Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution of Capillary Transit Times in Isolated Lungs of Distribution of Capillary Transit Times in Isolated Lungs of Oxygen-Tolerant Rats Oxygen-Tolerant Rats

: Rats pre-exposed to 85% O 2 for 5–7 days tolerate the otherwise lethal effects of 100% O 2 . The objective was to evaluate the effect of rat exposure to 85% O 2 for 7 days on lung capillary mean transit time ( t ̄ c ) and distribution of capillary transit times ( h c ( t )). This information is important for subsequent evaluation of the effect of this hyperoxia model on the redox metabolic functions of the pulmonary capillary endothelium. The venous concentration vs. time outflow curves of fluorescein isothiocyanate labeled dextran (FITC-dex), an intravascular indicator, and coenzyme Q 1 hydroquinone (CoQ 1 H 2 ), a compound which rapidly equilibrates between blood and tissue on passage through the pulmonary circulation, were measured following their bolus injection into the pulmonary artery of isolated perfused lungs from rats exposed to room air (normoxic) or 85% O 2 for 7 days (hyperoxic). The moments (mean transit time and variance) of the measured FITC-dex and CoQ 1 H 2 outflow curves were determined for each lung
Development and application of WetQual-C, a mechanistic model for simulating carbon dynamics, GHG gas emissions and carbon export from wetlands

ii Acknowledgments. iv List of Tables. xi List of Figures. xii 1. Chapter 1: Introduction . 1 1. Objectives of this study . 3 2. Classifications of existing wetland carbon models . 4 3. Greenhouse gas (CH4 and CO2) emission models . 5 3.1 Allocation of available carbon substrate to microbial decomposition . 6 3.2 Production of CO2 and CH4 . 9 3.3 Aerobic and anaerobic oxidation of CH4 . 10 3.4 Transport processes governing CH4 flux to atmosphere . 11 4. Dissertation organization . 12 2. Chapter 2: Carbon dynamics and export from flooded Wetlands: A modeling approach . 14
Constructing the three-dimensional extinction density maps using V-net

One of the major challenges we face is how to quickly and accurately create the three-dimensional (3D) density distributions of interstellar dust in the Milky Way using extinction and distance measurements of large samples of stars. In this study, we introduce a novel machine-learning approach that utilizes a convolution neural network, specifically a V-net, to infer the 3D distribution of dust density. Experiments are performed within two regions located towards the Galactic anti-center. The neural network is trained and tested using 10,000 simulations of dust density and line-of-sight extinction maps. Evaluation of the test sample confirms the successful generation of dust density maps from extinction maps by our model. Additionally, the performance of the trained network is evaluated using data from the literature. Our results demonstrate that our model is capable of capturing detailed dust density variations and can recover dust density maps while reducing the ``fingers of god"effect. Moving forward, we plan to apply this model to real observational data to obtain the fine distribution of dust at large and small scales in the Milky Way.
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms

Despite the recent advances in model compression techniques for deep neural networks, deploying such models on ultra-low-power embedded devices still proves challenging. In particular, quantization schemes for Gated Recurrent Units (GRU) are difficult to tune due to their dependence on an internal state, preventing them from fully benefiting from sub-8bit quantization. In this work, we propose a modular integer quantization scheme for GRUs where the bit width of each operator can be selected independently. We then employ Genetic Algorithms (GA) to explore the vast search space of possible bit widths, simultaneously optimising for model size and accuracy. We evaluate our methods on four different sequential tasks and demonstrate that mixed-precision solutions exceed homogeneous-precision ones in terms of Pareto efficiency. In our results, we achieve a model size reduction between 25% and 55% while maintaining an accuracy comparable with the 8-bit homogeneous equivalent.
Promoting Multiculturalism in Technology Based-Education: A Framework for the Improvement of Compatibility between Educators of Special Groups and their Learners and a Case Study on Educators of Roma in Greece to Prove its Efficiency

In multicultural education and especially in the education of special groups (Roma, immigrants, etc.), the compatibility between educator and learners directly affects the efficiency and the outcome of the educational programs. This chapter presents a framework for the e-education of educators of special groups based on and aiming to improve their compatibility with their learners, code named “the ESG Framework,” as well as a case study on educators of Roma in Greece, through which the efficiency of the ESG Framework was confirmed. The ESG Framework, standing in the crossroads of learning technology, multicultural education, and adults’ education, combines the necessary elements from all these main fields. In each phase of its structure, the ESG Framework provides clear guidelines to be followed by designers, developers, administrators, and instructors for the effective and measurable improvement of compatibility between educators of special groups and their learners. DOI: 10.4018/978-1-4666-2101-5.ch007
Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading

Lipreading involves using visual data to recognize spoken words by analyzing the movements of the lips and surrounding area. It is a hot research topic with many potential applications, such as human-machine interaction and enhancing audio speech recognition. Recent deep-learning based works aim to integrate visual features extracted from the mouth region with landmark points on the lip contours. However, employing a simple combination method such as concatenation may not be the most effective approach to get the optimal feature vector. To address this challenge, firstly, we propose a cross-attention fusion-based approach for large lexicon Arabic vocabulary to predict spoken words in videos. Our method leverages the power of cross-attention networks to efficiently integrate visual and geometric features computed on the mouth region. Secondly, we introduce the first large-scale Lip Reading in the Wild for Arabic (LRW-AR) dataset containing 20,000 videos for 100-word classes, uttered by 36 speakers. The experimental results obtained on LRW-AR and ArabicVisual databases showed the effectiveness and robustness of the proposed approach in recognizing Arabic words. Our work provides insights into the feasibility and effectiveness of applying lipreading techniques to the Arabic language, opening doors for further research in this field. Link to the project page: https://crns-smartvision.github. io/lrwar
Building a Hierarchical Architecture and Communication Model for the Quantum Internet

The research of architecture has tremendous significance in realizing quantum Internet. Although there is not yet a standard quantum Internet architecture, the distributed architecture is one of the possible solutions, which utilizes quantum repeaters or dedicated entanglement sources in a flat structure for entanglement preparation&distribution. In this paper, we analyze the distributed architecture in detail and demonstrate that it has three limitations: 1) possible high maintenance overhead, 2) possible low-performance entanglement distribution, and 3) unable to support optimal entanglement routing. We design a hierarchical quantum Internet architecture and a communication model to solve the problems above. We also present a W-state Based Centralized Entanglement Preparation&Distribution (W-state Based CEPD) scheme and a Centralized Entanglement Routing (CER) algorithm within our hierarchical architecture and perform an experimental comparison with other entanglement preparation&distribution schemes and entanglement routing algorithms within the distributed architecture. The evaluation results show that the entanglement distribution efficiency of hierarchical architecture is 11.5% higher than that of distributed architecture on average (minimum 3.3%, maximum 37.3%), and the entanglement routing performance of hierarchical architecture is much better than that of a distributed architecture according to the fidelity and throughput.
Mathematical Approaches to Sustainability Assessment and Protocol Development for the Bioenergy Sustainability Target Assessment Resource (Bio-STAR)

Bioenergy is renewable energy made of materials derived from biological, non-fossil sources. In addition to the benefits of utilizing an energy source that is renewable, bioenergy is being researched for its potential positive impact on climate change mitigation, job creation, and regional energy security. It has also been studied to investigate possible challenges related to indirect and direct land-use change and food security. Bioenergy sustainability assessment provides a method to identify, quantify, and interpret indicators, or metrics, of bioenergy sustainability in order to study trade-offs between environmental, social, and economic aspects of bioenergy production and use. Assessment is crucial to inform policymakers, researchers, and stakeholders as they make decisions to support the development of a sustainable bioeconomy in the United States and globally. It is the purpose of this dissertation to identify and derive mathematical techniques that aid in the development of the Bioenergy Sustainability Target Assessment Resource (Bio-STAR). Guiding principles for BioSTAR include (i) adaptability for assessing diverse bioenergy production pathways, (ii) flexibility to support a range of analyses that researchers and policymakers may seek to undertake, and (iii) mathematical robustness with respect to the operations utilized. Key components of sustainability assessment are defined and presented in the first chapter. Of the key components, Normalization and Aggregation represent areas in which the mathematical processes utilized are critical to assessment outcomes. As such, mathematical theory is developed for Normalization and Aggregation in sustainability assessment and presented in the second and third chapters, respectively. This theory is applied in the fourth chapter to inform the development of protocols for the Bioenergy Sustainability Target Assessment Resource. Bioenergy is seen as component of a sustainable energy future in the United States. Bioenergy is unique among renewable energy sources in that it can be produced in a variety of ways. Bio-STAR is a tool that will enable policymakers, researchers, and stakeholders to explore these many bioenergy options from a sustainability viewpoint and make decisions that will guide the U.S. and the world towards a sustainable energy future.
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback

The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery.
Targeted Parallelization of Conflict-Based Search for Multi-Robot Path Planning

Multi-Robot Path Planning (MRPP) on graphs, equivalently known as Multi-Agent Path Finding (MAPF), is a well-established NP-hard problem with critically important applications. As serial computation in (near)-optimally solving MRPP approaches the computation efficiency limit, parallelization offers a promising route to push the limit further, especially in handling hard or large MRPP instances. In this study, we initiated a \emph{targeted} parallelization effort to boost the performance of conflict-based search for MRPP. Specifically, when instances are relatively small but robots are densely packed with strong interactions, we apply a decentralized parallel algorithm that concurrently explores multiple branches that leads to markedly enhanced solution discovery. On the other hand, when instances are large with sparse robot-robot interactions, we prioritize node expansion and conflict resolution. Our innovative multi-threaded approach to parallelizing bounded-suboptimal conflict search-based algorithms demonstrates significant improvements over baseline serial methods in success rate or runtime. Our contribution further pushes the understanding of MRPP and charts a promising path for elevating solution quality and computational efficiency through parallel algorithmic strategies.
Knowledge Graph Assisted Automatic Sports News Writing

In this paper, we present a novel method for automatically generating sports news, which employs a unique algorithm that extracts pivotal moments from live text broadcasts and uses them to create an initial draft of the news. This draft is further refined by incorporating key details and background information from a specially designed sports knowledge graph. This graph contains 5,893 entities, which are classified into three distinct conceptual categories, interconnected through four relationship types, and characterized by 27 unique attributes. In addition, we create a multi-stage learning model by combining convolutional neural networks and a transformer encoder. This model expresses entity-task interactions using convolutional neural networks and enriches entity representations in the query set with the transformer encoder. It also includes a processor to compute matching scores for incomplete triples, addressing few-shot knowledge graph completion problem. The efficiency of this approach has been confirmed through both subjective and objective evaluations of 50 selected test cases, demonstrating its capability in revolutionizing the creation of sports news.
UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking

Multi-object tracking (MOT) methods have seen a significant boost in performance recently, due to strong interest from the research community and steadily improving object detection methods. The majority of tracking methods follow the tracking-by-detection (TBD) paradigm, blindly trust the incoming detections with no sense of their associated localization uncertainty. This lack of uncertainty awareness poses a problem in safety-critical tasks such as autonomous driving where passengers could be put at risk due to erroneous detections that have propagated to downstream tasks, including MOT. While there are existing works in probabilistic object detection that predict the localization uncertainty around the boxes, no work in 2D MOT for autonomous driving has studied whether these estimates are meaningful enough to be leveraged effectively in object tracking. We introduce UncertaintyTrack, a collection of extensions that can be applied to multiple TBD trackers to account for localization uncertainty estimates from probabilistic object detectors. Experiments on the Berkeley Deep Drive MOT dataset show that the combination of our method and informative uncertainty estimates reduces the number of ID switches by around 19\% and improves mMOTA by 2-3%. The source code is available at https://github.com/TRAILab/UncertaintyTrack
Measuring Privacy Loss in Distributed Spatio-Temporal Data

Statistics about traffic flow and people's movement gathered from multiple geographical locations in a distributed manner are the driving force powering many applications, such as traffic prediction, demand prediction, and restaurant occupancy reports. However, these statistics are often based on sensitive location data of people, and hence privacy has to be preserved while releasing them. The standard way to do this is via differential privacy, which guarantees a form of rigorous, worst-case, person-level privacy. In this work, motivated by several counter-intuitive features of differential privacy in distributed location applications, we propose an alternative privacy loss against location reconstruction attacks by an informed adversary. Our experiments on real and synthetic data demonstrate that our privacy loss better reflects our intuitions on individual privacy violation in the distributed spatio-temporal setting.
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students

