Photoacoustic detection of stimulated emission pumping in p-difluorobenzene

Photoacoustic detection has been used to monitor a stimulated emission pumping process in p‐difluorobenzene. Using the Ã^(1)B_(2u)5^1 state as an intermediate, several vibrational levels of the ground electronic state were populated. The photoacoustic method is an attractive alternative to other detection techniques because of its sensitivity, simplicity, and its ability to differentiate between stimulated emission pumping and excited state absorption. An example of excited state absorption in aniline is given

Atomic structure of Mn wires on Si(001) resolved by scanning tunneling microscopy

At submonolayer coverage, Mn forms atomic wires on the Si(001) surface oriented perpendicular to the underlying Si dimer rows. While many other elements form symmetric dimer wires at room temperature, we show that Mn wires have an asymmetric appearance and pin the Si dimers nearby. We find that an atomic configuration with a Mn trimer unit cell can explain these observations due to the interplay between the Si dimer buckling phase near the wire and the orientation of the Mn trimer. We study the resulting four wire configurations in detail using high-resolution scanning tunneling microscopy (STM) imaging and compare our findings with STM images simulated by density functional theory.Comment: 4 pages, 4 figure

In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations

Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data

Total variation denoising in l1l^1 anisotropy

We aim at constructing solutions to the minimizing problem for the variant of Rudin-Osher-Fatemi denoising model with rectilinear anisotropy and to the gradient flow of its underlying anisotropic total variation functional. We consider a naturally defined class of functions piecewise constant on rectangles (PCR). This class forms a strictly dense subset of the space of functions of bounded variation with an anisotropic norm. The main result shows that if the given noisy image is a PCR function, then solutions to both considered problems also have this property. For PCR data the problem of finding the solution is reduced to a finite algorithm. We discuss some implications of this result, for instance we use it to prove that continuity is preserved by both considered problems.Comment: 34 pages, 9 figure

The Cauchy-Schlomilch transformation

The Cauchy-Schl\"omilch transformation states that for a function $f$ and $a, \, b > 0$, the integral of $f(x^{2})$ and $af((ax-bx^{-1})^{2}$ over the interval $[0, \infty)$ are the same. This elementary result is used to evaluate many non-elementary definite integrals, most of which cannot be obtained by symbolic packages. Applications to probability distributions is also given

Field induced density wave in the heavy fermion compound CeRhIn5

Metals containing Ce often show strong electron correlations due to the proximity of the 4f state to the Fermi energy, leading to strong coupling with the conduction electrons. This coupling typically induces a variety of competing ground states, including heavy-fermion metals, magnetism and unconventional superconductivity. The d-wave superconductivity in CeTMIn5 (TM=Co, Rh, Ir) has attracted significant interest due to its qualitative similarity to the cuprate high-Tc superconductors. Here, we show evidence for a field induced phase-transition to a state akin to a density-wave (DW) in the heavy fermion CeRhIn5, existing in proximity to its unconventional superconductivity. The DW state is signaled by a hysteretic anomaly in the in-plane resistivity accompanied by the appearance of non-linear electrical transport at high magnetic fields (>27T), which are the distinctive characteristics of density-wave states. The unusually large hysteresis enables us to directly investigate the Fermi surface of a supercooled electronic system and to clearly associate a Fermi surface reconstruction with the transition. Key to our observation is the fabrication of single crystal microstructures, which are found to be highly sensitive to "subtle" phase transitions involving only small portions of the Fermi surface. Such subtle order might be a common feature among correlated electron systems, and its clear observation adds a new perspective on the similarly subtle CDW state in the cuprates.Comment: Accepted in Nature Communication

XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera

We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates in generic scenes and is robust to difficult occlusions both by other people and objects. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that neither extracted global body positions nor joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes