ShantanuT01/DACTYL-Pretraining huggingface dataset:
We report the observation of the fractional quantum Hall effect in the lowest Landau level of a two-dimensional electron system (2DES), residing in the diluted magnetic semiconductor Cd(1-x)Mn(x)Te. The presence of magnetic impurities results in a giant Zeeman splitting leading to an unusual ordering of composite fermion Landau levels. In experiment, this results in an unconventional opening and closing of fractional gaps around filling factor v = 3/2 as a function of an in-plane magnetic field, i.e. of the Zeeman energy. By including the s-d exchange energy into the composite Landau level spectrum the opening and closing of the gap at filling factor 5/3 can be modeled quantitatively. The widely tunable spin-splitting in a diluted magnetic 2DES provides a novel means to manipulate fractional states.
Using a Gibbs sampling algorithm for joint CMB estimation and component separation, we compute the large-scale CMB and foreground posteriors of the 3-yr WMAP temperature data. Our parametric data model includes the cosmological CMB signal and instrumental noise, a single power law foreground component with free amplitude and spectral index for each pixel, a thermal dust template with a single free overall amplitude, and free monopoles and dipoles at each frequency. This simple model yields a surprisingly good fit to the data over the full frequency range from 23 to 94 GHz. We obtain a new estimate of the CMB sky signal and power spectrum, and a new foreground model, including a measurement of the effective spectral index over the high-latitude sky. A particularly significant result is the detection of a common spurious offset in all frequency bands of ~ -13muK, as well as a dipole in the V-band data. Correcting for these is essential when determining the effective spectral index of the foregrounds. We find that our new foreground model is in good agreement with template-based model presented by the WMAP team, but not with their MEM reconstruction. We believe the latter may be at least partially compromised by the residual offsets and dipoles in the data. Fortunately, the CMB power spectrum is not significantly affected by these issues, as our new spectrum is in excellent agreement with that published by the WMAP team. The corresponding cosmological parameters are also virtually unchanged.
This paper can be seen as an update to part of the author's dissertation. We study the mod $p$ cohomology of the pro-$p$ Iwahori subgroups $I$ of $\operatorname{SL}_{n}(\mathbb Q_{p})$ (and $\operatorname{GL}_{n}(\mathbb{Q}_{p})$) for $n=2$ and $n=3$. Here we use the spectral sequence $E_{1}^{s,t} = H^{s,t}(\mathfrak{g},\mathbb{F}_{p}) \Longrightarrow H^{s+t}(I,\mathbb{F}_{p})$ due to Sorensen, and we do explicit calculations with an ordered basis of $I$, which gives us a basis of $\mathfrak{g} = \mathbb{F}_{p} \otimes_{\mathbb{F}_{p}[\pi]} \operatorname{gr} I$ that we use to calculate $H^{s,t}(\mathfrak{g},\mathbb{F}_{p})$. We note that the multiplicative spectral sequence $E_{1}^{s,t} = H^{s,t}(\mathfrak{g},\mathbb{F}_{p})$ collapses on the first page by noticing that all maps on each page are necessarily trivial, and this allows us to describe the above group cohomology groups and all cup products. Finally we note some connections to cohomology of central division algebras over $\mathbb{Q}_{p}$ and point out some future research directions.
We investigated the influence of the Ta buffer layer on the thermal stability of polycrystalline Ta/ MnN/ CoFeB exchange bias systems, showing high exchange bias of about 1800 Oe at room temperature. The thermal stability of those trilayer systems is limited by nitrogen diffusion that occurs during annealing processes. Most of the nitrogen diffuses into the Ta buffer layer, which is necessary for good crystal growth of MnN and thus a crucial component of the exchange bias system. In order to improve the thermal stability, we prepared exchange bias stacks where we varied the Ta thickness to look for an optimum value that guarantees stable and high exchange over a broad temperature range. Our findings show that thin layers of 2-5 nm Ta indeed support stable exchange bias up to annealing temperatures of more than $550^{\circ}$C. Furthermore, we found that the introduction of a TaN$_{\text{x}}$ layer between MnN and Ta, acting as a barrier, can prevent nitrogen diffusion. However, our results show that those measures, even though being beneficial in terms of thermal stability, often lead to decreased crystallinity and thus lower the exchange bias.
