Miracle-dz/UniFAR-FTUs huggingface dataset:
SCIENCE EDUCATION IN NIGERIA: THE JOURNEY SO FAR

Science education is a field of study that exposes learners to the contents as well as the methodology (processes) of acquiring scientific knowledge for practical application in relevant and applicable areas of life endeavors such as life on the planet, production of essential human needs like soap of all kinds, creams, drinks, petroleum and its bi-products, clothing, drugs, household utensils and chemicals for preservations of food items as well as textiles are all a product and principles of Chemistry. Other likely forms of production derived from science education is the production and supply of energy from different sources, means of transportation and communication as well as the inventions of machines and materials that has made life comfortable and enjoyable, all of this makes work easier and faster as it is an acquisition of the basic knowledge and principles of Physics. This type of education takes place in a formal school setting under the auspices of the science educator who is professionally qualified to moderate the scientific activities in the classroom with the use of appropriate pedagogies. The article specifically focuses on the history and journey so far of Science Education in Nigeria, its policy, status of science education in Nigeria, budgetary allocation of some sampled African Countries, problems of science teaching in Nigeria and way forward to its effective teaching.
The Indefinite Metric of R. Mrugala and the The Indefinite Metric of R. Mrugala and the Geometry of the Thermodynamical Phase Space Geometry of the Thermodynamical Phase Space

A BSTRACT . We study an indeﬁnite metric G which was introduced by R. Mrugala and is deﬁned on the contact phase space ( P,θ ) of a homogeneous thermodynamical system. We describe the curvature properties and the isometry group of the metric G . We established an isomorphism of the space ( P,θ,G ) with the Heisenberg Lie group H n , endowed with the right invariant contact structure and the right invariant indeﬁnite metric. The lift ˜ G of the metric G to the symplectization ˜ P of contact space ( P,θ ) and its properties are studied. Finally we introduce the ”hyperbolic projectivization” of the space ( ˜ P, ˜ θ, ˜ G ) that can be considered as the natural compactiﬁcation of the thermodynamical phase space ( P,θ,G ) .
Improved Branch and Bound Global Optimization Algorithm for a Class of Sum of Linear Ratios Problems

—Sum of linear ratios problem is a particularly special kind of the fractional programming problem. Its solution usually has multiple levels and many local optimal solutions of non-global solutions. In this paper, an improved branch and bound global optimization is proposed for a class of sum of linear ratios problems. Firstly, the original problem is transformed into its equivalent form by introduced variables, and the equivalence of the two optimal solutions is proved. Secondly, the linear relaxation programming of the equivalent problem is constructed by using linear relaxation technique. The upper and lower bounds of the optimal value of the original problem are continuously improved by solving a sequence of linear relaxation programming problems, then we can get an approximate optimal solution of the original problem. Finally, with the combination of branch rules and bounding techniques, an improved branch-and-bound global optimization algorithm is designed to solve the optimal solution of the original problem. The numerical experimental results show that the improved algorithm is feasible and effective.
The Integrating Framework For The Technology Acceptance Method Of E-Services By Numerical Acceptance

: Innovations acceptance is attracting increasing attention. Researchers are particularly interested in factors that affect the adoption of IS and IT innovations. However, despite numerous advantages of e-payment usage such as convenience, speed, efficiency and reduced cost, the economic ecosystem is skeptical to embrace e-payment systems (Adeyelure, Pretorius & Kalema, 2013)[1][2]. This research develops extended TAM model by integrating the three dimensions of trust (integrity, benevolence and competence) and two key constructs from technology acceptance models (perceived ease of use and perceived usefulness of e-payment) to explain online consumers’ intentions to engage in the business relationship with e-payment. Through this framework, researchers can have a more accurate explanation of the consumer behavior and intention to accept new technology[3][4]. This framework will be used to examine consumer’s behavioral intention to accept e-payment by researchers[4].
Identification of potential novel combination antibiotic regimens based on drug-susceptibility and genetic diversity of Gram-negative bacteria causing neonatal sepsis in low- and middle-income countries

