Digital Commons Network^™

Personalizing Ai Models Using Low-Rank Adaptation And Direct Preference Optimization, Saravanan Ganesh, Yi Li, Yunfei Hu, Krystal Kallarackal, Kelvin Nguyen, Chu-Cheng Lin

Defensive Publications Series

Aligning a large language model (LLM) to individual preferences is difficult to perform at scale. This disclosure describes techniques that leverage direct preference optimization (DPO) and low-rank adaptation (LoRA) to enable scalable alignment of artificial intelligence (AI) models. With user permission, user’s edits to suggestions from the model are obtained. The context of the user’s written data or interaction with the LLM is obtained. The user’s edits serve as training data to contextually fine-tune the model using LoRA and DPO. Training data is created as a side product of the user's tasks assisted by the LLM. The training data is …

Remote Cryptographic Device Verification For Theft Deterrence, Siddarth Pandit, Max Bires

Defensive Publications Series

This disclosure describes techniques that leverage remotely provisioned attestation certificates (RPAC) to actively deter device theft. Devices that are reported as lost are identified, and a robust access restriction mechanism is activated that denies both device-local and backend services to the device. To deny service, a bipartite mechanism is deployed that includes enabling a user to declare that their device is stolen and conveying to various services that the device is stolen.

Enhanced Deixis In Video Conferencing With Floating Video And Synthesized Gestures, Xun Qian, David Kim, Ruofei Du

Defensive Publications Series

In traditional video conferencing with screensharing, presenters struggle to reference specific on-screen elements effectively due to the limitations of small video feeds and basic pointer tools. This disclosure describes video conferencing (VC) techniques for enhancing the ability to point (deixis) in a videoconference using synthesized hand gestures. The video feed of the presenter is integrated into the shared screen, while synthesized hand gestures are overlaid to align with speech and pointer movements. With user permission, a multimodal machine learning pipeline accepts as input shared screen content, pointer/controller data, and ongoing speech to generate gestures and to determine optimal video placement. …

Dualprompt Gnn: Tackling Graph Heterogeneity Via Bi- Perspective Prompting

Defensive Publications Series

The challenges tied to unstructured graph data are manifold, primarily falling into node, edge, and graph-level problem categories. Graph Neural Networks (GNNs) serve as effective tools to tackle these issues. However, individual tasks often demand distinct model architectures, and training these models typically requires abundant labeled data, a luxury often unavailable in practical settings. Recently, various "prompt tuning" methodologies have emerged to empower GNNs to adapt to multitask learning with limited labels. The crux of these methods lies in bridging the gap between pretraining tasks and downstream objectives. Nonetheless, a prevalent oversight in existing studies is the homophily-centric nature of …

Heterophily-Aware Personalized Graph Masked Autoencoder

Defensive Publications Series

Graph Masked AutoEncoder (GMAE) has recently attracted vast interest in handling graph-related tasks by adopting the 'masking-reconstruction' learning paradigm. Most existing GMAE-based methods adhere to the homophily assumption, i.e., connected nodes share the same attributes or labels. However, this assumption is not always right because most graphs from real-world applications are mixed by both homophilic and heterophilic edges. Therefore, it is necessary to distinguish them to improve the representative ability of GMAE. In this paper, we propose a heterophily-aware personalized graph masked autoencoder (HAP-GMAE). Specifically, we design a teacher-guided edge discriminator that distinguishes homophilic and heterophilic edges by leveraging the …

Adaptive Node-Subgraph Contrastive Learning For Heterophilic Graph Fraud Detection

Defensive Publications Series

Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the aforementioned issues, we present a novel adaptive node-subgraph contrastive learning approach for graph-based fraud detection, dubbed ANS-GFD. …

Consistent And Homophily-Aware Representation Learning For Multiplex Graphs

Defensive Publications Series

Although unsupervised multiplex graph representation learning (UMGRL) has been a hot research topic, existing UMGRL methods still has limitations to be addressed. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency. To address these issues, in this paper, we propose a new UMGRL method, CH-MGRL (Consistency and Homophily-Aware Multiplex Graph Representation Learning), to explore both homophily and consistency in the multiplex graph. Specifically, we propose to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to …

Federated Ai Learning In Healthcare: Comprehensive Framework For Managing Urgent Care Denials, Kush Sharma

Defensive Publications Series

Federated learning (FL) represents a paradigm shift in artificial intelligence (AI) by enabling collaborative model training across decentralized entities—such as hospitals, insurance providers, and clinics—without requiring the exchange of raw patient data. This approach is particularly transformative in healthcare, where data privacy regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and impose strict limitations on data sharing. In the context of urgent care denials—such as rejected pre-authorizations, triage prioritization errors, or resource allocation decisions—FL offers a way to improve AI-driven decision-making while preserving patient confidentiality.

This elaborates on a federated AI framework designed to address …

Aggn: Adaptive Granularity Graph Networks For Heterophilic Environments

Defensive Publications Series

Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Existing methods addressing this issue often overlook the importance of information granularity and rarely consider implicit relationships between distant nodes. To overcome these limitations, we propose the Adaptive Granularity Graph Network (AGGN), a novel GNN model specifically designed for heterophilous graphs. AGGN enhances node embeddings by aggregating multi-view information at various granularity levels and incorporating implicit data from distant, …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

Learning Resilient Graph Structures In Heterophilic Settings

Defensive Publications Series

Graphs provide a fundamental way to model relationships between entities and are central to numerous machine learning tasks. Standard graph-based methods often assume the provided graph structure is both accurate and complete. However, real-world graphs frequently suffer from noise and sparsity, negatively impacting downstream tasks like node classification and clustering. While graph representation learning has advanced significantly, many methods implicitly assume graph homophily (connections predominantly between nodes of the same class), struggling when faced with heterophily (connections predominantly between different classes). This paper introduces a novel method, Resilient Graph Learning for Heterophily (RGLH), designed to learn high-quality graph structures directly …

Robust Graph Learning Through Spatial-Spectral Synergy Against Structural Attacks

Defensive Publications Series

Graph Convolutional Networks (GCNs) are powerful tools for learning from graph data but exhibit significant vulnerability to adversarial structural attacks that manipulate node connections. While various defense strategies focusing independently on the spatial or spectral domains exist, they often fail to leverage the complementary strengths of both perspectives. This paper introduces the Spatial- Spectral Graph Convolutional Network (S²-GCN), a novel framework designed to enhance GCN robustness against structural attacks by synergistically combining spatial and spectral defense mechanisms. S²-GCN comprises two core GCN-based modules operating in parallel. The spectral module utilizes a graph structure derived from learnable low-frequency spectral components, adaptively …

Balancedgraphformer: Enhancing Graph Transformers With Localized Training Against Over-Globalization

Defensive Publications Series

As Transformers gain traction in graph machine learning, the issue of "over-globalization" has emerged, where their global attention mechanisms excessively emphasize distant vertices, potentially diluting vital local information. This is particularly detrimental in graphs where local neighborhoods hold significant predictive value. Existing methods often lack flexibility in local processing or fail to effectively integrate local and global contexts. This paper introduces BalancedGraphFormer, a novel framework designed to localize graph transformer training. It integrates a dedicated local module with a complementary global module. The local module captures fine-grained neighborhood patterns, while the global module integrates broader context without overshadowing local details. …

Quantum-Coherent Graphene Via Entangled Plasma Deposition (Coherium™) Room Temp Superconductor, Peter Branton

Defensive Publications Series

Twisted bilayer graphene (TBG) at a magic angle (~1.1°) has demonstrated superconductivity below ~1.7 K due to emergent flat-band behaviour. However, its mechanical assembly limitations, thermal fragility, and scalability issues hinder real-world applications. The Coherium™ process addresses these limitations by synthesizing entangled carbon plasma into a structurally coherent graphene lattice with integrated AI stabilization, targeting room-temperature Superconducting properties.

Efficient Partitioning For Devices With Multiple User Modes, Harshad Dhabu, Abhijit Adsule

Defensive Publications Series

This document describes techniques that enable a computing device (e.g., a smart watch, ring, glasses, etc.) to more efficiently transition between multiple user modes, such as an adult mode and a child mode. Using a single system image including applications installed on the computing device for every user mode may lead to system image bloat, diminished performance and power inefficiency. Alternatively, creating separate stock keeping units (SKUs) for each user mode may introduce manufacturing and inventory complexities. Instead, the device may divide the device's storage and the applications corresponding to each user mode into three partitions. The first partition may …

Machine Learning System And Method For Predicting Risk Of Drilling Component Failure, Baker Hughes Company

Defensive Publications Series

The invention addresses the problem of drilling tool failure risk assessment in the oil and gas industry. Tool failure in drilling operations can lead to significant financial losses, downtime, and safety risks. The invention aims to provide a solution for predicting and preventing these failures, which is crucial for maintaining safe and efficient drilling operations.

The invention proposes a solution by combining data-driven techniques with expert knowledge. It begins with data collection, including historical data on tool performance, and involves data cleaning and pre-processing. Relevant features are identified, and a predictive model is developed using machine learning techniques. The model …

Stop Encoding And Transferring Remote Imaging Data When The Client Window Is Minimized Or Covered By Other Windows, Hp Inc

Defensive Publications Series

The document discusses a system designed to pause the encoding and transferring of remote imaging data when the client window is minimized or covered by other windows. This system aims to reduce CPU/GPU and network bandwidth consumption, thereby conserving resources and minimizing costs for users. When the client window is minimized or covered, the host stops encoding imaging data, which saves network bandwidth and enhances security by preventing remote pixels from being captured by other applications. The system resumes encoding and transferring imaging data when the client window is restored or becomes visible.

Security Approach To Prevent Data Leak, Hp Inc

Defensive Publications Series

The system is capable to detect QR code-like pattern and further identify it is static or changing. If it's changing, we block it by applying mask image.

