Miracle-dz/UniFAR-FTUs huggingface dataset:
Orofacial pain for clinicians: A review of constant and attack-like facial pain syndromes

Background Primary headache syndromes such as migraine are among the most common neurological syndromes. Chronic facial pain syndromes of non-odontogenic cause are less well known to neurologists despite being highly disabling. Given the pain localization, these patients often consult dentists first who may conduct unnecessary dental interventions even if a dental cause is not identified. Once it becomes clear that dental modalities have no effect on the pain, patients may be referred to another dentist or orofacial pain specialist, and later to a neurologist. Unfortunately, neurologists are also often not familiar with chronic orofacial pain syndromes although they share the neural system, i.e., trigeminal nerve and central processing areas for headache disorders. Conclusion In essence, three broad groups of orofacial pain patients are important for clinicians: (i) Attack-like orofacial pain conditions, which encompass neuralgias of the cranial nerves and less well-known facial variants of primary headache syndromes; (ii) persistent orofacial pain disorders, including neuropathic pain and persistent idiopathic facial/dentoalveolar pain; and (iii) other differential diagnostically relevant orofacial pain conditions encountered by clinicians such as painful temporomandibular disorders, bruxism, sinus pain, dental pain, and others which may interfere (trigger) and overlap with headache. It is rewarding to know and recognize the clinical picture of these facial pain syndromes, given that, just like for headache, an internationally accepted classification system has been published and many of these syndromes can be treated with medications generally used by neurologists for other pain syndromes.
GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images

While current face animation methods can manipulate expressions individually, they suffer from several limitations. The expressions manipulated by some motion-based facial reenactment models are crude. Other ideas modeled with facial action units cannot generalize to arbitrary expressions not covered by annotations. In this paper, we introduce a novel Geometry-aware Facial Expression Translation (GaFET) framework, which is based on parametric 3D facial representations and can stably decoupled expression. Among them, a Multi-level Feature Aligned Transformer is proposed to complement non-geometric facial detail features while addressing the alignment challenge of spatial features. Further, we design a De-expression model based on StyleGAN, in order to reduce the learning difficulty of GaFET in unpaired"in-the-wild"images. Extensive qualitative and quantitative experiments demonstrate that we achieve higher-quality and more accurate facial expression transfer results compared to state-of-the-art methods, and demonstrate applicability of various poses and complex textures. Besides, videos or annotated training data are omitted, making our method easier to use and generalize.
High-rate discretely-modulated continuous-variable quantum key distribution using quantum machine learning

We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (DM CVQKD) using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., the initialization part that is used for training and estimating quantum classifier, the prediction part that is used for generating highly correlated raw keys, and the data-postprocessing part that generates the final secret key string shared by Alice and Bob. To this end, a low-complexity quantum k-nearest neighbor (QkNN) classifier is designed for predicting the lossy discretely-modulated coherent states (DMCSs) at Bob's side. The performance of the proposed QkNN-based CVQKD especially in terms of machine learning metrics and complexity is analyzed, and its theoretical security is proved by using semi-definite program (SDP) method. Numerical simulation shows that the secret key rate of our proposed scheme is explicitly superior to the existing DM CVQKD protocols, and it can be further enhanced with the increase of modulation variance.
General spherically symmetric solution of Cotton gravity

In this paper we present the general spherically symmetric static solution to the vacuum equations of Cotton gravity. The obtained metric solution reveals the presence of singularities at the photosphere of a spherical source, which probably obstruct the formation of the stellar Schwarzschild-radius black holes. The solution is characterized by two integration constants, whose values can be restricted by association with the Hubble horizon. We examine the diverse features of the solution, including the long-range modifications to Newton’s force through the incorporation of the velocity-squared repulsive term to model the dark energy.
THE COMPENSATORY RESERVE INDEX FOR PREDICTING HEMORRHAGIC SHOCK IN PREHOSPITAL TRAUMA

