A detection-based pattern recognition framework and its applications

The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation. Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages. A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage. This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed in more applications in the future.Ph.D.Committee Chair: Lee, Chin-Hui; Committee Member: Clements, Mark; Committee Member: Ghovanloo, Maysam; Committee Member: Romberg, Justin; Committee Member: Yuan, Min

A super-Eddington wind scenario for the progenitors of type Ia supernovae: binary population synthesis calculations

The super-Eddington wind scenario has been proposed as an alternative way for producing type Ia supernovae (SNe Ia). The super-Eddington wind can naturally prevent the carbon--oxygen white dwarfs (CO WDs) with high mass-accretion rates from becoming red-giant-like stars. Furthermore, it works in low-metallicity environments, which may explain SNe Ia observed at high redshifts. In this article, we systematically investigated the most prominent single-degenerate WD+MS channel based on the super-Eddington wind scenario. We combined the Eggleton stellar evolution code with a rapid binary population synthesis (BPS) approach to predict SN Ia birthrates for the WD+MS channel by adopting the super-Eddington wind scenario and detailed mass-accumulation efficiencies of H-shell flashes on the WDs. Our BPS calculations found that the estimated SN Ia birthrates for the WD+MS channel are ~0.009-0.315*10^{-3}{yr}^{-1} if we adopt the Eddington accretion rate as the critical accretion rate, which are much lower than that of the observations (<10% of the observed SN Ia birthrates). This indicates that the WD+MS channel only contributes a small proportion of all SNe Ia. The birthrates in this simulation are lower than previous studies, the main reason of which is that new mass-accumulation efficiencies of H-shell flashes are adopted. We also found that the critical mass-accretion rate has a significant influence on the birthrates of SNe Ia. Meanwhile, the results of our BPS calculations are sensitive to the values of the common-envelope ejection efficiency.Comment: 14 pages, 9 figures, 1 table, accepted for publication in Astronomy and Astrophysic

Prediction of drilling fluid lost-circulation zone based on deep learning

Lost circulation has become a crucial technical problem that restricts the quality and efficiency improvement of the drilling operation in deep oil and gas wells. The lost-circulation zone prediction has always been a hot and difficult research topic on the prevention and control of lost circulation. This study applied machine learning and statistical methods to deeply mine 105 groups and 29 features of loss data from typical loss block M. After removing 10 sets of noise data, the methods of mean removal, range scaling and normalization were used to pre-treat the 95 sets of the loss data. The multi-factor analysis of variance (ANOVA) and random forest algorithm were adopted to determine the 13 main factors affecting the lost circulation. The three typical deep learning neural network models were improved, the parameters in the models were adjusted, the neural network models with different structures were compared according to the PR curves, and the best model structure was built. The pre-treated loss data in 95 sets with 13 features were divided into the training set and test set by a ratio of 4:1. The model performance was evaluated using F1 score, accuracy, and recall rate. The trained model was successfully applied to the G block with severe leakage. The results show that the capsule network model is better than the BP neural network model and the convolutional neural network model. It stabilizes at 300 training rounds, with a prediction accuracy of 94.73%. The improved model can be applied to lost-circulation control in the field and provide guidance on leakage prevention and plugging operations

Language-Grounded Control for Coordinated Robot Motion and Speech

Recent advancements have enabled human-robot collaboration through physical assistance and verbal guidance. However, limitations persist in coordinating robots' physical motions and speech in response to real-time changes in human behavior during collaborative contact tasks. We first derive principles from analyzing physical therapists' movements and speech during patient exercises. These principles are translated into control objectives to: 1) guide users through trajectories, 2) control motion and speech pace to align completion times with varying user cooperation, and 3) dynamically paraphrase speech along the trajectory. We then propose a Language Controller that synchronizes motion and speech, modulating both based on user cooperation. Experiments with 12 users show the Language Controller successfully aligns motion and speech compared to baselines. This provides a framework for fluent human-robot collaboration.Comment: Under review in ICRA 202

Induction of heat shock protein 70 (Hsp70) prevents neuregulin-induced demyelination by enhancing the proteasomal clearance of c-Jun

Modulating molecular chaperones is emerging as an attractive approach to treat neurodegenerative diseases associated with protein aggregation, DPN (diabetic peripheral neuropathy) and possibly, demyelinating neuropathies. KU-32 [N-(7-((2R,3R,4S,5R)-3,4-dihydroxy-5-methoxy-6,6-dimethyl-tetrahydro-2H-pyran-2-yloxy)-8-methyl-2-oxo-2H-chromen-3-yl)acetamide] is a small molecule inhibitor of Hsp90 (heat shock protein 90) and reverses sensory deficits associated with myelinated fibre dysfunction in DPN. Additionally, KU-32 prevented the loss of myelinated internodes induced by treating myelinated SC (Schwann cell)-DRG (dorsal root ganglia) sensory neuron co-cultures with NRG1 (neuregulin-1 Type 1). Since KU-32 decreased NRG1-induced demyelination in an Hsp70-dependent manner, the goal of the current study was to clarify how Hsp70 may be mechanistically linked to preventing demyelination. The activation of p42/p44 MAPK (mitogen-activated protein kinase) and induction of the transcription factor c-Jun serve as negative regulators of myelination. NRG1 activated MAPK, induced c-Jun expression and promoted a loss of myelin segments in DRG explants isolated from both WT (wild-type) and Hsp70 KO (knockout) mice. Although KU-32 did not block the activation of MAPK, it blocked c-Jun induction and protected against a loss of myelinated segments in WT mice. In contrast, KU-32 did not prevent the NRG1-dependent induction of c-Jun and loss of myelin segments in explants from Hsp70 KO mice. Overexpression of Hsp70 in myelinated DRG explants prepared from WT or Hsp70 KO mice was sufficient to block the induction of c-Jun and the loss of myelin segments induced by NRG1. Lastly, inhibiting the proteasome prevented KU-32 from decreasing c-Jun levels. Collectively, these data support that Hsp70 induction is sufficient to prevent NRG1-induced demyelination by enhancing the proteasomal degradation of c-Jun.This work was supported the Juvenile Diabetes Research Foundation and The National Institutes of Health [grant numbers NS054847 (to R.T.D.), CA120458 and CA109265 (to B.S.J.B.) and NS075311 (to B.S.J.B. and R.T.D.)]

Multicolor emission based on a N, N′—Disubstituted dihydrodibenzo [a, c] phenazine crown ether macrocycle

Dynamic fluorophore 9,14-diphenyl-9,14-dihydrodibenzo[a,c]phenazine (DPAC) affords a new platform to produce diverse emission outputs. In this paper, a novel DPAC-containing crown ether macrocycle D-6 is synthesized and characterized. Host-guest interactions of D-6 with different ammonium guests produced a variety of fluorescence with hypsochromic shifts up to 130 nm, which are found to be affected by choice of solvent or guest and host/guest stoichiometry. Formation of supramolecular complexes were confirmed by UV-vis titration, 1H NMR and HRMS spectroscopy

The multiplexed light storage of Orbital Angular Momentum based on atomic ensembles

The improvement of the multi-mode capability of quantum memory can further improve the utilization efficiency of the quantum memory and reduce the requirement of quantum communication for storage units. In this letter, we experimentally investigate the multi-mode light multiplexing storage of orbital angular momentum (OAM) mode based on rubidium vapor, and demultiplexing by a photonic OAM mode splitter which combines a Sagnac loop with two dove prisms. Our results show a mode extinction ratio higher than 80$\%$ at 1 $\mu$s of storage time. Meanwhile, two OAM modes have been multiplexing stored and demultiplexed in our experimental configuration. We believe the experimental scheme may provide a possibility for high channel capacity and multi-mode quantum multiplexed quantum storage based on atomic ensembles

Structure–property relation and relevance of beam theories for microtubules: a coupled molecular and continuum mechanics study

Quasi-one-dimensional microtubules (MTs) in cells enjoy high axial rigidity but large transverse flexibility due to the inter-protofilament (PF) sliding. This study aims to explore the structure–property relation for MTs and examine the relevance of the beam theories to their unique features. A molecular structural mechanics (MSM) model was used to identify the origin of the inter-PF sliding and its role in bending and vibration of MTs. The beam models were then fitted to the MSM to reveal how they cope with the distinct mechanical responses induced by the inter-PF sliding. Clear evidence showed that the inter-PF sliding is due to the soft inter-PF bonds and leads to the length-dependent bending stiffness. The Euler beam theory is found to adequately describe MT deformation when the inter-PF sliding is largely prohibited. Nevertheless, neither shear deformation nor the nonlocal effect considered in the ‘more accurate’ beam theories can fully capture the effect of the inter-PF sliding. This reflects the distinct deformation mechanisms between an MT and its equivalent continuous body