Tootfinder

Integrating Simulation Budget Management into Drum-Buffer-Rope: A Study on Parametrization and Reducing Computational Effort
Balwin Bokor, Wolfgang Seiringer, Klaus Altendorfer
https://arxiv.org/abs/2402.14832

Integrating Simulation Budget Management into Drum-Buffer-Rope: A Study on Parametrization and Reducing Computational Effort
In manufacturing, a bottleneck workstation frequently emerges, complicating production planning and escalating costs. To address this, Drum-Buffer-Rope (DBR) is a widely recognized production planning and control method that fo-cuses on centralizing the bottleneck workstation, thereby improving production system performance. Although DBR is primarily focused on creating a bottleneck schedule, the selection of planning parameters is crucial, as they significantly in-fluence the scheduling proces…

This https://arxiv.org/abs/2404.12457 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has shown significant improvements in various natural language processing tasks by integrating the strengths of large language models (LLMs) and external knowledge databases. However, RAG introduces long sequence generation and leads to high computation and memory costs. We propose Thoth, a novel multilevel dynamic caching system tailored for RAG. Our analysis benchmarks current RAG systems, pinpointing the performance bottleneck (i.e., long sequence due to …

This https://arxiv.org/abs/2302.10681 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing
The rise of mobile AI accelerators allows latency-sensitive applications to execute lightweight Deep Neural Networks (DNNs) on the client side. However, critical applications require powerful models that edge devices cannot host and must therefore offload requests, where the high-dimensional data will compete for limited bandwidth. This work proposes shifting away from focusing on executing shallow layers of partitioned DNNs. Instead, it advocates concentrating the local resources on variationa…

ByteCard: Enhancing Data Warehousing with Learned Cardinality Estimation
Yuxing Han, Haoyu Wang, Lixiang Chen, Yifeng Dong, Xing Chen, Benquan Yu, Chengcheng Yang, Weining Qian
https://arxiv.org/abs/2403.16110

ByteCard: Enhancing Data Warehousing with Learned Cardinality Estimation
Cardinality estimation is a critical component and a longstanding challenge in modern data warehouses. ByteHouse, ByteDance's cloud-native engine for big data analysis in exabyte-scale environments, serves numerous internal decision-making business scenarios. With the increasing demand of ByteHouse, cardinality estimation becomes the bottleneck for efficiently processing queries. Specifically, the existing query optimizer of ByteHouse uses the traditional Selinger-like cardinality estimator, wh…

This https://arxiv.org/abs/2401.07809 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimal Data Splitting in Distributed Optimization for Machine Learning
The distributed optimization problem has become increasingly relevant recently. It has a lot of advantages such as processing a large amount of data in less time compared to non-distributed methods. However, most distributed approaches suffer from a significant bottleneck - the cost of communications. Therefore, a large amount of research has recently been directed at solving this problem. One such approach uses local data similarity. In particular, there exists an algorithm provably optimally …

Watched Gamers Nexus' benchmark review of #DragonsDogma2 and damn. No wonder the game is getting destroyed in reviews. Literally every CPU ends up being a bottleneck as soon as you hit any cluster of NPCs, which explains why the 1% and .1% FPS lows are so consistent down the chart. If you look at most performance HUDs, they wouldn't show a CPU bind, but PresentMon has added new …

This https://arxiv.org/abs/2402.16731 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. In this work, we introduce PyGim, an efficient ML framew…

This https://arxiv.org/abs/2403.07869 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces. In this work, we demonstrate TeleMoMa, a general and modular interface for whole-body teleoperation of mobile manipulators. TeleMoMa unifies multiple human interfaces including RGB and depth cameras, virtual reality con…

This https://arxiv.org/abs/2206.02217 has been replaced.
link: https://scholar.google.com/scholar?q=a

Learning Mesh Motion Techniques with Application to Fluid-Structure Interaction
Mesh degeneration is a bottleneck for fluid-structure interaction (FSI) simulations and for shape optimization via the method of mappings. In both cases, an appropriate mesh motion technique is required. The choice is typically based on heuristics, e.g., the solution operators of partial differential equations (PDE), such as the Laplace or biharmonic equation. Especially the latter, which shows good numerical performance for large displacements, is expensive. Moreover, from a continuous perspec…

Breaching the Bottleneck: Evolutionary Transition from Reward-Driven Learning to Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Nets
Solvi Arnold, Reiji Suzuki, Takaya Arita, Kimitoshi Yamazaki
https://arxiv.org/abs/2404.12631

Breaching the Bottleneck: Evolutionary Transition from Reward-Driven Learning to Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Nets
Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally provided measures of behaviour quality to acquire fit behaviour. This imposes an information bottleneck that precludes learning from diverse …

This https://arxiv.org/abs/2404.10824 has been replaced.
link: https://scholar.google.com/scholar?q=a

Decoupled Weight Decay for Any $p$ Norm
With the success of deep neural networks (NNs) in a variety of domains, the computational and storage requirements for training and deploying large NNs have become a bottleneck for further improvements. Sparsification has consequently emerged as a leading approach to tackle these issues. In this work, we consider a simple yet effective approach to sparsification, based on the Bridge, or $L_p$ regularization during training. We introduce a novel weight decay scheme, which generalizes the standar…

This https://arxiv.org/abs/2404.11912 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and hi…

This https://arxiv.org/abs/2401.07809 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimal Data Splitting in Distributed Optimization for Machine Learning
The distributed optimization problem has become increasingly relevant recently. It has a lot of advantages such as processing a large amount of data in less time compared to non-distributed methods. However, most distributed approaches suffer from a significant bottleneck - the cost of communications. Therefore, a large amount of research has recently been directed at solving this problem. One such approach uses local data similarity. In particular, there exists an algorithm provably optimally …

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Christina Giannoula, Peiming Yang, Ivan Fernandez Vega, Jiacheng Yang, Yu Xin Li, Juan Gomez Luna, Mohammad Sadrosadati, Onur Mutlu, Gennady Pekhimenko
https://arxiv.org/abs/2402.16731

