Error Correction of Transversal CNOT Gates for Scalable Surface-Code Computation

Recent experimental advances have made it possible to implement logical multiqubit transversal gates on surface codes in a multitude of platforms. A transversal controlled-not (tcnot) gate on two surface codes introduces correlated errors across the code blocks and thus requires modified decoding compared to established methods of decoding surface-code quantum memory (SCQM) or lattice-surgery operations. In this work, we examine and benchmark the performance of three different decoding strategies for the tcnot for scalable fault-tolerant quantum computation. In particular, we present a low-complexity decoder based on minimum-weight perfect matching (MWPM) that achieves the same threshold as the SCQM MWPM decoder. We extend our analysis with a study of tailored decoding of a transversal-teleportation circuit, along with a comparison between the performance of lattice-surgery and transversal operations under Pauli- and erasure-noise models. Our investigation builds toward systematic estimation of the cost of implementing large-scale quantum algorithms based on transversal gates in the surface code

Concentrated confinement effect of FRP jacket on compressive behavior of confined UHPC

Drawing upon the interplay between crack evolution within the confined ultra-high-performance concrete (UHPC) and external fiber reinforced polymer (FRP) jackets, an innovative concept dubbed the concentrated confinement effect is put forward to illuminate the occurrence of the first ascending branch in the axial compressive stress-strain response of FRP jacket-confined UHPC. Further, the crack evolution, dilation characteristics and stress-strain response of compressive behavior of FRP jacket-confined UHPC are systematically investigated to verify the rationality of concentrated confinement effect. Also, a model is established to predict the compressive strength of the first ascending branch of FRP jacket-confined UHPC with varying confinement stiffness. The practical engineering significance of the first ascending branch to the compressive properties of FRP jacket-confined UHPC is emphasized: the ultimate strength offers a safety margin, while the first ascending branch strength is more appropriate for the design requirements of structural engineering. © 2025 Elsevier Lt

A 108-to-141.8GHz 27.1%-Tuning-Range Synthesizer Employing a Dual-Reference-FTL Sub-Sampling PLL and 3rd-Harmonic-Enhancement Class-F VCO and Injection-Locked Frequency Tripler

A sub-THz frequency synthesizer employs a cascade of a sub-sampling PLL featuring a frequency-tracking loop with dual co-prime sub-sampling factors for an infinite lock-in range, a 3rd harmonic-amplitude-enhancement class-F VCO, a harmonic-rejection-enhancement class-F ILFT, and a feed-forward locking-range enhancement. The proposed synthesizer measures 27.1% frequency tuning range from 108 GHz to 141.8 GHz with maximum output power of -22dBm, -3dB bandwidth of 25GHz while consuming 38mW

PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent

Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content. To address these challenges, we introduce PropaInsight, a conceptual framework grounded in foundational social science research, which systematically dissects propaganda into techniques, arousal appeals, and underlying intent. PropaInsight offers a more granular understanding of how propaganda operates across different contexts. Additionally, we present PropaGaze, a novel dataset that combines human-annotated data with high-quality synthetic data generated through a meticulously designed pipeline. Our experiments show that off-the-shelf LLMs struggle with propaganda analysis, but PropaGaze significantly improves performance. Fine-tuned Llama-7B-Chat achieves 203.4% higher text span IoU in technique identification and 66.2% higher BertScore in appeal analysis compared to 1-shot GPT-4-Turbo. Moreover, PropaGaze complements limited human-annotated data in data-sparse and cross-domain scenarios, demonstrating its potential for comprehensive and generalizable propaganda analysis

Distributions and evolution of trap states in non-fullerene organic solar cells

The photovoltaic performance of non-fullerene organic solar cells (OSCs) is essentially determined by the presence of charge traps. However, their exact distributions in OSCs have remained unclear. Here, we report the successful profiling of spatial and energetic distributions of trap states via the drive-level capacitance profiling (DLCP) method. Our DLCP results unveil that the trap densities at device interfaces are 1 to 2 orders of magnitude greater than those of the film interior, and improving film crystallinity helps reduce trap density. Furthermore, the DLCP method enables operando monitoring of trap evolution during OSC operation, which reveals that trap evolution is strongly correlated with film morphology stability. The OSCs with stable morphology show minimal changes in trap distributions and can operate for 500 h without significant efficiency loss. With this method, we establish the correlations between trap distributions/evolution and device efficiency/stability and provide insightful guidance toward more efficient and stable OSCs. © 2024 Elsevier Inc

Enhanced removal of methylene blue using KMnO4-modified kitchen waste-derived lignin

Adsorption is considered a simple and efficient method for treating dye wastewater. However, poor adsorption capacity, high cost, and complex production processes significantly limit the widespread use of adsorption. Kitchen waste-derived lignin, an inert component, is challenging to treat and underutilized. Nevertheless, the presence of various functional groups in lignin makes it a potential adsorbent for organic pollutants. Therefore, kitchen waste-derived lignin adsorption enables efficient waste management and cost-effective pollutant removal. Herein, potassium permanganate-modified lignin powder (KMnO4-LP) was synthesized using methylene blue (MB) as a target pollutant and kitchen waste-derived lignin as a raw material. The effects of factors such as dosage, pH, contact time, and initial MB concentration on the adsorption capacity of KMnO4-LP were investigated. Batch experiments revealed that KMnO4-LP achieved an MB removal rate of 95.94% under optimal conditions (MB concentration of 50 mg L−1, KMnO4-LP dosage of 3 g L−1, and pH 13). The adsorption process was well described by the quasi-secondary kinetic and Langmuir isotherm models. KMnO4-LP exhibited a significantly higher maximum adsorption capacity (48.19 mg g−1) than the unmodified lignin (16.18 mg g−1). Thermodynamic analysis indicated that the negative ΔG values (−0.43, −0.87, and −6.33 kJ mol−1) and the positive ΔH value (86.66 kJ mol−1) indicated that the adsorption process was both spontaneous and endothermic. The adsorption site energy distribution highlighted that KMnO4-LP had a strong affinity for MB. After five regeneration cycles using NaOH solution as the desorbent, the removal efficiency of KMnO4-LP for MB decreased from 93.1% to 86.3%. This study indicates that KMnO4-lignin is an effective adsorbent for MB removal from water and provides novel insights into the coupling of MB removal with the utilization of kitchen waste-derived lignin. © 2025 The Royal Society of Chemistry

