Buffer gas induced collision shift for the 88^{88}Sr 1S0−3P1\bf{^1S_0-^3P_1} clock transition

Precision saturation spectroscopy of the $^{88}{\rm Sr} ^1S_0-^3P_1$ is performed in a vapor cell filled with various rare gas including He, Ne, Ar, and Xe. By continuously calibrating the absolute frequency of the probe laser, buffer gas induced collision shifts of $\sim$kHz are detected with gas pressure of 1-20 mTorr. Helium gave the largest fractional shift of $1.6 \times 10^{-9} {\rm Torr}^{-1}$. Comparing with a simple impact calculation and a Doppler-limited experiment of Holtgrave and Wolf [Phys. Rev. A {\bf 72}, 012711 (2005)], our results show larger broadening and smaller shifting coefficient, indicating effective atomic loss due to velocity changing collisions. The applicability of the result to the $^1S_0-^3P_0$ optical lattice clock transition is also discussed

Comment on the orthogonality of the Macdonald functions of imaginary order

Recently, Yakubovich [Opuscula Math. 26 (2006) 161--172] and Passian et al. [J. Math. Anal. Appl. doi:10.1016/j.jmaa.2009.06.067] have presented alternative proofs of an orthogonality relation obeyed by the Macdonald functions of imaginary order. In this note, we show that the validity of that relation may be also proved in a simpler way by applying a technique occasionally used in mathematical physics to normalize scattering wave functions to the Dirac delta distribution.Comment: LaTeX, 4 page

Emergence of Object Segmentation in Perturbed Generative Models

We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Spotlight presentatio

Extracting textual overlays from social media videos using neural networks

Textual overlays are often used in social media videos as people who watch them without the sound would otherwise miss essential information conveyed in the audio stream. This is why extraction of those overlays can serve as an important meta-data source, e.g. for content classification or retrieval tasks. In this work, we present a robust method for extracting textual overlays from videos that builds up on multiple neural network architectures. The proposed solution relies on several processing steps: keyframe extraction, text detection and text recognition. The main component of our system, i.e. the text recognition module, is inspired by a convolutional recurrent neural network architecture and we improve its performance using synthetically generated dataset of over 600,000 images with text prepared by authors specifically for this task. We also develop a filtering method that reduces the amount of overlapping text phrases using Levenshtein distance and further boosts system's performance. The final accuracy of our solution reaches over 80A% and is au pair with state-of-the-art methods.Comment: International Conference on Computer Vision and Graphics (ICCVG) 201

The Benefits of Music Therapy and the Integration of Music Therapy into a Standard Curriculum for Special Needs Students

Music therapy and its benefits to students with exceptionalities in special education programs have been proven through many social-scientific studies discussed in the literature review. Music therapy is the use of music as a therapeutic intervention for those with mental health, emotional/behavioral, and learning exceptionalities. Definitions of music therapy depend on many variables such as the philosophy, techniques, aims, and objectives of the therapists (Toolan & Coleman, 1994). Students with challenging behaviors, such as aggression and self-injurious behavior (SIB), benefit greatly from interventions in music therapy (Savarimuthu & Bunnell, 2002). The specific goal of this project is to create greater awareness of the benefits of music therapy to students with special needs and to create a unique curriculum that would incorporate music therapy into the special education program. The methodology for this research includes an in-depth analysis of multiple literature on music education, music therapy interventions, and curriculum models in an effort to understand the curricular elements of music therapy that would benefit Special Education programs in Massachusetts. The anticipated outcome is to develop a curriculum specific for students with exceptionalities that includes music therapy. Researching music therapy is important because my career goal is to work with the special needs population and to ascertain that music therapy is a part in these students’ effort to become productive members of society