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Hui Yang

Publications and source records attributed to Hui Yang.

At least 127 records · Page 7Linked to original sources
arXiv2022

Dynamics of the lithium metal electrodeposition: Effects of a gas bubble

Rechargeable lithium metal batteries have been widely investigated recently, driven by the global trend for the electrification of transportation. Understanding the dynamics of lithium metal electrodeposition is crucial to design safe and reliable lithium metal anodes. In this study, we developed a grand potential-based phase-field model to investigate the effect of a static gas bubble, which forms due to the complicated internal side reactions, on the dynamics of the dendrite growth during electrodeposition. It is observed that with the presence of a gas bubble, the dendrite growth is largely accelerated, due to the accumulation of lithium ions on the far side of the bubble away from the anode surface, which could serve as an ion "reservoir" for the dendrite growth, leading to the bending/tilting of the lithium dendrites toward the bubble. Meanwhile, the effects of the bubble size and distance to the anode are further studied, demonstrating that the larger the bubble size and the closer to the anode, the longer the lithium dendrites grow. We hope this study could serve as an example to exploit the effect of extrinsic factors on the dendrite growth dynamics.

cond-mat.mtrl-sci↗
arXiv2022

Advancing the Landscape of Multimessenger Science in the Next Decade

The last decade has brought about a profound transformation in multimessenger science. Ten years ago, facilities had been built or were under construction that would eventually discover the nature of objects in our universe could be detected through multiple messengers. Nonetheless, multimessenger science was hardly more than a dream. The rewards for our foresight were finally realized through IceCube's discovery of the diffuse astrophysical neutrino flux, the first observation of gravitational waves by LIGO, and the first joint detections in gravitational waves and photons and in neutrinos and photons. Today we live in the dawn of the multimessenger era. The successes of the multimessenger campaigns of the last decade have pushed multimessenger science to the forefront of priority science areas in both the particle physics and the astrophysics communities. Multimessenger science provides new methods of testing fundamental theories about the nature of matter and energy, particularly in conditions that are not reproducible on Earth. This white paper will present the science and facilities that will provide opportunities for the particle physics community renew its commitment and maintain its leadership in multimessenger science.

astro-ph.HE↗
arXiv2022

The Future of Gamma-Ray Experiments in the MeV-EeV Range

Gamma-rays, the most energetic photons, carry information from the far reaches of extragalactic space with minimal interaction or loss of information. They bring messages about particle acceleration in environments so extreme they cannot be reproduced on earth for a closer look. Gamma-ray astrophysics is so complementary with collider work that particle physicists and astroparticle physicists are often one in the same. Gamma-ray instruments, especially the Fermi Gamma-ray Space Telescope, have been pivotal in major multi-messenger discoveries over the past decade. There is presently a great deal of interest and scientific expertise available to push forward new technologies, to plan and build space- and ground-based gamma-ray facilities, and to build multi-messenger networks with gamma rays at their core. It is therefore concerning that before the community comes together for planning exercises again, much of that infrastructure could be lost to a lack of long-term planning for support of gamma-ray astrophysics. Gamma-rays with energies from the MeV to the EeV band are therefore central to multiwavelength and multi-messenger studies to everything from astroparticle physics with compact objects, to dark matter studies with diffuse large scale structure. These goals and new discoveries have generated a wave of new gamma-ray facility proposals and programs. This paper highlights new and proposed gamma-ray technologies and facilities that have each been designed to address specific needs in the measurement of extreme astrophysical sources that probe some of the most pressing questions in fundamental physics for the next decade. The proposed instrumentation would also address the priorities laid out in the recent Astro2020 Decadal Survey, a complementary study by the astrophysics community that provides opportunities also relevant to Snowmass.

