ShantanuT01/DACTYL-Pretraining huggingface dataset:
In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether this modality (as compared to 3D kinematics) can still be reliable for the prediction of human intentions, defined as the overarching goal embedded in an action sequence. Since the same action can be performed with different intentions, this problem is more challenging but yet affordable as proved by quantitative cognitive studies which exploit the 3D kinematics acquired through motion capture systems. In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions. Precisely, we propose Intention from Motion, a new paradigm where, without using any contextual information, we consider instantaneous grasping motor acts involving a bottle in order to forecast why the bottle itself has been reached (to pass it or to place in a box, or to pour or to drink the liquid inside). We process only the grasping onsets casting intention prediction as a classification framework. Leveraging on our multimodal acquisition (3D motion capture data and 2D optical videos), we compare the most commonly used 3D descriptors from cognitive studies with state-of-the-art video-based techniques. Since the two analyses achieve an equivalent performance, we demonstrate that computer vision tools are effective in capturing the kinematics and facing the cognitive problem of human intention prediction.
We study the dynamics of a quantum Ising chain after the sudden introduction of a non-integrable long-range interaction. Via an exact mapping onto a fully-connected lattice of hard-core bosons, we show that a pre-thermal state emerges and we investigate its features by focusing on a class of physically relevant observables. In order to gain insight into the eventual thermalization, we outline a diagrammatic approach which complements the study of the previous quasi-stationary state and provides the basis for a self-consistent solution of the kinetic equation. This analysis suggests that both the temporal decay towards the pre-thermal state and the crossover to the eventual thermal one may occur algebraically.
We investigate the sensitivity of the time evolution of a kinetic Ising model with Glauber dynamics against the initial conditions. To do so we apply the "damage spreading" method, i.e., we study the simultaneous evolution of two identical systems subjected to the same thermal noise. We derive a master equation for the joint probability distribution of the two systems. We then solve this master equation within an effective-field approximation which goes beyond the usual mean-field approximation by retaining the fluctuations though in a quite simplistic manner. The resulting effective-field theory is applied to different physical situations. It is used to analyze the fixed points of the master equation and their stability and to identify regular and chaotic phases of the Glauber Ising model. We also discuss the relation of our results to directed percolation.
A scheme, where three atomic ensembles can be prepared in the states of the W-class via Raman type interaction of strong classical field and a projection measurement involved three single-photon detectors and two beamsplitters, are considered. The obtained atomic entanglement consists of the Dicke or W-states of each of the ensembles.
To unravel the interplay between the strong electronic correlation and itinerant-localized dual nature in atypical f electron systems, we employed the density functional theory in combination with the single-site dynamical mean-field theory to systematically investigate the electronic structures of CeSb and USb. We find that the 4f states in CeSb are mostly localized which show a weak quasi-particle resonance peak near the Fermi level. Conversely, the 5f electrons in USb display partially itinerant feature, accompanied by mixed-valence behavior and prominent valence state fluctuations. Particularly, the 4f electronic correlations in CeSb are distinctly orbital-selective with strikingly renormalized 4f5/2 states, according to the low-energy behaviors of 4f self-energy functions. It is believed that the strong electronic correlation and fantastic bonding of f states contribute to elucidate the fascinating magnetism.
We present the analysis of the first NuSTAR observations ($\sim 220$ ks), simultaneous with the last SUZAKU observations ($\sim 50$ ks), of the active galactic nucleus of the bright Seyfert 1 galaxy Mrk 509. The time-averaged spectrum in the $1-79$ keV X-ray band is dominated by a power-law continuum ($\Gamma\sim 1.8-1.9$), a strong soft excess around 1 keV, and signatures of X-ray reflection in the form of Fe K emission ($\sim 6.4$ keV), an Fe K absorption edge ($\sim 7.1$ keV), and a Compton hump due to electron scattering ($\sim 20-30$ keV). We show that these data can be described by two very different prescriptions for the soft excess: a warm ($kT\sim 0.5-1$ keV) and optically thick ($\tau\sim10-20$) Comptonizing corona, or a relativistically blurred ionized reflection spectrum from the inner regions of the accretion disk. While these two scenarios cannot be distinguished based on their fit statistics, we argue that the parameters required by the warm corona model are physically incompatible with the conditions of standard coronae. Detailed photoionization calculations show that even in the most favorable conditions, the warm corona should produce strong absorption in the observed spectrum. On the other hand, while the relativistic reflection model provides a satisfactory description of the data, it also requires extreme parameters, such as maximum black hole spin, a very low and compact hot corona, and a very high density for the inner accretion disk. Deeper observations of this source are thus necessary to confirm the presence of relativistic reflection, and to further understand the nature of its soft excess.
We propose a radiative seesaw model with a light dark matter candidate (DM) under modular $A_4$ and gauged $U(1)_{B-L}$ symmetries, in which neutrino masses are generated via one-loop level, and we have a bosonic DM candidate $\sim 0.01-50$ GeV. DM is stabilized by nonzero modular weight and a remnant $Z_2$ symmetry of $U(1)_{B-L}$ symmetry. The naturalness of tiny DM mass is indirectly realized by the radiatively induced mass of neutral fermions that interact with our DM candidate. Thus, DM mass does not have to exceed the neutral fermion masses, whose scale is assumed to be 50 GeV. Taking several benchmark points of DM mass in DM analysis, we show the numerical analysis for the neutrino oscillation data in the normal and inverted hierarchy cases and find several features for both cases.
Teutsch145 and Teutsch146 are shown to be open clusters (OCs) orbiting well inside the Solar circle, a region where several dynamical processes combine to disrupt most OCs on a time-scale of a few 10^8yrs. BVI photometry from the GALILEO telescope is used to investigate the nature and derive the fundamental and structural parameters of the optically faint and poorly-known OCs Teutsch145 and 146. These parameters are computed by means of field-star decontaminated colour-magnitude diagrams (CMDs) and stellar radial density profiles (RDPs). Cluster mass estimates are made based on the intrinsic mass functions (MFs). We derive the ages 200+100-50Myr and 400+/-100Myr, and the distances from the Sun 2.7+/-0.3kpc and 3.8+/-0.2kpc, respectively for Teutsch145 and 146. Their integrated apparent and absolute magnitudes are m_V ~ 12.4, m_V ~ 13.3, M_V ~- 5.6 and M_V ~- 5.3. The MFs (detected for stars with m>1Msun) have slopes similar to Salpeter's IMF. Extrapolated to the H-burning limit, the MFs would produce total stellar masses of ~1400Msun, typical of relatively massive OCs. Both OCs are located deep into the inner Galaxy and close to the Crux-Scutum arm. Since cluster-disruption processes are important, their primordial masses must have been higher than the present-day values. The conspicuous stellar density excess observed in the innermost bin of both RDPs might reflect the dynamical effects induced by a few 10^8yrs of external tidal stress.
Next-generation concepts for solid-state memory devices are based on current-driven domain wall propagation, where the wall velocity governs the device performance. It has been shown that the domain wall velocity and the direction of travel is controlled by the nature of the wall and its chirality. This chirality is attributed to effects emerging from the lack of inversion symmetry at the interface between a ferromagnet and a heavy metal, leading to an interfacial Dzyaloshinskii-Moriya interaction that can control the shape and chirality of the magnetic domain wall. Here we present direct imaging of domain walls in Pt/Co/AlO$_x$ films using Lorentz transmission electron microscopy, demonstrating the presence of homochiral, and thus topologically protected, N\'{e}el walls. Such domain walls are good candidates for dense data storage, bringing the bit size down close to the limit of the domain wall width.
