Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial cells drive organ fibrosis in mice by inducing expression of the transcription factor SOX9

Fibrosis is a hallmark of chronic disease. Although fibroblasts are involved, it is unclear to what extent endothelial cells also might contribute. We detected increased expression of the transcription factor Sox9 in endothelial cells in several different mouse fibrosis models. These models included systolic heart failure induced by pressure overload, diastolic heart failure induced by high-fat diet and nitric oxide synthase inhibition, pulmonary fibrosis induced by bleomycin treatment, and liver fibrosis due to a choline-deficient diet. We also observed up-regulation of endothelial SOX9 in cardiac tissue from patients with heart failure. To test whether SOX9 induction was sufficient to cause disease, we generated mice with endothelial cell–specific overexpression of Sox9, which promoted fibrosis in multiple organs and resulted in signs of heart failure. Endothelial Sox9 deletion prevented fibrosis and organ dysfunction in the two mouse models of heart failure as well as in the lung and liver fibrosis mouse models. Bulk and single-cell RNA sequencing of mouse endothelial cells across multiple vascular beds revealed that SOX9 induced extracellular matrix, growth factor, and inflammatory gene expression, leading to matrix deposition by endothelial cells. Moreover, mouse endothelial cells activated neighboring fibroblasts that then migrated and deposited matrix in response to SOX9, a process partly mediated by the secreted growth factor CCN2, a direct SOX9 target; endothelial cell–specific Sox9 deletion reversed these changes. These findings suggest a role for endothelial SOX9 as a fibrosis-promoting factor in different mouse organs during disease and imply that endothelial cells are an important regulator of fibrosis. SOX9 in endothelial cells regulates organ fibrosis in mice by inducing extracellular matrix, inflammatory, and growth factor gene expression. Editor’s summary Chronic disease is often characterized by organ fibrosis, but how specific cell types contribute to fibrosis is unclear. Here, Trogisch et al. showed that endothelial cells are important drivers of fibrosis in heart failure, pulmonary fibrosis, and liver fibrosis mouse models. Up-regulation of the transcription factor SOX9 specifically in endothelial cells was associated with organ fibrosis in these mouse models. Overexpression of endothelial SOX9 resulted in fibrosis pathology in multiple mouse organs, whereas deletion of SOX9 in endothelial cells prevented organ fibrosis and dysfunction. SOX9 was also increased in cardiac tissue from patients with heart failure, suggesting that endothelial cell SOX9 may be a therapeutic target for human fibrotic disease. —Brandon Berry
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay

The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue (SAR) missions presents a promising avenue for enhancing operational efficiency and effectiveness. However, the success of these missions is not solely dependent on the technical capabilities of the drones but also on their acceptance and interaction with humans on the ground. This paper explores the effect of human-centric factor in UAV trajectory planning for SAR missions. We introduce a novel approach based on the reinforcement learning augmented with Analytic Hierarchy Process and novel similarity-based experience replay to optimize UAV trajectories, balancing operational objectives with human comfort and safety considerations. Additionally, through a comprehensive survey, we investigate the impact of gender cues and anthropomorphism in UAV design on public acceptance and trust, revealing significant implications for drone interaction strategies in SAR. Our contributions include (1) a reinforcement learning framework for UAV trajectory planning that dynamically integrates multi-objective considerations, (2) an analysis of human perceptions towards gendered and anthropomorphized drones in SAR contexts, and (3) the application of similarity-based experience replay for enhanced learning efficiency in complex SAR scenarios. The findings offer valuable insights into designing UAV systems that are not only technically proficient but also aligned with human-centric values.
The characteristics and evaluation of water pollution in Ganjiang Tail River

