bluuebunny/crossref_metadata_2025 huggingface dataset:
AS-Parser: Log Parsing Based on Adaptive Segmentation
Authors: Chen Xiaolei , Wang Peng , Chen Jia , Wang Wei
Published: December 2023

System logs have long been recognized as valuable data for analyzing and diagnosing system failures. One fundamental task of log processing is to convert unstructured logs into structured logs through log parsing. All previous log parsing approaches follow a general framework that first segments each log into a token sequence and then computes similarity between two sequences. However, all existing approaches share the common drawback: the flat segmentation with fixed delimiters fails to understand the structural information of logs, which causes low parsing accuracy. To address this problem, we propose a novel log parsing approach, AS-Parser. Our approach introduces a hierarchical log segmentation mechanism that can adaptively segment logs into a tree structure. It can automatically recognize the appropriate delimiters and capture the common structural information. Moreover, we propose three improvements that enhance both the effectiveness and efficiency of our approach. On the public benchmark, AS-Parser performs best on 14 out of 16 datasets, with an average parsing accuracy of 0.943, far exceeding existing approaches.
ChainedFilter: Combining Membership Filters by Chain Rule
Authors: Li Haoyu , Wang Liuhui , Chen Qizhi , Ji Jianan , Wu Yuhan , Zhao Yikai , Yang Tong , Akella Aditya
Published: December 2023

Membership (membership query/membership testing) is a fundamental problem across databases, networks and security. However, previous research has primarily focused on either approximate solutions, such as Bloom Filters, or exact methods, like perfect hashing and dictionaries, without attempting to develop an integral theory. In this paper, we propose a unified and complete theory, namely chain rule, for general membership problems, which encompasses both approximate and exact membership as extreme cases. Building upon the chain rule, we introduce a straightforward yet versatile algorithm framework, namely ChainedFilter, to combine different elementary filters without losing information. Our evaluation results demonstrate that ChainedFilter improves performance of many applications including static dictionary, lossless data compression, Cuckoo Hashing, LSM-Tree and Learned Filters.
Correlation Joins over Time Series Data Streams Utilizing Complementary Dimension Reduction and Transformation
Authors: Alizade Nikoo AmirReza , Böhlen Michael H. , Helmer Sven
Published: December 2023

A common analysis task over a stream of time series is to find all pairs of windows whose correlation is above a given threshold. For a large number of streams, doing so naively, i.e., checking the Cartesian product, is too expensive. In essence, finding correlated pairs in a non-naive way boils down to a high-dimensional similarity join in a Euclidean space. While there are similarity join algorithms, such as Quickjoin and ε-kdB tree, they are inefficient for high-dimensional data. We propose CorrJoin, short for Correlation Join, that combines a complementary dimension reduction and transformation step with a subsequent double-filtering step. In the first step, we reduce the dimensionality of data stream windows by combining a fast but inaccurate method, Piecewise Aggregate Approximation (PAA), with an accurate and slow one, Singular Value Decomposition (SVD). Not only does SVD compensate for the weaknesses of PAA, it also transforms the data to make the first filter based on bucketing more effective. The second filter, which uses Euclidean distances, reduces the number of false positives before computing exact correlations. Our experiments reveal that in common settings, CorrJoin is an order of magnitude faster than state-of-the-art approaches (up to 20 times faster than Quickjoin).
Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet
Authors: Li Yanan , Deng Guangqing , Bai Changming , Yang Jingyu , Wang Gang , Zhang Hao , Bai Jin , Yuan Haitao , Xu Mengwei , Wang Shangguang
Published: December 2023

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. However, there is currently very little understanding of how QoS, QoE, and the impact of QoS on QoE for VSAs on edge platforms in the wild and at scale. To close the knowledge gap, we collect SNESet, an active measurement dataset comprising QoS and QoE telemetry metrics of 8 VSAs over four months, covering end-users from 798 edge sites,30 cities, and 3 ISPs in one country.We characterize and compare the QoS and QoE metrics in SNESet with existing publicly available datasets, highlighting that SNESet includes a significantly greater number of metrics (horizontal diversity and vertical hierarchy) and provides more comprehensive coverage of specific metrics.Moreover, we qualitatively and quantitatively analyze the impact of QoS on QoE in both domain-general and domain-specific scenarios. Our findings can inform the system design decisions that different entities in the video ecosystem (content providers, video player designers, third-party optimizers, edge vendors) make to maximize end-users experience and ultimately maximize the business outcomes. We hope SNESet can attract more research efforts in the data management community, computer network community, and beyond.
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks
Authors: Chen Fahao , Li Peng , Wu Celimuge
Published: December 2023

Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning dynamic graphs by exploiting both spatial and temporal features. Although DGNN has recently received considerable attention by AI community and various DGNN models have been proposed, building a distributed system for efficient DGNN training is still challenging. It has been well recognized that how to partition the dynamic graph and assign workloads to multiple GPUs plays a critical role in training acceleration. Existing works partition a dynamic graph into snapshots or temporal sequences, which only work well when the graph has uniform spatio-temporal structures. However, dynamic graphs in practice are not uniformly structured, with some snapshots being very dense while others are sparse. To address this issue, we propose DGC, a distributed DGNN training system that achieves a 1.25× - 7.52× speedup over the state-of-the-art in our testbed. DGC's success stems from a new graph partitioning method that partitions dynamic graphs into chunks, which are essentially subgraphs with modest training workloads and few inter connections. This partitioning algorithm is based on graph coarsening, which can run very fast on large graphs. In addition, DGC has a highly efficient run-time, powered by the proposed chunk fusion and adaptive stale aggregation techniques. Extensive experimental results on 3 typical DGNN models and 4 popular dynamic graph datasets are presented to show the effectiveness of DGC.
Equitable Top-k Results for Long Tail Data
Authors: Islam Md Mouinul , Asadi Mahsa , Basu Roy Senjuti
Published: December 2023

For datasets exhibiting long tail phenomenon, we identify a fairness concern in existing top-k algorithms, that return a "fixed" set of k results for a given query. This causes a handful of popular records (products, items, etc) getting overexposed and always be returned to the user query, whereas, there exists a long tail of niche records that may be equally desirable (have similar utility). To alleviate this, we propose θ-Equiv-top-k-MMSP inside existing top-k algorithms - instead of returning a fixed top-k set, it generates all (or many) top-k sets that are equivalent in utility and creates a probability distribution over those sets. The end user will be returned one of these sets during the query time proportional to its associated probability, such that, after many draws from many end users, each record will have as equal exposure as possible (governed by uniform selection probability). θ-Equiv-top-k-MMSP is formalized with two sub-problems. (a) θ-Equiv-top-k-Sets to produce a set S of sets, each set has k records, where the sets are equivalent in utility with the top-k set; (b) MaxMinFair to produce a probability distribution over S, that is, PDF(S), such that the records in S have uniform selection probability. We formally study the hardness of θ-Equiv-top-k-MMSP. We present multiple algorithmic results - (a) An exact solution for θ-Equiv-top-k-Sets, and MaxMinFair. (b) We design highly scalable algorithms that solve θ-Equiv-top-k-Sets through a random walk and is backed by probability theory, as well as a greedy solution designed for MaxMinFair. (c) We finally present an adaptive random walk based algorithm that solves θ-Equiv-top-k-Sets and MaxMinFair at the same time. We empirically study how θ-Equiv-top-k-MMSP can alleviate a equitable exposure concerns that group fairness suffers from. We run extensive experiments using 6 datasets and design intuitive baseline algorithms that corroborate our theoretical analysis.
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries
Authors: Mo Songsong , Chen Yile , Wang Hao , Cong Gao , Bao Zhifeng
Published: December 2023

With the expansion of modern database services, multi-user access has become a crucial feature in various practical application scenarios, including enterprise applications and e-commerce platforms. However, if multiple users submit queries within a short time frame, it can result in potential issues such as redundant computation and query concurrency. Unfortunately, most existing multi-query optimization methods, which aim to enhance query processing efficiency, have not adequately addressed these two problems, especially in the setting where multiple queries are being executed concurrently. To this end, we propose a novel method named Lemo for the multi-query optimization problem. Specifically, we propose a novel value network to predict latencies of concurrent queries as the foundation model for query plan generation. Furthermore, we introduce a shared buffer manager component to cache the intermediate results of sub-queries. The shared buffer manager applies a novel replacement policy to maintain the cached buffer with the objective of maximizing the opportunity for the reuse of the cached sub-queries. Based on the shared buffer, our proposed value network can incorporate the cached results into cost estimation to further guide Lemo in generating query plans, thus avoiding redundant computation. Lemo has been integrated into PostgreSQL and experiments conducted on real datasets with PostgreSQL show that it outperforms all the baselines in efficiency.
Lightweight Materialization for Fast Dashboards Over Joins
Authors: Huang Zezhou , Wu Eugene
Published: December 2023

Dashboards are vital in modern business intelligence tools, providing non-technical users with an interface to access comprehensive business data. With the rise of cloud technology, there is an increased number of data sources to provide enriched contexts for various analytical tasks, leading to a demand for interactive dashboards over a large number of joins. Nevertheless, joins are among the most expensive operations in DBMSes, making the support of interactive dashboards over joins challenging.

In this paper, we present Treant, a dashboard accelerator for queries over large joins. Treant uses factorized query execution to handle aggregation queries over large joins, which alone is still insufficient for interactive speeds. To address this, we exploit the incremental nature of user interactions using Calibrated Junction Hypertree (CJT), a novel data structure that applies lightweight materialization of the intermediates during factorized execution. CJT ensures that the work needed to compute a query is proportional to how different it is from the previous query, rather than the overall complexity. Treant manages CJTs to share work between queries and performs materialization offline or during user "think-times." Implemented as a middleware that rewrites SQL, Treant is portable to any SQL-based DBMS. Our experiments on single node and cloud DBMSes show that Treant improves dashboard interactions by two orders of magnitude, and provides 10x improvement for ML augmentation compared to SOTA factorized ML system.
MOST: Model-Based Compression with Outlier Storage for Time Series Data
Authors: Yang Zehai , Chen Shimin
Published: December 2023

Time series data are used in a wide variety of applications. The explosive growth of the amount of time series data poses a significant challenge in efficient data storage and query processing. Unfortunately, existing compression techniques either show only low to medium compression ratio on time series data, or incur significant decompression overhead during query processing.

