Miracle-dz/UniFAR-FTUs huggingface dataset:
Endocrine Responses to Critical Illness

Hormones mediate much of the body's homeostatic response to serious illness or injury. Insufficient endocrine responses may hamper a critically ill patient's ability to survive. This introductory article briefly surveys some endocrine responses to critical illness, and we have purposely allowed for some redundancy with subsequent articles that cover some of the topics in greater detail. In each section of this article we briefly review normal physiology, then discuss specific hormonal changes that occur with stress, and finally consider the clinical implications of these changes.
The Application of the Solar Chimney for Ventilating Buildings

This study sought to demonstrate the potential applications of the solar chimney for the naturally ventilating a building. Computational fluid dynamics (CFD) was used to model various room configurations to assess ventilation strategies. A parametric study of the solar chimney system was executed, and three-dimensional simulations were compared and validated with experiments. A new definition for the hydraulic diameter that incorporated the chimney geometry was developed to predict the flow regime in the solar chimney system. To mitigate the cost and effort to use experiments to analyze building energy, a mathematical approach was considered. A relationship between smalland full-scale models was investigated using non-dimensional analysis. Multiple parameters were involved in the mathematical model to predict the air velocity, where the predictions were in good agreement with experimental data as well as the numerica l simulations from the present study. The second part of the study considered building design optimization to improve ventila t ion using air changes per hour (ACH) as a metric, and air circulation patterns within the building. An upper vent was introduced near the ceiling of the chimney system, which induced better air circulation by removing the warm air in the building. The study pursued to model a realistic scenario for the solar chimney system, where it investigated the effect of the vent sizes, insulat ion, and a reasonable solar chimney size. It was shown that it is critical to insulate the backside of the absorber and that the ratio of the conditioned area to chimney volume should be at least 10. Lastly, the application of the solar chimney system for basement ventilation was discussed. Appropriate vent locations in the basement were determined, where the best ventilation was achieved when the duct inlet was located near the ceiling and the exhaust vent was located near the floor of the chimney. Sufficient ventilation was also achieved even for scenarios of a congested building when modeling the presence of multiple people. The Application of the Solar Chimney for Ventilating Buildings
Multilevel lattice codes from Hurwitz quaternion integers

This work presents an extension of the Construction $\pi_A$ lattices proposed in \cite{huang2017construction}, to Hurwitz quaternion integers. This construction is provided by using an isomorphism from a version of the Chinese remainder theorem applied to maximal orders in contrast to natural orders in prior works. Exploiting this map, we analyze the performance of the resulting multilevel lattice codes, highlight via computer simulations their notably reduced computational complexity provided by the multistage decoding. Moreover it is shown that this construction effectively attain the Poltyrev-limit.
Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review

Vision is a major component in several digital technologies and tools used in agriculture. The object detector, You Look Only Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance. YOLO offers real-time detection with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics. The research and application of YOLO in agriculture are accelerating rapidly but are fragmented and multidisciplinary. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Thus, the study aims to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition. First, we conducted a bibliometric review of 257 articles to understand the scholarly landscape of YOLO in agricultural domain. Secondly, we conducted a systematic review of 30 articles to identify current knowledge, gaps, and modifications in YOLO for specific agricultural tasks. The study critically assesses and summarizes the information on YOLO's end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies show the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community.
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search

End-to-end (E2E) automatic speech recognition (ASR) methods exhibit remarkable performance. However, since the performance of such methods is intrinsically linked to the context present in the training data, E2E-ASR methods do not perform as desired for unseen user contexts (e.g., technical terms, personal names, and playlists). Thus, E2E-ASR methods must be easily contextualized by the user or developer. This paper proposes an attention-based contextual biasing method that can be customized using an editable phrase list (referred to as a bias list). The proposed method can be trained effectively by combining a bias phrase index loss and special tokens to detect the bias phrases in the input speech data. In addition, to improve the contextualization performance during inference further, we propose a bias phrase boosted (BPB) beam search algorithm based on the bias phrase index probability. Experimental results demonstrate that the proposed method consistently improves the word error rate and the character error rate of the target phrases in the bias list on both the Librispeech-960 (English) and our in-house (Japanese) dataset, respectively.
PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks

Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters. Traditional defense mechanisms, such as adversarial training, gradient masking, and input transformations, either impose substantial computational costs or compromise the test accuracy of non-adversarial inputs. To address these challenges, we propose an efficient defense mechanism, PuriDefense, that employs random patch-wise purifications with an ensemble of lightweight purification models at a low level of inference cost. These models leverage the local implicit function and rebuild the natural image manifold. Our theoretical analysis suggests that this approach slows down the convergence of query-based attacks by incorporating randomness into purifications. Extensive experiments on CIFAR-10 and ImageNet validate the effectiveness of our proposed purifier-based defense mechanism, demonstrating significant improvements in robustness against query-based attacks.
DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing

Users often rely on GUIs to edit and interact with visualizations - a daunting task due to the large space of editing options. As a result, users are either overwhelmed by a complex UI or constrained by a custom UI with a tailored, fixed subset of options with limited editing flexibility. Natural Language Interfaces (NLIs) are emerging as a feasible alternative for users to specify edits. However, NLIs forgo the advantages of traditional GUI: the ability to explore and repeat edits and see instant visual feedback. We introduce DynaVis, which blends natural language and dynamically synthesized UI widgets. As the user describes an editing task in natural language, DynaVis performs the edit and synthesizes a persistent widget that the user can interact with to make further modifications. Study participants (n=24) preferred DynaVis over the NLI-only interface citing ease of further edits and editing confidence due to immediate visual feedback.
Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems

Monte Carlo Search gives excellent results in multiple difficult combinatorial problems. Using a prior to perform non uniform playouts during the search improves a lot the results compared to uniform playouts. Handmade heuristics tailored to the combinatorial problem are often used as priors. We propose a method to automatically compute a prior. It uses statistics on solved problems. It is a simple and general method that incurs no computational cost at playout time and that brings large performance gains. The method is applied to three difficult combinatorial problems: Latin Square Completion, Kakuro, and Inverse RNA Folding.
Approximation of Solution Operators for High-dimensional PDEs

We propose a finite-dimensional control-based method to approximate solution operators for evolutional partial differential equations (PDEs), particularly in high-dimensions. By employing a general reduced-order model, such as a deep neural network, we connect the evolution of the model parameters with trajectories in a corresponding function space. Using the computational technique of neural ordinary differential equation, we learn the control over the parameter space such that from any initial starting point, the controlled trajectories closely approximate the solutions to the PDE. Approximation accuracy is justified for a general class of second-order nonlinear PDEs. Numerical results are presented for several high-dimensional PDEs, including real-world applications to solving Hamilton-Jacobi-Bellman equations. These are demonstrated to show the accuracy and efficiency of the proposed method.
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models

Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e.g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e.g. approximating the adequate dosage of a drug for a patient in personalized pharmacology). In this work, we demonstrate that non-uniformity in the observed value distributions of individual entities leads to severely biased predictions in state-of-the-art models, skewing predictions towards the average of observed past values for the entity and providing worse-than-random predictive power in eccentric yet equally important cases. We show that the usage of global error metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) is insufficient to capture this phenomenon, which we name eccentricity bias, and we introduce Eccentricity-Area Under the Curve (EAUC) as a new complementary metric that can quantify it in all studied models and datasets. We also prove the adequateness of EAUC by using naive de-biasing corrections to demonstrate that a lower model bias correlates with a lower EAUC and vice-versa. This work contributes a bias-aware evaluation of dyadic regression models to avoid potential unfairness and risks in critical real-world applications of such systems.
Intensive gamma-ray light sources based on oriented single crystals

The feasibility of gamma-ray light sources based on the channeling phenomenon of ultra-relativistic electrons and positrons in oriented single crystals is demonstrated by means of rigorous numerical modeling. Case studies presented refer to 10 GeV $e^{-}/e^{+}$ beams incident on $10^{-1}-10^0$ mm thick diamond and silicon crystals. It is shown that for moderate values of the beam average current ($\lesssim 1$ mA) the average photon flux in the energy range $10^1-10^2$ MeV emitted within the $10^1-10^2$ $\mu$rad cone and 1 \% bandwidth can be on the level of $10^{12}$ photon/s for electrons and $10^{14}$ photon/s for positrons. These values are higher than the fluxes available at modern laser-Compton gamma ray light sources.
Changing-look NLS1 galaxies, their detection with SVOM, and the case of NGC 1566

