This paper presents a heuristic for simplifying resource allocation in wireless access systems using the concept of comparative advantage. It reduces allocation complexity in multi-cell networks by halving the dimensionality of the search space before optimization. Simulations and experiments confirm its effectiveness, highlighting its potential for practical deployment in next-generation wireless access networks.

We expand the scope of cache memory to include LEO constellations, which are highly distributed systems with thousands of satellites connected with free-space optics inter-satellite links (ISL) with numerous nodes only one hop from any point on earth. We show how to increase the number of cache hits and improve the speed of inference for the important use case of LLMs. These benefits apply not only to LLMs, both terrestrially hosted and on satellites, but also generalize to any cache distributed over multiple locations that needs to be accessed in a timely manner. We show the benefit of our key value cache (KVC) protocol in simulations and present a proof-of-concept implementation of the protocol for KVCs on a testbed comprising 5 Intel NUC Linux mini PCs hosting a 95 node, 19x5 constellation, with an NVIDIA Jetson Nano 8GB GPU hosting the LLM.

We propose a resource allocation mechanism to improve the efficiency of low earth orbit satellite communication systems or similar communication systems that require lightweight downlink transmitters. The mechanism uses distributed auction with locality-sensitive-hashing (LSH) based prediction for dynamic allocation of downlink resources such as spectrum, satellite links, and radios, without the need to send channel status back to satellites. The method minimizes the computational complexity at satellites while keeping the complexity of ground stations low. Simulation and experimental results show that this mechanism improves total channel capacity by dynamically leveraging the diversity among satellite-station links, reduces uplink overhead by providing lightweight and effective channel status feedback, and also provides implicit resource information stemming from the auction dynamics. This new mechanism provides a feasible solution for low earth orbit satellites which are sensitive to power consumption and overheating.

We present a Web survey system demo that computes aggregates of sensitive data while protecting individual contributions using a novel secure aggregation algorithm implemented in a Web browser.

Communications standards are designed via committees of humans holding repeated meetings over months or even years until consensus is achieved. This includes decisions regarding the modulation and coding schemes to be supported over an air interface. We propose a way to “automate” the selection of the set of modulation and coding schemes to be supported over a given air interface and thereby streamline both the standards design process and the ease of extending the standard to support new modulation schemes applicable to new higher-level applications and services. Our scheme involves machine learning, whereby a constructor entity submits proposals to an evaluator entity, which returns a score for the proposal. The constructor employs reinforcement learning to iterate on its submitted proposals until a score is achieved that was previously agreed upon by both constructor and evaluator to be indicative of satisfying the required design criteria (including performance metrics for transmissions over the interface).

Information overload is a systemic problem for knowledge workers in enterprise. For a long time, information was scarce and therefore valuable. While, the explosion of digital information has made information plentiful, time to read and process that content is now scarce. This problem is only exacerbated by our increased mobility, and the expectation to be "on top" of the continuous barrage of documents while on the go. Knowledge workers in enterprise need solutions that are designed with quick methods for finding what to read in a large collection of documents (e.g. financial reports, legal documents, news), and ways of presenting it within small visual real estate. Unlike reviews, document collections are long, more varied, and context is extremely important. In response, we present Odin, a mobile web-based window onto a user's document corpus. Rather than performing corpus summarization, Odin users can quickly find opinions and documents that are Aligned or Divergent from the corpus' consensus, or those that are the most Relevant given the overall corpus' of opinions. Odin presents this information through a simple and intuitive mobile interface. To the authors' knowledge, this is the first UI/system (and support algorithm) to allow mobile users to place documents and their opinions in context through alignment rather than raw word count or sentiment. Positive results from two evaluations are also presented.

Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank them using a proposed metric within distributed systems. We evaluate our approach on passengers' records of an urbanized city's public transportation system and correlate our findings with passengers' postings on social media micro blogs. Our experiments show that the metric derived using our localization algorithm gives a better ranking of anomalies as compared to standard deviation measures from statistical models. Our case studies also demonstrate that transportation events reported in social media micro blogs matches the locations of our detect anomalies, suggesting that our algorithm performs well in locating the anomalies within distributed systems.

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone.

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, images, users, or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources in social tagging systems may become semantically stable over time, as more and more users tag them and implicitly agree on the relative importance of tags for a resource. At the same time, previous work has raised an array of new questions such as: 1) how can we assess semantic stability in a robust and methodical way? 2) does the semantic stabilization varies across different social tagging systems and ultimately, and 3) what are the factors that can explain semantic stabilization in such systems? In this work, we tackle these questions by: 1) presenting a novel and robust method, which overcomes a number of limitations in existing methods; 2) empirically investigating semantic stabilization in different social tagging systems with distinct domains and properties; and 3) detecting potential causes of stabilization and implicit consensus, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams that are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams that are generated via imitation dynamics or natural language phenomena alone.

We designed and ran an experiment to measure social influence in online recommender systems, specifically how often people's choices are changed by others' recommendations when facing different levels of confirmation and conformity pressures. In our experiment participants were first asked to provide their preferences between pairs of items. They were then asked to make second choices about the same pairs with knowledge of others' preferences. Our results show that others people's opinions significantly sway people's own choices. The influence is stronger when people are required to make their second decision sometime later (22.4%) than immediately (14.1%). Moreover, people seem to be most likely to reverse their choices when facing a moderate, as opposed to large, number of opposing opinions. Finally, the time people spend making the first decision significantly predicts whether they will reverse their decisions later on, while demographics such as age and gender do not. These results have implications for consumer behavior research as well as online marketing strategies.

We present an analysis of user conversations in on-line social media and their evolution over time. We propose a dynamic model that predicts the growth dynamics and structural properties of conversation threads. The model reconciles the differing observations that have been reported in existing studies. By separating artificial factors from user behavior, we show that there are actually underlying rules in common for on-line conversations in different social media websites. Results of our model are supported by empirical measurements throughout a number of different social media websites.

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present the design, implementation and evaluation of a new geotagging service, Gloe, that makes it easy to find, rate and recommend arbitrary on-line content in a mobile setting. The service automates the content search process by taking advantage of geographic and social context, while using crowdsourced expertise to present a personalized feed of targeted information ranked by a novel geo-aware rating and incentive mechanism.

In recent years, social media has become ubiquitous and important for social networking and content sharing.And yet, the content that is generated from these websites remains largely untapped.In this paper, we demonstrate how social media content can be used to predict real-world outcomes.In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies.We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors.We further demonstrate how sentiments extracted from Twitter can be further utilized to improve the forecasting power of social media.

Opinions play an important role in trust building and the creation of consensus about issues and products and a number of studies have focused on the design, evaluation, and utilization of online opinion systems. However, little effort has been spent on the dynamic aspects of online opinion formation. In this article, we study the dynamics of online opinion expression by analyzing the temporal evolution of vey large sets of user views and determine that in the course of time, later opinions tend to show a big difference with earlier opinions, which moderates the average opinion to the less extreme. Online posters also tend to disagree with previous opinions when the cost of expression is high.

We have designed and implemented Friendlee, a mobile social networking application for close relationships. Friendlee analyzes the user's call and messaging activity to form an intimate network of the user's closest social contacts while providing ambient awareness of the user' social network in a compelling, yet non-intrusive manner.

We analyze the role that popularity and novelty play in attracting the attention of users to dynamic websites. We do so by determining the performance of three different strategies that can be utilized to maximize attention. The first one prioritizes novelty while the second emphasizes popularity. A third strategy looks myopically into the future and prioritizes stories that are expected to generate the most clicks within the next few minutes. We show that the first two strategies should be selected on the basis of the rate of novelty decay, while the third strategy performs sub-optimally in most cases. We also demonstrate that the relative performance of the first two strategies as a function of the rate of novelty decay changes abruptly around a critical value, resembling a phase transition in the physical world.

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We then establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies product and pricing categories for which viral marketing seems to be very effective.