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. In this work, we introduce PyGim, an efficient ML framew…

This https://arxiv.org/abs/2312.09062 has been replaced.
initial toot: https://mastoxiv.page/@ar…

W4$Λ$: leveraging $Λ$ coupled cluster for accurate computational thermochemistry approaches
High-accuracy composite wavefunction methods like Weizmann-4 (W4) theory, high-accuracy extrapolated \textit{ab initio} thermochemistry (HEAT), and Feller-Peterson-Dixon (FPD) enable sub-kJ/mol accuracy in gas-phase thermochemical properties. Their biggest computational bottleneck is the evaluation of the valence post-CCSD(T) correction term. We demonstrate here, for the W4-17 thermochemistry benchmark and subsets thereof, that the lambda coupled cluster expansion converges more rapidly and smo…

This https://arxiv.org/abs/2403.07786 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Generative deep learning-enabled ultra-large field-of-view lens-free imaging
Advancements in high-throughput biomedical applications necessitate real-time, large field-of-view (FOV) imaging capabilities. Conventional lens-free imaging (LFI) systems, while addressing the limitations of physical lenses, have been constrained by dynamic, hard-to-model optical fields, resulting in a limited one-shot FOV of approximately 20 $mm^2$. This restriction has been a major bottleneck in applications like live-cell imaging and automation of microfluidic systems for biomedical researc…

If the grid and electricity system is the bottleneck, why not go off-grid? That's what this 🇿🇦 South African charging network wants to do - first for cars, now for trucks. But wouldn't it be better to stay connected and serve the grid? That would enable greater social benefits and battery solar income.

This https://arxiv.org/abs/2404.11284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Amplifying Main Memory-Based Timing Covert and Side Channels using Processing-in-Memory Operations
The adoption of processing-in-memory (PiM) architectures has been gaining momentum because they provide high performance and low energy consumption by alleviating the data movement bottleneck. Yet, the security of such architectures has not been thoroughly explored. The adoption of PiM solutions provides a new way to directly access main memory, which can be potentially exploited by malicious user applications. We show that this new way to access main memory opens opportunities for high-through…

Two-Stage Block Orthogonalization to Improve Performance of $s$-step GMRES
Ichitaro Yamazaki, Andrew J. Higgins, Erik G. Boman, Daniel B. Szyld
https://arxiv.org/abs/2402.15033

Two-Stage Block Orthogonalization to Improve Performance of $s$-step GMRES
On current computer architectures, GMRES' performance can be limited by its communication cost to generate orthonormal basis vectors of the Krylov subspace. To address this performance bottleneck, its $s$-step variant orthogonalizes a block of $s$ basis vectors at a time, potentially reducing the communication cost by a factor of $s$. Unfortunately, for a large step size $s$, the solver can generate extremely ill-conditioned basis vectors, and to maintain stability in practice, a conservatively…

Upper Bound of Bayesian Generalization Error in Partial Concept Bottleneck Model (CBM): Partial CBM outperforms naive CBM
Naoki Hayashi, Yoshihide Sawada
https://arxiv.org/abs/2403.09206

Upper Bound of Bayesian Generalization Error in Partial Concept Bottleneck Model (CBM): Partial CBM outperforms naive CBM
Concept Bottleneck Model (CBM) is a methods for explaining neural networks. In CBM, concepts which correspond to reasons of outputs are inserted in the last intermediate layer as observed values. It is expected that we can interpret the relationship between the output and concept similar to linear regression. However, this interpretation requires observing all concepts and decreases the generalization performance of neural networks. Partial CBM (PCBM), which uses partially observed concepts, ha…

Entirely reassuring! Yes, start cutting corners to "accelerate" deliveries. #boing #boeing

Ryanair says Boeing moving to accelerate jet deliveries
Move may ease bottleneck on seat supply for peak summer months

This https://arxiv.org/abs/2309.15877 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Neuro-Inspired Hierarchical Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalit…

Hydro: Adaptive Query Processing of ML Queries
Gaurav Tarlok Kakkar, Jiashen Cao, Aubhro Sengupta, Joy Arulraj, Hyesoon Kim
https://arxiv.org/abs/2403.14902

Hydro: Adaptive Query Processing of ML Queries
Query optimization in relational database management systems (DBMSs) is critical for fast query processing. The query optimizer relies on precise selectivity and cost estimates to effectively optimize queries prior to execution. While this strategy is effective for relational DBMSs, it is not sufficient for DBMSs tailored for processing machine learning (ML) queries. In ML-centric DBMSs, query optimization is challenging for two reasons. First, the performance bottleneck of the queries shifts t…

BERT: Accelerating Vital Signs Measurement for Bioradar with An Efficient Recursive Technique
Chengyao Tang, Yongpeng Dai, Zhi Li, Yongping Song, Fulai Liang, Tian Jin
https://arxiv.org/abs/2404.13315

BERT: Accelerating Vital Signs Measurement for Bioradar with An Efficient Recursive Technique
Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring. As an important part of VS sensing process, VS measurement aims to capture the chest wall micromotion induced by the human respiratory and cardiac activities. Unfortunately, the existing VS measurement methods using bioradar have encountered bottlenecks in making a trade-off between time cost and measurement accuracy. To break this bottleneck, this letter propos…

Efficient and Provably Convergent Computation of Information Bottleneck: A Semi-Relaxed Approach
Lingyi Chen, Shitong Wu, Jiachuan Ye, Huihui Wu, Wenyi Zhang, Hao Wu
https://arxiv.org/abs/2404.04862

Efficient and Provably Convergent Computation of Information Bottleneck: A Semi-Relaxed Approach
Information Bottleneck (IB) is a technique to extract information about one target random variable through another relevant random variable. This technique has garnered significant interest due to its broad applications in information theory and deep learning. Hence, there is a strong motivation to develop efficient numerical methods with high precision and theoretical convergence guarantees. In this paper, we propose a semi-relaxed IB model, where the Markov chain and transition probability co…

If the grid and electricity system is the bottleneck, why not go off-grid? That's what this 🇿🇦 South African charging network wants to do - first for cars, now for trucks. But wouldn't it be better to stay connected and serve the grid? That would enable greater social benefits and battery solar income.

