85 research outputs found
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
Federated adversarial training can effectively complement adversarial
robustness into the privacy-preserving federated learning systems. However, the
high demand for memory capacity and computing power makes large-scale federated
adversarial training infeasible on resource-constrained edge devices. Few
previous studies in federated adversarial training have tried to tackle both
memory and computational constraints simultaneously. In this paper, we propose
a new framework named Federated Adversarial Decoupled Learning (FADE) to enable
AT on heterogeneous resource-constrained edge devices. FADE differentially
decouples the entire model into small modules to fit into the resource budget
of each device, and each device only needs to perform AT on a single module in
each communication round. We also propose an auxiliary weight decay to
alleviate objective inconsistency and achieve better accuracy-robustness
balance in FADE. FADE offers theoretical guarantees for convergence and
adversarial robustness, and our experimental results show that FADE can
significantly reduce the consumption of memory and computing power while
maintaining accuracy and robustness.Comment: Preprint versio
Single channel based interference-free and self-powered human-machine interactive interface using eigenfrequency-dominant mechanism
The recent development of wearable devices is revolutionizing the way of
human-machine interaction (HMI). Nowadays, an interactive interface that
carries more embedded information is desired to fulfil the increasing demand in
era of Internet of Things. However, present approach normally relies on sensor
arrays for memory expansion, which inevitably brings the concern of wiring
complexity, signal differentiation, power consumption, and miniaturization.
Herein, a one-channel based self-powered HMI interface, which uses the
eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is
reported. When manually vibrated, the inherent recovery of the MMP caused a
damped oscillation that generates current signals because of Faraday's Law of
induction. The time-to-frequency conversion explores the MMP-related
eigenfrequency, which provides a specific solution to allocate diverse commands
in an interference-free behavior even with one electric channel. A cylindrical
cantilever model was built to regulate the MMP eigenfrequencies via precisely
designing the dimensional parameters and material properties. We show that
using one device and two electrodes, high-capacity HMI interface can be
realized when the MMPs with different eigenfrequencies have been integrated.
This study provides the reference value to design the future HMI system
especially for situations that require a more intuitive and intelligent
communication experience with high-memory demand.Comment: 35 pages, 6 figure
A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape
As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually
Markers of fungal translocation are elevated during post-acute sequelae of SARS-CoV-2 and induce NF-ÎşB signaling
Long COVID, a type of post-acute sequelae of SARS-CoV-2 (PASC), has been associated with sustained elevated levels of immune activation and inflammation. However, the mechanisms that drive this inflammation remain unknown. Inflammation during acute coronavirus disease 2019 could be exacerbated by microbial translocation (from the gut and/or lung) to blood. Whether microbial translocation contributes to inflammation during PASC is unknown. We did not observe a significant elevation in plasma markers of bacterial translocation during PASC. However, we observed higher levels of fungal translocation - measured as β-glucan, a fungal cell wall polysaccharide - in the plasma of individuals experiencing PASC compared with those without PASC or SARS-CoV-2-negative controls. The higher β-glucan correlated with higher inflammation and elevated levels of host metabolites involved in activating N-methyl-d-aspartate receptors (such as metabolites within the tryptophan catabolism pathway) with established neurotoxic properties. Mechanistically, β-glucan can directly induce inflammation by binding to myeloid cells (via Dectin-1) and activating Syk/NF-κB signaling. Using a Dectin-1/NF-κB reporter model, we found that plasma from individuals experiencing PASC induced higher NF-κB signaling compared with plasma from negative controls. This higher NF-κB signaling was abrogated by piceatannol (Syk inhibitor). These data suggest a potential targetable mechanism linking fungal translocation and inflammation during PASC
Targeting the αvβ3/NGR2 Pathway in Neuroendocrine Prostate Cancer
Highly aggressive, metastatic, neuroendocrine prostate cancer, which typically develops from prostate cancer cells acquiring resistance to androgen deprivation therapy, is associated with limited treatment options and hence poor prognosis. We have previously demonstrated that the αVβ3 integrin is over-expressed in neuroendocrine prostate cancer. We now show that LM609, a monoclonal antibody that specifically targets the human αVβ3 integrin, hinders the growth of neuroendocrine prostate cancer patient-derived xenografts in vivo. Our group has recently identified a novel αVβ3 integrin binding partner, NgR2, responsible for regulating the expression of neuroendocrine markers and for inducing neuroendocrine differentiation in prostate cancer cells. Through in vitro functional assays, we here demonstrate that NgR2 is crucial in promoting cell adhesion to αVβ3 ligands. Moreover, we describe for the first time co-fractionation of αVβ3 integrin and NgR2 in small extracellular vesicles derived from metastatic prostate cancer patients\u27 plasma. These prostate cancer patient-derived small extracellular vesicles have a functional impact on human monocytes, increasing their adhesion to fibronectin. The monocytes incubated with small extracellular vesicles do not show an associated change in conventional polarization marker expression and appear to be in an early stage that may be defined as adhesion competent . Overall, these findings allow us to better understand integrin-directed signaling and cell-cell communication during cancer progression. Furthermore, our results pave the way for new diagnostic and therapeutic perspectives for patients affected by neuroendocrine prostate cancer
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
Incident type 2 diabetes attributable to suboptimal diet in 184 countries
The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.8–14.4 million) incident T2D cases, representing 70.3% (68.8–71.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.0–27.1%)), excess refined rice and wheat intake (24.6% (22.3–27.2%)) and excess processed meat intake (20.3% (18.3–23.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.4–87.7%)) and Latin America and the Caribbean (81.8% (80.1–83.4%)); and lowest proportional burdens were in South Asia (55.4% (52.1–60.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally.publishedVersio
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