arXiv subjects

Ning Li

Publications and source records attributed to Ning Li.

At least 145 records · Page 8Linked to original sources

arXiv2022

CoNet: Borderless and decentralized server cooperation in edge computing

In edge computing (EC), by offloading tasks to edge server or remote cloud, the system performance can be improved greatly. However, since the traffic distribution in EC is heterogeneous and dynamic, it is difficult for an individual edge server to provide satisfactory computation service anytime and anywhere. This issue motivated the researchers to study the cooperation between edge servers. The previous server cooperation algorithms have disadvantages since the cooperated region is limited within one-hop. However, the performance of EC can be improved further by releasing the restriction of cooperation region. Even some works have extended the cooperated region to multi-hops, they fail to support the task offloading which is one of the core issues of edge computing. Therefore, we propose a new decentralized and borderless server cooperation algorithm for edge computing which takes task offloading strategy into account, named CoNet. In CoNet, the cooperation region is not limited. Each server forms its own basic cooperation unit (BCU) and calculates its announced capability based on BCU. The server's capability, the processing delay, the task and calculation result forwarding delay are considered during the calculation. The task division strategy bases on the real capability of host-server and the announced capability of cooperation-servers. This cooperation process is recursive and will be terminated once the terminal condition is satisfied. The simulation results demonstrate the advantages of CoNet over previous works.

cs.NI↗

arXiv2022

In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory

Continually learning new classes from a few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage, in which new examples and classes can be incrementally stored and efficiently retrieved. One viable architectural solution is to tightly couple a stationary deep neural network to a dynamically evolving explicit memory (EM). As the centerpiece of this architecture, we propose an EM unit that leverages energy-efficient in-memory compute (IMC) cores during the course of continual learning operations. We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM). Specifically, the physical superposition of a few encoded training examples is realized via in-situ progressive crystallization of PCM devices. The classification accuracy achieved on the IMC core remains within a range of 1.28%--2.5% compared to that of the state-of-the-art full-precision baseline software model on both the CIFAR-100 and miniImageNet datasets when continually learning 40 novel classes (from only five examples per class) on top of 60 old classes.

cs.LG↗

arXiv2022

Perturbative quantum Monte Carlo method for nuclear physics

While first order perturbation theory is routinely used in quantum Monte Carlo (QMC) calculations, higher-order terms present significant numerical challenges. We present a new approach for computing perturbative corrections in projection QMC calculations. We demonstrate the method by computing nuclear ground state energies up to second order for a realistic chiral interaction. We calculate the binding energies of several light nuclei up to $^{16}$O by expanding the Hamiltonian around the Wigner SU(4) limit and find good agreement with data. In contrast to the natural ordering of the perturbative series, we find remarkably large second order energy corrections. This occurs because the perturbing interactions break the symmetries of the unperturbed Hamiltonian. Our method is free from the sign problem and can be applied to QMC calculations for many-body systems in nuclear physics, condensed matter physics, ultracold atoms, and quantum chemistry.

nucl-th↗

arXiv2022

Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach

In this study, a probability density-based approach for constructing trajectories is proposed and validated through an typical use-case application: Estimated Time of Arrival (ETA) prediction given origin-destination pairs. The ETA prediction is based on physics and mathematical laws given by the extracted information of probability density-based trajectories constructed. The overall ETA prediction errors are about 0.106 days (i.e. 2.544 hours) on average with 0.549 days (i.e. 13.176 hours) standard deviation, and the proposed approach has an accuracy of 92.08% with 0.959 R-Squared value for overall trajectories between Singapore and Australia ports selected.

eess.SY↗

arXiv2022

Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1

In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of the proposed approach are investigated, which are batch method and rolling horizon method. The experimental results show that both paradigm methods of proposed approach can effectively enhance port efficiency. The average wait time could be significantly reduced by 86.0% - 95.5%, and the average turnaround time could eventually save 38.2% - 42.4% with respect to historical benchmarks. Moreover, the paradigm method of rolling horizon could reduce to 20 mins on running time over 3-month datasets, rather than 4 hrs on batch method at corresponding maximum performance.

