152 research outputs found
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?
ChatGPT embarks on a new era of artificial intelligence and will
revolutionize the way we approach intelligent traffic safety systems. This
paper begins with a brief introduction about the development of large language
models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety
issues. Furthermore, we discuss the controversies surrounding LLMs, raise
critical questions for their deployment, and provide our solutions. Moreover,
we propose an idea of multi-modality representation learning for smarter
traffic safety decision-making and open more questions for application
improvement. We believe that LLM will both shape and potentially facilitate
components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended
Clustering framework to identify traffic conflicts and determine thresholds based on trajectory data
Traffic conflict indicators are essential for evaluating traffic safety and
analyzing trajectory data, especially in the absence of crash data. Previous
studies have used traffic conflict indicators to predict and identify
conflicts, including time-to-collision (TTC), proportion of stopping distance
(PSD), and deceleration rate to avoid a crash (DRAC). However, limited research
is conducted to understand how to set thresholds for these indicators while
accounting for traffic flow characteristics at different traffic states. This
paper proposes a clustering framework for determining surrogate safety measures
(SSM) thresholds and identifying traffic conflicts in different traffic states
using high-resolution trajectory data from the Citysim dataset. In this study,
unsupervised clustering is employed to identify different traffic states and
their transitions under a three-phase theory framework. The resulting clusters
can then be utilized in conjunction with surrogate safety measures (SSM) to
identify traffic conflicts and assess safety performance in each traffic state.
From different perspectives of time, space, and deceleration, we chose three
compatible conflict indicators: TTC, DRAC, and PSD, considering functional
differences and empirical correlations of different SSMs. A total of three
models were chosen by learning these indicators to identify traffic conflict
and non-conflict clusters. It is observed that Mclust outperforms the other
two. The results show that the distribution of traffic conflicts varies
significantly across traffic states. A wide moving jam (J) is found to be the
phase with largest amount of conflicts, followed by synchronized flow phase (S)
and free flow phase(F). Meanwhile, conflict risk and thresholds exhibit similar
levels across transitional states
TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety
Large Language Models (LLMs) have shown remarkable effectiveness in various
general-domain natural language processing (NLP) tasks. However, their
performance in transportation safety domain tasks has been suboptimal,
primarily attributed to the requirement for specialized transportation safety
expertise in generating accurate responses [1]. To address this challenge, we
introduce TrafficSafetyGPT, a novel LLAMA-based model, which has undergone
supervised fine-tuning using TrafficSafety-2K dataset which has human labels
from government produced guiding books and ChatGPT-generated instruction-output
pairs. Our proposed TrafficSafetyGPT model and TrafficSafety-2K train dataset
are accessible at https://github.com/ozheng1993/TrafficSafetyGPT
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection
Cereal grain plays a crucial role in the human diet as a major source of
essential nutrients. Grain Appearance Inspection (GAI) serves as an essential
process to determine grain quality and facilitate grain circulation and
processing. However, GAI is routinely performed manually by inspectors with
cumbersome procedures, which poses a significant bottleneck in smart
agriculture.
In this paper, we endeavor to develop an automated GAI system:AI4GrainInsp.
By analyzing the distinctive characteristics of grain kernels, we formulate GAI
as a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible
kernels are considered normal samples while damaged grains or unknown objects
are regarded as anomalies. We further propose an AD model, called AD-GAI, which
is trained using only normal samples yet can identify anomalies during
inference. Moreover, we customize a prototype device for data acquisition and
create a large-scale dataset including 220K high-quality images of wheat and
maize kernels. Through extensive experiments, AD-GAI achieves considerable
performance in comparison with advanced AD methods, and AI4GrainInsp has highly
consistent performance compared to human experts and excels at inspection
efficiency over 20x speedup. The dataset, code and models will be released at
https://github.com/hellodfan/AI4GrainInsp.Comment: Accepted by ECAI2023. https://github.com/hellodfan/AI4GrainIns
Exploratory analysis of injury severity under different levels of driving automation (SAE Level 2-5) using multi-source data
Vehicles equipped with automated driving capabilities have shown the
potential to improve safety and operations. Advanced driver assistance systems
(ADAS) and automated driving systems (ADS) have been widely developed to
support vehicular automation. Although the studies on the injury severity
outcomes that involve automated driving systems are ongoing, there is limited
research investigating the difference between injury severity outcomes of the
ADAS and ADS vehicles using real-world crash data. To ensure comprehensive
analysis, a multi-source dataset that includes the NHTSA crash database (752
cases), CA DMV crash reports (498 cases), and news outlet data (30 cases) is
used. Two random parameters multinomial logit models with heterogeneity in the
means and variances are estimated to gain a better understanding of the
variables impacting the crash injury severity outcome for the ADAS (SAE Level
2) and ADS (SAE Levels 3-5) vehicles. We found that while 56 percent of crashes
involving ADAS vehicles took place on a highway, 84 percent of crashes
involving ADS took place in more urban settings. The model estimation results
indicate that the weather indicators, traffic incident or work zone indicator,
differences in the system sophistication that are captured by both manufacture
year and high or low mileage, type of collision, as well as rear and front
impact indicators all play a significant role in the crash injury severity. The
results offer an exploratory assessment of the safety performance of the ADAS
and ADS equipped vehicles in the real-world environment and can be used by the
manufacturers and other stakeholder to dictate the direction of their
deployment and usage
Secure transmission via joint precoding optimization for downlink MISO NOMA
Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. When the eavesdropping channel state information (CSI) is available at BS, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. When the CSI of the eavesdropping channel is not available, we first consider the case that the secure user is not the farthest from BS, and the transmit power of the farther users is maximized via joint precoding optimization to guarantee its security. Then, we consider the case when the farthest user from BS requires secure transmission, and the modified successive interference cancellation order and joint precoding optimization can be adopted to ensure its security. Similar method can be exploited to solve the two non-convex problems when the CSI is unknown. Simulation results demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively, with and without eavesdropping CSI
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
The ChatGPT, a lite and conversational variant of Generative Pretrained
Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large
Language Models (LLMs) with billions of parameters. LLMs have stirred up much
interest among researchers and practitioners in their impressive skills in
natural language processing tasks, which profoundly impact various fields. This
paper mainly discusses the future applications of LLMs in dentistry. We
introduce two primary LLM deployment methods in dentistry, including automated
dental diagnosis and cross-modal dental diagnosis, and examine their potential
applications. Especially, equipped with a cross-modal encoder, a single LLM can
manage multi-source data and conduct advanced natural language reasoning to
perform complex clinical operations. We also present cases to demonstrate the
potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical
application. While LLMs offer significant potential benefits, the challenges,
such as data privacy, data quality, and model bias, need further study.
Overall, LLMs have the potential to revolutionize dental diagnosis and
treatment, which indicates a promising avenue for clinical application and
research in dentistry
Identification of RNA silencing suppressor encoded by citrus chlorotic dwarf-associated virus
IntroductionCitrus chlorotic dwarf-associated virus (CCDaV) is an economically important citrus virus associated with leaf curling, deformation, and chlorosis found in China. Plants have evolved RNA silencing to defend against viral infections; however, the mechanism by which CCDaV suppresses RNA silencing in citrus remains unknown.MethodsSix proteins encoded by CCDaV were ectopically expressed in Nicotiana benthamiana 16c using the pCHF3 vector to identify RNA-silencing suppression activities.ResultsV2 protein encoded by CCDaV suppressed local RNA silencing and systemic RNA silencing triggered by GFP RNA, but did not impede short-distance movement of the RNA silencing signal in N. benthamiana 16c. GFP fluorescence observations showed that the ability of V2 protein to suppress RNA silencing was weaker than tomato bushy stunt virus P19. Deletion analysis showed that the putative nuclear localization signal (NLS, 25–54 aa) was involved in the RNA silencing suppression activity of V2 protein. Furthermore, V2 protein cannot block dsRNA-triggered RNA silencing. The subcellular localization assay suggested that V2 protein was localized to nucleus of N. benthamiana.ConclusionOverall, the results of this study demonstrate that CCDaV-V2 acts as an activity of silencing suppression. This is the first reported RNA-silencing suppressor encoded by Citlodavirus and will be valuable in revealing the molecular mechanism of CCDaV infection
Light Controllable Electronic Phase Transition in Ionic Liquid Gated Monolayer Transition Metal Dichalcogenides
Ionic
liquid gating has proved to be effective in inducing emergent
quantum phenomena such as superconductivity, ferromagnetism, and topological
states. The electrostatic doping at two-dimensional interfaces relies
on ionic motion, which thus is operated at sufficiently high temperature.
Here, we report the in situ tuning of quantum phases
by shining light on an ionic liquid-gated interface at cryogenic temperatures.
The light illumination enables flexible switching of the quantum transition
in monolayer WS2 from an insulator to a superconductor.
In contrast to the prevailing picture of photoinduced carriers, we
find that in the presence of a strong interfacial electric field conducting
electrons could escape from the surface confinement by absorbing photons,
mimicking the field emission. Such an optical tuning tool in conjunction
with ionic liquid gating greatly facilitates continuous modulation
of carrier densities and hence electronic phases, which would help
to unveil novel quantum phenomena and device functionality in various
materials
Robust 3.7 V-Na[CuMn]O Cathode for Na-ion Batteries
Na-ion batteries (NIBs), which are recognized as a next-generation
alternative technology for energy storage, still suffer from commercialization
constraints due to the lack of low-cost, high-performance cathode materials.
Since our first discovery of Cu/Cu electrochemistry in 2014,
numerous Cu-substituted/doped materials have been designed for NIBs. However
for almost ten years, the potential of Cu/Cu electrochemistry has
been grossly underappreciated and normally regarded as a semielectrochemically
active redox. Here, we re-synthesized P2-Na[CuMn]O
and reinterpreted it as a high-voltage, cost-efficient, air-stable, long-life,
and high-rate cathode material for NIBs, which demonstrates a high operating
voltage of 3.7 V and a completely active Cu/Cu redox reaction.
The 2.3 Ah cylindrical cells exhibit excellent cycling (93.1% capacity after
2000 cycles), high rate (97.2% capacity at 10C rate), good low-temperature
performance (86.6% capacity at -30C), and high safety, based on which,
a 56 V-11.5 Ah battery pack for E-bikes is successfully constructed, exhibiting
stable cycling (96.5% capacity at the 800th cycle) and a long driving distance
(36 km, tester weight 65 kg). This work offers a commercially feasible cathode
material for low-cost, high-voltage NIBs, paving the way for advanced NIBs in
power and stationary energy storage applications.Comment: 15 pages, 3 figures, 1 tabl
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