Miracle-dz/UniFAR-FTUs huggingface dataset:
SYSFLOW: Efficient Execution Platform for IoT Devices

Traditional executable delivery models pose challenges for IoT devices with limited storage, necessitating the download of complete executables and dependencies. Network solutions like NFS, designed for data files, encounter high IO overhead for irregular access patterns. This paper introduces SYSFLOW, a lightweight network-based executable delivery system for IoT. SYSFLOW delivers on-demand, redirecting local disk IO to the server through optimized network IO. To optimize cache hit rates, SYSFLOW employs server-side action-based prefetching, reducing latency by 45.1% to 75.8% compared to native Linux filesystems on SD cards. In wired environments, SYSFLOW's latency is up to 67.7% lower than NFS. In wireless scenarios, SYSFLOW performs 22.9% worse than Linux, comparable with Linux and outperforming NFS by up to 60.7%. While SYSFLOW's power consumption may be 6.7% higher than NFS, it offers energy savings due to lower processing time.
Distributed Optimization via Kernelized Multi-armed Bandits

Multi-armed bandit algorithms provide solutions for sequential decision-making where learning takes place by interacting with the environment. In this work, we model a distributed optimization problem as a multi-agent kernelized multi-armed bandit problem with a heterogeneous reward setting. In this setup, the agents collaboratively aim to maximize a global objective function which is an average of local objective functions. The agents can access only bandit feedback (noisy reward) obtained from the associated unknown local function with a small norm in reproducing kernel Hilbert space (RKHS). We present a fully decentralized algorithm, Multi-agent IGP-UCB (MA-IGP-UCB), which achieves a sub-linear regret bound for popular classes for kernels while preserving privacy. It does not necessitate the agents to share their actions, rewards, or estimates of their local function. In the proposed approach, the agents sample their individual local functions in a way that benefits the whole network by utilizing a running consensus to estimate the upper confidence bound on the global function. Furthermore, we propose an extension, Multi-agent Delayed IGP-UCB (MAD-IGP-UCB) algorithm, which reduces the dependence of the regret bound on the number of agents in the network. It provides improved performance by utilizing a delay in the estimation update step at the cost of more communication.
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

Surrogate models are statistical or conceptual approximations for more complex simulation models. In this context, it is crucial to propagate the uncertainty induced by limited simulation budget and surrogate approximation error to predictions, inference, and subsequent decision-relevant quantities. However, quantifying and then propagating the uncertainty of surrogates is usually limited to special analytic cases or is otherwise computationally very expensive. In this paper, we propose a framework enabling a scalable, Bayesian approach to surrogate modeling with thorough uncertainty quantification, propagation, and validation. Specifically, we present three methods for Bayesian inference with surrogate models given measurement data. This is a task where the propagation of surrogate uncertainty is especially relevant, because failing to account for it may lead to biased and/or overconfident estimates of the parameters of interest. We showcase our approach in two detailed case studies for both linear and nonlinear modeling scenarios. Uncertainty propagation in surrogate models enables more reliable and safe approximation of expensive simulators and will therefore be useful in various fields of applications.
The socioeconomic gap in life expectancy in Germany: a decomposition of age- and cause-specific mortality contributions, 2003--2021

Background Earlier death among people in socioeconomically deprived circumstances has been found internationally and for various causes of death, resulting in a considerable life expectancy gap between socioeconomic groups. This study examines how age- and cause-specific mortality contributions to the socioeconomic gap in life expectancy have changed in Germany over time. Methods Official German population and cause-of-death statistics from 2003 to 2021 were linked to the district-level German Index of Socioeconomic Deprivation. Life-table and decomposition methods were applied to calculate life expectancy by deprivation quintile and decompose the life expectancy gap ({triangleup}LE) between the most and least deprived quintiles into age- and cause-specific mortality contributions. Findings From 2003 to 2019, the {triangleup}LE between the most and least deprived quintiles of districts increased from 1.1 to 1.8 years among women and from 3.0 to 3.1 years among men. Thereafter, in the COVID-19 pandemic, it increased more rapidly to 2.2 and 3.5 years respectively in 2021. The causes of death contributing most to the {triangleup}LE were cardiovascular diseases (CVD) and cancer, with declining contributions of CVD deaths at age 70 and above and increasing contributions of cancer deaths at ages 40-74 over time. COVID-19 mortality at ages 45+ was the strongest contributor to the increase in {triangleup}LE after 2019. Interpretation To reduce the socioeconomic gap in life expectancy, effective efforts are needed to prevent early deaths from CVD and cancer in deprived populations, with cancer prevention and control becoming an increasingly important field of action in this respect.
Speech-induced suppression and vocal feedback sensitivity in human cortex

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity.
Comprehensive chromatome profiling identifies metabolic enzymes on chromatin in healthy and cancer cells

