Ballistic Josephson junctions based on CVD graphene

Josephson junctions with graphene as the weak link between superconductors have been intensely studied in recent years, with respect to both fundamental physics and potential applications. However, most of the previous work was based on mechanically exfoliated graphene, which is not compatible with mass production. Here we present our research using graphene grown by chemical vapour deposition (CVD) as the weak link of Josephson junctions. We demonstrate that CVD-graphene-based Josephson junctions with Nb electrodes can work effectively without any thermal hysteresis from 1.5 K down to a base temperature of 320 mK, and they show an ideal Fraunhofer-like interference pattern in a perpendicular magnetic field. We also show that the critical current of the junction can be tuned by a gate voltage. Furthermore, for our shortest junctions (50 nm in length), we find that the normal state resistance oscillates with the gate voltage, indicating that the junctions are in the ballistic regime, a feature not previously observed in CVD-graphene-based Josephson junctions.Comment: 14 pages, 4 figure

Direct van der Waals simulation (DVS) of phase-transforming fluids

[Abstract:] We present the method of direct van der Waals simulation (DVS) to study computationally flows with liquid-vapor phase transformations. Our approach is based on a discretization of the Navier-Stokes-Korteweg equations, which couple flow dynamics with van der Waals’ nonequilibrium thermodynamic theory of phase transformations, and opens an opportunity for first-principles simulation of a wide range of boiling and cavitating flows. The proposed algorithm enables unprecedented simulations of the Navier-Stokes-Korteweg equations involving cavitating flows at strongly under-critical conditions and �(10 to 5) Reynolds number. The proposed technique provides a pathway for a fundamental understanding of phase-transforming flows with multiple applications in science, engineering, and medicine.This work is funded partially by the U.S. Department of Defense (award no. FA9550-20-1-0165), PO Dr. Yin Lu (Julie) Young and partially by National Science Foundation, United States (award no. 1805817). This work uses the Bridges-2 system at the Pittsburgh Supercomputing Center (PSC) through allocation no. MCH220014 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support (ACCESS) program, which is supported by the National Science Foundation, grant nos. 2138259, 2138286, 2138307, 2137603, and 2138296.Estados Unidos. Department of Defense; FA9550-20-1-0165Estados Unidos. National Science Foundation; 1805817Estados Unidos. National Science Foundation; 2138259Estados Unidos. National Science Foundation; 2138286Estados Unidos. National Science Foundation; 2138307Estados Unidos. National Science Foundation; 2137603Estados Unidos. National Science Foundation; 213829

A quantum-inspired tensor network method for constrained combinatorial optimization problems

Combinatorial optimization is of general interest for both theoretical study and real-world applications. Fast-developing quantum algorithms provide a different perspective on solving combinatorial optimization problems. In this paper, we propose a quantum inspired algorithm for general locally constrained combinatorial optimization problems by encoding the constraints directly into a tensor network state. The optimal solution can be efficiently solved by borrowing the imaginary time evolution from a quantum many-body system. We demonstrate our algorithm with the open-pit mining problem numerically. Our computational results show the effectiveness of this construction and potential applications in further studies for general combinatorial optimization problems

Temporal Sentence Grounding in Videos: A Survey and Future Directions

Temporal sentence grounding in videos (TSGV), \aka natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate the methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.Comment: 29 pages, 32 figures, 9 table

Deep N-ary Error Correcting Output Codes

Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original multi-class classification problem into a series of independent simpler classification subproblems. Unfortunately, integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as deep N-ary ECOC, is not straightforward and yet fully exploited in the literature, due to the high expense of training base learners. To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct experiments by varying the backbone with different deep neural network architectures for both image and text classification tasks. Furthermore, extensive ablation studies on deep N-ary ECOC show its superior performance over other deep data-independent ensemble methods.Comment: EAI MOBIMEDIA 202

A Review of Static Pressure Reset Control in Variable Air Volume Air Condition System

AbstractFor the sake of energy saving of variable air volume (VAV) system, this paper presents the a review of static pressure control around the optimization problem of static pressure reset in VAV air conditioning system. Then, main control methods of static pressure reset are described, and existing problems are analyzed and concluded. Finally, it is pointed out that the critical technology and the development trend of static pressure reset control. This overview is not intended to be an exhaustive survey on this topic, and any omission of other works is purely unintentional