Miracle-dz/UniFAR-FTUs huggingface dataset:
Inertial Inchworm Crawling

Inchworm crawling allows for both quasistatic and dynamic gaits at a wide range of actuation frequencies. This locomotion mechanism is common in nonskeletal animals and exploited extensively in the bio-inspired field of soft robotics. In this work we develop and simulate the hybrid dynamic crawling of a three-link robot, with passive frictional contacts. We fabricate and experimentally test such robot under periodic inputs of joints' angles, with good agreement to the theoretical predictions. This allows to comprehend and exploit the effects of inertia in order to find optimal performance in inputs' parameters. A simple criterion of robustness to uncertainties in friction is proposed. Tuning the inputs according to this criterion improves the robustness of low-frequency actuation, while increasing the frequency allows for gaits with both high advancement velocity and robustness. Finally, the advantages of uneven mass distribution are studied. Time-scaling technique is introduced to shape inputs that achieve similar effect without reassembling the robot. A machine-learning based optimization is applied to these inputs to further improve the robot's performance.
Ecological Distribution and Assemblage Structure of Neritid Gastropods in an Okinawan Mangrove Swamp, Southern Japan

Diversification in habitat use by neritid gastropods is remarkable.In intertidal areas neritid gastro pods exhibit marked habitat segregation along environmental gradients such as salinity,tidal level,wave ex posure,and particle size of substratum.We studied habitat use of neritid gastropods in a mangrove swamp and adjacent seashore of Iriomote Island in the Ryukyu Islands,southern Japan.We found16neritid species, and multivariate analyses(TWINSPAN and CCA)revealed that salinity and water conditions were decisive factors affecting the habitat use of these neritids.Neritina turrita and N.violacea had high densities and wide distributions.These two species were generally found together,but they showed an interspecific habitat seg regation particularly with regard to prevailing water conditions of the habitat.Intraspecific differences of habitat use among individuals of different sizes were also detected in these two species,and this differences seemed to be caused by habitat shift with growth.
Comparing Moment-based and Monte Carlo Methods of Radiation Transport Modeling for Type II-Plateau Supernova Light Curves

Time-dependent electromagnetic signatures from core-collapse supernovae are the result of detailed transport of the shock-deposited and radioactively powered radiation through the stellar ejecta. Due to the complexity of the underlying radiative processes, considerable approximations are made to simplify key aspects of the radiation transport problem. We present a systematic comparison of the moment-based radiation hydrodynamical code STELLA and the Monte Carlo radiation transport code Sedona in the 1D modeling of Type II-Plateau supernovae. Based on explosion models generated from the Modules for Experiments in Stellar Astrophysics instrument, we find remarkable agreements in the modeled light curves and the ejecta structure thermal evolution, affirming the fidelity of both radiation transport modeling approaches. The radiative moments computed directly by the Monte Carlo scheme in Sedona also verify the accuracy of the moment-based scheme. We find that the coarse resolutions of the opacity tables and the numerical approximations in STELLA have an insignificant impact on the resulting bolometric light curves, making it an efficient tool for the specific task of optical light-curve modeling.
Designing Differentially Private Estimators in High Dimensions

We study differentially private mean estimation in a high-dimensional setting. Existing differential privacy techniques applied to large dimensions lead to computationally intractable problems or estimators with excessive privacy loss. Recent work in high-dimensional robust statistics has identified computationally tractable mean estimation algorithms with asymptotic dimension-independent error guarantees. We incorporate these results to develop a strict bound on the global sensitivity of the robust mean estimator. This yields a computationally tractable algorithm for differentially private mean estimation in high dimensions with dimension-independent privacy loss. Finally, we show on synthetic data that our algorithm significantly outperforms classic differential privacy methods, overcoming barriers to high-dimensional differential privacy.
Non-drug altered states as psychedelic experiences

An exploration of personality predictor variables (absorption and temporal lobe lability) and acute experience measures (5D-ASC) in the comparison of several non-drug induced altered states (darkroom anechoic chamber, floatation tank and holotropic breathwork) with psychedelic altered states (e.g., DMT, psilocybin, cannabis).
Observation of Supercavity Modes in Subwavelength Dielectric Resonators

