Miracle-dz/UniFAR-FTUs huggingface dataset:
Extensions for Inference in Difference-in-Differences with Few Treated Clusters

In settings with few treated units, Difference-in-Differences (DID) estimators are not consistent, and are not generally asymptotically normal. This poses relevant challenges for inference. While there are inference methods that are valid in these settings, some of these alternatives are not readily available when there is variation in treatment timing and heterogeneous treatment effects; or for deriving uniform confidence bands for event-study plots. We present alternatives in settings with few treated units that are valid with variation in treatment timing and/or that allow for uniform confidence bands.
Capturing Topic Framing via Masked Language Modeling

Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.
On the Convergence of Federated Averaging with Cyclic Client Participation

Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full client participation or partial client participation where the clients can be uniformly sampled. However, in practical cross-device FL systems, only a subset of clients that satisfy local criteria such as battery status, network connectivity, and maximum participation frequency requirements (to ensure privacy) are available for training at a given time. As a result, client availability follows a natural cyclic pattern. We provide (to our knowledge) the first theoretical framework to analyze the convergence of FedAvg with cyclic client participation with several different client optimizers such as GD, SGD, and shuffled SGD. Our analysis discovers that cyclic client participation can achieve a faster asymptotic convergence rate than vanilla FedAvg with uniform client participation under suitable conditions, providing valuable insights into the design of client sampling protocols.
Structured variational approximations with skew normal decomposable graphical models and implicit copulas

Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This paper considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. Different parametrizations are explored for this variational family, and the speed of convergence and quality of the approximation can depend on the parametrization used. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. Our parametrization of the implicit copula approximation is novel, even in the special case of a Gaussian approximation. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models, and we conclude that our copula approaches are most accurate, but that the SDGM methods are often nearly as good and have lower computational demands.
A Nonlocal diffusion model with $H^1$ convergence for Dirichlet Boundary

In this paper, we present a nonlocal model for Poisson equation and corresponding eigenproblem with Dirichlet boundary condition. In the direct derivation of the nonlocal model, normal derivative is required which is not known for Dirichlet boundary. To overcome this difficulty, we treat the normal derivative as an auxiliary variable and derive corresponding nonlocal approximation of the boundary condition. For this specifically designed nonlocal mode, we can prove its well-posedness and convergence to the counterpart continuous model. The nonlocal model is carefully designed such that coercivity and symmetry are preserved. Based on these good properties, we can prove the nonlocal model converges with first order rate in $H^1$ norm. Our model can be naturally extended to Poisson problems with Robin boundary and corresponding eigenvalue problem.
MPC-based Motion Planning for Autonomous Truck-Trailer Maneuvering

Time-optimal motion planning of autonomous vehicles in complex environments is a highly researched topic. This paper describes a novel approach to optimize and execute locally feasible trajectories for the maneuvering of a truck-trailer Autonomous Mobile Robot (AMR), by dividing the environment in a sequence or route of freely accessible overlapping corridors. Multi-stage optimal control generates local trajectories through advancing subsets of this route. To cope with the advancing subsets and changing environments, the optimal control problem is solved online with a receding horizon in a Model Predictive Control (MPC) fashion with an improved update strategy. This strategy seamlessly integrates the computationally expensive MPC updates with a low-cost feedback controller for trajectory tracking, for disturbance rejection, and for stabilization of the unstable kinematics of the reversing truck-trailer AMR. This methodology is implemented in a flexible software framework for an effortless transition from offline simulations to deployment of experiments. An experimental setup showcasing the truck-trailer AMR performing two reverse parking maneuvers validates the presented method.
Online Reinforcement Learning with Uncertain Episode Lengths

Existing episodic reinforcement algorithms assume that the length of an episode is fixed across time and known a priori. In this paper, we consider a general framework of episodic reinforcement learning when the length of each episode is drawn from a distribution. We first establish that this problem is equivalent to online reinforcement learning with general discounting where the learner is trying to optimize the expected discounted sum of rewards over an infinite horizon, but where the discounting function is not necessarily geometric. We show that minimizing regret with this new general discounting is equivalent to minimizing regret with uncertain episode lengths. We then design a reinforcement learning algorithm that minimizes regret with general discounting but acts for the setting with uncertain episode lengths. We instantiate our general bound for different types of discounting, including geometric and polynomial discounting. We also show that we can obtain similar regret bounds even when the uncertainty over the episode lengths is unknown, by estimating the unknown distribution over time. Finally, we compare our learning algorithms with existing value-iteration based episodic RL algorithms on a grid-world environment.
Qualitative quantum simulation of resonant tunneling and localization with the shallow quantum circuits

In a circuit-based quantum computer, the computing is performed via the discrete-time evolution driven by quantum gates. Accurate simulation of continuoustime evolution requires a large number of quantum gates and therefore suffers from more noise. In this paper, we find that shallow quantum circuits are sufficient to qualitatively observe some typical quantum phenomena in the continuous-time evolution limit, such as resonant tunneling and localization phenomena. We study the propagation of a spin excitation in Trotter circuits with a large step size. The circuits are formed of two types of two-qubit gates, i.e. XY gates and controlled- Rx gates, and single-qubit Rz gates. The configuration of the Rz gates determines the distribution of the spin excitation at the end of evolution. We demonstrate the resonant tunneling with up to four steps and the localization phenomenon with dozens of steps in Trotter circuits. Our results show that the circuit depth required for qualitative observation of some significant quantum phenomena is much smaller than that required for quantitative computation, suggesting that it is feasible to apply qualitative observations to near-term quantum computers. We also provide a way to use the physics laws to understand the error propagation in quantum circuits.
The Fractional Schrödinger Equation with the Generalized Woods-Saxon Potential

The bound state energy eigenvalues and the corresponding eigenfunctions of the generalized Woods-Saxon potential reported in [Phys. Rev. C, 72, 027001 (2005)] is extended to the fractional forms using the generalized fractional derivative and the fractional Nikiforov-Uvarov (NU) technique. Analytical solutions of bound states of the Schrodinger equation for the present potential are obtained in the terms of fractional Jacobi polynomials. It is demonstrated that the classical results are a special case of the present results at α=β=1. Therefore, the present results play important role in molecular chemistry and nuclear physics.
Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

Smart traffic lights in intelligent transportation systems (ITSs) are envisioned to greatly increase traffic efficiency and reduce congestion. Deep reinforcement learning (DRL) is a promising approach to adaptively control traffic lights based on the real-time traffic situation in a road network. However, conventional methods may suffer from poor scalability. In this paper, we investigate deep reinforcement learning to control traffic lights, and both theoretical analysis and numerical experiments show that the intelligent behavior ``greenwave"(i.e., a vehicle will see a progressive cascade of green lights, and not have to brake at any intersection) emerges naturally a grid road network, which is proved to be the optimal policy in an avenue with multiple cross streets. As a first step, we use two DRL algorithms for the traffic light control problems in two scenarios. In a single road intersection, we verify that the deep Q-network (DQN) algorithm delivers a thresholding policy; and in a grid road network, we adopt the deep deterministic policy gradient (DDPG) algorithm. Secondly, numerical experiments show that the DQN algorithm delivers the optimal control, and the DDPG algorithm with passive observations has the capability to produce on its own a high-level intelligent behavior in a grid road network, namely, the ``greenwave"policy emerges. We also verify the ``greenwave"patterns in a $5 \times 10$ grid road network. Thirdly, the ``greenwave"patterns demonstrate that DRL algorithms produce favorable solutions since the ``greenwave"policy shown in experiment results is proved to be optimal in a specified traffic model (an avenue with multiple cross streets). The delivered policies both in a single road intersection and a grid road network demonstrate the scalability of DRL algorithms.
Role of the Mycobacterium tuberculosis ESX-4 Secretion System in Heme Iron Utilization and Pore Formation by PPE Proteins

M. tuberculosis (Mtb) is completely dependent on iron acquisition in the host to cause disease. The largest source of iron for Mtb in the human host is heme. ABSTRACT Mycobacterium tuberculosis (Mtb) is transmitted through aerosols and primarily colonizes within the lung. The World Health Organization estimates that Mtb kills ~1.4 million people every year. A key aspect that makes Mtb such a successful pathogen is its ability to overcome iron limitation mounted by the host immune response. In our previous studies, we have shown that Mtb can utilize iron from heme, the largest source of iron in the human host, and that it uses two redundant heme utilization pathways. In this study, we show that the ESX-4 type VII secretion system (T7SS) is necessary for extracellular heme uptake into the Mtb cell through both heme utilization pathways. ESX-4 influences the secretion of the culture filtrate proteins Rv0125 and Rv1085c, which are also necessary for efficient heme utilization. We also discovered that deletion of the alternative sigma factor SigM significantly reduced Mtb heme utilization through both pathways and predict that SigM is a global positive regulator of core heme utilization genes of both pathways. Finally, we present the first direct evidence that some mycobacterial PPE (proline-proline-glutamate motif) proteins of the PPE protein family are pore-forming membrane proteins. Altogether, we identified core components of both Mtb Heme utilization pathways that were previously unknown and identified a novel channel-forming membrane protein of Mtb. IMPORTANCE M. tuberculosis (Mtb) is completely dependent on iron acquisition in the host to cause disease. The largest source of iron for Mtb in the human host is heme. Here, we show that the ancestral ESX-4 type VII secretion system is required for the efficient utilization of heme as a source of iron, which is an essential nutrient. This is another biological function identified for ESX-4 in Mtb, whose contribution to Mtb physiology is poorly understood. A most exciting finding is that some mycobacterial PPE (proline-proline-glutamate motif) proteins that have been implicated in the nutrient acquisition are membrane proteins that can form channels in a lipid bilayer. These observations have far-reaching implications because they support an emerging theme that PPE proteins can function as channel proteins in the outer mycomembrane for nutrient acquisition. Mtb has evolved a heme uptake system that is drastically different from all other known bacterial heme acquisition systems.
Cayley trees, the Ising problem, and the thermodynamic limit

