There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

A survey on coverage path planning for robotics

What is Twitter

Mass spectrometry is the main analytical technique currently used to address the challenges of glycomics as it offers unrivalled levels of sensitivity and the ability to handle complex mixtures of different glycan variations. Determination of glycan structures from analysis of MS data is a major bottleneck in high-throughput glycomics projects, and robust solutions to this problem are of critical importance. However, all the approaches currently available have inherent restrictions to the type of glycans they can identify, and none of them have proved to be a definitive tool for glycomics. GlycoWorkbench is a software tool developed by the EUROCarbDB initiative to assist the manual interpretation of MS data. The main task of GlycoWorkbench is to evaluate a set of structures proposed by the user by matching the corresponding theoretical list of fragment masses against the list of peaks derived from the spectrum. The tool provides an easy to use graphical interface, a comprehensive and increasing set of str...

/pdf/glycoworkbench-a-tool-for-the-computer-assisted-annotation-49vo5ousfe.pdf

GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans

Several recent works considered multi-a(ge)nt robotics in static environments. In this work we examine ways of operating in dynamic environments, where changes take place independently of the agents??? activity. The work focuses on a dynamic variant of the cooperative cleaners problem, a problem that requires several simple agents to clean a connected region of ???dirty??? pixels in Z2. A number of simple agents move in this dirty region, each having the ability to ???clean??? the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a deterministic expansion of dirt in the environment, simulating spreading of contamination or fire. Theoretical lower bounds for the problem are presented, as well as various impossibility results. A cleaning protocol for the problem is presented, and a wealth of experimental results testing its performance in comparison to the lower bounds. Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

/pdf/multi-agent-cooperative-cleaning-of-expanding-domains-1w33aspzls.pdf

Multi-agent Cooperative Cleaning of Expanding Domains

Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today's smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, predicting outcomes, and so on. In this work we investigate the properties of learning and inference of real world data collected via mobile phones over time. In particular, we look at the dynamic learning process over time, and how the ability to predict individual parameters and social links is incrementally enhanced with the accumulation of additional data. To do this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year, and is one of the most comprehensive mobile phone datasets gathered in academia to date. We develop several models that predict social and individual properties from sensed mobile phone data, including detection of life-partners, ethnicity, and whether a person is a student or not. Then, for this set of diverse learning tasks, we investigate how the prediction accuracy evolves over time, as new data is collected. Finally, based on gained insights, we propose a method for advance prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. This has practical implications, like informing the design of mobile data collection campaigns, or evaluating analysis strategies.

Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data

Measuring and predicting the human mobility along the links of a transportation network has always been of a great importance to researchers in the field. Hitherto, producing such data relied heavily on expensive and time con- suming surveying and on-field observational methods. In this work we propose an efficient estimation method for the assessment of the flow through links in trans- portation networks that is based on the Betweenness Centrality measure of the network's nodes. Furthermore, we show that the correlation between those two features can be significantly increased when additional (pre-defined and known) properties of the network are taken into account, generating an augmented Mo- bility Oriented Betweenness Centrality measure. We validate the results using a transportation dataset, constructed using cellular phones data, that contains a high resolution network of the Israeli transportation system. We show that the flow that was measured using this expensive and complicated method can be accurately es- timated using our proposed Augmented Betweenness technique.

/pdf/augmented-betweenness-centrality-for-mobility-prediction-in-2k23wz8zwd.pdf

Augmented Betweenness Centrality for Mobility Prediction in Transportation Networks

Security and Privacy in Social Networks brings to the forefront innovative approaches for analyzing and enhancing the security and privacy dimensions in online social networks, and is the first comprehensive attempt dedicated entirely to this field. In order to facilitate the transition of such methods from theory to mechanisms designed and deployed in existing online social networking services, the book aspires to create a common language between the researchers and practitioners of this new area- spanning from the theory of computational social sciences to conventional security and network engineering.

Security and Privacy in Social Networks

https://web.media.mit.edu/~yanival/Theoretical_Computer_Science.pdf

Yaniv Altshuler

Papers

Multi-agent Cooperative Cleaning of Expanding Domains

Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data

Augmented Betweenness Centrality for Mobility Prediction in Transportation Networks

Security and Privacy in Social Networks

Static and expanding grid coverage with ant robots: Complexity results