We propose a novel compression technique, MOST (Model-based compression with Outlier STorage) for time series data. As measurement values often change smoothly in a period of time, we divide a time series into segments of smooth changes, then compute a linear model for each segment. Since tiny errors are often acceptable in analysis tasks, we omit data points whose computed values are within a pre-specified error threshold from the actual values, thereby effectively reducing the data size. Outliers are rare but important for many applications, and therefore we store outliers explicitly. Moreover, for processing MOST compressed data, we propose a segment-outlier dual-mode query engine that computes segments as a whole as much as possible, and build a prototype MostDB. Experimental results on real-world data sets show that MOST achieves 9.45-15.04x compression ratios. Compared to existing time series databases, MostDB achieves up to 11.68x speedups for common queries from the IoTDB Benchmark.
Rethink Query Optimization in HTAP Databases
Authors: Song Haoze , Zhou Wenchao , Li Feifei , Peng Xiang , Cui Heming
Published: December 2023

The advent of data-intensive applications has fueled the evolution of hybrid transactional and analytical processing (HTAP). To support mixed workloads, distributed HTAP databases typically maintain two data copies that are specially tailored for data freshness and performance isolation. In particular, a copy in a row-oriented format is well-suited for OLTP workloads, and a second copy in a column-oriented format is optimized for OLAP workloads. Such a hybrid design opens up a new design space for query optimization: plans can be optimized over different data formats and can be executed over isolated resources, which we term hybrid plans. In this paper, we demonstrate that hybrid plans can largely benefit query execution (e.g., up to 11x speedups in our evaluation). However, we also found these benefits will potentially be at the cost of sacrificing data freshness or performance isolation since traditional optimizers may not precisely model and schedule the execution of hybrid plans on real-time updated HTAP databases. Therefore, we propose Metis, an HTAP-aware optimizer. We show, both theoretically and experimentally, that using the proposed optimizations, a system can largely benefit from hybrid plans while preserving isolated performance for OLTP and OLAP, and these optimizations are robust to the changes in workloads.
Veil: A Storage and Communication Efficient Volume-Hiding Algorithm
Authors: Han Shanshan , Chakraborty Vishal , Goodrich Michael T. , Mehrotra Sharad , Sharma Shantanu
Published: December 2023

This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent volume leakage, entitled Veil, that partitions the dataset by randomly mapping keys to a set of equi-sized buckets. Veil provides a tunable mechanism for data owners to explore a trade-off between storage and communication overheads. To make buckets indistinguishable to the adversary, Veil uses a novel padding strategy that allow buckets to overlap, reducing the need to add fake records. Both theoretical and experimental results show Veil to significantly outperform existing state-of-the-art.
R2D2: Reducing Redundancy and Duplication in Data Lakes
Authors: Shah Raunak , Mukherjee Koyel , Tyagi Atharv , Karnam Sai Keerthana , Joshi Dhruv , Bhosale Shivam Pravin , Mitra Subrata
Published: December 2023

Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for these datasets. In this work, we focus on identifying and reducing redundancy in enterprise data lakes by addressing the problem of "dataset containment". To the best of our knowledge, this is one of the first works that addresses table-level containment at a large scale.

We propose R2D2: a three-step hierarchical pipeline that efficiently identifies almost all instances of containment by progressively reducing the search space in the data lake. It first builds (i) a schema containment graph, followed by (ii) statistical min-max pruning, and finally, (iii) content level pruning. We further propose minimizing the total storage and access costs by optimally identifying redundant datasets that can be deleted (and reconstructed on demand) while respecting latency constraints.

We implement our system on Azure Databricks clusters using Apache Spark for enterprise data stored in ADLS Gen2, and on AWS clusters for open-source data. In contrast to existing modified baselines that are inaccurate or take several days to run, our pipeline can process an enterprise customer data lake at the TB scale in approximately 5 hours with high accuracy. We present theoretical results as well as extensive empirical validation on both enterprise (scale of TBs) and open-source datasets (scale of MBs - GBs), which showcase the effectiveness of our pipeline.
Splitting Tuples of Mismatched Entities
Authors: Fan Wenfei , Han Ziyan , Ren Weilong , Wang Ding , Wang Yaoshu , Xie Min , Yan Mengyi
Published: December 2023

There has been a host of work on entity resolution (ER), to identify tuples that refer to the same entity. This paper studies the inverse of ER, to identify tuples to which distinct real-world entities are matched by mistake, and split such tuples into a set of tuples, one for each entity. We formulate the tuple splitting problem. We propose a scheme to decide what tuples to split and what tuples to correct without splitting, fix errors/assign attribute values to the split tuples, and impute missing values. The scheme introduces a class of rules, which embed predicates for aligning entities across relations and knowledge graphs G, assessing correlation between attributes, and extracting data from G. It unifies logic deduction, correlation models, and data extraction by chasing the data with the rules. We train machine learning models to assess attribute correlation and predict missing values. We develop algorithms for the tuple splitting scheme. Using real-life data, we empirically verify that the scheme is efficient and accurate, with F-measure 0.92 on average.
Homomorphic Compression: Making Text Processing on Compression Unlimited
Authors: Guan Jiawei , Zhang Feng , Ma Siqi , Chen Kuangyu , Hu Yihua , Chen Yuxing , Pan Anqun , Du Xiaoyong
Published: December 2023

Lossless data compression is an effective way to handle the huge transmission and storage overhead of massive text data. Its utility is even more significant today when data volumes are skyrocketing. The concept of operating on compressed data infuses new blood into efficient text management by enabling mainly access-oriented text processing tasks to be done directly on compressed data without decompression. Facing limitations of the existing compressed text processing schemes such as limited types of operations supported, low efficiency, and high space occupation, we address these problems by proposing a homomorphic compression theory. It enables the generalization and characterization of algorithms with compression processing capabilities. On this basis, we develop HOCO, an efficient text data management engine that supports a variety of processing tasks on compressed text. We select three representative compression schemes and implement them combined with homomorphism in HOCO. HOCO supports the extension of homomorphic compression schemes through a modular and object-oriented design and has convenient interfaces for text processing tasks. We evaluate HOCO on six real-world datasets. The three schemes implemented in HOCO show trade-offs in terms of compression ratio, supported operation types, and efficiency. Experiments also show that HOCO can achieve higher throughput in random access and modification operations (averagely 9.18× than the state-of-the-art) and lower latency in text analytic tasks (averagely 7.16× than processing on uncompressed text) without compromising compression efficacy.
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation
Authors: Miao Zhengjie , Wang Jin
Published: December 2023

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite recent efforts to improve different tasks in table understanding by using the power of large pre-trained language models, existing methods heavily rely on large-scale and high-quality labeled instances, while they still suffer from the data sparsity problem due to the imbalanced data distribution among different classes. In this paper, we propose the Watchog framework, which employs contrastive learning techniques to learn robust representations for tables by leveraging a large-scale unlabeled table corpus with minimal overhead. Our approach enables the learned table representations to enhance fine tuning with much fewer additional labeled instances than in prior studies for downstream column annotation tasks. Besides, we further proposed optimization techniques for semi-supervised settings. Experimental results on popular benchmarking datasets illustrate the superiority of our proposed techniques in two column annotation tasks under different settings. In particular, our Watchog framework effectively alleviates the class imbalance issue caused by a long-tailed label distribution. In the semi-supervised setting, Watchog outperforms the best-known method by up to 26% and 41% in Micro and Macro F1 scores, respectively, on the task of semantic type detection.
POMACS V7, N3, December 2023 Editorial
Authors: Ciucu Florin , Fanti Giulia , Righter Rhonda
Published: December 2023

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS/Performance 2024 conference on June 10-14, 2024, in Venice, Italy. These papers have been selected during the summer submission round by the 84 members of the ACM SIGMETRICS/Performance 2024 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two SIGMETRICS/Performance deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the summer deadline, POMACS is publishing 20 papers out of 91 submissions, of which 11 had previously received a one-shot revision decision. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 27% of the submissions were in the Theory track, 46% were in the Measurement & Applied Modeling track, 41% were in the Systems track, and 14% were in the Learning track. Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/Performance/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinions on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Mor Harchol-Balter, the IFIP Working Group 7.3 Chair, Mark S. Squillante, the previous SIGMETRICS Board Chair, Giuliano Casale, and the past program committee Chairs, Konstantin Avratchenkov, Phillipa Gill, and Bhuvan Urgaonkar, who provided a wealth of information and guidance. Finally, we are grateful to the organization committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2024.
Agents of Autonomy: A Systematic Study of Robotics on Modern Hardware
Authors: Bakhshalipour Mohammad , Gibbons Phillip B.
Published: December 2023

As robots increasingly permeate modern society, it is crucial for the system and hardware research community to bridge its long-standing gap with robotics. This divide has persisted due to the lack of (i) a systematic performance evaluation of robotics on different computing platforms and (ii) a comprehensive, open-source, cross-platform benchmark suite.

To address these gaps, we present a systematic performance study of robotics on modern hardware and introduce RoWild, an open-source benchmark suite for robotics that is comprehensive and cross-platform. Our workloads encompass a broad range of robots, including driverless vehicles, pilotless drones, and stationary robotic arms, and we evaluate their performance on a spectrum of modern computing platforms, from low-end embedded CPUs to high-end server-grade GPUs. The source code of the benchmark suite is available in https://cmu-roboarch.github.io/rowild/.

