Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Exploration of Magnetic Saturation and Anisotropy Energy for Nonstoichiometric Ferrite Compositions

A grand challenge in materials research is identifying the relationship between composition and performance. Herein, we explore this relationship for magnetic properties, specifically magnetic saturation (M$_s$) and magnetic anisotropy energy (MAE) of ferrites. Ferrites are materials derived from magnetite (which has the chemical formulae Fe$_3$O$_4$) that comprise metallic elements in some combination such as Fe, Mn, Ni, Co, Cu and Zn. They are used in a variety of applications such as electromagnetism, magnetic hyperthermia, and magnetic imaging. Experimentally, synthesis and characterization of magnetic materials is time consuming. In order to create insight to help guide synthesis, we compute the relationship between ferrite composition and magnetic properties using density functional theory (DFT). Specifically, we compute M$_s$ and MAE for 571 ferrite structures with the formulae M1$_x$M2$_y$Fe$_{3-x-y}$O$_4$, where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn and 0 $\le$ x $\le$ 1 and y = 1 - x. By varying composition, we were able to vary calculated values of M$_s$ and MAE by up to 9.6$\times$10$^5$ A m$^{-1}$ and 14.1$\times$10$^5$ J m$^{-3}$, respectively. Our results suggest that composition can be used to optimize magnetic properties for applications in heating, imaging, and recording. This is mainly achieved by varying M$_s$, as these applications are more sensitive to variation in M$_s$ than MAE.
The Use of Multi-Scale Fiducial Markers To Aid Takeoff and Landing Navigation by Rotorcraft

This paper quantifies the performance of visual SLAM that leverages multi-scale fiducial markers (i.e., artificial landmarks that can be detected at a wide range of distances) to show its potential for reliable takeoff and landing navigation in rotorcraft. Prior work has shown that square markers with a black-and-white pattern of grid cells can be used to improve the performance of visual SLAM with color cameras. We extend this prior work to allow nested marker layouts. We evaluate performance during semi-autonomous takeoff and landing operations in a variety of environmental conditions by a DJI Matrice 300 RTK rotorcraft with two FLIR Blackfly color cameras, using RTK GNSS to obtain ground truth pose estimates. Performance measures include absolute trajectory error and the fraction of the number of estimated poses to the total frame. We release all of our results -- our dataset and the code of the implementation of the visual SLAM with fiducial markers -- to the public as open-source.
The Influence of Pain on Reduced Quality of Life in Patients with Hidradenitis Suppurativa: A Single-Center Retrospective Study

Background: Pain and itch are impactful and burdensome symptoms of hidradenitis suppurativa (HS). Elucidating factors associated with pain and itch severity may identify groups disproportionally affected by HS-related pain and itch and further our understanding of how pain and itch impact quality of life (QoL) in patients with HS. Objective: The objective of the study was to determine factors associated with pain severity, itch severity, and reduced QoL in patients with HS. Methods: This is a retrospective cross-sectional study of 257 adults with HS who received care in an HS Specialty Clinic from January 2019 to August 2021. Multivariable mixed-effects linear regression was used to determine the relationships between clinical and demographic patient factors and the outcomes of pain severity, itch severity, and skin-specific QoL. Results: Factors associated with reduced QoL were Hurley stage II (β = 19.66, 95% CI: 1.40–37.93) and III (β = 21.98, 95% CI: 1.57–42.39) disease as well as severity of pain (β = 13.74, 95% CI: 11.93–15.55), itch (β = 4.57, 95% CI: 2.59–6.55), anxiety (β = 2.55 95% CI: 1.29–3.81), and depression (β = 1.43, 95% CI: 0.30–2.56). Increasing HS pain severity was associated with Hurley stage III disease (β = 2.04, 95% Cl: 0.99–3.09), black race (β = 1.23, 95% Cl: 0.40, 2.06), depression severity (β = 0.08, 95% Cl: 0.02, 0.14), and anxiety severity (β = 0.10 95% Cl: 0.04, 0.17). Factors associated with HS itch severity were Hurley stage III disease (β = 2.23, 95% Cl: 1.19, 3.27), black race (β = 0.92, 95% Cl: 0.07, 1.78), depression severity (β = 0.09, 95% Cl: 0.04, 0.14), and anxiety severity (β = 0.07, 95% Cl: 0.01, 0.13). Conclusion: Pain is one of the largest contributors to QoL in patients with HS; on a 0–10 numeric rating scale, a 2-point increase in HS pain had a similar independent effect on QoL as having Hurley stage III disease compared to Hurley stage I.
An Ontology of Dark Patterns Knowledge: Foundations, Definitions, and a Pathway for Shared Knowledge-Building

Deceptive and coercive design practices are increasingly used by companies to extract profit, harvest data, and limit consumer choice. Dark patterns represent the most common contemporary amalgamation of these problematic practices, connecting designers, technologists, scholars, regulators, and legal professionals in transdisciplinary dialogue. However, a lack of universally accepted definitions across the academic, legislative and regulatory space has likely limited the impact that scholarship on dark patterns might have in supporting sanctions and evolved design practices. In this paper, we seek to support the development of a shared language of dark patterns, harmonizing ten existing regulatory and academic taxonomies of dark patterns and proposing a three-level ontology with standardized definitions for 65 synthesized dark patterns types across low-, meso-, and high-level patterns. We illustrate how this ontology can support translational research and regulatory action, including pathways to extend our initial types through new empirical work and map across application domains.
From Knowing to Doing: Learning Diverse Motor Skills through Instruction Learning

Recent years have witnessed many successful trials in the robot learning field. For contact-rich robotic tasks, it is challenging to learn coordinated motor skills by reinforcement learning. Imitation learning solves this problem by using a mimic reward to encourage the robot to track a given reference trajectory. However, imitation learning is not so efficient and may constrain the learned motion. In this paper, we propose instruction learning, which is inspired by the human learning process and is highly efficient, flexible, and versatile for robot motion learning. Instead of using a reference signal in the reward, instruction learning applies a reference signal directly as a feedforward action, and it is combined with a feedback action learned by reinforcement learning to control the robot. Besides, we propose the action bounding technique and remove the mimic reward, which is shown to be crucial for efficient and flexible learning. We compare the performance of instruction learning with imitation learning, indicating that instruction learning can greatly speed up the training process and guarantee learning the desired motion correctly. The effectiveness of instruction learning is validated through a bunch of motion learning examples for a biped robot and a quadruped robot, where skills can be learned typically within several million steps. Besides, we also conduct sim-to-real transfer and online learning experiments on a real quadruped robot. Instruction learning has shown great merits and potential, making it a promising alternative for imitation learning.
Diverse Neural Audio Embeddings - Bringing Features back !

With the advent of modern AI architectures, a shift has happened towards end-to-end architectures. This pivot has led to neural architectures being trained without domain-specific biases/knowledge, optimized according to the task. We in this paper, learn audio embeddings via diverse feature representations, in this case, domain-specific. For the case of audio classification over hundreds of categories of sound, we learn robust separate embeddings for diverse audio properties such as pitch, timbre, and neural representation, along with also learning it via an end-to-end architecture. We observe handcrafted embeddings, e.g., pitch and timbre-based, although on their own, are not able to beat a fully end-to-end representation, yet adding these together with end-to-end embedding helps us, significantly improve performance. This work would pave the way to bring some domain expertise with end-to-end models to learn robust, diverse representations, surpassing the performance of just training end-to-end models.
A Continual Learning Paradigm for Non-differentiable Visual Programming Frameworks on Visual Reasoning Tasks

Recently, the visual programming framework (VisProg) has emerged as a significant framework for executing compositional visual tasks due to its interpretability and flexibility. However, the performance of VisProg on specific Visual Reasoning (VR) tasks is markedly inferior compared to well-trained task-specific models since its employed visual sub-modules have limited generalization capabilities. Due to the non-differentiability of VisProg, it is quite challenging to improve these visual sub-modules within VisProg for the specific VR task while maintaining their generalizability on the un-seen tasks. Attempt to overcome these difficulties, we propose CLVP, a Continuous Learning paradigm for VisProg across various visual reasoning tasks. Specifically, our CLVP distills the capabilities of well-trained task-specific models into the visual sub-modules in a stepwise and anti-forgetting manner. This can continually improve the performance of VisProg on multiple visual tasks while preserving the flexibility of VisProg. Extensive and comprehensive experimental results demonstrate that our CLVP obtains significant performance gains on specific VR benchmarks, i.e., GQA (+1.4%) and NLVRv2 (+5.6%), compared to the VisProg baseline, and also maintains a promising generalizability for VR on un-seen and previous learned tasks.
Zero-Shot Policy Transferability for the Control of a Scale Autonomous Vehicle