Objectives: Several recent studies highlight the high prevalence of resistance to multiple antibiotic classes used in current treatment regimens for neonatal sepsis and new treatment options are urgently needed. We aimed to identify potential new combination antibiotic treatment regimens by investigating the drug-resistance and genetic profiles of the most frequently isolated Gram-negative bacteria causing neonatal sepsis in low- and middle-income countries (LMICs) in the NeoOBS study. Material and methods: Gram-negative bacteria isolated from neonates with culture-confirmed sepsis from 13 clinical sites in nine countries, mainly LMICs, were analyzed. Culture-based identification was followed by whole-genome sequencing (WGS). Minimal inhibitory concentrations (MICs) for 8 antibiotics were determined for a representative subset of 108 isolates. Results: Five bacterial species, Klebsiella pneumoniae (n=135), Acinetobacter baumannii (n=80), Escherichia coli (n=34), Serratia marcescens (n=33) and Enterobacter cloacae complex (ECC) (n=27) accounted for most Gram-negative bacterial isolates received (309/420, 74%). Extended-spectrum {beta}-lactamases (ESBL) genes mostly belonging to CTX-M-15 were found in 107 (79%) K. pneumoniae isolates and 13 (38%) E. coli, as well as in 6 (18%) and 10 (37%) S. marcescens and ECC isolates, respectively. Carbapenem resistance genes were present in 41 (30%) K. pneumoniae, while 73 (91%) of A. baumannii isolates were predicted to be MDR based on carbapenem resistance genes. Apart from A. baumannii, in which two major pandemic lineages predominated, a wide genetic diversity occurred at the intraspecies level with different MDR clones occurring at the different sites. Phenotypic testing showed resistance to the WHO first- and second- line recommended treatment regimens: 74% of K. pneumoniae isolates were resistant to gentamicin and 85% to cefotaxime; E. coli isolates showed resistance to ampicillin, gentamicin and cefotaxime in 90%, 38% and 47%, respectively. For the novel antibiotic regimens involving different combinations of flomoxef, fosfomycin and amikacin, the overall predicted MIC-determined susceptibility for Enterobacterales isolates was 71% (n=77) to flomoxef-amikacin, 76% (n=82) to flomoxef-fosfomycin and 79% (n=85) to fosfomycin-amikacin combinations, compared to 31% and 22% isolates susceptible to ampicillin-gentamicin and cefotaxime, respectively. ESBL-producing Enterobacterales isolates were 100% susceptible both to flomoxef-fosfomycin and flomoxef-amikacin and 92% to fosfomycin-amikacin. Conclusion: Enterobacterales carried multiple resistance genes to cephalosporins, carbapenems and aminoglycosides. ESBL-producing K. pneumoniae and E. coli isolates were highly susceptible to the three new antibiotic combination regimens planned to be evaluated in the currently recruiting GARDP-sponsored NeoSep1 trial.
Anomalous Response of Mercury’s Magnetosphere to Solar Wind Compression: Comparison to Earth

Magnetic field intensity increases when solar wind compresses a planet’s magnetosphere. The compression can be measured using the ratio of compressed magnetic fields to purely dipolar magnetic fields just inside the magnetopause. For Earth, the ratio is proportional to the subsolar standoff distance of the magnetopause. Data from in-orbit observations by the MESSENGER spacecraft indicate an opposite ratio for Mercury; the compression ratio is inversely proportional to the subsolar standoff distance. The additional magnetic fields induced by currents at the top of Mercury’s core enhance the total magnetic field strength. We also evaluated differences in the subsolar standoff of Mercury’s magnetopause according to the north–south polarity of the interplanetary magnetic field (IMF). Previous studies have not identified meaningful differences in subsolar standoff distance between those in northward versus southward IMF polarities for Mercury; however, we found that the difference is statistically significant at a large IMF B Z (15–20 nT). The magnetic reconnection that occurs behind the cusp for a large northward IMF transfers the magnetic flux to the dayside and increases the subsolar standoff distance. The eroded magnetic flux for a large southward IMF is compensated by the induced magnetic fields.
JWST CEERS and JADES Active Galaxies at z = 4–7 Violate the Local M •–M ⋆ Relation at >3σ: Implications for Low-mass Black Holes and Seeding Models

