Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper gastrointestinal tract microbiota with oral origin in relation to oesophageal squamous cell carcinoma

Abstract Introduction: Poor oral hygiene is linked to high risks of many systemic diseases, including cancers. Oral dysbiosis is closely associated with poor oral hygiene, causing tooth loss, gingivitis, and periodontitis. We provide a summary of studies and discuss the risk factors for oesophageal squamous cell carcinoma (ESCC) from a microbial perspective in this review.Methods: A literature search of studies published before December 31, 2022 from PubMed, Web of Science, and The Cochrane Library was performed. The search strategies included the following keywords: (1) oral care, oral health, oral hygiene, dental health, dental hygiene, tooth loss, teeth loss, tooth absence, missing teeth, edentulism, tooth brushing, mouthwash, and tooth cleaning; (2) esophageal, esophagus, oesophagus, and oesophageal; (3) cancer, carcinoma, tumor, and neoplasm.Discussion: Poor oral health, indicated by infrequent tooth brushing, chronic periodontitis, and tooth loss, has been associated with an increased risk of squamous dysplasia and ESCC. Oral microbial diversity and composition are profoundly dysregulated during oesophageal tumorigenesis. Similar to the oral microbiota, the oesophageal microbiota varies distinctly in multiple bacterial taxa in ESCC and gastric cardia adenocarcinoma, both of which have high co-occurrence rates in the “Oesophageal Cancer Belt”. In addition, the potential roles of oncogenic viruses in ESCC have also been discussed. We also briefly explore the potential mechanisms underlying the tumor-promoting role of dysregulated microbiota for the development of therapeutic targeting strategies.Conclusion: Poor oral health is an established risk indicator of ESCC. The dysbiosis of microbiota in upper gastrointestinal tract that highly resembles the oral microbial ecosystem but with distinct features at individual sites contributes to the development and progression of ESCC.
Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data

Urban arterial corridors are landscapes that give rise to short and long-term exposures to transportation-related pollution. With high traffic volumes and a wide mix of road users, urban arterial environments are important targets for improved exposure assessment to traffic-related pollution. A common method to estimate exposure is to use traffic volumes as a proxy. The study presented here analyzes a unique yearlong dataset of simultaneous roadside air quality and traffic observations for a U.S. arterial to assess the reliability of using traffic volumes as a proxy for traffic-related exposure. Results show how the relationships of traffic volumes with NO and NO2 vary not only by time of day and season but also by time aggregation. At short-term aggregations (15 minutes) nitrogen oxides were found to have a significant linear relationship with traffic volumes during morning hours for all seasons although variability was still high (r= 0.1 0.45 for NO, r=0.14 0.27 for NO2), and little to no relationship during evening periods (r <0.01 0.03 for NO, r<0.01 0.05 for NO2). Comparisons with annual aggregations validate the use of traffic volumes to estimate annual exposure concentrations for morning periods (r= 0.89 for NO, r=0.87 for NO2) and evening NO2 (r =0.46). Traffic volumes are a 8 weak or poor predictor for annual evening NO (r= -0.09) and short-term 15 minute aggregations, particularly evening periods. Seasonal and diurnal characterizations show that roadside PM2.5 (mass) measurements do not have a relationship with local traffic volumes, leading us to conclude that PM2.5 mass is more tied to regional sources and meteorological conditions. As epidemiology and personal exposure assessment research aims to study health impacts and pollutant levels encountered by pedestrians, bicyclists, those waiting for transit, and other road users, these results show when traffic volumes alone can be a reliable proxy for exposure and when this approach is not warranted. 2.
Seeking for Innovation with Magnetic Resonance Imaging Paramagnetic Contrast Agents: Relaxation Enhancement via Weak and Dynamic Electrostatic Interactions with Positively Charged Groups on Endogenous Macromolecules

