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Jing Liu

Jing Liu contributes to research discovery and scholarly infrastructure.

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preprint2026arXiv

LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)

This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and 4D generation settings. The challenge is motivated by a central gap in current evaluation practice: perceptual quality alone is insufficient to judge whether generated dynamics are physically plausible, temporally coherent, and consistent with input conditions. Participants are required to build a metric that jointly predicts four dimensions, i.e., Video Quality, Physical Realism, Condition-Video Alignment, and Temporal Consistency. Depart from that, participants also need to localize physical anomaly timestamps for fine-grained diagnosis. The benchmark dataset contains 1,554 videos generated by seven representative world generative models, organized into three tracks (text-2D, image-to-4D, and video-to-4D) and spanning 26 categories. These categories explicitly cover physics-relevant scenarios, including dynamics, optics, and thermodynamics, together with diverse real-world and creative content. To ensure label reliability, scores and anomaly timestamps are produced through trained human annotation with an additional automated quality-control pass. Evaluation is based on both score prediction and anomaly localization, with a composite protocol that combines TimeStamp_IOU and SRCC/PLCC. This report summarizes the challenge design and provides method-level insights from submitted solutions.

preprint2026arXiv

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state per decoding step and extending the KV storage to CPU memory. In practice, however, these algorithmic savings rarely translate into end-to-end system-level gains because sparse methods typically operate at different granularities and thus rely on ad hoc, per-algorithm implementations. At the same time, hierarchical KV storage introduces a new systems bottleneck: retrieving fine-grained, irregular KV subsets across the GPU-CPU boundary can easily erase the benefits of sparsity. We present SPIN, a sparse-attention-aware inference framework that co-designs the execution pipeline with hierarchical KV storage through three techniques: (1) a unified partition abstraction that maps different sparsity granularities onto a shared page-based KV substrate; (2) a locality-aware KV cache manager that dynamically sizes per-request HBM budgets and uses a GPU-friendly bucketed LRU policy to cut PCIe round-trips; and (3) a two-level hierarchical metadata layout sized to the active working set rather than the worst-case address space. Built on vLLM with three representative sparse attention algorithms, SPIN delivers 1.66-5.66x higher end-to-end throughput and 7-9x lower TTFT than vLLM, and reduces TPOT by up to 58% over the original sparse-attention implementations.

preprint2024arXiv

Distinguishing the nanohertz gravitational-wave sources by the observations of compact dark matter subhalos

The latest pulsar timing array data reveals evidence of nanohertz gravitational waves (GWs), which have been explained by both cosmological and astrophysical sources. However, current observations lack the precision needed to differentiate between different models from the spectral index. We find that the cosmological GW sources, including bubble collisions, sound waves, domain walls, condensate fragmentations, and primordial curvature perturbations, induce large energy density perturbations so that most dark matter will exist in gravitationally self-bound subhalos. Then, the observation of such substructures of dark matter can serve as a novel independent method to confirm or exclude the cosmological GW sources.

preprint2024arXiv

Temporal Adaptive RGBT Tracking with Modality Prompt

RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

preprint2023arXiv

LGN-Net: Local-Global Normality Network for Video Anomaly Detection

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies. However, they often consider only local or global normality in the temporal dimension. Some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events. But powerful representation allows these methods to represent some anomalies and causes miss detection. In contrast, the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies, which also restricts them from representing diverse normal patterns and causes false alarm. To this end, we propose a two-branch model, Local-Global Normality Network (LGN-Net), to simultaneously learn local and global normality. Specifically, one branch learns the evolution regularities of appearance and motion from consecutive frames as local normality utilizing a spatiotemporal prediction network, while the other branch memorizes prototype features of the whole videos as global normality by a memory module. LGN-Net achieves a balance of representing normal and abnormal instances by fusing local and global normality. In addition, the fused normality enables LGN-Net to generalize to various scenes more than exploiting single normality. Experiments demonstrate the effectiveness and superior performance of our method. The code is available online: https://github.com/Myzhao1999/LGN-Net.

preprint2022arXiv

An Interpretability Evaluation Benchmark for Pre-trained Language Models

While pre-trained language models (LMs) have brought great improvements in many NLP tasks, there is increasing attention to explore capabilities of LMs and interpret their predictions. However, existing works usually focus only on a certain capability with some downstream tasks. There is a lack of datasets for directly evaluating the masked word prediction performance and the interpretability of pre-trained LMs. To fill in the gap, we propose a novel evaluation benchmark providing with both English and Chinese annotated data. It tests LMs abilities in multiple dimensions, i.e., grammar, semantics, knowledge, reasoning and computation. In addition, it provides carefully annotated token-level rationales that satisfy sufficiency and compactness. It contains perturbed instances for each original instance, so as to use the rationale consistency under perturbations as the metric for faithfulness, a perspective of interpretability. We conduct experiments on several widely-used pre-trained LMs. The results show that they perform very poorly on the dimensions of knowledge and computation. And their plausibility in all dimensions is far from satisfactory, especially when the rationale is short. In addition, the pre-trained LMs we evaluated are not robust on syntax-aware data. We will release this evaluation benchmark at \url{http://xyz}, and hope it can facilitate the research progress of pre-trained LMs.

preprint2022arXiv

Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might "funnel" students towards a normative answer or "focus" students to reflect on their own thinking, deepening their understanding of math concepts. When teachers focus, they treat students' contributions as resources for collective sensemaking, and thereby significantly improve students' achievement and confidence in mathematics. We propose the task of computationally detecting funneling and focusing questions in classroom discourse. We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither. We introduce supervised and unsupervised approaches to differentiating these questions. Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model's potential for use in automated teacher feedback tools. Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions. The high performance of the supervised measure indicates its promise for supporting teachers in their instruction.

preprint2022arXiv

Contextual Adapters for Personalized Speech Recognition in Neural Transducers

Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is with shallow fusion methods at inference time. However, due to their dependence on external language models and the deterministic approach to weight boosting, their performance is limited. In this paper, we propose training neural contextual adapters for personalization in neural transducer based ASR models. Our approach can not only bias towards user-defined words, but also has the flexibility to work with pretrained ASR models. Using an in-house dataset, we demonstrate that contextual adapters can be applied to any general purpose pretrained ASR model to improve personalization. Our method outperforms shallow fusion, while retaining functionality of the pretrained models by not altering any of the model weights. We further show that the adapter style training is superior to full-fine-tuning of the ASR models on datasets with user-defined content.

preprint2022arXiv

Dependence of the amplitude of gravitational waves from preheating on the inflationary energy scale

Stochastic gravitational wave backgrounds (SGWBs) receive increasing attention and provide a new possibility to directly probe the early Universe. In the preheating process at the end of inflation, parametric resonance can generate large energy density perturbations and efficiently produce gravitational waves (GWs) which carry unique information about inflation. Since the peak frequency of such GWs is approximately proportional to the inflationary energy scale, $Λ_{\mathrm{inf}}$, GWs from preheating are expected to be observed by interferometer GW detectors in low-scale inflationary models. We investigate the dependence of the amplitude of such GWs on $Λ_{\mathrm{inf}}$, and find that the present energy spectrum of these GWs does not depend on $Λ_{\mathrm{inf}}$ only in the case of $Λ_{\mathrm{inf}}$ is above a critical value $Λ_{c}$, a parameter depending on the resonance strength. We numerically obtain $Λ_{c}$ in terms of the model parameters in linear approximation and then conduct lattice simulations to verify this result. For $Λ_{\mathrm{inf}}\lesssimΛ_{c}$, the amplitude of GWs quickly decreases with $Λ_{\mathrm{inf}}$ and becomes challenging to observe. In turn, observing such GWs in interferometer detectors also helps to determine $Λ_{\mathrm{inf}}$ and the resonance strength during the preheating.

