RealNetworks (United States)

Abstract This paper investigates structural properties of monotone function classes within the framework of three-valued logic (3VL), aiming to characterize dependencies and constraints that ensure structural finiteness and order-preserving properties. This research delves into characteristics of structurally finite classes of order-preserving 3VL map. Monotonicity plays a critical role in understanding functional behaviour, which is essential for structuring closed logical operations within $ P_{k} $. We define $ F $ as a closed class in $ P_{k} $, consisting only of mappings that comply with specified operations and are strictly confined to $ P_{k} $. The notation $ F(n) $ represents the subset of mappings in $ F $ dependent on $ n $ variables, highlighting the limited scope of inputs and their respective outputs. Further analysis through $ CR(F) $ facilitates the identification of precursor subclasses within $ F $, which have not yet achieved closure under all necessary operations—a pivotal step in constructing intermediate mapping classes. Within the framework of 3VL, the study examines order-preserving maps $ f_{D}, f_{K}, f_{M^{(2)}}, f_{DM^{(2)}}, f_{KM^{(2)}} $, deliberately excluding the sets $ D, K, M^{(2)} $, where $ DM^{(2)} $ is defined as $ D \cap M^{(2)} $ and $ KM^{(2)} $ as $ K \cap M^{(2)} $. The lattice framework in $ P_{k}(1) $ for $ k = 3 $ systematically organizes these mapping classes based on their completeness properties, with particular emphasis on classes such as $ M $, which is characterized as a comprehensive set governed by the operations of maximum and minimum. Our theorems establish structural finiteness for classes of unary order-preserving maps, emphasizing their two-variable dependency in class construction. This study extends existing findings on single-variable monotonic functions within 3VL by integrating criteria of linear order. Key results include the classification of mapping classes $ F \subseteq M $ containing $ M^{(1)}_{3} $, categorized into distinct groups: $ M^{(1)}_{3}, KM^{(2)}, DM^{(2)}, M^{(2)}, K, D, M $. Inclusion relations are visualized diagrammatically, showing the hierarchical structure of these monotone classes, ultimately validating the structural finiteness of classes incorporating all unary order-preserving maps within the broader context of 3VL.

Recently, various types of Video Inpainting models have been released. Video Inpainting is used to naturally erase the object you want to erase in the video. However, to use inpainting models, we usually need frames extracted from a video and masks and most people make these data manually. We propose a novel End-to-End Video Object Removal framework with Cropping Interested Region and Video Quality Assessment (ORCA). ORCA is built in an end-to-end way by combining the Detection, Segmentation, and Inpainting modules. The characteristics of proposed framework are going through the cropping step before inpainting step. In addition, We propose our own video quality assessment since ORCA use two models for inpainting. Our new metric indicates the higher quality of the results between two models. Experimental results show the superior performance of the proposed methods.

This paper presents and evaluates a simple but effective approach for substantially reducing inter-frequency handover (HO) failure rate. We build a machine learning model to forecast inter-frequency HO failures. For improved accuracy compared to the state-of-the-art models, we use domain knowledge to identify and leverage the model input features. These features include reference signal received power (RSRP) of the source and target base stations as well as the RSRP of the interferers for both the source and the target layers. Six machine learning classifiers are tested with the highest accuracy of 93% observed for the XGBoost classifier. The novel idea to include the RSRP of the interferes improved the accuracy of XGBoost by 10%.

