Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Efficiency of Finely Stratified Experiments

This paper studies the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. Here, efficiency is understood to be with respect to a broad class of treatment assignment schemes for which the marginal probability that any unit is assigned to treatment equals a pre-specified value, e.g., one half. Importantly, we do not require that treatment status is assigned in an i.i. d. fashion, thereby accommodating complicated treatment assignment schemes that are used in practice, such as stratified block randomization and matched pairs. The class of parameters considered are those that can be expressed as the solution to a set of moment conditions involving a known function of the observed data, including possibly the pre-specified value for the marginal probability of treatment assignment. We show that this class of parameters includes, among other things, average treatment effects, quantile treatment effects, local average treatment effects as well as the counterparts to these quantities in experiments in which the unit is itself a cluster. In this setting, we establish two results. First, we derive a lower bound on the asymptotic variance of estimators of the parameter of interest in the form of a convolution theorem. Second, we show that the naive method of moments estimator achieves this bound on the asymptotic variance quite generally if treatment is assigned using a"finely stratified"design. By a"finely stratified"design, we mean experiments in which units are divided into groups of a fixed size and a proportion within each group is assigned to treatment uniformly at random so that it respects the restriction on the marginal probability of treatment assignment. In this sense,"finely stratified"experiments lead to efficient estimators of treatment effect parameters"by design"rather than through ex post covariate adjustment.
YOLOv8 for defect inspection of hexagonal directed self-assembly patterns: a data-centric approach

Shrinking pattern dimensions leads to an increased variety of defect types in semiconductor devices. This has spurred innovation in patterning approaches such as Directed Self-Assembly (DSA) for which no traditional, automatic defect inspection software exists. Machine Learning-based SEM image analysis has become an increasingly popular research topic for defect inspection with supervised ML models often showing the best performance. However, little research has been done on obtaining a dataset with high-quality labels for these supervised models. In this work, we propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns while requiring minimal quality control effort from a DSA expert. We show that YOLOv8, a state-of-the-art neural network, achieves defect detection precisions of more than 0.9 mAP on our final dataset which best reflects DSA expert defect labeling expectations. We discuss the strengths and limitations of our proposed labeling approach and suggest directions for future work in data-centric ML-based defect inspection.
Compound heat wave and PM2.5 pollution episodes in U.S. cities

This study analyzes heat waves (HWs), air pollution (AP) episodes, and compound HW and AP events (CE) in the urban environment and provides a comparison between events in urban areas (UAs) and rural areas (RAs). A 1-km gridded daily minimum temperature dataset and a 1-km gridded daily PM2.5 concentration dataset were used along with geospatial data to characterize events by their frequency, intensity in heat, intensity in pollution, and duration. The greatest differences between UAs and RAs in frequency, heat intensity, pollution intensity, and duration for all events were seen in the West and Southwest regions. For both UAs and RAs, it was found that HWs were the most frequent, intense, and longest lasting in the West and Southwest regions, AP episodes were the most frequent and longest lasting in the Northeast, Ohio Valley, and Southeast regions, and AP episodes were the most intense in the Northern Rockies and Plains and Upper Midwest regions. It was concluded that HWs (AP episodes) had a greater impact on CEs than AP episodes (HWs) in regions with more prominent HWs (AP episodes).
Effective statistical control strategies for complex turbulent dynamical systems

Control of complex turbulent dynamical systems involving strong nonlinearity and high degrees of internal instability is an important topic in practice. Different from traditional methods for controlling individual trajectories, controlling the statistical features of a turbulent system offers a more robust and efficient approach. Crude first-order linear response approximations were typically employed in previous works for statistical control with small initial perturbations. This paper aims to develop two new statistical control strategies for scenarios with more significant initial perturbations and stronger nonlinear responses, allowing the statistical control framework to be applied to a much wider range of problems. First, higher-order methods, incorporating the second-order terms, are developed to resolve the full control-forcing relation. The corresponding changes to recovering the forcing perturbation effectively improve the performance of the statistical control strategy. Second, a mean closure model for the mean response is developed, which is based on the explicit mean dynamics given by the underlying turbulent dynamical system. The dependence of the mean dynamics on higher-order moments is closed using linear response theory but for the response of the second-order moments to the forcing perturbation rather than the mean response directly. The performance of these methods is evaluated extensively on prototype nonlinear test models, which exhibit crucial turbulent features, including non-Gaussian statistics and regime switching with large initial perturbations. The numerical results illustrate the feasibility of different approaches due to their physical and statistical structures and provide detailed guidelines for choosing the most suitable method based on the model properties.
Med-HALT: Medical Domain Hallucination Test for Large Language Models