JWST is revolutionizing our understanding of the high-z Universe by expanding the black hole horizon, looking farther and to smaller masses, and revealing the stellar light of their hosts. By examining JWST galaxies at z = 4–7 that host Hα-detected black holes, we investigate (i) the high-z M •–M ⋆ relation and (ii) the black hole mass distribution, especially in its low-mass range (M • ≲ 106.5 M ⊙). With a detailed statistical analysis, our findings conclusively reveal a high-z M •–M ⋆ relation that deviates at >3σ confidence level from the local relation. The high-z relation is log(M•/M⊙)=−2.43−0.83+0.83+1.06−0.09+0.09log(M⋆/M⊙) . Black holes are overmassive by ∼10–100× compared to their low-z counterparts in galactic hosts of the same stellar mass. This fact is not due to a selection effect in surveys. Moreover, our analysis predicts the possibility of detecting in high-z JWST surveys 5–15× more black holes with M • ≲ 106.5 M ⊙, and 10–30× more with M • ≲ 108.5 M ⊙, compared to local relation’s predictions. The lighter black holes preferentially occupy galaxies with a stellar mass of ∼107.5–108 M ⊙. We have yet to detect these sources because (i) they may be inactive (duty cycles 1%–10%), (ii) the host overshines the active galactic nucleus (AGN), or (iii) the AGN is obscured and not immediately recognizable by line diagnostics. A search of low-mass black holes in existing JWST surveys will further test the M •–M ⋆ relation. Current JWST fields represent a treasure trove of black hole systems at z = 4–7; their detection will provide crucial insights into their early evolution and coevolution with their galactic hosts.
The Accretion History of AGN: The Spectral Energy Distributions of X-Ray-luminous Active Galactic Nuclei

Spectral energy distributions (SEDs) from X-ray to far-infrared (FIR) wavelengths are presented for a sample of 1246 X-ray-luminous active galactic nuclei (AGNs; L 0.5–10 keV > 1043 erg s−1), with z spec < 1.2, selected from Stripe 82X, COSMOS, and GOODS-N/S. The rest-frame SEDs show a wide spread (∼2.5 dex) in the relative strengths of broad continuum features at X-ray, ultraviolet (UV), mid-infrared (MIR), and FIR wavelengths. A linear correlation (log–log slope of 0.7 ± 0.04) is found between L MIR and L X. There is significant scatter in the relation between the L UV and L X owing to heavy obscuration; however, the most luminous and unobscured AGNs show a linear correlation (log–log slope of 0.8 ± 0.06) in the relation above this scatter. The relation between L FIR and L X is predominantly flat, but with decreasing dispersion at L X > 1044 erg s−1. The ratio between the “galaxy-subtracted” bolometric luminosity and the intrinsic L X increases from a factor of ∼10 to 70 from log L bol/(erg s−1) = 44.5 to 46.5. Characteristic SED shapes have been determined by grouping AGNs based on relative strengths of the UV and MIR emission. The average L 1μm is constant for the majority of these SED shapes, while AGNs with the strongest UV and MIR emission have elevated L 1μm, consistent with the AGN emission dominating their SEDs at optical and near-infrared wavelengths. A strong correlation is found between the SED shape and both the L X and L bol, such that L bol/L X = 20.4 ± 1.8, independent of the SED shape. This is consistent with an evolutionary scenario of increasing L bol with decreasing obscuration as the AGN blows away circumnuclear gas.
CalibrationPhys: Self-Supervised Video-Based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras

Video-based heart and respiratory rate measurements using facial videos are more useful and user-friendly than traditional contact-based sensors. However, most of the current deep learning approaches require ground-truth pulse and respiratory waves for model training, which are expensive to collect. In this paper, we propose CalibrationPhys, a self-supervised video-based heart and respiratory rate measurement method that calibrates between multiple cameras. CalibrationPhys trains deep learning models without supervised labels by using facial videos captured simultaneously by multiple cameras. Contrastive learning is performed so that the pulse and respiratory waves predicted from the synchronized videos using multiple cameras are positive and those from different videos are negative. CalibrationPhys also improves the robustness of the models by means of a data augmentation technique and successfully leverages a pre-trained model for a particular camera. Experimental results utilizing two datasets demonstrate that CalibrationPhys outperforms state-of-the-art heart and respiratory rate measurement methods. Since we optimize camera-specific models using only videos from multiple cameras, our approach makes it easy to use arbitrary cameras for heart and respiratory rate measurements.
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO.
Evaluating the change of consumption and culinary practices at the transition to agriculture : a multi-disciplinary approach from a Danish kitchen midden

Due to the excellent conditions for preservation of anthropogenic materials, the introduction of domesticated fauna and flora into southern Scandinavia, ca. 3950 cal BC, has been debated for over 165 years. In order to test questions relating to the nature and timing of this cultural change, the Mesolithic-Neolithic transition, a number of archaeological and biomolecular techniques have been applied in recent decades. The carbon and nitrogen stable isotope analyses of human bone collagen is one such method that has been regularly performed. Interestingly, every study has argued for a dramatic or sudden dietary change (Tauber 1981) despite evidence to the contrary (Milner et al. 2004). However, there has not been a single study that has employed a range of techniques to materials derived from one site in order to evaluate the change of consumption and culinary practices across the Mesolithic-Neolithic transition. This thesis uses a combination of stable isotope analyses (carbon, nitrogen and sulphur) and radiocarbon dating of human bone collagen to reconstruct long-term dietary practices, and place these individuals into context. In order to provide an indication as to when the site was occupied, incremental growth line analysis of the European oyster (Ostrea edulis) was undertaken. In addition, organic residue analysis of ceramic vessels was performed to explore food consumption as well as culinary practices. Furthermore, an archaeoichthyological analysis was carried out to reconstruct the subsistence economy. This marks the first large-scale application of these methods to materials derived from one kitchen midden. Furthermore, it includes materials derived from 32 additional contemporaneous sites. The combined results demonstrate a degree of complexity: some things changed, others remained the same and there was variation both between sites and regions.
Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models

Pre-trained and frozen large language models (LLMs) can effectively map simple scene rearrangement instructions to programs over a robot's visuomotor functions through appropriate few-shot example prompting. To parse open-domain natural language and adapt to a user's idiosyncratic procedures, not known during prompt engineering time, fixed prompts fall short. In this paper, we introduce HELPER, an embodied agent equipped with an external memory of language-program pairs that parses free-form human-robot dialogue into action programs through retrieval-augmented LLM prompting: relevant memories are retrieved based on the current dialogue, instruction, correction, or VLM description, and used as in-context prompt examples for LLM querying. The memory is expanded during deployment to include pairs of user's language and action plans, to assist future inferences and personalize them to the user's language and routines. HELPER sets a new state-of-the-art in the TEACh benchmark in both Execution from Dialog History (EDH) and Trajectory from Dialogue (TfD), with a 1.7x improvement over the previous state-of-the-art for TfD. Our models, code, and video results can be found in our project's website: https://helper-agent-llm.github. io.
Meta- (out-of-context) learning in neural networks

Brown et al. (2020) famously introduced the phenomenon of in-context learning in large language models (LLMs). We establish the existence of a phenomenon we call meta-out-of-context learning (meta-OCL) via carefully designed synthetic experiments with LLMs. Our results suggest that meta-OCL leads LLMs to more readily"internalize"the semantic content of text that is, or appears to be, broadly useful (such as true statements, or text from authoritative sources) and use it in appropriate circumstances. We further demonstrate meta-OCL in a synthetic computer vision setting, and propose two hypotheses for the emergence of meta-OCL: one relying on the way models store knowledge in their parameters, and another suggesting that the implicit gradient alignment bias of gradient-descent-based optimizers may be responsible. Finally, we reflect on what our results might imply about capabilities of future AI systems, and discuss potential risks. Our code can be found at https://github.com/krasheninnikov/internalization.
Joint Non-Linear MRI Inversion with Diffusion Priors

Magnetic resonance imaging (MRI) is a potent diagnostic tool, but suffers from long examination times. To accelerate the process, modern MRI machines typically utilize multiple coils that acquire sub-sampled data in parallel. Data-driven reconstruction approaches, in particular diffusion models, recently achieved remarkable success in reconstructing these data, but typically rely on estimating the coil sensitivities in an off-line step. This suffers from potential movement and misalignment artifacts and limits the application to Cartesian sampling trajectories. To obviate the need for off-line sensitivity estimation, we propose to jointly estimate the sensitivity maps with the image. In particular, we utilize a diffusion model -- trained on magnitude images only -- to generate high-fidelity images while imposing spatial smoothness of the sensitivity maps in the reverse diffusion. The proposed approach demonstrates consistent qualitative and quantitative performance across different sub-sampling patterns. In addition, experiments indicate a good fit of the estimated coil sensitivities.
Oka tubes in holomorphic line bundles

Let $(E,h)$ be a semipositive hermitian holomorphic line bundle on a compact complex manifold $X$ with $\dim X>1$. Assume that for each point $x\in X$ there exists a divisor $D\in |E|$ in the complete linear system determined by $E$ whose complement $X\setminus D$ is a Stein neighbourhood of $x$ with the density property. Then, the disc bundle $\Delta_h(E)=\{e\in E:|e|_h<1\}$ is an Oka manifold while $E\setminus \overline{\Delta_h(E)}=\{e\in E:|e|_h>1\}$ is a Kobayashi hyperbolic domain. In particular, the zero section of $E$ admits a basis of Oka neighbourhoods $\{|e|_h0$. We show that this holds if $X$ is a projective space, a complex Grassmannian, or a product of Grassmannians. This phenomenon contributes to the heuristic principle that Oka properties are related to metric positivity of complex manifolds.
Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model

Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers.