Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Dysfunction Underlying Working Memory Processing at Different Stages of the Illness Course in Schizophrenia:A Comparative Meta-analysis

Schizophrenia (SCZ), as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk (CHR), first-episode psychosis (FEP), and long-term SCZ, using meta-analyses of functional magnetic resonance imaging (fMRI) studies. Following a systematic literature search, fifty-six whole-brain task-based fMRI studies (15 for CHR, 16 for FEP, 25 for long-term SCZ) were included. The separate and pooled neurofunctional mechanisms among CHR, FEP and long-term SCZ were generated by Seed-based d Mapping toolbox. The CHR and FEP groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of SCZ. Individuals with FEP showed lower activation in left inferior parietal lobule than those with long-term SCZ, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.
Comparison of Inorganic Carbon System Parameters Measured in the Atlantic Ocean from 1990 to 1998 and Recommended Adjustments

As part of the global synthesis effort sponsored by the Global Carbon Cycle project of the National Oceanic and Atmospheric Administration (NOAA) and U.S. Department of Energy, a comprehensive comparison was performed of inorganic carbon parameters measured on oceanographic surveys carried out under auspices of the Joint Global Ocean Flux Study and related programs. Many of the cruises were performed as part of the World Hydrographic Program of the World Ocean Circulation Experiment and the NOAA Ocean-Atmosphere Carbon Exchange Study. Total dissolved inorganic carbon (DIC), total alkalinity (TAlk), fugacity of CO{sub 2}, and pH data from twenty-three cruises were checked to determine whether there were systematic offsets of these parameters between cruises. The focus was on the DIC and TAlk state variables. Data quality and offsets of DIC and TAlk were determined by using several different techniques. One approach was based on crossover analyses, where the deep-water concentrations of DIC and TAlk were compared for stations on different cruises that were within 100 km of each other. Regional comparisons were also made by using a multiple-parameter linear regression technique in which DIC or TAlk was regressed against hydrographic and nutrient parameters. When offsets of greater than 4 {micro}mol/kg were observed for DIC and/or 6 {micro}mol/kg were observed for TAlk, the data taken on the cruise were closely scrutinized to determine whether the offsets were systematic. Based on these analyses, the DIC data and TAlk data of three cruises were deemed of insufficient quality to be included in the comprehensive basinwide data set. For several of the cruises, small adjustments in TAlk were recommended for consistency with other cruises in the region. After these adjustments were incorporated, the inorganic carbon data from all cruises along with hydrographic, chlorofluorocarbon, and nutrient data were combined as a research quality product for the scientific community.
Systematic Evaluation of Generative Machine Learning Capability to Simulate Distributions of Observables at the Large Hadron Collider

Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider. This paper aims to explore the performance of generative models for complementing the statistics of classical Monte Carlo simulations in the final stage of data analysis by generating additional synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several normalizing flow architectures are adapted for this task and their performance is systematically evaluated using a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background. The applicability of normalizing flows under different model parameters and a restricted number of initial events used in training is investigated. The best performing model is then chosen for further evaluation with a set of statistical procedures and a simplified physics analysis. We demonstrate that the the number of events used in training coupled with the flow architecture are crucial for the physics performance of the generative models. By implementing and performing a series of statistical tests and evaluations we show that a machine-learning-based generative procedure can can be used to generate synthetic data that matches the original samples closely enough and that it can therefore be incorporated in the final stage of a physics analysis with some given systematic uncertainty.
Coherence length and penetration depth in strongly correlated superconductors

Superconductivity emerges from the spatial coherence of a macroscopic condensate characterized by two intrinsic length scales: the coherence length $\xi_0$ and the London penetration depth $\lambda_{\mathrm{L}}$. While their description is well established in weak-coupling Bardeen-Cooper-Schrieffer (BCS) theory and Eliashberg theory, $\xi_0$ and $\lambda_{\mathrm{L}}$ are generally unknown quantities in strongly correlated superconductors. In this work, we establish a framework to calculate these length scales in microscopic theories and from first principles. Central to this idea are Nambu-Gor'kov Green functions under a constraint of finite-momentum pairing and their analysis with respect to the superconducting order parameter and resultant supercurrents. We illustrate with a multi-orbital model of alkali-doped fullerides (A$_3$C$_{60}$) using Dynamical Mean-Field Theory (DMFT) how proximity of superconductivity, Jahn-Teller metallic, and Mott-localized states impact superconducting coherence, order parameter stiffness, and critical temperature. Our analysis reveals a"localized"superconducting regime with robustly short $\xi_0$. Multi-orbital effects cause a domeless rise in the critical temperature as the pairing interaction is increased, setting this system apart from the BCS to Bose-Einstein-Condensate (BEC) crossover phenomenology.
Theory of infrared double-resonance Raman spectrum in graphene: The role of the zone-boundary electron-phonon enhancement

