The Hope of Salman Masalha: Re-Territorializing Hebrew

Israeli poetry can be depicted as a triangle composed of three elements: territory (the State of Israel); language (Hebrew); and identity (Jewish). In his Hebrew collection of poetry Eḥad Mikan (in place, 2004), Salman Masalha—a bilingual author publishing in both Arabic and Hebrew—challenges this interrelation of territory, language and identity. The debate between the literary scholars Hannan Hever and Reuven Snir explore the central expressions of this challenge. For it points, on the one hand, to the subversive potential of such work towards the Israeli canon while, on the other hand, to its connection to Arabic literature. Writing in the language of the other often invokes the seminal essay by Deleuze and Guattay Kafka: Toward Minor Literature (1986). In that context, it was assumed that Hebrew works by Arab authors de-territorialize the Hebrew language, detaching it from its natural users. In the present essay, however, we point to the ways in which Masalha’s Hebrew poetry in fact re-territorializes the Hebrew language; that is, it turns Hebrew from the language of the Jewish people to the language of the region, to the language of “Someone from Here” (as the Hebrew title of the book implies). We will present close readings of the poems, as well as of the para-textual features of the collection, in order to demonstrate how Masalha’s work not only comments on his identity as an Arab living in Israel, but on the identity of Hebrew poetry as well. Our reading therefore perceives Masalha’s collection as a milestone in the evolution of Hebrew poetry; while in 1948 Hebrew poetry was transformed into Israeli poetry, in the 21st century, it is being transformed into poetry “written from here.

Photo Editing with Face Selection and Replacement

This disclosure describes techniques to enable users to edit photos to include selected faces or facial expressions. A user can select a photo from a burst or other collection of photos. Detected faces in the selected photo are highlighted in a user interface that enables a user to select a face in the photo to modify. In response, a set of candidate faces that are suitable to replace the selected face are presented in the user interface. With user permission, the candidate faces can be obtained and/or modified from other accessible photos, such as from a burst of photos. The user can select any candidate face that seamlessly replaces the selected face in the displayed photo. The described interface allows users to quickly and easily replace undesired facial expressions in photos with preferred facial expressions

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

From Distant to Public Reading

From its very beginning, the term “distant reading” (Moretti 2000) was controversial, displacing ‘close reading’ by relying on literary histories and thereby reflecting on the entire global literary system. One of the weaknesses of this approach lies in its exclusive reliance on canonical and authoritative historiographies, one or two for each national literature, something which is bound to over-simplify the complexities of national literatures. As is known, Moretti’s proposal became a ‘slogan’ for Digital Humanities while algorithmic manipulation of texts has taken the place of reading literary (human) histories. Yet the problem of over-simplification remains, albeit differently. As an alternative, we offer a fusion approach, radicalising Moretti’s idea. In this article, we demonstrate how computer-based analysis of different readings carried out by many readers – not necessarily professionals – produces a relatively minute picture. Our case study will be the Hebrew novel, from its emergence in 1853 to the present day; a manageable corpus on which we gather information using questionnaires we have carefully created in our lab. Alongside the presentation of our approach, the actual research, and its initial findings, we will reflect theoretically on the conceptual benefits, as well as the limits, of public distance reading

Finding Hidden Cliques in Linear Time with High Probability

We are given a graph G with n vertices, where a random subset of k vertices has been made into a clique, and the remaining edges are chosen independently with probability 1 2, k). The hidden clique problem is to design an algorithm that finds the k-clique in polynomial time with high probability. An algorithm due to Alon, Krivelevich and Sudakov [3] uses spectral techniques to find the hidden clique with high probability when k = c √ n for a sufficiently large constant c> 0. Recently, an algorithm that solves the same problem was proposed by Feige and Ron [14]. It has the advantages of being simpler and more intuitive, and of an improved running time of O(n 2). However, the analysis in [14] gives success probability of only 2/3. In this paper we present a new algorithm for finding hidden cliques that both runs in time O(n 2), and has a failure probability that is less than polynomially small.. This random graph model is denoted G(n, 1