Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey

Automated co-located human-human interaction analysis has been addressed by the use of nonverbal communication as measurable evidence of social and psychological phenomena. We survey the computing studies (since 2010) detecting phenomena related to social traits (e.g., leadership, dominance, personality traits), social roles/relations, and interaction dynamics (e.g., group cohesion, engagement, rapport). Our target is to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings (free-standing conversations, meetings, dyads, and crowds). We also present a comprehensive summary of the related datasets and outline future research directions which are regarding the implementation of artificial intelligence, dataset curation, and privacy-preserving interaction analysis. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively; multimodal features are prominently performing better; deep learning architectures showed improved performance in overall, but there exist many phenomena whose detection has never been implemented through deep models. We also identified several limitations such as the lack of scalable benchmarks, annotation reliability tests, cross-dataset experiments, and explainability analysis.
Chemical reaction motifs driving non-equilibrium behaviours in phase separating materials

Chemical reactions that couple to systems that phase separate have been implicated in diverse contexts from biology to materials science. However, how a particular set of chemical reactions (chemical reaction network, CRN) would affect the behaviours of a phase separating system is difficult to fully predict theoretically. In this paper, we analyse a mean field theory coupling CRNs to a combined system of phase separating and non-phase separating materials and analyse how the properties of the CRNs affect different classes of non-equilibrium behaviour: microphase separation or temporally oscillating patterns. We examine the problem of achieving microphase separated condensates by statistical analysis of the Jacobians, of which the most important motifs are negative feedback of the phase separating component and combined inhibition/activation by the non-phase separating components. We then identify CRN motifs that are likely to yield microphase by examining randomly generated networks and parameters. Molecular sequestration of the phase separating motif is shown to be the most robust towards yielding microphase separation. Subsequently, we find that dynamics of the phase separating species is promoted most easily by inducing oscillations in the diffusive components coupled to the phase separating species. Our results provide guidance towards the design of CRNs that manage the formation, dissolution and organization of compartments.
ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. To fulfill this, we propose a novel LNL framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples. Extensive experiments demonstrate that ProMix significantly advances the current state-of-the-art results on multiple benchmarks with different types and levels of noise. It achieves an average improvement of 2.48% on the CIFAR-N dataset.
Positive solutions of the Gross–Pitaevskii equation for energy critical and supercritical nonlinearities

We consider positive and spatially decaying solutions to the following Gross–Pitaevskii equation with a harmonic potential: −Δu+|x|2u=ωu+|u|p−2uin Rd, where d⩾3 , p > 2 and ω > 0. For p=2dd−2 (energy-critical case) there exists a ground state u ω if and only if ω∈(ω∗,d) , where ω∗=1 for d = 3 and ω∗=0 for d⩾4 . We give a precise description on asymptotic behaviours of u ω as ω→ω∗ up to the leading order term for different values of d⩾3 . When p>2dd−2 (energy-supercritical case) there exists a singular solution u∞ for some ω∈(0,d) . We compute the Morse index of u∞ in the class of radial functions and show that the Morse index of u∞ is infinite in the oscillatory case, is equal to 1 or 2 in the monotone case for p not large enough and is equal to 1 in the monotone case for p sufficiently large.
Liquid spreading

The authors review the wetting and spreading properties of simple liquids on solid surfaces, putting emphasis on the role of the heterogeneities of the solid surface and on the spreading kinetics. In a situation of partial wetting, the liquid does not spread completely and shows a finite contact angle on a solid surface. The partial wetting behaviour on perfect solid surfaces is well described by classical capillarity. Heterogeneities of the solid surface lead to contact angle hysteresis. In a complete wetting situation, the liquid forms a film on a solid surface with a thickness in the mesoscopic range. The direct long range interaction between liquid and solid described by the so-called disjoining pressure governs the physics of these films. Films of mesoscopic thickness also appear in the spreading kinetics of liquids. These precursor films form ahead of macroscopic advancing liquid fronts. The spreading kinetics is extremely slow.
Geometric properties of eigenfunctions

We give an overview of some new and old results on geometric properties of eigenfunctions of Laplacians on Riemannian manifolds. We discuss properties of nodal sets and critical points, the number of nodal domains, and asymptotic properties of eigenfunctions in the high-energy limit (such as weak * limits, the rate of growth of Lp norms, and relationships between positive and negative parts of eigenfunctions).
Whole body [O-15]water pharmacokinetics measured in blood

A simple pharmacokinetic model to explain the time course of [O-15]water in human whole blood after bolus injection is described. The model has been derived from measurements in twelve healthy volunteers who were measured repeatedly, resulting in 67 datasets, made in the context of PET blood flow studies. In contrast to traditional volume of distribution estimates of total body water (TBW) which rely on measurements after many hours, the model and data provide insights into the fast uptake components in the distribution of water in the body. Data fitting shows that the volume of distribution of fast exchanging tissues is 21 l, TBW was calculated to be 37 l. Monte Carlo simulation showed that the expected inaccuracy of determination of parameters due to unsystematic sources in the measurement data was around 5% for most parameters. Our data show that water extraction to tissue is somewhat higher than would be predicted from the tabulated values, probably because skeletal blood flow is sensitive to physiological status and environmental conditions. The study provides valuable reference data on the distribution and kinetics of water in man. Using the parameters and model from this study, reference input time-activity curves can be calculated, e.g. for the Monte Carlo study of error propagation in PET studies.
Novel Class Discovery without Forgetting

Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. Importantly, they achieve this without deteriorating the performance on their earlier learning. Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting, which tasks a machine learning model to incrementally discover novel categories of instances from unlabeled data, while maintaining its performance on the previously seen categories. We propose 1) a method to generate pseudo-latent representations which act as a proxy for (no longer available) labeled data, thereby alleviating forgetting, 2) a mutual-information based regularizer which enhances unsupervised discovery of novel classes, and 3) a simple Known Class Identifier which aids generalized inference when the testing data contains instances form both seen and unseen categories. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery. Our extensive evaluations reveal that existing models catastrophically forget previously seen categories while identifying novel categories, while our method is able to effectively balance between the competing objectives. We hope our work will attract further research into this newly identified pragmatic problem setting.
Permittivity measurement of thin liquid layers using open-ended coaxial probes

The composition of a thin emulsion layer backed by air can be determined from permittivity measurements with an open-ended coaxial probe. In this work, techniques for probe design and determination of liquid permittivity are studied. Measurements on thin liquid layers backed by a low-permittivity material show that the measured apparent permittivity varies exponentially with the layer thickness, with a probe constant that is independent of measurement frequency and liquid permittivity. It is found that an electrostatic model is applicable for design of probes with regard to the probe's sensitivity depth. Models of different complexity for determination of permittivity are compared and it is shown that a bilinear model in combination with an empirical exponential model estimates the permittivity of thin liquid layers with high sensitivity provided that the thickness of the layer is known. The bilinear model uses calibration measurements on three samples with known permittivities. A somewhat lower precision is obtained with a full-wave model of the probe, but this model has the advantage that no calibration except a simple reference plane rotation is needed. However, the computation time of the full-wave model is high.
Discrete-Fresnel Domain Channel Estimation in OCDM-Based Radar Systems

Orthogonal chirp-division multiplexing (OCDM) has been recently proposed as an alternative scheme to orthogonal frequency-division multiplexing (OFDM) for data communication. Among the reasons for that are its superior robustness, e.g., to frequency selectivity of channels and intersymbol interference. Furthermore, channel estimation in the discrete-Fresnel domain has been investigated for both communication and sensing, without, however, considering the effects of frequency shifts. This article investigates the discrete-Fresnel domain channel estimation in OCDM-based radar systems as an alternative to the correlation-based processing from the conventional OCDM (C-OCDM) radar-communication (RadCom) system, which yields high, transmit-data-dependent sidelobe level. In this context, a mathematical formulation for the investigated channel estimation approach is presented. In addition, extensions to multiuser/multiple-input–multiple-output (MIMO) and to a sector-modulated OCDM (SM-OCDM) RadCom operation are introduced. Finally, the performance of the proposed schemes is analyzed based on simulation and measurement results for an emulated automotive scenario. In summary, both OCDM-based radar and SM-OCDM RadCom systems yield improved peak-to-average power ratio and comparable or improved radar performance in terms of sidelobe level and Doppler shift robustness to their C-OCDM and OFDM counterparts for practical range and velocity values. In addition, equal communication performance robustness to C-OCDM, which outperforms OFDM, is experienced in the SM-OCDM case.
Human-centric Image Cropping with Partition-aware and Content-preserving Features

Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two novel feature designs for the candidate crop: partition-aware feature and content-preserving feature. For partition-aware feature, we divide the whole image into nine partitions based on the human bounding box and treat different partitions in a candidate crop differently conditioned on the human information. For content-preserving feature, we predict a heatmap indicating the important content to be included in a good crop, and extract the geometric relation between the heatmap and a candidate crop. Extensive experiments demonstrate that our method can perform favorably against state-of-the-art image cropping methods on human-centric image cropping task. Code is available at https://github.com/bcmi/Human-Centric-Image-Cropping.
CATASTROPHE BONDS An investment analysis of their performance and diversification benefits

This thesis employs total return indices to investigate if catastrophe bonds are zero-beta assets and how they have performed compared to other assets. We conduct time series regressions and conclude that catastrophe bond returns are correlated with both the return of the equity- and the high yield corporate bond market during the subprime financial crisis, but find no significant correlation after the crisis. We include a proxy for risk aversion and find that investors’ level of risk aversion affects the correlation during the crisis, something that previous researchers have discussed theoretically but not shown statistically. Using Sharpe ratios, we examine the risk-adjusted return of catastrophe bonds and show that catastrophe bonds noticeably out-performed the equities and the high yield corporate bonds, both during and after the crisis. The high risk-adjusted return, in combination with the low correlation with the other financial markets, make catastrophe bonds an attractive asset to investors. return a both 4 risk-adjusted return been higher in the Cat bond market compared the
Active galactic nuclei

Our current knowledge of active galactic nuclei is reviewed. The importance of observational data taken over a wide range of frequencies, from radio and infrared through optical and ultraviolet to X-rays and gamma -rays, is emphasized. Important overall principles include the continuity from quasars and QSOs through Seyfert and radiogalaxies to low-luminosity LINERs, the importance of considering roughly cylindrically symmetric (rather than spherically symmetric) structures, and that the various regions generally have different axes and planes of symmetry, and are often warped.
A nonstationary spatial covariance model for data on graphs

Spatial data can exhibit dependence structures more complicated than can be represented using models that rely on the traditional assumptions of stationarity and isotropy. Several statistical methods have been developed to relax these assumptions. One in particular, the"spatial deformation approach"defines a transformation from the geographic space in which data are observed, to a latent space in which stationarity and isotropy are assumed to hold. Taking inspiration from this class of models, we develop a new model for spatially dependent data observed on graphs. Our method implies an embedding of the graph into Euclidean space wherein the covariance can be modeled using traditional covariance functions such as those from the Mat\'{e}rn family. This is done via a class of graph metrics compatible with such covariance functions. By estimating the edge weights which underlie these metrics, we can recover the"intrinsic distance"between nodes of a graph. We compare our model to existing methods for spatially dependent graph data, primarily conditional autoregressive (CAR) models and their variants and illustrate the advantages our approach has over traditional methods. We fit our model and competitors to bird abundance data for several species in North Carolina. We find that our model fits the data best, and provides insight into the interaction between species-specific spatial distributions and geography.
Diffuse-interface approach to competition between viscous flow and diffusion in pinch-off dynamics

The pinch-off dynamics of a liquid thread has been studied through numerical simulations and theoretical analysis. Occurring at small length scales, the pinch-off dynamics admits similarity solutions that can be classified into the Stokes regime and the diffusion-dominated regime, with the latter being recently experimentally observed in aqueous two-phase systems [Phys. Rev. Lett. 123, 134501 (2019)]. Derived by applying Onsager's variational principle, the Cahn-Hilliard-Navier-Stokes model is employed as a minimal model capable of describing the interfacial motion driven by not only advection but also diffusion. By analyzing the free energy dissipation mechanisms in the model, a characteristic length scale is introduced to measure the competition between diffusion and viscous flow in interfacial motion. This length scale is typically of nanometer scale for systems far from the critical point, but can approach micrometer scale for aqueous two-phase systems close to the critical point. The Cahn-Hilliard-Navier-Stokes model is solved by using an accurate and efficient spectral method in a cylindrical domain with axisymmetry. Ample numerical examples are presented to show the pinch-off processes in the Stokes regime and the diffusion-dominated regime respectively. In particular, the crossover between these two regimes is investigated numerically and analytically to reveal how the scaling behaviors of similarity solutions are to be qualitatively changed as the characteristic length scale is inevitably accessed by the pinching neck of the interface. Discussions are also provided for numerical examples that are performed for the breakup of long liquid filaments and show qualitatively different phenomena in different scaling regimes.
Optimal precision for GANs

When learning disconnected distributions, Generative adversarial networks (GANs) are known to face model misspeciﬁcation. Indeed, a continuous mapping from a unimodal latent distribution to a disconnected one is impossible, so GANs necessarily generate samples outside of the support of the target distribution. This raises a fundamental question: what is the latent space partition that minimizes the measure of these areas? Building on a recent result of geometric measure theory, we prove that an optimal GANs must structure its latent space as a ’simplicial cluster’ - a Voronoi partition where cells are convex cones - when the dimension of the latent space is larger than the number of modes. In this conﬁguration, each Voronoi cell maps to a distinct mode of the data. We derive both an upper and a lower bound on the optimal precision of GANs learning disconnected manifolds. Interestingly, these two bounds have the same order of decrease: √ log m , m being the number of modes. Finally, we perform several experiments to exhibit the geometry of the latent space and experimentally show that GANs have a geometry with similar properties to the theoretical one.
Correspondence Matters for Video Referring Expression Comprehension

We investigate the problem of video Referring Expression Comprehension (REC), which aims to localize the referent objects described in the sentence to visual regions in the video frames. Despite the recent progress, existing methods suffer from two problems: 1) inconsistent localization results across video frames; 2) confusion between the referent and contextual objects. To this end, we propose a novel Dual Correspondence Network (dubbed as DCNet) which explicitly enhances the dense associations in both the inter-frame and cross-modal manners. Firstly, we aim to build the inter-frame correlations for all existing instances within the frames. Specifically, we compute the inter-frame patch-wise cosine similarity to estimate the dense alignment and then perform the inter-frame contrastive learning to map them close in feature space. Secondly, we propose to build the fine-grained patch-word alignment to associate each patch with certain words. Due to the lack of this kind of detailed annotations, we also predict the patch-word correspondence through the cosine similarity. Extensive experiments demonstrate that our DCNet achieves state-of-the-art performance on both video and image REC benchmarks. Furthermore, we conduct comprehensive ablation studies and thorough analyses to explore the optimal model designs. Notably, our inter-frame and cross-modal contrastive losses are plug-and-play functions and are applicable to any video REC architectures. For example, by building on top of Co-grounding, we boost the performance by 1.48% absolute improvement on Accu. @0.5 for VID-Sentence dataset.
2D GANs Meet Unsupervised Single-view 3D Reconstruction

Recent research has shown that controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. However, less attention has been devoted to 3D vision tasks. In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images and perform the single-view reconstruction of generic objects. Firstly, a novel offline StyleGAN-based generator is presented to generate plausible pseudo images with full control over the viewpoint. Then, we propose to utilize a neural implicit function, along with a differentiable renderer to learn 3D geometry from pseudo images with object masks and rough pose initializations. To further detect the unreliable supervisions, we introduce a novel uncertainty module to predict uncertainty maps, which remedy the negative effect of uncertain regions in pseudo images, leading to a better reconstruction performance. The effectiveness of our approach is demonstrated through superior single-view 3D reconstruction results of generic objects.
An Investigation Into The Drag Increase on Roughen Surface due to Marine Fouling Growth

Marine biofouling attached to underwater ship hull has caused problems for many years to ship operators. It has been reported to increase the total drag thus can reduce the speed of ship and disturb the overall operation of marine vehicles. Changes laminar to turbulent flow becomes fast when passing through rough ships surface. The current study models the growth of marine fouling for one year periods basis on general cargo vessel. The methods have been used in this studied was model scale 1 : 53. The use of smooth model is also included in order to analyses the difference between the two conditions. Two models of the hull roughness are regular and irregular roughness. The modeling of roughness using sand with a specific size and have been scaled. The results show that an increase in skin friction drag of about 41% per year for biofouling spread not evenly at wetted surface area (WSA).