This paper presents a 6.78-MHz inductive wireless power transfer (WPT) system designed for implantable medical devices (IMDs). To meet the stringent IMD limits on form factor, power conversion efficiency (PCE), and regulation, the proposed system adopts a resonant current-mode (RCM) method. A compact RX coil, under spatial constraints, necessitates the adoption of the RCM method, which provides a voltage conversion ratio greater than unity, enabling efficient transfer of the energy stored in the LC tank to the output. To achieve high regulation performance, a deficit-energy-quantifying (DEQ) resonant regulating rectifier (3R) is introduced. The DEQ-3R operates by quantifying the energy deficit to reach the target output voltage and comparing it with the LC-tank energy. Comparison result detects the transition timing from the resonance phase to the charging phase with a delay-compensated comparator (DCC) adaptive to the LC-tank energy level. The proposed DEQ-3R is implemented in a 180-nm CMOS process, occupying 1.04 mm2 active area. Experiment results using an 80-nH RX coil demonstrate regulated 2.5-V operation over a coupling-coefficient range of 0.1-0.16 and a load power range of 5-45 mW. The prototype achieves a peak PCE of 81.28%, reflecting a 6.08% improvement over a counterpart without the DEQ, while reducing the VOUT ripple from 432mVPP to 168mVPP with a 20-nF output capacitor.

MOTIVATION: LLMs like GPT-4, despite their advancements, often produce hallucinations and struggle with integrating external knowledge effectively. While Retrieval-Augmented Generation (RAG) attempts to address this by incorporating external information, it faces significant challenges such as context length limitations and imprecise vector similarity search. ESCARGOT aims to overcome these issues by combining LLMs with a dynamic Graph of Thoughts and biomedical knowledge graphs, improving output reliability, and reducing hallucinations. RESULT: ESCARGOT significantly outperforms industry-standard RAG methods, particularly in open-ended questions that demand high precision. ESCARGOT also offers greater transparency in its reasoning process, allowing for the vetting of both code and knowledge requests, in contrast to the black-box nature of LLM-only or RAG-based approaches. AVAILABILITY AND IMPLEMENTATION: ESCARGOT is available as a pip package and on GitHub at: https://github.com/EpistasisLab/ESCARGOT.

MOTIVATION: Answering and solving complex problems using a large language model (LLM) given a certain domain such as biomedicine is a challenging task that requires both factual consistency and logic, and LLMs often suffer from some major limitations, such as hallucinating false or irrelevant information, or being influenced by noisy data. These issues can compromise the trustworthiness, accuracy, and compliance of LLM-generated text and insights. RESULTS: Knowledge Retrieval Augmented Generation ENgine (KRAGEN) is a new tool that combines knowledge graphs, Retrieval Augmented Generation (RAG), and advanced prompting techniques to solve complex problems with natural language. KRAGEN converts knowledge graphs into a vector database and uses RAG to retrieve relevant facts from it. KRAGEN uses advanced prompting techniques: namely graph-of-thoughts (GoT), to dynamically break down a complex problem into smaller subproblems, and proceeds to solve each subproblem by using the relevant knowledge through the RAG framework, which limits the hallucinations, and finally, consolidates the subproblems and provides a solution. KRAGEN's graph visualization allows the user to interact with and evaluate the quality of the solution's GoT structure and logic. AVAILABILITY AND IMPLEMENTATION: KRAGEN is deployed by running its custom Docker containers. KRAGEN is available as open-source from GitHub at: https://github.com/EpistasisLab/KRAGEN.

In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalance in the distribution of the auxiliary OOD data across classes. We propose a balanced energy regularization loss that is simple but generally effective for a variety of tasks. Our balanced energy regularization loss utilizes class-wise different prior probabilities for auxiliary data to address the class imbalance in OOD data. The main concept is to regularize auxiliary samples from majority classes, more heavily than those from minority classes. Our approach performs better for OOD detection in semantic segmentation, long-tailed image classification, and image classification than the prior energy regularization loss. Furthermore, our approach achieves state-of-the-art performance in two tasks: OOD detection in semantic segmentation and long-tailed image classification.

MOTIVATION: Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training. RESULTS: Aliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data. AVAILABILITY AND IMPLEMENTATION: Aliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro.

This paper proposes a contents security technique for digital holographic display systems. A digital holographic display system assumes that it follows faithfully the current system frame in processes such as image acquisition, processing, transmission, reception and reconstruction to provide 2D or 3D video services. For the image acquisition in this system, RGB and depth cameras are used. An image contents encryption technique, which is executed in the digital holographic display system, is proposed. This technique encrypts only a part of data by performing the frequency transformation of depth-maps and RGB images. In this paper, two applications of the proposed encryption technique are discussed. In the first one, only depth-maps are encrypted and in the second one, depth-maps and RGB image information are encrypted simultaneously. The experiment results reveal that 0.73% of the entire data are encrypted and that the proposed technique can effectively hide the visual information of an original RGB image and depth-map.

In this paper, we propose a distributed, infrastructure-free algorithm for supporting self-localization and location-tracking of portable devices in home networks that do not rely on any positioning infrastructure, such as GPS (Global Positioning System). The proposed algorithm employs the received signal strength (RSS) to estimate the current position of each portable device and then elaborates the position with the box-based sequential Monte Carlo (BSMC) method. Simulation results indicate that the proposed algorithm is superior to the well-received Centroid algorithm in terms of the distance estimation error.