cs.CE↗

arXiv2022

Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 2

In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing weighted average turnaround time. The proposed approach is developed as a heuristic algorithm applied and investigated through different observation windows with weekly rolling horizon paradigm method. The experimental results show that the proposed approach is effective and promising on mitigating the turnaround time of vessels. The results demonstrate that largest potential savings of turnaround time (weighted average) are around 17 hours (28%) reduction on baseline of 1-week observation, 45 hours (37%) reduction on baseline of 2-week observation and 70 hours (40%) reduction on baseline of 3-week observation. Even though the experimental results are based on historical datasets, the results potentially present significant benefits if real-time applications were applied under a quadratic computational complexity.

cs.CE↗

arXiv2022

Traps and transport resistance: the next frontier for stable state-of-the-art non-fullerene acceptor solar cells

Stability is one of the most important challenges facing organic solar cells (OSC) on their path to commercialization. In the high-performance material system PM6:Y6 studied here, investigate degradation mechanisms of inverted photovoltaic devices. We have identified two distinct degradation pathways: one requires presence of both illumination and oxygen and features a short-circuit current reduction, the other one is induced thermally and marked by severe losses of open-circuit voltage and fill factor. We focus our investigation on the thermally accelerated degradation. Our findings show that bulk material properties and interfaces remain remarkably stable, however, aging-induced defect state formation in the active layer remains the primary cause of thermal degradation. The increased trap density leads to higher non-radiative recombination, which limits open-circuit voltage and lowers charge carrier mobility in the photoactive layer. Furthermore, we find the trap-induced transport resistance to be the major reason for the drop in fill factor. Our results suggest that device lifetimes could be significantly increased by marginally suppressing trap formation, leading to a bright future for OSC.

cond-mat.mtrl-sci↗

arXiv2022

SWIPENET: Object detection in noisy underwater images

In recent years, deep learning based object detection methods have achieved promising performance in controlled environments. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) images in the underwater datasets and real applications are blurry whilst accompanying severe noise that confuses the detectors and (2) objects in real applications are usually small. In this paper, we propose a novel Sample-WeIghted hyPEr Network (SWIPENET), and a robust training paradigm named Curriculum Multi-Class Adaboost (CMA), to address these two problems at the same time. Firstly, the backbone of SWIPENET produces multiple high resolution and semantic-rich Hyper Feature Maps, which significantly improve small object detection. Secondly, a novel sample-weighted detection loss function is designed for SWIPENET, which focuses on learning high weight samples and ignore learning low weight samples. Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data. Then, based on the clean detector, multiple detectors focusing on learning diverse noisy data are trained and incorporated into a unified deep ensemble of strong noise immunity. Experiments on two underwater robot picking contest datasets (URPC2017 and URPC2018) show that the proposed SWIPENET+CMA framework achieves better accuracy in object detection against several state-of-the-art approaches.

cs.CV↗

arXiv2022

Majorana corner states in an attractive quantum spin Hall insulator with opposite in-plane Zeeman energy at two sublattice sites

Higher-order topological superconductors and superfluids host lower-dimensional Majorana corner and hinge states since novel topology exhibitions on boundaries. While such topological nontrivial phases have been explored extensively, more possible schemes are necessary for engineering Majorana states. In this paper we propose Majorana corner states could be realized in a two-dimensional attractive quantum spin-Hall insulator with opposite in-plane Zeeman energy at two sublattice sites. The appropriate Zeeman field leads to the opposite Dirac mass for adjacent edges of a square sample, and naturally induce Majorana corner states. This topological phase can be characterized by Majorana edge polarizations, and it is robust against perturbations on random potentials as long as the edge gap remains open. Our work provides a new possibility to realize a second-order topological superfluid in two dimensions and engineer Majorana corner states.

cond-mat.mes-hall↗

arXiv2022

Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data

Semiparametric joint models of longitudinal and competing risks data are computationally costly and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risks survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from $O(n^2)$ or $O(n^3)$ to $O(n)$ in various components including numerical integration, risk set calculation, and standard error estimation, where $n$ is the number of subjects. Using both simulated and real world biobank data, we demonstrate that these linear scan algorithms generate drastic speed-up of up to hundreds of thousands fold when $n>10^4$, sometimes reducing the run-time from days to minutes. We have developed an R-package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and time-to-event data with and without competing risks, and made it publicly available on the Comprehensive R Archive Network (CRAN).

