COMPLETE STREETS CODE FOR ROADWAY FACILITY IMPROVEMENT IN COLLEGE PARK CAMPUS, THE UNIVERSITY OF MARYLAND - A CONTEXT-SENSITIVE APPROACH

This design-research thesis suggests that the improvement of campus roadway facilities using Complete Streets principle and practices can enhance the overall pedestrian experience. Campus Drive, one of the main arterials in the College Park campus of the University of Maryland, will be used as a case study. Heavily used by a variety of users, often conflicting with one another, University of Maryland Campus Drive would benefit from a major planning and design amelioration to meet the increasing demands of serving as a university main street. The goal of this thesis project is to prioritize the benefits for pedestrians in the right-of-way and improve the pedestrian experience on campus. This goal also responds to the recent Facilities Master Plan vision of building a more walkable campus. The goal of this design-research thesis will be achieved focusing on four aspects. First, design and plans will discourage cut-through driving to reduce vehicular traffic volume on Campus Drive in order to reduce pedestrian and vehicle conflicts. Second, plans and designs will clarify cyclists' use of the right-of-way and create a built environment that will reduce and hopefully eliminate current riding on pedestrian sidewalk. Third, the case study seeks to improve public transit facilities on Campus Drive to better serve users of which the majorities travel as pedestrians on campus. Finally, the case study seeks to improve pedestrian facilities to enhance pedestrian connectivity, accessibility, and overall experience on University of Maryland Campus Drive. Campus Drive roadway facilities will be inventoried. Roadway segments typologies will be identified and classified. A toolkit, road improvement design interventions, will be developed based on this classification. An improved master plan will be developed utilizing the toolkit while considering the specific site context around specific segments and the overall functions carried by Campus Drive as a campus main street. Detailed plans and designs will be developed for focus areas that demonstrate the goals and objectives. The outcome of the design-research thesis project is expected to serve as an example of implementing Complete Streets principles and practices in urban commuter university campuses, where transportation needs and institutional functions interact with each other

Hybrid algorithms to solve linear systems of equations with limited qubit resources

The solution of linear systems of equations is a very frequent operation and thus important in many fields. The complexity using classical methods increases linearly with the size of equations. The HHL algorithm proposed by Harrow et al. achieves exponential acceleration compared with the best classical algorithm. However, it has a relatively high demand for qubit resources and the solution $\left| x \right\rangle$ is in a normalized form. Assuming that the eigenvalues of the coefficient matrix of the linear systems of equations can be represented perfectly by finite binary number strings, three hybrid iterative phase estimation algorithms (HIPEA) are designed based on the iterative phase estimation algorithm in this paper. The complexity is transferred to the measurement operation in an iterative way, and thus the demand of qubit resources is reduced in our hybrid algorithms. Moreover, the solution is stored in a classical register instead of a quantum register, so the exact unnormalized solution can be obtained. The required qubit resources in the three HIPEA algorithms are different. HIPEA-1 only needs one single ancillary qubit. The number of ancillary qubits in HIPEA-2 is equal to the number of nondegenerate eigenvalues of the coefficient matrix of linear systems of equations. HIPEA-3 is designed with a flexible number of ancillary qubits. The HIPEA algorithms proposed in this paper broadens the application range of quantum computation in solving linear systems of equations by avoiding the problem that quantum programs may not be used to solve linear systems of equations due to the lack of qubit resources.Comment: 22 pages, 6 figures, 6 tables, 48 equation

Hedgehog Spin-vortex Crystal Antiferromagnetic Quantum Criticality in CaK(Fe1-xNix)4As4 Revealed by NMR

Two ordering states, antiferromagnetism and nematicity, have been observed in most iron-based superconductors (SCs). In contrast to those SCs, the newly discovered SC CaK(Fe$_{1-x}$Ni$_x$)$_4$As$_4$ exhibits an antiferromagnetic (AFM) state, called hedgehog spin-vortex crystal structure, without nematic order, providing the opportunity for the investigation into the relationship between spin fluctuations and SC without any effects of nematic fluctuations. Our $^{75}$As nuclear magnetic resonance studies on CaK(Fe$_{1-x}$Ni$_x$)$_4$As$_4$ (0$\le x\le$ 0.049) revealed that CaKFe$_4$As$_4$ is located close to a hidden hedgehog SVC AFM quantum-critical point (QCP). The magnetic QCP without nematicity in CaK(Fe$_{1-x}$Ni$_x$)$_4$As$_4$ highlights the close connection of spin fluctuations and superconductivity in iron-based SCs. The advantage of stoichiometric composition also makes CaKFe$_4$As$_4$ an ideal platform for further detailed investigation of the relationship between magnetic QCP and superconductivity in iron-based SCs without disorder effects.Comment: 6 pages, 5 figures, accepted for publication in Phys. Rev. Let

CURRENT DENSITY EFFECTS ON PLASMA EMISSION DURING PLASMA ELECTROLYTIC OXIDATION (PEO) ON AZ91D-MAGNESIUM ALLOY

The effect of bipolar pulse mode current ratio on plasma behavior was investigated in PEO on AZ91D Mg-Alloy. Two cases of current ratio including 1.20 and 0.88 were applied to the sample. Plasma emission behavior was studied using plasma images and plasma emission measured by photodetector and Intensified Charged-Couple Device (ICCD) camera. The current ratio of greater than 1 shows the continuous increase and then stabilization in emission intensity with a gradual increase in voltage throughout the PEO process. In contrast, the current ratio of less than 1, a sudden drop in plasma emission intensity with voltage was found after 786s. Therefore, PEO process can be divided into two regimes, arc regime and soft regime, before and after voltage drop respectively. Results of measured spectra show that a soft regime does not have atomic or ionic excitation during PEO process. It is demonstrated that the growth of porous layer during PEO can be controlled, which is benefit for the protective oxide coating of sample

Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition

Automatic recognition of disordered and elderly speech remains a highly challenging task to date due to the difficulty in collecting such data in large quantities. This paper explores a series of approaches to integrate domain adapted SSL pre-trained models into TDNN and Conformer ASR systems for dysarthric and elderly speech recognition: a) input feature fusion between standard acoustic frontends and domain adapted wav2vec2.0 speech representations; b) frame-level joint decoding of TDNN systems separately trained using standard acoustic features alone and with additional wav2vec2.0 features; and c) multi-pass decoding involving the TDNN/Conformer system outputs to be rescored using domain adapted wav2vec2.0 models. In addition, domain adapted wav2vec2.0 representations are utilized in acoustic-to-articulatory (A2A) inversion to construct multi-modal dysarthric and elderly speech recognition systems. Experiments conducted on the UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest TDNN and Conformer ASR systems integrated domain adapted wav2vec2.0 models consistently outperform the standalone wav2vec2.0 models by statistically significant WER reductions of 8.22% and 3.43% absolute (26.71% and 15.88% relative) on the two tasks respectively. The lowest published WERs of 22.56% (52.53% on very low intelligibility, 39.09% on unseen words) and 18.17% are obtained on the UASpeech test set of 16 dysarthric speakers, and the DementiaBank Pitt test set respectively.Comment: accepted by ICASSP 202

Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition

Accurate recognition of cocktail party speech containing overlapping speakers, noise and reverberation remains a highly challenging task to date. Motivated by the invariance of visual modality to acoustic signal corruption, an audio-visual multi-channel speech separation, dereverberation and recognition approach featuring a full incorporation of visual information into all system components is proposed in this paper. The efficacy of the video input is consistently demonstrated in mask-based MVDR speech separation, DNN-WPE or spectral mapping (SpecM) based speech dereverberation front-end and Conformer ASR back-end. Audio-visual integrated front-end architectures performing speech separation and dereverberation in a pipelined or joint fashion via mask-based WPD are investigated. The error cost mismatch between the speech enhancement front-end and ASR back-end components is minimized by end-to-end jointly fine-tuning using either the ASR cost function alone, or its interpolation with the speech enhancement loss. Experiments were conducted on the mixture overlapped and reverberant speech data constructed using simulation or replay of the Oxford LRS2 dataset. The proposed audio-visual multi-channel speech separation, dereverberation and recognition systems consistently outperformed the comparable audio-only baseline by 9.1% and 6.2% absolute (41.7% and 36.0% relative) word error rate (WER) reductions. Consistent speech enhancement improvements were also obtained on PESQ, STOI and SRMR scores.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin

Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Speaker adaptation techniques provide a powerful solution to customise automatic speech recognition (ASR) systems for individual users. Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors. To address these issues, a set of compact and data efficient speaker-dependent (SD) parameter representations are used to facilitate both speaker adaptive training and test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR systems. The sensitivity to supervision quality is reduced using a confidence score-based selection of the less erroneous subset of speaker-level adaptation data. Two lightweight confidence score estimation modules are proposed to produce more reliable confidence scores. The data sparsity issue, which is exacerbated by data selection, is addressed by modelling the SD parameter uncertainty using Bayesian learning. Experiments on the benchmark 300-hour Switchboard and the 233-hour AMI datasets suggest that the proposed confidence score-based adaptation schemes consistently outperformed the baseline speaker-independent (SI) Conformer model and conventional non-Bayesian, point estimate-based adaptation using no speaker data selection. Similar consistent performance improvements were retained after external Transformer and LSTM language model rescoring. In particular, on the 300-hour Switchboard corpus, statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute (9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also obtained on the AMI development and evaluation sets.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin