821 research outputs found
Source Coding for a Multihop Network
Summary form only given. In this paper, we bound the rate-distortion region for a four-node network. The results are the first known expansion of rate-distortion theory from single-hop networks (every source has a direct connection to each of its destinations), to multihop networks, which allow intermediate nodes. While single-hop network source coding solutions may be applied in multihop networks, such applications require explicit rate allocation for each source-destination pair, and the resulting solutions may be suboptimal. We therefore tackle the multihop network source coding problem directly using a diamond network
On the Concavity of Rate Regions for Lossless Source Coding in Networks
For a family of network source coding problems, we prove that the lossless rate region is concave in the distribution of sources. While the proof of concavity is straightforward for the few examples where a single-letter characterization of the lossless source coding region is known, it is more difficult for the vast majority of networks where the lossless source coding region remains unsolved. The class of networks that we investigate includes both solved and unsolved examples. We further conjecture that the same property applies more widely and sketch an avenue for investigating that conjecture
On Multi-Resolution Coding and a Two-Hop Network
We study the source coding problem on a simple two-hop network with side information on the middle and end nodes. For the degraded case, where the side information at the end node is weaker than the side information at the middle node, a complete characterization of the rate-distortion region is derived
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
On-device machine learning (ML) inference can enable the use of private user
data on user devices without revealing them to remote servers. However, a pure
on-device solution to private ML inference is impractical for many applications
that rely on embedding tables that are too large to be stored on-device. In
particular, recommendation models typically use multiple embedding tables each
on the order of 1-10 GBs of data, making them impractical to store on-device.
To overcome this barrier, we propose the use of private information retrieval
(PIR) to efficiently and privately retrieve embeddings from servers without
sharing any private information. As off-the-shelf PIR algorithms are usually
too computationally intensive to directly use for latency-sensitive inference
tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR
with the downstream ML application to obtain further speedup. Our GPU
acceleration strategy improves system throughput by more than over
an optimized CPU PIR implementation, and our PIR-ML co-design provides an over
additional throughput improvement at fixed model quality. Together,
for various on-device ML applications such as recommendation and language
modeling, our system on a single V100 GPU can serve up to queries per
second -- a throughput improvement over a CPU-based baseline --
while maintaining model accuracy
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collection
of cloud services and products, constituting significant compute demand of the
cloud infrastructure. Thus, improving the execution efficiency of neural
recommendation directly translates into infrastructure capacity saving. In this
paper, we devise a novel end-to-end modeling infrastructure, DeepRecInfra, that
adopts an algorithm and system co-design methodology to custom-design systems
for recommendation use cases. Leveraging the insights from the recommendation
characterization, a new dynamic scheduler, DeepRecSched, is proposed to
maximize latency-bounded throughput by taking into account characteristics of
inference query size and arrival patterns, recommendation model architectures,
and underlying hardware systems. By doing so, system throughput is doubled
across the eight industry-representative recommendation models. Finally,
design, deployment, and evaluation in at-scale production datacenter shows over
30% latency reduction across a wide variety of recommendation models running on
hundreds of machines
Activation of Thromboxane A2 Receptor (TP) Increases the Expression of Monocyte Chemoattractant Protein -1 (MCP-1)/Chemokine (C-C motif) Ligand 2 (CCL2) and Recruits Macrophages to Promote Invasion of Lung Cancer Cells
Thromboxane synthase (TXAS) and thromboxane A2 receptor (TP), two critical components for thromboxane A2 (TXA2) signaling, have been suggested to be involved in cancer invasion and metastasis. However, the mechanisms by which TXA2 promotes these processes are still unclear. Here we show that TXA2 mimetic, I-BOP, induced monocyte chemoattractant protein -1(MCP-1)/chemokine (C-C motif) ligand 2 (CCL2) expression at both mRNA and protein levels in human lung adenocarcinoma A549 cells stably over-expressing TP receptor α isoform (A549-TPα). The induction of MCP-1 was also found in other lung cancer cells H157 and H460 that express relatively high levels of endogenous TP. Using specific inhibitors of several signaling molecules and promoter/luciferase assay, we identified that transcription factor SP1 mediates I-BOP-induced MCP-1 expression. Furthermore, supernatants from I-BOP-treated A549-TPα cells enhanced MCP-1-dependent migration of RAW 264.7 macrophages. Moreover, co-culture of A549 cells with RAW 264.7 macrophages induced expression of MMPs, VEGF and MCP-1 genes, and increased the invasive potential in A549 cells. These findings suggest that TXA2 may stimulate invasion of cancer cells through MCP-1-mediated macrophage recruitment
Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels
Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets
A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations
Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes' functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.</p
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