82 research outputs found
Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications
This article introduces a novel lightweight framework using ambient
backscattering communications to counter eavesdroppers. In particular, our
framework divides an original message into two parts: (i) the active-transmit
message transmitted by the transmitter using conventional RF signals and (ii)
the backscatter message transmitted by an ambient backscatter tag that
backscatters upon the active signals emitted by the transmitter. Notably, the
backscatter tag does not generate its own signal, making it difficult for an
eavesdropper to detect the backscattered signals unless they have prior
knowledge of the system. Here, we assume that without decoding/knowing the
backscatter message, the eavesdropper is unable to decode the original message.
Even in scenarios where the eavesdropper can capture both messages,
reconstructing the original message is a complex task without understanding the
intricacies of the message-splitting mechanism. A challenge in our proposed
framework is to effectively decode the backscattered signals at the receiver,
often accomplished using the maximum likelihood (MLK) approach. However, such a
method may require a complex mathematical model together with perfect channel
state information (CSI). To address this issue, we develop a novel deep
meta-learning-based signal detector that can not only effectively decode the
weak backscattered signals without requiring perfect CSI but also quickly adapt
to a new wireless environment with very little knowledge. Simulation results
show that our proposed learning approach, without requiring perfect CSI and
complex mathematical model, can achieve a bit error ratio close to that of the
MLK-based approach. They also clearly show the efficiency of the proposed
approach in dealing with eavesdropping attacks and the lack of training data
for deep learning models in practical scenarios
Commune agroecosystem analysis to support decision making for water allocation for fisheries and agriculture in the Tonle Sap wetland system
The Project on Commune Agroecosystem Analysis to Support Decision Making for Water Allocation
for Fisheries and Agriculture in the Tonle Sap Wetland System was undertaken with the aim of
improving fisheries considerations in the Commune Agroecosystem Analysis (CAEA) process
undertaken in Cambodia, to facilitate better planning at the commune level. Under this project a
number of changes were made to the CAEA tools and process and pilot tested in an adaptive,
iterative manner in four communes – two that had conducted a CAEA previously and two that had
not. Results and analyses indicated that the project had significantly strengthened the manner in
which livelihoods, water resources and fisheries are now addressed by CAEA. The revised CAEA
guidance manual has also shown potential for having wider uptake, and a number of tools have
been used by several other projects within Cambodia
Recherche écorégionale sur la gestion intégrée des ressources naturelles dans les écosystèmes de riziculture en Asie du sud-est
Ecoregional research for integrated natural research management in Southeast Asian rice ecosystems
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN
- …