Miracle-dz/UniFAR-FTUs huggingface dataset:
Standard Versus Natural: Assessing the Impact of Environmental Variables on Organic Matter Decomposition in Streams Using Three Substrates

The decomposition of allochthonous organic matter, such as leaves, is a crucial ecosystem process in low‐order streams. Microbial communities, including fungi and bacteria, colonize allochthonous organic material, break up large molecules, and increase the nutritional value for macroinvertebrates. Environmental variables are known to affect microbial as well as macroinvertebrate communities and alter their ability to decompose organic matter. Studying the relationship between environmental variables and decomposition has mainly been realized using leaves, with the drawbacks of differing substrate composition and consequently between‐study variability. To overcome these drawbacks, artificial substrates have been developed, serving as standardizable surrogates. In the present study, we compared microbial and total decomposition of leaves with the standardized substrates of decotabs and, only for microbial decomposition, of cotton strips, across 70 stream sites in a Germany‐wide study. Furthermore, we identified the most influential environmental variables for the decomposition of each substrate from a range of 26 variables, including pesticide toxicity, concentrations of nutrients, and trace elements, using stability selection. The microbial as well as total decomposition of the standardized substrates (i.e., cotton strips and decotabs) were weak or not associated with that of the natural substrate (i.e., leaves, r² < 0.01 to r² = 0.04). The decomposition of the two standardized substrates, however, showed a moderate association (r² = 0.21), which is probably driven by their similar composition, with both being made of cellulose. Different environmental variables were identified as the most influential for each of the substrates and the directions of these relationships contrasted between the substrates. Our results imply that these standardized substrates are unsuitable surrogates when investigating the decomposition of allochthonous organic matter in streams. Environ Toxicol Chem 2023;42:2007–2018. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Sex hormones, SHBG and cognitive performance among older Australian women: an observational study

Abstract Objective This study aims to explore the associations between sex hormones and cognitive performance in older women. Methods Associations between sex hormones, sex hormone binding globulin (SHBG) and cognitive performance were examined in women aged at least 70 years, without dementia and not using medications that influence sex hormones. Linear and generalized linear regression models included age, body mass index, education, smoking, alcohol, living circumstances, diabetes, hypertension, depression and impaired renal function. Results The included 5511 women had a median (interquartile range) age of 73.9 (71.6–77.6) years. No associations were found for estrone, estradiol, testosterone or dehydroepiandrosterone and cognitive performance. SHBG concentrations above quartile 1 (Q1) were significantly inversely associated with processing speed (Q2, β = –0.94, 95% confidence interval [CI] − 1.64 to −0.24, p = 0.009; Q3, β = –0.82, 95% CI −1.53 to −0.10, p = 0.025; and Q4, β = –0.95, 95% CI −1.70 to −0.20, p = 0.013). Conclusions Sex hormones were not associated with cognitive performance. The finding that low SHBG is associated with better processing speed warrants further investigation. The null findings for the sex hormones establish a firm baseline to confidently explore the association between sex hormones and longitudinal cognitive performance in this population. Trial Registration International Standard Randomized Controlled Trial Number Register (ISRCTN83772183) and ClinicalTrials.gov (NCT01038583).
LOW-TEMPERATURE ION TRAP STUDIES OF N+(3Pja) + H2(j) → NH+ + H

Using a low-temperature 22-pole ion trap apparatus, detailed measurements for the title reaction have been performed between 10 K and 100 K in order to get some state specific information about this fundamental hydrogen abstraction process. The relative population of the two lowest H2 rotational states, j = 0 and 1, has been varied systematically. NH+ formation is nearly thermo-neutral; however, to date, the energetics are not known with the accuracy required for low-temperature astrochemistry. Additional complications arise from the fact that, so far, there is no reliable theoretical or experimental information on how the reactivity of the N+ ion depends on its fine-structure (FS) state 3Pja. Since in the present trapping experiment, thermalization of the initially hot FS population competes with hydrogen abstraction, the evaluation of the decay of N+ ions over long storage times and at various He and H2 gas densities provides information on these processes. First assuming strict adiabatic behavior, a set of state specific rate coefficients is derived from the measured thermal rate coefficients. In addition, by recording the disappearance of the N+ ions over several orders of magnitude, information on nonadiabatic transitions is extracted including FS-changing collisions.
Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multistudy magnetic resonance imaging consortia for diagnosing Alzheimer’s disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference, etc. We find that models that incorporate multisource data from demographic, clinical, genetic factors, and cognitive scores are also unbiased. These models have a better predictive AUC across subgroups than those trained only with imaging features, but there are also situations when these additional features do not help.
Social Safety Theory: Conceptual Foundation, Underlying Mechanisms, and Future Directions

