Combining X-ray tomography with simultaneous shear force measurement, we investigate shear-induced granular avalanches using spherical particles with different surface roughness. We find that systems consisting of particles with large surface roughne
ss display quasi-periodic avalanches interrupted by crackling-like small ones. In contrast, systems consisting of particles with small roughness display no detectable avalanches. The stress drop of quasi-periodic avalanche shows a linear relation with the correlation length of particle non-affine displacement, suggesting that roughness enhances inter-particle locking and hence particle-level dynamic correlation length. However, the nonaffine displacement is two orders of magnitude smaller than particle size, indicating that stress is mainly released on the length scale of roughness. The correlation length of non-affine displacements abruptly increases when a quasi-periodic avalanche occurs, suggesting that quasi-periodic avalanches can be interpreted as a spinodal nucleation event in a first-order phase transition.
We report an x-ray emission spectroscopy (XES) study of the local fluctuating magnetic moment ($mu_{bare}$) in $mathrm{NaFe_{1-x}Co_{x}As}$ and $mathrm{NaFe_{1-x}Cu_{x}As}$. In NaFeAs, the reduced height of the As ions induces a local magnetic moment
higher than $mathrm{Ba_2As_2}$, despite lower T$_N$ and ordered magnetic moment. As NaFeAs is doped with Co $mu_{bare}$ is slightly reduced, whereas Cu doping leaves it unaffected, indicating a different doping mechanism: based on electron counting for Co whereas impurity scattering dominates in the case of Cu. Finally, we observe an increase of $mu_{bare}$ with temperature in all samples as observed in electron- and hole-doped $mathrm{BaFe_2As_2}$. Since both Co and Cu doping display superconductivity, our findings demonstrate that the formation of Cooper pairs is not connected with the complete loss of fluctuating paramagnetic moments.
Using X-ray tomography, we experimentally investigate granular packings subject to mechanical tapping for three types of beads with different friction coefficients. We validate Edwards volume ensemble in these three-dimensional granular systems and e
stablish a granular version of thermodynamic zeroth law. Within Edwards framework, we also explicitly clarify how friction influences granular statistical mechanics as modifying the density of states, which allows us to determine the entropy as a function of packing fraction and friction subsequently. Additionally, we obtain a granular jamming phase diagram based on geometric coordination number and packing fraction.
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is pro
posed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $0.495 pm 0.309$ mm and Dice coefficient of $0.985 pm 0.011$. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.
We present an X-ray tomography study of the segregation mechanisms of tracer particles in a three-dimensional cyclically sheared bi-disperse granular medium. Big tracers are dragged by convection to rise to the top surface and then remain trapped the
re due to the small downward convection cross-section, which leads to segregation. Additionally, we also find that the local structural up-down asymmetry due to arching effect around big tracers will induce the tracers to have a net upward displacement against its smaller neighbors, which is another mechanism for segregation.
4FGL J0935.3+0901 is a gamma-ray source detected by the Large Area Telescope (LAT) onboard the Fermi Gamma-Ray Space Telescope. We have conducted detailed analysis of the LAT data for this source and multi-wavelength studies of the source field. Its
gamma-ray emission can be described with a power law ($Gamma=2.0pm0.2$) with an exponential cutoff ($E_c=2.9pm1.6$ GeV), while the flux shows significant long-term variations. From analysis of archival Neil Gehrels Swift X-ray Telescope data, we find only one X-ray source in the LATs 2$sigma$ error region. Within a 3.7 radius error circle of the X-ray source, there is only one optical object down to $rsim$23 mag. Time-resolved photometry of the optical object indicates a likely 2.5~hr periodic modulation, while its spectrum shows double-peaked hydrogen and helium emission lines (similar to those seen in accretion discs in low-mass X-ray binaries). Combining these results, we conclude that we have discovered a compact X-ray emitting binary in likely association with 4FGL J0935.3+0901, i.e., a millisecond pulsar (MSP) binary. We discuss the implication of the optical spectral features: this binary could be a transitional MSP system at a sub-luminous disc state, although the other possibility, the binary in a rotation-powered state showing the optical emission lines due to intrabinary interaction processes, can not be excluded. Further observational studies will help determine detailed properties of this candidate MSP binary and thus clarify its current state.
In the search for superconductivity in BaAu2Sb2-type monoclinic structure, we have successfully synthesized a new compound BaPt2Bi2, which crystallizes in the space group P21/m (S.G. 11; Pearson symbol mP10) according to a combination of powder and s
ingle crystal X-ray diffraction and scanning electron microscopy. Sharp electrical resistivity drop and large diamagnetic magnetization below 2.0 K indicates it owns the superconducting ground state. This makes BaPt2Bi2 the first reported superconductor in mono-clinic BaAu2Sb2-type structure, a previously unappreciated structure for superconductivity. First-principles calculations considering the spin-orbit coupling indicate that Pt-Bi anti-bonding interaction plays a critical role in inducing superconductivity.
Motivation: Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1)
quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments. Results: We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x--6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x--3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm. Availability: The code is available online at: https://github.com/CMU-SAFARI/GRIM
Motivation: Seed filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. Read mappers 1) quickly generate possible
mapping locations (i.e., seeds) for each read, 2) extract reference sequences at each of the mapping locations, and then 3) check similarity between each read and its associated reference sequences with a computationally expensive dynamic programming algorithm (alignment) to determine the origin of the read. Location filters come into play before alignment, discarding seed locations that alignment would have deemed a poor match. The ideal location filter would discard all poor matching locations prior to alignment such that there is no wasted computation on poor alignments. Results: We propose a novel filtering algorithm, GRIM-Filter, optimized to exploit emerging 3D-stacked memory systems that integrate computation within a stacked logic layer, enabling processing-in-memory (PIM). GRIM-Filter quickly filters locations by 1) introducing a new representation of coarse-grained segments of the reference genome and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for 5% error acceptance rates, GRIM-Filter eliminates 5.59x-6.41x more false negatives and exhibits end-to-end speedups of 1.81x-3.65x compared to mappers employing the best previous filtering algorithm.
Motivation: Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the m
apper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the potential of the mapper to select less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds. Results: We present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-bp read in $O(x times L)$ operations on average and in $O(x times L^{2})$ operations in the worst case. We compared OSS against four state-of-the-art seed selection schemes and observed that OSS provides a 3-fold reduction of average seed frequency over the best previous seed selection optimizations.
