Abstract

The pyrimidine-pyrimidone (6-4) photoproduct (6-4PP) is a UV-induced DNA lesion implicated in skin disorders and cancers. The repair protein XPC/RAD4 detects this lesion and initiates nucleotide excision repair to safeguard genomic integrity. While the X-ray crystallographic structure of the 6–4PP containing DNA-RAD4/XPC complex reveals DNA distortion and extrusion of the lesion and partner bases, the precise mechanism by which RAD4/XPC initiates lesion repair remains unclear. To investigate this, we employed molecular dynamics simulations, umbrella sampling, and the nudged elastic band method to map the minimum energy path (MEP) from RAD4’s initial encounter with damaged DNA to its bound state. Our results reveal that the initial interrogation phase involves partial opening of the DNA, marked by the partial extrusion of the lesion while its partner bases largely remain intrahelical, accompanied by significant DNA unwinding near the damage site. This partially opened state represents the rate-limiting distortion, with 5′ base flipping as the bottleneck for downstream events leading to the bound state. Upon overcoming the bottleneck, the DNA adopts a final untwisted conformation, with the lesion flipping out first, followed by the complete sequential flipping of the 5′ base and then the 3′ partner bases, while full β-hairpin insertion ultimately stabilizes the final bound DNA complex. The energetics and structural intermediates along the MEP provide key insights into conformational changes that drive lesion recognition, extrusion, and stable binding, advancing our understanding of nucleotide excision repair.

Abstract

Determining spin state energy gaps (SSE) of 3d transition metal complexes (TMCs) is a major challenge in theoretical chemistry, as high-level quantum methods, though reliable, are computationally impractical for large-scale studies. This work explores a machine learning (ML)-based approach to predict DFT adiabatic SSE gaps using descriptors derived from a single high-spin DFT calculation. This approach is adopted to eliminate the differential treatment of electronic correlation between high-spin and low-spin structures. Our descriptors aim to incorporate the knowledge of crystal field theory into the ML model. They include atomic energy levels of bare metal ions, natural charges of ligating atoms, d-orbital molecular orbital eigenvalues derived from an high spin calculation, HOMO–LUMO gaps of free ligands, and simple identity-based features. We train ML models on 1434 SSE values spanning 934 complexes and demonstrate their transferability to more challenging complexes having bidentate π-bonding ligands despite being trained on simpler Werner-type monodentate complexes. We achieved a minimum MAE of 4.0 kcal mol−1 on the monodentate test set, and maintained a comparable MAE of 6.6 kcal mol−1 in the transferability assessment. This approach bypasses the need for multi-reference low-spin optimizations while retaining predictive accuracy, offering a cost-effective strategy for SSE estimation in transition metal chemistry. We hope the insights covered in this study will contribute to the development of additional electronic structure-based descriptors for SSE predictions.

Abstract

Recognizing textual attributes such as bold, italic, underline, and strikeout is essential for understanding text semantics, structure, and visual presentation. These attributes highlight key information, making them crucial for document analysis. Existing methods struggle with com putational efficiency or adaptability in noisy, multilingual settings. To address this, we introduce TexTAR, a multi-task, context-aware Trans former for Textual Attribute Recognition (TAR). Our novel data selec tion pipeline enhances context awareness, and our architecture employs a 2D RoPE (Rotary Positional Embedding)-style mechanism to incorpo rate input context for more accurate attribute predictions. We also in troduce MMTAD, a diverse, multilingual, multi-domain dataset annotated with text attributes across real-world documents such as legal records, notices, and textbooks. Extensive evaluations show TexTAR outperforms existing methods, demonstrating that contextual awareness contributes to state-of-the-art TAR performance

Abstract

This paper presents a dual-loop reference-less Clock and Data Recovery (CDR) architecture implemented in TSMC 28 nm CMOS technology for PCIe Gen3 applications. The proposed design features an extended BiDirectional Frequency Detector (BFD) that enables rapid frequency acquisition in both directions over a wide locking range. By utilizing DNFAST and DNSLOW signals, the BFD dynamically adjusts the correction rate based on frequency error, ensuring accurate frequency tracking and a seamless transition from frequency acquisition to phase tracking. This approach significantly enhances the locking performance and stability of the CDR system. The proposed CDR achieves a locking time of 4.1μs when transitioning from PCIe Gen1 (2. 5 G b p s) to PCIe Gen3 (8 G b p s) under a PRBS7 pattern with a power consumption of 3.9 mW at 0.9 V power supply.

