Tootfinder

Efficient reallocation of indivisible resources: Pair-efficiency versus Pareto-efficiency
Pinaki Mandal
https://arxiv.org/abs/2506.15169 https://

Efficient reallocation of indivisible resources: Pair-efficiency versus Pareto-efficiency
In the object reallocation problem, achieving Pareto-efficiency is desirable, but may be too demanding for implementation purposes. In contrast, pair-efficiency, which is the minimal efficiency requirement, is more suitable. Despite being a significant relaxation, however, pair-efficiency ensures Pareto-efficiency for any strategy-proof and individually rational rule when agents' preferences are unrestricted. What if agents' preferences have specific restricted structures, such as single-peak…

High-Efficiency Quantum Memory of Full-Bandwidth Squeezed Light
Jinxian Guo, Meihong Wang, Zeliang Wu, Chenyu Qiao, Fengyi Xu, Xiaoran Zhang, Xiaolong Su, Liqing Chen, Weiping Zhang
https://arxiv.org/abs/2506.15399

High-Efficiency Quantum Memory of Full-Bandwidth Squeezed Light
In continuous-variable quantum information processing, it is crucial to develop high-efficiency and broadband quantum memory of squeezed light, which enables the storage of full-bandwidth information. Here, we present a quantum memory of squeezed light with up to 24 MHz bandwidth, which is at least 12 times that of previous narrowband resonant memory systems, via a far-off resonant Raman process. We achieve output squeezing of as high as 1.0 dB with fidelity above 92% and a memory efficiency of…

In a memo to employees, Amazon CEO Andy Jassy says AI "will reduce our total corporate workforce as we get efficiency gains" over time (Steve Kopack/NBC News)
https://www.nbcnews.com/business/business-

Amazon expects to cut corporate jobs as it relies more on AI
Amazon CEO Andy Jassy said Tuesday that the company expects artificial intelligence "will reduce our total corporate workforce as we get efficiency gains" over time.

Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
Asifullah khan, Muhammad Zaeem Khan, Saleha Jamshed, Sadia Ahmad, Aleesha Zainab, Kaynat Khatib, Faria Bibi, Abdul Rehman
https://arxiv.org/abs/2506.12365

Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
This survey paper outlines the key developments in the field of Large Language Models (LLMs), such as enhancing their reasoning skills, adaptability to various tasks, increased computational efficiency, and ability to make ethical decisions. The techniques that have been most effective in bridging the gap between human and machine communications include the Chain-of-Thought prompting, Instruction Tuning, and Reinforcement Learning from Human Feedback. The improvements in multimodal learning and…

High-efficiency WSe$_2$ photovoltaics enabled by ultra-clean van der Waals contacts
Kamal Kumar Paul, Cullen Chosy, Soumya Sarkar, Zhuangnan Li, Han Yan, Ye Wang, Leyi Loh, Lixin Liu, Hu Young Jeong, Samuel D. Stranks, Yan Wang, Manish Chhowalla
https://arxiv.org/abs/2506.14733

High-efficiency WSe$_2$ photovoltaics enabled by ultra-clean van der Waals contacts
Layered transition metal dichalcogenide semiconductors are interesting for photovoltaics owing to their high solar absorbance and efficient carrier diffusion. Tungsten diselenide (WSe$_2$), in particular, has emerged as a promising solar cell absorber. However, defective metal-semiconductor interfaces have restricted the power conversion efficiency (PCE) to approximately 6%. Here we report WSe$_2$ photovoltaics with a record-high PCE of approximately 11% enabled by ultra-clean indium/gold (In/A…

A Geometric Solution to the Isoperimetric Problem and its Quantitative Inequalities
Lakshya Chaudhary
https://arxiv.org/abs/2506.12864 https://

A Geometric Solution to the Isoperimetric Problem and its Quantitative Inequalities
This paper introduces a geometric solution to the classical isoperimetric problem by analysing the area efficiency of n-sided regular polygons through a novel apothem-hypotenuse ratio framework. A new formula is derived to quantify polygonal efficiency based on apothem, hypotenuse, and perimeter, demonstrating how regular polygons approach the optimal area-enclosing efficiency of the circle as the number of sides increases. The work further presents an original formula for area lost relative to…

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Zhirui Sun, Xingrong Diao, Yao Wang, Bi-Ke Zhu, Jiankun Wang
https://arxiv.org/abs/2506.14305

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Navigation in human-robot shared crowded environments remains challenging, as robots are expected to move efficiently while respecting human motion conventions. However, many existing approaches emphasize safety or efficiency while overlooking social awareness. This article proposes Learning-Risk Model Predictive Control (LR-MPC), a data-driven navigation algorithm that balances efficiency, safety, and social awareness. LR-MPC consists of two phases: an offline risk learning phase, where a Prob…

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging
Seyed Moein Abtahi, Akramul Azim
https://arxiv.org/abs/2506.15026

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging
The rapid advancements in autonomous vehicle (AV) technology promise enhanced safety and operational efficiency. However, frequent lane changes and merging maneuvers continue to pose significant safety risks and disrupt traffic flow. This paper introduces the Minimizing Lane Change Algorithm (MLCA), a state-machine-based approach designed to reduce unnecessary lane changes, thereby enhancing both traffic safety and efficiency. The MLCA algorithm prioritizes maintaining lane stability unless saf…

How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI
Christina Bremer, Harshit Gujral, Michelle Lin, Lily Hinkers, Christoph Becker, Vlad C. Coroam\u{a}
https://arxiv.org/abs/2506.14653

How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI
As part of global climate action, digital technologies are seen as a key enabler of energy efficiency savings. A popular application domain for this work is smart homes. There is a risk, however, that these efficiency gains result in rebound effects, which reduce or even overcompensate the savings. Rebound effects are well-established in economics, but it is less clear whether they also inform smart energy research in other disciplines. In this paper, we ask: to what extent have rebound effects…

The efficiency of mixed modes for angular momentum transport
Beatriz Bordad\'agua, Felix Ahlborn, Quentin Copp\'ee, Jo\~ao P. Marques, K\'evin Belkacem, Saskia Hekker
https://arxiv.org/abs/2506.13283

The efficiency of mixed modes for angular momentum transport
Core rotation rates of red-giant stars inferred from asteroseismic observations are substantially lower than predicted by current stellar models. This indicates the lack of an efficient angular momentum transport mechanism in radiative interiors. Mixed pressure-gravity modes are a promising candidate to extract angular momentum from the core of red giants. We focus on determining the effect of mixed modes on the rotation rates of stars evolving along the red giant branch (RGB). We developed a p…

You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality
Xingyu Chen, Xinmin Fang, Shuting Zhang, Xinyu Zhang, Liang He, Zhengxiong Li
https://arxiv.org/abs/2506.15183

You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality
Mobile Virtual Reality (VR) is essential to achieving convenient and immersive human-computer interaction and realizing emerging applications such as Metaverse. However, existing VR technologies require two separate renderings of binocular images, causing a significant bottleneck for mobile devices with limited computing capability and power supply. This paper proposes an approach to rendering optimization for mobile VR called EffVR. By utilizing the per-pixel attribute, EffVR can generate bino…

Designing Deep Learning Frameworks for LLMs:Challenges, Expectations, and Opportunities
Yanzhou Mu, Rong Wang, Juan Zhai, Chunrong Fang, Xiang Chen, Jiacong Wu, An Guo, Jiawei Shen, Bingzhuo Li, Zhenyu Chen
https://arxiv.org/abs/2506.13114

Designing Deep Learning Frameworks for LLMs:Challenges, Expectations, and Opportunities
Large language models (LLMs) drive significant advancements in real industry applications. LLMs rely on DL frameworks for efficient model construction, distributed execution, and optimized deployment. Their large parameter scale and long execution cycles place extreme demands on DL frameworks in terms of scalability, stability, and efficiency. Therefore, poor usability, limited functionality, and subtle bugs in DL frameworks may hinder development efficiency and cause severe failures or resourc…

HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
Miguel Camelo Botero, Esra Aycan Beyazit, Nina Slamnik-Krije\v{s}torac, Johann M. Marquez-Barja
https://arxiv.org/abs/2506.13408

HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement…

A new guide focusing on renewables-based electrification of road transport by #IRENA, embedded into wider sustainable transport planning (e.g. prioritize public transport). Efficiency of electrification renewables smart charging & V2G key to enable system-wide benefits

Energy Efficiency Maximization for Movable Antenna Communication Systems
Jingze Ding, Zijian Zhou, Lipeng Zhu, Yuping Zhao, Bingli Jiao, Rui Zhang
https://arxiv.org/abs/2506.07129

Energy Efficiency Maximization for Movable Antenna Communication Systems
This paper investigates energy efficiency maximization for movable antenna (MA)-aided multi-user uplink communication systems by considering the time delay and energy consumption incurred by practical antenna movement. We first examine the special case with a single user and propose an optimization algorithm based on the one-dimensional (1D) exhaustive search to maximize the user's energy efficiency. Moreover, we derive an upper bound on the energy efficiency and analyze the conditions required…

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Madhavrao Dukre, Feilong Tang, Sara Atito, Sudipta Roy, Muhammad Awais, Muhammad Haris Khan, Imran Razzak
https://arxiv.org/abs/2506.15649

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Despite significant advances in inference-time search for vision-language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-confidence generations which often lead to persistent hallucinations. We introduce \textbf{Value-guided Inference with Margin-based Reward (ViMaR)}, a two-stage inference framework that improves both efficiency and output fidelity by combining a temporal-difference value model with a margin-aware reward adjustment. In th…

WaPo's Matt Murray notifies print subscribers that the paper is reordering its sections to improve efficiency, including eliminating standalone Metro sections (Ben Mullin/@benmullin)
https://x.com/benmullin/status/1934701401193447845

Ben Mullin (@BenMullin) on X
Matt Murray notifies Washington Post print subscribers their newspaper is changing: “We are reordering our sections to improve our efficiency….”

MECHA: Multithreaded and Efficient Cryptographic Hardware Access
Pratama Derry, Laksmono Agus Mahardika Ari, Iqbal Muhammad, Howon Kim
https://arxiv.org/abs/2506.15034

MECHA: Multithreaded and Efficient Cryptographic Hardware Access
This paper presents a multithread and efficient cryptographic hardware access (MECHA) for efficient and fast cryptographic operations that eliminates the need for context switching. Utilizing a UNIX domain socket, MECHA manages multiple requests from multiple applications simultaneously, resulting in faster processing and improved efficiency. We comprise several key components, including the Server thread, Client thread, Transceiver thread, and a pair of Sender and Receiver queues. MECHA design…

Life Cycle Analysis for Emissions of Scientific Computing Centres
Wim Vanderbauwhede, Mattias Wadenstein
https://arxiv.org/abs/2506.14365 https://

Life Cycle Analysis for Emissions of Scientific Computing Centres
We propose a dedicated model to assist with the life cycle analysis of emissions of scientific computing centres. The model takes into account both the embodied carbon and emissions from use, as well as other factors such as data centre power usage efficiency, data centre expansion, hardware replacement, increase in energy efficiency of next-generation hardware, reduction in carbon intensity of the electricity supply and potential for heat reuse. If differs from existing models in its detailed …

AviationLLM: An LLM-based Knowledge System for Aviation Training
Jia'ang Wan, Feng Shen, Fujuan Li, Yanjin Sun, Yan Li, Shiwen Zhang
https://arxiv.org/abs/2506.14336

AviationLLM: An LLM-based Knowledge System for Aviation Training
Aviation training is a core link in ensuring flight safety, improving industry efficiency and promoting sustainable development. It not only involves flight simulation but also requires the learning of a great deal of professional aviation theory knowledge. In the existing training system, the knowledge is mainly imparted by the the instructors. However, the number of instructors is limited and the professional answers obtained from the Internet are not accurate enough, resulting in low trainin…

Efficient Serving of LLM Applications with Probabilistic Demand Modeling
Yifei Liu, Zuo Gan, Zhenghao Gan, Weiye Wang, Chen Chen, Yizhou Shan, Xusheng Chen, Zhenhua Han, Yifei Zhu, Shixuan Sun, Minyi Guo
https://arxiv.org/abs/2506.14851

Efficient Serving of LLM Applications with Probabilistic Demand Modeling
Applications based on Large Language Models (LLMs) contains a series of tasks to address real-world problems with boosted capability, which have dynamic demand volumes on diverse backends. Existing serving systems treat the resource demands of LLM applications as a blackbox, compromising end-to-end efficiency due to improper queuing order and backend warm up latency. We find that the resource demands of LLM applications can be modeled in a general and accurate manner with Probabilistic Demand G…

Sodium induced beneficial effects in wide bandgap Cu(In,Ga)S2 solar cell with 15.7% efficiency
Arivazhagan Valluvar Oli, Kulwinder Kaur, Michele Melchiorre, Aubin Jean-Claude Mireille Prot, Sevan Gharabeiki, Yucheng Hu, Gunnar Kusch, Adam Hultqvist, Tobias T\"orndahl, Wolfram Hempel, Wolfram Witte, Rachel A. Oliver, Susanne Siebentritt
https://

Sodium induced beneficial effects in wide bandgap Cu(In,Ga)S2 solar cell with 15.7% efficiency
This study underscores the pivotal role of sodium (Na) supply in optimizing the optoelectronic properties of wide bandgap (~1.6 eV) Cu(In,Ga)S2 (CIGS) thin film absorbers for high efficiency solar cells. Our findings demonstrate that the synergistic use of Na from the glass substrate, in conjunction with in-situ sodium fluoride (NaF) co-evaporation, significantly enhances the structural and optoelectronic properties of the CIGS. CIGS grown under either Na-deficient or excess conditions exhibits…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/4]:
- Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency
Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, Tianyi Zhou

Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
Jesmin Jahan Tithi, Hanjiang Wu, Avishaii Abuhatzera, Fabrizio Petrini
https://arxiv.org/abs/2506.15006

Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
The explosive growth of Large Language Models (LLMs) - such as GPT-4 with 1.8 trillion parameters - demands a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness. Our work provides a comprehensive co-design framework that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (two-tier vs. FullFlat optical), the size of the scale-out domain, and popular parallelism/optimization strategies used in LLMs. We introduce an…

Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations
Jing Li, Zhengqi Zhang
https://arxiv.org/abs/2506.11518

Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations
We propose transformed Diffsuion-Wave fractional Physics-Informed Neural Networks (tDWfPINNs) for efficiently solving time-fractional diffusion-wave equations with fractional order $α\in(1,2)$. Conventional numerical methods for these equations often compromise the mesh-free advantage of Physics-Informed Neural Networks (PINNs) or impose high computational costs when computing fractional derivatives. The proposed method avoids first-order derivative calculations at quadrature points by introdu…

Understanding Learning Invariance in Deep Linear Networks
Hao Duan, Guido Mont\'ufar
https://arxiv.org/abs/2506.13714 https://arx…

Understanding Learning Invariance in Deep Linear Networks
Equivariant and invariant machine learning models exploit symmetries and structural patterns in data to improve sample efficiency. While empirical studies suggest that data-driven methods such as regularization and data augmentation can perform comparably to explicitly invariant models, theoretical insights remain scarce. In this paper, we provide a theoretical comparison of three approaches for achieving invariance: data augmentation, regularization, and hard-wiring. We focus on mean squared e…

A detailed and comprehensive account of fractional Physics-Informed Neural Networks: From implementation to efficiency
Donya Dabiri, Joshua DaRosa, Milad Saadat, Deepak Mangal, Safa Jamali
https://arxiv.org/abs/2506.11241

A detailed and comprehensive account of fractional Physics-Informed Neural Networks: From implementation to efficiency
Fractional differential equations are powerful mathematical descriptors for intricate physical phenomena in a compact form. However, compared to integer ordinary or partial differential equations, solving fractional differential equations can be challenging considering the intricate details involved in their numerical solutions. Robust data-driven solutions hence can be of great interest for solving fractional differential equations. In the recent years, fractional physics-informed neural netwo…

Here's the article. It's a fun piece of history. Lots of well-known names.
https://www.nytimes.com/1990/10/17/business/business-technology-harnessing-hundreds-computers-improve-speed-efficiency.html
https://social.v.st/@th/114437577349237616

Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience
Niloufar Mirzavand Boroujeni, Nasim Mirzavand Boroujeni, Nima Moradi, Saeed Jamalzadeh
https://arxiv.org/abs/2506.12671

Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience
Autonomous vehicles (AVs) can improve efficiency, reduce costs, and enhance road safety. They optimize traffic flow, minimize congestion, and support sustainability through shared mobility and reduced fuel consumption. A key challenge in AV deployment is allocating vehicles to parking lots across regions to meet fluctuating demand. Proper allocation reduces delays, lowers costs, and boosts user satisfaction by ensuring timely vehicle availability. This paper explores the impact of customer wait…

Energy Efficiency Optimization of Finite Block Length STAR-RIS-aided MU-MIMO Broadcast Channels
Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Robert Schober, Lajos Hanzo
https://arxiv.org/abs/2506.11594

Energy Efficiency Optimization of Finite Block Length STAR-RIS-aided MU-MIMO Broadcast Channels
Energy-efficient designs are proposed for multi-user (MU) multiple-input multiple-output (MIMO) broadcast channels (BC), assisted by simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) operating at finite block length (FBL). In particular, we maximize the sum energy efficiency (EE), showing that STAR-RIS can substantially enhance it. Our findings demonstrate that the gains of employing STAR-RIS increase when the codeword length and the maximum tolerable b…

Under Randy Clarke, WMATA has cut the end-to-end travel time on the Metro Red Line by 8 minutes. This is a 68 minute trip, so it's a gain in efficiency of over 10% on top of faster trips for riders.
This is a combination of automated train operation (with doors closing sooner), raising the speed limit back to the original design of 75 mph, and I imagine other fixes.

WMATA | Metro to launch Automatic Train Operation on the Blue, Orange, and Silver lines on June 15
Metro news and service information for the Washington, DC, Maryland, and Virginia region.

This https://arxiv.org/abs/2501.01271 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Energy-and Spectral-Efficiency Trade-off in Distributed Massive-MIMO Networks
This paper investigates a fundamental yet under-explored trade-off between energy efficiency (EE) and spectral efficiency (SE) in distributed massive MIMO (D-mMIMO) systems. Unlike conventional EE-SE trade-off studies that primarily focus on transmission power, D-mMIMO systems introduce new energy consumption factors including fronthaul signaling and distributed signal processing, which are heavily influenced by AP-UE association. This work highlights the critical need for a system-level EE-SE …

All-Angle Scanning Leaky-Wave Antennas and Surface-Wave Routing by Reconfigurable Metasurfaces
Yongming Li, Xikui Ma, Viktar Asadchy, Sergei A. Tretyakov
https://arxiv.org/abs/2506.14954

All-Angle Scanning Leaky-Wave Antennas and Surface-Wave Routing by Reconfigurable Metasurfaces
In this work, we show that propagating waves can be fully converted into surface waves and back using geometrically periodic arrays of simple electrically small metal elements loaded by adjustable reactive loads. The proposed approach allows the creation of all-angle scanning leaky-wave antennas with perfect or even superdirective aperture efficiency at all scan angles. Moreover, it is possible to co-design such leaky-wave antenna arrays with surface-wave waveguides that can guide the received …

Engineering Supercomputing Platforms for Biomolecular Applications
Robert Welch, Charles Laughton, Oliver Henrich, Tom Burnley, Daniel Cole, Alan Real, Sarah Harris, James Gebbie-Rayet
https://arxiv.org/abs/2506.15585

Engineering Supercomputing Platforms for Biomolecular Applications
A range of computational biology software (GROMACS, AMBER, NAMD, LAMMPS, OpenMM, Psi4 and RELION) was benchmarked on a representative selection of HPC hardware, including AMD EPYC 7742 CPU nodes, NVIDIA V100 and AMD MI250X GPU nodes, and an NVIDIA GH200 testbed. The raw performance, power efficiency and data storage requirements of the software was evaluated for each HPC facility, along with qualitative factors such as the user experience and software environment. It was found that the diversit…

HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search
Qian Xu, Feng Zhang, Chengxi Li, Lei Cao, Zheng Chen, Jidong Zhai, Xiaoyong Du
https://arxiv.org/abs/2506.14707

HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search
Approximate Nearest Neighbor Search (ANNS) is essential for various data-intensive applications, including recommendation systems, image retrieval, and machine learning. Scaling ANNS to handle billions of high-dimensional vectors on a single machine presents significant challenges in memory capacity and processing efficiency. To address these challenges, distributed vector databases leverage multiple nodes for the parallel storage and processing of vectors. However, existing solutions often suf…

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
Fabian C. Pe\~na, Steffen Herbold
https://arxiv.org/abs/2506.08581

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
This work evaluates Sentence-BERT for a multi-label code comment classification task seeking to maximize the classification performance while controlling efficiency constraints during inference. Using a dataset of 13,216 labeled comment sentences, Sentence-BERT models are fine-tuned and combined with different classification heads to recognize comment types. While larger models outperform smaller ones in terms of F1, the latter offer outstanding efficiency, both in runtime and GFLOPS. As result…

Towards High-Efficiency Solar Cells: Insights into AsNCa 3 Antiperovskite as Active Layer
M. Irfan, B. D. Aparicio-Huacarpuma, C. M. de Oliveira Bastos, M. J. Piotrowski, C. R. C. R\^ego, D. Guedes-Sobrinho, R. Besse, A. M. Almeida Silva, Alexandre C. Dias, L. A. Ribeiro Jr
https://arxiv.org/abs/2506.15403

Towards High-Efficiency Solar Cells: Insights into AsNCa 3 Antiperovskite as Active Layer
Advances in photovoltaic technology are a viable route to contribute to cleaner and more sustainable energy solutions, placing perovskite-based materials among the best candidates for solar energy conversion. However, some challenges must be addressed to enhance their performance and stability. Herein, we report an investigation of the AsNCa3 antiperovskite system for its potential in photovoltaic devices. We consider eight distinct crystalline phases, their structural parameters, dynamical sta…

UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
Vasiliki Balaska, Ioannis Tsampikos Papapetros, Katerina Maria Oikonomou, Loukas Bampis, Antonios Gasteratos
https://arxiv.org/abs/2506.13505

UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UAV-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accur…

Particle Masses Spectrum from Harmonic Cascade Principles
Jonathan Washburn, Elshad Allahyarov
https://arxiv.org/abs/2506.12859 https://

Particle Masses Spectrum from Harmonic Cascade Principles
We present a parameter-free framework, Recognition Science (RS), that predicts the full spectrum of Standard-Model particle masses from first principles. The derived mass formula $m(n,d,s,g) = m_0 \cdot (X_{opt})^{n} \cdot (X_{opt})^{R_{RS}} \cdot E(d,s,g)$ prescribes to each particle a discrete harmonic lattice site $n$ involving only geometric constants: the optimal recognition scale $X_{opt} = ϕ/π$, a resonance index $R_{RS} = 7/12$, the Planck-derived base mass $m_0$, and an efficiency fa…

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
Ashish Gautam, Prasanna Date, Shruti Kulkarni, Robert Patton, Thomas Potok
https://arxiv.org/abs/2506.14138

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
Spiking Neural Networks (SNNs) are computational models inspired by the structure and dynamics of biological neuronal networks. Their event-driven nature enables them to achieve high energy efficiency, particularly when deployed on neuromorphic hardware platforms. Unlike conventional Artificial Neural Networks (ANNs), which primarily rely on layered architectures, SNNs naturally support a wide range of connectivity patterns, from traditional layered structures to small-world graphs characterize…

How we decreased #GitLab repo backup times from 48 hours to 41 minutes
https://about.gitlab.com/blog/2025/06/05/how-we-decr…

Controlling energy delivery with bistable nanostructures
Andreas Ehrmann, Carl P. Goodrich
https://arxiv.org/abs/2506.14266 https://a…

Controlling energy delivery with bistable nanostructures
Countless biological processes are fueled by energy-rich molecules like ATP and GTP that supply energy with extreme efficiency. However, designing similar energy-delivery schemes from the bottom up, essential for the development of powered nanostructures and other {\it de novo} machinery, presents a significant challenge: how can an energy-rich structure be stable in solution yet still deliver this energy at precisely the right time? In this paper, we present a purely physical mechanism that so…

Aerial Grasping via Maximizing Delta-Arm Workspace Utilization
Haoran Chen, Weiliang Deng, Biyu Ye, Yifan Xiong, Ximin Lyu
https://arxiv.org/abs/2506.15539

Aerial Grasping via Maximizing Delta-Arm Workspace Utilization
The workspace limits the operational capabilities and range of motion for the systems with robotic arms. Maximizing workspace utilization has the potential to provide more optimal solutions for aerial manipulation tasks, increasing the system's flexibility and operational efficiency. In this paper, we introduce a novel planning framework for aerial grasping that maximizes workspace utilization. We formulate an optimization problem to optimize the aerial manipulator's trajectory, incorporating t…

Thermodynamic Constraints in DRAM cells: Experimental Verification of Energy Efficiency Limits in Information Erasure
Takase Shimizu, Kensaku Chida, Gento Yamahata, Katsuhiko Nishiguchi
https://arxiv.org/abs/2505.23087

Thermodynamic Constraints in DRAM cells: Experimental Verification of Energy Efficiency Limits in Information Erasure
We measured the energy efficiency of information erasure using silicon DRAM cells capable of counting charges on capacitors at the single-electron level. Our measurements revealed that the efficiency decreased as the erasure error rate decreased, and notably, the Landauer limit was not achieved even under effectively infinite-time operation. By comparing the measured efficiency with the Landauer limit, we identified a thermodynamic constraint that prevents DRAM from reaching this limit: the ina…

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
Francesco Chiossi, Julian Rasch, Robin Welsch, Albrecht Schmidt, Florian Michahelles
https://arxiv.org/abs/2506.15293

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we …

Emission Properties of a Hybrid Metallized Diamond(001) Photocathode
Louis A. Angeloni, Ir-Jene Shan, W. Andreas Schroeder
https://arxiv.org/abs/2506.12550

Emission Properties of a Hybrid Metallized Diamond(001) Photocathode
The spectral emission characteristics of a proof-of-concept hybrid metallized diamond(001) photocathode are presented. The quantum efficiency (QE) is shown to be determined by the photo-injection efficiency across the ohmic contact at the back metallized face, whereas the mean transverse energy (MTE) of the photoemitted electrons is consistent with a (optical)phonon-assisted and momentum-resonant Franck-Condon mechanism [Phys. Rev. Applied 23, 054065 (2025)] following electron drift transport t…

An Entropy-Stable/Double-Flux scheme for the multi-component compressible Navier-Stokes equations
Vahid Badrkhani, T. Jeremy P. Karpowsk, Christian Hasse
https://arxiv.org/abs/2506.13231

An Entropy-Stable/Double-Flux scheme for the multi-component compressible Navier-Stokes equations
We present a novel combination of numerical techniques to improve the efficiency, accuracy, and robustness of multi-component compressible flow simulations. At the core of our approach is an Entropy-Stable formulation that preserves kinetic energy and integrates a Double-Flux scheme tailored for multi-component flows with variable specific heat ratios. This formulation yields low-dissipation, oscillation-free solutions and enhances stability compared to standard fully conservative methods. To f…

