Miracle-dz/UniFAR-FTUs huggingface dataset:
A Methodological Note: An Introduction to Autoregressive Models

The autoregressive model is a useful tool to analyze longitudinal data. It is particularly suitable for gerontological research as autoregressive models can be used to establish the causal relationship within a single variable over time as well as the causal ordering between two or more variables (e.g., physical health and psychological well-being) over time through bivariate autoregressive cross-lagged or contemporaneous models. Specifically, bivariate autoregressive models can explore the cross-lagged effects between two variables over time to determine the proper causal ordering between these variables. The advantage of analyzing cross-lagged effects is to test for the strength of prediction between two variables controlling for each variable's previous time score as well as the autoregressive component of the model. Bivariate autoregressive contemporaneous models can also be used to determine causal ordering within the same time point when compared to cross-lagged effects. Since the technique uses structural equation modeling, models are also adjusted for measurement error. This paper will present an introduction to setting up models and a step-by-step approach to analyzing univariate simplex autoregressive models, bivariate autoregressive cross-lagged models, and bivariate autoregressive contemporaneous models.
Identifying correlations driven by influential observations in large datasets

Although high-throughput data allow researchers to interrogate thousands of variables simultaneously, it can also introduce a significant number of spurious results. Here we demonstrate that correlation analysis of large datasets can yield numerous false positives due to the presence of outliers that canonical methods fail to identify. We present Correlations Under The InfluencE (CUTIE), an open-source jackknifing-based method to detect such cases with both parametric and non-parametric correlation measures, and which can also uniquely rescue correlations not originally deemed significant or with incorrect sign. Our approach can additionally be used to identify variables or samples that induce these false correlations in high proportion. A meta-analysis of various omics datasets using CUTIE reveals that this issue is pervasive across different domains, although microbiome data are particularly susceptible to it. Although the significance of a correlation eventually depends on the thresholds used, our approach provides an efficient way to automatically identify those that warrant closer examination in very large datasets.
Association of emotion reactivity and distress intolerance with suicide attempts in U.S. Army soldiers.

INTRODUCTION Emotion reactivity (ER) and distress intolerance (DI) may be associated with increased suicide attempt (SA) risk among U.S. Army soldiers. METHOD In this case-control study, 74 soldiers recently hospitalized for SA (cases) were compared with 133 control soldiers from the same Army installations selected based on either propensity score matching (n = 103) or reported 12-month suicide ideation (SI) (n = 30). Controls were weighted to represent the total Army population at the study sites and the subpopulation of 12-month ideators. Participants completed questionnaires assessing ER, DI, and other psychosocial variables. Logistic regression analyses examined whether ER and DI differentiated SA cases from the general population and from 12-month ideators before and after controlling for additional important risk factors (sociodemographic characteristics, stressors, mental disorders). RESULTS In univariate analyses, ER differentiated SA cases from both the general population (OR = 2.5[95%CI = 1.7-3.6]) and soldiers with 12-month SI (OR = 2.5[95%CI = 1.3-4.6]). DI also differentiated cases from the general population (OR = 2.9[95%CI = 2.0-4.1]) and 12-month ideators (OR = 1.9[95%CI = 1.1-3.5]). These associations persisted after controlling for sociodemographic variables, stressors, and mental disorders. CONCLUSION Findings provide evidence that higher ER and DI are associated with increased risk of SA among soldiers, even after adjusting for known risk factors. Prospective research with larger samples is needed.
Fictional Inquiry—design collaboration in a shared narrative space

In this paper we present Fictional Inquiry, a collaborative Participatory Design technique that provides an approach that allows designers to shape the context of collaborative design activities. Fictional Inquiry allows designers to address specific issues when inquiring into existing use practices, or exploring the future in the collaborative design process. Fictional Inquiry entails bypassing existing socio-cultural structures by creating partially fictional situations, artifacts, and narratives that mediate collaborative design activities. In this paper we present the Fictional Inquiry technique through three cases that highlight the applicability of the technique when staging the design situation, evoking ideas for possible futures, and initiating organizational change. We present a general framework for understanding and staging Fictional Inquiry, and provide an account of how Fictional Inquiry was used in three quite different design situations.
Counterfactual Fairness in Mortgage Lending via Matching and Randomization

Unfairness in mortgage lending has created generational inequality among racial and ethnic groups in the US. Many studies address this problem, but most existing work focuses on correlation-based techniques. In our work, we use the framework of counterfactual fairness to train fair machine learning models. We propose a new causal graph for the variables available in the Home Mortgage Disclosure Act (HMDA) data. We use a matching-based approach instead of the latent variable modeling approach, because the former approach does not rely on any modeling assumptions. Furthermore, matching provides us with counterfactual pairs in which the race variable is isolated. We first demonstrate the unfairness in mortgage approval and interest rates between African-American and non-Hispanic White sub-populations. Then, we show that having balanced data using matching does not guarantee perfect counterfactual fairness of the machine learning models.
Adaptive Channel Encoding for Point Cloud Analysis