Our findings reveal that current architectures experience significant inefficiencies when executing robotic workloads, highlighting the need for architectural advancements that satisfy the primary requirements of robotic tasks. We discuss approaches for meeting these requirements, offering insights for improving the performance of robotics.
A Hop Away from Everywhere: A View of the Intercontinental Long-haul Infrastructure
Authors: Carisimo Esteban , Wang Caleb J. , Weaver Mia , Bustamante Fabián E. , Barford Paul
Published: December 2023

We present a longitudinal study of intercontinental long-haul links (LHLs) - links with latencies significantly higher than that of all other links in a traceroute path. Our study is motivated by the recognition of these LHLs as a network-layer manifestation of critical transoceanic undersea cables. We present a methodology and associated processing system for identifying long-haul links in traceroute measurements. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. We identify 85,620 layer-3 links (out of 2.7M links in a large traceroute dataset) that satisfy our definition for intercontinental long haul with many of them terminating in a relatively small number of nodes. An analysis of connected components shows a clearly dominant component with a relative size that remains stable despite a significant growth of the long-haul infrastructure.
Miracle or Mirage? A Measurement Study of NFT Rug Pulls
Authors: Huang Jintao , He Ningyu , Ma Kai , Xiao Jiang , Wang Haoyu
Published: December 2023

NFT rug pull is one of the most prominent type of NFT scam, whose definition is that the developers of an NFT project abandon it and run away with investors' funds. Although they have drawn attention from our community, to the best of our knowledge, the NFT rug pulls have not been systematically measured. To fill the void, this paper presents the first in-depth measurement study of NFT rug pulls. Specifically, we first compile a list of 253 known NFT rug pulls as our initial confirmed rug pulls (i.e., ground truth), based on which we perform a pilot study, highlighting the key symptoms of NFT rug pulls. Then, we design an effective rule-based detector to measure the prevalence of NFT rug pulls in the ecosystem. We have labelled 7,487 happened NFT rug pull projects which were not revealed by our community. To eliminate the potential damage brought by rug pull scams, we take a step further towards designing a real-time prediction model to proactively identify the potential rug pull projects in an early stage ahead of the scam happens. We have implemented a prototype system, and deployed it in the real-world setting for over 6 months. Our system has raised alarms for 5,557 new NFT projects by the time of this writing, which works as a whistle blower that pinpoints rug pull scams timely, thus mitigating the impacts.
CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency
Authors: Hanafy Walid A. , Liang Qianlin , Bashir Noman , Irwin David , Shenoy Prashant
Published: December 2023

Cloud platforms are increasing their emphasis on sustainability and reducing their operational carbon footprint. A common approach for reducing carbon emissions is to exploit the temporal flexibility inherent to many cloud workloads by executing them in periods with the greenest energy and suspending them at other times. Since such suspend-resume approaches can incur long delays in job completion times, we present a new approach that exploits the elasticity of batch workloads in the cloud to optimize their carbon emissions. Our approach is based on the notion of "carbon scaling," similar to cloud autoscaling, where a job dynamically varies its server allocation based on fluctuations in the carbon cost of the grid's energy. We develop a greedy algorithm for minimizing a job's carbon emissions via carbon scaling that is based on the well-known problem of marginal resource allocation. We implement a CarbonScaler prototype in Kubernetes using its autoscaling capabilities and an analytic tool to guide the carbon-efficient deployment of batch applications in the cloud. We then evaluate CarbonScaler using real-world machine learning training and MPI jobs on a commercial cloud platform and show that it can yield i) 51% carbon savings over carbon-agnostic execution; ii) 37% over a state-of-the-art suspend-resume policy; and iii) 8 over the best static scaling policy.
Optimized Cross-Path Attacks via Adversarial Reconnaissance
Authors: Huang Yudi , Lin Yilei , He Ting
Published: December 2023

While softwarization and virtualization technologies make modern communication networks appear easier to manage, they also introduce highly complex interactions within the networks that can cause unexpected security threats. In this work, we study a particular security threat due to the sharing of links between high-security paths and low-security paths, which enables a new type of DoS attacks, called cross-path attacks, that indirectly attack a set of targeted high-security paths (target paths) by congesting the shared links through a set of attacker-controlled low-security paths (attack paths). While the feasibility of such attacks has been recently demonstrated in the context of SDN, their potential performance impact has not been characterized. To this end, we develop an approach for designing an optimized cross-path attack under a constrained total attack rate, consisting of (i) novel reconnaissance algorithms that can provide consistent estimates of the locations and parameters of the shared links via network tomography, and (ii) efficient optimization methods to design the optimal allocation of attack rate over the attack paths to maximally degrade the performance of the target paths. The proposed attack has achieved a significantly larger performance impact than its non-optimized counterparts in extensive evaluations based on multiple network settings, signaling the importance of addressing such intelligent attacks in network design.
Anoctamin-1 is induced by TGF-β and contributes to lung myofibroblast differentiation
Authors: Reed Eleanor B. , Orbeta Shaina , Miao Bernadette A. , Sitikov Albert , Chen Bohao , Levitan Irena , Solway Julian , Mutlu Gökhan M. , Fang Yun , Mongin Alexander A. , Dulin Nickolai O.
Published: December 2023

This study describes a novel mechanism of differentiation of human lung fibroblasts (HLFs) to myofibroblasts: the key process in the pathogenesis of pulmonary fibrosis. Transforming growth factor-β (TGF-β) drives the expression of calcium-activated chloride channel anoctmin-1 (ANO1) leading to an increase in intracellular levels of chloride. The latter recruits chloride-sensitive with-no-lysine (K) kinase (WNK1) to activate profibrotic RhoA and AKT signaling pathways, possibly through activation of mammalian target of rapamycin complex-2 (mTORC2), altogether promoting myofibroblast differentiation.
Molecular mechanisms underlying TNFα-induced mitochondrial fragmentation in human airway smooth muscle cells
Authors: Dasgupta Debanjali , Mahadev Bhat Sanjana , Creighton Claire , Cortes Catherin , Delmotte Philippe , Sieck Gary C.
Published: December 2023

Airway inflammation is increasing worldwide. Proinflammatory cytokines mediate an adaptive mechanism to overcome inflammation-induced cellular stress. Previously, we reported that TNFα mediates hASM cellular responses, leading to increased force and ATP consumption associated with increased O2 consumption, and oxidative stress. This study indicates that TNFα induces ER stress, which induces mitochondrial fragmentation via pIRE1αS724/XBP1s mediated CDK1/5 upregulation and pDRP1S616 phosphorylation. Mitochondrial fragmentation may promote hASM mitochondrial biogenesis to maintain healthy mitochondrial pool.
Single-cell resolution of human airway epithelial cells exposed to bronchiolitis obliterans-associated chemicals
Authors: Chu Chin-Yi , Kim So-Young , Pryhuber Gloria S. , Mariani Thomas J. , McGraw Matthew D.
Published: December 2023

Bronchiolitis obliterans (BO) is a devastating fibrotic lung disease of the small airways, or bronchioles. This original manuscript uses single-cell RNA sequencing for identifying common signatures of chemically exposed airway epithelial cells in BO induction. Chemical exposure reduced the proportion of keratin 5+ basal cells while increasing the proportion of keratin 4+ suprabasal cells. Functional pathways contributory to these shifts differed significantly across exposures. These new results highlight similarities and differences in BO induction across exposures.
Topographic Effects on the Seismic Response of Trapezoidal Canyons Subjected to Obliquely Incident SV Waves
Authors: Shen Hui , Liu Yaqun , Li Haibo , Liu Bo
Published: December 2023

The topography and the incident angle of seismic waves both have considerable effects on the seismic ground motions of canyons in a half-space. In this paper, the theory of wavefield decomposition and the artificial boundary is used to develop a method for inputting obliquely incident SV waves. Formulas for the equivalent nodal forces applied to the truncated boundary are derived and implemented in the finite element method. The validity of the proposed method is verified by a test case. A parametric study is then performed to investigate the influence of canyon geometry and incident angle of SV waves on the seismic response of trapezoidal canyons. The numerical results indicate that the canyon inclination has a more significant effect on the ground motion amplification than its height and width. The amplification effects are strongly related to the canyon inclination and the incident angle of SV waves. Additionally, the dominant frequency corresponding to the acceleration of the canyon crests is not sensitive to the incident angle of SV waves.
Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data
Authors: Wang Junhua , Li Chengmin , Fu Ting , Zhang Lanfang , Sobhani Anae , Xue Jiangtian
Published: December 2023

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.
Supplementary Table S5 from Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression
Authors: Hsu Wen-Hao , LaBella Kyle A. , Lin Yiyun , Xu Ping , Lee Rumi , Hsieh Cheng-En , Yang Lei , Zhou Ashley , Blecher Jonathan M. , Wu Chang-Jiun , Lin Kangyu , Shang Xiaoying , Jiang Shan , Spring Denise J. , Xia Yan , Chen Peiwen , Shen John Paul , Kopetz Scott , DePinho Ronald A.
Published: December 2023

Supplementary Table S5 shows the human CRC clinical information.
Supplementary Table S2 from Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression
Authors: Hsu Wen-Hao , LaBella Kyle A. , Lin Yiyun , Xu Ping , Lee Rumi , Hsieh Cheng-En , Yang Lei , Zhou Ashley , Blecher Jonathan M. , Wu Chang-Jiun , Lin Kangyu , Shang Xiaoying , Jiang Shan , Spring Denise J. , Xia Yan , Chen Peiwen , Shen John Paul , Kopetz Scott , DePinho Ronald A.
Published: December 2023

Supplementary Table S2 includes Lipid-rich CAFs gene signature
Supplementary Figure S7 from Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression
Authors: Hsu Wen-Hao , LaBella Kyle A. , Lin Yiyun , Xu Ping , Lee Rumi , Hsieh Cheng-En , Yang Lei , Zhou Ashley , Blecher Jonathan M. , Wu Chang-Jiun , Lin Kangyu , Shang Xiaoying , Jiang Shan , Spring Denise J. , Xia Yan , Chen Peiwen , Shen John Paul , Kopetz Scott , DePinho Ronald A.
Published: December 2023

