Miracle-dz/UniFAR-FTUs huggingface dataset:
Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These foundation models or 'Jacks of All Trades' (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, we formulate the Set of Sets to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively 'Masters of All Trades'.
Scaling Theory of Wave Confinement in Classical and Quantum Periodic Systems

Completely controlling the propagation of waves in periodic media is a key challenge that is essential for a large variety of applications. An especially interesting type of propagation control is wave confinement, achievable by introducing disorder and functional defects into an otherwise periodic medium [1]. Based on the character of the introduced defect, such waves may exhibit various dimensionalities of confinement, as depicted in Fig. 1.
Modeling and mitigation of occupational safety risks in dynamic industrial environments

Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and reinforce good safety behaviors. The analytic capabilities to analyze the data acquired through such systems, however, are still lacking in terms of their ability to robustly quantify risks posed by various occupational hazards. Moreover, best practices and modern SMSs are unable to account for dynamically evolving environments/behavioral characteristics commonly found in many industrial settings. This article proposes a method to address these issues by enabling continuous and quantitative assessment of safety risks in a data-driven manner. The backbone of our method is an intuitive hierarchical probabilistic model that explains sparse and noisy safety data collected by a typical SMS. A fully Bayesian approach is developed to calibrate this model from safety data in an online fashion. Thereafter, the calibrated model holds necessary information that serves to characterize risk posed by different safety hazards. Additionally, the proposed model can be leveraged for automated decision making, for instance solving resource allocation problems -- targeted towards risk mitigation -- that are often encountered in resource-constrained industrial environments. The methodology is rigorously validated on a simulated test-bed and its scalability is demonstrated on real data from large maintenance projects at a petrochemical plant.
Manipulating Elections by Changing Voter Perceptions

The integrity of elections is central to democratic systems. However, a myriad of malicious actors aspire to influence election outcomes for financial or political benefit. A common means to such ends is by manipulating perceptions of the voting public about select candidates, for example, through misinformation. We present a formal model of the impact of perception manipulation on election outcomes in the framework of spatial voting theory, in which the preferences of voters over candidates are generated based on their relative distance in the space of issues. We show that controlling elections in this model is, in general, NP-hard, whether issues are binary or real-valued. However, we demonstrate that critical to intractability is the diversity of opinions on issues exhibited by the voting public. When voter views lack diversity, and we can instead group them into a small number of categories---for example, as a result of political polarization---the election control problem can be solved in polynomial time in the number of issues and candidates for arbitrary scoring rules.
Uniform Manifold Approximation with Two-phase Optimization

We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately. In UMATO, optimization is divided into two phases so that the resulting embeddings can depict the global structure reliably while preserving the local structure with sufficient accuracy. In the first phase, hub points are identified and projected to construct a skeletal layout for the global structure. In the second phase, the remaining points are added to the embedding preserving the regional characteristics of local areas. Through quan-titative experiments, we found that UMATO (1) outperformed widely used DR techniques in preserving the global structure while (2) pro-ducing competitive accuracy in representing the local structure. We also verified that UMATO is preferable in terms of robustness over diverse initialization methods, numbers of epochs, and subsampling techniques.
ONCE-3DLanes: Building Monocular 3D Lane Detection

We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus necessary and enables effective and safe driving. However, existing 3D lane detection datasets are either unpublished or synthesized from a simulated environment, severely hampering the development of this field. In this paper, we take steps towards addressing these issues. By exploiting the explicit relationship between point clouds and image pixels, a dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations in 211K road scenes. In addition, we present an extrinsic-free, anchorfree method, called SALAD, regressing the 3D coordinates of lanes in image view without converting the feature map into the bird's-eye view (BEV). To facilitate future research on 3D lane detection, we benchmark the dataset and provide a novel evaluation metric, performing extensive experiments of both existing approaches and our proposed method. The aim of our work is to revive the interest of 3D lane detection in a real-world scenario. We believe our work can lead to the expected and unexpected innovations in both academia and industry.
A Faster Algorithm for Betweenness Centrality Based on Adjacency Matrices

