Miracle-dz/UniFAR-FTUs huggingface dataset:
Enterohormone therapy for short bowel syndrome

Purpose of review Short bowel syndrome (SBS) patients are at risk to develop intestinal failure when the decreased absorption of macronutrients, water, and electrolytes necessitates parenteral support for survival. The adverse effects of SBS and parenteral support negatively affect the quality of life (QoL) of SBS-intestinal failure patients. However, spontaneous intestinal adaptation along with disease-modifying therapies allow reducing parenteral support, thereby improving QoL. Recent findings During the first years following extensive surgery, spontaneous structural and functional intestinal changes take place which stimulate a more efficient nutrient and fluid absorption in the remaining bowel. Given their potential role in the ileal braking mechanism, enterohormones, such as glucagon-like peptide (GLP)-2, GLP-1, and peptide YY (PYY), promote an accelerated adaptation or hyperadaptation. While the exact role of GLP-1 and PYY in SBS is still being explored, GLP-2 analogs have clearly shown to be effective in improving outcome in SBS. Summary Whereas spontaneous intestinal adaptation improves the nutritional status of SBS patients to a certain extent, GLP-2 analogs can further decrease parenteral support needs through hyperadaptation. There are, however, other promising candidates on the horizon that – alone or in combination – could possibly establish additional disease-modifying effects.
First intention high-frequency jet ventilation for periviable infants

Purpose of review Ventilation of periviable infants born at 22–23 weeks gestation remains a challenge in neonatology. This review highlights the evidence surrounding the use of first intention high-frequency jet ventilation (HFJV) in infants born near the limits of viability with a review of pulmonary fetal development and a focused overview of HFJV strategies including an in-depth analysis of the management strategies used in the initial randomized trials. Recent findings A paucity of recent trials exists, with no randomized control trials assessing the use of first intention HFJV performed in the last 25 years. A retrospective observational cohort trial of the use of HFJV for infants born at less than 750 g has been recently published demonstrating the efficacy of HFJV for this population even with 2.0-mm endotracheal tubes. Summary The lack of recent randomized trials contributes to the controversy surrounding the use of first intention HFJV. Although new research is needed in the area, this review includes the ventilation strategy of an experienced center with a focus on the use of first intention HFJV for the care of premature infants born less than 24 weeks gestation.
Airway Fire Prevention in the Operating Room

Airway fires during surgical procedures are preventable events that result in devastating outcomes for patients, healthcare providers, and healthcare facilities. Minimization of the risk of fires is a subsection of Standard 6 of the American Association of Nurse Anesthetists’ Standards for Nurse Anesthesia Practice. A central priority of operating room fire mitigation is the minimized use of oxidizing agents. Oxygen is one component of the fire triad responsible for increasing the likelihood of these events, which is controlled by anesthesia providers in the operating room. The primary aim of this quality improvement project was to increase anesthesia provider knowledge pertaining to airway fire risk mitigation strategies through the implementation of an evidenced-based educational intervention. The secondary aim of this quality improvement project was to demonstrate a quantitative reduction of airway fire risk through a comparison of pre-intervention and post-intervention supplemental oxygen administration levels during surgical procedures. The overall goal of this project was to highlight the value of optimized provider knowledge regarding airway fire risk mitigation techniques, especially among high-risk airway fire procedures.
The Role of Stigma Cost in Child Labor

The nuisance of child labor in underdeveloped and developing countries is a matter of grave concern. Exposing children into a vast world of the labor market at a susceptible phase of life leads to cutting down on their education, loss of creation of human capital and never-ending bad days for the country as a whole. These unfortunate children grow up and enter into the formal labor markets as unskilled laborers, destined to earn abysmally low wages throughout their adulthood. This vicious cycle of illegal, yet forced child labor continues for generations. Hence, the study and an effective course of action in this regard are peremptory. This paper tries to establish a model-theoretic framework of child labor from the supply sided point of view. It tries to investigate the determinants of supply of child labor through the parent’s problem of household time allocation for a child in the face of a trichotomy of working in the market, working at home, and study time. Assuming an altruistic attitude of parents, the paper solves a simple household utilization problem of family welfare, taking child labor in the household and markets as a constraint. It is established that the time allotted for child labor in both household and markets reduce significantly with the increase in adult income. The study further tries to analyze the effects of imposition of a socio-psychological stigma cost on the incidence of child labor. Finally, the paper discusses the various probable policy implementations which can be considered by the government either through direct abolition of child labor or by indirectly raising the stigma cost among masses.
TARGET-HF: developing a model for detecting incident heart failure among symptomatic patients in general practice using routine health care data

