What is Fair Pay for Executives? An Information Theoretic Analysis of Wage Distributions

The high pay packages of U.S. CEOs have raised serious concerns about what would constitute a fair pay.Comment: 16 page

Recognizing complex faces and gaits via novel probabilistic models

In the field of computer vision, developing automated systems to recognize people under unconstrained scenarios is a partially solved problem. In unconstrained sce- narios a number of common variations and complexities such as occlusion, illumi- nation, cluttered background and so on impose vast uncertainty to the recognition process. Among the various biometrics that have been emerging recently, this dissertation focus on two of them namely face and gait recognition. Firstly we address the problem of recognizing faces with major occlusions amidst other variations such as pose, scale, expression and illumination using a novel PRObabilistic Component based Interpretation Model (PROCIM) inspired by key psychophysical principles that are closely related to reasoning under uncertainty. The model basically employs Bayesian Networks to establish, learn, interpret and exploit intrinsic similarity mappings from the face domain. Then, by incorporating e cient inference strategies, robust decisions are made for successfully recognizing faces under uncertainty. PROCIM reports improved recognition rates over recent approaches. Secondly we address the newly upcoming gait recognition problem and show that PROCIM can be easily adapted to the gait domain as well. We scienti cally de ne and formulate sub-gaits and propose a novel modular training scheme to e ciently learn subtle sub-gait characteristics from the gait domain. Our results show that the proposed model is robust to several uncertainties and yields sig- ni cant recognition performance. Apart from PROCIM, nally we show how a simple component based gait reasoning can be coherently modeled using the re- cently prominent Markov Logic Networks (MLNs) by intuitively fusing imaging, logic and graphs. We have discovered that face and gait domains exhibit interesting similarity map- pings between object entities and their components. We have proposed intuitive probabilistic methods to model these mappings to perform recognition under vari- ous uncertainty elements. Extensive experimental validations justi es the robust- ness of the proposed methods over the state-of-the-art techniques.

AI-driven Hypernetwork of Organic Chemistry: Network Statistics and Applications in Reaction Classification

Rapid discovery of new reactions and molecules in recent years has been facilitated by the advancements in high throughput screening, accessibility to a much more complex chemical design space, and the development of accurate molecular modeling frameworks. A holistic study of the growing chemistry literature is, therefore, required that focuses on understanding the recent trends and extrapolating them into possible future trajectories. To this end, several network theory-based studies have been reported that use a directed graph representation of chemical reactions. Here, we perform a study based on representing chemical reactions as hypergraphs where the hyperedges represent chemical reactions and nodes represent the participating molecules. We use a standard reactions dataset to construct a hypernetwork and report its statistics such as degree distributions, average path length, assortativity or degree correlations, PageRank centrality, and graph-based clusters (or communities). We also compute each statistic for an equivalent directed graph representation of reactions to draw parallels and highlight differences between the two. To demonstrate the AI applicability of hypergraph reaction representation, we generate dense hypergraph embeddings and use them in the reaction classification problem. We conclude that the hypernetwork representation is flexible, preserves reaction context, and uncovers hidden insights that are otherwise not apparent in a traditional directed graph representation of chemical reactions

Soft Regulation with Crowd Recommendation: Coordinating Self-Interested Agents in Sociotechnical Systems under Imperfect Information

Regulating emerging industries is challenging, even controversial at times. Under-regulation can result in safety threats to plant personnel, surrounding communities, and the environment. Over-regulation may hinder innovation, progress, and economic growth. Since one typically has limited understanding of, and experience with, the novel technology in practice, it is difficult to accomplish a properly balanced regulation. In this work, we propose a control and coordination policy called soft regulation that attempts to strike the right balance and create a collective learning environment. In soft regulation mechanism, individual agents can accept, reject, or partially accept the regulator’s recommendation. This non-intrusive coordination does not interrupt normal operations. The extent to which an agent accepts the recommendation is mediated by a confidence level (from 0 to 100%). Among all possible recommendation methods, we investigate two in particular: the best recommendation wherein the regulator is completely informed and the crowd recommendation wherein the regulator collects the crowd’s average and recommends that value. We show by analysis and simulations that soft regulation with crowd recommendation performs well. It converges to optimum, and is as good as the best recommendation for a wide range of confidence levels. This work sheds a new theoretical perspective on the concept of the wisdom of crowds

Scaling up vaccine production through ‘copying exactly’

Waiving intellectual property rights and donating vaccines are not enough to vaccinate the world quickly, write Arnab Acharya, Aaron Moment (Columbia), Sanjay Reddy (The New School) and Venkat Venkatasubramanian (Columbia). The solution is to scale up production by sharing production methods freely, giving companies a financial incentive where necessary

Robust and Efficient Swarm Communication Topologies for Hostile Environments

Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents and hence the information available to agents, ultimately influencing the performance of the algorithm. In this paper, we present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration. We use particle swarm optimization to optimize an objective function with multiple sub-optimal regions in a hostile environment and study its performance for a range of network topologies with loss of agents. The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies that are subsequently leveraged to discover general properties of networks that maximize performance. Moreover, networks with small-world properties are seen to maximize performance under hostile conditions