ShantanuT01/DACTYL-Pretraining huggingface dataset:
We show that new physics can show up in dileptonic events through its radiative contributions to the dilepton invariant mass, leading to unique "monocline" features in $m_{\ell\ell}$, as well as the angular distribution of the leptons. We focus in particular on the case of dark matter with scalar messengers coupling it to the quarks and leptons. Consistent thermal models require the dark matter to have masses of 100's GeV and have $\gsim 1$ couplings to the Standard Model (SM), implying that radiative corrections to the SM Drell-Yan rate can be sizeable. We consider the case of Majorana, Dirac, and pseudo-Dirac dark matter and show that there are regions of parameter space where the non-existence of a monocline, which starts at roughly twice the dark matter mass, $m_{\ell\ell} \sim 2 m_\chi$, places the strongest constraint on the model. We make predictions for the sensitivities at the high luminosity 14 TeV LHC as well as a future 100 TeV proton-proton collider. We find that our dilepton signal is most sensitive when the mediator and the dark matter are nearly degenerate and conventional MET-based searches are least sensitive.
We study the effect of sputter-deposition conditions, namely substrate temperature and chamber base pressure, upon the interface quality of epitaxial Pt/Co/Pt thin films with perpendicular magnetic anisotropy. Here we define interface quality to be the inverse of the sum in quadrature of roughness and intermixing. We find that samples with the top Co/Pt layers grown at 250 degrees C exhibit a local maximum in roughness-intermixing and that the interface quality is better for lower or higher deposition temperatures, up to 400 degrees C, above which the interface quality degrades. Imaging the expansion of magnetic domains in an in-plane field using wide-field Kerr microscopy, we determine the interfacial Dzyaloshinskii-Moriya interaction (DMI) in films in the deposition temperature range 100 degrees C to 300 degrees C. The net DMI is linked to the difference in top and bottom Co interface qualities; the net DMI increases as the difference between top and bottom Co interface quality increases. Furthermore, for sufficiently low base pressures, the net DMI increases linearly with the deposition temperature, indicating that fine-tuning of the DMI may be achieved via the deposition conditions.
A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.
The most distant quasar yet discovered sets constraints on the formation mechanism of black holes. Its light spectrum has tantalizing features that are expected to be observed before the reionization epoch ended.
In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to accurately estimate jet energy. Different machine learning methods were explored, including linear regression, support vector regression and decision tree. The best result is obtained by linear regression with predictive features and the performance is improved significantly from the baseline method.
One large glitch was detected in PSR B1737$-$30 using data spanning from MJD 57999 to 58406 obtained with the newly built Shanghai Tian Ma Radio Telescope (TMRT). The glitch took place at the time around MJD 58232.4 when the pulsar underwent an increase in the rotation frequency of $\Delta \nu$ about 1.38$\times 10^{-6}$ Hz, corresponding to a fractional step change of $\Delta \nu / \nu$ $\thicksim$ 8.39$\times 10^{-7}$. Post$\textrm{-}$glitch $\nu$ gradually decreased to the pre$\textrm{-}$glitch value. The frequency derivative was observed to undergo a step change of about $-$9$\times 10^{-16}$ s$^{-2}$. Since July 1987, there are 36 glitches already reported in PSR B1737$-$30 including this one. According to our analysis, the glitch size distribution is well described by the power law with index of 1.13. The distribution of the interval between two adjacent glitches (waiting time $\Delta T$) follows a Poissonian probability density function. For PSR B1737$-$30, the interval is prone to be long after a large glitch. But no correlation is found between glitch size and the interval since previous glitch.
Continuum coupling correction to binding energies in the neutron rich oxygen and fluorine isotopes is studied using the Shell Model Embedded in the Continuum. We discuss the importance of different effects, such as the position of one-neutron emission threshold, the effective interaction or the number of valence particles on the magnitude of this correction.