Background: Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data. Methods: Our study involved a dynamic cohort of patients (>=35 years) who consulted their GP with either dyspnea and/or peripheral edema within the Amsterdam metropolitan area in 2011-2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor. Results: Data from 31,905 patients were included (40% male, median age 60) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n=25,524) and validation (n=6,381) set. TARGET-HF attained a C-statistic of 0.853 (95%-CI:0.834-0.872) on the validation set, which proved to provide a better discrimination than C=0.822 for age alone (95% CI:0.801-0.842, p<0.001) and C=0.824 for the hospital-based model (95% CI:0.802-0.843, p<0.001). Conclusion: The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at time of GP consultation.
Efficacy and safety of Yangxue Qingnao granules for the treatment of essential hypertension

Abstract Background: Essential hypertension is a major risk factor for many fatal cardiovascular and cerebrovascular diseases and has become a heavy burden on families and society. At present, the prevention and treatment of essential hypertension is still unsatisfactory. Yangxue Qingnao granules is a kind of Chinese patent medicine that has been used to treat essential hypertension. The objective of this protocol is to systematically evaluate the efficacy and safety of Yangxue Qingnao granules in the treatment of essential hypertension. Methods: Randomized controlled trials on Yangxue Qingnao granules for essential hypertension will be searched from the following databases: PubMed, EMbase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, China Science and Technology Journal Database, and China Biology Medicine disc from inception to August 27, 2021, regardless of language. Study screening and data extraction will be carried out by two independent reviewers. The quality of the included studies will be assessed using Cochrane risk-of-bias tool for randomized trials. Statistic analysis will be performed using RevMan 5.3 software. The quality of evidence will be assessed using GRADE approach. Results: This study will systematically evaluate the efficacy and safety of Yangxue Qingnao granules in the treatment of essential hypertension and provide high-quality evidence for clinical practice. Conclusion: The findings of this systematic review will provide high-quality evidence to verify the efficacy and safety of Yangxue Qingnao granules in the treatment of essential hypertension. INPLASY registration number: INPLASY202190015.
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution

Convolutional neural network (CNN) has achieved great success on image super-resolution (SR). However, most deep CNN-based SR models take massive computations to obtain high performance. Downsampling features for multi-resolution fusion is an efficient and effective way to improve the performance of visual recognition. Still, it is counter-intuitive in the SR task, which needs to project a low-resolution input to high-resolution. In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task. The network contains pixel-unshuffled downsampling and Self-Residual Depthwise Separable Convolutions. Specifically, we utilize pixel-unshuffle operation to downsample the input features and use grouped convolution to reduce the channels. Besides, we enhance the depthwise convolution's performance by adding the input feature to its output. The comparison findings demonstrate that, with fewer parameters and computational costs, our HPUN achieves and surpasses the state-of-the-art performance on SISR. All results are provided in the github https://github.com/Sun1992/HPUN.
Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching

Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations that are not relevant to the task. We hypothesize that enriching models with speaker information in a controlled, educated way can guide them to pick up on relevant inductive biases. For the speaker-driven task of predicting code-switching points in English–Spanish bilingual dialogues, we show that adding sociolinguistically-grounded speaker features as prepended prompts significantly improves accuracy. We find that by adding influential phrases to the input, speaker-informed models learn useful and explainable linguistic information. To our knowledge, we are the first to incorporate speaker characteristics in a neural model for code-switching, and more generally, take a step towards developing transparent, personalized models that use speaker information in a controlled way.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation

Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE methods. We first show that a residual block of layers in Transformer can be described as a higher-order solution to ODE. Inspired by this, we design a new architecture, ODE Transformer, which is analogous to the Runge-Kutta method that is well motivated in ODE. As a natural extension to Transformer, ODE Transformer is easy to implement and efficient to use. Experimental results on the large-scale machine translation, abstractive summarization, and grammar error correction tasks demonstrate the high genericity of ODE Transformer. It can gain large improvements in model performance over strong baselines (e.g., 30.77 and 44.11 BLEU scores on the WMT’14 English-German and English-French benchmarks) at a slight cost in inference efficiency.
Dimensionality Reduction and Wasserstein Stability for Kernel Regression

