ShantanuT01/DACTYL-Pretraining huggingface dataset:
We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild. In contrast to previous methods that directly map 3D models and RGB images to an embedding space, we establish a common low-level representation in the form of location fields from which we compute pose invariant 3D shape descriptors. Location fields encode correspondences between 2D pixels and 3D surface coordinates and, thus, explicitly capture 3D shape and 3D pose information without appearance variations which are irrelevant for the task. This early fusion of 3D models and RGB images results in three main advantages: First, the bottleneck location field prediction acts as a regularizer during training. Second, major parts of the system benefit from training on a virtually infinite amount of synthetic data. Finally, the predicted location fields are visually interpretable and unblackbox the system. We evaluate our proposed approach on three challenging real-world datasets (Pix3D, Comp, and Stanford) with different object categories and significantly outperform the state-of-the-art by up to 20% absolute in multiple 3D retrieval metrics.
This paper proposes and studies new quantum version of $f$-divergences, a class of convex functionals of a pair of probability distributions including Kullback-Leibler divergence, Rnyi-type relative entropy and so on. There are several quantum versions so far, including the one by Petz. We introduce another quantum version ($\mathrm{D}_{f}^{\max}$, below), defined as the solution to an optimization problem, or the minimum classical $f$- divergence necessary to generate a given pair of quantum states. It turns out to be the largest quantum $f$-divergence. The closed formula of $\mathrm{D}_{f}^{\max}$ is given either if $f$ is operator convex, or if one of the state is a pure state. Also, concise representation of $\mathrm{D}_{f}^{\max}$ as a pointwise supremum of linear functionals is given and used for the clarification of various properties of the quality.   Using the closed formula of $\mathrm{D}_{f}^{\max}$, we show: Suppose $f$ is operator convex. Then the\ maximum $f\,$- divergence of the probability distributions of a measurement under the state $\rho$ and $\sigma$ is strictly less than $\mathrm{D}_{f}^{\max}\left( \rho\Vert\sigma\right) $. This statement may seem intuitively trivial, but when $f$ is not operator convex, this is not always true. A counter example is $f\left( \lambda\right) =\left\vert 1-\lambda\right\vert $, which corresponds to total variation distance.   We mostly work on finite dimensional Hilbert space, but some results are extended to infinite dimensional case.
We introduce a new class of non-isothermal models describing the evolution of nematic liquid crystals and prove their consistency with the fundamental laws of classical Thermodynamics. The resulting system of equations captures all essential features of physically relevant models, in particular, the effect of stretching of the director field is taken into account. In addition, the associated initial-boundary value problem admits global-in-time weak solutions without any essential restrictions on the size of the initial data.
We have investigated a THz detection scheme based on mixing of electrical signals in a voltage-dependent capacitance made out of suspended graphene. We have analyzed both coherent and incoherent detection regimes and compared their performance with the state of the art. Using a high-amplitude local oscillator, we anticipate potential for quantum limited detection in the coherent mode. The sensitivity stems from the extraordinary mechanical and electrical properties of atomically thin graphene or graphene-related 2D materials.
By using the gauge Ward identities, we study correlation functions of gauged WZNW models. We show that the gauge dressing of the correlation functions can be taken into account as a solution of the Knizhnik-Zamolodchikov equation. Our method is analogous to the analysis of the gravitational dressing of 2D field theories.
A new alternate method for evaluating linear response theory is formally developed, and results are presented. This method involves the time-evolution of the system using TDHF and is constructed directly on top of a static Hartree-Fock calculation. By Fourier transforming the time-dependent result the response function and the total probability amplitude are extracted. This method allows for a coherent description of static properties of nuclei, such as binding energies and deformations, while also providing a method for calculating collective modes and reaction rates. A full 3-D Cartesian Basis-Spline collocation representation is used with several Skyrme interactions. Sample results are presented for the giant multipole resonances of 16O, 40Ca, and 32S and compared to other calculations.
