LHGI leverages subgraph sampling, structured by metapaths, to condense the network while preserving the majority of its semantic information. Adopting the methodology of contrastive learning, LHGI defines the mutual information between normal/negative node vectors and the global graph vector as the objective to shape the learning process. LHGI employs the maximization of mutual information to solve the network training problem in the absence of supervised data. Experimental findings reveal the LHGI model's superior feature extraction ability, outperforming baseline models in both medium-sized and large-sized unsupervised heterogeneous networks. The LHGI model's node vectors show heightened effectiveness and efficiency in their application to downstream mining activities.
Dynamical wave function collapse models elucidate the disintegration of quantum superposition, as the system's mass grows, by implementing stochastic and nonlinear corrections to the Schrödinger equation's framework. Among the subjects examined, Continuous Spontaneous Localization (CSL) was a focus of significant theoretical and experimental inquiry. FHD-609 cell line The demonstrable impacts of the collapse phenomenon are dependent on diverse configurations of the model's phenomenological parameters, such as strength and correlation length rC, and have, until now, resulted in the rejection of regions within the permissible (-rC) parameter space. Our innovative approach to disentangling the and rC probability density functions uncovers a more profound statistical interpretation.
Presently, the Transmission Control Protocol (TCP) remains the dominant protocol for trustworthy transport layer communication in computer networks. Unfortunately, TCP suffers from drawbacks such as substantial handshake latency, head-of-line blocking phenomena, and more. Google proposed the Quick User Datagram Protocol Internet Connection (QUIC) protocol to address these issues, enabling a 0-1 round-trip time (RTT) handshake and user-mode congestion control algorithm configuration. Currently, the QUIC protocol's integration with traditional congestion control algorithms is not optimized for numerous situations. This problem necessitates a novel congestion control mechanism, leveraging deep reinforcement learning (DRL). We propose Proximal Bandwidth-Delay Quick Optimization (PBQ) for QUIC, merging conventional bottleneck bandwidth and round-trip propagation time (BBR) metrics with the proximal policy optimization (PPO) algorithm. The PPO agent, within the PBQ framework, generates a congestion window (CWnd) value, adapting its behavior in response to network conditions. Simultaneously, BBR dictates the client's pacing rate. Applying the introduced PBQ mechanism to QUIC, we obtain a refined QUIC version, termed PBQ-fortified QUIC. FHD-609 cell line Comparative analysis of the PBQ-enhanced QUIC protocol against existing QUIC implementations, including QUIC with Cubic and QUIC with BBR, shows substantial improvements in both throughput and round-trip time (RTT), as evidenced by experimental results.
We propose a refined strategy for diffusely exploring complex networks, using stochastic resetting, with the resetting site identified from node centrality scores. In contrast to previous methods, this approach enables the random walker to probabilistically jump from its current node to a specifically selected reset node; however, it further enhances the walker's capability to hop to the node providing the fastest route to all other nodes. From the standpoint of this approach, the resetting site is designated as the geometric center, the node that minimizes the mean journey time to every other node. We calculate the Global Mean First Passage Time (GMFPT) using Markov chain theory to evaluate random walk performance with resetting, examining the individual effects of each resetting node choice. Furthermore, we evaluate the effectiveness of various node sites as resetting points through a comparison of their respective GMFPT values. The application of this method is examined across a spectrum of network topologies, including abstract and real-world implementations. We observe that centrality-focused resetting of directed networks, based on real-life relationships, yields more significant improvements in search performance than similar resetting applied to simulated undirected networks. In real networks, the average travel time to all other nodes can be reduced by this advocated central reset. We also unveil a connection between the longest shortest path (diameter), the average node degree, and the GMFPT, when the initial node is the center. For undirected scale-free networks, stochastic resetting proves effective specifically when the network structure is extremely sparse and tree-like, features that translate into larger diameters and smaller average node degrees. FHD-609 cell line Even for directed networks containing loops, resetting remains a beneficial strategy. Numerical results align with the expected outcomes of analytic solutions. This study highlights the effectiveness of the proposed random walk algorithm, enhanced by centrality-based resetting procedures, in decreasing the search time for targets across various network topologies.
