arXiv subjects

Hao Wu

Publications and source records attributed to Hao Wu.

At least 613 records · Page 34Linked to original sources

arXiv2022

Move aside pentacene: Diazapentacene doped para-terphenyl as a zero-field room-temperature maser with strong coupling for cavity quantum electrodynamics

Masers, the microwave analogue of lasers, promise to deliver ultra-low noise amplification of microwave signals for use in medical MRI imaging and deep-space communication. Research on masers in modern times was rekindled thanks to the discovery of gain media that were operable at room-temperature, eschewing bulky cryogenics that hindered their use. However, besides the two known materials of pentacene doped in para-terphenyl and negatively-charged nitrogen-vacancy defects in diamond, there has been scarce progress in the search for completely new room-temperature gain media. Here we show the discovery of 6,13-diazapentacene doped in para-terphenyl as a maser gain medium that can operate at room-temperature and without an external magnetic field. A measured maser pulse power of -10 dBm shows it is on par with pentacene-doped para-terphenyl in absolute power, while possessing compelling advantages against its pentacene predecessor in that it has a faster amplification startup time, can be excited with longer wavelength light at 620 nm and enjoys greater chemical stability from added nitrogen groups. Furthermore, we show that the maser bursts allow 6,13-diazapentacene-doped para-terphenyl to reach the strong coupling regime for cavity quantum electrodynamics, where it has a high cooperativity of 182. We study the optical and microwave spin dynamics of 6,13-diazapentacene-doped para-terphenyl in order to evaluate its behavior as a maser gain medium, where it features fast intersystem crossing and an advantageously higher triplet quantum yield. Our results pave the way for the future discovery of other similar maser materials and help point to such materials as promising candidates for the study of cavity quantum electrodynamic effects at room-temperature.

quant-ph↗

arXiv2022

Room-Temperature Spin-Valve Effect in Fe$_3$GaTe$_2$/MoS$_2$/Fe$_3$GaTe$_2$ 2D van der Waals Heterojunction Devices

Spin-valve effect has been the focus of spintronics over the last decades due to its potential in many spintronic devices. Two-dimensional (2D) van der Waals (vdW) materials are highly expected to build the spin-valve heterojunction. However, the Curie temperatures (TC) of the vdW ferromagnetic 2D crystals are mostly below room temperature (~30-220 K). It is very challenging to develop room temperature, ferromagnetic (FM) 2D crystals based spin-valve devices which are still not available to date. We report the first room temperature, FM 2D crystal based all-2D vdW Fe3GaTe2/MoS2/Fe3GaTe2 spin valve devices. The Magnetoresistance (MR) of the all- devices is up to 15.89% at 2.3 K and 11.97% at 10 K, 4-30 times of MR from the spin valves of Fe$_3$GaTe$_2$/MoS$_2$/Fe$_3$GaTe$_2$ and conventional NiFe/MoS$_2$/NiFe. Typical spin valve effect shows strong dependence on MoS2 spacer thickness in the vdW heterojunction. Importantly, the spin valve effect (0.31%) still robustly exists at 300 K with low working currents down to 10 nA (0.13 A/cm$^2$). The results provide a general vdW platform to room temperature, 2D FM crystals based 2D spin valve devices.

cond-mat.mes-hall↗

arXiv2022

A Labelled Sequent Calculus for Public Announcement Logic

Public announcement logic(PAL) is an extension of epistemic logic (EL) with some reduction axioms. In this paper, we propose a cut-free labelled sequent calculus for PAL, which is an extension of that for EL with sequent rules adapted from the reduction axioms. This calculus admits cut and allows terminating proof search.

cs.LO↗

arXiv2022

Crossing Probabilities of Multiple Ising Interfaces

We prove that in the scaling limit, the crossing probabilities of multiple interfaces in the critical planar Ising model with alternating boundary conditions are conformally invariant expressions given by the pure partition functions of multiple SLE(κ) with κ=3. In particular, this identifies the scaling limits with ratios of specific correlation functions of conformal field theory.

math-ph↗

arXiv2022

Prediction for the synthesis cross sections of new moscovium isotopes in fusion-evaporation reactions

