Miracle-dz/UniFAR-FTUs huggingface dataset:
Stocks and ecological significance of soil carbon in Tanzania

Numerous documentations have paid a few attentions to the mapping of soil organic carbon (SOC) in Tanzania. To any country, the estimation of SOC is very important as provide social, ecological and economic values just to mention a few. Here we reviewed over 55 publications be journal papers, government and international organization reports to ascertain on the amount of SOC, factors influencing its accumulation, and highlight its ecological and socio-economic significance. Despite of the limited information, the Eastern Arc Mountains seem to have an average carbon stock of 100 - 400MgCha -1 in the undisturbed and 85 MgCha -1 in the disturbed areas; while the semi-arid areas have about 0.4-10 MgCha -1 only. In most cases, SOC were highest on the surface (0-20cm) and decreased with increasing soil depth. This is because most anthropogenic activities i.e. organic fertilizations take place at the top sub-soils. Edaphic and climate factors had as well significant contribution to the accumulation of SOC. Rainfall and temperature appeared to facilitate numerous soil processes that lead to SOC accumulation. Potentially, SOC improves soil fertility for higher crop yields, mitigates the emission of greenhouse gases and eventually improves the people's livelihoods. For example, maize yields were significantly higher under SOC 1.9 ton per hectare than has without it 0.9 ton per hectare. In addition thousands of carbons dioxides tones are seized in the soil. Otherwise, its offset would have serious atmospheric pollution. We therefore, recommends for more original researches on SOC to harness its economic, social and ecological potentials for the benefits of the people.
Association of Risk Factors and Comorbidities With Chronic Pain in the Elderly Population

Introduction/Objective: Chronic pain disorders affect about 20% of adults in the United States, and it disproportionately affects individuals living in the neighborhoods of extreme socioeconomic disadvantage. In many instances, chronic pain has been noted to arise from an aggregation of multiple risk factors and events. Therefore, it is of importance to recognize the modifiable risk factors. The aim of this study was to investigate the comorbid medical conditions and risk factors associated with chronic pain disorders in patients aged 65 years and older. Methods: Our team retrospectively reviewed medical records of elderly patients (65 years and older) who were evaluated in our outpatient medicine office between July 1, 2020 and June 30, 2021 for acute problems, management of chronic medical problems, or well visits. We divided our patients into a group who suffered from chronic pain disorder, and another group who did not have chronic pain disorder. The association of variables were compared between those groups. Results: Of the 2431 patients, 493 (20.3%) had a chronic pain disorder. A higher frequency of females in the group with chronic pain disorder was found compared to the group without a chronic pain disorder (60.6% vs 55.2%; P = .033). The mean ages between the two groups were similar in the group with a chronic pain disorder compared to the group without (76.35 ± 7.5 year vs 76.81 ± 7.59 year; P = .228). There were significant associations of certain comorbidities in the group with a chronic pain disorder compared to the group without a chronic pain disorder, such as depression (21.9% vs 15.2%; P < .001), anxiety (27.0% vs 17.1%; P < .001), chronic obstructive pulmonary disease (8.7% vs 6.1%; P = .036), obstructive sleep apnea (16.8% vs 11.6%; P = .002), gastroesophageal reflux disease (40.8% vs 29.0%; P < .001), osteoarthritis (49.3% vs 26.1%; P < .001), other rheumatologic diseases (24.9% vs 19.4%; P = .006), and peripheral neuropathy (14.4% vs 5.3%; P < .001). Conclusion: Female sex, depression, anxiety, chronic obstructive pulmonary disease, obstructive sleep apnea, gastroesophageal reflux disease, osteoarthritis, other rheumatologic diseases, and peripheral neuropathy were significantly associated with chronic pain disorder in elderly patients, while BMI was not associated with chronic pain disorder.
Abundance of harbor porpoise (Phocoena phocoena) in three Alaskan regions, corrected for observer errors due to perception bias and species misidentification, and corrected for animals submerged from view