Data warehouses, lakes, lakehouses, and more – our choices significantly affect operational costs and development speed. Join Lars Albertsson at Berlin Buzzwords to explore how different data processing paradigms impact deployment, failure handling, and data quality. Learn strategies to minimise costs and latency, bridge between paradigms, and enhance development iteration and operational efficiency.
Learn more:

Elon Musk’s goons at the Department of Government Efficiency
transmitted a large amount of data
—all of it undetected
—using a Starlink Wi-Fi terminal they installed on top of the White House, according to a new report.
Starlink is a satellite Wi-Fi service owned and operated by Musk’s SpaceX.
The DOGE goons installed it in mid-March with the sign-off of Donald Trump’s administration,
but against concerns raised by security officials, according to The Washing…

Musk’s DOGE Goons Surreptitiously Transmitted Reams of White House Data
Musk’s team installed a Starlink Wi-Fi terminal on top of the White House, allowing them to bypass data-tracking security measures.

Finite sample-optimal adjustment sets in linear Gaussian causal models
Nadja Rutsch, Sara Magliacane, St\'ephanie van der Pas
https://arxiv.org/abs/2506.12869

Finite sample-optimal adjustment sets in linear Gaussian causal models
Traditional covariate selection methods for causal inference focus on achieving unbiasedness and asymptotic efficiency. In many practical scenarios, researchers must estimate causal effects from observational data with limited sample sizes or in cases where covariates are difficult or costly to measure. Their needs might be better met by selecting adjustment sets that are finite sample-optimal in terms of mean squared error. In this paper, we aim to find the adjustment set that minimizes the me…

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…

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Xiao Wang, Junru Yu, Jun Huang, Qiong Wu, Ljubo Vacic, Changyin Sun
https://arxiv.org/abs/2506.14502

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Despite the recent advancements in artificial intelligence technologies have shown great potential in improving transport efficiency and safety, autonomous vehicles(AVs) still face great challenge of driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, human have free wills and usually do not make the same decisions even situate in the exactly same scenarios, leading to the data-driven methods suffer from poor migratability and high search cost proble…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
Mohsen Jenadeleh, Jon Sneyers, Davi Lazzarotto, Shima Mohammadi, Dominik Keller, Atanas Boev, Rakesh Rao Ramachandra Rao, Ant\'onio Pinheiro, Thomas Richter, Alexander Raake, Touradj Ebrahimi, Jo\~ao Ascenso, Dietmar Saupe
https://arxiv…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual…

Spectral Efficiency Maximization for DMA-enabled Multiuser MISO with Statistical CSI
Hao Xu, Boyu Ning, Chongjun Ouyang, Hongwen Yang
https://arxiv.org/abs/2506.09570

Spectral Efficiency Maximization for DMA-enabled Multiuser MISO with Statistical CSI
Dynamic metasurface antennas (DMAs) offer the potential to achieve large-scale antenna arrays with low power consumption and reduced hardware costs, making them a promising technology for future communication systems. This paper investigates the spectral efficiency (SE) of DMA-enabled multiuser multiple-input single-output (MISO) systems in both uplink and downlink transmissions, using only statistical channel state information (CSI) to maximize the ergodic sum rate of multiple users. For the u…

Tady: A Neural Disassembler without Structural Constraint Violations
Siliang Qin, Fengrui Yang, Hao Wang, Bolun Zhang, Zeyu Gao, Chao Zhang, Kai Chen
https://arxiv.org/abs/2506.13323

Tady: A Neural Disassembler without Structural Constraint Violations
Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread e…

Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration
Omar Numan, Gaurav Singh, Kazybek Adam, Jelin Leslin, Aleksi Korsman, Otto Simola, Marko Kosunen, Jussi Ryyn\"anen, Martin Andraud
https://arxiv.org/abs/2506.15440

Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration
Developing accurate and reliable Compute-In-Memory (CIM) architectures is becoming a key research focus to accelerate Artificial Intelligence (AI) tasks on hardware, particularly Deep Neural Networks (DNNs). In that regard, there has been significant interest in analog and mixed-signal CIM architectures aimed at increasing the efficiency of data storage and computation to handle the massive amount of data needed by DNNs. Specifically, resistive mixed-signal CIM cores are pushed by recent progre…

Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy
Ruonan Lia, Chang Wena, Mingyu Yan, Congcong Wu, Ahmed Lotfy Elrefai, Xiaotong Zhang, Sahban Wael Saeed Alnaser
https://arxiv.org/abs/2506.15398

Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy
This study focuses on the novel municipal-scale rural integrated energy system (RIES), which encompasses energy supply and application. By constructing a seven-dimensional evaluation system including energy efficiency, energy supply, low-carbon sustainability, environmental impact, energy economy, social benefits, and integrated energy system development, this research combines the improved analytic hierarchy process (IAHP) and entropy weight method (EWM) by sum of squares of deviations to bala…

Spatial and Pulse Efficiency Constraints in Atom Interferometric Gravitational Wave Detectors
Patrik Schach, Enno Giese
https://arxiv.org/abs/2506.09511 ht…

Spatial and Pulse Efficiency Constraints in Atom Interferometric Gravitational Wave Detectors
Currently planned and constructed terrestrial detectors for gravitational waves and dark matter based on differential light-pulse atom interferometry are designed around three primary strategies to enhance their sensitivity: (i) Resonant-mode enhancement using multiple diamonds, (ii) large-momentum-transfer techniques to increase arm separation within the interferometer, and (iii) very-long baseline schemes that increase the distance between the two interferometers. Both resonant-mode enhanceme…

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters
Sergio Iserte, Iker Mart\'in-\'Alvarez, Krzysztof Rojek, Jos\'e I. Aliaga, Maribel Castillo, Weronika Folwarska, Antonio J. Pe\~na
https://arxiv.org/abs/2506.14743

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters
Dynamic resource management is essential for optimizing computational efficiency in modern high-performance computing (HPC) environments, particularly as systems scale. While research has demonstrated the benefits of malleability in resource management systems (RMS), the adoption of such techniques in production environments remains limited due to challenges in standardization, interoperability, and usability. Addressing these gaps, this paper extends our prior work on the Dynamic Management of…

Fast automated adjoints for spectral PDE solvers
Calum S. Skene, Keaton J. Burns
https://arxiv.org/abs/2506.14792 https://arxiv.org/p…

Fast automated adjoints for spectral PDE solvers
We present a general and automated approach for computing model gradients for PDE solvers built on sparse spectral methods, and implement this capability in the widely used open-source Dedalus framework. We apply reverse-mode automatic differentiation to symbolic graph representations of PDEs, efficiently constructing adjoint solvers that retain the speed and memory efficiency of this important class of modern numerical methods. This approach enables users to compute gradients and perform optim…

