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Graph analysis involves a high number of random memory access patterns. Earlier research has shownthat the cache miss latency is responsible for more than half of the graph processing time, with the CPU execution having the smaller share. There has been significant study on decreasing the CPU computing time for example, by employing better cache prefetching and replacement policies. In thispaper, we study the various methods that do so by attempting to decrease the CPU cache miss ratio.Graph Reordering attempts to exploit the power-law distribution of graphs -- few sparsely-populated vertices in the graph have high number of connections -- to keep the frequently accessed vertices together locally and hence decrease the cache misses. However, reordering the graph by keeping the hot vertices together may affect the spatial locality of the graph, and thus add to the total CPU compute time.Also, we also need to have a control over the total reordering time and its inverse relation with thefinal CPU execution timeIn order to exploit this trade-off between reordering as per vertex hotness and spatial locality, we introduce the light-weight Community-based Reordering. We attempt to maintain the community-structureof the graph by storing the hot-members in the community locally together. The implementation also takes into consideration the impact of graph diameter on the execution time. We compare our implementation with other reordering implementations and find a significantly better result on five graph processing algorithms: BFS, CC, CCSV, PR and BC. Lorder achieved speed-up of upto 7x and an average speed-up of 1.2x as compared to other reordering algorithms

We study the probability distribution of age of information (AoI) in arbitrary networks with memoryless service times. A source node generates packets following a Poisson process, and then the packets are forwarded across the network in such a way that newer updates preempt older ones. This model is equivalent to gossip networks that was recently studied by Yates, and for which he obtained a recursive formula allowing the computation for the average AoI. In this paper, we obtain a very simple characterization of the stationary distribution of AoI at every node in the network. This allows for the computation of the average of an arbitrary function of the age. In particular, we can compute age-violation probabilities. Furthermore, we show how it is possible to use insights from our simple characterization in order to substantially reduce the computation time of average AoIs in some structured networks. Finally, we describe how it is possible to use our characterization in order to obtain faster and more accurate Monte Carlo simulations estimating the average AoI, or the average of an arbitrary function of the age.

Rust is a modern systems language focused on performance and reliability. Complementing Rust's promise to provide "fearless concurrency", developers frequently exploit asynchronous message passing. Unfortunately, arbitrarily ordering sending and receiving messages to maximise computation-communication overlap (a popular optimisation to message-passing applications) opens up a Pandora's box of further subtle concurrency bugs. To guarantee deadlock-freedom by construction, we present Rumpsteak: a new Rust framework based on multiparty session types. Previous session type implementations in Rust are either built upon synchronous and blocking communication and/or limited to two-party interactions. Crucially, none support the arbitrary ordering of messages for efficiency. Rumpsteak instead targets asynchronous async/await code. Its unique ability is allowing developers to arbitrarily order send/receive messages while preserving deadlock-freedom. For this, Rumpsteak incorporates two recent advanced session type theories: (1) k-multiparty compatibility (kmc), which globally verifies the safety of a set of participants, and (2) asynchronous multiparty session subtyping, which locally verifies optimisations in the context of a single participant. Specifically, we propose a novel algorithm for asynchronous subtyping that is both sound and decidable. We first evaluate the performance and expressiveness of Rumpsteak against three previous Rust implementations. We discover that Rumpsteak is around 1.7--8.6x more efficient and can safely express many more examples by virtue of offering arbitrary message ordering. Secondly, we analyse the complexity of our new algorithm and benchmark it against kmc and a binary session subtyping algorithm. We find they are exponentially slower than Rumpsteak's.

Recently, Information Retrieval community has witnessed fast-paced advances in Dense Retrieval (DR), which performs first-stage retrieval by encoding documents in a low-dimensional embedding space and querying them with embedding-based search. Despite the impressive ranking performance, previous studies usually adopt brute-force search to acquire candidates, which is prohibitive in practical Web search scenarios due to its tremendous memory usage and time cost. To overcome these problems, vector compression methods, a branch of Approximate Nearest Neighbor Search (ANNS), have been adopted in many practical embedding-based retrieval applications. One of the most popular methods is Product Quantization (PQ). However, although existing vector compression methods including PQ can help improve the efficiency of DR, they incur severely decayed retrieval performance due to the separation between encoding and compression. To tackle this problem, we present JPQ, which stands for Joint optimization of query encoding and Product Quantization. It trains the query encoder and PQ index jointly in an end-to-end manner based on three optimization strategies, namely ranking-oriented loss, PQ centroid optimization, and end-to-end negative sampling. We evaluate JPQ on two publicly available retrieval benchmarks. Experimental results show that JPQ significantly outperforms existing popular vector compression methods in terms of different trade-off settings. Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size. The compressed index further brings 10x speedup on CPU and 2x speedup on GPU in query latency.