We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants.

We propose using the information revealed through auctions, including in particular the unsuccessful bids, to identify latent demand. Applied to combinatorial auctions for bundles of goods, this information can identify new bundles with particularly high valuations, expressed by their high complementarity. We present a simple algorithm for identifying these bundles, suitable for use with agent-based ecommerce systems.

A bstra& A major impediment to using recommendation systems and collective knowledge for electronic commerce is the reluctance of individuals to reveal preferences in order to find groups of people that share them.An equally important barrier to fluid electronic commerce is the lack of agreed upon trusted third parties.We propose new non-third party mechanisms to overcome these barriers.Our solutions facilitate finding shared preferences, discovering communities with shared values, removing disincentives posed by liabilities, and negotiating on behalf of a group.We adapt known techniques from the cryptographic literature to enable these new capabilities.

Since it has been suggested that the brain binds its fragmentary representations of perceptual events via phase-locking of stimulated neural oscillators, it is important to determine how extended synchronization can occur in a clustered organization of cells possessing a distribution of firing rates. To answer that question, we establish the basic conditions for the existence of a binding mechanism based on synchronized oscillations. In addition, we present a simple hierarchical architecture of feedback units that not only induces robust phase-locking within and segregation between perceptual groups, but also serves as a generic binding machine.

The authors have designed and implemented an open, market-based computational system called Spawn. The Spawn system utilizes idle computational resources in a distributed network of heterogeneous computer workstations. It supports both coarse-grain concurrent applications and the remote execution of many independent tasks. Using concurrent Monte Carlo simulations as prototypical applications, the authors explore issues of fairness in resource distribution, currency as a form of priority, price equilibria, the dynamics of transients, and scaling to large systems. In addition to serving the practical goal of harnessing idle processor time in a computer network, Spawn has proven to be a valuable experimental workbench for studying computational markets and their dynamics.>

A simple and robust procedure for freezing out chaotic behavior in systems composed of interacting agents making decisions based on imperfect and delayed information is described. It is based on a reward mechanism whereby the relative number of computational agents following effective strategies is increased at the expense of the others. This procedure, which generates a diverse population out of an essentially homogeneous one, is able to control chaos through a series of dynamical bifurcations into a stable fixed point. Stability boundaries are computed and the minimal amount of diversity required in the system is established.>

We investigate the effectiveness of connectionist architectures for predicting the future behavior of nonlinear dynamical systems. We focus on real-world time series of limited record length. Two examples are analyzed: the benchmark sunspot series and chaotic data from a computational ecosystem. The problem of overfitting, particularly serious for short records of noisy data, is addressed both by using the statistical method of validation and by adding a complexity term to the cost function ("back-propagation with weight-elimination"). The dimension of the dynamics underlying the time series, its Liapunov coefficient, and its nonlinearity can be determined via the network. We also show why sigmoid units are superior in performance to radial basis functions for high-dimensional input spaces. Furthermore, since the ultimate goal is accuracy in the prediction, we find that sigmoid networks trained with the weight-elimination algorithm outperform traditional nonlinear statistical approaches.

The performance of fault-stealing algorithms for very large, multipipeline systolic arrays is considered. Extensions of an existing algorithm are proposed, and with these extensions the algorithm is shown to work for large array sizes. Using the modified algorithms as a testbed, a scaling theory that predicts, on the basis of performance for a single small array, the performance of the algorithm for arbitrary array size, defect rate, and number of spares is introduced. The theory differs from current approaches in that it has both analytical and empirical components, and in that it accurately predicts system performance, rather than providing bounds on it.>

Since their invention, computers have been designed, used and improved using the human mind as metaphor. The appearance of a distributed form of computation in large networks in shifting the emphasis towards social structures as a source of inspiration for the design of new systems. I suggest that a reverse strategy could prove to be useful as well. If our results are any indication of future developments, the workings of distributed computational systems could provide a flexible and controlled environment for the study of complex interactions that are seldom accessible in the natural world.