Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-Collective Collaborative Site Selection

Robotic collectives are large groups (at least 50) of locally sensing and communicating robots that encompass characteristics of swarms and colonies, whose emergent behaviors accomplish complex tasks. Future human-collective teams will extend the ability of operators to monitor, respond, and make decisions in disaster response, search and rescue, and environmental monitoring problems. This manuscript evaluates two collective best-of-n decision models for enabling collectives to identify and choose the highest valued target from a finite set of n targets. Two challenges impede the future use of human-collective shared decisions: 1) environmental bias reduces collective decision accuracy when poorer targets are easier to evaluate than higher quality targets, and 2) little is understood about shared human-collective decision making interaction strategies. The two evaluated collective best-of-n models include an existing insect colony decision model and an extended bias-reducing model that attempts to reduce environmental bias in order to improve accuracy. Collectives using these two strategies are compared independently and as members of human-collective teams. Independently, the extended model is slower than the original model, but the extended algorithm is 57% more accurate in decisions where the optimal option is more difficult to evaluate. Human-collective teams using the bias-reducing model require less operator influence and achieve 25% higher accuracy with difficult decisions, than the human-collective teams using the original model. Further, a novel human-collective interaction strategy enables operators to adjust collective autonomy while making multiple simultaneous decisions.
Light 
p
-shell nuclei with cluster structures (
4≤A≤16
) in nuclear matter

The composition of hot and dense nuclear matter is calculated including the 1p-shell nuclei 4 ≤ A ≤ 16. In-medium shifts, in particular Pauli blocking, are determined by the intrinsic wave function of the nuclei. Results are given within a shell-model approach for the nucleon wave function. Light nuclei are not always well described by the shell model. The ’clustered’ nucleus Be exhibits strong correlation effects because of α-like clustering. Intrinsic cluster structures are also significant for the nuclei Li, Li, Be, and Be. The contribution of the relatively rare elements Li, Be, and B, to the equation of state (EoS) of matter near the saturation density is overestimated in simple approaches such as the nuclear statistical equilibrium (NSE) model. Both, the treatment of continuum correlations and the account of in-medium modifications are considered for the contribution of He and H clusters. Compared to the extended NSE including unstable nuclei, the contributions of the corresponding P3/2 channel with A = 5, Z = 2 and P2 channel with A = 4, Z = 1, respectively, to the EoS are strongly suppressed at high densities owing to Pauli blocking effects. For the shifts of the binding energies of the light p-shell nuclei, simple fit formula are given to calculate the composition of hot and dense matter in a wide parameter range.
Hα Distances to the Leading Arm of the Magellanic Stream

The Leading Arm (LA) is a tidal feature that is in front of the Magellanic Clouds (MCs) on their orbit through the Galaxy’s halo. Many physical properties of the LA, such as its mass and size, are poorly constrained because it has few distance measurements. While Hα measurements have been used to estimate the distances to halo clouds, many studies have been unsuccessful in detecting Hα from the LA. In this study, we explore a group of H i clouds which lie 75°–90° from the MCs. Through ultraviolet and 21 cm radio spectroscopy, this region, dubbed the LA Extension, was found to have chemical and kinematic similarities to the LA. Using the Wisconsin Hα Mapper, we detect Hα emission in four out of seven of our targets. Assuming that this region is predominantly photoionized, we use a radiation model that incorporates the contributions of the Galaxy, MCs, and the extragalactic background at z = 0 to derive a heliocentric distance of d⊙ ≥ 13.4 . We also use this model to rederive Hα distances of d⊙ ≥ 5.0 kpc and d⊙ ≥ 22.9 to two clouds in the literature that might also be associated with the LA. Using these new measurements, and others in the literature, we provide a general trend of the variation of LA heliocentric distance as a function of Magellanic Stream longitude, and explore its implications for the origin and closest point of approach of the LA.
Fermi-liquid behaviors in three-dimensional polaron system with tunable dispersion