The water quality in Ganjiang River has an important impact on the ecological environment of Poyang Lake, because Ganjiang River is an important water supply of Poyang Lake. In this paper, the electrical conductivity (ED), turbidity (NTU), suspended solids (SS), total phosphorus (NP), total nitrogen (NT), ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), and chemical oxygen demand quantity (COD) have been considered as indicators of water quality while performing an assessment of water in Ganjiang River. We evaluated and analyzed comprehensively the quality of surface and underground water by using the Water Quality Identification Index Method. The sample water was retrieved every 50 days from eight monitoring points located in three sections of downstream Ganjiang River in Nanchang city; the study was conducted from September 10, 2015 to June 1, 2016. The results indicate that the pollution index of northern, central, and southern tributaries in Ganjiang River downstream are 3.807, 3.567, and 3.795, respectively; these results were obtained by performing the primary pollutants quality identification index method (PP-WQI); the pollution index for the same tributaries was found to be 3.8077, 3.5003, 3.7465, respectively when we performed comprehensive water quality identification index method (CWQI). The water pollution grades are between level 3 and level 4. The main pollutants are COD, TN, and SS; moreover, there is a linear relationship between the pollution index in groundwater and surface water. The water quality is the best in the central branch, and worst in the south; the water quality is moderate in the north. Furthermore, the water of upstream is better than that of downstream. Finally, the water quality is worst in summer but best in winter.
Multimodal Sleep Apnea Detection with Missing or Noisy Modalities

Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods assume that each sleep study is associated with a fixed set of observed modalities and that all modalities are available for each sample. However, noisy and missing modalities are a common issue in real-world clinical settings. In this study, we propose a comprehensive pipeline aiming to compensate for the missing or noisy modalities when performing sleep apnea detection. Unlike other existing studies, our proposed model works with any combination of available modalities. Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0.9) even in the presence of high levels of noise or missingness. This is especially relevant in settings where the level of noise and missingness is high (such as pediatric or outside-of-clinic scenarios).
Scaling quantum computing with dynamic circuits

Quantum computers process information with the laws of quantum mechanics. Current quantum hardware is noisy, can only store information for a short time, and is limited to a few quantum bits, i.e., qubits, typically arranged in a planar connectivity. However, many applications of quantum computing require more connectivity than the planar lattice offered by the hardware on more qubits than is available on a single quantum processing unit (QPU). Here we overcome these limitations with error mitigated dynamic circuits and circuit-cutting to create quantum states requiring a periodic connectivity employing up to 142 qubits spanning multiple QPUs connected in real-time with a classical link. In a dynamic circuit, quantum gates can be classically controlled by the outcomes of mid-circuit measurements within run-time, i.e., within a fraction of the coherence time of the qubits. Our real-time classical link allows us to apply a quantum gate on one QPU conditioned on the outcome of a measurement on another QPU which enables a modular scaling of quantum hardware. Furthermore, the error mitigated control-flow enhances qubit connectivity and the instruction set of the hardware thus increasing the versatility of our quantum computers. Dynamic circuits and quantum modularity are thus key to scale quantum computers and make them useful.
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient

Sentence Representation Learning (SRL) is a crucial task in Natural Language Processing (NLP), where contrastive Self-Supervised Learning (SSL) is currently a mainstream approach. However, the reasons behind its remarkable effectiveness remain unclear. Specifically, in other research fields, contrastive SSL shares similarities in both theory and practical performance with non-contrastive SSL (e.g., alignment&uniformity, Barlow Twins, and VICReg). However, in SRL, contrastive SSL outperforms non-contrastive SSL significantly. Therefore, two questions arise: First, what commonalities enable various contrastive losses to achieve superior performance in SRL? Second, how can we make non-contrastive SSL, which is similar to contrastive SSL but ineffective in SRL, effective? To address these questions, we start from the perspective of gradients and discover that four effective contrastive losses can be integrated into a unified paradigm, which depends on three components: the Gradient Dissipation, the Weight, and the Ratio. Then, we conduct an in-depth analysis of the roles these components play in optimization and experimentally demonstrate their significance for model performance. Finally, by adjusting these components, we enable non-contrastive SSL to achieve outstanding performance in SRL.
Evolving machine learning workflows through interactive AutoML