Betweenness centrality is essential in complex network analysis; it characterizes the importance of nodes and edges in networks. It is a crucial problem that exactly computes the betweenness centrality in large networks faster, which urgently needs to be solved. We propose a novel algorithm for betweenness centrality based on the parallel computing of adjacency matrices, which is faster than the existing algorithms for large networks. The time complexity of the algorithm is only related to the number of nodes in the network, not the number of edges. Experimental evidence shows that the algorithm is effective and efficient. This novel algorithm is faster than Brandes' algorithm on small and dense networks and offers excellent solutions for betweenness centrality index computing on large-scale complex networks.
VICE: Variational Interpretable Concept Embeddings

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.
Evocube: A Genetic Labelling Framework for Polycube‐Maps

Polycube‐maps are used as base‐complexes in various fields of computational geometry, including the generation of regular all‐hexahedral meshes free of internal singularities. However, the strict alignment constraints behind polycube‐based methods make their computation challenging for CAD models used in numerical simulation via finite element method (FEM). We propose a novel approach based on an evolutionary algorithm to robustly compute polycube‐maps in this context.
Extracting Symbolic Models of Collective Behaviors with Graph Neural Networks and Macro-Micro Evolution

Collective behaviors are typically hard to model. The scale of the swarm, the large number of interactions, and the richness and complexity of the behaviors are factors that make it difficult to distill a collective behavior into simple symbolic expressions. In this paper, we propose a novel approach to symbolic regression designed to facilitate such modeling. Using raw and post-processed data as an input, our approach produces viable symbolic expressions that closely model the target behavior. Our approach is composed of two phases. In the first, a graph neural network (GNN) is trained to extract an approximation of the target behavior. In the second phase, the GNN is used to produce data for a nested evolutionary algorithm called macro-micro evolution (MME). The macro layer of this algorithm selects candidate symbolic expressions, while the micro layer tunes its parameters. Experimental evaluation shows that our approach outperforms competing solutions for symbolic regression, making it possible to extract compact expressions for complex swarm behaviors.
State-of-the-art in Open-domain Conversational AI: A Survey

We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI.
Security Analysis about Long-Range Ballistic Missile Flying in the Environment of Space Debris

As the flying space environment has become deteriorated,according to the analysis of missile′s trajectory and space debris′ forecast model,a model of impact alert between long-range ballistic missile and space debris is established.The security influence for long-range ballistic missile from space debris is analyzed according to simulation.The simulation results indicate that there is underlying threatening to the long-range ballistic missile,and the research results can be used in flying security alert and selecting launch windows of missile.
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

Under the semi-supervised framework, we propose an end-to-end memory-based segmentation network (MemSeg) to detect surface defects on industrial products. Considering the small intra-class variance of products in the same production line, from the perspective of differences and commonalities, MemSeg introduces artificially simulated abnormal samples and memory samples to assist the learning of the network. In the training phase, MemSeg explicitly learns the potential differences between normal and simulated abnormal images to obtain a robust classification hyperplane. At the same time, inspired by the mechanism of human memory, MemSeg uses a memory pool to store the general patterns of normal samples. By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner. Through experimental validation, MemSeg achieves the state-of-the-art (SOTA) performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the image-level and pixel-level, respectively. In addition, MemSeg also has a significant advantage in inference speed benefiting from the end-to-end and straightforward network structure, which better meets the real-time requirement in industrial scenarios.
Optimization-Based Ramping Reserve Allocation of Aggregated BESS for AGC Enhancement

—This paper considers Automatic Generation Control (AGC) enhancement by exploring the synergy between conventional generators and an aggregated Battery Energy Storage System (BESS). In practice, it has been observed that BESSs sometimes inefﬁciently contribute to the minimization of Area Control Error (ACE). This phenomenon is open to several interpretations. On the one hand, cost-effective aggregation of BESSs has not been well-established. On the other hand, BESSs could be driven into an inefﬁcient operation due to miscalculated ACE. Moreover, some BESSs may have to operate in the opposite direction than desired to maintain energy neutrality. Therefore, this paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for BESS aggregation, addressing the three issues simultaneously. The underlying methodology is to formulate the BESS aggregation problem as an distributed online optimization problem and solve it in real-time, where corrected ACE is leveraged to quantify the instantaneous ramping requirements. Another distinctive feature of ORRA lies in its ability to immediately deploy and smoothly un-deploy the aggregated BESS with respect to a frequency event, thereby contributing to energy-neutral operation. The optimization algorithm is fully distributed and can guarantee fair and near-optimal allocation in real-time. Simulations on a modiﬁed IEEE 14-bus system are performed to illustrate the effectiveness of ORRA.
High-speed imaging of spatiotemporal correlations in Hong-Ou-Mandel interference.

The Hong-Ou-Mandel interference effect lies at the heart of many emerging quantum technologies whose performance can be significantly enhanced with increasing numbers of entangled modes one could measure and thus utilize. Photon pairs generated through the process of spontaneous parametric down conversion are known to be entangled in a vast number of modes in the various degrees of freedom (DOF) the photons possess such as time, energy, and momentum, etc. Due to limitations in detection technology and techniques, often only one such DOFs can be effectively measured at a time, resulting in much lost potential. Here, we experimentally demonstrate, with the aid of a time tagging camera, high speed measurement and characterization of two-photon interference. With a data acquisition time of only a few seconds, we observe a bi-photon interference and coalescence visibility of ∼64% with potentially up to ∼2 × 103 spatial modes. These results open up a route for practical applications of using the high dimensionality of spatiotemporal DOF in two-photon interference, and in particular, for quantum sensing and communication.
Managing stage 1 hypertension: Consider the risks, stop the progression

The 2017 American College of Cardiology and American Heart Association Task Force on Clinical Practice Guidelines on the treatment of hypertension recommended lifestyle modification and monitoring every 3 to 6 months for patients with stage 1 hypertension. However, the guidelines did not include recommendations for patients whose blood pressure is unresponsive to lifestyle therapy. The authors review the updated AHA position statement, which is meant to help clinicians manage patients with stage 1 hypertension and a low 10-year risk of atherosclerotic cardiovascular disease. Guidelines on managing patients with stage 1 hypertension and a low 10-year risk of atherosclerotic cardiovascular disease.
Comparison of dietary share of ultra-processed foods assessed with a FFQ against a 24-h dietary recall in adults: results from KNHANES 2016

Abstract Objective: To evaluate the performance of an FFQ for estimating dietary contributions of NOVA groups to individuals’ diets with a specific focus on ultra-processed foods (UPF) compared with a single 24-h dietary recall (24HR). Design: All consumed foods and beverages assess with both a 109-item FFQ and a single 24HR were classified into one of four NOVA groups: natural or minimally processed foods (MPF), processed culinary ingredients (PCI), processed foods (PF) and UPF. The contributions of each NOVA group to daily intakes of energy, protein, fat, saturated fat, carbohydrate, fibre and Na were expressed as crude intake, energy-adjusted intake and percentage intake. Mean differences, correlation coefficients and joint classification were calculated for intakes of energy and nutrients from each NOVA group between the FFQ and the 24HR. Setting: The Korea National Health and Nutrition Examination Survey 2016. Participants: Adults aged 19–64 years (n 3189). Results: The smallest group-mean differences between the two methods were observed in UPF (2–40 %). The greatest average Pearson’s correlation coefficients between the FFQ and 24HR were shown in dietary contributions of UPF (r = 0·22–0·25 for MPF; r = 0·02–0·05 for PCI; r = 0·11–0·18 for PF; r = 0·26–0·30 for UPF). The greatest agreement in quartile classification between the FFQ and the 24HR was observed in dietary contributions of UPF (70·0–71·5 % for MPF; 64·2–68·8 % for PCI; 66·9–69·2 % for PF; 71·8–73·9 % for UPF). Conclusions: The use of the FFQ for estimating absolute intake of UPF may not be encouraged in its current form, but it still may be used for relative comparisons such as quantile categorisation.
Development of a Low-Level Control System for the ROV Visor3

