The Zoned Namespace (ZNS) SSD is an innovative technology that aims to mitigate the block interface tax associated with conventional SSDs. However, constructing a RAID system using ZNS SSDs presents a significant challenge in managing partial parity for incomplete stripes. Previous research permanently logs partial parity in a limited number of reserved zones, which not only creates bottlenecks in throughput but also exacerbates write amplification, thereby reducing the device's lifetime. We refer to these inefficiencies as the partial parity tax.

Recently, open-source large Vision-Language Models (VLMs) have progressed toward achieving performance comparable to closed-source VLMs. However, open-source VLMs struggle to recognize unfamiliar texts depicted in the images, where the texts are written in underrepresented languages like Korean. This illiteracy problem is primarily due to insufficient training data for the underrepresented languages. To address this problem, we propose a novel augmentation scheme that generates large-scale image data for the underrepresented languages with minimal manual annotations. Our scheme synthetically combines a text image depicting words or sentences with a template image containing textual contexts, such as a receipt, a sign, a book, and a product label. Specifically, the text image is cut and pasted into a patch of the template image to generate a synthetic image, which is labeled with the corresponding texts in the text image. Therefore, fine-tuning a VLM with our synthetic data can enhance its ability to generalize to real-world text recognition tasks. Experimental results demonstrate the effectiveness of our scheme, showing a significant performance improvement in text recognition.

Identifying therapeutic genes is crucial for developing treatments targeting genetic causes of diseases, but experimental trials are costly and time-consuming. Although many deep learning approaches aim to identify biomarker genes, predicting therapeutic target genes remains challenging due to the limited number of known targets. To address this, we propose HIT (Hypergraph Interaction Transformer), a deep hypergraph representation learning model that identifies a gene's therapeutic potential, biomarker status, or lack of association with diseases. HIT uses hypergraph structures of genes, ontologies, diseases, and phenotypes, employing attention-based learning to capture complex relationships. Experiments demonstrate HIT's state-of-the-art performance, explainability, and ability to identify novel therapeutic targets.

OBJECTIVE: Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality. MATERIALS AND METHODS: We propose the RIAS framework, an end-to-end framework that is designed with reliability and interpretability at its core and automatically optimizes the given model. Using RIAS, clinicians get accurate and reliable predictions which can be used as likelihood, with global and local explanations, and "what if" scenarios to achieve desired outcomes as well. RESULTS: We apply RIAS to AMI prognosis prediction data which comes from the Korean Acute Myocardial Infarction Registry. We compared FT-Transformer with XGBoost and MLP and found that FT-Transformer has superiority in sensitivity and comparable performance in AUROC and F1 score to XGBoost. Furthermore, RIAS reveals the significance of statin-based medications, beta-blockers, and age on mortality regardless of time period. Lastly, we showcase reliable and interpretable results of RIAS with local explanations and counterfactual examples for several realistic scenarios. DISCUSSION: RIAS addresses the "black-box" issue in AI by providing both global and local explanations based on SHAP values and reliable predictions, interpretable as actual likelihoods. The system's "what if" counterfactual explanations enable clinicians to simulate patient-specific scenarios under various conditions, enhancing its practical utility. CONCLUSION: The proposed framework provides reliable and interpretable predictions along with counterfactual examples.

In this paper, we present an SSD design that is resilient to sudden power-off failures. Modern SSDs use a large number of capacitors that act as energy reserves to persist both host data and SSD metadata in the unforeseen event of a power outage. However, these capacitors take up a large footprint that limits the SSD’s density. We present a series of design choices that significantly reduce the SSD’s dependence on capacitors, all the while meeting the durability, consistency, and power-on time constraints. We demonstrate that at a modest performance overhead of 11%, the amount of required capacitance is reduced by 97.87%.

Radial visualization continues to be a popular design choice in information visualization systems, due perhaps in part to its aesthetic appeal. However, it is an open question whether radial visualizations are truly more effective than their Cartesian counterparts. In this paper, we describe an initial user trial from an ongoing empirical study of the SQiRL (Simple Query interface with a Radial Layout) visualization system, which supports both radial and Cartesian projections of stacked bar charts. Participants were shown 20 diagrams employing a mixture of radial and Cartesian layouts and were asked to perform basic analysis on each. The participants' speed and accuracy for both visualization types were recorded. Our initial findings suggest that, in spite of the widely perceived advantages of Cartesian visualization over radial visualization, both forms of layout are, in fact, equally usable. Moreover, radial visualization may have a slight advantage over Cartesian for certain tasks. In a follow-on study, we plan to test users' ability to create, as well as read and interpret, radial and Cartesian diagrams in SQiRL.