preprint2022arXiv

DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection

As moving objects always draw more attention of human eyes, the temporal motive information is always exploited complementarily with spatial information to detect salient objects in videos. Although efficient tools such as optical flow have been proposed to extract temporal motive information, it often encounters difficulties when used for saliency detection due to the movement of camera or the partial movement of salient objects. In this paper, we investigate the complimentary roles of spatial and temporal information and propose a novel dynamic spatiotemporal network (DS-Net) for more effective fusion of spatiotemporal information. We construct a symmetric two-bypass network to explicitly extract spatial and temporal features. A dynamic weight generator (DWG) is designed to automatically learn the reliability of corresponding saliency branch. And a top-down cross attentive aggregation (CAA) procedure is designed so as to facilitate dynamic complementary aggregation of spatiotemporal features. Finally, the features are modified by spatial attention with the guidance of coarse saliency map and then go through decoder part for final saliency map. Experimental results on five benchmarks VOS, DAVIS, FBMS, SegTrack-v2, and ViSal demonstrate that the proposed method achieves superior performance than state-of-the-art algorithms. The source code is available at https://github.com/TJUMMG/DS-Net.

preprint2022arXiv

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models

In this paper, we focus on studying robustness evaluation of Chinese question matching. Most of the previous work on analyzing robustness issue focus on just one or a few types of artificial adversarial examples. Instead, we argue that it is necessary to formulate a comprehensive evaluation about the linguistic capabilities of models on natural texts. For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models. DuQM contains 3 categories and 13 subcategories with 32 linguistic perturbations. The extensive experiments demonstrate that DuQM has a better ability to distinguish different models. Importantly, the detailed breakdown of evaluation by linguistic phenomenon in DuQM helps us easily diagnose the strength and weakness of different models. Additionally, our experiment results show that the effect of artificial adversarial examples does not work on the natural texts.

preprint2022arXiv

First operation of undoped CsI directly coupled with SiPMs at 77 Kelvin

The light yield of a small undoped cesium iodide (CsI) crystal directly coupled with two silicon photomultipliers (SiPMs) at about 77~Kelvin was measured to be $43.0 \pm 1.1$~photoelectrons (PE) per keV electron-equivalent (keV$_\text{ee}$) using $X$ and $γ$-ray peaks from an $^{241}$Am radioactive source from 18 to 60 keV. The high light yield together with some other technical advantages illustrate the great potential of this novel combination for neutrino and low-mass dark matter detection, particularly at accelerator-based neutrino sources, where random background can be highly suppressed by requiring coincident triggers between SiPMs and beam pulse timing signals. Some potential drawbacks of using cryogenic SiPMs instead of photomultiplier tubes (PMTs) were identified, such as worse energy resolution and optical cross-talks between SiPMs. Their influence to rare-event detection was discussed and possible solutions were provided.

preprint2022arXiv

First-principles perspective on full-spectrum infrared photodetectors from doping an excitonic insulator

Innovations in imaging technology involves finding strategies and materials suitable for detection applications over the entire infrared range. Herein, we propose a new design concept based on the unique feature of an excitonic insulator, namely, negative exciton transition energy ($E_t$). We demonstrate this concept using first-principles $GW$-BSE calculations on one-dimensional organometallic wire (CrBz)$_\infty$. The pristine (CrBz)$_\infty$ exhibits an excitonic instability due to a negative $E_t$ for the lowest exciton. Substitutional doping can continuously tune the $E_t$ from $\sim$0 to $\sim$0.6 eV, which shows the ability of photon detection from terahertz to near-infrared. This type of detectors have advantages of outstanding wavelength selectivity, reduced thermal disturbance and elevated working temperature. Our work not only adds another member in the family of rare one-dimensional excitonic insulators, but also opens a new avenue for the development of high-performance infrared photodetectors in the future.

preprint2022arXiv

FocusFormer: Focusing on What We Need via Architecture Sampler

Vision Transformers (ViTs) have underpinned the recent breakthroughs in computer vision. However, designing the architectures of ViTs is laborious and heavily relies on expert knowledge. To automate the design process and incorporate deployment flexibility, one-shot neural architecture search decouples the supernet training and architecture specialization for diverse deployment scenarios. To cope with an enormous number of sub-networks in the supernet, existing methods treat all architectures equally important and randomly sample some of them in each update step during training. During architecture search, these methods focus on finding architectures on the Pareto frontier of performance and resource consumption, which forms a gap between training and deployment. In this paper, we devise a simple yet effective method, called FocusFormer, to bridge such a gap. To this end, we propose to learn an architecture sampler to assign higher sampling probabilities to those architectures on the Pareto frontier under different resource constraints during supernet training, making them sufficiently optimized and hence improving their performance. During specialization, we can directly use the well-trained architecture sampler to obtain accurate architectures satisfying the given resource constraint, which significantly improves the search efficiency. Extensive experiments on CIFAR-100 and ImageNet show that our FocusFormer is able to improve the performance of the searched architectures while significantly reducing the search cost. For example, on ImageNet, our FocusFormer-Ti with 1.4G FLOPs outperforms AutoFormer-Ti by 0.5% in terms of the Top-1 accuracy.

preprint2022arXiv

Learning Appearance-motion Normality for Video Anomaly Detection

Video anomaly detection is a challenging task in the computer vision community. Most single task-based methods do not consider the independence of unique spatial and temporal patterns, while two-stream structures lack the exploration of the correlations. In this paper, we propose spatial-temporal memories augmented two-stream auto-encoder framework, which learns the appearance normality and motion normality independently and explores the correlations via adversarial learning. Specifically, we first design two proxy tasks to train the two-stream structure to extract appearance and motion features in isolation. Then, the prototypical features are recorded in the corresponding spatial and temporal memory pools. Finally, the encoding-decoding network performs adversarial learning with the discriminator to explore the correlations between spatial and temporal patterns. Experimental results show that our framework outperforms the state-of-the-art methods, achieving AUCs of 98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.

preprint2022arXiv

Mesa: A Memory-saving Training Framework for Transformers

There has been an explosion of interest in designing high-performance Transformers. While Transformers have delivered significant performance improvements, training such networks is extremely memory intensive owing to storing all intermediate activations that are needed for gradient computation during backpropagation, especially for long sequences. To this end, we present Mesa, a memory-saving training framework for Transformers. Specifically, Mesa uses exact activations during forward pass while storing a low-precision version of activations to reduce memory consumption during training. The low-precision activations are then dequantized during back-propagation to compute gradients. Besides, to address the heterogeneous activation distributions in the multi-head self-attention layers, we propose a head-wise activation quantization strategy, which quantizes activations based on the statistics of each head to minimize the approximation error. To further boost training efficiency, we learn quantization parameters by running estimates. More importantly, by re-investing the saved memory in employing a larger batch size or scaling up model size, we may further improve the performance under constrained computational resources. Extensive experiments on ImageNet, CIFAR-100 and ADE20K demonstrate that Mesa can achieve flexible memory-savings (up to 50%) during training while achieving comparable or even better performance. Code is available at https://github.com/ziplab/Mesa.

preprint2022arXiv

Multi-scale Analysis of Nitrogen Loss Mitigation in the US Corn Belt

Reducing the size of the hypoxic zone in the Gulf of Mexico has proven to be a challenging task. A variety of mitigation options have been proposed, each likely to produce markedly different patterns of mitigation with widely varying consequences for the economy. The general consensus is that no single measure alone is sufficient to achieve the EPA Task Force goal for reducing the Gulf hypoxic zone and it appears that a combination of management practices must be employed. However, absent a highly resolved, multi-scale framework for assessing these policy combinations, it has been unclear what pattern of mitigation is likely to emerge from different policies and what the consequences would be for local, regional and national land use, food prices and farm returns. We address this research gap by utilizing a novel multi-scale framework for evaluating alternative N loss management policies in the Mississippi River basin. This combines fine-scale agro-ecosystem responses with an economic model capturing domestic and international market and price linkages. We find that wetland restoration combined with improved N use efficiency, along with a leaching tax could reduce the Mississippi River N load by 30-53\% while only modestly increasing corn prices. This study underscores the value of fine-resolution analysis and the potential of combined economic and ecological instruments in tackling nonpoint source nitrate pollution.

preprint2022arXiv

Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding

End-to-end Spoken Language Understanding (E2E SLU) has attracted increasing interest due to its advantages of joint optimization and low latency when compared to traditionally cascaded pipelines. Existing E2E SLU models usually follow a two-stage configuration where an Automatic Speech Recognition (ASR) network first predicts a transcript which is then passed to a Natural Language Understanding (NLU) module through an interface to infer semantic labels, such as intent and slot tags. This design, however, does not consider the NLU posterior while making transcript predictions, nor correct the NLU prediction error immediately by considering the previously predicted word-pieces. In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system. In this work, we propose a streamable multi-task semantic transducer model to address these considerations. Our proposed architecture predicts ASR and NLU labels auto-regressively and uses a semantic decoder to ingest both previously predicted word-pieces and slot tags while aggregating them through a fusion network. Using an industry scale SLU and a public FSC dataset, we show the proposed model outperforms the two-stage E2E SLU model for both ASR and NLU metrics.

preprint2022arXiv

New analysis of the fraction of observable nights at astronomical sites based on FengYun-2 satellite data

The fraction of observable nights is an essential parameter for selecting astronomical sites. In recent years, meteorological satellite data have played an essential role in recognising and providing statistics of observable nights. We present a method to estimate the fraction of observable nights based on the FengYun-2 series of geostationary meteorological satellites and weather records of multiple astronomical sites. We have calculated the fraction of observable nights at 27 sites in Indonesia and two astronomical sites in China to validate the method. The results derived from our method show good agreement with previous works. Furthermore, we have derived the yearly distribution of the fraction of observable nights above China, which indicates the area near 40$^{\circ}$N has more observable nights than other areas in China.

preprint2022arXiv

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh

preprint2022arXiv

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

preprint2022arXiv

Observation of three superconducting transitions in the pressurized CDW-bearing compound TaTe2

Transition metal dichalcogenides host a wide variety of lattice and electronic structures, as well as corresponding exotic physical properties, especially under certain tuning conditions. Here, we are the first to report the observation of pressure-induced three superconducting transitions in TaTe2, a charge density wave (CDW) - bearing layered transition-metal dichalcogenide that is metallic but not superconducting at ambient pressure. We find that its CDW state can be easily suppressed upon increasing pressure up to ~ 1 GPa. A superconducting state then emerges from the suppressed CDW state and persists to the pressure about 7 GPa. Unexpectedly, another superconducting state appears at ~ 11 GPa within the same monoclinic (M) structure of its ambient-pressure one. Upon further compression to 21 GPa, a third superconducting state with higher Tc appears from a high-pressure (HP) phase. Our experimental results suggest that the pressure-induced three superconducting transitions in TaTe2 are respectively driven by the suppression of the CDW state, the change of the angle in the M phase and the transition of M-to-HP phase. These results demonstrate not only the versatile nature of this correlated electron system, but also the first experimental example that shows the pressure-induced evolution from a CDW state to three superconducting states driven by different mechanisms.

preprint2022arXiv

One-off Negative Sequential Pattern Mining

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this paper discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this paper proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency, but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

preprint2022arXiv

Primordial black hole production during first-order phase transitions

Primordial black holes (PBHs) produced in the early Universe have attracted wide interest for their ability to constitute dark matter and explain the compact binary coalescence. We propose a new mechanism of PBH production during first-order phase transitions (PTs) and find that PBHs are naturally produced during PTs model-independently. Because of the randomness of the quantum tunneling, there always exists some probability that the vacuum decay is postponed in a whole Hubble volume. Since the vacuum energy density remains constant while radiation is quickly redshifted in the expanding Universe, the postponed vacuum decay then results in overdense regions, which finally collapse into PBHs as indicated by numerical simulations. Utilizing this result one can obtain mutual predictions and constraints between PBHs and GWs from PTs. The predicted mass function of PBHs is nearly monochromatic. We investigate two typical cases and find that 1) PBHs from a PT constitute all dark matter and GWs peak at $1$Hz, 2) PBHs from a PT can explain the coalescence events observed by LIGO-Virgo collaboration, and meanwhile GWs can explain the common-spectrum process detected by NANOGrav collaboration.

preprint2022arXiv

Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks

The robustness of deep neural networks is crucial to modern AI-enabled systems and should be formally verified. Sigmoid-like neural networks have been adopted in a wide range of applications. Due to their non-linearity, Sigmoid-like activation functions are usually over-approximated for efficient verification, which inevitably introduces imprecision. Considerable efforts have been devoted to finding the so-called tighter approximations to obtain more precise verification results. However, existing tightness definitions are heuristic and lack theoretical foundations. We conduct a thorough empirical analysis of existing neuron-wise characterizations of tightness and reveal that they are superior only on specific neural networks. We then introduce the notion of network-wise tightness as a unified tightness definition and show that computing network-wise tightness is a complex non-convex optimization problem. We bypass the complexity from different perspectives via two efficient, provably tightest approximations. The results demonstrate the promising performance achievement of our approaches over state of the art: (i) achieving up to 251.28% improvement to certified lower robustness bounds; and (ii) exhibiting notably more precise verification results on convolutional networks.

preprint2022arXiv

Quantum Magnetometer with Dual-Coupling Optomechanics

An experimentally feasible magnetometer based on a dual-coupling optomechanical system is proposed, where the radiation-pressure coupling transduces the magnetic signal to the optical phase, and the quadratic optomechanical interaction induces a periodic squeezing effect. The latter not only amplifies the signal to be measured, but also accelerates the signal transducing rate characterized by an experimentally observable phase accumulation efficiency. In the vicinity of opto-mechanical decoupled time, the ultimate bound to the estimability of magnetic signal is proportional to $\exp(-6r)$, and then the optimized accuracy of estimation can be enhanced nearly 3 orders with a controllable squeezing parameter $r<1$. Moreover, our proposal is robust against the mechanical thermal noise, and the sensitivity of a specific measurement can reach to the order of $10^{-17}{\rm T/\sqrt{Hz}}$ in the presence of dissipations and without ground state cooling of mechanical oscillator. Our proposal fundamentally broadens the fields of quantum metrology and cavity optomechanics, with potential application for on-chip magnetic detection with high precision.

preprint2022arXiv

Rapid Elastic Architecture Search under Specialized Classes and Resource Constraints

In many real-world applications, we often need to handle various deployment scenarios, where the resource constraint and the superclass of interest corresponding to a group of classes are dynamically specified. How to efficiently deploy deep models for diverse deployment scenarios is a new challenge. Previous NAS approaches seek to design architectures for all classes simultaneously, which may not be optimal for some individual superclasses. A straightforward solution is to search an architecture from scratch for each deployment scenario, which however is computation-intensive and impractical. To address this, we present a novel and general framework, called Elastic Architecture Search (EAS), permitting instant specializations at runtime for diverse superclasses with various resource constraints. To this end, we first propose to effectively train an over-parameterized network via a superclass dropout strategy during training. In this way, the resulting model is robust to the subsequent superclasses dropping at inference time. Based on the well-trained over-parameterized network, we then propose an efficient architecture generator to obtain promising architectures within a single forward pass. Experiments on three image classification datasets show that EAS is able to find more compact networks with better performance while remarkably being orders of magnitude faster than state-of-the-art NAS methods, e.g., outperforming OFA (once-for-all) by 1.3% on Top-1 accuracy at a budget around 361M #MAdds on ImageNet-10. More critically, EAS is able to find compact architectures within 0.1 second for 50 deployment scenarios.

preprint2022arXiv

Reducing US Biofuels Requirements Mitigates Short-term Impacts of Global Population and Income Growth on Agricultural Environmental Outcomes

Biobased energy, particularly corn starch-based ethanol and other liquid renewable fuels, are a major element of federal and state energy policies in the United States. These policies are motivated by energy security and climate change mitigation objectives, but corn ethanol does not substantially reduce greenhouse gas emissions when compared to petroleum-based fuels. Corn production also imposes substantial negative externalities (e.g., nitrogen leaching, higher food prices, water scarcity, and indirect land use change). In this paper, we utilize a partial equilibrium model of corn-soy production and trade to analyze the potential of reduced US demand for corn as a biobased energy feedstock to mitigate increases in nitrogen leaching, crop production and land use associated with growing global populations and income from 2020 to 2050. We estimate that a 23% demand reduction would sustain land use and nitrogen leaching below 2020 levels through the year 2025, and a 41% reduction would do so through 2030. Outcomes are similar across major watersheds where corn and soy are intensively farmed.

preprint2022arXiv

Room-temperature printing of ultrathin Quasi-2D GaN semiconductor via liquid metal gallium surface confined nitridation reaction

Outstanding wide-bandgap semiconductor material such as gallium nitride (GaN) has been extensively utilized in power electronics, radiofrequency amplifiers, and harsh environment devices. Due to its quantum confinement effect in enabling desired deep-ultraviolet emission, excitonic impact, and electronic transport features, two-dimensional (2D) or ultrathin quasi-2D GaN semiconductors have been one of the most remarkable candidates for future growth of microelectronic devices. Here, for the first time, we reported a large area, wide bandgap, and room-temperature quasi-2D GaN synthesis and printing strategy through introducing the plasma medicated liquid metal gallium surface-confined nitridation reaction mechanism. The developed direct fabrication and compositional process is consistent with various electronics manufacturing approaches and thus opens an easy going way for cost-effective growth of the third-generation semiconductor. In particular, the fully printed field-effect transistors relying on the GaN thus made show p-type switching with an on/off ratio greater than 105, maximum field-effect hole mobility of 53 cm2/(V*s), and a small sub-threshold swing. As it was demonstrated, the present method allows to produce at room temperature the GaN with thickness spanning from 1nm to nanometers. This basic method can be further extended, generalized, and utilized for making various electronic and photoelectronic devices in the coming time.

preprint2022arXiv

Tracking the nematicity in cuprate superconductors: a resistivity study under uniaxial pressure

Overshadowing the superconducting dome in hole-doped cuprates, the pseudogap state is still one of the mysteries that no consensus can be achieved. It has been suggested that the rotational symmetry is broken in this state and may result in a nematic phase transition, whose temperature seems to coincide with the onset temperature of the pseudogap state $T^*$ around optimal doping level, raising the question whether the pseudogap results from the establishment of the nematic order. Here we report results of resistivity measurements under uniaxial pressure on several hole-doped cuprates, where the normalized slope of the elastoresistivity $ζ$ can be obtained as illustrated in iron-based superconductors. The temperature dependence of $ζ$ along particular lattice axis exhibits kink feature at $T_{k}$ and shows Curie-Weiss-like behavior above it, which may suggest a spontaneous nematic transition. While $T_{k}$ seems to be the same as $T^*$ around the optimal doping and in the overdoped region, they become very different in underdoped La$_{2-x}$Sr$_{x}$CuO$_4$. Our results suggest that the nematic order, if indeed existing, is an electronic phase within the pseudogap state.

preprint2022arXiv

Ultrafast photothermoelectric effect in Dirac semimetallic Cd3As2 revealed by terahertz emission

The thermoelectric effects of topological semimetals have attracted tremendous research interest because many topological semimetals are excellent thermoelectric materials and thermoelectricity serves as one of their most important potential applications. In this work, we reveal the transient photothermoelectric response of Dirac semimetallic Cd3As2, namely the photo-Seebeck effect and photo-Nernst effect, by studying the terahertz (THz) emission from the transient photocurrent induced by these effects. Our excitation polarization and power dependence confirm that the observed THz emission is due to photothermoelectric effect instead of other nonlinear optical effect. Furthermore, when a weak magnetic field (~0.4 T) is applied, the response clearly indicates an order of magnitude enhancement on transient photothermoelectric current generation compared to the photo-Seebeck effect. Such enhancement supports an ambipolar transport nature of the photo-Nernst current generation in Cd3As2. These results highlight the enhancement of thermoelectric performance can be achieved in topological Dirac semimetals based on the Nernst effect, and our transient studies pave the way for thermoelectric devices applicable for high field circumstance when nonequilibrium state matters. The large THz emission due to highly efficient photothermoelectric conversion is comparable to conventional semiconductors through optical rectification and photo-Dember effect.

preprint2021arXiv

CPTR: Full Transformer Network for Image Captioning

In this paper, we consider the image captioning task from a new sequence-to-sequence prediction perspective and propose CaPtion TransformeR (CPTR) which takes the sequentialized raw images as the input to Transformer. Compared to the "CNN+Transformer" design paradigm, our model can model global context at every encoder layer from the beginning and is totally convolution-free. Extensive experiments demonstrate the effectiveness of the proposed model and we surpass the conventional "CNN+Transformer" methods on the MSCOCO dataset. Besides, we provide detailed visualizations of the self-attention between patches in the encoder and the "words-to-patches" attention in the decoder thanks to the full Transformer architecture.

preprint2021arXiv

Current-induced magnetization switching in a chemically disordered A1 CoPt single layer

We report the first demonstration of the current-induced magnetization switching in a perpendicularly magnetized A1 CoPt single layer. We show that good perpendicular magnetic anisotropy can be obtained in a wide composition range of the A1 Co1-xPtx single layers, which allows to fabricate perpendicularly magnetized CoPt single layer with composition gradient to break the inversion symmetry of the structure. By fabricating the gradient CoPt single layer, we have evaluated the SOT efficiency and successfully realized the SOT-induced magnetization switching. Our study provides an approach to realize the current-induced magnetization in the ferromagnetic single layers without attaching SOT source materials.

preprint2021arXiv

Fast Sequence Generation with Multi-Agent Reinforcement Learning

Autoregressive sequence Generation models have achieved state-of-the-art performance in areas like machine translation and image captioning. These models are autoregressive in that they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a simple and efficient model for Non-Autoregressive sequence Generation (NAG) with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAG as a multi-agent reinforcement learning system where element positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. On MSCOCO image captioning benchmark, our NAG method achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup. On WMT14 EN-DE machine translation dataset, our method outperforms cross-entropy trained baseline by 6.0 BLEU points while achieves the greatest decoding speedup of 17.46x.

preprint2021arXiv

Generation and storage of spin squeezing via learning-assisted optimal control

The generation and storage of spin squeezing is an attracting topic in quantum metrology and the foundations of quantum mechanics. The major models to realize the spin squeezing are the one- and two-axis twisting models. Here, we consider a collective spin system coupled to a bosonic field, and show that proper constant-value controls in this model can simulate the dynamical behaviors of these two models. More interestingly, a better performance of squeezing can be obtained when the control is time-varying, which is generated via a reinforcement learning algorithm. However, this advantage becomes limited if the collective noise is involved. To deal with it, we propose a four-step strategy for the construction of a new type of combined controls, which include both constant-value and time-varying controls, but performed at different time intervals. Compared to the full time-varying controls, the combined controls not only give a comparable minimum value of the squeezing parameter over time, but also provides a better lifetime and larger full amount of squeezing. Moreover, the amplitude form of a combined control is simpler and more stable than the full time-varying control. Therefore, our scheme is very promising to be applied in practice to improve the generation and storage performance of squeezing.

preprint2021arXiv

Global-Local Propagation Network for RGB-D Semantic Segmentation

Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images. Most existing methods exploit a multi-stage fusion strategy to propagate depth feature to the RGB branch. However, at the very deep stage, the propagation in a simple element-wise addition manner can not fully utilize the depth information. We propose Global-Local propagation network (GLPNet) to solve this problem. Specifically, a local context fusion module(L-CFM) is introduced to dynamically align both modalities before element-wise fusion, and a global context fusion module(G-CFM) is introduced to propagate the depth information to the RGB branch by jointly modeling the multi-modal global context features. Extensive experiments demonstrate the effectiveness and complementarity of the proposed fusion modules. Embedding two fusion modules into a two-stream encoder-decoder structure, our GLPNet achieves new state-of-the-art performance on two challenging indoor scene segmentation datasets, i.e., NYU-Depth v2 and SUN-RGBD dataset.

preprint2021arXiv

Gravitational waves from resonant amplification of curvature perturbations during inflation

Parametric resonance in a single-field inflationary model with a periodic structure on the potential gives rise to curvature perturbations with large amplitudes on small scales, which could result in observable primordial black holes (PBHs) and concomitant gravitational waves (GWs) induced by curvature perturbations in the radiation-dominated era. In such a model, GWs associated with the PBH formation were investigated in Ref. [1]. In this paper, we consider a stochastic GW background sourced by inflaton perturbations resonantly amplified during inflation. We compute the energy spectra of induced GWs produced both during inflation and in the radiation-dominated era, and find that the peak of the energy spectrum of the former is much higher than that of the latter, but is located at a lower frequency. Moreover, the energy spectrum of induced GWs produced during inflation exhibits a unique oscillating character in the ultraviolet region. Both the stochastic GW backgrounds are expected to be detected by future space-based laser interferometers.

preprint2021arXiv

Momentum-Resolved Visualization of Electronic Evolution in Doping a Mott Insulator