This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. Hallucination, wherein these models generate plausible yet unverified or incorrect information, can have serious consequences in healthcare applications. We propose a new benchmark and dataset, Med-HALT (Medical Domain Hallucination Test), designed specifically to evaluate and reduce hallucinations. Med-HALT provides a diverse multinational dataset derived from medical examinations across various countries and includes multiple innovative testing modalities. Med-HALT includes two categories of tests reasoning and memory-based hallucination tests, designed to assess LLMs’ problem-solving and information retrieval abilities. Our study evaluated leading LLMs, including Text Davinci, GPT-3.5, LlaMa-2, MPT, and Falcon, revealing significant differences in their performance. The paper provides detailed insights into the dataset, promoting transparency and reproducibility. Through this work, we aim to contribute to the development of safer and more reliable language models in healthcare. Our benchmark can be found at medhalt.github. io
A Secure Open-Source Intelligence Framework For Cyberbullying Investigation

Cyberbullying has become a pervasive issue based on the rise of cell phones and internet usage affecting individuals worldwide. This paper proposes an open-source intelligence pipeline using data from Twitter to track keywords relevant to cyberbullying in social media to build dashboards for law enforcement agents. We discuss the prevalence of cyberbullying on social media, factors that compel individuals to indulge in cyberbullying, and the legal implications of cyberbullying in different countries also highlight the lack of direction, resources, training, and support that law enforcement officers face in investigating cyberbullying cases. The proposed interventions for cyberbullying involve collective efforts from various stakeholders, including parents, law enforcement, social media platforms, educational institutions, educators, and researchers. Our research provides a framework for cyberbullying and provides a comprehensive view of the digital landscape for investigators to track and identify cyberbullies, their tactics, and patterns. An OSINT dashboard with real-time monitoring empowers law enforcement to swiftly take action, protect victims, and make significant strides toward creating a safer online environment.
D2S: Representing local descriptors and global scene coordinates for camera relocalization

State-of-the-art visual localization methods mostly rely on complex procedures to match local descriptors and 3D point clouds. However, these procedures can incur significant cost in terms of inference, storage, and updates over time. In this study, we propose a direct learning-based approach that utilizes a simple network named D2S to represent local descriptors and their scene coordinates. Our method is characterized by its simplicity and cost-effectiveness. It solely leverages a single RGB image for localization during the testing phase and only requires a lightweight model to encode a complex sparse scene. The proposed D2S employs a combination of a simple loss function and graph attention to selectively focus on robust descriptors while disregarding areas such as clouds, trees, and several dynamic objects. This selective attention enables D2S to effectively perform a binary-semantic classification for sparse descriptors. Additionally, we propose a new outdoor dataset to evaluate the capabilities of visual localization methods in terms of scene generalization and self-updating from unlabeled observations. Our approach outperforms the state-of-the-art CNN-based methods in scene coordinate regression in indoor and outdoor environments. It demonstrates the ability to generalize beyond training data, including scenarios involving transitions from day to night and adapting to domain shifts, even in the absence of the labeled data sources. The source code, trained models, dataset, and demo videos are available at the following link: https://thpjp.github. io/d2s
Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features.
Rationale-Guided Few-Shot Classification to Detect Abusive Language

Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain evaluation settings. To overcome this, a common strategy is to use a few samples from the target domain to train models to get better performance in that domain (cross-domain few-shot training). However, this might cause the models to overfit the artefacts of those samples. A compelling solution could be to guide the models toward rationales, i.e., spans of text that justify the text's label. This method has been found to improve model performance in the in-domain setting across various NLP tasks. In this paper, we propose RGFS (Rationale-Guided Few-Shot Classification) for abusive language detection. We first build a multitask learning setup to jointly learn rationales, targets, and labels, and find a significant improvement of 6% macro F1 on the rationale detection task over training solely rationale classifiers. We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains. Furthermore, RGFS-based models outperform LIME/SHAP-based approaches in terms of plausibility and are close in performance in terms of faithfulness.
Aging and Interstitial Lung Diseases: Unraveling an Old Forgotten Player in the Pathogenesis of Lung Fibrosis

