Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoothed Analysis of Social Choice Revisited

A canonical problem in voting theory is: which voting rule should we use to aggregate voters’ preferences into a collective decision over alternatives? When applying the axiomatic approach to evaluate and compare voting rules, we are faced with prohibitive impossibilities. However, these impossibilities occur under the assumption that voters’ preferences (collectively called a proﬁle) will be worst-case with respect to the desired criterion. In this paper, we study the axiomatic approach slightly beyond the worst-case: we present and apply a “smoothed” model of the voting setting, which assumes that while inputs (proﬁles) may be worst-case, all inputs will be perturbed by a small amount of noise. In deﬁning and analyzing our noise model, we do not aim to substantially technically innovate on Lirong Xia’s recently-proposed smoothed model of social choice; rather, we oﬀer an alternative model and approach to analyzing it that aims to strike a diﬀerent balance of simplicity and technical generality, and to correspond closely to Spielman and Teng’s original work on smoothed analysis [29]. Within our model, we then give simple proofs of smoothed-satisfaction or smoothed-violation of several axioms and paradoxes, including most of those studied by Xia as well as some previously unstudied. Novel results include smoothed analysis of Arrow’s theorem and analyses of the axioms Consistency and Independence of Irrelevant Alternatives. In independent work from Xia’s recent pa-per [34], we also show the smoothed-satisfaction of coalition-based notions of Strategy-Proofness, Monotonocity, and Participation. A ﬁnal, central component of our contributions are the high-level insights and future directions we identify based on this work, which we describe in detail to maximally
Synthesizing Programmatic Policies with Actor-Critic Algorithms and ReLU Networks

Programmatically Interpretable Reinforcement Learning (PIRL) encodes policies in human-readable computer programs. Novel algorithms were recently introduced with the goal of handling the lack of gradient signal to guide the search in the space of programmatic policies. Most of such PIRL algorithms first train a neural policy that is used as an oracle to guide the search in the programmatic space. In this paper, we show that such PIRL-specific algorithms are not needed, depending on the language used to encode the programmatic policies. This is because one can use actor-critic algorithms to directly obtain a programmatic policy. We use a connection between ReLU neural networks and oblique decision trees to translate the policy learned with actor-critic algorithms into programmatic policies. This translation from ReLU networks allows us to synthesize policies encoded in programs with if-then-else structures, linear transformations of the input values, and PID operations. Empirical results on several control problems show that this translation approach is capable of learning short and effective policies. Moreover, the translated policies are at least competitive and often far superior to the policies PIRL algorithms synthesize.
Palm problems arising in BAR approach and its applications

We consider Palm distributions arising in a Markov process with time homogeneous transitions which is jointly stationary with multiple point processes. Motivated by a BAR approach studied in the recent paper Braverman, Dai and Miyazawa (2023}, we are interested in two problems; when this Markov process inherits the same Markov structure under the Palm distributions, and how the state changes at counting instants of the point processes can be handled to derive stationary equations when there are simultaneous counts and each of them influences the state changes. We affirmatively answer to the first problem, and propose a framework for resolving the second problem. We also discuss how those results can be applied in deriving BAR's for the diffusion approximation of queueing models in heavy traffic. In particular, as their new application, the heavy traffic limit of the stationary distribution is derived for a single server queue with a finite waiting room. Those examples show how the BAR approach is useful under the proposed framework.
The effects of a national, voluntary agreement for a more inclusive working life on work participation following long-term sickness absence: a Norwegian cohort study

Objectives This study aimed to estimate the average individual effect of the company-level Norwegian Agreement on a More Inclusive Working Life (IA Agreement) on individuals’ (i) sustained return to work after a sickness absence (SA) episode, and (ii) recurrent SA. Methods Using register data, 79 253 men and 94 914 women born in Norway 1967–1976 were followed for one year between 2005 and 2010 after returning to work from an SA episode (>16 days). Weighted Cox proportional hazard models analysed time to first exit from work by companies’ IA status (IA/non-IA). Weighted cumulative incidence differences between IA and non-IA groups with 95% bootstrapped confidence intervals (CI) were calculated for the competing events of full SA, graded (<100%) SA, unemployment/economic inactivity, education, disability pension, and death/emigration. Stabilised inverse probability of treatment weights balanced IA/non-IA groups according to nine covariates. Analyses were stratified by gender, and separately for two initial SA diagnoses (musculoskeletal and psychological). Results Both men [adjusted hazard ratio (HR) 0.96, 95% CI 0.93–0.99] and women (adjusted HR 0.97, 95% CI 0.94–0.99) in IA companies were less likely to exit work in the year following SA. Similar findings were seen among individuals with musculoskeletal diagnoses and women with psychological diagnoses. Men with psychological diagnoses were more likely to exit work. Recurrent full and graded SA were more likely, and unemployment/economic inactivity less likely, in IA companies. However, the estimated effects were small and the CI often included the null. Conclusions Individuals working in IA companies were more likely to remain in work. This was mainly due to reduced unemployment/economic inactivity, suggesting the IA Agreement may have influenced work participation through other means than reduced SA.
SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for Soil Organic Carbon Prediction with Digital Soil Mapping in Europe

