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Fingerprint localization is emerging as one of the most important indoor positioning methods, especially with the development of deep learning (DL). To avoid potential privacy leakage of clients from the large-scale fingerprinting samples for DL training, federated learning (FL) has been introduced to implement distributed learning without exchanging raw local data. However, FL-based localization suffers from severe accuracy degradation caused by heterogeneity among distributed data. To tackle this problem, this letter builds a model self-supervised federated learning (MSS-FL) framework for fingerprint localization. In MSS-FL, we propose to conduct self-supervised learning at the model level, and incorporate a model self-supervised loss into the local training objective to relieve the bias between global model and local model. Experiments on a real-world dataset are conducted to validate the improved localization accuracy with faster convergence of MSS-FL, compared with other state-of-the-art FL methods.

Belief propagation-based positioning is a promising technique to provide accurate geographical locations for location-based services without central coordination, but its performance can be severely affected by the choice of anchor locations. To solve this problem, we formulate an epidemic model to capture the propagation process, by which the localization time can be upper-bounded by functions of topological characteristics of the agent network. With this knowledge, we design a graph-based anchor deployment strategy that ranks the agents by its topological importance, and deploy anchors to best cover the most important ones. Numerical results show that the proposed graph-based approach outperforms existing solutions in both convergence speed and localization errors, providing a practical solution to BP-based localization schemes.

AbstractWe consider a one-dimensional superprocess with a supercritical local branching mechanism $\psi$ , where particles move as a Brownian motion with drift $-\rho$ and are killed when they reach the origin. It is known that the process survives with positive probability if and only if $\rho<\sqrt{2\alpha}$ , where $\alpha=-\psi'(0)$ . When $\rho<\sqrt{2 \alpha}$ , Kyprianou et al.[18] proved that $\lim_{t\to \infty}R_t/t =\sqrt{2\alpha}-\rho$ almost surely on the survival set, where $R_t$ is the rightmost position of the support at time t. Motivated by this work, we investigate its large deviation, in other words, the convergence rate of $\mathbb{P}_{\delta_x} (R_t >\gamma t+\theta)$ as $t \to \infty$ , where $\gamma >\sqrt{2 \alpha} -\rho$ , $\theta \ge 0$ . As a by-product, a related Yaglom-type conditional limit theorem is obtained. Analogous results for branching Brownian motion can be found in Harris et al.[13].

Pigments are often used in water borne barrier coatings but tend to make the coatings prone to failure. The pigment properties effects on this issue is lacking in literature. In this work, coatings that used pigments with different moduli but with similar size and aspect ratio were characterized in terms of water vapor resistance before and after folding. Coatings with talc had better water vapor resistance than coatings with similar sized kaolin. Talc also limited the degradation of barrier properties when folded. Coatings with metalized poly(ethylene terephthalate) (PET) flakes had better failure resistance than coatings with similarly sized rigid mica. Both results are likely caused by the ability of the low modulus pigment to deform and allow for strain to occur in the pigment as well as the latex phase. Styrene-butadiene (SB) and natural rubber (NR) latex coatings had a better failure resistance than styrene-acrylate (SA) latex, which is likely due to their low glass transition temperatures and high strain-to-failure values. However, coatings with high amounts of SB or NR latex may lead to blocking issues in production. Adding kaolin into SA and SB latex mixtures resulted in improved water vapor barrier property and failure resistance.

The anchors in wireless network localization system suffer from the risk of location leakage, which may result in some catastrophic dangers. To preserve the location privacy of anchors, this letter proposes to hide the anchor’s position in range-based localization through a secret sharing method. By decomposing the private input of anchors into several secret shares and concealing it in the constant term of the secret polynomial, the location information of anchors cannot be disclosed by any other node in the network including the server, during the entire localization process. The performance of the proposed scheme in privacy preservation, positioning accuracy and efficiency improvement (i.e., low communication and computation costs) are evaluated through simulations.

ABSTRACTThis research revealed that 15 modules were obtained through weighted gene co-expression network analysis, among which the magenta and blue modules were significantly associated with Alzheimer’s Disease (AD). There were 121 genes in the magenta module and 1022 genes in the blue module. Through differently expressed genes analysis, significant differences were shown in 134 genes (88 were up-regulated and 46 were down-regulated). 34 immune-key genes were obtained after three types of genes were crossed. Functional enrichment analysis showed that genes were mainly enriched in cytokine receptor activity and immune receptor activity. Through protein–protein interaction (PPI) network analysis, 10 hub genes were obtained: SERPINE1, ZBTB16, CD44, BCL6, HMOX1, SLC11A1, CEACAM8, ITGA5, SOCS3, and IL4R. Through immune-infiltration analysis, significant differences were demonstrated in four immune cells: CD8 + T cells, resting NK cells, M2 macrophages, and activated dendritic cells, and a significant positive correlation was shown between CD8 + T cells and macrophages M2, or between the other two cells. CEACAM8 was positively correlated with CD8 + T cells and macrophages M2, and negatively correlated with the other two cells while the remaining nine genes showed the opposite. Receiver operating characteristic (ROC) curve analysis demonstrated that both the differential immune cells and 10 hub genes had good diagnostic values. In GSE122063, the hub genes were verified and BCL6, CD44, HMOX1, IL4R, ITGA5,and SOCS3were up-regulated. Meanwhile, hub genes was up-regulated in the brain tissues of AD rats. This study is of great significance for the diagnosis and therapy of AD.