In a high-dimensional regression framework, we study consequences of the naive two-step procedure where first the dimension of the input variables is reduced and second, the reduced input variables are used to predict the output variable with kernel regression. In order to analyze the resulting regression errors, a novel stability result for kernel regression with respect to the Wasserstein distance is derived. This allows us to bound errors that occur when perturbed input data is used to fit the regression function. We apply the general stability result to principal component analysis (PCA). Exploiting known estimates from the literature on both principal component analysis and kernel regression, we deduce convergence rates for the two-step procedure. The latter turns out to be particularly useful in a semi-supervised setting.
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation

Data Augmentation (DA) is known to improve the generalizability of deep neural networks. Most existing DA techniques naively add a certain number of augmented samples without considering the quality and the added computational cost of these samples. To tackle this problem, a common strategy, adopted by several state-of-the-art DA methods, is to adaptively generate or re-weight augmented samples with respect to the task objective during training. However, these adaptive DA methods: (1) are computationally expensive and not sample-efficient, and (2) are designed merely for a specific setting. In this work, we present a universal DA technique, called Glitter, to overcome both issues. Glitter can be plugged into any DA method, making training sample-efficient without sacrificing performance. From a pre-generated pool of augmented samples, Glitter adaptively selects a subset of worst-case samples with maximal loss, analogous to adversarial DA. Without altering the training strategy, the task objective can be optimized on the selected subset. Our thorough experiments on the GLUE benchmark, SQuAD, and HellaSwag in three widely used training setups including consistency training, self-distillation and knowledge distillation reveal that Glitter is substantially faster to train and achieves a competitive performance, compared to strong baselines.
DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications

The ability to predict the future trajectory of a patient is a key step toward the development of therapeutics for complex diseases such as Alzheimer's disease (AD). However, most machine learning approaches developed for prediction of disease progression are either single-task or single-modality models, which can not be directly adopted to our setting involving multi-task learning with high dimensional images. Moreover, most of those approaches are trained on a single dataset (i.e. cohort), which can not be generalized to other cohorts. We propose a novel multimodal multi-task deep learning model to predict AD progression by analyzing longitudinal clinical and neuroimaging data from multiple cohorts. Our proposed model integrates high dimensional MRI features from a 3D convolutional neural network with other data modalities, including clinical and demographic information, to predict the future trajectory of patients. Our model employs an adversarial loss to alleviate the study-specific imaging bias, in particular the inter-study domain shifts. In addition, a Sharpness-Aware Minimization (SAM) optimization technique is applied to further improve model generalization. The proposed model is trained and tested on various datasets in order to evaluate and validate the results. Our results showed that 1) our model yields significant improvement over the baseline models, and 2) models using extracted neuroimaging features from 3D convolutional neural network outperform the same models when applied to MRI-derived volumetric features.
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training

Due to the limitations of the model structure and pre-training objectives, existing vision-and-language generation models cannot utilize pair-wise images and text through bi-directional generation. In this paper, we propose DU-VLG, a framework which unifies vision-and-language generation as sequence generation problems. DU-VLG is trained with novel dual pre-training tasks: multi-modal denoising autoencoder tasks and modality translation tasks. To bridge the gap between image understanding and generation, we further design a novel commitment loss. We compare pre-training objectives on image captioning and text-to-image generation datasets. Results show that DU-VLG yields better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss. We also obtain higher scores compared to previous state-of-the-art systems on three vision-and-language generation tasks. In addition, human judges further confirm that our model generates real and relevant images as well as faithful and informative captions.
Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle

Deep learning-based methods have made significant achievements for image dehazing. However, most of existing dehazing networks are concentrated on training models using simulated hazy images, resulting in generalization performance degradation when applied on real-world hazy images because of domain shift. In this paper, we propose a mutual learning dehazing framework for domain adaption. Specifically, we first devise two siamese networks: a teacher network in the synthetic domain and a student network in the real domain, and then optimize them in a mutual learning manner by leveraging EMA and joint loss. Moreover, we design a sample-cycle strategy based on density augmentation (HDA) module to introduce pseudo real-world image pairs provided by the student network into training for further improving the generalization performance. Extensive experiments on both synthetic and real-world dataset demonstrate that the propose mutual learning framework outperforms state-of-the-art dehazing techniques in terms of subjective and objective evaluation.
Multipartite electron-nuclear gates in defect systems through dynamical decoupling sequences

Quantum networks play an indispensable role in quantum information tasks such as secure communications, enhanced quantum sensing, and distributed computing. Among the most mature and promising platforms for quantum networking are color centers in solids. One of the challenges in using these systems for networking applications is to controllably created and manipulate entanglement between the electron and the nuclear spin register. Here, we develop a general formalism to quantify and control the generation of entanglement in an arbitrarily large nuclear spin register coupled to a defect electronic spin. We provide a reliable measure of nuclear spin selectivity, taking into account the presence of unwanted nuclei. We show how to realize direct multipartite gates through the use of dynamical decoupling sequences, reducing drastically the total gate time compared to protocols based on sequential entanglement with individual nuclear spins. We quantify the performance of such gate operations in the presence of unwanted residual entanglement links, capturing the dynamics of the entire nuclear spin register.
Behavior-Aware Account De-Anonymization on Ethereum Interaction Graph

Blockchain technology has the characteristics of decentralization, traceability and tamper-proof, which creates a reliable decentralized trust mechanism, further accelerating the development of blockchain finance. However, the anonymization of blockchain hinders market regulation, resulting in increasing illegal activities such as money laundering, gambling and phishing fraud on blockchain financial platforms. Thus, financial security has become a top priority in the blockchain ecosystem, calling for effective market regulation. In this paper, we consider identifying Ethereum accounts from a graph classification perspective, and propose an end-to-end graph neural network framework named Ethident, to characterize the behavior patterns of accounts and further achieve account de-anonymization. Specifically, we first construct an Account Interaction Graph (AIG) using raw Ethereum data. Then we design a hierarchical graph attention encoder named HGATE as the backbone of our framework, which can effectively characterize the node-level account features and subgraph-level behavior patterns. For alleviating account label scarcity, we further introduce contrastive self-supervision mechanism as regularization to jointly train our framework. Comprehensive experiments on Ethereum datasets demonstrate that our framework achieves superior performance in account identification, yielding 1.13% $\sim ~4.93$ % relative improvement over previous state-of-the-art. Furthermore, detailed analyses illustrate the effectiveness of Ethident in identifying and understanding the behavior of known participants in Ethereum (e.g. exchanges, miners, etc.), as well as that of the lawbreakers (e.g. phishing scammers, hackers, etc.), which may aid in risk assessment and market regulation.
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an $\epsilon$-optimal policy with probability $1-\delta$. While minimax optimal algorithms exist for this problem, its instance-dependent complexity remains elusive in episodic Markov decision processes (MDPs). In this paper, we propose the first nearly matching (up to a horizon squared factor and logarithmic terms) upper and lower bounds on the sample complexity of PAC RL in deterministic episodic MDPs with finite state and action spaces. In particular, our bounds feature a new notion of sub-optimality gap for state-action pairs that we call the deterministic return gap. While our instance-dependent lower bound is written as a linear program, our algorithms are very simple and do not require solving such an optimization problem during learning. Their design and analyses employ novel ideas, including graph-theoretical concepts (minimum flows) and a new maximum-coverage exploration strategy.
Ask to Understand: Question Generation for Multi-hop Question Answering

“Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thusachieving good performance. At the same time, the latter provides a clear reasoning logical routeby decomposing multi-hop questions into simple single-hop sub-questions. In this paper, wepropose a novel method to complete multi-hop QA from the perspective of Question Genera-tion (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classicalQA module, which could help the model understand the context by asking inherently logicalsub-questions, thus inheriting interpretability from the QD-based method and showing superiorperformance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of ourproposed QG module, human evaluation further clarifies its interpretability quantitatively, andthorough analysis shows that the QG module could generate better sub-questions than QD meth-ods in terms of fluency, consistency, and diversity. ”