Metabolic and epigenetic rewiring are widely considered hallmarks of cancer, with emerging evidence of crosstalk between them. Anecdotal evidence of metabolic enzymes moonlighting in the chromatin environment has suggested how this crosstalk might be facilitated, but the extent of nuclear relocalization of metabolic enzymes remains elusive. Here, we provide a comprehensive chromatin proteomics resource across cancer lineages as well as healthy samples and demonstrate that metabolic enzyme moonlighting on chromatin is widespread across tissues and pathways. We show that the abundance of metabolic enzymes on chromatin is tissue-specific, with oxidative phosphorylation proteins depleted in lung cancer samples, perhaps suggesting an interplay between cell identity and nuclear metabolism. Finally, we explore metabolic functions in the chromatin environment and show that one-carbon folate enzymes are associated with DNA damage and repair processes, providing an approach to explore non-canonical functions of metabolic enzymes.
MORPHIX: Resolving sample identification bias in morphometrics analysis with a supervised machine learning package

Evolutionary biologists, primarily anatomists and ontogenists, employ modern geometric morphometrics to quantitatively analyse physical forms (e.g., skull morphology) and explore relationships, variations, and differences between samples and taxa using landmark coordinates. The standard approach comprises two steps, Generalised Procrustes Analysis (GPA) followed by Principal Component Analysis (PCA). PCA projects the superimposed data produced by GPA onto a set of uncorrelated variables, which can be visualised on scatterplots and used to draw phenetic, evolutionary, and ontogenetic conclusions. Recently, the use of PCA in genetic studies has been challenged. Due to PCA’s central role in morphometrics, we sought to evaluate the standard approach and claims based on PCA outcomes. To test PCA’s accuracy, robustness, and reproducibility using benchmark data of the crania of five papionin genera, we developed MORPHIX, a Python package containing the necessary tools for processing superimposed landmark data with classifier and outlier detection methods, which can be further visualised using various plots. We discuss the case of Homo Nesher Ramla, an archaic human with a questionable taxonomy. We found that PCA outcomes are artefacts of the input data and are neither reliable, robust, nor reproducible as field members may assume and that supervised machine learning classifiers are more accurate both for classification and detecting new taxa. Our findings raise concerns about PCA-based findings in 18,000 to 32,900 studies. Our work can be used to evaluate prior and novel claims concerning the origins and relatedness of inter- and intra-species and improve phylogenetic and taxonomic reconstructions.
Searchlight-based trial-wise fMRI decoding in the presence of trial-by-trial correlations

In multivariate pattern analysis (MVPA) for functional magnetic resonance imaging (fMRI) signals, trial-wise response amplitudes are sometimes estimated using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates can be highly correlated due to the temporally smoothed shape of the hemodynamic response function. In previous work (Soch, J., Allefeld, C., & Haynes, J.-D. (2020). Inverse transformed encoding models – a solution to the problem of correlated trialby-trial parameter estimates in fMRI decoding. NeuroImage, 209, 116449, 1-19. https://doi.org/10.1016/j.neuroimage. 2019.116449), we have proposed inverse transformed encoding modelling (ITEM), a principled approach for trial-wise decoding from fMRI signals in the presence of trial-by-trial correlations. Here, we (i) perform simulation studies addressing its performance for multivariate signals and (ii) present searchlightbased ITEM analysis – which allows to predict a variable of interest from the vicinity of each voxel in the brain. We empirically validate the approach by confirming a priori plausible hypotheses about the well-understood visual system.
Structural Basis of μ-Opioid Receptor-Targeting by a Nanobody Antagonist

The μ-opioid receptor (μOR), a prototypical member of the G protein-coupled receptor (GPCR) family, is the molecular target of opioid analgesics such as morphine and fentanyl. Due to the limitations and severe side effects of currently available opioid drugs, there is considerable interest in developing novel modulators of μOR function. Most GPCR ligands today are small molecules, however biologics, including antibodies and nanobodies, are emerging as alternative therapeutics with clear advantages such as affinity and target selectivity. Here, we describe the nanobody NbE, which selectively binds to the μOR and acts as an antagonist. We functionally characterize NbE as an extracellular and genetically encoded µOR ligand and uncover the molecular basis for µOR antagonism by solving the cryo-EM structure of the NbE-µOR complex. NbE displays a unique ligand binding mode and achieves µOR selectivity by interactions with the orthosteric pocket and extracellular receptor loops. Based on a β-hairpin loop formed by NbE that deeply inserts into the µOR and centers most binding contacts, we design short peptide analogues that retain µOR antagonism. The work illustrates the potential of nanobodies to uniquely engage with GPCRs and describes novel μOR ligands that can serve as a basis for therapeutic developments.
Therapeutic validation of MMR-associated genetic modifiers in a human ex vivo model of Huntington’s disease