High temperature superconductivity in cuprates arises from doping a parent Mott insulator by electrons or holes. A central issue is how the Mott gap evolves and the low-energy states emerge with doping. Here we report angle-resolved photoemission spectroscopy measurements on a cuprate parent compound by sequential in situ electron doping. The chemical potential jumps to the bottom of the upper Hubbard band upon a slight electron doping, making it possible to directly visualize the charge transfer band and the full Mott gap region. With increasing doping, the Mott gap rapidly collapses due to the spectral weight transfer from the charge transfer band to the gapped region and the induced low-energy states emerge in a wide energy range inside the Mott gap. These results provide key information on the electronic evolution in doping a Mott insulator and establish a basis for developing microscopic theories for cuprate superconductivity.

preprint2021arXiv

Partial Hadamard Encoded Synthetic Transmit Aperture for High Frame Rate Imaging with Minimal l2-Norm Least Square Method

Synthetic transmit aperture (STA) ultrasound imaging is well known for ideal focusing in the full field of view. However, it suffers from low signal-to-noise ratio (SNR) and low frame rate, because each array element must be activated individually. In our previous study, we encoded all the array elements with partial Hadamard matrix and reconstructed the complete STA dataset with compressed sensing (CS) algorithm (CS-STA). As all the elements are activated in each transmission and the number of transmissions is smaller than that of STA, this method can achieve higher SNR and higher frame rate. Its main drawback is the time-consuming CS reconstruction. In this study, we accelerate the complete STA dataset reconstruction with minimal l2-norm least square method. Thanks of the orthogonality of partial Hadamard matrix, the minimal l2-norm least square solution can be easily calculated. The proposed method is tested with simulation data and experimental phantom and in-vivo data. The results demonstrate that the proposed method achieves ~5*10^3 times faster reconstruction speed than CS algorithm. The simulation results demonstrate that the proposed method is capable of achieving the same accuracy for STA dataset reconstruction as conventional CS-STA method. The simulations, phantom and in-vivo experiments show that the proposed method is capable of improving the generalized contrast-to-noise ratio (gCNR) and SNR with maintained spatial resolution and fewer transmissions, compared with STA. In conclusion, the improved image quality and reduced computational time of LS-STA pave the way for its real-time applications in the clinics.

preprint2021arXiv

Resonance instability of primordial gravitational waves during inflation in Chern-Simons gravity

We investigate axion inflation where the gravitational Chern-Simons term is coupled to a periodic function of the inflaton. We find that tensor perturbations with different polarizations are amplified in different ways by the Chern-Simons coupling. Depending on the model parameters, the resonance amplification results in a parity-violating peak or a board plateau in the energy spectrum of gravitational waves, and the sharp cutoff in the infrared region constitutes a characteristic distinguishable from stochastic gravitational wave backgrounds produced by matter fields in Einstein gravity.

preprint2020arXiv

An analytical approximation of the scalar spectrum in the ultra-slow-roll inflationary models

The ultra-slow-roll (USR) inflationary models predict large-amplitude scalar perturbations at small scales which can lead to the primordial black hole production and scalar-induced gravitational waves. In general scalar perturbations in the USR models can only be obtained using numerical method because the usual slow-roll approximation breaks. In this work, we propose an analytical approach to estimate the scalar spectrum which is consistent with the numerical result. We find that the USR inflationary models predict a peak with power-law slopes in the scalar spectrum and energy spectrum of gravitational waves, and we derive the expression of the spectral indexes in terms of the inflationary potential. In turn, the inflationary potential near the USR regime can be reconstructed from the negative spectral index of the gravitational wave energy spectrum.

preprint2020arXiv

CoKE: Contextualized Knowledge Graph Embedding

Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static embedding for each entity or relation, ignoring their intrinsic contextual nature, i.e., entities and relations may appear in different graph contexts, and accordingly, exhibit different properties. This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings. Two types of graph contexts are studied: edges and paths, both formulated as sequences of entities and relations. CoKE takes a sequence as input and uses a Transformer encoder to obtain contextualized representations. These representations are hence naturally adaptive to the input, capturing contextual meanings of entities and relations therein. Evaluation on a wide variety of public benchmarks verifies the superiority of CoKE in link prediction and path query answering. It performs consistently better than, or at least equally well as current state-of-the-art in almost every case, in particular offering an absolute improvement of 21.0% in H@10 on path query answering. Our code is available at \url{https://github.com/PaddlePaddle/Research/tree/master/KG/CoKE}.

preprint2020arXiv

Electric transitions of the charmed-strange mesons in a relativistic quark model

In the present work, we adopt a relativistic constituent quark model to depict the charmed strange meson spectroscopy, in which $D_{s0}(2317)$ and $D_{s1}(2460)$ are considered as the $1^3P_0$ and $1P_1^\prime$ charmed strange mesons, respectively. By using the wave function obtained from the relativistic quark model, we further investigate the electric transitions between charmed strange mesons. We find the long wave length approximation is reasonable for the charmed strange meson radiative decay by comparing the results with different approximations. The estimated partial widths are all safely under the upper limits of the experimental data. Moreover, we find the branching ratio of $D_{s1}(2536) \to D_s^\ast γ/D_s γ$ are large enough to be detected, which could be searched by further experiments in Belle II and LHCb.

preprint2020arXiv

Electronic Evolution from the Parent Mott Insulator to a Superconductor in Lightly Hole-Doped Bi2Sr2CaCu2O8+delta

High temperature superconductivity in cuprates is realized by doping the Mott insulator with charge carriers. A central issue is how such an insulating state can evolve into a conducting or superconducting state when charge carriers are introduced. Here, by in situ vacuum annealing and Rb deposition on the Bi2Sr2Ca0.6Dy0.4Cu2O8+delta (Bi2212) sample surface to push its doping level continuously from deeply underdoped (Tc=25 K, doping level p-0.066) to the near zero doping parent Mott insulator, angle-resolved photoemission spectroscopy measurements are carried out to observe the detailed electronic structure evolution in lightly hole-doped region for the first time. Our results indicate that the chemical potential lies at about 1 eV above the charge transfer band for the parent state at zero doping which is quite close to the upper Hubbard band. With increasing hole doping, the chemical potential moves continuously towards the charge transfer band and the band structure evolution exhibits a rigid band shift-like behavior. When the chemical potential approaches the charge transfer band at a doping level of -0.05, the nodal spectral weight near the Fermi level increases, followed by the emergence of the coherent quasiparticle peak and the insulator-superconductor transition. Our observations provide key insights in understanding the insulator-superconductor transition in doping the parent cuprate compound and for establishing related theories.

preprint2020arXiv

Fluctuation-enhanced quantum metrology

The main obstacle for practical quantum technology is the noise, which can induce the decoherence and destroy the potential quantum advantages. The fluctuation of a field, which induces the dephasing of the system, is one of the most common noises and widely regarded as detrimental to quantum technologies. Here we show, contrary to the conventional belief, the fluctuation can be used to improve the precision limits in quantum metrology for the estimation of various parameters. Specifically, we show that for the estimation of the direction and rotating frequency of a field, the achieved precisions at the presence of the fluctuation can even surpass the highest precision achievable under the unitary dynamics which have been widely taken as the ultimate limit. We provide explicit protocols, which employs the adaptive quantum error correction, to achieve the higher precision limits with the fluctuating fields. Our study provides a completely new perspective on the role of the noises in quantum metrology. It also opens the door for higher precisions beyond the limit that has been believed to be ultimate.

preprint2020arXiv

Generative Low-bitwidth Data Free Quantization

Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedded devices. Existing quantization methods require original data for calibration or fine-tuning to get better performance. However, in many real-world scenarios, the data may not be available due to confidential or private issues, thereby making existing quantization methods not applicable. Moreover, due to the absence of original data, the recently developed generative adversarial networks (GANs) cannot be applied to generate data. Although the full-precision model may contain rich data information, such information alone is hard to exploit for recovering the original data or generating new meaningful data. In this paper, we investigate a simple-yet-effective method called Generative Low-bitwidth Data Free Quantization (GDFQ) to remove the data dependence burden. Specifically, we propose a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model. With the help of generated data, we can quantize a model by learning knowledge from the pre-trained model. Extensive experiments on three data sets demonstrate the effectiveness of our method. More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method. Code is available at https://github.com/xushoukai/GDFQ.

