91 research outputs found
Plan4MC: Skill Reinforcement Learning and Planning for Open-World Minecraft Tasks
We study building a multi-task agent in Minecraft. Without human
demonstrations, solving long-horizon tasks in this open-ended environment with
reinforcement learning (RL) is extremely sample inefficient. To tackle the
challenge, we decompose solving Minecraft tasks into learning basic skills and
planning over the skills. We propose three types of fine-grained basic skills
in Minecraft, and use RL with intrinsic rewards to accomplish basic skills with
high success rates. For skill planning, we use Large Language Models to find
the relationships between skills and build a skill graph in advance. When the
agent is solving a task, our skill search algorithm walks on the skill graph
and generates the proper skill plans for the agent. In experiments, our method
accomplishes 24 diverse Minecraft tasks, where many tasks require sequentially
executing for more than 10 skills. Our method outperforms baselines in most
tasks by a large margin. The project's website and code can be found at
https://sites.google.com/view/plan4mc.Comment: 19 page
Tidal variation shaped microplastic enrichment patterns in mangrove blue carbon ecosystem of northern Beibu Gulf, China
Mangroves are considered to be a sink for microplastics (MPs) due to their unique characteristics. Previous studies mainly focused on the spatial distribution of MPs, but few researchers have addressed the influence of tidal variation on this distribution, especially since the MP total number in mangroves was unknown. In this study, surface sediment samples were collected in mangroves from the Beibu Gulf, South China Sea, and the abundance, composition, and number of MPs were investigated. The results showed that MPs were widely present in all mangrove sediment samples, with abundances ranging from 26.67 ± 9.43 to 239.94 ± 37.80 items/kg. The distribution of MPs was heterogeneous among different sampling sites, with the highest levels in the Shankou (SK) area. The MP abundance in the same mangrove forest gradually increased from the low tidal zone to the high tidal zone, with the enrichment factor ranging from 1.50 to 4.00. The MP abundance was significantly correlated with particulate organic carbon (POC) (n = 12, R = 0.664, p < 0.05). Results showed that mangroves had an interception effect on MPs and factors affecting MP distribution in mangrove sediments included not only tides but also human activities, such as aquaculture, agriculture, and residential life. Finally, this paper estimated the MP total number in mangroves at different sampling areas and tidal zones, and the middle tidal zone was considered to be more accurate for MP pollution assessment in mangroves
PUMA: Secure Inference of LLaMA-7B in Five Minutes
With ChatGPT as a representative, tons of companies have began to provide
services based on large Transformers models. However, using such a service
inevitably leak users' prompts to the model provider. Previous studies have
studied secure inference for Transformer models using secure multiparty
computation (MPC), where model parameters and clients' prompts are kept secret.
Despite this, these frameworks are still limited in terms of model performance,
efficiency, and deployment. To address these limitations, we propose framework
PUMA to enable fast and secure Transformer model inference. Our framework
designs high quality approximations for expensive functions, such as GeLU and
Softmax, which significantly reduce the cost of secure inference while
preserving the model performance. Additionally, we design secure Embedding and
LayerNorm procedures that faithfully implement the desired functionality
without undermining the Transformer architecture. PUMA is about 2x faster than
the state-of-the-art MPC framework MPCFORMER(ICLR 2023) and has similar
accuracy as plaintext models without fine-tuning (which the previous works
failed to achieve).
One more thing, PUMA can evaluate LLaMA-7B in around 5 minutes to generate 1
token. To our best knowledge, this is the first time that a model with such a
parameter size is able to be evaluated under MPC. PUMA has been open-sourced in
the Github repository of SecretFlow-SPU
Neutrino Physics with JUNO
The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe
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
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
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