The BCS Pairing Instability in the Thermodynamic Limit

The superconducting pairing instability---as determined by a divergence of the two-particle susceptibility---is obtained in the mean field (BCS) approximation in the thermodynamic limit. The usual practice is to examine this property for a finite lattice. We illustrate that, while the conclusions remain unchanged, the technical features are very different in the thermodynamic limit and conform more closely with the usual treatment of phase transitions encountered in, for example, the mean-field paramagnetic-ferromagnetic transition. Furthermore, by going to the extreme dilute limit, one can distinguish three dimensions from one and two dimensions, in which a pairing instability occurs even for two particles.Comment: 4 pages + references, 4 figure

Microscopic Simulation and Evaluation of the Roundabout Capacity Model in Highway Capacity Manual

University of Minnesota M.S. thesis. January 2018. Major: Civil Engineering. Advisor: John Hourdos. 1 computer file (PDF); vi, 70 pages.Shown to be an effective intersection design, the roundabout is receiving increasing attention and popularity. Several models, described in this work, have been developed to predict roundabout capacity. One of them, the roundabout capacity model included in the Highway Capacity Manual (HCM), is widely used in the US, using a gap-acceptance foundation based on data collected in US roundabouts. This study explored the accuracy of the two-lane variants of the roundabout capacity models in HCM 6th Edition and HCM 2010 by comparing them with an exponential regression model fitted on flow rate measurements collected at a two-lane roundabout in Richfield, Minnesota. Based on the same gap-acceptance foundation proposed in HCM, two other models were developed by recalculating coefficients. Each followed a different calibration strategy and compared with the Richfield model. It was found that calibration can significantly enhance the accuracy of the default HCM model and calibrating only the intercept of the default HCM model can produce a model with similar accuracy as the model resulting by calibrating both coefficients. To further assist traffic engineers, this work validated the capability of the popular traffic simulator AIMSUN to build a roundabout model with realistic capacities. A sensitivity analysis, exploring the impact of different simulation parameters, further assisted in proposing an efficient and reliable simulation calibration methodology. Initial safety margin, visibility along main stream, reaction time at stop, and max acceleration were selected to calibrate driver’s gap acceptance behavior. The result showed that if a calibrated model in AIMSUN could produce the same critical headway and follow-up headway as those in the HCM6 model, it will also result in similar capacities as the HCM6 model

Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics

Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new lyrics, source lyrics)} are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the system is available at \href{https://Sudowoodo.apps-hp.danlu.netease.com/}{Sudowoodo} and \href{https://youtu.be/u5BBT_j1L5M}{https://youtu.be/u5BBT\_j1L5M}.Comment: 7 pages,3 figures, submit to emnlp 2023 demo trac

Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting

Pre-trained language models (PLMs) have played an increasing role in multimedia research. In terms of vision-language (VL) tasks, they often serve as a language encoder and still require an additional fusion network for VL reasoning, resulting in excessive memory overhead. In this paper, we focus on exploring PLMs as a stand-alone model for VL reasoning tasks. Inspired by the recently popular prompt tuning, we first prove that the processed visual features can be also projected onto the semantic space of PLMs and act as prompt tokens to bridge the gap between single- and multi-modal learning. However, this solution exhibits obvious redundancy in visual information and model inference, and the placement of prompt tokens also greatly affects the final performance. Based on these observations, we further propose a novel transfer learning approach for PLMs, termed Dynamic Visual Prompting (DVP). Concretely, DVP first deploys a cross-attention module to obtain text-related and compact visual prompt tokens, thereby greatly reducing the input length of PLMs. To obtain the optimal placement, we also equip DVP with a reinforcement-learning based search algorithm, which can automatically merge DVP with PLMs for different VL tasks via a very short search process. In addition, we also experiment DVP with the recently popular adapter approach to keep the most parameters of PLMs intact when adapting to VL tasks, helping PLMs achieve a quick shift between single- and multi-modal tasks. We apply DVP to two representative PLMs, namely BERT and T5, and conduct extensive experiments on a set of VL reasoning benchmarks including VQA2.0, GQA and SNLIVE. The experimental results not only show the advantage of DVP on efficiency and performance, but also confirm its superiority in adapting pre-trained language models to VL tasks

IvyGPT: InteractiVe Chinese pathwaY language model in medical domain

General large language models (LLMs) such as ChatGPT have shown remarkable success. However, such LLMs have not been widely adopted for medical purposes, due to poor accuracy and inability to provide medical advice. We propose IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality medical question-answer (QA) instances and Reinforcement Learning from Human Feedback (RLHF). After supervised fine-tuning, IvyGPT has good multi-turn conversation capabilities, but it cannot perform like a doctor in other aspects, such as comprehensive diagnosis. Through RLHF, IvyGPT can output richer diagnosis and treatment answers that are closer to human. In the training, we used QLoRA to train 33 billion parameters on a small number of NVIDIA A100 (80GB) GPUs. Experimental results show that IvyGPT has outperformed other medical GPT models.Comment: 5 pages, 3 figure

Enhancement of the Antibacterial Activity of Silver Nanoparticles against Phytopathogenic Bacterium Ralstonia solanacearum

In this paper, the enhanced antibacterial activity of silver nanoparticles (AgNPs) against the phytopathogenic bacterium Ralstonia solanacearum after stabilization using selected surfactants (SDS, SDBS, TX-100, and Tween 80) was examined, in comparison with silver ion. Tween 80 was found to be the most preferable stabilizer of AgNPs due to the beneficial synergistic effects of the AgNPs and surfactant. However, all the surfactants nearly had no effects on the antibacterial activity of Ag+. In vitro, Tween 80-stabilized AgNPs showed the highest bactericidal activity against R. solanacearum. Further measurements using TEM, fluorescence microscopy, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) revealed that though Ag+ and Tween 80-Ag+ induced high toxicity, Tween 80-stabilized AgNPs displayed most severe damage when in direct contact with cells, causing mechanistic injury to the cell membrane and strongly modifying and destructing the cellular proteins. Meanwhile, in vivo, the pot experiments data indicated that the control efficiency of Tween 80-stabilized AgNPs on tobacco bacterial wilt was 96.71%, 90.11%, and 84.21%, at 7 days, 14 days, and 21 days, respectively. Based on the results evidencing their advantageous low dosage requirements and strong antimicrobial activity, Tween 80-stabilized AgNPs are a promising antibacterial agent for use in alternative crop disease control approaches

Non-linear spacing policy and network analysis for shared-road platooning

Connected vehicle technology creates new opportunities for obtaining knowledge about the surrounding traffic and using that knowledge to optimize individual vehicle behaviors. This project creates an interdisciplinary group to study vehicle connectivity, and this report discusses three activities of this group. First, we study the problem of traffic state (flows and densities) using position reports from connected vehicles. Even if the market penetration of connected vehicles is limited, speed information can be inverted through the flow-density relationship to estimate space-and time-specific flows and densities. Propagation, according to the kinematic wave theory, is combined with measurements through Kalman filtering. Second, the team studies the problem of cyber-attack communications. Malicious actors could hack the communications to incorrectly report position, speed, or accelerations to induce a collision. By comparing the communications with radar data, the project team develops an analytical method for vehicles using cooperative adaptive cruise control to detect erroneous or malicious data and respond accordingly (by not relying on connectivity for safe following distances). Third, the team considers new spacing policies for cooperative adaptive cruise control and how they would affect city traffic. Due to the computational complexity of microsimulation, the team elects to convert the new spacing policy into a flow-density relationship. A link transmission model is constructed by creating a piecewise linear approximation. Results from dynamic traffic assignment on a city network shows that improvements in capacity reduces delays on freeways, but surprisingly route choice increased congestion for the overall city

FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM