Tootfinder

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
Debasish Jana, Pinakpani Pal, Pawan Kumar
https://arxiv.org/abs/2506.12616

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitations. This paper leverages the ROOF (Real-time Onsite Operations Facilitation) framework with decentralized computing at intermediary fog and peripheral edge network layers to reduce latency by processing …

Phonikud: Hebrew Grapheme-to-Phoneme Conversion for Real-Time Text-to-Speech
Yakov Kolani, Maxim Melichov, Cobi Calev, Morris Alper
https://arxiv.org/abs/2506.12311

Phonikud: Hebrew Grapheme-to-Phoneme Conversion for Real-Time Text-to-Speech
Real-time text-to-speech (TTS) for Modern Hebrew is challenging due to the language's orthographic complexity. Existing solutions ignore crucial phonetic features such as stress that remain underspecified even when vowel marks are added. To address these limitations, we introduce Phonikud, a lightweight, open-source Hebrew grapheme-to-phoneme (G2P) system that outputs fully-specified IPA transcriptions. Our approach adapts an existing diacritization model with lightweight adaptors, incurring ne…

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
Valentin Ackva, Fares Schulz
https://arxiv.org/abs/2506.12665 https://

ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
Numerous tools for neural network inference are currently available, yet many do not meet the requirements of real-time audio applications. In response, we introduce anira, an efficient cross-platform library. To ensure compatibility with a broad range of neural network architectures and frameworks, anira supports ONNX Runtime, LibTorch, and TensorFlow Lite as backends. Each inference engine exhibits real-time violations, which anira mitigates by decoupling the inference from the audio callback…

Non-equilibrium real-time dynamics and transport coefficients in Light-Front Holographic QCD
Fidele J. Twagirayezu
https://arxiv.org/abs/2506.12653 https:/…

Non-equilibrium real-time dynamics and transport coefficients in Light-Front Holographic QCD
We propose an extension of Light-Front Holographic QCD (LFHQCD) to investigate non-equilibrium real-time dynamics and transport properties of strongly coupled QCD matter. While LFHQCD has been successfully applied to hadronic spectroscopy and parton distributions, its potential for modeling transport phenomena remains unexplored. We develop a light-front framework to compute key transport coefficients-such as shear viscosity, bulk viscosity, and the jet quenching parameter-by introducing finite…

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
Ashutosh Shankar (Indian Institute Of Technology Kharagpur), Astha Kumari (Indian Institute Of Technology Kharagpur)
https://arxiv.org/abs/2506.12415

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed acyclic graphs (DAGs) within heterogeneous, pre-occupied Mobile Edge Computing (MEC) networks. We propose a modified version of the Heterogeneous Earliest Finish Time (HEFT) algorithm designed to exploit residual processing capacity in preoccupied MEC enviro…

Scheduling Agile Earth Observation Satellites with Onboard Processing and Real-Time Monitoring
Antonio M. Mercado-Mart\'inez, Beatriz Soret, Antonio Jurado-Navas
https://arxiv.org/abs/2506.11556

Scheduling Agile Earth Observation Satellites with Onboard Processing and Real-Time Monitoring
The emergence of Agile Earth Observation Satellites (AEOSs) has marked a significant turning point in the field of Earth Observation (EO), offering enhanced flexibility in data acquisition. Concurrently, advancements in onboard satellite computing and communication technologies have greatly enhanced data compression efficiency, reducing network latency and congestion while supporting near real-time information delivery. In this paper, we address the Agile Earth Observation Satellite Scheduling …

Learning Before Filtering: Real-Time Hardware Learning at the Detector Level
Bo\v{s}tjan Ma\v{c}ek
https://arxiv.org/abs/2506.11981 https://

Learning Before Filtering: Real-Time Hardware Learning at the Detector Level
Advances in sensor technology and automation have ushered in an era of data abundance, where the ability to identify and extract relevant information in real time has become increasingly critical. Traditional filtering approaches, which depend on a priori knowledge, often struggle to adapt to dynamic or unanticipated data features. Machine learning offers a compelling alternative-particularly when training can occur directly at or near the detector. This paper presents a digital hardware archit…

Mariposa Fire
Northbound 101 South of Calle Mariposa Reina,
Santa Barbara County, CA
https://share.watchduty.org/i/51947?ts=1750206799000

Watch Duty - Wildfire Maps & Alerts
Real-time information about wildfire and firefighting efforts nearby

Experimental Verification of a Time-Domain Load Identification Method for Single-Phase Circuits
Francisco M. Arrabal-Campos, Francisco G. Montoya, Jorge Ventura, Santiago S\'anchez-Acevedo, Raymundo E. Torres-Olguin, Francisco de Le\'on
https://arxiv.org/abs/2506.12593

Experimental Verification of a Time-Domain Load Identification Method for Single-Phase Circuits
This paper presents experimental validation of a time-domain load parameter determination method for single-phase circuits. The verification is performed in a state-of-the-art smart grid laboratory equipped with power hardware and real-time emulators. The proposed method enables the identification of circuit parameters using only instantaneous voltage and current measurements at the point of common coupling. The experimental setup includes a range of test cases covering linear and non-sinusoida…

Good morning all! A rare day without any real meetings, and only one calendar entry (and that's a fun one, a networking thing to talk about how social media research can go forward in Germany). I'm not sure I can be trusted with that much freedom from time constraints #academicChatter

Turns out the reason that the GPU accelerated level detector made the CDR slower is that I was working on test data loaded from a file, and not moving the data from CPU to GPU before starting the filter graph. So of *course* it was slower, due to the unnecessary data movement.
But that's unrealistic since on a real high-performance scope driver (e.g. thunderscope) the first thing we typically do is push data from CPU to GPU to do the int-float conversion there.
Time to repea…

Combining this with a text to speech reader would be an interesting thing for sight-challenged folk. https://github.com/ngxson/smolvlm-realtime-webcam

Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments
Zaiqiang Wu, I-Chao Shen, Takeo Igarashi
https://arxiv.org/abs/2506.12348

Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments
Per-garment virtual try-on methods collect garment-specific datasets and train networks tailored to each garment to achieve superior results. However, these approaches often struggle with loose-fitting garments due to two key limitations: (1) They rely on human body semantic maps to align garments with the body, but these maps become unreliable when body contours are obscured by loose-fitting garments, resulting in degraded outcomes; (2) They train garment synthesis networks on a per-frame basi…

At this year's Berlin Buzzwords, Viola Rädle & Raphael Franke will present gamma_flow, an open-source Python package for real-time spectral data analysis.
Learn more: https://2025.berlinbuzzwords.de/session/gamma_flow-deno…

Towards real-time additive-free dopamine detection at $10^{-8}$ mM with hardware accelerated platform integrated on camera
Ning Li, Qizhou Wang, Zhao He, Arturo Burguete-Lopez, Fei Xiang, Andrea Fratalocchi
https://arxiv.org/abs/2506.13447

Towards real-time additive-free dopamine detection at $10^{-8}$ mM with hardware accelerated platform integrated on camera
Tracing physiological neurotransmitters such as dopamine (DA) with detection limits down to $\mathrm{1\times10^{-8}}$ mM is a critical goal in neuroscience for studying brain functions and progressing the understanding of cerebral disease. Addressing this problem requires enhancing the current state-of-the-art additive-free electrochemical workstation methods by over two orders of magnitude. In this work, we implement an ultra-sensitive, additive-free platform exploiting suitably engineered lig…

StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling
Hui Wang, Yifan Yang, Shujie Liu, Jinyu Li, Lingwei Meng, Yanqing Liu, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin
https://arxiv.org/abs/2506.12570

StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling
Recent advances in zero-shot text-to-speech (TTS) synthesis have achieved high-quality speech generation for unseen speakers, but most systems remain unsuitable for real-time applications because of their offline design. Current streaming TTS paradigms often rely on multi-stage pipelines and discrete representations, leading to increased computational cost and suboptimal system performance. In this work, we propose StreamMel, a pioneering single-stage streaming TTS framework that models continu…

PuDHammer: Experimental Analysis of Read Disturbance Effects of Processing-using-DRAM in Real DRAM Chips
Ismail Emir Yuksel, Akash Sood, Ataberk Olgun, O\u{g}uzhan Canpolat, Haocong Luo, F. Nisa Bostanc{\i}, Mohammad Sadrosadati, A. Giray Ya\u{g}l{\i}k\c{c}{\i}, Onur Mutlu
https://arxiv.org/abs/2506.12947

PuDHammer: Experimental Analysis of Read Disturbance Effects of Processing-using-DRAM in Real DRAM Chips
Processing-using-DRAM (PuD) is a promising paradigm for alleviating the data movement bottleneck using DRAM's massive internal parallelism and bandwidth to execute very wide operations. Performing a PuD operation involves activating multiple DRAM rows in quick succession or simultaneously, i.e., multiple-row activation. Multiple-row activation is fundamentally different from conventional memory access patterns that activate one DRAM row at a time. However, repeatedly activating even one DRAM ro…

Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling
Connor Yang, Joey Wagner, Emily Dolson, Luis Zaman, Matthew Andres Moreno
https://arxiv.org/abs/2506.12975

Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling
Due to ongoing accrual over long durations, a defining characteristic of real-world data streams is the requirement for rolling, often real-time, mechanisms to coarsen or summarize stream history. One common data structure for this purpose is the ring buffer, which maintains a running downsample comprising most recent stream data. In some downsampling scenarios, however, it can instead be necessary to maintain data items spanning the entirety of elapsed stream history. Fortunately, approaches g…

Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving
Boris Ivanovic, Cristiano Saltori, Yurong You, Yan Wang, Wenjie Luo, Marco Pavone
https://arxiv.org/abs/2506.12251

Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving
Autoregressive Transformers are increasingly being deployed as end-to-end robot and autonomous vehicle (AV) policy architectures, owing to their scalability and potential to leverage internet-scale pretraining for generalization. Accordingly, tokenizing sensor data efficiently is paramount to ensuring the real-time feasibility of such architectures on embedded hardware. To this end, we present an efficient triplane-based multi-camera tokenization strategy that leverages recent advances in 3D ne…

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

So cool to see KakaoMap has real time train location on the map 🤩

Real-Time Capable, Low-latency Upstream Scheduling in Multi-Tenant, SLA Compliant TWDM PON
Arijeet Ganguli, Marco Ruffini
https://arxiv.org/abs/2506.12118 …

Real-Time Capable, Low-latency Upstream Scheduling in Multi-Tenant, SLA Compliant TWDM PON
Virtualized Passive Optical Networks (vPONs) offer a promising solution for modern access networks, bringing enhanced flexibility, reduced capital expenditures (CapEx), and support for multi-tenancy. By decoupling network functions from physical infrastructure, vPONs enable service providers to efficiently share network resources among multiple tenants. In this paper, we propose a novel merging DBA algorithm, called the Dynamic Time and Wavelength Allocation (DTWA) algorithm, for a virtualized …

Shelter Soul: Bridging Shelters and Adopters Through Technology
Yashodip Dharmendra Jagtap
https://arxiv.org/abs/2506.12739 https://a…

Shelter Soul: Bridging Shelters and Adopters Through Technology
Pet adoption processes often face inefficiencies, including limited accessibility, lack of real-time information, and mismatched expectations between shelters and adopters. To address these challenges, this study presents Shelter Soul, a technology-based solution designed to streamline pet adoption through an integrated, web-based platform. Developed using the MERN stack and GraphQL, Shelter Soul is a prototype system built to improve pet matching accuracy, shelter management efficiency, and se…

Robustness of Floquet topological phase at room temperature: a first-principles dynamics study
Ruiyi Zhou, Yosuke Kanai
https://arxiv.org/abs/2506.12005 ht…

Robustness of Floquet topological phase at room temperature: a first-principles dynamics study
Nonadiabatic Thouless pumping of electrons is studied within the framework of topological Floquet engineering, particularly focused on how atomic lattice dynamics affect the emergent Floquet topological phase in trans-polyacetylene under the driving electric field. As similarly done in the earlier work [Zhou and Kanai, J. Phys. Chem. Lett., 12, 4496 (2021)], the real-time time-dependent density functional theory and Ehrenfest dynamics simulations were used to investigate the extent to which the…

Dynamic Collaborative Material Distribution System for Intelligent Robots In Smart Manufacturing
Ziren Xiao, Ruxin Xiao, Chang Liu, Xinheng Wang
https://arxiv.org/abs/2506.11723

Dynamic Collaborative Material Distribution System for Intelligent Robots In Smart Manufacturing
The collaboration and interaction of multiple robots have become integral aspects of smart manufacturing. Effective planning and management play a crucial role in achieving energy savings and minimising overall costs. This paper addresses the real-time Dynamic Multiple Sources to Single Destination (DMS-SD) navigation problem, particularly with a material distribution case for multiple intelligent robots in smart manufacturing. Enumerated solutions, such as in \cite{xiao2022efficient}, tackle t…

Instance-Specific Test-Time Training for Speech Editing in the Wild
Taewoo Kim, Uijong Lee, Hayoung Park, Choongsang Cho, Nam In Park, Young Han Lee
https://arxiv.org/abs/2506.13295

Instance-Specific Test-Time Training for Speech Editing in the Wild
Speech editing systems aim to naturally modify speech content while preserving acoustic consistency and speaker identity. However, previous studies often struggle to adapt to unseen and diverse acoustic conditions, resulting in degraded editing performance in real-world scenarios. To address this, we propose an instance-specific test-time training method for speech editing in the wild. Our approach employs direct supervision from ground-truth acoustic features in unedited regions, and indirect …

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Haoqing Li, Ricardo Borsoi, Tales Imbiriba, Pau Closas
https://arxiv.org/abs/2506.13733

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solve the trade-off between temporal and spatial resolution in remote sensing instruments. Although several algorithms have been proposed for this problem, the presence of outliers such as clouds downgrades their performance. Moreover, strategies that integrate robustness, recursive operation and learned models are missin…

