Miracle-dz/UniFAR-FTUs huggingface dataset:
Patient Diversion Across Primary Health Centers Using Real Time Delay Predictors

In the current work, we consider diversion of childbirth patients who arrive seeking emergency admission to public primary health centers (PHCs). PHCs are the first point of contact for an Indian patient with formal medical care, and offer medical care on an outpatient basis, and limited inpatient and childbirth care. In this context, real-time prediction of the wait time of the arriving patient becomes important in order to determine whether the patient must be diverted to another PHC or not. We study this problem using a discrete event simulation that we develop of medical care operations in two PHCs in India. We approximate the labour room service at each PHC as an M/G/1 queueing system and show how the accuracy of real-time delay predictors impacts the extent of the change in operational outcomes at each PHC. We simulate patient diversion using actual delays as well as the delay estimates generated by various delay predictors based on the state of the system such as queue-length, elapsed service time, and observed delay histories. The simulation of the diversion process also incorporates travel time between the PHCs. We also propose a new delay predictor that incorporates information regarding the system state as well as the service time distribution. We compare the operational outcomes at both PHCs without diversion and with diversion using the above delay predictors. We show numerically that more accurate delay predictors lead to more equitable distribution of resources involved in provision of childbirth care across both PHCs.
A Note on the Representation Power of GHHs

In this note we prove a sharp lower bound on the necessary number of nestings of nested absolute-value functions of generalized hinging hyperplanes (GHH) to represent arbitrary CPWL functions. Previous upper bound states that $n+1$ nestings is sufficient for GHH to achieve universal representation power, but the corresponding lower bound was unknown. We prove that $n$ nestings is necessary for universal representation power, which provides an almost tight lower bound. We also show that one-hidden-layer neural networks don't have universal approximation power over the whole domain. The analysis is based on a key lemma showing that any finite sum of periodic functions is either non-integrable or the zero function, which might be of independent interest.
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT

We investigate how Multilingual BERT (mBERT) encodes grammar by examining how the high-order grammatical feature of morphosyntactic alignment (how different languages define what counts as a “subject”) is manifested across the embedding spaces of different languages. To understand if and how morphosyntactic alignment affects contextual embedding spaces, we train classifiers to recover the subjecthood of mBERT embeddings in transitive sentences (which do not contain overt information about morphosyntactic alignment) and then evaluate them zero-shot on intransitive sentences (where subjecthood classification depends on alignment), within and across languages. We find that the resulting classifier distributions reflect the morphosyntactic alignment of their training languages. Our results demonstrate that mBERT representations are influenced by high-level grammatical features that are not manifested in any one input sentence, and that this is robust across languages. Further examining the characteristics that our classifiers rely on, we find that features such as passive voice, animacy and case strongly correlate with classification decisions, suggesting that mBERT does not encode subjecthood purely syntactically, but that subjecthood embedding is continuous and dependent on semantic and discourse factors, as is proposed in much of the functional linguistics literature. Together, these results provide insight into how grammatical features manifest in contextual embedding spaces, at a level of abstraction not covered by previous work.
Modeling surrender risk in life insurance: theoretical and experimental insight

Surrender poses one of the major risks to life insurance and a sound modeling of its true probability has direct implication on the risk capital demanded by the Solvency II directive. We add to the existing literature by performing extensive experiments that present highly practical results for various modeling approaches, including XGBoost, random forest, GLM and neural networks. Further, we detect shortcomings of prevalent model assessments, which are in essence based on a confusion matrix. Our results indicate that accurate label predictions and a sound modeling of the true probability can be opposing objectives. We illustrate this with the example of resampling. While resampling is capable of improving label prediction in rare event settings, such as surrender, and thus is commonly applied, we show theoretically and numerically that models trained on resampled data predict significantly biased event probabilities. Following a probabilistic perspective on surrender, we further propose time-dependent confidence bands on predicted mean surrender rates as a complementary assessment and demonstrate its benefit. This evaluation takes a very practical, going concern perspective, which respects that the composition of a portfolio, as well as the nature of underlying risk drivers might change over time.
Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels; only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.
Fine-Grained Named Entity Typing over Distantly Supervised Data via Refinement in Hyperbolic Space

Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context. While distant supervision is the most common way to acquire supervised training data, it brings in label noise, as it assigns type labels to the entity mentions irrespective of mentions’ context. In attempts to deal with the label noise, leading research on the FG-NET assumes that the ﬁne-grained entity typing data possesses a euclidean nature, which restraints the ability of the existing models in combating the label noise. Given the fact that the ﬁne-grained type hierarchy exhibits a hierarchal structure, it makes hyperbolic space a natural choice to model the FG-NET data. In this research, we propose FGNET-RH, a novel framework that beneﬁts from the hyperbolic geometry in combination with the graph structures to perform entity typing in a performance-enhanced fashion. FGNET-RH initially uses LSTM networks to encode the mention in relation with its context, later it forms a graph to distill/reﬁne the mention’s encodings in the hyperbolic space. Finally, the reﬁned mention encoding is used for entity typing. Experimentation using different benchmark datasets shows that FGNET-RH improves the performance on FG-NET by up to 3.5% in terms of strict accuracy.
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction

Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset LSOIE. Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available.
Progenitor mass distribution for 22 historic core-collapse supernovae

We infer the progenitor mass distribution for 22 historic core-collapse supernovae (CCSNe) using a Bayesian hierarchical model. For this inference, we use the local star formation histories to estimate the age for each supernova (SN). These star formation histories often show multiple bursts of star formation; our model assumes that one burst is associated with the SN progenitor and the others are random bursts of star formation. The primary inference is the progenitor age distribution. Due to the limited number of historic SNe and highly uncertain star formation at young ages, we restrict our inference to the slope of the age distribution and the maximum age for CCSNe. Using single-star evolutionary models, we transform the progenitor age distribution into a progenitor mass distribution. Under these assumptions, the minimum mass for CCSNe is Mmin = 8.60 +0.37 −0.41 M and the slope of the progenitor mass distribution is α = −2.61 −1.18. The power-law slope for the progenitor mass distribution is consistent with the standard Salpeter initial mass function (α = −2.35). These values are consistent with previous estimates using precursor imaging and the age-dating technique, further confirming that using stellar populations around SN and supernova remnants is a reliable way to infer the progenitor masses.
On the Twinnability of Face-Centered Cubic Metals in Deformed Microstructures

On the Twinnability of Face-Centered Cubic Metals in Deformed Microstructures Matthew Daly* Ritesh Jagatramka Department of Civil and Materials Engineering, University of Illinois at Chicago – 842 W. Taylor St., 2095 ERF (MC 246), Chicago, IL, 60607, United States ABSTRACT The competition between deformation twinning and dislocation slip underpins the evolution of mesoscale plasticity in face-centered cubic materials. While competition between these mechanisms is known to be related to the critical features of the generalized stacking fault energy landscape, a physical theory that tracks competition over extended plasticity has yet to emerge. Here, we report a methodology to predict the evolution of this competition in deformed microstructures using intrinsic material parameters. Our approach implements kinetic Monte Carlo simulations and analytical modeling to produce a constitutive framework for deformation twin evolution in face-centered cubic metals that is free of empirical fitting or phenomenology. These efforts are leveraged to derive a set of physical relations for strain partitioning, which provide kinetically-weighted predictions for the contributions of each mechanism to plasticity. The culmination of this effort results in a new twinnability parameter that tracks the competition between deformation twinning and dislocation slip over extended plastic deformation. In contrast to previous twinnability parameters, this criterion considers deformation history when weighing mechanistic competition. This parameter therefore extends previous theories for the competition between these mechanisms to include deformation beyond incipient events. This analysis finds direct applications in work hardening and crystal plasticity models that have previously relied on
COVID-19 Agent-Based Model with Multi-objective Optimization for Vaccine Distribution

Now that SARS-CoV-2 (COVID-19) vaccines are developed, it is very important to plan its distribution strategy. In this paper, we formulated a multi-objective linear programming model to optimize vaccine distribution and applied it to the agent-based version of our age-stratified and quarantine-modified SEIR with non-linear incidence rates (ASQ-SEIR-NLIR) compartmental model. Simulations were performed using COVID-19 data from Quezon City and results were analyzed under various scenarios: (1) no vaccination, (2) base vaccination (prioritizing essential workers and vulnerable population), (3) prioritizing mobile workforce, (4) prioritizing elderly, and (5) prioritizing mobile workforce and elderly;in terms of (a) reducing infection rates and (b) reducing mortality incidence. After 10 simulations on distributing 500,000 vaccine courses, results show that prioritizing mobile workforce minimizes further infections by 24.14%, which is better than other scenarios. On the other hand, prioritizing the elderly yields the highest protection (439%) for the Quezon City population compared to other scenarios. This could be due to younger people, when contracted the disease, has higher chances of recovery than the elderly. Thus, this leads to reduction of mortality cases.
Lack of trust and social media echo chambers predict COVID-19 vaccine hesitancy

