Reference Based Genome Compression

DNA sequencing technology has advanced to a point where storage is becoming the central bottleneck in the acquisition and mining of more data. Large amounts of data are vital for genomics research, and generic compression tools, while viable, cannot offer the same savings as approaches tuned to inherent biological properties. We propose an algorithm to compress a target genome given a known reference genome. The proposed algorithm first generates a mapping from the reference to the target genome, and then compresses this mapping with an entropy coder. As an illustration of the performance: applying our algorithm to James Watson's genome with hg18 as a reference, we are able to reduce the 2991 megabyte (MB) genome down to 6.99 MB, while Gzip compresses it to 834.8 MB.Comment: 5 pages; Submitted to the IEEE Information Theory Workshop (ITW) 201

Prince Albert No. 56 Teachers Association to James H. Meredith (Undated)

https://egrove.olemiss.edu/mercorr_pro/1666/thumbnail.jp

Path finding strategies in scale-free networks

We numerically investigate the scale-free network model of Barab{\'a}si and Albert [A. L. Barab{\'a}si and R. Albert, Science {\bf 286}, 509 (1999)] through the use of various path finding strategies. In real networks, global network information is not accessible to each vertex, and the actual path connecting two vertices can sometimes be much longer than the shortest one. A generalized diameter depending on the actual path finding strategy is introduced, and a simple strategy, which utilizes only local information on the connectivity, is suggested and shown to yield small-world behavior: the diameter $D$ of the network increases logarithmically with the network size $N$, the same as is found with global strategy. If paths are sought at random, $D \sim N^{0.5}$ is found.Comment: 4 pages, final for

Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback

Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs undesirable images. If so, we simply need to prevent the generation of the bad images, and we call this task censoring. In this work, we present censored generation with a pre-trained diffusion model using a reward model trained on minimal human feedback. We show that censoring can be accomplished with extreme human feedback efficiency and that labels generated with a mere few minutes of human feedback are sufficient. Code available at: https://github.com/tetrzim/diffusion-human-feedback.Comment: Published in NeurIPS 202

Nanodiamond-Gutta Percha Composite Biomaterials for Root Canal Therapy.

Root canal therapy (RCT) represents a standard of treatment that addresses infected pulp tissue in teeth and protects against future infection. RCT involves removing dental pulp comprising blood vessels and nerve tissue, decontaminating residually infected tissue through biomechanical instrumentation, and root canal obturation using a filler material to replace the space that was previously composed of dental pulp. Gutta percha (GP) is typically used as the filler material, as it is malleable, inert, and biocompatible. While filling the root canal space with GP is the standard of care for endodontic therapies, it has exhibited limitations including leakage, root canal reinfection, and poor mechanical properties. To address these challenges, clinicians have explored the use of alternative root filling materials other than GP. Among the classes of materials that are being explored as novel endodontic therapy platforms, nanodiamonds (NDs) may offer unique advantages due to their favorable properties, particularly for dental applications. These include versatile faceted surface chemistry, biocompatibility, and their role in improving mechanical properties, among others. This study developed a ND-embedded GP (NDGP) that was functionalized with amoxicillin, a broad-spectrum antibiotic commonly used for endodontic infection. Comprehensive materials characterization confirmed improved mechanical properties of NDGP over unmodified GP. In addition, digital radiography and microcomputed tomography imaging demonstrated that obturation of root canals with NDGP could be achieved using clinically relevant techniques. Furthermore, bacterial growth inhibition assays confirmed drug functionality of NDGP functionalized with amoxicillin. This study demonstrates a promising path toward NDGP implementation in future endodontic therapy for improved treatment outcomes

Fully Quantized Always-on Face Detector Considering Mobile Image Sensors

Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors are essential in various practical applications, such as energy-efficient mobile phones and surveillance systems with always-on capabilities. One noteworthy limitation is the absence of suitable face detectors for the always-on scenario, a crucial aspect of image sensor-level applications. These detectors must operate directly with sensor RAW data before the image signal processor (ISP) takes over. This gap poses a significant challenge in achieving optimal performance in such scenarios. Further research and development are necessary to bridge this gap and fully leverage the potential of iCIS applications. In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications. To achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs, simulating always-on face detection processed "before" the ISP chain. Our approach employs ternary (-1, 0, 1) weights for potential implementations in image sensors, resulting in a relatively simple network architecture with shallow layers and extremely low-bitwidth. Our method demonstrates reasonable face detection performance and excellent efficiency in simulation studies, offering promising possibilities for practical always-on face detectors in real-world applications.Comment: Accepted to ICCV 2023 Workshop on Low-Bit Quantized Neural Networks (LBQNN), Ora