In the framework of the dinuclear system model, the synthesis mechanism of the superheavy nuclides with atomic number $Z=112, 114, 115$ in the reactions of projectiles $^{40,48}$Ca bombarding on targets $^{238}$U, $^{242}$Pu, and $^{243}$Am at a wide incident energies (excitation energy from 0-100 MeV) have been investigated systematically. Based on the available experimental excitation functions, the dependence of calculated synthesis cross sections on collision orientations has been studied thoroughly. The TKEs of these collisions with the fixed collision orientation show its orientation dependence which can be used to predict the tendency of kinetic energy diffusion. The TKEs are dependent on incident energies which have been discussed. The method of Coulomb barrier distribution function has been applied in our calculations which could treat all of the collision orientations from the tip-tip to side-side approximately. The calculations of excitation functions of $^{48}$Ca + $^{238}$U, $^{48}$Ca + $^{242}$Pu, and $^{48}$Ca + $^{243}$Am have a nice agreement with the available experimental data. The isospin effect of projectiles on production cross sections of moscovium isotopes and the influence of entrance channel effect on the synthesis cross sections of superheavy nuclei have been discussed. The synthesis cross section of new moscovium isotopes $^{278-286}$Mc have been predicted as large as hundreds pb, in the fusion-evaporation reactions of $^{35,37}$Cl + $^{248}$Cf, $^{38,40}$Ar + $^{247}$Bk, $^{39,41}$K + $^{247}$Cm, $^{40,42,44,46}$Ca + $^{238}$Am, $^{45}$Sc + $^{242}$Pu, and $^{46,48,50}$Ti + $^{243}$Np, $^{51}$V + $^{238}$U at the excitation energy interval of 0-100 MeV.

nucl-th↗

arXiv2022

Track Targets by Dense Spatio-Temporal Position Encoding

In this work, we propose a novel paradigm to encode the position of targets for target tracking in videos using transformers. The proposed paradigm, Dense Spatio-Temporal (DST) position encoding, encodes spatio-temporal position information in a pixel-wise dense fashion. The provided position encoding provides location information to associate targets across frames beyond appearance matching by comparing objects in two bounding boxes. Compared to the typical transformer positional encoding, our proposed encoding is applied to the 2D CNN features instead of the projected feature vectors to avoid losing positional information. Moreover, the designed DST encoding can represent the location of a single-frame object and the evolution of the location of the trajectory among frames uniformly. Integrated with the DST encoding, we build a transformer-based multi-object tracking model. The model takes a video clip as input and conducts the target association in the clip. It can also perform online inference by associating existing trajectories with objects from the new-coming frames. Experiments on video multi-object tracking (MOT) and multi-object tracking and segmentation (MOTS) datasets demonstrate the effectiveness of the proposed DST position encoding.

cs.CV↗

arXiv2022

Enhancing Spatiotemporal Prediction Model using Modular Design and Beyond

Predictive learning uses a known state to generate a future state over a period of time. It is a challenging task to predict spatiotemporal sequence because the spatiotemporal sequence varies both in time and space. The mainstream method is to model spatial and temporal structures at the same time using RNN-based or transformer-based architecture, and then generates future data by using learned experience in the way of auto-regressive. The method of learning spatial and temporal features simultaneously brings a lot of parameters to the model, which makes the model difficult to be convergent. In this paper, a modular design is proposed, which decomposes spatiotemporal sequence model into two modules: a spatial encoder-decoder and a predictor. These two modules can extract spatial features and predict future data respectively. The spatial encoder-decoder maps the data into a latent embedding space and generates data from the latent space while the predictor forecasts future embedding from past. By applying the design to the current research and performing experiments on KTH-Action and MovingMNIST datasets, we both improve computational performance and obtain state-of-the-art results.

cs.CV↗

arXiv2022

FP8 Formats for Deep Learning

FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit floating point (FP8) binary interchange format consisting of two encodings - E4M3 (4-bit exponent and 3-bit mantissa) and E5M2 (5-bit exponent and 2-bit mantissa). While E5M2 follows IEEE 754 conventions for representatio of special values, E4M3's dynamic range is extended by not representing infinities and having only one mantissa bit-pattern for NaNs. We demonstrate the efficacy of the FP8 format on a variety of image and language tasks, effectively matching the result quality achieved by 16-bit training sessions. Our study covers the main modern neural network architectures - CNNs, RNNs, and Transformer-based models, leaving all the hyperparameters unchanged from the 16-bit baseline training sessions. Our training experiments include large, up to 175B parameter, language models. We also examine FP8 post-training-quantization of language models trained using 16-bit formats that resisted fixed point int8 quantization.