The core-cusp problem remains as a challenging discrepancy between observations and simulations in the standard $\Lambda$CDM model for the formation of galaxies. The problem is that $\Lambda$CDM simulations predict a steep power-law mass density profile at the center of galactic dark matter halos. However, observations of dwarf galaxies in the Local Group reveal a density profile consistent with a nearly flat distribution of dark matter near the center. A number of solutions to this dilemma have been proposed. Here, we summarize investigations into the possibility that the dark matter particles themselves self interact and scatter. Such self-interacting dark matter (SIDM) particles can smooth out the dark-matter profile in high-density regions. We also review the theoretical proposal that self-interacting dark matter may arise as an additional Higgs scalar in the 3-3-1 extension of the standard model. We present new simulations of galaxy formation and evolution for this formulation of self-interacting dark matter. Current constraints on this self-interacting dark matter are then summarized.
We study certain self-interacting walks on the set of integers, that choose to jump to the right or to the left randomly but influenced by the number of times they have previously jumped along the edges in the finite neighbourhood of their current position (in the present paper, typically, we will discuss the case where one considers the neighbouring edges and the next-to-neighbouring edges).   We survey a variety of possible behaviours, including some where the walk is eventually confined to an interval of large length. We also focus on certain "asymmetric" drifts, where we prove that with positive probability, the walks behave deterministically on large scale and move like a constant times the square root of time, or like a constant times the logarithm of time.
In this paper, we establish interior $C^{1,\alpha}$ estimates for solutions of the linearized Monge-Amp$\grave{e}$re equation $$\mathcal{L}_{\phi}u:=\mathrm{tr}[\Phi D^2 u]=f,$$ where the density of the Monge-Amp$\grave{e}$re measure $g:=\mathrm{det}D^2\phi$ satisfies a $\mathrm{VMO}$-type condition and $\Phi:=(\mathrm{det}D^2\phi)(D^2\phi)^{-1}$ is the cofactor matrix of $D^2\phi$.
We find general relations between the on-shell gravitational trace anomaly A_N, and the logarithmic correction Delta S_N to the entropy of "large" BPS extremal black holes in N>1 supergravity theories in D=4 space-time dimensions (recently computed by Sen [arXiv:1108.3842]). For (generalized) self-mirror theories (all having A_N = 0), we obtain the result DeltaS_N = - Delta S_(8-N) = 2 - N/2, whereas for generic theories the trace anomaly tildeA_N of the fully dualized theory turns out to coincide with 2Delta S_N, up to a model-independent shift: tildeA_N = 2Delta S_N - 1. We also speculate on N=1 theories displaying "large" extremal black hole solutions.
Despite the fact that architectural administration information in Korea has been providing high-quality information for a long period of time, the level of utility of the information is not high because it focuses on administrative information. While this is the case, a three-dimensional (3D) map with higher resolution has emerged along with the technological development. However, it cannot function better than visual transmission, as it includes only image information focusing on the exterior of the building. If information related to the exterior of the building can be extracted or identified from a 3D map, it is expected that the utility of the information will be more valuable as the national architectural administration information can then potentially be extended to include such information regarding the building exteriors to the level of BIM(Building Information Modeling). This study aims to present and assess a basic method of extracting information related to the appearance of the exterior of a building for the purpose of 3D mapping using deep learning and digital image processing. After extracting and preprocessing images from the map, information was identified using the Fast R-CNN(Regions with Convolutional Neuron Networks) model. The information was identified using the Faster R-CNN model after extracting and preprocessing images from the map. As a result, it showed approximately 93% and 91% accuracy in terms of detecting the elevation and window parts of the building, respectively, as well as excellent performance in an experiment aimed at extracting the elevation information of the building. Nonetheless, it is expected that improved results will be obtained by supplementing the probability of mixing the false detection rate or noise data caused by the misunderstanding of experimenters in relation to the unclear boundaries of windows.
