Tootfinder

Frequency comb generation in low-loss, low-stress, high-Q deuterated silicon nitride microring resonators in an 8-inch photonics platform
Y. Cao, G. F. Chen, C. Lau, L. Y. M. Tobing, S. L. H. Jang, Y. F. Tsang, J. O. Yoo, Y. T. Toh, J. S. Goh, L. W. Lim, C. W. Wong, D. K. T. Ng, D. T. H. Tan, X. Luo
https://arxiv.org/abs/2507.17287

Frequency comb generation in low-loss, low-stress, high-Q deuterated silicon nitride microring resonators in an 8-inch photonics platform
Systematic studies on different SiN films in terms of propagation losses are presented, and deuterated SiN emerges as a good candidate for ultralow loss (< 0.1 dB/cm) and reliability by simple 8-inch process with low thermal budget. Frequency comb generation in high-Q (~1 million) deuterated silicon nitride microring is demonstrated and used for intensity modulated direct detection transmission. Negligible power penalty for 25.78 GBaud/s NRZ and PAM4 is achieved at error rates <10-6, below the …

Robust quantum communication through lossy microwave links
James D. Teoh, Nathanael Cottet, Patrick Winkel, Luke D. Burkhart, Luigi Frunzio, Robert J. Schoelkopf
https://arxiv.org/abs/2509.18547

Language Generation in the Limit: Noise, Loss, and Feedback
Yannan Bai, Debmalya Panigrahi, Ian Zhang
https://arxiv.org/abs/2507.15319 https://

Language Generation in the Limit: Noise, Loss, and Feedback
Kleinberg and Mullainathan (2024) recently proposed a formal framework called language generation in the limit and showed that given a sequence of example strings from an unknown target language drawn from any countable collection, an algorithm can correctly generate unseen strings from the target language within finite time. This notion was further refined by Li, Raman, and Tewari (2024), who defined stricter categories of non-uniform and uniform generation. They showed that a finite union of …

Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
Minsoo Kim, Jip Kim
https://arxiv.org/abs/2507.17194

Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
Optimal transmission switching (OTS) improves optimal power flow (OPF) by selectively opening transmission lines, but its mixed-integer formulation increases computational complexity, especially on large grids. To deal with this, we propose a dispatch-aware deep neural network (DA-DNN) that accelerates DC-OTS without relying on pre-solved labels. DA-DNN predicts line states and passes them through a differentiable DC-OPF layer, using the resulting generation cost as the loss function so that al…

Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation
Vasiliki Ismiroglou, Malte Pedersen, Stefan H. Bengtson, Andreas Aakerberg, Thomas B. Moeslund
https://arxiv.org/abs/2509.15011

Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation
In recent years, the underwater image formation model has found extensive use in the generation of synthetic underwater data. Although many approaches focus on scenes primarily affected by discoloration, they often overlook the model's ability to capture the complex, distance-dependent visibility loss present in highly turbid environments. In this work, we propose an improved synthetic data generation pipeline that includes the commonly omitted forward scattering term, while also considering a …

Active RISs: Modeling and Optimization
Recep Akif Tasci, Panagiotis Gavriilidis, Ertugrul Basar, George C. Alexandropoulos
https://arxiv.org/abs/2507.16499 https://

Active RISs: Modeling and Optimization
Reconfigurable Intelligent Surfaces (RIS)-empowered communication has emerged as a transformative technology for next generation wireless networks, enabling the programmable shaping of the propagation environment. However, conventional RISs are fundamentally limited by the double path loss effect, which severely attenuates the reflected signals. To overcome this, active RIS architectures, capable of amplifying impinging signals, have been proposed. This chapter investigates the modeling, perfor…

Spatially tailored spin wave excitation for spurious-free, low-loss magnetostatic wave filters with ultra-wide frequency tunability
Shuxian Wu, Shun Yao, Xingyu Du, Chin-Yu Chang, Roy H. Olsson III
https://arxiv.org/abs/2507.14469

