Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutral gas coma dynamics: modeling of flows and attempts to link inner coma structures to properties of the nucleus

Deriving properties of cometary nuclei from coma data is of significant importance for our understanding of cometary activity and has implications beyond. Ground-based data represent the bulk of measurements available for comets. Yet, to date these observations only access a comet's gas and dust coma at rather large distances from the surface and do not directly observe its surface. In contrast, spacecraft missions are one of the only tools that gain direct access to surface measurements. However, such missions are limited to roughly one per decade. We can overcome these challenges by recognising that the coma contains information about the nucleus's properties. In particular, the near-surface gas environment is most representative of the nucleus. It can inform us about the composition, regionality of activity, and sources of coma features and how they link to the topography, morphology, or other surface properties. The inner coma data is a particularly good proxy because it has not yet, or only marginally, been contaminated by coma chemistry or secondary gas sources. Additionally, the simultaneous observation of the innermost coma with the surface provides the potential to make a direct link between coma measurements and the nucleus. If we hope to link outer coma measurements obtained by Earth-based telescopes to the surface, we must first understand how the inner coma measurements are linked to the surface. Numerical models that describe the flow from the surface into the immediate surroundings are needed to make this connection. This chapter focuses on the advances made to understand the flow of the neutral gas coma from the surface to distances up to a few tens of nuclei radii. We describe both simple/heuristic models and state-of-the-art physically consistent models. Model limitations and what they each are best suited for are discussed.
Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips

Two distinguishing features of state-of-the-art mobile and autonomous systems are 1) there are often multiple workloads, mainly deep neural network (DNN) inference, running concurrently and continuously; and 2) they operate on shared memory system-on-chips (SoC) that embed heterogeneous accelerators tailored for specific operations. State-of-the-art lacks efficient performance and resource management techniques necessary to either maximize total system throughput or minimize end-to-end workload latency. In this work, we propose HaX-CoNN, a novel scheme that characterizes and maps layers in concurrently executing DNN inference workloads to a diverse set of accelerators within a SoC. Our scheme uniquely takes per-layer execution characteristics, shared memory (SM) contention, and inter-accelerator transitions into account to find optimal schedules. We evaluate HaX-CoNN on NVIDIA Orin, NVIDIA Xavier, and Qualcomm Snapdragon 865 SoCs. Our experimental results indicate that HaX-CoNN minimizes memory contention by up to 45% and can improve latency and total throughput by up to 32% and 29%, respectively, compared to the state-of-the-art approaches.
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

Multi-stakeholder recommender systems involve various roles, such as users, providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak providers. However, when considering MMF, the features or parameters of these roles vary over time, how to ensure long-term provider MMF has become a significant challenge. We observed that recommendation feedback loops (named RFL) will influence the provider MMF greatly in the long term. RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback. When utilizing the feedback, the recommender model will regard unexposed item as negative. In this way, tail provider will not get the opportunity to be exposed, and its items will always be considered as negative samples. Such phenomenons will become more and more serious in RFL. To alleviate the problem, this paper proposes an online ranking model named Long-Term Provider Max-min Fairness (named LTP-MMF). Theoretical analysis shows that the long-term regret of LTP-MMF enjoys a sub-linear bound. Experimental results on three public recommendation benchmarks demonstrated that LTP-MMF can outperform the baselines in the long term.
Understanding and Remediating Open-Source License Incompatibilities in the PyPI Ecosystem

The reuse and distribution of open-source software must be in compliance with its accompanying open-source license. In modern packaging ecosystems, maintaining such compliance is challenging because a package may have a complex multi-layered dependency graph with many packages, any of which may have an incompatible license. Although prior research finds that license incompatibilities are prevalent, empirical evidence is still scarce in some modern packaging ecosystems (e.g., PyPI). It also remains unclear how developers remediate the license incompatibilities in the dependency graphs of their packages (including direct and transitive dependencies), let alone any automated approaches. To bridge this gap, we conduct a large-scale empirical study of license incompatibilities and their remediation practices in the PyPI ecosystem. We find that 7.27% of the PyPI package releases have license incompatibilities and 61.3 % of them are caused by transitive dependencies, causing challenges in their remediation; for remediation, developers can apply one of the five strategies: migration, removal, pinning versions, changing their own licenses, and negotiation. Inspired by our findings, we propose Silence, an SMT-solver-based approach to recommend license incompatibility remediations with minimal costs in package dependency graph. Our evaluation shows that the remediations proposed by Silencecan match 19 historical real-world cases (except for migrations not covered by an existing knowledge base) and have been accepted by five popular PyPI packages whose developers were previously unaware of their license incompatibilities.
Encode-Store-Retrieve: Enhancing Memory Augmentation through Language-Encoded Egocentric Perception