ABSTRACT Background: The compensatory reserve index (CRI) is a noninvasive, continuous measure designed to detect intravascular volume loss. CRI is derived from the pulse oximetry waveform and reflects the proportion of physiologic reserve remaining before clinical hemodynamic decompensation. Methods: In this prospective, observational, prehospital cohort study, we measured CRI in injured patients transported by emergency medical services (EMS) to a single Level I trauma center. We determined whether the rolling average of CRI values over 60 s (CRI trend [CRI-T]) predicts in-hospital diagnosis of hemorrhagic shock, defined as blood product administration in the prehospital setting or within 4 h of hospital arrival. We hypothesized that lower CRI-T values would be associated with an increased likelihood of hemorrhagic shock and better predict hemorrhagic shock than prehospital vital signs. Results: Prehospital CRI was collected on 696 adult trauma patients, 21% of whom met our definition of hemorrhagic shock. The minimum CRI-T was 0.14 (interquartile range [IQR], 0.08–0.31) in those with hemorrhagic shock and 0.31 (IQR 0.15–0.50) in those without (P = <0.0001). The positive likelihood ratio of a CRI-T value <0.2 predicting hemorrhagic shock was 1.85 (95% confidence interval [CI], 1.55–2.22). The area under the ROC curve (AUC) for the minimum CRI-T predicting hemorrhagic shock was 0.65 (95% CI, 0.60–0.70), which outperformed initial prehospital HR (0.56; 95% CI, 0.50–0.62) but underperformed EMS systolic blood pressure and shock index (0.74; 95% CI, 0.70–0.79 and 0.72; 95% CI, 0.67–0.77, respectively). Conclusions: Low prehospital CRI-T predicts blood product transfusion by EMS or within 4 hours of hospital arrival but is less prognostic than EMS blood pressure or shock index. The evaluated version of CRI may be useful in an austere setting at identifying injured patients that require the most significant medical resources. CRI may be improved with noise filtering to attenuate the effects of vibration and patient movement.
Body Fat Estimation from Surface Meshes using Graph Neural Networks

Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.
Prototype Learning for Out-of-Distribution Polyp Segmentation

Existing polyp segmentation models from colonoscopy images often fail to provide reliable segmentation results on datasets from different centers, limiting their applicability. Our objective in this study is to create a robust and well-generalized segmentation model named PrototypeLab that can assist in polyp segmentation. To achieve this, we incorporate various lighting modes such as White light imaging (WLI), Blue light imaging (BLI), Linked color imaging (LCI), and Flexible spectral imaging color enhancement (FICE) into our new segmentation model, that learns to create prototypes for each class of object present in the images. These prototypes represent the characteristic features of the objects, such as their shape, texture, color. Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers. We first generate a coarse mask that is used to learn prototypes for the main object class, which are then employed to generate the final segmentation mask. By using prototypes to represent the main class, our approach handles the variability present in the medical images and generalize well to new data since prototype capture the underlying distribution of the data. PrototypeLab offers a promising solution with a dice coefficient of $\geq$ 90\% and mIoU $\geq$ 85\% with a near real-time processing speed for polyp segmentation. It achieved superior performance on OOD datasets compared to 16 state-of-the-art image segmentation architectures, potentially improving clinical outcomes. Codes are available at https://github.com/xxxxx/PrototypeLab.
Nonlocal theory of elasticity in the natural vibrations of curved CNTs with defects

The current study presents the natural vibrations of single-walled carbon nanotubes (SWCNTs) using the nonlocal theory of elasticity. The study will help to develop nanodevices. The study focuses on the natural vibrations of curved or arch-like carbon nanotubes (CNTs). It is considered that the CNT has a crack-like defect and is simply supported (SS) at both ends. To model the equations of motion for the CNTs, a curved nanobeam approach is adopted, and the governing equations are solved using the separation of variables method. This research investigates the natural frequencies of three types of CNTs: armchair, zigzag, and chiral. A qualitative validation study demonstrates that the obtained results align with those published in the literature. Notably, the study reveals that small-scale effects and defects in the nanotubes influence the natural frequencies of CNTs.
Towards Fast, Adaptive, and Hardware-Assisted User-Space Scheduling

