467 research outputs found
On the Asymptotic Capacity of Information Theoretical Privacy-preserving Epidemiological Data Collection
We formulate a new secure distributed computation problem, where a simulation
center can require any linear combination of users' data through a
caching layer consisting of servers. The users, servers, and data
collector do not trust each other. For users, any data is required to be
protected from up to servers; for servers, any more information than the
desired linear combination cannot be leaked to the data collector; and for the
data collector, any single server knows nothing about the coefficients of the
linear combination. Our goal is to find the optimal download cost, which is
defined as the size of message uploaded to the simulation center by the
servers, to the size of desired linear combination. We proposed a scheme with
the optimal download cost when . We also prove that when ,
the scheme is not feasible
Z' Mediated right-handed Neutrinos from Meson Decays at the FASER
We investigate the pair production of right-handed neutrinos mediated by a
from the meson decays at the FASER detector of the HL-LHC. The
can be either the additional gauge boson in the or
sterile -specific model. Taking the gauge coupling or the kinetic
mixing at the current limits, we analyses the sensitivity to the masses of the
heavy neutrinos, , and active-sterile mixing, , of the FASER-2.
In a background free scenario, FASER-2 is able to probe when GeV, which is comparable to the current limits
from the beam dump experiments for the right-handed neutrinos dominantly
coupled to electron and muon flavours, and exceed three magnitude for tau. When
comes to the model, FASER-2 can probe ,
which is better than the current limits in all three flavours. A proposed
long-lived particle detector, FACET, is also studied, while no significant
difference from FASER-2 is derived.Comment: 14 pages, 8 figure
Coreset Selection with Prioritized Multiple Objectives
Coreset selection is powerful in reducing computational costs and
accelerating data processing for deep learning algorithms. It strives to
identify a small subset from large-scale data, so that training only on the
subset practically performs on par with full data. When coreset selection is
applied in realistic scenes, under the premise that the identified coreset has
achieved comparable model performance, practitioners regularly desire the
identified coreset can have a size as small as possible for lower costs and
greater acceleration. Motivated by this desideratum, for the first time, we
pose the problem of "coreset selection with prioritized multiple objectives",
in which the smallest coreset size under model performance constraints is
explored. Moreover, to address this problem, an innovative method is proposed,
which maintains optimization priority order over the model performance and
coreset size, and efficiently optimizes them in the coreset selection
procedure. Theoretically, we provide the convergence guarantee of the proposed
method. Empirically, extensive experiments confirm its superiority compared
with previous strategies, often yielding better model performance with smaller
coreset sizes
Testing the seesaw mechanisms via displaced right-handed neutrinos from a light scalar at the HL-LHC
We investigate the pair production of right-handed neutrinos from a light
scalar decays based at the model. The scalar mixes to
the SM Higgs, and the physical scalar is required to be lighter than the
observed Higgs. The pair-produced right-handed neutrinos are predicted to be
long-lived by the type-I seesaw mechanism, and yield potential distinct
signatures such as displaced vertex and time-delayed leptons at the
CMS/ATLAS/LHCb, as well as signatures at the far detectors including the
CODEX-b, FACET, FASER, MoEDAL-MAPP and MATHUSLA. We analyse the sensitivity
reach at the HL-LHC for the RH neutrinos with masses from 2.5-30 GeV, showing
that the active-sterile mixing to muons can be probed
at the CMS/ATLAS/LHCb, and one magnitude lower at the MATHUSLA, reaching the
parameter space interesting for type-I seesaw mechanisms.Comment: 11 pages, 7 figure
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
With the increasing adoption of data-hungry machine learning algorithms,
personal data privacy has emerged as one of the key concerns that could hinder
the success of digital transformation. As such, Privacy-Preserving Machine
Learning (PPML) has received much attention from both academia and industry.
However, organizations are faced with the dilemma that, on the one hand, they
are encouraged to share data to enhance ML performance, but on the other hand,
they could potentially be breaching the relevant data privacy regulations.
