Conditional Hardness of Earth Mover Distance

The Earth Mover Distance (EMD) between two sets of points A, B subseteq R^d with |A| = |B| is the minimum total Euclidean distance of any perfect matching between A and B. One of its generalizations is asymmetric EMD, which is the minimum total Euclidean distance of any matching of size |A| between sets of points A,B subseteq R^d with |A| <= |B|. The problems of computing EMD and asymmetric EMD are well-studied and have many applications in computer science, some of which also ask for the EMD-optimal matching itself. Unfortunately, all known algorithms require at least quadratic time to compute EMD exactly. Approximation algorithms with nearly linear time complexity in n are known (even for finding approximately optimal matchings), but suffer from exponential dependence on the dimension. In this paper we show that significant improvements in exact and approximate algorithms for EMD would contradict conjectures in fine-grained complexity. In particular, we prove the following results: - Under the Orthogonal Vectors Conjecture, there is some c>0 such that EMD in Omega(c^{log^* n}) dimensions cannot be computed in truly subquadratic time. - Under the Hitting Set Conjecture, for every delta>0, no truly subquadratic time algorithm can find a (1 + 1/n^delta)-approximate EMD matching in omega(log n) dimensions. - Under the Hitting Set Conjecture, for every eta = 1/omega(log n), no truly subquadratic time algorithm can find a (1 + eta)-approximate asymmetric EMD matching in omega(log n) dimensions

Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks

Microscopic analysis of histological sections is considered the "gold standard" to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2um resolution. It is based on a convolutional neural network that combines topological information from probabilistic atlases with the texture features learned from high-resolution cell-body stained images. The model is applied to visual areas and trained on a sparse set of partial annotations. We show how predictions are transferable to new brains and spatially consistent across sections.Comment: Accepted for oral presentation at International Symposium of Biomedical Imaging (ISBI) 201

Diattenuation of Brain Tissue and its Impact on 3D Polarized Light Imaging

3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological brain sections by measuring their birefringence. This study investigates another effect caused by the optical anisotropy of brain tissue - diattenuation. Based on numerical and experimental studies and a complete analytical description of the optical system, the diattenuation was determined to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation effect has negligible impact on the fiber orientations derived by 3D-PLI. The diattenuation signal, however, was found to highlight different anatomical structures that cannot be distinguished with current imaging techniques, which makes Diattenuation Imaging a promising extension to 3D-PLI.Comment: 32 pages, 15 figure

A Jones matrix formalism for simulating three-dimensional polarized light imaging of brain tissue

The neuroimaging technique three-dimensional polarized light imaging (3D-PLI) provides a high-resolution reconstruction of nerve fibres in human post-mortem brains. The orientations of the fibres are derived from birefringence measurements of histological brain sections assuming that the nerve fibres - consisting of an axon and a surrounding myelin sheath - are uniaxial birefringent and that the measured optic axis is oriented in direction of the nerve fibres (macroscopic model). Although experimental studies support this assumption, the molecular structure of the myelin sheath suggests that the birefringence of a nerve fibre can be described more precisely by multiple optic axes oriented radially around the fibre axis (microscopic model). In this paper, we compare the use of the macroscopic and the microscopic model for simulating 3D-PLI by means of the Jones matrix formalism. The simulations show that the macroscopic model ensures a reliable estimation of the fibre orientations as long as the polarimeter does not resolve structures smaller than the diameter of single fibres. In the case of fibre bundles, polarimeters with even higher resolutions can be used without losing reliability. When taking the myelin density into account, the derived fibre orientations are considerably improved.Comment: 20 pages, 8 figure

Передача данных сетью БПЛА вдоль линейного объекта на противололожных курсах

The process of data transmission by a network of unmanned aerial vehicles (UAVs) based on DPMR (Digital Private Mobile Radio) along an extended linear object is considered. A method of forming a network with the help of two UAVs moving in opposite directions with equal intervals between the devices within each of them is shown

Increased representation of the non-dominant hand in pianists demonstrated by measurement of 3D morphology of the central sulcus

Health and self-regulatio

Picosecond fluorescence of intact and dissolved PSI-LHCI crystals

Over the last years many crystal structures of photosynthetic pigment-protein complexes have been determined, and used extensively to model spectroscopic results obtained on the same proteins in solution. However, the crystal structure is not necessarily identical to the structure of the protein in solution. Here we studied picosecond fluorescence of Photosystem I-Light Harvesting Complex I (PSI-LHCI), a multisubunit pigment protein complex that catalyzes the first steps of photosynthesis. The ultrafast fluorescence of PSI-LHCI crystals is identical to that of dissolved crystals, but differs considerably from most kinetics presented in literature. In contrast to most studies, the present data can be modeled quantitatively with only 2 compartments: PSI core and LHCI. This yields the rate of charge separation from an equilibrated core (22.5+/-2.5 ps) and rates of excitation energy transfer from LHCI to core (kLC) and vice versa (kCL). The ratio R=kCL/kLC between these rates appears to be wavelength-dependent and scales with the ratio of the absorption spectra of LHCI and core, indicating the validity of a detailed balance relation between both compartments. kLC depends slightly but non systematically on detection wavelength, averaging (9.4+/-4.9 ps)(-1). R ranges from 0.5 (below 690 nm) to around 1.3 above 720 nm

Проблема утилизации и вторичной переработки пластиковых бутылок

Происходящие глобальные изменения преобразовывают обычную сырьевую экономику в высокотехнологичную, позволяющую рационально использовать имеющиеся ресурсы и при этом не загрязнять окружающую нас среду. Переработка ПЭТ-бутылок позволит решить проблему утилизации пластикового мусора и может стать прибыльным бизнесом. Результаты исследования показали, что сырье, полученное в процессе переработки пластиковых бутылок, может быть использовано для изготовления востребованной продукции.The ongoing global changes transform the conventional raw material economy into a high-tech one, allowing rational use of available resources and at the same time to not polluting the environment around us. Recycling of PET bottles will solve the problem of recycling plastic trash and can become a profitable business. The results of the research showed that secondary raw material, obtained during the processing of plastic bottles can be used for the production of the demanded products

Contour Proposal Networks for Biomedical Instance Segmentation

We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available