ShantanuT01/DACTYL-Pretraining huggingface dataset:
Cosmic voids defined as a subset of Delaunay Triangulation (DT) circumspheres have been used to measure the Baryon Acoustic Oscillations (BAO) scale; providing tighter constraints on cosmological parameters when combined with matter tracers. These voids are defined as spheres larger than a given radius threshold, which is constant over the survey volume. However, the response of these void tracers to observational systematics has not yet been studied. In this work we analyse the response of void clustering to selection effects. We find for the case of moderate (<20 per cent) incompleteness, void selection based on a constant radius cut yields robust measurements. This is particularly true for BAO-reconstructed galaxy samples, where large-scale void exclusion effects are mitigated. Moreover, we observe for the case of severe (up to 90 per cent) incompleteness -- such as can be found at the edges of the radial selection function -- that an accurate estimation of the void distribution is necessary for unbiased clustering measurements. In addition, we find that without reconstruction, using a constant threshold under these conditions produces a stronger void exclusion effect that can affect the clustering on large scales. A new void selection criteria dependent on the (local) observed tracer density that maximises the BAO peak significance prevents the aforementioned exclusion features from contaminating the BAO signal. Finally, we verify, with large simulations including light cone evolution, that both void sample definitions (local and constant) yield unbiased and consistent BAO scale measurements.
Gamma-ray bursts (GRBs) have variable lightcurves. Although most models attribute the observed variability to one physical origin (e.g. central engine activity, clumpy circumburst medium, relativistic turbulence), some models invoke two physically distinct variability components. We develop a method, namely, the stepwise filter correlation (SFC) method, to decompose the variability components in a GRB lightcurve. Based on a low-pass filter technique, we progressively filter the high frequency signals from the lightcurve, and then perform a correlation analysis between each adjunct pair of filtered lightcurves. Our simulations suggest that if a mock lightcurve contains a slow variability component superposed on a rapidly varying time sequence, the correlation coefficient as a function of the filter frequency would display a prominent dip feature around the frequency of the slow component. Through simulations, we demonstrate that this method can identify significant clustering structures of a lightcurve in the frequency domain, and proved that it can catch superposed signals that are otherwise not easy to retrieve based on other methods (e.g. the power density spectrum analysis method). We apply this method to 266 BATSE bright GRBs. We find that the majority of the bursts have clear evidence of such a superposition effect. We perform a statistical analysis of the identified variability components, and discuss the implications for GRB physics.
An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the views in texts, and record clinically relevant organ measurements. Hence, automatic view classification and landmark detection of the organs can be instrumental to streamline the examination workflow. However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e.g., low contrast and large variations, but also the heterogeneity across tasks, i.e., one classification task for all views, and then one landmark detection task for each relevant view. While convolutional neural networks (CNN) have demonstrated more promising outcomes on ultrasound image analytics than traditional machine learning approaches, it becomes impractical to deploy multiple networks (one for each task) due to the limited computational and memory resources on most existing ultrasound scanners. To overcome such limits, we propose a multi-task learning framework to handle all the tasks by a single network. This network is integrated to perform view classification and landmark detection simultaneously; it is also equipped with global convolutional kernels, coordinate constraints, and a conditional adversarial module to leverage the performances. In an experimental study based on 187,219 ultrasound images, with the proposed simplified approach we achieve (1) view classification accuracy better than the agreement between two clinical experts and (2) landmark-based measurement errors on par with inter-user variability. The multi-task approach also benefits from sharing the feature extraction during the training process across all tasks and, as a result, outperforms the approaches that address each task individually.
The trapped-ion QCCD (quantum charge-coupled device) architecture proposal lays out a blueprint for a universal quantum computer. The design begins with electrodes patterned on a two-dimensional surface configured to trap multiple arrays of ions (or ion crystals). Communication within the ion crystal network allows for the machine to be scaled while keeping the number of ions in each crystal to a small number, thereby preserving the low error rates demonstrated in trapped-ion experiments. By proposing to communicate quantum information by moving the ions through space to interact with other distant ions, the architecture creates a quantum computer endowed with full-connectivity. However, engineering this fully-connected computer introduces a host of difficulties that have precluded the architecture from being fully realized in the twenty years since its proposal. Using a Honeywell cryogenic surface trap, we report on the integration of all necessary ingredients of the QCCD architecture into a programmable trapped-ion quantum computer. Using four and six qubit circuits, the system level performance of the processor is quantified by the fidelity of a teleported CNOT gate utilizing mid-circuit measurement and a quantum volume measurement of $2^6=64$. By demonstrating that the low error rates achievable in small ion crystals can be successfully integrated with a scalable trap design, parallel optical delivery, and fast ion transport, the QCCD architecture is shown to be a viable path toward large quantum computers. Atomic ions provide perfectly identical, high-fidelity qubits. Our work shows that the QCCD architecture built around these qubits will provide high performance quantum computers, likely enabling important near-term demonstrations such as quantum error correction and quantum advantage.