In this paper we explore the scientific synergies between Athena and some of the key multi-messenger facilities that should be operative concurrently with Athena. These facilities include LIGO A+, Advanced Virgo+ and future detectors for ground-based observation of gravitational waves (GW), LISA for space-based observations of GW, IceCube and KM3NeT for neutrino observations, and CTA for very high energy observations. These science themes encompass pressing issues in astrophysics, cosmology and fundamental physics such as: the central engine and jet physics in compact binary mergers, accretion processes and jet physics in Super-Massive Binary Black Holes (SMBBHs) and in compact stellar binaries, the equation of state of neutron stars, cosmic accelerators and the origin of Cosmic Rays (CRs), the origin of intermediate and high-Z elements in the Universe, the Cosmic distance scale and tests of General Relativity and the Standard Model. Observational strategies for implementing the identified science topics are also discussed.   A significant part of the sources targeted by multi-messenger facilities is of transient nature. We have thus also discussed the synergy of \textsl{Athena} with wide-field high-energy facilities, taking THESEUS as a case study for transient discovery. This discussion covers all the Athena science goals that rely on follow-up observations of high-energy transients identified by external observatories, and includes also topics that are not based on multi-messenger observations, such as the search for missing baryons or the observation of early star populations and metal enrichment at the cosmic dawn with Gamma-Ray Bursts (GRBs).
We address the problem of the maximal finite number of real points of a real algebraic curve (of a given degree and, sometimes, genus) in the projective plane. We improve the known upper and lower bounds and construct close to optimal curves of small degree. Our upper bound is sharp if the genus is small as compared to the degree. Some of the results are extended to other real algebraic surfaces, most notably ruled.
We combine observations of the Coronal Multi-channel Polarimeter (CoMP) and the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to study the characteristic properties of (propagating) Alfvenic motions and quasi-periodic intensity disturbances in polar plumes. This unique combination of instruments highlights the physical richness of the processes taking place at the base of the (fast) solar wind. The (parallel) intensity perturbations with intensity enhancements around 1% have an apparent speed of 120 km/s (in both the 171A and 193A passbands) and a periodicity of 15 minutes, while the (perpendicular) Alfvenic wave motions have a velocity amplitude of 0.5 km/s, a phase speed of 830 km/s, and a shorter period of 5 minutes on the same structures. These observations illustrate a scenario where the excited Alfvenic motions are propagating along an inhomogeneously loaded magnetic field structure such that the combination could be a potential progenitor of the magnetohydrodynamic turbulence required to accelerate the fast solar wind.
We present a method to synthesize novel views from a single $360^\circ$ panorama image based on the neural radiance field (NeRF). Prior studies in a similar setting rely on the neighborhood interpolation capability of multi-layer perceptions to complete missing regions caused by occlusion, which leads to artifacts in their predictions. We propose 360FusionNeRF, a semi-supervised learning framework where we introduce geometric supervision and semantic consistency to guide the progressive training process. Firstly, the input image is re-projected to $360^\circ$ images, and auxiliary depth maps are extracted at other camera positions. The depth supervision, in addition to the NeRF color guidance, improves the geometry of the synthesized views. Additionally, we introduce a semantic consistency loss that encourages realistic renderings of novel views. We extract these semantic features using a pre-trained visual encoder such as CLIP, a Vision Transformer trained on hundreds of millions of diverse 2D photographs mined from the web with natural language supervision. Experiments indicate that our proposed method can produce plausible completions of unobserved regions while preserving the features of the scene. When trained across various scenes, 360FusionNeRF consistently achieves the state-of-the-art performance when transferring to synthetic Structured3D dataset (PSNR~5%, SSIM~3% LPIPS~13%), real-world Matterport3D dataset (PSNR~3%, SSIM~3% LPIPS~9%) and Replica360 dataset (PSNR~8%, SSIM~2% LPIPS~18%).
We have investigated a brane world model in which the gravitational field in the bulk is described both by a metric tensor and a minimally coupled scalar field. This scalar field is taken to be a chameleon with an appropriate potential function. The scalar field interacts with matter and there is an energy transfer between the two components. We find a late-time asymptotic solution which exhibits late-time accelerating expansion. We also show that the Universe recently crosses the phantom barrier without recourse to any exotic matter. We provide some thermodynamic arguments which constrain both the direction of energy transfer and dynamics of the extra dimension.
We report the observation of layer breathing mode (LBM) vibrations in few-layer graphene (FLG) samples of thickness from 2 to 6 layers, exhibiting both Bernal (AB) and rhombohedral (ABC) stacking order. The LBM vibrations are identified using a Raman combination band lying around 1720 cm-1. From double resonance theory, we identify the feature as the LOZO' combination mode of the out-of-plane LBM (ZO') and the in-plane longitudinal optical mode (LO). The LOZO' Raman band is found to exhibit multiple peaks, with a unique line shape for each layer thickness and stacking order. These complex line shapes of the LOZO'-mode arise both from the material-dependent selection of different phonons in the double-resonance Raman process and from the detailed structure of the different branches of LBM in FLG.