This https://arxiv.org/abs/2309.06629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations be…

Hydro: Adaptive Query Processing of ML Queries
Gaurav Tarlok Kakkar, Jiashen Cao, Aubhro Sengupta, Joy Arulraj, Hyesoon Kim
https://arxiv.org/abs/2403.14902

Hydro: Adaptive Query Processing of ML Queries
Query optimization in relational database management systems (DBMSs) is critical for fast query processing. The query optimizer relies on precise selectivity and cost estimates to effectively optimize queries prior to execution. While this strategy is effective for relational DBMSs, it is not sufficient for DBMSs tailored for processing machine learning (ML) queries. In ML-centric DBMSs, query optimization is challenging for two reasons. First, the performance bottleneck of the queries shifts t…

This https://arxiv.org/abs/2404.09403 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Different from most traditional fusion models that incorporate all modalities identically in neural networks, our model de…

This https://arxiv.org/abs/2309.12681 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients (also known as barren plateaus) are a central bottleneck in the scalability of variational quantum algorithms (VQAs), and are known to arise in various ways, from circuit depth and hardware noise to global observables. However, a caveat of most existing gradient bound results is the requirement of t-design circuit assumptions that are typically not satisfied…

Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-Hopper
Junjie Li, Yinzhi Wang, Xiao Liang, Hang Liu
https://arxiv.org/abs/2404.13195

Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-Hopper
Porting codes to GPU often requires major efforts. While several tools exist for automatically offload numerical libraries such as BLAS and LAPACK, they often prove impractical due to the high cost of mandatory data transfer. The new unified memory architecture in NVIDIA Grace-Hopper allows high bandwidth cache-coherent memory access of all memory from both CPU and GPU, potentially eliminating bottleneck faced in conventional architecture. This breakthrough opens up new avenues for application …

This https://arxiv.org/abs/2311.13681 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Compact 3D Gaussian Representation for Radiance Field
Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hinders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetri…

The high CPU / low GPU usage and low FPS (i5-13400F/RX 7800 XT) on Spider Man Remastered for PC was driving me crazy. It's hard to believe disabling Hyper-Threading brought up a crisp, native QHD with full ray-tracing to this game.
This way, it also runs at native 2160p 60/90 FPS with/without ray-tracing, although it's still underutilizing the GPU a bit, while not even getting close to CPU bottleneck.

PyTOaCNN: Topology optimization using an adaptive convolutional neural network in Python
Khaish Singh Chadha, Prabhat Kumar
https://arxiv.org/abs/2404.12244

PyTOaCNN: Topology optimization using an adaptive convolutional neural network in Python
This paper introduces an adaptive convolutional neural network (CNN) architecture capable of automating various topology optimization (TO) problems with diverse underlying physics. The proposed architecture has an encoder-decoder-type structure with dense layers added at the bottleneck region to capture complex geometrical features. The network is trained using datasets obtained by the problem-specific open-source TO codes. Tensorflow and Keras are the main libraries employed to develop and to …

This https://arxiv.org/abs/2007.01834 has been replaced.
link: https://scholar.google.com/scholar?q=a

Universal Distances for Extended Persistence
The extended persistence diagram is an invariant of piecewise linear functions, which is known to be stable under perturbations of functions with respect to the bottleneck distance as introduced by Cohen-Steiner, Edelsbrunner, and Harer. We address the question of universality, which asks for the largest possible stable distance on extended persistence diagrams, showing that a more discriminative variant of the bottleneck distance is universal. Our result applies more generally to settings wher…

The EU installed a record 16.2 GW of wind power capacity last year, and WindEurope expects this to grow to an average of 29 GW/year in the period up to 2030.
Permitting is speeding up, grids seen as the main bottleneck now.
#windpower #windenergy

I've spent some of today finding bottlenecks in my world-simulating cellular automaton, and I can now simulate 10,000 years of history from the middle of the Ice Age to the middle of the Iron Age over 40,000 square kilometres (at kilometre scale) in slightly under two minutes of wall-clock time. It's utterly caning my poor computer, but it doesn't crash.
#Clojure

This https://arxiv.org/abs/2311.16581 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

GeoScaler: Geometry and Rendering-Aware Downsampling of 3D Mesh Textures
High-resolution texture maps are necessary for representing real-world objects accurately with 3D meshes. The large sizes of textures can bottleneck the real-time rendering of high-quality virtual 3D scenes on devices having low computational budgets and limited memory. Downsampling the texture maps directly addresses the issue, albeit at the cost of visual fidelity. Traditionally, downsampling of texture maps is performed using methods like bicubic interpolation and the Lanczos algorithm. Thes…

This https://arxiv.org/abs/2403.06324 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge
The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the first bandwidth estimation challenge which was hosted at ACM MMSys 2021, we learnt that bandwidt…

This https://arxiv.org/abs/2404.09218 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
The Information Bottleneck (IB) method is an information theoretical framework to design a parsimonious and tunable feature-extraction mechanism, such that the extracted features are maximally relevant to a specific learning or inference task. Despite its theoretical value, the IB is based on a functional optimization problem that admits a closed form solution only on specific cases (e.g., Gaussian distributions), making it difficult to be applied in most applications, where it is necessary to …

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
William Hornsby, Ander Gray, James Buchanan, Daniel Kenndy, Bhavin Patel, Francis Casson, Colin Roach, Mikkel Lykkegaard, Huy Nguyen, Nikolaos Papadimas, Ben Fourcin, Jordan Hart
https://arxiv.org/abs/2403.13006

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
Spherical tokamaks (STs) have many desirable features that make them a suitable choice for fusion power plants. To understand their confinement properties, accurate calculation of turbulent micro-instabilities is necessary for tokamak design. Presented is a novel surrogate model for Micro-tearing modes (MTMs), the micro-instability thought to be dominant in high beta STs. Direct numerical calculation of micro-instabilities is computationally expensive and is a significant bottleneck in integrat…

Machine Learning LSST 3x2pt analyses -- forecasting the impact of systematics on cosmological constraints using neural networks
Supranta S. Boruah, Tim Eifler, Vivian Miranda, Elyas Farah, Jay Motka, Elisabeth Krause, Xiao Fang, Paul Rogozenski, The LSST Dark Energy Science Collaboration
https://arxiv.org/abs/2403.11797

Machine Learning LSST 3x2pt analyses -- forecasting the impact of systematics on cosmological constraints using neural networks
Validating modeling choices through simulated analyses and quantifying the impact of different systematic effects will form a major computational bottleneck in the preparation for 3$\times$2 analysis with Stage-IV surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). We can significantly reduce the computational requirements by using machine learning based emulators, which allow us to run fast inference while maintaining the full realism of the data analysis pipeline.…

A Distance Metric Learning Model Based On Variational Information Bottleneck
YaoDan Zhang, Zidong Wang, Ru Jia, Ru Li
https://arxiv.org/abs/2403.02794 http…

A Distance Metric Learning Model Based On Variational Information Bottleneck
In recent years, personalized recommendation technology has flourished and become one of the hot research directions. The matrix factorization model and the metric learning model which proposed successively have been widely studied and applied. The latter uses the Euclidean distance instead of the dot product used by the former to measure the latent space vector. While avoiding the shortcomings of the dot product, the assumption of Euclidean distance is neglected, resulting in limited recommend…

Introducing Combi-Stations in Robotic Mobile Fulfilment Systems: A Queueing-Theory-Based Efficiency Analysis
Lin Xie, Sonja Otten
https://arxiv.org/abs/2403.12798

Introducing Combi-Stations in Robotic Mobile Fulfilment Systems: A Queueing-Theory-Based Efficiency Analysis
In the era of digital commerce, the surge in online shopping and the expectation for rapid delivery have placed unprecedented demands on warehouse operations. The traditional method of order fulfilment, where human order pickers traverse large storage areas to pick items, has become a bottleneck, consuming valuable time and resources. Robotic Mobile Fulfilment Systems (RMFS) offer a solution by using robots to transport storage racks directly to human-operated picking stations, eliminating the …

SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
Xinyu Shi, Zecheng Hao, Zhaofei Yu
https://arxiv.org/abs/2403.14302 htt…

SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling methods, and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address …

This https://arxiv.org/abs/2311.12772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation
Numerous quantum algorithms require the use of quantum error correction to overcome the intrinsic unreliability of physical qubits. However, error correction imposes a unique performance bottleneck, known as T-complexity, that can make an implementation of an algorithm as a quantum program run more slowly than on idealized hardware. In this work, we identify that programming abstractions for control flow, such as the quantum if-statement, can introduce polynomial increases in the T-complexity o…

Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
Lucas R. Timmerman, Shashikant Kumar, Phanish Suryanarayana, Andrew J. Medford
https://arxiv.org/abs/2404.07961

Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme.

This https://arxiv.org/abs/2312.06608 has been replaced.
initial toot: https://mastoxiv.page/@…

Information theory for data-driven model reduction in physics and biology
Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant variables. A prerequisite to model reduction is the identification of these relevant variables, a task for which no general method exists. Here, we develop a systematic approach based on the information bottleneck to identify the relevant variables, defined as those most predictive of the future. We elucidate analytically the relation between these relevant v…

This https://arxiv.org/abs/2311.11833 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Towards Perturbation-Induced Static Pivoting on GPU-Based Linear Solvers
Linear system solving is a key tool for computational power system studies, e.g., optimal power flow, transmission switching, or unit commitment. CPU-based linear system solver speeds, however, have saturated in recent years. Emerging research shows that GPU-based linear system solvers are beginning to achieve notable speedup over CPU-based alternatives in some applications. Due to the architecture of GPU memory access, numerical pivoting represents the new bottleneck which prevents GPU-based s…

Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms
Sebastian Kokott, Florian Merz, Yi Yao, Christian Carbogno, Mariana Rossi, Ville Havu, Markus Rampp, Matthias Scheffler, Volker Blum
https://arxiv.org/abs/2403.10343

Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms
Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drasti…

A unified generalization of inverse regression via adaptive column selection
Yin Jin, Wei Luo
https://arxiv.org/abs/2404.08284 https://

A unified generalization of inverse regression via adaptive column selection
A bottleneck of sufficient dimension reduction (SDR) in the modern era is that, among numerous methods, only the sliced inverse regression (SIR) is generally applicable under the high-dimensional settings. The higher-order inverse regression methods, which form a major family of SDR methods that are superior to SIR in the population level, suffer from the dimensionality of their intermediate matrix-valued parameters that have an excessive number of columns. In this paper, we propose the generic…

A Fast Algorithm to Simulate Nonlinear Resistive Networks
Benjamin Scellier
https://arxiv.org/abs/2402.11674 https://arxiv.org/pdf/24…

A Fast Algorithm to Simulate Nonlinear Resistive Networks
In the quest for energy-efficient artificial intelligence systems, resistor networks are attracting interest as an alternative to conventional GPU-based neural networks. These networks leverage the physics of electrical circuits for inference and can be optimized with local training techniques such as equilibrium propagation. Despite their potential advantage in terms of power consumption, the challenge of efficiently simulating these resistor networks has been a significant bottleneck to asses…

This https://arxiv.org/abs/2312.02269 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Ultrafast compact binary mergers
The duration of the gravitational waves (GW) induced orbital decay is often the bottleneck of the evolutionary phases going from star formation to a merger. We show here that kicks imparted to the newly born compact object during the second collapse generically result in a GW merger times distribution behaving like $dN/d\log t \propto t^{2/7}$ at short durations, leading to ultrafast mergers. Namely, a non-negligible fraction of neutron star binaries, formed in this way, will merge on a time sc…

