Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards optimum smoking cessation interventions during pregnancy: a household model to explore cost‐effectiveness

Abstract Background and Aims Previous economic evaluations of smoking cessation interventions for pregnant women are limited to single components, which do not in isolation offer sufficient potential impact to address smoking cessation targets. To inform the development of more appropriate complex interventions, we (1) describe the development of the Economics of Smoking in Pregnancy: Household (ESIP.H) model for estimating the life‐time cost‐effectiveness of smoking cessation interventions aimed at pregnant women and (2) use a hypothetical case study to demonstrate how ESIP.H can be used to identify the characteristics of optimum smoking cessation interventions. Methods The hypothetical intervention was based on current evidence relating to component elements, including financial incentives, partner smoking, intensive behaviour change support, cigarettes consumption and duration of support to 12 months post‐partum. ESIP.H was developed to assess the life‐time health and cost impacts of multi‐component interventions compared with standard National Health Service (NHS) care in England. ESIP.H considers cigarette consumption, partner smoking and some health conditions (e.g. obesity) that were not included in previous models. The Markov model's parameters were estimated based on published literature, expert judgement and evidence‐based assumptions. The hypothetical intervention was evaluated from an NHS perspective. Results The hypothetical intervention was associated with an incremental gain in quitters (mother and partner) at 12 months postpartum of 249 [95% confidence interval (CI) = 195–304] per 1000 pregnant smokers. Over the long‐term, it had an incremental negative cost of £193 (CI = –£779 to 344) and it improved health, with a 0.50 (CI = 0.36–0.69) increase in quality‐adjusted life years (QALYs) for mothers, partners and offspring, with a 100% probability of being cost‐effective. Conclusions The Economics of Smoking in Pregnancy: Household model for estimating cost‐effectiveness of smoking cessation interventions aimed at pregnant women found that a hypothetical smoking cessation intervention would greatly extend reach, reduce smoking and be cost‐effective.
Shocks in Radiatively Driven Time-dependent, Relativistic Jets around Black Holes

We study time-dependent relativistic jets under the influence of the radiation field of the accretion disk. The accretion disk consists of an inner compact corona and an outer sub-Keplerian disk. The thermodynamics of the fluid is governed by a relativistic equation of state (EOS) for multispecies fluid that enables us to study the effect of composition on jet dynamics. Jets originate from the vicinity of the central black hole, where the effect of gravity is significant and traverses large distances where only special relativistic treatment is sufficient. So we have modified the flat metric to include the effect of gravity. In this modified relativistic framework we have developed a new total variation diminishing routine along with a multispecies EOS for the purpose. We show that the acceleration of jets crucially depends on flow composition. All the results presented are transonic in nature; starting from very low injection velocities, the jets can achieve high Lorentz factors. For sub-Eddington luminosities, lepton-dominated jets can be accelerated to Lorentz factors >50. The change in radiation field due to variation in the accretion disk dynamics will be propagated to the jet in a finite amount of time. Hence, any change in radiation field due to a change in disk configuration will affect the lower part of the jet before it affects the outer part. This can drive shock transition in the jet flow. Depending on the disk oscillation frequency, amplitude, and jet parameters, these shocks can collide with each other and may trigger shock cascades.
Chain of Thought Imitation with Procedure Cloning

Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning problem in which one fits a function approximator to the input-output mapping exhibited by the logged demonstrations (input observations to output actions). While the framing of imitation learning as a supervised input-output learning problem allows for applicability in a wide variety of settings, it is also an overly simplistic view of the problem in situations where the expert demonstrations provide much richer insight into expert behavior. For example, applications such as path navigation, robot manipulation, and strategy games acquire expert demonstrations via planning, search, or some other multi-step algorithm, revealing not just the output action to be imitated but also the procedure for how to determine this action. While these intermediate computations may use tools not available to the agent during inference (e.g., environment simulators), they are nevertheless informative as a way to explain an expert's mapping of state to actions. To properly leverage expert procedure information without relying on the privileged tools the expert may have used to perform the procedure, we propose procedure cloning, which applies supervised sequence prediction to imitate the series of expert computations. This way, procedure cloning learns not only what to do (i.e., the output action), but how and why to do it (i.e., the procedure). Through empirical analysis on navigation, simulated robotic manipulation, and game-playing environments, we show that imitating the intermediate computations of an expert's behavior enables procedure cloning to learn policies exhibiting significant generalization to unseen environment configurations, including those configurations for which running the expert's procedure directly is infeasible.
PSO-Convolutional Neural Networks With Heterogeneous Learning Rate

Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computationally expensive to train them. A myriad of architectures and training strategies have been proposed to overcome this challenge and address several problems in image processing such as speech, image and action recognition as well as object detection. In this article, we propose a novel Particle Swarm Optimization (PSO) based training for ConvNets. In such framework, the vector of weights of each ConvNet is typically cast as the position of a particle in phase space whereby PSO collaborative dynamics intertwines with Stochastic Gradient Descent (SGD) in order to boost training performance and generalization. Our approach goes as follows: $i$ ) [regular phase] each ConvNet is trained independently via SGD; $ii$ ) [collaborative phase] ConvNets share among themselves their current vector of weights (or particle-position) along with their gradient estimates of the Loss function. Distinct step sizes are coined by distinct ConvNets. By properly blending ConvNets with large (possibly random) step-sizes along with more conservative ones, we propose an algorithm with competitive performance with respect to other PSO-based approaches on Cifar-10 and Cifar-100 (accuracy of 98.31% and 87.48%). These accuracy levels are obtained by resorting to only four ConvNets– such results are expected to scale with the number of collaborative ConvNets accordingly. We make our source codes available for download https://github.com/leonlha/ PSO-ConvNet-Dynamics.
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering

Data artifacts incentivize machine learning models to learn non-transferable generalizations by taking advantage of shortcuts in the data, and there is growing evidence that data artifacts play a role for the strong results that deep learning models achieve in recent natural language processing benchmarks. In this paper, we focus on task-oriented dialogue and investigate whether popular datasets such as MultiWOZ contain such data artifacts. We found that by only keeping frequent phrases in the training examples, state-of-the-art models perform similarly compared to the variant trained with full data, suggesting they exploit these spurious correlations to solve the task. Motivated by this, we propose a contrastive learning based framework to encourage the model to ignore these cues and focus on learning generalisable patterns. We also experiment with adversarial filtering to remove"easy"training instances so that the model would focus on learning from the"harder"instances. We conduct a number of generalization experiments -- e.g., cross-domain/dataset and adversarial tests -- to assess the robustness of our approach and found that it works exceptionally well.
PAC-Wrap: Semi-Supervised PAC Anomaly Detection

Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve this goal in the semi-supervised setting, we propose to provide Probably Approximately Correct (PAC) guarantees on the false negative and false positive detection rates for anomaly detection algorithms. Our method (PAC-Wrap) can wrap around virtually any existing semi-supervised and unsupervised anomaly detection method, endowing it with rigorous guarantees. Our experiments with various anomaly detectors and datasets indicate that PAC-Wrap is broadly effective.
Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark

The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport, which are drastically imbalanced with missing attributes sometimes approaching 50\% of the overall data dimensionality. The paper presents the data analysis pipeline starting from the publicly available data of road traffic accidents and ending with predictors of possible injuries and their degree of severity. It addresses the huge incompleteness of public data with a MissForest model. The paper also introduces two baseline approaches to create injury predictors: a supervised artificial neural network and a reinforcement learning model. The dataset can potentially stimulate diverse aspects of machine learning research on imbalanced datasets and the two approaches can be used as baseline references when researchers test more advanced learning algorithms in this area.
ALGEBRAIC COMBINATORICS

This paper is a follow-up to [5], in which the first author studied primitive association schemes lying between a tensor power T dm of the trivial association scheme and the Hamming scheme H ( d,m ). A question which arose naturally in that study was whether all primitive fusions of T d m lie between T d/e m e and H ( d/e,m e ) for some e | d . This note answers this question positively provided that m is large enough. We similarly classify primitive fusions of the d th tensor power of a Johnson scheme on (cid:0) mk (cid:1) points when m is large enough in terms of k and d .
Single-cell gene regulatory network analysis for mixed cell populations with applications to COVID-19 single cell data (preprint)

Gene regulatory network (GRN) refers to the complex network formed by regulatory interactions between genes in living cells. In this paper, we consider inferring GRNs in single cells based on single cell RNA sequencing (scRNA-seq) data. In scRNA-seq, single cells are often profiled from mixed populations and their cell identities are unknown. A common practice for single cell GRN analysis is to first cluster the cells and infer GRNs for every cluster separately. However, this two-step procedure ignores uncertainty in the clustering step and thus could lead to inaccurate estimation of the networks. To address this problem, we propose to model scRNA-seq by the mixture multivariate Poisson log-normal (MPLN) distribution. The precision matrices of the MPLN are the GRNs of different cell types and can be jointly estimated by maximizing MPLN's lasso-penalized log-likelihood. We show that the MPLN model is identifiable and the resulting penalized log-likelihood estimator is consistent. To avoid the intractable optimization of the MPLN's log-likelihood, we develop an algorithm called VMPLN based on the variational inference method. Comprehensive simulation and real scRNA-seq data analyses reveal that VMPLN performs better than the state-of-the-art single cell GRN methods.
Dendrification of Hom-Malcev algebras

