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 $K$ users' data through a caching layer consisting of $N$ servers. The users, servers, and data collector do not trust each other. For users, any data is required to be protected from up to $E$ 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 $E < N-1$. We also prove that when $E\geq N-1$, 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 $Z^\prime$ from the meson decays at the FASER detector of the HL-LHC. The $Z^\prime$ can be either the additional gauge boson in the $U(1)_{B-L}$ or sterile $\nu$-specific $U(1)_s$ model. Taking the gauge coupling or the kinetic mixing at the current limits, we analyses the sensitivity to the masses of the heavy neutrinos, $m_N$, and active-sterile mixing, $V_{lN}^2$, of the FASER-2. In a background free scenario, FASER-2 is able to probe $V_{lN}^2 \approx 10^{-8}$ when $m_N \sim 0.2$ 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 $U(1)_s$ model, FASER-2 can probe $V_{lN}^2 \approx 10^{-10}$, 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 $B-L$ scalar decays based at the $U(1)_{B-L}$ model. The $B-L$ 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 $V_{\mu N} \sim 10^{-5}$ 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$\times$ 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

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&nbsp;1.4 and Review Manager&nbsp;5.3&nbsp;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:&nbsp;CRD42022369491). Results: In total, 18&nbsp;articles with&nbsp;2521&nbsp;patients were assessed as meeting the study criteria. The overall sensitivity was&nbsp;0.90 (95%&nbsp;CI [Confidence Interval], 0.87‒0.91; p&nbsp;=&nbsp;0.00), the overall specificity was&nbsp;0.99 (95%&nbsp;CI&nbsp;0.98‒1.00; p&nbsp;=&nbsp;0.62), the overall area under the curve (AUC) was&nbsp;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&nbsp;0.88 (95%&nbsp;CI&nbsp;0.84‒0.91; p&nbsp;=&nbsp;0.12) vs.&nbsp;0.73&nbsp;(95%&nbsp;CI&nbsp;0.69‒0.76; p&nbsp;=&nbsp;0.91), the specificity was&nbsp;1.00 (95%&nbsp;CI&nbsp;0.99‒1.00; p&nbsp;=&nbsp;1.00) vs.&nbsp;0.99 (95%&nbsp;CI&nbsp;0.67‒0.74; p&nbsp;=&nbsp;0.92), and the AUC was&nbsp;0.99&nbsp;vs.&nbsp;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

Effects of pore connectivity and water saturation on matrix permeability of deep gas shale

Shale matrix permeability is an important indicator for evaluating gas transport and production. However, the effects of pore connectivity and water saturation on the matrix permeability in deep gas shales have not been adequately studied. In this study, the permeability of deep shales in the Yichang area of the Middle Yangtze was characterized using three methods. These included the determination of apparent permeability in different directions via pulse-decay, also matrix permeability obtained via the Gas Research Institute method, and the connected pore network permeability via the mercury injection capillary pressure technique. The results revealed a significant difference between the horizontal and vertical permeability of deep shales. The smaller the size of the multiple connected pore network, the larger was the effective tortuosity and the lower the permeability. Comparison of the three permeabilities and combined microscopic observations revealed that microfractures and laminae were the dominant gas transport channels. Importantly, the matrix permeability decreased exponentially with increasing water saturation, with water vapor adsorption experiments revealing that water occupation of pores and pore-throat spaces smaller than 10 nm in diameter was the main reason for this decrease in matrix permeability. Collectively, proposed method of evaluating effective permeability with an index for shale gas reservoirs is significant for sweet spot selection and production prediction of shale gas reservoirs around the globe.Cited as: Zhao, J., Sun, M., Pan, Z., Liu, B., Ostadhassan, M., Hu, Q. Effects of pore connectivity and water saturation on matrix permeability of deep gas shale. Advances in Geo-Energy Research, 2022, 6(1): 54-68. https://doi.org/10.46690/ager.2022.01.0