Animals as Social Actors: Cases of Equid Resistance in the Ancient Near East

This paper examines the concept of animals as social actors in the ancient Near East through a case study of human–equid relations. In particular, examples where equids may be seen as expressing resistance, as depicted in the iconography of the third and second millenniabc, are analysed. The first part of the paper discusses how animals have been perceived in scholarly debates in philosophy, archaeology and human–animal studies. It is argued that an acknowledgement of animals as social actors can improve our understanding of the human past, and the relation of humans to their broader environment. The second part of the paper presents three examples from the ancient Near East where equids may be interpreted as pushing back or resisting the boundaries placed by humans, resulting in a renegotiation of the relationship.MSC

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1

Effects of high-energy protons on selected cells Final report, Jun. 1966 - Aug. 1966

Irradiation effects of high energy protons studied on silver-cadmium and nickel-cadmium cells containing battery electrodes and potassium hydroxide electrolyte

Radiation effects on silver and zinc battery electrodes. V Interim report, Apr. - Jul. 1966

Gamma radiation effects determined on silver and zinc battery electrodes and silver-cadmium cell

Radiation effects on silver and zinc battery electrodes, i interim report, apr. - jul. 1965

Radiation effects on silver and zinc battery electrode

Radiation effects on silver and zinc battery electrodes, II Interim report, Jul. - Oct. 1965

Radiation effects on silver and zinc electrodes in silver-zinc batter

Radiation effects on silver and zinc battery electrodes, III Interim report, Oct. 1965 - Jan. 1966

Radiation effects on silver-zinc battery electrode

The effects of radiation on nickel-cadmium battery electrodes, i final report, jun. 1963 - apr. 1965

Effect of radiation on nickel-cadmium battery electrode

A Cost-based Optimizer for Gradient Descent Optimization

As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will invoke the appropriate algorithms and system configurations to execute it. An important observation towards designing such a framework is that many ML tasks can be expressed as mathematical optimization problems, which take a specific form. Furthermore, these optimization problems can be efficiently solved using variations of the gradient descent (GD) algorithm. Thus, to decouple a user specification of an ML task from its execution, a key component is a GD optimizer. We propose a cost-based GD optimizer that selects the best GD plan for a given ML task. To build our optimizer, we introduce a set of abstract operators for expressing GD algorithms and propose a novel approach to estimate the number of iterations a GD algorithm requires to converge. Extensive experiments on real and synthetic datasets show that our optimizer not only chooses the best GD plan but also allows for optimizations that achieve orders of magnitude performance speed-up.Comment: Accepted at SIGMOD 201