Matter makes a transition from non-relativistic to relativistic regime, as it falls onto a black hole. We employ a relativistic equation of state, abbreviated as RC, to study multi-species fluid flow around black holes. We show that pair-plasma fluid around a black hole is thermally not relativistic. In order to make it relativistic, a finite baryon loading is necessary. As a consequence of this, pair-plasma flow do not suffer centrifugal pressure driven shock in accretion. However, fluid with finite baryon content may undergo shock transition.
In 2010 Zagier introduced the notion of a quantum modular form. One of his first examples was the "strange" function $F(q)$ of Kontsevich. Here we produce a new example of a quantum modular form by making use of some of Ramanujan's mock theta functions. Using these functions and their transformation behaviour, we also compute asymptotic expansions similar to expansions of $F(q)$.
We present a detailed study of the finite one-dimensional quantum Ising chain in a transverse field in the presence of boundary magnetic fields coupled with the order-parameter spin operator. We consider two magnetic fields located at the boundaries of the chain that have the same strength and that are aligned in the same or in the opposite direction. We derive analytic expressions for the gap in all phases for large values of the chain length L, as a function of the boundary field strength. We also investigate the behavior of the chain in the quantum ferromagnetic phase for oppositely aligned fields, focusing on the magnet-to-kink transition that occurs at a finite value of the magnetic field strength. At this transition we compute analytically the finite-size crossover functions for the gap, the magnetization profile, the two-point correlation function, and the density of fermionic modes. As the magnet-to-kink transition is equivalent to the wetting transition in two-dimensional classical Ising models, our results provide new analytic predictions for the finite-size behavior of Ising systems in a strip geometry at this transition.
Exploiting known semantic relationships between fine-grained tasks is critical to the success of recent model agnostic approaches. These approaches often rely on meta-optimization to make a model robust to systematic task or domain shifts. However, in practice, the performance of these methods can suffer, when there are no coherent semantic relationships between the tasks (or domains). We present Invenio, a structured meta-learning algorithm to infer semantic similarities between a given set of tasks and to provide insights into the complexity of transferring knowledge between different tasks. In contrast to existing techniques such as Task2Vec and Taskonomy, which measure similarities between pre-trained models, our approach employs a novel self-supervised learning strategy to discover these relationships in the training loop and at the same time utilizes them to update task-specific models in the meta-update step. Using challenging task and domain databases, under few-shot learning settings, we show that Invenio can discover intricate dependencies between tasks or domains, and can provide significant gains over existing approaches in terms of generalization performance. The learned semantic structure between tasks/domains from Invenio is interpretable and can be used to construct meaningful priors for tasks or domains.
In this paper we consider an Hartree-Fock type system made by two Schr\"odinger equations in presence of a Coulomb interacting term and a cooperative pure power and subcritical nonlinearity, driven by a suitable parameter $\beta \geq 0$. We show the existence of semitrivial and vectorial ground states solutions depending on the parameters involved. The asymptotic behavior with respect to the parameter $\beta$ of these solutions is also studied.
Neural volume rendering became increasingly popular recently due to its success in synthesizing novel views of a scene from a sparse set of input images. So far, the geometry learned by neural volume rendering techniques was modeled using a generic density function. Furthermore, the geometry itself was extracted using an arbitrary level set of the density function leading to a noisy, often low fidelity reconstruction. The goal of this paper is to improve geometry representation and reconstruction in neural volume rendering. We achieve that by modeling the volume density as a function of the geometry. This is in contrast to previous work modeling the geometry as a function of the volume density. In more detail, we define the volume density function as Laplace's cumulative distribution function (CDF) applied to a signed distance function (SDF) representation. This simple density representation has three benefits: (i) it provides a useful inductive bias to the geometry learned in the neural volume rendering process; (ii) it facilitates a bound on the opacity approximation error, leading to an accurate sampling of the viewing ray. Accurate sampling is important to provide a precise coupling of geometry and radiance; and (iii) it allows efficient unsupervised disentanglement of shape and appearance in volume rendering. Applying this new density representation to challenging scene multiview datasets produced high quality geometry reconstructions, outperforming relevant baselines. Furthermore, switching shape and appearance between scenes is possible due to the disentanglement of the two.