The pathological huntingtin (HTT) trinucleotide repeat underlying Huntington’s disease (HD) continues to expand throughout life. Repeat length correlates both with earlier age at onset (AaO) and faster progression, making slowing its expansion an attractive therapeutic approach. Genome-wide association studies have identified candidate variants associated with altered AaO and progression, with many found in DNA mismatch repair (MMR) associated genes. We examine whether lowering expression of these genes affects the rate of somatic expansion in human ex vivo models using HD iPSCs and HD iPSC-derived striatal neurons. We have generated a stable CRISPR interference HD iPSC line in which we can specifically and efficiently lower gene expression from a donor carrying over 125 CAG repeats. Lowering expression of each member of the MMR complexes MutS (MSH2, MSH3 & MSH6), MutL (MLH1, PMS1, PMS2 & MLH3) and LIG1 resulted in characteristic MMR deficiencies. Reduced MSH2, MSH3 and MLH1 slowed repeat expansion to the largest degree, while lowering either PMS1, PMS2 and MLH3 slowed it to a lesser degree. These effects were recapitulated in iPSC derived striatal cultures where MutL factor expression was lowered. Here, reducing the expression of MMR factors by CRISPRi to levels typically reached by current therapeutics effectively slows the pathogenic expansion of the HTT CAG repeat tract. We highlight members of the MutL family as potential therapeutic targets to slow repeat expansion with the aim to delay onset and progression of HD, and potentially other repeat expansion disorders exhibiting somatic instability. GRAPHICAL ABSTRACT
Targeting NGF but not VEGF or BDNF signaling reduces endometriosis-associated pain in mice

Introduction Endometriosis is a chronic inflammatory disease that affects ∼10% of women. A significant fraction of patients experience limited or no efficacy with current therapies. Tissue adjacent to endometriosis lesions often exhibits increased neurite and vascular density, suggesting that disease pathology involves neurotrophic activity and angiogenesis. Objectives We aim to evaluate the potential for key tyrosine-kinase-receptor-coupled neurotrophic molecules to contribute to endometriosis-associated pain in mice. Methods The levels of VEGFR1 regulators (VEGFA, VEGFB, PLGF, and sVEGFR1) were quantified by ELISA in peritoneal fluid from endometriosis patients undergoing surgery and used to calculate VEGFR1 occupancy. We used genetic depletion, neutralizing antibody, and pharmacological approaches to specifically block ligand (NGF or BDNF) or neurotrophic receptor (VEGFR1, TRKs) in a murine model of endometriosis-associated pain. Endometriosis-associated pain was determined using the von Frey filaments method, quantification of spontaneous abdominal pain-related behavior, and thermal discomfort. Diseases parameters were evaluated by lesion size and prevalence. Results We found that entrectinib (pan-Trk inhibitor) or anti-NGF treatments reduced evoked pain, spontaneous pain, and thermal discomfort. In contrast, even though receptor occupancy revealing that VEGFR1 agonist levels are sufficient to support pain, blocking VEGFR1 signaling via antibody or tamoxifen-induced knockout did not reduce pain or lesion size in mice. Targeting BDNF-TrkB with an anti-BDNF antibody also proved ineffective. Conclusions This suggests NGF-TrkA signaling, but not BDNF-TrkB or VEGF-VEGFR1, mediates endometriosis-associated pain. Moreover, entrectinib blocks endometriosis-associated pain and reduces lesion sizes. Our results also indicated that entrectinib-like molecules are promising candidates for endometriosis treatment. Credit author statement Conceptualization: T.H. Zaninelli, V. Fattori, and M.S. Rogers; investigation and data curation: T.H. Zaninelli, V. Fattori, O.K. Heintz, K.R. Wright; A.C. Andrello, W.A. Verri Jr, M.S. Rogers; funding acquisition: M.S. Rogers,, S.A. Missmer, R.M. Anchan; methodology: T.H. Zaninelli, V. Fattori, and M.S. Rogers; human sample collection: S.A. Missmer, A.F. Vitonis, K.L. Terry, R.M. Anchan; animal breading and VEGFR1 ablation: D. Sim and H. Bukhari; resources: A.C. Andrello; D. Bree, T. Zheng, J. Wagner, W.A. Verri Jr, and M.S. Rogers; project administration: T.H. Zaninelli; supervision: V. Fattori, W.A. Verri Jr, and M.S. Rogers; visualization: T.H. Zaninelli, V. Fattori, W.A. Verri Jr, and M.S. Rogers; writing–original draft: T.H. Zaninelli; writing – editing and reviewing: all authors. All authors have read and approved the final version of the manuscript.
Toward a Uniﬁed Parameterization of the Boundary Layer and Moist Convection. Part I: A New Type of Mass-Flux Model