The characterization of physical systems is intrinsically tied to the fundamental and essential concept of constitutive relations. By means of -deformed functions, some constitutive relations are extended in scope. Employing the inverse hyperbolic sine function, this paper demonstrates applications of Kaniadakis distributions in areas of statistical physics and natural science.
Student-LMS interaction logs are used in this study to model learning pathways via constructed networks. Enrolled students' examination of course materials, in a sequential manner, is cataloged by these networks. Prior research demonstrated a fractal property in the social networks of students who excelled, while those of students who struggled exhibited an exponential structure. This research strives to empirically validate the emergent and non-additive qualities of student learning trajectories on a macro level, while simultaneously introducing the concept of equifinality—different learning paths achieving similar educational outcomes—at a micro level. In addition, the learning progressions of the 422 students enrolled in a blended learning course are classified by their learning achievements. Individual learning pathways are mapped by networks, from which a fractal-based method extracts the relevant learning activities in a sequential manner. Through fractal procedures, the quantity of crucial nodes is lessened. A deep learning network assesses each student's sequence, designating it as either a pass or a fail. Learning performance prediction's accuracy reached 94%, the area under the ROC curve stood at 97%, and the Matthews correlation scored 88%, showcasing deep learning networks' capability to model equifinality in complex systems.
A significant upward trend is evident in the number of incidents of torn archival images across recent years. Leakage tracking presents a considerable obstacle to the development of robust anti-screenshot digital watermarking methods for archival images. The prevalent, single-texture characteristic of archival images is a factor contributing to the low detection rate of watermarks in many existing algorithms. Using a Deep Learning Model (DLM), we propose in this paper an anti-screenshot watermarking algorithm tailored for archival images. Currently, screenshot image watermarking algorithms employing DLM technology are effective against screenshot attacks. However, the application of these algorithms to archival images causes a substantial and noticeable surge in the image watermark's bit error rate (BER). Due to the widespread use of archival images, we introduce ScreenNet, a novel DLM for enhancing the resilience of anti-screenshot systems for archival imagery. It employs style transfer to elevate the background and create a richer texture. To counteract the influence of cover image screenshots, a style transfer-based preprocessing is applied to archival images prior to their input into the encoder. Secondly, the fragmented images are commonly adorned with moiré patterns, thus a database of damaged archival images with moiré patterns is formed using moiré network algorithms. Finally, the watermark is encoded/decoded through the improved ScreenNet model, where the extracted archive database serves as the disruptive noise layer. Empirical evidence from the experiments validates the proposed algorithm's capability to withstand anti-screenshot attacks while simultaneously providing the means to detect and thus reveal watermark information from ripped images.
The innovation value chain's perspective on scientific and technological innovation recognizes two stages: research and development, and the subsequent transition and implementation of achievements. This study employs panel data, encompassing 25 Chinese provinces, as its dataset. We employ a two-way fixed effects model, a spatial Dubin model, and a panel threshold model to explore the effect of two-stage innovation efficiency on the worth of a green brand, the spatial dimensions of this influence, and the threshold impact of intellectual property protections in this process. Innovation efficiency's dual phases positively impact green brand valuations, the effect being markedly stronger in the eastern region compared to the central and western regions. A clear spatial spillover effect exists in the valuation of green brands, stemming from the two phases of regional innovation efficiency, particularly within the eastern sector. The innovation value chain is marked by a prominent spillover effect. Intellectual property protection's pronounced single threshold effect is noteworthy. The positive influence of two innovation phases' efficiency on the valuation of green brands is markedly amplified when the threshold is breached. Economic development, openness, market size, and marketization levels demonstrate a noteworthy variation in the value attributed to green brands across different regions.