We show that in SU(3)_C X SU(3)_L X U(1)_N (3-3-1) models embedded with a singlet scalar playing the role of the axion, after imposing scale invariance, dynamical symmetry breaking of Peccei-Quinn symmetry occurs through the one-loop effective potential for the singlet field. We, then, analyze the structure of spontaneous symmetry breaking by studying the new scalar potential for the model, and verify that electroweak symmetry breaking is tightly connected to the 3-3-1 breaking by the strong constraints among their vacuum expectation values. This offers a valuable guide to write down the correct pattern of symmetry breaking for multi-scalar theories. We also obtained that the accompanying massive pseudo-scalar, instead of acquiring mass of order of Peccei-Quinn scale as we would expect, develops a mass at a much lower scale, a consequence solely of the dynamical breaking.
In this paper, we propose a novel Convolutional Neural Network (CNN) architecture for learning multi-scale feature representations with good tradeoffs between speed and accuracy. This is achieved by using a multi-branch network, which has different computational complexity at different branches. Through frequent merging of features from branches at distinct scales, our model obtains multi-scale features while using less computation. The proposed approach demonstrates improvement of model efficiency and performance on both object recognition and speech recognition tasks,using popular architectures including ResNet and ResNeXt. For object recognition, our approach reduces computation by 33% on object recognition while improving accuracy with 0.9%. Furthermore, our model surpasses state-of-the-art CNN acceleration approaches by a large margin in accuracy and FLOPs reduction. On the task of speech recognition, our proposed multi-scale CNNs save 30% FLOPs with slightly better word error rates, showing good generalization across domains. The codes are available at https://github.com/IBM/BigLittleNet
We analyze the duration of the unbiased Avoider-Enforcer game for three basic positional games. All the games are played on the edges of the complete graph on $n$ vertices, and Avoider's goal is to keep his graph outerplanar, diamond-free and $k$-degenerate, respectively. It is clear that all three games are Enforcer's wins, and our main interest lies in determining the largest number of moves Avoider can play before losing.   Extremal graph theory offers a general upper bound for the number of Avoider's moves. As it turns out, for all three games we manage to obtain a lower bound that is just an additive constant away from that upper bound. In particular, we exhibit a strategy for Avoider to keep his graph outerplanar for at least $2n-8$ moves, being just 6 short of the maximum possible. A diamond-free graph can have at most $d(n)=\lceil\frac{3n-5}{2}\rceil$ edges, and we prove that Avoider can play for at least $d(n)-3$ moves. Finally, if $k$ is small compared to $n$, we show that Avoider can keep his graph $k$-degenerate for as many as $e(n)$ moves, where $e(n)$ is the maximum number of edges a $k$-degenerate graph can have.
In the present paper we study the phase diagram of the Heisenberg model on the honeycomb lattice with antiferromagnetic interactions up to third neighbors along the line $J_2=J_3$ that include the point $J_2=J_3=J_1/2$, corresponding to the highly frustrated point where the classical ground state has macroscopic degeneracy. Using the Linear Spin-Wave, Schwinger boson technique followed by a mean field decoupling and exact diagonalization for small systems we find an intermediate phase with a spin gap and short range N\'eel correlations in the strong quantum limit (S=1/2). All techniques provide consistent results which allow us to predict the existence of a quantum disordered phase, which may have been observed in recent high-field ESR measurements in manganites.
In this article a multilayer parking system with screening of size n=3 is studied with a focus on the time-dependent particle density. We prove that the asymptotic limit of the particle density increases from an average density of 1/3 on the first layer to the value of (10 - \sqrt 5 )/19 in higher layers.