Flashcard schedulers are tools that rely on 1) student models to predict the flashcards a student knows; and 2) teaching policies to schedule cards based on these predictions. Existing student models, however, only use flashcard-level features, like the student's past responses, ignoring the semantic ties of flashcards. Deep Knowledge Tracing (DKT) models can capture semantic relations with language models, but are inefficient, lack content-rich datasets for evaluation, and require robust teaching policies. To address these issues, we design KARL, a DKT-inspired student model that uses retrieval and BERT embeddings for efficient and accurate student recall predictions. To test KARL, we collect a new dataset of diverse study history on trivia questions. KARL bests existing student models in AUC and calibration error. Finally, we propose a novel teaching policy that exploits the predictive power of DKT models to deploy KARL online. Based on 27 learners and 32 6-day study trajectories, KARL shows the ability to enhance medium-term educational learning, proving its efficacy for scheduling.
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models

Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. However, most existing instruction datasets include only single instructions, and they struggle to follow complex instructions composed of multiple subtasks (Wang et al., 2023a). In this work, we propose a novel concept of compositional instructions called chain-of-instructions (CoI), where the output of one instruction becomes an input for the next like a chain. Unlike the conventional practice of solving single instruction tasks, our proposed method encourages a model to solve each subtask step by step until the final answer is reached. CoI-tuning (i.e., fine-tuning with CoI instructions) improves the model's ability to handle instructions composed of multiple subtasks. CoI-tuned models also outperformed baseline models on multilingual summarization, demonstrating the generalizability of CoI models on unseen composite downstream tasks.
Groups without unitary representations, submeasures, and the escape property

We give new examples of topological groups that do not have non-trivial continuous unitary representations, the so-called exotic groups. We prove that all groups of the form $L^0(\phi, G)$, where $\phi$ is a pathological submeasure and $G$ is a topological group, are exotic. This result extends, with a different proof, a theorem of Herer and Christensen on exoticness of $L^0(\phi,\mathbb{R})$ for $\phi$ pathological. It follows that every topological group embeds into an exotic one. In our arguments, we introduce the escape property, a geometric condition on a topological group, inspired by the solution to Hilbert's fifth problem and satisfied by all locally compact groups, all non-archimedean groups, and all Banach--Lie groups. Our key result involving the escape property asserts triviality of all continuous homomorphisms from $L^0(\phi, G)$ to $L^0(\mu, H)$, where $\phi$ is pathological, $\mu$ is a measure, $G$ is a topological group, and $H$ is a topological group with the escape property.
A Landscape Study of Open Source and Proprietary Tools for Software Bill of Materials (SBOM)

Modern software applications heavily rely on diverse third-party components, libraries, and frameworks sourced from various vendors and open source repositories, presenting a complex challenge for securing the software supply chain. To address this complexity, the adoption of a Software Bill of Materials (SBOM) has emerged as a promising solution, offering a centralized repository that inventories all third-party components and dependencies used in an application. Recent supply chain breaches, exemplified by the SolarWinds attack, underscore the urgent need to enhance software security and mitigate vulnerability risks, with SBOMs playing a pivotal role in this endeavor by revealing potential vulnerabilities, outdated components, and unsupported elements. This research paper conducts an extensive empirical analysis to assess the current landscape of open-source and proprietary tools related to SBOM. We investigate emerging use cases in software supply chain security and identify gaps in SBOM technologies. Our analysis encompasses 84 tools, providing a snapshot of the current market and highlighting areas for improvement.
Derivative-Free iterative One-Step Reconstruction for Multispectral CT

Image reconstruction in Multispectral Computed Tomography (MSCT) requires solving a challenging nonlinear inverse problem, commonly tackled via iterative optimization algorithms. Existing methods necessitate computing the derivative of the forward map and potentially its regularized inverse. In this work, we present a simple yet highly effective algorithm for MSCT image reconstruction, utilizing iterative update mechanisms that leverage the full forward model in the forward step and a derivative-free adjoint problem. Our approach demonstrates both fast convergence and superior performance compared to existing algorithms, making it an interesting candidate for future work. We also discuss further generalizations of our method and its combination with additional regularization and other data discrepancy terms.
Uncovering Latent Human Wellbeing in Language Model Embeddings

Do language models implicitly learn a concept of human wellbeing? We explore this through the ETHICS Utilitarianism task, assessing if scaling enhances pretrained models' representations. Our initial finding reveals that, without any prompt engineering or finetuning, the leading principal component from OpenAI's text-embedding-ada-002 achieves 73.9% accuracy. This closely matches the 74.6% of BERT-large finetuned on the entire ETHICS dataset, suggesting pretraining conveys some understanding about human wellbeing. Next, we consider four language model families, observing how Utilitarianism accuracy varies with increased parameters. We find performance is nondecreasing with increased model size when using sufficient numbers of principal components.