arXiv subjects

Baohua Sun

Publications and source records attributed to Baohua Sun.

At least 19 recordsLinked to original sources

arXiv2026

Reaction Cross Sections and $\alpha$-Cluster Geometry in $^{12}$C and Be Isotopes

Reaction cross sections $\sigma_{\rm R}$ are widely used to infer matter radii, yet their sensitivity to nuclear structure beyond radial one-body distributions is less well understood. We combine complete $A$-body nucleon configurations sampled from \textit{ab initio} nuclear lattice effective field theory (NLEFT) with event-by-event Monte Carlo Glauber calculations, thereby retaining the many-body correlations encoded in NLEFT. Using a fixed binary-collision prescription determined by the measured energy- and isospin-dependent total nucleon-nucleon cross sections, the calculations capture the overall magnitudes and energy dependence simultaneously for the available data on $^{12}$C and $^{9}$Be projectiles on carbon and hydrogen. Controlled randomization of angular correlations at fixed matter root-mean-square radius and spherically averaged one-body radial density produces only a weak change in $\sigma_{\rm R}$ for $^{12}$C but approximately a $10\%$ increase for $^{9}\mathrm{Be}+{}^{1}\mathrm{H}$. The calculations also capture the measured rise--plateau--sharp-rise--reduction trend across $^{7,9\text{--}12}$Be, a distinctive pattern reflecting the evolution of cluster and halo structures along the isotopic chain. These results show that $\sigma_{\rm R}$ retains sensitivity to intrinsic many-body geometry beyond a single inferred matter radius, opening a route to studies of exotic $\alpha$-cluster geometries and spatial nucleon correlations through reaction cross sections.

nucl-th↗

arXiv2026

Purifying one-neutron removal as a probe of single-particle strength

One-neutron removal reactions exhibit a strong proton-neutron asymmetry dependence in the inclusive reduction factor $R_s$, a long-standing issue that has been discussed in terms of both possible intrinsic isospin dependence of single-particle strength and reaction-mechanism effects. We address this issue by reframing inclusive removal as a coupled fast-dynamics and deexcitation process, and by validating this transport-deexcitation chain against a global, mutually constraining data set. Confronting 73 one-neutron removal cross sections and 28 residue parallel-momentum distributions with isospin-dependent quantum molecular dynamics followed by GEMINI evaporation shows that the apparent $R_s$-$\Delta S$ trend is correlated with evaporation feeding and evaporation loss. By subtracting the feeding contribution and correcting for the loss component in the measured cross sections, we construct a purified reduction factor $R_{\rm dir}$, that more closely reflects single-particle strength than the inclusive $R_s$. The resulting $R_{\rm dir}$ exhibits a much weaker $\Delta S$ dependence within current uncertainties, consistent with the weak isospin-asymmetry dependence observed in nucleon-transfer and quasifree-knockout systematics.

nucl-th↗

arXiv2025

Identification and online monitoring of experimental measurement states via Cuscore statistic

We present a statistical method for detecting and analysing state changes in experimental measurements using the Cuscore statistic and its special case, the Centred Cuscore statistic. These statistics are designed to identify deviations in detector responses using sequential hypothesis testing relative to a defined reference state. Applications to charge-changing reaction experiments at the FRagment Separator facility at the GSI Helmholtz Centre for Heavy Ion Research, Germany, and the Second Radioactive Ion Beam Line in Lanzhou at the Institute of Modern Physics, China, demonstrate the ability of these tools to quantify state changes, identify the change point, and classify data segments based on measured states. For long-term online monitoring, we use the exponentially weighted moving average to continuously update computations, enabling the detection of successive changes. This method supports both real-time and post-experiment diagnostics and provides a robust approach for enhancing data integrity and experimental control in nuclear physics and related fields.

physics.ins-det↗

arXiv2025

Point-proton density distributions of stable nuclei

Point-proton density distributions are deduced for 130 stable nuclei from $^{7}\mathrm{Li}$ to $^{232}\mathrm{Th}$ from nuclear charge densities determined in elastic electron scattering. There are 171 cases are presented in model-dependent forms, including the modified Harmonic-oscillator function, two-parameter Femi function (2pF), three-parameter Femi function, three-parameter Gaussian function, and 97 in Fourier-Bessel series model-independent forms. Independent of density functions, the point-proton root-mean-square (rms) radii of the derived point-proton density show excellent agreement with each other. We identify cases where the tabulated data of charge densities and charge radii are inconsistent, and the deduced point-proton density distributions are inaccurate due to insufficient experimental momentum transfer coverage or inconsistent scattering experiments. For the widely used 2pF distribution, it is found that the surface diffuseness parameters can be empirically calculated from those of charge density, while the half-density radius parameters follow the $A^{1/3}$ rule. The derived point-proton density distributions can be used as input in nuclear reaction studies and compared with nuclear model predictions.

