Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Gradient Methods With Time-Varying Uncoordinated Stepsizes: Convergence Analysis and Privacy Design

Decentralized optimization enables a network of agents to cooperatively optimize an overall objective function without a central coordinator and is gaining increased attention in domains as diverse as control, sensor networks, data mining, and robotics. However, the information sharing among agents in decentralized optimization also discloses agents' information, which is undesirable or even unacceptable when involved data are sensitive. This paper proposes two gradient based decentralized optimization algorithms that can protect participating agents' privacy without compromising optimization accuracy or incurring heavy communication/computational overhead. This is in distinct difference from differential privacy based approaches which have to trade optimization accuracy for privacy, or encryption based approaches which incur heavy communication and computational overhead. Both algorithms leverage a judiciously designed mixing matrix and time-varying uncoordinated stepsizes to enable privacy, one using diminishing stepsizes while the other using non-diminishing stepsizes. In both algorithms, when interacting with any one of its neighbors, a participating agent only needs to share one message in each iteration to reach convergence to an exact optimal solution, which is in contrast to most gradient-tracking based algorithms requiring every agent to share two messages (an optimization variable and an auxiliary gradient-tracking variable) under non-diminishing stepsizes. Furthermore, both algorithms can guarantee the privacy of a participating agent even when all information shared by the agent are accessible to an adversary, a scenario in which most existing accuracy-maintaining privacy approaches will fail to protect privacy. Simulation results confirm the effectiveness of the proposed algorithms.
Channel-specific core-valence projectors for determining partial Auger decay widths

ABSTRACT Auger decay is a relaxation process of core-vacant states in atoms and molecules, in which one valence electron fills the core vacancy while a second one is emitted. These states pose a challenge to electronic-structure theory, because they are embedded in the ionisation continuum. Recently, we showed that molecular Auger decay can be described using complex-variable coupled-cluster (CC) methods and that partial widths and branching ratios can be computed based on a decomposition of the CC energy. Here, we introduce channel-specific core-valence projectors, dubbed Auger channel projectors, as a more general technique to evaluate partial widths from complex-variable treatments. We apply this new method to core-ionised states of neon, water, ammonia and methane using CC singles and doubles (CCSD), equation-of-motion ionisation potential CCSD and configuration interaction singles (CIS) wave functions. Even though a single CIS calculation can never describe all Auger decay channels at once, we show that a combination of CIS calculations based on different reference states is able to recover partial and total decay widths from CC calculations to an excellent degree. GRAPHICAL ABSTRACT
Lung Cancer and Radon: Pooled Analysis of Uranium Miners Hired in 1960 or Later

Background: Despite reductions in exposure for workers and the general public, radon remains a leading cause of lung cancer. Prior studies of underground miners depended heavily upon information on deaths among miners employed in the early years of mine operations when exposures were high and tended to be poorly estimated. Objectives: To strengthen the basis for radiation protection, we report on the follow-up of workers employed in the later periods of mine operations for whom we have more accurate exposure information and for whom exposures tended to be accrued at intensities that are more comparable to contemporary settings. Methods: We conducted a pooled analysis of cohort studies of lung cancer mortality among 57,873 male uranium miners in Canada, Czech Republic, France, Germany, and the United States, who were first employed in 1960 or later (thereby excluding miners employed during the periods of highest exposure and focusing on miners who tend to have higher quality assessments of radon progeny exposures). We derived estimates of excess relative rate per 100 working level months (ERR/100 WLM) for mortality from lung cancer. Results: The analysis included 1.9 million person-years of observation and 1,217 deaths due to lung cancer. The relative rate of lung cancer increased in a linear fashion with cumulative exposure to radon progeny (ERR/100 WLM=1.33; 95% CI: 0.89, 1.88). The association was modified by attained age, age at exposure, and annual exposure rate; for attained ages <55 y, the ERR/100 WLM was 8.38 (95% CI: 3.30, 18.99) among miners who were exposed at ≥35 years of age and at annual exposure rates of <0.5 working levels. This association decreased with older attained ages, younger ages at exposure, and higher exposure rates. Discussion: Estimates of association between radon progeny exposure and lung cancer mortality among relatively contemporary miners are coherent with estimates used to inform current protection guidelines. https://doi.org/10.1289/EHP10669
Simultaneous state and unknown input set‐valued observers for quadratically constrained nonlinear dynamical systems