Digital soil mapping (DSM) is an advanced approach that integrates statistical modeling and cutting-edge technologies, including machine learning (ML) methods, to accurately depict soil properties and their spatial distribution. Soil organic carbon (SOC) is a crucial soil attribute providing valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. This study highlights the significance of spatial-temporal deep learning (DL) techniques within the DSM framework. A novel architecture is proposed, incorporating spatial information using a base convolutional neural network (CNN) model and spatial attention mechanism, along with climate temporal information using a long short-term memory (LSTM) network, for SOC prediction across Europe. The model utilizes a comprehensive set of environmental features, including Landsat-8 images, topography, remote sensing indices, and climate time series, as input features. Results demonstrate that the proposed framework outperforms conventional ML approaches like random forest commonly used in DSM, yielding lower root mean square error (RMSE). This model is a robust tool for predicting SOC and could be applied to other soil properties, thereby contributing to the advancement of DSM techniques and facilitating land management and decision-making processes based on accurate information.
Fatal Drug Overdose Risks of Health Care Workers in the United States

BACKGROUND Despite an unprecedented increase in drug overdose deaths in the United States, the risks faced by U.S. health care workers, who often have access to controlled prescription drugs, are not known. OBJECTIVE To estimate risks for drug overdose death among health care workers relative to non-health care workers. DESIGN Prospective cohort study. SETTING United States. PARTICIPANTS Health care workers (n = 176 000) and non-health care workers (n = 1 662 000) aged 26 years or older surveyed in 2008 and followed for cause of death through 2019. MEASUREMENTS Age- and sex-standardized drug overdose deaths were determined for 6 health care worker groups (physicians, registered nurses, other treating or diagnosing health care workers, health technicians, health care support workers, and social or behavioral health workers) and non-health care workers. Adjusted drug overdose death hazards (and 95% CIs) were also evaluated, with adjustment for age, sex, race/ethnicity, marital status, education, income, urban or rural residence, and region. RESULTS Approximately 0.07% of our study sample died of a drug overdose during follow-up. Among health care workers, annual standardized rates of drug overdose death per 100 000 persons ranged from 2.3 (95% CI, 0 to 4.8) for physicians to 15.5 (CI, 9.8 to 21.2) for social or behavioral health workers. Compared with those for non-health care workers, the adjusted hazards of total drug overdose death were significantly increased for social or behavioral health workers (adjusted hazard ratio, 2.55 [CI, 1.74 to 3.73]), registered nurses (adjusted hazard ratio, 2.22 [CI, 1.57 to 3.13]), and health care support workers (adjusted hazard ratio, 1.60 [CI, 1.19 to 2.16]), but not for physicians (adjusted hazard ratio, 0.61 [CI, 0.19 to 1.93]), other treating or diagnosing health care workers (adjusted hazard ratio, 0.93 [CI, 0.44 to 1.95]), or health technicians (adjusted hazard ratio, 1.13 [CI, 0.75 to 1.68]). Results were generally similar for opioid-related overdose deaths and unintentional overdose deaths. LIMITATION Unmeasured confounding, uncertain validity of cause of death, and one-time assessment of occupation. CONCLUSION Registered nurses, social or behavioral health workers, and health care support workers were at increased risk for drug overdose death, suggesting the need to identify and intervene on those at high risk. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute.
Two Candidate Pulsar TeV Halos Identified from Property-similarity Studies

