Decoherence Patterns of Topological Qubits from Majorana Modes

We investigate the decoherence patterns of topological qubits in contact with the environment by a novel way of deriving the open system dynamics other than the Feynman-Vernon. Each topological qubit is made of two Majorana modes of a 1D Kitaev's chain. These two Majorana modes interact with the environment in an incoherent way which yields peculiar decoherence patterns of the topological qubit. More specifically, we consider the open system dynamics of the topological qubits which are weakly coupled to the fermionic/bosonic Ohmic-like environments. We find atypical patterns of quantum decoherence. In contrast to the cases of non-topological qubits for which they always decohere completely in all Ohmic-like environments, the topological qubits decohere completely in the Ohmic and sub-Ohmic environments but not in the super-Ohmic ones. Moreover, we find that the fermion parities of the topological qubits though cannot prevent the qubit states from decoherence in the sub-Ohmic environments, can prevent from thermalization turning into Gibbs state. We also study the cases in which each Majorana mode can couple to different Ohmic-like environments and the time dependence of concurrence for two topological qubits.Comment: 20 pages, 10 figures; v2 ref updated to match NJP versio

Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201

The effects of rear-wheel camber on the kinematics of upper extremity during wheelchair propulsion

BACKGROUND: The rear-wheel camber, defined as the inclination of the rear wheels, is usually used in wheelchair sports, but it is becoming increasingly employed in daily propulsion. Although the rear-wheel camber can increase stability, it alters physiological performance during propulsion. The purpose of the study is to investigate the effects of rear-wheel cambers on temporal-spatial parameters, joint angles, and propulsion patterns. METHODS: Twelve inexperienced subjects (22.3±1.6 yr) participated in the study. None had musculoskeletal disorders in their upper extremities. An eight-camera motion capture system was used to collect the three-dimensional trajectory data of markers attached to the wheelchair-user system during propulsion. All participants propelled the same wheelchair, which had an instrumented wheel with cambers of 0°, 9°, and 15°, respectively, at an average velocity of 1 m/s. RESULTS: The results show that the rear-wheel camber significantly affects the average acceleration, maximum end angle, trunk movement, elbow joint movement, wrist joint movement, and propulsion pattern. The effects are especially significant between 0° and 15°. For a 15° camber, the average acceleration and joint peak angles significantly increased (p < 0.01). A single loop pattern (SLOP) was adopted by most of the subjects. CONCLUSIONS: The rear-wheel camber affects propulsion patterns and joint range of motion. When choosing a wheelchair with camber adjustment, the increase of joint movements and the base of support should be taken into consideration

Structural and cognitive deficits in chronic carbon monoxide intoxication: a voxel-based morphometry study

BACKGROUND: Patients with carbon monoxide (CO) intoxication may develop ongoing neurological and psychiatric symptoms that ebb and flow, a condition often called delayed encephalopathy (DE). The association between morphologic changes in the brain and neuropsychological deficits in DE is poorly understood. METHODS: Magnetic resonance imaging and neuropsychological tests were conducted on 11 CO patients with DE, 11 patients without DE, and 15 age-, sex-, and education-matched healthy subjects. Differences in gray matter volume (GMV) between the subgroups were assessed and further correlated with diminished cognitive functioning. RESULTS: As a group, the patients had lower regional GMV compared to controls in the following regions: basal ganglia, left claustrum, right amygdala, left hippocampus, parietal lobes, and left frontal lobe. The reduced GMV in the bilateral basal ganglia, left post-central gyrus, and left hippocampus correlated with decreased perceptual organization and processing speed function. Those CO patients characterized by DE patients had a lower GMV in the left anterior cingulate and right amygdala, as well as lower levels of cognitive function, than the non-DE patients. CONCLUSIONS: Patients with CO intoxication in the chronic stage showed a worse cognitive and morphologic outcome, especially those with DE. This study provides additional evidence of gray matter structural abnormalities in the pathophysiology of DE in chronic CO intoxicated patients