A Reflection on Change Classification in the Era of Large Language Models

Change classification, today known as Just-in-Time Defect Prediction, is a technique for predicting software bugs at the change level of granularity. Several ideas came together to form change classification: predictions on code changes, using word-level textual features, use of machine learning classifiers, and leveraging open source code repositories. While change classification has led to a robust line of research, it has not yet had significant industrial adoption. A key recommendation is to explore explainability features so developers can better understand why a prediction is being made. We explore how large language models can advance this work by providing prediction explanations and bug fix suggestions. © 1976-2012 IEEE

아카이브 자료의 맥락화 강화: 인공지능을 활용한 이미지 처리

디지털화가 가속화되는 시대에 도서관은 단순히 물리적인 자료를 보관하고 전시하는 전통적 역할을 넘어 진화하고 있다. 현재 많은 도서관은 소장 자료의 접근성과 맥락화를 강화하기 위해 첨단 기술을 활용한 새로운 전략을 도입하고 있다. 본 글에서는 새로운 전략 중 특히 소장 이미지 및 사진 자료 측면에 초점을 맞춰 혁신적인 전략으로 도서관의 시각 자료를 관리, 분류, 제시하는 방식을 어떻게 바꿀 수 있는지 논의한다. 인공지능(Artificial Intelligence, AI), 특히 컴퓨터 비전 모델은 도서관에서 사진, 예술 작품 등 시각적 콘텐츠를 보다 효율적으로 관리할 수 있는 기술적 도약을 제공한다. 도서관에서는 객체 탐지, 이미지 태깅, 얼굴 인식 등의 기술을 활용해 대용량의 시각 데이터를 보다 효율적으로 분류할 수 있다. 뿐만 아니라 다양한 계층과 연령의 사람들이 개별 자료를 더 쉽게 찾고 이용할 수 있도록 더욱 풍부한 맥락 정보를 제공하며, 이는 사용자 참여와 연구 기회의 증가로 이어진다. 이러한 기술을 통해 이미지가 자동으로 분석되므로 도서관 직원은 더욱 효율적으로 정보를 분류하고 검색할 수 있다. 또한, 이미지 콘텐츠와 맥락에 대한 더 깊은 통찰력을 제공함으로써 스토리텔링을 강화하며, 사용자와 자료 간의 연결성을 증진시킨다

Telling Data Stories with the Hero's Journey: Design Guidance for Creating Data Videos

Data videos increasingly becoming a popular data storytelling form represented by visual and audio integration. In recent years, more and more researchers have explored many narrative structures for effective and attractive data storytelling. Meanwhile, the Hero's Journey provides a classic narrative framework specific to the Hero's story that has been adopted by various mediums. There are continuous discussions about applying Hero's Journey to data stories. However, so far, little systematic and practical guidance on how to create a data video for a specific story type like the Hero's Journey, as well as how to manipulate its sound and visual designs simultaneously. To fulfill this gap, we first identified 48 data videos aligned with the Hero's Journey as the common storytelling from 109 high-quality data videos. Then, we examined how existing practices apply Hero's Journey for creating data videos. We coded the 48 data videos in terms of the narrative stages, sound design, and visual design according to the Hero's Journey structure. Based on our findings, we proposed a design space to provide practical guidance on the narrative, visual, and sound custom design for different narrative segments of the hero's journey (i.e., Departure, Initiation, Return) through data video creation. To validate our proposed design space, we conducted a user study where 20 participants were invited to design data videos with and without our design space guidance, which was evaluated by two experts. Results show that our design space provides useful and practical guidance for data storytellers effectively creating data videos with the Hero's Journey. © 1995-2012 IEEE

Effect of Acceptor-Type Traps in GaN Buffer Layer on Current Collapse of ε-Ga₂O₃/GaN HEMTs

In this paper, we investigate the effects of acceptor-type traps in the GaN buffer layer on current collapse in ε-Ga2O3/GaN high-electron-mobility transistors (HEMTs). Numerical simulations were conducted across a wide range of trap densities (1 × 1015 cm−3 to 1 × 1018 cm−3) and energy levels (0.4 eV to 1.0 eV). The results show that as trap density increased, current dispersion increased to a peak value of 0.34 A/mm, with a dispersion percentage of 30.91%. Higher trap energy levels (0.6 eV, 0.8 eV, and 1.0 eV) reduced current collapse due to limited electron trapping. Conversely, at a lower energy level of 0.4 eV, rapid recovery prevented significant net current loss despite initial current collapse. For comparison, Al0.28Ga0.72N/GaN HEMTs were also analyzed, showing a similar trend in the effect of trap energy levels, but with a non-monotonic dependence on trap density due to the lower two-dimensional electron gas (2DEG) concentration. These findings highlight the importance of optimizing trap density and energy levels to mitigate current collapse and improve device performance. Such optimizations can make ε-Ga2O3/GaN HEMTs more reliable and efficient for high-power applications requiring stability and robustness. © The Minerals, Metals & Materials Society 2025