In this article, we propose fixed‐order set‐valued (in the form of ℓ2$$ {\ell}_2 $$ ‐norm hyperballs) observers for several classes of quadratically constrained nonlinear dynamical systems with unknown input signals that simultaneously/jointly find bounded hyperballs of states and unknown inputs that include the true states and inputs. Necessary and sufficient conditions in the form of linear matrix inequalities (LMIs) for the stability (in the sense of quadratic stability) of the proposed observers are derived for ( ℳ,γ$$ \mathcal{M},\gamma $$ )‐quadratically constrained (( ℳ,γ$$ \mathcal{M},\gamma $$ )‐QC) systems, which includes several classes of nonlinear systems: (I) Lipschitz continuous, (II) ( 𝒜,γ )‐QC* and (III) linear parameter‐varying (LPV) systems. This new quadratic constraint property is at least as general as the incremental quadratic constraint property for nonlinear systems and is proven in the paper to embody a broad range of nonlinearities. In addition, we design the optimal ℋ∞$$ {\mathscr{H}}_{\infty } $$ observer among those that satisfy the quadratic stability conditions and show that the design results in uniformly bounded‐input bounded‐state (UBIBS) estimate radii/error dynamics and uniformly bounded sequences of the estimate radii. Furthermore, we provide closed‐form upper bound sequences for the estimate radii and sufficient conditions for their convergence to steady state. Finally, the effectiveness of the proposed set‐valued observers is demonstrated through illustrative examples, where we compare the performance of our observers with some existing observers.
RL with KL penalties is better viewed as Bayesian inference

Reinforcement learning (RL) is frequently employed in fine-tuning large language models (LMs), such as GPT-3, to penalize them for undesirable features of generated sequences, such as offensiveness, social bias, harmfulness or falsehood. The RL formulation involves treating the LM as a policy and updating it to maximise the expected value of a reward function which captures human preferences, such as non-offensiveness. In this paper, we analyze challenges associated with treating a language model as an RL policy and show how avoiding those challenges requires moving beyond the RL paradigm. We start by observing that the standard RL approach is flawed as an objective for fine-tuning LMs because it leads to distribution collapse: turning the LM into a degenerate distribution. Then, we analyze KL-regularised RL, a widely used recipe for fine-tuning LMs, which additionally constrains the fine-tuned LM to stay close to its original distribution in terms of Kullback-Leibler (KL) divergence. We show that KL-regularised RL is equivalent to variational inference: approximating a Bayesian posterior which specifies how to update a prior LM to conform with evidence provided by the reward function. We argue that this Bayesian inference view of KL-regularised RL is more insightful than the typically employed RL perspective. The Bayesian inference view explains how KL-regularised RL avoids the distribution collapse problem and offers a first-principles derivation for its objective. While this objective happens to be equivalent to RL (with a particular choice of parametric reward), there exist other objectives for fine-tuning LMs which are no longer equivalent to RL. That observation leads to a more general point: RL is not an adequate formal framework for problems such as fine-tuning language models. These problems are best viewed as Bayesian inference: approximating a pre-defined target distribution.
An Information-Theoretic Framework for Deep Learning

Each year, deep learning demonstrates new and improved empirical results with deeper and wider neural networks. Meanwhile, with existing theoretical frameworks, it is difficult to analyze networks deeper than two layers without resorting to counting parameters or encountering sample complexity bounds that are exponential in depth. Perhaps it may be fruitful to try to analyze modern machine learning under a different lens. In this paper, we propose a novel information-theoretic framework with its own notions of regret and sample complexity for analyzing the data requirements of machine learning. We use this framework to study the sample complexity of learning from data generated by deep ReLU neural networks and deep networks that are infinitely wide but have a bounded sum of weights. We establish that the sample complexity of learning under these data generating processes is at most linear and quadratic, respectively, in network depth.
An Experimental Adult Zebrafish Model for Shigella Pathogenesis, Transmission, and Vaccine Efficacy Studies

