Miracle-dz/UniFAR-FTUs huggingface dataset:
The relative risk of ischemic cerebrovascular accident in patients with Von Willebrand disease

Cerebrovascular accident (CVA) is one of the leading causes of death in the United States. Von Willebrand factor plays an important role in platelet activation and adhesion. It remains unclear whether Von Willebrand disease (vWD) is associated with a decreased risk of developing CVA. The study aimed to compare the relative risk (RR) of CVA in patients with and without vWD. We queried the National Inpatient Sample from 2009 to 2014 for discharge data and records for vWD and CVA using International Classification of Diseases, Ninth-Revision codes. The unadjusted and adjusted RR of CVA in patients with and without vWD were estimated using log-binomial model. Descriptive measures including means, medians, standard deviations, and range were presented based on normality test of continuous data. The prevalence of CVA was lower in patients with vWD than in those without vWD (1.31% vs 2.04%), with a RR of 0.64 (95% confidence interval (CI): 0.60–0.68). After adjusting for common CVA risk factors, the RR remained lower in vWD patients: 0.81 (95% CI: 0.76–0.86). vWD is associated with a lower RR of developing CVA. This suggests that deficiency of Von Willebrand factor is potentially protective against the development of CVA. To the best of our knowledge, this is the first study in humans to compare the RR of CVA in patients with and without vWD. Future studies are needed to explore causal relationships and therapeutic benefits.
Transition from wave turbulence to acousticlike shock-wave regime

We report on the experimental observation of a transition from a dispersive wave turbulence regime to a nondispersive regime involving shock waves on the surface of a fluid. We use a magnetic fluid in a canal subjected to an external horizontal magnetic field to tune the dispersivity of the system. For a low magnetic field, gravity-capillary wave turbulence is observed, whereas for a high enough field, random steep coherent structures arise which are found to be shock waves. These shock waves create singularities in the second-order difference of the surface elevation, leading to an $\omega^{-4}$ frequency power spectrum. This spectrum is also found to be controlled by the number and amplitude of the shocks and is well captured by a model based on a random Dirac-$\delta$ distribution (Kuznetsov-like spectrum). Finally, the shock-amplitude statistics exhibits a power-law distribution with an exponent close to the predictions of the one-dimensional random-forced Burgers equation. This shock-wave regime, discovered here for surface waves, thus paves the way to better explore their properties.
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning

Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner. However, the data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model severely biased to the head classes with the majority of the training samples. To alleviate this issue, decoupled training has recently been introduced to FL, considering it has achieved promising results in centralized long-tailed learning by re-balancing the biased classifier after the instance-balanced training. However, the current study restricts the capacity of decoupled training in federated long-tailed learning with a sub-optimal classifier re-trained on a set of pseudo features, due to the unavailability of a global balanced dataset in FL. In this work, in order to re-balance the classifier more effectively, we integrate the local real data with the global gradient prototypes to form the local balanced datasets, and thus re-balance the classifier during the local training. Furthermore, we introduce an extra classifier in the training phase to help model the global data distribution, which addresses the problem of contradictory optimization goals caused by performing classifier re-balancing locally. Extensive experiments show that our method consistently outperforms the existing state-of-the-art methods in various settings.
Audience-Centric Natural Language Generation via Style Infusion

Adopting contextually appropriate, audience-tailored linguistic styles is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons. First, it is difficult to collect large volumes of audience-specific stylistic data. Second, some stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this paper, we propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive/polite/empathic than Sample-B - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We then infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. The code and data are accessible at https://github.com/CrowdDynamicsLab/StyleInfusion.
Biotic Interactivity between Grazers and Plants: Relationships Contributing to Atmospheric Boundary Layer Dynamics

Abstract During 1987 and 1988 First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) studies conducted in the tallgrass prairie of central Kansas, variations in ungulate grazing intensity produced a patchy spatial and temporal distribution of remaining vegetation. Equally variable plant regrowth patterns contributed further to a broad array of total primary production that resulted in a pronounced mosaic of grazing impacts. This regrowth potential, derived from a relative growth rate (RGR) equation comparing ungrazed and grazed plants, determines much of the ecosystem dynamics within and among the grazed pastures and between years. Rates of change in new plant growth (ΔRGRg) ranged from −100% to +40%; however, 78% of the time in 1987 and 71% in 1988, productivity increased as a function of grazing intensity. Since plant growth potential in ungrazed (RGRug) and grazed systems (RGRg) have inherently different attributes, interactions with the abiotic environment may .
Rejoinder to "Modification of the Computational Procedure in Parker and Bregman's Method of Calculating Sample Size from Matched Case–Control Studies with a Dichotomous Exposure"