Abstract CD27 is a member of the TNF-Receptor superfamily expressed on CD4+ and CD8+ T cells, on NK and NKT cells and on B cells. It promotes T cell co-activation, proliferation, clonal expansion and differentiation into antigen specific cytotoxic and memory T cells after stimulation with its ligand CD70. Its stimulatory signal is mediated via the NFkB pathway, but also via the phosphatidylinositol 3 kinase and the protein kinase B. Moreover, CD27 signaling influences the innate immune response via a direct activation of NK cells and a subsequent secretion of interferon-gamma (IFN-γ). CD27 plays a central role in immunological responses and by promoting T cell and NK cell activation it contributes to anti-tumor immunity. Previous studies have demonstrated tumor growth inhibition with anti-CD27 agonistic monoclonal antibodies in different mice models for solid and hematological tumors. This mechanism of action can be partly explained by the recruitment of IFN-γ producing CD8+ T cells within the tumor. CD27 is a promising target for antitumor therapy. Kineta has generated a library of 147 fully human anti-CD27 monoclonal antibodies after immunization of Trianni mice. From this library, a lead candidate with strong agonistic proprieties has been selected. This anti-CD27 antibody originates from a unique clade after alignment of the variable heavy chains. Kineta’s lead candidate demonstrates selectivity and cross-reactivity with Non-Human Primate (NHP)-CD27 but not with the mouse-CD27. It also induces strong NFkB signaling in a Jurkat T cell-reporter, either soluble or cross-linked. It also induces T cell proliferation and secretion of pro-inflammatory cytokines in vitro. This T cell activation occurs only in the presence of a TCR engagement preventing future risks of spontaneous activation of naïve T cells in vivo. Our lead antibody also induces direct activation of NK cells demonstrated by the expression of CD69 on their surface. We have evaluated the anti-tumor properties of our lead antibody as a single agent in vivo in human CD27 Knock-In (KI) mice. Our anti-CD27 candidate induces a significant anti-tumor activity in the EG7 thymoma model. We have also demonstrated the anti-tumor efficacy of this lead candidate in Raji cells implanted in Scid mice. Preliminary experiments performed in human CD27 KI mice have demonstrated a long half-life of our antibody at different concentrations. Epitope characterization, NHP pharmacokinetic analysis and additional in vivo studies of our lead anti-CD27 antibody in different tumor models use as a single agent and in combination with different check-point inhibitors are on-going. Citation Format: Shawn Iadonato, Jessica Cross, Nathan Eyde, Emily Frazier, Neda Kabi, Chen Katz, Remington Lance, Kurt Lustig, Yulia Ovechkina, David Peckham, Shaarwari Sridhar, Carla Talbaux, Isabelle Tihista, Mei Xu, Thierry Guillaudeux. CD27 an emerging immuno-oncology target at the cross-roads of innate and adaptive anti-tumor immune responses [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A60.

Measuring the influence of users in social networks is key for numerous applications. A recently proposed influence metric, coined as Ψ-score, allows to go beyond traditional centrality metrics, which only assess structural graph importance, by further incorporating the rich information provided by the posting and re-posting activity of users. The Ψ-score is shown in fact to generalize PageRank for non-homogeneous node activity. Despite its significance, it scales poorly to large datasets; for a network of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> users, it requires to solve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> linear systems of equations of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> . To address this problem, this work introduces a novel scalable algorithm for the fast approximation of Ψ- score, named Power-Ψ. The proposed algorithm is based on a novel equation indicating that it suffices to solve one system of equations of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> to compute the Ψ-score. Then, our algorithm exploits the fact that such a system can be recursively and distributedly approximated to any desired error. This permits the Ψ-score, summarizing both structural and behavioral information for the nodes, to run as fast as PageRank. We validate the effectiveness of the proposed algorithm, which we release as an open source Python library, on several real-world datasets.

In the first phase of 5G network deployment, User Equipment (UE) will camp traditionally on LTE network. Later on, if the UE requests a 5G service, it will be made to camp simultaneously on LTE and 5G. This dual-camping is enabled through a 3GPP-standardized approach known as E-UTRAN New-Radio Dual-Connectivity (EN-DC). Unlike single-networkcamping, where poor RF conditions of only one network affect user Quality-of-Experience (QoE), in EN-DC, poor RF condition in either LTE or 5G network can be detrimental to user QoE. Sub-optimal parameter configuration to activate EN-DC can hamper retainability KPI as UE may observe increased radio link failure (RLF). While the need to maximize the EN-DC activation is obvious for 5G network maximum utility, RLF avoidance is equally important to maintain the QoE requirements. We address this problem by first using Tomek Link to counter data imbalance problem and then building an AI model to predict RLF from real network low level measurements. We then propose and evaluate an RLF risk-aware EN-DC activation scheme that draws on insights from the developed RLF prediction model. Simulation using a 3GPP-compliant 5G simulator show that compared to no-conditioning on EN-DC activation, in the evaluated cell cluster, the proposed scheme can help reduce the potential RLF instances by 99%. This RLF reduction happens at the cost of 50% reduction in EN-DC activation. This is first study to present a framework and insights for operators to optimally conFigure the EN-DC activation parameters to achieve desired trade-off between maximizing 5G sites utility and QoE.