We theoretically investigate the double-resonance Raman spectrum of monolayer graphene down to infrared laser excitation energies. By using first-principles density functional theory calculations, we improve upon previous theoretical predictions based on conical models or tight-binding approximations, and rigorously justify the evaluation of the electron-phonon enhancement found in Ref. [Venanzi, T. , Graziotto, L. et al., Phys. Rev. Lett. 130, 256901 (2023)]. We proceed to discuss the effects of such enhancement on the room temperature graphene resistivity, hinting towards a possible reconciliation of theoretical and experimental discrepancies.
Impact of climate variability on NDVI over the Indian region during 1981–2010

The normalized difference vegetation index (NDVI), derived from the Advanced Very High Resolution Radiometer (AVHRR) (1981–2000), and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data (2000–2010) are analysed to examine their spatio-temporal variability over the Indian region. Climatic factors are well known to be associated with vegetation. Therefore, an attempt has also been made in this study to examine the impact of climate variability on NDVI over the Indian region. The average spatio-temporal patterns of NDVI suggest that the variability in NDVI is well associated with climatic factors such as rainfall and temperature. During hot weather, the all-India NDVI shows the lowest values; the values increase from the onset of the summer monsoon season (June–September) onwards over the Indian region. The NDVI attains its peak value in the month of October. The composite annual cycles of NDVI during drought and flood years also show similar features. During drought years, there is a decrease in all-India NDVI for all months. Opposite features are seen during flood years, with a substantial increase in all-India NDVI from the month of October onwards compared to normal years. This clearly indicates the impact of heavy summer monsoon rainfall over the country on NDVI during winter (October–December) and suggests that soil moisture gained by flood conditions helps the NDVI to increase. In contrast, drought conditions show an immediate effect on NDVI but the incremental changes are of smaller magnitude. Spatial patterns also show similar features, with negative anomalies in NDVI over large parts of the country during drought years and positive anomalies during flood years. There exist year-to-year variations in NDVI depending on the performance of the monsoon. NDVI is positively correlated with rainfall during the southwest (June–September) and northeast (October–December) monsoons over a large part of the country. Also, there exists strong lag correlation between summer monsoon rainfall of the current year and NDVI of the next year, indicating that an increase (decrease) in rainfall during the rainy seasons is favourable (unfavourable) for vegetation during the winter (January and February) and the pre-monsoon season (March–May) of the following year. Thus, the analysis shows significant impact of inter-annual variability of climate on the NDVI over the Indian region. Strong lag correlations between rainfall and NDVI indicate the potential for estimating NDVI over India by the regression method.
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN

Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive performance, their practical application is often limited by the prohibitive amount of resources required for every inference. Early-exiting dynamic neural networks (EDNN) circumvent this issue by allowing a model to make some of its predictions from intermediate layers (i.e., early-exit). Training an EDNN architecture is challenging as it consists of two intertwined components: the gating mechanism (GM) that controls early-exiting decisions and the intermediate inference modules (IMs) that perform inference from intermediate representations. As a result, most existing approaches rely on thresholding confidence metrics for the gating mechanism and strive to improve the underlying backbone network and the inference modules. Although successful, this approach has two fundamental shortcomings: 1) the GMs and the IMs are decoupled during training, leading to a train-test mismatch; and 2) the thresholding gating mechanism introduces a positive bias into the predictive probabilities, making it difficult to readily extract uncertainty information. We propose a novel architecture that connects these two modules. This leads to significant performance improvements on classification datasets and enables better uncertainty characterization capabilities.
Mean and variance of the longest alternating subsequence in a random separable permutation

A permutation is \it separable \rm if it can be obtained from the singleton permutation by iterating direct sums and skew sums. Equivalently, it is separable if and only it avoids the patterns 2413 and 3142. Under the uniform probability on separable permutations of $[n]$, let the random variable $A_n$ denote the length of the longest alternating subsequence. Also, let $A_n^{+,-}$ denote the length of the longest alternating subsequence that begins with an ascent and ends with a descent, and define $A_n^{-,+}, A_n^{+,+}, A_n^{-,-}$ similarly. By symmetry, the first two and the last two of these latter four random variables are equi-distributed. We prove that the expected value of any of these five random variables behaves asymptotically as $(2-\sqrt2)n\approx0.5858\thinspace n$. We also prove that the variance of any of the four random variables $A_n^{\pm,\pm}$ behaves asymptotically as $\frac{16-11\sqrt2}2n\approx0.2218\thinspace n$.
Are transit times useful process‐based tools for flow prediction and classification in ungauged basins in montane regions?

