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Design and Analysis of Intellectual Property Protection Strategies Based on Differential Equations
No full textThis paper constructs a novel intellectual property (IP) protection strategy using differential equation theory, aiming to analyze and optimize the effectiveness of IP protection. By developing a mathematical model, it explores the dynamic impact of IP protection intensity on both innovative enterprises and infringement activities. The study finds that a well-designed IP protection strategy can effectively reduce infringement while promoting technological innovation. The paper also discusses the effects of strategies under varying parameter conditions and verifies the model\u27s rationality and effectiveness through numerical simulation. The findings provide theoretical support and references for formulating IP protection policies
Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology
No full textArtificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration might introduce automation bias, where users uncritically follow automated cues. This bias may worsen when time pressure strains practitioners\u27 cognitive resources. We quantified automation bias by measuring the adoption of negative system consultations and examined the role of time pressure in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results indicate that while AI integration led to a statistically significant increase in overall performance, it also resulted in a 7% automation bias rate, where initially correct evaluations were overturned by erroneous AI advice. Conversely, time pressure did not exacerbate automation bias occurrence, but appeared to increase its severity, evidenced by heightened reliance on the system\u27s negative consultations and subsequent performance decline. These findings highlight potential risks of AI use in healthcare
AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators
No full textNovel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of spin orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our AI guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices
A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation
No full textTo automatically detect Anterior Mediastinum Lesions (AMLs) in the Anterior Mediastinum (AM), the primary requirement will be an automatic segmentation model specifically designed for the AM. The prevalence of AML is extremely low, making it challenging to conduct screening research similar to lung cancer screening. Retrospectively reviewing chest CT scans over a specific period to investigate the prevalence of AML requires substantial time. Therefore, developing an Artificial Intelligence (AI) model to find location of AM helps radiologist to enhance their ability to manage workloads and improve diagnostic accuracy for AMLs. In this paper, we introduce a U-shaped structure network to segment AM. Two attention mechanisms were used for maintaining long-range dependencies and localization. In order to have the potential of Multi-Head Self-Attention (MHSA) and a lightweight network, we designed a parallel MHSA named Wide-MHSA (W-MHSA). Maintaining long-range dependencies is crucial for segmentation when we upsample feature maps. Therefore, we designed a Dilated Depth-Wise Parallel Path connection (DDWPP) for this purpose. In order to design a lightweight architecture, we introduced an expanding convolution block and combine it with the proposed W-MHSA for feature extraction in the encoder part of the proposed U-shaped network. The proposed network was trained on 2775 AM cases, which obtained an average Dice Similarity Coefficient (DSC) of 87.83%, mean Intersection over Union (IoU) of 79.16%, and Sensitivity of 89.60%. Our proposed architecture exhibited superior segmentation performance compared to the most advanced segmentation networks, such as Trans Unet, Attention Unet, Res Unet, and Res Unet++
Uniqueness Results for Mixed Local and Nonlocal Equations with Singular Nonlinearities and Source Terms
No full textThis paper considers a local and non-local problem characterized by singular nonlinearity and a source term. Specifically, we focus on the following problem:
\begin{equation}\label{A}\tag{P}
-Δ_{p} u + (-Δ)^{s}_{q} u = f(x) u^{-α} + g(x) u^β, \quad u > 0 \quad \text{in } Ω; \quad u = 0, \quad \text{in } \mathbb{R}^{N} \setminus Ω,
\end{equation}
where is an open bounded domain with a boundary , and . We assume that . The function is non-zero and belongs to a suitable Lebesgue space for some , or satisfies a growth condition involving negative powers of the distance function near the boundary . Additionally, is a nonnegative function within appropriate Lebesgue spaces. The primary objectives of this paper are twofold. First, we establish the uniqueness of infinite energy solutions to problem \eqref{A} by introducing a novel comparison principle under certain conditions. Second, we derive several existence results for weak solutions in various senses, accompanied by regularity results for problem \eqref{A}. Furthermore, we present a non-existence result when the function and is near the boundary, under the condition . Our approach leverages the Picone identities on one hand and the interaction between the local and non-local terms on the other hand
Low-temperature thermoelectric transport in the disordered two-dimensional electron gas
No full textWe study interaction corrections to the thermoelectric transport coefficient and the thermopower in the two-dimensional disordered electron gas with long-range Coulomb interactions. To this end, we analyze the heat density-density correlation function using the recently derived generalized non-linear sigma model with particle-hole asymmetry. We present a detailed account of the perturbative calculation of the correlation function, the analysis of its structure, and the derivation of the final results for the transport coefficients. We show that both real and virtual processes give rise to logarithmic interaction corrections to and with and in the diffusive transport regime , where is the temperature and is the disorder scattering rate. A short account of our findings is presented in an accompanying letter.22 pages, 6 figure
NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference
No full textOnline LLM inference powers many exciting applications such as intelligent chatbots and autonomous agents. Modern LLM inference engines widely rely on request batching to improve inference throughput, aiming to make it cost-efficient when running on expensive GPU accelerators. However, the limited GPU memory has largely limited the batch size achieved in practice, leaving significant GPU compute resources wasted.