This https://arxiv.org/abs/2402.19364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

Digital Over-the-Air Computation: Achieving High Reliability via Bit-Slicing
Jiawei Liu, Yi Gong, Kaibin Huang
https://arxiv.org/abs/2404.07121 https://

Digital Over-the-Air Computation: Achieving High Reliability via Bit-Slicing
6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning, sensing, and data analytics. Their common operation is to aggregate high-dimensional data, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution, called over-the-air computation (AirComp), exploits channels' waveform superposition property to enable simultaneous access, thereby overcoming the bottleneck. Neverth…

This https://arxiv.org/abs/2403.04643 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

QAQ: Quality Adaptive Quantization for LLM KV Cache
The emergence of LLMs has ignited a fresh surge of breakthroughs in NLP applications, particularly in domains such as question-answering systems and text generation. As the need for longer context grows, a significant bottleneck in model deployment emerges due to the linear expansion of the Key-Value (KV) cache with the context length. Existing methods primarily rely on various hypotheses, such as sorting the KV cache based on attention scores for replacement or eviction, to compress the KV cac…

An Information Bottleneck Approach for Markov Model Construction
Dedi Wang, Yunrui Qiu, Eric Beyerle, Xuhui Huang, Pratyush Tiwary
https://arxiv.org/abs/2404.02856

An Information Bottleneck Approach for Markov Model Construction
Markov state models (MSMs) are valuable for studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with the dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constructing the Markovian model at a specific lag time requires state defined without significant internal energy barriers, enabling internal dyn…

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Christophe Bonneville, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung, Yeonjong Shin, David M. Bortz, Debojyoti Ghosh, Jiun-Shyan Chen, Jonathan Belof, Youngsoo Choi
https://

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this…

Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup
Touchchai Chotisorayuth, Andreas Tiffeau-Mayer
https://arxiv.org/abs/2403.09010

Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup
An individual's adaptive immune receptor (AIR) repertoire records immune history due to the exquisite antigen specificity of AIRs. Reading this record requires computational approaches for inferring receptor function from sequence, as the diversity of possible receptor-antigen pairs vastly outstrips experimental knowledge. Identification of AIRs with similar sequence and thus putatively similar function is a common performance bottleneck in these approaches. Here, we benchmark the time complexi…

This https://arxiv.org/abs/2403.06324 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge
The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the first bandwidth estimation challenge which was hosted at ACM MMSys 2021, we learnt that bandwidt…

This https://arxiv.org/abs/2311.01598 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption
Homomorphic encryption (HE) is a privacy-preserving computation technique that enables computation on encrypted data. Today, the potential of HE remains largely unrealized as it is impractically slow, preventing it from being used in real applications. A major computational bottleneck in HE is the key-switching operation, accounting for approximately 70% of the overall HE execution time and involving a large amount of data for inputs, intermediates, and keys. Prior research has focused on hardw…

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
William Hornsby, Ander Gray, James Buchanan, Daniel Kenndy, Bhavin Patel, Francis Casson, Colin Roach, Mikkel Lykkegaard, Huy Nguyen, Nikolaos Papadimas, Ben Fourcin, Jordan Hart
https://arxiv.org/abs/2403.13006

Gaussian process surrogate models for the properties of micro-tearing modes in spherical tokamaks
Spherical tokamaks (STs) have many desirable features that make them a suitable choice for fusion power plants. To understand their confinement properties, accurate calculation of turbulent micro-instabilities is necessary for tokamak design. Presented is a novel surrogate model for Micro-tearing modes (MTMs), the micro-instability thought to be dominant in high beta STs. Direct numerical calculation of micro-instabilities is computationally expensive and is a significant bottleneck in integrat…

This https://arxiv.org/abs/2210.03357 has been replaced.
link: https://scholar.google.com/scholar?q=a

Queue replacement principle for corridor problems with heterogeneous commuters
This study investigates the theoretical properties of a departure time choice problem considering commuters' heterogeneity with respect to the value of schedule delay in corridor networks. Specifically, we develop an analytical method to solve the dynamic system optimal (DSO) and dynamic user equilibrium (DUE) problems. To derive the DSO solution, we first demonstrate the bottleneck-based decomposition property, i.e., the DSO problem can be decomposed into multiple single bottleneck problems. S…

This https://arxiv.org/abs/2402.19364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

This https://arxiv.org/abs/2402.10930 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters
The self-attention mechanism sets transformer-based large language model (LLM) apart from the convolutional and recurrent neural networks. Despite the performance improvement, achieving real-time LLM inference on silicon is challenging due to the extensively used Softmax in self-attention. Apart from the non-linearity, the low arithmetic intensity greatly reduces the processing parallelism, which becomes the bottleneck especially when dealing with a longer context. To address this challenge, we…

This https://arxiv.org/abs/2307.10917 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Efficient quantum amplitude encoding of polynomial functions
Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on $n$ qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approxim…

Provably Sample Efficient RLHF via Active Preference Optimization
Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury
https://arxiv.org/abs/2402.10500

Provably Sample Efficient RLHF via Active Preference Optimization
Reinforcement Learning from Human Feedback (RLHF) is pivotal in aligning Large Language Models (LLMs) with human preferences. While these aligned generative models have demonstrated impressive capabilities across various tasks, the dependence on high-quality human preference data poses a costly bottleneck in practical implementation of RLHF. Hence better and adaptive strategies for data collection is needed. To this end, we frame RLHF as a contextual preference bandit problem with prompts as co…

Distance Comparison Operators for Approximate Nearest Neighbor Search: Exploration and Benchmark
Zeyu Wang, Haoran Xiong, Zhenying He, Peng Wang, Wei wang
https://arxiv.org/abs/2403.13491