Adding pigments into waterborne barrier coatings improves barrier properties and cost-effectiveness but increases the risk of crack formation during folding. Crack formation is affected by pigment shape, aspect ratio, and concentration; however, the exact mechanism for these effects is still not well understood. In this work, a systematic model was used to understand the influence of the paper and coating thickness, the latex and pigment modulus, the pigment shape and aspect ratio, and pigment concentration on the failure of waterborne barrier coatings during folding. A finite element method-based model was solved with a commercial package to simulate the folding process. These simulations were compared to experimental results to verify the key parameters that affect coating failure. High paper and coating thickness, pigment loadings, pigment aspect ratios, and modulus differences between latex and pigment increased the likelihood of failure. Experiments and models using lower modulus spherical plastic pigments were more difficult to fail than coatings made with higher modulus kaolin. The maximum strain for coatings bent to a set curvature was the smallest when the modulus of latex and pigment were similar. The model agreed closely with experimental results for two pigment types at various pigment loadings.

The structural modification of graphene at the atomic level is crucial for electrochemical applications. Doping heteroatoms to modify the structure of graphene has widely been adopted. However, the construction and controllable doping of heteroatom-doped graphene remains a challenge. Herein, a novel semisynthetic method is developed to synthesize a borazine (B3N3)-containing acetylenic compound as a precursor, and a series of B3N3-doped few-layered graphene nanosheets are prepared after annealing at different temperatures. To form graphene sheets, the in situ-forming MgBrCl salt is used as an intercalation agent to enlarge the mutual distance between molecules, which can inhibit the unwanted cross-linking reaction. Nanosheets with different thicknesses of 2.5, 3.5, and 4.1 nm can be obtained at annealing temperatures of 1500, 1200, and 1000 °C, respectively. The results demonstrate that the B and N atoms are co-doped in the graphene by the structure of B3N3, and the doping site can be changed with different annealing temperatures. The optical gap of graphene can be successfully opened by doping with B3N3, and the resultant material can be potentially utilized as a catalyst and semiconductor material. Furthermore, this new semisynthetic strategy will offer the opportunity to fabricate more carbon materials via controllable heteroatom doping.

Aim:We aimed to demonstrate noninvasive prenatal diagnosis (NIPD) of hemophilia A (HA) using a haplotype-based approach. Methods:Two families at risk for HA were recruited for this study. First, maternal haplotypes associated with pathogenic variants were constructed using the genotypes of the mothers and probands. Then, fetal haplotypes were deduced using a maternal haplotype-assisted hidden Markov model. Finally, the NIPD results were further confirmed by invasive prenatal diagnosis. Results:Two fetal genotypes were successfully inferred, with one normal fetus and one carrier fetus. The NIPD results were confirmed by invasive prenatal diagnosis, with a 100% consistency rate. Conclusion:Our test has been shown to be accurate and reliable. With further validation in a large patient cohort, this haplotype-based approach could be feasible for the NIPD of HA and other X-linked single-gene disorders.

ABSTRACTInformation Systems (IS) researchers and practitioners have to deal with the concept of causality. IS researchers explicitly model and test causal relationships, like Information Technology (IT) investments lead to economic outcomes. IS practitioners implicitly assume that their initiatives (investment, training, strategic initiatives) cause the desirable outcomes they seek. While causality has always been a tricky concept to assess, in the digital world of today, it becomes even more challenging. Below, we describe the changes in “embeddedness” and “agency” due to digitalization that convolute establishment of causality in studying relationships between IT and the social enterprise. We also offer projections into the future based on these changes and some implications for researchers.

Expanded carrier screening (ECS) has been demonstrated to increase the detection rate of carriers compared with traditional tests. The aim of this study was to assess the potential value of ECS for clinical application in Southern China, a region with high prevalence of thalassemia and with diverse ethnic groups, and to provide a reference for future implementations in areas with similar population characteristics. A total of 10,476 prenatal/preconception couples from 34 self-reported ethnic groups were simultaneously tested and analyzed anonymously for 11 Mendelian disorders using targeted next-generation sequencing. Overall, 27.49% of individuals without self-reported family history of disorders were found to be carriers of at least 1 of the 11 conditions, and the carrier frequency varied greatly between ethnic groups, ranging from 4.15% to 81.35%. Furthermore, 255 couples (2.43%) were identified as carrier couples at an elevated risk having an affected baby, sixty-five of which would not have been identified through the existing screening strategy, which only detects thalassemia. The modeled risk of fetuses being affected by any of the selected disorders was 531 per 100,000 (95% CI, 497–567 per 100,000). Our data demonstrate the feasibility of ECS, and provide evidence that ECS is a promising alternative to traditional one-condition screening strategies. The lessons learned from this experience should be applicable for other countries or regions with diverse ethnic groups.