Estimating the abundance of cetaceans from aerial survey data requires careful attention to survey design and analysis. Once an aerial observer perceives a marine mammal or group of marine mammals, he or she has only a few seconds to identify and enumerate the individuals sighted, as well as to determine the distance to the sighting and record this information. In line-transect survey analyses, it is assumed that the observer has correctly identified and enumerated the group or individual. We describe methods used to test this assumption and how survey data should be adjusted to account for observer errors. Harbor porpoises (Phocoena phocoena) were censused during aerial surveys in the summer of 1997 in Southeast Alaska (9844 km survey effort), in the summer of 1998 in the Gulf of Alaska (10,127 km), and in the summer of 1999 in the Bering Sea (7849 km). Sightings of harbor porpoise during a beluga whale (Phocoena phocoena) survey in 1998 (1355 km) provided data on harbor porpoise abundance in Cook Inlet for the Gulf of Alaska stock. Sightings by primary observers at side windows were compared to an independent observer at a belly window to estimate the probability of misidentification, underestimation of group size, and the probability that porpoise on the surface at the trackline were missed (perception bias, g(0)). There were 129, 96, and 201 sightings of harbor porpoises in the three stock areas, respectively. Both g(0) and effective strip width (the realized width of the survey track) depended on survey year, and g(0) also depended on the visibility reported by observers. Harbor porpoise abundance in 1997–99 was estimated at 11,146 animals for the Southeast Alaska stock, 31,046 animals for the Gulf of Alaska stock, and 48,515 animals for the Bering Sea stock.
Impacts of urbanization on stream water quantity and quality in the United States

Since the 1950s, the world’s urban population has grown more than 400% to 3.9 billion today. About 60% of the total population is expected to live in urban areas by the year 2025. For the United States (U.S.), 80% of the population lives in urban areas. The Earth has entered into the Anthropocene, a new geological epoch dominated by urbanization and people. It has become increasingly important to understand the impacts of urbanization on ecosystem structure and functions, society, and culture (McDonald et al., 2011). The most obvious and direct impact of urbanization on watersheds is altering the hydrologic cycle that controls the flow of energy and matter in ecosystems. Water resources in urban environments are increasingly stressed around the world due to increasing population, rapid land use change, and climate change and variability (McDonald et al., 2011). Anthropogenic structures such as irrigation canals, wells, reservoirs, dams, and paved roads have shaped the natural watershed landscapes. In many population centers, megacities in particular, water availability has severely limited environmental, social, and economic development. Civilizations developed around water, and in turn human activities, have altered the quantity, quality, and distribution of water on earth. This article reviews our understanding about the impacts of urbanization on water quantity and quality with a focus on linkages of the physical, biological, chemical, and socioeconomic processes (Paul and Meyer, 2001). We discuss current approaches to minimizing the urbanization footprint on streamflow across developing landscapes.
All-cause and suicide mortalities among adolescents and young adults who contacted specialised gender identity services in Finland in 1996–2019: a register study

Background All-cause and suicide mortalities of gender-referred adolescents compared with matched controls have not been studied, and particularly the role of psychiatric morbidity in mortality is unknown. Objective To examine all-cause and suicide mortalities in gender-referred adolescents and the impact of psychiatric morbidity on mortality. Methods Finnish nationwide cohort of all <23 year-old gender-referred adolescents in 1996–2019 (n=2083) and 16 643 matched controls. Cox regression models with HRs and 95% CIs were used to analyse all-cause and suicide mortalities. Findings Of the 55 deaths in the study population, 20 (36%) were suicides. In bivariate analyses, all-cause mortality did not statistically significantly differ between gender-referred adolescents and controls (0.5% vs 0.3%); however, the proportion of suicides was higher in the gender-referred group (0.3% vs 0.1%). The all-cause mortality rate among gender-referred adolescents (controls) was 0.81 per 1000 person-years (0.40 per 1000 person-years), and the suicide mortality rate was 0.51 per 1000 person-years (0.12 per 1000 person-years). However, when specialist-level psychiatric treatment was controlled for, neither all-cause nor suicide mortality differed between the two groups: HR for all-cause mortality among gender-referred adolescents was 1.0 (95% CI 0.5 to 2.0) and for suicide mortality was 1.8 (95% CI 0.6 to 4.8). Conclusions Clinical gender dysphoria does not appear to be predictive of all-cause nor suicide mortality when psychiatric treatment history is accounted for. Clinical implications It is of utmost importance to identify and appropriately treat mental disorders in adolescents experiencing gender dysphoria to prevent suicide.
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling

Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds. However, such models are often not useful in practice because even compounds with high docking scores do not consistently show experimental activity. More accurate methods for activity prediction exist, such as molecular dynamics based binding free energy calculations, but they are too computationally expensive to use in a generative model. We propose a multi-fidelity approach, Multi-Fidelity Bind (MFBind), to achieve the optimal trade-off between accuracy and computational cost. MFBind integrates docking and binding free energy simulators to train a multi-fidelity deep surrogate model with active learning. Our deep surrogate model utilizes a pretraining technique and linear prediction heads to efficiently fit small amounts of high-fidelity data. We perform extensive experiments and show that MFBind (1) outperforms other state-of-the-art single and multi-fidelity baselines in surrogate modeling, and (2) boosts the performance of generative models with markedly higher quality compounds.
A theorem of Strichartz for multipliers on homogeneous trees