Attention mechanism plays a more and more important role in point cloud analysis and channel attention is one of the hotspots. With so much channel information, it is difficult for neural networks to screen useful channel information. Thus, an adaptive channel encoding mechanism is proposed to capture channel relationships in this paper. It improves the quality of the representation generated by the network by explicitly encoding the interdependence between the channels of its features. Specifically, a channel-wise convolution (Channel-Conv) is proposed to adaptively learn the relationship between coordinates and features, so as to encode the channel. Different from the popular attention weight schemes, the ChannelConv proposed in this paper realizes adaptability in convolution operation, rather than simply assigning different weights for channels. Extensive experiments on existing benchmarks verify our method achieves the state of the arts.
Inf-CP: A Reliable Channel Pruning based on Channel Influence

One of the most effective methods of channel pruning is to trim on the basis of the importance of each neuron. However, measuring the importance of each neuron is an NP-hard problem. Previous works have proposed to trim by considering the statistics of a single layer or a plurality of successive layers of neurons. These works cannot eliminate the influence of different data on the model in the reconstruction error, and currently, there is no work to prove that the absolute values of the parameters can be directly used as the basis for judging the importance of the weights. A more reasonable approach is to eliminate the difference between batch data that accurately measures the weight of influence. In this paper, we propose to use ensemble learning to train a model for different batches of data and use the influence function (a classic technique from robust statistics) to learn the algorithm to track the model's prediction and return its training parameter gradient, so that we can determine the responsibility for each parameter, which we call"influence", in the prediction process. In addition, we theoretically prove that the back-propagation of the deep network is a first-order Taylor approximation of the influence function of the weights. We perform extensive experiments to prove that pruning based on the influence function using the idea of ensemble learning will be much more effective than just focusing on error reconstruction. Experiments on CIFAR shows that the influence pruning achieves the state-of-the-art result.
Distilled Domain Randomization

Deep reinforcement learning is an effective tool to learn robot control policies from scratch. However, these methods are notorious for the enormous amount of required training data which is prohibitively expensive to collect on real robots. A highly popular alternative is to learn from simulations, allowing to generate the data much faster, safer, and cheaper. Since all simulators are mere models of reality, there are inevitable differences between the simulated and the real data, often referenced as the 'reality gap'. To bridge this gap, many approaches learn one policy from a distribution over simulators. In this paper, we propose to combine reinforcement learning from randomized physics simulations with policy distillation. Our algorithm, called Distilled Domain Randomization (DiDoR), distills so-called teacher policies, which are experts on domains that have been sampled initially, into a student policy that is later deployed. This way, DiDoR learns controllers which transfer directly from simulation to reality, i.e., without requiring data from the target domain. We compare DiDoR against three baselines in three sim-to-sim as well as two sim-to-real experiments. Our results show that the target domain performance of policies trained with DiDoR is en par or better than the baselines'. Moreover, our approach neither increases the required memory capacity nor the time to compute an action, which may well be a point of failure for successfully deploying the learned controller.
Flexible Option Learning

Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more efficiently over time. Although option learning was initially formulated in a way that allows updating many options simultaneously, using off-policy, intra-option learning (Sutton, Precup&Singh, 1999), many of the recent hierarchical reinforcement learning approaches only update a single option at a time: the option currently executing. We revisit and extend intra-option learning in the context of deep reinforcement learning, in order to enable updating all options consistent with current primitive action choices, without introducing any additional estimates. Our method can therefore be naturally adopted in most hierarchical RL frameworks. When we combine our approach with the option-critic algorithm for option discovery, we obtain significant improvements in performance and data-efficiency across a wide variety of domains.
Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data.