ABSTRACT Classic theories of stress and health are largely based on assumptions regarding how different psychosocial stressors influence biological processes that, in turn, affect human health and behavior. Although theoretically rich, this work has yielded little consensus and led to numerous conceptual, measurement, and reproducibility issues. Social Safety Theory aims to address these issues by using the primary goal and regulatory logic of the human brain and immune system as the basis for specifying the social-environmental situations to which these systems should respond most strongly to maximize reproductive success and survival. This analysis gave rise to the integrated, multi-level formulation described herein, which transforms thinking about stress biology and provides a biologically based, evolutionary account for how and why experiences of social safety and social threat are strongly related to health, well-being, aging, and longevity. In doing so, the theory advances a testable framework for investigating the biopsychosocial roots of health disparities as well as how health-relevant biopsychosocial processes crystalize over time and how perceptions of the social environment interact with childhood microbial environment, birth cohort, culture, air pollution, genetics, sleep, diet, personality, and self-harm to affect health. The theory also highlights several interventions for reducing social threat and promoting resilience.
Depressive symptoms and social support among Australian men: A 7-year longitudinal study

Background: Mental health disorders are ranked globally as the single largest contributor to non-fatal ill-health. Social support can be a means of reducing and managing depression. However, depression can also impact on a person’s level of social support. Objective: As men typically have fewer sources of social support than females, this study investigated the bi-directional associations between depressive symptoms and perceived levels of social support among Australian males, aged 18–63. Methods: Three waves of panel data from Ten to Men: The Australian Longitudinal Study on Male Health collected over 7 years (2013–2020) were used. A random intercept cross-lagged panel analysis with 5112 participants was undertaken. Mediating effects and indirect and total effects for lagged and cross-lagged pathways were also examined. Results: Over time, greater social support was found to be associated with lower depression levels, and simultaneously greater levels of depression was found to be associated with lower levels of social support. Standardised cross-lagged effects between waves were mostly similar (β = 0.10). However, mediation analyses identified that only the total effect size of the association for depression at wave 1 predicting social support at wave 3 (β = −0.29) was significant. Mediated effects of social support at wave 1 predicting depression at wave 3 were not significant. Limitations: These include the number of years between each wave, and data were collected during the COVID pandemic. Conclusion: The study provides robust longitudinal evidence supporting the notion that social support and depression are both a cause and consequence of the other. However, the long-term effects of depression reducing social support were longer lasting than the effects of social support reducing depression.
The data processing pipeline for the Herschel/SPIRE imaging Fourier Transform Spectrometer

We present the data processing pipeline to generate calibrated data products from the Spectral and Photometric Imaging Receiver (SPIRE) imaging Fourier Transform Spectrometer. The pipeline processes telemetry from SPIRE point source, jiggle- and raster-map observations, producing calibrated spectra in low-, medium-, high-, and mixed low- and highresolution modes. The spectrometer pipeline shares some elements with the SPIRE photometer pipeline, including the conversion of telemetry packets into data timelines and the calculation of bolometer voltages from the raw telemetry. We present the following fundamental processing steps unique to the spectrometer: temporal and spatial interpolation of the stage mechanism and detector data to create interferograms; apodization; Fourier transform, and creation of a hyperspectral data cube. We also describe the corrections for various instrumental effects including first- and secondlevel glitch identification and removal, correction of the effects due to the Herschel primary mirror and the spectrometer calibrator, interferogram baseline correction, channel fringe correction, temporal and spatial phase correction, non-linear response of the bolometers, variation of instrument performance across the focal plane arrays, and variation of spectral efficiency. Astronomical calibration is based on combinations of observations of standard astronomical sources and regions of space known to contain minimal emission.
Ho\v{r}ava models as palladium of unitarity and renormalizability in quantum gravity

We give a review of UV renormalization of Ho\v{r}ava gravity (HG) models introduced as a remedy against violation of unitarity in quantum gravity theory. Projectable and non-projectable low-dimensional HG models and the spectra of their physical degrees of freedom are considered in the linearized approximation on flat spacetime background. The problem of regularity of their propagators depending on the choice of gauge fixing procedure is discussed along with the role of this regularity in UV renormalization. With the choice of a special class of quasi-relativistic gauge conditions perturbative renormalizability of projectable HG models is proven in any spacetime dimension and the status of renormalization in non-projectable models is briefly discussed. We show how the covariance of counterterms is provided within the class of background covariant gauge conditions and show asymptotic freedom of $(2+1)$-dimensional HG. We also present the calculation of beta-functions in $(3+1)$-dimensional theory by the generalized Schwinger-DeWitt technique of universal functional traces and obtain fixed points of its renormalization group flow, some of them being good candidates for asymptotic freedom.
Differentially Private Distributed Bayesian Linear Regression with MCMC

We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the data and share certain summary statistics of their portions in privacy-preserving noise. We develop a novel generative statistical model for privately shared statistics, which exploits a useful distributional relation between the summary statistics of linear regression. Bayesian estimation of the regression coefficients is conducted mainly using Markov chain Monte Carlo algorithms, while we also provide a fast version to perform Bayesian estimation in one iteration. The proposed methods have computational advantages over their competitors. We provide numerical results on both real and simulated data, which demonstrate that the proposed algorithms provide well-rounded estimation and prediction.
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning

Many real-time applications (e.g., Augmented/Virtual Reality, cognitive assistance) rely on Deep Neural Networks (DNNs) to process inference tasks. Edge computing is considered a key infrastructure to deploy such applications, as moving computation close to the data sources enables us to meet stringent latency and throughput requirements. However, the constrained nature of edge networks poses several additional challenges to the management of inference workloads: edge clusters can not provide unlimited processing power to DNN models, and often a trade-off between network and processing time should be considered when it comes to end-to-end delay requirements. In this paper, we focus on the problem of scheduling inference queries on DNN models in edge networks at short timescales (i.e., few milliseconds). By means of simulations, we analyze several policies in the realistic network settings and workloads of a large ISP, highlighting the need for a dynamic scheduling policy that can adapt to network conditions and workloads. We therefore design ASET, a Reinforcement Learning based scheduling algorithm able to adapt its decisions according to the system conditions. Our results show that ASET effectively provides the best performance compared to static policies when scheduling over a distributed pool of edge resources.
Breaking Out of the Ivory Tower: A Large-scale Analysis of Patent Citations to HCI Research

What is the impact of human-computer interaction research on industry? While it is impossible to track all research impact pathways, the growing literature on translational research impact measurement offers patent citations as one measure of how industry recognizes and draws on research in its inventions. In this paper, we perform a large-scale measurement study primarily of 70, 000 patent citations to premier HCI research venues, tracing how HCI research are cited in United States patents over the last 30 years. We observe that 20.1% of papers from these venues, including 60–80% of papers at UIST and 13% of papers in a broader dataset of SIGCHI-sponsored venues overall, are cited by patents—far greater than premier venues in science overall (9.7%) and NLP (11%). However, the time lag between a patent and its paper citations is long (10.5 years) and getting longer, suggesting that HCI research and practice may not be efficiently connected.
Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis

In this paper, we propose HyperVision, a realtime unsupervised machine learning (ML) based malicious traffic detection system. Particularly, HyperVision is able to detect unknown patterns of encrypted malicious traffic by utilizing a compact inmemory graph built upon the traffic patterns. The graph captures flow interaction patterns represented by the graph structural features, instead of the features of specific known attacks. We develop an unsupervised graph learning method to detect abnormal interaction patterns by analyzing the connectivity, sparsity, and statistical features of the graph, which allows HyperVision to detect various encrypted attack traffic without requiring any labeled datasets of known attacks. Moreover, we establish an information theory model to demonstrate that the information preserved by the graph approaches the ideal theoretical bound. We show the performance of HyperVision by real-world experiments with 92 datasets including 48 attacks with encrypted malicious traffic. The experimental results illustrate that HyperVision achieves at least 0.92 AUC and 0.86 F1, which significantly outperform the state-of-the-art methods. In particular, more than 50% attacks in our experiments can evade all these methods. Moreover, HyperVision achieves at least 80.6 Gb/s detection throughput with the average detection latency of 0.83s.
Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints

In this paper, we consider a general non-convex constrained optimization problem, where the objective function is weakly convex and the constraint function is convex while they can both be non-smooth. This class of problems arises from many applications in machine learning such as fairness-aware supervised learning. To solve this problem, we consider the classical switching subgradient method by [62], which is an intuitive and easily implementable ﬁrst-order method. Before this work, its iteration complexity was only known for convex optimization. We prove its oracle complexity for ﬁnding a nearly stationary point when the objective function is non-convex. The analysis is derived separately when the constraint function is deterministic and stochastic. Compared to existing methods, especially the double-loop methods, the switching gradient method can be applied to non-smooth problems and only has a single loop, which saves the eﬀort on tuning the number of inner iterations.
In-line devices and sensors based on air-silica microstructure optical fibers

This paper reports some of our recent work on in-line devices based on air-silica microstructrue optical fibers. These devices are fabricated by use of a CO2 laser/a femtosecond infrared laser and include strong long period gratings in index-guiding fibers and air-core photonic bandgap fibers, in-fiber polarizers, polarimeters, and modal interferometers. Applications of such devices for strain, temperature, directional bend, twist, and gas sensing are discussed.
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization

Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference. The main advantages of Stein thinning are the automatic remove of the burn-in period, the correction of the bias introduced by recent MCMC algorithms, and the asymptotic properties of convergence towards the target distribution. Nevertheless, Stein thinning suffers from several empirical pathologies, which may result in poor approximations, as observed in the literature. In this article, we conduct a theoretical analysis of these pathologies, to clearly identify the mechanisms at stake, and suggest improved strategies. Then, we introduce the regularized Stein thinning algorithm to alleviate the identified pathologies. Finally, theoretical guarantees and extensive experiments show the high efficiency of the proposed algorithm. An implementation of regularized Stein thinning as the kernax library in python and JAX is available at https://gitlab.com/drti/kernax.
Manipulation of polarization topology using a Fabry-Pérot fiber cavity with a higher-order mode optical nanofiber

Optical nanofiber cavity research has mainly focused on the fundamental mode. Here, a Fabry-P\'erot fiber cavity with an optical nanofiber supporting the higher-order modes, TE01, TM01, HE21o, and HE21e, is demonstrated. Using cavity spectroscopy, with mode imaging and analysis, we observe cavity resonances that exhibit complex, inhomogeneous states of polarization with topological features containing Stokes singularities such as C-points, Poincar\'e vortices, and L-lines. In situ tuning of the intracavity birefringence enables the desired profile and polarization of the cavity mode to be obtained. These findings open new research possibilities for cold atom manipulation and multimode cavity quantum electrodynamics using the evanescent fields of higher-order mode optical nanofibers.
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks

From natural language processing to genome sequencing, large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices. This has motivated the research of new compute and network systems capable of handling such tasks. In particular, recent work has focused on developing management schemes which decide how to allocate distributed resources such that some overall objective, such as minimising the job completion time (JCT), is optimised. However, such studies omit explicit consideration of how much a job should be distributed, usually assuming that maximum distribution is desirable. In this work, we show that maximum parallelisation is sub-optimal in relation to user-critical metrics such as throughput and blocking rate. To address this, we propose PAC-ML (partitioning for asynchronous computing with machine learning). PAC-ML leverages a graph neural network and reinforcement learning to learn how much to partition computation graphs such that the number of jobs which meet arbitrary user-defined JCT requirements is maximised. In experiments with five real deep learning computation graphs on a recently proposed optical architecture across four user-defined JCT requirement distributions, we demonstrate PAC-ML achieving up to 56.2% lower blocking rates in dynamic job arrival settings than the canonical maximum parallelisation strategy used by most prior works.