Proofs have been given that the Bethe-Peierls approximation solves exactly the Ising problem on a Cayley tree. For a tree with coordination number $\ensuremath{\gamma}g2$, the approximation predicts, among other things, a phase transition in zero field at ${T}_{c}=2J {{\mathrm{ln}[\frac{\ensuremath{\gamma}}{(\ensuremath{\gamma}\ensuremath{-}2)}]}}^{\ensuremath{-}1}$, with a discontinuity in the specific heat. On the other hand, the partition function in zero field can be calculated exactly and turns out to be analytic for all $T$. This paradox is analyzed and resolved. The transition occurring on a Cayley tree is found not to be of the type usually studied in thermodynamics.
Association Between Ergonomic Burden Assessed Using 20-Item Agricultural Work-Related Ergonomic Risk Questionnaire and Shoulder, Low Back, and Leg Pain in Korean Farmers

ABSTRACT Objectives:This study aimed to determine the agricultural ergonomic burden in Korean farmers and to analyze its correlation with musculoskeletal pain. Methods: In total, 1001 farmers (525 females and 476 males; mean age, 59.6±7.5years) who owned or rented a farm and belonged to an agricultural cooperative unit were recruited. Ergonomic burdens were assessed using a 20-item Agricultural Work-related Ergonomic Risk Questionnaire (20 agricultural works). The presence of musculoskeletal pain (shoulder, low back, and leg/foot), Farm Stressor Inventory, subjective stress index, and agricultural workload (low, moderate, somewhat hard, or hard) were collected using structured questionnaires. Results: Factor analysis of the Agricultural Work-related Ergonomic Risk Questionnaire revealed a four-factor solution: neck and upper limb, trunk and push – pull, machine and heavy lifting, and repetitive trauma. Cronbach’s alpha was greater than 0.65. For 18 of the 20 items, there was a significant association with the Farm Stressor Inventory, subjective stress index, and agricultural workload. The most frequent ergonomic burdens were squatting (51.2%), highly repetitive wrist movements (53.5%), shoulder flexion at 45–90° (51.2%), and trunk flexion or twisting at≥45° (48.8%). Ergonomic burdens were significantly different in 13 items between sexes. The musculoskeletal pain was associated with increased agricultural burdens in 10 items in male farmers and 14 items in female farmers. Conclusion: Increased agricultural ergonomic burdens were associated with musculoskeletal pain. Ergonomic burden showed different patterns between male and female farmers, with female farmers appearing to be more affected by ergonomic burden than male farmers.
Self‐reported quality of life in symptomatic and asymptomatic women with X‐linked adrenoleukodystrophy

Up to 80% of women with X‐linked adrenoleukodystrophy (X‐ALD) develop symptoms of myelopathy and peripheral neuropathy during their lifetime. The study's objective was to compare symptomatic versus asymptomatic women with X‐ALD regarding their physical and mental well‐being and quality of life.
Body adiposity markers and insulin resistance in patients with type 1 diabetes