astro-ph.HE↗
arXiv2022

Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss

Accurate emotion classification for online reviews is vital for business organizations to gain deeper insights into markets. Although deep learning has been successfully implemented in this area, accuracy and processing time are still major problems preventing it from reaching its full potential. This paper proposes an Enhanced Leaky Rectified Linear Unit activation and Weighted Loss (ELReLUWL) algorithm for enhanced text emotion classification and faster parameter convergence speed. This algorithm includes the definition of the inflection point and the slope for inputs on the left side of the inflection point to avoid gradient saturation. It also considers the weight of samples belonging to each class to compensate for the influence of data imbalance. Convolutional Neural Network (CNN) combined with the proposed algorithm to increase the classification accuracy and decrease the processing time by eliminating the gradient saturation problem and minimizing the negative effect of data imbalance, demonstrated on a binary sentiment problem. The results show that the proposed solution achieves better classification performance in different data scenarios and different review types. The proposed model takes less convergence time to achieve model optimization with seven epochs against the current convergence time of 11.5 epochs on average. The proposed solution improves accuracy and reduces the processing time of text emotion classification. The solution provides an average class accuracy of 96.63% against a current average accuracy of 91.56%. It also provides a processing time of 23.3 milliseconds compared to the current average processing time of 33.2 milliseconds. Finally, this study solves the issues of gradient saturation and data imbalance. It enhances overall average class accuracy and decreases processing time.

cs.NE↗
arXiv2022

A priori estimates versus arbitrarily large solutions for fractional semi-linear elliptic equations with critical Sobolev exponent

We study positive solutions to the fractional semi-linear elliptic equation $$ (- Δ)^σu = K(x) u^\frac{n + 2 σ}{n - 2 σ} ~~~~~~ in ~ B_2 \setminus \{ 0 \} $$ with an isolated singularity at the origin, where $K$ is a positive function on $B_2$, the punctured ball $B_2 \setminus \{ 0 \} \subset \mathbb{R}^n$ with $n \geq 2$, $σ\in (0, 1)$, and $(- Δ)^σ$ is the fractional Laplacian. In lower dimensions, we show that, for any $K \in C^1 (B_2)$, a positive solution $u$ always satisfies that $u(x) \leq C |x|^{ - (n - 2 σ)/2 }$ near the origin. In contrast, we construct positive functions $K \in C^1 (B_2)$ in higher dimensions such that a positive solution $u$ could be arbitrarily large near the origin. In particular, these results also apply to the prescribed boundary mean curvature equations on $\mathbb{B}^{n+1}$.

math.AP↗
arXiv2022

Lessons from the AdKDD'21 Privacy-Preserving ML Challenge

Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed under the Improving Web Advertising Business Group at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21, a premier workshop on Advertising Science with data provided by advertising company Criteo. In this paper, we describe the challenge tasks, the structure of the available datasets, report the challenge results, and enable its full reproducibility. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap. We also run additional experiments to observe the sensitivity of winning methods to different parameters such as privacy budget or quantity of available privileged side information. We conclude that the industry needs either alternate designs for private data sharing or a breakthrough in learning with aggregated data only to keep ad relevance at a reasonable level.

cs.LG↗
arXiv2021

Unambiguous state discrimination with intrinsic coherence

We investigate the discrimination of pure-mixed (quantum filtering) and mixed-mixed states and compare their optimal success probability with the one for discriminating other pairs of pure states superposed by the vectors included in the mixed states. We prove that under the equal-fidelity condition, the pure-pure state discrimination scheme is superior to the pure-mixed (mixed-mixed) one. With respect to quantum filtering, the coherence exists only in one pure state and is detrimental to the state discrimination for lower dimensional systems; while it is the opposite for the mixed-mixed case with symmetrically distributed coherence. Making an extension to infinite-dimensional systems, we find that the coherence which is detrimental to state discrimination may become helpful and vice versa.