For the unsorted database quantum search with the unknown fraction $\lambda$ of target items, there are mainly two kinds of methods, i.e., fixed-point or trail-and-error. (i) In terms of the fixed-point method, Yoder et al. [Phys. Rev. Lett. 113, 210501 (2014)] claimed that the quadratic speedup over classical algorithms has been achieved. However, in this paper, we point out that this is not the case, because the query complexity of Yoder's algorithm is actually in $O(1/\sqrt{\lambda_0})$ rather than $O(1/\sqrt{\lambda})$, where $\lambda_0$ is a known lower bound of $\lambda$. (ii) In terms of the trail-and-error method, currently the algorithm without randomness has to take more than 1 times queries or iterations than the algorithm with randomly selected parameters. For the above problems, we provide the first hybrid quantum search algorithm based on the fixed-point and trail-and-error methods, where the matched multiphase Grover operations are trialed multiple times and the number of iterations increases exponentially along with the number of trials. The upper bound of expected queries as well as the optimal parameters are derived. Compared with Yoder's algorithm, the query complexity of our algorithm indeed achieves the optimal scaling in $\lambda$ for quantum search, which reconfirms the practicality of the fixed-point method. In addition, our algorithm also does not contain randomness, and compared with the existing deterministic algorithm, the query complexity can be reduced by about 1/3. Our work provides an new idea for the research on fixed-point and trial-and-error quantum search.
In this paper, we present three remarkable properties of the normal distribution: first that if two independent variables's sum is normally distributed, then each random variable follows a normal distribution (which is referred to as the Levy Cramer theorem), second a variation of the Levy Cramer theorem that states that if two independent symmetric random variables with finite variance have their sum and their difference independent, then each random variable follows a standard normal distribution, and third that the normal distribution is characterized by the fact that it is the only distribution for which the sample mean and variance are independent (which is a central property for deriving the Student distribution and referred as the Geary theorem). The novelty of this paper is to provide new, quicker or self contained proofs of theses theorems.
Previous work has studied the pattern count on singly restricted permutations. In this work, we focus on patterns of length 3 in multiply restricted permutations, especially for double and triple pattern-avoiding permutations. We derive explicit formulae or generating functions for various occurrences of length 3 patterns on multiply restricted permutations, as well as some combinatorial interpretations for non-trivial pattern relationships.
We first classify the possible configurations of fibrations which are not semi-stable on extremal elliptic K3 surfaces. Then we give a complete list of extremal elliptic K3 surfaces whose singular fibers are all not of type $I_n$.
An effective texture is presented for six Majorana neutrinos, three active and three (conventional) sterile, based on a 6x6 mass matrix, whose 3x3 active--sterile component (i.e., Dirac component) is conjectured to get a fermion universal form similar to the constructed previously 3x3 mass matrix for charged leptons and 3x3 mass matrices for up and down quarks. This is true, however, when the bimaximal mixing, specific for neutrinos, is transformed out unitarily from the neutrino mass matrix. The 3x3 active--active component (i.e., lefthanded component) of neutrino 6x6 mass matrix is diagonal and gets degenerate entries. It dominates over the whole neutrino mass matrix. In such a texture, three neutrino masses are nearly degenerate, $ m_1 \simeq m_2 \simeq m_3 $, but their mass-squared differences appear hierarchical, $\Delta m^2_{21} \ll \Delta m^2_{32} \simeq \Delta m^2_{31}$, while the remaining three neutrino masses can be constructed to vanish, $ m_4 = m_5 = m_6 = 0 $, in contrast to the familiar seesaw mechanism.