Comparison of Innovative Strategies for the Coverage Problem: Path Planning, Search Optimization, and Applications in Underwater Robotics
Ahmed Ibrahim, Francisco F. C. Rego, \'Eric Busvelle
https://arxiv.org/abs/2506.15376

Comparison of Innovative Strategies for the Coverage Problem: Path Planning, Search Optimization, and Applications in Underwater Robotics
In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path planning strategies to enhance the efficiency of underwater gliders, particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), M…

AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
Junhang Cheng, Fang Liu, Chengru Wu, Li Zhang
https://arxiv.org/abs/2506.10525

AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
While Large Language Models (LLMs) have significantly advanced code generation efficiency, they face inherent challenges in balancing performance and inference costs across diverse programming tasks. Dynamically selecting the optimal LLM based on task difficulty and resource constraints offers a promising approach to achieve an optimal balance between efficiency and performance. However, existing model selection methods are resource-intensive and often neglect cost efficiency. Moreover, these a…

What Pareto-Efficiency Adjustments Cannot Fix
Josue Ortega, Gabriel Ziegler, R. Pablo Arribillaga, Geng Zhao
https://arxiv.org/abs/2506.11660 https://

What Pareto-Efficiency Adjustments Cannot Fix
The Deferred Acceptance (DA) algorithm is stable and strategy-proof, but can produce outcomes that are Pareto-inefficient for students, and thus several alternative mechanisms have been proposed to correct this inefficiency. However, we show that these mechanisms cannot correct DA's rank-inefficiency and inequality, because these shortcomings can arise even in cases where DA is Pareto-efficient. We also examine students' segregation in settings with advantaged and marginalized students. We pr…

From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment
Bin Xie, Bingbing Xu, Yige Yuan, Shengmao Zhu, Huawei Shen
https://arxiv.org/abs/2506.12446 …

From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment
Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However, existing dominant approaches using reward-guided search (RGS) primarily rely on outcome reward models (ORMs), which suffer from a critical granularity mismatch: ORMs are designed to provide outcome rewards for complete responses, while RGS methods rely on process rewards to guide the policy, leading to inconsistent sc…

STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support
Chenqi Zhang, Yu Feng, Jieru Zhao, Guangda Liu, Wenchao Ding, Chentao Wu, Minyi Guo
https://arxiv.org/abs/2506.09070

STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support
3D Gaussian Splatting (3DGS) has gained popularity for its efficiency and sparse Gaussian-based representation. However, 3DGS struggles to meet the real-time requirement of 90 frames per second (FPS) on resource-constrained mobile devices, achieving only 2 to 9 FPS.Existing accelerators focus on compute efficiency but overlook memory efficiency, leading to redundant DRAM traffic. We introduce STREAMINGGS, a fully streaming 3DGS algorithm-architecture co-design that achieves fine-grained pipelin…

Optimizing Length Compression in Large Reasoning Models
Zhengxiang Cheng, Dongping Chen, Mingyang Fu, Tianyi Zhou
https://arxiv.org/abs/2506.14755 https://…

Optimizing Length Compression in Large Reasoning Models
Large Reasoning Models (LRMs) have achieved remarkable success, yet they often suffer from producing unnecessary and verbose reasoning chains. We identify a core aspect of this issue as "invalid thinking" -- models tend to repeatedly double-check their work after having derived the correct answer. To address this specific inefficiency, we move beyond the general principles of Efficacy and Efficiency to propose two new, fine-grained principles: Brevity, which advocates for eliminating redundancy…

Energy efficiency of DMAs vs. conventional MIMO: a sensitivity analysis
Pablo Ram\'irez-Espinosa, David Morales-Jim\'enez, Beatriz Soret
https://arxiv.org/abs/2506.09181

Energy efficiency of DMAs vs. conventional MIMO: a sensitivity analysis
Motivated by the stringent and challenging need for `greener communications' in increasingly power-hungry 5G networks, this paper presents a detailed energy efficiency analysis for three different multi-antenna architectures, namely fully-digital arrays, hybrid arrays, and dynamic metasurface antennas (DMAs). By leveraging a circuital model, which captures mutual coupling, insertion losses, propagation through the waveguides in DMAs and other electromagnetic phenomena, we design a transmit Wien…

Tady: A Neural Disassembler without Structural Constraint Violations
Siliang Qin, Fengrui Yang, Hao Wang, Bolun Zhang, Zeyu Gao, Chao Zhang, Kai Chen
https://arxiv.org/abs/2506.13323

Tady: A Neural Disassembler without Structural Constraint Violations
Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread e…

How Warm-Glow Alters the Usability of Technology
Antonios Saravanos (New York University)
https://arxiv.org/abs/2506.14720 https://ar…

How Warm-Glow Alters the Usability of Technology
As technology increasingly aligns with users' personal values, traditional models of usability, focused on functionality and specifically effectiveness, efficiency, and satisfaction, may not fully capture how people perceive and evaluate it. This study investigates how the warm-glow phenomenon, the positive feeling associated with doing good, shapes perceived usability. An experimental approach was taken in which participants evaluated a hypothetical technology under conditions designed to evok…

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Ziwei Liu, Borui Kang, Wei Li, Hangjie Yuan, Yanbing Yang, Wenbin Li, Jun Luo, Yifan Zhu, Tao Feng
https://arxiv.org/abs/2506.12409

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Continual learning in vision-language models (VLMs) faces critical challenges in balancing parameter efficiency, memory consumption, and optimization stability. While First-Order (FO) optimization (e.g., SGD) dominate current approaches, their deterministic gradients often trap models in suboptimal local minima and incur substantial memory overhead. This paper pioneers a systematic exploration of Zeroth-Order (ZO) optimization for vision-language continual learning (VLCL). We first identify the…

Context Matters: Learning Generalizable Rewards via Calibrated Features
Alexandra Forsey-Smerek, Julie Shah, Andreea Bobu
https://arxiv.org/abs/2506.15012 …

Context Matters: Learning Generalizable Rewards via Calibrated Features
A key challenge in reward learning from human input is that desired agent behavior often changes based on context. Traditional methods typically treat each new context as a separate task with its own reward function. For example, if a previously ignored stove becomes too hot to be around, the robot must learn a new reward from scratch, even though the underlying preference for prioritizing safety over efficiency remains unchanged. We observe that context influences not the underlying preference…

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Huong Nguyen, Hong-Tri Nguyen, Praveen Kumar Donta, Susanna Pirttikangas, Lauri Lov\'en
https://arxiv.org/abs/2506.10607

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Federated learning has emerged as a privacy-preserving technique for collaborative model training across heterogeneously distributed silos. Yet, its reliance on a single central server introduces potential bottlenecks and risks of single-point failure. Decentralizing the server, often referred to as decentralized learning, addresses this problem by distributing the server role across nodes within the network. One drawback regarding this pure decentralization is it introduces communication ineff…

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu
https://arxiv.org/abs/2506.10399