We depend on our own memory to encode, store, and retrieve our experiences. However, memory lapses can occur. One promising avenue for achieving memory augmentation is through the use of augmented reality head-mounted displays to capture and preserve egocentric videos, a practice commonly referred to as life logging. However, a significant challenge arises from the sheer volume of video data generated through life logging, as the current technology lacks the capability to encode and store such large amounts of data efficiently. Further, retrieving specific information from extensive video archives requires substantial computational power, further complicating the task of quickly accessing desired content. To address these challenges, we propose a memory augmentation system that involves leveraging natural language encoding for video data and storing them in a vector database. This approach harnesses the power of large vision language models to perform the language encoding process. Additionally, we propose using large language models to facilitate natural language querying. Our system underwent extensive evaluation using the QA-Ego4D dataset and achieved state-of-the-art results with a BLEU score of 8.3, outperforming conventional machine learning models that scored between 3.4 and 5.8. Additionally, in a user study, our system received a higher mean response score of 4.13/5 compared to the human participants' score of 2.46/5 on real-life episodic memory tasks.
Snowballing Nested Sampling

A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. This stabilizes MCMC proposals over time, and leads to pleasant properties, including that the number of live points and number of MCMC steps do not have to be calibrated, that the evidence and posterior approximation improves as more compute is added and can be diagnosed with convergence diagnostics from the MCMC literature. Snowballing nested sampling converges to a ``perfect'' nested sampling run with infinite number of MCMC steps.
Efficient Large-scale AUV-based Visual Seafloor Mapping

Driven by the increasing number of marine data science applications, there is a growing interest in surveying and exploring the vast, uncharted terrain of the deep sea with robotic platforms. Despite impressive results achieved by many on-land visual mapping algorithms in the past decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions. Typically, deep-sea exploration involves the use of autonomous underwater vehicles (AUVs) equipped with high-resolution cameras and artificial illumination systems. However, images obtained in this manner often suffer from heterogeneous illumination and quality degradation due to attenuation and scattering, on top of refraction of light rays. All of this together often lets on-land SLAM approaches fail underwater or makes Structure-from-Motion approaches drift or omit difficult images, resulting in gaps, jumps or weakly registered areas. In this work, we present a system that incorporates recent developments in underwater imaging and visual mapping to facilitate automated robotic 3D reconstruction of hectares of seafloor. Our approach is efficient in that it detects and reconsiders difficult, weakly registered areas, to avoid omitting images and to make better use of limited dive time; on the other hand it is computationally efficient; leveraging a hybrid approach combining benefits from SLAM and Structure-from-Motion that runs much faster than incremental reconstructions while achieving at least on-par performance. The proposed system has been extensively tested and evaluated during several research cruises, demonstrating its robustness and practicality in real-world conditions.
PDE Discovery for Soft Sensors Using Coupled Physics-Informed Neural Network with Akaike's Information Criterion

Soft sensors have been extensively used to monitor key variables using easy-to-measure variables and mathematical models. Partial differential equations (PDEs) are model candidates for soft sensors in industrial processes with spatiotemporal dependence. However, gaps often exist between idealized PDEs and practical situations. Discovering proper structures of PDEs, including the differential operators and source terms, can remedy the gaps. To this end, a coupled physics-informed neural network with Akaike's criterion information (CPINN-AIC) is proposed for PDE discovery of soft sensors. First, CPINN is adopted for obtaining solutions and source terms satisfying PDEs. Then, we propose a data-physics-hybrid loss function for training CPINN, in which undetermined combinations of differential operators are involved. Consequently, AIC is used to discover the proper combination of differential operators. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-AIC for soft sensors. The proposed CPINN-AIC is a data-driven method to discover proper PDE structures and neural network-based solutions for soft sensors.
Versatile Face Animator: Driving Arbitrary 3D Facial Avatar in RGBD Space