Transit times are being increasingly explored as process‐based tools for conceptualizing hydrological function at a range of scales. Despite this, little effort has been made to relate transit times to conventional hydrometric flow statistics. Rather, the identification of the appropriate transit time distribution (TTD) for a hydrological system and the derivation of metrics such as the mean transit time (MTT) have required quantitative assessment of input–output relationships for conservative tracers using lumped parameter models. This has allowed the main landscape controls to be identified and has facilitated the prediction of MTTs in ungauged basins in particular geomorphic provinces, though relationships with streamflow measures have been unexamined. We used estimated MTTs (with uncertainty) for 16 experimental catchments (0·5–690 km2 in area) with contrasting geologic, topographic, pedologic and climatic characteristics in Scotland. The MTT was highly variable ranging from 60 days to ca > 1500 days, reflecting differences in catchment soil cover, geomorphic properties and precipitation. The MTT was closely correlated with key hydrometric design statistics such as the mean annual flood (MAF) and percentiles of high (Q5) and low (Q95) flows. Analysis of MTT estimates, in conjunction with geographic information system (GIS)‐based assessment of landscape controls, showed that MTT could be predicted to within 30% for ungauged basins from catchment soil cover and drainage density. Furthermore, hydrometric design statistics for ungauged basins could also be forecast from MTT predictions with median relative errors of 14, 11 and 28% for the MAF, Q5 and Q95 respectively. We suggest that MTTs—predicted from mapped landscape characteristics—can be useful diagnostic metrics for ungauged montane basins, as well as a useful similarity index for process‐based catchment classification. This is important as montane headwaters are often critical, but data‐poor environments influencing the quantity, quality and ecology of downstream flows. Copyright © 2010 John Wiley & Sons, Ltd.
SeqXGPT: Sentence-Level AI-Generated Text Detection

Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level detection challenge by synthesizing a dataset that contains documents that are polished with LLMs, that is, the documents contain sentences written by humans and sentences modified by LLMs. Then we propose \textbf{Seq}uence \textbf{X} (Check) \textbf{GPT}, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection. These features are composed like \textit{waves} in speech processing and cannot be studied by LLMs. Therefore, we build SeqXGPT based on convolution and self-attention networks. We test it in both sentence and document-level detection challenges. Experimental results show that previous methods struggle in solving sentence-level AIGT detection, while our method not only significantly surpasses baseline methods in both sentence and document-level detection challenges but also exhibits strong generalization capabilities.
Arithmetic Deformation of Line Bundles

We introduce a new method to study mixed characteristic deformation of line bundles. In particular, for sufficiently large smooth projective families $f : \mathcal{X} \to \mathcal{S}$ defined over the ring of $N$-integers $\mathcal{O}_{L}[1/N]$ of a number field $L$, we produce a proper closed subscheme $\mathcal{E} \subsetneq \mathcal{S}$ outside of which all line bundles appearing in positive characteristic fibres of $f$ admit characteristic zero lifts. This in particular applies to elliptic surfaces over $\mathbb{P}^1$ and projective hypersurfaces in $\mathbb{P}^3$ of degree $d \geq 5$. We also study the locus in $\mathcal{E}$ in more detail in the $h^{0, 2} = 2$ case.
Estimation of ground surface temperatures from borehole temperature data

To infer past climatic changes from temperatures measured in boreholes, one must obtain reliable estimates of ground surface temperature (GST) histories from these data. This paper presents a method that uses a Bayesian inverse technique to estimate the GST in the Fourier frequency domain. By assuming the a priori GST to be stationary, with a prescribed standard deviation and a cutoff period, the time series is constrained to be bounded and smooth. Because accounting for conductivity variations with depth is crucial to the estimation of GST, a layered Earth medium has been used, for which the forward analytical solution of one-dimensional heat conduction is available. Borehole data from Flin Flon and Lac Dufault in Canada are inverted with this method. The Flin Flon hole, logged to a depth of 2900 m, provides an opportunity to study long-term GST variations, and the estimated GST from this hole shows some effects of deglaciation at the end of Pleistocene. At Lac Dufault, similar GSTs were obtained independently from three holes ranging from 550 to 900 m, and hence the results are considered more reliable. In the Lac Dufault GSTs, there is a warm period centered about 1000 years BP and a cold period about 400 years B.P., confirming the presence of the Little Climatic Optimum and the Little Ice Age, respectively. The Flin Flon result does not show the Little Climatic Optimum, and the Little Ice Age occurs about 100–200 years earlier. However, in both locations, the GSTs show another brief cold period around the turn of the century followed by rapid warming until 1940–1950, in good agreement with the trend of northern hemisphere surface air temperatures.
Surveillance and Stability of SARS-CoV-2 Wastewater Samples in Minnesota