Gd-L1 is a macrocyclic Gd-HPDO3A derivative functionalized with a short spacer to a trisulfonated pyrene. When compared to Gd-HPDO3A, the increased relaxivity appears to be determined by both the higher molecular weight and the occurrence of an intramolecularly catalyzed prototropic exchange of the coordinated OH moiety. In water, Gd-L1 displayed a relaxivity of 7.1 mM–1 s–1 (at 298 K and 0.5 T), slightly increasing with the concentration likely due to the onset of intermolecular aggregation. A remarkably high and concentration-dependent relaxivity was measured in human serum (up to 26.5 mM–1 s–1 at the lowest tested concentration of 0.005 mM). The acquisition of 1H-nuclear magnetic relaxation dispersion (NMRD) and 17O-R2 vs T profiles allowed to get an in-depth characterization of the system. In vitro experiments in the presence of human serum albumin, γ-globulins, and polylysine, as well as using media mimicking the extracellular matrix, provided strong support to the view that the trisulfonated pyrene fosters binding interactions with the exposed positive groups on the surface of proteins, responsible for a remarkable in vivo hyperintensity in T1w MR images. The in vivo MR images of the liver, kidneys, and spleen showed a marked contrast enhancement in the first 10 min after the i.v. injection of Gd-L1, which was 2–6-fold higher than that for Gd-HPDO3A, while maintaining a very similar excretion behavior. These findings may pave the way to an improved design of MRI GBCAs, for the first time, based on the setup of weak and dynamic interactions with abundant positive groups on serum and ECM proteins.
Application in Pesticide Analysis: Liquid Chromatography—A Review of the State of Science for Biomarker Discovery and Identification

Throughout history economic growth has led to an increased utilization of natural resources. This increased usage has manifested in the deposition of industrial wastewater, medical waste, and other waste residuals into the environment and the generation of polluted air. The widespread use of biohazardous pesticides, artificial food additives, pharmaceuticals, neutraceuticals, nanomaterials, and mineral-based beauty and skin care products also contribute to this environmental burden. The delicate balance between the growth of living organisms, resource utilization, and environmental stressors is being challenged. The persistence of environmental contaminants in many environmental compartments presents a potential risk to humans and ecosystems. Human exposures leading to adverse health outcomes can increase as the variety and quantity of contamination sources increase. Environmental contaminants can enter into the body through different routes of exposure (e.g., dermal, inhalation, ingestion) leading to various outcomes. The harmful effect caused by a pollutant depends on the route of exposure as well as its innate toxicity, the effective exposure dose, and host factors such as age, gender, nutritional state, and developmental stage [1].
Environmental sustainability assessment of algae production systems : methodological development and case studies

A growing demand for resources and competition for land use, especially critical for Europe, is fueling the search for renewable and sustainable products. Innovative, resource-efficient and integrated approaches such as the development of marine biomass fed into integrated biorefineries may bring sustainable and cost-effective solutions to meet the growing needs. Algal biomass is no doubt considered a promising and valuable feedstock for a bio-based economy. Algae are fast-growing aquatic organisms and most of them are autotrophs: they convert sunlight, carbon dioxide, nitrogen and phosphorous (often available in excess) into valuable biomass components, which are of interest for a diverse group of industries, e.g., the food, feed, cosmetics and energy sectors (chapter 1). Despite the large potential of products derived from algae, its cultivation in Europe is still in its early stages and an estimation of the environmental sustainability may guide a further development and scale-up of this sector by highlighting its competitiveness (chapter 2). Chapter 3 illustrates the objectives of this PhD dissertation (addressed in Part I). In Part II, the environmental sustainability (specifically the life cycle resource footprint) of algae production under temperate climate conditions is assessed in an attempt to unravel the bottlenecks of current European production pathways. In chapter 4 and 5, the environmental footprint of microalgae production for higher value applications, and more specific as a feed ingredient, is examined. A first case study concerns an LCA study of microalgae production in Belgium in an innovative cultivation system for aquaculture purposes (chapter 4). Thereafter, the environmental resource footprint of an integrated algal biorefinery located in the Netherlands is assessed (chapter 5). Both case studies consider waste stream mitigation and a comparison was made with the footprint of alternative biomass plants such as soybeans. However, most envFpironmental sustainability assessment methods are rather semi-mature than well-established, which might result in incomplete LCA comparisons. Therefore, Part III of this PhD addresses the need to better quantify the environmental impacts related to surface use, both terrestrial land and sea surface, as this is not straightforward yet in life cycle assessment (LCA). Advanced LCA indicators are proposed in an attempt to better account for the impact of anthropogenic land and sea surface occupation. This development in the field of LCA enables a more fair comparison between the environmental resource footprint of aquatic algae, cultivated on marginal land or in the sea, versus terrestrial crops, of which most of them are grown on fertile land.
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis

Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition. Despite their large dimensionality, and even though these representations are not tailored for emotion recognition tasks, they are frequently used to train large speech emotion models with high memory and computational costs. In this work, we show that there exist lower-dimensional subspaces within the these pre-trained representational spaces that offer a reduction in downstream model complexity without sacrificing performance on emotion estimation. In addition, we model label uncertainty in the form of grader opinion variance, and demonstrate that such information can improve the models generalization capacity and robustness. Finally, we compare the robustness of the emotion models against acoustic degradations and observed that the reduced dimensional representations were able to retain the performance similar to the full-dimensional representations without significant regression in dimensional emotion performance.
Improved Approximation Coflows Scheduling Algorithms for Minimizing the Total Weighted Completion Time and Makespan in Heterogeneous Parallel Networks

Coflow is a network abstraction used to represent communication patterns in data centers. The coflow scheduling problem encountered in large data centers is a challenging $\mathcal{NP}$-hard problem. This paper tackles the scheduling problem of coflows with release times in heterogeneous parallel networks, which feature an architecture consisting of multiple network cores running in parallel. Two polynomial-time approximation algorithms are presented in this paper, designed to minimize the total weighted completion time and makespan in heterogeneous parallel networks, respectively. For any given $\epsilon>0$, our proposed approximation algorithm for minimizing the total weighted completion time achieves approximation ratios of $3 + \epsilon$ and $2 + \epsilon$ in the cases of arbitrary and zero release times, respectively. Additionally, we introduce an approximation algorithm for minimizing the makespan, achieving an approximation ratio of $2 + \epsilon$ for $\epsilon>0$. Notably, these advancements surpass the previously best-known approximation ratio of $O(\log m/ \log \log m)$ for both minimizing the total weighted completion time and makespan. This result also improves upon the previous approximation ratios of $6-\frac{2}{m}$ and $5-\frac{2}{m}$ for arbitrary and zero release times, respectively, in identical parallel networks.
Kirchhoff-Law Johnson Noise Meets Web 3.0: A Statistical Physical Method of Random Key Generation for Decentralized Identity Protocols

This paper presents a statistical physical generation of random keys for a decentralized identity ecosystem that uses Web 3.0 protocols. Web 3.0 is driven by secure keys, typically represented in hexadecimal, that are pseudo-randomly generated by an initialization vector and complex computational algorithms. We demonstrate that the statistical physical Kirchhoff-law-Johnson-noise (KLJN) scheme eliminates the additional computational power by naturally generating truly random binary keys to drive the creation of decentralized identifiers (DIDs) that are appended to an Ethereum blockchain.
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources

We consider the problem of reward maximization in the dueling bandit setup along with constraints on resource consumption. As in the classic dueling bandits, at each round the learner has to choose a pair of items from a set of $K$ items and observe a relative feedback for the current pair. Additionally, for both items, the learner also observes a vector of resource consumptions. The objective of the learner is to maximize the cumulative reward, while ensuring that the total consumption of any resource is within the allocated budget. We show that due to the relative nature of the feedback, the problem is more difficult than its bandit counterpart and that without further assumptions the problem is not learnable from a regret minimization perspective. Thereafter, by exploiting assumptions on the available budget, we provide an EXP3 based dueling algorithm that also considers the associated consumptions and show that it achieves an $\tilde{\mathcal{O}}\left({\frac{OPT^{(b)}}{B}}K^{1/3}T^{2/3}\right)$ regret, where $OPT^{(b)}$ is the optimal value and $B$ is the available budget. Finally, we provide numerical simulations to demonstrate the efficacy of our proposed method.
Active Third-Person Imitation Learning

We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert. In our setting, each perspective provides only limited information about the expert's behavior, and the learning agent must carefully select and combine information from different perspectives to achieve competitive performance. This setting is inspired by real-world imitation learning applications, e.g., in robotics, a robot might observe a human demonstrator via camera and receive information from different perspectives depending on the camera's position. We formalize the aforementioned active third-person imitation learning problem, theoretically analyze its characteristics, and propose a generative adversarial network-based active learning approach. Empirically, we demstrate that our proposed approach can effectively learn from expert demonstrations and explore the importance of different architectural choices for the learner's performance.
ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments

The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions. This paper introduces a new semantic segmentation dataset specifically tailored for construction sites, taking into account the diverse challenges posed by adverse weather and environmental conditions. The dataset is designed to enhance the training and evaluation of object detection models, fostering their adaptability and reliability in real-world construction applications. Our dataset comprises annotated images captured under a wide range of different weather conditions, including but not limited to sunny days, rainy periods, foggy atmospheres, and low-light situations. Additionally, environmental factors such as the existence of dirt/mud on the camera lens are integrated into the dataset through actual captures and synthetic generation to simulate the complex conditions prevalent in construction sites. We also generate synthetic images of the annotations including precise semantic segmentation masks for various objects commonly found in construction environments, such as wheel loader machines, personnel, cars, and structural elements. To demonstrate the dataset's utility, we evaluate state-of-the-art object detection algorithms on our proposed benchmark. The results highlight the dataset's success in adversarial training models across diverse conditions, showcasing its efficacy compared to existing datasets that lack such environmental variability.
Amodal Ground Truth and Completion in the Wild

The problem we study in this paper is amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work, the amodal segmentation ground truth on real images is usually predicted by manual annotaton and thus is subjective. In contrast, we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for partially occluded objects in real images. This pipeline is used to construct an amodal completion evaluation benchmark, MP3D-Amodal, consisting of a variety of object categories and labels. To better handle the amodal completion task in the wild, we explore two architecture variants: a two-stage model that first infers the occluder, followed by amodal mask completion; and a one-stage model that exploits the representation power of Stable Diffusion for amodal segmentation across many categories. Without bells and whistles, our method achieves a new state-of-the-art performance on Amodal segmentation datasets that cover a large variety of objects, including COCOA and our new MP3D-Amodal dataset. The dataset, model, and code are available at https://www.robots. ox.ac. uk/~vgg/research/amodal/.
LeanVec: Search your vectors faster by making them fit

Modern deep learning models have the ability to generate high-dimensional vectors whose similarity reflects semantic resemblance. Thus, similarity search, i.e., the operation of retrieving those vectors in a large collection that are similar to a given query, has become a critical component of a wide range of applications that demand highly accurate and timely answers. In this setting, the high vector dimensionality puts similarity search systems under compute and memory pressure, leading to subpar performance. Additionally, cross-modal retrieval tasks have become increasingly common, e.g., where a user inputs a text query to find the most relevant images for that query. However, these queries often have different distributions than the database embeddings, making it challenging to achieve high accuracy. In this work, we present LeanVec, a framework that combines linear dimensionality reduction with vector quantization to accelerate similarity search on high-dimensional vectors while maintaining accuracy. We present LeanVec variants for in-distribution (ID) and out-of-distribution (OOD) queries. LeanVec-ID yields accuracies on par with those from recently introduced deep learning alternatives whose computational overhead precludes their usage in practice. LeanVec-OOD uses a novel technique for dimensionality reduction that considers the query and database distributions to simultaneously boost the accuracy and the performance of the framework even further (even presenting competitive results when the query and database distributions match). All in all, our extensive and varied experimental results show that LeanVec produces state-of-the-art results, with up to 3.7x improvement in search throughput and up to 4.9x faster index build time over the state of the art.
On Carrollian and Galilean contractions of BMS algebra in 3 and 4 dimensions

In this paper, we find a class of Carrollian and Galilean contractions of (extended) BMS algebra in 3+1 and 2+1 dimensions. To this end, we investigate possible embeddings of 3D/4D Poincar\'{e} into the BMS${}_3$ and BMS${}_4$ algebras, respectively. The contraction limits in the 2+1-dimensional case are then enforced by appropriate contractions of its Poincar\'{e} subalgebras. In 3+1 dimensions, we have to apply instead the analogy between the structures of Poincar\'{e} and BMS algebra. In the case of non-vanishing cosmological constant in 2+1 dimensions, we consider the contractions of $\Lambda$-BMS${}_3$ algebra in an analogous manner.
Control Barrier Function Based UAV Safety Controller in Autonomous Airborne Tracking and Following Systems

Safe operations of UAVs are of paramount importance for various mission-critical and safety-critical UAV applications. In context of airborne target tracking and following, UAVs need to track a flying target avoiding collision and also closely follow its trajectory. The safety situation becomes critical and more complex when the flying target is non-cooperative and has erratic movements. This paper proposes a method for collision avoidance in an autonomous fast moving dynamic quadrotor UAV tracking and following another target UAV. This is achieved by designing a safety controller that minimally modifies the control input from a trajectory tracking controller and guarantees safety. This method enables pairing our proposed safety controller with already existing flight controllers. Our safety controller uses a control barrier function based quadratic program (CBF-QP) to produce an optimal control input enabling safe operation while also follow the trajectory of the target closely. We implement our solution on AirSim simulator over PX4 flight controller and with numerical results, we validate our approach through several simulation experiments with multiple scenarios and trajectories.