Electromagnetic response of dielectric resonators with high refractive index is governed by optically induced electric and magnetic Mie resonances facilitating confinement of light with the amplitude enhancement. Traditionally, strong subwavelength trapping of light was associated only with plasmonic or epsilon‐near‐zero structures, which however suffer from material losses. Recently, an alternative localization mechanism was proposed allowing the trapping of light in individual subwavelength optical resonators with a high quality factor in the regime of a supercavity mode. Here, the experimental observation of the supercavity modes in subwavelength ceramic resonators in the radio‐frequency range is presented. It is experimentally demonstrated that the regime of supercavity modes can be achieved via precise tuning of the resonator's dimensions. A huge growth of the unloaded quality factor is achieved with experimental values up to 1.25 × 104, limited only by material losses of ceramics. It is revealed that the supercavity modes can be excited efficiently both in the near‐ and far‐field. In both cases, the supercavity mode manifests itself explicitly as a Fano resonance with characteristic peculiarities of spectral shape and radiation pattern. A comparison of supercavities made of diversified materials for the visible, infrared, THz, and radio‐frequency regimes is provided.
A Scalable and Cloud-Native Hyperparameter Tuning System

In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework. Though there are multiple hyperparameter tuning systems available, this is the first one that caters to the needs of both users and administrators of the system. We present the motivation and design of the system and contrast it with existing hyperparameter tuning systems, especially in terms of multi-tenancy, scalability, fault-tolerance, and extensibility. It can be deployed on local machines, or hosted as a service in on-premise data centers, or in private/public clouds. We demonstrate the advantage of our system using experimental results as well as real-world, production use cases. Katib has active contributors from multiple companies and is open-sourced at \emph{this https URL} under the Apache 2.0 license.
Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)

Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others. This paper presents new funding, publication, and scholarly network metrics and visualizations that were validated via expert surveys. The metrics and visualizations exemplify the emergence and convergence of three areas of strategic interest: artificial intelligence (AI), robotics, and internet of things (IoT) over the last 20 years (1998-2017). For 32,716 publications and 4,497 NSF awards, we identify their topical coverage (using the UCSD map of science), evolving co-author networks, and increasing convergence. The results support data-driven decision making when setting proper research and development (R&D) priorities; developing future S&T investment strategies; or performing effective research program assessment.
Variational Mutual Information Maximization Framework for VAE Latent Codes with Continuous and Discrete Priors

Learning interpretable and disentangled representations of data is a key topic in machine learning research. Variational Autoencoder (VAE) is a scalable method for learning directed latent variable models of complex data. It employs a clear and interpretable objective that can be easily optimized. However, this objective does not provide an explicit measure for the quality of latent variable representations which may result in their poor quality. We propose Variational Mutual Information Maximization Framework for VAE to address this issue. In comparison to other methods, it provides an explicit objective that maximizes lower bound on mutual information between latent codes and observations. The objective acts as a regularizer that forces VAE to not ignore the latent variable and allows one to select particular components of it to be most informative with respect to the observations. On top of that, the proposed framework provides a way to evaluate mutual information between latent codes and observations for a fixed VAE model. We have conducted our experiments on VAE models with Gaussian and joint Gaussian and discrete latent variables. Our results illustrate that the proposed approach strengthens relationships between latent codes and observations and improves learned representations.
Female choice of matings to maximise parental care.

We address the question of how many males a female should mate with in the context of species in which males provide care for potential offspring. Our analysis is based on the evolutionarily stable levels of parental effort of a female and a group of males. In the case of one female and two males we give a complete analysis of how the female's preference for monogamy or polyandry depends on the fitness functions assumed in the model. We then go on to consider the question of how many males a female should mate with to receive the highest level of male care. Our results are relevant in particular to the mating system of the dunnock (Prunella modularis) and also to the general question of whether a species should be monogamous as opposed to polyandrous.
The magnetic helicity density patterns from non-axisymmetric solar dynamo

We study the helicity density patterns which can result from the emerging bipolar regions. Using the relevant dynamo model and the magnetic helicity conservation law we find that the helicity density patterns around the bipolar regions depend on the configuration of the ambient large-scale magnetic field, and in general they show a quadrupole distribution. The position of this pattern relative to the equator can depend on the tilt of the bipolar region. We compute the time–latitude diagrams of the helicity density evolution. The longitudinally averaged effect of the bipolar regions shows two bands of sign for the density distributions in each hemisphere. Similar helicity density patterns are provided by the helicity density flux from the emerging bipolar regions subjected to surface differential rotation.
Land-use effects on the functional distinctness of arthropod communities

