Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and estimation of causal effects in the presence of confounded principal strata

The principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation-by-death problems. The causal effects within principal strata which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. Analyses of principal causal effects from observed data in the literature mostly rely on ignorability of the treatment assignment, which requires practitioners to accurately measure as many as covariates so that all possible confounding sources are captured. However, collecting all potential confounders in observational studies is often difficult and costly, the ignorability assumption may thus be questionable. In this paper, by leveraging available negative controls that have been increasingly used to deal with uncontrolled confounding, we consider identification and estimation of causal effects when the treatment and principal strata are confounded by unobserved variables. Specifically, we show that the principal causal effects can be nonparametrically identified by invoking a pair of negative controls that are both required not to directly affect the outcome. We then relax this assumption and establish identification of principal causal effects under various semiparametric or parametric models. We also propose an estimation method of principal causal effects. Extensive simulation studies show good performance of the proposed approach and a real data application from the National Longitudinal Survey of Young Men is used for illustration.
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning

Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forgetting issue could be mitigated by storing historical data for replay, which yet would cause memory overheads as well as imbalanced prediction updates. To address this dilemma, we propose to leverage"free"external unlabeled data querying in continual learning. We first present a CIL with Queried Unlabeled Data (CIL-QUD) scheme, where we only store a handful of past training samples as anchors and use them to query relevant unlabeled examples each time. Along with new and past stored data, the queried unlabeled are effectively utilized, through learning-without-forgetting (LwF) regularizers and class-balance training. Besides preserving model generalization over past and current tasks, we next study the problem of adversarial robustness for CIL-QUD. Inspired by the recent success of learning robust models with unlabeled data, we explore a new robustness-aware CIL setting, where the learned adversarial robustness has to resist forgetting and be transferred as new tasks come in continually. While existing options easily fail, we show queried unlabeled data can continue to benefit, and seamlessly extend CIL-QUD into its robustified versions, RCIL-QUD. Extensive experiments demonstrate that CIL-QUD achieves substantial accuracy gains on CIFAR-10 and CIFAR-100, compared to previous state-of-the-art CIL approaches. Moreover, RCIL-QUD establishes the first strong milestone for robustness-aware CIL. Codes are available in https://github.com/VITA-Group/CIL-QUD.
Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small‐scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain, and partitioning of stress and strain fields in individual grains. Herein, a methodology is developed using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. Three important field variables are focused on: total strain, elastic strain, and microrotation. The results show that the elastic strain in individual grains exhibits a unimodal lognormal distribution, while the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, the identified peaks are evaluated in terms of deformation mechanisms in a grain, which, e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models.
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting

Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have hierarchical relations. Previous works assume rigid consistency over the given hierarchies and do not adapt well to real-world data that show deviation from this assumption. Moreover, recent state-of-art neural probabilistic methods also impose hierarchical relations on point predictions and samples of the predictive distribution. This does not account for full forecast distributions being consistent with the hierarchy and leading to poorly calibrated forecasts. We close both these gaps and propose PROFHiT, a probabilistic hierarchical forecasting model that jointly models forecast distributions over the entire hierarchy. PROFHiT (1) uses a flexible probabilistic Bayesian approach and (2) introduces soft distributional consistency regularization that enables end-to-end learning of the entire forecast distribution leveraging information from the underlying hierarchy. This enables calibrated forecasts as well as adaptation to real-life data with varied hierarchical consistency. PROFHiT provides 41-88% better performance in accuracy and significantly better calibration over a wide range of dataset consistency. Furthermore, PROFHiT adapts to missing data and can provide reliable forecasts even if up to 10% of input time-series data is missing, whereas other methods' performance severely degrades by over 70%
High-Resolution Bathymetric Reconstruction From Sidescan Sonar With Deep Neural Networks