Creating realistic 3D facial animation is crucial for various applications in the movie production and gaming industry, especially with the burgeoning demand in the metaverse. However, prevalent methods such as blendshape-based approaches and facial rigging techniques are time-consuming, labor-intensive, and lack standardized configurations, making facial animation production challenging and costly. In this paper, we propose a novel self-supervised framework, Versatile Face Animator, which combines facial motion capture with motion retargeting in an end-to-end manner, eliminating the need for blendshapes or rigs. Our method has the following two main characteristics: 1) we propose an RGBD animation module to learn facial motion from raw RGBD videos by hierarchical motion dictionaries and animate RGBD images rendered from 3D facial mesh coarse-to-fine, enabling facial animation on arbitrary 3D characters regardless of their topology, textures, blendshapes, and rigs; and 2) we introduce a mesh retarget module to utilize RGBD animation to create 3D facial animation by manipulating facial mesh with controller transformations, which are estimated from dense optical flow fields and blended together with geodesic-distance-based weights. Comprehensive experiments demonstrate the effectiveness of our proposed framework in generating impressive 3D facial animation results, highlighting its potential as a promising solution for the cost-effective and efficient production of facial animation in the metaverse.
A Neural-Network-Based Convex Regularizer for Image Reconstruction

—The emergence of deep-learning-based methods for solving inverse problems has enabled a signiﬁcant increase in reconstruction quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these shortcomings while retaining the performance. In this work, this problem is tackled by revisiting regularizers that are the sum of convex-ridge functions. The gradient of such regularizers is parametrized by a neural network that has a single hidden layer with increasing and learnable activation functions. This neural network is trained within a few minutes as a multi-step Gaussian denoiser. The numerical experiments for denoising, CT, and MRI reconstruction show improvements over methods that offer similar reliability guarantees.
Mapping Patterns for Virtual Knowledge Graphs

Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mappings that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we propose a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of patterns present therein.
Prediction of the phase difference between large-scale velocity and Reynolds stress fluctuations in wall turbulence

Abstract A resolvent-based model was used to predict the phase-difference profile between velocity and stress coherent motions measured in a high Reynolds number channel flow as a proxy for predicting large- and small-scale turbulent interactions. The resolvent model is based on the transfer-function approach for scale interactions in wall turbulence proposed in Jacobi et al. (J. Fluid Mech., vol 914, 2021, pp. 1–27), but incorporates a quasi-empirical weighting scheme to construct composite mode shapes that represent the realistic dispersion of convection velocities associated with the large scales of turbulence. The weighting scheme was derived from the observed similarity between the spectral region where the resolvent operator is low rank and the streamwise spectral energy density of wall-bounded turbulence, and was found to be superior to both single-convection velocity models and models based on linearly weighted modes, when compared with cross-spectral phase calculations from a channel flow computation. The ability to predict the phase relationship between large-scale coherent motions and their associated stress fluctuations allows for refining and extending resolvent-based models of turbulence to describe small-scale features of wall-bounded flows.
3D Face Recognition: Two Decades of Progress and Prospects

Three-dimensional (3D) face recognition has been extensively investigated in the last two decades due to its wide range of applications in many areas, such as security and forensics. Numerous methods have been proposed to deal with the challenges faced by 3D face recognition, such as facial expressions, pose variations, and occlusions. These methods have achieved superior performances on several small-scale datasets, including FRGC v2.0, Bosphorus, BU-3DFE, and Gavab. However, deep learning–based 3D face recognition methods are still in their infancy due to the lack of large-scale 3D face datasets. To stimulate future research in this area, we present a comprehensive review of the progress achieved by both traditional and deep learning–based 3D face recognition methods in the last two decades. Comparative results on several publicly available datasets under different challenges of facial expressions, pose variations, and occlusions are also presented.
ULK1-regulated AMP sensing by AMPK and its application for the treatment of chronic kidney disease

