Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

International audienceIn urban canyons, non-line-of-sight (NLOS) multipath interferences affect position estimation based on global navigation satellite systems (GNSS). This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test (MLRT) is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen's inequal- ity allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.Comment: 14 pages, 7 tables, 6 figures, accepted by AAAI 202

A Randomized, Double‐Blind Study Comparing Pharmacokinetics and Pharmacodynamics of Proposed Biosimilar ABP 798 With Rituximab Reference Product in Subjects With Moderate to Severe Rheumatoid Arthritis

ABP 798 is a proposed biosimilar to rituximab reference product (RP), an anti-CD20 monoclonal antibody. Pharmacokinetics (PK), pharmacodynamics (PD), and safety results from the comparative clinical study that evaluated the PK, PD, safety, efficacy, and immunogenicity of ABP 798 versus rituximab RP are presented here. Subjects with moderate to severe rheumatoid arthritis (RA) received 2 doses of ABP 798, United States-sourced RP (rituximab US) or European Union-sourced RP (rituximab EU), each consisting of two 1000-mg infusions 2 weeks apart. For the second dose (week 24), ABP 798- and rituximab EU-treated subjects received the same treatment; rituximab US-treated subjects transitioned to ABP 798. End points included area under the serum concentration-time curve from time 0 extrapolated to infinity and maximum observed serum concentration following the second infusion of the first dose (PK) and percentage of subjects with complete CD19+ cell depletion days 1-33 (PD). Primary analysis established PK similarity between ABP 798 and rituximab RP based on 90% confidence intervals of the adjusted geometric mean ratios being within a prespecified equivalence margin of 0.8 and 1.25. Complete CD19+ B-cell depletion on day 3 among groups confirmed PD similarity. These findings demonstrated PK/PD similarity between ABP 798 and rituximab RP in subjects with moderate to severe RA

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-prone edges tend to connect nodes with the same class (i.e., intra-class nodes), heterophily-prone edges tend to build relationships between nodes with different classes (i.e., inter-class nodes). Existing GNNs only take the original graph during training. The problem with this approach is that it forgets to take into consideration the ``missing-half" structural information, that is, heterophily-prone topology for homophily-prone graphs and homophily-prone topology for heterophily-prone graphs. In our paper, we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two components: graph complementation and complemented graph convolution. The first component finds the missing-half structural information for a given graph to complement it. The complemented graph has two sets of graphs including both homophily- and heterophily-prone topology. In the latter component, to handle complemented graphs, we design a new graph convolution from the perspective of optimisation. The experiment results show that GOAL consistently outperforms all baselines in eight real-world datasets.Comment: Accepted by ICML 202

Electrode Side Reactions, Capacity Loss and Mechanical Degradation in Lithium-Ion Batteries

For advancing lithium-ion battery (LIB) technologies, a detailed understanding of battery degradation mechanisms is important. In this article, experimental observations are provided to elucidate the relation between side reactions, mechanical degradation, and capacity loss in LIBs. Graphite/Li(Ni1/3Mn1/3Co1/3)O2 cells of two very different initial anode/cathode capacity ratios (R, both R \u3e 1) are assembled to investigate the electrochemical behavior. The initial charge capacity of the cathode is observed to be affected by the anode loading, indicating that the electrolyte reactions on the anode affect the electrolyte reactions on the cathode. Additionally, the rate of “marching” of the cathode is found to be affected by the anode loading. These findings attest to the “cross-talk” between the two electrodes. During cycling, the cell with the higher R value display a lower columbic efficiency, yet a lower capacity fade rate as compared to the cell with the smaller R. This supports the notion that columbic efficiency is not a perfect predictor of capacity fade. Capacity loss is attributed to the irreversible production of new solid electrolyte interphase (SEI) facilitated by the mechanical degradation of the SEI. The higher capacity fade in the cell with the lower R is explained with the theory of diffusion-induced stresses (DISs)