Aging is a natural process characterized by a progressive functional impairment and reduced capacity to respond adaptively to environmental stimuli. Aging is associated with increased susceptibility to a variety of chronic diseases, including type 2 diabetes mellitus, cancer, and neurological diseases. Lung pathologies are not the exception, and the prevalence of several interstitial lung diseases (ILDs), primarily idiopathic pulmonary ﬁbrosis, has been found to increase considerably with age. Although our understanding of the biology of aging has advanced remarkably in the last 2 decades, the molecular mechanisms linking aging to ILD remain unclear. Immunosenescence, oxidative stress, abnormal shortening of telomeres, apoptosis, and epigenetic changes affecting gene expression have been proposed to contribute to the aging process, and aging-associated diseases. Here, we review the emerging concepts highlighting the putative aging-associated abnormalities involved in some human ILDs.
Rationale for Investigation of Epidermal Growth Factor Receptor Inhibitors in Definitive Treatment of Locally Advanced Non-Small Cell Lung Cancer and Head and Neck Cancer

Designing targeted therapies has become an important ﬁeld in cancer therapeutics. The epidermal growth factor receptor (EGFR) is a molecular target that has gained immense attention as preclinical and clinical studies have supported its potential role for therapy of a variety of cancers, including non–small cell lung cancer (NSCLC) and head and neck (HN) cancer. Several compounds that speciﬁcally inhibit EGFR have been developed, including ZD1839, C225, and OSI-774. Interestingly, studies suggesting a potential role for EGFR inhibitors as an adjunct to the current combined-modality approach for therapy of NSCLC and HN cancer have been performed in the preclinical and clinical setting. Therefore, determining the potential of EGFR inhibitors to improve the efﬁcacy of standard combined-modality regimens (chemotherapy/radiation therapy (cid:1) surgery) for NSCLC and HN cancer patients is of the utmost importance. An overview of the rationale and the ongoing/proposed studies aimed at determining the role for EGFR inhibitors in combination with radiation therapy for NSCLC and HN cancer patients will be presented.
How Should We Manage Empyema: Antibiotics Alone, Fibrinolytics, or Primary Video-Assisted Thoracoscopic Surgery (VATS)?

Empyema is a well-recognized complication of pneumonia and its prevalence is increasing in the childhood population. The management of these patients requires a strategy for diagnosis and treatment that results in prompt resolution of infection and discharge with minimal morbidity. Traditionally conservative treatment has been the standard with insertion of a chest drain and intravenous antibiotics and, for those who fail to respond, an open thoracotomy and formal decortication. Since the 1990s two new treatment modalities have been described; ﬁbrinolysis (promoting pleural drainage and circulation) and early VATS (video-assisted thoracoscopic surgery). Many institutions are now using one of these methods as ﬁrst-line therapy. Both of these treatments result in shorter hospital stay and fewer complications than the conservative approach. In this review we will appraise the evidence for all three treatments and consider which treatment is optimal in children.
Germ Cell Transplantation

Transplantation of male germ line stem cells from a fertile donor to the testis of an infertile recipient restores donor-derived spermatogenesis in the recipient testis. The resulting sperm pass the donor genotype to the offspring of the recipient. Germ cell transplantation helped to elucidate the biology of male germ line stem cells and their niche in the testis, develop systems to isolate and culture spermatogonial stem cells, examine defects in spermatogenesis, correct male infertility, and introduce genetic changes into the male germ line. Although most widely studied in rodents, germ cell transplantation has been applied to larger mammals, including primates. Potential clinical applications include restoration of fertility in patients who underwent sterilizing treatments for cancer or targeted correction of genetic defects in testicular somatic cells. Recently, ectopic grafting of testis tissue from diverse donor species, including primates, into a mouse host has opened an additional possibility to study spermatogenesis and to produce fertile sperm from immature donors. Testis xenografts are ideally suitable to study toxicants or drugs with the potential to enhance or suppress male fertility without the necessity of performing experiments in the target species. Therefore, transplantation of germ cells and xenografting of testis tissue represent powerful approaches for the study, preservation, and manipulation of male fertility.
Effect of Strokes on Musical Ability and Performance