The main goal of this work is to introduce the notion of Hom-M-dendriform algebras which are the dendriform version of Hom-Malcev algebras. In fact they are the algebraic structures behind the O -operator of Hom-pre-Malcev algebras. They also ﬁt into a bigger framework as Hom-Malcev algebraic analogues of Hom-L-dendriform algebras. Furthermore, we show a connections between Hom-M-Dendriform algebras and Hom-alternative quadri-algebras.
Domain Adaptation for Memory-Efficient Dense Retrieval

Dense retrievers encode documents into ﬁxed dimensional embeddings. However, storing all the document embeddings within an index produces bulky indexes which are expensive to serve. Recently, BPR (Yamada et al., 2021) and JPQ (Zhan et al., 2021a) have been proposed which train the model to produce binary document vectors, which reduce the index 32 × and more. The authors showed these binary embedding models signiﬁcantly outperform more traditional index compression techniques like Product Quantization (PQ). Previous work evaluated these approaches just in-domain, i.e. the methods were evaluated on tasks for which training data is available. In practice, retrieval models are often used in an out-of-domain setting, where they have been train on a publicly available dataset, like MS MARCO, but are then used for some custom dataset for which no training data is available. In this work, we show that binary embedding models like BPR and JPQ can perform significantly worse than baselines once there is a domain-shift involved. We propose a modiﬁ-cation to the training procedure of BPR and JPQ and combine it with a corpus speciﬁc generative procedure which allow the adaptation of BPR and JPQ to any corpus without requiring labeled training data. Our domain-adapted strategy known as GPL is model ag-nostic, achieves an improvement by up-to 19.3 and 11.6 points in nDCG@10 across the BEIR benchmark in comparison to BPR and JPQ while maintaining its 32x memory efﬁciency. JPQ+GPL even outperforms our upper base-line: uncompressed TAS-B model on average by 2.0 points. 1
Sample Efficient Approaches for Idiomaticity Detection

Deep neural models, in particular Transformer-based pre-trained language models, require a significant amount of data to train. This need for data tends to lead to problems when dealing with idiomatic multiword expressions (MWEs), which are inherently less frequent in natural text. As such, this work explores sample efficient methods of idiomaticity detection. In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection. In addition, to further explore generalisability, we focus on the identification of MWEs not present in the training data. Our experiments show that while these methods improve performance on English, they are much less effective on Portuguese and Galician, leading to an overall performance about on par with vanilla mBERT. Regardless, we believe sample efficient methods for both identifying and representing potentially idiomatic MWEs are very encouraging and hold significant potential for future exploration.
Unsupervised Sign Language Phoneme Clustering using HamNoSys Notation

Traditionally, sign language resources have been collected in controlled settings for speciﬁc tasks involving supervised sign classiﬁcation or linguistic studies accompanied by speciﬁc annotation type. To date, very few who explored signing videos found online on social media platforms as well as the use of unsupervised methods applied to such resources. Due to the fact that the ﬁeld is striving to achieve acceptable model performance on the data that differs from that seen during training calls for more diversity in sign language data, stepping away from the data obtained in controlled laboratory settings. Moreover, since the sign language data collection and annotation carries large overheads, it is desirable to accelerate the annotation process. Considering the aforementioned tendencies, this paper takes the side of harvesting online data in a pursuit for automatically generating and annotating sign language corpora through phoneme clustering.
Self-Supervised Speech Representation Learning: A Review

Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Such methods have shown success in natural language processing and computer vision domains, achieving new levels of performance while reducing the number of labels required for many downstream scenarios. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. Other approaches rely on multi-modal data for pre-training, mixing text or visual data streams with speech. Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years. This review presents approaches for self-supervised speech representation learning and their connection to other research areas. Since many current methods focus solely on automatic speech recognition as a downstream task, we review recent efforts on benchmarking learned representations to extend the application beyond speech recognition.
The effect of curricular reform on gross anatomy laboratory examination performance: An institutional analysis