Modern datacenter applications are prone to high tail latencies since their requests typically follow highly-dispersive distributions. Delivering fast interrupts is essential to reducing tail latency. Prior work has proposed both OS- and system-level solutions to reduce tail latencies for microsecond-scale workloads through better scheduling. Unfortunately, existing approaches like customized dataplane OSes, require significant OS changes, experience scalability limitations, or do not reach the full performance capabilities hardware offers. The emergence of new hardware features like UINTR exposed new opportunities to rethink the design paradigms and abstractions of traditional scheduling systems. We propose LibPreemptible, a preemptive user-level threading library that is flexible, lightweight, and adaptive. LibPreemptible was built with a set of optimizations like LibUtimer for scalability, and deadline-oriented API for flexible policies, time-quantum controller for adaptiveness. Compared to the prior state-of-the-art scheduling system Shinjuku, our system achieves significant tail latency and throughput improvements for various workloads without modifying the kernel. We also demonstrate the flexibility of LibPreemptible across scheduling policies for real applications experiencing varying load levels and characteristics.
Long‐term follow‐up after endovascular treatment of aortic coarctation with bare and covered Cheatham platinum stents

Endovascular treatment of aortic coarctation (CoA) in children and adults frequently requires stent implantation. The aim of this study was to analyze long‐term results after CoA treatment with bare and covered Cheatham‐PlatinumTM (CP) stents in our institution and to derive recommendations for the differential use of these stent types.
Doubly heterogeneous networks facilitate the emergence of collective cooperation

There is growing recognition that the network structures arising from interactions between different entities in physical, social and biological systems fundamentally alter the evolutionary outcomes. Previous paradigm exploring evolutionary game dynamics has assumed that individuals update their strategies at an identical rate, reporting that structurally heterogeneous networks -- despite their ubiquity in real systems -- generally hinder the emergence of collective cooperation compared to their homogeneous counterparts. Here we solve this paradox by creating a new paradigm where individuals on arbitrary networks are allowed to update strategies at arbitrary, personalized rates, and provide the precise condition under which universal collective cooperation is favored. We find that when individuals' update rates vary inversely with their number of connections, heterogeneous networks actually outperform homogeneous ones in promoting cooperation. This surprising property of such"doubly heterogeneous"networks cautions against the conventional wisdom that heterogeneous networks are antagonistic to cooperation. We further develop an efficient protocol for optimizing the promotion of cooperation by tuning individuals' update rates in any structure. Our findings highlight that personalized interaction dynamics, beyond structure, in complex networks are fundamental to understanding and promoting collective cooperation.
Kea, Bird of Paradox: The Evolution and Behavior of a New Zealand Parrot

The kea, a crow-sized parrot that lives in the rugged mountains of New Zealand, is considered by some a playful comic and by others a vicious killer. Its true character is a mystery that biologists have debated for more than a century. Judy Diamond and Alan Bond have written a comprehensive account of the kea's contradictory nature, and their conclusions cast new light on the origins of behavioral flexibility and the problem of species survival in human environments everywhere. New Zealand's geological remoteness has made the country home to a bizarre assemblage of plants and animals that are wholly unlike anything found elsewhere. Keas are native only to the South Island, breeding high in the rigorous, unforgiving environment of the Southern Alps. Bold, curious, and ingeniously destructive, keas have a complex social system that includes extensive play behavior. Like coyotes, crows, and humans, keas are 'open-program' animals with an unusual ability to learn and to create new solutions to whatever problems they encounter. Diamond and Bond present the kea's story from historical and contemporary perspectives and include observations from their years of field work. A comparison of the kea's behavior and ecology with that of its closest relative, the kaka of New Zealand's lowland rain forests, yields insights into the origins of the kea's extraordinary adaptability. The authors conclude that the kea's high level of sociality is a key factor in the flexible lifestyle that probably evolved in response to the alpine habitat's unreliable food resources and has allowed the bird to survive the extermination of much of its original ecosystem. But adaptability has its limits, as the authors make clear when describing present-day interactions between keas and humans and the attempts to achieve a peaceful coexistence.
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions

Most recommender systems (RecSys) do not provide an indication of confidence in their decisions. Therefore, they do not distinguish between recommendations of which they are certain, and those where they are not. Existing confidence methods for RecSys are either inaccurate heuristics, conceptually complex or computationally very expensive. Consequently, real-world RecSys applications rarely adopt these methods, and thus, provide no confidence insights in their behavior. In this work, we propose learned beta distributions (LBD) as a simple and practical recommendation method with an explicit measure of confidence. Our main insight is that beta distributions predict user preferences as probability distributions that naturally model confidence on a closed interval, yet can be implemented with the minimal model-complexity. Our results show that LBD maintains competitive accuracy to existing methods while also having a significantly stronger correlation between its accuracy and confidence. Furthermore, LBD has higher performance when applied to a high-precision targeted recommendation task. Our work thus shows that confidence in RecSys is possible without sacrificing simplicity or accuracy, and without introducing heavy computational complexity. Thereby, we hope it enables better insight into real-world RecSys and opens the door for novel future applications.
Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs

Neural Radiance Fields from Sparse inputs (NeRF-S) have shown great potential in synthesizing novel views with a limited number of observed viewpoints. However, due to the inherent limitations of sparse inputs and the gap between non-adjacent views, rendering results often suffer from over-fitting and foggy surfaces, a phenomenon we refer to as "CONFUSION" during volume rendering. In this paper, we analyze the root cause of this confusion and attribute it to two fundamental questions: "WHERE" and "HOW". To this end, we present a novel learning framework, WaH-NeRF, which effectively mitigates confusion by tackling the following challenges: (i) "WHERE" to Sample? in NeRF-S-we introduce a Deformable Sampling strategy and a Weight-based Mutual Information Loss to address sample-position confusion arising from the limited number of viewpoints; and (ii) "HOW" to Predict? in NeRF-S-we propose a Semi-Supervised NeRF learning Paradigm based on pose perturbation and a Pixel-Patch Correspondence Loss to alleviate prediction confusion caused by the disparity between training and testing viewpoints. By integrating our proposed modules and loss functions, WaH-NeRF outperforms previous methods under the NeRF-S setting. Code is available https://github.com/bbbbby-99/WaH-NeRF.
Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification. Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platform-specific and computational tool-specific. We believe that a community effort to evaluate long-read RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses.
A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems

Integrated sensing and communications (ISAC) is emerging as a key technique for next-generation wireless systems. In order to expedite the practical implementation of ISAC within pervasive mobile networks, it is essential to equip widely-deployed base stations with radar sensing capabilities. Thus, the utilization of standardized multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) hardware architectures and waveforms becomes pivotal for realizing seamless integration of effective communication and sensing functionalities. In this paper, we introduce a novel joint angle-range-velocity estimation algorithm for the MIMO-OFDM ISAC system. This approach exclusively depends on conventional MIMO-OFDM communication waveforms, which are widely adopted in wireless communications. Specifically, the angle-range-velocity information of potential targets is jointly extracted by utilizing all the received echo signals within a coherent processing interval (CPI). Therefore, the proposed joint estimation algorithm can achieve larger processing gains and higher resolution by fully exploiting echo signals and jointly estimating the angle-range-velocity information. Theoretical analysis for maximum unambiguous range, resolution, and processing gains are provided to verify the advantages of the proposed joint estimation algorithm. Finally, extensive numerical experiments are presented to demonstrate that the proposed joint estimation approach can achieve significantly lower root-mean-square-error (RMSE) of angle/range/velocity estimation for both single-target and multi-target scenarios.
Applied metamodelling for ATM performance simulations

The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, those usually of interest in policy analysis. Our experiments show XALM's predictive performance comparable to the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels. Using the `Mercury' (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager's range and scope by analysing six variables. This case study illustrates XALM's effectiveness in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, XALM complements traditional simulation-based analyses. Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.