Introduction: Symposium on ‘Convicting the Innocent

Examining what went wrong in the first 250 DNA exonerations was a sobering occupation, and I describe what I found in my book Convicting the Innocent, published by Harvard University Press in 2011. Still more haunting is the question of how many other wrongful convictions have not been uncovered and will never see the light of day. The New England Law Review has brought together a remarkable group of scholars who have each made leading contributions to the study of wrongful convictions from different disciplines and scholarly perspectives: Simon Cole, Deborah Davis, Gisli H. Gudjonsson, Richard Leo, and Elizabeth Loftus. Each has done ground-breaking work focusing on evidence in criminal investigations and prosecutions, looking beyond just what we know from the wrongful convictions that do come to light. This Symposium issue returns the focus to research that can tell us more about the causes of wrongful convictions, and in this introduction I try to do justice to their remarkable contributions

Amnestically induced persistence in random walks

We study how the Hurst exponent $\alpha$ depends on the fraction $f$ of the total time $t$ remembered by non-Markovian random walkers that recall only the distant past. We find that otherwise nonpersistent random walkers switch to persistent behavior when inflicted with significant memory loss. Such memory losses induce the probability density function of the walker's position to undergo a transition from Gaussian to non-Gaussian. We interpret these findings of persistence in terms of a breakdown of self-regulation mechanisms and discuss their possible relevance to some of the burdensome behavioral and psychological symptoms of Alzheimer's disease and other dementias.Comment: 4 pages, 3 figs, subm. to Phys. Rev. Let

The Deformed Transformed

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Infinity and the Sublime

In this paper we intend to connect two different strands of research concerning the origin of what I shall loosely call "formal" ideas: firstly, the relation between logic and rhetoric - the theme of the 2006 Cambridge conference to which this paper was a contribution -, and secondly, the impact of religious convictions on the formation of certain twentieth century mathematical concepts, as brought to the attention recently by the work of L. Graham and J.-M. Kantor. In fact, we shall show that the latter question is a special case of the former, and that investigation of the larger question adds to our understanding of the smaller one. Our approach will be primarily historical.Comment: 29 pages and 3 figure

“Let's Make a List”: James Schuyler's taxonomic autobiography

August 21, 1970 A few sound[s] are embedded in the fog – a gull mewing, different far off fog horns – like unset polished stones laid out in cotton wool. Tuesday, March 5, 1985 At six AM the heavy gray burns a heavier blue. Rain, water drops clinging to the balcony. There is an ethical consideration in James Schuyler's Diary. While we have spent the last fifty years grappling with the aesthetic problems of how to represent the unrepresentable, how to present the unpresentable, and how to signify the significant, little time has been spent considering the status of representations of the unremarkable. There is a whole history in American poetry and literature of validating the everyday, making it special, but Schuyler never really does that. Are things special just because we say so, or rather because we note them down? Do we name things into being, at least linguistic or literary being? The Diary asks these questions and in doing so it broaches the kind of postmodern ethical questions that one finds in the recent work of Lyotard, Derrida, and Nancy. These questions are significant not in the normal sense of the reasons for such interrogations or the answers expected, but rather because they represent a desire on the part of Schuyler to ask after otherness, to try to elicit a response from the other while respecting that such a response may not be comprehensible even if it is forthcoming. I would like here to posit a desire to ask after the other first before one asks after oneself, to enquire without any hope of a satisfactory answer as such as the postmodern ethical position, and to suggest that the autobiographical slant of Schuyler's work is, paradoxically considering the nature of autobiography, just such a positioning of his self in relation to the world

Online learning and detection of faces with low human supervision

The final publication is available at link.springer.comWe present an efficient,online,and interactive approach for computing a classifier, called Wild Lady Ferns (WiLFs), for face learning and detection using small human supervision. More precisely, on the one hand, WiLFs combine online boosting and extremely randomized trees (Random Ferns) to compute progressively an efficient and discriminative classifier. On the other hand, WiLFs use an interactive human-machine approach that combines two complementary learning strategies to reduce considerably the degree of human supervision during learning. While the first strategy corresponds to query-by-boosting active learning, that requests human assistance over difficult samples in function of the classifier confidence, the second strategy refers to a memory-based learning which uses ¿ Exemplar-based Nearest Neighbors (¿ENN) to assist automatically the classifier. A pre-trained Convolutional Neural Network (CNN) is used to perform ¿ENN with high-level feature descriptors. The proposed approach is therefore fast (WilFs run in 1 FPS using a code not fully optimized), accurate (we obtain detection rates over 82% in complex datasets), and labor-saving (human assistance percentages of less than 20%). As a byproduct, we demonstrate that WiLFs also perform semi-automatic annotation during learning, as while the classifier is being computed, WiLFs are discovering faces instances in input images which are used subsequently for training online the classifier. The advantages of our approach are demonstrated in synthetic and publicly available databases, showing comparable detection rates as offline approaches that require larger amounts of handmade training data.Peer ReviewedPostprint (author's final draft