Automatic workflow composition (AWC) is a relevant problem in automated machine learning (AutoML) that allows finding suitable sequences of preprocessing and prediction models together with their optimal hyperparameters. This problem can be solved using evolutionary algorithms and, in particular, grammar-guided genetic programming (G3P). Current G3P approaches to AWC define a fixed grammar that formally specifies how workflow elements can be combined and which algorithms can be included. In this paper we present \ourmethod, an interactive G3P algorithm that allows users to dynamically modify the grammar to prune the search space and focus on their regions of interest. Our proposal is the first to combine the advantages of a G3P method with ideas from interactive optimisation and human-guided machine learning, an area little explored in the context of AutoML. To evaluate our approach, we present an experimental study in which 20 participants interact with \ourmethod to evolve workflows according to their preferences. Our results confirm that the collaboration between \ourmethod and humans allows us to find high-performance workflows in terms of accuracy that require less tuning time than those found without human intervention.
FSL Model can Score Higher as It Is

In daily life, we tend to present the front of our faces by staring squarely at a facial recognition machine, instead of facing it sideways, in order to increase the chance of being correctly recognised. Few-shot-learning (FSL) classification is challenging in itself because a model has to identify images that belong to classes previously unseen during training. Therefore, a warped and non-typical query or support image during testing can make it even more challenging for a model to predict correctly. In our work, to increase the chance of correct prediction during testing, we aim to rectify the test input of a trained FSL model by generating new samples of the tested classes through image-to-image translation. An FSL model is usually trained on classes with sufficient samples, and then tested on classes with few-shot samples. Our proposed method first captures the style or shape of the test image, and then identifies a suitable trained class sample. It then transfers the style or shape of the test image to the train-class images for generation of more test-class samples, before performing classification based on a set of generated samples instead of just one sample. Our method has potential in empowering a trained FSL model to score higher during the testing phase without any extra training nor dataset. According to our experiments, by augmenting the support set with just 1 additional generated sample, we can achieve around 2% improvement for trained FSL models on datasets consisting of either animal faces or traffic signs. By augmenting both the support set and the queries, we can achieve even more performance improvement. Our Github Repository is publicly available.
Brownian symmetrization of planar domains

One of aims of this note is to capture the interest of the mathematical community to a novel transformation, which we shall call Brownian symmetrization. This transformation arises from the solution of the planar Skorokhod embedding problem. Brownian symmetrization shares some properties with the famous Steiner symmetrization. However, we show that these two transformations are not the same and they do not affect each other.
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.
[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation

This paper focuses on reproducing and extending the results of the paper:"Modeling Personalized Item Frequency Information for Next-basket Recommendation"which introduced the TIFU-KNN model and proposed to utilize Personalized Item Frequency (PIF) for Next Basket Recommendation (NBR). We utilized publicly available grocery shopping datasets used in the original paper and incorporated additional datasets to assess the generalizability of the findings. We evaluated the performance of the models using metrics such as Recall@K, NDCG@K, personalized-hit ratio (PHR), and Mean Reciprocal Rank (MRR). Furthermore, we conducted a thorough examination of fairness by considering user characteristics such as average basket size, item popularity, and novelty. Lastly, we introduced novel $\beta$-VAE architecture to model NBR. The experimental results confirmed that the reproduced model, TIFU-KNN, outperforms the baseline model, Personal Top Frequency, on various datasets and metrics. The findings also highlight the challenges posed by smaller basket sizes in some datasets and suggest avenues for future research to improve NBR performance.
PiShield: A NeSy Framework for Learning with Requirements

Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first framework ever allowing for the integration of the requirements into the neural networks' topology. PiShield guarantees compliance with these requirements, regardless of input. Additionally, it allows for integrating requirements both at inference and/or training time, depending on the practitioners' needs. Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains. Here, we explore three application scenarios: functional genomics, autonomous driving, and tabular data generation.
COPR -- Efficient, large-scale log storage and retrieval

Modern, large scale monitoring systems have to process and store vast amounts of log data in near real-time. At query time the systems have to find relevant logs based on the content of the log message using support structures that can scale to these amounts of data while still being efficient to use. We present our novel Compressed Probabilistic Retrieval algorithm (COPR), capable of answering Multi-Set Multi-Membership-Queries, that can be used as an alternative to existing indexing structures for streamed log data. In our experiments, COPR required up to 93% less storage space than the tested state-of-the-art inverted index and had up to four orders of magnitude less false-positives than the tested state-of-the-art membership sketch. Additionally, COPR achieved up to 250 times higher query throughput than the tested inverted index and up to 240 times higher query throughput than the tested membership sketch.
Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects

When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may not be designed to estimate quantile treatment effects (QTE), which is the effect of treatment on the quantiles of outcome distribution. In QTE estimation, we consider two relation types with the outcome as the expected value and quantile point. To achieve this, we propose a data-driven covariate selection method for propensity score models that allows for the selection of covariates related to the expected value and quantile of the outcome for QTE estimation. Assuming the quantile regression model as an outcome regression model, covariate selection was performed using a regularization method with the partial regression coefficients of the quantile regression model as weights. The proposed method was applied to artificial data and a dataset of mothers and children born in King County, Washington, to compare the performance of existing methods and QTE estimators. As a result, the proposed method performs well in the presence of covariates related to both the expected value and quantile of the outcome.
In-Person, Hybrid or Remote? Employers' Perspectives on the Future of Work Post-Pandemic

We present an employer-side perspective on remote work through the pandemic using data from top executives of 129 employers in North America. Our analysis suggests that at least some of the pandemic-accelerated changes to the work location landscape will likely stick; with some form of hybrid work being the norm. However, the patterns will vary by department (HR/legal/sales/IT, etc.) and by sector of operations. Top three concerns among employers include: supervision and mentoring, reduction in innovation, and creativity; and the top three benefits include their ability to retain / recruit talent, positive impact on public image and their ability to compete. An Ordered Probit model of the expected April 2024 work location strategy revealed that those in transportation, warehousing, and manufacturing sectors, those with a fully in-person approach to work pre-COVID, and those with a negative outlook towards the impact of remote work are likely to be more in-person-centered, while those with fully remote work approach in April 2020 are likely to be less in-person-centered. Lastly, we present data on resumption of business travel, in-person client interactions and changes in office space reconfigurations that employers have made since the beginning of the pandemic.
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning

A common failure mode for policies trained with imitation is compounding execution errors at test time. When the learned policy encounters states that were not present in the expert demonstrations, the policy fails, leading to degenerate behavior. The Dataset Aggregation, or DAgger approach to this problem simply collects more data to cover these failure states. However, in practice, this is often prohibitively expensive. In this work, we propose Diffusion Meets DAgger (DMD), a method to reap the benefits of DAgger without the cost for eye-in-hand imitation learning problems. Instead of collecting new samples to cover out-of-distribution states, DMD uses recent advances in diffusion models to create these samples with diffusion models. This leads to robust performance from few demonstrations. In experiments conducted for non-prehensile pushing on a Franka Research 3, we show that DMD can achieve a success rate of 80% with as few as 8 expert demonstrations, where naive behavior cloning reaches only 20%. DMD also outperform competing NeRF-based augmentation schemes by 50%.
Accelerated Real-time Cine and Flow under In-magnet Staged Exercise

Background: Cardiovascular magnetic resonance imaging (CMR) is a well-established imaging tool for diagnosing and managing cardiac conditions. The integration of exercise stress with CMR (ExCMR) can enhance its diagnostic capacity. Despite recent advances in CMR technology, ExCMR remains technically challenging due to motion artifacts and limited spatial and temporal resolution. Methods: This study investigates the feasibility of biventricular functional and hemodynamic assessment using real-time (RT) ExCMR during a staged exercise protocol in 26 healthy volunteers. We introduce a coil reweighting technique to minimize motion artifacts. In addition, we identify and analyze heartbeats from the end-expiratory phase to enhance the repeatability of cardiac function quantification. To demonstrate clinical feasibility, qualitative results from five patients are also presented. Results: Our findings indicate a consistent decrease in end-systolic volume (ESV) and stable end-diastolic volume (EDV) across exercise intensities, leading to increased stroke volume (SV) and ejection fraction (EF). Coil reweighting effectively reduces motion artifacts, improving image quality in both healthy volunteers and patients. The repeatability of cardiac function parameters, demonstrated by scan-rescan tests in nine volunteers, improves with the selection of end-expiratory beats. Conclusions: The study demonstrates that RT ExCMR with in-magnet exercise is a feasible and effective method for dynamic cardiac function monitoring during exercise. The proposed coil reweighting technique and selection of end-expiratory beats significantly enhance image quality and repeatability.