quant-ph↗
arXiv2021

Union and Intersection of all Justifications

We present new algorithm for computing the union and intersection of all justifications for a given ontological consequence without first computing the set of all justifications. Through an empirical evaluation, we show that our approach works well in practice for expressive DLs. In particular, the union of all justifications can be computed much faster than with existing justification-enumeration approaches. We further discuss how to use these results to repair ontologies efficiently.

cs.LO↗
arXiv2021

Quadrangular embeddings of complete graphs and the Even Map Color Theorem (with details)

Hartsfield and Ringel constructed orientable quadrangular embeddings of the complete graph $K_n$ for $n\equiv 5 \pmod 8$, and nonorientable ones for $n \ge 9$ and $n\equiv 1 \pmod 4$. These provide minimal quadrangulations of their underlying surfaces. We extend these results to determine, for every complete graph $K_n$, $n \ge 4$, the minimum genus, both orientable and nonorientable, for the surface in which $K_n$ has an embedding with all faces of degree at least $4$, and also for the surface in which $K_n$ has an embedding with all faces of even degree. These last embeddings provide sharpness examples for a result of Hutchinson bounding the chromatic number of graphs embedded with all faces of even degree, completing the proof of the Even Map Color Theorem. We also show that if a connected simple graph $G$ has a perfect matching and a cycle then the lexicographic product $G[K_4]$ has orientable and nonorientable quadrangular embeddings; this provides new examples of minimal quadrangulations.

math.CO↗
arXiv2021

Local estimates for conformal $Q$-curvature equations

We derive local estimates of positive solutions to the conformal $Q$-curvature equation $$ (-Δ)^m u = K(x) u^{\frac{n+2m}{n-2m}} ~~~~~~ in ~ Ω\backslash Λ$$ near their singular set $Λ$, where $Ω\subset \mathbb{R}^n$ is an open set, $K(x)$ is a positive continuous function on $Ω$, $Λ$ is a closed subset of $\mathbb{R}^n$, $2 \leq m < n/2$ and $m$ is an integer. Under certain flatness conditions at critical points of $K$ on $Λ$, we prove that $u(x) \leq C [{dist}(x, Λ)]^{-(n-2m)/2}$ when the upper Minkowski dimension of $Λ$ is less than $(n-2m)/2$.

math.AP↗
arXiv2021

Confining photonic nanojet in a microwell on microsphere lens for highly efficient light focusing, signal amplification and quantitative detection

Dielectric microspheres or microbeads can squeeze light into the subwavelength scale via photonic nanojet (PNJ) focusing. This enables strong light-matter interactions within its focus and induces extraordinary effects such as enhancements in light emission, signal collection and various other applications. However, critical challenges exist on how to efficiently and precisely loading the studied objects into the desired location of the PNJ focusing, and subsequently measure the field and signal of interests precisely and reliably. Such technique is currently missing. We present for the first time a unique microwell-decorated microsphere lens (u-well lens), with a semi-open microwell sample compartment directly fabricated on top of the microsphere lens. The u-well lens confines PNJ in a semi-open microwell and allows passive trapping of individual micro-object into the PNJ focusing area with high efficiency and spatial accuracy. We demonstrate that individual fluorescent microsphere of different sizes can be readily introduced to the designated location with loading efficiency >70% and generates reproducible enhanced fluorescence signals with standard deviation better than that can be quantitatively measured. A comprehensive analysis on the optical properties of the u-well lens reveals the synergistic effect of field enhancement and collection efficiency on the optical enhancement. We finally employ this special microsphere lens for fluorescent-bead-based biotin concentration analysis. The results suggest a greatly enhanced sensitivity and highly improved detection limit, opening the door for its application in highly sensitive and quantitative detection.

physics.optics↗
arXiv2021

Tunable photon blockade in a two-mode second-order nonlinear system embedded with a two-level atom