Figure S7 shows the replicates of staining images.
Supplementary Figure S5 from Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression
Authors: Hsu Wen-Hao , LaBella Kyle A. , Lin Yiyun , Xu Ping , Lee Rumi , Hsieh Cheng-En , Yang Lei , Zhou Ashley , Blecher Jonathan M. , Wu Chang-Jiun , Lin Kangyu , Shang Xiaoying , Jiang Shan , Spring Denise J. , Xia Yan , Chen Peiwen , Shen John Paul , Kopetz Scott , DePinho Ronald A.
Published: December 2023

Figure S5 demonstrates how KRAS* controls pro-adipogenesis cytokines
via TFCP2.
Supplementary Figure S3 from Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression
Authors: Hsu Wen-Hao , LaBella Kyle A. , Lin Yiyun , Xu Ping , Lee Rumi , Hsieh Cheng-En , Yang Lei , Zhou Ashley , Blecher Jonathan M. , Wu Chang-Jiun , Lin Kangyu , Shang Xiaoying , Jiang Shan , Spring Denise J. , Xia Yan , Chen Peiwen , Shen John Paul , Kopetz Scott , DePinho Ronald A.
Published: December 2023

Figure S3 demonstrates the upregulation of proadipogenic cytokines by Kras* and its role in driving lipo-fibrogenesis.
Supplementary Table S4 from Structural Basis of PML-RARA Oncoprotein Targeting by Arsenic Unravels a Cysteine Rheostat Controlling PML Body Assembly and Function
Authors: Bercier Pierre , Wang Qian Qian , Zang Ning , Zhang Jie , Yang Chang , Maimaitiyiming Yasen , Abou-Ghali Majdouline , Berthier Caroline , Wu Chengchen , Niwa-Kawakita Michiko , Dirami Thassadite , Geoffroy Marie-Claude , Ferhi Omar , Quentin Samuel , Benhenda Shirine , Ogra Yasumitsu , Gueroui Zoher , Zhou Chun , Naranmandura Hua , de Thé Hugues , Lallemand-Breitenbach Valérie
Published: December 2023

Table displaying the information regarding the data collection and refinement statistics for the crystal structures of MBP-B2 and MBP-B2 C213A-A216V
Supplementary Movie S1 from Structural Basis of PML-RARA Oncoprotein Targeting by Arsenic Unravels a Cysteine Rheostat Controlling PML Body Assembly and Function
Authors: Bercier Pierre , Wang Qian Qian , Zang Ning , Zhang Jie , Yang Chang , Maimaitiyiming Yasen , Abou-Ghali Majdouline , Berthier Caroline , Wu Chengchen , Niwa-Kawakita Michiko , Dirami Thassadite , Geoffroy Marie-Claude , Ferhi Omar , Quentin Samuel , Benhenda Shirine , Ogra Yasumitsu , Gueroui Zoher , Zhou Chun , Naranmandura Hua , de Thé Hugues , Lallemand-Breitenbach Valérie
Published: December 2023

Supplementary movie 1 shows PML NBs fusion in MEFs, where two PML NBs undergo complete fusion and relaxation into one body
Supplementary Figure 15 from Formate Supplementation Enhances Antitumor CD8+ T-cell Fitness and Efficacy of PD-1 Blockade
Authors: Rowe Jared H. , Elia Ilaria , Shahid Osmaan , Gaudiano Emily F. , Sifnugel Natalia E. , Johnson Sheila , Reynolds Amy G. , Fung Megan E. , Joshi Shakchhi , LaFleur Martin W. , Park Joon Seok , Pauken Kristen E. , Rabinowitz Joshua D. , Freeman Gordon J. , Haigis Marcia C. , Sharpe Arlene H.
Published: December 2023

Supplementary Figure 15 shows the effects of formate treatment on TCF1 and TIM3 expression in tumor infiltrating "bystander cells."
Supplementary Figure 12 from Formate Supplementation Enhances Antitumor CD8+ T-cell Fitness and Efficacy of PD-1 Blockade
Authors: Rowe Jared H. , Elia Ilaria , Shahid Osmaan , Gaudiano Emily F. , Sifnugel Natalia E. , Johnson Sheila , Reynolds Amy G. , Fung Megan E. , Joshi Shakchhi , LaFleur Martin W. , Park Joon Seok , Pauken Kristen E. , Rabinowitz Joshua D. , Freeman Gordon J. , Haigis Marcia C. , Sharpe Arlene H.
Published: December 2023

Supplementary Figure 12 shows single cell RNA sequencing data for changes in metabolic pathway transcriptional activity in tumor infiltrating CD8+ T cells from formate versus control mice receiving isotype control antibodies.
Supplementary Figure S3 from Structural Basis of PML-RARA Oncoprotein Targeting by Arsenic Unravels a Cysteine Rheostat Controlling PML Body Assembly and Function
Authors: Bercier Pierre , Wang Qian Qian , Zang Ning , Zhang Jie , Yang Chang , Maimaitiyiming Yasen , Abou-Ghali Majdouline , Berthier Caroline , Wu Chengchen , Niwa-Kawakita Michiko , Dirami Thassadite , Geoffroy Marie-Claude , Ferhi Omar , Quentin Samuel , Benhenda Shirine , Ogra Yasumitsu , Gueroui Zoher , Zhou Chun , Naranmandura Hua , de Thé Hugues , Lallemand-Breitenbach Valérie
Published: December 2023

Figure S3 shows that PML B2 trimer controls NB distribution and dynamics ranging from a liquid-like bodies to filaments
Supplementary Figure S2 from Structural Basis of PML-RARA Oncoprotein Targeting by Arsenic Unravels a Cysteine Rheostat Controlling PML Body Assembly and Function
Authors: Bercier Pierre , Wang Qian Qian , Zang Ning , Zhang Jie , Yang Chang , Maimaitiyiming Yasen , Abou-Ghali Majdouline , Berthier Caroline , Wu Chengchen , Niwa-Kawakita Michiko , Dirami Thassadite , Geoffroy Marie-Claude , Ferhi Omar , Quentin Samuel , Benhenda Shirine , Ogra Yasumitsu , Gueroui Zoher , Zhou Chun , Naranmandura Hua , de Thé Hugues , Lallemand-Breitenbach Valérie
Published: December 2023

Figure S2 shows Crystal structure of MBP-B2 fusion protein, and the similarity between the 3D structure of PML B2 and other TRIM proteins, yet its formation relies on specific amino acids
Supplementary Figure S6 from p53 Gain-of-Function Mutation Induces Metastasis via BRD4-Dependent CSF-1 Expression
Authors: Efe Gizem , Dunbar Karen J. , Sugiura Kensuke , Cunningham Katherine , Carcamo Saul , Karaiskos Spyros , Tang Qiaosi , Cruz-Acuña Ricardo , Resnick-Silverman Lois , Peura Jessica , Lu Chao , Hasson Dan , Klein-Szanto Andres J. , Taylor Alison M. , Manfredi James J. , Prives Carol , Rustgi Anil K.
Published: December 2023

Supplementary Figure S6. Graphical abstract depicting the pro-metastatic role and mechanism of Trp53R172H-dependent Csf-1/Csf-1r signaling.
Supplementary Figure S3 from p53 Gain-of-Function Mutation Induces Metastasis via BRD4-Dependent CSF-1 Expression
Authors: Efe Gizem , Dunbar Karen J. , Sugiura Kensuke , Cunningham Katherine , Carcamo Saul , Karaiskos Spyros , Tang Qiaosi , Cruz-Acuña Ricardo , Resnick-Silverman Lois , Peura Jessica , Lu Chao , Hasson Dan , Klein-Szanto Andres J. , Taylor Alison M. , Manfredi James J. , Prives Carol , Rustgi Anil K.
Published: December 2023

Supplementary Figure S3. Stat3 and EMT pathways are enriched in Trp53R172H- dependent metastasis.
Data from Formate Supplementation Enhances Antitumor CD8+ T-cell Fitness and Efficacy of PD-1 Blockade
Authors: Rowe Jared H. , Elia Ilaria , Shahid Osmaan , Gaudiano Emily F. , Sifnugel Natalia E. , Johnson Sheila , Reynolds Amy G. , Fung Megan E. , Joshi Shakchhi , LaFleur Martin W. , Park Joon Seok , Pauken Kristen E. , Rabinowitz Joshua D. , Freeman Gordon J. , Haigis Marcia C. , Sharpe Arlene H.
Published: December 2023

Abstract
The tumor microenvironment (TME) restricts antitumor CD8+ T-cell function and immunotherapy responses. Cancer cells compromise the metabolic fitness of CD8+ T cells within the TME, but the mechanisms are largely unknown. Here we demonstrate that one-carbon (1C) metabolism is enhanced in T cells in an antigen-specific manner. Therapeutic supplementation of 1C metabolism using formate enhances CD8+ T-cell fitness and antitumor efficacy of PD-1 blockade in B16-OVA tumors. Formate supplementation drives transcriptional alterations in CD8+ T-cell metabolism and increases gene signatures for cellular proliferation and activation. Combined formate and anti–PD-1 therapy increases tumor-infiltrating CD8+ T cells, which are essential for enhanced tumor control. Our data demonstrate that formate provides metabolic support to CD8+ T cells reinvigorated by anti–PD-1 to overcome a metabolic vulnerability in 1C metabolism in the TME to further improve T-cell function.
Significance:
This study identifies that deficiencies in 1C metabolism limit the efficacy of PD-1 blockade in B16-OVA tumors. Supplementing 1C metabolism with formate during anti–PD-1 therapy enhances CD8+ T-cell fitness in the TME and CD8+ T-cell–mediated tumor clearance. These findings demonstrate that formate supplementation can enhance exhausted CD8+ T-cell function.

See related commentary by Lin et al., p. 2507.

This article is featured in Selected Articles from This Issue, p. 2489