We produce three-dimensional Monte-Carlo radiative transfer models of the edge-on spiral galaxy NGC 891, a fast-rotating galaxy thought to be an analogue to the Milky Way. The models contain realistic spiral arms and a fractal distribution of clumpy dust. We fit our models to Hubble Space Telescope images corresponding to the B and I bands, using shapelet analysis and a genetic algorithm to generate 30 statistically best-fitting models. These models have a strong preference for spirality and clumpiness, with average face-on attenuation decreasing from 0.24(0.16) to 0.03(0.03) mag in the B(I) band between 0.5 and 2 radial scale-lengths. Most of the attenuation comes from small high-density clumps with low (<10%) filling factors. The fraction of dust in clumps is broadly consistent with results from fitting NGC 891's spectral energy distribution. Because of scattering effects and the intermixed nature of the dust and starlight, attenuation is smaller and less wavelength-dependent than the integrated dust column-density. Our clumpy models typically have higher attenuation at low inclinations than previous radiative transfer models using smooth distributions of stars and dust, but similar attenuation at inclinations above 70 degrees. At all inclinations most clumpy models have less attenuation than expected from previous estimates based on minimizing scatter in the Tully-Fisher relation. Mass-to-light ratios are higher and the intrinsic scatter in the Tully-Fisher relation is larger than previously expected for galaxies similar to NGC 891. The attenuation curve changes as a function of inclination, with R_(B,B-I)=A_(B)/E(B-I) increasing by ~0.75 from face-on to near-edge-on orientations.
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.
As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imaging data is often scarce and short on annotations. In this paper, we present an assessment of unsupervised feature learning approaches for images in the biomedical literature, which can be applied to automatic biomedical concept detection. Six unsupervised representation learning methods were built, including traditional bags of visual words, autoencoders, and generative adversarial networks. Each model was trained, and their respective feature space evaluated using images from the ImageCLEF 2017 concept detection task. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches, in contrast with previously popular computer vision methods. Although generative adversarial networks can provide good results, they are harder to succeed in highly varied data sets. The possibility of semi-supervised learning, as well as their use in medical information retrieval problems, are the next steps to be strongly considered.
We look for very low-mass members in open clusters with different ages placed at different distances. The main goal is to produce a reliable census of low-mass stars for each of the clusters and derive the Initial Mass Function. To achieve this, we combine deep optical and infrared photometry from our own observing runs and from different public databases. We also characterise the individual stellar parameters of our targets.
The present approach to SERS does not rely on a hihj electrical conductivity substrate or admixture to enable the SERS process. The novelty of our method is to create an artificial periodic electron distribution on a surface by illumination with ultraviolet radiation whose photon energy is above the photoelectric work function of the material.
The gamma function is a good approximation to the luminosity function of astrophysical objects, and a truncated gamma distribution would permit a more rigorous analysis. This paper examines the generalized gamma distribution (GG) and then introduces the scale and the new double truncation. The magnitude version of the truncated GG distribution with scale is adopted in order to fit the luminosity function (LF) for galaxies or quasars. The new truncated GG LF is applied to the five bands of SDSS galaxies, to the 2dF QSO Redshift Survey in the range of redshifts between 0.3 and 0.5, and to the COSMOS QSOs in the range of redshifts between 3.7 and 4.7. The average absolute magnitude versus redshifts for SDSS galaxies and QSOs of 2dF was modeled adopting a redshift dependence for the lower and upper absolute magnitude of the new truncated GG LF.
In form of a case study for the KdV and the KdV2 equations, we present a novel approach of representing the frequencies of integrable PDEs which allows to extend them analytically to spaces of low regularity and to study their asymptotics. Applications include convexity properties of the Hamiltonians and wellposedness results in spaces of low regularity. In particular, it is proved that on $H^{s}$ the KdV2 equation is $C^{0}$-wellposed if $s\ge 0$ and illposed (in a strong sense) if $s < 0$.
This paper deals with the problem of existence and uniqueness of a solution for a backward stochastic differential equation (BSDE for short) with one reflecting barrier in the case when the terminal value, the generator and the obstacle process are Lp-integrable with p in ]1,2[. To construct the solution we use two methods: penalization and Snell envelope. As an application we broaden the class of functions for which the related obstacle partial differential equation problem has a unique viscosity solution.
We review results on several interesting phenomena in warped compactifications of M theory, as presented at Strings 2000. The behavior of gauge fields in dimensional reduction from $d+1$ to $d$ dimensions in various backgrounds is explained from the point of view of the holographic duals (and a point raised in the question session at the conference is addressed). We summarize the role of additional fields (in particular scalar fields) in 5d warped geometries in making it possible for Poincare-invariant domain wall solutions to exist to a nontrivial order in a controlled approximation scheme without fine-tuning of parameters in the $5d$ action (and comment on the status of the singularities arising in the general relativistic description of these solutions). Finally, we discuss briefly the emergence of excitations of wrapped branes in warped geometries whose effective thickness, as measured along the Poincare slices in the geometry, grows as the energy increases.
