Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial movement synergies and Action Unit detection from distal wearable Electromyography and Computer Vision

Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalizes on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction of the facial expression production, whilst allowing EMG measurements. However, measuring EMG distally entails that the exact source of the facial movement is unknown. We propose a novel method to estimate specific Facial Action Units (AUs) from distal facial EMG and Computer Vision (CV). This method is based on Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NNMF), and sorting of the resulting components to determine which is the most likely to correspond to each CV-labeled action unit (AU). Performance on the detection of AU06 (Orbicularis Oculi) and AU12 (Zygomaticus Major) was estimated by calculating the agreement with Human Coders. The results of our proposed algorithm showed an accuracy of 81% and a Cohen's Kappa of 0.49 for AU6; and accuracy of 82% and a Cohen's Kappa of 0.53 for AU12. This demonstrates the potential of distal EMG to detect individual facial movements. Using this multimodal method, several AU synergies were identified. We quantified the co-occurrence and timing of AU6 and AU12 in posed and spontaneous smiles using the human-coded labels, and for comparison, using the continuous CV-labels. The co-occurrence analysis was also performed on the EMG-based labels to uncover the relationship between muscle synergies and the kinematics of visible facial movement.
Localizing Anomalies From Weakly-Labeled Videos

Video anomaly detection under video-level labels is currently a challenging task. Previous works have made progresses on discriminating whether a video sequence contains anomalies. However, most of them fail to accurately localize the anomalous events within videos in the temporal domain. In this paper, we propose a Weakly Supervised Anomaly Localization (WSAL) method focusing on temporally localizing anomalous segments within anomalous videos. Inspired by the appearance difference in anomalous videos, the evolution of adjacent temporal segments is evaluated for the localization of anomalous segments. To this end, a high-order context encoding model is proposed to not only extract semantic representations but also measure the dynamic variations so that the temporal context could be effectively utilized. In addition, in order to fully utilize the spatial context information, the immediate semantics are directly derived from the segment representations. The dynamic variations as well as the immediate semantics, are efficiently aggregated to obtain the final anomaly scores. An enhancement strategy is further proposed to deal with noise interference and the absence of localization guidance in anomaly detection. Moreover, to facilitate the diversity requirement for anomaly detection benchmarks, we also collect a new traffic anomaly (TAD) dataset which specifies in the traffic conditions, differing greatly from the current popular anomaly detection evaluation benchmarks. Thedataset and the benchmark test codes, as well as experimental results, are made public on http://vgg-ai.cn/pages/Resource/ and https://github.com/ktr-hubrt/WSAL. Extensive experiments are conducted to verify the effectiveness of different components, and our proposed method achieves new state-of-the-art performance on the UCF-Crime and TAD datasets.
Equivalence of elastic and Ising models for spin crossover materials

Spin crossover (SCO) materials are reversible molecular switches; and occur in a wide range of near octahedral transition metal complexes and frameworks with $d^4-d^7$ electron configurations. SCO systems present collective spin-state phase transitions that show hysteresis, multistep transitions, gradual transitions, and anti-ferroelastic phases. Ising models have often been employed to model these behaviors, as they are far easier to solve than more realistic elastic models. However previously Ising models have required phenomenological parameters that do not have a clear physical origin. We present an exact mapping from an elastic model of balls and springs to the Ising model. The resulting Ising coupling constants arise only from the elastic interactions, and are independent of the lattice dynamics, i.e., there are no isotope effects. The elastic interactions, and hence the Ising coupling constants can be determined from the measurements of the bulk and shear moduli. The Ising coupling constants can be frustrated, their signs can be negative or positive, and their magnitude agrees well with previous estimates from fits of experimental spin-transition curves. The Ising coupling constants follow a power law for large separations between metal centers, in particular an inverse square law for the square lattice. For the square lattice with nearest neighbor elastic interactions this model predicts a diverse range of orders including multistep transitions.
Nutrient-dependent patterns in the sex ratio of Ovomermis sinensis (Nematoda: Mermithidae), a biological control agent of Helicoverpa armigera (Lepidoptera: Noctuidae)

ABSTRACT Ovomermis sinensis (Nematoda: Mermithidae) is an entomophilic nematode and a potential biocontrol agent of lepidopteran pests, including Helicoverpa armigera. The sex ratio of a species can be used to regulate the size of the reproductive population. Parasitic load, parasitic period, host instar, and body size were examined to identify factors affecting the O. sinensis sex ratio. We tested the hypothesis that the O. sinensis sex ratio is correlated with host nutrient supply and the nutrients absorbed by the nematodes. The results show that the proportion of male O. sinensis increased with parasitic load but decreased with host instars and body size. Moreover, the parasitic period of males was significantly shorter than that of females. However, all the factors (host and nematodes) affecting the sex ratio were significantly modified by restricting the host diet, which increased the proportion of males. In turn, juveniles that absorbed fewer nutrients tended to develop into males. Taken together, our findings suggest that factors impacting the O. sinensis sex ratio are related to host nutrient status and provide parameters for mass rearing and a release strategy for this natural enemy.
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this article we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion. The proposed model is end-to-end trainable and self-supervised without any external annotations. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise a network to extract the shared latent features, which are then used for MRI synthesis. Since paired data is unavailable for our study (and rare in practice), pixel-level constraints are infeasible to apply. We instead propose to enforce the distributions to be statistically indistinguishable, by adversarial learning in both the image domain and feature space. To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint. A new cross-modal attention technique is proposed to utilise non-local spatial information, by encouraging multi-modal knowledge fusion and propagation. We extend the approach to consider the case where 3D auxiliary information (e.g., 3D neighbours and a 3D location index) from volumetric data is also available, and show that this improves image synthesis. The proposed approach is evaluated quantitatively and qualitatively with comparison to real fetal MR images and other approaches to synthesis, demonstrating its feasibility of synthesising realistic MR images.
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect the subsurface objects (i.e. rebars, utility pipes) and reveal the underground scene. One of the biggest challenges in GPR based inspection is the subsurface targets reconstruction. In order to address this issue, this paper presents a 3D GPR migration and dielectric prediction system to detect and reconstruct underground targets. This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i.e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets. Our proposed DepthNet processes the GPR data by removing the noise in B-scan image as well as predicting depth of subsurface objects. For DepthNet model training and testing, we collect the real GPR data in the concrete test pit at Geophysical Survey System Inc. (GSSI) and create the synthetic GPR data by using gprMax3.0 simulator. The dataset we create includes 350 labeled GPR images. The DepthNet achieves an average accuracy of 92.64% for B-scan feature detection and an 0.112 average error for underground target depth prediction. In addition, the experimental results verify that our proposed method improve the migration accuracy and performance in generating 3D GPR image compared with the traditional migration methods.
How biased are halo properties in cosmological simulations?

Cosmological N-body simulations have been a major tool of theorists for decades, yet many of the numerical issues that these simulations face are still unexplored. This paper measures numerical biases in these large, dark matter-only simulations that affect the properties of their dark matter haloes. We compare many simulation suites in order to provide several tools for simulators and analysts which help mitigate these biases. We summarise our comparisons with practical `convergence limits' that can be applied to a wide range of halo properties, including halo properties which are traditionally overlooked by the testing literature. We also find that the halo properties predicted by different simulations can diverge from one another at unexpectedly high resolutions. We demonstrate that many halo properties depend strongly on force softening scale and that this dependence leads to much of the measured divergence between simulations. We offer an empirical model to estimate the impact of such effects on the rotation curves of a halo population. This model can serve as a template for future empirical models of the biases in other halo properties.
Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning

Data holders are increasingly seeking to protect their user's privacy, whilst still maximizing their ability to produce machine learning (ML) models with high quality predictions. In this work, we empirically evaluate various implementations of differential privacy (DP), and measure their ability to fend off real-world privacy attacks, in addition to measuring their core goal of providing accurate classifications. We establish an evaluation framework to ensure each of these implementations are fairly evaluated. Our selection of DP implementations add DP noise at different positions within the framework, either at the point of data collection/release, during updates while training of the model, or after training by perturbing learned model parameters. We evaluate each implementation across a range of privacy budgets and datasets, each implementation providing the same mathematical privacy guarantees. By measuring the models' resistance to real world attacks of membership and attribute inference, and their classification accuracy. we determine which implementations provide the most desirable tradeoff between privacy and utility. We found that the number of classes of a given dataset is unlikely to influence where the privacy and utility tradeoff occurs, a counter-intuitive inference in contrast to the known relationship of increased privacy vulnerability in datasets with more classes. Additionally, in the scenario that high privacy constraints are required, perturbing input training data before applying ML modeling does not trade off as much utility, as compared to noise added later in the ML process.
The Organization of Software Teams in the Quest for Continuous Delivery: A Grounded Theory Approach

Context: Continuous delivery practices accelerate time to market and improve customer satisfaction. Although recent related work suggests that organizations employing continuous delivery should promote a collaborative culture among different IT teams, there is no substantial literature tackling how organizations should organize their teams to excel in continuous delivery. Objective: In this study, we investigate how organizations pursuing continuous delivery organize their development and operations teams. Method: We collected and analyzed data from interviews with 46 IT professionals, following Grounded Theory guidelines. Results: After a careful analysis, we identified four patterns of organizational structures: (1) siloed departments, (2) classical DevOps, (3) cross-functional teams, and (4) platform teams. The main contribution of this study is a taxonomy that organizes these structures along with their properties. This taxonomy is our theory for organizing software teams in the context of continuous delivery.
Research Note A new procedure for quantification of lignin in soybean (Glycine max (L.) Merrill) seed coat and their relationship with the resistance to mechanical damage

The quantitative assay for protein-free lignin measured by thioglycolic acid reaction was applied to soybean (Glycine max L.) seed coat. The lignothioglycolic acid (LTGA) produced by the reaction of thioglycolic and lignin was recorded (at A280 nm) for various dilutions and a wavelength-dependent absorptivity value (e = 17.87 g -1 L cm -1 ) was obtained. Subsequently, the lignin contents of eleven soybean cultivars (Savana, Davis, FT-10, Paranagoina, Bossier, IAC-8, Santa Rosa, Doko, Parana, FT-2 and IAS-5) were determined. The lignin contents correlated with the index of resistance to seed mechanical damage. These results indicate that the LTGA method for lignin quantification can be used for screening genotypes for mechanical damage resistance in a breeding program for soybean seed quality.
Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. One of this tasks is in-bed human pose estimation, which has significant values in many healthcare applications. In-bed pose monitoring in natural settings could involve complete darkness or full occlusion. Furthermore, the lack of publicly available in-bed pose datasets hinders the use of many successful pose estimation algorithms for this task. In this paper, we introduce our Simultaneously-collected multimodal Lying Pose (SLP) dataset, which includes in-bed pose images from 109 participants captured using multiple imaging modalities including RGB, long wave infrared, depth, and pressure map. We also present a physical hyper parameter tuning strategy for ground truth pose label generation under extreme conditions such as lights off and being fully covered by a sheet/blanket. SLP design is compatible with the mainstream human pose datasets, therefore, the state-of-the-art 2D pose estimation models can be trained effectively with SLP data with promising performance as high as 95% at PCKh@0.5 on a single modality. The pose estimation performance can be further improved by including additional modalities through collaboration.
Food Habits of the Feral House Cat in East-Central Texas

were shot along roadsides or in fields and woodlots with the exception of three that were taken in steel traps and three that were found dead on the road. Stomach contents are listed in Table 1. Errington (Jour. Mamm. 17(1): 64-65, 1936), in discussing the food habits of southern Wisconsin house cats, is of the opinion that the manner in which the cats are collected does not present a really true cross-section of food habits. Those cats that hunted along roadsides, in stubble and hayfields or those picked up dead on the highways were readily seen and collected, while "the elusive woodsdwellers, which are most likely to be feral and to have the best access to avian prey," are seldom taken. These observations may hold true, in part at least, for the Post Oak Region, although a larger number of song birds and quail frequent the roadsides and the more open fields and semi-brushy areas. Of the 31 stomachs examined, two contained food that had apparently been fed to them by The owners. The cotton rat was taken more than any other single food item. This is probably because more cats were collected along roadsides and in brushy areas in and bordering pastures, where populations of TABLE 1. -QUALITATIVE ANALYSIS OF 31 STOMACHS OF FERAL HOUSE CATS COLLECTED IN THE CENTRAL POST OAK REGION
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization

In this paper, we consider algorithm-independent lower bounds for the problem of black-box optimization of functions having a bounded norm is some Reproducing Kernel Hilbert Space (RKHS), which can be viewed as a non-Bayesian Gaussian process bandit problem. In the standard noisy setting, we provide a novel proof technique for deriving lower bounds on the regret, with benefits including simplicity, versatility, and an improved dependence on the error probability. In a robust setting in which every sampled point may be perturbed by a suitably-constrained adversary, we provide a novel lower bound for deterministic strategies, demonstrating an inevitable joint dependence of the cumulative regret on the corruption level and the time horizon, in contrast with existing lower bounds that only characterize the individual dependencies. Furthermore, in a distinct robust setting in which the final point is perturbed by an adversary, we strengthen an existing lower bound that only holds for target success probabilities very close to one, by allowing for arbitrary success probabilities in $(0,1)$.
Inferring (sub)millimetre dust opacities and temperature structure in edge-on protostellar discs from resolved multiwavelength continuum observations: the case of the HH 212 disc

(Sub)millimetre dust opacities are required for converting the observable dust continuum emission to the mass, but their values have long been uncertain, especially in discs around young stellar objects. We propose a method to constrain the opacity κν in edge-on discs from a characteristic optical depth τ0,ν, the density ρ0, and radius R0 at the disc outer edge through κν = τ0,ν/(ρ0R0), where τ0,ν is inferred from the shape of the observed flux along the major axis, ρ0 from gravitational stability considerations, and R0 from direct imaging. We applied the 1D semi-analytical model to the embedded, Class 0, HH 212 disc, which has high-resolution data in Atacama Large Millimetre/submillimetre Array (ALMA) bands 9, 7, 6, and 3 and Very Large Array Ka band (λ = 0.43, 0.85, 1.3, 2.9, and 9.1 mm). The modelling is extended to 2D through RADMC-3D radiative transfer calculations. We find a dust opacity of κν ≈ 1.9 × 10−2, 1.3 × 10−2, and 4.9 × 10−3 cm2 g−1 of gas and dust for ALMA bands 7, 6, and 3, respectively, with uncertainties dependent on the adopted stellar mass. The inferred opacities lend support to the widely used prescription κλ = 2.3 × 10−2(1.3mm/λ) cm2 g−1 . We inferred a temperature of ∼45 K at the disc outer edge that increases radially inwards. It is well above the sublimation temperatures of ices such as CO and N2, which supports the notion that the disc chemistry cannot be completely inherited from the protostellar envelope.
Predicting the Time of Arrival of Coronal Mass Ejections at Earth From Heliospheric Imaging Observations

The time of arrival (ToA) of coronal mass ejections (CMEs) at Earth is a key parameter due to the space weather phenomena associated with the CME arrival, such as intense geomagnetic storms. Despite the incremental use of new instrumentation and the development of novel methodologies, ToA estimated errors remain above 10 h on average. Here, we investigate the prediction of the ToA of CMEs using observations from heliospheric imagers, i.e., from heliocentric distances higher than those covered by the existent coronagraphs. In order to perform this work, we analyze 14 CMEs observed by the heliospheric imagers HI‐1 onboard the twin STEREO spacecraft to determine their front location and speed. The kinematic parameters are derived with a new technique based on the Elliptical Conversion (ElCon) method, which uses simultaneous observations from the two viewpoints from STEREO. Outside the field of view of the instruments, we assume that the dynamics of the CME evolution is controlled by aerodynamic drag, i.e., a force resulting from the interaction with particles from the background solar wind. To model the drag force, we use a physical model that allows us to derive its parameters without the need to rely on drag coefficients derived empirically. We found a CME ToA mean error of 1.6 ± 8.0 h ToA and a mean absolute error of 6.9 ± 3.9 h for a set of 14 events. The results suggest that observations from HI‐1 lead to estimates with similar errors to observations from coronagraphs.
Neural networks and quantum field theory

We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory. The correspondence relies on the fact that many asymptotic neural networks are drawn from Gaussian processes (GPs), the analog of non-interacting field theories. Moving away from the asymptotic limit yields a non-Gaussian process (NGP) and corresponds to turning on particle interactions, allowing for the computation of correlation functions of neural network outputs with Feynman diagrams. Minimal NGP likelihoods are determined by the most relevant non-Gaussian terms, according to the flow in their coefficients induced by the Wilsonian renormalization group. This yields a direct connection between overparameterization and simplicity of neural network likelihoods. Whether the coefficients are constants or functions may be understood in terms of GP limit symmetries, as expected from ’t Hooft’s technical naturalness. General theoretical calculations are matched to neural network experiments in the simplest class of models allowing the correspondence. Our formalism is valid for any of the many architectures that becomes a GP in an asymptotic limit, a property preserved under certain types of training.