Improving controllability of complex networks by rewiring links regularly

Network science have constantly been in the focus of research for the last decade, with considerable advances in the controllability of their structural. However, much less effort has been devoted to study that how to improve the controllability of complex networks. In this paper, a new algorithm is proposed to improve the controllability of complex networks by rewiring links regularly which transforms the network structure. Then it is demonstrated that our algorithm is very effective after numerical simulation experiment on typical network models (Erd\"os-R\'enyi and scale-free network). We find that our algorithm is mainly determined by the average degree and positive correlation of in-degree and out-degree of network and it has nothing to do with the network size. Furthermore, we analyze and discuss the correlation between controllability of complex networks and degree distribution index: power-law exponent and heterogeneit

Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives

Name ambiguity is common in academic digital libraries, such as multiple authors having the same name. This creates challenges for academic data management and analysis, thus name disambiguation becomes necessary. The procedure of name disambiguation is to divide publications with the same name into different groups, each group belonging to a unique author. A large amount of attribute information in publications makes traditional methods fall into the quagmire of feature selection. These methods always select attributes artificially and equally, which usually causes a negative impact on accuracy. The proposed method is mainly based on representation learning for heterogeneous networks and clustering and exploits the self-attention technology to solve the problem. The presentation of publications is a synthesis of structural and semantic representations. The structural representation is obtained by meta-path-based sampling and a skip-gram-based embedding method, and meta-path level attention is introduced to automatically learn the weight of each feature. The semantic representation is generated using NLP tools. Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method. The experimental results show the superiority of our new method for capturing the most attributes from publications and reducing the impact of redundant information

EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an indirect metric for evaluating such task understanding from videos. To make a direct evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. These questions are divided into four types, including descriptive (what status?), predictive (what will?), explanatory (what caused?), and counterfactual (what if?) to provide diagnostic analyses on spatial, temporal, and causal understandings of goal-oriented tasks. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos. We hope this effort will drive the vision community to move onward with goal-oriented video understanding and reasoning.Comment: Published at NeurIPS Track on Datasets and Benchmarks 202

A hybrid method for quantum dynamics simulation

We propose a hybrid approach to simulate quantum many body dynamics by combining Trotter based quantum algorithm with classical dynamic mode decomposition. The interest often lies in estimating observables rather than explicitly obtaining the wave function's form. Our method predicts observables of a quantum state in the long time by using data from a set of short time measurements from a quantum computer. The upper bound for the global error of our method scales as $O(t^{3/2})$ with a fixed set of the measurement. We apply our method to quench dynamics in Hubbard model and nearest neighbor spin systems and show that the observable properties can be predicted up to a reasonable error by controlling the number of data points obtained from the quantum measurements.Comment: 9 pages, 4 figure

Heat Dissipation Performance of Micro-channel Heat Sink with Various Protrusion Designs

This research will focus on studying the effect of aperture size and shape of the micro-channel heat sink on heat dissipation performance for chip cooling. The micro-channel heat sink is considered to be a porous medium with fluid subject inter-facial convection. Derivation based on energy equation gives a set of governing partial differential equations describing the heat transfer through the micro-channels. Numerical simulation, including steady-state thermal analysis based on CFD software, is used to create a finite element solver to tackle the derived partial differential equations with properly defined boundary conditions related to temperature. After simulating three types of heat sinks with various protrusion designs including micro-channels fins, curly micro-channels fins, and Micro-pin fins, the result shows that the heat sink with the maximum contact area per unit volume will have the best heat dissipation performance, we will interpret the result by using the volume averaging theorem on the porous medium model of the heat sink

Engineered Production of Fungal Anticancer Cyclooligomer Depsipeptides in Saccharo-Myces Cerevisiae

Two fungal cyclooligomer depsipeptide synthetases (CODSs), BbBEAS (352 kDa) and BbBSLS (348 kDa) from Beauveria bassiana ATCC 7159, were reconstituted in Saccharomyces cerevisiae BJ5464-NpgA, leading to the production of the corresponding anticancer natural products, beauvericins and bassianolide, respectively. The titers of beauvericins (33.82±1.41 mg/l) and bassianolide (21.74±0.08 mg/l) in the engineered S. cerevisiae BJ5464-NpgA strains were comparable to those in the native producer B. bassiana. Feeding D-hydroxyisovaleric acid (D-Hiv) and the corresponding L-amino acid precursors improved the production of beauvericins and bassianolide. However, the high price of D-Hiv limits its application in large-scale production of these cyclooligomer depsipeptides. Alternatively, we engineered another enzyme, ketoisovalerate reductase (KIVR) from B. bassiana, into S. cerevisiae BJ5464-NpgA for enhanced in situ synthesis of this expensive substrate. Co-expression of BbBEAS and KIVR in the yeast led to significant improvement of the production of beauvericins. The total titer of beauvericin and its congeners (beauvericins A, B and C) was increased to 61.73±2.96 mg/l and reached 2.6-fold of that in the native producer B. bassiana ATCC 7159. Supplement of L-Val at 10 mM improved the supply of ketoisovalerate, the substrate of KIVR, which consequently further increased the total titer of beauvericins to 105.76±2.12 mg/l. Using this yeast system, we functionally characterized an unknown CODS from Fusarium venenatum NRRL 26139 as a beauvericin synthetase, which was named as FvBEAS. Our work thus provides a useful approach for functional reconstitution and engineering of fungal CODSs for efficient production of this family of anticancer molecules