Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Same

This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distribution network to provide service elements that prompt the content generator, rather than distributing encoded data of fully finished programs. The service elements include fine-tailored text descriptions, lightweight image data of some objects, or application programming interfaces, comprehensively referred to as semantic sources, and the user terminal translates the received semantic data into video frames. Empowered by the random nature of generative AI, the users could then experience super-personalized services accordingly. The proposed idea incorporates the situations in which the user receives different service providers' element packages; a sequence of packages over time, or multiple packages at the same time. Given promised in-context coherence and content integrity, the combinatory dynamics will amplify the service diversity, allowing the users to always chance upon new experiences. This work particularly aims at short-form videos and advertisements, which the users would easily feel fatigued by seeing the same frame sequence every time. In those use cases, the content provider's role will be recast as scripting semantic sources, transformed from a thorough producer. Overall, this work explores a new form of media ecosystem facilitated by receiver-embedded generative models, featuring both random content dynamics and enhanced delivery efficiency simultaneously.Comment: 13 pages, 7 figure

Shape optimization of superconducting transmon qubit for low surface dielectric loss

Surface dielectric loss of superconducting transmon qubit is believed as one of the dominant sources of decoherence. Reducing surface dielectric loss of superconducting qubit is known to be a great challenge for achieving high quality factor and a long relaxation time ($T_{1}$). Changing the geometry of capacitor pads and junction wire of transmon qubit makes it possible to engineer the surface dielectric loss. In this paper, we present the shape optimization approach for reducing Surface dielectric loss in transmon qubit. The capacitor pad and junction wire of the transmon qubit are shaped as spline curves and optimized through the combination of the finite-element method and global optimization algorithm. Then, we compared the surface participation ratio, which represents the portion of electric energy stored in each dielectric layer and proportional to two-level system (TLS) loss, of optimized structure and existing geometries to show the effectiveness of our approach. The result suggests that the participation ratio of capacitor pad, and junction wire can be reduced by 16% and 26% compared to previous designs through shape optimization, while overall footprint and anharmonicity maintain acceptable value. As a result, the TLS-limited quality factor and corresponding $T_{1}$ were increased by approximately 21.6%

Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning

Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage hyperbolic or spherical spaces to learn representations that embed such structures more accurately, these methods are confined under the message-passing paradigm, making the models vulnerable against side-effects such as oversmoothing and oversquashing. More recent work have proposed global attention-based graph Transformers that can easily model long-range interactions, but their extensions towards non-Euclidean geometry are yet unexplored. To bridge this gap, we propose Fully Product-Stereographic Transformer, a generalization of Transformers towards operating entirely on the product of constant curvature spaces. When combined with tokenized graph Transformers, our model can learn the curvature appropriate for the input graph in an end-to-end fashion, without the need of additional tuning on different curvature initializations. We also provide a kernelized approach to non-Euclidean attention, which enables our model to run in time and memory cost linear to the number of nodes and edges while respecting the underlying geometry. Experiments on graph reconstruction and node classification demonstrate the benefits of generalizing Transformers to the non-Euclidean domain.Comment: 19 pages, 7 figure

Bandwidth-Effective DRAM Cache for GPUs with Storage-Class Memory

We propose overcoming the memory capacity limitation of GPUs with high-capacity Storage-Class Memory (SCM) and DRAM cache. By significantly increasing the memory capacity with SCM, the GPU can capture a larger fraction of the memory footprint than HBM for workloads that oversubscribe memory, achieving high speedups. However, the DRAM cache needs to be carefully designed to address the latency and BW limitations of the SCM while minimizing cost overhead and considering GPU's characteristics. Because the massive number of GPU threads can thrash the DRAM cache, we first propose an SCM-aware DRAM cache bypass policy for GPUs that considers the multi-dimensional characteristics of memory accesses by GPUs with SCM to bypass DRAM for data with low performance utility. In addition, to reduce DRAM cache probes and increase effective DRAM BW with minimal cost, we propose a Configurable Tag Cache (CTC) that repurposes part of the L2 cache to cache DRAM cacheline tags. The L2 capacity used for the CTC can be adjusted by users for adaptability. Furthermore, to minimize DRAM cache probe traffic from CTC misses, our Aggregated Metadata-In-Last-column (AMIL) DRAM cache organization co-locates all DRAM cacheline tags in a single column within a row. The AMIL also retains the full ECC protection, unlike prior DRAM cache's Tag-And-Data (TAD) organization. Additionally, we propose SCM throttling to curtail power and exploiting SCM's SLC/MLC modes to adapt to workload's memory footprint. While our techniques can be used for different DRAM and SCM devices, we focus on a Heterogeneous Memory Stack (HMS) organization that stacks SCM dies on top of DRAM dies for high performance. Compared to HBM, HMS improves performance by up to 12.5x (2.9x overall) and reduces energy by up to 89.3% (48.1% overall). Compared to prior works, we reduce DRAM cache probe and SCM write traffic by 91-93% and 57-75%, respectively.Comment: Published in 2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA'24

Quantitative Sasang Constitution Diagnosis Method for Distinguishing between Tae-eumin and Soeumin Types Based on Elasticity Measurements of the Skin of the Human Hand

The usefulness of constitutional diagnoses based on skin measurements has been established in oriental medicine. However, it is very difficult to standardize traditional diagnosis methods. According to Sasang constitutional medicine, humans can be distinguished based on properties of the skin, including its texture, roughness, hardness and elasticity. The elasticity of the skin was previously used to distinguish between people with Tae-eumin (TE) and Soeumin (SE) constitutions. The present study designed a system that uses a compression method to measure the elasticity of hand skin and evaluated its measurement repeatability. The proposed system was used to compare the skin elasticity between SE and TE subjects, which produced a measurement repeatability error of <3%. The proposed system is suitable for use as a quantitative constitution diagnosis method for distinguishing between TE and SE subjects with an acceptable level of uncertainty

Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction

This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of difficulty, previous KT research has yet to exploit its potential for model optimization and has struggled to predict difficulty from unseen data. To address these problems, we propose a difficulty-centered contrastive learning method for KT models and a Large Language Model (LLM)-based framework for difficulty prediction. These innovative methods seek to improve the performance of KT models and provide accurate difficulty estimates for unseen data. Our ablation study demonstrates the efficacy of these techniques by demonstrating enhanced KT model performance. Nonetheless, the complex relationship between language and difficulty merits further investigation.Comment: 10 pages, 4 figures, 2 table

CardioGuard: A Brassiere-based Reliable ECG Monitoring Sensor System for Supporting Daily Smartphone Healthcare Applications

We propose CardioGuard, a brassiere-based reliable electrocardiogram (ECG) monitoring sensor system, for supporting daily smartphone healthcare applications. It is designed to satisfy two key requirements for user-unobtrusive daily ECG monitoring: reliability of ECG sensing and usability of the sensor. The system is validated through extensive evaluations. The evaluation results showed that the CardioGuard sensor reliably measure the ECG during 12 representative daily activities including diverse movement levels; 89.53% of QRS peaks were detected on average. The questionnaire-based user study with 15 participants showed that the CardioGuard sensor was comfortable and unobtrusive. Additionally, the signal-to-noise ratio test and the washing durability test were conducted to show the high-quality sensing of the proposed sensor and its physical durability in practical use, respectively

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use