Radiation hardness of small-pitch 3D pixel sensors up to HL-LHC fluences

A new generation of 3D silicon pixel detectors with a small pixel size of 50$\times$50 and 25$\times$100 $\mu$m$^{2}$ is being developed for the HL-LHC tracker upgrades. The radiation hardness of such detectors was studied in beam tests after irradiation to HL-LHC fluences up to $1.4\times10^{16}$ n$_{\mathrm{eq}}$/cm$^2$. At this fluence, an operation voltage of only 100 V is needed to achieve 97% hit efficiency, with a power dissipation of 13 mW/cm$^2$ at -25$^{\circ}$C, considerably lower than for previous 3D sensor generations and planar sensors.Comment: 5 pages, 2 figures, Proceedings of TIPP 2017, Beijing (International Conference on The Technology and Instrumentation in Particle Physics 2017

Research on mechanisms of alloy strengthening. Part 1 - Strengthening through fine particle dispersion. Part 2 - Control of structure and properties by means of rapid quenching of liquid metals /splat cooling/ Semiannual report

Alloy strengthening mechanisms - strengthening by fine particle dispersion, and structure and properties control by rapid quenching or splat cooling of liquid metal

Tracing Galaxy Assembly: Tadpole Galaxies in the Hubble Ultra Deep Field

In the Hubble Ultra Deep Field (HUDF) an abundance of galaxies is seen with a knot at one end plus an extended tail, resembling a tadpole. These "tadpole galaxies" appear dynamically unrelaxed--presumably in an early merging state--where tidal interactions likely created the distorted knot-plus-tail morphology. Here we systematically select tadpole galaxies from the HUDF and study their properties as a function of their photometric redshifts. In a companion HUDF variability study, Cohen et al. (2005) revealed a total of 45 variable objects believed to be Active Galactic Nuclei (AGN). Here we show that this faint AGN sample has no overlap with the tadpole galaxy sample, as predicted by theoretical work. The tadpole morphology--combined with the lack of overlap with the variable objects--supports the idea that these galaxies are in the process of an early-stage merger event, i.e., at a stage that likely precedes the "turn-on" of any AGN component and the onset of any point-source variability.Comment: 7 pages, 4 figures. Accepted for publication by Astrophysical Journa

On the expressive power of read-once determinants

We introduce and study the notion of read-$k$ projections of the determinant: a polynomial $f \in \mathbb{F}[x_1, \ldots, x_n]$ is called a {\it read-$k$ projection of determinant} if $f=det(M)$, where entries of matrix $M$ are either field elements or variables such that each variable appears at most $k$ times in $M$. A monomial set $S$ is said to be expressible as read-$k$ projection of determinant if there is a read-$k$ projection of determinant $f$ such that the monomial set of $f$ is equal to $S$. We obtain basic results relating read-$k$ determinantal projections to the well-studied notion of determinantal complexity. We show that for sufficiently large $n$, the $n \times n$ permanent polynomial $Perm_n$ and the elementary symmetric polynomials of degree $d$ on $n$ variables $S_n^d$ for $2 \leq d \leq n-2$ are not expressible as read-once projection of determinant, whereas $mon(Perm_n)$ and $mon(S_n^d)$ are expressible as read-once projections of determinant. We also give examples of monomial sets which are not expressible as read-once projections of determinant

Far-Infrared and Sub-Millimeter Observations and Physical Models of the Reflection Nebula Ced 201

ISO [C II] 158 micron, [O I] 63 micron, and H_2 9 and 17 micron observations are presented of the reflection nebula Ced 201, which is a photon-dominated region illuminated by a B9.5 star with a color temperature of 10,000 K (a cool PDR). In combination with ground based [C I] 609 micron, CO, 13CO, CS and HCO+ data, the carbon budget and physical structure of the reflection nebula are constrained. The obtained data set is the first one to contain all important cooling lines of a cool PDR, and allows a comparison to be made with classical PDRs. To this effect one- and three-dimensional PDR models are presented which incorporate the physical characteristics of the source, and are aimed at understanding the dominant heating processes of the cloud. The contribution of very small grains to the photo-electric heating rate is estimated from these models and used to constrain the total abundance of PAHs and small grains. Observations of the pure rotational H_2 lines with ISO, in particular the S(3) line, indicate the presence of a small amount of very warm, approximately 330 K, molecular gas. This gas cannot be accommodated by the presented models.Comment: 32 pages, 7 figures, in LaTeX. To be published in Ap

