1,278 research outputs found
Computer Aided Verification
This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book
Spatial Ecology of Coyotes and Cougars: Understanding the Influence of Multiple Prey on the Spatial Interactions of Two Predators
The extent to which predators regulate prey populations remains a subject of debate. Yet, when predator control is employed as a management strategy, it is often assumed that predators can and do regulate prey populations. From 2011 through 2015, I monitored the demography and space use of coyotes (Canis latrans) and cougars (Puma concolor) on Monroe Mountain in Fishlake National Forest, Utah as part of a larger collaboration investigating the impacts of coyote aerial control on mule deer (Odocoileus hemionus) neonate survival. My primary objective was to assess the impacts of anthropogenic regulation on the respective populations and identify any cascading effects relevant to mule deer management. To meet this objective, I established a monitoring program for both predators by deploying radio-telemetry collars (VHF and GPS) on each, documented predation events, established surveys for small mammals and lagomorphs to monitor primary prey populations during deer parturition (June – August), and collected data on the location and demographic composition of winter-removed coyotes. I analyzed these data primarily in a community-based, animal movement and resource selection framework permitting the integration of data from multiple sources. When evaluating coyote aerial removal as a management strategy, I identified a spatial dependency in the ability to match removals with indices of deer recruitment as Wildlife Services Operations personnel were primarily limited by terrain and tree cover. Thus, matching treatment with deer fawning was highly variable with only a small number of sites where removals were effective. In addition, I found that coyotes selected for sites with the highest densities of lagomorphs while avoiding areas with a high probability of encountering cougars. Coyotes did not select for mule deer fawning sites, although individual coyotes that occupied resource-poor home ranges were more likely to do so. Cougars strongly selected for mule deer high use areas throughout much of the year, only switching to elk (Cervus elaphus) during the cougar harvest season (i.e., winter). Data from cougar kill site investigations match the observed patterns in cougar space use. My results suggest that predator-prey processes are multi-dimensional and dynamic through time, which likely contribute to the lack of resolution regarding the efficacy of predator control and the regulatory potential of predators in general
FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to
run at the edge, where data analysis and decision-making can be performed in
real-time and close to data sources. To protect data privacy and unify data
silos among end devices in EI, Federated Learning (FL) is proposed for
collaborative training of shared AI models across devices without compromising
data privacy. However, the prevailing FL approaches cannot guarantee model
generalization and adaptation on heterogeneous clients. Recently, Personalized
Federated Learning (PFL) has drawn growing awareness in EI, as it enables a
productive balance between local-specific training requirements inherent in
devices and global-generalized optimization objectives for satisfactory
performance. However, most existing PFL methods are based on the Parameters
Interaction-based Architecture (PIA) represented by FedAvg, which causes
unaffordable communication burdens due to large-scale parameters transmission
between devices and the edge server. In contrast, Logits Interaction-based
Architecture (LIA) allows to update model parameters with logits transfer and
gains the advantages of communication lightweight and heterogeneous on-device
model allowance compared to PIA. Nevertheless, previous LIA methods attempt to
achieve satisfactory performance either relying on unrealistic public datasets
or increasing communication overhead for additional information transmission
other than logits. To tackle this dilemma, we propose a knowledge cache-driven
PFL architecture, named FedCache, which reserves a knowledge cache on the
server for fetching personalized knowledge from the samples with similar hashes
to each given on-device sample. During the training phase, ensemble
distillation is applied to on-device models for constructive optimization with
personalized knowledge transferred from the server-side knowledge cache.Comment: 14 pages, 6 figures, 9 tables. arXiv admin note: text overlap with
arXiv:2301.0038
The Performance Cost of Security
Historically, performance has been the most important feature when optimizing computer hardware. Modern processors are so highly optimized that every cycle of computation time matters. However, this practice of optimizing for performance at all costs has been called into question by new microarchitectural attacks, e.g. Meltdown and Spectre. Microarchitectural attacks exploit the effects of microarchitectural components or optimizations in order to leak data to an attacker. These attacks have caused processor manufacturers to introduce performance impacting mitigations in both software and silicon.
To investigate the performance impact of the various mitigations, a test suite of forty-seven different tests was created. This suite was run on a series of virtual machines that tested both Ubuntu 16 and Ubuntu 18. These tests investigated the performance change across version updates and the performance impact of CPU core number vs. default microarchitectural mitigations. The testing proved that the performance impact of the microarchitectural mitigations is non-trivial, as the percent difference in performance can be as high as 200%
Hybrid2: Combining Caching and Migration in Hybrid Memory Systems
This paper considers a hybrid memory system composed of memory technologies with different characteristics; in particular a small, near memory exhibiting high bandwidth, i.e., 3D-stacked DRAM, and a larger, far memory offering capacity at lower bandwidth, i.e., off-chip DRAM. In the past,the near memory of such a system has been used either as a DRAM cache or as part of a flat address space combined with a migration mechanism. Caches and migration offer different tradeoffs (between performance, main memory capacity, data transfer costs, etc.) and share similar challenges related todata-transfer granularity and metadata management. This paper proposes Hybrid2 , a new hybrid memory system architecture that combines a DRAM cache with a migration scheme. Hybrid 2 does not deny valuable capacity from the memory system because it uses only a small fraction of the near memory as a DRAM cache; 64MB in our experiments.It further leverages the DRAM cache as a staging area to select the data most suitable for migration. Finally, Hybrid2 alleviates the metadata overheads of both DRAM caches and migration using a common mechanism. Using near to far memory ratios of 1:16, 1:8 and 1:4 in our experiments, Hybrid2 on average outperforms current state-of-the-art migration schemes by 7.9%, 9.1% and 6.4%, respectively. In the same system configurations, compared to DRAM caches Hybrid2 gives away on average only 0.3%, 1.2%, and 5.3% of performance offering 5.9%, 12.1%, and 24.6% more main memory capacity, respectively
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
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