The concept of percolation is combined with a self-consistent treatment of the interaction between the dynamics on a lattice and the external drive. Such a treatment can provide a mechanism by which the system evolves to criticality without fine tuning, thus offering a route to self-organized criticality (SOC) which in many cases is more natural than the weak random drive combined with boundary loss/dissipation as used in standard sand-pile formulations. We introduce a new metaphor, the e-pile model, and a formalism for electric conduction in random media to compute critical exponents for such a system. Variations of the model apply to a number of other physical problems, such as electric plasma discharges, dielectric relaxation, and the dynamics of the Earth's magnetotail.
We show the critique of the Pan et al experiment, given in quant-ph/0503108, is unfounded.
In this report, the polarization properties of the photoluminescence emitted by single nitrogen-vacancy (NV) color centers in diamond are investigated using resonant excitation at cryogenic temperature. We first underline that the two excited-state orbital branches are associated with two orthogonal transition dipoles. Using selective excitation of one dipole, we then show that the photoluminescence is partially unpolarized owing to fast relaxation between the two orbitals induced by the thermal bath. This result might be important in the context of the realization of indistinguishable single photons using NV defect in diamond.
Active Galactic Nuclei are powered by accretion of matter onto a supermassive black hole (SMBH) of mass Mbh ~ 10^{5}-10^{9} Msun. The accretion process is indeed the most efficient mechanism for energy release we currently know of, with up to ~30-40% of the gravitational rest mass energy that can be converted into radiation. The vast majority of this energy is released at high energy (UV-X-rays) within the central $100$ gravitational radii from the central SMBH. This energy release occurs through a variety of emission and absorption mechanisms, spanning the entire electromagnetic spectrum. The UV emission being commonly explained by the presence of an optically thick accretion flow, while the X-rays generally require a hotter, optically thinner, plasma, the so-called X-ray corona. If outflows are present, they can also extract a significant part of the gravitational power. With an origin in the deep potential well of the SMBH, the study of the high-energy emission of AGN give a direct insight into the physical properties of the accretion, ejection and radiative mechanisms occurring in the SMBH close environment. While not exhaustive, we discuss in this chapter our present understanding of these mechanisms, the limitations we are currently facing and the expected advances in the future.
We study Le Potier's strange duality conjecture on $\mathbb{P}^2$. We show the conjecture is true for the pair ($W(2,0,2),~M(d,0)$) with $d>0$, where $W(2,0,2)$ is the moduli space of semistable sheaves of rank 2, zero first Chern class and second Chern class 2, and $M(d,0)$ is the moduli space of 1-dimensional semistable sheaves of first Chern class $dH$ and Euler characteristic 0.
In an attempt to find minimal scenarios we study the implications of Dirac and Majorana mass matrices with texture zeros within the type I seesaw mechanism. For the Dirac mass matrices we consider 5 zero textures which we show to be the most minimal form that can successfully account for low energy phenomenology if the Majorana mass matrices are chosen minimal as well. For those, we consider both diagonal and even more minimal non-diagonal forms. The latter can be motivated e.g. by simple U(1) flavour symmetries and have two degenerate eigenvalues. We classify the allowed textures and discuss the ramifications for leptogenesis and lepton flavour violation.
In this paper, we will discuss the long-term variations in the optical and the infrared bands of some AGNs. It is interesting to note that the reported periods of AGNs are of the similar values (about 10 years, see Fan et al. 1998a, ApJ, 507 in press, and references therein). DCF method shows the optical and infrared bands are strongly correlated suggesting that the emission mechanisms in the two bands have a common origin.