We discuss applications of the study of the new and barely explored class of changing-look (CL) narrow-line Seyfert 1 (NLS1) galaxies and comment on their detection with the space mission SVOM (Space Variable Objects Monitor). We highlight the case of NGC 1566, which is outstanding in many respects, for instance as one of the nearest known CL AGN undergoing exceptional outbursts. Its NLS1 nature is discussed, and we take it as a nearby prototype for systems that could be discovered and studied in the near future, including with SVOM. Finally, we briefly examine the broader implications and applications of CL events in NLS1 galaxies and show that such systems, once discovered in larger numbers, will greatly advance our understanding of the physics of the environment of rapidly growing supermassive black holes. This White Paper is part of a sequence of publications which explore aspects of our understanding of (CL) NLS1 galaxy physics with future missions.
K\H{o}v\'ari-S\'os-Tur\'an theorem for hereditary families

The celebrated K\H{o}v\'ari-S\'os-Tur\'an theorem states that any $n$-vertex graph containing no copy of the complete bipartite graph $K_{s,s}$ has at most $O_s(n^{2-1/s})$ edges. In the past two decades, motivated by the applications in discrete geometry and structural graph theory, a number of results demonstrated that this bound can be greatly improved if the graph satisfies certain structural restrictions. We propose the systematic study of this phenomenon, and state the conjecture that if $H$ is a bipartite graph, then an induced $H$-free and $K_{s,s}$-free graph cannot have much more edges than an $H$-free graph. We provide evidence for this conjecture by considering trees, cycles, the cube graph, and bipartite graphs with degrees bounded by $k$ on one side, obtaining in all the cases similar bounds as in the non-induced setting. Our results also have applications to the Erd\H{o}s-Hajnal conjecture, the problem of finding induced $C_4$-free subgraphs with large degree and bounding the average degree of $K_{s, s}$-free graphs which do not contain induced subdivisions of a fixed graph.
Curriculum Design Helps Spiking Neural Networks to Classify Time Series

Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption. However, it is difficult to demonstrate their superiority in classification accuracy, because current efforts mainly focus on designing better network structures. In this work, enlighten by brain-inspired science, we find that, not only the structure but also the learning process should be human-like. To achieve this, we investigate the power of Curriculum Learning (CL) on SNNs by designing a novel method named CSNN with two theoretically guaranteed mechanisms: The active-to-dormant training order makes the curriculum similar to that of human learning and suitable for spiking neurons; The value-based regional encoding makes the neuron activity to mimic the brain memory when learning sequential data. Experiments on multiple time series sources including simulated, sensor, motion, and healthcare demonstrate that CL has a more positive effect on SNNs than ANNs with about twice the accuracy change, and CSNN can increase about 3% SNNs' accuracy by improving network sparsity, neuron firing status, anti-noise ability, and convergence speed.
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the remarkable resilience of AENet to noise.
Semantic-Aware Resource Allocation in Constrained Networks with Limited User Participation

Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the need to tailor the resource allocation to meet the specific requirements of semantic transmission. In this paper, we focus on networks with limited resources where devices are constrained to transmit with limited bandwidth and power over large distance. Specifically, we devise an efficient strategy to select the most pertinent semantic features and participating users, taking into account the channel quality, the transmission time, and the recovery accuracy. To this end, we formulate an optimization problem with the goal of selecting the most relevant and accurate semantic features over devices while satisfying constraints on transmission time and quality of the channel. This involves optimizing communication resources, identifying participating users, and choosing specific semantic information for transmission. The underlying problem is inherently complex due to its non-convex nature and combinatorial constraints. To overcome this challenge, we efficiently approximate the optimal solution by solving a series of integer linear programming problems. Our numerical findings illustrate the effectiveness and efficiency of our approach in managing semantic communications in networks with limited resources.
SCENES: Subpixel Correspondence Estimation With Epipolar Supervision