Interference Mitigation in STAR-RIS-Aided Multi-User Networks with Statistical CSI
Abuzar B. M. Adam, Mohammed A. M. Elhassan, Elhadj Moustapha Diallo, Mohamed Amine Ouamri
https://arxiv.org/abs/2506.12964

Interference Mitigation in STAR-RIS-Aided Multi-User Networks with Statistical CSI
In this paper, we investigate real-time interference mitigation in multiuser wireless networks assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). Unlike conventional methods that rely on instantaneous channel state information (CSI), we consider a practical scenario where only statistical CSI is available, and the STAR-RIS phase shifts are impaired by random phase errors modeled via the Von Mises distribution. To tackle the resulting nonconve…

Personalized LLM Decoding via Contrasting Personal Preference
Hyungjune Bu, Chanjoo Jung, Minjae Kang, Jaehyung Kim
https://arxiv.org/abs/2506.12109 https:…

Personalized LLM Decoding via Contrasting Personal Preference
As large language models (LLMs) are progressively deployed in various real-world applications, personalization of LLMs has become increasingly important. While various approaches to LLM personalization such as prompt-based and training-based methods have been actively explored, the development of effective decoding-time algorithms remains largely overlooked, despite their demonstrated potential. In this paper, we propose CoPe (Contrasting Personal Preference), a novel decoding-time approach app…

Molecular Control of Floquet Topological Phase in Non-adiabatic Thouless Pumping
Ruiyi Zhou, Yosuke Kanai
https://arxiv.org/abs/2506.12147 https://

Molecular Control of Floquet Topological Phase in Non-adiabatic Thouless Pumping
Nonadiabatic Thouless pumping of electrons is studied in the framework of topological Floquet engineering, particularly focused on how changes to chemical moieties can control the emergence of the Floquet topological phase. We employ real-time time-dependent density functional theory to investigate the extent to which the topological invariant, the winding number, is impacted by molecular-level changes to trans-polyacetylene. In particular, several substitutions to trans-polyacetylene are studi…

London-based Noah, which is developing API-focused stablecoin infrastructure to enable real-time cross-border payments, raised a $22M seed led by LocalGlobe (Ben Weiss/Fortune)
https://fortune.com/crypto/2025/06/10/noah-thijn-lamers-stable…

Exclusive: Stablecoin startup Noah raises $22 million, adds Adyen vet as cofounder
“I feel like I have the energy of [when I was] 25,” said Thijn Lamers, who is 66 years old.

Real-time adaptive tracking of fluctuating relaxation rates in superconducting qubits
Fabrizio Berritta, Jacob Benestad, Jan A. Krzywda, Oswin Krause, Malthe A. Marciniak, Svend Kr{\o}jer, Christopher W. Warren, Emil Hogedal, Andreas Nylander, Irshad Ahmad, Amr Osman, Janka Bizn\'arov\'a, Marcus Rommel, Anita Fadavi Roudsari, Jonas Bylander, Giovanna Tancredi, Jeroen Danon, Jacob Hastrup, Ferdinand Kuemmeth, Morten Kjaergaard

Real-time adaptive tracking of fluctuating relaxation rates in superconducting qubits
The fidelity of operations on a solid-state quantum processor is ultimately bounded by decoherence effects induced by a fluctuating environment. Characterizing environmental fluctuations is challenging because the acquisition time of experimental protocols limits the precision with which the environment can be measured and may obscure the detailed structure of these fluctuations. Here we present a real-time Bayesian method for estimating the relaxation rate of a qubit, leveraging a classical co…

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Oscar Boullosa Dapena
https://arxiv.org/abs/2506.12111

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Real-time continuous learning over streaming data remains a central challenge in deep learning and AI systems. Traditional gradient-based models such as backpropagation through time (BPTT) face computational and stability limitations when dealing with temporally unbounded data. In this paper, we introduce a novel architecture, Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs), which leverages the Feynman technique of differentiation under the integral sign to formulate neural u…

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In an earlier post I introduced the principle that there were four categories of side hustle. Let’s now talk about the fourth and final category.
4️⃣ 𝐈 – 𝐈𝐧𝐯𝐞𝐬𝐭𝐨𝐫 𝐒𝐢𝐝𝐞 𝐇𝐮𝐬𝐭𝐥𝐞𝐬: 𝐌𝐚𝐤𝐢𝐧𝐠 𝐌𝐨𝐧𝐞𝐲 𝐖𝐨𝐫𝐤 𝐟𝐨𝐫 𝐘𝐨𝐮
The ultimate level of financial freedom? Investing (I quadrant) – where your money generates income instead of your time.
🔹 Examples: Stocks, real estate, cryptocurrency, digital assets, angel investing.

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Jan C. Schulze, Alexander Mitsos
https://arxiv.org/abs/2506.12819

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Computationally cheap yet accurate enough dynamical models are vital for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order simplified model can enable such real-time applications. Herein, we review state-of-the-art nonlinear model order reduction methods and provide a theoretical comparison of method properties. Additionally, we discuss both general-purpose methods and tailored appro…

I love communism because of its vision of a society based on equality, solidarity, and the abolition of class and state oppression. The idea of real communism, as envisioned by thinkers like Peter Kropotkin and Karl Marx, inspires me deeply. It’s a world where people freely associate, manage their own workplaces, and share resources according to need. This ideal has never truly been realized in history, which makes it all the more important to keep striving for it.
At the same time, I …

Simple quintessence models in light of DESI-BAO observations
James M. Cline, Varun Muralidharan
https://arxiv.org/abs/2506.13047 https://

Simple quintessence models in light of DESI-BAO observations
Recent analyses from the DESI collaboration suggest that the dark energy density of the Universe may be decreasing with time, slowing the acceleration of the scale factor $a$. Typically these studies are performed assuming an ansatz for the equation of state $w(a)$. In this work, we instead consider simple models of a scalar quintessence potential with linear and quadratic behavior, which could be more representative of real models than particular parametrizations of $w(a)$. We observe a signif…

Modelado y gemelos digitales en el contexto fotovoltaico
Franco Bertani Matung, Juan Cruz Esquembre Santamar\'ia, Ricardo R. Palma, Fabricio Orlando Sanchez Varretti
https://arxiv.org/abs/2506.12102

Modelado y gemelos digitales en el contexto fotovoltaico
The photovoltaic industry faces the challenge of optimizing the performance and management of its systems in an increasingly digitalized environment. In this context, digital twins offer an innovative solution: virtual models that replicate in real time the behavior of solar installations. This technology makes it possible to anticipate failures, improve operational efficiency and facilitate data-driven decision-making. This report analyzes its application in the photovoltaic sector, highlighti…

Dynamic allocation: extremes, tail dependence, and regime Shifts
Yin Luo, Sheng Wang, Javed Jussa
https://arxiv.org/abs/2506.12587 https://

