Order-of-Magnitude Influence Diagrams

In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams

Multi-objective influence diagrams with possibly optimal policies

The formalism of multi-objective influence diagrams has recently been developed for modeling and solving sequential decision problems under uncertainty and multiple objectives. Since utility values representing the decision maker’s preferences are only partially ordered (e.g., by the Pareto order) we no longer have a unique maximal value of expected utility, but a set of them. Computing the set of maximal values of expected utility and the corresponding policies can be computationally very challenging. In this paper, we consider alternative notions of optimality, one of the most important one being the notion of possibly optimal, namely optimal in at least one scenario compatible with the inter-objective tradeoffs. We develop a variable elimination algorithm for computing the set of possibly optimal expected utility values, prove formally its correctness, and compare variants of the algorithm experimentally

Best-first and/or search for graphical models

Abstract The paper presents and evaluates the power of best-first search over AND/OR search spaces in graphical models. The main virtue of the AND/OR representation is its sensitivity to the structure of the graphical model, which can translate into significant time savings. Indeed, in recent years depth-first AND/OR Branch-and-Bound algorithms were shown to be very effective when exploring such search spaces, especially when using caching. Since best-first strategies are known to be superior to depth-first when memory is utilized, exploring the best-first control strategy is called for. In this paper we introduce two classes of best-first AND/OR search algorithms: those that explore a context-minimal AND/OR search graph and use static variable orderings, and those that use dynamic variable orderings but explore an AND/OR search tree. The superiority of the best-first search approach is demonstrated empirically on various real-world benchmarks

Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO

Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks. However, certain important tasks are less amenable to these technologies, benefiting from innovations to traditional inference schemes. One such task is protein re-design. Recently a new re-design algorithm, AOBB-K*, was introduced and was competitive with state-of-the-art BBK* on small protein re-design problems. However, AOBB-K* did not scale well. In this work we focus on scaling up AOBB-K* and introduce three new versions: AOBB-K*-b (boosted), AOBB-K*-DH (with dynamic heuristics), and AOBB-K*-UFO (with underflow optimization) that significantly enhance scalability.Comment: In proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023) and published in Proceedings of Machine Learning Research (PMLR

PMA-Treatment of Human Monocytes Induces a M1 Phenotype in Adherent Macrophages

Background: Human monocyte lines are widely used in basic research as model of inflammation, mostly following adherence with phorbol 12-myristate 13-acetate (PMA). However, the SC line, of normal human monocytes is not well documented, unlike tumour-derived cell lines, such as THP-1. Aim: The purpose of this study was to determine the phenotype of adherent macrophages, induced after the treatment with PMA in three different concentrations, starting from the most widely reported concentration in the literature. Methods: Normal human monocytes SC (ATCC CRL-9855) were routinely maintained according to manufacturer’s instructions. Cells were treated with Phorbol 12-myristate 13-acetate (PMA Sigma Aldrich P1585), in concentrations of 200 ng/mL, 100 ng/mL, 25 ng/mL and adhesion was documented using an Evos phase-contrast inverted microscope. Cell behaviour was validated by real-time impedance readings. The adhered cells were treated with bacterial lipopolysaccharide (LPS) in concentrations of 50 ng/mL (mimicking chronic inflammation) and 1 μg/mL (mimicking acute inflammation). The supernatant was collected twice, after 4 hours, respectively after 18 hours of treatment with LPS. A screening of pro- and anti-inflammatory cytokines was performed using the multiplexing platform Luminex 200. ELISA tests were performed to validate the cytokines secretion: IL-6, IL-8, IL-10, IL-23 and TNF-ɑ, using a LEGEND MAX Human ELISA kit specific to each cytokine.   Results: Cell adhesion was studied by time-lapse microscopy for 48 hrs. The lowest concentration of PMA which induced cell adherence was 25 ng/mL. Multiplex screening of cytokines showed a pro-inflammatory phenotype of macrophages stimulated with LPS. This finding was validated by ELISA tests for IL-6, IL-8, IL-23 and TNF-ɑ (as pro-inflammatory cytokine) and IL-10 (an anti-inflammatory molecule). For the first category, we noticed a time-dependent response, present in adherent macrophages, but not in circulating monocytes. Regarding the second category of cytokines, the secretion is present only for the adhered and LPS treated cells. It is also present in a time-dependent manner (a higher concentration can be noticed in the collected supernatant after 18 hours of treatment compared with the one collected after 4 hours of treatment). Conclusion: The macrophages obtained from normal human monocytes with PMA are M1 type, regardless of the concentration used for differentiation

Computing possibly optimal solutions for multi-objective constraint optimisation with tradeoffs

Computing the set of optimal solutions for a multiobjective constraint optimisation problem can be computationally very challenging. Also, when solutions are only partially ordered, there can be a number of different natural notions of optimality, one of the most important being the notion of Possibly Optimal, i.e., optimal in at least one scenario compatible with the inter-objective tradeoffs. We develop an AND/OR Branch-and-Bound algorithm for computing the set of Possibly Optimal solutions, and compare variants of the algorithm experimentally

An Extensible Technique for High-Precision Testing of Recovery Code

Thorough testing of software systems requires ways to productively employ fault injection. We describe a technique for automatically identifying the errors exposed by shared libraries, finding good injection targets in program binaries, and producing corresponding injection scenarios. We present a framework for writing precise custom triggers that inject the desired faults--in the form of error return codes and corresponding side effects--at the boundary between shared libraries and applications. We incorporated these ideas in the LFI tool chain. With no developer assistance and no access to source code, this new version of LFI found 11 serious, previously unreported bugs in the BIND name server, the Git version control system, the MySQL database server, and the PBFT replication system. LFI achieved entirely automatically 35%-60% improvement in recovery-code coverage, without requiring any new tests. LFI can be downloaded from http://lfi.epfl.ch