New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift

Testing theories of hierarchical structure formation requires estimating the distribution of galaxy morphologies and its change with redshift. One aspect of this investigation involves identifying galaxies with disturbed morphologies (e.g., merging galaxies). This is often done by summarizing galaxy images using, e.g., the CAS and Gini-M20 statistics of Conselice (2003) and Lotz et al. (2004), respectively, and associating particular statistic values with disturbance. We introduce three statistics that enhance detection of disturbed morphologies at high-redshift (z ~ 2): the multi-mode (M), intensity (I), and deviation (D) statistics. We show their effectiveness by training a machine-learning classifier, random forest, using 1,639 galaxies observed in the H band by the Hubble Space Telescope WFC3, galaxies that had been previously classified by eye by the CANDELS collaboration (Grogin et al. 2011, Koekemoer et al. 2011). We find that the MID statistics (and the A statistic of Conselice 2003) are the most useful for identifying disturbed morphologies. We also explore whether human annotators are useful for identifying disturbed morphologies. We demonstrate that they show limited ability to detect disturbance at high redshift, and that increasing their number beyond approximately 10 does not provably yield better classification performance. We propose a simulation-based model-fitting algorithm that mitigates these issues by bypassing annotation.Comment: 15 pages, 14 figures, accepted for publication in MNRA

Dancing for Parkinson’s: A Gateway for Connectedness to Peers and Social Assurance

The first-year student experience in college is a crucial time for personal and professional development, especially for students entering science, technology, education, and mathematics (STEM) disciplines. Unfortunately, it is also the time when students most commonly leave STEM, largely due to disconnection from faculty and peers. The Freshman Research Initiative (FRI) is a program that introduces first-year undergraduates to research in a variety of fields. The program has shown positive outcomes for student success and retention in STEM fields. However, it has not been demonstrated whether this program can increase social connectedness and assurance, potentially contributing to students’ longer-term retention in STEM. In this pilot study, we measured social connectedness/assurance among students before and after a 16-week course in neurophysiology. We found that combined scores of social connectedness and assurance significantly increased by the end of the course. We also found that individual constructs of social connectedness and assurance significantly increased. Furthermore, the majority of students from FRI were retained in STEM fields. We plan future studies to include collection of longitudinal data and measures to identify additional reasons that the FRI increased these positive outcomes among our student participants

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Moreover, people have difficulty to trust models that cannot be explained. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer to these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations

Metastatic Esophageal Carcinoma Cells Exhibit Reduced Adhesion Strength and Enhanced Thermogenesis

Despite continuous improvements in multimodal therapeutic strategies, esophageal carcinoma maintains a high mortality rate. Metastases are a major life-limiting component; however, very little is known about why some tumors have high metastatic potential and others not. In this study, we investigated thermogenic activity and adhesion strength of primary tumor cells and corresponding metastatic cell lines derived from two patients with metastatic adenocarcinoma of the esophagus. We hypothesized that the increased metastatic potential of the metastatic cell lines correlates with higher thermogenic activity and decreased adhesion strength. Our data show that patient-derived metastatic esophageal tumor cells have a higher thermogenic profile as well as a decreased adhesion strength compared to their corresponding primary tumor cells. Using two paired esophageal carcinoma cell lines of primary tumor and lymph nodes makes the data unique. Both higher specific thermogenesis profile and decreased adhesion strength are associated with a higher metastatic potential. They are in congruence with the clinical patient presentation. Understanding these functional, biophysical properties of patient derived esophageal carcinoma cell lines will enable us to gain further insight into the mechanisms of metastatic potential of primary tumors and metastases. Microcalorimetric evaluation will furthermore allow for rapid assessment of new treatment options for primary tumor and metastases aimed at decreasing the metastatic potential

On the problem of reconstructing a tournament from subtournaments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41629/1/605_2005_Article_BF01299955.pd

Evaluation of probabilistic photometric redshift estimation approaches for the Rubin Observatory Legacy Survey of Space and Time (LSST)

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing 12 photo-z algorithms applied to mock data produced for The Rubin Observatory Legacy Survey of Space and Time Dark Energy Science Collaboration. By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. We report systematic biases and overall over/underbreadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performance metrics

Theoretical and experimental evidence of level repulsion states and evanescent modes in sonic crystal stubbed waveguides

The complex band structures calculated using the Extended Plane Wave Expansion (EPWE) reveal the presence of evanescent modes in periodic systems, never predicted by the classical \omega(\vec{k}) methods, providing novel interpretations of several phenomena as well as a complete picture of the system. In this work we theoretically and experimentally observe that in the ranges of frequencies where a deaf band is traditionally predicted, an evanescent mode with the excitable symmetry appears changing drastically the interpretation of the transmission properties. On the other hand, the simplicity of the sonic crystals in which only the longitudinal polarization can be excited, is used to interpret, without loss of generality, the level repulsion between symmetric and antisymmetric bands in sonic crystals as the presence of an evanescent mode connecting both repelled bands. These evanescent modes, obtained using EPWE, explain both the attenuation produced in this range of frequencies and the transfer of symmetry from one band to the other in good agreement with both experimental results and multiple scattering predictions. Thus, the evanescent properties of the periodic system have been revealed necessary for the design of new acoustic and electromagnetic applications based on periodicity

Optimal Duration and Timing of Adjuvant Chemotherapy After Definitive Surgery for Ductal Adenocarcinoma of the Pancreas: Ongoing Lessons From the ESPAC-3 Study

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Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing 12 photo-z algorithms applied to mock data produced for The Rubin Observatory Legacy Survey of Space and Time Dark Energy Science Collaboration. By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. We report systematic biases and overall over/underbreadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performance metrics