336 research outputs found
Early Pliocene Mice and Rats from the Gray Fossil Site of Eastern Tennessee: Implications for the Evolution of Cricetidae and Understanding of the Past Ecosystem
Cricetidae ranks as the second-most species-rich and abundant mammalian family, with limited studies on eastern North American records prior to the Pleistocene. While cricetids has been previously noted at the early Pliocene Gray Fossil Site (GFS), this study provides a detailed description of eight taxa: Postcopemys (two species), Symmetrodontomys, Oryzomyini, Peromyscus, Neotoma, Neotomodon, and Xenomys. Postcopemys is the most common cricetid taxon at GFS, followed by Peromyscus and Neotoma. These records expand the stratigraphic and geographic range of multiple genera. Distinctive morphological features of GFS taxa suggest presence of several new species. The GFS cricetid assemblage exhibits diverse body sizes and dietary preferences, setting GFS apart from other contemporaneous sites and emphasizing its spatial and temporal uniqueness. The Appalachian region represents a biodiversity hotspot today, and GFS was likely an important habitat for cricetid evolution during the Pliocene
Calculation and finite element analysis of the temperature field for high-speed rail bearing based on vibrational characteristics
The complicated temperature environment of the high-speed rail bearing will generate the thermal stress and thermal deformation, which will change the vibrational characteristics of the bearing. If the vibration is serious, it will result in bearing failure and destructive accidents. Thus, the steady temperature field and the relationship between temperature field and the critical speed of the bearing were researched based on the vibrational characteristics in the paper. According to the specific work conditions and structure characteristics of the double row tapered roller bearing assembly, the heat transfer model of high-speed rail bearing was developed. The heat source and the external heat dissipation of the bearing were calculated, the reasonable boundary conditions of lubrication were set, and then the finite element model was established in ANSYS. According to four different distribution methods of heat source, the temperature field of the inner ring, outer ring and rollers were simulated and analyzed. Comparing the four different results, a reasonable distribution method of the heat source was put forward. Finally the effects of steady temperature field on critical speed of high-speed rail bearing were discussed. The simulation results showed that the bearing temperature distribution was basically consistent with the actual working conditions. The steady temperature field has stronger effect on vibration mode of low-order critical speed then high-order critical speed of bearing. The results of this study provide a basis of vibration characteristics for the use and optimal design of high-speed rail bearing
Man-in-the-Middle Attack Resistant Secret Key Generation via Channel Randomization
Physical-layer based key generation schemes exploit the channel reciprocity
for secret key extraction, which can achieve information-theoretic secrecy
against eavesdroppers. Such methods, although practical, have been shown to be
vulnerable against man-in-the-middle (MitM) attacks, where an active adversary,
Mallory, can influence and infer part of the secret key generated between Alice
and Bob by injecting her own packet upon observing highly correlated
channel/RSS measurements from Alice and Bob. As all the channels remain stable
within the channel coherence time, Mallory's injected packets cause Alice and
Bob to measure similar RSS, which allows Mallory to successfully predict the
derived key bits. To defend against such a MitM attack, we propose to utilize a
reconfigurable antenna at one of the legitimate transceivers to proactively
randomize the channel state across different channel probing rounds. The
randomization of the antenna mode at every probing round breaks the temporal
correlation of the channels from the adversary to the legitimate devices, while
preserving the reciprocity of the channel between the latter. This prevents key
injection from the adversary without affecting Alice and Bob's ability to
measure common randomness. We theoretically analyze the security of the
protocol and conduct extensive simulations and real-world experiments to
evaluate its performance. Our results show that our approach eliminates the
advantage of an active MitM attack by driving down the probability of
successfully guessing bits of the secret key to a random guess.Comment: 13 pages, 8 figures, 4 table
MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics
DNA methylation is a crucial regulator of gene transcription and has been
linked to various diseases, including autoimmune diseases and cancers. However,
diagnostics based on DNA methylation face challenges due to large feature sets
and small sample sizes, resulting in overfitting and suboptimal performance. To
address these issues, we propose MIRACLE, a novel interpretable neural network
that leverages autoencoder-based multi-task learning to integrate multiple
datasets and jointly identify common patterns in DNA methylation.
MIRACLE's architecture reflects the relationships between methylation sites,
genes, and pathways, ensuring biological interpretability and meaningfulness.
The network comprises an encoder and a decoder, with a bottleneck layer
representing pathway information as the basic unit of heredity. Customized
defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency
matrix information, which provides explainability and expresses the
site-gene-pathway hierarchical structure explicitly. And from the embedding,
there are different multi-task classifiers to predict diseases.
