Random assignments with uniform preferences: An impossibility result

Agents have uniform preferences if a weakly decreasing utility function determines each agent's preference ranking over the same order of alternatives. We show that the impossibility in the random assignment problem between strategyproofness, ordinally efficiency, and fairness in the sense of equal division lower bound, prevails even if agents have uniform preferences. Furthermore, it continues to hold even if we weaken the strategyproofness to upper-contour strategyproofness, or the ordinal efficiency to robust ex-post Pareto efficiency

Real-Time Structure and Object Aware Semantic SLAM

Simultaneous Localization And Mapping (SLAM) is one of the fundamental problems in mobile robotics and addresses the reconstruction of a previously unseen environment while simultaneously localising a mobile robot with respect to it. For visual-SLAM, the simplest representation of the map is a collection of 3D points that is sparse and efficient to compute and update, particularly for large-scale environments, however it lacks semantic information and is not useful for high-level tasks such as robotic grasping and manipulation. Although methods to compute denser representations have been proposed, these reconstructions remain equivalent to a collection of points and therefore carry no additional semantic information or relationship. Man-made environments contain many structures and objects that carry high-level semantics and can potentially act as landmarks of a SLAM map, while encapsulating semantic information as opposed to a set of points. For instance, planes are good representations for feature deprived regions, where they provide information complimentary to points and can also model dominant planar layouts of the environment with very few parameters. Furthermore, a generic representation for previously unseen objects can be used as a general landmark that carries semantics in the reconstructed map. Integrating visual semantic understanding and geometric reconstruction has been studied before, however due to various reasons, including high- level geometric entities in the SLAM framework has been restricted to a slow, offline structure-from-motion context, or high-level entities merely act as regulators for points in the map instead of independent landmarks. One of those critical reasons is the lack of proper mathematical representation for high-level landmarks and the other main reasons are the challenge of detection and tracking of these landmarks and formulating an observation model – a mapping between corresponding image observable quantities and estimated parameters of the representations. In this work, we address these challenges to achieve an online real-time SLAM framework with scalable maps consisting of both sparse points and high-level structural and semantic landmarks such as planes and objects. We explicitly target real-time performance and keep that as a beacon which influences critically the representation choice and all the modules of our SLAM system. In the context of factor graphs, we propose novel representations for structural entities as planes and general unseen and not-predefined objects as bounded dual quadrics that decompose to permit clean, fast and effective real-time implementation that is amenable to the nonlinear leastsquare formulation and respects the sparsity pattern of the SLAM problem. In this representation we are not concerned with high-fidelity reconstruction of individual objects, but rather to represent the general layout and orientation of objects in the environment. Also the minimal representations of planes is explored leading to a representation that can be constructed and updated online in a least-squares framework. Another challenge that we address in this work is to marry high-level landmark detections based on deep-learned frameworks, with geometric SLAM systems. Due to the recent success of CNN-based object detections and also depth and surface normal estimations from single image, it is feasible now to detect and estimate these semantic landmarks from single RGB images, therefore leading us seamlessly from RGB-D SLAM system to pure monocular SLAM thanks to the real-time predictions of the trained CNN and appropriate representations. Furthermore, to benefit from deep-learned priors, we incorporate high-fidelity single-image reconstructions and hallucinations of objects on top of the coarse quadrics to enrich the sparse map semantically, while constraining the shape of the coarse quadrics even more. Pertinent to our beacon, proposed landmark representations in the map also provide the potential for imposing additional constraints and priors that carry crucial semantic information about the scene, without incurring great extra computational cost. In this work, we have explored and proposed constraints such as priors on the extent and shape of the objects, point-plane regularizer, plane-plane (Manhattan assumption), and plane-object (supporting affordance) constraints. We evaluate our proposed SLAM system extensively using different input sensor modalities from RGB-D to monocular in almost all publicly available benchmarks both indoors and outdoors to show its applicability as a general-purpose SLAM solution. The extensive experiments show the efficacy of our SLAM through different comparisons and ablation studies including high-level structures and objects with imposed constraints among them in various scenarios. In particular, the estimated camera trajectories have been improved significantly in varied sequences of visual SLAM datasets and also our own captured sequences with UR5 robotic arm equipped with a depth camera. In addition to more accurate camera trajectories, our system yields enriched sparse maps with semantically meaningful planar structures and generic objects in the scene along with their mutual relationshipsThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Effect of operational parameters and internal recycle on rhenium solvent extraction from leach liquors using a mixer-settler