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhea…

ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
Ryan Quach, Yidi Wang, Ali Jahanshahi, Daniel Wong, Hyoseung Kim
https://arxiv.org/abs/2506.12598

ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
As AI inference becomes mainstream, research has begun to focus on improving the energy consumption of inference servers. Inference kernels commonly underutilize a GPU's compute resources and waste power from idling components. To improve utilization and energy efficiency, multiple models can co-locate and share the GPU. However, typical GPU spatial partitioning techniques often experience significant overheads when reconfiguring spatial partitions, which can waste additional energy through rep…

The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks
Md Istiak Hossain Shihab, Christopher Hundhausen, Ahsun Tariq, Summit Haque, Yunhan Qiao, Brian Mulanda
https://arxiv.org/abs/2506.10051

The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks
When graduates of computing degree programs enter the software industry, they will most likely join teams working on legacy code bases developed by people other than themselves. In these so-called brownfield software development settings, generative artificial intelligence (GenAI) coding assistants like GitHub Copilot are rapidly transforming software development practices, yet the impact of GenAI on student programmers performing brownfield development tasks remains underexplored. This paper i…

$\xi$-Based adaptive phase field model for quasi-static anti-plane fracture
Maria P. Fernando, S. M. Mallikarjunaiah
https://arxiv.org/abs/2506.12360 https…

$ξ$-Based adaptive phase field model for quasi-static anti-plane fracture
The $ξ$-based spatially adaptive three-field variable phase-field model for quasi-static anti-plane crack propagation is introduced. A dynamically optimized regularization length is integrated to improve computational efficiency and accuracy in numerical approximations. A local adaptive mesh refinement strategy is developed, which maintains an optimal balance between mesh resolution and the accurate depiction of fractures using the \textsf{AT1} diffuse interface model. The total energy functio…

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Hongbi Zhou, Zhangkai Ni
https://arxiv.org/abs/2506.12400 https://

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training proce…

Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Maria Voreakou, George Kousiouris, Mara Nikolaidou
https://arxiv.org/abs/2506.10693

Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and the Internet of Things, messaging systems have become an integral and mainstream part of modern computing infrastructures, carrying out significant workload in a majority of applications. In this paper, we describe an experimental process to explore energy-ba…

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective
Sibo Zhang, Bruno Clerckx
https://arxiv.org/abs/2506.12646

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective
Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information, fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an infor…

Barrier Method for Inequality Constrained Factor Graph Optimization with Application to Model Predictive Control
Anas Abdelkarim, Holger Voos, Daniel G\"orges
https://arxiv.org/abs/2506.14341

Barrier Method for Inequality Constrained Factor Graph Optimization with Application to Model Predictive Control
Factor graphs have demonstrated remarkable efficiency for robotic perception tasks, particularly in localization and mapping applications. However, their application to optimal control problems -- especially Model Predictive Control (MPC) -- has remained limited due to fundamental challenges in constraint handling. This paper presents a novel integration of the Barrier Interior Point Method (BIPM) with factor graphs, implemented as an open-source extension to the widely adopted g2o framework. O…

Low-Latency Terrestrial Interference Detection for Satellite-to-Device Communications
Runnan Liu, Weifeng Zhu, Shu Sun, Wenjun Zhang
https://arxiv.org/abs/2506.12908

Low-Latency Terrestrial Interference Detection for Satellite-to-Device Communications
Direct satellite-to-device communication is a promising future direction due to its lower latency and enhanced efficiency. However, intermittent and unpredictable terrestrial interference significantly affects system reliability and performance. Continuously employing sophisticated interference mitigation techniques is practically inefficient. Motivated by the periodic idle intervals characteristic of burst-mode satellite transmissions, this paper investigates online interference detection fram…

Watermarking LLM-Generated Datasets in Downstream Tasks
Yugeng Liu, Tianshuo Cong, Michael Backes, Zheng Li, Yang Zhang
https://arxiv.org/abs/2506.13494 ht…

Watermarking LLM-Generated Datasets in Downstream Tasks
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated data to train their models. However, the inability to track content produced by LLMs poses a significant challenge, potentially leading to copyright infringement for the LLM owners. In this paper, we pr…

EPC Framework for BESS Projects
Zeenat Hameed, Chresten Tr{\ae}holt
https://arxiv.org/abs/2506.13281 https://arxiv.org/pdf/2506.13281…

EPC Framework for BESS Projects
Battery Energy Storage Systems (BESS) are critical for modern power networks, supporting grid services such as frequency regulation, peak shaving, and black start. Delivering a BESS under an Engineering, Procurement, and Construction (EPC) model requires a concise methodology that balances regulatory compliance, technical details, and schedule efficiency. This paper presents a streamlined, five step EPC framework covering feasibility assessment, permitting, procurement, construction, and commis…

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
Haotian Zhang, Liu Liu, Baosheng Yu, Jiayan Qiu, Yanwei Ren, Xianglong Liu
https://arxiv.org/abs/2506.12394

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
The advent of parameter-efficient fine-tuning methods has significantly reduced the computational burden of adapting large-scale pretrained models to diverse downstream tasks. However, existing approaches often struggle to achieve robust performance under domain shifts while maintaining computational efficiency. To address this challenge, we propose Low-rAnk Regulated Gradient Projection (LARGO) algorithm that integrates dynamic constraints into low-rank adaptation methods. Specifically, LARGO …

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…

A Cooperative Contactless Object Transport with Acoustic Robots
Narsimlu Kemsaram, Akin Delibasi, James Hardwick, Bonot Gautam, Diego Martinez Plasencia, Sriram Subramanian
https://arxiv.org/abs/2506.13957

A Cooperative Contactless Object Transport with Acoustic Robots
Cooperative transport, the simultaneous movement of an object by multiple agents, has been widely observed in biological systems such as ant colonies, which improve efficiency and adaptability in dynamic environments. Inspired by these natural phenomena, we present a novel acoustic robotic system for the transport of contactless objects in mid-air. Our system leverages phased ultrasonic transducers and a robotic control system onboard to generate localized acoustic pressure fields, enabling pre…

250 Magnetic Tunnel Junctions-Based Probabilistic Ising Machine
Shuhan Yang, Andrea Grimaldi, Youwei Bao, Eleonora Raimondo, Jia Si, Giovanni Finocchio, Hyunsoo Yang
https://arxiv.org/abs/2506.14590

250 Magnetic Tunnel Junctions-Based Probabilistic Ising Machine
In combinatorial optimization, probabilistic Ising machines (PIMs) have gained significant attention for their acceleration of Monte Carlo sampling with the potential to reduce time-to-solution in finding approximate ground states. However, to be viable in real applications, further improvements in scalability and energy efficiency are necessary. One of the promising paths toward achieving this objective is the development of a co-design approach combining different technology layers including …

Watermarking LLM-Generated Datasets in Downstream Tasks
Yugeng Liu, Tianshuo Cong, Michael Backes, Zheng Li, Yang Zhang
https://arxiv.org/abs/2506.13494 ht…