Abstact: We introduce new models of energy redistribution in stochastic chemical kinetics with several molecule types and energy parameters. The main results concern the situations when there are product form measures. Using a probabilistic interpretation of the related Boltzmann equation, we find some invariant measures explicitly and prove convergence to them.
A tight binding model of electrons interacting via bare Coulomb repulsion is numerically investigated by use of the Density Matrix Renormalization Group method which we prove applicable also to very long range potentials. From the analysis of the elementary excitations, of the spin and charge correlation functions and of the momentum distribution, a picture consistent with the formation of a one dimensional "Wigner crystal" emerges, in quantitative agreement with a previous bosonization study. At finite doping, Umklapp scattering is shown to be ineffective in the presence of long range forces.
Let $f$ be a holomorphic cusp form of weight $k$ with respect to full modular group $SL_2(\mathbb{Z})$ satisfying a normalized Hecke eigenform, $L_f(s)$ the $L$-function attached to the form $f$. Good gave the approximate functional equation and mean square formula of $L_f(s)$. In this paper, we shall generalize these formulas for the derivatives of $L_f(s)$.
We describe efficient protocols for quantum oblivious transfer and for one-out-of-two quantum oblivious transfer. These protocols, which can be implemented with present technology, are secure against general attacks as long as the cheater can not store the bit for an arbitrarily long period of time.
We experimentally demonstrate the simultaneous reconstruction of the bulk and the edge energy spectra caused by strong spin-orbit interaction, at the two lowest filling factors $\nu=1$ and $\nu=2$ for $In_xGa_{1-x}As$ two-dimensional electron system with $x = 0.75$. The observed reconstruction is of different character at these filling factors: it is characterized by zeroing of the energy gap at the levels crossing point at $\nu=2$, while the reconstruction at $\nu=1$ goes through the coexistence of two $\nu=1$ quantum Hall phases with different spin projections. An analysis indicates a strong influence of many-body interaction on the reconstruction at $\nu=1$.
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators' preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution Learning (PADL) framework to address it from the perspective of disentangling an annotator's preference from stochastic errors using distribution learning so as to produce not only a meta segmentation but also the segmentation possibly made by each annotator. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator's segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks.
Prediction of low-mass $W_R$ and $Z_R$ gauge bosons in popular grand unified theories has been the subject of considerable attention over the last three decades. In this work we show that when gravity induced corrections due to dim.5 operator are included the minimal symmetry breaking chain of $SO(10)$ and $E_6$ GUTs can yield $W_R^{\pm}$ and $Z_R$ bosons with masses in the range $(3-10)$ TeV which are accessible to experimental tests at the Large Hordan Collider. The RH neutrinos turn out to be heavy pseudo-Dirac fermions. The model can fit all fermion masses and manifest in rich structure of lepton flavor violation while proton life time is predicted to be much longer than the accessible limit of Super-Kamiokande or planned Hyper-Kamiokande collaborations.
We describe a certain "self-similar" family of solutions to the free Schroedinger equation in all dimensions, and derive some consequences of such solutions for two specific problems.
Electromagnetic particle is considered as appropriate particle solution of nonlinear electrodynamics. Mass, spin, charge, and dipole moment for the electromagnetic particle are defined. Classical motion equations for massive charged particle with spin and dipole moment are obtained from integral conservation laws for the field.
Using ab initio thermodynamics, the stability of a wide range of hydrocarbon adsorbates under various chemical vapor deposition (CVD) conditions (temperature, methane and hydrogen pressures) used in experimental graphene growth protocols at solid and liquid Cu surfaces has been explored. At the employed high growth temperatures around the melting point of Cu, we find that commonly used thermodynamic models such as the harmonic oscillator model may no longer be accurate. Instead, we account for the translational and rotational mobility of adsorbates using a recently developed hindered translator and rotator model or a two-dimensional ideal gas model. The thermodynamic considerations turn out to be crucial for explaining experimental results and allow us to improve and extend the findings of earlier theoretical studies regarding the role of hydrogen and hydrocarbon species in CVD. In particular, we find that smaller hydrocarbons will completely dehydrogenate under most CVD conditions. For larger clusters our results show that metal-terminated and hydrogen-terminated edges have very similar stabilities. While both cluster types might thus form during the experiment, we show that the low binding strength of clusters with hydrogen-terminated edges could result in instability towards desorption.