Abstract

In this paper, we propose an efficient and robust framework for estimating leakage power while considering statistical variations in process parameters, supply voltage, and temperature (PVT) in FinFET-based digital circuits. Extensive testing reports an average error of less than 1 % in the ML models for standard digital cells, with an average MAPE (Mean Absolute Percentage Error) of 0.764%,0.770%, and 0.814% in the 7 nm,10 nm, and 16 nm FinFET technology nodes, respectively, which ensures a highly accurate leakage estimate for larger circuits. Additionally, we investigated the transistor-level approach, which distinctively estimates subthreshold, gate, and body leakage power using only transistor-level models and stacks, and investigated its trade-offs to gate-level models. Using ML techniques, we achieve a speedup of over 400 times compared to HSPICE for the characterization of digital blocks, using which complex cell designs can be analyzed across various input test combinations, significantly saving the time required for extensive simulations.

Abstract

Document layout analysis is essential for downstream tasks such as information retrieval, extraction, OCR, and digitization. However, existing large-scale datasets like PubLayNet and DocBank lack fine-grained region labels and multilingual diversity, making them insufficient for representing complex document layouts. Human-annotated datasets such as M6Doc and D4LA offer richer labels and greater domain diversity, but are too small to train robust models and lack adequate multilingual coverage. This gap is especially pronounced for Indic documents, which encompass diverse scripts yet remain underrepresented in current datasets, further limiting progress in this space. To address these shortcomings, we introduce IndicDLP, a large-scale foundational document layout dataset spanning 11 representative Indic languages alongside English and 12 common document domains. Additionally, we curate UED-mini, a dataset derived from DocLayNet and M6Doc, to enhance pretraining and provide a solid foundation for Indic layout models. Our experiments demonstrate that fine-tuning existing English models on IndicDLP significantly boosts performance, validating its effectiveness. Moreover, models trained on IndicDLP generalize well beyond Indic layouts, making it a valuable resource for document digitization. This work bridges gaps in scale, diversity, and annotation granularity, driving inclusive and efficient document understanding

Abstract

A 16-Gb/s continuous-time linear equalizer (CTLE) is designed in 28-nm CMOS technology, featuring an active inductor load and feedforward compensation to enhance high-frequency performance. The proposed design features a power-efficient architecture, achieving a remarkable eye opening of 45.75 ps and a vertical differential eye opening of 781.19 mV. The design achieves reliable operation for channel losses of 9 dB, consuming a total power of 3.3 mW from a 0.9 V supply. This CTLE provides an energy-efficient solution for high-speed SerDes applications, with a calculated figure-of-merit of 0.022 pJ/hit/dB.

Abstract

Segment matching is an important intermediate task in computer vision that establishes correspondences between semantically or geometrically coherent regions across images. Unlike keypoint matching, which focuses on localized features, segment matching captures structured regions, offering greater robustness to occlusions, lighting variations, and viewpoint changes. In this paper, we leverage the spatial understanding of 3D foundation models to tackle wide-baseline segment matching, a challenging setting involving extreme viewpoint shifts. We propose an architecture that uses the inductive bias of these 3D foundation models to match segments across image pairs with up to 180 degree view-point change rotation. Extensive experiments show that our approach outperforms state-of-the-art methods, including the SAM2 video propagator and local feature matching methods, by up to 30% on the AUPRC metric, on ScanNet++ and Replica datasets. We further demonstrate benefits of the proposed model on relevant downstream tasks, including 3D instance mapping and object-relative navigation. Project Page: https://segmast3r.github.io/

Abstract

We propose an alignment-free, end-to-end Non-Audible Murmur (NAM)-to-Speech conversion model. Existing methods rely on large NAM-text pairs per speaker to generate high-quality alignments for training non-autoregressive models. However, alignment quality deteriorates when trained on multispeaker data, limiting their ability to generalize and effectively utilize the available training data. To address this, we introduce a streamlined autoregressive approach that eliminates the need for explicit alignment learning. By leveraging multi-speaker samples, synthetic training pairs, and multitask character recognition training, our method reduces the word error rate (WER) by 59.19% compared to the state-of-the-art (SOTA) on two public datasets. We demonstrate the model’s zero-shot capability and validate the effectiveness of multitask training through ablation studies. Speech samples are available at https: //noalignnam.github.io/autoregressiveNAM/

Abstract

We use the spin-boson model to describe the dynamics of a two-level atom interacting with Fabry-Pérot cavity modes. We solve the Schrödinger equation for the system-bath model without the Born-Markov approximation to derive the non-Markovian reduced dynamics of the qubit. We further construct an exact Lindblad-type master equation for it. Similar to the quantum Otto cycle, we construct a non-Markovian quasicyclic process based on the atom-cavity interactions, which we call the quasi-Otto cycle. For judicious choices of input state and parameters, the quasicycle can be more efficient as a quantum engine than the Otto cycle. We also show that if the quasicycle is repeated multiple times, the efficiency of the quasi-Otto engine asymptotically approaches that of the Otto engine.