Tailors: Accelerating Sparse Tensor Algebra by Overbooking Buffer Capacity

Sparse tensor algebra is a challenging class of workloads to accelerate due to low arithmetic intensity and varying sparsity patterns. Prior sparse tensor algebra accelerators have explored tiling sparse data to increase exploitable data reuse and improve throughput, but typically allocate tile size in a given buffer for the worst-case data occupancy. This severely limits the utilization of available memory resources and reduces data reuse. Other accelerators employ complex tiling during preprocessing or at runtime to determine the exact tile size based on its occupancy. This paper proposes a speculative tensor tiling approach, called overbooking, to improve buffer utilization by taking advantage of the distribution of nonzero elements in sparse tensors to construct larger tiles with greater data reuse. To ensure correctness, we propose a low-overhead hardware mechanism, Tailors, that can tolerate data overflow by design while ensuring reasonable data reuse. We demonstrate that Tailors can be easily integrated into the memory hierarchy of an existing sparse tensor algebra accelerator. To ensure high buffer utilization with minimal tiling overhead, we introduce a statistical approach, Swiftiles, to pick a tile size so that tiles usually fit within the buffer's capacity, but can potentially overflow, i.e., it overbooks the buffers. Across a suite of 22 sparse tensor algebra workloads, we show that our proposed overbooking strategy introduces an average speedup of $52.7\times$ and $2.3\times$ and an average energy reduction of $22.5\times$ and $2.5\times$ over ExTensor without and with optimized tiling, respectively.Comment: 17 pages, 13 figures, in MICRO 202

An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances

In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (ASC). Our approach is built upon a universal set of acoustic segment units covering the overall acoustic scene space. First, those units are modeled with acoustic segment models (ASMs) used to tokenize acoustic scene utterances into sequences of acoustic segment units. Next, paralleling the idea of stop words in information retrieval, stop ASMs are automatically detected. Finally, acoustic segments associated with the stop ASMs are blocked, because of their low indexing power in retrieval of most acoustic scenes. In contrast to building scene models with whole utterances, the ASM-removed sub-utterances, i.e., acoustic utterances without stop acoustic segments, are then used as inputs to the AlexNet-L back-end for final classification. On the DCASE 2018 dataset, scene classification accuracy increases from 68%, with whole utterances, to 72.1%, with segment selection. This represents a competitive accuracy without any data augmentation, and/or ensemble strategy. Moreover, our approach compares favourably to AlexNet-L with attention.Comment: Accepted by Interspeech 202

Early detection and lesion visualization of pear leaf anthracnose based on multi-source feature fusion of hyperspectral imaging

Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases

Dual Characters of GH-IGF1 Signaling Pathways in Radiotherapy and Post-radiotherapy Repair of Cancers

Radiotherapy remains one of the most important cancer treatment modalities. In the course of radiotherapy for tumor treatment, the incidental irradiation of adjacent tissues could not be completely avoided. DNA damage is one of the main factors of cell death caused by ionizing radiation, including single-strand (SSBs) and double-strand breaks (DSBs). The growth hormone-Insulin-like growth factor 1 (GH-IGF1) axis plays numerous roles in various systems by promoting cell proliferation and inhibiting apoptosis, supporting its effects in inducing the development of multiple cancers. Meanwhile, the GH-IGF1 signaling involved in DNA damage response (DDR) and DNA damage repair determines the radio-resistance of cancer cells subjected to radiotherapy and repair of adjacent tissues damaged by radiotherapy. In the present review, we firstly summarized the studies on GH-IGF1 signaling in the development of cancers. Then we discussed the adverse effect of GH-IGF1 signaling in radiotherapy to cancer cells and the favorable impact of GH-IGF1 signaling on radiation damage repair to adjacent tissues after irradiation. This review further summarized recent advances on research into the molecular mechanism of GH-IGF1 signaling pathway in these effects, expecting to specify the dual characters of GH-IGF1 signaling pathways in radiotherapy and post-radiotherapy repair of cancers, subsequently providing theoretical basis of their roles in increasing radiation sensitivity during cancer radiotherapy and repairing damage after radiotherapy

A Two-Stage Approach to Device-Robust Acoustic Scene Classification

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finer-grained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set, where our best system, a two-stage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.Comment: Submitted to ICASSP 2021. Code available: https://github.com/MihawkHu/DCASE2020_task

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effect of electric field on Fe₂O₃ nanowire growth during thermal oxidation

A direct current of 5 A was applied to narrow strips of iron foil in air to synthesize iron oxide nanowires (NWs) via thermal oxidation route of resistive heating. Transverse electric fields of 0–4000 V/m were applied perpendicularly to the surface of the iron foil during thermal oxidations. Results showed that the Fe2O3 NW array can grow perpendicularly on that surface by using this kind of thermal oxidation method. Transverse electric fields applied during thermal oxidation significantly affected the morphology of the Fe2O3 NW array. With increasing strength of the transverse electric fields, the Fe2O3 NWs became much longer, thinner and denser in distribution, and the diameters became more uniform. Furthermore, solid state based-up diffusion growth mechanism for the Fe2O3 NW array was confirmed by thermal oxidation. </jats:p

Hydrothermal synthesis of core-shell structured PS@GdPO4:Tb3+/Ce3+ spherical particles and their luminescence properties

AbstractNon-aggregated spherical polystyrene (PS) particles were coated with GdPO4:Tb3+/Ce3+ phosphor layers by a conventional hydrothermal synthesis using poly(vinylpyrrolidone) (PVP) as an additive without further annealing treatment. X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), photoluminescence (PL), as well as luminescence decay experiments were used to characterise the resulting core-shell structured PS@GdPO4:Tb3+/Ce3+ samples. The results of XRD indicated that the PS particles were successfully coated with the GdPO4:Tb3+/Ce3+ phosphor layers, which could be further verified by the images of FESEM. Under ultraviolet excitation, the PS@GdPO4:Tb3+/Ce3+ phosphors show Tb3+ characteristic emission, i.e. 5D4-7FJ (J = {6, 5, 4, 3}) emission lines with green emission 5D4-7F5 (543 nm) as the most prominent group. The core-shell phosphors so obtained have potential applications in field emission display (FED) and plasma display panels (PDP).</jats:p