Recent work suggests that fundamental and Dirichlet strings, and their (p,q) bound states, may be observed as cosmic strings. The evolution of cosmic string networks, and therefore their observational signals, depends on what happens when two strings collide. We study this in string perturbation theory for collisions between all possible pairs of strings; different cases involve sphere, disk, and annulus amplitudes. The result also depends on the details of compactification; the dependence on ratios of scales is only logarithmic, but this is still numerically important. We study a range of models and parameters, and find that in most cases these strings can be distinguished from cosmic strings that arise as gauge theory solitons.
Machine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature that exhibits the effectiveness of ML algorithms is feature extraction that can be instrumental in the applications for digital forensics. Convolutional Neural Networks are further used to identify parts of the file. To this end, we observed that the literature does not include sufficient information about the identification of the algorithms used to compress file fragments. With this research, we attempt to address this gap as compression algorithms are beneficial in generating higher entropy comparatively as they make the data more compact. We used a base dataset, compressed every file with various algorithms, and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.
Micrometer width and nanometer thick wires with different shapes were produced $\approx 3~\upmu$m below the surface of a diamond crystal using a microbeam of He$^+$ ions with 1.8~MeV energy. Initial samples are amorphous and after annealing at $T\approx 1475$~K, the wires crystallized into a graphite-like structures, according to confocal Raman spectroscopy measurements. The electrical resistivity at room temperature is only one order of magnitude larger than the in-plane resistivity of highly oriented pyrolytic bulk graphite and shows a small resistivity ratio($\rho(2{\rm K})/\rho(315{\rm K}) \approx 1.275$). A small negative magnetoresistance below $T=200$~K was measured and can be well understood taking spin-dependent scattering processes into account. The used method provides the means to design and produce millimeter to micrometer sized conducting circuits with arbitrary shape embedded in a diamond matrix.
We propose a non-convex training objective for robust binary classification of data sets in which label noise is present. The design is guided by the intention of solving the resulting problem by adiabatic quantum optimization. Two requirements are imposed by the engineering constraints of existing quantum hardware: training problems are formulated as quadratic unconstrained binary optimization; and model parameters are represented as binary expansions of low bit-depth. In the present work we validate this approach by using a heuristic classical solver as a stand-in for quantum hardware. Testing on several popular data sets and comparing with a number of existing losses we find substantial advantages in robustness as measured by test error under increasing label noise. Robustness is enabled by the non-convexity of our hardware-compatible loss function, which we name q-loss.
We obtain results on the so-called Andre-Pink-Zannier conjecture which is a special case of a the Zilber-Pink conjecture on unlikely intersections in Shimura varieties. Our methods rely on an ergodic theorem of Richard-Zamojski and we are able to obtain stronger conclusions that those of the Andre-Pink-Zannier conjecture in the special case we consider. We work under the assumption of the S-Shafarevich conjecture and S-semisimplicity conjecture which hold for Shimura varieties of abelian type.
We discuss various recent advances on weak forms of the Twin Prime Conjecture.
This paper is concerned with the homogenization of the Dirichlet eigenvalue problem, posed in a bounded domain $\Omega\subset\mathbb R^2$, for a vectorial elliptic operator $-\nabla\cdot A^\epsilon(\cdot)\nabla$ with $\epsilon$-periodic coefficients. We analyse the asymptotics of the eigenvalues $\lambda^{\epsilon,k}$ when $\epsilon\rightarrow 0$, the mode $k$ being fixed. A first-order asymptotic expansion is proved for $\lambda^{\epsilon,k}$ in the case when $\Omega$ is either a smooth uniformly convex domain, or a convex polygonal domain with sides of slopes satisfying a small divisors assumption. Our results extend those of Moskow and Vogelius restricted to scalar operators and convex polygonal domains with sides of rational slopes. We take advantage of the recent progress due to G\'erard-Varet and Masmoudi in the homogenization of boundary layer type systems.