Spatially tailored spin wave excitation for spurious-free, low-loss magnetostatic wave filters with ultra-wide frequency tunability
Yttrium iron garnet magnetostatic wave (MSW) radio frequency (RF) cavity filters are promising for sixth-generation (6G) communication systems due to their wide frequency tunability. However, the presence of severe spurious modes arising from the finite cavity dimensions severely degrades the filter performance. We present a half-cone transducer that spatially tailors spin wave excitation to selectively enhance the primary cavity modes comprising the MSW filter passband, while strongly suppress…

A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation
Frederik Marinus Trudslev, Matteo Lissandrini, Juan Manuel Rodriguez, Martin B{\o}gsted, Daniele Dell'Aglio
https://arxiv.org/abs/2507.11324

A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation
Privacy Preserving Synthetic Data Generation (PP-SDG) has emerged to produce synthetic datasets from personal data while maintaining privacy and utility. Differential privacy (DP) is the property of a PP-SDG mechanism that establishes how protected individuals are when sharing their sensitive data. It is however difficult to interpret the privacy loss ($\varepsilon$) expressed by DP. To make the actual risk associated with the privacy loss more transparent, multiple privacy metrics (PMs) have b…

Imbalance in Balance: Online Concept Balancing in Generation Models
Yukai Shi, Jiarong Ou, Rui Chen, Haotian Yang, Jiahao Wang, Xin Tao, Pengfei Wan, Di Zhang, Kun Gai
https://arxiv.org/abs/2507.13345

Imbalance in Balance: Online Concept Balancing in Generation Models
In visual generation tasks, the responses and combinations of complex concepts often lack stability and are error-prone, which remains an under-explored area. In this paper, we attempt to explore the causal factors for poor concept responses through elaborately designed experiments. We also design a concept-wise equalization loss function (IMBA loss) to address this issue. Our proposed method is online, eliminating the need for offline dataset processing, and requires minimal code changes. In o…

$\Delta L$ Normalization: Rethink Loss Aggregation in RLVR
Zhiyuan He, Xufang Luo, Yike Zhang, Yuqing Yang, Lili Qiu
https://arxiv.org/abs/2509.07558 https://

$ΔL$ Normalization: Rethink Loss Aggregation in RLVR
We propose $ΔL$ Normalization, a simple yet effective loss aggregation method tailored to the characteristic of dynamic generation lengths in Reinforcement Learning with Verifiable Rewards (RLVR). Recently, RLVR has demonstrated strong potential in improving the reasoning capabilities of large language models (LLMs), but a major challenge lies in the large variability of response lengths during training, which leads to high gradient variance and unstable optimization. Although previous methods…

On the Trade-Off Between Sum-Rate and Energy Efficiency through the Convergence of HAPS and Active RIS Technologies
Bilal Karaman, Ilhan Basturk, Ferdi Kara, Metin Ozturk, Sezai Taskin, Halil Yanikomeroglu
https://arxiv.org/abs/2507.13889

On the Trade-Off Between Sum-Rate and Energy Efficiency through the Convergence of HAPS and Active RIS Technologies
This paper investigates the integration of active reconfigurable intelligent surfaces (RIS) relay with high-altitude platform stations (HAPS) to enhance non-terrestrial network (NTN) performance in next-generation wireless systems. While prior studies focused on passive RIS architectures, the severe path loss and double fading in long-distance HAPS links make active RIS a more suitable alternative due to its inherent signal amplification capabilities. We formulate a sum-rate maximization proble…

Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
Yichen Han, Xiaoyang Hao, Keming Chen, Weibo Xiong, Jun He, Ruonan Zhang, Junjie Cao, Yue Liu, Bowen Li, Dongrui Zhang, Hui Xia, Huilei Fu, Kai Jia, Kaixuan Guo, Mingli Jin, Qingyun Meng, Ruidong Ma, Ruiqian Fang, Shaotong Guo, Xuhui Li, Yang Xiang, Ying Zhang, Yulong Liu, Yunfeng Li, Yuyi Zhang, Yuze Zhou, Zhen Wang, Zhaowen Chen

Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
Text-to-speech (TTS) synthesis has seen renewed progress under the discrete modeling paradigm. Existing autoregressive approaches often rely on single-codebook representations, which suffer from significant information loss. Even with post-hoc refinement techniques such as flow matching, these methods fail to recover fine-grained details (e.g., prosodic nuances, speaker-specific timbres), especially in challenging scenarios like singing voice or music synthesis. We propose QTTS, a novel TTS fra…