preprint2020arXiv

Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs

In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve because most of them are NP-hard or NP-complete. Although traditional general methods such as simulated annealing (SA), genetic algorithms (GA) and so forth have been devised to these hard problems, their accuracy and time consumption are not satisfying in practice. In this work, we proposed a simple, fast, and general algorithm framework based on advanced automatic differentiation technique empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm on three representative optimization problems on graph including modularity optimization from network science, Sherrington-Kirkpatrick (SK) model from statistical physics, maximum independent set (MIS) and minimum vertex cover (MVC) problem from combinatorial optimization on graph. High-quality solutions can be obtained with much less time consuming compared to traditional approaches.

preprint2020arXiv

HPGe detector field calculation methods demonstrated with an educational program, GeFiCa

A review of tools and methods to calculate electrostatic potentials and fields inside high-purity germanium detectors in various configurations is given. The methods are illustrated concretely with a new educational program named GeFiCa - Germanium detector Field Calculator. Demonstrated in GeFiCa are generic numerical calculations based on the successive over-relaxation method as well as analytic ones whenever simplification is possible due to highly symmetric detector geometries. GeFiCa is written in C++, and provided as an extension to the CERN ROOT libraries widely used in the particle physics community. Calculation codes for individual detectors, provided as ROOT macros and python scripts, are distributed along with the GeFiCa core library, serving as both examples showing the usage of GeFiCa and starting points for customized calculations. They can be run without compilation in a ROOT interactive session or directly from a Linux shell. The numerical results are saved in a ROOT tree, making full use of the I/O optimization and plotting functionalities in ROOT. The speed and precision of the calculation are comparable to other commonly used packages, which qualifies GeFiCa as a scientific research tool. However, the main focus of GeFiCa is to clearly explain and demonstrate the analytic and numeric methods to solve Poisson's equation, practical coding considerations and visualization methods, with intensive documentation and example macros. It serves as a one-stop resource for people who want to understand the operating mechanism of such a package under the hood.

preprint2020arXiv

Load-balanced Service Function Chaining in Edge Computing over FiWi Access Networks for Internet of Things

Service function chaining (SFC) is promising to implement flexible and scalable virtual network infrastructure for the Internet of Things (IoT). Edge computing is envisioned to be an effective solution to process huge amount of IoT application data. In order to uniformly provide services to IoT applications among the distributed edge computing nodes (ECNs), we present a unified SFC orchestration framework based on the coordination of SDN and NFV, which provides a synergic edge cloud platform by exploiting the connectivity of FiWi access networks. In addition, we study the VNF deployment problem under our synergic framework, and we formulate it as a mixed-integer nonlinear programming (MINLP) problem jointly considering the load balancing of networking and computing for chaining VNFs. We also propose two approximation optimal deployment algorithms named Greedy-Bisection Multi-Path (GBMP) and KSP MultiPath (KSMP) taking advantage of the multi-instance virtual network functions (VNFs) deployed in ECNs and the multipath capacity in FiWi access networks. Extensive simulations are conducted in two types of IoT application scenarios in the EC over FiWi access networks. The numerical results show that our proposed algorithms are superior to single path and ECMP based deployment algorithms in terms of load balancing, service acceptance ratio, and network utilization in both two typical scenarios.

preprint2020arXiv

Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning

Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAIC as a multi-agent reinforcement learning system where positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. Besides, we propose to utilize massive unlabeled images to boost captioning performance. Extensive experiments on MSCOCO image captioning benchmark show that our NAIC model achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup.

preprint2020arXiv

Normalized and Geometry-Aware Self-Attention Network for Image Captioning

Self-attention (SA) network has shown profound value in image captioning. In this paper, we improve SA from two aspects to promote the performance of image captioning. First, we propose Normalized Self-Attention (NSA), a reparameterization of SA that brings the benefits of normalization inside SA. While normalization is previously only applied outside SA, we introduce a novel normalization method and demonstrate that it is both possible and beneficial to perform it on the hidden activations inside SA. Second, to compensate for the major limit of Transformer that it fails to model the geometry structure of the input objects, we propose a class of Geometry-aware Self-Attention (GSA) that extends SA to explicitly and efficiently consider the relative geometry relations between the objects in the image. To construct our image captioning model, we combine the two modules and apply it to the vanilla self-attention network. We extensively evaluate our proposals on MS-COCO image captioning dataset and superior results are achieved when comparing to state-of-the-art approaches. Further experiments on three challenging tasks, i.e. video captioning, machine translation, and visual question answering, show the generality of our methods.

preprint2020arXiv

Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision

Violence detection has been studied in computer vision for years. However, previous work are either superficial, e.g., classification of short-clips, and the single scenario, or undersupplied, e.g., the single modality, and hand-crafted features based multimodality. To address this problem, in this work we first release a large-scale and multi-scene dataset named XD-Violence with a total duration of 217 hours, containing 4754 untrimmed videos with audio signals and weak labels. Then we propose a neural network containing three parallel branches to capture different relations among video snippets and integrate features, where holistic branch captures long-range dependencies using similarity prior, localized branch captures local positional relation using proximity prior, and score branch dynamically captures the closeness of predicted score. Besides, our method also includes an approximator to meet the needs of online detection. Our method outperforms other state-of-the-art methods on our released dataset and other existing benchmark. Moreover, extensive experimental results also show the positive effect of multimodal (audio-visual) input and modeling relationships. The code and dataset will be released in https://roc-ng.github.io/XD-Violence/.

preprint2020arXiv

NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration

Deep reinforcement learning has been applied more and more widely nowadays, especially in various complex control tasks. Effective exploration for noisy networks is one of the most important issues in deep reinforcement learning. Noisy networks tend to produce stable outputs for agents. However, this tendency is not always enough to find a stable policy for an agent, which decreases efficiency and stability during the learning process. Based on NoisyNets, this paper proposes an algorithm called NROWAN-DQN, i.e., Noise Reduction and Online Weight Adjustment NoisyNet-DQN. Firstly, we develop a novel noise reduction method for NoisyNet-DQN to make the agent perform stable actions. Secondly, we design an online weight adjustment strategy for noise reduction, which improves stable performance and gets higher scores for the agent. Finally, we evaluate this algorithm in four standard domains and analyze properties of hyper-parameters. Our results show that NROWAN-DQN outperforms prior algorithms in all these domains. In addition, NROWAN-DQN also shows better stability. The variance of the NROWAN-DQN score is significantly reduced, especially in some action-sensitive environments. This means that in some environments where high stability is required, NROWAN-DQN will be more appropriate than NoisyNets-DQN.

preprint2020arXiv

NTIRE 2020 Challenge on NonHomogeneous Dehazing

This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. NH-Haze is the first realistic nonhomogeneous haze dataset that provides ground truth images. The nonhomogeneous haze has been produced using a professional haze generator that imitates the real conditions of haze scenes. 168 participants registered in the challenge and 27 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing.

preprint2020arXiv

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor 16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

preprint2020arXiv

NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results

This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets. In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner. In track 2, algorithms are required to learn the quality mapping from one device to another when their quality varies substantially and weakly-aligned video pairs are available. For track 1, in total 7 teams competed in the final test phase, demonstrating novel and effective solutions to the problem. For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.

preprint2020arXiv

Operational definition of a quantum speed limit

The quantum speed limit is a fundamental concept in quantum mechanics, which aims at finding the minimum time scale or the maximum dynamical speed for some fixed targets. In a large number of studies in this field, the construction of valid bounds for the evolution time is always the core mission, yet the physics behind it and some fundamental questions like which states can really fulfill the target, are ignored. Understanding the physics behind the bounds is at least as important as constructing attainable bounds. Here we provide an operational approach for the definition of the quantum speed limit, which utilizes the set of states that can fulfill the target to define the speed limit. Its performances in various scenarios have been investigated. For time-independent Hamiltonians, it is inverse-proportional to the difference between the highest and lowest energies. The fact that its attainability does not require a zero ground-state energy suggests it can be used as an indicator of quantum phase transitions. For time-dependent Hamiltonians, it is shown that contrary to the results given by existing bounds, the true speed limit should be independent of the time. Moreover, in the case of spontaneous emission, we find a counterintuitive phenomenon that a lousy purity can benefit the reduction of the quantum speed limit.

preprint2020arXiv

Primordial black holes and gravitational waves from parametric amplification of curvature perturbations