We derive, using the spherical collapse model, a generalized Layzer-Irvine equation which can be used to describe the gravitational collapse of cold dark matter in a dark energy background. We show that the usual Layzer-Irvine equation is valid if the dark matter and the dark energy are minimally coupled to each other and the dark energy distribution is homogeneous, independently of its equation of state. We compute the corrections to the standard Layzer-Irvine equation which arise in the presence of dark energy inhomogeneities. We show that, in the case of a dark energy component with a constant equation of state parameter consistent with the latest observational constraints, these corrections are expected to be small, even if the dark energy has a negligible sound speed. However, we find that, in more general models, the impact of dark energy perturbations on the dynamics of clusters of galaxies, which will be constrained by ESA's Euclid mission with unprecedented precision, might be significant.
Suppose $X$ is a Markov process on the real line (or some interval). Do the distributions of its first passage times downwards (fptd) determine its law? In this paper we treat some special cases of this question. We prove that if the fptd process has the law of a subordinator, then necessarily $X$ is a L\'evy process with no negative jumps; specifying the law of the subordinator determines the law of $X$ uniquely. We further show that, likewise, the classes of continuous-state branching processes and of self-similar processes without negative jumps are also respectively characterised by a certain structure of their fptd distributions; and each member of these classes separately is determined uniquely by the precise family of its fptd laws. The road to these results is paved by (i) the identification of Markov processes without negative jumps in terms of the nature of their fptd laws, and (ii) some general results concerning the identification of the fptd distributions for such processes.
We report on our discovery and observations of the Pan-STARRS1 supernova (SN) PS1-12sk, a transient with properties that indicate atypical star formation in its host galaxy cluster or pose a challenge to popular progenitor system models for this class of explosion. The optical spectra of PS1-12sk classify it as a Type Ibn SN (c.f. SN 2006jc), dominated by intermediate-width (3x10^3 km/s) and time variable He I emission. Our multi-wavelength monitoring establishes the rise time dt = 9-23 days and shows an NUV-NIR SED with temperature > 17x10^3 K and a peak rise magnitude of Mz = -18.9 mag. SN Ibn spectroscopic properties are commonly interpreted as the signature of a massive star (17 - 100 M_sun) explosion within a He-enriched circumstellar medium. However, unlike previous Type Ibn supernovae, PS1-12sk is associated with an elliptical brightest cluster galaxy, CGCG 208-042 (z = 0.054) in cluster RXC J0844.9+4258. The expected probability of an event like PS1-12sk in such environments is low given the measured infrequency of core-collapse SNe in red sequence galaxies compounded by the low volumetric rate of SN Ibn. Furthermore, we find no evidence of star formation at the explosion site to sensitive limits (Sigma Halpha < 2x10^-3 M_sun/yr/kpc^2). We therefore discuss white dwarf binary systems as a possible progenitor channel for SNe Ibn. We conclude that PS1-12sk represents either a fortuitous and statistically unlikely discovery, evidence for a top-heavy IMF in galaxy cluster cooling flow filaments, or the first clue suggesting an alternate progenitor channel for Type Ibn SNe.
The relation between early type galaxy size, surface brightness and velocity dispersion, "the fundamental plane", has long been understood as resulting from equilibrium in their largely pressure supported stellar dynamics. The dissipation and feedback involved in reaching such an equilibrium through merger formation of these galaxies over cosmic time can be responsible for the orientation of the plane. We see a correlation between surface brightness enhancement and youth in the 6dF Galaxy Survey. Correlations of this `tilt' with stellar mass, age, concentration, shape and metallicity now point the direction for further work on the resolved kinematics and structure of these nearby galaxies and on their initial mass function and dark matter component.   On the face of it, the Tully Fisher relation is a simpler one dimensional scaling relation. However, as late type galaxies have bulges as well as disks, and, as the surface density of disks is only standard for the more massive galaxies, additional parameters are involved.