stat.ME↗

arXiv2022

Higher-order topological corner states induced solely by onsite potentials with mirror symmetry

Higher-order topological insulators have triggered great interests because of exhibitions of non-trivial bulk topology on lower-dimensional boundaries like corners and hinges. While such interesting phases have been investigated in a plethora of systems by tuning staggered tunneling strength or manipulating existing topological phases, here we show that a higher-order topological phase can be driven solely by mirror-symmetric onsite potentials. We first introduce a simple chain model in one dimension that mimics the Su-Schrieffer-Heeger-like model. However, due to the lack of internal symmetries like chiral or particle-hole symmetry, the energies of the topological edge modes are not pinned at zero. Once the model is generalized to two dimensions, we observe the emergence of topological corner modes. These corner modes are intrinsic manifestation of non-trivial bulk band topology protected by mirror symmetry, and thus, they are robust against symmetry-preserved perturbations. Our study provides a concise proposal for realizing a class of higher-order topological insulators, which involves only tuning onsite energies. This can be easily accessible in experiments and provides a different playground for engineering topological corner modes.

cond-mat.mes-hall↗

arXiv2022

Projected mushroom-type phase-change memory

Phase-change memory devices have found applications in in-memory computing where the physical attributes of these devices are exploited to compute in place without the need to shuttle data between memory and processing units. However, non-idealities such as temporal variations in the electrical resistance have a detrimental impact on the achievable computational precision. To address this, a promising approach is projecting the phase configuration of phase change material onto some stable element within the device. Here we investigate the projection mechanism in a prominent phase-change memory device architecture, namely mushroom-type phase-change memory. Using nanoscale projected Ge2Sb2Te5 devices we study the key attributes of state-dependent resistance, drift coefficients, and phase configurations, and using them reveal how these devices fundamentally work.

physics.app-ph↗

arXiv2021

Approaching the Purcell factor limit with whispering-gallery hyperbolic phonon polaritons in hBN nanotubes

Enhanced light-matter interaction at the nanoscale is pivotal in the foundation of nonlinear optics, quantum optics, and nanophotonics, which are essential for a vast range of applications including single-photon sources, nanolasers, and nanosensors. In this context, the combination of strongly confined polaritons and low-loss nanocavities provides a promising way to enhance light-matter interaction, thus giving rise to a high density of optical states, as quantified by the so-called Purcell factor - the ratio of the decay rate of an optical quantum emitter to its value in free space. Here, we exploit whispering-gallery hyperbolic-phonon-polariton (WG-HPhP) modes in hBN nanotubes (BNNTs) to demonstrate record-high Purcell factors (~10^12) driven by the deep-subwavelength confinement of phonon polaritons and the low intrinsic losses in these atomically smooth nanocavities. Furthermore, the measured Purcell factor increases with decreasing BNNT radius down to 5 nm, a result that extrapolates to ~10^14 in a single-walled BNNT. Our study supports WG-HPhP modes in one-dimensional nanotubes as a powerful platform for investigating ultrastrong light-matter interactions, which open exciting perspectives for applications in single-molecular sensors and nanolasers.

physics.optics↗

arXiv2021

Ab initio nuclear thermodynamics from lattice effective field theory

We show that the {\it ab initio} calculations of nuclear thermodynamics can be performed efficiently using lattice effective field theory. The simulations use a new approach called the pinhole trace algorithm to calculate thermodynamic observables for a fixed number of protons and neutrons enclosed in a finite box. In this framework, we calculate the equation of state, the liquid-vapor coexistence line and the critical point of neutral symmetric nuclear matter with high precision. Since the algorithm uses a canonical ensemble with a fixed number of particles, it provides a sizable computational advantage over grand canonical ensemble simulations that can be a factor of several thousands to as much as several millions for large volume simulations.

nucl-th↗

arXiv2021

Real-time division-of-focal-plane polarization imaging system with progressive networks

