Fair attribution of functional contribution in artificial and biological networks

This letter presents the multi-perturbation Shapley value analysis (MSA), an axiomatic, scalable, and rigorous method for deducing causal function localization from multiple perturbations data. The MSA, based on fundamental concepts from game theory, accurately quantifies the contributions of network elements and their interactions, overcoming several shortcomings of previous function localization approaches. Its successful operation is demonstrated in both the analysis of a neurophysiological model and of reversible deactivation data. The MSA has a wide range of potential applications, including the analysis of reversible deactivation experiments, neuronal laser ablations, and transcranial magnetic stimulation "virtual lesions," as well as in providing insight into the inner workings of computational models of neurophysiological systems.     

Learning to Match the Schemas of Data Sources: A Multistrategy Approach

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are element location maps to address and price maps to listed-price. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.     

Controlled analysis of neurocontrollers with informational lesioning

How does one aim to understand neural information processing? One of the difficult first challenges is to identify the roles of the network's elements. To this end a functional contribution analysis (FCA) method has been developed and applied for studying the neurocontrollers of evolutionary autonomous agents (EAAs). The FCA processes data composed of multiple lesion experiments and the corresponding performance levels that the agent obtains under these lesions. It calculates the contribution values (CVs) of the network's elements such that the ability to predict the agent's performance under new, unseen lesions is maximized. Previous analysis has found a strong dependence of the CVs and the prediction error on the specific type of lesioning method used, i.e. on the way in which the activity of lesioned neurons is disrupted. We present a new, informational lesioning method (ILM), which views a lesion as a noisy channel and applies a controlled lesion to the network by varying the lesioning level from large to arbitrarily small magnitudes. Studying the ILM within the FCA framework, our main results are threefold: first, that lower lesioning levels permit more accurate FCA predictions; second, that the usage of minute ILM lesioning levels can uncover the long-term effects of elements on the network's functioning; and third, that as the lesioning level decreases, the CVs tend to approach limit values, reflecting the importance of these elements in the intact, normal-functioning neurocontroller.     

Crowds by Example

We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.     

Using and determining location in a context-sensitive tour guide

In a study that provided unique insights into the challenges associated with developing location-based applications, the Lancaster Guide project used members of the general public to test a network-centric electronic tourist guide. We discuss two main topics. The first is our choice of positioning technology - beacons that broadcast using an IEEE 802.11 wireless network combined with user input. The second topic concerns techniques for generating custom tours for electronic city-guide systems. Guide generates these custom tours by taking into account multiple contextual triggers and user preferences. In practice, producing good tours and, indeed, assessing the quality of a tour are difficult tasks. While our analysis of techniques for producing custom tours is somewhat specific to the city-guide domain we believe that the majority of our work is relevant to location-based systems in general     

Tell Me Who I Am: An Interactive Recommendation System

We consider a model of recommendation systems, where each member from a given set of players has a binary preference to each element in a given set of objects: intuitively, each player either likes or dislikes each object. However, the players do not know their preferences. To find his preference of an object, a player may probe it, but each probe incurs unit cost. The goal of the players is to learn their complete preference vector (approximately) while incurring minimal cost. This is possible if many players have similar preference vectors: such a set of players with similar “taste” may split the cost of probing all objects among them, and share the results of their probes by posting them on a public billboard. The problem is that players do not know a priori whose taste is close to theirs. In this paper we present a distributed randomized peer-to-peer algorithm in which each player outputs a vector which is close to the best possible approximation of the player’s real preference vector after a polylogarithmic number of rounds. The algorithm works under adversarial preferences. Previous algorithms either made severely limiting assumptions on the structure of the preference vectors, or had polynomial overhead. Research of N. Alon supported in part by the Israel Science Foundation and by the Von Neumann Fund. B. Awerbuch supported by NSF grants ANIR-0240551, CCF-0515080 and CCR-0311795. Research of Y. Azar supported in part by the German-Israeli Foundation and by the Israel Science Foundation. Research of B. Patt-Shamir supported in part by Israel Ministry of Science and Technology and by the Israel Science Foundation (grant 664/05).     

The trithorax group gene moira encodes a brahma-associated putative chromatin-remodeling factor in Drosophila melanogaster

The genes of the trithorax group (trxG) in Drosophila melanogaster are required to maintain the pattern of homeotic gene expression that is established early in embryogenesis by the transient expression of the segmentation genes. The precise role of each of the diverse trxG members and the functional relationships among them are not well understood. Here, we report on the isolation of the trxG gene moira (mor) and its molecular characterization. mor encodes a fruit fly homolog of the human and yeast chromatin-remodeling factors BAF170, BAF155, and SWI3. mor is widely expressed throughout development, and its 170-kDa protein product is present in many embryonic tissues. In vitro, MOR can bind to itself and it interacts with Brahma (BRM), an SWI2-SNF2 homolog, with which it is associated in embryonic nuclear extracts. The leucine zipper motif of MOR is likely to participate in self-oligomerization; the equally conserved SANT domain, for which no function is known, may be required for optimal binding to BRM. MOR thus joins BRM and Snf5-related 1 (SNR1), two known Drosophila SWI-SNF subunits that act as positive regulators of the homeotic genes. These observations provide a molecular explanation for the phenotypic and genetic relationships among several of the trxG genes by suggesting that they encode evolutionarily conserved components of a chromatin-remodeling complex.     

Synthesis and Characterization of Submicron-to-Micron Scale, Monodisperse, Spherical, and Nonporous Zirconia Particles

Monodisperse, spherical, and nonporous zirconia particles ranging from submicrons to microns for use as supports for capillary electrophoresis and chromatography were synthesized by the controlled hydrolysis of zirconium tetra-alkoxides in alcohol solutions in the presence of long-chain organic acids. Particle characteristics varied as a function of water concentration, the chain length of the organic acid, aging time, temperature, and extent of stirring. Changing these experimental parameters affected particle size, particle size distribution, and the degree of particle aggregation. Submicron particles were made by increasing the water concentration and decreasing aging time. Particle size increases as temperature during the aging period increases. Most importantly, large monodisperse particles (4 μm) could be made by extended stirring. In general, we found that particle size increases as the chain length of the organic acid increases. We also found that gentle rotation during the aging period prevented both particle settling and secondary nucleation. This allowed the production of large particles. BET nitrogen adsorption uptake measurements demonstrate that the particles could be sintered, without aggregating them, to remove virtually all internal porosity.     

Growth and Adhesion Enhancement of Diamond Films Deposited on Steel Substrates by a Cr–N Interlayer

Diamond film deposition onto iron-based substrates by chemical vapor deposition methods is complicated by the formation of black carbon or graphitic soot on the substrate surface prior to diamond nucleation and growth, by fast diffusion of carbon into the iron substrate, and by poor adhesion of the deposited film. These complications suggested the use of a buffer layer between the deposited diamond film and the iron-based substrate. We review different methods used to improve the adhesion of diamond film to steel substrates. In particular we describe in detail our own studies which involve the use of a Cr-N interlayer. The use of a chromium nitride interlayer has been found to improve significantly the adhesion of diamond films deposited on ferrous substrates. This is achieved by hindering diffusion processes of carbon and iron, very stable mechanical and chemical bonding between the interlayer and the diamond film, and good adhesion of the interlayer to the steel substrate. We also report on our studies related to residual stress present in the films, as well as a correlation between the interlayer properties and adhesion strength of deposited films.