Early Stopping Chain-of-thoughts in Large Language Models
Minjia Mao, Bowen Yin, Yu Zhu, Xiao Fang
https://arxiv.org/abs/2509.14004 https://arxiv.org/pdf/2…

Early Stopping Chain-of-thoughts in Large Language Models
Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (CoT), but such a lengthy CoT incurs high inference costs. In this study, we introduce ES-CoT, an inference-time method that shortens CoT generation by detecting answer convergence and stopping early with minimal performance loss. At the end of each reasoning step, we prompt the LLM to output its current final answer, denoted as a step answer. We then…

Loss Functions for Detecting Outliers in Panel Data
Charles D. Coleman, Thomas Bryan
https://arxiv.org/abs/2509.07014 https://arxiv.org/pdf/2509.07014

Loss Functions for Detecting Outliers in Panel Data
The detection of outliers is of critical importance in the assurance of data quality. Outliers may exist in observed data or in data derived from these observed data, such as estimates and forecasts. An outlier may indicate a problem with its data generation process or may simply be a true statement about the world. Without making any distributional assumptions, this paper proposes the use of loss functions to detect these outliers in panel data. An unsigned loss function is derived axiomatical…

Loss-tolerant parallelized Bell-state generation with a hybrid cat qudit
Z. M. McIntyre, W. A. Coish
https://arxiv.org/abs/2509.08577 https://arxiv.org/pdf…

Loss-tolerant parallelized Bell-state generation with a hybrid cat qudit
Having multiple Bell pairs shared by distant quantum registers provides a key resource for both quantum networks and distributed quantum computing. In this paper, we present a protocol for parallelized Bell-pair generation that uses the phase of a coherent light pulse to encode a qudit, enabling the simultaneous generation of multiple Bell pairs. By encoding a qudit in a basis of light-matter Schrödinger's cat states, the loss of a photon in transit can be detected through an $XX$ parity syndr…

Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation
Tanjim Islam Riju, Shuchismita Anwar, Saman Sarker Joy, Farig Sadeque, Swakkhar Shatabda
https://arxiv.org/abs/2508.13068

Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation
We propose a two-stage multimodal framework that enhances disease classification and region-aware radiology report generation from chest X-rays, leveraging the MIMIC-Eye dataset. In the first stage, we introduce a gaze-guided contrastive learning architecture for disease classification. It integrates visual features, clinical labels, bounding boxes, and radiologist eye-tracking signals and is equipped with a novel multi-term gaze-attention loss combining MSE, KL divergence, correlation, and cen…

The Impact of Generative AI on Code Expertise Models: An Exploratory Study
Ot\'avio Cury, Guilherme Avelino
https://arxiv.org/abs/2507.08160 https://…

The Impact of Generative AI on Code Expertise Models: An Exploratory Study
Generative Artificial Intelligence (GenAI) tools for source code generation have significantly boosted productivity in software development. However, they also raise concerns, particularly the risk that developers may rely heavily on these tools, reducing their understanding of the generated code. We hypothesize that this loss of understanding may be reflected in source code knowledge models, which are used to identify developer expertise. In this work, we present an exploratory analysis of how…

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
Juhi Soni, Markus Lange-Hegermann, Stefan Windmann
https://arxiv.org/abs/2508.11528

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
We propose an unsupervised anomaly detection approach based on a physics-informed diffusion model for multivariate time series data. Over the past years, diffusion model has demonstrated its effectiveness in forecasting, imputation, generation, and anomaly detection in the time series domain. In this paper, we present a new approach for learning the physics-dependent temporal distribution of multivariate time series data using a weighted physics-informed loss during diffusion model training. A …

Second-Generation Planet Formation in Post-AGB Discs: Testing the Role of Gravitational Instability
Ali Pourmand, Devika Kamath, Orsola De Marco, Mark Wardle
https://arxiv.org/abs/2509.03894

Second-Generation Planet Formation in Post-AGB Discs: Testing the Role of Gravitational Instability
Post-Asymptotic Giant Branch (post-AGB) binary stars are evolved systems that host circumbinary discs formed through mass loss during late-stage binary interaction. Their structural, kinematic, and chemical similarities to planet-forming discs have motivated the idea that these systems may act as sites of "second-generation" planet formation. In this study, we assess whether the gravitational instability mechanism - a proposed pathway for giant planet formation in some protoplanetary discs-can …

