Chronic pain is a wide-spread condition that is debilitating and expensive to manage, costing the United States alone around $600 billion in 2010. In a common symptom of chronic pain called allodynia, non-painful stimuli produce painful responses with highly variable presentations across individuals. While the specific mechanisms remain unclear, allodynia is hypothesized to be caused by the dysregulation of excitatory-inhibitory (E-I) balance in pain-processing neural circuitry in the dorsal horn of the spinal cord. In this work, we analyze biophysically-motivated subcircuit structures that represent common motifs in neural circuits in laminae I-II of the dorsal horn. These circuits are hypothesized to be part of the neural pathways that mediate two different types of allodynia: static and dynamic. We use neural firing rate models to describe the activity of populations of excitatory and inhibitory interneurons within each subcircuit. By accounting for experimentally-observed responses under healthy conditions, we specify model parameters defining populations of subcircuits that yield typical behavior under normal conditions. Then, we implement a sensitivity analysis approach to identify the mechanisms most likely to cause allodynia-producing dysregulation of the subcircuit's E-I signaling. We find that disruption of E-I balance generally occurs either due to downregulation of inhibitory signaling so that excitatory neurons are "released" from inhibitory control, or due to upregulation of excitatory neuron responses so that excitatory neurons "escape" their inhibitory control. Which of these mechanisms is most likely to occur, the subcircuit components involved in the mechanism, and the proportion of subcircuits exhibiting the mechanism can vary depending on the subcircuit structure. These results suggest specific hypotheses about diverse mechanisms that may be most likely responsible for allodynia, thus offering predictions for the high interindividual variability observed in allodynia and identifying targets for further experimental studies on the underlying mechanisms of this chronic pain symptom.

This paper presents a drug development platform based on virtual screening technology. The platform integrates key components such as pocket prediction, molecular docking, molecular dynamics simulation, and ADMET evaluation to achieve an efficient and automated drug virtual screening process. The platform utilizes Docker for modular encapsulation, ensuring environment isolation and convenient deployment. It also provides standardized input-output formats and a task allocation system, enabling users to quickly deploy and customize the workflow. Experimental results demonstrate the effectiveness of the platform in identifying real drugs and evaluating virtual screening results, providing an efficient and reliable solution for drug development. The platform features easy deployment and migration, independent module execution, automated workflow implementation, personalized customization and replacement, task allocation for computationally intensive steps, and complex operations in molecular dynamics simulation.

We present LocalPose, a novel method for 9 DoF object pose estimation from object point clouds. Existing works regress pose directly from the global shape embedding and are limited to a fixed set of shapes. We identify that the global object pose is closely related to local geometry properties like surface orientations at representative regions. Therefore, our key idea is to summarize local geometry properties as pose signatures at each point and aggregate them into the global pose, where local pose signatures are easier to learn by the network and generalize to novel shapes. We find two types of pose signatures that benefit pose estimation. First, we learn a neural network to predict 9 DoF pose signatures as pose candidates, and the process of voting them for the object pose. Second, we treat surface normals as direct pose regulators who help to select a subset of pose candidates to achieve the best accuracy. Experiments show that our method outperforms the state-of-the-art in terms of fine-grained pose accuracy on synthetic and real datasets, contributed by both pose signatures as candidates and regulators.

We present a novel vectorized indoor modeling approach that converts point clouds into building information models (BIM) with concise and semantically segmented polygonal meshes. Existing methods detect planar shapes and connect them to complete the scene. Some focus on floor plan reconstruction as a simplified problem to better analyze connectivity between planes of floors and walls. However, the connectivity analysis is still challenging and ill-posed with incomplete point clouds as input. We propose ArrangementNet to estimate scene arrangements from an incomplete point cloud, which we can easily convert into a BIM model. ArrangementNet is a novel graph neural network that consumes noisy over-partitioned initial arrangements extracted through non-learning techniques and outputs high-quality scene arrangement. The core of ArrangementNet is an extended graph convolution that leverages co-linear and co-face relationships in the arrangement and improves the quality of prediction in complex scenes. We apply ArrangementNet to improve floor plan and ceiling arrangements and enrich them with semantic objects as scene arrangements for scene generation. Our approach faithfully models challenging scenes obtained from laser scans or multiview stereo and shows significant improvement in BIM model reconstruction compared to the state-of-the-art. Our code is available at https://github.com/zssjh/ArrangementNet.

