Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of Fermion Masses, Mixing Angles, Lagrangian Potentials and Beta Decays, Based on Higgs Bosons Arising from the Scaler Fields of a Kaluza Klein Theory Given Five-Dimensional General Covariance by Dirac’s Quantum Theory of the Electron

Why the twelve elementary fermions have the masses they have, (and what the neutrino masses actually are) is one of the deepest unsolved mysteries of modern physics. We crack this puzzle using a theory of fermion masses which succeeds in reparameterizing all twelve fermion masses in terms of other known parameters for which the theoretical interconnection to these masses have not heretofore been understood. The first step is to “repair” long-recognized perplexities of Kaluza-Klein theory using Dirac’s quantum theory of the electron to enforce general covariance across all five dimensions. One consequence of this is the emergence of a modified Dirac equation for fermions which naturally contains the Kaluza-Klein scaler. After establishing a connection between this Kaluza-Klein scaler and the standard model Higgs scaler, we use the latter to connect the known masses of all the quarks and charged leptons to the CKM and PMNS mixing angles and several other parameters which have heretofore not been theoretically connected to these masses. Then, after using the Newton gravitational constant and the Fermi vacuum to establish a sum of neutrino masses in the exact range expected from experiments, it also becomes possible to predict the rest masses of the three neutrinos. Also predicted are the existence and rest mass of a second leptonic Higgs boson, and tighter values for several other known parameters including the mass of the established Higgs boson. Also uncovered is a new, deep role for the cosmological neutrino background (CvB) and the Higgs boson in triggering and facilitating weak interaction beta decay events. Jay R. Yablon, November 7, 2018
The Gauss-Bonnet topological scalar in the Geometric Trinity of Gravity

The Gauss-Bonnet topological scalar is presented in metric-teleparallel formalism as well as in the symmetric and general teleparallel formulations. In all of the aforementioned frameworks, the full expressions are provided explicitly in terms of torsion, non-metricity and Levi-Civita covariant derivative. The number of invariant terms of this form is counted and compared with the number which can appear in the corresponding effective field theory. Although the difference in this number is not very large, it is found that the Gauss-Bonnet invariant excludes some of the effective field theory terms. This result sheds new light on how General Relativity symmetries can be maintained at higher order in teleparallel theories: this fact appears to be highly nontrivial in the teleparallel formulation. The importance of the so-called ``pseudo-invariant'' theories like $f(T)$- and $f(T,T_\mathcal{G})$-gravity is further discussed in the context of teleparallel Gauss-Bonnet gravity.
Soil respiration in a high arctic glacier foreland in Ny-Alesund, Svalbard.

Soil respiration rates were measured in a successional glacier foreland in Ny-A Õlesund, Svalbard, and the amount of CO, e%ux during the plant-growing season was estimated using a simple regression model. Three study sites (Site +, Site , and Site -) were set up along with the primary succession in the deglaciated area of East Br gger Glacier in Ny-A Õlesund, Svalbard, Norway (13 N +, E). Another study site, Site RB, was set up on a riverbed in the Bay River between Site , and Site -. Soil respiration (SR), air temperature at +* cm height (AT), soil surface temperature (SST) and soil temperature at + cm depth (ST) were measured at the four study sites with an open-airflow system using an infra-red gas analyzer from July to August, +33/. The mean soil respiration rate varied among the four sites: 0. ,, . , 0and -. 1mg CO, m h + at Site +, Site ,, Site and Site RB, respectively. These di#erences in the soil respiration rate among the four sites corresponded with the soil organic amount, microbial biomass, and root biomass. The soil respiration rate showed the best correlation with AT at Site +, Site , and Site RB, and with ST at Site -. The cumulative amount of CO, e%ux calculated using correlation equations obtained from the above relationships between SR and AT or ST was /. 2, . 0, 03 and -. g CO, m , at Site +, Site ,, Site and Site RB, respectively, for two months (from July to August, +33/). These values were extremely low compared to those of warmer ecosystems, such as low-arctic tundra, temperate mixed forests, and tropical moist forests. key words: high arctic, primary succession, soil respiration rate
CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to account for complex interactions across genes and pathways under specific tissues and cell types. We introduce CellGO, a tool for cell type-specific gene functional analysis. CellGO employs a deep learning model to simulate signaling propagation within a cell, enabling the development of a heuristic pathway activity measuring system to identify cell type-specific active pathways given a single gene or a gene set. It is featured with additional functions to uncover pathway communities and the most active genes within pathways to facilitate mechanistic interpretation. This study demonstrated that CellGO can effectively capture cell type-specific pathways even when working with mixed cell-type markers. CellGO’s performance was benchmarked using gene knockout datasets, and its implementation effectively infers the cell type-specific pathogenesis of risk genes associated with neurodevelopmental and neurodegenerative disorders, suggesting its potential in understanding complex polygenic diseases. CellGO is accessible through a python package and a four-mode web interface for interactive usage with pretrained models on 71 single-cell datasets from human and mouse fetal and postnatal brains.
Nonstructural protein 1 of swine arterivirus PRRSV downregulates promyelocytic leukemia nuclear bodies and promotes viral replication