Dynamic allocation: extremes, tail dependence, and regime Shifts
By capturing outliers, volatility clustering, and tail dependence in the asset return distribution, we build a sophisticated model to predict the downside risk of the global financial market. We further develop a dynamic regime switching model that can forecast real-time risk regime of the market. Our GARCH-DCC-Copula risk model can significantly improve both risk- and alpha-based global tactical asset allocation strategies. Our risk regime has strong predictive power of quantitative equity fac…

Reimagining Dance: Real-time Music Co-creation between Dancers and AI
Olga Vechtomova, Jeff Bos
https://arxiv.org/abs/2506.12008 https://

Reimagining Dance: Real-time Music Co-creation between Dancers and AI
Dance performance traditionally follows a unidirectional relationship where movement responds to music. While AI has advanced in various creative domains, its application in dance has primarily focused on generating choreography from musical input. We present a system that enables dancers to dynamically shape musical environments through their movements. Our multi-modal architecture creates a coherent musical composition by intelligently combining pre-recorded musical clips in response to dance…

Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning
G. F. Subbotin, D. I. Sorokin, M. R. Nurgaliev, A. A. Granovskiy, I. P. Kharitonov, E. V. Adishchev, E. N. Khairutdinov, R. Clark, H. Shen, W. Choi, J. Barr, D. M. Orlov
https://arxiv.org/abs/2506.13267

Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning
Precise control of plasma shape and position is essential for stable tokamak operation and achieving commercial fusion energy. Traditional control methods rely on equilibrium reconstruction and linearized models, limiting adaptability and real-time performance. Here,the first application of deep reinforcement learning (RL) for magnetic plasma control on the mid-size DIII-D tokamak is presented, demonstrating a nonlinear approach that improves robustness and flexibility across plasma scenarios. …

Design and Implementation of Washing Machine HUD Using FPGAs
Norman Stites, D. G. Perera
https://arxiv.org/abs/2506.11287 https://arx…

Design and Implementation of Washing Machine HUD Using FPGAs
In contemporary digital design education, practical field programmable gate array (FPGA) projects are indispensable for bridging theoretical concepts with real-world applications. This project focuses on developing a hardware-based simulation of a domestic washing machine controller using the Xilinx Spartan-3E development board. A critical component of the design is the graphical heads-up display (HUD), which renders real-time information about the machine's operational state and cycle selectio…

From Flat to Feeling: A Feasibility and Impact Study on Dynamic Facial Emotions in AI-Generated Avatars
Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita, P{\aa}l Halvorsen
https://arxiv.org/abs/2506.13477

From Flat to Feeling: A Feasibility and Impact Study on Dynamic Facial Emotions in AI-Generated Avatars
Dynamic facial emotion is essential for believable AI-generated avatars; however, most systems remain visually inert, limiting their utility in high-stakes simulations such as virtual training for investigative interviews with abused children. We introduce and evaluate a real-time architecture fusing Unreal Engine 5 MetaHuman rendering with NVIDIA Omniverse Audio2Face to translate vocal prosody into high-fidelity facial expressions on photorealistic child avatars. We implemented a distributed t…

Controlled manipulation of solitons in a recirculating fiber loop using external potentials
Fran\c{c}ois Copie, Pierre Suret, St\'ephane Randoux
https://arxiv.org/abs/2506.13544

Controlled manipulation of solitons in a recirculating fiber loop using external potentials
Optical solitons are self-sustained wave packets that propagate without distortion due to a balance between dispersion and nonlinearity. Their unique stability underpins key photonic applications while also playing a central role in nonlinear wave physics. However, real-time control over soliton dynamics in non-dissipative systems remains a major challenge, limiting their practical applications in photonic systems. Here, we introduce a fiber-based platform for soliton manipulation, by creating …

The more time goes on, the more conflicted I get about #AI. (or, more specifically, generative AI. I like causal AI a fair bit)
On one hand, I hate so much about it:
the needless environmental and electronic parts waste, the impact on labor, the monopolistic nature of main organizations driving it, the endless conversations about AGI and other absurdly utopian (or dystopian) futures, the widespread theft of IP and human work, the devaluation of labor and craft… so much of it is not okay at all.
On the other hand, I kinda get it?
I like to test software for myself, so I've been dipping my toes into some popular AI tools over the past couple of years. I have a paid subscription to ChatGPT (which I don’t feel great about, I know), and I have found genuine utility in it.
More so, I feel so conflicted when I talk to people I respect and who I think are very smart and creative and they tell me how in love they are with all of these AI tools, about the complex workflows they build, about their experiments with agentic AI and integrations... These people seem so excited, so alive, so joyous about the things that this technology allows them to do. They often wouldn't have had the skills / knowledge / financial capital to do some of those things with human efforts alone. And now I see them coding their own tools, doing complex data analysis, trying creative experiments with graphics / text / video...
And I feel like a total jerk going "BUT ACTUALLY THIS IS UNETHICAL AND INEFFICIENT AND YOU SHOULD STOP IT BECAUSE AI SUCKS".
The technology behind all these gen AI models does have real utility, and it has kicked off a lot of creativity from people who wouldn't have dabbled in those kinds of projects otherwise.
So I don't know how to feel. Because I can't let go of the guilt and the real problems and the awareness of how much empty hype there is.
#technology #artificialintelligence #genAI #ChatGPT

Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Mingcong Li
https://arxiv.org/abs/2506.13168 https://

Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Real-time adaptive control of nonlinear systems with unknown dynamics and time-varying disturbances demands precise modeling and robust parameter adaptation. While existing neural network-based strategies struggle with computational inefficiency or inadequate temporal dependencies, this study proposes a hybrid control framework integrating a Temporal-Gated Radial Basis Function (TGRBF) network with a nonlinear robust controller. The TGRBF synergizes radial basis function neural networks (RBFNNs…

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In an earlier post I introduced the principle that there were four categories of side hustle. Let’s now talk about the fourth and final category.
4️⃣ 𝐈 – 𝐈𝐧𝐯𝐞𝐬𝐭𝐨𝐫 𝐒𝐢𝐝𝐞 𝐇𝐮𝐬𝐭𝐥𝐞𝐬: 𝐌𝐚𝐤𝐢𝐧𝐠 𝐌𝐨𝐧𝐞𝐲 𝐖𝐨𝐫𝐤 𝐟𝐨𝐫 𝐘𝐨𝐮
The ultimate level of financial freedom? Investing (I quadrant) – where your money generates income instead of your time.
🔹 Examples: Stocks, real estate, cryptocurrency, digital assets, angel investing.