Tested on six datasets, including rheumatoid arthritis, systemic lupus
erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and
type 1 diabetes, MIRACLE demonstrates robust performance in identifying common
functions of DNA methylation across different phenotypes, with higher accuracy
in prediction dieseases than baseline methods. By incorporating biological
prior knowledge, MIRACLE offers a meaningful and interpretable framework for
DNA methylation data analysis in the context of autoimmune diseases
Joint Inference on Truth/Rumor and Their Sources in Social Networks
In the contemporary era of information explosion, we are often faced with the
mixture of massive \emph{truth} (true information) and \emph{rumor} (false
information) flooded over social networks. Under such circumstances, it is very
essential to infer whether each claim (e.g., news, messages) is a truth or a
rumor, and identify their \emph{sources}, i.e., the users who initially spread
those claims. While most prior arts have been dedicated to the two tasks
respectively, this paper aims to offer the joint inference on truth/rumor and
their sources. Our insight is that a joint inference can enhance the mutual
performance on both sides.
To this end, we propose a framework named SourceCR, which alternates between
two modules, i.e., \emph{credibility-reliability training} for truth/rumor
inference and \emph{division-querying} for source detection, in an iterative
manner. To elaborate, the former module performs a simultaneous estimation of
claim credibility and user reliability by virtue of an Expectation Maximization
algorithm, which takes the source reliability outputted from the latter module
as the initial input. Meanwhile, the latter module divides the network into two
different subnetworks labeled via the claim credibility, and in each subnetwork
launches source detection by applying querying of theoretical budget guarantee
to the users selected via the estimated reliability from the former module. The
proposed SourceCR is provably convergent, and algorithmic implementable with
reasonable computational complexity. We empirically validate the effectiveness
of the proposed framework in both synthetic and real datasets, where the joint
inference leads to an up to 35\% accuracy of credibility gain and 29\% source
detection rate gain compared with the separate counterparts
Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks
Long Range (LoRa) wireless technology, characterized by low power consumption
and a long communication range, is regarded as one of the enabling technologies
for the Industrial Internet of Things (IIoT). However, as the network scale
increases, the energy efficiency (EE) of LoRa networks decreases sharply due to
severe packet collisions. To address this issue, it is essential to
appropriately assign transmission parameters such as the spreading factor and
transmission power for each end device (ED). However, due to the sporadic
traffic and low duty cycle of LoRa networks, evaluating the system EE
performance under different parameter settings is time-consuming. Therefore, we
first formulate an analytical model to calculate the system EE. On this basis,
we propose a transmission parameter allocation algorithm based on multiagent
reinforcement learning (MALoRa) with the aim of maximizing the system EE of
LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED
to better learn how much ''attention'' should be given to the parameter
assignments for relevant EDs when seeking to improve the system EE. Simulation
results demonstrate that MALoRa significantly improves the system EE compared
with baseline algorithms with an acceptable degradation in packet delivery rate
(PDR).Comment: 6 pages, 3 figures, This paper has been accepted for publication in
IEEE Global Communications Conference (GLOBECOM) 202
Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis
Improving model robustness against potential modality noise, as an essential
step for adapting multimodal models to real-world applications, has received
increasing attention among researchers. For Multimodal Sentiment Analysis
(MSA), there is also a debate on whether multimodal models are more effective
against noisy features than unimodal ones. Stressing on intuitive illustration
and in-depth analysis of these concerns, we present Robust-MSA, an interactive
platform that visualizes the impact of modality noise as well as simple defence
methods to help researchers know better about how their models perform with
imperfect real-world data.Comment: Accept by AAAI 2023. Code is available at
https://github.com/thuiar/Robust-MS
Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography
This paper presents a closed-loop magnetic manipulation framework for robotic
transesophageal echocardiography (TEE) acquisitions. Different from previous
work on intracorporeal robotic ultrasound acquisitions that focus on continuum
robot control, we first investigate the use of magnetic control methods for
more direct, intuitive, and accurate manipulation of the distal tip of the
probe. We modify a standard TEE probe by attaching a permanent magnet and an
inertial measurement unit sensor to the probe tip and replacing the flexible
gastroscope with a soft tether containing only wires for transmitting
ultrasound signals, and show that 6-DOF localization and 5-DOF closed-loop
control of the probe can be achieved with an external permanent magnet based on
the fusion of internal inertial measurement and external magnetic field sensing
data. The proposed method does not require complex structures or motions of the
actuator and the probe compared with existing magnetic manipulation methods. We
have conducted extensive experiments to validate the effectiveness of the
framework in terms of localization accuracy, update rate, workspace size, and
tracking accuracy. In addition, our results obtained on a realistic cardiac
tissue-mimicking phantom show that the proposed framework is applicable in real
conditions and can generally meet the requirements for tele-operated TEE
acquisitions.Comment: Accepted by IEEE Transactions on Robotics. Copyright may be
transferred without notice, after which this version may no longer be
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