Distance Comparison Operators for Approximate Nearest Neighbor Search: Exploration and Benchmark
Approximate nearest neighbor search (ANNS) on high-dimensional vectors has become a fundamental and essential component in various machine learning tasks. Prior research has shown that the distance comparison operation is the bottleneck of ANNS, which determines the query and indexing performance. To overcome this challenge, some novel methods have been proposed recently. The basic idea is to estimate the actual distance with fewer calculations, at the cost of accuracy loss. Inspired by this, w…

Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
Zhenxiao Cheng, Jie Zhou, Wen Wu, Qin Chen, Liang He
https://arxiv.org/abs/2402.18145

Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-level importance using dimension-level gradient values through a norm function, often presuming equal significance for all gradient dimensions. However, in the context of Aspect-based Sentiment Analysis (ABSA), our preliminary research suggests that only specific dimensions are pertinent. To address this, we propose the …

Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data
Xu Gan, Chongwen Huang, Zhaohui Yang, Caijun Zhong, Xiaoming Chen, Zhaoyang Zhang, Qinghua Guo, Chau Yuen, Merouane Debbah
https://arxiv.org/abs/2402.10593

Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data
Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol sch…

A unified generalization of inverse regression via adaptive column selection
Yin Jin, Wei Luo
https://arxiv.org/abs/2404.08284 https://

A unified generalization of inverse regression via adaptive column selection
A bottleneck of sufficient dimension reduction (SDR) in the modern era is that, among numerous methods, only the sliced inverse regression (SIR) is generally applicable under the high-dimensional settings. The higher-order inverse regression methods, which form a major family of SDR methods that are superior to SIR in the population level, suffer from the dimensionality of their intermediate matrix-valued parameters that have an excessive number of columns. In this paper, we propose the generic…

From Pixel to Cancer: Cellular Automata in Computed Tomography
Yuxiang Lai, Xiaoxi Chen, Angtian Wang, Alan Yuille, Zongwei Zhou
https://arxiv.org/abs/2403.06459

From Pixel to Cancer: Cellular Automata in Computed Tomography
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in medical images, which can greatly diversify the data and annotations for AI training. However, current tumor synthesis approaches are not applicable across different organs due to their need for specific expertise and design. This paper establishes a set of generic rules to simulate tumor development. Each cell (pixel…

The future of document indexing: GPT and Donut revolutionize table of content processing
Degaga Wolde Feyisa, Haylemicheal Berihun, Amanuel Zewdu, Mahsa Najimoghadam, Marzieh Zare
https://arxiv.org/abs/2403.07553

The future of document indexing: GPT and Donut revolutionize table of content processing
Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts information directly from scanned documents without OCR, and OpenAI GPT-3.5 Turbo, a robust large language model. The proposed methodology is initiated by acquiring the table of conte…

Symbolic Autoencoding for Self-Supervised Sequence Learning
Mohammad Hossein Amani, Nicolas Mario Baldwin, Amin Mansouri, Martin Josifoski, Maxime Peyrard, Robert West
https://arxiv.org/abs/2402.10575

Symbolic Autoencoding for Self-Supervised Sequence Learning
Traditional language models, adept at next-token prediction in text sequences, often struggle with transduction tasks between distinct symbolic systems, particularly when parallel data is scarce. Addressing this issue, we introduce \textit{symbolic autoencoding} ($Σ$AE), a self-supervised framework that harnesses the power of abundant unparallel data alongside limited parallel data. $Σ$AE connects two generative models via a discrete bottleneck layer and is optimized end-to-end by minimizing …

Accelerating Distributed Deep Learning using Lossless Homomorphic Compression
Haoyu Li, Yuchen Xu, Jiayi Chen, Rohit Dwivedula, Wenfei Wu, Keqiang He, Aditya Akella, Daehyeok Kim
https://arxiv.org/abs/2402.07529

Accelerating Distributed Deep Learning using Lossless Homomorphic Compression
As deep neural networks (DNNs) grow in complexity and size, the resultant increase in communication overhead during distributed training has become a significant bottleneck, challenging the scalability of distributed training systems. Existing solutions, while aiming to mitigate this bottleneck through worker-level compression and in-network aggregation, fall short due to their inability to efficiently reconcile the trade-offs between compression effectiveness and computational overhead, hinder…

TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
Shivin Dass, Wensi Ai, Yuqian Jiang, Samik Singh, Jiaheng Hu, Ruohan Zhang, Peter Stone, Ben Abbatematteo, Roberto Martin-Martin
https://arxiv.org/abs/2403.07869

TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces. In this work, we demonstrate TeleMoMa, a general and modular interface for whole-body teleoperation of mobile manipulators. TeleMoMa unifies multiple human interfaces including RGB and depth cameras, virtual reality con…

This https://arxiv.org/abs/2108.07460 has been replaced.
link: https://scholar.google.com/scholar?q=a

Persistent Homology with Selective Rips complexes detects geodesic circles
This paper introduces a method to detect each geometrically significant loop that is a geodesic circle (an isometric embedding of $S^1$) and a bottleneck loop (meaning that each of its perturbations increases the length) in a geodesic space using persistent homology. Under fairly mild conditions we show that such a loop either terminates a $1$-dimensional homology class or gives rise to a $2$-dimensional homology class in persistent homology. The main tool in this detection technique are select…

Condensed-Phase Quantum Chemistry
Paul J. Robinson, Adam Rettig, Hieu Q. Dinh, Meng-Fu Chen, Joonho Lee
https://arxiv.org/abs/2403.13207 https://

Condensed-Phase Quantum Chemistry
Molecular quantum chemistry has seen enormous progress in the last few decades thanks to the more advanced and sophisticated numerical techniques and computing power. Following the recent interest in extending these capabilities to condensed-phase problems, we summarize basic knowledge of condensed-phase quantum chemistry for ones with experience in molecular quantum chemistry. We highlight recent efforts in this direction, including solving the electron repulsion integrals bottleneck and imple…

ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters
Shiwei Liu, Guanchen Tao, Yifei Zou, Derek Chow, Zichen Fan, Kauna Lei, Dennis Sylvester, Gregory Kielian, Mehdi Saligane
https://arxiv.org/abs/2402.10930

ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters
The self-attention mechanism sets transformer-based large language model (LLM) apart from the convolutional and recurrent neural networks. Despite the performance improvement, achieving real-time LLM inference on silicon is challenging due to the extensively used Softmax in self-attention. Apart from the non-linearity, the low arithmetic intensity greatly reduces the processing parallelism, which becomes the bottleneck especially when dealing with a longer context. To address this challenge, we…

A Robust Semantic Communication System for Image
Xiang Peng, Zhijin Qin, Xiaoming Tao, Jianhua Lu, Khaled B. Letaief
https://arxiv.org/abs/2403.09222 https…

A Robust Semantic Communication System for Image
Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in image, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Fu…

Network mechanism for generating genuinely correlative Gaussian states
Zhaofang Bai, Shuanping Du
https://arxiv.org/abs/2402.07489 https://

Network mechanism for generating genuinely correlative Gaussian states
Generating a long-distance quantum state with genuine quantum correlation (GQC) is one of the most essential functions of quantum networks to support quantum communication. Here, we provide a deterministic scheme for generating multimode Gaussian states with certain GQC (including genuine entanglement). Efficient algorithms of generating multimode states are also proposed. Our scheme is useful for resolving the bottleneck in generating some multimode Gaussian states and may pave the way towards…

This https://arxiv.org/abs/2209.14019 has been replaced.
link: https://scholar.google.com/scholar?q=a

Inertial Quasi-Newton Methods for Monotone Inclusion: Efficient Resolvent Calculus and Primal-Dual Methods
We introduce an inertial quasi-Newton Forward-Backward Splitting Algorithm to solve a class of monotone inclusion problems. While the inertial step is computationally cheap, in general, the bottleneck is the evaluation of the resolvent operator. A change of the metric makes its computation hard even for (otherwise in the standard metric) simple operators. In order to fully exploit the advantage of adapting the metric, we develop a new efficient resolvent calculus for a low-rank perturbed standa…

FaiRTT: An Empirical Approach for Enhanced RTT Fairness and Bottleneck Throughput in BBR
Akshita Abrol, Purnima Murali Mohan, Tram Truong-Huu
https://arxiv.org/abs/2403.19973

FaiRTT: An Empirical Approach for Enhanced RTT Fairness and Bottleneck Throughput in BBR
In next-generation networks, achieving Round-trip Time (RTT) fairness is essential for ensuring fair bandwidth distribution among diverse flow types, enhancing overall network utilization. The TCP congestion control algorithm -- BBR, was proposed by Google to dynamically adjust sending rates in response to changing network conditions. While BBRv2 was implemented to overcome the unfairness limitation of BBRv1, it still faces intra-protocol fairness challenges in balancing the demands of high-ban…

JUMBO: Fully Asynchronous BFT Consensus Made Truly Scalable
Hao Cheng, Yuan Lu, Zhenliang Lu, Qiang Tang, Yuxuan Zhang, Zhenfeng Zhang
https://arxiv.org/abs/2403.11238

JUMBO: Fully Asynchronous BFT Consensus Made Truly Scalable
Recent progresses in asynchronous Byzantine fault-tolerant (BFT) consensus, e.g. Dumbo-NG (CCS' 22) and Tusk (EuroSys' 22), show promising performance through decoupling transaction dissemination and block agreement. However, when executed with a larger number $n$ of nodes, like several hundreds, they would suffer from significant degradation in performance. Their dominating scalability bottleneck is the huge authenticator complexity: each node has to multicast $\bigO(n)$ quorum certificates (Q…

From Pixel to Cancer: Cellular Automata in Computed Tomography
Yuxiang Lai, Xiaoxi Chen, Angtian Wang, Alan Yuille, Zongwei Zhou
https://arxiv.org/abs/2403.06459

From Pixel to Cancer: Cellular Automata in Computed Tomography
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in medical images, which can greatly diversify the data and annotations for AI training. However, current tumor synthesis approaches are not applicable across different organs due to their need for specific expertise and design. This paper establishes a set of generic rules to simulate tumor development. Each cell (pixel…

Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
Steve Rhyner, Haocong Luo, Juan G\'omez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu
https://arxiv.org/abs/2404.07164

Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
Machine Learning (ML) training on large-scale datasets is a very expensive and time-consuming workload. Processor-centric architectures (e.g., CPU, GPU) commonly used for modern ML training workloads are limited by the data movement bottleneck, i.e., due to repeatedly accessing the training dataset. As a result, processor-centric systems suffer from performance degradation and high energy consumption. Processing-In-Memory (PIM) is a promising solution to alleviate the data movement bottleneck b…

Speeding up 6-DoF Grasp Sampling with Quality-Diversity
Johann Huber, Fran\c{c}ois H\'el\'enon, Mathilde Kappel, Elie Chelly, Mahdi Khoramshahi, Fa\"iz Ben Amar, St\'ephane Doncieux
https://arxiv.org/abs/2403.06173

Speeding up 6-DoF Grasp Sampling with Quality-Diversity
Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critical challenge, as this skill is required to complete many manipulation tasks. Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse, high-performing solutions to a given problem…

FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines
Jiaao He, Jidong Zhai
https://arxiv.org/abs/2403.11421 https://<…

FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines
Cost of serving large language models (LLM) is high, but the expensive and scarce GPUs are poorly efficient when generating tokens sequentially, unless the batch of sequences is enlarged. However, the batch size is limited by some constantly reused intermediate results, namely KV-Cache. They occupy too much memory to fit more sequences into a GPU simultaneously. While they could be offloaded to host memory, the CPU-GPU bandwidth is an inevitable bottleneck. We find a way to decompose the tran…

