Michael Millemann: Putting Maryland\u27s Legal Clinics on the Map

Co-Director Michael Millemann\u27s tireless work has gained national prominence for the law school\u27s Clinical Law Program

Shaping the Landscape with Landmark Advocacy

For Stan Herr, public service is not something to do on the side

Evolution of column density distributions within Orion~A

We compare the structure of star-forming molecular clouds in different regions of Orion A to determine how the column density probability distribution function (N-PDF) varies with environmental conditions such as the fraction of young protostars. A correlation between the N-PDF slope and Class 0 protostar fraction has been previously observed in a low-mass star-formation region (Perseus) by Sadavoy; here we test if a similar correlation is observed in a high-mass star-forming region. We use Herschel data to derive a column density map of Orion A. We use the Herschel Orion Protostar Survey catalog for accurate identification and classification of the Orion A young stellar object (YSO) content, including the short-lived Class 0 protostars (with a $\sim$ 0.14 Myr lifetime). We divide Orion A into eight independent 13.5 pc$^2$ regions; in each region we fit the N-PDF distribution with a power-law, and we measure the fraction of Class 0 protostars. We use a maximum likelihood method to measure the N-PDF power-law index without binning. We find that the Class 0 fraction is higher in regions with flatter column density distributions. We test the effects of incompleteness, YSO misclassification, resolution, and pixel-scale. We show that these effects cannot account for the observed trend. Our observations demonstrate an association between the slope of the power-law N-PDF and the Class 0 fractions within Orion A. Various interpretations are discussed including timescales based on the Class 0 protostar fraction assuming a constant star-formation rate. The observed relation suggests that the N-PDF can be related to an "evolutionary state" of the gas. If universal, such a relation permits an evaluation of the evolutionary state from the N-PDF power-law index at much greater distances than those accesible with protostar counts. (abridged)Comment: A&A Letter, accepte

Superpixels: An Evaluation of the State-of-the-Art

Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003. By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/

Disentangling Adversarial Robustness and Generalization

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.Comment: Conference on Computer Vision and Pattern Recognition 201

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass

Dust-temperature of an isolated star-forming cloud: Herschel observations of the Bok globule CB244

We present Herschel observations of the isolated, low-mass star-forming Bok globule CB244. It contains two cold sources, a low-mass Class 0 protostar and a starless core, which is likely to be prestellar in nature, separated by 90 arcsec (~ 18000 AU). The Herschel data sample the peak of the Planck spectrum for these sources, and are therefore ideal for dust-temperature and column density modeling. With these data and a near-IR extinction map, the MIPS 70 micron mosaic, the SCUBA 850 micron map, and the IRAM 1.3 mm map, we model the dust-temperature and column density of CB244 and present the first measured dust-temperature map of an entire star-forming molecular cloud. We find that the column-averaged dust-temperature near the protostar is ~ 17.7 K, while for the starless core it is ~ 10.6K, and that the effect of external heating causes the cloud dust-temperature to rise to ~ 17 K where the hydrogen column density drops below 10^21 cm^-2. The total hydrogen mass of CB244 (assuming a distance of 200 pc) is 15 +/- 5 M_sun. The mass of the protostellar core is 1.6 +/- 0.1 M_sun and the mass of the starless core is 5 +/- 2 M_sun, indicating that ~ 45% of the mass in the globule is participating in the star-formation process.Comment: Accepted for A&A Herschel Special Issue; 5 pages, 2 figure