Wastewater-based epidemiology provides an approach for assessing the prevalence of pathogens such as COVID-19 in a sewer service area. In this study, SARS-CoV-2 RNA was measured serially in 44 wastewater treatment plants of varying service capacities comprising approximately 67% of the population of Minnesota, from September 2020 through December 2022. We employed linear regression models to establish a predictive relationship between the weekly SARS-CoV-2 RNA concentrations in wastewater and clinical case counts. Metrics were assessed under specified transformation and normalization methods which we confirmed by cross-validation averaged across the enrolled treatment plants. We report that the relationship between COVID-19 incidence and SARS-CoV-2 RNA in wastewater may be treatment plant-specific. Toward establishing guidelines for pathogen surveillance, we further studied storage and time-to-analysis for RNA wastewater data and observed large effects of storage temperature, indicating that collection methods may have an important effect on the utility and validity of wastewater data for infectious disease monitoring. Our findings are additive for any large-scale wastewater surveillance program.
Towards an integrated understanding of dietary phenotypes

Diet and nutrition comprise a complex, multi-faceted interface between animal biology and food environments. With accumulating information on the many facets of this association arises a need for systems-based approaches that integrate dietary components and their links with ecology, feeding, post-ingestive processes and the functional and ecological consequences of these interactions. We briefly show how a modelling approach, nutritional geometry, has used the experimental control afforded in laboratory studies to begin to unravel these links. Laboratory studies, however, have limited ability to establish whether and how the feeding and physiological mechanisms interface with realistic ecological environments. We next provide an overview of observational field studies of free-ranging primates that have examined this, producing largely correlative data suggesting that similar feeding mechanisms operate in the wild as in the laboratory. Significant challenges remain, however, in establishing causal links between feeding, resource variation and physiological processes in the wild. We end with a more detailed account of two studies of temperate primates that have capitalized on the discrete variation provided by seasonal environments to strengthen causal inference in field studies and link patterns of intake to dynamics of nutrient processing. This article is part of the theme issue ‘Food processing and nutritional assimilation in animals’.
The Cretaceous Succession in Jamaica

Abstract There are wide divergencies between the views of different authors as to the correlation of the Jamaican Cretaceous rocks, the extreme views being (a) that the range covered is Albian to Turonian, and (b) that the rocks are mainly or wholly Maestrichtian in age. An examination of all known occurrences of the rudist genera Barrettia and Titanosarcolites in the Antillean and Central American area suggests that the oft-repeated statement that these two genera occur in association is erroneous. In Jamaica the Barrettia Limestone lies at least 2,500 ft. below the Titanosarcolites Limestone. Six palaeontological zones may be recognized in the Cretaceous of Jamaica, each containing its own peculiar assemblage of fossils, and with very few species common to two zones. Evidence is brought forward that only the highest part, comprising the uppermost two zones, should be included in the Maestrichtian, that the underlying shale is Campanian, that the Barrettia Limestone and associated shales are Turonian, and that the lowest zone at present known is Cenomanian.
Chemical and functional characteristics to detect sugar syrup adulteration in honey from different botanical origins

ABSTRACT The objective of this study was to detect the changes in physicochemical and functional properties of raw and processed divergent honey varieties after adulterating with different sugar syrups. Specifically, when 25% to 55% cane syrup was added to raw Acacia honey, the HMF content increased from 46.25 to 101.6 mg/kg. In processed honey that contained 55% cane syrup, HMF content was even higher, reaching up to 402.47 mg/kg. The reported values for HMF (>80 mg/kg) content and DN (>8 DN) were above the described quality evaluation standard of honey. The results indicated that adulterating Acacia honey with up to 55% corn and corn syrup caused a reduction in diastase content to below 3.5 DN, and an increase in HMF content above 93.28 mg/kg. Similarly, adding 10% corn syrup to raw Ziziphus honey resulted in a decrease in both DPPH and ABTS+ values from 83.16% and 88.58%, respectively, to 72.83% and 76.97%. The findings in this study demonstrated that the addition of malt syrup (55%) to raw Trifolium honey resulted in an increase in the phytochemical content, with the TPC value reaching 805.15 mg GAE/100 g. However, processing Trifolium honey caused a significant decrease in TPC content to 505.15 mg GAE/100 g. This work highlights the need for more research into unexplored honey varieties and adulteration methods to improve quality evaluation standards and mitigate authenticity issues.