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report. To automate this process, we propose a multitask universal lesion analysis network (MULAN) for joint detection, tagging, and segmentation of lesions in a variety of body parts, which greatly extends existing work of single-task lesion analysis on specific body parts. MULAN is based on an improved Mask R-CNN framework with three head branches and a 3D feature fusion strategy. It achieves the state-of-the-art accuracy in the detection and tagging tasks on the DeepLesion dataset, which contains 32K lesions in the whole body. We also analyze the relationship between the three tasks and show that tag predictions can improve detection accuracy via a score refinement layer.
We provide an $A_4$ based flavor symmetric scenario to accommodate the inverse seesaw mechanism for explaining light neutrino masses and mixing. We find that the lepton mixing, in particular the tri-bimaximal mixing pattern and its deviation through nonzero $\theta_{13}$, is originated solely from the flavor structure of the lepton number violating contribution of the neutral lepton mass matrix. Here we discuss in detail how a nonzero value of $\theta_{13}$ is correlated with the other parameters in the framework and its impact on the Dirac CP phase $\delta$. We also analyze the non-unitarity effects on lepton mixing matrix and its implication in terms of the lepton flavor violating decays, etc..
The XMM-Newton observation of the optically Type 1 AGN AXJ0447-0627 (z=0.214) unambiguously reveals a complex, bright and prominent set of lines in the 4-8 keV rest frame energy range. Although, from a phenomenological point of view, the observed properties can be described by a simple power law model plus 5 narrow Gaussian lines (at rest frame energies of nearly 4.49, 5.55, 6.39, 7.02 and 7.85 keV), we find that a model comprising a power law (Gamma of the order of 2.2), a reflected relativistic continuum, a narrow Fe I Kalpha line from neutral material as well as a broad Fe Kalpha relativistic line from a ionized accretion disk represents a good physical description of the data. The ''double horned'' profile of the relativistic line implies an inclination of the accretion disk of the order of 45 degree, and an origin in a narrow region of the disk, from R_in of the order of 19 GM/c^2 to R_out of the order of 30 GM/c^2. The narrow Fe I Kalpha line from neutral material is probably produced far from the central black hole, most likely in the putative molecular torus. Although some of these properties have been already found in other Type 1 AGN and discussed in the literature, at odd with the objects reported so far we measure high equivalent widths (EWs) of the observed lines: nearly 1.4 keV for the ``double horned'' relativistic line and nearly 0.4 keV for the narrow line.
We investigate the degree of $\alpha$ ($^4$He nucleus) clustering in the ground-state density profiles of $^{44}$Ti and $^{52}$Ti. Two types of density distributions, shell- and cluster-model configurations, are generated fully microscopically with the antisymmetrized quasi-cluster model, which can describe both the j-j coupling shell and $\alpha$-cluster configurations in a single scheme. Despite both the models reproducing measured charge radius data, we found that the $\alpha$ clustering significantly diffuses the density profiles near the nuclear surface compared to the ideal j-j coupling shell model configuration. The effect is most significant for $^{44}$Ti, while it is less for $^{52}$Ti due to the occupation of the $0f_{7/2}$ orbits in the $^{48}$Ca core. This difference can be detected by measuring proton-nucleus elastic scattering or the total reaction cross section on a carbon target at intermediate energies.
With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Fur-thermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct re-alistic scenes. In this paper, we propose a structure-based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing, such as image localization, object retrieval, and scene reconstruction. Our experiments show that this approach achieves favorable results that outperform existing state-of-the-art techniques.