The conventional photon blockade for high-frequency mode is investigated in a two-mode second-order nonlinear system embedded with a two-level atom. By solving the master equation and calculating the zero-delay-time second-order correlation function $g^{(2)}(0)$, we obtain that strong photon antibunching can be achieved in this scheme. In particular, we find that by increasing the linear coupling coefficient of the system, a perfect blockade region will be formed near the zero second-order nonlinear coupling coefficient. Similarly, by increasing the nonlinear coupling coefficient of the system, the perfect blockade zone will appear. And this scheme is not sensitive to the reservoir temperature, both of which make the current system easier to implement experimentally.

quant-ph↗
arXiv2021

Rare earth engineering in RMn$_6$Sn$_6$ topological kagome magnets

Exploration of the topological quantum materials with electron correlation is at the frontier of physics, as the strong interaction may give rise to new topological phases and transitions. Here we report that a family of kagome magnets RMn$_6$Sn$_6$ manifest the quantum transport properties analogical to those in the quantum-limit Chern magnet TbMn$_6$Sn$_6$. The topological transport in the family, including quantum oscillations with nontrivial Berry phase and large anomalous Hall effect arising from Berry curvature field, points to the existence of massive Dirac fermions. Our observation demonstrates a close relationship between rare-earth magnetism and topological electron structure, indicating the rare-earth elements can effectively engineer the Chern quantum phase in kagome magnets.

cond-mat.mtrl-sci↗
arXiv2021

Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems.

cs.IR↗
arXiv2021

Visualizing Multiwavelength Properties of Classified X-ray Sources from Chandra Source Catalog

We present a simple but informative online tool to visualize the multiwavelength properties of about 2,700 X-ray sources from Chandra Source Catalog version 2.0 with literature verified classifications. Here we describe the catalogs that we used to collect the classifications and extract the MW properties of these sources, and the properties themselves. We also describe the design and functionality of the tool.

astro-ph.HE↗
arXiv2021

Thermally-driven formation of Ge quantum dots on self-catalysed thin GaAs nanowires

Embedding quantum dots (QDs) on nanowire (NW) sidewalls allows the integration of multi-layers of QDs into the active region of radial p-i-n junctions to greatly enhance light emission/absorption. However, the surface curvature makes the growth much more challenging compared with growths on thin-films, particularly on NWs with small diameters (Ø <100 nm). Moreover, the {110} sidewall facets of self-catalyzed NWs favor two-dimensional growth (2D), with the realization of three-dimensional (3D) Stranski-Krastanow growth becoming extremely challenging. Here, we demonstrate thermally-driven formation of Ge dots on the {110} sidewalls facets of thin self-catalyzed NWs without using any surfactant or surface treatment. The 2D-3D transition of the pseudomorphic Ge layer grown on GaAs NWs is driven by energy minimization under high-temperature annealing. This method opens a new avenue to integrate QDs on NWs without any restriction on NW diameter or elastic strain, which can allow the formation of QDs in a wider range of materials systems where the growth of islands by traditional mechanisms is not possible, with benefits for novel NWQD-based optoelectronic devices.

cond-mat.mes-hall↗
arXiv2021

Design of High-Quality Reflectors for Vertical Nanowire Lasers on Si

Nanowires (NWs) with a unique one-dimensional structure can monolithically integrate high-quality III-V semiconductors onto Si platform, which is highly promising to build lasers for Si photonics. However, the lasing from vertically-standing NWs on silicon is much more difficult to achieve compared with NWs broken off from substrates, causing significant challenges in the integration. Here, the challenge of achieving vertically-standing NW lasers is systematically analyzed. The poor optical reflectivity at the NW/Si interface results severe optical field leakage to the substrate, and the commonly used SiO2 or Si2N3 dielectric mask at the interface can only improve it to ~10%, which is the major obstacle for achieving low-threshold lasing. A NW super lattice distributed Bragg reflector is therefore proposed, which is able to greatly improve the reflectivity to >97%. This study provides a highly-feasible method to greatly improve the performance of vertically-standing NW lasers, which can boost the rapid development of Si photonics.

physics.optics↗