Consolidating Streams to Improve DASH Cache Utilisation

Existing HTTP caches interact poorly with multiple Dynamic Adaptive Streaming over HTTP (DASH) streams of the same content: time and quality differences prevent a complete representation from being cached, reducing hit-ratios. We propose to consolidate near-simultaneous streams based on time or quality, where the improved cache performance makes this worthwhile. We estimate that there is a sufficient number of near-simultaneous streams for our proposed techniques to improve cache hit-ratios

Consolidating Streams to Improve DASH Cache Utilisation

Existing HTTP caches interact poorly with multiple Dynamic Adaptive Streaming over HTTP (DASH) streams of the same content: time and quality differences prevent a complete representation from being cached, reducing hit-ratios. We propose to consolidate near-simultaneous streams based on time or quality, where the improved cache performance makes this worthwhile. We estimate that there is a sufficient number of near-simultaneous streams for our proposed techniques to improve cache hit-ratios

Predictive caching and prefetching of query results in search engines

We study the caching of query result pages in Web search engines. Popular search engines receive millions of queries per day, and ecient policies for caching query results may enable them to lower their response time and reduce their hardware requirements. We present PDC (probability driven cache), a novel scheme tailored for caching search results, that is based on a probabilistic model of search engine users. We then use a trace of over seven million queries submitted to the search engine AltaVista to evaluate PDC, as well as traditional LRU and SLRU based caching schemes. The trace driven simulations show that PDC outperforms the other policies. We also examine the prefetching of search results, and demonstrate that prefetching can increase cache hit ratios by 50% for large caches, and can double the hit ratios of small caches. When integrating prefetching into PDC, we attain hit ratios of over 0:53.

Query Load Balancing by Caching Search Results in Peer-to-Peer Information Retrieval Networks

For peer-to-peer web search engines it is important to keep the delay between receiving a query and providing search results within an acceptable range for the end user. How to achieve this remains an open challenge. One way to reduce delays is by caching search results for queries and allowing peers to access each others cache. In this paper we explore the limitations of search result caching in large-scale peer-to-peer information retrieval networks by simulating such networks with increasing levels of realism. We find that cache hit ratios of at least thirty-three percent are attainable

Prediction of Federal Funds Target Rate: a dynamic logistic Bayesian Model averaging approach

In this paper we examine which macroeconomic and financial variables have most predictive power for the target repo rate decisions made by the Federal Reserve -- We conduct the analysis for the FOMC decisions during the period June 1998-April 2015 using dynamic logistic models with dynamic Bayesian Model Averaging that allows to perform predictions in real-time with great flexibility -- The computational burden of the algorithm is reduced by adapting a Markov Chain Monte Carlo Model Composition: MC3 -- We found that the outcome of the FOMC meetings during the sample period are predicted well: Logistic DMA-Up and Dynamic Logit-Up models present hit ratios of 87,2 and 88,7; meanwhile, hit ratios for the Logistic DMA-Down and Dynamic Logit-Down models are 79,8 and 68,0, respectivel

RDGC: A Reuse Distance-Based Approach to GPU Cache Performance Analysis

In the present paper, we propose RDGC, a reuse distance-based performance analysis approach for GPU cache hierarchy. RDGC models the thread-level parallelism in GPUs to generate appropriate cache reference sequence. Further, reuse distance analysis is extended to model the multi-partition/multi-port parallel caches and employed by RDGC to analyze GPU cache memories. RDGC can be utilized for architectural space exploration and parallel application development through providing hit ratios and transaction counts. The results of the present study demonstrate that the proposed model has an average error of 3.72 % and 4.5 % (for L1 and L2 hit ratios, respectively). The results also indicate that the slowdown of RDGC is equal to 47 000 times compared to hardware execution, while it is 59 times faster than GPGPU-Sim simulator

On Sampling Top-K Recommendation Evaluation

Recently, Rendle has warned that the use of sampling-based top-$k$ metrics might not suffice. This throws a number of recent studies on deep learning-based recommendation algorithms, and classic non-deep-learning algorithms using such a metric, into jeopardy. In this work, we thoroughly investigate the relationship between the sampling and global top-$K$ Hit-Ratio (HR, or Recall), originally proposed by Koren[2] and extensively used by others. By formulating the problem of aligning sampling top-$k$ ($SHR@k$) and global top-$K$ ($HR@K$) Hit-Ratios through a mapping function $f$, so that $SHR@k\approx HR@f(k)$, we demonstrate both theoretically and experimentally that the sampling top-$k$ Hit-Ratio provides an accurate approximation of its global (exact) counterpart, and can consistently predict the correct winners (the same as indicate by their corresponding global Hit-Ratios)

Poor neural and perceptual phoneme discrimination during acoustic variation in dyslexia

Whereas natural acoustic variation in speech does not compromise phoneme discrimination in healthy adults, it was hypothesized to be a challenge for developmental dyslexics. We investigated dyslexics’ neural and perceptual discrimination of native language phonemes during acoustic variation. Dyslexics and non-dyslexics heard /æ/ and /i/ phonemes in a context with fo variation and then in a context without it. Mismatch negativity (MMN) and P3a responses to phoneme changes were recorded with electroencephalogram to compare groups during ignore and attentive listening. perceptual phoneme discrimination in the variable context was evaluated with hit-ratios and reaction times. MMN/N2bs were diminished in dyslexics in the variable context. Hit-ratios were smaller in dyslexics than controls. MMNs did not differ between groups in the context without variation. These results suggest that even distinctive vowels are challenging to discriminate for dyslexics when the context resembles natural variability of speech. This most likely reflects poor categorical perception of phonemes in dyslexics. Difficulties to detect linguistically relevant invariant information during acoustic variation in speech may contribute to dyslexics’ deficits in forming native language phoneme representations during infancy. Future studies should acknowledge that simple experimental paradigms with repetitive stimuli can be insensitive to dyslexics’ speech processing deficits.Peer reviewe