As COVID-19 vaccines are rolled out across the world, there are growing concerns about the role that trust, belief in conspiracy theories and spread of misinformation through social media impact vaccine hesitancy. We use a nationally representative survey of 1,476 adults in the UK between December 12 to 18, 2020 and five focus groups conducted in the same period. Trust is a core predictor, with distrust in vaccines in general and mistrust in government raising vaccine hesitancy. Trust in health institutions and experts and perceived personal threat are vital, with focus groups revealing that COVID-19 vaccine hesitancy is driven by a misunderstanding of herd immunity as providing protection, fear of rapid vaccine development and side effects, belief the virus is man-made and related to population control. Particularly those who obtain information from relatively unregulated social media sources such as YouTube that have recommendations tailored by watch history are less likely to be willing to become vaccinated. Those who hold general conspiratorial beliefs are less willing to be vaccinated. Since an increasing number of individuals use social media for gathering health information, interventions require action from governments, health officials and social media companies. More attention needs to help people understand their own risks, unpack complex concepts and fill knowledge voids.
The genetic architecture of obsessive-compulsive disorder: alleles across the frequency spectrum contribute liability to OCD

Objective: Obsessive-compulsive disorder (OCD) is known to be substantially heritable; however, the contribution of common genetic variation across the alleleic frequency spectrum to this heritability remains uncertain. We use two new, homogenous cohorts to estimate heritability of OCD from common genetic variation and contrast results with prior studies. Methods: The sample consisted of 2096 Swedish-born individuals diagnosed with OCD and 4609 controls, all genotyped for common genetic variants, specifically >400,000 single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) [≥] 0.01. Using genotypes of these SNPs to estimate distant familial relationships among individuals, we estimated heritability of OCD, both overall and partitioned according to MAF bins. Results: We estimated narrow-sense heritability of 28% (SE=4%). The estimate was robust, varying only modestly under different models. Contrary to an earlier study, however, SNPs with MAF between 0.01 and 0.05 accounted for 8% of heritability and estimated heritability per bin roughly follows expectations based on a simple model for SNP-based heritability. Conclusions: These results indicate that common inherited risk variation (MAF [≥] 0.01) accounts for most of the heritable variation in OCD. SNPs with low MAF contribute meaningfully to the heritability of OCD and the results are consistent with expectation under the "infinitesimal model," where risk is influenced by a large number of loci across the genome and across MAF bins.
Individual factors underlie temperature variation in sickness and in health: influence of age, BMI and genetic factors in a multi-cohort study

Introduction: Ageing affects immune function resulting in aberrant fever response to infection. We assess the effects of biological variables on basal temperature and temperature in COVID-19 infection, proposing an updated temperature threshold for older adults. Methods: Participants: a) Unaffected twin volunteers: 1089 adult TwinsUK participants. b) London hospitalised COVID-19+: 520 adults with emergency admission. c) Birmingham hospitalised COVID-19+: 757 adults with emergency admission. d) Community-based COVID-19+: 3972 adults self-reporting a positive test using the COVID Symptom Study mobile application. Analysis: Heritability assessed using saturated and univariate ACE models: Linear mixed-effect and multivariable linear regression analysing associations between temperature, age, sex and BMI; multivariable logistic regression analysing associations between fever (>=37.8degC) and age; receiver operating characteristic (ROC) analysis to identify temperature threshold for adults >=65 years. Results: Among unaffected volunteers, lower BMI (p=0.001), and older age (p<0.001) associated with lower basal temperature. Basal temperature showed a heritability of 47% (95% Confidence Interval 18-57%). In COVID-19+ participants, increasing age associated with lower temperatures in cohorts (c) and (d) (p<0.001). For each additional year of age, participants were 1% less likely to demonstrate a fever (OR 0.99; p<0.001). Combining healthy and COVID-19+ participants, a temperature of 37.4degC in adults >=65 years had similar sensitivity and specificity to 37.8degC in adults <65 years for discriminating fever in COVID-19. Conclusions: Ageing affects temperature in health and acute infection. Significant heritability indicates biological factors contribute to temperature regulation. Our observations indicate a lower threshold (37.4degC) should be considered for assessing fever in older adults.
Probing a bino NLSP at lepton colliders