Watermarking LLM-Generated Datasets in Downstream Tasks
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated data to train their models. However, the inability to track content produced by LLMs poses a significant challenge, potentially leading to copyright infringement for the LLM owners. In this paper, we pr…

Dynamic Scheduling for Enhanced Performance in RIS-assisted Cooperative Network with Interference
Yomali Lokugama, Saman Atapattu, Nathan Ross, Sithamparanathan Kandeepan, Chintha Tellambura
https://arxiv.org/abs/2506.12778

Dynamic Scheduling for Enhanced Performance in RIS-assisted Cooperative Network with Interference
Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technologies, enhancing spectral efficiency and improving interference management in multi-user cooperative communications. This paper investigates the integration of RIS with Flexible-Duplex (FlexD) communication, featuring dynamic scheduling capabilities, to mitigate unintended external interference in multi-user wireless networks. By leveraging the reconfigurability of RIS and dynamic scheduling, we propose a user-pair …

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Yupeng Jiang, Yao Deng, Sebastian Schroder, Linfeng Liang, Suhaas Gambhir, Alice James, Avishkar Seth, James Pirrie, Yihao Zhang, Xi Zheng
https://arxiv.org/abs/2506.11400

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety, reliability, and efficiency of these systems is paramount as they transition from research prototypes to mission-critical platforms. This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline, covering each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardwa…

HarMoEny: Efficient Multi-GPU Inference of MoE Models
Zachary Douchet, Rishi Sharma, Martijn de Vos, Rafael Pires, Anne-Marie Kermarrec, Oana Balmau
https://arxiv.org/abs/2506.12417

HarMoEny: Efficient Multi-GPU Inference of MoE Models
Mixture-of-Experts (MoE) models offer computational efficiency during inference by activating only a subset of specialized experts for a given input. This enables efficient model scaling on multi-GPU systems that use expert parallelism without compromising performance. However, load imbalance among experts and GPUs introduces waiting times, which can significantly increase inference latency. To address this challenge, we propose HarMoEny, a novel solution to address MoE load imbalance through t…

A 40.68-MHz, 200-ns-Settling Active Rectifier and TX-Side Load Monitoring for Minimizing Radiated Power in Biomedical Implants
Ronald Wijermars, Yi-Han Ou-Yang, Sijun Du, Dante Gabriel Muratore
https://arxiv.org/abs/2506.07710

A 40.68-MHz, 200-ns-Settling Active Rectifier and TX-Side Load Monitoring for Minimizing Radiated Power in Biomedical Implants
This letter describes a 40.68 MHz wireless power transfer receiver for implantable applications focused on minimizing tissue heating. The system features a novel power radiated efficiency optimization strategy and a fast-settling active rectifier that maintains high efficiency during load and link variations required for downlink communication. The power radiated efficiency optimization explicitly reduces tissue heating while enabling transmitter-side load monitoring for closed-loop control. Th…

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu, Yinghao Cai, Shuo Wang
https://arxiv.org/abs/2506.14648

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback- and sample- efficiency still remain the problems that hinder the application of PbRL. In this paper, we present a novel efficient query selection and preference-guided exploration method, called SENIOR, which could select the meaningful and easy-to-comparison behavior segment pairs to improve human feedback-efficiency…

Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Yonggang Tao, Jingling Xue
https://arxiv.org/abs/2506.11614 https://

Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Delta debugging assumes search space monotonicity: if a program causes a failure, any supersets of that program will also induce the same failure, permitting the exclusion of subsets of non-failure-inducing programs. However, this assumption does not always hold in practice. This paper introduces Probabilistic Monotonicity Assessment (PMA), enhancing the efficiency of DDMIN-style algorithms without sacrificing effectiveness. PMA dynamically models and assesses the search space's monotonicity ba…

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
Minwoo Kim, Youngchol Choi, Yeongjun Kim, Eojin Seo, Hyun Jong Yang
https://arxiv.org/abs/2506.13008

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, {the} low propagation speed of UWA signals, combined w…

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Vincent Lannurien, Cam\'elia Slimani, Louis Morge-Rollet, Laurent Lemarchand, David Espes, Fr\'ed\'eric Le Roy, Jalil Boukhobza
https://arxiv.org/abs/2506.11800

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems ((N)IDS) are promising solutions to detect malicious behavior on network traffic. However, modern NIDS rely on resource-hungry machine learning techniques, that can be difficult to deploy on a swarm of drones. The goal of the DISPEED project is to leverage the heterogeneity (exec…

Analysis of cost-efficiency of serverless approaches
Nakhat Syeda, Harsh Shah, Rajvinder Singh, Suraj Jaju, Sumedha Kumar, Gourav Chhabra, Maria Spichkova
https://arxiv.org/abs/2506.05836

Analysis of cost-efficiency of serverless approaches
In this paper, we present a survey of research studies related to the cost-effectiveness of serverless approach and corresponding cost savings. We conducted a systematic literature review using Google Scholar search engine, covering the period from 2010 to 2024. We identified 34 related studies, from which we extracted 17 parameters that might influence the relative cost savings of applying the serverless approach.

AI Magnetic Levitation (Maglev) Conveyor for Automated Assembly Production
Ray Wai Man Kong
https://arxiv.org/abs/2506.08039 https://…

AI Magnetic Levitation (Maglev) Conveyor for Automated Assembly Production
Efficiency, speed, and precision are essential in modern manufacturing. AI Maglev Conveyor system, combining magnetic levitation (maglev) technology with artificial intelligence (AI), revolutionizes automated production processes. This system reduces maintenance costs and downtime by eliminating friction, enhancing operational efficiency. It transports goods swiftly with minimal energy consumption, optimizing resource use and supporting sustainability. AI integration enables real-time monitorin…

Spectral Efficiency Maximization for mmWave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints
Jitendra Singh, Anand Mehrotra, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
https://arxiv.org/abs/2506.04683

Spectral Efficiency Maximization for mmWave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints
A hybrid transmit precoder (TPC) and receive combiner (RC) pair is conceived for millimeter wave (mmWave) multiple input multiple output (MIMO) integrated sensing and communication (ISAC) systems. The proposed design considers a practical mean squared error (MSE) constraint between the desired and the achieved beampatterns constructed for identifying radar targets (RTs). To achieve optimal performance, we formulate an optimization problem relying on sum spectral efficiency (SE) maximization of …

This https://arxiv.org/abs/2409.08665 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles
Efficiency is critical for autonomous vehicles (AVs), especially for emergency AVs. However, most existing methods focus on regular vehicles, overlooking the distinct strategies required by emergency vehicles to address the challenge of maximizing efficiency while ensuring safety. In this paper, we propose an Integrated Agile Decision-Making with Active and Safety-Critical Motion Planning System (IDEAM). IDEAM focuses on enabling emergency AVs, such as ambulances, to actively attain efficiency …