Abstract

Abstract- Data is useful information collected, programmed, generated and/or altered for a specific application/s or for future use. Such data sampling/data record is of utmost importance in single use airborne systems especially the military warfare. The operator will be interested in acquisition of high speed data during flight but will be denied such opportunity due to protocol and bandwidth restrictions therein. This paper deals with one such data network which is popular with many avionics designers, the conventional command response protocol MIL-STD- 1553B which restricts the overall baud rate to 1Mbps. This paper proposes a unique data fusion technique to combine (i) a 100x baud rate Multi Carrier Wave (MCW) with (ii) conventional baud rate of 1Mbps on standard MIL-STD-1553B hardware. OFDM (Orthogonal Frequency Division Multiplexing) technique] and bus protocol that is used to achieve the data flow and fusion are explained in detail.

Abstract

Background Music Emotion Recognition (MER) systems primarily rely on audio features (Wang et al., 2021), with recent approaches incorporating lyrics to analyze sentiment and structure for improved accuracy (Agrawal et al., 2021). Musical content, particularly melody, has been found to have a stronger capacity to convey perceived emotional expression than lyrics, although lyrics tend to enhance negative emotions more easily than positive emotions (Ali & Peynircioğlu, 2006). This variability raises important questions: Which component carries the greatest emotional information? Do listeners rely on music, vocals, or lyrics when assessing emotional content? Aim Here, we systematically analyse the contribution of music, vocals, and lyrics to perception of emotion in music. Methods For this study, we used the DEAM (Soleymani et al., 2013) and PMEmo (Zhang et al., 2018) datasets, comprising a total of 2,700 songs. The DEAM dataset includes 1,802 audio items annotated for both dynamic and static emotion using continuous valence and arousal ratings. Participants utilised a two-dimensional interface based on the Self‑Assessment Manikins (SAM) to continuously rate the emotional content at a 2‑Hz sampling rate. The PMEmo dataset contains 794 popular music choruses sourced from international music charts (Billboard Hot 100, iTunes Top 100, and UK Top 40) and was annotated using a similar interface to capture continuous dynamic ratings for valence and arousal at the same sampling rate. Songs were source-separated using Ultimate Vocal Remover (Takahashi et al., 2017) to isolate music and vocals, while lyrics were transcribed using OpenAI’s Whisper model (Radford et al., 2022) and verified manually. Deep learning models trained on diverse song and speech datasets predicted VA values for musical and vocal components, while lyrics were analyzed using models trained on general and lyrically annotated texts (Çano, 2017). Spearman's correlation was calculated between predicted and human-annotated (HA) VA values. Additionally, quadrant-based (Q1: positive V, high A; Q2: negative V, high A; Q3: negative V, low A; Q4: positive V, low A) concurrency analyses evaluated prediction alignment with human-identified emotional quadrants. Results The musical component exhibited the highest correlation with HA ratings (valence: r = 0.70, arousal: r = 0.74), followed by vocals (valence: r = 0.54, arousal: r = 0.65; all p < .001), while lyrics contributed the least (valence: r = 0.11, arousal: r = 0.01). In terms of overall quadrant concurrency, the musical component showed the highest concurrency (60.41%) followed by vocals (48.84%) and then lyrics (31.45%). Specifically, the highest quadrant concurrency was observed in Q3 (musical: 90.53%, vocal: 93.53%, lyrics: 66.16%) followed by Q1 for musical (64.51%) and vocals (39.10%), and Q4 for lyrics (42.50%). Discussion Our findings emphasize the dominant role of the musical component in shaping perceived emotional expression in Western tonal music, aligning with prior work highlighting melody’s emotional salience (Ali & Peynircioğlu, 2006). Furthermore, for music signifying negative valence and low arousal (Q3), there is a higher degree of congruence between the components in conveying sadness and its related emotions. These results suggest that MER systems could prioritize musical and vocal components over lyrical content (Wang et al., 2021; Agrawal et al., 2021), as they appear to primarily drive the perception of emotional expression. References Agrawal, Y., Shanker, R. G. R., & Alluri, V. (2021). Transformer-based approach towards music emotion recognition from lyrics. In V. U. Rao & R. Kailash (Eds.), Advances in information retrieval (pp. 167–175). Springer. https://doi.org/10.1007/978-3-030-72240-1_12 Ali, S. O., & Peynircioğlu, Z. F. (2006). Songs and emotions: Are lyrics and melodies equal partners? Psychology of Music, 34(4), 511–534. https://doi.org/10.1177/0305735606067168 Çano, E. (2017). MoodyLyrics: A sentiment-annotated lyrics dataset. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2017) (pp. 118–124). Association for Computational Linguistics. https://doi.org/10.18653/v1/S17-1017 Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022). Robust speech recognition via large-scale weak supervision. arXiv. https://doi.org/10.48550/arXiv.2212.04356 Soleymani, M., Caro, M., Schmidt, E., Sha, C.-Y., & Yang, Y.-H. (2013). 1000 songs for emotional analysis of music. In P. Herrera, E. Gómez, & T. Kaneko (Eds.), Proceedings of the 2nd ACM International Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies (pp. 1–6). Association for Computing Machinery. Takahashi, N., Huang, P.-S., & Lee, P. (2017). Multi-scale multi-band DenseNets for audio source separation. arXiv. https://doi.org/10.48550/arXiv.1706.09588 Wang, S., Xu, C., Ding, A. S., & Tang, Z. (2021). A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network. Electronics, 10(15), 1769. https://doi.org/10.3390/electronics10151769 Zhang, K., Zhang, H., Li, S., Yang, C., & Sun, L. (2018). The PMEmo dataset for music emotion recognition. In Proceedings of the 2018 ACM International Conference on Multimedia Retrieval (pp. 135–142). Association for Computing Machinery. https://doi.org/10.1145/3206025.3206037

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

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.