Music2Palette: Emotion-aligned Color Palette Generation via Cross-Modal Representation Learning
Jiayun Hu, Yueyi He, Tianyi Liang, Changbo Wang, Chenhui Li
https://arxiv.org/abs/2507.04758

Music2Palette: Emotion-aligned Color Palette Generation via Cross-Modal Representation Learning
Emotion alignment between music and palettes is crucial for effective multimedia content, yet misalignment creates confusion that weakens the intended message. However, existing methods often generate only a single dominant color, missing emotion variation. Others rely on indirect mappings through text or images, resulting in the loss of crucial emotion details. To address these challenges, we present Music2Palette, a novel method for emotion-aligned color palette generation via cross-modal rep…

Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Method
Leqian Li, Dianxi Shi, Jialu Zhou, Xinyu Wei, Mingyue Yang, Songchang Jin, Shaowu Yang
https://arxiv.org/abs/2508.17862

Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Method
Large Language Models (LLMs) have shown remarkable capabilities across diverse tasks, yet they face inherent limitations such as constrained parametric knowledge and high retraining costs. Retrieval-Augmented Generation (RAG) augments the generation process by retrieving externally stored knowledge absent from the models internal parameters. However, RAG methods face challenges such as information loss and redundant retrievals during multi-round queries, accompanying the difficulties in precise…

The loss tolerance of cat breeding for fault-tolerant grid state generation
Olga Solodovnikova, Ulrik L. Andersen, Jonas S. Neergaard-Nielsen
https://arxiv.org/abs/2508.06193 ht…

The loss tolerance of cat breeding for fault-tolerant grid state generation
The development of a continuous-variable photonic quantum computer depends on the reliable preparation of high-quality Gottesman-Kitaev-Preskill states. The most promising GKP preparation scheme is the cat breeding protocol, which can generate GKP states deterministically given a source of squeezed cat states, using beam splitters, homodyne detectors and a feedforward displacement. However, analyzing the performance of the protocol under loss is cumbersome due to the exponential scaling of the …

WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
Zijian Li, Xin Guan, Bo Zhang, Shen Huang, Houquan Zhou, Shaopeng Lai, Ming Yan, Yong Jiang, Pengjun Xie, Fei Huang, Jun Zhang, Jingren Zhou
https://arxiv.org/abs/2509.13312

WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
This paper tackles open-ended deep research (OEDR), a complex challenge where AI agents must synthesize vast web-scale information into insightful reports. Current approaches are plagued by dual-fold limitations: static research pipelines that decouple planning from evidence acquisition and one-shot generation paradigms that easily suffer from long-context failure issues like "loss in the middle" and hallucinations. To address these challenges, we introduce WebWeaver, a novel dual-agent framewo…

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
NextStep Team, Chunrui Han, Guopeng Li, Jingwei Wu, Quan Sun, Yan Cai, Yuang Peng, Zheng Ge, Deyu Zhou, Haomiao Tang, Hongyu Zhou, Kenkun Liu, Ailin Huang, Bin Wang, Changxin Miao, Deshan Sun, En Yu, Fukun Yin, Gang Yu, Hao Nie, Haoran Lv, Hanpeng Hu, Jia Wang, Jian Zhou, Jianjian Sun, Kaijun Tan, Kang An, Kangheng Lin, Liang Zhao, Mei Chen, Peng Xing, Rui Wang, Shiyu Liu, Shutao Xia, Tianhao You, Wei Ji…

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive paradigm forward with NextStep-1, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives. Next…

Nonlinear periodic orbit solutions and their bifurcation structure at the origin of soliton hopping in coupled microresonators
Savyaraj Deshmukh, Aleksandr Tusnin, Alexey Tikan, Tobias J. Kippenberg, Tobias M. Schneider
https://arxiv.org/abs/2508.09921

