325 research outputs found
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
Scalable and controllable synthesis of atomic metal electrocatalysts assisted by an egg-box in alginate
Herein, a general strategy is developed to synthesize atomic metal catalysts using sustainable and earth-abundant sodium alginate (Na-Alg), a common seaweed extract, as a precursor. The “egg-box” structure in Na-Alg after ion-exchange with metal cations (Zn2+, Co2+, Ni2+, Cu2+, etc.) is the key to achieve a scalable and controllable synthesis of highly dispersed atomic metals. For instance, atomic Co, Ni and Cu have been successfully synthesized using this method. As a representative, the as-synthesized atomically dispersed Co on reduced graphene oxide (A-Co/r-GO) can reach a maximum metal loading of 3.6 wt%, showing outstanding catalytic activity and stability for the oxygen reduction reaction (ORR) with a half-wave potential (E1/2) of 0.842 V vs. RHE that is more positive than that of 20 wt% Pt/C (0.827 V vs. RHE) in alkaline solutions. The A-Co/r-GO catalyzed zinc-air batteries (ZABs) outperform Pt/C-based ZABs in the aspects of discharge voltage and specific zinc capacity, and can work robustly for more than 250 h with negligible voltage loss with refueling the Zn anode and KOH electrolyte periodically. This work opens up a new strategy for a general, practical and scalable synthesis of atomic metal catalysts at very low cost.No Full Tex
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