Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching Intimate Partner Violence Education: A Quasi-Experimental Study Exploring Gaming and Storytelling

Abstract The purpose of this quasi-experimental study was to evaluate best strategies for teaching intimate partner violence (IPV) education in an undergraduate community health nursing course. Results suggest gaming was a more effective strategy than storytelling for knowledge acquisition and storytelling was more effective for knowledge retention. IPV-related nursing interventions can impact client outcomes; therefore, education is needed prior to entering the workforce. The evaluation of strategies to improve knowledge acquisition and retention of IPV content is essential to ensure best practices for detection and intervention.
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots. This work proposes a data informed residual reinforcement learning (DR-RL) based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms its standard RL from scratch counterpart regarding sample efficiency and scalability. Specifically, we first decouple the original robot into low-dimensional robotic subsystems; and further utilize one-step backward (OSBK) data to construct incremental subsystems that are equivalent model-free representations of the above decoupled robotic subsystems. The formulated incremental subsystems allow for parallel learning to relieve computation load and offer us mathematical descriptions of robotic movements for conducting theoretical analysis. Then, we apply DR-RL to learn the tracking control policy, a combination of incremental base policy and incremental residual policy, under a parallel learning architecture. The incremental residual policy uses the guidance from the incremental base policy as the learning initialization and further learns from interactions with environments to endow the tracking control policy with adaptability towards dynamically changing environments. Our proposed DR-RL based tracking control scheme is developed with rigorous theoretical analysis of system stability and weight convergence, and validated numerically on comparative simulations and also experimentally on a 3-DoF robot manipulator that would fail for other counterpart RL methods.
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels

We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels. We prove that, with normalized multi-channel input and ReLU activation, the eigenfunctions of these kernels with the uniform measure are formed by products of spherical harmonics, defined over the channels of the different pixels. We next use hierarchical factorizable kernels to bound their respective eigenvalues. We show that the eigenvalues decay polynomially, quantify the rate of decay, and derive measures that reflect the composition of hierarchical features in these networks. Our results provide concrete quantitative characterization of over-parameterized convolutional network architectures.
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents

environment: Observations for the player include the current position, a one-hot vector with one entry per location, hazard presence or absence via a boolean indicating if the wind is currently blowing, and heat, a scalar in [0, heat capacity] indicating how much heat the player has collected. Actions for the player take the form of movement: an integer in [-1, 0, 1], that moves the agent deterministically to an adjacent location (-1, 1) or allows the agent to stay in the same location (0). Virtual reality environment: Observations for the player include vision via A 1440x1600 pixel display running at 120 frames/second, presented to the human participant using the Valve Index virtual reality headset. The visual scene conveys the position, hazard, and heat information to the human participant. The player also receives input via controller vibration; by turning the controller vibration on or off, the player can perceive hazards and other input sources (e.g., signals from another agent). Actions for the player include movement, here effected by the human participant walking and shifting using their body, where the coordinates of their headset is used to determine their position in the virtual reality environment. Using their body movement, the player can also convert heat while standing in the heat region by raising their hand to trigger the exchange of a full heat capacity into a point of reward. Additional detail on the virtual reality environment are provided in Sec. 8. 4.2. Experimental Conditions: Normal, Random,and Drift Intervals Throughout the experimental results which we will present, a key element that we varied was the schedule and stochastic nature of the wind hazard. The hazard is described by two key variables: the inter-stimulus interval which is the time between hazards, as measured from the start of one hazard to the start of the following hazard, and the stimulus length which is the duration of the hazard; see Figure 4a for an example. With this framework, we considered three types of hazard, illustrated in Figures 4a-c. In each experiment, the type and parameters for the hazard were held constant during the player’s interaction with the environment, and only varied across independent experiments. These conditions are as follows: Fixed: The hazard is parameterized with fixed values for the inter-stimulus interval and stimulus length (e.g., 8 and 2 steps respectively in the abstract environment). Random: The hazard has a fixed stimulus length (e.g., 2 steps in the abstract environment), but the inter-stimulus interval is chosen at uniform random from a range [x-m, x+n] (e.g., a total ISI range of [8, 13] steps in the abstract environment). Drift: The hazard has a fixed stimulus length and initial inter-stimulus interval (e.g., 2 and 8 steps in the abstract environment, as in the Fixed condition), but the inter-stimulus interval varies
Latent Image Animator: Learning to Animate Images via Latent Space Navigation

Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current animation-approaches commonly exploit structure representation extracted from driving videos. Such structure representation is instrumental in transferring motion from driving videos to still images. However, such approaches fail in case the source image and driving video encompass large appearance variation. Moreover, the extraction of structure information requires additional modules that endow the animation-model with increased complexity. Deviating from such models, we here introduce the Latent Image Animator (LIA), a self-supervised autoencoder that evades need for structure representation. LIA is streamlined to animate images by linear navigation in the latent space. Specifically, motion in generated video is constructed by linear displacement of codes in the latent space. Towards this, we learn a set of orthogonal motion directions simultaneously, and use their linear combination, in order to represent any displacement in the latent space. Extensive quantitative and qualitative analysis suggests that our model systematically and significantly outperforms state-of-art methods on VoxCeleb, Taichi and TED-talk datasets w.r. t. generated quality.
Three-dimensional third-order gas-kinetic scheme on hybrid unstructured meshes for Euler and Navier-Stokes equations

In this paper, a third order gas kinetic scheme is developed on the three dimensional hybrid unstructured meshes for the numerical simulation of compressible inviscid and viscous flows. A third-order WENO reconstruction is developed on the hybrid unstructured meshes, including tetrahedron, pyramid, prism and hexahedron. A simple strategy is adopted for the selection of big stencil and sub-stencils. Incorporate with the two-stage fourth-order temporal discretization and lower-upper symmetric Gauss-Seidel methods, both explicit and implicit high-order gas-kinetic schemes are developed. A variety of numerical examples, from the subsonic to supersonic flows, are presented to validate the accuracy and robustness for both inviscid and viscous flows.
Adversarial Support Alignment

We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matched. We propose symmetric support difference as a divergence measure to quantify the mismatch between supports. We show that select discriminators (e.g. discriminator trained for Jensen-Shannon divergence) are able to map support differences as support differences in their one-dimensional output space. Following this result, our method aligns supports by minimizing a symmetrized relaxed optimal transport cost in the discriminator 1D space via an adversarial process. Furthermore, we show that our approach can be viewed as a limit of existing notions of alignment by increasing transportation assignment tolerance. We quantitatively evaluate the method across domain adaptation tasks with shifts in label distributions. Our experiments show that the proposed method is more robust against these shifts than other alignment-based baselines.
Dynamic groups in complex task environments: To change or not to change a winning team?

Organisations rely upon group formation to solve complex tasks, and groups often adapt to the demands of the task they face by changing their composition periodically. Previous research comes to ambiguous results regarding the effects of group adaptation on task performance. This paper aims to understand the impact of group adaptation, defined as a process of periodically changing a group’s composition, on complex task performance and considers the moderating role of individual learning and task complexity in this relationship. We base our analyses on an agent-based model of adaptive groups in a complex task environment based on the NK framework. The results indicate that reorganising well-performing groups might be beneficial, but only if individual learning is restricted. However, there are also cases in which group adaptation might unfold adverse effects. We provide extensive analyses that shed additional light on and, thereby, help explain the ambiguous results of previous research.
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages

Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation. We investigate a wide variety of supervised and unsupervised morphological segmentation methods for four polysynthetic languages: Nahuatl, Raramuri, Shipibo-Konibo, and Wixarika. Then, we compare the morphologically inspired segmentation methods against Byte-Pair Encodings (BPEs) as inputs for machine translation (MT) when translating to and from Spanish. We show that for all language pairs except for Nahuatl, an unsupervised morphological segmentation algorithm outperforms BPEs consistently and that, although supervised methods achieve better segmentation scores, they under-perform in MT challenges. Finally, we contribute two new morphological segmentation datasets for Raramuri and Shipibo-Konibo, and a parallel corpus for Raramuri–Spanish.
Revealing charge carrier dynamics and transport in Te-doped GaAsSb and GaAsSbN nanowires by correlating ultrafast terahertz spectroscopy and optoelectronic characterization

