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We introduce a representation for generating explanations for the outcomes of combinatorial optimization algorithms. The two key ideas are (A) to maintain fine-grained representations of the values manipulated by these algorithms and (B) to derive explanations from these representations through merge, filter, and aggregation operations. An explanation in our model presents essentially a high-level comparison of the solution to a problem with a hypothesized alternative, illuminating why the solution is better than the alternative. Our value representation results in explanations smaller than other dynamic program representations, such as traces. Based on a measure for the conciseness of explanations we demonstrate through a number of experiments that the explanations produced by our approach are small and scale well with problem size across a number of different applications. 

The incessant mutations of viruses, variable immune responses, and likely emergence of new viral threats necessitate multiple approaches to novel antiviral therapeutics. Furthermore, the new antiviral agents should have broad-spectrum activity and be environmentally stable. Here, we show that biocompatible tapered CuS nanoparticles (NPs) efficiently agglutinate coronaviruses with binding affinity dependent on the chirality of surface ligands and particle shape.L-penicillamine-stabilized NPs with left-handed curved apexes display half-maximal inhibitory concentrations (IC₅₀) as low as 0.66 pM (1.4 ng/mL) and 0.57 pM (1.2 ng/mL) for pseudo-type SARS-CoV-2 viruses and wild-type Wuhan-1 SARS-CoV-2 viruses, respectively, which are about 1,100 times lower than those for antibodies (0.73 nM). Benefiting from strong NPs–protein interactions, the same particles are also effective against other strains of coronaviruses, such as HCoV-HKU1, HCoV-OC43, HCoV-NL63, and SARS-CoV-2 Omicron variants with IC₅₀values below 10 pM (21.8 ng/mL). Considering rapid response to outbreaks, exposure to elevated temperatures causes no change in the antiviral activity of NPs while antibodies are completely deactivated. Testing in mice indicates that the chirality-optimized NPs can serve as thermally stable analogs of antiviral biologics complementing the current spectrum of treatments. 

MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An important feature of this DSL is the ability to generate explanations of why a computed optimal solution is better than its alternatives. The functional approach and Haskell's type system support a high-level formulation of decision-making problems, which facilitates a number of innovations, including the gradual evolution and adaptation of problem representations, a more user-friendly form of sensitivity analysis based on problem domain data, and fine-grained control over explanations. 

Abstract In this paper, we present a method for explaining the results produced by dynamic programming (DP) algorithms. Our approach is based on retaining a granular representation of values that are aggregated during program execution. The explanations that are created from the granular representations can answer questions of why one result was obtained instead of another and therefore can increase the confidence in the correctness of program results. Our focus on dynamic programming is motivated by the fact that dynamic programming offers a systematic approach to implementing a large class of optimization algorithms which produce decisions based on aggregated value comparisons. It is those decisions that the granular representation can help explain. Moreover, the fact that dynamic programming can be formalized using semirings supports the creation of a Haskell library for dynamic programming that has two important features. First, it allows programmers to specify programs by recurrence relationships from which efficient implementations are derived automatically. Second, the dynamic programs can be formulated generically (as type classes), which supports the smooth transition from programs that only produce result to programs that can run with granular representation and also produce explanations. Finally, we also demonstrate how to anticipate user questions about program results and how to produce corresponding explanations automatically in advance. 

Unmanned vehicles, equipped with radiation detection sensors, can serve as a valuable aid to personnel responding to radiological incidents. The use of tele-operated ground vehicles avoids human exposure to hazardous environments, which in addition to radioactive contamination, might present other risks to personnel. Autonomous unmanned vehicles using algorithms for radioisotope classification, source localization, and efficient exploration allow these vehicles to conduct surveys with reduced human supervision allowing teams to address larger areas in less time. This work presents systems for autonomous radiation search with results presented in several proof-of-concept demonstrations.