Ballistic annihilation is an interacting system in which particles placed throughout the real line move at preassigned velocities and annihilate upon colliding. The longstanding conjecture that in the symmetric three-velocity setting there exists a phase transition for the survival of middle-velocity particles was recently resolved by Haslegrave, Sidoravicius, and Tournier. We develop a framework based on a mass transport principle to analyze three-velocity ballistic annihilation with asymmetric velocities assigned according to an asymmetric probability measure. We show the existence of a phase transition in all cases by deriving universal bounds. In particular, all middle-speed particles perish almost surely if their initial density is less than 1/5, regardless of the velocities, relative densities, and spacing of initial particles. We additionally prove the continuity of several fundamental statistics as the probability measure is varied.
We present a study of planar physical solutions to the Lorentz-Dirac equation in a constant electromagnetic field. In this case, we reduced the Lorentz-Dirac equation to the one second order differential equation. We obtained the asymptotics of physical solutions to this equation at large proper times. It turns out that, in the crossed constant uniform electromagnetic field with vanishing invariants, a charged particle goes to a universal regime at large times. We found the ratio of momentum components which tends to a constant determined only by the external field. This effect is essentially due to a radiation reaction. There is not such an effect for the Lorentz equation in this field.
We present theoretical study of revival phenomena for a wave packet initially well localized in a one-dimensional potential in the presence of an external periodic modulating field. The classical motion, revival, and super-revival time scale are derived exactly for wave packet excited in the one dimension box potential.
Using the Resonant Valence Bond (RVB) concept and the Schwinger-boson mean field approximation, we investigate a two dimensional anisotropic Heisenberg antiferromagnet. We find that increasing the coupling ratio ($\alpha=J_{y}/J_{x}$), a disordered-ordered phase transition appears beyond a critical value ($\alpha_{c}$). When $\alpha>\alpha_{c}$ there exists a finite $T_{N}$ beyond which an ordered-disordered phase transition occurs, while for $\alpha<\alpha_{c}$ there always exists a gap. Also we find that a decoupling temperature $T_{D}$ exists for 1D and 2D isotropic case, when $T$ approaches $T_{D}$ the RVB order parameter decreases rapidly to zero.
With the rapid growth of video data and the increasing demands of various applications such as intelligent video search and assistance toward visually-impaired people, video captioning task has received a lot of attention recently in computer vision and natural language processing fields. The state-of-the-art video captioning methods focus more on encoding the temporal information, while lack of effective ways to remove irrelevant temporal information and also neglecting the spatial details. However, the current RNN encoding module in single time order can be influenced by the irrelevant temporal information, especially the irrelevant temporal information is at the beginning of the encoding. In addition, neglecting spatial information will lead to the relationship confusion of the words and detailed loss. Therefore, in this paper, we propose a novel recurrent video encoding method and a novel visual spatial feature for the video captioning task. The recurrent encoding module encodes the video twice with the predicted key frame to avoid the irrelevant temporal information often occurring at the beginning and the end of a video. The novel spatial features represent the spatial information in different regions of a video and enrich the details of a caption. Experiments on two benchmark datasets show superior performance of the proposed method.
The key role of the orbital degree of freedom to understand the magnetic properties of layered ruthenates is here discussed. In the G-type antiferromagnetic phase of Ca$_2$RuO$_4$, recent X-ray experiments reported the presence of 0.5 hole per site in the $d_{xy}$ orbital, while the $d_{\rm yz}$ and $d_{zx}$ orbitals contain 1.5 holes. This unexpected $t_{2g}$ hole distribution is explained by a novel state with orbital ordering (OO), stabilized by a combination of Coulomb interactions and lattice distortions. In addition, the rich phase diagram presented here suggests the possibility of large magnetoresistance effects, and predicts a new ferromagnetic OO phase in ruthenates.