Several authors have addressed the problem of calculating sample size for a matched case– control study with a dichotomous exposure. The approach of Parker and Bregman (1986, Biometrics 42, 919–926) is, in our view, one of the most satisfactory, since it requires specification of quantities that are often easily available to the investigator. However, its recommended implementation involves a computational approximation. We show here that the approximation performs poorly in extreme situations and can be easily replaced with a more exact calculation.
Multiepoch Detections of the Extended Atmosphere and Transmission Spectra of KELT-9b with a 1.5 m Telescope

Irradiated Jovian atmospheres are complex and dynamic and can undergo temporal variations due to the close proximity of their parent stars. Of the Jovian planets that have been cataloged to date, KELT-9b is the hottest gas giant known, with an equilibrium temperature of 4050 K. We probe the temporal variability of transmission spectroscopic signatures from KELT-9b via a set of archival multiyear ground-based transit observations, performed with the TRES facility on the 1.5 m reflector at the Fred Lawrence Whipple Observatory. Our observations confirm past detections of Fe i, Fe ii, and Mg i over multiple epochs, in addition to excess absorption at Hα, which is an indicator for ongoing mass loss. From our multiyear data set, the Hα light curve consistently deviates from a standard transit and follows a “W” shape that is deeper near ingress and egress and shallower midtransit. To search for and quantify any seasonal variations that may be present, we parameterize a “cometary tail” model to fit for the Hα transit. We find no detectable variations between the different observed epochs. Though a “cometary tail” describes the Hα flux variations well, we note that such a scenario requires a high density of neutral hydrogen in the n = 2 excited state far beyond the planetary atmosphere. Other scenarios, such as center-to-limb variations larger than that expected from 1D atmosphere models, may also contribute to the observed Hα transit shape. These multiepoch observations highlight the capabilities of small telescopes to provide temporal monitoring of the dynamics of exoplanet atmospheres.
Multivariable orthogonal polynomials and coupling coefficients for discrete series representations

We study polynomials of several variables which occur as coupling coefficients for the analytic continuation of the holomorphic discrete series of SU(1,1). There are three types of such polynomials, one corresponding to each conjugacy class of one-parameter subgroups. They may be viewed as multivariable generalizations of Hahn, Jacobi, and continuous Hahn polynomials and include many orthogonal and biorthogonal families occurring in the literature. We give a simple and unified approach to these polynomials using the group theoretic interpretation. We prove many formal properties, in particular a number of convolution and linearization formulas. We also develop the corresponding theory for the Heisenberg group, leading to multivariable generalizations of Krawtchouk and Hermite polynomials.
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives

Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh","um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives influencing Spoken Language Understanding (SLU) on these speech events. This aim of this article is to survey a breadth of perspectives in a holistic way; i.e. from considering underlying (psycho)linguistic theory, to their annotation and consideration in Automatic Speech Recognition (ASR) and SLU systems, to lastly, their study from a generation standpoint. This article aims to present the perspectives in an approachable way to the SLU and Conversational AI community, and discuss moving forward, what we believe are the trends and challenges in each area.
Star formation rates and the far-infrared luminosity of Galactic molecular clouds

The CO luminosity, far-IR luminosity, and virial mass of 55 molecular clouds are determined and related to star formation rates (SFRs) in the clouds. The SFR of OB stars per unit of available molecular mass is found to be independent of cloud mass and varies widely over a range of about 100 for clouds of mass between 30,000 and five million solar masses. The far-IR luminosity is proportional to the first power of the CO luminosity for clouds undergoing high-mass star formation. There are massive clouds without any current high-mass star formation. The average gas depletion time is about 2.5 billion yr. The far-IR luminosity-to-mass ratio for isolated or weakly interacting spiral galaxies observed by IRAS is twice that for the average Galactic molecular cloud. The star formation mechanism operating in strongly interacting galaxies is five times more efficient than that of the most active Galactic cloud and 30 times that of the average Galactic cloud. 20 references.
LTL Reactive Synthesis with a Few Hints