—We propose a novel data-driven approach for high- resolution bathymetric reconstruction from sidescan. Sidescan sonar (SSS) intensities as a function of range do contain some information about the slope of the seabed. However, that information must be inferred. Additionally, the navigation system provides the estimated trajectory, and normally the altitude along this trajectory is also available. From these we obtain a very coarse seabed bathymetry as an input. This is then combined with the indirect but high-resolution seabed slope information from the sidescan to estimate the full bathymetry. This sparse depth could be acquired by single-beam echo sounder, Doppler Velocity Log (DVL), other bottom tracking sensors or bottom tracking algorithm from sidescan itself. In our work, a fully convolutional network is used to estimate the depth contour and its aleatoric uncertainty from the sidescan images and sparse depth in an end- to-end fashion. The estimated depth is then used together with the range to calculate the point’s 3D location on the seaﬂoor. A high-quality bathymetric map can be reconstructed after fusing the depth predictions and the corresponding conﬁdence measures from the neural networks. We show the improvement of the bathymetric map gained by using sparse depths with sidescan over estimates with sidescan alone. We also show the beneﬁt of conﬁdence weighting when fusing multiple bathymetric estimates into a single map.
Second order perturbation theory of two-scale systems in fluid dynamics

In the present paper we study slow-fast systems of coupled equations from fluid dynamics, where the fast component is perturbed by additive noise. We prove that, under a suitable limit of infinite separation of scales, the slow component of the system converges in law to a solution of the initial equation perturbed with transport noise, and subject to the influence of an additional It\=o-Stokes drift. The obtained limit equation is very similar to turbulent models derived heuristically. Our results apply to the Navier-Stokes equations in dimension $d=2,3$; the Surface Quasi-Geostrophic equations in dimension $d=2$; and the Primitive equations in dimension $d=2,3$.
Multimodal Dialogue State Tracking

Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tracking has largely been limited to unimodality, in which slots and slot values are limited by knowledge domains (e.g. restaurant domain with slots of restaurant name and price range) and are defined by specific database schema. In this paper, we propose to extend the definition of dialogue state tracking to multimodality. Specifically, we introduce a novel dialogue state tracking task to track the information of visual objects that are mentioned in video-grounded dialogues. Each new dialogue utterance may introduce a new video segment, new visual objects, or new object attributes and a state tracker is required to update these information slots accordingly. We created a new synthetic benchmark and designed a novel baseline, Video-Dialogue Transformer Network (VDTN), for this task. VDTN combines both object-level features and segment-level features and learns contextual dependencies between videos and dialogues to generate multimodal dialogue states. We optimized VDTN for a state generation task as well as a self-supervised video understanding task which recovers video segment or object representations. Finally, we trained VDTN to use the decoded states in a response prediction task. Together with comprehensive ablation and qualitative analysis, we discovered interesting insights towards building more capable multimodal dialogue systems.
Linear cross-entropy benchmarking with Clifford circuits

With the advent of quantum processors exceeding $100$ qubits and the high engineering complexities involved, there is a need for holistically benchmarking the processor to have quality assurance. Linear cross-entropy benchmarking (XEB) has been used extensively for systems with $50$ or more qubits but is fundamentally limited in scale due to the exponentially large computational resources required for classical simulation. In this work we propose conducting linear XEB with Clifford circuits, a scheme we call Clifford XEB. Since Clifford circuits can be simulated in polynomial time, Clifford XEB can be scaled to much larger systems. To validate this claim, we run numerical simulations for particular classes of Clifford circuits with noise and observe exponential decays. When noise levels are low, the decay rates are well-correlated with the noise of each cycle assuming a digital error model. We perform simulations of systems up to 1,225 qubits, where the classical processing task can be easily dealt with by a workstation. Furthermore, using the theoretical guarantees in Chen et al. (arXiv:2203.12703), we prove that Clifford XEB with our proposed Clifford circuits must yield exponential decays under a general error model for sufficiently low errors. Our theoretical results explain some of the phenomena observed in the simulations and shed light on the behavior of general linear XEB experiments.
Catastrophic overfitting can be induced with discriminative non-robust features

Adversarial training (AT) is the de facto method for building robust neural networks, but it can be computationally expensive. To mitigate this, fast single-step attacks can be used, but this may lead to catastrophic overfitting (CO). This phenomenon appears when networks gain non-trivial robustness during the first stages of AT, but then reach a breaking point where they become vulnerable in just a few iterations. The mechanisms that lead to this failure mode are still poorly understood. In this work, we study the onset of CO in single-step AT methods through controlled modifications of typical datasets of natural images. In particular, we show that CO can be induced at much smaller $\epsilon$ values than it was observed before just by injecting images with seemingly innocuous features. These features aid non-robust classification but are not enough to achieve robustness on their own. Through extensive experiments we analyze this novel phenomenon and discover that the presence of these easy features induces a learning shortcut that leads to CO. Our findings provide new insights into the mechanisms of CO and improve our understanding of the dynamics of AT. The code to reproduce our experiments can be found at https://github.com/gortizji/co_features.
Nonwords Pronunciation Classification in Language Development Tests for Preschool Children

This work aims to automatically evaluate whether the language development of children is age-appropriate. Validated speech and language tests are used for this purpose to test the auditory memory. In this work, the task is to determine whether spoken nonwords have been uttered correctly. We compare different approaches that are motivated to model specific language structures: Low-level features (FFT), speaker embeddings (ECAPA- TDNN), grapheme-motivated embeddings (wav2vec 2.0), and phonetic embeddings in form of senones (ASR acoustic model). Each of the approaches provides input for VGG-like 5-layer CNN classifiers. We also examine the adaptation per non- word. The evaluation of the proposed systems was performed using recordings from different kindergartens of spoken non- words. ECAPA-TDNN and low-level FFT features do not ex-plicitly model phonetic information; wav2vec2.0 is trained on grapheme labels, our ASR acoustic model features contain (sub-)phonetic information. We found that the more granular the phonetic modeling is, the higher are the achieved recognition rates. The best system trained on ASR acoustic model features with VTLN achieved an accuracy of 89.4% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 0.923. This corresponds to an improvement in accuracy of 20.2% and AUC of 0.309 relative compared to the FFT-baseline.
Anonymous Expression in an Online Community for Women in China

Gender issues faced by women can range from workplace harassment to domestic violence. While publicly disclosing these issues on social media can be hard, some may incline to express themselves anonymously. We approached such an anonymous female community on Chinese social media where discussion on gender issues takes place with a qualitative content analysis. By observing anonymous experiences contributed by female users and made publicly available by an influencer, we identified 20 issues commonly discussed, with cheating-partner, controlling parents and age anxiety taking the lead. The results are placed into context with Chinese culture and expectations about gender. By describing the results in context with the social challenges faced by women in China, and understanding how these issues are anonymously and openly discussed by them, we aim to motivate more policies and platform designs to accommodate the needs of the affected population.
Visualization of internal forces inside the proton in a classical relativistic model

A classical model of a stable particle of finite size is studied. The model parameters can be chosen such that the described particle has the mass and radius of a proton. Using the energy-momentum tensor (EMT), we show how the presence of long-range forces alters some notions taken for granted in short-range systems. We focus our attention on the D-term form factor. The important conclusion is that a more careful definition of the D-term may be required when long-range forces are present.
When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

Prescriptive process monitoring approaches leverage histori-cal data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process’s performance. A centerpiece of a prescriptive process monitoring method is its intervention policy: a decision function determining if and when to trigger an intervention on an ongoing case. Previous proposals in this ﬁeld rely on intervention policies that consider only the current state of a given case. These approaches do not consider the tradeoﬀ between triggering an intervention in the current state, given the level of uncertainty of the underlying predictive models, versus delaying the intervention to a later state. Moreover, they assume that a resource is always available to perform an intervention (inﬁnite capacity). This paper addresses these gaps by introducing a prescriptive process monitoring method that ﬁlters and ranks ongoing cases based on prediction scores, prediction uncertainty, and causal ef-fect of the intervention, and triggers interventions to maximize a gain function, considering the available resources. The proposal is evaluated using a real-life event log. The results show that the proposed method outperforms existing baselines regarding total gain.
The ALMA REBELS Survey: The Cosmic H i Gas Mass Density in Galaxies at z ≈ 7

