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Learning Gradients of Convex Functions with Monotone Gradient networks

Learning Gradients of Convex Functions with Monotone Gradien...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shreyas Chaudhari Srinivasa Pranav José M.F. Moura Electrical and Computer Engineering Carnegie Mellon University

While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical applications ranging from gradient-based optimization to optimal transport. Recent works have explored data-driven methods for learning convex objective functions, but learning their monotone gradients is seldom studied. In this work, we propose C-MGN and M-MGN, two monotone gradient neural network architectures for directly learning the gradients of convex functions. We show that, compared to state of the art methods, our networks are easier to train, learn monotone gradient fields more accurately, and use significantly fewer parameters. We further demonstrate their ability to learn optimal transport mappings to augment driving image data.

关键词： Neural networks Signal processing network architecture Linear programming Convex functions Distance measurement Acoustics

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Vaccinating a Population is a Changing programming Problem

Vaccinating a Population is a <i>Changing</i> Programming Pr...

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18th ieee International conference on Computational Intelligence in Bioinformatics and Computational Biology (ieee CIBCB)

作者： Amin, Sumaiya Houghten, Sheridan Hughes, James Alexander St Francis Xavier Univ Comp Sci Antigonish NS Canada Brock Univ Comp Sci St Catharines ON Canada

ISBN: (纸本)9781665401128

How best to apply vaccines to a population is an open problem. It is trivial to derive intuitive strategies, but until tested, their efficacy is not known. This problem is particularly challenging when considering the dynamics of social contact networks and their changes over time. A system for automatically discovering tested vaccination strategies with evolutionary computation has been improved upon to include additional graph metrics and to generate vaccination strategies for dynamic graphs, something that is expected of real social networks within communities. The system's ability to generate effective strategies was demonstrated along with a comparison of the strategies developed when fit to a static graph versus a dynamic graph. It was observed that the additional computational resources required to generate strategies on a dynamic graph may not be necessary as strategies developed for static graphs performed similarly well;however, the authors are careful to acknowledge that results may differ significantly when adjusting the systems many parameters.

关键词： COVID-19 Dynamic Epidemic Genetic programming Graphs SEIR Model Simulation Vaccinations

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Optimizing Parallel Java Streams 26

Optimizing Parallel Java Streams

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26th International conference on Engineering of Complex Computer Systems (ICECCS)

作者： Basso, Matteo Schiavio, Filippo Rosa, Andrea Binder, Walter Univ Svizzera Italiana USI Fac Informat Lugano Switzerland

ISBN: (纸本)9781665401623

The Java Stream API increases developer productivity and greatly simplifies exploiting parallel computation by providing a high-level abstraction on top of complex data processing, parallelization, and synchronization algorithms. However, the usage of the Java Stream API often incurs significant runtime overhead. Method inlining and the automated translation of code using the Java Stream API into imperative code using loops can reduce such overhead;however, existing approaches and tools are applicable only to sequential stream pipelines, leaving the optimization of parallel streams an open issue. We bridge this gap by presenting a novel method to exploit high-level static analysis to characterize stream pipelines, detect parallel streams, and apply transformations removing the abstraction overhead. We evaluate our method on a set of benchmarks, showing that our approach significantly reduces execution time and memory allocation.

关键词： Java Parallel Streams Functional programming Program Optimization Bytecode Transformation

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Schema-Aware Deep Graph Convolutional networks for Heterogeneous Graphs

Schema-Aware Deep Graph Convolutional Networks for Heterogen...

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9th ieee International conference on Big Data (ieee BigData)

作者： Manchanda, Saurav Zheng, Da Karypis, George Univ Minnesota Minneapolis MN 55455 USA AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA

ISBN: (纸本)9781665439022

Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure information and the node (or edge) features through message passing and computes 'deep' node representations. Despite significant progress in the field, designing GCN architectures for heterogeneous graphs still remains an open challenge. Due to the schema of a heterogeneous graph, useful information may reside multiple hops away. A key question is how to perform message passing to incorporate information of neighbors multiple hops away while avoiding the well-known over-smoothing problem in GCNs. To address this question, we propose our GCN framework Deep Heterogeneous Graph Convolutional network (DHGCN), which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away. It first computes representations of the target nodes based on their schema-derived ego-network (SEN). It then links the nodes of the same type with various pre-defined metapaths and performs message passing along these links to compute final node representations. Our design choices naturally capture the way a heterogeneous graph is generated from the schema. The experimental results on real and synthetic datasets corroborate the design choice and illustrate the performance gains relative to competing alternatives.