A Compact Dynamic Omnidirectional Antenna
Sheng Huang, Jacob R. Randall, Cory Hilton, Jeffrey A. Nanzer
https://arxiv.org/abs/2506.11351 https://

A Compact Dynamic Omnidirectional Antenna
We propose a novel omnidirectional antenna design incorporating directional modulation for secure narrow planar information transmission. The proposed antenna features a compact size and stable omnidirectional radiation performance by employing two tightly spaced, printed meander line monopole antennas, acting as a single radiating element. To achieve a narrow information secure region, the proposed antenna is fed by differential power excitation of two ports with real-time dynamic switching. T…

Agentic AI as the enemy's agent.
It is a bad idea to allow an LLM access to internal data and external communication (web pages, APIs, email, …) at the same time.
#AgenticAI #DataLeak #LLM

Pendulum Tracker -- SimuF\'isica: A Web-based Tool for Real-time Measurement of Oscillatory Motion
Marco P. M. de Souza, Juciane G. Maia, Lilian N. de Andrade
https://arxiv.org/abs/2506.07301

Pendulum Tracker -- SimuFísica: A Web-based Tool for Real-time Measurement of Oscillatory Motion
We present Pendulum Tracker, a computer vision-based application that enables real-time measurement of the oscillatory motion of a physical pendulum. Integrated into the educational platform SimuFísica, the system uses the OpenCV.js library and runs directly in the browser, working on computers, tablets, and smartphones. The application automatically detects the pendulum's position via the device's camera, displaying in real time the angle-versus-time graph and estimates of the oscillation per…

"The real threat posed by generative AI is not that it will eliminate work on a mass scale, rendering human labour obsolete. It is that, left unchecked, it will continue to transform work in ways that deepen precarity, intensify surveillance, and widen existing inequalities."
"The current trajectory of generative AI reflects the priorities of firms seeking to lower costs, discipline workers, and consolidate profits — not any drive to enhance human flourishing. If we allo…

Is the AI Bubble About to Burst?
In the years since Automation and the Future of Work first appeared, a new wave of technological enthusiasm has swept across the popular imagination. The catalyst this time has been the rapid advances in generative artificial intelligence, spearheaded by companies like OpenAI, Google DeepMind, and Meta. Once again, a c

Im Normalfall poste ich von dem trottel nichts, aber...
https://xcancel.com/elonmusk/status/1930703865801810022#m
"Time to drop the really big bomb: realDonaldTrump is in the Epstein files. That is the real reason they have not been made public. Have…

On-The-Fly Symbolic Algorithm for Timed ATL with Abstractions
Nicolaj {\O}. Jensen, Kim G. Larsen, Didier Lime, Ji\v{r}\'i Srba
https://arxiv.org/abs/2506.07802

On-The-Fly Symbolic Algorithm for Timed ATL with Abstractions
Verification of real-time systems with multiple components controlled by multiple parties is a challenging task due to its computational complexity. We present an on-the-fly algorithm for verifying timed alternating-time temporal logic (TATL), a branching-time logic with quantifiers over outcomes that results from coalitions of players in such systems. We combine existing work on games and timed CTL verification in the abstract dependency graph (ADG) framework, which allows for easy creation of…

Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks
Kamran Shafafi, Alaa Awad Abdellatif, Manuel Ricardo, Rui Campos
https://arxiv.org/abs/2506.13287

Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks
Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler for next-generation wireless networks due to their on-demand deployment, high mobility, and ability to provide Line-of-Sight (LoS) connectivity. These features make UAVs particularly well-suited for dynamic and mission-critical applications such as intelligent transportation systems and emergency communications. However, effectively positioning multiple UAVs in real-time to meet non-uniform, time-varying traffic demands remains a sig…

Novel software for continuous wavelet analysis enable EEG real-time analysis on portable computers
Shoichiro Nakanishi
https://arxiv.org/abs/2506.07793 htt…

Novel software for continuous wavelet analysis enable EEG real-time analysis on portable computers
Continuous Wavelet Transform (CWT) is frequently used for waveform analysis. For example, in the field of neuroscience research, CWT is performed to analyze electroencephalograms (EEG) and calculate the index of brain activity. Recent advancements in computer technology, such as general-purpose computing on Graphics Processing Units (GPGPU), have enabled the application of CWT to real-time waveform analysis. However, the computational complexity of CWT is large, and it is challenging to employ …

Apache Flink is uniquely positioned to serve as the backbone for AI agents, equipping them with the powerful new tool of stream processing. Join Steffen Hoellinger at this year's Berlin Buzzwords to explore how Flink jobs can be transformed into “Agents”—autonomous, goal-driven entities that dynamically interact with data streams, trigger actions, and adapt their behaviour based on real-time insights.
Learn more:

Enabling automatic transcription of child-centered audio recordings from real-world environments
Daniil Kocharov, Okko R\"as\"anen
https://arxiv.org/abs/2506.11747

Enabling automatic transcription of child-centered audio recordings from real-world environments
Longform audio recordings obtained with microphones worn by children-also known as child-centered daylong recordings-have become a standard method for studying children's language experiences and their impact on subsequent language development. Transcripts of longform speech audio would enable rich analyses at various linguistic levels, yet the massive scale of typical longform corpora prohibits comprehensive manual annotation. At the same time, automatic speech recognition (ASR)-based transcri…

Secure API-Driven Research Automation to Accelerate Scientific Discovery
Tyler J. Skluzacek, Paul Bryant, A. J. Ruckman, Daniel Rosendo, Suzanne Prentice, Michael J. Brim, Ryan Adamson, Sarp Oral, Mallikarjun Shankar, Rafael Ferreira da Silva
https://arxiv.org/abs/2506.11950

Secure API-Driven Research Automation to Accelerate Scientific Discovery
The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M revolutionizes programmatic access to high performance computing (HPC) while maintaining uncompromising security. This framework allows intelligent agents and experimental facilities to dyn…

InstaInpaint: Instant 3D-Scene Inpainting with Masked Large Reconstruction Model
Junqi You, Chieh Hubert Lin, Weijie Lyu, Zhengbo Zhang, Ming-Hsuan Yang
https://arxiv.org/abs/2506.10980

InstaInpaint: Instant 3D-Scene Inpainting with Masked Large Reconstruction Model
Recent advances in 3D scene reconstruction enable real-time viewing in virtual and augmented reality. To support interactive operations for better immersiveness, such as moving or editing objects, 3D scene inpainting methods are proposed to repair or complete the altered geometry. However, current approaches rely on lengthy and computationally intensive optimization, making them impractical for real-time or online applications. We propose InstaInpaint, a reference-based feed-forward framework t…

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
Nitik Bhatia, Patrick Rinke, Ondrej Krejci
https://arxiv.org/abs/2506.13486

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR spectra often requires high-fidelity simulations, such as density functional theory based ab-initio molecular dynamics, which are computationally expensive and therefore limited in the tractable system size and complexity. In this work, we present a novel active learning-based framework…

TransGI: Real-Time Dynamic Global Illumination With Object-Centric Neural Transfer Model
Yijie Deng, Lei Han, Lu Fang
https://arxiv.org/abs/2506.09909 http…

TransGI: Real-Time Dynamic Global Illumination With Object-Centric Neural Transfer Model
Neural rendering algorithms have revolutionized computer graphics, yet their impact on real-time rendering under arbitrary lighting conditions remains limited due to strict latency constraints in practical applications. The key challenge lies in formulating a compact yet expressive material representation. To address this, we propose TransGI, a novel neural rendering method for real-time, high-fidelity global illumination. It comprises an object-centric neural transfer model for material repres…

Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
Ye Niu, Sanping Zhou, Yizhe Li, Ye Den, Le Wang
https://arxiv.org/abs/2506.09422

Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
In many complex scenarios, robotic manipulation relies on generative models to estimate the distribution of multiple successful actions. As the diffusion model has better training robustness than other generative models, it performs well in imitation learning through successful robot demonstrations. However, the diffusion-based policy methods typically require significant time to iteratively denoise robot actions, which hinders real-time responses in robotic manipulation. Moreover, existing dif…

Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Jarek Duda
https://arxiv.org/abs/2506.05354

Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{τ

Treefera, which uses satellite imagery, drone imagery, and AI to provide real-time insights into supply chains, raised a $30M Series B led by Notion Capital (Cate Lawrence/Tech.eu)
https://tech.eu/2025/06/03/treefera-secure…

Treefera secures $20M Series B for AI-driven supply chain resilience solutions
Supply chain resilience startup Treeferachas secured $20 million in its Series B funding round, led by Notion Capital with participation from other investors.  In the face of growing economic uncertainty, a stringent regulatory landscape, and increasing environmental risk, Treefera’s platform utilises satellite imagery, drone imagery, and adaptive AI in order to provide real-time, tailored insights into supply chains.  This is especially significant within the first mile of supply chains,…

Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
Songqi Wang, Yue Zhang, Jia Chen, Xinyuan Zhang, Yi Li, Ning Lin, Yangu He, Jichang Yang, Yingjie Yu, Yi Li, Zhongrui Wang, Xiaojuan Qi, Han Wang
https://arxiv.org/abs/2506.13151

Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
The human brain simultaneously optimizes synaptic weights and topology by growing, pruning, and strengthening synapses while performing all computation entirely in memory. In contrast, modern artificial-intelligence systems separate weight optimization from topology optimization and depend on energy-intensive von Neumann architectures. Here, we present a software-hardware co-design that bridges this gap. On the algorithmic side, we introduce a real-time dynamic weight-pruning strategy that moni…

Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning
Kai Zhou, Youbiao He, Chong Zhong, Yifu Wu
https://arxiv.org/abs/2506.08893 …

Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning
Despite high reliability, modern power systems with growing renewable penetration face an increasing risk of cascading outages. Real-time cascade mitigation requires fast, complex operational decisions under uncertainty. In this work, we extend the influence graph into a Markov decision process model (MDP) for real-time mitigation of cascading outages in power transmission systems, accounting for uncertainties in generation, load, and initial contingencies. The MDP includes a do-nothing action …

Real-Time Confidence Detection through Facial Expressions and Hand Gestures
Tanjil Hasan Sakib, Samia Jahan Mojumder, Rajan Das Gupta, Md Imrul Hasan Showmick, Md. Yeasin Rahat, Md. Jakir Hossen
https://arxiv.org/abs/2506.09153

Real-Time Confidence Detection through Facial Expressions and Hand Gestures
Real-time face orientation recognition is a cutting-edge technology meant to track and analyze facial movements in virtual environments such as online interviews, remote meetings, and virtual classrooms. As the demand for virtual interactions grows, it becomes increasingly important to measure participant engagement, attention, and overall interaction. This research presents a novel solution that leverages the Media Pipe Face Mesh framework to identify facial landmarks and extract geometric dat…

Microservices and Real-Time Processing in Retail IT: A Review of Open-Source Toolchains and Deployment Strategies
Aaditaa Vashisht (Department of Information Science,Engineering, RV College of Engineering, India), Rekha B S (Department of Information Science,Engineering, RV College of Engineering, India)
https://arxiv.org/abs/25…

Microservices and Real-Time Processing in Retail IT: A Review of Open-Source Toolchains and Deployment Strategies
With the rapid pace of digital transformation, the retail industry is increasingly depending on real-time, scalable, and resilient systems to manage financial transactions, analyze customer behavior, and streamline order processing. This literature review explores how modern event-driven and microservices-based architectures, particularly those leveraging Apache Kafka, Spring Boot, MongoDB, and Kubernetes are transforming retail and financial systems. By systematically reviewing academic public…

Oh, absolutely, I live in Norway, because apparently, people need proof before they believe anything these days. Next time you see me, I’ll be casually dropping the phrase “Norsk Syndikalistisk Forbund” in perfect Norwegian, just to remind you that I’m not a figment of your imagination but a real, live, radicalized-by-proxy Norwegian who reads obscure early 20th-century syndicalist newspapers in my spare time.
Don’t worry, I’ll also make sure to quote both Emma Goldman and Rudolf Rocke…

RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding
Yisi Liu, Chenyang Wang, Hanjo Kim, Raniya Khan, Gopala Anumanchipalli
https://arxiv.org/abs/2506.10289

RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding
Voice conversion has emerged as a pivotal technology in numerous applications ranging from assistive communication to entertainment. In this paper, we present RT-VC, a zero-shot real-time voice conversion system that delivers ultra-low latency and high-quality performance. Our approach leverages an articulatory feature space to naturally disentangle content and speaker characteristics, facilitating more robust and interpretable voice transformations. Additionally, the integration of differentia…

Generalised Rate Control Approach For Stream Processing Applications
Ziren Xiao
https://arxiv.org/abs/2506.11710 https://arxiv.org/pd…

Generalised Rate Control Approach For Stream Processing Applications
Distributed stream processing systems are widely deployed to process real-time data generated by various devices, such as sensors and software systems. A key challenge in the system is overloading, which leads to an unstable system status and consumes additional system resources. In this paper, we use a graph neural network-based deep reinforcement learning to collaboratively control the data emission rate at which the data is generated in the stream source to proactively avoid overloading scen…

Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models
Gon\c{c}alo Mesquita, Ana Rita C\'oias, Artur Dubrawski, Alexandre Bernardino
https://arxiv.org/abs/2506.03752

Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models
The growing demands of stroke rehabilitation have increased the need for solutions to support autonomous exercising. Virtual coaches can provide real-time exercise feedback from video data, helping patients improve motor function and keep engagement. However, training real-time motion analysis systems demands frame-level annotations, which are time-consuming and costly to obtain. In this work, we present a framework that learns to classify individual frames from video-level annotations for real…

SONIC: Sound Optimization for Noise In Crowds
Pranav M N, Gandham Sai Santhosh, Tejas Joshi, S Sriniketh Desikan, Eswar Gupta
https://arxiv.org/abs/2506.13272

SONIC: Sound Optimization for Noise In Crowds
This paper presents SONIC, an embedded real-time noise suppression system implemented on the ARM Cortex-M7-based STM32H753ZI microcontroller. Using adaptive filtering (LMS), the system improves speech intelligibility in noisy environments. SONIC focuses on a novel approach to noise suppression in audio signals, specifically addressing the limitations of traditional Active Noise Cancellation (ANC) systems. The paper explores various signal processing algorithms in a micro-controller point of vie…

