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To address the issue of poor embedding performance in the knowledge graph of a programming design course, a joint represen-tation learning model that combines entity neighborhood infor-mation and description information is proposed. Firstly, a graph at-tention network is employed to obtain the features of entity neigh-boring nodes, incorporating relationship features to enrich the structural information. Next, the BERT-WWM model is utilized in conjunction with attention mechanisms to obtain the representation of entity description information. Finally, the final entity vector representation is obtained by combining the vector representations of entity neighborhood information and description information. Experimental results demonstrate that the proposed model achieves favorable performance on the knowledge graph dataset of the pro-gramming design course, outperforming other baseline models.

News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability., Comment: 12 pages, 2 figures

Large language models(LLMS)have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes PatternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In this paper, we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets based on silicon photomultipliers (SiPM) as main transducers. In the toy experiment, the neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. In the electromagnetic calorimeter experiment, several neural network models (FC, CNN and LSTM) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely., Comment: 26 pages, 12 figures

Recent experiment reported the self-passivated electride Hf2S with excellent stability and continuous electrocatalytic ability [S. H. Kang et al., Sci. Adv. 6, eaba7416 (2020)]. Starting from its 2H-type layered structure, we have studied the electronic, magnetic, and transport properties of the electride Hf2S in the monolayer and multilayer forms by combining first-principles electronic structure calculations and Kubo formula approach. Our calculations indicate that these thin films of Hf2S electride are both dynamically and thermodynamically stable. Astonishingly, the calculations further show that the outmost Hf atoms and the surface electron gas of the Hf2S multilayers are spin polarized, while the inner Hf atoms and the electron gas in the interlayer regions remain nonmagnetic. Due to the magnetic surface, the multilayer Hf2S exhibits many unusual transport properties such as the surface anomalous Hall effect and the electric-field-induced layer Hall effect. Our theoretical predictions on Hf2S call for future experimental verification., Comment: 5 pages, 5 figures, 34 references

The exploration of superconductivity in low-dimensional materials has attracted intensive attention for decades. Based on first-principles electronic structure calculations, we have systematically investigated the electronic and superconducting properties of the two-dimensional electride Ba$_2$N in the monolayer limit. Our results show that monolayer Ba$_2$N has a low work function of 3.0 eV and a predicted superconducting transition temperature ($T_c$) of 3.4 K. The superconductivity can be further improved with the tensile strain, which results from the increase of density of states at the Fermi level as well as the enhanced coupling between inner-layer electrons and phonons. Remarkably, at the 4$\%$ tensile strain, the acoustic branches have noticeable softening at the K point of Brillouin zone and the superconducting $T_c$ can reach 10.8 K. The effect of lattice strain on the electron transfer from the superficial region to the inner-layer region of monolayer Ba$_2$N may also apply to other electride materials and influence their physical properties., Comment: 8 pages, 9 figures

The Topmetal-M is a large area pixel sensor (18 mm * 23 mm) prototype fabricated in a new 130 nm high-resistivity CMOS process in 2019. It contains 400 rows * 512 columns square pixels with the pitch of 40 {\mu}m. In Topmetal-M, a novel charge collection method combing the Monolithic Active Pixel Sensor (MAPS) and the Topmetal sensor has been proposed for the first time. Both the ionized charge deposited by the particle in the sensor and along the track over the sensor can be collected. The in-pixel circuit mainly consists of a low-noise charge sensitive amplifier to establish the signal for the energy reconstruction, and a discriminator with a Time-to-Amplitude Converter (TAC) for the Time of Arrival (TOA) measurement. With this mechanism, the trajectory, particle hit position, energy and arrival time of the particle can be measured. The analog signal from each pixel is accessible through time-shared multiplexing over the entire pixel array. This paper will discuss the design and preliminary test results of the Topmetal-M sensor.

The significantly enhanced superconducting transition temperature ($T_c$) of an FeSe monolayer on SrTiO$_3$(001) substrate has attracted extensive attention in recent years. Here, based on first-principles electronic structure calculations, we propose another candidate substrate LaO(001) for the epitaxial growth of FeSe monolayer to realize superconductivity. Our calculations show that for the optimal adsorption structure of FeSe monolayer on LaO(001), the stripe antiferromagnetic state and the dimer antiferromagnetic state are almost energetically degenerate, indicating the existence of strong magnetic fluctuation that is beneficial to the appearance of superconductivity. According to the Bader charge analysis, the calculated electron doping from the LaO substrate to the FeSe monolayer is about 0.18 electrons per Fe atom, even larger than that in case of FeSe/SrTiO$_3$(001). Since LaO was also reported to be a superconductor with $T_c$ ~ 5 K, it may have a superconducting proximity effect on the epitaxial FeSe film and vice versa. These results suggest that LaO would be an interesting substrate to study the interface-related superconductivity., Comment: 7 pages, 6 figures, 1 table

In this paper, we present the design and test results of LOCx2, a transmitter ASIC for the ATLAS Liquid Argon Calorimeter trigger upgrade. LOCx2 consists of two channels and each channel encodes ADC data with an overhead of 14.3% and transmits serial data at 5.12 Gbps with a latency of less than 27.2 ns. LOCx2 is fabricated with a commercial 0.25-um Silicon-on-Sapphire CMOS technology and is packaged in a 100-pin QFN package. The power consumption of LOCx2 is about 843 mW.

A Liquid-argon Trigger Digitizer Board (LTDB) is being developed to upgrade the ATLAS Liquid Argon Calorimeter Phase-I trigger electronics. The LTDB located at the front end needs to obtain the clock signals and be configured and monitored remotely from the back end. A clock and control system is being developed for the LTDB and the major functions of the system have been evaluated. The design and evaluation of the clock and control system are presented in this paper.

We report the development of a mid-board, TOSA and ROSA based miniature dual channel optical transmitter (MTx) and a transceiver (MTRx). The design transmission data rate is 5.12 Gbps per channel and receiving data rate 4.8 Gbps. MTx and MTRx are only 6 mm tall and are electrically and optically pluggable. Although the fiber TOSA/ROSA coupling is through a custom latch, the fiber uses the standard LC ferrule, flange and spring. Light coupling is ensured by the TOSA and ROSA with the LC coupling mechanism. With the dual channel serializer LOCx2 sits under MTx, one achieves high data transmission with a small PCB footprint, and enjoys the reliability of the hermetically packaged TOSA. MTx and MTRx are designed for detector front-end readout of the ATLAS Liquid Argon Calorimeter (LAr) trigger upgrade.

A serializer ASIC and a VCSEL driver ASIC are needed for the front-end optical data transmission in the ATLAS liquid argon calorimeter readout phase-I upgrade. The baseline ASICs are the serializer LOCx2 and the VCSEL driver LOCld, designed in a 0.25-{\mu}m Silicon-on-Sapphire (SoS) CMOS technology and consumed 843 mW and 320 mW, respectively. Based on a 130-nm CMOS technology, we design two pin-to-pin-compatible backup ASICs, LOCx2-130 and LOCld-130. Their power consumptions are much lower then of their counterparts, whereas other performance, such as the latency, data rate, and radiation tolerance, meet the phase-I upgrade requirements. We present the design of LOCx2-130 and LOCld-130. The test results of LOCx2-130 are also presented., Comment: 12 pages, 12 figures