Recent advances in the growth of III-V semiconductor nanowires (NWs) hold great promise for nanoscale optoelectronic device applications. It is established that a small amount of nitrogen (N) incorporation in III-V semiconductor NWs can effectively red-shift their wavelength of operation and tailor their electronic properties for specific applications. However, understanding the impact of N incorporation on non-equilibrium charge carrier dynamics and transport in semiconducting NWs is critical in achieving efficient semiconducting NW devices. In this work, ultrafast optical pump-terahertz probe spectroscopy has been used to study non-equilibrium carrier dynamics and transport in Te-doped GaAsSb and dilute nitride GaAsSbN NWs, with the goal of correlating these results with electrical characterization of their equilibrium photo-response under bias and low-frequency noise characteristics. Nitrogen incorporation in GaAsSb NWs led to a significant increase in the carrier scattering rate, resulting in a severe reduction in carrier mobility. Carrier recombination lifetimes of 33 ± 1 picoseconds (ps) and 147 ± 3 ps in GaAsSbN and GaAsSb NWs, respectively, were measured. The reduction in the carrier lifetime and photoinduced optical conductivities are due to the presence of N-induced defects, leading to deterioration in the electrical and optical characteristics of dilute nitride NWs relative to the non-nitride NWs. Finally, we observed a very fast rise time of ∼2 ps for both NW materials, directly impacting their potential use as high-speed photodetectors.
The Aemulus Project. V. Cosmological Constraint from Small-scale Clustering of BOSS Galaxies

We analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of 0.1 ∼ 60 h −1 Mpc. Although our simulations are based on wCDM with general relativity (GR), we include a scaling parameter of the halo velocity field, γ f , defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we find f σ 8(z = 0.25) = 0.413 ± 0.031, f σ 8(z = 0.4) = 0.470 ± 0.026, and f σ 8(z = 0.55) = 0.396 ± 0.022. Compared with Planck observations using a flat Lambda cold dark matter model, our results are lower by 1. 9σ, 0. 3σ, and 3. 4σ, respectively. These results are consistent with other recent simulation-based results at nonlinear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of γf1/2σ8≈0.75 . We note, however, that the BOSS data is well fit assuming GR, i.e., γ f = 1. We cannot rule out an unknown systematic error in the galaxy bias model at nonlinear scales, but near-future data and modeling will enhance our understanding of the galaxy–halo connection, and provide a strong test of new physics beyond the standard model.
Video Prediction at Multiple Scales with Hierarchical Recurrent Networks

—Autonomous systems not only need to understand their current environment, but should also be able to predict future actions conditioned on past states, for instance based on captured camera frames. For certain tasks, detailed predictions such as future video frames are required in the near future, whereas for others it is beneﬁcial to also predict more abstract representations for longer time horizons. However, existing video prediction models mainly focus on forecasting detailed possible outcomes for short time-horizons, hence being of limited use for robot perception and spatial reasoning. We propose Multi-Scale Hierarchical Prediction (MSPred), a novel video prediction model able to forecast future possible outcomes of different levels of granularity at different time-scales simultaneously. By combining spatial and temporal downsampling, MSPred is able to efﬁciently predict abstract representations such as human poses or object locations over long time horizons, while still maintaining a competitive performance for video frame prediction. In our experiments, we demonstrate that our proposed model accurately predicts future video frames as well as other representations (e.g. keypoints or positions) on various scenarios, including bin-picking scenes or action recognition datasets, consistently outperforming popular approaches for video frame prediction. Furthermore, we conduct an ablation study to investigate the importance of the different modules and design choices in MSPred. In the spirit of reproducible research, we open-source VP-Suite , a general framework for deep-learning-based video prediction, as well as pretrained models to reproduce our results.
Quantum Sensors for High Precision Measurements of Spin-dependent Interactions

