266 research outputs found
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
The performance of a semantic segmentation model for remote sensing (RS)
images pretrained on an annotated dataset would greatly decrease when testing
on another unannotated dataset because of the domain gap. Adversarial
generative methods, e.g., DualGAN, are utilized for unpaired image-to-image
translation to minimize the pixel-level domain gap, which is one of the common
approaches for unsupervised domain adaptation (UDA). However, the existing
image translation methods are facing two problems when performing RS images
translation: 1) ignoring the scale discrepancy between two RS datasets which
greatly affects the accuracy performance of scale-invariant objects, 2)
ignoring the characteristic of real-to-real translation of RS images which
brings an unstable factor for the training of the models. In this paper,
ResiDualGAN is proposed for RS images translation, where an in-network resizer
module is used for addressing the scale discrepancy of RS datasets, and a
residual connection is used for strengthening the stability of real-to-real
images translation and improving the performance in cross-domain semantic
segmentation tasks. Combined with an output space adaptation method, the
proposed method greatly improves the accuracy performance on common benchmarks,
which demonstrates the superiority and reliability of ResiDuanGAN. At the end
of the paper, a thorough discussion is also conducted to give a reasonable
explanation for the improvement of ResiDualGAN. Our source code is available at
https://github.com/miemieyanga/ResiDualGAN-DRDG
Crocs: Cross-Technology Clock Synchronization for WiFi and ZigBee
Clock synchronization is a key function in embedded wireless systems and
networks. This issue is equally important and more challenging in IoT systems
nowadays, which often include heterogeneous wireless devices that follow
different wireless standards. Conventional solutions to this problem employ
gateway-based indirect synchronization, which suffers low accuracy. This paper
for the first time studies the problem of cross-technology clock
synchronization. Our proposal called Crocs synchronizes WiFi and ZigBee devices
by direct cross-technology communication. Crocs decouples the synchronization
signal from the transmission of a timestamp. By incorporating a barker-code
based beacon for time alignment and cross-technology transmission of
timestamps, Crocs achieves robust and accurate synchronization among WiFi and
ZigBee devices, with the synchronization error lower than 1 millisecond. We
further make attempts to implement different cross-technology communication
methods in Crocs and provide insight findings with regard to the achievable
accuracy and expected overhead
Federated Learning Incentive Mechanism under Buyers' Auction Market
Auction-based Federated Learning (AFL) enables open collaboration among
self-interested data consumers and data owners. Existing AFL approaches are
commonly under the assumption of sellers' market in that the service clients as
sellers are treated as scarce resources so that the aggregation servers as
buyers need to compete the bids. Yet, as the technology progresses, an
increasing number of qualified clients are now capable of performing federated
learning tasks, leading to shift from sellers' market to a buyers' market. In
this paper, we shift the angle by adapting the procurement auction framework,
aiming to explain the pricing behavior under buyers' market. Our modeling
starts with basic setting under complete information, then move further to the
scenario where sellers' information are not fully observable. In order to
select clients with high reliability and data quality, and to prevent from
external attacks, we utilize a blockchain-based reputation mechanism. The
experimental results validate the effectiveness of our approach
RF-Transformer: A Unified Backscatter Radio Hardware Abstraction
This paper presents RF-Transformer, a unified backscatter radio hardware
abstraction that allows a low-power IoT device to directly communicate with
heterogeneous wireless receivers at the minimum power consumption. Unlike
existing backscatter systems that are tailored to a specific wireless
communication protocol, RF-Transformer provides a programmable interface to the
micro-controller, allowing IoT devices to synthesize different types of
protocol-compliant backscatter signals sharing radically different PHY-layer
designs. To show the efficacy of our design, we implement a PCB prototype of
RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating
standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our
extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1
Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi,
ZigBee, Bluetooth, and LoRa signals while consuming 7.6-74.2 less power than
their active counterparts. Our ASIC simulation based on the 65-nm CMOS process
shows that the power gain of RF-Transformer can further grow to 92-678. We
further integrate RF-Transformer with pressure sensors and present a case study
on detecting foot traffic density in hallways. Our 7-day case studies
demonstrate RFTransformer can reliably transmit sensor data to a commodity
gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental
results also verify the compatibility of RF-Transformer with commodity
receivers. Code and hardware schematics can be found at:
https://github.com/LeFsCC/RF-Transformer
High-efficient screening method for identification of key genes in breast cancer through microarray and bioinformatics
Background/Aim: The aim of the present study was to identify key pathways and genes in breast cancer and develop a new method for screening key genes with abnormal expression based on bioinformatics. Materials and Methods: Three microarray datasets GSE21422, GSE42568 and GSE45827 were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were analyzed using GEO2R. The gene ontology (GO) and pathway enrichment analysis were established through DAVID database. The protein–protein interaction (PPI) network was performed through the Search Tool for the Retrieval of Interacting Genes (STRING) database and managed by Cytoscape. The overall survival (OS) analysis of the 4 genes including AURKA, CDH1, CDK1 and PPARG that had higher degrees in this network was uncovered Kaplan-Meier analysis. Results: A total of 811 DEGs were identified in breast cancer, which were enriched in biological processes, including cell cycle, mitosis, vessel development and lipid metabolic. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the up-regulated DEGs were particularly involved in cell cycle, progesterone-mediated oocyte maturation and leukocyte transendothelial migration, while the down-regulated DEGs were mainly involved in regulation of lipolysis, fatty acid degradation and glycerolipid metabolism. Through PPI network analysis, 14 hub genes were identified. Among them, the high expression of AURKA, CDH1 and CDK1 were associated with worse OS of breast cancer patients; while the high expression of PPARG was linked with better OS. Conclusion: The present study identified key pathways and genes involved in breast cancer which are potential molecular targets for breast cancer treatment and diagnosis
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Large Language Models (LLMs) have shown immense potential in multimodal
applications, yet the convergence of textual and musical domains remains
relatively unexplored. To address this gap, we present MusiLingo, a novel
system for music caption generation and music-related query responses.
MusiLingo employs a single projection layer to align music representations from
the pre-trained frozen music audio model MERT with the frozen LLaMA language
model, bridging the gap between music audio and textual contexts. We train it
on an extensive music caption dataset and fine-tune it with instructional data.
Due to the scarcity of high-quality music Q&A datasets, we created the
MusicInstruct (MI) dataset from MusicCaps, tailored for open-ended music
inquiries. Empirical evaluations demonstrate its competitive performance in
generating music captions and composing music-related Q&A pairs. Our introduced
dataset enables notable advancements beyond previous ones
Measuring Online Emotional Reactions to Offline Events
The rich and dynamic information environment on social media provides
researchers, policy makers, and entrepreneurs with opportunities to learn about
social phenomena in a timely manner. However, using this data to understand
human affect and behavior poses multiple challenges, such as heterogeneity of
topics and events discussed in the highly dynamic online information
environment. To address these challenges, we present a methodology for
systematically detecting and measuring emotional reactions to offline events
using change point detection on the time series of collective affect, and
further explaining these reactions using a transformer-based topic model. We
demonstrate the utility of the methodology on a corpus of tweets collected from
a large US metropolitan area between January and August, 2020, covering a
period of great social change, including the COVID-19 pandemic and racial
justice protests. We demonstrate that our method is able to disaggregate topics
to measure population's emotional and moral reactions to events. This
capability allows for better monitoring of population's reactions to offline
events using online data
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