We present NEO, an online LLM inference system that offloads part of attention compute and KV cache states from the GPU to the local host CPU, effectively increasing the GPU batch size and thus inference throughput. To this end, NEO proposes asymmetric GPU-CPU pipelining and load-aware scheduling to balance GPU and CPU loads and fully utilize their compute and memory resources. We evaluate NEO on a wide range of workloads (i.e., code generation, text summarization), GPUs (i.e., T4, A10G, H100), and LLM models (i.e., 7B, 8B, 70B). NEO achieves up to 7.5, 26%, and 14% higher throughput compared to GPU-only approach on T4, A10G, and H100 GPUs, respectively, while maintaining the same latency; with more powerful CPUs, NEO achieves up to 79.3% throughput gain on A10G GPU
A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns
No full textIn the digital world, influencers are pivotal as opinion leaders, shaping the views and choices of their influencees. Modern advertising often follows this trend, where marketers choose appropriate influencers for product endorsements, based on thorough market analysis. Previous studies on influencer selection have typically relied on numerical representations of individual opinions and interactions, a method that simplifies the intricacies of social dynamics. In this work, we first introduce a Time-aware Influencer Simulator (TIS), helping promoters identify and select the right influencers to market their products, based on LLM simulation. To validate our approach, we conduct experiments on the public advertising campaign dataset SAGraph which encompasses social relationships, posts, and user interactions. The results show that our method outperforms traditional numerical feature-based approaches and methods using limited LLM agents. Our research shows that simulating user timelines and content lifecycles over time simplifies scaling, allowing for large-scale agent simulations in social networks. Additionally, LLM-based agents for social recommendations and advertising offer substantial benefits for decision-making in promotional campaigns.10 pages, 5 figure
Our Solar System Neighborhood: Three Diverging Tales of Planetary Habitability and Windows to Earth\u27s Past and Future
No full textUnderstanding planetary habitability is one of the major challenges of the current scientific era, particularly given the discovery of a large and diverse terrestrial exoplanet population. Discerning the primary factors that contribute to planetary habitability may be extracted through a detailed examination of the terrestrial planets within the Solar System, most particularly Venus, Earth, and Mars, and the evolution of their interiors and atmospheres through time. Here, we provide a detailed description of the fundamental properties of these three planets, the effects of solar evolution, and the potential contributions of these various aspects toward driving their evolutionary pathways. We argue that evolution of Venus, Earth, and Mars provide essential templates from which a more comprehensive approach toward the study of planetary habitability may be derived.45 pages, 3 figures, accepted for publication in The Archean Earth (ed.: M. Homan, P. Mason, R. Ernst, T. Lyons, C. Heubeck, D. Papineau, E. Stueeken, R. Mazumder, A. Webb, W. Altermann), Elsevie
Negative-Free Self-Supervised Gaussian Embedding of Graphs
No full textGraph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of GCL benefits from two key properties: \emph{alignment} and \emph{uniformity}, which align representations of positive node pairs while uniformly distributing all representations on the hypersphere. The uniformity property plays a critical role in preventing representation collapse and is achieved by pushing apart augmented views of different nodes (negative pairs). As such, existing GCL methods inherently rely on increasing the quantity and quality of negative samples, resulting in heavy computational demands, memory overhead, and potential class collision issues. In this study, we propose a negative-free objective to achieve uniformity, inspired by the fact that points distributed according to a normalized isotropic Gaussian are uniformly spread across the unit hypersphere. Therefore, we can minimize the distance between the distribution of learned representations and the isotropic Gaussian distribution to promote the uniformity of node representations. Our method also distinguishes itself from other approaches by eliminating the need for a parameterized mutual information estimator, an additional projector, asymmetric structures, and, crucially, negative samples. Extensive experiments over seven graph benchmarks demonstrate that our proposal achieves competitive performance with fewer parameters, shorter training times, and lower memory consumption compared to existing GCL methods.Accepted by Neural Network