126 research outputs found
Concatenation of the Gottesman-Kitaev-Preskill code with the XZZX surface code
Bosonic codes provide an alternative option for quantum error correction. An
important category of bosonic codes called the Gottesman-Kitaev-Preskill (GKP)
code has aroused much interest recently. Theoretically, the error correction
ability of GKP code is limited since it can only correct small shift errors in
position and momentum quadratures. A natural approach to promote the GKP error
correction for large-scale, fault-tolerant quantum computation is concatenating
encoded GKP states with a stabilizer code. The performance of the XZZX
surface-GKP code, i.e., the single-mode GKP code concatenated with the XZZX
surface code is investigated in this paper under two different noise models.
Firstly, in the code-capacity noise model, the asymmetric rectangular GKP code
with parameter is introduced. Using the minimum weight perfect
matching decoder combined with the continuous-variable GKP information, the
optimal threshold of the XZZX-surface GKP code reaches when
, compared with the threshold of the standard
surface-GKP code. Secondly, we analyze the shift errors of two-qubit gates in
the actual implementation and build the full circuit-level noise model. By
setting the appropriate bias parameters, the logical error rate is reduced by
several times in some cases. These results indicate the XZZX surface-GKP codes
are more suitable for asymmetric concatenation under the general noise models.
We also estimate the overhead of the XZZX-surface GKP code which uses about 291
GKP states with the noise parameter 18.5 dB () to
encode a logical qubit with the error rate , compared with
the qubit-based surface code using 3041 qubits to achieve almost the same
logical error rate.Comment: 17 pages, 10 figure
Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
We propose a balanced coarsening scheme for multilevel hypergraph
partitioning. In addition, an initial partitioning algorithm is designed to
improve the quality of k-way hypergraph partitioning. By assigning vertex
weights through the LPT algorithm, we generate a prior hypergraph under a
relaxed balance constraint. With the prior hypergraph, we have defined the
Wasserstein discrepancy to coordinate the optimal transport of coarsening
process. And the optimal transport matrix is solved by Sinkhorn algorithm. Our
coarsening scheme fully takes into account the minimization of connectivity
metric (objective function). For the initial partitioning stage, we define a
normalized cut function induced by Fiedler vector, which is theoretically
proved to be a concave function. Thereby, a three-point algorithm is designed
to find the best cut under the balance constraint
DFL: High-Performance Blockchain-Based Federated Learning
Many researchers are trying to replace the aggregation server in federated
learning with a blockchain system to achieve better privacy, robustness and
scalability. In this case, clients will upload their updated models to the
blockchain ledger, and use a smart contract on the blockchain system to perform
model averaging. However, running machine learning applications on the
blockchain is almost impossible because a blockchain system, which usually
takes over half minute to generate a block, is extremely slow and unable to
support machine learning applications.
This paper proposes a completely new public blockchain architecture called
DFL, which is specially optimized for distributed federated machine learning.
This architecture inherits most traditional blockchain merits and achieves
extremely high performance with low resource consumption by waiving global
consensus. To characterize the performance and robustness of our architecture,
we implement the architecture as a prototype and test it on a physical
four-node network. To test more nodes and more complex situations, we build a
simulator to simulate the network. The LeNet results indicate our system can
reach over 90% accuracy for non-I.I.D. datasets even while facing model
poisoning attacks, with the blockchain consuming less than 5% of hardware
resources.Comment: 11 pages, 17 figure
Evaluating Large Language Models: A Comprehensive Survey
Large language models (LLMs) have demonstrated remarkable capabilities across
a broad spectrum of tasks. They have attracted significant attention and been
deployed in numerous downstream applications. Nevertheless, akin to a
double-edged sword, LLMs also present potential risks. They could suffer from
private data leaks or yield inappropriate, harmful, or misleading content.
Additionally, the rapid progress of LLMs raises concerns about the potential
emergence of superintelligent systems without adequate safeguards. To
effectively capitalize on LLM capacities as well as ensure their safe and
beneficial development, it is critical to conduct a rigorous and comprehensive
evaluation of LLMs.
This survey endeavors to offer a panoramic perspective on the evaluation of
LLMs. We categorize the evaluation of LLMs into three major groups: knowledge
and capability evaluation, alignment evaluation and safety evaluation. In
addition to the comprehensive review on the evaluation methodologies and
benchmarks on these three aspects, we collate a compendium of evaluations
pertaining to LLMs' performance in specialized domains, and discuss the
construction of comprehensive evaluation platforms that cover LLM evaluations
on capabilities, alignment, safety, and applicability.
We hope that this comprehensive overview will stimulate further research
interests in the evaluation of LLMs, with the ultimate goal of making
evaluation serve as a cornerstone in guiding the responsible development of
LLMs. We envision that this will channel their evolution into a direction that
maximizes societal benefit while minimizing potential risks. A curated list of
related papers has been publicly available at
https://github.com/tjunlp-lab/Awesome-LLMs-Evaluation-Papers.Comment: 111 page
MXene nanomaterials in biomedicine: A bibliometric perspective
Purpose: MXene is two-dimensional (2D) nanomaterials that comprise transition metal carbides, nitrides, and carbonitrides. Their unique nanostructure attributes it a special role in medical applications. However, bibliometric studies have not been conducted in this field. Therefore, the aim of the present study was to conduct a bibliometric analysis to evaluate the global scientific output of MXene in biomedical research, explore the current situation of this field in the past years and predicte its research hotpots.Methods: We utilized visual analysis softwares Citespace and Bibliometrix to analyze all relevant documents published in the period of 2011–2022. The bibliometric records were obtained from the Web of Science Core Collection.Results: A total of 1,489 publications were analyzed in this study. We observed that China is the country with the largest number of publications, with Sichuan University being the institution with the highest number of publications in this field. The most publications on MXene medicine research in the past year were found primarily in journals about Chemistry/Materials/Physics. Moreover, ACS Applied Materials and Interfaces was found to be the most productive journal in this field. Co-cited references and keyword cluster analysis revealed that #antibacterial# and #photothermal therapy# are the research focus keyword and burst detection suggested that driven wearable electronics were newly-emergent research hot spots.Conclusion: Our bibliometric analysis indicates that research on MXene medical application remains an active field of study. At present, the research focus is on the application of MXene in the field of antibacterial taking advantage of its photothermal properties. In the future, wearable electronics is the research direction of MXene medical application
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
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
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
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