We present MVLayoutNet, a network for holistic 3D reconstruction from multi-view panoramas. Our core contribution is to seamlessly combine learned monocular layout estimation and multi-view stereo (MVS) for accurate layout reconstruction in both 3D and image space. We jointly train a layout module to produce an initial layout and a novel MVS module to obtain accurate layout geometry. Unlike standard MVSNet, our MVS module takes a newly-proposed layout cost volume, which aggregates multi-view costs at the same depth layer into corresponding layout elements. We additionally provide an attention-based scheme that guides the MVS module to focus on structural regions. Such a design considers both local pixel-level costs and global holistic information for better reconstruction. Experiments show that our method outperforms state-of-the-arts in terms of depth rmse by 21.7% and 41.2% on the 2D-3D-S [1] and ZInD [4] datasets. For complex scenes with multiple rooms, our method can be applied to each layout element of a precomputed topology to accurately reconstruct a globally coherent layout geometry.

Abstract DNA storage is a new digital data storage technology based on specific encoding and decoding methods between 0 and 1 binary codes of digital data and A-T-C-G quaternary codes of DNAs, which and is expected to develop into a major data storage form in the future due to its advantages (such as high data density, long storage time, low energy consumption, convenience for carrying, concealed transportation and multiple encryptions). In this review, we mainly summarize the recent research advances of four main encoding and decoding methods of DNA storage technology: direct mapping method between 0 and 1 binary and A-T-C-G quaternary codes in early-stage, fountain code for higher logical storage density, inner and outer codes for random access DNA storage data, and CRISPR mediated in vivo DNA storage method. The first three encoding/decoding methods belong to in vitro DNA storage, representing the mainstream research and application in DNA storage. Their advantages and disadvantages are also reviewed: direct mapping method is easy and efficient, but has high error rate and low logical density; fountain code can achieve higher storage density without random access; inner and outer code has error-correction design to realize random access at the expense of logic density. This review provides important references and improved understanding of DNA storage methods. Development of efficient and accurate DNA storage encoding and decoding methods will play a very important and even decisive role in the transition of DNA storage from the laboratory to practical application, which may fundamentally change the information industry in the future.

The explosion of Next Generation Sequencing (NGS) data with over one billion reads per day poses a great challenge to the capability of current computing systems. In this paper, we proposed a CPU-FPGA heterogeneous architecture for accelerating a short reads mapping algorithm, which was built upon the concept of hash-index. In particular, by extracting and mapping the most time-consuming and basic operations to specialized processing elements (PEs), our new algorithm is favorable to efficient acceleration on FPGAs. The proposed architecture is implemented and evaluated on a customized FPGA accelerator card with a Xilinx Virtex5 LX330 FPGA resided. Limited by available data transfer bandwidth, our NGS mapping accelerator, which operates at 175MHz, integrates up to 100 PEs. Compared to an Intel six-cores CPU, the speedup of our accelerator ranges from 22.2 times to 42.9 times.

The wide acceptance of bioinformatics, medical imaging and multimedia applications, which have a data-centric favor to them, require more efficient and application-specific systems to be built. Due to the advances in modern FPGA technologies recently, there has been a resurgence in research aimed at accelerator design that leverages FPGAs to accelerate large-scale scientific applications. In this paper, we exploit this trend towards FPGA-based accelerator design and provide a proof-of-concept and comprehensive case study on FPGA-based accelerator design for a single-particle 3D reconstruction application in single-precision floating-point format. The proposed stream architecture is built by first offloading computing-intensive software kernels to dedicated hardware modules, which emphasizes the importance of optimizing computing dominated data access patterns. Then configurable computing streams are constructed by arranging the hardware modules and bypass channels to form a linear deep pipeline. The efficiency of the proposed stream architecture is justified by the reported 2.54 times speedup over a 4-cores CPU. In terms of power efficiency, our FPGA-based accelerator introduces a 7.33 and 3.4 times improvement over a 4-cores CPU and an up-to-date GPU device, respectively.

Over the past decades, we noticed huge advances in FPGA technologies. The topic of floating-point accelerator on FPGA has gained renewed interests due to the increased device size and the emergence of fast hardware floating-point library. The popularity of FFT makes it easier to justify spending lots of effort doing detailed optimization. However, the ever increasing data size in some compelling application domains remains beyond the capability of existing FFT accelerators. The demand for more performance remains an active research topic. In this paper, leveraging structured description of FFT algorithms, we propose a FPGA-based FFT core generation framework, which emits Verilog HDL code given high-level algorithmic description and can handle radix-2 as well as prime-radix problem size. In particular, the proposed framework is optimized for 2D FFT and real FFT. The performance of our implementation is comparable with a commercial FFT IP. When compared with the latest results on GPU and CPU, measured in peak floating-point performance and energy efficiency, it shows that GPUs have outperformed FPGAs for FFT acceleration. However, we consider that FPGAs still have advantage in some situations.