We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM). Given an input text with masked tokens, we rely on conventional masks to learn inter-relations between corrupted tokens and context via autoencoding, and pseudo masks to learn intra-relations between masked spans via partially autoregressive modeling. With well-designed position embeddings and self-attention masks, the context encodings are reused to avoid redundant computation. Moreover, conventional masks used for autoencoding provide global masking information, so that all the position embeddings are accessible in partially autoregressive language modeling. In addition, the two tasks pre-train a unified language model as a bidirectional encoder and a sequence-to-sequence decoder, respectively. Our experiments show that the unified language models pre-trained using PMLM achieve new state-of-the-art results on a wide range of natural language understanding and generation tasks across several widely used benchmarks.

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually approximated by message passing between direct (one-hop) neighbors. In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC). GDC leverages generalized graph diffusion, examples of which are the heat kernel and personalized PageRank. It alleviates the problem of noisy and often arbitrarily defined edges in real graphs. We show that GDC is closely related to spectral-based models and thus combines the strengths of both spatial (message passing) and spectral methods. We demonstrate that replacing message passing with graph diffusion convolution consistently leads to significant performance improvements across a wide range of models on both supervised and unsupervised tasks and a variety of datasets. Furthermore, GDC is not limited to GNNs but can trivially be combined with any graph-based model or algorithm (e.g. spectral clustering) without requiring any changes to the latter or affecting its computational complexity. Our implementation is available online.

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific models are learned in the inner-loop, by using only a small amount of data from the current task. A key challenge in scaling these approaches is the need to differentiate through the inner loop learning process, which can impose considerable computational and memory burdens. By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer. This effectively decouples the meta-gradient computation from the choice of inner loop optimizer. As a result, our approach is agnostic to the choice of inner loop optimizer and can gracefully handle many gradient steps without vanishing gradients or memory constraints. Theoretically, we prove that implicit MAML can compute accurate meta-gradients with a memory footprint that is, up to small constant factors, no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost. Experimentally, we show that these benefits of implicit MAML translate into empirical gains on few-shot image recognition benchmarks.

Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves state-of-the-art results across the board in both extractive and abstractive settings. Our code is available at //github.com/nlpyang/PreSumm

Existing methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results. However, most current systems rely on restricted forms of user feedback, such as binary relevance responses, or feedback based on a fixed set of relative attributes, which limits their impact. In this paper, we introduce a new approach to interactive image search that enables users to provide feedback via natural language, allowing for more natural and effective interaction. We formulate the task of dialog-based interactive image retrieval as a reinforcement learning problem, and reward the dialog system for improving the rank of the target image during each dialog turn. To avoid the cumbersome and costly process of collecting human-machine conversations as the dialog system learns, we train our system with a user simulator, which is itself trained to describe the differences between target and candidate images. The efficacy of our approach is demonstrated in a footwear retrieval application. Extensive experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.

Being intensively studied, visual object tracking has witnessed great advances in either speed (e.g., with correlation filters) or accuracy (e.g., with deep features). Real-time and high accuracy tracking algorithms, however, remain scarce. In this paper we study the problem from a new perspective and present a novel parallel tracking and verifying (PTAV) framework, by taking advantage of the ubiquity of multi-thread techniques and borrowing ideas from the success of parallel tracking and mapping in visual SLAM. The proposed PTAV framework is typically composed of two components, a (base) tracker T and a verifier V, working in parallel on two separate threads. The tracker T aims to provide a super real-time tracking inference and is expected to perform well most of the time; by contrast, the verifier V validates the tracking results and corrects T when needed. The key innovation is that, V does not work on every frame but only upon the requests from T; on the other end, T may adjust the tracking according to the feedback from V. With such collaboration, PTAV enjoys both the high efficiency provided by T and the strong discriminative power by V. Meanwhile, to adapt V to object appearance changes over time, we maintain a dynamic target template pool for adaptive verification, resulting in further performance improvements. In our extensive experiments on popular benchmarks including OTB2015, TC128, UAV20L and VOT2016, PTAV achieves the best tracking accuracy among all real-time trackers, and in fact even outperforms many deep learning based algorithms. Moreover, as a general framework, PTAV is very flexible with great potentials for future improvement and generalization.