AMP-activated protein kinase (AMPK) is a central kinase involved in energy homeostasis. Increased intracellular adenosine monophosphate (AMP) levels result in AMPK activation through the binding of AMP to the γ-subunit of AMPK. Recently, we reported that AMP-induced AMPK activation is impaired in the kidneys in chronic kidney disease (CKD) despite an increase in the AMP/ATP ratio. However, the mechanisms by which AMP sensing is disrupted in CKD are unclear. In this study, we identified mechanisms of energy homeostasis in which Unc-51-like kinase 1 (ULK1)-dependent phosphorylation of AMPKγ1 at Ser260/Thr262 promotes AMP sensitivity of AMPK. AMPK activation by AMP was impaired in Ulk1−/− mice despite an increased AMP/ATP ratio. We also demonstrated that MK8722, an allosteric AMPK activator, activates AMPK in the kidneys of a CKD mouse model via a pathway that is independent of AMP sensing. MK8722 treatment significantly attenuates the deterioration of renal function in CKD and is a potential therapeutic option in CKD therapeutics.
Female specific dysfunction of sensory neocortical circuits in a mouse model of autism mediated by mGluR5 and Estrogen Receptor α

Autism manifests differently in males and females and the brain mechanisms that mediate these sex-dependent differences are unknown. Here, we demonstrate that deletion of the ASD-risk gene, Pten, in neocortical pyramidal neurons (NSEPten KO) results in robust hyperexcitability of local neocortical circuits in female, but not male, mice, observed as prolonged, spontaneous persistent activity states (UP states). Circuit hyperexcitability in NSEPten KO mice is mediated by enhanced and/or altered signaling of metabotropic glutamate receptor 5 (mGluR5) and estrogen receptor α (ERα) to ERK and protein synthesis selectively in Pten deleted female neurons. In support of this idea, Pten deleted Layer 5 cortical neurons have female-specific increases in mGluR5 and mGluR5-driven protein synthesis. In addition, mGluR5-ERα complexes are elevated in female cortex and genetic reduction of ERα in Pten KO cortical neurons rescues circuit excitability, protein synthesis and enlarged neurons selectively in females. Abnormal timing and hyperexcitability of neocortical circuits in female NSEPten KO mice are associated with deficits in temporal processing of sensory stimuli and social behaviors as well as mGluR5-dependent seizures. Female-specific cortical hyperexcitability and mGluR5-dependent seizures are also observed in a human disease relevant mouse model, germline Pten+/- mice. Our results reveal molecular mechanisms by which sex and a high impact ASD-risk gene interact to affect brain function and behavior.
Uncertainty and Sensitivity Analysis of the 2010 Environmental Performance Index

Abstract An assessment of the robustness of the 2010 Environmental Performance Index (EPI) ranks requires the evaluation of uncertainties underlying the index and the sensitivity of the country rankings to the methodological choices made during the development of the Index. To test this robustness, the Yale and Columbia University have continued their partnership with the Joint Research Centre (JRC) of the European Commission in Ispra, Italy. This JRC report shows that although the theoretical framework and the indicators for the EPI were carefully chosen by experts, the issue of weighting is crucial to obtain a robust performance index. The current weighting and normalization schemes result in an EPI that is dominated by very few indicators while having an almost random association with several other underlying indicators. With respect to the five main assumptions tested in the uncertainty and sensitivity analysis, the country ranks are relatively reliable for 109 countries, while any conclusion on the ranks for the remaining countries should be made with great caution. An equal weighting approach or factor analysis-derived weights at the indicator level, as opposed to the current weighting scheme greatly influences the ranks. Thus, the choice of the weights must be evaluated according to the EPI’s analytical rationale, policy relevance, and implied value judgments. If the objective of EPI is to promote action on all policies categories more work would be needed to ensure that all policy fields have an impact on the aggregated EPI or, alternatively, policy categories should be given more emphasis than the aggregated measure. The 2010 EPI is developed for 163 countries and is based on twenty five indicators grouped in ten policy categories: Environmental burden of disease, Air pollution (effects on humans), Water (effects on humans), Air Pollution (effects on ecosystem), Water (effects on ecosystem), Biodiversity & Habitat, Forestry, Fisheries, Agriculture and Climate Change. The EPI ranking is assessed by evaluating how sensitive the country ranks are to the assumptions made on the index structure and the aggregation of the 25 underlying indicators. The assumptions tested by the JRC-IPSC are: • measurement error of the raw data, • EPI structure – grouping at policy categories, • weights assigned to the indicators and/or to the policy categories, • aggregation function at the policy or at the objectives level, and • number of indicators or policy categories.