This paper develops computationally efficient algorithms for the analysis of acoustic data to localize a target through improved angle of arrival estimation. The passive target localization problem has a wide range of applications in wireless communication, navigation, acoustic sensor networks, indoor localization, to name a few. We have focused on novel formulations and solution methods for target localization using Time Differences of Arrival (TDOA) among distinct pairs of passive sensor nodes in an acoustic sensor network with known sensor positions.

Infrared (IR) small target detection under complex environments is an essential part of IR search and track systems. However, previously proposed IR small target detection algorithms cannot achieve complete suppression of complex and significant backgrounds. The spatial–temporal information of image sequences is not fully exploited. In this article, we present a sparse regularization-based twist tensor model for IR small target detection. First, the twist tensor model is built via perspective conversion based on the target’s local continuity in the spatial–temporal domain, which makes the original complicated background components more structured and increases the difference between the background and the target. Then, the structured sparsity-inducing norm is introduced to define the locality and continuity of the target. To further minimize the sparse background structures and global noise, the structured sparsity-inducing norm and the$l_{1}$norm are combined as the target’s parse constraint. Experimental results on real scenes reveal that the suggested method can process images with high detection accuracy and outstanding background suppression ability compared to various state-of-the-art methods.

We show the O(log n) time extract minimum function of efficient priority queues can be generalized to the extraction of the k smallest elements in O(k log(n/k)) time1, which we prove optimal for comparison-based priority queues with o(log n) time insertion. We show heap-ordered tree selection (Kaplan et al., SOSA '19) can be applied on the heap-ordered trees of the classic Fibonacci heap and Brodal queue, in O(k log(n/k)) amortized and worst-case time, respectively. We additionally show the deletion of k elements or selection without extraction can be performed on both heaps, also in O(k log(n/k)) time. Surprisingly, all operations are possible with no modifications to the original Fibonacci heap and Brodal queue data structures. We then apply the result to lazy search trees (Sandlund & Wild, FOCS '20), creating a new interval data structure based on selectable heaps. This gives optimal O(B+n) time lazy search tree performance, lowering insertion complexity into a gap Δi from O(log(n/|Δi|) + log log n) to O(log(n/|Δi|)) time. An O(1) time merge operation is also made possible when used as a priority queue, among other situations. If Brodal queues are used, all runtimes of the lazy search tree can be made worst-case.

Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities. Unlike existing settings, we allow each agent to specify requirements to only accept allocations from a subset of the total supply for each resource. These requirements can take form in location constraints (e.g. A hospital can only accept volunteers who live nearby due to commute limitations). This can also model a type of substitution effect where some agents need 1 unit of resource A \emph{or} B, both belonging to the same meta-type. But some agents specifically want A, and others specifically want B. We propose a new mechanism called Dominant Resource Fairness with Meta Types which determines the allocations by solving a small number of linear programs. The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and a notion of sharing incentive for our setting. To the best of our knowledge, we are the first to study this problem formulation, which improved upon existing work by capturing more constraints that often arise in real life situations. Finally, we show numerically that our method scales better to large problems than alternative approaches.

Infrared (IR) small-target detection has been a widely studied task in IR search and tracking systems. It remains a challenging problem, especially in heterogeneous scenarios, where it is very difficult to discriminate true targets from sparse residuals in the background. A novel edge and corner awareness-based spatial–temporal tensor (ECA-STT) model is presented in this article. First, we construct an STT based on a spatial–temporal correlation analysis of the IR video background. Then, we propose an indicator to highlight the target through adjustable importance measurements of the edge and corner. The tensor-based nonlocal total variation is also adopted to describe the edges in the background. The target–background separation problem is modeled as a tensor robust principal component analysis (TRPCA) problem with the tensor rank function replaced by the tensor truncated nuclear norm. The proposed model is solved by an effective optimization algorithm derived from the alternating direction method of multipliers (ADMM). Extensive experiments verify the superior abilities of the proposed model in target enhancement and background suppression.

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force-South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans.

In recent years, research has revealed gender biases in numerous software products. But although some researchers have found ways to improve gender participation in specific software projects, general methods focus mainly on detecting gender biases -- not fixing them. To help fill this gap, we investigated whether the GenderMag bias detection method can lead directly to designs with fewer gender biases. In our 3-step investigation, two HCI researchers analyzed an industrial software product using GenderMag; we derived design changes to the product using the biases they found; and ran an empirical study of participants using the original product versus the new version. The results showed that using the method in this way did improve the software's inclusiveness: women succeeded more often in the new version than in the original; men's success rates improved too; and the gender gap entirely disappeared.