We present a minimalistic but effective neural network that computes dense facial correspondences in highly unconstrained RGB images. Our network learns a per-pixel flow and a matchability mask between 2D input photographs of a person and the projection of a textured 3D face model. To train such a network, we generate a massive dataset of synthetic faces with dense labels using renderings of a morphable face model with variations in pose, expressions, lighting, and occlusions. We found that a training refinement using real photographs is required to drastically improve the ability to handle real images. When combined with a facial detection and 3D face fitting step, we show that our approach outperforms the state-of-the-art face alignment methods in terms of accuracy and speed. By directly estimating dense correspondences, we do not rely on the full visibility of sparse facial landmarks and are not limited to the model space of regression-based approaches. We also assess our method on video frames and demonstrate successful per-frame processing under extreme pose variations, occlusions, and lighting conditions. Compared to existing 3D facial tracking techniques, our fitting does not rely on previous frames or frontal facial initialization and is robust to imperfect face detections.
What are topological phases of matter? First, they are phases of matter at zero temperature. Second, they have a non-zero energy gap for the excitations above the ground state. Third, they are disordered liquids that seem have no feature. But those disordered liquids actually can have rich patterns of many-body entanglement representing new kinds of order. This paper will give a simple introduction and a brief survey of topological phases of matter. We will first discuss topological phases that have topological order (ie with long range entanglement). Then we will cover topological phases that have no topological order (ie with only short-range entanglement).
(Abriged) Intranight polarization variability in AGN has not been studied extensively so far. Studying the variability in polarization makes it possibly to distinguish between different emission mechanisms. Thus it can help answering the question if intranight variability in radio-loud and radio-quiet AGN is of the same or of fundamentally different origin. In this paper we investigate intranight polarization variability in AGN. Our sample consists of 28 AGN at low to moderate redshifts (0.048 < z < 1.036), 12 of which are radio-quiet quasars (RQQs) and 16 are radio-loud blazars. The subsample of blazars consists of eight flat-spectrum radio-quasars (FSRQs) and eight BL Lac objects. We find clear differences between the two samples. A majority of the radio-loud AGN show moderate to high degrees of polarization, more than half of them also show variability in polarization. There seems to be a dividing line for polarization intranight variability at P~5 per cent over which all objects vary in polarization. Only two out of 12 radio-quiet quasars show polarized emission, both at levels of P<1 per cent. The lack of polarization intranight variability in radio-quiet AGN points towards accretion instabilities being the cause for intranight flux variability whereas the high duty cycle of polarization variability in radio-loud objects is more likely caused by instabilities in the jet or changes of physical conditions in the jet plasma.
We derive the optimal N to M phase-covariant quantum cloning for equatorial states in dimension d with M=kd+N, k integer. The cloning maps are optimal for both global and single-qudit fidelity. The map is achieved by an ``economical'' cloning machine, which works without ancilla.
(Abridged) By asymptotically matching a post-Newtonian (PN) metric to two tidally perturbed Schwarzschild metrics, we generate approximate initial data (in the form of a 4-metric) for a nonspinning black hole binary in a circular orbit. We carry out this matching through O(v^4) in the binary's orbital velocity v, so the resulting data are conformally curved. Far from the holes, we use the appropriate PN metric that accounts for retardation, which we construct using the highest-order PN expressions available to compute the binary's past history. The data set's uncontrolled remainders are thus O(v^5) throughout the timeslice; we also generate an extension to the data set that has uncontrolled remainders of O(v^6) in the purely PN portion of the timeslice (i.e., not too close to the holes). The resulting data are smooth, since we join all the metrics together by smoothly interpolating between them. We perform this interpolation using transition functions constructed to avoid introducing excessive additional constraint violations. Due to their inclusion of tidal deformations and outgoing radiation, these data should substantially reduce the initial spurious ("junk") radiation observed in current simulations that use conformally flat initial data. Such reductions in the nonphysical components of the initial data will be necessary for simulations to achieve the accuracy required to supply Advanced LIGO and LISA with the templates necessary for parameter estimation.
In this note, we introduce the notion of a singular principal G-bundle, associated to a reductive algebraic group G over the complex numbers by means of a faithful representation $\varrho^\p\colon G\lra \SL(V)$. This concept is meant to provide an analogon to the notion of a torsion free sheaf as a generalization of the notion of a vector bundle. We will construct moduli spaces for these singular principal bundles which compactify the moduli spaces of stable principal bundles.
Hysteresis dispersion under a varying external field Hex is investigated through numerical simulations based on the Landau-Lifshitz-Gilbert (LLG) equation, indicating the energy dissipation can be determined by W({\eta}) = A (f, H0). A linear relation between area of hysteresis and magnitude of external field is discovered. Evolution of hysteresis is also investigated under oscillating external field.