We investigate the electronic properties and electron correlations in a three-dimensional (3D) polaron system with tunable dispersion parameter in Fermi-liquid picture. The polaronic coupling is considered as a weakly attractive bare coupling. Both the single and many-polaron, zero and finite-temperature cases are considered in this paper. The physical quantities, including the self-energy, spectral function, optical conductivity (both the planar and 3D one), free energy, and momentum distribution function, are studied and are been verified exhibit Fermi-liquid features. Both the analytic derivation and numerical calculation are done in this paper to make our results reliable.
ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric

Estimation of perceptual quality in audio and speech is possible using a variety of methods. The combined v3 release of ViSQOL and ViSQOLAudio (for speech and audio, respectively,) provides improvements upon previous versions, in terms of both design and usage. As an open source C++ library or binary with permissive licensing, ViSQOL can now be deployed beyond the research context into production usage. The feedback from internal production teams at Google has helped to improve this new release, and serves to show cases where it is most applicable, as well as to highlight limitations. The new model is benchmarked against real-world data for evaluation purposes. The trends and direction of future work is discussed.
#COVID-19 on Twitter: Bots, Conspiracies, and Social Media Activism

With people moving out of physical public spaces due to containment measures to tackle the novel coronavirus (COVID-19) pandemic, online platforms become even more prominent tools to understand social discussion. Studying social media can be informative to assess how we are collectively coping with this unprecedented global crisis. However, social media platforms are also populated by bots, automated accounts that can amplify certain topics of discussion at the expense of others, possibly distorting the perception of the ongoing conversation. In this paper, we study 43.3M English tweets about COVID-19 and provide early evidence of the use of bots to promote political conspiracies in the US, but also as a tool to enable participatory activism to surface information that could otherwise be censored in China.
Softly broken conformal symmetry with higher curvature terms

In a scalar-coupled-gravity model, the quadratically divergent counter term appearing in the mass renormalization of the scalar fields must inherit corrections arising out of gravitational interactions. In this work we have explicitly demonstrated that there are no such corrections of gravitational origin to the quadratic divergences in the mass counter terms. This statement holds true irrespective of the nature of the gravitational interaction, i.e., whether gravity is described by general relativity or f(R) theory. Interestingly, it also turns out that the one loop effective action of scalar-coupled-gravity system will be well-behaved \emph{if and only if} the $f(R)$ theory is free from ghosts. In particular, the results derived in the context of f(R) theory are shown to be in exact agreement with the corresponding results derived from the equivalent scalar-tensor representation. Our analysis suggests the tantalizing possibility that the masses of the scalar fields can be consistently kept smaller than some Ultra Violet (UV) cutoff scale and is independent of the nature of the gravity theory, which may involve higher curvature corrections. All these will be true provided the matter fields and the gravity theory can be embedded consistently into a UV complete theory at the Planck scale.
Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.
The assembly history of the nearest S0 galaxy NGC 3115 from its kinematics out to six half-light radii

Using new and archival data, we study the kinematic properties of the nearest field S0 galaxy, NGC 3115, out to $\sim6.5$ half-light radii ($R_\mathrm{e}$) from its stars (integrated starlight), globular clusters (GCs) and planetary nebulae (PNe). We find evidence of three kinematic regions with an inner transition at $\sim0.2\ R_\mathrm{e}$ from a dispersion-dominated bulge ($V_\mathrm{rot}/\sigma 1$), and then an additional transition from the disk to a slowly rotating spheroid at $\sim2-2.5\, R_\mathrm{e}$, as traced by the red GCs and PNe (and possibly by the blue GCs beyond $\sim5\, R_\mathrm{e}$). From comparison with simulations, we propose an assembly history in which the original progenitor spiral galaxy undergoes a gas-rich minor merger that results in the embedded kinematically cold disk that we see today in NGC 3115. At a later stage, dwarf galaxies, in mini mergers (mass-ratio $<$ 1:10), were accreted building-up the outer slowly rotating spheroid, with the central disk kinematics largely unaltered. Additionally, we report new spectroscopic observations of a sample of ultra-compact dwarfs (UCDs) around NGC 3115 with the Keck/KCWI instrument. We find that five UCDs are inconsistent with the general rotation field of the GCs, suggesting an \textit{ex-situ} origin for these objects, i.e. perhaps the remnants of tidally stripped dwarfs. A further seven UCDs follow the GC rotation pattern, suggesting an \textit{in-situ} origin and, possibly a GC-like nature.
TW Hya: an old protoplanetary disc revived by its planet

