biometric, clinical, and para-clinical measures. Registration number: IDRCB number 2021-A02108-33, Clinical Trial: NCT 05451394
Mobilisation towards formal employment in the healthcare system: A qualitative study of community health workers in South Africa

In low and middle-income countries (LMICs), community health workers (CHWs) play a critical role in delivering primary health care (PHC) services to vulnerable populations. In these settings, they often receive low stipends, function with a lack of basic resources and have little bargaining power with which to demand better working conditions. In this article, we examine CHWs employment status, their struggle for recognition as health workers, and their activities to establish labour representation in South Africa. Using a case study approach, we studied seven CHW teams located in semi-urban and rural areas of Gauteng and Mpumalanga Provinces, South Africa. We used in-depth interviews, focus group discussions and observations to gather data from CHWs and their representatives, supervisors and PHC facility staff members. The rural and semi-urban sites CHWs were poorly supervised, resourced and received meagre remuneration, their employment outsourced, without employment benefits and protection. The lack of career progression opportunities demotivated the CHWs, particularly those keen to establish a career in health. In the semi-urban sites, CHWs established a task team to represent them that held regular meetings and often used violent and disruptive strategies against clinic, district and provincial management, which often led to tensions and conflicts with facility staff and programme coordinators. After a meeting with the local provincial legislature, the task team joined a labour union (NEHAWU) in order to be able to participate in the local Bargaining Council. Though they were not successful in getting the government to provide permanent employment, the union negotiated an increase in stipend from R2 500 (136 USD) to R3 500 (192 USD). In contrast, in the rural sites, the CHWs were not actively demanding permanent employment due to their employment contracts being partly managed by non-government organisations (NGOs) managements; they were fearful of being recalled from the government programme. After the study ended, during the height of COVID-19 in 2020, when the need for motivated and effective CHWs became much more obvious to decision makers, the semi-urban-based teams received permanent employment with remuneration between R9-11,000 (500-600 USD). The task team and their protests raised awareness of the plight of the CHWs, and joining a formal union enabled them to negotiate a modest salary increase. However, it was the emergency created by the world-wide COVID-19 pandemic that forced decision-makers to acknowledge their reliance on this community-based cadre. Hopefully this recognition, and the associated gains, will not fade as the pandemic recedes.
On the limitations of large language models in clinical diagnosis

Background: The potential of large language models (LLM) such as GPT to support complex tasks such as differential diagnosis has been a subject of debate, with some ascribing near sentient abilities to the models and others claiming that LLMs merely perform "autocomplete on steroids". A recent study reported that the Generative Pretrained Transformer 4 (GPT-4) model performed well in complex differential diagnostic reasoning. The authors assessed the performance of GPT-4 in identifying the correct diagnosis in a series of case records from the New England Journal of Medicine. The authors constructed prompts based on the clinical presentation section of the case reports, and compared the results of GPT-4 to the actual diagnosis. GPT-4 returned the correct diagnosis as a part of its response in 64% of cases, with the correct diagnosis being at rank 1 in 39% of cases. However, such concise but comprehensive narratives of the clinical course are not typically available in electronic health records (EHRs). Further, if they were available, EHR records contain identifying information whose transmission is prohibited by Health Insurance Portability and Accountability Act (HIPAA) regulations. Methods: To assess the expected performance of GPT on comparable datasets that can be generated by text mining and by design cannot contain identifiable information, we parsed the texts of the case reports and extracted Human Phenotype Ontology (HPO) terms, from which prompts for GPT were constructed that contain largely the same clinical abnormalities but lack the surrounding narrative. Results: While the performance of GPT-4 on the original narrative-based text was good, with the final diagnosis being included in its differential in 29/75 cases (38.7%; rank 1 in 17.3% of cases; mean rank of 3.4), the performance of GPT-4 on the feature-based approach that includes the major clinical abnormalities without additional narrative texas substantially worse, with GPT-4 including the final diagnosis in its differential in 8/75 cases (10.7%; rank 1 in 4.0% of cases; mean rank of 3.9). Interpretation: We consider the feature-based queries to be a more appropriate test of the performance of GPT-4 in diagnostic tasks, since it is unlikely that the narrative approach can be used in actual clinical practice. Future research and algorithmic development is needed to determine the optimal approach to leveraging LLMs for clinical diagnosis.
Water, sanitation, and hygiene in selected health facilities in Ethiopia: risks for healthcare acquired antibiotic resistant infections

Abstract Background Inadequate water, sanitation and hygiene (WASH) in health facilities and the low adherence to infection control protocols can increase the risk of hospital-acquired (nosocomial) infections, which in turn can increase morbidity and mortality, health care cost, but also contribute to increased microbial resistance. Objectives The study aimed to assess WASH facilities and practices, and levels of nosocomial pathogens in surface and water samples collected from selected health facilities in Oromia Region and Southern, Nations and Nationalities and Peoples Region (SNNPR). Methods WASH in health care facilities in Bulle and Doyogena (SNNPR) and Bidre (Oromia) were assessed through interviews and direct observations (n= 26 facilities). Water and surface samples were collected from major hospitals and health centers. A total of 90 surface swabs and 14 water samples were collected from which a number of bacteria (n=224) were identified, characterized and tested for antimicrobial susceptibility. Results Water supply, toilet facilities, and waste management procedures were suboptimal. Only 11/26 of the health facilities had access to water at the time of the survey. The lowest hand-hygiene compliance was for Bidre (4%), followed by Doyogena (14%), and Bulle (36%). Over 70% of the identified bacteria were from four categories: Staphylococcus spp, Bacillus spp, E. coli, and Klebsiella spp. These bacteria were also found in high risk locations including neonatal intensive care units, delivery and surgical rooms. Antimicrobial susceptibility was detected in [≥] 50% of the isolates for penicillin, cefazolin, ampicillin, oxacillin, and cotrimoxazole, and [≥] 50% of the isolates displayed multi-drug resistance. Conclusion Investing in WASH infrastructures, promotion of handwashing practices, implementing infection prevention and control (IPC) measures and antibiotic stewardship is critical to ensure quality care in these settings.
Livelihood Resilience of Households Affected by The Upper Cisokan Pumped Storage Hydropower Development Project

This article examines the impact of the large-scale Upper Cisokan Pumped Storage Hydropower (UCPSH) development project, in West Java, Indonesia on household livelihoods. The process of land acquisition and resettlement has taken away land-based resources that are the source of livelihood for households in 4 villages around the project. This article discusses how the resilience of household livelihoods occurs 5 years after land acquisition and resettlement. This study was conducted by interviewing 134 households affected by land acquisition and resettlement living in the vicinity of the UCPSH project site. The level of severity is studied to see how the household can respond to disturbances. Livelihood resilience in affected households occurs when the household’s livelihood system can absorb disturbances, then responds by re-establishing livelihood capital and assets. Livelihood strategies are carried out based on the capacity possessed to maintain and improve the quality of life. As a result, there are two livelihood strategies carried out by the affected households, the agricultural intensification strategy has improved 9% of living conditions and the livelihood diversification strategy has improved 2.5% of living conditions.
A geometry‐based two‐step method for nonlinear classification using quasi‐linear support vector machine

This paper proposes a two‐step method to construct a nonlinear classifier consisting of multiple local linear classifiers interpolated with a basis function. In the first step, a geometry‐based approach is first introduced to detect local linear partitions and build local linear classifiers. A coarse nonlinear classifier can then be constructed by interpolating the local linear classifiers. In the second step, a support vector machine (SVM) formulation is used to further implicitly optimize the linear parameters of the nonlinear classifier. In this way, the nonlinear classifier is constructed in exactly the same way as a standard SVM, using a special data‐dependent quasi‐linear kernel composed of the information of the local linear partitions. Numerical experiments on several real‐world datasets demonstrate the effectiveness of the proposed classifier and show that, in cases where traditional nonlinear SVMs run into overfitting problems, the proposed classifier is effective in improving the classification performance. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Panchromatic Photometry of Low-redshift, Massive Galaxies Selected from SDSS Stripe 82

The broadband spectral energy distribution of a galaxy encodes valuable information on its stellar mass, star formation rate (SFR), dust content, and possible fractional energy contribution from nonstellar sources. We present a comprehensive catalog of panchromatic photometry, covering 17 bands from the far-ultraviolet to 500 μm, for 2685 low-redshift (z = 0.01–0.11), massive (M * > 1010 M ⊙) galaxies selected from the Stripe 82 region of the Sloan Digital Sky Survey, one of the largest areas with relatively deep, uniform observations over a wide range of wavelengths. Taking advantage of the deep optical coadded images, we develop a hybrid approach for matched-aperture photometry of the multiband data. We derive robust uncertainties and upper limits for undetected galaxies, deblend interacting/merging galaxies and sources in crowded regions, and treat contamination by foreground stars. We perform spectral energy distribution fitting to derive the stellar mass, SFR, and dust mass, critically assessing the influence of flux upper limits for undetected photometric bands and applying corrections for systematic uncertainties based on extensive mock tests. Comparison of our measurements with those of commonly used published catalogs reveals good agreement for the stellar masses. While the SFRs of galaxies on the star-forming main sequence show reasonable consistency, galaxies in and below the green valley show considerable disagreement between different sets of measurements. Our analysis suggests that one should incorporate the most accurate and inclusive photometry into the spectral energy distribution analysis, and that care should be exercised in interpreting the SFRs of galaxies with moderate to weak star formation activity.
A study examining the correlation between serum 25-OH-VitD levels, CD3-CD19+ B lymphocytes, and the risk of early spontaneous abortion in pregnant women

