Miracle-dz/UniFAR-FTUs huggingface dataset:
Concomitant occurrence of appendiceal mucocele and ulcerative colitis: Case reports

Background: Appendiceal mucocele (AM) is a rare disease, manifested by accumulation of mucus in appendiceal lumen. The role of ulcerative colitis (UC) in the occurrence of appendiceal mucocele is not known. However, it is suggested that AM may be a presentation of colorectal cancer in IBD patients. Case Presentation: Here, we presented 3 cases of concomitant AM and ulcerative colitis. The first patient was a 55-year-old woman with 2-year history of left sided UC; the second person was a 52-year-old woman with 12-year history of pan-UC; and the third patient was a 60-year-old man with 11-year history of pan-colitis. They were all referred due to indolent right lower quadrant abdominal pain. Imaging evaluations suggested the presence of appendiceal mucocele and therefore, they all went under operation. Pathologic evaluation reported AM of mucinous cyst adenoma type; low-grade appendiceal mucinous neoplasm of appendix with intact serosa; and cyst-adenoma type AM for the three above-mentioned patients, respectively. Conclusion: Although concomitant occurrence of AM and ulcerative colitis is rare, regarding the potential of neoplastic changes in AM, physicians must keep in mind the diagnosis of AM in UC patients with non-specific abdominal RLQ pain or bulged appendiceal orifice during colonoscopy.
Impact of early telemedicine follow-up on 30-Day hospital readmissions

Introduction Telemedicine is increasing in popularity but the impact of this shift on patient outcomes has not been well described. Prior data has shown that early post-discharge office visits can reduce readmissions. However, it is unknown if routine use of telemedicine visits for this purpose is similarly beneficial. Materials and methods We conducted a retrospective observational study using electronic health records data to assess if the rate of 30-day hospital readmissions differed between modality of visit for primary care or cardiology post-discharge follow-up visits. Results Compared to discharges with completed in-person follow-up visits, the adjusted odds of readmission for those with telemedicine follow-up visits was not significantly different (odds ratio [OR] 0.96, 95% confidence interval [CI] 0.61 to 1.51, P = 0.86). Conclusions Our study showed that 30-day readmission rate did not differ significantly according to the modality of visit. These results provide reassurance that telemedicine visits are a safe and viable alternative for primary care or cardiology post-hospitalization follow-up.
Clinical, endoscopic, and demographic characteristics of idiopathic duodenal ulcers compared with helicobacter pylori positive ulcers

Background: Helicobacter pylori infection is the most common cause of peptic ulcer disease. However, the prevalence rates of non-helicobacter pylori idiopathic peptic ulcers have increased over the past few years. This study aims to compare the characteristics of Helicobacter pylori-positive with idiopathic duodenal ulcers. Methods: A cross-sectional cohort study was conducted on 950 patients which were excluded from the analysis process duo to the concomitant presence of gastric ulcer, malignancy, Zollinger Ellison syndrome, Crohn's disease, esophageal varices, history of taking anti-Helicobacter pylori therapy, and history of taking NSAID or aspirin. Eventually, 647 subjects were enrolled for the analysis process. In this case, these subjects were divided into two groups: (I) Helicobacter pylori-positive ulcer group and (II) Helicobacter pylori-negative and non-NSAID (idiopathic) ulcer group. Results: The findings showed that 417 patients (64.5%) had duodenal ulcers induced by Helicobacter pylori, and 111 patients (17.1%) had Helicobacter pylori-negative and non-NSAID ulcers. The mean ages of patients in Helicobacter pylori-positive and idiopathic ulcer groups were 39±15 and 42±17, respectively. In this case, 33 patients (29.7%) with idiopathic ulcers and 56 patients (25.1%) with Helicobacter pylori-positive ulcers had upper gastrointestinal bleeding. Also, 22 patients (21%) with idiopathic ulcers and 31 patients (16.5%) with Helicobacter pylori-positive ulcers had multiple duodenal ulcers Conclusion: The present study demonstrated that the idiopathic ulcers included 17.1% of duodenal ulcers. Also, it was concluded that patients with idiopathic ulcers were predominantly male with an age range older than the other group. In addition, patients in this group had more ulcers.
Impact of divergence of residual feed intake on triglyceride metabolism-related gene expression in meat-type ducks

