251 research outputs found
CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities
Detecting norm violations in online communities is critical to maintaining
healthy and safe spaces for online discussions. Existing machine learning
approaches often struggle to adapt to the diverse rules and interpretations
across different communities due to the inherent challenges of fine-tuning
models for such context-specific tasks. In this paper, we introduce
Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a
novel method that employs prompt-based learning to detect norm violations
across various types of rules. CPL-NoViD outperforms the baseline by
incorporating context through natural language prompts and demonstrates
improved performance across different rule types. Significantly, it not only
excels in cross-rule-type and cross-community norm violation detection but also
exhibits adaptability in few-shot learning scenarios. Most notably, it
establishes a new state-of-the-art in norm violation detection, surpassing
existing benchmarks. Our work highlights the potential of prompt-based learning
for context-sensitive norm violation detection and paves the way for future
research on more adaptable, context-aware models to better support online
community moderators
Affective Polarization in Social Networks
Affective polarization has grown dramatically in recent years, with surveys
showing that liberals and conservatives not only disagree on policy issues but
also dislike and distrust each other. While studies have implicated social
media in amplifying polarization, there is a lack of agreement on the
mechanisms driving affective polarization and methods to measure it. Our paper
addresses these gaps. First, we directly measure affective polarization on
social media by quantifying the emotional tone of reply interactions between
users. As predicted by affective polarization, in-group interactions between
same-partisanship users tend to be positive, while out-group interactions
between opposite-partisanship users are characterized by negativity and
toxicity. Second, we show that affective polarization generalizes beyond the
in-group/out-group dichotomy and can be considered a structural property of
social networks. Specifically, we show that emotions vary with network distance
between users, with closer interactions eliciting positive emotions and more
distant interactions leading to anger, disgust, and toxicity. These findings
are consistent across diverse datasets and languages, spanning discussions on
topics such as the Covid-19 pandemic, abortion, and the 2017 French Election.
Our research provides new insights into the complex social dynamics of
affective polarization in the digital age and its implications for political
discourse
Compressive Holographic Video
Compressed sensing has been discussed separately in spatial and temporal
domains. Compressive holography has been introduced as a method that allows 3D
tomographic reconstruction at different depths from a single 2D image. Coded
exposure is a temporal compressed sensing method for high speed video
acquisition. In this work, we combine compressive holography and coded exposure
techniques and extend the discussion to 4D reconstruction in space and time
from one coded captured image. In our prototype, digital in-line holography was
used for imaging macroscopic, fast moving objects. The pixel-wise temporal
modulation was implemented by a digital micromirror device. In this paper we
demonstrate temporal super resolution with multiple depths recovery
from a single image. Two examples are presented for the purpose of recording
subtle vibrations and tracking small particles within 5 ms.Comment: 12 pages, 6 figure
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
Previous studies have shown that leveraging domain index can significantly
boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628).
However, such domain indices are not always available. To address this
challenge, we first provide a formal definition of domain index from the
probabilistic perspective, and then propose an adversarial variational Bayesian
framework that infers domain indices from multi-domain data, thereby providing
additional insight on domain relations and improving domain adaptation
performance. Our theoretical analysis shows that our adversarial variational
Bayesian framework finds the optimal domain index at equilibrium. Empirical
results on both synthetic and real data verify that our model can produce
interpretable domain indices which enable us to achieve superior performance
compared to state-of-the-art domain adaptation methods. Code is available at
https://github.com/Wang-ML-Lab/VDI.Comment: ICLR 2023 Spotlight (notable-top-25%
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
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Patterns of spatial genetic structures in Aedes albopictus (Diptera: Culicidae) populations in China.
BACKGROUND:The Asian tiger mosquito, Aedes albopictus, is one of the 100 worst invasive species in the world and the vector for several arboviruses including dengue, Zika and chikungunya viruses. Understanding the population spatial genetic structure, migration, and gene flow of vector species is critical to effectively preventing and controlling vector-borne diseases. Little is known about the population structure and genetic differentiation of native Ae. albopictus in China. The aim of this study was to examine the patterns of the spatial genetic structures of native Ae. albopictus populations, and their relationship to dengue incidence, on a large geographical scale. METHODS:During 2016-2018, adult female Ae. albopictus mosquitoes were collected by human landing catch (HLC) or human-bait sweep-net collections in 34 localities across China. Thirteen microsatellite markers were used to examine the patterns of genetic diversity, population structure, and gene flow among native Ae. albopictus populations. The correlation between population genetic indices and dengue incidence was also examined. RESULTS:A total of 153 distinct alleles were identified at the 13 microsatellite loci in the tested populations. All loci were polymorphic, with the number of distinct alleles ranging from eight to sixteen. Genetic parameters such as PIC, heterozygosity, allelic richness and fixation index (FST) revealed highly polymorphic markers, high genetic diversity, and low population genetic differentiation. In addition, Bayesian analysis of population structure showed two distinct genetic groups in southern-western and eastern-central-northern China. The Mantel test indicated a positive correlation between genetic distance and geographical distance (R2Â =Â 0.245, PÂ =Â 0.01). STRUCTURE analysis, PCoA and GLS interpolation analysis indicated that Ae. albopictus populations in China were regionally clustered. Gene flow and relatedness estimates were generally high between populations. We observed no correlation between population genetic indices of microsatellite loci in Ae. albopictus populations and dengue incidence. CONCLUSION:Strong gene flow probably assisted by human activities inhibited population differentiation and promoted genetic diversity among populations of Ae. albopictus. This may represent a potential risk of rapid spread of mosquito-borne diseases. The spatial genetic structure, coupled with the association between genetic indices and dengue incidence, may have important implications for understanding the epidemiology, prevention, and control of vector-borne diseases
The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape
The rich and dynamic information environment of social media provides
researchers, policy makers, and entrepreneurs with opportunities to learn about
social phenomena in a timely manner. However, using these data to understand
social behavior is difficult due to heterogeneity of topics and events
discussed in the highly dynamic online information environment. To address
these challenges, we present a method 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 method
by successfully detecting major and smaller events on three different datasets,
including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in which
we revealed the complex psychological impact of the BlackLivesMatter movement
and the COVID-19 pandemic, (2) a dataset related to abortion rights discussions
in USA, in which we uncovered the strong emotional reactions to the overturn of
Roe v. Wade and state abortion bans, and (3) a dataset about the 2022 French
presidential election, in which we discovered the emotional and moral shift
from positive before voting to fear and criticism after voting. The capability
of our method allows for better sensing and monitoring of population's
reactions during crises using online data.Comment: arXiv admin note: substantial text overlap with arXiv:2307.1024
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
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