Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

This paper studies the order acceptance and scheduling problem on unrelated parallel machines with machine eligibility constraints. Two objectives are considered to maximize total net profit and minimize the makespan, and the mathematical model of this problem is formulated as multiobjective mixed integer linear programming. Some properties with respect to the objectives are analysed, and then a classic list scheduling (LS) rule named the first available machine rule is extended, and three new LS rules are presented, which focus on the maximization of the net profit, the minimization of the makespan, and the trade-off between the two objectives, respectively. Furthermore, a list-scheduling-based multiobjective parthenogenetic algorithm (LS-MPGA) is presented with parthenogenetic operators and Pareto-ranking and selection method. Computational experiments on randomly generated instances are carried out to assess the effectiveness and efficiency of the four LS rules under the framework of LS-MPGA and discuss their application environments. Results demonstrate that the performance of the LS-MPGA developed for trade-off is superior to the other three algorithms

An Iterated Greedy Algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry

CofiFab: Coarse-to-fine fabrication of large 3D objects

This paper presents CofiFab, a coarse-to-fine 3D fabrication solution, which combines 3D printing and 2D laser cutting for cost-effective fabrication of large objects at lower cost and higher speed. Our key approach is to first build coarse internal base structures within the given 3D object using laser-cutting, and then attach thin 3D-printed parts, as an external shell, onto the base to recover the fine surface details. CofiFab achieves this with three novel algorithmic components. First, we formulate an optimization model to compute fabricatable polyhedrons of maximized volume, as the geometry of the internal base. Second, we devise a new interlocking scheme to tightly connect laser-cut parts into a strong internal base, by iteratively building a network of nonorthogonal interlocking joints and locking parts around polyhedral corners. Lastly, we also optimize the partitioning of the external object shell into 3D-printable parts, while saving support material and avoiding overhangs. These components also consider aesthetics, stability and balancing in addition to cost saving. As a result, CofiFab can efficiently produce large objects by assembly. To evaluate its effectiveness, we fabricate objects of varying shapes and sizes, where CofiFab significantly improves compared to previous methods

Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method

Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.Comment: IJCAI202

A fungal phylogeny based on 82 complete genomes using the composition vector method

<p>Abstract</p> <p>Background</p> <p>Molecular phylogenetics and phylogenomics have greatly revised and enriched the fungal systematics in the last two decades. Most of the analyses have been performed by comparing single or multiple orthologous gene regions. Sequence alignment has always been an essential element in tree construction. These alignment-based methods (to be called the standard methods hereafter) need independent verification in order to put the fungal Tree of Life (TOL) on a secure footing. The ever-increasing number of sequenced fungal genomes and the recent success of our newly proposed alignment-free composition vector tree (CVTree, see Methods) approach have made the verification feasible.</p> <p>Results</p> <p>In all, 82 fungal genomes covering 5 phyla were obtained from the relevant genome sequencing centers. An unscaled phylogenetic tree with 3 outgroup species was constructed by using the CVTree method. Overall, the resultant phylogeny infers all major groups in accordance with standard methods. Furthermore, the CVTree provides information on the placement of several currently unsettled groups. Within the sub-phylum Pezizomycotina, our phylogeny places the Dothideomycetes and Eurotiomycetes as sister taxa. Within the Sordariomycetes, it infers that <it>Magnaporthe grisea </it>and the Plectosphaerellaceae are closely related to the Sordariales and Hypocreales, respectively. Within the Eurotiales, it supports that <it>Aspergillus nidulans </it>is the early-branching species among the 8 aspergilli. Within the Onygenales, it groups <it>Histoplasma </it>and <it>Paracoccidioides </it>together, supporting that the Ajellomycetaceae is a distinct clade from Onygenaceae. Within the sub-phylum Saccharomycotina, the CVTree clearly resolves two clades: (1) species that translate CTG as serine instead of leucine (the CTG clade) and (2) species that have undergone whole-genome duplication (the WGD clade). It places <it>Candida glabrata </it>at the base of the WGD clade.</p> <p>Conclusion</p> <p>Using different input data and methodology, the CVTree approach is a good complement to the standard methods. The remarkable consistency between them has brought about more confidence to the current understanding of the fungal branch of TOL.</p

Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques

Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs). In this study, we empirically investigate improving an LM's generalization in semantic parsing with two simple techniques: at the token level, we introduce a token preprocessing method to preserve the semantic boundaries of tokens produced by LM tokenizers; at the sequence level, we propose to use special tokens to mark the boundaries of components aligned between input and output. Our experimental results on two text-to-SQL semantic parsing datasets show that our token preprocessing, although simple, can substantially improve the LM performance on both types of generalization, and our component boundary marking method is particularly helpful for compositional generalization.Comment: 9 pages, to be published in ACL202

Scalable Remote Rendering using Synthesized Image Quality Assessment

Depth-image-based rendering (DIBR) is widely used to support 3D interactive graphics on low-end mobile devices. Although it reduces the rendering cost on a mobile device, it essentially turns such a cost into depth image transmission cost or bandwidth consumption, inducing performance bottleneck to a remote rendering system. To address this problem, we design a scalable remote rendering framework based on synthesized image quality assessment. Specially, we design an efficient synthesized image quality metric based on Just Noticeable Distortion (JND), properly measuring human perceived geometric distortions in synthesized images. Based on this, we predict quality-aware reference viewpoints, with viewpoint intervals optimized by the JND-based metric. An adaptive transmission scheme is also developed to control depth image transmission based on perceived quality and network bandwidth availability. Experiment results show that our approach effectively reduces transmission frequency and network bandwidth consumption with perceived quality on mobile devices maintained. A prototype system is implemented to demonstrate the scalability of our proposed framework to multiple clients