Man is not the only musical animal. Most male birds sing to proclaim their territory or to woo a mate. Moreover, musical ability confers advantages. A simple repetitive song announces mastery over a territory, but, according to John Krebs,' great tits sing several slightly different songs to delude potential intruders into thinking that the territory has several defenders. Female birds adopt a special inviting posture when enticing males to mate. Females in-jected with estr-adiol will assume this posture on hearing tape recordings of the appropriate male song. Female song sparrows display for copulation more readily to several song types than to repetition of one song. ' Female sedge warblers respond in direct proportion to the size of the repertoire.Vemale canaries that hear more complex male songs build nests sooner and lay more eggs than female mates of musically less adept males. "edge warblers have 21 vocabulary of up to 50 musical syllables, which they rearrange into a constantly changing repertoire. " It is unclear whether originality translates into an advantage, although among great tits females prefer tn mate with males singing slightly unfamiliar songs compared with the familiar song of the female's father.5 One can only speculate on the evolutionary advantage of musical ability and the atavistic meaning of martial music and serenades. The fact that music-evoked thrills can be abolished by naloxone" suggests that musical pleasure is mediated by opioid systems and probably has a long and primeval evolutionary history.
Diagnosis and Management of Parapneumonic Effusions

Parapneumonic effusions affect many patients and are associated with considerable morbidity and mortality. It is necessary to differentiate complicated effusions requiring intervention from uncomplicated effusions. Differentiation is achieved using clinical, pleural ﬂuid, and imaging parameters. Intervention takes the form of blind catheter placement and drainage, image-guided catheter placement and drainage, and surgical decortication [video-assisted thoracoscopic surgery (VATS) or open thoracotomy]. Image-guided drainage and management of complicated effusions in adults and pediatric patients are safe and highly effective in select patients. The use of intrapleural ﬁbrinolytic agents to facilitate resolution of complicated effusions is widespread and considered effective by many despite a lack of conclusive data supporting this method. We propose an algorithmic approach to patients with parapneumonic effusions and advocate image-guided drainage and management in patients likely to beneﬁt from this treatment.
Review: Role of Genetics in Susceptibility and Outcome of Sarcoidosis

Sarcoidosis develops in genetically predisposed individuals that are exposed to unknown antigens. There is a statistically signiﬁcant increased risk for the disease among family members of sarcoidosis patients, and the disease differs in different ethnic groups. It is a genetically complex disease, with many genes contributing, both as risk factors but also with an inﬂuence on the disease course. The strongest genetic associations with sarcoidosis are found within the major histocompatibility complex (MHC) [human leukocyte antigen (HLA) in humans]—region on chromosome six. This region includes, besides the HLA-class I and class-II genes, a large number of genes important for the immune response and inﬂammation. The well studied associations between sarcoidosis and HLA-class II molecules indicate speciﬁc antigen presentation for T helper cells, which is in agreement with the characteristic ﬁnding of lung-accumulated CD4 þ T helper cells expressing a limited set of T cell receptors. Strong associations between distinct HLA-DRB1 alleles and the disease phenotype and course have been described, especially so in patient subgroups. Although a large number of genes are associated with sarcoidosis, this review focuses on gene associations and their inﬂuence on the disease phenotype, including the disease course.
Thrombotic Microangiopathy in Transplantation and Malignancy

Thrombotic microangiopathy (TMA) after hematopoietic stem-cell transplantation (HSCT) or solid-organ transplantation is a serious complication that may be associated with diverse clinical conditions. The reported incidence varies widely, in part due to different diagnostic criteria. Currently, the diagnosis is based mostly on clinical features and is often uncertain; many disease or therapy-related complications in transplantation and malignancy can manifest clinical features of TMA. Risk factors for TMA post HSCT include the type of conditioning regimen, the presence graft-versus-host disease (GVHD), the use of calcineurin inhibitors (cyclosporine and tacrolimus) for GVHD prophylaxis, and infection. Cyclosporin and tacrolimus are the most commonly reported agents associated with TMA in solid-organ (mainly kidney) transplantations. Cancer-related TMA may be associated with chemotherapy or the malignancy itself. Compared with idiopathic TMA (thrombotic thrombocytopenic purpura), the outcome for patients with TMA post-HSCT or disseminated malignancy is poor. The efﬁcacy of plasma exchange in the treatment of TMA post-HSCT or malignancy is uncertain. In the future, objective criteria integrating laboratory features (including tissue pathology, quantitative hematology, and endothelial cell functionality) with clinical features may assist in the diagnostic accuracy of TMA post HSCT, which would allow better evaluation of treatment modalities and better prediction of prognosis and outcomes.