Teraelectronvolt halos have been suggested to be a common phenomenon associated with middle-aged pulsars. Based on our recent work on the middle-aged pulsar J0631+1036, which is the only known source positionally coincident with a hard teraelectronvolt γ-ray source and likely powers the latter as a teraelectronvolt halo, we select three candidate teraelectronvolt halos from the first Large High Altitude Air Shower Observatory (LHAASO) catalog of γ-ray sources. The corresponding pulsars, given by the positional coincidences and property similarities, are PSR J1958+2846, PSR J2028+3332, and PSR J1849-0001. We analyze the gigaelectronvolt γ-ray data obtained with the Large Area Telescope on board the Fermi Gamma-ray Space Telescope for the first two pulsars, as the last is γ-ray quiet. We remove the pulsed emissions of the pulsars from the source regions from timing analysis, and determine that there are no residual gigaelectronvolt emissions in the regions as any possible counterparts to the teraelectronvolt sources. Considering the previous observational results for the source regions and comparing the two pulsars to Geminga (and Monogem), the LHAASO-detected teraelectronvolt sources are likely the pulsars’ respective teraelectronvolt halos. We find that the candidate and identified teraelectronvolt halos, including that of PSR J1849-0001, have luminosities at 50 TeV (estimated from the differential fluxes) approximately proportional to the spin-down energy Ė of the pulsars, and the ratios of the former to the latter are ∼6 × 10−4.
Pitfalls in soft tissue cytopathology

Fine needle aspiration biopsy (FNAB) is a diagnostic modality for the evaluation of suspicious soft tissue masses. Despite its reasonable sensitivity, specificity and positive predictive value in differentiating benign from malignant neoplasms, the exact subtyping of the primary soft tissue tumours can be challenging. Certain tumours constitute “pitfalls” and add to the diagnostic challenge. This review provides a detailed account of the diagnostic challenges in soft tissue cytopathology, including pitfalls and, more importantly, the ways to overcome these challenges by integrating clinical details, key cytomorphological features and judicious application of ancillary techniques.
Participation in the Georgia Food for Health programme and CVD risk factors: a longitudinal observational study

Abstract Objective: To assess the relationship between programme attendance in a produce prescription (PRx) programme and changes in cardiovascular risk factors. Design: The Georgia Food for Health (GF4H) programme provided six monthly nutrition education sessions, six weekly cooking classes and weekly produce vouchers. Participants became programme graduates attending at least 4 of the 6 of both the weekly cooking classes and monthly education sessions. We used a longitudinal, single-arm approach to estimate the association between the number of monthly programme visits attended and changes in health indicators. Setting: GF4H was implemented in partnership with a large safety-net health system in Atlanta, GA. Participants: Three hundred thirty-one participants living with or at-risk of chronic disease and food insecurity were recruited from primary care clinics. Over three years, 282 participants graduated from the programme. Results: After adjusting for programme site, year, participant sex, age, race and ethnicity, Supplemental Nutrition Assistance Program participation and household size, we estimated that each additional programme visit attended beyond four visits was associated with a 0·06 kg/m2 reduction in BMI (95 % CI –0·12, –0·01; P = 0·02), a 0·37 inch reduction in waist circumference (95 % CI –0·48, –0·27; P < 0·001), a 1·01 mmHg reduction in systolic blood pressure (95 % CI –1·45, –0·57; P < 0·001) and a 0·43 mmHg reduction in diastolic blood pressure (95 % CI –0·69, –0·17; P = 0·001). Conclusions: Each additional cooking and nutrition education visit attended beyond the graduation threshold was associated with modest but significant improvements in CVD risk factors, suggesting that increased engagement in educational components of a PRx programme improves health outcomes.
Understanding the Effectiveness of Large Language Models in Code Translation

Code translation aims to convert source code from one programming language (PL) to another. Given the promising abilities of large language models (LLMs) in code synthesis, researchers are actively exploring their potential to automate code translation, i.e., generating code in target PL from its equivalent in another PL. The pre-requisite for advancing the state of LLM-based code translation is to understand their limitations. To that end, we present a large-scale empirical study to investigate the ability of LLMs, including general LLMs and code LLMs, for code translation across pairs of different languages, including C, C++, Go, Java, and Python. Our analysis involves the translation of 1,700 code samples from three distinct benchmarks and real-world projects, revealing LLMs are yet to be reliably used to automate code translation -- with incorrect translations ranging from 52.7% to 97.9% across the studied LLMs. Further manual investigation of unsuccessful translations among all PLs identifies 14 root causes for translation bugs. Based on the insights from the empirical study, we propose a prompt-crafting approach to provide additional context for LLMs, improving the performance of LLM-based code translation by 5.5% on average across different PLs, LLMs, and benchmarks. Our study is the first of its kind, in terms of its scale and breadth, that provides insights into the current limitations of LLMs in code translation and opportunities for improving them. Our collected extensive dataset -- consisting of 1,700 code samples written in five PLs with 10K+ tests, 43K+ translated code, 1,725 manually labeled bugs, and 1,365 bug-fix pairs generated using LLMs -- can help drive research in this area.
TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment

Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the human visual system, existing methods typically use a combination of global and local representations (i.e., multi-scale features) to achieve superior performance. However, most of them adopt simple linear fusion of multi-scale features, and neglect their possibly complex relationship and interaction. In contrast, humans typically first form a global impression to locate important regions and then focus on local details in those regions. We therefore propose a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions, named as TOPIQ. Our approach to IQA involves the design of a heuristic coarse-to-fine network (CFANet) that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner. A key component of our approach is the proposed cross-scale attention mechanism, which calculates attention maps for lower level features guided by higher level features. This mechanism emphasizes active semantic regions for low-level distortions, thereby improving performance. TOPIQ can be used for both Full-Reference (FR) and No-Reference (NR) IQA. We use ResNet50 as its backbone and demonstrate that TOPIQ achieves better or competitive performance on most public FR and NR benchmarks compared with state-of-the-art methods based on vision transformers, while being much more efficient (with only ${\sim }13\%$ FLOPS of the current best FR method). Codes are released at https://github.com/chaofengc/IQA-PyTorch.
Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience

Machine learning (ML) has shown great promise for revolutionizing a number of areas, including healthcare. However, it is also facing a reproducibility crisis, especially in medicine. ML models that are carefully constructed from and evaluated on a training set might not generalize well on data from different patient populations or acquisition instrument settings and protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests.
Deepfake Detection: A Comparative Analysis

This paper present a comprehensive comparative analysis of supervised and self-supervised models for deepfake detection. We evaluate eight supervised deep learning architectures and two transformer-based models pre-trained using self-supervised strategies (DINO, CLIP) on four benchmarks (FakeAVCeleb, CelebDF-V2, DFDC, and FaceForensics++). Our analysis includes intra-dataset and inter-dataset evaluations, examining the best performing models, generalisation capabilities, and impact of augmentations. We also investigate the trade-off between model size and performance. Our main goal is to provide insights into the effectiveness of different deep learning architectures (transformers, CNNs), training strategies (supervised, self-supervised), and deepfake detection benchmarks. These insights can help guide the development of more accurate and reliable deepfake detection systems, which are crucial in mitigating the harmful impact of deepfakes on individuals and society.
Estimation of reindeer lichen biomass by image analysis

During consultation procedures between forest owners and the Sami, data on, e.g., reindeer lichen biomass on the current site is needed. Hitherto, the existing methods of measuring lichen cover and biomass has been either objective methods such as the Point Intercept method, which is time consuming, or some sort of subjective visual estimation, which is faster but less accurate. However, both these methods are sensitive to different observers and/or to different inventories. This paper addresses the further development and evaluation of a photographical inventory method that uses colour distribution in images to estimate lichen biomass. During the autumn of 2011 six different locations, with different grazing pressure and lichen cover, in Norrbotten county were inventoried using both the Point Intercept method and the photographical method, complemented with collection of biomass samples. The sample images were analyzed with respect to lichen and background areas (cover) using WinCAM™. Statistical analyses were used to create equations for estimation of lichen biomass from lichen cover, as well as to compare photographically estimated lichen cover and lichen cover from the Point Intercept method. Results shows that it is possible to estimate lichen biomass from lichen cover analyzed via image analysis. Results also indicate that different equations should be used for different sites depending degree of lichen cover and ground vegetation.
On the formality problem for manifolds with special holonomy

In sections 1 and 2 we follow our online talk at the 21st Geometrical Seminar (Beograd, Serbia) on June 30, 2022 by giving a survey of the formality problem for manifold with special holonomy and exposing recent results by M. Amann and the author on the formality of Joyce's examples of G_2-manifolds. In section 3 we expose the approach to establishing the formality by using the intersection Massey products.