231 research outputs found
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Recently, neural heuristics based on deep reinforcement learning have
exhibited promise in solving multi-objective combinatorial optimization
problems (MOCOPs). However, they are still struggling to achieve high learning
efficiency and solution quality. To tackle this issue, we propose an efficient
meta neural heuristic (EMNH), in which a meta-model is first trained and then
fine-tuned with a few steps to solve corresponding single-objective
subproblems. Specifically, for the training process, a (partial)
architecture-shared multi-task model is leveraged to achieve parallel learning
for the meta-model, so as to speed up the training; meanwhile, a scaled
symmetric sampling method with respect to the weight vectors is designed to
stabilize the training. For the fine-tuning process, an efficient hierarchical
method is proposed to systematically tackle all the subproblems. Experimental
results on the multi-objective traveling salesman problem (MOTSP),
multi-objective capacitated vehicle routing problem (MOCVRP), and
multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform
the state-of-the-art neural heuristics in terms of solution quality and
learning efficiency, and yield competitive solutions to the strong traditional
heuristics while consuming much shorter time.Comment: Accepted at NeurIPS 202
Solution structure of the second bromodomain of Brd2 and its specific interaction with acetylated histone tails
<p>Abstract</p> <p>Background</p> <p>Brd2 is a transcriptional regulator and belongs to BET family, a less characterized novel class of bromodomain-containing proteins. Brd2 contains two tandem bromodomains (BD1 and BD2, 46% sequence identity) in the N-terminus and a conserved motif named ET (extra C-terminal) domain at the C-terminus that is also present in some other bromodomain proteins. The two bromodomains have been shown to bind the acetylated histone H4 and to be responsible for mitotic retention on chromosomes, which is probably a distinctive feature of BET family proteins. Although the crystal structure of Brd2 BD1 is reported, no structure features have been characterized for Brd2 BD2 and its interaction with acetylated histones.</p> <p>Results</p> <p>Here we report the solution structure of human Brd2 BD2 determined by NMR. Although the overall fold resembles the bromodomains from other proteins, significant differences can be found in loop regions, especially in the ZA loop in which a two amino acids insertion is involved in an uncommon <it>Ļ</it>-helix, termed <it>Ļ</it>D. The helix <it>Ļ</it>D forms a portion of the acetyl-lysine binding site, which could be a structural characteristic of Brd2 BD2 and other BET bromodomains. Unlike Brd2 BD1, BD2 is monomeric in solution. With NMR perturbation studies, we have mapped the H4-AcK12 peptide binding interface on Brd2 BD2 and shown that the binding was with low affinity (2.9 mM) and in fast exchange. Using NMR and mutational analysis, we identified several residues important for the Brd2 BD2-H4-AcK12 peptide interaction and probed the potential mechanism for the specific recognition of acetylated histone codes by Brd2 BD2.</p> <p>Conclusion</p> <p>Brd2 BD2 is monomeric in solution and dynamically interacts with H4-AcK12. The additional secondary elements in the long ZA loop may be a common characteristic of BET bromodomains. Surrounding the ligand-binding cavity, five aspartate residues form a negatively charged collar that serves as a secondary binding site for H4-AcK12. We suggest that Brd2 BD1 and BD2 may possess distinctive roles and cooperate to regulate Brd2 functions. The structure basis of Brd2 BD2 will help to further characterize the functions of Brd2 and its BET members.</p
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Most of existing neural methods for multi-objective combinatorial
optimization (MOCO) problems solely rely on decomposition, which often leads to
repetitive solutions for the respective subproblems, thus a limited Pareto set.
Beyond decomposition, we propose a novel neural heuristic with diversity
enhancement (NHDE) to produce more Pareto solutions from two perspectives. On
the one hand, to hinder duplicated solutions for different subproblems, we
propose an indicator-enhanced deep reinforcement learning method to guide the
model, and design a heterogeneous graph attention mechanism to capture the
relations between the instance graph and the Pareto front graph. On the other
hand, to excavate more solutions in the neighborhood of each subproblem, we
present a multiple Pareto optima strategy to sample and preserve desirable
solutions. Experimental results on classic MOCO problems show that our NHDE is
able to generate a Pareto front with higher diversity, thereby achieving
superior overall performance. Moreover, our NHDE is generic and can be applied
to different neural methods for MOCO.Comment: Accepted at NeurIPS 202
A high energy output and low onset temperature nanothermite based on three-dimensional ordered macroporous nano-NiFe2O4
Three-dimensional ordered macroporous (3DOM) Al/NiFe2O4 nanothermite has been obtained by colloidal crystal templating method combined with magnetron sputtering processing. Owing to the superior material properties and unique 3DOM structural characteristics of composite metal oxides, the heat output of the Al/NiFe2O4 nanothermite is up to 2921.7 J gā 1, which is more than the values of Al/NiO and Al/Fe2O3 nanothermites in literature. More importantly, by comparison to the other two nanothermites, the onset temperature of 298.2 Ā°C from Al/NiFe2O4 is remarkably low, which means it can be ignited more easily. Laser ignition experiment indicate that the synthesized Al/NiFe2O4 nanothermite can be easily ignited by laser. In addition, the preparation process is highly compatible with the MEMS technology. These exciting achievements have great potential to expand the scope of nanothermite applications
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