关键词： graph convolutional networks graph neural networks metapath heterogeneous graph relational graph over-smoothing network schema

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Automatic Generation of Micro-kernels for Performance Portability of Matrix Multiplication on RISC-V Vector Processors

Automatic Generation of Micro-kernels for Performance Portab...

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2023 International conference on High Performance Computing, network, Storage, and Analysis, SC Workshops 2023

作者： Igual, Francisco Piñuel, Luis Catalán, Sandra Martínez, Héctor Castelló, Adrián Quintana-Ortí, Enrique Universidad Complutense de Madrid Madrid Spain Universidad de Córdoba Córdoba Spain Universitat Jaume I Castellón de la Plana Spain Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9798400707858

In this paper, we propose and evaluate several optimized implementations of the general matrix multiplication (gemm) on two different RISC-V architecture cores implementing the RISC-V vector extension (RVV): C906 and C910 from T-HEAD. Specifically, we address the performance portability problem across these processor cores by means of an automatic assembly code generator, written in Python, capable of emitting RVV code for high performance computing (HPC), with a variety of combinations of specific and general optimizations. Our experimental results using a number of automatically-generated micro-kernels for gemm, on both RISC-V architectures, reveal different impact of each optimization, depending on the target architecture, and highlight the importance of automatically generating HPC RVV code to achieve performance portability while reducing the developers' effort. In addition, these optimizations show important performance gains with resepcto to a state-of-the-art tuned BLAS library (openBLAS), reaching 3 × and 1.3 × speed-ups for the C910 and C906, respectively. © 2023 ACM.

关键词： Automatic programming

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Enriching Local Patterns with Multi-Token Attention for Broad-Sight Neural networks

Enriching Local Patterns with Multi-Token Attention for Broa...

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ieee Workshop on Applications of Computer Vision (WACV)

作者： Hankyul Kang Jongbin Ryu Ajou University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In neural networks, recognizing visual patterns is challenging because global average pooling disregards local patterns and solely relies on over-concentrated activation. Global average pooling enforces the network to learn objects regardless of their location, so features tend to be activated only in specific regions. To support this claim, we provide a novel analysis of the problems that over-concentration brings about in networks with extensive experiments. We analyze the over-concentration through problems arising from feature variance and dead neurons that are not activated. Based on our analysis, we introduce a multi-token attention pooling layer to alleviate the over-concentration problem. Our attention-pooling layer captures broad-sight local patterns by learning multiple tokens with the proposed distillation algorithm. It resolves the high bias and high variance errors of learned multi-tokens, which is crucial when aggregating local patterns with multi-tokens. Our method applies to various vision tasks and network architectures such as CNN, ViT, and MLP-Mixer. The proposed method improves baselines with few extra resources, and a network employing our pooling method works favorably against state-of-the-art networks. We open-source the code at https://***/Lab-LVM/imagenet-models.

关键词： Visualization Computer vision Codes Neurons Object detection network architecture Pattern recognition Object recognition Biological neural networks

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Standardization of Cloud Interfaces Using the Matter Protocol for Interoperable Smart Homes

Standardization of Cloud Interfaces Using the Matter Protoco...

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Consumer Communications and networking conference, CCNC ieee

作者： Luca Pizzocolo Mattia Cerutti Sanders Batista Felix Mattia Brambilla Politecnico di Milano Milan Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The lack of interoperability between smart home devices prevents the implementation of advanced services and use cases in home and building automation. The latest standardization attempt is the release of the Matter protocol, whose goal is to enable interoperability among devices in a local network. In this paper, we present the integration of the Matter protocol in an interoperable, open-source smart home platform capable of managing heterogeneous Matter networks and exchanging data and commands with a cloud infrastructure, standardizing a Message Queuing Telemetry Transport (MQTT) device-to-cloud interface according to the Matter ontology. We evaluate the integration experience and customization level and measure the latency of different MQTT cloud brokers on cellular and fiber internet connectivity. The qualitative results confirm that Matter core features work properly, even if they are still difficult to configure at this early stage of protocol rollout. The quantitative results show the impact of MQTT broker, internet connectivity, and cloud microservices. Overall, the results demonstrate the potentialities of Matter in becoming the definitive standard for smart home communications and the feasibility of its application outside the local network boundaries, i.e., towards the standard-ization of the device-to-cloud communication.

关键词： Performance evaluation Cloud computing Protocols Microservice architectures Smart homes Optical fiber networks Optical fiber devices Telemetry Interoperability Standards

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Enabling Pipeline Parallelism in Heterogeneous Managed Runtime Environments via Batch Processing 2022

Enabling Pipeline Parallelism in Heterogeneous Managed Runti...