Porcine reproductive and respiratory syndrome virus (PRRSV) inhibits the type I interferon (IFN) production and signaling pathways during infection, and the nonstructural protein 1 (nsp1) has been identified as a potent IFN antagonist. Promyelocytic leukemia (PML) protein is a major scaffold protein organizing the PML nuclear bodies (NBs) of the cell and plays a diverse role in maintaining the cellular homeostasis including antiviral response among many other processes. The present study reveals a significant reduction of PML NBs in cells during infection of PRRSV, implicating the negative regulation of PML gene expression by PRRSV. Subsequently, the nsp1β protein was identified as the viral regulator for PML expression. The overexpression of PML isoforms restricted viral replication, while the gene silencing of endogenous PML promoted viral replication. The downregulation of PML expression by PRRSV was post-translational via the ubiquitination-proteasome pathway. Of six isoforms, PML-II and PML-IV exhibited the most potent suppressive activity against viral replication. PRRSV nsp1β bound to PML directly, and this interaction was mediated through the small ubiquitin-like modifier (SUMO)-interacting motifs (SIMs) on nsp1β. Further studies revealed that double mutations in SIM1 and SIM4 abolished the binding of nsp1β to PML and prevented the PML degradation. The PML downregulation by nsp1 was common in other arteriviruses including equine arteritis virus, murine lactate dehydrogenase elevating virus, and simian hemorrhagic fever virus. Our study unveils the evolutionary conservation of the viral immune evasion strategy employed by arteriviruses, which promotes their replication by targeting PML for downregulation. IMPORTANCE OF THE STUDY Porcine reproductive and respiratory syndrome (PRRS) is an economically significant disease in the swine industry worldwide. One of the immunological hallmarks in virus-infected animals is the suppression of type I interferon response during an early-stage infection, leading to the consequence of adaptive immunity and viral persistence. In the present study, we report that the nsp1-beta protein of PRRS virus degrades the promyelocytic leukemia (PML) protein and downregulates PML nuclear body (NB) formation. The PML downregulation by PRRS virus results in enhanced viral replication. The PML downregulation by nsp1 is common in other arteriviruses, unveiling the basic understanding of cell-virus interactions and immune evasion strategies for arteriviruses.
A bi-objective hierarchical hub location model with facility failure

In the most past hub location problem (HLP) studies, failure probability is limited to a constant value; while in reality, it depends on various factors including natural disasters such as floods and earthquakes, commodity flow, institutional elements such as strikes, and etc. This paper looks into the problem of hub network design through a new model. The problem is to maximize the network reliability and to minimize the total routing cost simultaneously. In order to provide a more realistic model, the hub’s failure probabilities were considered as a function of the hub’s incoming traffic. Reserve hub elements are also taken into account in the model with the aim of increasing network reliability. To solve the model, a non-dominated sorting genetic algorithm (NSGA-II) is presented. The parameters of the algorithm are tuned using Taguchi method. The proposed solution is then carried out in a case study where the results confirm the acceptable performance of the model and also sensitivity analysis is performed in order to describe the effects of critical parameters into objective functions.
LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning

The low-rank adaptation (LoRA) method can largely reduce the amount of trainable parameters for fine-tuning large language models (LLMs), however, it still requires expensive activation memory to update low-rank weights. Reducing the number of LoRA layers or using activation recomputation could harm the fine-tuning performance or increase the computational overhead. In this work, we present LoRA-FA, a memory-efficient fine-tuning method that reduces the activation memory without performance degradation and expensive recomputation. LoRA-FA chooses to freeze the projection-down weight of $A$ and update the projection-up weight of $B$ in each LoRA layer. It ensures the change of model weight reside in a low-rank space during LLMs fine-tuning, while eliminating the requirement to store full-rank input activations. We conduct extensive experiments across multiple model types (RoBERTa, T5, LLaMA) and model scales. Our results show that LoRA-FA can always achieve close fine-tuning accuracy across different tasks compared to full parameter fine-tuning and LoRA. Furthermore, LoRA-FA can reduce the overall memory cost by up to 1.4$\times$ compared to LoRA.
Machine learning methods for the search for L&T brown dwarfs in the data of modern sky surveys