Triglyceride (TG) metabolism is a key factor that affects residual feed intake (RFI); however, few studies have been conducted on the related gene expression in poultry. The aim of the present study was to investigate the expression of genes and their associations with RFI in meat-type ducks. Weight gain and feed intake (FI) at an age 21–42 days were measured and the RFI was calculated. Quantitative PCR was used to test the expression of the six identified genes, namely peroxisome proliferator activated receptor γ (PPARγ), glycerol kinase 2 (GK2), glycerol-3-phosphate dehydrogenase 1 (GPD1), glycerol kinase (GYK), lipase E (LIPE), and lipoprotein lipase (LPL) in the duodenum in the high RFI (HRFI) and low RFI (LRFI) groups. The results demonstrated that daily feed intake, feed conversion ratio (FCR), and RFI were markedly higher in HRFI ducks than those in LRFI ducks. Moreover, the levels of expression of PPARγ, GK2, and LIPE were significantly higher in the LRFI group than those in the HRFI group. Correlation analysis showed that PPARγ, GK2, and LIPE were significantly negatively associated with FCR and RFI. Furthermore, gene expression levels were negatively associated with the measured phenotype. The association of GK2 with PPARγ, GPD1, LPL, and LIPE was positive. The relationship between the TG related gene and RFI was further verified to potentially develop pedigree poultry breeding programs. The results of this study suggested that the expression of genes correlated with TG metabolism and transport is up-regulated in the duodenum of ducks with high feed efficiency. PPARγ, GK2, and LIPE are important genes that affect RFI. The results of the present study provide information that could facilitate further explorations of the mechanism of RFI and potential markers at the molecular and cellular levels.
The heart of the matter: Right heart imaging indicators for treatment escalation in pulmonary arterial hypertension

Abstract Right heart (RH) structure and function are major determinants of symptoms and prognosis in pulmonary arterial hypertension (PAH). RH imaging provides detailed information, but evidence and guidelines on the use of RH imaging in treatment decisions are limited. We conducted a Delphi study to gather expert opinion on the role of RH imaging in decision‐making for treatment escalation in PAH. A panel of 17 physicians with expertise in PAH and RH imaging used three surveys in a modified Delphi process to reach consensus on the role of RH imaging in PAH. Survey 1 used open‐ended questions to gather information. Survey 2 contained Likert scale and other questions intended to identify consensus on topics identified in Survey 1. Survey 3 contained Likert scale questions derived from Survey 2 and summary information on the results of Survey 2. The Delphi panel reached consensus that RH imaging is likely to improve the current risk stratification algorithms and help differentiate risk levels in patients at intermediate risk. Tricuspid annular plane systolic excursion, right ventricular fractional area change, right atrial area, tricuspid regurgitation, inferior venae cavae diameter, and pericardial effusion should be part of routine echocardiography in PAH. Cardiac magnetic resonance imaging is valuable but limited by cost and access. A pattern of abnormal RH imaging results should prompt consideration of hemodynamic evaluation and possible treatment escalation. RH imaging is an important tool for decisions about treatment escalation in PAH, but systematically collected evidence is needed to clarify its role.
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference

We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend on individual-specific covariates. The individualized DAG structure of the proposed approach can be uniquely identified at any given quantile, based on purely observational data without strong assumptions such as a known topological ordering. To scale the proposed method to a large number of variables and covariates, we propose for the model parameters a novel parameter expanded horseshoe prior that affords a number of attractive theoretical and computational benefits to our approach. By modeling the conditional quantiles, qDAGx overcomes the common limitations of mean regression for DAGs, which can be sensitive to the choice of likelihood, e.g., an assumption of multivariate normality, as well as to the choice of priors. We demonstrate the performance of qDAGx through extensive numerical simulations and via an application in precision medicine, which infers patient-specific protein--protein interaction networks in lung cancer.
Accessing Higher Dimensions for Unsupervised Word Translation

The striking ability of unsupervised word translation has been demonstrated with the help of word vectors / pretraining; however, they require large amounts of data and usually fails if the data come from different domains. We propose coocmap, a method that can use either high-dimensional co-occurrence counts or their lower-dimensional approximations. Freed from the limits of low dimensions, we show that relying on low-dimensional vectors and their incidental properties miss out on better denoising methods and useful world knowledge in high dimensions, thus stunting the potential of the data. Our results show that unsupervised translation can be achieved more easily and robustly than previously thought -- less than 80MB and minutes of CPU time is required to achieve over 50\% accuracy for English to Finnish, Hungarian, and Chinese translations when trained on similar data; even under domain mismatch, we show coocmap still works fully unsupervised on English NewsCrawl to Chinese Wikipedia and English Europarl to Spanish Wikipedia, among others. These results challenge prevailing assumptions on the necessity and superiority of low-dimensional vectors, and suggest that similarly processed co-occurrences can outperform dense vectors on other tasks too.
Simplifying Full Waveform Inversion via Domain-Independent Self-Supervised Learning

Geophysics has witnessed success in applying deep learning to one of its core problems: full waveform inversion (FWI) to predict subsurface velocity maps from seismic data. It is treated as an image-to-image translation problem, jointly training an encoder for seismic data and a decoder for the velocity map from seismic-velocity pairs. In this paper, we report a surprising phenomenon: when training an encoder and decoder separately in their own domains via self-supervised learning, a linear relationship is observed across domains in the latent spaces. Moreover, this phenomenon connects multiple FWI datasets in an elegant manner: these datasets can share the self-learned encoder and decoder with different linear mappings. Based on these findings, we develop SimFWI, a new paradigm that includes two steps: (a) learning a seismic encoder and a velocity decoder separately by masked image modeling over multiple datasets; (b) learning a linear mapping per dataset. Experimental results show that SimFWI can achieve comparable results to a jointly trained model from the supervision of paired seismic data and velocity maps.
On the Risk of Misinformation Pollution with Large Language Models

In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems. We establish a threat model and simulate potential misuse scenarios, both unintentional and intentional, to assess the extent to which LLMs can be utilized to produce misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of ODQA systems. To mitigate the harm caused by LLM-generated misinformation, we explore three defense strategies: prompting, misinformation detection, and majority voting. While initial results show promising trends for these defensive strategies, much more work needs to be done to address the challenge of misinformation pollution. Our work highlights the need for further research and interdisciplinary collaboration to address LLM-generated misinformation and to promote responsible use of LLMs.
Complementing GPT-3 with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata

As the largest knowledge base, Wikidata is a massive source of knowledge, complementing large language models with well-structured data. In this paper, we present WikiWebQuestions, a high-quality knowledge base question answering benchmark for Wikidata. This new benchmark uses real-world human data with SPARQL annotation to facilitate a more accurate comparison with large language models utilizing the up-to-date answers from Wikidata. Additionally, a baseline for this benchmark is established with an effective training data synthesis methodology and WikiSP, a Seq2Seq semantic parser, that handles large noisy knowledge graphs. Experimental results illustrate the effectiveness of this methodology, achieving 69% and 59% answer accuracy in the dev set and test set, respectively. We showed that we can pair semantic parsers with GPT-3 to provide a combination of verifiable results and qualified guesses that can provide useful answers to 97% of the questions in the dev set of our benchmark.
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale

Multilingual machine translation (MMT), trained on a mixture of parallel and monolingual data, is key for improving translation in low-resource language pairs. However, the literature offers conflicting results on the performance of different methods of including monolingual data. To resolve this, we examine how denoising autoencoding (DAE) and backtranslation (BT) impact MMT under different data conditions and model scales. Unlike prior studies, we use a realistic dataset of 100 translation directions and consider many domain combinations of monolingual and test data. We find that monolingual data generally helps MMT, but models are surprisingly brittle to domain mismatches, especially at smaller model scales. BT is beneficial when the parallel, monolingual, and test data sources are similar but can be detrimental otherwise, while DAE is less effective than previously reported. Next, we analyze the impact of scale (from 90M to 1.6B parameters) and find it is important for both methods, particularly DAE. As scale increases, DAE transitions from underperforming the parallel-only baseline at 90M to converging with BT performance at 1.6B, and even surpassing it in low-resource. These results offer new insights into how to best use monolingual data in MMT.
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces

Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity. Contemporary techniques enable the generation of highly intricate visuals which rapidly approach near-photorealistic quality. Nevertheless, as progress is achieved, the complexity of these methodologies increases, consequently intensifying the comprehension barrier between individuals within the field and those external to it. In an endeavor to mitigate this disparity, we propose a streamlined approach for text-to-image generation, which encompasses both the training paradigm and the sampling process. Despite its remarkable simplicity, our method yields aesthetically pleasing images with few sampling iterations, allows for intriguing ways for conditioning the model, and imparts advantages absent in state-of-the-art techniques. To demonstrate the efficacy of this approach in achieving outcomes comparable to existing works, we have trained a one-billion parameter text-conditional model, which we refer to as"Paella". In the interest of fostering future exploration in this field, we have made our source code and models publicly accessible for the research community.
Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection

Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex since anomalous events are rare and because it is an open set recognition task, i.e., what is anomalous at inference has not been observed at training. We propose COSKAD, a novel model that encodes skeletal human motion by a graph convolutional network and learns to COntract SKeletal kinematic embeddings onto a latent hypersphere of minimum volume for Video Anomaly Detection. We propose three latent spaces: the commonly-adopted Euclidean and the novel spherical and hyperbolic. All variants outperform the state-of-the-art on the most recent UBnormal dataset, for which we contribute a human-related version with annotated skeletons. COSKAD sets a new state-of-the-art on the human-related versions of ShanghaiTech Campus and CUHK Avenue, with performance comparable to video-based methods. Source code and dataset will be released upon acceptance.
MaskCL: Semantic Mask-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change

This paper considers a novel and challenging problem: unsupervised long-term person re-identification with clothes change. Unfortunately, conventional unsupervised person re-id methods are designed for short-term cases and thus fail to perceive clothes-independent patterns due to simply being driven by RGB prompt. To tackle with such a bottleneck, we propose a semantic mask-driven contrastive learning approach, in which silhouette masks are embedded into contrastive learning framework as the semantic prompts and cross-clothes invariance is learnt from hierarchically semantic neighbor structure by combining both RGB and semantic features in a two-branches network. Since such a challenging re-id task setting is investigated for the first time, we conducted extensive experiments to evaluate state-of-the-art unsupervised short-term person re-id methods on five widely-used clothes-change re-id datasets. Experimental results verify that our approach outperforms the unsupervised re-id competitors by a clear margin, remaining a narrow gap to the supervised baselines.
Autour de la platitude

© Bulletin de la S. M. F. , 1969, tous droits réservés. L’accès aux archives de la revue « Bulletin de la S. M. F. » (http://smf. emath.fr/Publications/Bulletin/Presentation.html) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/legal.php). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing

Discourse parsing, the task of analyzing the internal rhetorical structure of texts, is a challenging problem in natural language processing. Despite the recent advances in neural models, the lack of large-scale, high-quality corpora for training remains a major obstacle. Recent studies have attempted to overcome this limitation by using distant supervision, which utilizes results from other NLP tasks (e.g., sentiment polarity, attention matrix, and segmentation probability) to parse discourse trees. However, these methods do not take into account the differences between in-domain and out-of-domain tasks, resulting in lower performance and inability to leverage the high-quality in-domain data for further improvement. To address these issues, we propose a distant supervision framework that leverages the relations between topic structure and rhetorical structure. Specifically, we propose two distantly supervised methods, based on transfer learning and the teacher-student model, that narrow the gap between in-domain and out-of-domain tasks through label mapping and oracle annotation. Experimental results on the MCDTB and RST-DT datasets show that our methods achieve the best performance in both distant-supervised and supervised scenarios.
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision. Despite incurring no additional latency at test time, data augmentation often requires more epochs of training to be effective. For example, even the simple flips-and-crops augmentation requires training for more than 5 epochs to improve performance, whereas RandAugment requires more than 90 epochs. We propose a general framework called Tied-Augment, which improves the efficacy of data augmentation in a wide range of applications by adding a simple term to the loss that can control the similarity of representations under distortions. Tied-Augment can improve state-of-the-art methods from data augmentation (e.g. RandAugment, mixup), optimization (e.g. SAM), and semi-supervised learning (e.g. FixMatch). For example, Tied-RandAugment can outperform RandAugment by 2.0% on ImageNet. Notably, using Tied-Augment, data augmentation can be made to improve generalization even when training for a few epochs and when fine-tuning. We open source our code at https://github.com/ekurtulus/tied-augment/tree/main.
Policy Gradient Convergence in Potential Linear-Quadratic Games with Decoupled Dynamics

This paper characterizes the class of non-cooperative finite-horizon general-sum potential Linear Quadratic (LQ) games. We show that a general LQ game with full-state feedback is potential if and only if it is an identical interest game. Based on this result, we restrict the class of LQ games to those with decoupled agent dynamics and information structure. For this restricted subset, we derive conditions under which the game is potential. Furthermore, for the identified subset, we prove the convergence of the policy gradient to a stationary point.
LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages

Knowing the language of an input text/audio is a necessary first step for using almost every natural language processing (NLP) tool such as taggers, parsers, or translation systems. Language identification is a well-studied problem, sometimes even considered solved; in reality, most of the world's 7000 languages are not supported by current systems. This lack of representation affects large-scale data mining efforts and further exacerbates data shortage for low-resource languages. We take a step towards tackling the data bottleneck by compiling a corpus of over 50K parallel children's stories in 350+ languages and dialects, and the computation bottleneck by building lightweight hierarchical models for language identification. Our data can serve as benchmark data for language identification of short texts and for understudied translation directions such as those between Indian or African languages. Our proposed method, Hierarchical LIMIT, uses limited computation to expand coverage into excluded languages while maintaining prediction quality.