We investigate a new mechanism to create large curvature perturbations on small scales due to parameter resonance in a single-field inflationary model with a small periodic structure upon the potential. After reentering the horizon, the amplified curvature perturbations can lead to observable primordial black holes as well as stochastic gravitational waves. The mass of primordial black holes and frequency of the induced gravitational waves depend on the model parameters. The resulted primordial black hole could constitute all dark matter or a fraction of dark matter in the universe, and corresponding stochastic gravitational waves fall in the frequency band measurable for the pulsar timing array and the space-based gravitational wave detectors.

preprint2020arXiv

Production of $P-$wave charmed and charmed-strange mesons in pion and kaon induced reactions

In the present work, we investigate the production of $P-$wave charmed or charmed-strange mesons in the pion or kaon induced reactions on a proton target. The total cross sections as well as the differential cross sections depending on the scattering angle are evaluated in an effective Lagrangian approach. Our estimations indicate that magnitude of the cross sections strongly depends on the model parameter but such dependence can be almost completely canceled for the cross section ratios. These model independent ratios can be taken as a good criterion of the validity of heavy quark limit in the charmed region, which is helpful to understand $P$-wave charmed and charmed-strange mesons.

preprint2020arXiv

Prospect of undoped inorganic crystals at 77 Kelvin for low-mass dark matter search at Spallation Neutron Source

Investigated in this work were sensitivities of a prototype detector for the detection of low-mass dark matter particles produced at the Spallation Neutron Source at the Oak Ridge National Laboratory in two years of data taking. The presumed prototype consisted of 10 kg undoped CsI or NaI scintillation crystals directly coupled with SiPM arrays operated at 77 K. Compared to the COHERENT CsI(Na) detector, a much higher light yield was assumed for the prototype. An experiment with a cylindrical 1~kg undoped CsI crystal coupled directly to two photomultiplier tubes at about 77~K was conducted as the first step to verify the idea. A light yield of $26.0 \pm 0.4$ photoelectrons per keV electron-equivalent was achieved. This eliminated the concern of self light absorption in large crystals raised in some of the early studies.

preprint2020arXiv

Prospect of undoped inorganic scintillators at 77 Kelvin for the detection of non-standard neutrino interactions at the Spallation Neutron Source

Investigated in this work are sensitivities to non-standard neutrino interactions (NSI) of a prototype detector placed about 20 meters away from the Spallation Neutron Source at the Oak Ridge National Laboratory in two years of data taking. The presumed prototype consists of 10 kg undoped CsI scintillation crystals directly coupled with SiPM arrays operated at 77 K. Compared to the COHERENT CsI(Na) detector, a much higher light yield is assumed for the prototype. An experiment with a cylindrical undoped CsI crystal coupled directly to a photomultiplier tube at about 77 K was conducted to verify the light yield assumption. A yield of $33.5 \pm 0.7$ photoelectrons per keV electron-equivalent (PE/keVee) was achieved in [13, 60] keVee, which is much closer to the relevant energy region for the NSI search than some of the early studies.

preprint2020arXiv

Reconciling the X(2240) with the Y(2175)

In the present work, we reanalyzed the cross sections for $e^+ e^- \to K^+K^-$, where a new structure $X(2240)$ was reported by BES III Collaboration. By including the interference between the direct coupling and vector meson intermediate processes, we find the mass and width of $X(2240)$ are $2197.4\pm 4.4$ MeV and $75.6\pm 7.2 $ MeV, respectively, which are well consistent with the PDG average values of the resonance parameters for $Y(2175)$, thus, we conclude that the $X(2240)$ should be the same state as the $Y(2175)$.

preprint2020arXiv

Robust Mean Estimation in High Dimensions via $\ell_0$ Minimization

We study the robust mean estimation problem in high dimensions, where $α<0.5$ fraction of the data points can be arbitrarily corrupted. Motivated by compressive sensing, we formulate the robust mean estimation problem as the minimization of the $\ell_0$-`norm' of the outlier indicator vector, under second moment constraints on the inlier data points. We prove that the global minimum of this objective is order optimal for the robust mean estimation problem, and we propose a general framework for minimizing the objective. We further leverage the $\ell_1$ and $\ell_p$ $(0<p<1)$, minimization techniques in compressive sensing to provide computationally tractable solutions to the $\ell_0$ minimization problem. Both synthetic and real data experiments demonstrate that the proposed algorithms significantly outperform state-of-the-art robust mean estimation methods.

preprint2020arXiv

Topologically different spin disorder phases of the J1-J2 Heisenberg model on the honeycomb lattice

Searching for spin liquids on the honeycomb J1-J2 Heisenberg model has been attracting great attention in the past decade. In this Paper we investigate the topological properties of the J1-J2 Heisenberg model by introducing nearest-neighbour and next-nearest-neighbour bond parameters. We find that there exist two topologically different phases in the spin disordered regime 0.2<J2/J1<0.5: for J2/J1<0.32, the system is a zero-flux spin liquid which is topological trivial and gapless; for J2/J1>0.32, it is a pi-flux chiral spin liquid, which is topological nontrivial and gapped. These results suggest that there exist two topologically different spin disorder phases in honeycomb J1-J2 Heisenberg model.

preprint2020arXiv

Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM

In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM). In the multi-clustering integration module, we apply three unsupervised K-means, affinity propagation and spectral clustering algorithms to obtain three different clustering partitions (CPs) without any background knowledge or label. Then, an unanimous voting strategy is used to generate a local clustering partition (LCP). The novel MIRBM model is a core feature encoding part of the proposed unsupervised feature learning architecture. The novelty of it is that the LCP as an unsupervised guidance is integrated into one step contrastive divergence (CD1) learning to guide the distribution of the hidden layer features. For the instance in the same LCP cluster, the hidden and reconstructed hidden layer features of the MIRBM model in the proposed architecture tend to constrict together in the training process. Meanwhile, each LCP center tends to disperse from each other as much as possible in the hidden and reconstructed hidden layer during training. The experiments demonstrate that the proposed unsupervised feature learning architecture has more powerful feature representation and generalization capability than the state-of-the-art graph regularized RBM (GraphRBM) for clustering tasks in the Microsoft Research Asia Multimedia (MSRA-MM)2.0 dataset.

preprint2020arXiv

Vatex Video Captioning Challenge 2020: Multi-View Features and Hybrid Reward Strategies for Video Captioning

This report describes our solution for the VATEX Captioning Challenge 2020, which requires generating descriptions for the videos in both English and Chinese languages. We identified three crucial factors that improve the performance, namely: multi-view features, hybrid reward, and diverse ensemble. Based on our method of VATEX 2019 challenge, we achieved significant improvements this year with more advanced model architectures, combination of appearance and motion features, and careful hyper-parameters tuning. Our method achieves very competitive results on both of the Chinese and English video captioning tracks.

preprint2019arXiv

Controlling exciton dynamics in two-dimensional MoS2 on hyperbolic metamaterial-based nanophotonic platform

The discovery of two-dimensional transition metal dichalcogenides (2D TMDs) has promised next-generation photonics and optoelectronics applications, particularly in the realm of nanophotonics. Arguably, the most crucial fundamental processes in these applications are the exciton migration and charge transfer in 2D TMDs. However, exciton dynamics in 2D TMDs have never been studied on a nanophotonic platform and more importantly, the control of exciton dynamics by means of nanophotonic structures has yet to be explored. Here, for the first time, we demonstrate the control of exciton dynamics in MoS2 monolayers by introducing a hyperbolic metamaterial (HMM) substrate. We reveal the migration mechanisms of various excitons in MoS2 monolayers. Furthermore, we demonstrate the Förster radius of the A-excitons can be increased by introducing HMMs through the nonlocal effects of HMMs due to the Purcell effect. On the other hand, the diffusion coefficient is unchanged for the C-excitons on HMMs. This study provides a revolutionary step forward in enabling 2D TMD nanophotonics hybrid devices.

preprint2019arXiv

Evidence for an Additional Symmetry Breaking from Direct Observation of Band Splitting in the Nematic State of FeSe Superconductor