The applications of spin-based quantum sensors to measurements probing fundamental physics are surveyed. Experimental methods and technologies developed for quantum information science have rapidly advanced in recent years, and these tools enable increasingly precise control and measurement of spin dynamics. Theories of beyond-the-Standard-Model physics predict, for example, symmetry violating electromagnetic moments aligned with particle spins, exotic spin-dependent forces, coupling of spins to ultralight bosonic dark matter fields, and changes to the local environment that affect spins. Spin-based quantum sensors can be used to search for these myriad phenomena, and offer a methodology for tests of fundamental physics that is complementary to particle colliders and large scale particle detectors. Areas of technological development that can significantly enhance the sensitivity of spin-based quantum sensors to new physics are highlighted.
DATA: Domain-Aware and Task-Aware Pre-training

The paradigm of training models on massive data without label through self-supervised learning (SSL) and fine-tuning on many downstream tasks has become a trend recently. However, due to the high training costs and the un-consciousness of downstream usages, most self-supervised learning methods lack the capability to correspond to the diversities of downstream scenarios, as there are various data domains, different vision tasks and latency constraints on models. Neural architecture search (NAS) is one universally acknowledged fashion to conquer the issues above, but applying NAS on SSL seems impossible as there is no label or metric provided for judging model selection. In this paper, we present DATA, a simple yet effective NAS approach specialized for SSL that provides D omain-A ware and T ask-A ware pre-training. Specifically, we (i) train a supernet which could be deemed as a set of millions of networks covering a wide range of model scales without any label, (ii) propose a flexible searching mechanism compatible with SSL that enables finding networks of different computation costs, for various downstream vision tasks and data domains without explicit metric provided. Instan-tiated With MoCo v2, our method achieves promising re-sults across a wide range of computation costs on down-stream tasks, including image classification, object
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation

Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention. However, due to their complex architectures and large numbers of parameters, measuring the generalization ability of specific DNN models remains an open challenge. In this paper, we propose to use multiple factors to measure and rank the relative generalization of DNNs based on a new concept of confidence dimension (CD). Furthermore, we provide a feasible framework in our CD to theoretically calculate the upper bound of generalization based on the conventional Vapnik-Chervonenk dimension (VC-dimension) and Hoeffding's inequality. Experimental results on image classification and object detection demonstrate that our CD can reflect the relative generalization ability for different DNNs. In addition to full-precision DNNs, we also analyze the generalization ability of binary neural networks (BNNs), whose generalization ability remains an unsolved problem. Our CD yields a consistent and reliable measure and ranking for both full-precision DNNs and BNNs on all the tasks.
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks

Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs that contain the adversary trigger. The major challenge for defending against these attacks is that only the attacker knows the secret trigger and the target class. The problem is further exacerbated by the recent introduction of"Hidden Triggers", where the triggers are carefully fused into the input, bypassing detection by human inspection and causing backdoor identification through anomaly detection to fail. To defend against such imperceptible attacks, in this work we systematically analyze how representations, i.e., the set of neuron activations for a given DNN when using the training data as inputs, are affected by backdoor attacks. We propose PiDAn, an algorithm based on coherence optimization purifying the poisoned data. Our analysis shows that representations of poisoned data and authentic data in the target class are still embedded in different linear subspaces, which implies that they show different coherence with some latent spaces. Based on this observation, the proposed PiDAn algorithm learns a sample-wise weight vector to maximize the projected coherence of weighted samples, where we demonstrate that the learned weight vector has a natural"grouping effect"and is distinguishable between authentic data and poisoned data. This enables the systematic detection and mitigation of backdoor attacks. Based on our theoretical analysis and experimental results, we demonstrate the effectiveness of PiDAn in defending against backdoor attacks that use different settings of poisoned samples on GTSRB and ILSVRC2012 datasets. Our PiDAn algorithm can detect more than 90% infected classes and identify 95% poisoned samples.
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos

Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the “Happy” expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event. To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the well-designed workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frame-works were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project are available on https://github.com/wangyanckxx/FERV39k.