Land-use change is a major driver of the global loss of biodiversity, but it is unclear to what extent this also results in a loss of ecological traits. Therefore, a better understanding of how land-use change affects ecological traits is crucial for efforts to sustain functional diversity. To this end we tested whether higher species richness or taxonomic distinctness generally leads to increased functional distinctness and whether intensive land use leads to functionally more narrow arthropod communities. We compiled species composition and trait data for 350 species of terrestrial arthropods (Araneae, Carabidae and Heteroptera) in different land-use types (forests, grasslands and arable fields) of low and high land-use intensity. We calculated the average functional and taxonomic distinctness and the rarified trait richness for each community. These measures reflect the range of traits, taxonomic relatedness and number of traits that are observed in local communities. Average functional distinctness only increased significantly with species richness in Carabidae communities. Functional distinctness increased significantly with taxonomic distinctness in communities of all analyzed taxa suggesting a high functional redundancy of taxonomically closely related species. Araneae and Heteroptera communities had the expected lower functional distinctness at sites with higher land-use intensity. More frequently disturbed land-use types such as managed grasslands or arable fields were characterized by species with smaller body sizes and higher dispersal abilities and communities with lower functional distinctness or trait richness. Simple recommendations about the conservation of functional distinctness of arthropod communities in the face of future land-use intensification and species loss are not possible. Our study shows that these relationships depend on the studied taxa and land-use type. However, for some arthropod groups functional distinctness is threatened by intensification and conversion from less to more frequently disturbed land-uses.
Equivariant Flows: exact likelihood generative learning for symmetric densities

Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution of interest. Recent work showed that such generative models can be utilized in statistical mechanics to sample equilibrium states of many-body systems in physics and chemistry. To scale and generalize these results, it is essential that the natural symmetries in the probability density - in physics defined by the invariances of the target potential - are built into the flow. We provide a theoretical sufficient criterion showing that the distribution generated by equivariant normalizing flows is invariant with respect to these symmetries by design. Furthermore, we propose building blocks for flows which preserve symmetries which are usually found in physical/chemical many-body particle systems. Using benchmark systems motivated from molecular physics, we demonstrate that those symmetry preserving flows can provide better generalization capabilities and sampling efficiency.
In vivo and in vitro assessment of Lactobacillus acidophilus as probiotic for tilapia (Oreochromis niloticus, Perciformes:Cichlidae) culture improvement

In this study, the use of Lactobacillus acidophilus as a probiotic for Nile tilapia (Oreochromis niloticus) culture was studied. Fish survival and the expression of some genes involved in the immune response were assessed. Diet supplementation with L. acidophilus for 15 days caused a significant increase in fish survival during a challenge with Aeromonas hydrophila and variations in immune response related to IL-1β and transferrin expression in Nile tilapia spleen and kidney. Moreover, extracellular products (ECPs) of L. acidophilus showed high antibacterial activity against fish pathogens such as A. hydrophila and Streptococcus agalactiae in vitro. It was also observed that viable L. acidophilus was able to disrupt quorum sensing activity in Chromobacterium violaceum.
Evidence for an Ancient Sea Level on Mars

Mars shares many similarities and characteristics to Earth including various geological features and planetary structure. The remarkable bimodal distribution of elevations in both planets is one of the most striking global features suggesting similar geodynamic processes of crustal differentiation on Earth and Mars. There also exists much evidence, based on geographic features resembling ancient shorelines, for the existence of an ancient Martian ocean in the northern hemisphere that covers nearly one-third of the planet’s surface. However, the interpretation of some features as ancient shorelines has been thoroughly challenged, which left the existence of a primordial Martian ocean controversial. Moreover, if oceans were formerly present on Mars, there is still significant ambiguity about the volume of water with the estimations ranging over 4 orders of magnitude. Here we map the Martian sea level problem onto a percolation model that provides strong evidence that the longest isoheight line on Mars that separates the northern and southern hemispheres acts as a critical level height with divergent correlation length and plays the same role as the present mean sea level does on Earth. Our results unravel remarkable similarities between Mars and Earth, posing a testable hypothesis about the level of the ancient ocean on Mars that can be answered experimentally by the future investigations and spacecraft exploration.
New timing measurement results of 16 pulsars

A pulsar’s position, proper motion, and parallax are important parameters in timing equations. It is challenging to fit astrometric parameters accurately through pulsar timing, especially for pulsars that show irregular timing properties. With the fast development of related techniques, it is possible to measure astrometric parameters of more and more pulsars in a model-independent manner with very-long-baseline interferometry (VLBI). In this work we select 16 normal pulsars, whose parallax and proper motion have not been successfully fitted with timing observations or show obvious differences from the corresponding latest VLBI solutions, and do further studies on their timing properties. After updating the astrometric parameters in pulsar ephemerides with the latest VLBI measurements, we derive the latest rotation solutions of these pulsars with observation data in the S and C bands obtained from the Shanghai Tian Ma Radio Telescope. Compared with the spin frequency ν inferred from previous rotation solutions, the newly fitted ν show differences larger than 10−9 Hz for most pulsars. The contribution of the Shklovsky effect to the period derivative $\dot{P}$ can be properly removed by taking advantage of the accurate proper motion and distance of target pulsars measured by VLBI astrometry. This further leads to a precise estimate of the intrinsic characteristic age τc. The differences between the newly measured τc and corresponding previous results are as large as 2% for some pulsars. VLBI astrometric parameter solutions also lead to better measurements of timing irregularities. For PSR B0154+61, the glitch epoch (MJD 58279.5) measured with a previous ephemeris is about 13 d later than the result (MJD 58266.4) obtained with VLBI astrometric parameter solutions.
Distinct Accretion Modes of Cygnus X-1 Revealed from Hard X-Rays

Thanks to recurrent observations of the black hole binary Cyg X-1 carried out over 15 years the INTEGRAL satellite has collected the largest data set in the hard X-ray band for this source. We have analyzed these data, complemented by data collected by other X-ray satellites and radio flux at 15 GHz. To characterize the spectral and variability properties of the system we have examined parameters such as the hard X-ray flux, photon index, and fractional variability. Our main result is that the 2D distribution of the photon index and flux determined for the 22–100 keV band forms six clusters. This result, interpreted within the Comptonization scenario as the dominant process responsible for the hard X-ray emission, leads to a conclusion that the hot plasma in Cyg X-1 takes the form of six specific geometries. The distinct character of each of these plasma states is reinforced by their different X-ray and radio variability patterns. In particular, the hardest and softest plasma states show no short-term flux–photon index correlation typical for the four other states, implying a lack of interaction between the plasma and accretion disk. The system evolves between these two extreme states, with the spectral slope regulated by a variable cooling of the plasma by the disk photons.
SimPool: Towards Topology Based Graph Pooling with Structural Similarity Features

Deep learning methods for graphs have seen rapid progress in recent years with much focus awarded to generalising Convolutional Neural Networks (CNN) to graph data. CNNs are typically realised by alternating convolutional and pooling layers where the pooling layers subsample the grid and exchange spatial or temporal resolution for increased feature dimensionality. Whereas the generalised convolution operator for graphs has been studied extensively and proven useful, hierarchical coarsening of graphs is still challenging since nodes in graphs have no spatial locality and no natural order. This paper proposes two main contributions, the first is a differential module calculating structural similarity features based on the adjacency matrix. These structural similarity features may be used with various algorithms however in this paper the focus and the second main contribution is on integrating these features with a revisited pooling layer DiffPool arXiv:1806.08804 to propose a pooling layer referred to as SimPool. This is achieved by linking the concept of network reduction by means of structural similarity in graphs with the concept of hierarchical localised pooling. Experimental results demonstrate that as part of an end-to-end Graph Neural Network architecture SimPool calculates node cluster assignments that functionally resemble more to the locality preserving pooling operations used by CNNs that operate on local receptive fields in the standard grid. Furthermore the experimental results demonstrate that these features are useful in inductive graph classification tasks with no increase to the number of parameters.
Universal thermodynamics in the Kitaev fractional liquid

In the Kitaev honeycomb model, the quantum spin fractionalizes into itinerant Majorana and gauge flux spontaneously upon cooling, leading to rich experimental ramifications at finite temperature and an upsurge of research interest. In this work, we employ the exponential tensor renormalization group approach to explore the Kitaev model under various perturbations, including the external fields, Heisenberg, and the off-diagonal couplings that are common in the Kitaev materials. Through large-scale manybody calculations, we find a Kitaev fractional liquid at intermediate temperature that is robust against perturbations. The fractional liquid exhibits universal thermodynamic behaviors, including the fractional thermal entropy, metallic specific heat, and an intermediate-temperature Curie law of magnetic susceptibility. The emergent universal susceptibility behavior, with a modified Curie constant, can be ascribed to the strongly fluctuating $\mathbb{Z}_2$ fluxes as well as the extremely short-ranged and bond-directional spin correlations. With this insight, we revisit the susceptibility measurements of Na$_2$IrO$_3$ and $\alpha$-RuCl$_3$, and find evident signatures of finite-temperature fractionalization and ferromagnetic Kitaev couplings. Moreover, the peculiar spin correlation in the fractional liquid corresponds to a stripy structure factor which rotates in the extended Brillouin zone as the spin component changes. Therefore, our findings encourage future experimental exploration of fractional liquid in the Kitaev materials by thermodynamic measurements and spin-resolved structure factor probes.