In order to process efficiently ever-higher dimensional data such as images, sentences, or audio recordings, one needs to find a proper way to reduce the dimensionality of such data. In this regard, SVD-based methods including PCA and Isomap have been extensively used. Recently, a neural network alternative called autoencoder has been proposed and is often preferred for its higher flexibility. This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification. To this purpose, we evaluated the performance of PCA compared to Isomap, a deep autoencoder, and a variational autoencoder. Experiments were conducted on three commonly used image datasets: MNIST, Fashion-MNIST, and CIFAR-10. The four different dimensionality reduction techniques were separately employed on each dataset to project data into a low-dimensional space. Then a k-NN classifier was trained on each projection with a cross-validated random search over the number of neighbours. Interestingly, our experiments revealed that k-NN achieved comparable accuracy on PCA and both autoencoders' projections provided a big enough dimension. However, PCA computation time was two orders of magnitude faster than its neural network counterparts.

This paper addresses the challenge of understanding the waiting dependencies between the threads and hardware resources required to complete a task. The objective is to improve software performance by detecting the underlying bottlenecks caused by system-level blocking dependencies. In this paper, we use a system level tracing approach to extract a Waiting Dependency Graph that shows the breakdown of a task execution among all the interleaving threads and resources. The method allows developers and system administrators to quickly discover how the total execution time is divided among its interacting threads and resources. Ultimately, the method helps detecting bottlenecks and highlighting their possible causes. Our experiments show the effectiveness of the proposed approach in several industry-level use cases. Three performance anomalies are analysed and explained using the proposed approach. Evaluating the method efficiency reveals that the imposed overhead never exceeds 10.1%, therefore making it suitable for in-production environments.

The execution of similar units can be compared by their internal behaviors to determine the causes of their potential performance issues. For instance, by examining the internal behaviors of different fast or slow web requests more closely, and by clustering and comparing their internal executions, one can determine what causes some requests to run slowly or behave in unexpected ways. In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes. First, low-level and fine-grained information regarding each request is gathered by tracing both the user space and the kernel space. Second, further information is extracted and fed into an outlier detector. Finally, these outliers are then clustered by their behavior, and each group is analyzed separately. Experiments revealed that this pipeline is indeed able to detect slow web requests and provide additional insights into their true root causes. Notably, we were able to identify a real PHP cache contention issue using the proposed approach.

The aim of this study was to determine the outcome of dogs with soft tissue sarcoma (STS) within the region of the ischiatic tuberosity (ITSTS) treated surgically. This was a multi‐institutional retrospective study. Fifty‐two dogs met the inclusion criteria, which were: histologically confirmed STS in the region of the IT treated with surgical resection between March 1st, 2009 and March 1st, 2021 with a minimum follow‐up time of 6 months. Data collected included patient signalment, preoperative diagnostics, surgical intent/method, surgical complications, histopathology, margins, outcome and cause of death. Statistical analyses were performed to determine significant factors in the treatment and prognosis of ITSTS. Overall survival time (OST) and disease progression were negatively associated with tumour grade, while recurrence was positively associated with grade and incomplete margins. Of the 52 included dogs, there were 24 grade I, 20 grade II and 7 grade III tumours. Forty dogs had reported histopathologic margins of which 26 were reported to be complete and 14 were incomplete. OST and progression‐free survival was not reached for tumours graded as I or II and was 255 and 268 days respectively, for grade III. Median time to recurrence was not reached for tumours excised with complete margins and was 398 days for those with incomplete margins. The surgical complication rate was 25%. ITSTS was not found to be a unique clinical entity in dogs as tumour behavior, treatment recommendations, and prognosis were similar to STS in other locations, with overall outcome and prognosis influenced by histologic grade and margins. While surgical complications were common, none resulted in significant morbidity or mortality.

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Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solution, Vaswani et al. introduced the Transformer, a model solely based on the attention mechanism that is able to relate any two positions of the input sequence, hence modelling arbitrary long dependencies. The Transformer has improved the state-of-the-art across numerous sequence modelling tasks. However, its effectiveness comes at the expense of a quadratic computational and memory complexity with respect to the sequence length, hindering its adoption. Fortunately, the deep learning community has always been interested in improving the models’ efficiency, leading to a plethora of solutions such as parameter sharing, pruning, mixed-precision, and knowledge distillation. Recently, researchers have directly addressed the Transformer’s limitation by designing lower-complexity alternatives such as the Longformer, Reformer, Linformer, and Performer. However, due to the wide range of solutions, it has become challenging for researchers and practitioners to determine which methods to apply in practice in order to meet the desired trade-off between capacity, computation, and memory. This survey addresses this issue by investigating popular approaches to make Transformers faster and lighter and by providing a comprehensive explanation of the methods’ strengths, limitations, and underlying assumptions.

Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solution, Vaswani et al. introduced the Transformer, a model solely based on the attention mechanism that is able to relate any two positions of the input sequence, hence modelling arbitrary long dependencies. The Transformer has improved the stateof-the-art across numerous sequence modelling tasks. However, its effectiveness comes at the expense of a quadratic computational and memory complexity with respect to the sequence length, hindering its adoption. Fortunately, the deep learning community has always been interested in improving the models' efficiency, leading to a plethora of solutions such as parameter sharing, pruning, mixed-precision, and knowledge distillation. Recently, researchers have directly addressed the Transformer's limitation by designing lower-complexity alternatives such as the Longformer, Reformer, Linformer, and Performer. However, due to the wide range of solutions, it has become challenging for the deep learning community to determine which methods to apply in practice to meet the desired trade-off between capacity, computation, and memory. This survey addresses this issue by investigating popular approaches to make the Transformer faster and lighter and by providing a comprehensive explanation of the methods' strengths, limitations, and underlying assumptions.

Due to the ever‐increasing number of computer nodes in distributed systems, efficient and effective tools have become crucial for their analysis. Although several efficient methods have been proposed to monitor and profile distributed systems, tracing remains the most effective solution for in‐depth system analysis. Tracing is the act of collecting a trace, which is a sequence of low‐level events generated by the kernel or the userspace. After data collection, the most important part is the event analysis. The paradigm and choice of graphs determine the ability of the user to detect abnormal behaviors and identify their root cause. Although tracing is a highly effective approach to analyzing complex systems, the scalability of the current analysis tools is limited. As a consequence, tracing is often impractical for large distributed systems. This paper identifies the shortcomings of the current approaches, most notably the critical path computation and the trace file transfer between nodes. Then, this paper proposes new solutions to these drawbacks, most notably a distributed algorithm to compute the critical path, that does not aggregate all traces in a single node, and an efficient architecture to perform tracing on distributed systems. These new solutions are made publically available.