ABSTRACT Objectives: Body composition changes are associated with adverse effects such as increased insulin resistance (IR) in individuals with diabetes mellitus. This study aims to evaluate the association between different body adiposity markers and IR in adults with type 1 diabetes (T1D). Subjects and methods: The cross-sectional study included outpatient adults with T1D from a university public hospital in southern Brazil. The body adiposity markers studied were waist circumference (WC), waist-height ratio (WHtR), body mass index (BMI), conicity index (CI), lipid accumulation product (LAP) and body adiposity index (BAI). IR was calculated using an Estimated Glucose Disposal Rate (EGDR) equation (analyzed in tertiles), considering an inverse relation between EGDR and IR. Poisson regression models were used to estimate the odds ratio (OR) and 95% CIs of association of adiposity markers with IR. Results: A total of 128 patients were enrolled (51% women), with a median EGDR of 7.2 (4.4-8.7) mg.kg−1.min−1. EGDR was negatively correlated with WC (r = −0.36, p < 0.01), WHtR (r = −0.39, p < 0.01), CI (r = −0.44, p < 0.01), LAP (r = −0.41, p < 0.01) and BMI (r = −0.24, p < 0.01). After regression analyses, WC (OR = 2.07; CIs: 1.12-3.337; p = 0.003), WHtR (OR = 2.77; CIs: 1.59-4.79; p < 0.001), CI (OR = 2.59; CIs: 1.43-4.66; p = 0.002), LAP (OR = 2.27; CIs: 1.25-4.11; p = 0.007) and BMI (OR = 1.78; CIs: 1.09-2.91; p = 0.019) remained associated with IR. Conclusions: The authors suggest using the studied adiposity markers as a routine since they were shown to be suitable parameters in association with IR.
Liquid-embedded (bio)printing of alginate-free, standalone, ultrafine, and ultrathin-walled cannular structures

Significance In addressing the limitations associated with conventional filament-(bio)printing strategies for producing solid or hollow fibers, we describe the development of a technique that allows facile fabrication of standalone, alginate-free, solid or cannular structures with diameters and wall thicknesses both down to as small as few micrometers. It is anticipated that our method would serve as an enabling platform that may satisfy the needs for the different types of biomedical and other applications involving the use of fibrous structures.
Anxiety, Depression and Burnout Levels of Nurses Working in COVID-19 Intensive Care Units

This descriptive and correlation-seeking study was planned to determine the depression, anxiety, and burnout levels experienced by nurses working in COVID intensive care units. The survey consisted of three instruments: Nurse Identification Form, Hospital Anxiety and Depression Scale (HADS), and Burnout Measure Short Form (BMS). It was determined that the mean anxiety score of the nurses was 11.31 ± 4.41, the mean depression score was 10.03 ± 3.54, and the mean of burnout was 3.92 ± 1.75. In addition, it was determined that 53.3% of the nurses experienced burnout and 27.0% were so exhausted that they needed professional help. Also, it was determined that there was a moderately significant positive relationship between anxiety and burnout and depression and burnout (p < 0.001). Protecting the mental health of nurses is very important in terms of both improving the quality of care and increasing the productivity of nurses.
Post-ischemic hyperemia following endovascular therapy for acute stroke is associated with lesion growth

A substantial proportion of acute stroke patients fail to recover following successful endovascular therapy (EVT) and injury to the brain and vasculature secondary to reperfusion may be a contributor. Acute stroke patients were included with: i) large vessel occlusion of the anterior circulation, ii) successful recanalization, and iii) evaluable MRI early after EVT. Presence of hyperemia on MRI perfusion was assessed by consensus using a modified ASPECTS. Three different approaches were used to quantify relative cerebral blood flow (rCBF). Sixty-seven patients with median age of 66 [59–76], 57% female, met inclusion criteria. Hyperemia was present in 35/67 (52%) patients early post-EVT, in 32/65 (49%) patients at 24 hours, and in 19/48 (40%) patients at 5 days. There were no differences in incomplete reperfusion, HT, PH-2, HARM, severe HARM or symptomatic ICH rates between those with and without early post-EVT hyperemia. A strong association (R2 = 0.81, p < 0.001) was found between early post-EVT hyperemia (p = 0.027) and DWI volume at 24 hours after adjusting for DWI volume at 2 hours (p < 0.001) and incomplete reperfusion at 24 hours (p = 0.001). Early hyperemia is a potential marker for cerebrovascular injury and may help select patients for adjunctive therapy to prevent edema, reperfusion injury, and lesion growth.
Is the mortality–fertility nexus gendered? A research note on sex differences in the impact of sibling mortality on fertility preferences

Research guided by demographic transition theory has shown that exposure to mortality influences women’s fertility preferences and behaviours. Despite the myriad contexts, methodological approaches, and linkages featured in past studies, they have shared a focus on women, leaving questions on the gendered salience of mortality exposures for adults’ fertility-related outcomes unanswered. In this research note, we analyse data from three African countries with distinct fertility profiles (Nigeria, Zambia, and Zimbabwe) to examine associations between sibling mortality exposure and ideal family size among women, men, and couples. We also investigate the stability of these associations over time. The associations between adults’ sibling mortality exposure and their own and their spouses’ ideal family sizes vary across countries. However, the gendered nature of the results in every country and evidence of cross-spousal effects uniformly demonstrate the need to incorporate sex differences into the study of the mortality–fertility link.