Many medical schools have undergone curricular reform recently. With these reforms, time spent teaching anatomy has been reduced, and there has been a general shift to a pass/fail grading system. At Indiana University School of Medicine (IUSM), a new curriculum was implemented in fall 2016. The year‐long human gross anatomy course taught in 2015 was condensed into an integrated, semester‐long course starting in 2016. Additionally, the grading scale shifted to pass/fail. This study examined first‐year medical student performance on anatomy practical laboratory examinations—specifically, among lower‐order (pure identification) questions and higher‐order (function, innervation) questions. Participants included medical students from a pre‐curricular reform cohort (year 2015, 34 students) and two post‐curricular reform cohorts (years 2016, 30 students and 2017, 33 students). A Kruskal–Wallis ANOVA test was used to determine differences of these questions among the three cohorts. Additionally, 40 of the same lower‐order questions that were asked on gross anatomy laboratory examinations from medical student cohort year 2015 and year 2016 were further analyzed using an independent samples t‐test. Results demonstrated that the pre‐curricular reform cohort scored significantly higher on both lower‐order (median = 81, p < 0.001) and higher‐order questions (median = 82.5, p < 0.05) than both post‐curricular reform cohorts. Additionally, when reviewing the selected 40 similar questions, it was found that the pre‐curricular reform cohort averaged significantly higher (82.1 ± 16.1) than the post‐curricular reform cohort from 2016 (69.3 ± 21.8, p = 0.004). This study provides evidence about the impact of curricular reform on medical student anatomical knowledge.
Please, Don’t Forget the Difference and the Confidence Interval when Seeking for the State-of-the-Art Status

This paper argues for the widest possible use of bootstrap confidence intervals for comparing NLP system performances instead of the state-of-the-art status (SOTA) and statistical significance testing. Their main benefits are to draw attention to the difference in performance between two systems and to help assessing the degree of superiority of one system over another. Two cases studies, one comparing several systems and the other based on a K-fold cross-validation procedure, illustrate these benefits.
ScholarBERT: Bigger is Not Always Better

Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have shown impressive performance on various downstream tasks. In-creasingly, researchers are “ﬁnetuning” these models to improve performance on domain-speciﬁc tasks. Here, we report a broad study in which we applied 14 transformer-based models to 11 scientiﬁc tasks in order to evaluate how downstream performance is affected by changes along various dimensions (e.g., training data, model size, pretraining time, ﬁnetun-ing length). In this process, we created the largest and most diverse scientiﬁc language model to date, ScholarBERT, by training a 770M-parameter BERT model on an 221B to-ken scientiﬁc literature dataset spanning many disciplines. Counterintuitively, our evaluation of the 14 BERT-based models (seven versions of ScholarBERT, ﬁve science-speciﬁc large language models from the literature, BERT-Base, and BERT-Large) reveals little difference in performance across the 11 science-focused tasks, despite major differences in model size and training data. We argue that our results establish an upper bound for the performance achievable with BERT-based architectures on tasks from the scientiﬁc domain.
Efficient PHY Layer Abstraction under Imperfect Channel Estimation

As most existing work investigate the PHY layer abstraction under an assumption of perfect channel estimation, it may become unreliable if there exists channel estimation error in a real communication system. This letter improves an efficient PHY layer method, EESM-log-SGN PHY layer abstraction, by considering the presence of channel estimation error. We develop two methods for implementing the EESM-log-SGN PHY abstraction under imperfect channel estimation. We show that the effective SINR is not impacted by the channel estimation error under multiple-input and single-output (MISO)/single-input and single-output (SISO) configuration, which is also verified by the full PHY simulation. The developed methods are then validated under different orthogonal frequency division multiplexing (OFDM) scenarios.
Masterful: A Training Platform for Computer Vision Models

Masterful is a software platform to train deep learning computer vision models. Data and model architecture are inputs to the platform, and the output is a trained model. The platform’s primary goal is to maximize a trained model’s accuracy, which it achieves through its regularization and semi-supervised learning implementations. The platform’s secondary goal is to minimize the amount of manual experimentation typically required to tune training hyperparameters, which it achieves via multiple metalearning algorithms which are custom built to control the platform’s regularization and semi-supervised learning implementations. The platform’s tertiary goal is to minimize the computing resources required to train a model, which it achieves via another set of metalearning algorithms which are purpose built to control Tensorﬂow’s optimization implementations. The platform builds on top of Tensorﬂow’s data management, architecture, automatic differentiation, and optimization implementations.