Analisa Gelombang Kejut Dan Pengaruhnya Terhadap Arus Lalu Lintas Di Jalan Sarapung Manado

Hubungan antara volume, kepadatan dan kecepatan merupakan elemen yang paling penting dalam teori arus lalu lintas. Ada banyak model yang menyatakan hubungan antara ketiga elemen sebagai unsur-unsur utama lalu lintas. Tiga model yang paling umum digunakan dalam praktek rekayasa lalu lintas adalah Greenshields, Greenberg dan Underwood. Karakteristik arus lalu lintas akan diperoleh berdasarkan model yang dipilih untuk mewakili data lapangan, kemudian menggunakan informasi tersebut untuk membuat analisa skenario insiden gelombang kejut.Penentuan model terpilih untuk perhitungan gelombang kejut didasarkan pada kriteria nilai uji R2 yang paling besar atau R2 > 0,5. Hasil analisa regresi diperoleh menggunakan bantuan software SPSS maupun dihitung dengan cara manual. Hasil yang diperoleh yaitu model Grenshield = 0,899, model Greenberg = 0,871, dan model Underwood = 0,928. Namun, disamping R2 perlu juga melihat karakteristik yang ditawarkan berdasarkan pada Kenyataan di lapangan. Hasil model harus benar secara logika dan statistik. Model Underwood tidak akan bekerja secara akurat ketika kondisi lalu lintas mengalami kemacetan. Oleh karena itu dipilih model Greenshields.Dari analisa yang dilakukan diperoleh hasil nilai Δt3-t2, QM dan Δt4-t yang dihitung tiap penambahan 5 menit (5, 10, 15, …, 60 menit.). Panjang antrian yang dapat terjadi selama durasi 5 menit adalah 980 meter (0,98 km). Waktu yang diperlukan kendaraan ketika memasuki kondisi macet dari kondisi normal adalah 5,64 menit, sedangkan waktu yang diperlukan untuk kembali ke keadaan normal dari kondisi macet adalah 5.839 menit

Emission-Line Galaxies from the HST PEARS Grism Survey I: The South Fields

We present results of a search for emission-line galaxies in the Southern Fields of the Hubble Space Telescope PEARS (Probing Evolution And Reionization Spectroscopically) grism survey. The PEARS South Fields consist of five ACS pointings (including the Hubble Ultra Deep Field) with the G800L grism for a total of 120 orbits, revealing thousands of faint object spectra in the GOODS-South region of the sky. Emission-line galaxies (ELGs) are one subset of objects that are prevalent among the grism spectra. Using a 2-dimensional detection and extraction procedure, we find 320 emission lines orginating from 226 galaxy "knots'' within 192 individual galaxies. Line identification results in 118 new grism-spectroscopic redshifts for galaxies in the GOODS-South Field. We measure emission line fluxes using standard Gaussian fitting techniques. At the resolution of the grism data, the H-beta and [OIII] doublet are blended. However, by fitting two Gaussian components to the H-beta and [OIII] features, we find that many of the PEARS ELGs have high [OIII]/H-beta ratios compared to other galaxy samples of comparable luminosities. The star-formation rates (SFRs) of the ELGs are presented, as well as a sample of distinct giant star-forming regions at z~0.1-0.5 across individual galaxies. We find that the radial distances of these HII regions in general reside near the galaxies' optical continuum half-light radii, similar to those of giant HII regions in local galaxies.Comment: 15 pages, 13 figures; Accepted for publication in A

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images