Shigellosis is a diarrheal disease that is prevalent in developing countries and especially dangerous in young children. Currently, animal models for shigellosis are unable to model some aspects of the infectious cycle. Here, we describe a new shigellosis model in adult zebrafish, an increasingly common model organism for studying bacterial pathogens. The zebrafish model can be used to study Shigella colonization, transmission, and immune responses, as well as test vaccine efficacy. ABSTRACT Shigellosis has been a menace to society for ages. The absence of an effective vaccine against Shigella, improper sanitation, and unhygienic use of food and water allow the disease to flourish. Shigella can also be transmitted via natural water bodies. In the absence of a good animal model, the actual nature of pathogenesis and transmission remains unclear. Zebrafish larvae have previously been described as a model for Shigella pathogenesis. However, larval fish lack a mature intestinal microbiota and immune system. Here, the adult zebrafish was assessed as a potential model for Shigella pathogenesis. Their well-developed innate and adaptive immune responses mimic the mammalian immune system. Shigella showed a clear dose-, time-, and temperature-dependent colonization of the adult zebrafish gut. Efficacy of a three-dose immunization regime was tested using bath immunization with heat-killed trivalent Shigella immunogen. The present study demonstrates the efficacy of an adult zebrafish model for pathogenesis, transmission, and vaccine efficacy studies. IMPORTANCE Shigellosis is a diarrheal disease that is prevalent in developing countries and especially dangerous in young children. Currently, animal models for shigellosis are unable to model some aspects of the infectious cycle. Here, we describe a new shigellosis model in adult zebrafish, an increasingly common model organism for studying bacterial pathogens. The zebrafish model can be used to study Shigella colonization, transmission, and immune responses, as well as test vaccine efficacy.
Current concepts regarding Graves’ orbitopathy

Graves’ orbitopathy (GO) is an orbital autoimmune disorder and the main extrathyroidal manifestation of Graves’ disease, the most common cause of hyperthyroidism. GO affects about 30% of Graves’ patients, although fewer than 10% have severe forms requiring immunosuppressive treatments. Management of GO requires a multidisciplinary approach. Medical therapies for active moderate‐to‐severe forms of GO (traditionally, high‐dose glucocorticoids) often provide unsatisfactory results, and subsequently surgeries are often needed to cure residual manifestations. The aim of this review is to provide an updated overview of current concepts regarding the epidemiology, pathogenesis, assessment, and treatment of GO, and to present emerging targeted therapies and therapeutic perspectives. Original articles, clinical trials, systematic reviews, and meta‐analyses from 1980 to 2021 were searched using the following terms: Graves’ disease, Graves’ orbitopathy, thyroid eye disease, glucocorticoids, orbital radiotherapy, rituximab, cyclosporine, azathioprine, teprotumumab, TSH‐receptor antibody, smoking, hyperthyroidism, hypothyroidism, thyroidectomy, radioactive iodine, and antithyroid drugs. Recent studies suggest a secular trend toward a milder phenotype of GO. Standardized assessment at a thyroid eye clinic allows for a better general management plan. Treatment of active moderate‐to‐severe forms of GO still relies in most cases on high‐dose systemic—mainly intravenous—glucocorticoids as monotherapy or in combination with other therapies—such as mycophenolate, cyclosporine, azathioprine, or orbital radiotherapy—but novel biological agents—including teprotumumab, rituximab, and tocilizumab—have achieved encouraging results.
A Pilot Study of Telehealth Mindfulness-Based Cognitive Therapy for Depression in Parkinson’s Disease

Introduction: Parkinson’s disease (PD) is characterized by high-rates of depression with limited evidence-based treatment options to improve mood. Objective: To expand therapeutic options, we evaluated the feasibility and effect of a telehealth mindfulness-based cognitive therapy intervention adapted for PD (MBCT-PD) in a sample of participants with DSM-5 depressive disorders. Methods: Fifteen participants with PD and clinically-significant depression completed 9 sessions of MBCT-PD. Depression, anxiety, and quality of life were evaluated at baseline, endpoint, and 1-month follow-up. Results: Telehealth MBCT-PD was feasible and beneficial. Completion rates exceeded 85% and treatment satisfaction rates were high. Notable improvements were observed for depression, anxiety, and quality of life over the course of the trial. Conclusion: Telehealth MBCT-PD shows promise and warrants further evaluation via randomized clinical trial with more diverse participants. Such research holds the potential to expand the range of therapeutic options for depression in PD, thereby setting the stage for personalized care.
The BEAR Toolbox

The Bayesian Estimation, Analysis and Regression toolbox (BEAR) is a comprehensive (Bayesian) (Panel) VAR toolbox for forecasting and policy analysis. BEAR is a MATLAB based toolbox which is easy for non-technical users to understand, augment and adapt. In particular, BEAR includes a user-friendly graphical interface which allows the tool to be used by country desk economists. Furthermore, BEAR is well documented, both within the code as well as including a detailed theoretical and user's guide. BEAR includes state-of-the art applications such as sign and magnitude restrictions, conditional forecasts, Bayesian forecast evaluation measures, Bayesian Panel VAR using different prior distributions (for example hierarchical priors), etc. BEAR is specifically developed for transparently supplying a tool for state-of-the-art research and is planned to be further developed to always be at the frontier of economic research. JEL Classification: C11, C30, C87, E00, F00
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP

While there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics. In this paper, we present a novel benchmark to evaluate the interpretability of both neural models and saliency methods. This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension, each provided with both English and Chinese annotated data. In order to precisely evaluate the interpretability, we provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive. We also design a new metric, i.e., the consistency between the rationales before and after perturbations, to uniformly evaluate the interpretability on different types of tasks. Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability. We will release this benchmark (https://www.luge.ai/#/luge/task/taskDetail? taskId=15) and hope it can facilitate the research in building trustworthy systems.
Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI

Purpose To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction. Methods Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values. Results Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10−6 mm2/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736–0.949 with specific instruction and 0.349–0.919 without specific instruction. Conclusion Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).
Viral Antigen and Inflammatory Biomarkers in Cerebrospinal Fluid in Patients With COVID-19 Infection and Neurologic Symptoms Compared With Control Participants Without Infection or Neurologic Symptoms

Key Points Question Are cerebrospinal fluid (CSF) SARS-CoV-2 antigens associated with central nervous system inflammation in patients with COVID-19? Findings Of 44 patients with COVID-19 (23 neurosymptomatic) included in this hospital-based cross-sectional study, CSF nucleocapsid antigen was detectable in 89% of patients with available data and was significantly correlated with immune activation markers (neopterin and interferon γ). Moreover, neurosymptomatic patients had a more pronounced inflammatory CSF profile compared with neuroasymptomatic patients that could not be attributed to differences in COVID-19 severity. Meaning These results suggest that viral components may contribute to central nervous system immune responses without direct viral invasion and highlight the clinical importance of neurologic symptoms.
Genetic Characterization of the Ibuprofen-Degradative Pathway of Rhizorhabdus wittichii MPO218

Ibuprofen, one of the most common pharmaceutical contaminants in natural environments, is toxic for some aquatic and terrestrial organisms. The main source of environmental ibuprofen is wastewater, so improving wastewater treatment is of relevant importance. ABSTRACT Ibuprofen is one of the most common drugs found as a contaminant in soils, sediments, and waters. Although several microorganisms able to metabolize ibuprofen have been described, the metabolic pathways and factors limiting biodegradation in nature remain poorly characterized. Among the bacteria able to grow on ibuprofen, three different strains belonging to Sphingomonadaceae and isolated from different geographical locations carry the same set of genes required for the upper part of the ibuprofen metabolic pathway. Here, we have studied the metabolic pathway of Rhizorhabdus wittichii MPO218, identifying new genes required for the lower part of the ibuprofen metabolic pathway. We have identified two new DNA regions in MPO218 involved in the metabolism of ibuprofen. One is located on the MPO218 chromosome and appears to be required for the metabolism of propionyl-CoA through the methylmalonyl-CoA pathway. Although involved in ibuprofen metabolism, this region is not strictly necessary for growing using ibuprofen. The second region belongs to the pIBU218 plasmid and comprises two gene clusters containing aromatic compound biodegradation genes, part of which are necessary for ibuprofen degradation. We have identified two genes required for the first two steps of the lower part of the ibuprofen metabolic pathway (ipfL and ipfM), and, based on our results, we propose the putative complete pathway for ibuprofen metabolism in strain MPO218. IMPORTANCE Ibuprofen, one of the most common pharmaceutical contaminants in natural environments, is toxic for some aquatic and terrestrial organisms. The main source of environmental ibuprofen is wastewater, so improving wastewater treatment is of relevant importance. Although several microorganisms capable of biodegrading ibuprofen have been described, the metabolic pathways and their genetic bases remain poorly understood. Three bacterial strains of the family Sphingomonadaceae capable of using ibuprofen as carbon and energy source have been described. Although the genes involved in the upper part of the degradation pathway (ipfABDEF cluster) have been identified, those required for the lower part of the pathway remained unknown. Here, we have confirmed the requirement of the ipf cluster for the generation of isobutyl catechol and have identified the genes involved in the subsequent transformation of the metabolic products. Identification of genes involved in ibuprofen degradation is essential to developing improved strains for the removal of this contaminant.