This https://arxiv.org/abs/2311.07710 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Practical and Optimal First-Order Method for Large-Scale Convex Quadratic Programming
Convex quadratic programming (QP) is an important class of optimization problem with wide applications in practice. The classic QP solvers are based on either simplex or barrier method, both of which suffer from the scalability issue because their computational bottleneck is solving linear equations. In this paper, we design and analyze a first-order method for QP, called restarted accelerated primal-dual hybrid gradient (rAPDHG), whose computational bottleneck is matrix-vector multiplication. …

Hierarchical storage management in user space for neuroimaging applications
Val\'erie Hayot-Sasson, Tristan Glatard
https://arxiv.org/abs/2404.11556 ht…

Hierarchical storage management in user space for neuroimaging applications
Neuroimaging open-data initiatives have led to increased availability of large scientific datasets. While these datasets are shifting the processing bottleneck from compute-intensive to data-intensive, current standardized analysis tools have yet to adopt strategies that mitigate the costs associated with large data transfers. A major challenge in adapting neuroimaging applications for data-intensive processing is that they must be entirely rewritten. To facilitate data management for standardi…

Speeding up 6-DoF Grasp Sampling with Quality-Diversity
Johann Huber, Fran\c{c}ois H\'el\'enon, Mathilde Kappel, Elie Chelly, Mahdi Khoramshahi, Fa\"iz Ben Amar, St\'ephane Doncieux
https://arxiv.org/abs/2403.06173

Speeding up 6-DoF Grasp Sampling with Quality-Diversity
Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critical challenge, as this skill is required to complete many manipulation tasks. Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse, high-performing solutions to a given problem…

This https://arxiv.org/abs/2402.00680 has been replaced.
link: https://scholar.google.com/scholar?q=a

LVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression
Existing learned video compression models employ flow net or deformable convolutional networks (DCN) to estimate motion information. However, the limited receptive fields of flow net and DCN inherently direct their attentiveness towards the local contexts. Global contexts, such as large-scale motions and global correlations among frames are ignored, presenting a significant bottleneck for capturing accurate motions. To address this issue, we propose a joint local and global motion compensation …

LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen
https://arxiv.org/abs/2403.00462

LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
Virtual assistants are poised to take a dramatic leap forward in terms of their dialogue capabilities, spurred by recent advances in transformer-based Large Language Models (LLMs). Yet a major bottleneck to achieving genuinely transformative task-oriented dialogue capabilities remains the scarcity of high quality and linguistically sophisticated data. Existing datasets, while impressive in scale, have limited domain coverage and contain few genuinely challenging conversational phenomena; those …

AdaGossip: Adaptive Consensus Step-size for Decentralized Deep Learning with Communication Compression
Sai Aparna Aketi, Abolfazl Hashemi, Kaushik Roy
https://arxiv.org/abs/2404.05919

AdaGossip: Adaptive Consensus Step-size for Decentralized Deep Learning with Communication Compression
Decentralized learning is crucial in supporting on-device learning over large distributed datasets, eliminating the need for a central server. However, the communication overhead remains a major bottleneck for the practical realization of such decentralized setups. To tackle this issue, several algorithms for decentralized training with compressed communication have been proposed in the literature. Most of these algorithms introduce an additional hyper-parameter referred to as consensus step-si…

This https://arxiv.org/abs/2209.14019 has been replaced.
link: https://scholar.google.com/scholar?q=a

Inertial Quasi-Newton Methods for Monotone Inclusion: Efficient Resolvent Calculus and Primal-Dual Methods
We introduce an inertial quasi-Newton Forward-Backward Splitting Algorithm to solve a class of monotone inclusion problems. While the inertial step is computationally cheap, in general, the bottleneck is the evaluation of the resolvent operator. A change of the metric makes its computation hard even for (otherwise in the standard metric) simple operators. In order to fully exploit the advantage of adapting the metric, we develop a new efficient resolvent calculus for a low-rank perturbed standa…

This https://arxiv.org/abs/2403.16110 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

ByteCard: Enhancing ByteDance's Data Warehouse with Learned Cardinality Estimation
Cardinality estimation is a critical component and a longstanding challenge in modern data warehouses. ByteHouse, ByteDance's cloud-native engine for extensive data analysis in exabyte-scale environments, serves numerous internal decision-making business scenarios. With the increasing demand for ByteHouse, cardinality estimation becomes the bottleneck for efficiently processing queries. Specifically, the existing query optimizer of ByteHouse uses the traditional Selinger-like cardinality estima…

This https://arxiv.org/abs/2401.12888 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
The aspiration of the next generation's autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology. Recent advancement in AD simulation, closed-loop mode…

A Segmentation Foundation Model for Diverse-type Tumors
Jianhao Xie, Ziang Zhang, Guibo Luo, Yuesheng Zhu
https://arxiv.org/abs/2403.06396 https://<…

A Segmentation Foundation Model for Diverse-type Tumors
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical image datasets do not have a sufficient amount of data so there are few large-scale models in medical imaging. We propose a large-scale Tumor Segmentation Foundation Model (TSFM) with 1.6 billion parameters using Resblock-backbone and Transformer-bottleneck,which has good transfer ability for downstream tasks. To make TS…

A Segmentation Foundation Model for Diverse-type Tumors
Jianhao Xie, Ziang Zhang, Guibo Luo, Yuesheng Zhu
https://arxiv.org/abs/2403.06396 https://<…

A Segmentation Foundation Model for Diverse-type Tumors
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical image datasets do not have a sufficient amount of data so there are few large-scale models in medical imaging. We propose a large-scale Tumor Segmentation Foundation Model (TSFM) with 1.6 billion parameters using Resblock-backbone and Transformer-bottleneck,which has good transfer ability for downstream tasks. To make TS…

This https://arxiv.org/abs/2401.15541 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise. However, many challenges persist in terms of feasibility, learning efficiency, and convergence. These hurdles stem from the bottleneck in communication, characterized by sporadic and irregular connectivity between LEO satellites and ground stations, coupled with the limited computation capability of satellite edge computing (SEC). This paper p…