Kinematics, workspace and singularity analysis of a multi-mode parallel robot

A family of reconfigurable parallel robots can change motion modes by passing through constraint singularities by locking and releasing some passive joints of the robot. This paper is about the kinematics, the workspace and singularity analysis of a 3-PRPiR parallel robot involving lockable Pi and R (revolute) joints. Here a Pi joint may act as a 1-DOF planar parallelogram if its lock-able P (prismatic) joint is locked or a 2-DOF RR serial chain if its lockable P joint is released. The operation modes of the robot include a 3T operation modes to three 2T1R operation modes with two different directions of the rotation axis of the moving platform. The inverse kinematics and forward kinematics of the robot in each operation modes are dealt with in detail. The workspace analysis of the robot allow us to know the regions of the workspace that the robot can reach in each operation mode. A prototype built at Heriot-Watt University is used to illustrate the results of this work.Comment: International Design Engineering Technical Conferences \& Computers and Information in Engineering Conference, Aug 2017, Cleveland, United States. 201

A Focused Study on Sequence Length for Dialogue Summarization

Output length is critical to dialogue summarization systems. The dialogue summary length is determined by multiple factors, including dialogue complexity, summary objective, and personal preferences. In this work, we approach dialogue summary length from three perspectives. First, we analyze the length differences between existing models' outputs and the corresponding human references and find that summarization models tend to produce more verbose summaries due to their pretraining objectives. Second, we identify salient features for summary length prediction by comparing different model settings. Third, we experiment with a length-aware summarizer and show notable improvement on existing models if summary length can be well incorporated. Analysis and experiments are conducted on popular DialogSum and SAMSum datasets to validate our findings.Comment: Preprint version - ICASSP submissio

Timing jitter and phase noise in electronic oscillators

In the first part of this dissertation, low frequency l/f or flicker noise in the frequency range of Hz to kHz has been identified and demonstrated to be described by temperature fluctuations in heat conduction in bipolar transistors operated at higher power densities. This noise phenomenon is not described by current SPICE programs used in circuit simulations. This noise in the kHz range can modulate LC oscillators and can be the determining factor in causing phase noise in modern wireless communication systems. At lower frequencies or lower power densities flicker noise may still result from number fluctuations or mobility fluctuations but this is not as important in determining the phase noise at kHz offsets from the carrier frequencies. In the second part of this dissertation work, we have developed a large signal nonlinear transient simulation technique to simulate phase noise due to device noise in electronic oscillators. Simulation results are consistent with Leeson’s theory and the magnitude of the sidebands directly scales with the magnitude of injected noise. Simulation also shows phase noise at 4.7 MHz frequency offset is white noise dominated and in good agreement with the experimental data reported in the literature. In the third part of this dissertation work, we have developed a large signal nonlinear transient simulation technique to simulate timing jitter in electronic oscillators. Simulation results are consistent with the accepted theory, analytical formula and A.Hajimiri's analytical model for white noise. Two important parameters cycle jitter, and cycle to cycle jitter used to describe jitter performance can be obtained from simulation. Simulation results are also compared with measurement and close agreement was observed between them. We have employed this methodology and investigated the timing jitter in silicon BJT /or SiGe HBT ECL ring oscillators, and we have shown silicon BJT /or SiGe HBT ring oscillators have lower jitter compared to their CMOS counterparts. As such silicon BJT and/or SiGe HBT ring oscillators are a potential choice for low jitter applications

Characterization of severe fever with thrombocytopenia syndrome in rural regions of Zhejiang, China.

Severe fever with thrombocytopenia syndrome virus (SFTSV) infections have recently been found in rural regions of Zhejiang. A severe fever with thrombocytopenia syndrome (SFTS) surveillance and sero-epidemiological investigation was conducted in the districts with outbreaks. During the study period of 2011-2014, a total of 51 SFTSV infection cases were identified and the case fatality rate was 12% (6/51). Ninety two percent of the patients (47/51) were over 50 years of age, and 63% (32/51) of laboratory confirmed cases occurred from May to July. Nine percent (11/120) of the serum samples from local healthy people without symptoms were found to be positive for antibodies to the SFTS virus. SFTSV strains were isolated by culture using Vero, and the whole genomic sequences of two SFTSV strains (01 and Zhao) were sequenced and submitted to the GenBank. Homology analysis showed that the similarity of the target nucleocapsid gene from the SFTSV strains from different geographic areas was 94.2-100%. From the constructed phylogenetic tree, it was found that all the SFTSV strains diverged into two main clusters. Only the SFTSV strains from the Zhejiang (Daishan) region of China and the Yamaguchi, Miyazakj regions of Japan, were clustered into lineage II, consistent with both of these regions being isolated areas with similar geographic features. Two out of eight predicted linear B cell epitopes from the nucleocapsid protein showed mutations between the SFTSV strains of different clusters, but did not contribute to the binding ability of the specific SFTSV antibodies. This study confirmed that SFTSV has been circulating naturally and can cause a seasonal prevalence in Daishan, China. The results also suggest that the molecular characteristics of SFTSV are associated with the geographic region and all SFTSV strains can be divided into two genotypes

In-Context Learning with Iterative Demonstration Selection

Spurred by advancements in scale, large language models (LLMs) have demonstrated strong few-shot learning ability via in-context learning (ICL). However, the performance of ICL has been shown to be highly sensitive to the selection of few-shot demonstrations. Selecting the most suitable examples as context remains an ongoing challenge and an open problem. Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample while ignoring the fact that the optimal selection dimension, i.e., diversity or similarity, is task-specific. Leveraging the merits of both dimensions, we propose Iterative Demonstration Selection (IDS). Using zero-shot chain-of-thought reasoning (Zero-shot-CoT), IDS iteratively selects examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. Specifically, IDS applies Zero-shot-CoT to the test sample before demonstration selection. The output reasoning path is then used to choose demonstrations that are prepended to the test sample for inference. The generated answer is accompanied by its corresponding reasoning path for extracting a new set of demonstrations in the next iteration. After several iterations, IDS adopts majority voting to obtain the final result. Through extensive experiments on tasks including commonsense reasoning, question answering, topic classification, and sentiment analysis, we demonstrate that IDS can consistently outperform existing ICL demonstration selection methods

Active Flow Control for Bluff Body Drag Reduction Using Reinforcement Learning with Partial Measurements

Active flow control for drag reduction with reinforcement learning (RL) is performed in the wake of a 2D square bluff body at laminar regimes with vortex shedding. Controllers parameterized by neural networks are trained to drive two blowing and suction jets. RL with full observability (sensors in the wake) successfully discovers a control policy which reduces the drag by suppressing the vortex shedding in the wake. However, a non-negligible performance degradation (~50\% less drag reduction) is observed when the controller is trained with partial measurements (sensors on the body). To mitigate this effect, we propose a dynamic, energy-efficient, maximum entropy RL control scheme. First, an energy-efficiency-based reward function is proposed to optimize the energy consumption of the controller while maximising drag reduction. Second, the controller is trained with an augmented state consisting of both current and past observations and actions, which can be formulated as a nonlinear autoregressive exogenous model, to alleviate the partial observability problem. Third, maximum entropy RL algorithms which promote exploration and exploitation in a sample efficient way are used and discover near-optimal policies in the challenging case of partial measurements. Complete stabilisation of the vortex shedding is achieved in the near wake using only surface pressure measurements on the rear of the body, resulting in similar drag reduction as in the case with wake sensors. The proposed approach opens new avenues for dynamic flow control using partial measurements for realistic configurations