We report on a study that employs an in-house developed simulation infrastructure to accomplish zero shot policy transferability for a control policy associated with a scale autonomous vehicle. We focus on implementing policies that require no real world data to be trained (Zero-Shot Transfer), and are developed in-house as opposed to being validated by previous works. We do this by implementing a Neural Network (NN) controller that is trained only on a family of circular reference trajectories. The sensors used are RTK-GPS and IMU, the latter for providing heading. The NN controller is trained using either a human driver (via human in the loop simulation), or a Model Predictive Control (MPC) strategy. We demonstrate these two approaches in conjunction with two operation scenarios: the vehicle follows a waypoint-defined trajectory at constant speed; and the vehicle follows a speed profile that changes along the vehicle's waypoint-defined trajectory. The primary contribution of this work is the demonstration of Zero-Shot Transfer in conjunction with a novel feed-forward NN controller trained using a general purpose, in-house developed simulation platform.
Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction

Recent span-based joint extraction models have demonstrated significant advantages in both entity recognition and relation extraction. These models treat text spans as candidate entities, and span pairs as candidate relationship tuples, achieving state-of-the-art results on datasets like ADE. However, these models encounter a significant number of non-entity spans or irrelevant span pairs during the tasks, impairing model performance significantly. To address this issue, this paper introduces a span-based multitask entity-relation joint extraction model. This approach employs the multitask learning to alleviate the impact of negative samples on entity and relation classifiers. Additionally, we leverage the Intersection over Union(IoU) concept to introduce the positional information into the entity classifier, achieving a span boundary detection. Furthermore, by incorporating the entity Logits predicted by the entity classifier into the embedded representation of entity pairs, the semantic input for the relation classifier is enriched. Experimental results demonstrate that our proposed SpERT.MT model can effectively mitigate the adverse effects of excessive negative samples on the model performance. Furthermore, the model demonstrated commendable F1 scores of 73.61%, 53.72%, and 83.72% on three widely employed public datasets, namely CoNLL04, SciERC, and ADE, respectively.
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style Transfer

The deployment of autonomous agents in real-world scenarios is challenged by"unknown unknowns", i.e. novel unexpected environments not encountered during training, such as degraded signs. While existing research focuses on anomaly detection and class imbalance, it often fails to address truly novel scenarios. Our approach enhances visual perception by leveraging the Variational Prototyping Encoder (VPE) to adeptly identify and handle novel inputs, then incrementally augmenting data using neural style transfer to enrich underrepresented data. By comparing models trained solely on original datasets with those trained on a combination of original and augmented datasets, we observed a notable improvement in the performance of the latter. This underscores the critical role of data augmentation in enhancing model robustness. Our findings suggest the potential benefits of incorporating generative models for domain-specific augmentation strategies.
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty

Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced predictive control techniques under uncertainty to ensure long-term economics and decarbonization. In this paper, we propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep learning-based forecasting and stochastic optimization, where these two stages are connected by the uncertainty estimation at multiple temporal resolutions; (ii) An efficient online data augmentation scheme, jointly involving model pre-training and online fine-tuning stages. In this way, the proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process, and finally to realize an optimal and robust dispatch solution. The proposed framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain. In addition, comprehensive experiments are conducted to interpret its effectiveness in the real-life scenario of smart building energy management.
Tunable Tesla-Scale Magnetic Attosecond Pulses through Ring-Current Gating