According to various estimates, brown dwarfs (BD) should account for up to 25 percent of all objects in the Galaxy. However, few of them are discovered and well-studied, both individually and as a population. Homogeneous and complete samples of brown dwarfs are needed for these kinds of studies. Due to their weakness, spectral studies of brown dwarfs are rather laborious. For this reason, creating a significant reliable sample of brown dwarfs, confirmed by spectroscopic observations, seems unattainable at the moment. Numerous attempts have been made to search for and create a set of brown dwarfs using their colours as a decision rule applied to a vast amount of survey data. In this work, we use machine learning methods such as Random Forest Classifier, XGBoost, SVM Classifier and TabNet on PanStarrs DR1, 2MASS and WISE data to distinguish L and T brown dwarfs from objects of other spectral and luminosity classes. The explanation of the models is discussed. We also compare our models with classical decision rules, proving their efficiency and relevance.
Quantum uncertainty of gravitational field and entanglement in superposed massive particles

Investigating the quantum nature of gravity is an important issue in modern physics. Recently, studies pertaining to the quantum superposition of gravitational potential have garnered significant interest. Inspired by Mari \textit{et al.} [Sci. Rep. {\bf 6} 22777 (2016)] and Baym and Ozawa [Proc. Natl. Acad. Sci. U.S.A. {\bf 106}, 3035 (2009)], Belenchia \textit{et al.} [Phys. Rev. D {\bf 98}, 126009 (2018)] considered a gedanken experiment involving such a quantum superposition and mentioned that the superposition renders causality and complementarity inconsistent. They resolved this inconsistency by considering the quantized dynamical degrees of freedom of gravity. This suggests a strong relationship between the quantum superposition of the gravitational potential and the quantization of the gravitational field. In our previous study [Phys. Rev. D {\bf 106}, 125002 (2022)], we have shown that the quantum uncertainty of a field guarantees the consistency between causality and complementarity. In this study, we focus on the entanglement between two particles' states due to the electromagnetic/gravitational potential and investigate its relationship with quantum uncertainty, causality, and complementarity. Our numerical analyses show that the quantum uncertainty of the electromagnetic/gravitational field results in vacuum fluctuations and prohibits the entanglement between two particles' states when causality is satisfied. We further demonstrate that complementarity holds when the particles do not get entangled. The uncertainty relation does not cause the entanglement between two particles' states, which guarantees complementarity.
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution. It also has an active professional competitive scene. StarCraft II is uniquely suited for advancing offline RL algorithms, both because of its challenging nature and because Blizzard has released a massive dataset of millions of StarCraft II games played by human players. This paper leverages that and establishes a benchmark, called AlphaStar Unplugged, introducing unprecedented challenges for offline reinforcement learning. We define a dataset (a subset of Blizzard's release), tools standardizing an API for machine learning methods, and an evaluation protocol. We also present baseline agents, including behavior cloning, offline variants of actor-critic and MuZero. We improve the state of the art of agents using only offline data, and we achieve 90% win rate against previously published AlphaStar behavior cloning agent.
Drift and dispersion studies of ocean-dumped waste using Landsat imagery and current drogues

The drift and dispersion of industrial acid wastes dumped 64 km off the Delaware coast were investigated using 16 Landsat images. Waste plume drift velocities and spread rates were obtained, and principal component analysis was performed to discriminate the acid waste from other pollutants. Waste plumes averaged drifting rates from 0.59 km/hr to 3.39 km/hr into the southwest quadrant, and remained above the thermocline, which formed at depths ranging from 13 m to 24 m. Rapid waste movement toward shore occurred primarily during storms, although plumes were rapidly dispersed and diluted at spread rates in excess of 4 cm/sec. Landsat data analysis also indicates plume width increase of about 1.5 cm/sec during calm sea conditions, which is also in agreement with Falk's (1974) estimates of plume dilution.
Results of Detailed Hydrologic Characterization Tests— Fiscal and Calendar Year 2005

This report provides the results of detailed hydrologic characterization tests conducted within selected Hanford Site wells during fiscal and calendar year 2005. Detailed characterization tests performed included groundwater-flow characterization, barometric response evaluation, slug tests, in-well vertical groundwater-flow assessments, and a single-well tracer and constant-rate pumping test. Hydraulic property estimates obtained from the detailed hydrologic tests include hydraulic conductivity, transmissivity, specific yield, effective porosity, in-well lateral and vertical groundwater-flow velocity, aquifer groundwater-flow velocity, and depth-distribution profiles of hydraulic conductivity. In addition, local groundwater-flow characteristics (i.e., hydraulic gradient and flow direction) were determined for a site where detailed well testing was performed. Results obtained from these tests provide hydrologic information that supports the needs of Resource Conservation and Recovery Act waste management area characterization as well as sitewide groundwater monitoring and modeling programs. These results also reduce the uncertainty of groundwater-flow conditions at selected locations on the Hanford Site.
A Common Genetic Factor Underlies Genetic Risk for Gynaecological and Reproductive Disorders and Is Correlated with Risk to Depression

Introduction: Sex steroid hormone fluctuations may underlie both reproductive disorders and sex differences in lifetime depression prevalence. Previous studies report high comorbidity among reproductive disorders and between reproductive disorders and depression. This study sought to assess the multivariate genetic architecture of reproductive disorders and their loading onto a common genetic factor and investigated whether this latent factor shares a common genetic architecture with female depression, including perinatal depression (PND). Method: Using UK Biobank and FinnGen data, genome-wide association meta-analyses were conducted for nine reproductive disorders, and genetic correlation between disorders was estimated. Genomic Structural Equation Modelling identified a latent genetic factor underlying disorders, accounting for their significant genetic correlations. SNPs significantly associated with both latent factor and depression were identified. Results: Excellent model fit existed between a latent factor underlying five reproductive disorders (χ2 (5) = 6.4; AIC = 26.4; CFI = 1.00; SRMR = 0.03) with high standardised loadings for menorrhagia (0.96, SE = 0.05); ovarian cysts (0.94, SE = 0.05); endometriosis (0.83, SE = 0.05); menopausal symptoms (0.77, SE = 0.10); and uterine fibroids (0.65, SE = 0.05). This latent factor was genetically correlated with PND (rG = 0.37, SE = 0.15, p = 1.4e−03), depression in females only (rG = 0.48, SE = 0.06, p = 7.2e−11), and depression in both males and females (MD) (rG = 0.35, SE = 0.03, p = 1.8e−30), with its top locus associated with FSHB/ARL14EP (rs11031006; p = 9.1e−33). SNPs intronic to ESR1, significantly associated with the latent factor, were also associated with PND, female depression, and MD. Conclusion: A common genetic factor, correlated with depression, underlies risk of reproductive disorders, with implications for aetiology and treatment. Genetic variation in ESR1 is associated with reproductive disorders and depression, highlighting the importance of oestrogen signalling for both reproductive and mental health.
Declines in ice cover induce light limitation in freshwater diatoms

The rediscovery of diatom blooms embedded within and beneath Lake Erie ice cover (2007-2012) ignited an intense interest in psychrophilic adaptations and winter limnology. Subsequent studies determined ice plays a vital role in winter diatom ecophysiology, as diatoms partition to the underside of ice thereby fixing their location within the photic zone. Yet, climate change has led to widespread ice decline across the Great Lakes, with Lake Erie presenting a nearly ice-free state in several recent winters. It has been hypothesized the resultant turbid, isothermal water column will induce light limitation amongst winter diatoms, serving as a detrimental competitive disadvantage. Here, we conducted a physiochemical and metatranscriptomic survey of the winter Lake Erie water column (2019-2020) that spanned spatial, temporal, and climatic gradients to investigate this hypothesis. We determined ice-free conditions decreased diatom bloom magnitude and altered diatom community composition. Diatoms increased the expression of various photosynthetic genes and iron transporters, suggesting they are attempting to increase their quantity of photosystems and light-harvesting components (a well-defined indicator of light limitation). Notably, we identified two gene families which serve to increase diatom fitness in the turbid ice-free water column: proton-pumping rhodopsins (a second means of light-driven energy acquisition) and fasciclins (a means to “raft” together to increase buoyancy and co-locate to the surface to optimize light acquisition). With large-scale climatic changes already underway, our observations provide insight into how diatoms respond to the dynamic ice conditions of today and shed light on how they will fare in a climatically altered tomorrow.
Identificacao de areas alagadas no Bioma Pantanal -Brasil- utilizando dados multitemporais TERRA/MODIS

Identification flooded areas of the Pantanal Biome – Brazil – using multitemporal terra/MODIS data The Pantanal is the largest tropical wetland on the planet and the major flooded area of Brazil. The seasonal floods are one of the major characteristics of this biome being a fundamental ecological factor in the region. The time series of remotely sensed images are an important tool to supervise the monitoring of these areas. The objective of this work was to identify the flooded areas of the Pantanal biome based on the spatial and temporal variation of the EVI (Enhanced Vegetation Index) values extracted from the MODIS (Moderate Resolution Imaging Spectroradiometer) images using principal components analysis. We used 217 EVI images from the period of 2000 to 2008, and precipitation data from the Tropical Rainfall Measuring Mission (TRMM). From the principal components analysis the variability in time of each pixel that composes the image was obtained for the entire region. Areas that presented the greatest variability were classified as flooded areas. In this work, it was possible to identify the dynamics of the region, i.e., periods of drought and flooding; and to obtain information about vegetation seasonality as well as the temporal profile of the EVI for flooded areas in the region.