Nonlinear periodic orbit solutions and their bifurcation structure at the origin of soliton hopping in coupled microresonators
Microresonator frequency combs, essential for future integrated optical systems, rely on dissipative Kerr solitons generated in a single microresonator to achieve coherent frequency comb generation. Recent advances in the nanofabrication of low-loss integrated nonlinear microresonators have paved the way for the exploration of coupled-resonator systems. These systems provide significant technological advantages, including higher conversion efficiency and the generation of dual dispersive waves.…

Accurate and Scalable Simulation of Cavity-Based Networks in Modular Quantum Architectures
Sahar Ben Rached, Zezhou Sun, Guilu Long, Santiago Rodrigo, Carmen G. Almud\'ever, Eduard Alarc\'on, Sergi Abadal
https://arxiv.org/abs/2508.13896

Accurate and Scalable Simulation of Cavity-Based Networks in Modular Quantum Architectures
Cavity-mediated interconnects are a promising platform for scaling modular quantum computers by enabling high-fidelity inter-chip quantum state transmission and entanglement generation. In this work, we first model the dynamics of deterministic inter-chip quantum state transfer using the Stimulated Raman Adiabatic Passage (STIRAP) protocol, analyzing fidelity loss mechanisms under experimentally achievable qubit-cavity coupling and decoherence parameters. We then extend the NetSquid simulator, …

MaskBeat: Loopable Drum Beat Generation
Luca A. Lanzend\"orfer, Florian Gr\"otschla, Karim Galal, Roger Wattenhofer
https://arxiv.org/abs/2507.03395

MaskBeat: Loopable Drum Beat Generation
We present MaskBeat, a transformer-based approach for loopable drum pattern generation. Rather than predicting drum hits sequentially, our method uses bidirectional attention with iterative refinement, allowing instruments to be generated in parallel while maintaining musical coherence. Additionally, we introduce custom loss functions that capture drum-specific musical relationships. Our experiments show that MaskBeat generates higher quality and more musically coherent drum patterns than basel…

Squeezed Light Generation in Periodically Poled Thin-Film Lithium Niobate Waveguides
Xiaodong Shi, Angela Anna Baiju, Xu Chen, Sakthi Sanjeev Mohanraj, Sihao Wang, Veerendra Dhyani, Biveen Shajilal, Mengyao Zhao, Ran Yang, Yue Li, Guangxing Wu, Hao Hao, Victor Leong, Ping Koy Lam, Di Zhu
https://arxiv.org/abs/2508.08599

Squeezed Light Generation in Periodically Poled Thin-Film Lithium Niobate Waveguides
Squeezed states of light play a key role in quantum-enhanced sensing and continuous-variable quantum information processing. Realizing integrated squeezed light sources is crucial for developing compact and scalable photonic quantum systems. In this work, we demonstrate on-chip broadband vacuum squeezing at telecommunication wavelengths on the thin-film lithium niobate (TFLN) platform. Our device integrates periodically poled lithium niobate (PPLN) nanophotonic waveguides with low-loss edge cou…

mmKey: Channel-Aware Beam Shaping for Reliable Key Generation in mmWave Wireless Networks
Poorya Mollahosseini, Yasaman Ghasempour
https://arxiv.org/abs/2508.19010 https://

mmKey: Channel-Aware Beam Shaping for Reliable Key Generation in mmWave Wireless Networks
Physical-layer key generation (PLKG) has emerged as a promising technique to secure next-generation wireless networks by exploiting the inherent properties of the wireless channel. However, PLKG faces fundamental challenges in the millimeter wave (mmWave) regime due to channel sparsity, higher phase noise, and higher path loss, which undermine both the randomness and reciprocity required for secure key generation. In this paper, we present mmKey, a novel PLKG framework that capitalizes on the a…

Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer
Xuanhao Mu, G\"okhan Demirel, Yuzhe Zhang, Jianlei Liu, Thorsten Schlachter, Veit Hagenmeyer
https://arxiv.org/abs/2508.10587

Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer
To bridge the temporal granularity gap in energy network design and operation based on Energy System Models, resampling of time series is required. While conventional upsampling methods are computationally efficient, they often result in significant information loss or increased noise. Advanced models such as time series generation models, Super-Resolution models and imputation models show potential, but also face fundamental challenges. The goal of time series generative models is to learn the…

APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction
Sasan Sharifipour, Constantino \'Alvarez Casado, Mohammad Sabokrou, Miguel Bordallo L\'opez
https://arxiv.org/abs/2509.08104

APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction
Training deep learning models for point cloud prediction tasks such as shape completion and generation depends critically on loss functions that measure discrepancies between predicted and ground-truth point sets. Commonly used functions such as Chamfer Distance (CD), HyperCD, and InfoCD rely on nearest-neighbor assignments, which often induce many-to-one correspondences, leading to point congestion in dense regions and poor coverage in sparse regions. These losses also involve non-differentiab…

FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction
Qiqing Liu, Guoquan Wei, Zekun Zhou, Yiyang Wen, Liu Shi, Qiegen Liu
https://arxiv.org/abs/2506.23466

FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction
Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise, potentially impacting diagnostic accuracy. Transformer combined with diffusion models has been a promising approach for image generation. Nevertheless, existing methods exhibit limitations in preserving finegrained image details. To address this issue, frequency domain-directed diffusion transformer (FD-DiT) is proposed for LDCT reconstruction. …

Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids
Mohini Bariya, Genevieve Flaspohler
https://arxiv.org/abs/2506.21311

Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids
Electric grids in low- and middle-income countries (LMICs) across the world face an acute challenge. To support global decarbonisation efforts and raise millions from energy poverty, these grids must shoulder substantial load growth while integrating distributed renewable generation. However, decades of rapid and poorly funded infrastructure expansions have led to national grids in many LMICs that are strained and weak, composed of aging, faulty, and undersized infrastructure. A cause and sympt…

"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
Beining Wu, Jun Huang, Shui Yu
https://arxiv.org/abs/2507.19657

"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
The development of next-generation networking systems has inherently shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize the quality, relevance, and utility of transmitted information, rather than sheer data volume. While classical network metrics, such as latency and packet loss, remain significant, they are insufficient to quantify the nuanced information quality requirements of modern intelligent applications, including autonomous vehicles, d…

Uplink RSMA for Pinching-Antenna Systems
Apostolos A. Tegos, Yue Xiao, Sotiris A. Tegos, George K. Karagiannidis, Panagiotis D. Diamantoulakis
https://arxiv.org/abs/2509.10076 h…

Uplink RSMA for Pinching-Antenna Systems
One of the key goals of next-generation wireless networks is to adapt to changing conditions and meet the growing demand for reliable, high-capacity communications from emerging applications. Overcoming the limitations of conventional technologies, such as fixed antenna positions, is essential to achieving this objective because it mitigates the impact of path loss on the received signal and creates strong line-of-sight links, enhancing system performance. With this in mind, the newly proposed …

One of our final topics in the #ISE2025 lecture were Knowledge Graph Embeddings. How to vectorise KG structures while preserving their inherent semantics?
#AI #KGE

Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image Generation
Sapir Esther Yiflach, Yuval Atzmon, Gal Chechik
https://arxiv.org/abs/2509.02295 https://

Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image Generation
Text-to-image diffusion models can generate stunning visuals, yet they often fail at tasks children find trivial--like placing a dog to the right of a teddy bear rather than to the left. When combinations get more unusual--a giraffe above an airplane--these failures become even more pronounced. Existing methods attempt to fix these spatial reasoning failures through model fine-tuning or test-time optimization with handcrafted losses that are suboptimal. Rather than imposing our assumptions abou…

Electrical control of quantum dots in GaAs-on-insulator waveguides for coherent single-photon generation
Hanna Salamon, Ying Wang, Arnulf Snedker-Nielsen, Atefeh Shadmani, R\"udiger Schott, Mircea Balauroiu, Nicolas Volet, Arne Ludwig, Leonardo Midolo
https://arxiv.org/abs/2508.04584

Electrical control of quantum dots in GaAs-on-insulator waveguides for coherent single-photon generation
The integration of coherent quantum emitters with silicon photonic platforms essential for scalable quantum technologies. We demonstrate electrically controlled self-assembled quantum dots embedded in GaAs waveguides bonded onto a SiO2/Si substrate and coupled to low-loss SiN waveguides. Our approach uses a die-to-die adhesive bonding process to realize a GaAs-on-insulator platform incorporating a p-i-n junction for charge noise suppression and Stark tuning of excitonic transitions. Resonance f…

