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Price-based revenue management is an important problem in operations management with many practical applications. The problem considers a seller who sells one or multiple products over T consecutive periods and is subject to constraints on the initial inventory levels of resources. Whereas, in theory, the optimal pricing policy could be obtained via dynamic programming, computing the exact dynamic programming solution is often intractable. Approximate policies, such as the resolving heuristics, are often applied as computationally tractable alternatives. In this paper, we show the following two results for price-based network revenue management under a continuous price set. First, we prove that a natural resolving heuristic attains O(1) regret compared with the value of the optimal policy. This improves the [Formula: see text] regret upper bound established in the prior work by Jasin in 2014. Second, we prove that there is an [Formula: see text] gap between the value of the optimal policy and that of the fluid model. This complements our upper bound result by showing that the fluid is not an adequate information-relaxed benchmark when analyzing price-based revenue management algorithms. Funding: This work was supported in part by the National Science Foundation [Grant CMMI-2145661].

We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload along geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incident reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone-redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high-priority 911 calls by 5.8% and the imbalance of police workload among Atlanta’s zones by 43%.

Airline booking data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class is much greater than the fraction of customers who choose the next cheapest available one, even if the price difference is small. To model this spike in demand for the cheapest available fare class, a choice model called the spiked multinomial logit (spiked-MNL) model was proposed. We study a network revenue management problem under the spiked-MNL choice model. We show that efficient sets, that is, assortments that offer a Pareto-optimal tradeoff between revenue and resource use, are nested-by-revenue when the spike effect is nonnegative. We use this result to show how a deterministic approximation of the stochastic dynamic program can be solved efficiently by solving a small linear program. The solution of the small linear program is used to construct a booking limit policy, and we prove that the policy is asymptotically optimal. This is the first such result for a booking limit policy under a choice model, and our proof uses anmore »approach that is different from those used for previous asymptotic optimality results. Finally, we evaluate different revenue management policies in numerical experiments using both synthetic and airline data.« less

Arabidopsis RESISTANCE TO POWDERY MILDEW 8.2 (RPW8.2) is specifically induced by the powdery mildew (PM) fungus (Golovinomyces cichoracearum) in the infected epidermal cells to activate immunity. However, the mechanism of RPW8.2-induction is not well understood. Here, we identify a G. cichoracearum effector that interacts with RPW8.2, named Gc-RPW8.2 interacting protein 1 (GcR8IP1), by a yeast two-hybrid screen of an Arabidopsis cDNA library. GcR8IP1 physically associated with RPW8.2 with its RING finger domain that is essential and sufficient for the association. GcR8IP1 was secreted and translocated into the nucleus of host cell infected with PM. Association of GcR8IP1 with RPW8.2 led to an increase of RPW8.2 in the nucleus. In turn, the nucleus-localised RPW8.2 promoted the activity of the RPW8.2 promoter, resulting in transcriptional self-amplification of RPW8.2 to boost immunity at infection sites. Additionally, ectopic expression or host-induced gene silencing of GcR8IP1 supported its role as a virulence factor in PM. Altogether, our results reveal a mechanism of RPW8.2-dependent defense strengthening via altered partitioning of RPW8.2 and transcriptional self-amplification triggered by a PM fungal effector, which exemplifies an atypical form of effector-triggered immunity.

In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories. Here the 9DoF pose, comprising 6D pose and 3D size, is equivalent to a 3D amodal bounding box representation with free 6D pose. Given the depth point cloud at the current frame and the estimated pose from the last frame, our novel end-to-end pipeline learns to accurately update the pose. Our pipeline is composed of three modules: 1) a pose canonicalization module that normalizes the pose of the input depth point cloud; 2) RotationNet, a module that directly regresses small interframe delta rotations; and 3) CoordinateNet, a module that predicts the normalized coordinates and segmentation, enabling analytical computation of the 3D size and translation. Leveraging the small pose regime in the pose-canonicalized point clouds, our method integrates the best of both worlds by combining dense coordinate prediction and direct rotation regression, thus yielding an end-to-end differentiable pipeline optimized for 9DoF pose accuracy (without using non-differentiable RANSAC). Our extensive experiments demonstratemore »that our method achieves new state-of-the-art performance on category-level rigid object pose (NOCSREAL275 [29]) and articulated object pose benchmarks (SAPIEN [34], BMVC [18]) at the fastest FPS ∼ 12.« less