Division-of-focal-plane (DoFP) polarization imaging technical recently has been applied in many fields. However, the images captured by such sensors cannot be used directly because they suffer from instantaneous field-of-view errors and low resolution problem. This paper builds a fast DoFP demosaicing system with proposed progressive polarization demosaicing convolutional neural network (PPDN), which is specifically designed for edge-side GPU devices like Navidia Jetson TX2. The proposed network consists of two parts: reconstruction stage and refining stage. The former recovers four polarization channels from a single DoFP image. The latter fine-tune the four channels to obtain more accurate polarization information. PPDN can be implemented in another version: PPDN-L (large), for the platforms of high computing resources. Experiments show that PPDN can compete with the best existing methods with fewer parameters and faster inference speed and meet the real-time demands of imaging system.

eess.IV↗

arXiv2021

High-responsivity, High-detectivity Photomultiplication Organic Photodetector Realized by a Metal-Insulator-Semiconductor Tunneling Junction

Organic photodetectors (OPDs) possess bright prospects in applications of medical imaging and wearable electronics due to the advantages such as low cost, good biocompatibility, and good flexibility. Photomultiplication OPDs (PM-OPDs) enabled by the trap-assisted carrier tunneling injection effect exhibit external quantum efficiencies far greater than unity, thus the acquired responsivities are extremely high. However, the reported PM-OPDs with high responsivity performances are all accompanied by high dark currents due to the introduction of carrier traps, which inevitably results in inferior detectivities. In this work, we modify a P3HT:PCBM donor-rich PM-OPD by introducing an atomically thin Al2O3 interfacial layer through the ALD technique, obtaining a high responsivity of 8294 A/W and high detectivity of 6.76*10^14 Jones, simultaneously, both of which are among the highest reported for bulk heterojunction PM-OPDs. Ascribed to the introduction of the atomically thin Al2O3 layer, the metal-insulator-semiconductor (MIS) tunneling junction is formed, which brings forward a suppressed dark current along with an increased amounts of holes tunneling under forward bias. Meanwhile, the weak light detection limit of the modified PM-OPD within the linear response range reaches the level of nW/cm2. Based on the proposed PM-OPD, a proof-of-concept image sensor with 26*26 pixels is demonstrated, which can respond to both ultraviolet light and visible light. The PM-OPD based sensor arrays can find broad applications for medical imaging, wearable electronics, etc.

physics.app-ph↗

arXiv2021

Perfect $DD^*$ molecular prediction matching the $T_{cc}$ observation at LHCb

In 2012, we investigated the possible molecular states composed of two charmed mesons [Phys.Rev. D 88, 114008 (2013), arXiv:1211.5007 [hep-ph](2012)]. The $D^*D$ system with the quantum numbers of $I(J^P)=0(1^+)$ was found to be a good candidate of the loosely bound molecular state. This state is very close to the $D^*D$ threshold with a binding energy around 0.47 MeV. This prediction was confirmed by the new LHCb observation of $T_{cc}^+$ [see Franz Muheim's talk at the European Physical Society conference on high energy physics 2021].

hep-ph↗

arXiv2021

Doubly charmed molecular pentaquarks

We perform a systematic exploration of the possible doubly charmed molecular pentaquarks composed of $Σ_c^{(*)}D^{(*)}$ with the one-boson-exchange potential model. After taking into account the $S-D$ wave mixing and the coupled channel effects, we predict several possible doubly charmed molecular pentaquarks, which include the $Σ_cD$ with $I(J^P) = 1/2(1/2^-)$, $Σ_c^*D$ with $1/2(3/2^-)$, and $Σ_cD^*$ with $1/2(1/2^-)$, $1/2(3/2^-)$. The $Σ_cD$ state with $3/2(1/2^-)$ and $Σ_cD^*$ state with $3/2(1/2^-)$ may also be suggested as candidates of doubly charmed molecular pentaquarks. The $Σ_cD$ and $Σ_c^*D$ states can be searched for by analyzing the $Λ_cDπ$ invariant mass spectrum of the bottom baryon and $B$ meson decays. The $Σ_cD^*$ states can be searched for in the invariant mass spectrum of $Λ_cD^*π$, $Λ_cDππ$ and $Λ_cDπγ$. Since the width of $Σ_c^*$ is much larger than that of $D^*$, $Σ_c^*D\rightarrow Λ_cDπ$ will be the dominant decay mode. We sincerely hope these candidates for the doubly charmed molecular pentaqurks will be searched by LHCb or BelleII collaboration in the near future.

hep-ph↗