Efficient Interleaved Speech Modeling through Knowledge Distillation
Mohammadmahdi Nouriborji, Morteza Rohanian
https://arxiv.org/abs/2506.23670 https://…

Efficient Interleaved Speech Modeling through Knowledge Distillation
Current speech language models exceed the size and latency constraints of many deployment environments. We build compact, expressive speech generation models through layer-aligned distillation, matching hidden states, attention maps, and softened logits to compress large multimodal transformers by 3x with minimal loss in performance. We introduce TinyWave, a family of 2B-parameter models for speech-to-speech and interleaved speech-text generation, trained on 50,000 hours of public audio. TinyWa…

Robust Tripartite Entanglement Generation via Correlated Noise in Spin Qubits
Sander Driessen, Ji Zou, Even Thingstad, Jelena Klinovaja, Daniel Loss
https://arxiv.org/abs/2506.20466

Robust Tripartite Entanglement Generation via Correlated Noise in Spin Qubits
We investigate the generation of genuine tripartite entanglement in a triangular spin-qubit system due to spatially correlated noise. In particular, we demonstrate how the formation of a highly entangled dark state -- a W state -- enables robust, long-lived tripartite entanglement. Surprisingly, we find that environmentally induced coherent coupling does not play a crucial role in sustaining this entanglement. This contrasts sharply with the two-qubit case, where the induced coupling significan…

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
Qifei Cui, Xinyu Lu
https://arxiv.org/abs/2506.21245 https://

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomaly detection module with a refined mask generation network, the proposed model accurately identifies tumor-sensitive regions and iteratively enhances segmentation precision using adversarial loss constraints. Multi-modal MRI data and synthetic image augmentation are employed to improve robustness and address the challenge of limited annotated dataset…

Mind the Gap: Conformative Decoding to Improve Output Diversity of Instruction-Tuned Large Language Models
Max Peeperkorn, Tom Kouwenhoven, Dan Brown, Anna Jordanous
https://arxiv.org/abs/2507.20956

Mind the Gap: Conformative Decoding to Improve Output Diversity of Instruction-Tuned Large Language Models
Instruction-tuning large language models (LLMs) reduces the diversity of their outputs, which has implications for many tasks, particularly for creative tasks. This paper investigates the ``diversity gap'' for a writing prompt narrative generation task. This gap emerges as measured by current diversity metrics for various open-weight and open-source LLMs. The results show significant decreases in diversity due to instruction-tuning. We explore the diversity loss at each fine-tuning stage for th…

Approaching transform-limited linewidths in telecom-wavelength transitions of ungated quantum dots
Andrew N. Wakileh, Dan Dalacu, Philip J. Poole, Boris Lamontagne, Simona Moisa, Robin L. Williams, Nir Rotenberg
https://arxiv.org/abs/2509.02320

Approaching transform-limited linewidths in telecom-wavelength transitions of ungated quantum dots
Highly coherent quantum emitters operating in the telecommunication C-band (1530 - 1565nm), where ultra-low-loss fibers and photonic circuits are available, are crucial to the development of scalable quantum technologies. In this work, we report on a modified Stranski-Krastanov growth scheme using chemical beam epitaxy to enable the generation of high-quality InAs/InP quantum dots, characterized by near-transform-limited linewidths ($Γ_{\mathrm{TL}}$). We demonstrate the growth of highly-symme…

An entangled photon source for the telecom C-band based on a semiconductor-confined spin
Petros Laccotripes, Junyang Huang, Ginny Shooter, Andrea Barbiero, Matthew S. Winnel, David A. Ritchie, Andrew J. Shields, Tina Muller, R. Mark Stevenson
https://arxiv.org/abs/2507.01648

An entangled photon source for the telecom C-band based on a semiconductor-confined spin
Multiphoton entangled states are a key resource for quantum networks and measurement-based quantum computation. Scalable protocols for generating such states using solid-state spin-photon interfaces have recently emerged, but practical implementations have so far relied on emitters operating at short wavelengths, incompatible with low-loss fibre transmission. Here, we take a key step towards the generation of telecom wavelength multi-qubit entangled states using an InAs/InP quantum dot. After e…