Routing in uncertain environments is challenging as it involves a number of contextual elements, such as different environmental conditions (forecast realizations with varying spatial and temporal uncertainty), changes in mission goals while en route, and asset status. In this paper, we use an approximate dynamic programming method with Q-factors to determine a cost-to-go approximation by treating the weather forecast realization information as a stochastic state. These types of algorithms take a large amount of offline computation time to determine the cost-to-go approximation, but once obtained, the online route recommendation is nearly instantaneous and several orders of magnitude faster than previously proposed ship routing algorithms. The proposed algorithm is robust to the uncertainty present in the weather forecasts. We compare this algorithm to a well-known shortest path algorithm and apply the approach to a real-world shipping tragedy using weather forecast realizations available prior to the event.

This paper presents a mixed-initiative tool for multiobjective planning and asset routing (TMPLAR) in dynamic and uncertain environments. TMPLAR is built upon multiobjective dynamic programming algorithms to route assets in a timely fashion, while considering fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). TMPLAR has the potential to be applied in a variety of contexts, including ship, helicopter, or unmanned aerial vehicle routing. The tool provides recommended schedules, consisting of waypoints, associated arrival and departure times, asset speed and bearing, that are optimized with respect to several objectives. The ship navigation is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather, multiple constraints on asset operation, and the added capability of waiting at a waypoint with the intent to avoid bad weather, conduct opportunistic training drills, or both. The key algorithmic contribution is a multiobjective shortest path algorithm for networks with stochastic nonconvex edge costs and the following problem features: 1) time windows on nodes; 2) ability to choose vessel speed to next node subject to (minimum and/or maximum) speed constraints; 3) ability to select the power plant configuration at each node; and 4) ability to wait at a node. The algorithm is demonstrated on six real world routing scenarios by comparing its performance against an existing operational routing algorithm.

Counter-smuggling operations constitute a high priority national security mission since drug-trafficking not only involves many criminals, but can also be a source of financing for many illicit activities such as narco-terrorism and arms trafficking. The counter-smuggling mission involves surveillance operations (to search, detect, track and identify potential threats) and interdiction operations (to intercept, investigate and potentially apprehend suspects). Potential smuggling activity is represented in the form of color-coded heat maps built using intelligence and meteorological and oceanographic information, which are interpreted in the form of probability of activity (PoA) surfaces. The PoA surfaces constitute the “sufficient statistics” for the asset allocation and scheduling processes. However, in the case of disconnected, intermittent, and low-bandwidth environments, the problem of allocating resources becomes very challenging as PoA information is unavailable or is not up to date. In this paper, we propose to utilize flow (historic PoA)-based surfaces, which provide cues on where the smugglers may have traversed in the past. Using the flow surfaces, we allocate the surveillance and interdiction assets to best thwart potential smuggling activities. We further evaluate the quality of our solution in terms of the number of targets interdicted and the amount of contraband seized.

3D packing is a fundamental problem for VLSI design. It arises from 3D-ICs floorplan design and task schedule in reconfigurable FPGA design. In this paper, we present a novel 3D floorplan representation by improving the 3D bounded slice-surface grid (3D-BSG) structure in two aspects: (1) optimize the evaluation time complexity from O(n3) to O(n2) by using three block relation graphs; (2) handle the infeasible assignment problem of 3D-BSG in linear time by giving the rule of null relation rooms. The effectiveness and efficiency of the fast 3D-BSG are shown by both theoretical analysis and experimental results. The average speedup compared to the original 3D-BSG is 44times high.

We study the pricing problem of end-to-end band-width service with loss assurance facilitated by a Multi-ISP Overlay Provider (MOP). The provider's strategies to construct end-to-end service contracts are investigated. By utilizing the MOP's contractual relationships with ISPs, we develop an options based approach for pricing end-to-end loss assurances. Application of options pricing techniques provides a mechanism for fair risk sharing between providers involved in end-to-end service delivery, as well as between providers and customers of Internet services.

We develop a spot pricing framework for intra-domain expected bandwidth contracts with loss based QoS guarantees. The framework accounts for both costs and risks associated with QoS delivery. A nonlinear pricing scheme is used for cost recovery and a utility based options pricing approach is developed for the risk related pricing. Application of options pricing techniques in Internet services provides a mechanism for fair risk sharing between the provider and the customer, and may be extended to price other uncertainties in QoS guarantees.