Method Of Energy-Based Low-Parametric Inversion In Lwd-Resistivity Geosteering, Baker Hughes Company

Defensive Publications Series

This submission outlines an innovative method, Energy-Based Low-Parametric Inversion, applied to logging while drilling (LWD) resistivity geosteering problems.

The submission points out a direct commercial relevance. The new capabilities of the method are linked to quality of LWD-resistivity service, its throughput, performance and scalability. Also, this method can provide the new service features and be closely synergetic with other logging methods.

Segmented Shield And Antenna For A Lwd Downhole Tool, Baker Hughes Company

Defensive Publications Series

One of the most promising concepts for a half shell LWD antenna is a concept with removable parts, especially an easy removable mechanical shield, coil and ferrites.

Quantum Smart Entangled Fusion Reactor, Peter Branton

Defensive Publications Series

The invention relates to a compact, high-efficiency energy generation system employing

quantum-entangled hydrogen isotope fusion within nanoscale confinement structures. It

comprises a multiplicity of individually controlled fusion traps formed at nanometer scale, each

configured to receive pre-entangled pairs of hydrogen isotopes such as protium, deuterium, or

tritium. Each trap is stimulated by attosecond-timed electromagnetic resonance pulses to induce

fusion via quantum tunneling. Fusion events are monitored and optimised through an integrated

artificial intelligence (AI) control layer that ensures only coherence-validated, phase-aligned

pairs are activated. The system includes capacitive and inductive energy reclamation

components, and operates in inert gas environments without …

Cover And Forewords, James W. Gallagher

The Geographical Bulletin

Cover and Forewords

Index The Geographical Bulletin, James W. Gallagher

The Geographical Bulletin

INDEX THE GEOGRAPHICAL BULLETIN

Volume 7 Complete Issue, James W. Gallagher

The Geographical Bulletin

Volume 7 Complete Issue

Spatial Variations In Women's Share Of The Manufacturing Labor Force In Tennessee: A Statistical Analysis, Ted Klimasewski

The Geographical Bulletin

A neglected facet of the geographic literature is the role of women employed in the labor force. Women make up a significant share of the labor force, and comprise approximately a third of the manufacturing employment in the United States during 1970. But the relative magnitude of female employment in manufacturing varies throughout the United States, and in some areas of the South, women make up more than half of the manufacturing employment. The purpose of this study is to introduce the manufactural aspect of women's role in the labor force by (1) examining the spatial variation in women's share …

Spatial Ordering In Social Area Typology, Larry B. Bubacz

The Geographical Bulletin

The primary purpose of this paper is to provide tentative evaluation of the general applicability of two statistics, the weighted mean areal center and standard distance, to the question of spatial ordering in social area analysis. More specifically, does the use of these statistics identify whether social phenomena tends to conform to either a concentric (doughnut) ring or a sector (pie-shaped) pattern? The weighted mean areal center is defined as the "balancing point" or "center of gravity" for a population distributed in twodimensions (x-y coordinates) which is comparable to the arithmetic mean of the conventional linear frequency distribution.' The descriptive …

Systems Approach In Physical Geography, Hubert B. Stroud

The Geographical Bulletin

The concept of a system is not new and the emergence of systems analysis in academia has caused controversy over the significance of this approach as a viable means of scientific analysis. Some geography scholars feel that the use of systems analysis would definitely put us on the research frontier,1 while others view the term " systems" as nothing more than jargon. Nevertheless, the simplest definition for system is " a set of interrelated elements." The demand for the systems approach arose because scholars in a number of disciplines recognized in their own research that individual components of a problem …

The American State Capital, A Twentieth Century Growth Pole, Frederick A. Hirsch

The Geographical Bulletin

A fall-out of our avoidance of " capes and bays" geography has been a " benign neglect" of political capitals. In addition to the mental block arising from past tedious memorizations of their existence has been a second factor of sparseness of cases. The United States with fifty state capitals has more than the number of capitals in most of the other federally organized sovereignties. The scientifically oriented among academic geographers have preferred to analyze phenomena in greater abundance such as central places or stream beds.

Utilization Of Plat Books For Determination Of The Rural-Urban Fringe, Douglas J. Kocher

The Geographical Bulletin

This paper has as its purpose the identification of the Valparaiso, Indiana rural-urban fringe' through an analysis of property boundary changes as evidence in county plat books. It is designed to test two hypotheses dealing with the nature of Valparaiso expansion and subdivision of land : one, greater urban expansion of Valparaiso will tend to be linear in nature and in the direction of major industrial areas and population centers; and two, the subdivision of land will show a marked increase with time as the urban area expands.

City Structure And City Change: A System Analysis Approach, A. Y. Abu-Ayyash

The Geographical Bulletin

The structure of the city and the transformation processes which generate changes in its structure have fascinated intellectuals from many disciplines. Sociologists are mainly concerned with the social structure and the demographic characteristics of the inhabitants of the city as a base for understanding its structure. Economists are concerned with economic forces in terms of locational competition as the main reasons for the changing structure of the city. Engineers are concerned with the architecture of the city.