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18th ACM SIGPLAN/SIGOPS International conference on Virtual Execution Environments (VEE)

作者： Blanaru, Florin Stratikopoulos, Athanasios Fumero, Juan Kotselidis, Christos Univ Manchester Manchester Lancs England

ISBN: (纸本)9781450392518

During the last decade, managed runtime systems have been constantly evolving to become capable of exploiting underlying hardware accelerators, such as GPUs and FPGAs. Regardless of the programming language and their corresponding runtime systems, the majority of the work has been focusing on the compiler front trying to tackle the challenging task of how to enable just-in-time compilation and execution of arbitrary code segments on various accelerators. Besides this challenging task, another important aspect that defines both functional correctness and performance of managed runtime systems is that of automatic memory management. Although automatic memory management improves productivity by abstracting away memory allocation and maintenance, it hinders the capability of using specific memory regions, such as pinned memory, in order to perform data transfer times between the CPU and hardware accelerators. In this paper, we introduce and evaluate a series of memory optimizations specifically tailored for heterogeneous managed runtime systems. In particular, we propose: (i) transparent and automatic "parallel batch processing" for overlapping data transfers and computation between the host and hardware accelerators in order to enable pipeline parallelism, and (ii) "off-heap pinned memory" in combination with parallel batch processing in order to increase the performance of data transfers without posing any on-heap overheads. These two techniques have been implemented in the context of the state-of-the-art open-source TornadoVM and their combination can lead up to 2.5x end-to-end performance speedup against sequential batch processing.

关键词： Data Transfers GPUs Heterogeneous architectures Memory Management Optimizations Virtual Machines

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A Compilation-and Remote-programming-as-a-Service Platform for IoT Devices 12

A Compilation-and Remote-Programming-as-a-Service Platform f...

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12th ieee International conference on Joint Cloud Computing (ieee JCC) / 9th ieee International conference on Mobile Cloud Computing, Services, and Engineering (ieee MobileCloud)

作者： Charalampidis, Pavlos Fragkiadakis, Alexandros Fdn Res & Technol Hellas FORTH ICS Inst Comp Sci Iraklion Greece Hellen Open Univ Sch Sci & Technol Patras Greece

ISBN: (纸本)9781665434799

The Internet-of-Things (IoT) presents itself as an emerging technology, which is able to interconnect a massive number of heterogeneous smart objects. These ubiquitous object-enabled networks, which may operate for several years in variable conditions, are used for supporting complex data-driven applications such as smart cities applications, home automation, health monitoring, etc. Throughout their extended lifetime, the devices forming the IoT networks need to be re-programmed, so that new features are added and software bugs or security vulnerabilities are resolved. In this work, we present work-in-progress on the design of a Compilation-and Remote-programming-as-a-Service platform that enables cloud-based compilation and build of device firmware, as well as remote firmware updates for deployed IoT devices, in a reliable and secure way. We introduce the functional architecture of our platform and elaborate on the interaction of functional components. Our solution can easily support various embedded operating systems and heterogeneous hardware platforms, as well as different communication patterns for firmware transfer.

关键词： Internet of Things Cloud computing Platform-as-a-Service Cloud compilation Over-the-air programming

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Demonstrating Isolation Principles for 5G network Slicing

Demonstrating Isolation Principles for 5G Network Slicing

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ieee conference on network Function Virtualization and Software Defined networks (NFV-SDN)

作者： Xhulio Limani Arno Troch Chieh-Chun Chen Chia-Yu Chang Andreas Gavrielides Miguel Camelo Johann M. Marquez-Barja Nina Slamnik-Kriještorac IDLab - Faculty of Applied Engineering University of Antwerp - Imec Belgium EURECOM France Nokia Bell Labs Antwerp Belgium

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

5G Standalone (SA) networks introduce a range of new applications, including enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communications (mMTC). Each of these applications has distinct network requirements, which current commercial network architectures, such as 4G and 5G Non-Standalone (NSA), struggle to meet simultaneously due to their one-size-fits-all design. The 5G SA architecture addresses this challenge through network Slicing, creating multiple isolated virtual networks on top of a single physical infrastructure. Isolation between slices is crucial for performance, security, and reliability. Each slice owns virtual resources, based on the physical resources (e.g., CPU, memory, antennas, and network interfaces) shared by the overall *** this demo, we define and showcase a real-life Proof of Concept (PoC), which enables network Slicing guaranteeing isolation between slices in 5G SA networks, for each network domain i.e., Radio Access network (RAN), Transport network (TN), and 5G Core (5GC) network.

关键词： Enhanced mobile broadband 5G mobile communication network slicing open RAN Ultra reliable low latency communication network function virtualization Security Reliability network interfaces Software defined networking

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