cs.LG↗

arXiv2022

Stochastic approximation to MBAR and TRAM: batch-wise free energy estimation

The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols have been introduced that involve using simulations with biases or changed temperatures. Two established statistically optimal estimators for obtaining unbiased equilibrium properties from such simulations are the multistate Bennett Acceptance Ratio (MBAR) and the transition-based reweighting analysis method (TRAM). Both MBAR and TRAM are solved iteratively and can suffer from long convergence times. Here we introduce stochastic approximators (SA) for both estimators, resulting in SAMBAR and SATRAM, which are shown to converge faster than their deterministic counterparts, without significant accuracy loss. Both methods are demonstrated on different molecular systems.

physics.chem-ph↗

arXiv2022

Spatial-temporal Transformers for EEG Emotion Recognition

Electroencephalography (EEG) is a popular and effective tool for emotion recognition. However, the propagation mechanisms of EEG in the human brain and its intrinsic correlation with emotions are still obscure to researchers. This work proposes four variant transformer frameworks~(spatial attention, temporal attention, sequential spatial-temporal attention and simultaneous spatial-temporal attention) for EEG emotion recognition to explore the relationship between emotion and spatial-temporal EEG features. Specifically, spatial attention and temporal attention are to learn the topological structure information and time-varying EEG characteristics for emotion recognition respectively. Sequential spatial-temporal attention does the spatial attention within a one-second segment and temporal attention within one sample sequentially to explore the influence degree of emotional stimulation on EEG signals of diverse EEG electrodes in the same temporal segment. The simultaneous spatial-temporal attention, whose spatial and temporal attention are performed simultaneously, is used to model the relationship between different spatial features in different time segments. The experimental results demonstrate that simultaneous spatial-temporal attention leads to the best emotion recognition accuracy among the design choices, indicating modeling the correlation of spatial and temporal features of EEG signals is significant to emotion recognition.

cs.RO↗

arXiv2022

A detail-enhanced sampling strategy in Hadamard single-pixel imaging

Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost. To improve imaging efficiency, many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples. In this study, we present an efficient HSI imaging method that employs an exponential probability function to sample Hadamard spectra along a direction with better energy concentration for obtaining Hadamard patterns. We also propose an XY order to further optimize the pattern-selection method with extremely fast Hadamard order generation while retaining the original performance. We used the compressed sensing algorithm for image reconstruction. The simulation and experimental results show that these pattern-selection method reliably reconstructs objects and preserves the edge and details of images.

eess.IV↗

arXiv2022

AVMiner: Expansible and Semantic-Preserving Anti-Virus Labels Mining Method

With the increase in the variety and quantity of malware, there is an urgent need to speed up the diagnosis and the analysis of malware. Extracting the malware family-related tokens from AV (Anti-Virus) labels, provided by online anti-virus engines, paves the way for pre-diagnosing the malware. Automatically extract the vital information from AV labels will greatly enhance the detection ability of security enterprises and equip the research ability of security analysts. Recent works like AVCLASS and AVCLASS2 try to extract the attributes of malware from AV labels and establish the taxonomy based on expert knowledge. However, due to the uncertain trend of complicated malicious behaviors, the system needs the following abilities to face the challenge: preserving vital semantics, being expansible, and free from expert knowledge. In this work, we present AVMiner, an expansible malware tagging system that can mine the most vital tokens from AV labels. AVMiner adopts natural language processing techniques and clustering methods to generate a sequence of tokens without expert knowledge ranked by importance. AVMiner can self-update when new samples come. Finally, we evaluate AVMiner on over 8,000 samples from well-known datasets with manually labeled ground truth, which outperforms previous works.

cs.CR↗

arXiv2022

Distinctive Image Captioning via CLIP Guided Group Optimization

Image captioning models are usually trained according to human annotated ground-truth captions, which could generate accurate but generic captions. In this paper, we focus on generating distinctive captions that can distinguish the target image from other similar images. To evaluate the distinctiveness of captions, we introduce a series of metrics that use large-scale vision-language pre-training model CLIP to quantify the distinctiveness. To further improve the distinctiveness of captioning models, we propose a simple and effective training strategy that trains the model by comparing target image with similar image group and optimizing the group embedding gap. Extensive experiments are conducted on various baseline models to demonstrate the wide applicability of our strategy and the consistency of metric results with human evaluation. By comparing the performance of our best model with existing state-of-the-art models, we claim that our model achieves new state-of-the-art towards distinctiveness objective.

cs.CV↗

arXiv2022

Production of neutron-rich actinide nuclides in isobaric collisions via multinucleon transfer reactions

