Energy and Time-Aware Inference Offloading for DNN-based Applications in LEO Satellites

In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology for remote sensing satellite image recognition. However, the substantial computation capability and energy demands of DNN models, coupled with the instability of the satellite-ground link, pose significant challenges, burdening satellites with limited power intake and hindering the timely completion of tasks. Existing approaches, such as transmitting all images to the ground for processing or executing DNN models on the satellite, is unable to effectively address this issue. By exploiting the internal hierarchical structure of DNNs and treating each layer as an independent subtask, we propose a satellite-ground collaborative computation partial offloading approach to address this challenge. We formulate the problem of minimizing the inference task execution time and onboard energy consumption through offloading as an integer linear programming (ILP) model. The complexity in solving the problem arises from the combinatorial explosion in the discrete solution space. To address this, we have designed an improved optimization algorithm based on branch and bound. Simulation results illustrate that, compared to the existing approaches, our algorithm improve the performance by 10%-18%Comment: Accepted by ICNP 2023 Worksho

Constructing tissue-like complex structures using cell-laden DNA hydrogel bricks

Tissue engineering has long been a challenge because of the difficulty of addressing the requirements that such an engineered tissue must meet. In this paper, we developed a new "brick-to-wall" based on unique properties of DNA supramolecular hydrogels to fabricate three-dimensional (3D) tissuelike structures: different cell types are encapsulated in DNA hydrogel bricks which are then combined to build 3D structures. Signal responsiveness of cells through the DNA gels was evaluated and it was discovered that the gel permits cell migration in 3D. The results demonstrated that this technology is convenient, effective and reliable for cell manipulation, and we believe that it will benefit artificial tissue fabrication and future large-scale production

Economic analysis and optimization of a renewable energy based power supply system with different energy storages for a remote island

This study investigates and compares the various combinations of renewable energies (solar, wind) and storage technologies (battery, pumped hydro storage, hybrid storage) for an off-grid power supply system. Four configurations (i.e., single RE source system, double RE source system, single storage, and double storage system) based on two scenarios (self-discharge equal to 0% and 1%) are considered, and their operational performance is compared and analyzed. The energy management strategy created for the hybrid pumped battery storage (HPBS) considers that batteries cover low energy surplus/shortages while pumped hydro storage (PHS) is the primary energy storage device for serving high-energy generations/deficits. The developed mathematical model is optimized using Particle Swarm Optimization and the performance and results of the optimizer are discussed in particular detail. The results evidence that self-discharge has a significant impact on the cost of energy (13%–50%) for all configurations due to the substantial increase in renewable energy (RE) generators size compared to the energy storage capacity. Even though solar-wind-PHS is the cost-optimal arrangement, it exhibits lower reliability when compared to solar-wind-HPBS. The study reveals the significance of HPBS in the off-grid RE environment, allowing more flexible energy management, enabling to guarantee a 100% power supply with minimum cost and reducing energy curtailment. Additionally, this study presents and discuss the results of a sensitivity analysis conducted by varying load demand and energy balance of all considered configurations is performed, which reveals the effectiveness of the supplementary functionality of both storages in hybrid mode. Overall, the role of energy storage in hybrid mode improved, and the total energy covered by hybrid storage increased (48%), which reduced the direct dependency on variable RE generation

Topologically controlled multiskyrmions in photonic gradient-index lenses

Skyrmions are topologically protected quasiparticles, originally studied in condensed-matter systems and recently in photonics, with great potential in high-density information storage. Despite the recent attention, most optical solutions require complex systems yet produce limited topologies. Here we demonstrate an extended family of quasiparticles beyond normal skyrmions that are controlled in compact photonic gradient-index media, extending to higher-order members such as multiskyrmions and multimerons, with increasingly complex topologies. We introduce multiple topological numbers (centrality, radiality, vorticity, and polarity) in addition to the skyrmion number to describe these photonic quasiparticles. Our compact creation system lends itself to integrated and programmable solutions of complex particle textures, with potential impacts on both photonic and condensed-matter systems for revolutionizing topological informatics and logic devices

ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations

Topologically controlled multiskyrmions in photonic gradient-index lenses

Skyrmions are topologically protected quasiparticles, originally studied in condensed-matter systems and recently in photonics, with great potential in ultra-high-capacity information storage. Despite the recent attention, most optical solutions require complex and expensive systems yet produce limited topologies. Here we demonstrate an extended family of quasiparticles beyond normal skyrmions, which are controlled in confined photonic gradient-index media, extending to higher-order members such as multiskyrmions and multimerons, with increasingly complex topologies. We introduce new topological numbers to describe these complex photonic quasiparticles and propose how this new zoology of particles could be used in future high-capacity information transfer. Our compact creation system lends integrated and programmable solutions of complex particle textures, with potential impacts on both photonic and condensed-matter systems for revolutionizing topological informatics and logic devices

Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

Clinical and Biological Implications of Mutational Spectrum in Acute Myeloid Leukemia of FAB Subtypes M0 and M1

Background/Aims: Acute myeloid leukemia (AML) of French-American-British (FAB) subtypes M0 and M1 are both poorly differentiated AML, but their mutational spectrum and molecular characteristics remain unknown. This study aimed to explore the mutational spectrum and prognostic factors of AML-M0 and M1. Methods: Sixty-five AML patients derived from The Cancer Genome Atlas (TCGA) database were enrolled in this study. Whole-genome sequencing was performed to depict the mutational spectrum of each patient. Clinical characteristics at diagnosis, including peripheral blood (PB) white blood cell counts (WBC), blast percentages in PB and bone marrow (BM), FAB subtypes and the frequencies of known recurrent genetic mutations were described. Survival was estimated using the Kaplan-Meier methods and log-rank test. Univariate and multivariate Cox proportional hazard models were constructed procedure. Results: Forty-six patients had more than five recurrent genetic mutations. FLT3 had the highest mutation frequency (n=20, 31%), followed by NPM1 (n=18, 28%), DNMT3A (n=16, 25%), IDH1 (n=14, 22%), IDH2 (n=12, 18%), RUNX1 (n=11, 17%) and TET2 (n=7, 11%). Univariate analysis showed that age >= 60 years and TP53 mutations had adverse effect on EFS (P=0.015, P=0.036, respectively) and OS (P=0.003, P=0.004, respectively), WBC count >= 50x10(9)/L and FLT3-ITD negatively affected EFS (P=0.003, P=0.034, respectively), whereas NPM1 mutations had favorable effect on OS (P=0.035) and allogeneic hematopoietic stem cell transplantation (allo-HSCT) on EFS and OS (all P= 50x10(9)/L was an independent risk factor for EFS (P=0.002) and TP53 mutations for OS (P=0.043). Conclusions: Our study provided new insights into the mutational spectrum and molecular signatures of AML-M0 and M1. We proposed that FLT3-ITD, NPM1 and TP53 be identified as markers for risk stratification of AML-M0 and M1. Patients with AML-M0 and M1 would likely benefit from allo-HSCT. (C) 2018 The Author(s) Published by S. Karger AG, Base