Wave packet dynamics in monolayer MoS2_2 with and without a magnetic field

We study the dynamics of electrons in monolayer Molybdenum Disulfide (MoS$_2$), in the absence as well as presence of a transverse magnetic field. Considering the initial electronic wave function to be a Gaussian wave packet, we calculate the time dependent expectation value of position and velocity operators. In the absence of the magnetic field, the time dependent average values of position and velocity show damped oscillations dependent on the width of the wave packet. In the presence of a transverse magnetic field, the wave packet amplitude shows oscillatory behaviour over short timescales associated with classical cyclotron orbit, followed by the phenomena of spontaneous collapse and revival over larger timescales. We relate the timescales of these effects and our results can be useful for the interpretation of experiments with trapped ions.Comment: 8pages, 3 figures. typos corrected and improved presentatio

Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion

Attention-based models such as transformers have shown outstanding performance on dense prediction tasks, such as semantic segmentation, owing to their capability of capturing long-range dependency in an image. However, the benefit of transformers for monocular depth prediction has seldom been explored so far. This paper benchmarks various transformer-based models for the depth estimation task on an indoor NYUV2 dataset and an outdoor KITTI dataset. We propose a novel attention-based architecture, Depthformer for monocular depth estimation that uses multi-head self-attention to produce the multiscale feature maps, which are effectively combined by our proposed decoder network. We also propose a Transbins module that divides the depth range into bins whose center value is estimated adaptively per image. The final depth estimated is a linear combination of bin centers for each pixel. Transbins module takes advantage of the global receptive field using the transformer module in the encoding stage. Experimental results on NYUV2 and KITTI depth estimation benchmark demonstrate that our proposed method improves the state-of-the-art by 3.3%, and 3.3% respectively in terms of Root Mean Squared Error (RMSE). Code is available at https://github.com/ashutosh1807/Depthformer.git

Application of Nanotechnology in the Remediation of Contaminated Groundwater: A Short Review

Nanotechnology is an emerging science that has shown promise in humanizing various life facets ranging from medicine to industrial materials. One such application of nanotechnology is for the remediation of contaminated groundwater. Groundwater pollution is becoming a major problem not only for the developing countries like India but also for most of the developed countries of the world. In this respect the application of nanotechnology may prove a boon to the mankind by providing an advance way for groundwater treatment. The status of groundwater quality, basic idea of nanotechnology for remediation and its practical applicability, ongoing projects and future scope in India has been reviewed through this article

Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts

Emotion Recognition in Conversations (ERC) is an important and active research area. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some stimuli evokes a change. There is a continuous ebb and flow of emotions in a conversation. Inspired by this observation, we propose a multimodal ERC model and augment it with an emotion-shift component that improves performance. The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications). We experiment with different variants of the model, and results show that the inclusion of emotion shift signal helps the model to outperform existing models for ERC on MOSEI and IEMOCAP datasets.Comment: 13 pages, Accepted at Workshop on Performance and Interpretability Evaluations of Multimodal, Multipurpose, Massive-Scale Models, COLING 202