We have calculated the multinucleon transfer reactions of $^{208}$Os, $^{208}$Pt, $^{208}$Hg, $^{208}$Pb,$^{208}$Po, $^{208}$Rn, $^{208}$Ra,$^{132,136}$Xe bombarding on $^{232}$Th and $^{248}$Cm at Coulomb barrier energies within the dinuclear system model, systematically. The results are in good agreement with the available experimental data. Coulomb effect and shell effect on production of actinides in these reactions have been investigated thoroughly. Potential energy surface and total kinetic energy mass distributions in the reactions $^{208}$Hg, $^{208}$Pb and$^{208}$Po colliding on $^{248}$Cm and $^{232}$Th are calculated and analyzed, respectively. It is found that PES and TKE spectra manifest the fragment formation mechanism in the multinucleon transfer reactions. The isospin effect and shell effect are shown in PES and TKE. Production cross-sections of multinucleon transfer products are highly dependent on the isobar projectiles with mass number $A=208$. The isobar projectiles with larger N/Z ratios are favorable for creating the neutron-rich target-like fragments. The isobar projectiles with larger charge number induced products tend to shift to proton-rich region. Coulomb potential coupled to shell effect is shown in production cross-sections of actinide isotopes. Based on the radioactive projectiles induced reactions, we have predicted massive new actinide isotopes around nuclear drip lines, even could access the superheavy nuclei region.

nucl-th↗

arXiv2022

Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expensive data annotation process. Limited labeled data increase the risk of distribution shift between test data and training data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared with current state-of-the-art methods. The in-depth analysis also demonstrates the effectiveness of the different-granularity filters. Our codes are available at https://github.com/TOWESSL/TOWESSL.

cs.CL↗

arXiv2022

A note on time hierarchies for reasonable semantic classes without advice

We show time hierarchies for reasonable semantic classes without advice by eliminating the constant bits of advice in previous results.The elimination is done by a contrapositive argument that for any reasonable computational model,let $\text{CTIME}(f(n))/{g(n)}$ denote the set of all languages decide by machines running in time $O(f(n))$ with advice of $g(n)$ bits in that model, if $\text{CTIME}(t(n))\subseteq \text{CTIME}(T(n))/{A(n)}$ then $\text{CTIME}(t(n))/a \subseteq \text{CTIME}(T(n))/{a+2^aA(n)}$ where $a$ is a constant integer.

cs.CC↗

arXiv2022

The Moment Passing Method for Wireless Channel Capacity Estimation

Wireless network capacity can be regarded as the most important performance metric for wireless communication systems. With the fast development of wireless communication technology, future wireless systems will become more and more complicated. As a result, the channel gain matrix will become a large-dimensional random matrix, leading to an extremely high computational cost to obtain the capacity. In this paper, we propose a moment passing method (MPM) to realize the fast and accurate capacity estimation for future ultra-dense wireless systems. It can determine the capacity with quadratic complexity, which is optimal considering that the cost of a single matrix operation is not less than quadratic complexity. Moreover, it has high accuracy. The simulation results show that the estimation error of this method is below 2 percent. Finally, our method is highly general, as it is independent of the distributions of BSs and users, and the shape of network areas. More importantly, it can be applied not only to the conventional multi-user multiple input and multiple output (MU-MIMO) networks, but also to the capacity-centric networks designed for B5G/6G.

cs.IT↗

arXiv2022

An Optimal Transport Approach to the Computation of the LM Rate

Mismatch capacity characterizes the highest information rate for a channel under a prescribed decoding metric, and is thus a highly relevant fundamental performance metric when dealing with many practically important communication scenarios. Compared with the frequently used generalized mutual information (GMI), the LM rate has been known as a tighter lower bound of the mismatch capacity. The computation of the LM rate, however, has been a difficult task, due to the fact that the LM rate involves a maximization over a function of the channel input, which becomes challenging as the input alphabet size grows, and direct numerical methods (e.g., interior point methods) suffer from intensive memory and computational resource requirements. Noting that the computation of the LM rate can also be formulated as an entropy-based optimization problem with constraints, in this work, we transform the task into an optimal transport (OT) problem with an extra constraint. This allows us to efficiently and accurately accomplish our task by using the well-known Sinkhorn algorithm. Indeed, only a few iterations are required for convergence, due to the fact that the formulated problem does not contain additional regularization terms. Moreover, we convert the extra constraint into a root-finding procedure for a one-dimensional monotonic function. Numerical experiments demonstrate the feasibility and efficiency of our OT approach to the computation of the LM rate.

cs.IT↗