Parallel random LiDAR with spatial multiplexing of a many-mode laser

We propose and experimentally demonstrate parallel LiDAR using random intensity fluctuations from a highly multimode laser. We optimize a degenerate cavity to have many spatial modes lasing simultaneously with different frequencies. Their spatio-temporal beating creates ultrafast random intensity fluctuations, which are spatially demultiplexed to generate hundreds of uncorrelated time traces for parallel ranging. The bandwidth of each channel exceeds 10 GHz, leading to a ranging resolution better than 1 cm. Our parallel random LiDAR is robust to cross-channel interference, and will facilitate high-speed 3D sensing and imaging.Comment: 12 pages, 7 figure

Deep Learning with Passive Optical Nonlinear Mapping

Deep learning has fundamentally transformed artificial intelligence, but the ever-increasing complexity in deep learning models calls for specialized hardware accelerators. Optical accelerators can potentially offer enhanced performance, scalability, and energy efficiency. However, achieving nonlinear mapping, a critical component of neural networks, remains challenging optically. Here, we introduce a design that leverages multiple scattering in a reverberating cavity to passively induce optical nonlinear random mapping, without the need for additional laser power. A key advantage emerging from our work is that we show we can perform optical data compression, facilitated by multiple scattering in the cavity, to efficiently compress and retain vital information while also decreasing data dimensionality. This allows rapid optical information processing and generation of low dimensional mixtures of highly nonlinear features. These are particularly useful for applications demanding high-speed analysis and responses such as in edge computing devices. Utilizing rapid optical information processing capabilities, our optical platforms could potentially offer more efficient and real-time processing solutions for a broad range of applications. We demonstrate the efficacy of our design in improving computational performance across tasks, including classification, image reconstruction, key-point detection, and object detection, all achieved through optical data compression combined with a digital decoder. Notably, we observed high performance, at an extreme compression ratio, for real-time pedestrian detection. Our findings pave the way for novel algorithms and architectural designs for optical computing.Comment: 16 pages, 7 figure

Electrically pumped semiconductor laser with low spatial coherence and directional emission

We design and fabricate an on-chip laser source that produces a directional beam with low spatial coherence. The lasing modes are based on the axial orbit in a stable cavity and have good directionality. To reduce the spatial coherence of emission, the number of transverse lasing modes is maximized by fine-tuning the cavity geometry. Decoherence is reached in a few nanoseconds. Such rapid decoherence will facilitate applications in ultrafast speckle-free full-field imaging

Hybrid approach to user intention modeling for dialog simulation

This paper proposes a novel user intention si-mulation method which is a data-driven ap-proach but able to integrate diverse user dis-course knowledge together to simulate various type of users. In Markov logic framework, lo-gistic regression based data-driven user inten-tion modeling is introduced, and human dialog knowledge are designed into two layers such as domain and discourse knowledge, then it is integrated with the data-driven model in gen-eration time. Cooperative, corrective and self-directing discourse knowledge are designed and integrated to mimic such type of users. Experiments were carried out to investigate the patterns of simulated users, and it turned out that our approach was successful to gener-ate user intention patterns which are not only unseen in the training corpus and but also per-sonalized in the designed direction.

Suppressing spatio-temporal lasing instabilities with wave-chaotic microcavities

Spatio-temporal instabilities are widespread phenomena resulting from complexity and nonlinearity. In broad-area edge-emitting semiconductor lasers, the nonlinear interactions of multiple spatial modes with the active medium can result in filamentation and spatio-temporal chaos. These instabilities degrade the laser performance and are extremely challenging to control. We demonstrate a powerful approach to suppress spatio-temporal instabilities using wave-chaotic or disordered cavities. The interference of many propagating waves with random phases in such cavities disrupts the formation of self-organized structures like filaments, resulting in stable lasing dynamics. Our method provides a general and robust scheme to prevent the formation and growth of nonlinear instabilities for a large variety of high-power lasers

Novel Nanocatalyst for the Selective Hydrogenation of Bio-Oil Model Compounds

This thesis focuses on the understanding the effect of various factors, such as physical structures of metal particles, chemical composition of supports and metal-support interactions, on the catalytic performance of Pd or Pt nanocatalysts for hydrodeoxygenation (HDO) of bio-oil model compounds. The first part of the thesis addressed the alternative catalyst synthesis strategy based on emerging double-flame spray pyrolysis method (FSP), which was able to tune the catalytic properties of nanocatalysts without changing their precursors and chemical compositions during the synthesis. A series of Pd catalysts on the silica-alumina supports, SiO2- , and Al2O3 supports have been synthesized with the tunable surface properties within micro-seconds. The characterization results showed that various flow rates of precursors and gases used for the synthesis of catalysts influenced the formation of the catalyst structures and further change the surface acidity of catalysts due to the correlation between acidity and structure, but, the flow rates did not influence the electronic properties of Pd particles. Therefore, the higher conversion but the similar chemoselectivity have been reached in the hydrogenation of the bio-oil model ketone compound-acetophenone The second part is to identify the dominant effects from size of metal catalysts (under uniform shape and face) or the support acidity in the hydrodeoxygenation of the bio-oil model compounds of acetophenone, benzaldehyde, and butyrophenone. The uniform cubic Pd particles with different size (8, 13, and 21 nm) have been synthesized and loaded on the most popular supports (SiO2-, Al2O3-, and silica-alumina) with various functional groups and acidity. The results showed different acidities on the supports (Brønsted acidic site for Silica-alumina, Lewis acidic site for Al2O3-, and non/weak silanol OH group for SiO2- support) could not influence the chemoselectivity of the reaction but effected the conversion obviously. The particle size has more significant influence than the acidity. The smallest (8nm) Pd particle catalysts regardless of kinds of supports revealed the highest conversion for the hydrogenation the bio-oil model compounds. The third part focused on the influence of various types of catalysts with different acidities, chemical composition, and metal-support interaction on enantioselective hydrogenation of several model compounds in two reaction systems: 1). Pt-cinchrona modified system, and 2). Pd-(S) proline modified system. The result indicated acidic supports promoted the both conversion and enantioselectivity. Specially, Pd/SA made by double-FSP method, which has the highest Brønsted acid sites, showed 100 % conversion of isopherone on 60 min with 99% ee values

POSBIOTM/W: A Development Workbench For Machine Learning Oriented Biomedical Text Mining System ∗

The POSBIOTM/W 1 is a workbench for machine-learning oriented biomedical text mining system. The POSTBIOTM/W is intended to assist biologist in mining useful information efficiently from biomedical text resources. To do so, it provides a suit of tools for gathering, managing, analyzing and annotating texts. The workbench is implemented in Java, which means that it is platform-independent.

Improving Speech Recognition Using Semantic and Reference Features in a Multimodal Dialog System

Abstract—Current Speech-based dialog system undergo a practical problem; a speech recognizer is defective due to inevitable errors. Even in multimodal dialog systems, which have multiple input channels, errors in the speech recognition are a major problem because speech contains a large portion of user’s intention. In this paper, we propose a re-ranking method to improve the performance of speech recognition in a multimodal dialog system. To re-rank the n-best speech recognition hypotheses, we use the multimodal understanding features that are orthogonal to the speech as well as the speech recognizer features. We demonstrate our method to smart home domain, and the results show that the multimodal understanding features are promising in overcoming many speech errors. I