Read mapping, which involves computationally intensive approximate string matching (ASM) on large datasets, is the primary performance bottleneck in genome sequence analysis. To accelerate read mapping, a processing-in-memory (PIM) architecture that conducts highly parallel computations within the memory to reduce energy-inefficient data movements can be a promising solution. In this paper, we present ReTAP, a processing-in-ReRAM Bitap accelerator for genomic analysis. Instead of using the intricate dynamic programming algorithm, our design incorporates the Bitap algorithm, which uses only simple bitwise operations to perform ASM. Additionally, we explore the opportunity to reduce redundant computations by dynamically adjusting the error tolerance of Bitap and co-design the hardware to enhance computation parallelism. Our evaluation demonstrates that ReTAP outperforms GenASM, the state-of-the-art Bitap accelerator, with a 5.74× throughput and 1.79× energy efficiency.

Read mapping, which involves computationally in-tensive approximate string matching (ASM) on large datasets, is the primary performance bottleneck in genome sequence analysis. To accelerate read mapping, a processing-in-memory (PIM) architecture that conducts highly parallel computations within the memory to reduce energy-inefficient data movements can be a promising solution. In this paper, we present ReTAP, a processing-in-ReRAM Bitap accelerator for genomic analysis. Instead of using the intricate dynamic programming algorithm, our design incorporates the Bitap algorithm, which uses only simple bitwise operations to perform ASM. Additionally, we explore the opportunity to reduce redundant computations by dynamically adjusting the error tolerance of Bitap and co-design the hardware to enhance computation parallelism. Our evaluation demonstrates that ReTAP outperforms GenASM, the state-of-the-art Bitap accelerator, with a 153.7 x higher throughput.

Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal geolocation information, rendering serious privacy concerns when working with raw data. A promising approach to address this issue is trajectory generation, which involves replacing original data with generated, privacy-free alternatives. Despite the potential of trajectory generation, the complex nature of human behavior and its inherent stochastic characteristics pose challenges in generating high-quality trajectories. In this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj). This model effectively combines the generative abilities of diffusion models with the spatial-temporal features derived from real trajectories. The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural network to embed conditional information and accurately estimate noise levels during the reverse process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories while retaining the original distributions. Moreover, the generated results can support downstream trajectory analysis tasks and significantly outperform other methods in terms of geo-distribution evaluations.

In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO–DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., “planning,” “operation,” “market,” “asset,” and “connection”) with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.

This paper presents a new approach to modeling call holding time of VoIP traffic on tandem networks. Call holding time is a key variable of traffic engineering models, and the traditional Erlang-B model uses the negative exponential function to model call holding time. Our study of hundreds of millions of telephone calls shows that the exponential assumption is not valid for modern large-scale VoIP networks. We propose to use time-to-event analysis which consists of fitting a parametric model to the call cease rate. Then we study several probability distribution functions and compare their capability to model the VoIP call departure rate. We find that both the log-logistic and the generalized gamma distributions provide a good fit for the data. Our statistical analysis shows that the approach of call cease rate provides more accurate results than the traditional exponential and log-normal holding time models.