Dark rings with bright rims are the indirect signposts of planets embedded in protoplanetary discs. In a recent first, an azimuthally elongated AU-scale blob, possibly a planet, was resolved with ALMA in TW Hya. The blob is at the edge of a cliff-like rollover in the dust disc rather than inside a dark ring. Here we build time-dependent models of TW Hya disc. We find that the classical paradigm cannot account for the morphology of the disc and the blob. We propose that ALMA-discovered blob hides a Neptune mass planet losing gas and dust. We show that radial drift of mm-sized dust particles naturally explains why the blob is located on the edge of the dust disc. Dust particles leaving the planet perform a characteristic U-turn relative to it, producing an azimuthally elongated blob-like emission feature. This scenario also explains why a 10 Myr old disc is so bright in dust continuum. Two scenarios for the dust-losing planet are presented. In the first, a dusty pre-runaway gas envelope of a ∼40 M⊕ Core Accretion planet is disrupted, e.g., as a result of a catastrophic encounter. In the second, a massive dusty pre-collapse gas giant planet formed by Gravitational Instability is disrupted by the energy released in its massive core. Future modelling may discriminate between these scenarios and allow us to study planet formation in an entirely new way – by analysing the flows of dust and gas recently belonging to planets, informing us about the structure of pre-disruption planetary envelopes.
Polarization control of radiation and energy flow in dipole-coupled nanorings

Collective optical excitations in dipole-coupled nanorings of sub-wavelength spaced quantum emitters exhibit extreme sub-radiance and field confinement facilitating an efficient and low-loss ring-to-ring energy transfer. We show that energy shifts, radiative lifetimes, and emission patterns of excitons and biexcitons in such rings can be tailored via the orientation of the individual dipoles. Tilting the polarization from perpendicular to tangential to the ring dramatically changes the lifetime of the symmetric exciton state from superradiance to subradiance with the radiated field acquiring orbital angular momentum. At a magic tilt angle all excitons are degenerate and the transport fidelity between two rings exhibits a minimum. Further simulations suggest that, for certain parameters, the decay decreases double-exponentially with the emitter’s density. Disorder in the rings’ structure can even enhance radiative lifetimes. The transport efficiency strongly depends on polarization and size, which we demonstrate by simulating a bio-inspired example of two rings with 9 and 16 dipoles as found in biological light harvesting complexes (LHC). The field distribution in the most superradiant state in a full LHC multi-ring structure shows tight sub-wavelength field confinement in the central ring, while long-lived subradiant states store energy in the outer rings.
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$. We propose a model-free algorithm UCB-Advantage and prove that it achieves $\tilde{O}(\sqrt{H^2SAT})$ regret where $T = KH$ and $K$ is the number of episodes to play. Our regret bound improves upon the results of [Jin et al., 2018] and matches the best known model-based algorithms as well as the information theoretic lower bound up to logarithmic factors. We also show that UCB-Advantage achieves low local switching cost and applies to concurrent reinforcement learning, improving upon the recent results of [Bai et al., 2019].
A Robust Method for Estimating Individualized Treatment Effect

We consider an additive model with a main effect and effects from multiple treatments. Our goal is to estimate the heterogeneous treatment effects among patients. Traditionally, one can fit standard regressions in which models for the main effect and the treatment effects are specified. However, mis-specification of either the main or the treatment effects could severely undermine the estimation. A set of recent proposals directly estimate the treatment effect, avoiding the potential mis-specification issue for the main effect. However, performance of these methods rely on either a known or accurate estimator of the propensity score. In this paper, we propose a doubly robust direct learning method (RD-Learning) to estimate the treatment effect. The double robustness comes form the fact that it is robust to the two issues of (1) main effect mis-specification and (2) inaccurate propensity score estimates. As long as these two do not occur at the same time, our estimate is consistent and has a smaller variance than competing methods. It can be used in both the binary and the multi-arm settings. As a by-product, we develop a competitive statistical inference tool for the treatment effect, assuming the propensity score is known. We provide theoretical insights to the proposed method using risk bounds under both linear and non-linear settings. Our method is further demonstrated by simulation studies and a real data example.
Effective halo model: Creating a physical and accurate model of the matter power spectrum and cluster counts

We introduce a physically-motivated model of the matter power spectrum, based on the halo model and perturbation theory. This model achieves 1\% accuracy on all $k-$scales between $k=0.02h\,\mathrm{Mpc}^{-1}$ to $k=1h\,\mathrm{Mpc}^{-1}$. Our key ansatz is that the number density of halos depends on the non-linear density contrast filtered on some unknown scale $R$. Using the Effective Field Theory of Large Scale Structure to evaluate the two-halo term, we obtain a model for the power spectrum with only two fitting parameters: $R$ and the effective `sound speed', which encapsulates small-scale physics. This is tested with two suites of cosmological simulations across a broad range of cosmologies and found to be highly accurate. Due to its physical motivation, the statistics can be easily extended beyond the power spectrum; we additionally derive the one-loop covariance matrices of cluster counts and their combination with the matter power spectrum. This yields a significantly better fit to simulations than previous models, and includes a new model for super-sample effects, which is rigorously tested with separate universe simulations. At low redshift, we find a significant ($\sim 10\%$) exclusion covariance from accounting for the finite size of halos which has not previously been modeled. Such power spectrum and covariance models will enable joint analysis of upcoming large-scale structure surveys, gravitational lensing surveys and cosmic microwave background maps on scales down to the non-linear scale. We provide a publicly released Python code.
The 1st Agriculture-Vision Challenge: Methods and Results

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture- Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found $\color{OrangeRed}{here}$.
A Review of Contemporary Atomic Frequency Standards.

Atomic frequency standards are used to generate accurate and precise time and frequency, enabling many communications, synchronization, and navigation systems in modern life. GPS and other satellite navigation systems, voice and data telecommunications, and timestamping of financial transactions all rely on precise time and frequency enabled by atomic frequency standards. This review provides a snapshot and outlook of contemporary atomic frequency standards and the applications they enable. We provide a concise summary of the performance and physics of operation of current and future atomic frequency standards. Additionally, examples of emerging frequency standard technologies and prototype demonstrations are presented, with a focus on technologies expected to provide commercial or military utility within the next decade. We include a comparison of performance vs. size and power for current atomic frequency standards, and we compare early prototypes of next-generation frequency standards to current product trends. An empirical relationship between frequency standard performance and product size is developed and discussed. Finally, we provide a mapping between applications and frequency standard technologies.
An Optimal Bearing-Only-Information Strategy for Unmanned Aircraft Collision Avoidance

This paper presents a novel collision avoidance strategy for unmanned aircraft detect and avoid that requires only information about the relative bearing angle between an aircraft and hazard. It is shown that this bearing-only strategy can be conceived as the solution to a novel differential game formulation of collision avoidance, and has several intuitive properties including maximising the instantaneous range acceleration in situations where the hazard is stationary or has a finite turn rate. The performance of the bearing-only strategy is illustrated in simulations based on test cases drawn from draft minimum operating performance standards for unmanned aircraft detect and avoid systems.
PAI-GCN: Permutable Anisotropic Graph Convolutional Networks for 3D Shape Representation Learning

Demand for efficient 3D shape representation learning is increasing in many 3D computer vision applications. The recent success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to 3D shapes. However, unlike images that are Euclidean structured, 3D shape data are irregular since each node's neighbors are inconsistent. Various convolutional graph neural networks for 3D shapes have been developed using isotropic filters or using anisotropic filters with predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that learns adaptive soft-permutation matrices for each node according to the geometric shape of its neighbors and performs shared anisotropic filters as CNN does. Comprehensive experiments demonstrate that our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods.
Identifiability and consistency of network inference using the hub model and variants: a restricted class of Bernoulli mixture models

Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The hub model belongs to the family of Bernoulli mixture models. Identifiability of parameters is a notoriously difficult problem for Bernoulli mixture models. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model -- the Bernoulli product. Identifiability and consistency are also proved for the new model. Numerical studies are provided to demonstrate the theoretical results.