Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for reconstruction is the detection of neuronal boundaries. Images acquired by serial section EM, a leading 3D EM technique, are highly anisotropic, with inferior quality along the third dimension. For such images, the 2D max-pooling convolutional network has set the standard for performance at boundary detection. Here we achieve a substantial gain in accuracy through three innovations. Following the trend towards deeper networks for object recognition, we use a much deeper network than previously employed for boundary detection. Second, we incorporate 3D as well as 2D filters, to enable computations that use 3D context. Finally, we adopt a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along with the original image to a second network that generates a final boundary map. Backpropagation training is accelerated by ZNN, a new implementation of 3D convolutional networks that uses multicore CPU parallelism for speed. Our hybrid 2D-3D architecture could be more generally applicable to other types of anisotropic 3D images, including video, and our recursive framework for any image labeling problem

PZnet: Efficient 3D ConvNet Inference on Manycore CPUs

Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volumetric (3D) data acquired by CT, MRI and Microscopy acquisition methods. To deploy convolutional nets in practical working systems, it is important to solve the efficient inference problem. Namely, one should be able to apply an already-trained convolutional network to many large images using limited computational resources. In this paper we present PZnet, a CPU-only engine that can be used to perform inference for a variety of 3D convolutional net architectures. PZNet outperforms MKL-based CPU implementations of PyTorch and Tensorflow by more than 3.5x for the popular U-net architecture. Moreover, for 3D convolutions with low featuremap numbers, cloud CPU inference with PZnet outperfroms cloud GPU inference in terms of cost efficiency

Utjecaj položaja furnira u strukturi furnirskih ploča na njihovu vlačnu čvrstoću

The aim of the research presented in this paper is to study the plywood tensile strength through a change of the position of layers in the panel structure around the central axis, without changing the number and thickness of veneers. So far, it has been known that the veneer layout in plywood structure has a significant impact on plywood bending properties. Besides these mechanical properties, the tensile strength of plywood is also a property that can define the use of plywood as a structural or non-structural panel. For studying the impact of veneer layout on plywood tensile strength, experimental models of nine-layer plywood were made. The models were made from peeled beech veneer with the thickness of 1.2, 1.5, 2.2 and 3.2 mm. The modelling was performed on the basis of changing the position of veneer, 3.2 mm thick, around the central axis. Pure water-soluble phenol-formaldehyde resin was used as plywood binder. The tensile strength of plywood panels was tested in five directions: parallel and perpendicular to the face grain, as well as at the angle of 22.5°, 45° and 67.5° to the face grain of the plywood panel. On the basis of the obtained data for tensile strength in different directions of plywood panel, the coefficient of equality of tensile strength of plywood models was calculated (Ket). The coefficient of mass quality (Kmq) was calculated, too. The research results showed that different veneer layouts in plywood structure have a significant impact on plywood tensile strength. All tested plywood models meet the defined values of tensile strength in accordance with the requirements of the national (МКС) standard for structural plywood for use in construction. Different layouts of veneer sheets in panel structure give opportunities for production of panels with different strength characteristics.U radu su opisana istraživanja čiji je cilj bio proučiti vlačnu čvrstoću furnirskih ploča s obzirom na promjenu položaja furnira u strukturi ploče oko središnje osi, bez promjene broja i debljine furnira. Do danas je poznato da položaj furnira u strukturi furnirskih ploča ima znatan utjecaj na savojna svojstva furnirskih ploča. Osim savojnih svojstava furnirskih ploča, važna je i njihova vlačna čvrstoća, koja može utjecati na to hoće li furnirske ploče biti primijenjene kao strukturni ili kao nestrukturni element. Za proučavanje utjecaja položaja furnira na vlačnu čvrstoću furnirske ploče izrađeni su eksperimentalni modeli devetoslojnih furnirskih ploča. Modeli su napravljeni od bukovih ljuštenih furnira debljine 1,2; 1,5; 2,2 i 3,2 mm. Modeliranje je obavljeno na temelju promjene položaja furnira debljine 3,2 mm oko središnje osi. Kao vezivo je upotrijebljena čista vodotopljiva fenol-formaldehidna smola. Vlačna čvrstoća furnirskih ploča ispitana je u pet smjerova: paralelno i okomito na smjer vlakanaca vanjskih furnira te pod kutom od 22,5°, 45° i 67,5° s obzirom na smjer vlakanaca vanjskih furniraploče. Na temelju dobivenih podataka o vlačnoj čvrstoći u različitim smjerovima ploče, izračunan je koeficijent jednakosti vlačne čvrstoće modela furnirskih ploča (Ket). Također je izračunan i koefi cijent masene kvalitete (Kmq). Rezultati istraživanja pokazali su da različit položaj furnira u strukturi furnirskih ploča znatno utječe na njihovu vlačnu čvrstoću. Svi ispitani modeli furniskih ploča zadovoljavaju vrijednosti vlačne čvrstoće definirane u skladu s makedonskim normama za strukturne furnirske ploče namijenjene uporabi u graditeljstvu. Različit položaj furnira u strukturi ploče omogućuje proizvodnju ploča različitih svojstava čvrstoće

LoopTune: Optimizing Tensor Computations with Reinforcement Learning

Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tuners have long search times and expert-optimized libraries introduce unsustainable costs. To address this, we developed LoopTune, a deep reinforcement learning compiler that optimizes tensor computations in deep learning models for the CPU. LoopTune optimizes tensor traversal order while using the ultra-fast lightweight code generator LoopNest to perform hardware-specific optimizations. With a novel graph-based representation and action space, LoopTune speeds up LoopNest by 3.2x, generating an order of magnitude faster code than TVM, 2.8x faster than MetaSchedule, and 1.08x faster than AutoTVM, consistently performing at the level of the hand-tuned library Numpy. Moreover, LoopTune tunes code in order of seconds

Automated computation of arbor densities: a step toward identifying neuronal cell types

The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference

Reversible magnetic mercury extraction from water

A facile and efficient way to decontaminate mercury(II) polluted water with the aid of magnetic, highly stable and recyclable carbon coated cobalt (Co/C) nanoparticles is reported. Comparing non-functionalised Co/C nanomagnets with particles that were functionalised with amino moieties, the latter one proved to be more effective for scavenging mercury with respect to extraction capacity and recyclability. A novel nanoparticle–poly(ethyleneimine) hybrid (Co/C–PEI) prepared by direct ring opening polymerization of aziridine initiated by an amine functionalised nanoparticle surface led to a high capacity material (10 mmol amino groups per g nanomaterial) and thus proved to be the best material for scavenging toxic mercury at relevant concentrations (mg L−1/μg L−1) for at least 6 consecutive cycles. On a large-scale, 20 L of drinking water with an initial Hg2+ concentration of 30 μg L−1 can be decontaminated to the level acceptable for drinking water (≤2 μg L−1) with just 60 mg of Co/C–PEI particles