Geometric Cover with Outliers Removal

We study the problem of partial geometric cover, which asks to find the minimum number of geometric objects (unit squares and unit disks in this work) that cover at least (n-t) of n given planar points, where 0 ? t ? n/2. When t = 0, the problem is the classical geometric cover problem, for which many existing works adopt a general framework called the shifting strategy. The shifting strategy is a divide and conquer paradigm which partitions the plane into equal-width strips, applies a local algorithm on each strip and then merges the local solutions with only a small loss on the overall approximation ratio. A challenge to extend the shifting strategy to the case of outliers is to determine the number of outliers in each strip. We develop a shifting strategy incorporating the outlier distribution, which runs in O(tn log n) time. We also develop local algorithms on strips for the outliers case, improving the running time over previous algorithms, and consequently obtain approximation algorithms to the partial geometric cover

Electromagnetically induced transparency of interacting Rydberg atoms with two-body dephasing

We study electromagnetically induced transparency in a three-level ladder type configuration in ultracold atomic gases, where the upper level is an electronically highly excited Rydberg state. An effective distance dependent two-body dephasing can be induced in a regime where dipole-dipoles interaction couple nearly degenerate Rydberg pair states. We show that strong two-body dephasing can enhance the excitation blockade of neighboring Rydberg atoms. Due to the dissipative blockade, transmission of the probe light is reduced drastically by the two-body dephasing in the transparent window. The reduction of transmission is accompanied by a strong photon-photon anti-bunching. Around the Autler-Townes doublets, the photon bunching is amplified by the two-body dephasing, while transmission is largely unaffected. Besides relevant to the ongoing Rydberg atom studies, our study moreover provides a setting to explore and understand two-body dephasing dynamics in many-body systems

Relaxed Attention for Transformer Models

The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly learned internal language model in the autoregressive transformer decoder complicating the integration of external language models. In this paper, we explore relaxed attention, a simple and easy-to-implement smoothing of the attention weights, yielding a two-fold improvement to the general transformer architecture: First, relaxed attention provides regularization when applied to the self-attention layers in the encoder. Second, we show that it naturally supports the integration of an external language model as it suppresses the implicitly learned internal language model by relaxing the cross attention in the decoder. We demonstrate the benefit of relaxed attention across several tasks with clear improvement in combination with recent benchmark approaches. Specifically, we exceed the former state-of-the-art performance of 26.90% word error rate on the largest public lip-reading LRS3 benchmark with a word error rate of 26.31%, as well as we achieve a top-performing BLEU score of 37.67 on the IWSLT14 (DE$\rightarrow$EN) machine translation task without external language models and virtually no additional model parameters. Code and models will be made publicly available

A new flywheel energy storage device for converting potential energy into kinetic energy

Generally, vehicles with axle structure do not use the gravitational potential energy of people and objects reasonably during transportation, but use the extra energy to make the vehicles operate. The purpose of this project is to study a flywheel energy storage device that converts potential energy into kinetic energy, so as to store gravitational potential energy and convert it into kinetic energy for output on demand, which is widely used in industry, civil transportation, medical rescue and other fields

Comparison of the safety and efficacy of propofol and dexmedetomidine as sedatives when used as a modified topical formulation

Purpose: To evaluate the safety and efficacy of propofol and dexmedetomidine as sedatives in patients with anticipated difficult airways, used as a modified topical preparation.Methods: A total of 432 patients were enrolled in this study. They were classified as ASA I and ASA II. The patients were equally divided into group A (propofol group) and group B (dexmedetomidine group). A modified Awake Fiberoptic Intubation (AFOI) was carried out for these patients, followed by airway assessment and evaluation of clinical outcome based on intubation scores, adverse events, and postoperative data.Results: Patients in both groups had successful intubation at the first attempt. There was no significant difference in baseline characteristics between the two groups. The SARI scores which characterized the overall score for tracheal intubation were 4.6 and 4.2 for groups A and B, respectively. With respect to rescue infusion and consciousness, 11 patients (5.09 %) in group A required rescue, as against 5 patients (2.31 %) in group B. Seven (7) patients (3.24 %) in group A (propofol group) had severe airway obstruction, while only 4 patients (1.85) in group B had the same adverse reaction. Patients in group B had more satisfactory and favourable outcomes than those in group A who were treated with modified AFOI.Conclusion: The use of dexmedetomidine based on modified topical anaesthesia is safe and comfortable in terms of patient convenience and difficult airway management. Thus, dexmedetomidine is a safe, feasible and effective method for managing difficult airway when applied using the modified AFOI

Eliminating Gradient Conflict in Reference-based Line-Art Colorization

Reference-based line-art colorization is a challenging task in computer vision. The color, texture, and shading are rendered based on an abstract sketch, which heavily relies on the precise long-range dependency modeling between the sketch and reference. Popular techniques to bridge the cross-modal information and model the long-range dependency employ the attention mechanism. However, in the context of reference-based line-art colorization, several techniques would intensify the existing training difficulty of attention, for instance, self-supervised training protocol and GAN-based losses. To understand the instability in training, we detect the gradient flow of attention and observe gradient conflict among attention branches. This phenomenon motivates us to alleviate the gradient issue by preserving the dominant gradient branch while removing the conflict ones. We propose a novel attention mechanism using this training strategy, Stop-Gradient Attention (SGA), outperforming the attention baseline by a large margin with better training stability. Compared with state-of-the-art modules in line-art colorization, our approach demonstrates significant improvements in Fr\'echet Inception Distance (FID, up to 27.21%) and structural similarity index measure (SSIM, up to 25.67%) on several benchmarks. The code of SGA is available at https://github.com/kunkun0w0/SGA .Comment: Accepted by ECCV202