67 research outputs found
TRAFFIC CONGESTION MODELING WITH DEEP ATTENTION HAWKES PROCESS
In this thesis, we focus on modeling the traffic congestion in the city of Atlanta. We are
trying to predict future congestion events on the main highways in Atlanta. We present a
novel framework for modeling traffic congestion events over road networks based on mutually
exciting Spatio-temporal point process models. We use multi-modal data by combining
traffic sensor networks data with police reports, which contain two types of triggering
mechanisms for congestion events. To capture the non-homogeneous temporal dependence
of the event on the past, we introduce a novel attention-based approach for the point process
model. To incorporate the directional spatial dependence induced by the road network, we
adapt the “tail-up” model from the spatial statistics context. We demonstrate the superior
performance of our approach compared to the state-of-the-art for both synthetic and real
data.M.S
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage
Deep Neural Networks (DNN) are vulnerable to adversarial perturbations-small
changes crafted deliberately on the input to mislead the model for wrong
predictions. Adversarial attacks have disastrous consequences for deep
learning-empowered critical applications. Existing defense and detection
techniques both require extensive knowledge of the model, testing inputs, and
even execution details. They are not viable for general deep learning
implementations where the model internal is unknown, a common 'black-box'
scenario for model users. Inspired by the fact that electromagnetic (EM)
emanations of a model inference are dependent on both operations and data and
may contain footprints of different input classes, we propose a framework,
EMShepherd, to capture EM traces of model execution, perform processing on
traces and exploit them for adversarial detection. Only benign samples and
their EM traces are used to train the adversarial detector: a set of EM
classifiers and class-specific unsupervised anomaly detectors. When the victim
model system is under attack by an adversarial example, the model execution
will be different from executions for the known classes, and the EM trace will
be different. We demonstrate that our air-gapped EMShepherd can effectively
detect different adversarial attacks on a commonly used FPGA deep learning
accelerator for both Fashion MNIST and CIFAR-10 datasets. It achieves a 100%
detection rate on most types of adversarial samples, which is comparable to the
state-of-the-art 'white-box' software-based detectors
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population
Understanding trajectory diversity is a fundamental aspect of addressing
practical traffic tasks. However, capturing the diversity of trajectories
presents challenges, particularly with traditional machine learning and
recurrent neural networks due to the requirement of large-scale parameters. The
emerging Transformer technology, renowned for its parallel computation
capabilities enabling the utilization of models with hundreds of millions of
parameters, offers a promising solution. In this study, we apply the
Transformer architecture to traffic tasks, aiming to learn the diversity of
trajectories within vehicle populations. We analyze the Transformer's attention
mechanism and its adaptability to the goals of traffic tasks, and subsequently,
design specific pre-training tasks. To achieve this, we create a data structure
tailored to the attention mechanism and introduce a set of noises that
correspond to spatio-temporal demands, which are incorporated into the
structured data during the pre-training process. The designed pre-training
model demonstrates excellent performance in capturing the spatial distribution
of the vehicle population, with no instances of vehicle overlap and an RMSE of
0.6059 when compared to the ground truth values. In the context of time series
prediction, approximately 95% of the predicted trajectories' speeds closely
align with the true speeds, within a deviation of 7.5144m/s. Furthermore, in
the stability test, the model exhibits robustness by continuously predicting a
time series ten times longer than the input sequence, delivering smooth
trajectories and showcasing diverse driving behaviors. The pre-trained model
also provides a good basis for downstream fine-tuning tasks. The number of
parameters of our model is over 50 million.Comment: 16 pages, 6 figures, under reviewed by Transportation Research Board
Annual Meeting, work in updat
Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree
Legged robots can pass through complex field environments by selecting gaits
and discrete footholds carefully. Traditional methods plan gait and foothold
separately and treat them as the single-step optimal process. However, such
processing causes its poor passability in a sparse foothold environment. This
paper novelly proposes a coordinative planning method for hexapod robots that
regards the planning of gait and foothold as a sequence optimization problem
with the consideration of dealing with the harshness of the environment as leg
fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the
entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve
some defeats of the standard MCTS applicating in the field of legged robot
planning. The proposed planning algorithm combines the fault-tolerant gait
method to improve the passability of the algorithm. Finally, compared with
other planning methods, experiments on terrains with different densities of
footholds and artificially-designed challenging terrain are carried out to
verify our methods. All results show that the proposed method dramatically
improves the hexapod robot's ability to pass through sparse footholds
environment
A New Species of the Genus Sinomicrurus Slowinski, Boundy and Lawson, 2001 (Squamata: Elapidae) from Hainan Province, China
A new species of the coral snake genus Sinomicrurus is described based on four specimens from southern Hainan Island (three specimens from Tianchi, Jianfengling National Nature Reserve, one specimen from Diaoluoshan National Nature Reserve), Hainan Province, China. Morphologically, the new species is rather similar to Sinomicrurus kelloggi. However, it is distinct from S. kelloggi by the pattern on the head, the head length, head length/width, the number of infralabial scales, number of bands on dorsal body, and number of blotches on the belly
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
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