Engineering A Workload-balanced Push-Relabel Algorithm for Massive Graphs on GPUs

The push-relabel algorithm is an efficient algorithm that solves the maximum flow/ minimum cut problems of its affinity to parallelization. As the size of graphs grows exponentially, researchers have used Graphics Processing Units (GPUs) to accelerate the computation of the push-relabel algorithm further. However, prior works need to handle the significant memory consumption to represent a massive residual graph. In addition, the nature of their algorithms has inherently imbalanced workload distribution on GPUs. This paper first identifies the two challenges with the memory and computational models. Based on the analysis of these models, we propose a workload-balanced push-relabel algorithm (WBPR) with two enhanced compressed sparse representations (CSR) and a vertex-centric approach. The enhanced CSR significantly reduces memory consumption, while the vertex-centric approach alleviates the workload imbalance and improves the utilization of the GPU. In the experiment, our approach reduces the memory consumption from O(V^2) to O(V + E). Moreover, we can achieve up to 7.31x and 2.29x runtime speedup compared to the state-of-the-art on real-world graphs in maximum flow and bipartite matching tasks, respectively. Our code will be open-sourced for further research on accelerating the push-relabel algorithm

The Application of Borehole Hydrogeological Investigation for Assessing Landslide Susceptibility

On the basis of 49 borehole studies at the mid- and upper-stream of the Dajia and Jhuoshuei river basins, landslide occurrence was found to not only be related to subsurface geological composition and hydrogeological characteristics, but also to groundwater level dynamics, which have seldom been addressed in previous works. It is suggested that the interplay between hydrogeologic and landslide factors be given further consideration in future investigations. This would be a crucial step towards effective disaster prevention for mountainous regions in Taiwan.本研究主要係嘗試透過孔內水文地質調查結果，評估大甲溪與濁水溪流域地表下岩層的地質材料特性、水力特性、地下水與集水區山崩潛勢之關聯性。過去研究較少針對崩塌地進行現地水文地質調查與試驗，本研究根據49處試驗場址成果顯示，水文地質特性與山崩的潛勢、滑動深度，以及地下水位有一定程度之關聯性。本研究建議進行調查區域的水文地質特性，以及各促崩因子所造成的山崩的易損性，係各類型山崩災害分析與潛勢評估之重要的研究各題，亦可提供相關台灣災害防治之參考

ERP Post-Implementation Learning, ERP Usage And Individual Performance Impact

In recent years, an increasing number of companies that have implemented ERP systems have expressed disappointment over a failure to reach anticipated goals. A major reason for this failure is the inefficient use of the ERP system by employees. Therefore, the critical issue is how users can most effectively take advantage of an ERP system. Post-implementation learning plays an important role in facilitating ERP usage and thus promotes individual performance. Particularly, the integrated and sophistic natures of ERP systems force users to learn continuously after ERP implementation. This study employed a survey method to examine the perceptions of a dataset of 659 ERP users. We found that ERP usage facilitates individual performance, including individual productivity, customer satisfaction and management control, and post-implementation learning contributes to all three types of ERP usage, including decision support, work integration and customer service. Our findings can provide academics and practitioners with knowledge of how to improve ERP usage and ensure individual performance impacts

An All Deep System for Badminton Game Analysis

The CoachAI Badminton 2023 Track1 initiative aim to automatically detect events within badminton match videos. Detecting small objects, especially the shuttlecock, is of quite importance and demands high precision within the challenge. Such detection is crucial for tasks like hit count, hitting time, and hitting location. However, even after revising the well-regarded shuttlecock detecting model, TrackNet, our object detection models still fall short of the desired accuracy. To address this issue, we've implemented various deep learning methods to tackle the problems arising from noisy detectied data, leveraging diverse data types to improve precision. In this report, we detail the detection model modifications we've made and our approach to the 11 tasks. Notably, our system garnered a score of 0.78 out of 1.0 in the challenge.Comment: Golden Award for IJCAI CoachAI Challenge 2023: Team NTNUEE AIoTLa