BACKGROUND Patients with acute interstitial nephritis (AIN) can present without typical clinical features, leading to a delay in diagnosis and treatment. We therefore developed and validated a diagnostic model to identify patients at risk of AIN using variables from the electronic health record. METHODS In patients who underwent a kidney biopsy at Yale between 2013-2018, we tested the association of over 150 variables with AIN including demographics, comorbidities, vital signs, and laboratory tests (training set, 70%). We used least absolute shrinkage and selection operator (LASSO) methodology to select pre-biopsy features associated with AIN. We performed AUC analysis with internal (held-out test set, 30%) and external validation (Biopsy Biobank Cohort of Indiana). We tested change in model performance after addition of urine biomarkers in Yale AIN study. RESULTS We included 393 patients (AIN = 22%) in training set, 158(AIN = 27%) in test set, 1118(AIN = 11%) in validation set, and 265(AIN = 11%) in Yale AIN study. Variables in selected model included serum creatinine (adjusted odds ratio, 2.31(1.42-3.76)), blood urea nitrogen to creatinine ratio (0.40(0.20-0.78)), and urine dipstick specific gravity (0.95(0.91-0.99)) and protein (0.39(0.23-0.68)). This model showed an AUC of 0.73(0.64-0.81) in the test set, which was similar to the AUC in the external validation cohort [0.74(0.69-0.79)]. The AUC improved to 0.84(0.76-0.91) upon addition of urine interleukin-9 and TNF-α. CONCLUSIONS We developed and validated a statistical model that showed a modest AUC for AIN diagnosis, which improved upon addition of urine biomarkers. Future studies could evaluate this model and biomarkers to identify unrecognized cases of AIN.
Institutional procedural discrimination, institutional racism, and other institutional discrimination: A nursing research example

Abstract Institutional discrimination matters. The purpose of this longitudinal community‐based participatory research study was to examine institutional procedural discrimination, institutional racism, and other institutional discrimination, and their relationships with participants' health during a maternal and child health program in a municipal initiative. Twenty participants from nine multilingual, multicultural community‐based organizations were included. Overall reported incidences of institutional procedural discrimination decreased from April 2019 (18.6%) to November 2019 (11.8%) although changes were not statistically significant and participants reporting incidences remained high (n = 15 in April and n = 14 in November). Participants reported experiencing significantly less “[when] different cultural ways of doing things were shared, the project did not support my way” from April 2019 (23.5%, n = 4) to November 2019 (0%, n = 0), Wilcoxon signed‐rank test Z = −2.00, p < 0.05. Some participants reported experiencing institutional racism (29.4%, n = 5) and other institutional discrimination (5.9%, n = 1). Participants experiencing institutional racism, compared to those who did not, reported a higher impact of the Initiative's program on their quality of life (t = 3.62, p < 0.01). Participatory survey designs enable nurse researchers to identify hidden pathways of institutional procedural discrimination, describe the impacts experienced, and examine types of institutional discrimination in health systems.
Thermodynamics of Darwinian evolution in molecular replicators

We consider the relationship between thermodynamics, fitness, and Darwinian evolution in autocatalytic molecular replicators. We uncover a thermodynamic bound that relates fitness, replication rate, and the Gibbs free energy dissipated per copy. This bound applies to a broad range of systems, including elementary and non-elementary autocatalytic reactions, polymer-based replicators, and certain kinds of autocatalytic sets. In addition, we show that the critical selection coefficient (the minimal fitness difference visible to selection) is bounded by the Gibbs free energy dissipated per replication. Our results imply fundamental thermodynamic bounds on the strength of selection in molecular evolution, complementary to other bounds that arise from finite population sizes and error thresholds. These bounds may be relevant for understanding thermodynamic constraints faced by early replicators at the origin of life. We illustrate our approach on several examples, including a classic model of replicators in a chemostat.
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons

With the motive of training all the parameters of a neural network, we study why and when one can achieve this by iteratively creating, training, and combining randomly selected subnetworks. Such scenarios have either implicitly or explicitly emerged in the recent literature: see e.g., the Dropout family of regularization techniques, or some distributed ML training protocols that reduce communication/computation complexities, such as the Independent Subnet Training protocol. While these methods are studied empirically and utilized in practice, they often enjoy partial or no theoretical support, especially when applied on neural network-based objectives. In this manuscript, our focus is on overparameterized single hidden layer neural networks with ReLU activations in the lazy training regime. By carefully analyzing $i)$ the subnetworks' neural tangent kernel, $ii)$ the surrogate functions' gradient, and $iii)$ how we sample and combine the surrogate functions, we prove linear convergence rate of the training error -- up to a neighborhood around the optimal point -- for an overparameterized single-hidden layer perceptron with a regression loss. Our analysis reveals a dependency of the size of the neighborhood around the optimal point on the number of surrogate models and the number of local training steps for each selected subnetwork. Moreover, the considered framework generalizes and provides new insights on dropout training, multi-sample dropout training, as well as Independent Subnet Training; for each case, we provide convergence results as corollaries of our main theorem.
Advanced solder bumping technology through super solder