AbstractThe extraction of rhenium from molybdenite roasting dust leach solution was performed using a mixer-settler extractor by tributyl phosphate (TBP) diluted in kerosene as the extractant. In the single-stage extraction experiments, effect of the aqueous to organic phase ratios, Qa/Qo, and the number of extraction stages, N, on the rhenium extraction was studied. It was found that using the phase ratio of 1:1 in a two-stage extraction, 87.5% depletion of rhenium was obtained. The comparison of experimental results with the continuous co-current extraction showed a good agreement. The effect of internal recycle of organic phase was investigated in the phase ratio of 1:1 by changing the flow rate ratio of recycle-to-fresh organic phase, Qro/Qfo. The optimum performance was achieved in the phase ratio, Qro/Qfo, equal to 3:7. It was found that improvement in the performance of the mixer-settler for the rhenium-TBP system can be obtained in the phase ratio of 1:1when Qro/Qfo = 3:7

Cement-Based Mortar Panels Reinforced with Recycled Steel Fibers in Flexural Strengthening of Concrete Beams

The effectiveness of a strengthening technique devised for the concrete beams subjected to bending is presented in this study, where recycled-steel fiber-reinforced mortar (RSFRM) panels are used as an eco-friendly replacement for ordinary steel fibers. Different mix designs for RSFRM are first investigated experimentally by testing 160 × 400 × 400 mm3 notched beam-like specimens in 3-point bending, while 100 × 100 × 100 mm3 cubes are tested in compression, to optimize the mix design. Finite element (FE) analyses are carried out on strengthened and non-strengthened beams to investigate the effectiveness of the proposed strengthening technique based on RSFRM panels. Starting from the tests on notched beams, an inverse FE analysis is used to optimize the RSFRM’s parameters to be implemented into the numerical model. The results show that applying RSFRM panels not only markedly increases the load-bearing capacity of the beams (up to 3.19 times with 3% of fibers by volume), but also changes their fracture mechanism from brittle to ductile fracture

Genetic algorithms for finding episodes in temporal networks

Castelli, M., Dondi, R., & Hosseinzadeh, M. M. (2020). Genetic algorithms for finding episodes in temporal networks. Procedia Computer Science, 176, 215-224. https://doi.org/10.1016/j.procs.2020.08.023The evolution of networks is a fundamental topic in network analysis and mining. One of the approaches that has been recently considered in this field is the analysis of temporal networks, where relations between elements can change over time. A relevant problem in the analysis of temporal networks is the identification of cohesive or dense subgraphs since they are related to communities. In this contribution, we present a method based on genetic algorithms and on a greedy heuristic to identify dense subgraphs in a temporal network. We present experimental results considering both synthetic and real-networks, and we analyze the performance of the proposed method when varying the size of the population and the number of generations. The experimental results show that our heuristic generally performs better in terms of quality of the solutions than the state-of-art method for this problem. On the other hand, the state-of-art method is faster, although comparable with our method, when the size of the population and the number of generations are limited to small values.publishersversionpublishe

The Mediating Role of Affect in the Relationship between the Big Five Factor Personality and Risk Aversion: A Structural Model

Introduction: The purpose of this study was to analyze the relationship between the personality characteristics and their effects on risk aversion by the intermediary role of affect. The study suggests that positive and negative affect in individuals can play an intermediary role in the relationship between personality characteristics,risk aversion, and decision making. Methods: 265 undergraduate and postgraduate students completed the Ten-Item Personality Inventory and the Positive and Negative Affect Schedule (PANAS. Data were analyzed using structural equations modeling. Results: Findings showed that the increase in extroversion characteristic was negatively and significantly associated with risk aversion; it was also found out that there was a negative and significant relationship between openness to experience and risk aversion. Furthermore, the relationship between adaptability and risk aversion in the presence of affect as the intermediary factor was not statistically significant. Conclusion: Risk aversion is closely interlaced and undeniably associated with the personality and mentality of the individuals. But, it has to be noted that the sciences related to the intended subjects are substantially new in this regard hence many of the intended topic’s angles are recognized and are worthy of discussion and study. Declaration of Interest: None