nucl-th↗

arXiv2023

Probing high-momentum component in nucleon momentum distribution by neutron-proton bremsstrahlung γ-rays in heavy ion reactions

The high momentum tail (HMT) of nucleons, as a signature of the short-range correlations in nuclei, has been investigated by the high-energy bremsstrahlung $γ$ rays produced in $^{86}$Kr + $^{124}$Sn at 25 MeV/u. The energetic photons are measured by a CsI(Tl) hodoscope mounted on the spectrometer CSHINE. The energy spectrum above 30 MeV can be reproduced by the IBUU model calculations incorporating the photon production channel from $np$ process in which the HMTs of nucleons is considered. A non-zero HMT ratio of about $15\%$ is favored by the data. The effect of the capture channel $np \to dγ$ is demonstrated.

nucl-ex↗

arXiv2021

GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS

Object detection is widely used on embedded devices. With the wide availability of CNN (Convolutional Neural Networks) accelerator chips, the object detection applications are expected to run with low power consumption, and high inference speed. In addition, the CPU load is expected to be as low as possible for a CNN accelerator chip working as a co-processor with a host CPU. In this paper, we optimize the object detection model on the CNN accelerator chip by minimizing the CPU load. The resulting model is called GnetDet. The experimental result shows that the GnetDet model running on a 224mW chip achieves the speed of 106FPS with excellent accuracy.

cs.AR↗

arXiv2021

GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS

Semantic segmentation is the task to cluster pixels on an image belonging to the same class. It is widely used in the real-world applications including autonomous driving, medical imaging analysis, industrial inspection, smartphone camera for person segmentation and so on. Accelerating the semantic segmentation models on the mobile and edge devices are practical needs for the industry. Recent years have witnessed the wide availability of CNN (Convolutional Neural Networks) accelerators. They have the advantages on power efficiency, inference speed, which are ideal for accelerating the semantic segmentation models on the edge devices. However, the CNN accelerator chips also have the limitations on flexibility and memory. In addition, the CPU load is very critical because the CNN accelerator chip works as a co-processor with a host CPU. In this paper, we optimize the semantic segmentation model in order to fully utilize the limited memory and the supported operators on the CNN accelerator chips, and at the same time reduce the CPU load of the CNN model to zero. The resulting model is called GnetSeg. Furthermore, we propose the integer encoding for the mask of the GnetSeg model, which minimizes the latency of data transfer between the CNN accelerator and the host CPU. The experimental result shows that the model running on the 224mW chip achieves the speed of 318FPS with excellent accuracy for applications such as person segmentation.

cs.CV↗

arXiv2020

SuperOCR: A Conversion from Optical Character Recognition to Image Captioning

Optical Character Recognition (OCR) has many real world applications. The existing methods normally detect where the characters are, and then recognize the character for each detected location. Thus the accuracy of characters recognition is impacted by the performance of characters detection. In this paper, we propose a method for recognizing characters without detecting the location of each character. This is done by converting the OCR task into an image captioning task. One advantage of the proposed method is that the labeled bounding boxes for the characters are not needed during training. The experimental results show the proposed method outperforms the existing methods on both the license plate recognition and the watermeter character recognition tasks. The proposed method is also deployed into a low-power (300mW) CNN accelerator chip connected to a Raspberry Pi 3 for on-device applications.

cs.CV↗

arXiv2020

Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device

Recent years NLP research has witnessed the record-breaking accuracy improvement by DNN models. However, power consumption is one of the practical concerns for deploying NLP systems. Most of the current state-of-the-art algorithms are implemented on GPUs, which is not power-efficient and the deployment cost is also very high. On the other hand, CNN Domain Specific Accelerator (CNN-DSA) has been in mass production providing low-power and low cost computation power. In this paper, we will implement the Super Characters method on the CNN-DSA. In addition, we modify the Super Characters method to utilize the multi-modal data, i.e. text plus tabular data in the CL-Aff sharedtask.