Furukawa Electric has developed method of eluding high-purity solder directly onto fine-pitch contacts through lattice substitution, called Super Solder (SS). The technology has already been adopted in precoating for boards mounting Pentium chips. SS supplies solder to fine-pitch electrode pads through a very simple process consisting of paste coat and heating, and is well suited to the needs of bump formation, especially on boards. This paper covers the configuration and characteristics of a production line providing fully-automatic bump formation with high yield, low labor requirements, and at a low capital investment.
Modification-Fair Cluster Editing

The classic Cluster Editing problem (also known as Correlation Clustering) asks to transform a given graph into a disjoint union of cliques (clusters) by a small number of edge modifications. When applied to vertex-colored graphs (the colors representing subgroups), standard algorithms for the NP-hard Cluster Editing problem may yield solutions that are biased towards subgroups of data (e.g., demographic groups), measured in the number of modifications incident to the members of the subgroups. We propose a modification fairness constraint which ensures that the number of edits incident to each subgroup is proportional to its size. To start with, we study Modification-Fair Cluster Editing for graphs with two vertex colors. We show that the problem is NP-hard even if one may only insert edges within a subgroup; note that in the classic "non-fair" setting, this case is trivially polynomial-time solvable. However, in the more general editing form, the modification-fair variant remains fixed-parameter tractable with respect to the number of edge edits. We complement these and further theoretical results with an empirical analysis of our model on real-world social networks where we find that the price of modification-fairness is surprisingly low, that is, the cost of optimal modification-fair differs from the cost of optimal "non-fair" solutions only by a small percentage.
Leonid Shilnikov and mathematical theory of dynamical chaos.

Leonid Shilnikov and mathematical theory of dynamical chaos Sergey Gonchenko,1, a) Alexey Kazakov,2, b) Dmitry Turaev,3, 2, c) and Andrey L. Shilnikov4, d) 1)Scientific and Educational Mathematical Center “Mathematics of Future Technologies”, Lobachevsky State University of Nizhny Novgorod, 23 Gagarina Ave., 603950 Nizhny Novgorod, Russia 2)National Research University Higher School of Economics, 25/12 Bolshaya Pecherskaya Ulitsa, 603155 Nizhny Novgorod, Russia 3)Imperial College, London, UK 4)Neuroscience Institute and Department of Mathematics & Statistics, Georgia State University, 100 Piedmont Ave., Atlanta, GA 30303, USA.
Biofilm Growth Under Elastic Confinement

Bacteria often form surface-bound communities, embedded in a self-produced extracellular matrix, called biofilms. Quantitative studies of their growth have typically focused on unconfined expansion above solid or semi-solid surfaces, leading to exponential radial growth. This geometry does not accurately reflect the natural or biomedical contexts in which biofilms grow in confined spaces. Here we consider one of the simplest confined geometries: a biofilm growing laterally in the space between a solid surface and an overlying elastic sheet. A poroelastic framework is utilised to derive the radial growth rate of the biofilm; it reveals an additional self-similar expansion regime, governed by the stiffness of the matrix, leading to a finite maximum radius, consistent with our experimental observations of growing Bacillus subtilis biofilms confined by PDMS.
The active element pattern

This review article discusses the use of the active element pattern for prediction of the scan performance of large phased array antennas. The introduction and application of the concept of the active element pattern goes back at least 30 years but the subject is generally not covered in modern antenna engineering textbooks or handbooks, and many contemporary workers are unfamiliar with this simple but powerful idea. In addition, early references on this subject do not provide a rigorous discussion or derivation of the active element pattern, relying instead on a more qualitative interpretation. The purpose of this article is to make the technique of active element patterns more accessible to antenna engineers, and to provide a new derivation of the basic active element pattern relations in terms of scattering parameters. >
Label Hierarchy Transition: Modeling Class Hierarchies to Enhance Deep Classifiers

Hierarchical classiﬁcation aims to sort the object into a hierarchy of categories. For example, a bird can be categorized according to a three-level hierarchy of order, family, and species. Existing methods commonly address hierarchical classiﬁcation by decoupling it into several multi-class classiﬁcation tasks. However, such a multi-task learning strategy fails to fully exploit the correlation among various categories across different hierarchies. In this paper, we propose Label Hierarchy Transition, a uniﬁed probabilistic framework based on deep learning, to address hierarchical classiﬁcation. Speciﬁcally, we explicitly learn the label hierarchy transition matrices, whose column vectors represent the conditional label distributions of classes between two adjacent hierarchies and could be capable of encoding the correlation embedded in class hierarchies. We further propose a confusion loss, which encourages the clas-siﬁcation network to learn the correlation across different label hierarchies during training. The proposed framework can be adapted to any existing deep network with only minor modiﬁcations. We experiment with three public benchmark datasets with various class hierarchies, and the re-sults demonstrate the superiority of our approach beyond the prior arts. Source code will be made publicly available.