Background: Store-operated calcium entry (SOCE), primarily mediated by Orai1 and stromal interaction molecule 1 (STIM1), is a major Ca2+ influx pathway that has been linked to human diseases including myopathy, epilepsy, immunodeficiency, and cancer. Despite of the recent rapid progress of dissecting molecular mechanisms underlying SOCE activation, the development of therapies against dysfunctional SOCE significantly lags behind, partly due to the lack of more specific pharmacological tools and poor understanding of currently available SOCE modifiers, including the a newly identified SOCE inhibitor, digitoxin. Objective and methods: Capitalizing on Ca2+ imaging and pharmacological tools, we aimed to systemically delineate the mechanism of action of digitoxin by defining how it impinges on Orai1 to exert its suppressive effect on SOCE. Results: The SOCE-suppressive function of digitoxin is dependent on S27-S30 residues of wild-type Orai1. With 8h-incubation of digitoxin with STIM1-prebound Orai1 or a constitutively active mutant Orai1-ANSGA, its inhibition was no longer dependent on S27-S30 residues. Instead, the inhibition may involve the pore region of Orai1 channels, as V102C mutant at the pore region would greatly diminish or abolish the inhibition on pre-activated Orai1. Conclusions: Our study identified two regions that are critical for the inhibition on Orai1 channels, providing valuable hotspots for future design of SOCE inhibitors.

Application Programming Interfaces (APIs) are frequent in software engineering domain texts, such as API references and Stack Overflow. These APIs and the comparison knowledge between them are not only important for solving programming issues (e.g., question answering), but they are also organized into structured knowledge to support many software engineering tasks (e.g., API misuse detection). As a result, extracting API comparison knowledge (API entities and semantic relations) from texts is essential. Existing rule-based and sequence labeling-based approaches must manually enumerate all linguistic patterns or label a large amount of data. Therefore, they involve a significant labor overhead and are exacerbated by morphological and common-word ambiguity. In contrast to matching or labeling API entities and relations, we formulates heterogeneous API extraction and API relation extraction tasks as a sequence-to-sequence generation task. It proposes APICKnow, an API entity-relation joint extraction model based on the large language model. To improve our model’s performance and quick learning ability, we adopt the prompt learning method to stimulate APICKnow to recognize API entities and relations. We systematically evaluate APICKnow on a set of sentences from Stack Overflow. The experimental results show that APICKnow can outperform the state-of-the-art baselines, and APICKnow has a quick learning ability and strong generalization ability.

We consider the supercritical super-Brownian motion with a general branching mechanism, where particles move as Brownian motion with drift − ρ and are killed when they reach the origin. We obtain a large-time asymptotic formula for the survival probability. [ABSTRACT FROM AUTHOR]

Maintaining quality of life (QOL) during chemotherapy is a critical aspect of cancer treatment. Instruments have been developed to assess symptom distress, self-efficacy, anxiety, depression, and other factors impacting QOL during cancer treatment, but Chinese versions have become available only recently.

Many network-management problems in large backbone networks need the answer to a seemingly simple question: where does a given IP packet, entering the network at a particular place and time, leave the network to continue on its path to the destination? Answering this question at scale and in real time is challenging for several reasons: (i) a destination IP address could match several IP prefixes, (ii) the longest-matching prefix may change over time, (iii) the number of IP prefixes and routing protocol messages is very large, and (iv) network-management applications often require answers to this question for a large number of destination IP addresses in real time. In this paper, we present an efficient algorithm for tracking prefix-match changes for ranges of IP addresses. We then present the design, implementation, and evaluation of the Route Oracle tool that answers queries about routing changes on behalf of network management applications. Our design of Route Oracle includes several performance optimizations, such as pre-processing of BGP update messages, and parallelization of query processing. Experiments with BGP measurement data from a large ISP backbone demonstrate that our system answers queries in real time and at scale.

Conventional wisdom has held that routing protocols cannot achieve both scalability and high availability. Despite scaling relatively well, today's Internet routing system does not react quickly to changing network conditions (e.g., link failures or excessive congestion). Overlay networks, on the other hand, can respond quickly to changing network conditions, but their reliance on aggressive probing does not scale to large topologies. The paper presents a layered routing architecture called UFO (Underlay Fused with Overlays), which achieves the best of both worlds by having the "underlay" provide explicit notification about network conditions to help improve the efficiency and scalability of routing overlays.