Coherent control over electron dynamics in atoms and molecules using high-intensity circularly polarized laser pulses gives rise to current loops, resulting in the emission of magnetic fields. We propose, and demonstrate with ab initio calculations, “current-gating” schemes to generate direct or alternating-current magnetic pulses in the infrared spectral region, with highly tunable waveform and frequency, and showing femtosecond-to-attosecond pulse duration. In optimal conditions, the magnetic pulse can be highly isolated from the driving laser and exhibits a high flux density (∼1 T at a few hundred nanometers from the source, with a pulse duration of 787 attoseconds) for application in forefront experiments of ultrafast spectroscopy. Our work paves the way toward the generation of attosecond magnetic fields to probe ultrafast magnetization, chiral responses, and spin dynamics.
Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints

3D scene reconstruction from 2D images has been a long-standing task. Instead of estimating per-frame depth maps and fusing them in 3D, recent research leverages the neural implicit surface as a unified representation for 3D reconstruction. Equipped with data-driven pre-trained geometric cues, these methods have demonstrated promising performance. However, inaccurate prior estimation, which is usually inevitable, can lead to suboptimal reconstruction quality, particularly in some geometrically complex regions. In this paper, we propose a two-stage training process, decouple view-dependent and view-independent colors, and leverage two novel consistency constraints to enhance detail reconstruction performance without requiring extra priors. Additionally, we introduce an essential mask scheme to adaptively influence the selection of supervision constraints, thereby improving performance in a self-supervised paradigm. Experiments on synthetic and real-world datasets show the capability of reducing the interference from prior estimation errors and achieving high-quality scene reconstruction with rich geometric details.
Evaluating Dynamic Topic Models

There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality with the model's temporal consistency. We demonstrate the utility of the proposed measure by applying it to synthetic data and data from existing DTMs. We also conducted a human evaluation, which indicates that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs, and guiding future research in this area.
Electric Aircraft Assignment, Routing, and Charge Scheduling Considering the Availability of Renewable Energy

Electric airplanes are expected to take to the skies soon, finding first use cases in small networks within hardly accessible areas, such as island communities. In this context, the environmental footprint of such airplanes will be strongly determined by the energy sources employed when charging them. This letter presents a framework to optimize aircraft assignment, routing and charge schedules explicitly accounting for the energy availability at the different airports, which are assumed to be equipped with renewable energy sources and stationary batteries. Specifically, considering the daily travel demand and weather conditions forecast in advance, we first capture the aircraft operations within a time-expanded directed acyclic graph, and combine it with a dynamic energy model of the individual airports. Second, aiming at minimizing grid-dependency, we leverage our models to frame the optimal electric aircraft and airport operational problem as a mixed-integer linear program that can be solved with global optimality guarantees. Finally, we showcase our framework in a real-world case-study considering one week of operations on the Dutch Leeward Antilles. Our results show that, depending on weather conditions and compared to current schedules, optimizing flights and operations in a renewable-energy-aware manner can reduce grid dependency from 18 to 100%, whilst significantly shrinking the operational window of the airplanes.
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms.
Outpatient Treatment of Confirmed COVID-19: Living, Rapid Practice Points From the American College of Physicians (Version 2)

DESCRIPTION Evidence for the use of outpatient treatments in adults with confirmed COVID-19 continues to evolve with new data. This is version 2 of the American College of Physicians (ACP) living, rapid practice points focusing on 22 outpatient treatments for COVID-19, specifically addressing the dominant SARS-CoV-2 Omicron variant. METHODS The Population Health and Medical Science Committee (formerly the Scientific Medical Policy Committee) developed this version of the living, rapid practice points on the basis of a living, rapid review done by the ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems (Danube University Krems). This topic will be maintained as living and rapid by continually monitoring and assessing the impact of new evidence. PRACTICE POINT 1 Consider molnupiravir to treat symptomatic patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 days of the onset of symptoms and at a high risk for progressing to severe disease. PRACTICE POINT 2 Consider nirmatrelvir-ritonavir combination therapy to treat symptomatic patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 days of the onset of symptoms and at a high risk for progressing to severe disease. PRACTICE POINT 3 Do not use ivermectin to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. PRACTICE POINT 4 Do not use sotrovimab to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting.