Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape

Equity of Attention: Amortizing Individual Fairness in Rankings

Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.Comment: Accepted to SIGIR 201

Data Repurposing through Compatibility: A Computational Perspective

Reuse of data in new contexts beyond the purposes for which it was originally collected has contributed to technological innovation and reducing the consent burden on data subjects. One of the legal mechanisms that makes such reuse possible is purpose compatibility assessment. In this paper, I offer an in-depth analysis of this mechanism through a computational lens. I moreover consider what should qualify as repurposing apart from using data for a completely new task, and argue that typical purpose formulations are an impediment to meaningful repurposing. Overall, the paper positions compatibility assessment as a constructive practice beyond an ineffective standard.Comment: To appear in the Special Issue of the Journal of Institutional and Theoretical Economics on "Machine Learning and the Law". Written for the Symposium on Machine Learning and the Law of the Max Planck Institute for Research on Collective Goods: https://www.coll.mpg.de/329557/segovia?c=6765

Halogen Vacancy Migration at Surfaces of CsPbBr3_3 Perovskites: Insights from Density Functional Theory

Migration of halogen vacancies is one of the primary sources of phase segregation and material degradation in lead-halide perovskites. Here we use first principles density functional theory to compare migration energy barriers and paths of bromine vacancies in the bulk and at a (001) surface of cubic CsPbBr$_3$. Our calculations indicate that surfaces might facilitate bromine vacancy migration in these perovskites, due to their soft structure that allows for bond lengths variations larger than in the bulk. We calculate the migration energy for axial-to-axial bromine vacancy migration at the surface to be only half of the value in the bulk. Furthermore, we study the effect of modifying the surface with four different alkali halide monolayers, finding an increase of the migration barrier to almost the bulk value for the NaCl-passivated system. Migration barriers are found to be correlated to the lattice mismatch between the CsPbBr$_3$ surface and the alkali halide monolayer. Our calculations suggest that surfaces might play a significant role in mediating vacancy migration in halide perovskites, a result with relevance for perovskite nanocrystals with large surface-to-volume ratios. Moreover, we propose viable ways for suppressing this undesirable process through passivation with alkali halide salts

A Dual Neural Network Architecture for Linear and Nonlinear Control of Inverted Pendulum on a Cart

The use of a self-contained dual neural network architecture for the solution of nonlinear optimal control problems is investigated in this study. The network structure solves the dynamic programming equations in stages and at the convergence, one network provides the optimal control and the second network provides a fault tolerance to the control system. We detail the steps in design and solve a linearized and a nonlinear, unstable, four-dimensional inverted pendulum on a cart problem. Numerical results are presented and compared with linearized optimal control. Unlike the previously published neural network solutions, this methodology does not need any external training, solves the nonlinear problem directly and provides a feedback control

A New Neural Architecture for Homing Missile Guidance

We present a new neural architecture which imbeds dynamic programming solutions to solve optimal target-intercept problems. They provide feedback guidance solutions, which are optimal with any initial conditions and time-to-go, for a 2D scenario. The method discussed in this study determines an optimal control law for a system by successively adapting two networks - an action and a critic network. This method determines the control law for an entire range of initial conditions; it simultaneously determines and adapts the neural networks to the optimal control policy for both linear and nonlinear systems. In addition, it is important to know that the form of control does not need to be known in order to use this metho

Adaptive Critic Based Neural Networks for Control (Low Order System Applications)

Dynamic programming is an exact method of determining optimal control for a discretized system. Unfortunately, for nonlinear systems the computations necessary with this method become prohibitive. This study investigates the use of adaptive neural networks that utilize dynamic programming methodology to develop near optimal control laws. First, a one dimensional infinite horizon problem is examined. Problems involving cost functions with final state constraints are considered for one dimensional linear and nonlinear systems. A two dimensional linear problem is also investigated. In addition to these examples, an example of the corrective capabilities of critics is shown. Synthesis of the networks in this study needs no external training; they do not need any apriori knowledge of the functional form of control. Comparison with specific optimal control techniques show that the networks yield optimal control over the entire range of trainin