cs.CL↗

arXiv2019

Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysis

The Super Characters method addresses sentiment analysis problems by first converting the input text into images and then applying 2D-CNN models to classify the sentiment. It achieves state of the art performance on many benchmark datasets. However, it is not as straightforward to apply in Latin languages as in Asian languages. Because the 2D-CNN model is designed to recognize two-dimensional images, it is better if the inputs are in the form of glyphs. In this paper, we propose SEW (Squared English Word) method generating a squared glyph for each English word by drawing Super Characters images of each English word at the alphabet level, combining the squared glyph together into a whole Super Characters image at the sentence level, and then applying the CNN model to classify the sentiment within the sentence. We applied the SEW method to Wikipedia dataset and obtained a 2.1% accuracy gain compared to the original Super Characters method. For multi-modal data with both structured tabular data and unstructured natural language text, the modified SEW method integrates the data into a single image and classifies sentiment with one unified CNN model.

cs.CL↗

arXiv2019

SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data

Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved the state of art result in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned two-dimensional CNN models for classification. Experimental results have shown that the proposed SuperTML method had achieved state-of-the-art results on both large and small datasets.

cs.CV↗

arXiv2019

SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding

Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop.

cs.CL↗

arXiv2019

System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications

Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 ontologies. The second demo classifies the Chinese online-shopping review into positive or negative.

cs.CL↗

arXiv2019

SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models

The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational response for open domain dialogues. The experimental results on a public dataset shows that the proposed SuperChat method generates high quality responses. An interactive demo is ready to show at the workshop.

cs.CL↗

arXiv2018

MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications

We designed a device for Convolution Neural Network applications with non-volatile MRAM memory and computing-in-memory co-designed architecture. It has been successfully fabricated using 22nm technology node CMOS Si process. More than 40MB MRAM density with 9.9TOPS/W are provided. It enables multiple models within one single chip for mobile and IoT device applications.

eess.SP↗

arXiv2018

Super Characters: A Conversion from Sentiment Classification to Image Classification

We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English.

cs.CL↗

arXiv2018

High precision nuclear mass predictions towards a hundred kilo-electron-volt accuracy

Mass is a fundamental property and an important fingerprint of atomic nucleus. It provides an extremely useful test ground for nuclear models and is crucial to understand energy generation in stars as well as the heavy elements synthesized in stellar explosions. Nuclear physicists have been attempting at developing a precise, reliable, and predictive nuclear model that is suitable for the whole nuclear chart, while this still remains a great challenge even in recent days. Here we employ the Fourier spectral analysis to examine the deviations of nuclear mass predictions to the experimental data and to present a novel way for accurate nuclear mass predictions. In this analysis, we map the mass deviations from the space of nucleon number to its conjugate space of frequency, and are able to pin down the main contributions to the model deficiencies. By using the radial basis function approach we can further isolate and quantify the sources. Taking a pedagogical mass model as an example, we examine explicitly the correlation between nuclear effective interactions and the distributions of mass deviations in the frequency domain. The method presented in this work, therefore, opens up a new way for improving the nuclear mass predictions towards a hundred kilo-electron-volt accuracy, which is argued to be the chaos-related limit for the nuclear mass predictions.

nucl-th↗

arXiv2018

Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications

Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size should all be taken into consideration for mobile and embedded applications. Domain Specific Architecture (DSA) for CNN is the efficient and practical solution for CNN deployment and implementation. We designed and produced a 28nm Two-Dimensional CNN-DSA accelerator with an ultra power-efficient performance of 9.3TOPS/Watt and with all processing done in the internal memory instead of outside DRAM. It classifies 224x224 RGB image inputs at more than 140fps with peak power consumption at less than 300mW and an accuracy comparable to the VGG benchmark. The CNN-DSA accelerator is reconfigurable to support CNN model coefficients of various layer sizes and layer types, including convolution, depth-wise convolution, short-cut connections, max pooling, and ReLU. Furthermore, in order to better support real-world deployment for various application scenarios, especially with low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also designed algorithms to fully utilize the CNN-DSA accelerator efficiently by reducing the dependency on external accelerator computation resources, including implementation of Fully-Connected (FC) layers within the accelerator and compression of extracted features from the CNN-DSA accelerator. Live demos with our CNN-DSA accelerator on mobile and embedded systems show its capabilities to be widely and practically applied in the real world.

cs.CV↗