91 research outputs found
Crime incidents embedding using restricted Boltzmann machines
We present a new approach for detecting related crime series, by unsupervised
learning of the latent feature embeddings from narratives of crime record via
the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a
drastically different approach from prior work on crime analysis, which
typically considers only time and location and at most category information.
After the embedding, related cases are closer to each other in the Euclidean
feature space, and the unrelated cases are far apart, which is a good property
can enable subsequent analysis such as detection and clustering of related
cases. Experiments over several series of related crime incidents hand labeled
by the Atlanta Police Department reveal the promise of our embedding methods.Comment: 5 pages, 3 figure
Bayesian Optimization with Hidden Constraints via Latent Decision Models
Bayesian optimization (BO) has emerged as a potent tool for addressing
intricate decision-making challenges, especially in public policy domains such
as police districting. However, its broader application in public policymaking
is hindered by the complexity of defining feasible regions and the
high-dimensionality of decisions. This paper introduces the Hidden-Constrained
Latent Space Bayesian Optimization (HC-LSBO), a novel BO method integrated with
a latent decision model. This approach leverages a variational autoencoder to
learn the distribution of feasible decisions, enabling a two-way mapping
between the original decision space and a lower-dimensional latent space. By
doing so, HC-LSBO captures the nuances of hidden constraints inherent in public
policymaking, allowing for optimization in the latent space while evaluating
objectives in the original space. We validate our method through numerical
experiments on both synthetic and real data sets, with a specific focus on
large-scale police districting problems in Atlanta, Georgia. Our results reveal
that HC-LSBO offers notable improvements in performance and efficiency compared
to the baselines.Comment: 8 pages, 8 figures (exclude appendix
Counterfactual Generative Models for Time-Varying Treatments
Estimating the counterfactual outcome of treatment is essential for
decision-making in public health and clinical science, among others. Often,
treatments are administered in a sequential, time-varying manner, leading to an
exponentially increased number of possible counterfactual outcomes.
Furthermore, in modern applications, the outcomes are high-dimensional and
conventional average treatment effect estimation fails to capture disparities
in individuals. To tackle these challenges, we propose a novel conditional
generative framework capable of producing counterfactual samples under
time-varying treatment, without the need for explicit density estimation. Our
method carefully addresses the distribution mismatch between the observed and
counterfactual distributions via a loss function based on inverse probability
weighting. We present a thorough evaluation of our method using both synthetic
and real-world data. Our results demonstrate that our method is capable of
generating high-quality counterfactual samples and outperforms the
state-of-the-art baselines
Enabling smartphone-based HD video chats by cooperative transmissions in CRNs
Smartphones have been equipped with the cameras that can shoot HD videos, and the video chat apps such as Skype are becoming popular. We can, therefore, intuitively predict the trend that users are expecting to enjoy HD video chats via utilizing their smartphones. Most of the current Internet services, however, cannot support the live HD video transmissions because of their low uplink rate. In order to overcome this limit, we propose to offload the uplink transmissions to cooperative users via cognitive radio networks. Specifically, we first divide the video stream into several substreams according to the H.264/SVC standard and the cooperative users’ uplink rates. Then, the cooperative users are selected by employing our proposed optimal multiple stopping method. Finally, the substreams are assigned to the selected cooperative users by a 0-1 Knapsack-based allocation algorithm. The simulation results demonstrate that our proposed scheme can successfully support 720P HD video chats
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