Higher-order closure (HOC) models have been proposed for parameterization of the turbulent planetary boundary layer (PBL). HOC models must include closures for higher-order moments (e.g., fourth moments in third-order closure models), for pressure terms, and for dissipation terms. Mass-ﬂux closure (MFC) models have been proposed for parameterization of cumulus convection and, more recently, the convective PBL. MFC models include closures for lateral mass exchanges and for pressure terms (which are usually ignored). The authors developed a new kind of model that combines HOC and MFC, which they hope will be useful for the parameterization of both the PBL and cumulus convection, in a uniﬁed framework. Such a model is particularly well suited to regimes in which the PBL turbulence and the cumulus convection are not well separated, for example, the broken stratocumulus and shallow cumulus regimes. The model makes use of an assumed joint probability distribution for the variables of interest, and the equations typically used in HOC models can be derived by integrating over the distribution. Accordingly, the model is called Assumed-Distribution Higher-Order Closure (ADHOC). The prognostic variables of ADHOC are the mean state, the second and third moments of the vertical velocity, and the vertical ﬂuxes of other quantities of interest. All of the parameters of the distribution can be determined from the predicted moments; thereafter the joint distribution is effectively known, and so any and all moments can be constructed as needed. In this way, the usual closure problem of ‘‘higher moments’’ is avoided. The pressure-term parameterizations previously developed for HOC models are used to predict the convective ﬂuxes and the moments of the vertical velocity. In companion papers, parameterizations of lateral mass exchanges and subplume-scale ﬂuxes are presented, and then ADHOC is applied to several observationally based tropical, subtropical, and dry convective boundary layers.
3223 Factor of Technology Acceptance of Clinicians in Central Infusion Pump Monitoring System: A Case Study at Ruamjairak Hospital in Thailand

s: This study investigates into the Central Infusion Pump Monitoring System, utilizing technology to oversee Infusion Pump operations and proactively prevent adverse drug events. The purpose of this study was to find the factors affecting the acceptance of technology in the use Central Infusion Pump Monitoring System by medical personnel at Ruamjairak Hospital in Thailand. It would be beneficial in terms of management and the decision to use a Central Infusion Pump Monitoring System. The sample consisted of 69 individuals that use infusion pump and working in Ruamjairak hospital for not less than 6 months. An online questionnaire was used to collect data by studying personnel factors, technological factors. brand factors, organizational factors, and price factors. The statistics used in this study were Spearman’s Rank Correlation Coefficient and the influence on the adoption Central Infusion Pump Monitoring System technology was determined using Stepwise Multiple Regression Analysis. The results find that technological factors, brand factors, price factors, and organizational factors exhibit a significant relationship with the adoption of technology for Central Infusion Pump Monitoring System. The regression coefficients, ordered by influence from most to least, are as follows: technology factor 0.545, brand factor 0.182, price factor 0.137, and organizational factor 0.136. Importantly, personnel factors were found to have no impact on the adoption of technology for Central Infusion Pump Monitoring System. The technological factors, brand factors, price factors, and organizational factors affect with statistical significance at the 0.05 level.
MASSIVELY PARALLEL HYPERPARAMETER TUNING

Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs. For such models, we cannot afford to train candidate models sequentially and wait months before finding a suitable hyperparameter configuration. Hence, we introduce the large-scale regime for parallel hyperparameter tuning, where we need to evaluate orders of magnitude more configurations than available parallel workers in a small multiple of the wall-clock time needed to train a single model. We propose a novel hyperparameter tuning algorithm for this setting that exploits both parallelism and aggressive earlystopping techniques, building on the insights of the Hyperband algorithm (Li et al., 2017). Finally, we conduct a thorough empirical study of our algorithm on several benchmarks, including large-scale experiments with up to 500 workers. Our results show that our proposed algorithm finds good hyperparameter settings nearly an order of magnitude faster than random search.
Graph-Based Autonomous Driving with Traffic-Rule-Enhanced Curriculum Learning

Training reinforcement learning (RL) agents for motion planning in heavily constrained solution spaces may require extensive exploration, leading to long training times. In automated driving, RL agents have to learn multiple skills at once, such as collision avoidance, traffic rule adherence, and goal reaching. In this work, we decompose this complicated learning task by applying curriculum learning for the first time onto an RL agent based on graph neural networks. The curriculum's sequence of sub-tasks gradually increases the difficulty of the longitudinal and lateral motion planning problem for the agent. Each of our sub-tasks contains a set of rewards, including novel rewards for temporal-logic-based traffic rules for speed, safety distance, and braking. Unlike prior work, the agent's state is extended by map and traffic rule information. Its performance is evaluated on prerecorded, real-world traffic data instead of simulations. Our numerical results show that the multi-stage curricula let the agent learn goal-seeking highway driving faster than in baseline setups trained from scratch. Including traffic rule information in both the RL state and rewards stabilizes the training and improves the agent's final goal-reaching performance.