The cosmological distance ladder crucially depends on classical Cepheids (with P=3-80 days), which are primary distance indicators up to 33 Mpc. Within this volume, very few SNe Ia have been calibrated through classical Cepheids, with uncertainty related to the non-linearity and the metallicity dependence of their period-luminosity (PL) relation. Although a general consensus on these effects is still not achieved, classical Cepheids remain the most used primary distance indicators. A possible extension of these standard candles to further distances would be important. In this context, a very promising new tool is represented by the ultra-long period (ULP) Cepheids (P \geq 80 days), recently identified in star-forming galaxies. Only a small number of ULP Cepheids have been discovered so far. Here we present and analyse the properties of an updated sample of 37 ULP Cepheids observed in galaxies within a very large metallicity range of 12+log(O/H) from ~7.2 to 9.2 dex. We find that their location in the colour(V-I)-magnitude diagram as well as their Wesenheit (V-I) index-period (WP) relation suggests that they are the counterparts at high luminosity of the shorter-period (P \leq 80 days) classical Cepheids. However, a complete pulsation and evolutionary theoretical scenario is needed to properly interpret the true nature of these objects. We do not confirm the flattening in the studied WP relation suggested by Bird et al. (2009). Using the whole sample, we find that ULP Cepheids lie around a relation similar to that of the LMC, although with a large spread (~0.4 mag).
Peripheral heavy ion reactions at ultra relativistic energies have large angular momentum that can be studied via two particle correlations using the Differential Hanbury Brown and Twiss method. In the present work we analyze the possibilities and sensitivity of the method in rotating, few source systems. Analytic results provide insight in the advantages of this method.
The number of supernovae known to be connected with long-duration gamma-ray bursts is increasing and the link between these events is no longer exclusively found at low redshift ($z \lesssim 0.3$) but is well established also at larger distances. We present a new case of such a liaison at $z = 0.33$ between GRB\,171010A and SN\,2017htp. It is the second closest GRB with an associated supernova of only three events detected by Fermi-LAT. The supernova is one of the few higher redshift cases where spectroscopic observations were possible and shows spectral similarities with the well-studied SN\,1998bw, having produced a similar Ni mass ($M_{\rm Ni}=0.33\pm0.02 ~\rm{M_{\odot}}$) with slightly lower ejected mass ($M_{\rm ej}=4.1\pm0.7~\rm{M_{\odot}}$) and kinetic energy ($E_{\rm K} = 8.1\pm2.5 \times 10^{51} ~\rm{erg}$). The host-galaxy is bigger in size than typical GRB host galaxies, but the analysis of the region hosting the GRB revealed spectral properties typically observed in GRB hosts and showed that the progenitor of this event was located in a very bright HII region of its face-on host galaxy, at a projected distance of $\sim$ 10 kpc from its galactic centre. The star-formation rate (SFR$_{GRB} \sim$ 0.2 M$_{\odot}$~yr$^{-1}$) and metallicity (12 + log(O/H) $\sim 8.15 \pm 0.10$) of the GRB star-forming region are consistent with those of the host galaxies of previously studied GRB-SN systems.
Systems living in complex non equilibrated environments often exhibit subdiffusion characterized by a sublinear power-law scaling of the mean square displacement. One of the most common models to describe such subdiffusive dynamics is the continuous time random walk (CTRW). Stochastic trajectories of a CTRW can be described mathematically in terms of a subordination of a normal diffusive process by an inverse Levy-stable process. Here, we propose a simpler Langevin formulation of CTRWs without subordination. By introducing a new type of non-Gaussian noise, we are able to express the CTRW dynamics in terms of a single Langevin equation in physical time with additive noise. We derive the full multi-point statistics of this noise and compare it with the noise driving scaled Brownian motion (SBM), an alternative stochastic model describing subdiffusive behaviour. Interestingly, these two noises are identical up to the level of the 2nd order correlation functions, but different in the higher order statistics. We extend our formalism to general waiting time distributions and force fields, and compare our results with those of SBM.
Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.
In the present work, we derive the motion of light in the weak-field limit of energy-momentum-squared gravity (EMSG). To do so, we introduce the post-Newtonian (PN) expansion of this modified theory of gravity. It is shown that in addition to the Newtonian potential, a new EMSG potential affects the trajectory of photons. As a result, in this theory, photons do not behave as predicted by general relativity (GR). To evaluate the EMSG theory by the solar system tests, we study light deflection and Shapiro time delay. Regarding the results obtained in \cite{bertotti2003test,shapiro2004measurement}, we restrict the free parameter of the theory and show that it lies within the range $-4.0\times 10^{-27}\, \text{m}\,\text{s}^2\,\text{kg}^{-1} < f_0' < 8.7\times 10^{-26}\,\text{m}\,\text{s}^2\,\text{kg}^{-1}$. This interval is in agreement with those derived in \cite{nazari2020Constraining,akarsu2018constraint}. This consistency manifests that this theory passes these solar system tests with flying colors. Interestingly, it turns out that the magnitude of the EMSG correction strongly depends on the density of the deflector. So, we investigate the possible effects of EMSG on images of a light source microlensed by a compact dense object such as neutron stars. It is estimated that the EMSG correction to the position of lensed images could be as large as $(1-0.1)$ micro-arcseconds which may be detected by future high-resolution missions. Moreover, the total magnification and the shape of light curves are obtained in the EMSG theory. It is revealed that except for a small deviation, the overall behavior of the EMSG light curves is similar to that in GR.