A theorem of Strichartz states that if a uniformly bounded bi-infinite sequence of functions on the Euclidean spaces, satisfies the condition that the Laplacian acting on a function in this sequence yields the next one, then each function in this sequence is an eigenfunction of the Laplacian. We consider a generalization of this result for homogeneous trees, where we replace bounded functions by tempered distributions and the Laplacian by multiplier operators acting on the tempered distributions. After establishing the result in this general context, we narrow our focus to specific cases, which includes important examples of multiplier operators such as heat and Schr\"odinger operator, ball and sphere averages of function.
Modeling Blood Alcohol Concentration Using Fractional Differential Equations Based on the ψ-Caputo Derivative

We propose a novel dynamical model for blood alcohol concentration that incorporates ‐Caputo fractional derivatives. Using the generalized Laplace transform technique, we successfully derive an analytic solution for both the alcohol concentration in the stomach and the alcohol concentration in the blood of an individual. These analytical formulas provide us a straightforward numerical scheme, which demonstrates the efficacy of the ‐Caputo derivative operator in achieving a better fit to real experimental data on blood alcohol levels available in the literature. In comparison with existing classical and fractional models found in the literature, our model outperforms them significantly. Indeed, by employing a simple yet nonstandard kernel function , we are able to reduce the error by more than half, resulting in an impressive gain improvement of 59%.
Electronic structure-property relationship in an Al0.5TiZrPdCuNi high-entropy alloy

The valence band (VB) structure of an Al0.5TiZrPdCuNi high-entropy alloy (HEA) obtained from X-ray photoelectron spectroscopy has been compared to that recently calculated by Odbadrakh et al, 2019. Both experimental and theoretical VBs show split-band structures typical of alloys composed from the early (TE) and late (TL) transition metals. Accordingly, several properties of this alloy (both in the glassy and crystalline state) associated with the electronic structure (ES), are compared with those of similar TE-TL alloys. The comparison shows in addition to the usual dependence on the total TL content strong effect of alloying with Al on the density of states at the Fermi level, N(EF) and on the magnetic susceptibility of Al0.5TiZrPdCuNi HEA, which is like that of conventional glassy alloys, such as Zr-Cu-Al ones. Despite some similarity between the shapes of theoretical and corresponding experimental VBs there are significant quantitative differences between them which should be taken into account in any future studies of ES in HEAs and other compositionally complex alloys (CCA).
Associative Memories in the Feature Space

An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. However, popular memory models fail to retrieve images even when the corruption is mild and easy to detect for a human evaluator. This is because similarities are evaluated in the raw pixel space, which does not contain any semantic information about the images. This problem can be easily solved by computing \emph{similarities} in an embedding space instead of the pixel space. We show that an effective way of computing such embeddings is via a network pretrained with a contrastive loss. As the dimension of embedding spaces is often significantly smaller than the pixel space, we also have a faster computation of similarity scores. We test this method on complex datasets such as CIFAR10 and STL10. An additional drawback of current models is the need of storing the whole dataset in the pixel space, which is often extremely large. We relax this condition and propose a class of memory models that only stores low-dimensional semantic embeddings, and uses them to retrieve similar, but not identical, memories. We demonstrate a proof of concept of this method on a simple task on the MNIST dataset.
Exploring child health risks of poultry keeping in Ethiopia: Insights from the 2015 Feed the Future Survey

The agricultural sector in Ethiopia and in other developing countries is increasingly asked to contribute to reducing undernutrition as well as poverty and food insecurity. Within agriculture, the livestock sector is thought to play a particularly important role in this respect, since the consumption of animal-sourced foods (ASFs) is a well-known determinant of child growth and the production of ASFs is an important source of income. However, there is growing evidence of associated health risks of poultry rearing in developing countries, particularly for young children who have been observed to directly ingest poultry feces. This is hypothesized to contribute to chronic gut damage – a condition termed environmental enteric disorder (EED) – that is widely believed to be a leading cause of child stunting in developing countries such as Ethiopia.
Real-Time Model-Based Quantitative Ultrasound and Radar

Ultrasound and radar signals are highly beneficial for medical imaging as they are non-invasive and non-ionizing. Traditional imaging techniques have limitations in terms of contrast and physical interpretation. Quantitative medical imaging can display various physical properties such as speed of sound, density, conductivity, and relative permittivity. This makes it useful for a wider range of applications, including improving cancer detection, diagnosing fatty liver, and fast stroke imaging. However, current quantitative imaging techniques that estimate physical properties from received signals, such as Full Waveform Inversion, are time-consuming and tend to converge to local minima, making them unsuitable for medical imaging. To address these challenges, we propose a neural network based on the physical model of wave propagation, which defines the relationship between the received signals and physical properties. Our network can reconstruct multiple physical properties in less than one second for complex and realistic scenarios, using data from only eight elements. We demonstrate the effectiveness of our approach for both radar and ultrasound signals.
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering

Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability. To address these issues, we propose iFairNMTF, an individual Fairness Nonnegative Matrix Tri-Factorization model with contrastive fairness regularization that achieves balanced and cohesive clusters. By introducing fairness regularization, our model allows for customizable accuracy-fairness trade-offs, thereby enhancing user autonomy without compromising the interpretability provided by nonnegative matrix tri-factorization. Experimental evaluations on real and synthetic datasets demonstrate the superior flexibility of iFairNMTF in achieving fairness and clustering performance.
A Dynamical View of the Question of Why

We address causal reasoning in multivariate time series data generated by stochastic processes. Existing approaches are largely restricted to static settings, ignoring the continuity and emission of variations across time. In contrast, we propose a learning paradigm that directly establishes causation between events in the course of time. We present two key lemmas to compute causal contributions and frame them as reinforcement learning problems. Our approach offers formal and computational tools for uncovering and quantifying causal relationships in diffusion processes, subsuming various important settings such as discrete-time Markov decision processes. Finally, in fairly intricate experiments and through sheer learning, our framework reveals and quantifies causal links, which otherwise seem inexplicable.
Revisiting Experience Replayable Conditions

Experience replay (ER) used in (deep) reinforcement learning is considered to be applicable only to off-policy algorithms. However, there have been some cases in which ER has been applied for on-policy algorithms, suggesting that off-policyness might be a sufficient condition for applying ER. This paper reconsiders more strict"experience replayable conditions"(ERC) and proposes the way of modifying the existing algorithms to satisfy ERC. To this end, instability of policy improvements is assumed to be a key in ERC. The instability factors are revealed from the viewpoint of metric learning as i) repulsive forces from negative samples and ii) replays of inappropriate experiences. Accordingly, the corresponding stabilization tricks are derived. As a result, it is confirmed through numerical simulations that the proposed stabilization tricks make ER applicable to an advantage actor-critic, an on-policy algorithm. In addition, its learning performance is comparable to that of a soft actor-critic, a state-of-the-art off-policy algorithm.
PPN meets EFT of dark energy: Post-Newtonian approximation in higher-order scalar-tensor theories

We study the post-Newtonian limit of higher-order scalar-tensor theories that are degenerate in the unitary gauge. They can be conveniently described by the effective field theory (EFT) of dark energy. We determine all the parametrized post-Newtonian (PPN) parameters in terms of the EFT of dark energy parameters. Experimental bounds on the PPN parameters are then translated to constraints on the EFT parameters. We present a Lagrangian of a unitary degenerate higher-order scalar-tensor theory characterized by a single function of the kinetic term of the scalar field whose PPN parameters have the same values as in general relativity.
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation

Quantifying the predictive uncertainty emerged as a possible solution to common challenges like overconfidence or lack of explainability and robustness of deep neural networks, albeit one that is often computationally expensive. Many real-world applications are multi-modal in nature and hence benefit from multi-task learning. In autonomous driving, for example, the joint solution of semantic segmentation and monocular depth estimation has proven to be valuable. In this work, we first combine different uncertainty quantification methods with joint semantic segmentation and monocular depth estimation and evaluate how they perform in comparison to each other. Additionally, we reveal the benefits of multi-task learning with regard to the uncertainty quality compared to solving both tasks separately. Based on these insights, we introduce EMUFormer, a novel student-teacher distillation approach for joint semantic segmentation and monocular depth estimation as well as efficient multi-task uncertainty quantification. By implicitly leveraging the predictive uncertainties of the teacher, EMUFormer achieves new state-of-the-art results on Cityscapes and NYUv2 and additionally estimates high-quality predictive uncertainties for both tasks that are comparable or superior to a Deep Ensemble despite being an order of magnitude more efficient.
On the universal exceptional structure of world-volume theories in string and M-theory

A universal structure of world-volume theories of half-BPS branes in string and M-theory in terms of exceptional generalised geometry is observed. Previous constructions are extended in two ways: from internal $d$-dimensional space to full 11- or 10-dimensional spacetimes, by coupling to the tensor hierarchy gauge fields, and from $E_{d(d)}$ with $d\leq 6$ to $E_{7(7)}$ and $E_{8(8)}$. This is done by a clarification of the role of the tensor hierarchy and its end in the context of brane world-volume theories. The exceptional structure of the gauged $\sigma$-model of the Kaluza-Klein monopole is provided as a new example.