The neutral atomic gas content of individual galaxies at large cosmological distances has until recently been difficult to measure due to the weakness of the hyperfine H i 21 cm transition. Here we estimate the H i gas mass of a sample of main-sequence star-forming galaxies at z ∼ 6.5–7.8 surveyed for [C ii] 158 μm emission as part of the Reionization Era Bright Emission Line Survey (REBELS), using a recent calibration of the [C ii]-to-H i conversion factor. We find that the H i gas mass excess in galaxies increases as a function of redshift, with an average of M Hi /M ⋆ ≈ 10, corresponding to H i gas mass fractions of f Hi = M Hi /(M ⋆ + M Hi ) = 90%, at z ≈ 7. Based on the [C ii] 158 μm luminosity function (LF) derived from the same sample of galaxies, we further place constraints on the cosmic H i gas mass density in galaxies (ρ Hi ) at this redshift, which we measure to be ρHI=7.1−3.0+6.4×106M⊙Mpc−3 . This estimate is substantially lower by a factor of ≈10 than that inferred from an extrapolation of damped Lyα absorber (DLA) measurements and largely depends on the exact [C ii] LF adopted. However, we find this decrease in ρ Hi to be consistent with recent simulations and argue that this apparent discrepancy is likely a consequence of the DLA sight lines predominantly probing the substantial fraction of H i gas in high-z galactic halos, whereas [C ii] traces the H i in the ISM associated with star formation. We make predictions for this buildup of neutral gas in galaxies as a function of redshift, showing that at z ≳ 5, only ≈10% of the cosmic H i gas content is confined in galaxies and associated with the star-forming ISM.
In ammation Related circRNAs Polymorphism and Ischemic Stroke Prognosis

xu liu The First Hospital of China Medical University: The First A liated Hospital of China Medical University qianwen wang China Medical College Hospital: China Medical University Hospital jingjing zhao China Medical College Hospital: China Medical University Hospital hongtao chang China Medical College Hospital: China Medical University Hospital ruixia zhu (  zrx_200626313@163.com ) the rst a liated hospital of china medical univercity https://orcid.org/0000-0002-2683-4674
Medicines Management Group (2018). Nurse-led medicines' monitoring in care homes study protocol: a process evaluation of the impact and sustainability of the adverse drug reaction (ADRe) profile for mental health

Introduction Improved medicines’ management could lead to real and sustainable improvements to the care of older adults. The overuse of mental health medicines has featured in many reports, and insufficient patient monitoring has been identified as an important cause of medicine-related harms. Nurse-led monitoring using the structured adverse drug reaction (ADRe) profile identifies and addresses the adverse effects of mental health medicines. Our study investigates clinical impact and what is needed to sustain utilisation in routine practice in care homes. Methods and analysis This process evaluation will use interviews and observations with the participants of all five homes involved in earlier research, and five newly recruited homes caring for people prescribed mental health medicines. The ADRe profile is implemented by nurses, within existing resources, to check for signs and symptoms of ADRs, initiate amelioration and share findings with pharmacists and prescribers for medication review. Outcome measures are the numbers and nature of problems addressed and understanding of changes needed to optimise clinical gain and sustain implementation. Data will be collected by 30 observations and 30 semistructured interviews. Clinical gains will be described and narrated. Interview analysis will be based on the constant comparative method. to policy-makers and sector
Gender differences in support for direct and indirect political aggression in the context of protracted conflict

The relationship between gender and political aggression is hotly debated and the empirical evidence is often mixed. While many surveys find a gender gap, with women less supportive of politically motivated aggression and violence than men, numerous case studies point to women’s active involvement in political violence and refute the association of women with peacefulness. This article argues that the gender–aggression relation depends upon (1) the type of political aggression under study (i.e. direct vs. indirect political aggression), and (2) contextual factors, notably the salience of a protracted conflict. Using original datasets representing Israeli Jews (N = 3,126) we found that in the context of protracted conflict, gender has a unique effect on support for indirect forms of political aggression, over and above other central predictors of political aggression (i.e. political orientation and threat perceptions), such that women are actually more supportive of politically motivated social distancing and exclusion of out-groups in conflict as compared to men. Women and men, however, do not differ in their support for direct, politically motivated, violent acts against government officials. Results also shed light on potential mechanisms underlying these differences (and lack thereof), in the context of protracted conflict. The findings cast further doubt on the stereotype of ‘peaceful women’ and point to the need for policymakers concerned with conflict resolution to address context-related factors when considering the gender-based differences in political aggression.
Epidermal necrolysis in the context of immuno‐oncologic medication as well as kinase inhibitors and biologics

Stevens‐Johnson syndrome and toxic epidermal necrolysis are severe, primarily drug‐induced reactions of skin and mucosa. Since they differ in the extent of skin detachment but not in etiology, they are grouped together as epidermal necrolysis (EN). Due to nationwide registration, representative data are available at the German Center for the Documentation of Severe Skin Reactions (dZh). Here, an increasing number of case notifications in the context with new immuno‐oncologic drugs, kinase inhibitors and biologics have been observed.