In this paper we propose augmenting Vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optionally preserving its capabilities on previously learned tasks. At each layer we introduce a set of learnable embedding vectors that provide contextual information useful for specific datasets. We call these "memory tokens". We show that augmenting a model with just a handful of such tokens per layer significantly improves accuracy when compared to conventional head-only fine-tuning, and performs only slightly below the significantly more expensive full fine-tuning. We then propose an attention-masking approach that enables extension to new downstream tasks, with a computation reuse. In this setup in addition to being parameters efficient, models can execute both old and new tasks as a part of single inference at a small incremental cost.
The late-type Be star $\beta$ CMi is remarkably stable compared to other Be stars that have been studied. This has led to a realistic model of the outflowing Be disk by Klement et al. These results showed that the disk is likely truncated at a finite radius from the star, which Klement et al.~suggest is evidence for an unseen binary companion in orbit. Here we report on an analysis of the Ritter Observatory spectroscopic archive of $\beta$ CMi to search for evidence of the elusive companion. We detect periodic Doppler shifts in the wings of the H$\alpha$ line with a period of 170 d and an amplitude of 2.25 km s$^{-1}$, consistent with a low-mass binary companion ($M\approx 0.42 M_\odot$). We then compared the small changes in the violet-to-red peak height changes ($V/R$) with the orbital motion. We find weak evidence that it does follow the orbital motion, as suggested by recent Be binary models by Panoglou et al. Our results, which are similar to those for several other Be stars, suggest that $\beta$ CMi may be a product of binary evolution where Roche lobe overflow has spun up the current Be star, likely leaving a hot subdwarf or white dwarf in orbit around the star. Unfortunately, no direct sign of this companion star is found in the very limited archive of {\it International Ultraviolet Explorer} spectra.
The aim of this note is to give some Burkholder-Davis-Gundy type inequalities which are valid for the Ito stochastic integral with respect to Banach valued Levy noise.
The multiple scattering of high-energy particles in a thick target is fromulated in an impact parameter representation. A formalism similar but not identical to that of Moliere is obtained. We show that calculations of particle beam broadening due to multiple Coulomb scattering alone can be given in closed form. The focus of this study is on whether or not the broadening of the Coulomb angular distribution prevents the retrieval of nuclear-interaction information from mesauring the angular distributions of charged partiles scattered from a thick target. For this purpose, we study multiple scatterings with both the nuclear and Coulomb interactions included and we do not make a small-angle expansion. Condition for retrieving nuclear infomration from high-energy protons propagating through a block of material are obtained.
We present new necessary and sufficient conditions for a function on $\partial\Omega\times S^1$ to be in the range of the attenuated Radon transform of a sufficiently smooth function support in the convex set $\bar\Omega\subset\mathbb{R}^2$. The approach is based on an explicit Hilbert transform associated with traces of the boundary of A-analytic functions in the sense of Bukhgeim.
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing voxel-based representations. While these approaches benefit greatly from advances in computer vision by generalizing 2D convolutions to the 3D setting, they also have several considerable drawbacks. The computational complexity of voxel-encodings grows cubically with the resolution thus limiting such representations to low-resolution 3D reconstruction. In an attempt to solve this problem, point cloud representations have been proposed. Although point clouds are more efficient than voxel representations as they only cover surfaces rather than volumes, they do not encode detailed geometric information about relationships between points. In this paper we propose a method to learn free-form deformations (FFD) for the task of 3D reconstruction from a single image. By learning to deform points sampled from a high-quality mesh, our trained model can be used to produce arbitrarily dense point clouds or meshes with fine-grained geometry. We evaluate our proposed framework on both synthetic and real-world data and achieve state-of-the-art results on point-cloud and volumetric metrics. Additionally, we qualitatively demonstrate its applicability to label transferring for 3D semantic segmentation.
In this work we illustrate the POWHEG BOX, a general computer code framework for implementing NLO calculations in shower Monte Carlo programs according to the POWHEG method. Aim of this work is to provide an illustration of the needed theoretical ingredients, a view of how the code is organized and a description of what a user should provide in order to use it.