Extracting point correspondences from two or more views of a scene is a fundamental computer vision problem with particular importance for relative camera pose estimation and structure-from-motion. Existing local feature matching approaches, trained with correspondence supervision on large-scale datasets, obtain highly-accurate matches on the test sets. However, they do not generalise well to new datasets with different characteristics to those they were trained on, unlike classic feature extractors. Instead, they require finetuning, which assumes that ground-truth correspondences or ground-truth camera poses and 3D structure are available. We relax this assumption by removing the requirement of 3D structure, e.g., depth maps or point clouds, and only require camera pose information, which can be obtained from odometry. We do so by replacing correspondence losses with epipolar losses, which encourage putative matches to lie on the associated epipolar line. While weaker than correspondence supervision, we observe that this cue is sufficient for finetuning existing models on new data. We then further relax the assumption of known camera poses by using pose estimates in a novel bootstrapping approach. We evaluate on highly challenging datasets, including an indoor drone dataset and an outdoor smartphone camera dataset, and obtain state-of-the-art results without strong supervision.
Classification with neural networks with quadratic decision functions

Neural network with quadratic decision functions have been introduced as alternatives to standard neural networks with affine linear one. They are advantageous when the objects to be identified are of compact basic geometries like circles, ellipsis etc. In this paper we investigate the use of such ansatz functions for classification. In particular we test and compare the algorithm on the MNIST dataset for classification of handwritten digits and for classification of subspecies. We also show, that the implementation can be based on the neural network structure in the software Tensorflow and Keras, respectively.
A HITRAN-formatted UV line list of S2 containing transitions involving X3Σ‒g, B3Σ‒u, and B″ 3Πu electronic states

The sulfur dimer (S2) is an important molecular constituent in cometary atmospheres and volcanic plumes on Jupiter’s moon Io. It is also expected to play an important role in the photochemistry of exoplanets. The UV spectrum of S2 contains transitions between vibronic levels above and below the dissociation limit, giving rise to a distinctive spectral signature. By using spectroscopic information from the literature, and the spectral simulation program PGOPHER, a UV line list of S2 is provided. This line list includes the primary $B\, ^{3}\Sigma ^{-}_{u}-X\, ^{3}\Sigma ^{-}_{g}$ (v′=0-27, v″=0-10) electronic transition, where vibrational bands with v′≥10 are predissociated. Intensities have been calculated from existing experimental and theoretical oscillator strengths, and semi-empirical strengths for the predissociated bands of (S2) have been derived from comparisons with experimental cross-sections. The S2 line list also includes the $B^{\prime \prime }\, ^{3}\Pi _{u}-X\, ^{3}\Sigma ^{-}_{g}$ (v′=0-19, v″=0-10) vibronic bands due to the strong interaction with the B state. In summary, we present the new HITRAN-formatted S2 line list and its validation against existing laboratory spectra. The extensive line list covers the spectral range 21 700−41 300 cm−1 (∼242 −461 nm) and can be used for modeling both absorption and emission.
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization

Federated Domain Generalization aims to learn a domain-invariant model from multiple decentralized source domains for deployment on unseen target domain. Due to privacy concerns, the data from different source domains are kept isolated, which poses challenges in bridging the domain gap. To address this issue, we propose a Multi-source Collaborative Gradient Discrepancy Minimization (MCGDM) method for federated domain generalization. Specifically, we propose intra-domain gradient matching between the original images and augmented images to avoid overfitting the domain-specific information within isolated domains. Additionally, we propose inter-domain gradient matching with the collaboration of other domains, which can further reduce the domain shift across decentralized domains. Combining intra-domain and inter-domain gradient matching, our method enables the learned model to generalize well on unseen domains. Furthermore, our method can be extended to the federated domain adaptation task by fine-tuning the target model on the pseudo-labeled target domain. The extensive experiments on federated domain generalization and adaptation indicate that our method outperforms the state-of-the-art methods significantly.