We study a variant of the problem of synthesizing Mealy machines that enforce LTL specifications against all possible behaviours of the environment including hostile ones. In the variant studied here, the user provides the high level LTL specification {\phi} of the system to design, and a set E of examples of executions that the solution must produce. Our synthesis algorithm works in two phases. First, it generalizes the decisions taken along the examples E using tailored extensions of automata learning algorithms. This phase generalizes the user-provided examples in E while preserving realizability of {\phi}. Second, the algorithm turns the (usually) incomplete Mealy machine obtained by the learning phase into a complete Mealy machine that realizes {\phi}. The examples are used to guide the synthesis procedure. We provide a completeness result that shows that our procedure can learn any Mealy machine M that realizes {\phi} with a small (polynomial) set of examples. We also show that our problem, that generalizes the classical LTL synthesis problem (i.e. when E = {\emptyset}), matches its worst-case complexity. The additional cost of learning from E is even polynomial in the size of E and in the size of a symbolic representation of solutions that realize {\phi}. This symbolic representation is computed by the synthesis algorithm implemented in Acacia-Bonzai when solving the plain LTL synthesis problem. We illustrate the practical interest of our approach on a set of examples.
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification

In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Recently, some attempts have been made for introducing adversarial-based UDA methods in the context of MLIC. However, these methods which rely on an additional discriminator subnet present one major shortcoming. The learning of domain-invariant features may harm their task-specific discriminative power, since the classification and discrimination tasks are decoupled. Herein, we propose to overcome this issue by introducing a novel adversarial critic that is directly deduced from the task-specific classifier. Specifically, a two-component Gaussian Mixture Model (GMM) is fitted on the source and target predictions in order to distinguish between two clusters. This allows extracting a Gaussian distribution for each component. The resulting Gaussian distributions are then used for formulating an adversarial loss based on a Frechet distance. The proposed method is evaluated on several multi-label image datasets covering three different types of domain shift. The obtained results demonstrate that DDA-MLIC outperforms existing state-of-the-art methods in terms of precision while requiring a lower number of parameters. The code will be made publicly available online.
On the Adversarial Robustness of Camera-based 3D Object Detection

In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness of these methods to adversarial attacks has not been thoroughly examined, especially when considering their deployment in safety-critical domains like autonomous driving. In this study, we conduct the first comprehensive investigation of the robustness of leading camera-based 3D object detection approaches under various adversarial conditions. We systematically analyze the resilience of these models under two attack settings: white-box and black-box; focusing on two primary objectives: classification and localization. Additionally, we delve into two types of adversarial attack techniques: pixel-based and patch-based. Our experiments yield four interesting findings: (a) bird's-eye-view-based representations exhibit stronger robustness against localization attacks; (b) depth-estimation-free approaches have the potential to show stronger robustness; (c) accurate depth estimation effectively improves robustness for depth-estimation-based methods; (d) incorporating multi-frame benign inputs can effectively mitigate adversarial attacks. We hope our findings can steer the development of future camera-based object detection models with enhanced adversarial robustness.
Bounded Solutions of Some Second Order Nonlinear Differential Equations

We prove Landesman–Lazer type existence conditions for the solutions bounded on the real line, together with their first derivatives, for some second order nonlinear differential equations of the form x″+cx′+f(t, x)=0. The proofs use upper and lower solutions techniques together with some limiting arguments.
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators

Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome the limited applicability of prior neural networks. One recent trend in GCNs is the use of deep network architectures. As opposed to the traditional GCNs, which only span only around two to five layers deep, modern GCNs now incorporate tens to hundreds of layers with the help of residual connections. From such deep GCNs, we find an important characteristic that they exhibit very high intermediate feature sparsity. This reveals a new opportunity for accelerators to exploit in GCN executions that was previously not present.In this paper, we propose SGCN, a fast and energy-efficient GCN accelerator which fully exploits the sparse intermediate features of modern GCNs. SGCN suggests several techniques to achieve significantly higher performance and energy efficiency than the existing accelerators. First, SGCN employs a GCN-friendly feature compression format. We focus on reducing the off-chip memory traffic, which often is the bottleneck for GCN executions. Second, we propose microarchitectures for seamlessly handling the compressed feature format. Specifically, we modify the aggregation phase of GCN to process compressed features, and design a combination engine that can output compressed features at no extra memory traffic cost. Third, to better handle locality in the existence of the varying sparsity, SGCN employs sparsity-aware cooperation. Sparsity-aware cooperation creates a pattern that exhibits multiple reuse windows, such that the cache can capture diverse sizes of working sets and therefore adapt to the varying level of sparsity. Through a thorough evaluation, we show that SGCN achieves 1.66× speedup and 44.1% higher energy efficiency compared to the existing accelerators in geometric mean.
Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

Crosslingual conditional generation (e.g., machine translation) has long enjoyed the benefits of scaling. Nonetheless, there are still issues that scale alone may not overcome. A source query in one language, for instance, may yield several translation options in another language without any extra context. Only one translation could be acceptable however, depending on the translator's preferences and goals. Choosing the incorrect option might significantly affect translation usefulness and quality. We propose a novel method interactive-chain prompting -- a series of question, answering and generation intermediate steps between a Translator model and a User model -- that reduces translations into a list of subproblems addressing ambiguities and then resolving such subproblems before producing the final text to be translated. To check ambiguity resolution capabilities and evaluate translation quality, we create a dataset exhibiting different linguistic phenomena which leads to ambiguities at inference for four languages. To encourage further exploration in this direction, we release all datasets. We note that interactive-chain prompting, using eight interactions as exemplars, consistently surpasses prompt-based methods with direct access to background information to resolve ambiguities.
The Structure and Origin of Switchbacks: Parker Solar Probe Observations

Switchbacks are rapid magnetic field reversals that last from seconds to hours. Current Parker Solar Probe (PSP) observations pose many open questions in regard to the nature of switchbacks. For example, are they stable as they propagate through the inner heliosphere, and how are they formed? In this work, we aim to investigate the structure and origin of switchbacks. In order to study the stability of switchbacks, we suppose the small-scale current sheets therein are generated by magnetic braiding, and they should work to stabilize the switchbacks. With more than 1000 switchbacks identified with PSP observations in seven encounters, we find many more current sheets inside than outside switchbacks, indicating that these microstructures should work to stabilize the S-shape structures of switchbacks. Additionally, we study the helium variations to trace the switchbacks to their origins. We find both helium-rich and helium-poor populations in switchbacks, implying that the switchbacks could originate from both closed and open magnetic field regions in the Sun. Moreover, we observe that the alpha-proton differential speeds also show complex variations as compared to the local Alfvén speed. The joint distributions of both parameters show that low helium abundance together with low differential speed is the dominant state in switchbacks. The presence of small-scale current sheets in switchbacks along with the helium features are in line with the hypothesis that switchbacks could originate from the Sun via interchange reconnection process. However, other formation mechanisms are not excluded.
E(n)-equivariant Graph Neural Cellular Automata

Cellular automata (CAs) are computational models exhibiting rich dynamics emerging from the local interaction of cells arranged in a regular lattice. Graph CAs (GCAs) generalise standard CAs by allowing for arbitrary graphs rather than regular lattices, similar to how Graph Neural Networks (GNNs) generalise Convolutional NNs. Recently, Graph Neural CAs (GNCAs) have been proposed as models built on top of standard GNNs that can be trained to approximate the transition rule of any arbitrary GCA. Existing GNCAs are anisotropic in the sense that their transition rules are not equivariant to translation, rotation, and reflection of the nodes' spatial locations. However, it is desirable for instances related by such transformations to be treated identically by the model. By replacing standard graph convolutions with E(n)-equivariant ones, we avoid anisotropy by design and propose a class of isotropic automata that we call E(n)-GNCAs. These models are lightweight, but can nevertheless handle large graphs, capture complex dynamics and exhibit emergent self-organising behaviours. We showcase the broad and successful applicability of E(n)-GNCAs on three different tasks: (i) pattern formation, (ii) graph auto-encoding, and (iii) simulation of E(n)-equivariant dynamical systems.