Miracle-dz/UniFAR-FTUs huggingface dataset:
Balanced Turbulence and the Helicity Barrier in Black Hole Accretion

Horizon-scale observations from the Event Horizon Telescope (EHT) have enabled precision study of supermassive black hole accretion. Contemporary accretion modeling often treats the inflowing plasma as a single, thermal fluid, but microphysical kinetic effects can lead to significant deviations from this idealized picture. We investigate how the helicity barrier influences EHT-accessible electromagnetic observables by employing a simple model for electron heating based on kinetic physics and the cascade of energy and helicity in unbalanced turbulence. Although the helicity barrier plays only a minor role in regions with high plasma β, like in standard and normal evolution (SANE) disks, it may substantially impact regions with more ordered magnetic fields, such as the jet and its surrounding wind in SANE flows as well as throughout the entire domain in magnetically arrested disk (MAD) flows. In SANE flows, emission shifts from the funnel wall toward the lower-magnetization disk region; in MAD flows the emission morphology remains largely unchanged. Including the helicity barrier leads to characteristically lower electron temperatures, and neglecting it can lead to underestimated accretion rates and inferred jet powers. The corresponding higher plasma densities result in increased depolarization and Faraday depths thereby decreasing the amplitude of the β 2 coefficient while leaving its angle unchanged. Both the increased jet power and lower β 2 may help alleviate outstanding tensions between modeling and EHT observations. We also find that the estimated ring diameter may be underestimated when the helicity barrier is neglected. Our results underscore the significance of the helicity barrier in shaping black hole observables and inferred accretion system parameters.
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products

We consider the problem of sufficient dimension reduction (SDR) for multi-index models. The estimators of the central mean subspace in prior works either have slow (non-parametric) convergence rates, or rely on stringent distributional conditions (e.g., the covariate distribution $P_{\mathbf{X}}$ being elliptical symmetric). In this paper, we show that a fast parametric convergence rate of form $C_d \cdot n^{-1/2}$ is achievable via estimating the \emph{expected smoothed gradient outer product}, for a general class of distribution $P_{\mathbf{X}}$ admitting Gaussian or heavier distributions. When the link function is a polynomial with a degree of at most $r$ and $P_{\mathbf{X}}$ is the standard Gaussian, we show that the prefactor depends on the ambient dimension $d$ as $C_d \propto d^r$.
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments

Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and lidar) datasets in urban environments. However, such annotated datasets are also needed for natural, unstructured environments to enable semantic perception for applications, including conservation, search and rescue, environment monitoring, and agricultural automation. Therefore, we introduce WildScenes, a bi-modal benchmark dataset consisting of multiple large-scale traversals in natural environments, including semantic annotations in high-resolution 2D images and dense 3D lidar point clouds, and accurate 6-DoF pose information. The data is (1) trajectory-centric with accurate localization and globally aligned point clouds, (2) calibrated and synchronized to support bi-modal inference, and (3) containing different natural environments over 6 months to support research on domain adaptation. Our 3D semantic labels are obtained via an efficient automated process that transfers the human-annotated 2D labels from multiple views into 3D point clouds, thus circumventing the need for expensive and time-consuming human annotation in 3D. We introduce benchmarks on 2D and 3D semantic segmentation and evaluate a variety of recent deep-learning techniques to demonstrate the challenges in semantic segmentation in natural environments. We propose train-val-test splits for standard benchmarks as well as domain adaptation benchmarks and utilize an automated split generation technique to ensure the balance of class label distributions. The data, evaluation scripts and pretrained models will be released upon acceptance at https://csiro-robotics.github. io/WildScenes.
RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair

Automated Program Repair (APR) has evolved significantly with the advent of Large Language Models (LLMs). Fine-tuning LLMs for program repair is a recent avenue of research, with many dimensions which have not been explored. Existing work mostly fine-tunes LLMs with naive code representations and is fundamentally limited in its ability to fine-tune larger LLMs. To address this problem, we propose RepairLLaMA, a novel program repair approach that combines 1) code representations for APR and 2) the state-of-the-art parameter-efficient LLM fine-tuning technique called LoRA. This results in RepairLLaMA producing a highly effective `program repair adapter' for fixing bugs with language models. Our experiments demonstrate the validity of both concepts. First, fine-tuning adapters with program repair specific code representations enables the model to use meaningful repair signals. Second, parameter-efficient fine-tuning helps fine-tuning to converge and contributes to the effectiveness of the repair adapter to fix data-points outside the fine-tuning data distribution. Overall, RepairLLaMA correctly fixes 125 Defects4J v2 and 82 HumanEval-Java bugs, outperforming all baselines.
Towards Detecting Cascades of Biased Medical Claims on Twitter

Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on Twitter and measure their spread. We propose a machine learning framework that uses two models in tandem: RoBERTa to detect medical claims and DistilBERT to classify bias. After identifying original biased medical claims, we conducted a retweet cascade analysis, computing their individual reach and rate of spread. Tweets containing biased claims were found to circulate faster and further than unbiased claims.
Full-Stack End-to-End Sub-THz Simulations at 140 GHz using NYUSIM Channel Model in ns-3

The next generation of wireless communication is expected to harness the potential of the sub-THz bands to achieve exceptional performance and ubiquitous connectivity. However, network simulators such as ns-3 currently lack support for channel models above 100 GHz. This limits the ability of researchers to study, design, and evaluate systems operating above 100 GHz. Here, we use the drop-based NYUSIM channel model to simulate channels above 100 GHz in all 3GPP scenarios including urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), indoor hotspot (InH), and indoor factory (InF). We evaluate the full stack downlink end-to-end performance (throughput, latency, and packet drop) experienced by a single user equipment (UE) connected to a Next Generation Node B (gNB) operating in the sub-THz bands for three gNB--UE antenna configurations: 8x8--4x4, 16x16--4x4, and 64x64--8x8 by using the NYUSIM channel model at 140 GHz in the ns-3 mmWave module. Our simulations demonstrate that sub-THz bands can enable high-fidelity applications that require data rates exceeding 1 Gbps and latency below 15 milliseconds (ms) using the current mmWave protocol stack, and large antenna arrays. In addition, we show the variation in throughput vs number of realizations and find the optimal number of realizations required to obtain statistically significant results. We strongly encourage researchers worldwide to adopt a similar approach, as it enables the readers to assess the accuracy and reliability of the reported results and enhance the findings' overall interpretability.
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the inclusion of item images may introduce substantial security challenges. Some existing works have shown that the item provider can manipulate item exposure rates to its advantage by constructing adversarial images. However, these works cannot reveal the real vulnerability of visually-aware recommender systems because (1) The generated adversarial images are markedly distorted, rendering them easily detectable by human observers; (2) The effectiveness of the attacks is inconsistent and even ineffective in some scenarios. To shed light on the real vulnerabilities of visually-aware recommender systems when confronted with adversarial images, this paper introduces a novel attack method, IPDGI (Item Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided diffusion model to generate adversarial samples designed to deceive visually-aware recommender systems. Taking advantage of accurately modeling benign images' distribution by diffusion models, the generated adversarial images have high fidelity with original images, ensuring the stealth of our IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct extensive experiments on two commonly used e-commerce recommendation datasets (Amazon Beauty and Amazon Baby) with several typical visually-aware recommender systems. The experimental results show that our attack method has a significant improvement in both the performance of promoting the long-tailed (i.e., unpopular) items and the quality of generated adversarial images.
Incremental Rotation Averaging Revisited and More: A New Rotation Averaging Benchmark

In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As the most significant feature of the IRAv4, a task-specific connected dominating set is extracted to serve as a more reliable and accurate reference for rotation global alignment. In addition, to further address the limitations of the existing rotation averaging benchmark of relying on the slightly outdated Bundler camera calibration results as ground truths and focusing solely on rotation estimation accuracy, this paper presents a new COLMAP-based rotation averaging benchmark that incorporates a cross check between COLMAP and Bundler, and employ the accuracy of both rotation and downstream location estimation as evaluation metrics, which is desired to provide a more reliable and comprehensive evaluation tool for the rotation averaging research. Comprehensive comparisons between the proposed IRAv4 and other mainstream rotation averaging methods on this new benchmark demonstrate the effectiveness of our proposed approach.
Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation

A precondition for the deployment of a Reinforcement Learning agent to a real-world system is to provide guarantees on the learning process. While a learning algorithm will eventually converge to a good policy, there are no guarantees on the performance of the exploratory policies. We study the problem of conservative exploration, where the learner must at least be able to guarantee its performance is at least as good as a baseline policy. We propose the first conservative provably efficient model-free algorithm for policy optimization in continuous finite-horizon problems. We leverage importance sampling techniques to counterfactually evaluate the conservative condition from the data self-generated by the algorithm. We derive a regret bound and show that (w.h.p.) the conservative constraint is never violated during learning. Finally, we leverage these insights to build a general schema for conservative exploration in DeepRL via off-policy policy evaluation techniques. We show empirically the effectiveness of our methods.
On the rank of two-dimensional simplicial distributions

Simplicial distributions provide a framework for studying quantum contextuality, a generalization of Bell's non-locality. Understanding extremal simplicial distributions is of fundamental importance with applications to quantum computing. We introduce a rank formula for twisted simplicial distributions defined for $2$-dimensional measurement spaces and provide a systematic approach for describing extremal distributions.
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration

Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment and bring the services closer to end-users. In this paper, a joint O-RAN/MEC orchestration using a Bayesian deep reinforcement learning (RL)-based framework is proposed that jointly controls the O-RAN functional splits, the allocated resources and hosting locations of the O-RAN/MEC services across geo-distributed platforms, and the routing for each O-RAN/MEC data flow. The goal is to minimize the long-term overall network operation cost and maximize the MEC performance criterion while adapting possibly time-varying O-RAN/MEC demands and resource availability. This orchestration problem is formulated as Markov decision process (MDP). However, the system consists of multiple BSs that share the same resources and serve heterogeneous demands, where their parameters have non-trivial relations. Consequently, finding the exact model of the underlying system is impractical, and the formulated MDP renders in a large state space with multi-dimensional discrete action. To address such modeling and dimensionality issues, a novel model-free RL agent is proposed for our solution framework. The agent is built from Double Deep Q-network (DDQN) that tackles the large state space and is then incorporated with action branching, an action decomposition method that effectively addresses the multi-dimensional discrete action with linear increase complexity. Further, an efficient exploration-exploitation strategy under a Bayesian framework using Thomson sampling is proposed to improve the learning performance and expedite its convergence. Trace-driven simulations are performed using an O-RAN-compliant model. The results show that our approach is data-efficient (i.e., converges faster) and increases the returned reward by 32\% than its non-Bayesian version.
Purdue e-Pubs Purdue e-Pubs

A mass transfer method has been employed to characterize tangential flow velocity around fan blades. In the current research, two commercial air-conditioning fan blade samples have been investigated. The blade surfaces were covered with a 10 µm yellow coating by a spray method. The fan and a camera are driven by a motor and shaft system that can synchronize the rotational speed. Therefore, a relative stay still image of the fan blade during rotation can be obtained. Subsequently, a small amount (50 ppm v ) of ammonia has been injected into an environmental chamber that has the fan installed and rotating. The coating material on the blade surfaces absorbs the ammonia from the airflow and responds with a color change from yellow to blue. At the same time, the color change was recorded by the camera. The surface color change can be quantified by image processing which represents the local mass transfer. According to the fluid mechanics and analogy between mass transfer, heat transfer, and Colburn’s relation for turbulent flow, local tangential flow velocity distribution at the external of the boundary layer on the blade surface can be quantified. Thereafter, friction factor and wall shear stress can be calculated accordingly. Comparing to other experimental and computational methods, this new experimental method provides a robust way to evaluate turbulent flow velocity around the rotational fan blades. The results show this method is promising to be employed to evaluate and optimize fan systems to improve efficiency and reduce noise.
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection

One of the core components of goal-oriented dialog systems is the task of Intent Detection. Few-shot Learning upon Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works making use of metric-based and optimzation-based methods have been proposed, the task is still challenging in large label spaces and much smaller number of shots. Generalized Few-shot learning is more difﬁcult due to the presence of both novel and seen classes during the testing phase. In this work, we propose a simple and effective method based on Natural Language Inference that not only tackles the problem of few shot intent detection, but also proves useful in zero-shot and generalized few shot learning problems. Our extensive experiments on a number of Natural Language Understanding (NLU) and Spoken Language Understanding (SLU) datasets show the effectiveness of our approach. In addition, we highlight the settings in which our NLI based method outperforms the baselines by huge margins.
The effects of purslane consumption on blood pressure, body weight, body mass index, and waist circumference: a systematic review and meta-analysis of randomised controlled

Abstract The effects of purslane consumption on anthropometric measurements and blood pressure have been studied in numerous experiments. However, the research findings conflict with one another. In order to assess the impact of purslane on weight, body mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP), this meta-analysis was carried out. Up until February 2023, PubMed, Web of Science, Scopus, Google Scholar, and the reference lists of the identified pertinent randomised controlled trials (RCTs) were all searched. The random-effects model was used to calculate the effect size and then to describe it as a weighted mean difference (WMD) and 95 % confidence interval (CI) (CRD42023427955). The systematic review was able to incorporate seven RCTs. Meta-analysis showed that purslane significantly decreased body weight (WMD): −0⋅73 kg, 95 % confidence interval (CI): −1⋅37, −0⋅09, P=0⋅025), BMI (WMD: −0⋅35 kg/m2, 95 % CI: −0⋅64, −0⋅07, P=0⋅016), and SBP (WMD: −3⋅64 mmHg, 95 % CI: −6⋅42, −0⋅87, P = 0⋅01), and for WC, there was no discernible effect (WMD: −0⋅86 cm; 95 % CI, −1⋅80 to 0⋅07; P = 0⋅06) and DBP (WMD: −0⋅36 mmHg; 95 % CI, −1⋅75 to 1⋅03; P = 0⋅61). Purslane consumption, especially in participants with a BMI of <30, might play a role in decreasing SBP, body weight, BMI, and WC. Purslane consumption significantly reduced body weight, BMI, and SBP; however, WC and DBP did not experience a reduction. More investigation is needed to verify the impact of purslane consumption on anthropometric parameters and blood pressure.
Heat transport and flow structure in thermal convection with two liquid layers

Abstract We report an experimental investigation of the heat transport and flow field in a rectangular Rayleigh–Bénard convection (RBC) cell with two immiscible fluids: silicone oil and glycerol. The global heat transport of the system is divided into three ranges corresponding to the different flow structures formed in the glycerol layer. In range I, the glycerol layer is dominated by conduction, and no plume is formed over the interface. In range II, cellular rolls are formed in the glycerol layer and the horizontal motion of rolls causes an oscillation of temperature in the interface. In range III, the cellular pattern is time-independent, and the interface forms a group of wavelets with wave numbers consistent with the mode of the cellular pattern. In lower-thin glycerol, the Nusselt (Nu) grows from conduction to convection through an oscillating subcritical bifurcation at critical Rayleigh number $Ra_c$. The value of $Ra_c$ in the present work is smaller than the theoretical prediction of both-rigid boundaries and greater than the prediction of one-rigid and one-free boundaries. In the upper-thick silicone oil layer, $Nu$ increases with increasing $Ra$, but it is smaller than that of traditional RBC. For the silicone oil layer in two-layer RBC, the hot plumes emitting over the liquid–liquid interface showed different shape and different velocity from cold plumes emitting from the top rigid plate. This implies that the velocity boundary condition strongly influences the flow structure in turbulent convection.
Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

Summary Objective : To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. Methods : In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. Results : Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. Conclusions : To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.