Establishing entangled links by entangling and swapping can enable end-to-end teleportation, avoiding eavesdropping and facilitating the critical applications, such as quantum key distribution (QKD) service. For these promising applications, a specific number of data qubits should be transmitted (periodically) to meet the quality of service (QoS) requirements for each request. Otherwise, the network provider cannot earn the profit from the request. In this paper, we present a new optimization problem and propose a novel approximation algorithm to maximize the (expected) total profit under the constraint of network resources. Finally, extensive simulation results manifest that our algorithm can outperform the existing approaches by up to 46%.

Electrocardiogram (ECG) is one of the most important information for cardiovascular diseases (CVDs) diagnosis. In recent year, several dry electrode-based smart clothes have been widely developed to improve the skin allergic reaction and gel-drying issue from conventional Ag/AgCl electrode under long-term measurement. However, most of these dry electrodes still have to contact with skin and may encounter the risk of skin irritation, and many smart clothing systems lack of automatic CVDs detection. In this article, a novel smart clothing was designed to automatically detect CVDs in daily life. Based on the technique of capacitive electrodes, the proposed smart clothing could access the bio-potential across the clothes to prevent the skin from irritation and discomfort, and could adapt to different body sizes by the specific belt mechanical design. Moreover, the CVDs detection algorithm was also designed and implemented in the field programmable gate array (FPGA) based ECG analysis module. The experiment results show that the proposed smart clothing could effectively real-time extract ECG features (P-, R-, and T-waves) and detect CVDs state via the front-end circuit, including bradycardia, tachycardia, atrial fibrillation, left ventricular hypertrophy, first-degree atrioventricular block, and hyperkalemia. The proposed FPGA architecture is also beneficial for future revisions or additions of CVD algorithms to improve more accurate diagnosis and monitoring of heart disease. It might reduce huge ECG data collected in daily life via only transmitting the abnormal ECG segment, and improve the diagnostic efficiency of CVDs in the future.

BACKGROUND: With the development of HiC technology, more and more HiC sequencing data have been produced. Although there are dozens of packages that can turn sequencing data into contact maps, there is no appropriate tool to query contact maps in order to extract biological information from HiC datasets. RESULTS: We present HiCmapTools, a tool for biologists to efficiently calculate and analyze HiC maps. The complete program provides multi-query modes and analysis tools. We have validated its utility on two real biological questions: TAD loop and TAD intra-density. CONCLUSIONS: HiCmapTools supports seven access options so that biologists can quantify contact frequency of the interest sites. The tool has been implemented in C++ and R and is freely available at https://github.com/changlabtw/hicmaptools and documented at https://hicmaptools.readthedocs.io/ .

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as$\mathcal{O}(1/\sqrt{K})$, where$K$is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger central DP guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as$\mathcal{O}(1/K^{3/4})$. In addition, we show that LDP is also boosted by user sampling.

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a fading multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as$\mathcal {O}(1/K^{1/2})$, where$K$is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as$\mathcal {O}(1/K^{3/4})$. In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are$(a)$known, or$(b)$unknown at the parameter server.

In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL over a MAC. We focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (e.g., rate or bandwidth) to different users based on a) the informativeness of the gradients at users, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness.

Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent interest in using coding to speed up distributed matrix multiplication, that are robust to stragglers (i.e., machines that may perform slower computations). In many scenarios, instead of exact computation, approximate matrix multiplication, i.e., allowing for a tolerable error is also sufficient. Such approximate schemes make use of randomization techniques to speed up the computation process. In this paper, we initiate the study of approximate coded matrix multiplication, and investigate the joint synergies offered by randomization and coding. Specifically, we propose two coded randomized sampling schemes that use (a) codes to achieve a desired recovery threshold and (b) random sampling to obtain approximation of the matrix multiplication. Tradeoffs between the recovery threshold and approximation error obtained through random sampling are investigated for a class of coded matrix multiplication schemes.

In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix A, with a matrix Bθ, where θ ∈ {1,..., M}. The set of candidate matrices {B1,..., BM} are public, and available at all the N servers. The goal of the user is to distributedly compute ABθ, such that (a) no information is leaked about the matrix A to any server; and (b) the index θ is kept private from each server. Our goal is to understand the fundamental tradeoff between the upload vs download cost for this problem. Our main contribution is to show that the lower convex hull of following (upload, download) pairs: (U,D) = (N/(K - 1), (K/(K - 1)) (1 + (K/N) + ··· + (K/N)M-1)) for K = 2, ..., N is achievable. The scheme improves upon state-of-the-art existing schemes for this problem, and leverages ideas from secret sharing and coded private information retrieval.

Matrix multiplication is one of the key operations in various engineering applications. Outsourcing large-scale matrix multiplication tasks to multiple distributed servers or cloud is desirable to speed up computation. However, security becomes an issue when these servers are untrustworthy. In this paper, we study the problem of secure distributed matrix multiplication from distributed untrustworthy servers. This problem falls in the category of secure function computation and has received significant attention in the cryptography community. However, characterizing the fundamental limits of information-theoretically secure matrix multiplication remain an open problem. We focus on information-theoretically secure distributed matrix multiplication with the goal of characterizing the minimum communication overhead. The capacity of secure matrix multiplication is defined as the maximum possible ratio of the desired information and the total communication received from N distributed servers. In particular, we study the following two models where we want to multiply two matrices A ∈ \mathbbFm×nand B ∈ \mathbbFn×p: (a) one-sided secure matrix multiplication with l colluding servers, in which B is a public matrix available at all servers and A is a private matrix. (b) fully secure matrix multiplication with l colluding servers, in which both A and B are private matrices. The goal is to securely multiply A and B when any l servers can collude. For model (a), we characterize the capacity as Cone-sided(l)= (N-l)/N by providing a secure matrix multiplication scheme and a matching converse. For model (b), we propose a novel scheme that lower bounds the capacity, i.e., Cfuly ≥ (√N-l)2/(√N-l+l)2.

The rapid development of Facebook makes a large amount of users to join the platform and share a variety of information on it. Facebook has become an innovative and effective platform for disseminating business information. It is thus interesting to investigate the issue of how to motivate the users to initiatively share the information of products or service provided by a certain brand which manages the Facebook Page. In addition, the privacy concern is one of the top issues of Facebook and would influence the sharing behavior. Therefore, this study intends to investigate the antecedents of the attitude toward sharing post on the Facebook from the perceived value perspective and how the privacy concern moderates the relationship between sharing attitude and sharing intention. According to the analysis results from 375 valid respondents, first, the utilitarian value and social value have significant effects on sharing attitude. Second, the privacy concern cannot moderate the relationship between the sharing attitude and sharing intention. In addition, we also divided the samples by gender and usage time to figure out the differences between each group. The implications of this study can provide some advices for Internet marketing decision makers to make proper marketing strategy of products and services.