This paper addresses the development of the simulation of the low-level control system for the underwater remotely operated vehicle Visor3. The 6-DOF mathematical model of Visor3 is presented using two coordinated systems: Earth-fixed and body-fixed frames. The navigation, guidance, and control (NGC) structure is divided into three layers: the high level or the mission planner; the mid-level or the path planner; and the low level formed by the navigation and control systems. The nonlinear model-based observer is developed using the extended Kalman filter (EKF) which uses the linearization of the model to estimate the current state. The behavior of the observer is verified through simulations using Simulink®. An experiment was conducted with a trajectory that describes changes in the and and yaw components. To accomplish this task, two algorithms are compared: a multiloop PID and PID with gravity compensation. These controllers and the nonlinear observer are tested using the 6-DOF mathematical model of Visor3. The control and navigation systems are a fundamental part of the low-level control system that will allow Visor3’s operators to take advantage of more advanced vehicle’s capabilities during inspection tasks of port facilities, hydroelectric dams, and oceanographic research.
Generative Wind Power Curve Modeling via Machine Vision: A Deep Convolutional Network Method With Data-Synthesis-Informed-Training

This study develops a novel data-synthesis-informed-training U-net (DITU-net) based method to automate the wind power curve (WPC) modeling without data pre-processing. The proposed data-synthesis-informed-training (DIT) process has following steps. First, different from traditional studies regarding the WPC modeling as a curve fitting problem, we renovate the WPC modeling formulation from a machine vision aspect. To develop sufficiently diversified training samples, we synthesize supervisory control and data acquisition (SCADA) WPC data based on a set of S-shape functions representing WPCs. These synthesized SCADA data and WPC functions are visualized as images, named the synthesized SCADA WPC and synthesized neat WPC, and paired as training samples. A deep generative model based on U-net is developed to approximate the projection recovering the synthesized neat WPC from the synthesized SCADA WPC. The developed U-net based model is applied into observed SCADA data and can successfully generate the neat WPC. Moreover, a pixel mapping and correction process is developed to derive a mathematical form depicting the neat WPC generated previously. The proposed DITU-net only needs to train once and does not require any data preprocessing in applications. Numerical experiments based on 76 WTs are conducted to validate the superiority of the proposed method via benchmarking against classical WPC modeling methods.
User-empowered Privacy-preserving Authentication Protocol for Electric Vehicle Charging Based on Decentralized Identity and Verifiable Credential

The use of Electric Vehicles (EVs) has been gaining traction in recent years due to various reasons. While charging their EVs, users expose their identity and personal details, and an adversary being able to identify and track where users charge their EVs is a potential privacy threat. In this article, we propose a user-empowered privacy-preserving authentication protocol for EV charging based on Decentralized Identifier (DID) and Verifiable Credential (VC) to provide Zero-Knowledge Proof (ZKP)-security. The proposed method gives users full control over their identities and allows them to remain anonymous while charging from any station. Giving control over their identities empowers users. At the same time, by making use of the concept of VC, other parties can verify that a user is legitimate and authenticate the user before providing charging services. Hence, the proposed protocol makes the charging service available in a secure way, while empowering users and preserving their privacy.
Experiences Porting NAMD to the Data Parallel C++ Programming Model

HPC applications have a growing need to leverage heterogeneous computing resources with a vendor-neutral programming paradigm. Data Parallel C++ is a programming language based on open standards SYCL, providing a vendor-neutral solution. We describe our experiences porting the NAMD molecular dynamics application with its GPU-offload force kernels to SYCL/DPC++. Results are shown that demonstrate correctness of the porting effort.