Hybrid of memory andprediction strategies for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs

A dynamic multi-objective evolutionary algorithm based on decision variable classification

The file attached to this record is the author's final peer reviewed version.In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms

Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models

Language models have been shown to exhibit positive scaling, where performance improves as models are scaled up in terms of size, compute, or data. In this work, we introduce NeQA, a dataset consisting of questions with negation in which language models do not exhibit straightforward positive scaling. We show that this task can exhibit inverse scaling, U-shaped scaling, or positive scaling, and the three scaling trends shift in this order as we use more powerful prompting methods or model families. We hypothesize that solving NeQA depends on two subtasks: question answering (task 1) and negation understanding (task 2). We find that task 1 has linear scaling, while task 2 has sigmoid-shaped scaling with an emergent transition point, and composing these two scaling trends yields the final scaling trend of NeQA. Our work reveals and provides a way to analyze the complex scaling trends of language models.Comment: Published at ACL 2023 Finding

Spatially Nonuniform Oscillations in Ferrimagnets Based on an Atomistic Model

The ferrimagnets, such as GdxFeCo(1-x), can produce ultrafast magnetic switching and oscillation due to the strong exchange field. The two-sublattices macrospin model has been widely used to explain the experimental results. However, it fails in describing the spatial nonuniform magnetic dynamics which gives rises to many important phenomenons such as the domain walls and skyrmions. Here we develop the two-dimensional atomistic model and provide a torque analysis method to study the ferrimagnetic oscillation. Under the spin-transfer torque, the magnetization oscillates in the exchange mode or the flipped exchange mode. When the Gd composition is increased, the exchange mode firstly disappears, and then appears again as the magnetization compensation point is reached. We show that these results can only be explained by analyzing the spatial distribution of magnetization and effective fields. In particular, when the sample is small, a spatial nonuniform oscillation is also observed in the square film. Our work reveals the importance of spatial magnetic distributions in understanding the ferrimagnetic dynamics. The method developed in this paper provides an important tool to gain a deeper understanding of ferrimagnets and antiferromagnets. The observed ultrafast dynamics can also stimulate the development of THz oscillators.Comment: 17 pages, 4 figure

SM3^3: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on extensively annotated datasets to model articulated objects within limited categories. However, this approach falls short of effectively addressing the diversity present in the real world. To tackle this issue, we propose a self-supervised interaction perception method, referred to as SM$^3$, which leverages multi-view RGB images captured before and after interaction to model articulated objects, identify the movable parts, and infer the parameters of their rotating joints. By constructing 3D geometries and textures from the captured 2D images, SM$^3$ achieves integrated optimization of movable part and joint parameters during the reconstruction process, obviating the need for annotations. Furthermore, we introduce the MMArt dataset, an extension of PartNet-Mobility, encompassing multi-view and multi-modal data of articulated objects spanning diverse categories. Evaluations demonstrate that SM$^3$ surpasses existing benchmarks across various categories and objects, while its adaptability in real-world scenarios has been thoroughly validated

Proton pump inhibitor has no effect in the prevention of post-endoscopic sphincterotomy delayed bleeding: a prospective randomized controlled trial

Background and aimsBleeding is one of the common adverse events of endoscopic retrograde cholangiopancreatography (ERCP), which is mainly caused by endoscopic sphincterotomy (EST). At present, it remains unclear whether proton pump inhibitor (PPI) should be used to prevent post-EST bleeding. Therefore, we performed a randomized controlled trial to investigate whether PPI is effective in the prevention of post-EST delayed bleeding.MethodsConsecutive eligible patients were randomly assigned (1:1) to experimental group (PPI group) or control group (normal saline, NS group). The patients in PPI group received intravenous esomeprazole 40  mg and normal saline 100  mL every 12  h for 2  days after ERCP immediately, and followed by oral esomeprazole (Nexium) 20  mg once a day for 7  days. Correspondingly, patients in the control group received intravenous normal saline 100  mL and did not take PPIs or any acid-suppressing drugs during hospitalization and after discharge. All patients were followed up for 30  days after ERCP. The primary endpoint was the incidence and severity of post-EST delayed bleeding.ResultsBetween July 2020 and July 2022, 290 patients were randomly assigned to PPI group (n = 146) or NS group (n = 144). 5 patients from each group were excluded from the final analysis. There were 6 patients with post-EST delayed bleeding, with an incidence rate of 2.14%. The median time of delayed bleeding was 2.5  days after ERCP. 3 cases (2.12%, 3/141) occurred in the PPI group, with 1 case of mild and 2 cases of moderate bleeding. 3 cases (2.16%, 3/139) occurred in the NS group, with 2 cases of mild and 1 case of moderate bleeding. There was no significant difference in the incidence and the severity of post-EST delayed bleeding between the two groups (p = 1.000).ConclusionProphylactic use of PPI after EST does not reduce the incidence and severity of post-EST delayed bleeding in patients.Clinical Trial Registrationhttps://www.chictr.org.cn/searchproj.aspx, identifier ChiCTR2000034697

Quinoline Group Modified Carbon Nanotubes for the Detection of Zinc Ions

Carbon nanotubes (CNTs) were covalently modified by fluorescence ligand (glycine-N-8-quinolylamide) and formed a hybrid material which could be used as a selective probe for metal ions detection. The anchoring to the surface of the CNTs was carried out by the reaction between the precursor and the carboxyl groups available on the surface of the support. Fourier transform infrared spectroscopy (FTIR) and Thermogravimetric analysis (TGA) unambiguously proved the existence of covalent bonds between CNTs and functional ligands. Fluorescence characterization shows that the obtained organic–inorganic hybrid composite is highly selective and sensitive (0.2 μM) to Zn(II) detection