Quasars (QSOs) hosting supermassive black holes are believed to reside in massive halos harboring galaxy overdensities. However, many observations revealed average or low galaxy densities around $z\gtrsim6$ QSOs. This could be partly because they measured galaxy densities in only tens of arcmin$^2$ around QSOs and might have overlooked potential larger scale galaxy overdensities. Some previous studies also observed only Lyman break galaxies (LBGs, massive older galaxies) and missed low mass young galaxies like Ly$\alpha$ emitters (LAEs) around QSOs. Here we present observations of LAE and LBG candidates in $\sim700$ arcmin$^2$ around a $z=6.61$ luminous QSO using Subaru Telescope Suprime-Cam with narrow/broadbands. We compare their sky distributions, number densities and angular correlation functions with those of LAEs/LBGs detected in the same manner and comparable data quality in our control blank field. In the QSO field, LAEs and LBGs are clustering in 4-20 comoving Mpc angular scales, but LAEs show mostly underdensity over the field while LBGs are forming $30\times60$ comoving Mpc$^2$ large scale structure containing 3-$7\sigma$ high density clumps. The highest density clump includes a bright (23.78 mag in the narrowband) extended ($\gtrsim 16$ kpc) Ly$\alpha$ blob candidate, indicative of a dense environment. The QSO could be part of the structure but is not located exactly at any of the high density peaks. Near the QSO, LAEs show underdensity while LBGs average to $4\sigma$ excess densities compared to the control field. If these environments reflect halo mass, the QSO may not be in the most massive halo, but still in a moderately massive one.
We prove that given any $\beta<1/3$, a time interval $[0,T]$, and given any smooth energy profile $e \colon [0,T] \to (0,\infty)$, there exists a weak solution $v$ of the three-dimensional Euler equations such that $v \in C^{\beta}([0,T]\times \mathbb{T}^3)$, with $e(t) = \int_{\mathbb{T}^3} |v(x,t)|^2 dx$ for all $t\in [0,T]$. Moreover, we show that a suitable $h$-principle holds in the regularity class $C^\beta_{t,x}$, for any $\beta<1/3$. The implication of this is that the dissipative solutions we construct are in a sense typical in the appropriate space of subsolutions as opposed to just isolated examples.
We present a comprehensive study of one method for measuring various parameters of global modes of oscillation of the Sun. Using velocity data taken by the Michelson Doppler Imager (MDI), we analyze spherical harmonic degrees l <= 300. Both current and historical methodologies are explained, and the various differences between the two are investigated to determine their effects on global-mode parameters and systematic errors in the analysis. These differences include a number of geometric corrections made during spherical harmonic decomposition; updated routines for generating window functions, detrending timeseries, and filling gaps; and consideration of physical effects such as mode profile asymmetry, horizontal displacement at the solar surface, and distortion of eigenfunctions by differential rotation. We apply these changes one by one to three years of data, and then reanalyze the entire MDI mission applying all of them, using both the original 72-day long timeseries and 360-day long timeseries. We find significant changes in mode parameters, both as a result of the various changes to the processing, as well as between the 72-day and 360-day analyses. We find reduced residuals of inversions for internal rotation, but seeming artifacts remain, such as the peak in the rotation rate near the surface at high latitudes. An annual periodicity in the f-mode frequencies is also investigated.
It is well-known that distances in random iid matrices are highly concentrated around their mean. In this note we extend this concentration phenomenon to Wigner matrices. Exponential bounds for the lower tail are also included.
We study $p$-adic families of eigenforms for which the $p$-th Hecke eigenvalue $a_p$ has constant $p$-adic valuation ("constant slope families"). We prove two separate upper bounds for the size of such families. The first is in terms of the logarithmic derivative of $a_p$ while the second depends only on the slope of the family. We also investigate the numerical relationship between our results and the former Gouv\^ea--Mazur conjecture.
Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis in computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proximity, grouping etc. to compute affinities between the objects in different frames. In this paper, we harness the power of deep learning for data association in tracking by jointly modelling object appearances and their affinities between different frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN) learns compact; yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. DAN also accounts for multiple objects appearing and disappearing between video frames. We exploit the resulting efficient affinity computations to associate objects in the current frame deep into the previous frames for reliable on-line tracking. Our technique is evaluated on popular multiple object tracking challenges MOT15, MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics demonstrates that our approach is among the best performing techniques on the leader board for these challenges. The open source implementation of our work is available at https://github.com/shijieS/SST.git.