We consider a scenario where light bino is the next-to-lightest supersymmetric particle (NLSP) and gravitino/axino is the lightest superysmmetric particle (LSP). For a bino mass less than or around hundred GeV, it can be pair produced at the future lepton colliders through t−channel slepton exchange, subsequently decaying into a gravitino/axino plus a photon. We study the prospects to look for such binos at the future colliders and find that a bino mass around 100 GeV can be probed at the 2σ (5σ) level for a slepton below 2 TeV (1.5 TeV) with a luminosity 3 ab−1. For a bino mass around 10 GeV, a slepton mass less than 4 TeV (3 TeV) can be probed at the 2σ (5σ) level, which is much beyond the reach of the LHC for direct slepton searches. ar X iv :2 10 1. 12 13 1v 1 [ he pph ] 2 8 Ja n 20 21
Experiments on the kinetics of field evaporation of small ions from droplets

The phenomenon of ion evaporation from charged liquid surfaces is at the basis of electrospray ionization, a source of a stunning variety of gas phase ions. It is studied here by producing a monodisperse cloud of charged droplets and measuring the charge q and diameter dr of the residue particles left after complete evaporation of the solvent. When the droplets contain small monovalent dissolved ions, the electric field E on the surface of their solid residues is found to be independent of dr. One can thus argue that the source of small ions in electrospray ionization is field‐emission, and not other proposed mechanisms such as Dole’s charged residue model. A consequence of the observed independence of E on dr is that the rate of ion ejection is simply related to the rate of solvent evaporation, estimated here as that for a clean surface of pure solvent.The reduction G(E) brought about by the electric field E in the activation energy for ion evaporation has thus been inferred as a function of the measured.
Thermo-optical bistability in silicon micro-cantilevers

We report a thermo-optical bistability observed in silicon micro-cantilevers irradiated by a laser beam with mW powers: reflectivity, transmissivity, absorption, and temperature can change by a factor of two between two stable states for the same input power. The temperature dependency of the absorption at the origin of the bistability results from interferences between internal reflections in the cantilever thickness, acting as a lossy Fabry-Pérot cavity. A theoretical model describing the thermo-optical coupling is presented. The experimental results obtained for silicon cantilevers irradiated in vacuum at two different visible wavelengths are in quantitative agreement with the predictions of this model.
Baseline Pruning-Based Approach to Trojan Detection in Neural Networks

This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing systematically pruned NN models. Our pruning-based approach consists of three main steps. First, detect any deviations from the reference look-up tables of model file sizes and model graphs. Next, measure the accuracy of a set of systematically pruned NN models following multiple pruning schemas. Finally, classify a NN model as clean or poisoned by applying a mapping between accuracy measurements and NN model labels. This work outlines a theoretical and experimental framework for finding the optimal mapping over a large search space of pruning parameters. Based on our experiments using Round 1 and Round 2 TrojAI Challenge datasets, the approach achieves average classification accuracy of 69.73 % and 82.41% respectively with an average processing time of less than 60 s per model. For both datasets random guessing would produce 50% classification accuracy. Reference model graphs and source code are available from GitHub.
AdaSpring

There are many deep learning (e.g. DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives. To enable robust and private mobile sensing, DNN tends to be deployed locally on the resource-constrained mobile devices via model compression. The current practice either hand-crafted DNN compression techniques, i.e., for optimizing DNN-relative performance (e.g. parameter size), or on-demand DNN compression methods, i.e., for optimizing hardware-dependent metrics (e.g. latency), cannot be locally online because they require offline retraining to ensure accuracy. Also, none of them have correlated their efforts with runtime adaptive compression to consider the dynamic nature of deployment context of mobile applications. To address those challenges, we present AdaSpring, a context-adaptive and self-evolutionary DNN compression framework. It enables the runtime adaptive DNN compression locally online. Specifically, it presents the ensemble training of a retraining-free and self-evolutionary network to integrate multiple alternative DNN compression configurations (i.e., compressed architectures and weights). It then introduces the runtime search strategy to quickly search for the most suitable compression configurations and evolve the corresponding weights. With evaluation on five tasks across three platforms and a real-world case study, experiment outcomes show that AdaSpring obtains up to 3.1x latency reduction, 4.2x energy efficiency improvement in DNNs, compared to hand-crafted compression techniques, while only incurring ≤ 6.2ms runtime-evolution latency.