Practical PPML typically allows multiple participants to individually train
their ML models, which are then aggregated to construct a global model in a
privacy-preserving manner, e.g., based on multi-party computation or
homomorphic encryption. Nevertheless, in most important applications of
large-scale PPML, e.g., by aggregating clients' gradients to update a global
model for federated learning, such as consumer behavior modeling of mobile
application services, some participants are inevitably resource-constrained
mobile devices, which may drop out of the PPML system due to their mobility
nature. Therefore, the resilience of privacy-preserving aggregation has become
an important problem to be tackled. In this paper, we propose a scalable
privacy-preserving aggregation scheme that can tolerate dropout by participants
at any time, and is secure against both semi-honest and active malicious
adversaries by setting proper system parameters. By replacing
communication-intensive building blocks with a seed homomorphic pseudo-random
generator, and relying on the additive homomorphic property of Shamir secret
sharing scheme, our scheme outperforms state-of-the-art schemes by up to
6.37 in runtime and provides a stronger dropout-resilience. The
simplicity of our scheme makes it attractive both for implementation and for
further improvements.Comment: 16 pages, 5 figures. Accepted by IEEE Transactions on Information
Forensics and Securit
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
In-context learning is a promising paradigm that utilizes in-context examples
as prompts for the predictions of large language models. These prompts are
crucial for achieving strong performance. However, since the prompts need to be
sampled from a large volume of annotated examples, finding the right prompt may
result in high annotation costs. To address this challenge, this paper
introduces an influence-driven selective annotation method that aims to
minimize annotation costs while improving the quality of in-context examples.
The essence of our method is to select a pivotal subset from a large-scale
unlabeled data pool to annotate for the subsequent sampling of prompts.
Specifically, a directed graph is first constructed to represent unlabeled
data. Afterward, the influence of candidate unlabeled subsets is quantified
with a diffusion process. A simple yet effective greedy algorithm for unlabeled
data selection is lastly introduced. It iteratively selects the data if it
provides a maximum marginal gain with respect to quantified influence. Compared
with previous efforts on selective annotations, our influence-driven method
works in an end-to-end manner, avoids an intractable explicit balance between
data diversity and representativeness, and enjoys theoretical support.
Experiments confirm the superiority of the proposed method on various
benchmarks, achieving better performance under lower time consumption during
subset selection. The project page is available at
https://skzhang1.github.io/IDEAL/.Comment: Accepted by ICLR 202
The effectiveness of ultrasound-guided core needle biopsy in detecting lymph node metastases in the axilla in patients with breast cancer: systematic review and meta-analysis
Objective: This study aimed to perform a meta-analysis to investigate the diagnostic safety and accuracy of Ultrasound-Guided Core Needle Biopsy (US-CNB) Axillary Lymph Nodes (ALNs) region in patients with Breast Cancer (BC).
Methods: The authors searched the electronic databases PubMed, Scopus, Embase, and Web of Science for clinical trials about US-CNB for the detection of ALNs in breast cancer patients. The authors extracted and pooled raw data from the included studies and performed statistical analyses using Meta-DiSc 1.4 and Review Manager 5.3 software. A random effects model was used to calculate the data. At the same time, data from the Ultrasound-guided Fine-Needle Aspiration (US-FNA) were introduced for comparison with the US-CNB. In addition, the subgroup was performed to explore the causes of heterogeneity. (PROSPERO ID: CRD42022369491).
Results: In total, 18 articles with 2521 patients were assessed as meeting the study criteria. The overall sensitivity was 0.90 (95% CI [Confidence Interval], 0.87‒0.91; p = 0.00), the overall specificity was 0.99 (95% CI 0.98‒1.00; p = 0.62), the overall area under the curve (AUC) was 0.98. Next, in the comparison of US-CNB and US-FNA, US-CNB is better than US-FNA in the diagnosis of ALNs metastases. The sensitivity was 0.88 (95% CI 0.84‒0.91; p = 0.12) vs. 0.73 (95% CI 0.69‒0.76; p = 0.91), the specificity was 1.00 (95% CI 0.99‒1.00; p = 1.00) vs. 0.99 (95% CI 0.67‒0.74; p = 0.92), and the AUC was 0.99 vs. 0.98. Subgroup analysis showed that heterogeneity may be related to preoperative Neoadjuvant Chemotherapy (NAC) treatment, region, size of tumor diameter, and the number of punctures.
Conclusion: US-CNB has a satisfactory diagnostic performance with good specificity and sensitivity in the preoperative diagnosis of ALNs in BC patients
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