Improved belief propagation is sufficient for real-time decoding of quantum memory
Tristan M\"uller, Thomas Alexander, Michael E. Beverland, Markus B\"uhler, Blake R. Johnson, Thilo Maurer, Drew Vandeth
https://arxiv.org/abs/2506.01779

Improved belief propagation is sufficient for real-time decoding of quantum memory
We introduce a new heuristic decoder, Relay-BP, targeting real-time quantum circuit decoding for large-scale quantum computers. Relay-BP achieves high accuracy across circuit-noise decoding problems: significantly outperforming BP+OSD+CS-10 for bivariate-bicycle codes and comparable to min-weight-matching for surface codes. As a lightweight message-passing decoder, Relay-BP is inherently parallel, enabling rapid low-footprint decoding with FPGA or ASIC real-time implementations, similar to stan…

This https://arxiv.org/abs/2409.18122 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

RT-GuIDE: Real-Time Gaussian splatting for Information-Driven Exploration
We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing dense maps. Further, we develop a GPU-accelerated motion planning algorithm that can exploit the Gaussian map for real-time navigation. The Gaussian map constructed onboard the robot is optimized for both photometric and geometric quality while enabling real-time situational awareness for autonomy. We show through simulation experiments that our method yields comparable Peak Signal-to-No…

2/3
🔹 Pros:
✅ Scalable – your income isn’t tied to hours worked
✅ Leverage – teams, automation, or systems do the heavy lifting
✅ More time freedom over the long term
🔹 Cons:
❌ Requires upfront effort to build systems
❌ May involve an initial investment
❌ Requires leadership & strategic thinking
💡 Best for: Those ready to shift from self-employed to system owner – turning their side hustle into a real asset.

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Lei Deng, Wenhan Xu, Jingwei Li, Danny H. K. Tsang
https://arxiv.org/abs/2506.09647

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic fo…

Light-Matter Entanglement in Real-Time Nuclear-Electronic Orbital Polariton Dynamics
Millan F. Welman, Tao E. Li, Sharon Hammes-Schiffer
https://arxiv.org/abs/2506.06490

Light-Matter Entanglement in Real-Time Nuclear-Electronic Orbital Polariton Dynamics
Molecular polaritons are hybrid light-matter states that enable the exploration of potential cavity-modified chemistry. The development of dynamical, first-principles approaches for simulating molecular polaritons is important for understanding their origins and properties. Herein, we present a hierarchy of first-principles methods to simulate the real-time dynamics of molecular polaritons in the strong coupling regime. These methods are based on real-time time-dependent density functional theo…

Israel-based Hypernative, which offers real-time threat prevention for crypto companies, raised a $40M Series B, taking its total funding to $67M (Ryan Lawler/Axios)
https://www.axios.com/pro/fintech-deals/2025/06/10/crypto-security-hypern…

Exclusive: Crypto security startup Hypernative raises $40M
Security remains a major barrier to mass adoption of crypto.

DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos
Chieh Hubert Lin, Zhaoyang Lv, Songyin Wu, Zhen Xu, Thu Nguyen-Phuoc, Hung-Yu Tseng, Julian Straub, Numair Khan, Lei Xiao, Ming-Hsuan Yang, Yuheng Ren, Richard Newcombe, Zhao Dong, Zhengqin Li
https://arxiv.org/abs/2506.09997

DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos
We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM), the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene. Feed-forward scene reconstruction has gained significant attention for its ability to rapidly create digital replicas of real-world environments. However, most existing models are limited to static scenes and fail to reconstruct the motion of moving objects. Developing a feed-forward model for …

Adaptive Bandwidth Sharing for Optimizing QoE of Real-Time Video
Sushi Anna George, Vinay Joseph
https://arxiv.org/abs/2506.09197 https://

Adaptive Bandwidth Sharing for Optimizing QoE of Real-Time Video
The concept of spectrum or bandwidth sharing has gained significant global attention as a means to enhance the efficiency of real-time traffic management in wireless networks. Effective bandwidth sharing enables optimal utilization of available resources, reducing congestion and improving QoE for delay-sensitive applications such as real-time video transmission. In this paper, we propose a novel iterative semi-static bandwidth sharing policy that balances the advantages of both static and dynam…

This https://arxiv.org/abs/2503.16217 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Enhanced quantum sensing in time-modulated non-Hermitian systems
Enhancing the sensitivity of quantum sensing near an exceptional point represents a significant phenomenon in non-Hermitian (NH) systems. However, the application of this property in time-modulated NH systems remains largely unexplored. In this work, we propose two theoretical schemes to achieve enhanced quantum sensing in time-modulated NH systems by leveraging the coalescence of eigenvalues and eigenstates. We conduct a comprehensive analysis of the full energy spectrum, including both real a…

2/3
🔹 Pros:
✅ Scalable – your income isn’t tied to hours worked
✅ Leverage – teams, automation, or systems do the heavy lifting
✅ More time freedom over the long term
🔹 Cons:
❌ Requires upfront effort to build systems
❌ May involve an initial investment
❌ Requires leadership & strategic thinking
💡 Best for: Those ready to shift from self-employed to system owner – turning their side hustle into a real asset.

StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology
William G. Coon, Preston Peranich, Griffin Milsap
https://arxiv.org/abs/2506.03442

StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology
The System to Augment Restorative Sleep (StARS) is a modular hardware/software platform designed for real-time sleep monitoring and intervention. Utilizing the compact DCM biosignal device, StARS captures electrophysiological signals (EEG, EMG, EOG) and synchronizes sensor data using the ezmsg real-time software framework. StARS supports interventions such as closed-loop auditory stimulation and dynamic thermal modulation guided by sleep-stage decoding via advanced neural network models and tra…

AffectMachine-Pop: A controllable expert system for real-time pop music generation
Kat R. Agres, Adyasha Dash, Phoebe Chua, Stefan K. Ehrlich
https://arxiv.org/abs/2506.08200

AffectMachine-Pop: A controllable expert system for real-time pop music generation
Music is a powerful medium for influencing listeners' emotional states, and this capacity has driven a surge of research interest in AI-based affective music generation in recent years. Many existing systems, however, are a black box which are not directly controllable, thus making these systems less flexible and adaptive to users. We present \textit{AffectMachine-Pop}, an expert system capable of generating retro-pop music according to arousal and valence values, which can either be pre-determ…

A Real-time 3D Desktop Display
Livio Tenze, Enrique Canessa
https://arxiv.org/abs/2506.08064 https://arxiv.org/pdf/2506.08064

A Real-time 3D Desktop Display
A new extended version of the altiro3D C++ Library -- initially developed to get glass-free holographic displays starting from 2D images -- is here introduced aiming to deal with 3D video streams from either 2D webcam images or flat video files. These streams are processed in real-time to synthesize light-fields (in Native format) and feed realistic 3D experiences. The core function needed to recreate multiviews consists on the use of MiDaS Convolutional Neural Network (CNN), which allows to ex…

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan, Kai Ma, Meng Yuan
https://arxiv.org/abs/2506.06824

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (…

Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
Chenglou Liu, Yufeng Lu, Fangfang Xie, Tingwei Ji, Yao Zheng
https://arxiv.org/abs/2506.02365

Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
As UAV popularity soars, so does the mission planning associated with it. The classical approaches suffer from the triple problems of decoupled of task assignment and path planning, poor real-time performance and limited adaptability. Aiming at these challenges, this paper proposes a dynamic real-time multi-UAV collaborative mission planning algorithm based on Dubins paths under a distributed formation structure. Dubins path with multiple advantages bridges the gap between task assignment and p…

6G Infrastructures for Edge AI: An Analytical Perspective
Kurt Horvath, Shpresa Tuda, Blerta Idrizi, Stojan Kitanov, Fisnik Doko, Dragi Kimovski
https://arxiv.org/abs/2506.10570

6G Infrastructures for Edge AI: An Analytical Perspective
The convergence of Artificial Intelligence (AI) and the Internet of Things has accelerated the development of distributed, network-sensitive applications, necessitating ultra-low latency, high throughput, and real-time processing capabilities. While 5G networks represent a significant technological milestone, their ability to support AI-driven edge applications remains constrained by performance gaps observed in real-world deployments. This paper addresses these limitations and highlights criti…

Grasp Prediction based on Local Finger Motion Dynamics
Dimitar Valkov, Pascal Kockwelp, Florian Daiber, Antonio Kr\"uger
https://arxiv.org/abs/2506.10818

Grasp Prediction based on Local Finger Motion Dynamics
The ability to predict the object the user intends to grasp offers essential contextual information and may help to leverage the effects of point-to-point latency in interactive environments. This paper explores the feasibility and accuracy of real-time recognition of uninstrumented objects based on hand kinematics during reach-to-grasp actions. In a data collection study, we recorded the hand motions of 16 participants while reaching out to grasp and then moving real and synthetic objects. Our…

NAT: Neural Acoustic Transfer for Interactive Scenes in Real Time
Xutong Jin, Bo Pang, Chenxi Xu, Xinyun Hou, Guoping Wang, Sheng Li
https://arxiv.org/abs/2506.06190

NAT: Neural Acoustic Transfer for Interactive Scenes in Real Time
Previous acoustic transfer methods rely on extensive precomputation and storage of data to enable real-time interaction and auditory feedback. However, these methods struggle with complex scenes, especially when dynamic changes in object position, material, and size significantly alter sound effects. These continuous variations lead to fluctuating acoustic transfer distributions, making it challenging to represent with basic data structures and render efficiently in real time. To address this c…

Neural-Augmented Kelvinlet: Real-Time Soft Tissue Deformation with Multiple Graspers
Ashkan Shahbazi, Kyvia Pereira, Jon S. Heiselman, Elaheh Akbari, Annie C. Benson, Sepehr Seifi, Xinyuan Liu, Garrison L. Johnston, Erwin Terpstra, Anne Draaisma, Jan-Jaap Severes, Jie Ying Wu, Nabil Simaan, Michael L. Miga, Soheil Kolouri
https://

Neural-Augmented Kelvinlet: Real-Time Soft Tissue Deformation with Multiple Graspers
Fast and accurate simulation of soft tissue deformation is a critical factor for surgical robotics and medical training. In this paper, we introduce a novel physics-informed neural simulator that approximates soft tissue deformations in a realistic and real-time manner. Our framework integrates Kelvinlet-based priors into neural simulators, making it the first approach to leverage Kelvinlets for residual learning and regularization in data-driven soft tissue modeling. By incorporating large-sca…

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
Bolin Li, Linwei Sun, Xuecong Huang, Yuzhi Jiang, Lijun Zhu
https://arxiv.org/abs/2506.08416

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
This paper presents a periodic bipedal gait learning method using reward composition, integrated with a real-time gait planner for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's lea…

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
Idriss Djiofack Teledjieu, Irzum Shafique
https://arxiv.org/abs/2506.01971 https://…

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
CityPulse is a proof-of-concept big data pipeline designed to enable real-time urban mobility analytics using scalable, containerized components -- without reliance on physical sensor infrastructure. The system simulates the ingestion of 11 million traffic-related records representing urban phenomena such as vehicle congestion, GPS coordinates, and weather conditions. Data is ingested through a Dockerized Apache Kafka cluster, coordinated by ZooKeeper, and processed in real time using Apache Sp…

Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On
Zaiqiang Wu, Yechen Li, Jingyuan Liu, Yuki Shibata, Takayuki Hori, I-Chao Shen, Takeo Igarashi
https://arxiv.org/abs/2506.10468

Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On
Existing image-based virtual try-on methods are often limited to the front view and lack real-time performance. While per-garment virtual try-on methods have tackled these issues by capturing per-garment datasets and training per-garment neural networks, they still encounter practical limitations: (1) the robotic mannequin used to capture per-garment datasets is prohibitively expensive for widespread adoption and fails to accurately replicate natural human body deformation; (2) the synthesized …

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams
Tauhid Tanjim, Jonathan St. George, Kevin Ching, Hee Rin Lee, Angelique Taylor
https://arxiv.org/abs/2506.08892

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams
The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-se…

Towards Autonomous In-situ Soil Sampling and Mapping in Large-Scale Agricultural Environments
Thien Hoang Nguyen, Erik Muller, Michael Rubin, Xiaofei Wang, Fiorella Sibona, Salah Sukkarieh
https://arxiv.org/abs/2506.05653

Towards Autonomous In-situ Soil Sampling and Mapping in Large-Scale Agricultural Environments
Traditional soil sampling and analysis methods are labor-intensive, time-consuming, and limited in spatial resolution, making them unsuitable for large-scale precision agriculture. To address these limitations, we present a robotic solution for real-time sampling, analysis and mapping of key soil properties. Our system consists of two main sub-systems: a Sample Acquisition System (SAS) for precise, automated in-field soil sampling; and a Sample Analysis Lab (Lab) for real-time soil property ana…

Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems
Mohammad Fakhruddin Babar, Zain A. H. Hammadeh, Mohammad Hamad, Monowar Hasan
https://arxiv.org/abs/2506.01991

Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems
Leaking information about the execution behavior of critical real-time tasks may lead to serious consequences, including violations of temporal constraints and even severe failures. We study information leakage for a special class of real-time tasks that have two execution modes, namely, typical execution (which invokes the majority of times) and critical execution (to tackle exceptional conditions). The data flow-driven applications inherit such a multimode execution model. In this paper, we i…

Neural Visibility Cache for Real-Time Light Sampling
Jakub Bok\v{s}ansk\'y, Daniel Meister
https://arxiv.org/abs/2506.05930 https://

Neural Visibility Cache for Real-Time Light Sampling
Direct illumination with many lights is an inherent component of physically-based rendering, remaining challenging, especially in real-time scenarios. We propose an online-trained neural cache that stores visibility between lights and 3D positions. We feed light visibility to weighted reservoir sampling (WRS) to sample a light source. The cache is implemented as a fully-fused multilayer perceptron (MLP) with multi-resolution hash-grid encoding, enabling online training and efficient inference o…