The iron-based superconductor FeSe has attracted much recent attention because of its simple crystal structure, distinct electronic structure and rich physics exhibited by itself and its derivatives. Determination of its intrinsic electronic structure is crucial to understand its physical properties and superconductivity mechanism. Both theoretical and experimental studies so far have provided a picture that FeSe consists of one hole-like Fermi surface around the Brillouin zone center in its nematic state. Here we report direct observation of two hole-like Fermi surface sheets around the Brillouin zone center, and the splitting of the associated bands, in the nematic state of FeSe by taking high resolution laser-based angle-resolved photoemission measurements. These results indicate that, in addition to nematic order and spin-orbit coupling, there is an additional order in FeSe that breaks either inversion or time reversal symmetries. The new Fermi surface topology asks for reexamination of the existing theoretical and experimental understanding of FeSe and stimulates further efforts to identify the origin of the hidden order in its nematic state.

preprint2019arXiv

Low-frequency Gravitational Waves from Double-inflection-point Inflation

We study the production of gravitational waves from primordial scalar perturbations in double-inflection-point inflation, in which one of the inflection points predicts the power spectra consistent with CMB observations at large scales and the other generates a large peak in the power spectrum of scalar perturbations at small scales. We find that the reduced gravitational waves at low frequencies can be detected by future space-based laser interferometers.

preprint2019arXiv

Primordial Black Holes from Cosmic Domain Walls

We investigate the formation of primordial black holes (PBHs) from the collapse of spherically symmetric domain wall bubbles, which spontaneously nucleate via quantum tunneling during inflation. Since the tension of domain walls changes with time and so domain walls nucleate in a short time interval, the mass function of PBHs in general has a spike-like structure. In contrast to models in which PBHs produced from overdense regions, our model avoids the uncertainties of PBHs production mechanism. PBHs from domain walls with mass around $10^{20}\mathrm{g}$ may constitute all dark matter, those with mass around $10^{34}\mathrm{g}$ can explain the merger events of binary black holes detected by LIGO.

preprint2019arXiv

Quantum Fisher information matrix and multiparameter estimation

Quantum Fisher information matrix (QFIM) is a core concept in theoretical quantum metrology due to the significant importance of quantum Cramér-Rao bound in quantum parameter estimation. However, studies in recent years have revealed wide connections between QFIM and other aspects of quantum mechanics, including quantum thermodynamics, quantum phase transition, entanglement witness, quantum speed limit and non-Markovianity. These connections indicate that QFIM is more than a concept in quantum metrology, but rather a fundamental quantity in quantum mechanics. In this paper, we summarize the properties and existing calculation techniques of QFIM for various cases, and review the development of QFIM in some aspects of quantum mechanics apart from quantum metrology. On the other hand, as the main application of QFIM, the second part of this paper reviews the quantum multiparameter Cramér-Rao bound, its attainability condition and the associated optimal measurements. Moreover, recent developments in a few typical scenarios of quantum multiparameter estimation and the quantum advantages are also thoroughly discussed in this part.

preprint2019arXiv

Selective Hybridization between Main Band and Superstructure Band in Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ Superconductor

High-resolution laser-based angle-resolved photoemission measurements have been carried out on Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi2212) and Bi$_2$Sr$_{2-x}$La$_x$CuO$_{6+δ}$ (Bi2201) superconductors. Unexpected hybridization between the main band and the superstructure band in Bi2212 is clearly revealed. In the momentum space where one main Fermi surface intersects with one superstructure Fermi surface, four bands are observed instead of two. The hybridization exists in both superconducting state and normal state, and in Bi2212 samples with different doping levels. Such a hybridization is not observed in Bi2201. This phenomenon can be understood by considering the bilayer splitting in Bi2212, the selective hybridization of two bands with peculiar combinations, and the altered matrix element effects of the hybridized bands. These observations provide strong evidence on the origin of the superstructure band which is intrinsic to the CuO$_2$ planes. Therefore, understanding physical properties and superconductivity mechanism in Bi2212 should consider the complete Fermi surface topology which involves the main bands, the superstructure bands and their interactions.

Jing Liu

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72 published item(s)

LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

Distinguishing the nanohertz gravitational-wave sources by the observations of compact dark matter subhalos

Temporal Adaptive RGBT Tracking with Modality Prompt

LGN-Net: Local-Global Normality Network for Video Anomaly Detection

An Interpretability Evaluation Benchmark for Pre-trained Language Models

Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

Contextual Adapters for Personalized Speech Recognition in Neural Transducers

Dependence of the amplitude of gravitational waves from preheating on the inflationary energy scale

DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models

First operation of undoped CsI directly coupled with SiPMs at 77 Kelvin

First-principles perspective on full-spectrum infrared photodetectors from doping an excitonic insulator

FocusFormer: Focusing on What We Need via Architecture Sampler

Learning Appearance-motion Normality for Video Anomaly Detection

Mesa: A Memory-saving Training Framework for Transformers

Multi-scale Analysis of Nitrogen Loss Mitigation in the US Corn Belt

Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding

New analysis of the fraction of observable nights at astronomical sites based on FengYun-2 satellite data

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Observation of three superconducting transitions in the pressurized CDW-bearing compound TaTe2

One-off Negative Sequential Pattern Mining

Primordial black hole production during first-order phase transitions

Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks

Quantum Magnetometer with Dual-Coupling Optomechanics

Rapid Elastic Architecture Search under Specialized Classes and Resource Constraints

Reducing US Biofuels Requirements Mitigates Short-term Impacts of Global Population and Income Growth on Agricultural Environmental Outcomes

Room-temperature printing of ultrathin Quasi-2D GaN semiconductor via liquid metal gallium surface confined nitridation reaction

Tracking the nematicity in cuprate superconductors: a resistivity study under uniaxial pressure

Ultrafast photothermoelectric effect in Dirac semimetallic Cd3As2 revealed by terahertz emission

CPTR: Full Transformer Network for Image Captioning

Current-induced magnetization switching in a chemically disordered A1 CoPt single layer

Fast Sequence Generation with Multi-Agent Reinforcement Learning

Generation and storage of spin squeezing via learning-assisted optimal control

Global-Local Propagation Network for RGB-D Semantic Segmentation

Gravitational waves from resonant amplification of curvature perturbations during inflation

Momentum-Resolved Visualization of Electronic Evolution in Doping a Mott Insulator

Partial Hadamard Encoded Synthetic Transmit Aperture for High Frame Rate Imaging with Minimal l2-Norm Least Square Method

Resonance instability of primordial gravitational waves during inflation in Chern-Simons gravity

An analytical approximation of the scalar spectrum in the ultra-slow-roll inflationary models

CoKE: Contextualized Knowledge Graph Embedding

Electric transitions of the charmed-strange mesons in a relativistic quark model

Electronic Evolution from the Parent Mott Insulator to a Superconductor in Lightly Hole-Doped Bi2Sr2CaCu2O8+delta

Fluctuation-enhanced quantum metrology

Generative Low-bitwidth Data Free Quantization

Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs

HPGe detector field calculation methods demonstrated with an educational program, GeFiCa

Load-balanced Service Function Chaining in Edge Computing over FiWi Access Networks for Internet of Things

Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning

Normalized and Geometry-Aware Self-Attention Network for Image Captioning

Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision

NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration

NTIRE 2020 Challenge on NonHomogeneous Dehazing

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results

Operational definition of a quantum speed limit

Primordial black holes and gravitational waves from parametric amplification of curvature perturbations

Production of $P-$wave charmed and charmed-strange mesons in pion and kaon induced reactions

Prospect of undoped inorganic crystals at 77 Kelvin for low-mass dark matter search at Spallation Neutron Source

Prospect of undoped inorganic scintillators at 77 Kelvin for the detection of non-standard neutrino interactions at the Spallation Neutron Source

Reconciling the X(2240) with the Y(2175)

Robust Mean Estimation in High Dimensions via $\ell_0$ Minimization

Topologically different spin disorder phases of the J1-J2 Heisenberg model on the honeycomb lattice

Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM

Vatex Video Captioning Challenge 2020: Multi-View Features and Hybrid Reward Strategies for Video Captioning

Controlling exciton dynamics in two-dimensional MoS2 on hyperbolic metamaterial-based nanophotonic platform

Evidence for an Additional Symmetry Breaking from Direct Observation of Band Splitting in the Nematic State of FeSe Superconductor

Low-frequency Gravitational Waves from Double-inflection-point Inflation

Primordial Black Holes from Cosmic Domain Walls

Quantum Fisher information matrix and multiparameter estimation

Selective Hybridization between Main Band and Superstructure Band in Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ Superconductor