The objective of this retrospective cohort study was to investigate the correlation between serum 25-OH-VitD levels, coagulation function, immune factors, and the risk of spontaneous abortion in the first trimester of pregnancy. Additionally, independent risk factors for spontaneous abortion in the first trimester of pregnancy were identified. A total of 412 pregnant women who attended Hubei Maternal and Child Health Care Hospital between October 2021 and February 2022 were included in the study. Of these, 221 met the eligibility criteria and were categorized into the early spontaneous abortion case group (n = 107) or the normal pregnancy control group (n = 114). The serum levels of 25-OH-VitD, CD3 + CD19- T lymphocytes, CD3-CD19 + B lymphocytes, NK (Natural Killer) cells, TNF-α (tumor necrosis factor-α) and coagulation factors (D-dimer, Protein C, Protein S) in both these groups were measured during early pregnancy (within 12 weeks) and evaluated using logistic regression analysis. Compared to the control group, body mass index, Protein S, CD19 + CD3-B lymphocytes, and 25-OH-VitD were significantly lower in the spontaneous abortion group during early pregnancy (P = .001; P = .004; P = .009; P = .001), blood glucose (fasting) and TNF-α significantly increased (P = .001; P = .046). Logistic regression analysis of potential mixed factors showed that fasting blood glucose and TNF-α were significantly different from the control group and were positively correlated (P = .001, P = .038). Fasting blood glucose 0R value is 2.264, 95% confidence interval is 0.043~0.25, TNF-α 0R value is 0.126, 95% confidence interval is 0.800~0.972. CD19 + CD3-B cells and 25-OH-VitD were correlated with spontaneous abortion (P = .005; P = .001), respectively 0R value and 95% confidence interval being −0.007 (1.002~1.012), −0.179 (1.139~1.256). Risk factors for spontaneous abortion in the first trimester (<12 weeks) of pregnancy include fasting glucose tolerance, decreased CD19 + CD3-energy B lymphocytes and 25-OH-VitD, and abnormal increase of TNF-α. Therefore, it is recommended that women with fertility needs be examined as early as possible to avoid adverse outcomes.
Use of fractional exhaled nitric oxide as a potential predictor of bronchodilator response

Nitric oxide (NO) is an important product of eosinophilic metabolism, and its increase is associated with bronchial remodeling and airway hyperresponsiveness. Fractional exhaled NO (FENO) in the expired air of patients with suspected or diagnosed asthma has been used as a marker for eosinophilic inflammation. This cohort study included asthmatic patients classified under steps 3, 4, or 5 of the global strategy for asthma management and prevention. In the morning of the same day, all patients underwent blood collection for eosinophil counts, followed by FENO measurement and spirometry. We considered 2 groups based on the bronchodilation (BD) response on spirometry (>10% of FVC or FEV1): positive (BD+) and negative (BD−). Differences between the 2 groups were analyzed for demographic features, FENO values, and predictive correlations between FENO and BD. Both groups of patients showed an increase in the eosinophil count (BD+, P = .03; BD−, P = .04) and FENO values (P = .015 for both) with an increase in the asthma severity from step 3 to step 5 of the global strategy for asthma management and prevention. The correlations of FENO and eosinophils as well as FENO values and BD + were 0.127 (95% confidence interval,–0.269 to –0.486) and 0.696 (95% confidence interval, 0.246–0.899; P = .007), respectively. Measuring FENO levels may be useful for identifying patients with BD+.
Constitutive ALS3 expression in Candida albicans enhances adhesion and biofilm formation of efg1, but not cph1 mutant strains

Adhesion to living and non-living surfaces is an important virulence trait of the fungal pathogen Candida albicans. Biofilm formation in this organism depends on the expression of a number of cell surface proteins including the hypha-specific protein Als3p. Loss of ALS3 impairs biofilm formation and decreases cell-cell adhesion. We wanted to test whether constitutively expressing ALS3 could compensate for defects in adhesion and biofilm formation observed in mutant strains that lack key transcriptional regulators of biofilm formation Efg1p and Cph1p. We found that ALS3 improved adhesion and biofilm formation in the efg1Δ and efg1Δ cph1Δ mutant strains, but had less effect on the cph1Δ strain.