arXiv subjects

Qichen Wang

Publications and source records attributed to Qichen Wang.

15 recordsLinked to original sources

arXiv2026

Rerootable Hypertree Decompositions

Hypertree decompositions are a cornerstone in the theory of answering conjunctive queries efficiently. However, they are not yet widely adopted in practice. Problems related to, e.g., the uniqueness of decompositions and succinct representations of all decompositions have so far mostly been neglected by the theory literature. In this paper, we present the first in-depth discussion of rerootability in hypertree decompositions---a property which we argue is essential for such problems. Rerootability leads us to projection-freeness, and we have to discuss normal form to recover tractability. Normal form, however, again obstructs rerootability, and for this reason, we define a relaxed notion of normal form which leads to a truly rerootable and tractable class. Experimental evidence suggests that the price we pay in terms of width increase for transitioning to this class of decompositions is moderate in practice.

cs.DB↗

arXiv2026

Answering Conjunctive Queries with Aggregations under Updates

Dynamic query processing keeps query answers up to date during insertions and deletions. For conjunctive queries (CQs) under set semantics, the maintainable classes are known exactly: the $q$-hierarchical CQs under arbitrary updates, widening to the free-connex CQs under insertion-only updates. But modern analytics aggregates, including bag counting, SUM/COUNT, provenance, access control, and shortest paths---all captured by evaluating a CQ over a positive commutative semiring. We ask whether aggregation changes what can be maintained efficiently, and if so, when. Under arbitrary updates, it does not: maintenance is at least as hard as over the Boolean semiring. Under insertion-only updates, it does: the boundary retreats from free-connex to a new class we call strong-connex, with $q$-hierarchical $\subsetneq$ strong-connex $\subsetneq$ free-connex $\subsetneq$ acyclic. For every ordered semiring carrying a suitable monotone sequence (e.g., sum-product and tropical), no free-connex but non-strong-connex CQ is maintainable in $O(|D|^{1/2-\epsilon})$ time under the OuMv and OMv conjectures. We further strengthen this lower bound into a family parameterized by the height and dimension of the query, under the combinatorial $k$-clique and generalized OuMv conjectures; these quantify how far the annotated hardness grows as the queries scale. On the algorithmic side, a single framework matches these boundaries by adapting CROWN to annotated relations. It maintains every strong-connex CQ in $O(1)$ amortized time under insertion-only updates, regardless of the underlying semiring. Moreover, under arbitrary updates, it maintains every $q$-hierarchical CQ in $O(1)$ amortized time if the semiring has $O(1)$-deletable aggregations. Together, the upper and lower bounds give query- and semiring-parameterized dichotomies that recover the Boolean picture and pinpoint the hardness aggregation adds.

cs.DB↗

arXiv2026

Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose a multi-agent deep reinforcement learning (MADRL) algorithm that enables each access point (AP) to autonomously control antenna re-configuration and advanced sleep mode (ASM) selection. After the training process, the proposed framework operates in a fully distributed manner, eliminating the need for centralized control and allowing each AP to dynamically adjust to real-time traffic fluctuations. Simulation results show that the proposed algorithm reduces power consumption (PC) by 56.23% compared to systems without any energy-saving scheme and by 30.12% relative to a non-learning mechanism that only utilizes the lightest sleep mode, with only a slight increase in drop ratio. Moreover, compared to the widely used deep Q-network (DQN) algorithm, it achieves a similar PC level but with a significantly lower drop ratio.

cs.IT↗

arXiv2026

Towards Parameterized Hardness on Maintaining Conjunctive Queries

We investigate the fine-grained complexity of dynamically maintaining the result of fixed self-join free conjunctive queries under single-tuple updates. Prior work shows that free-connex queries can be maintained in update time $O(|D|^{\delta})$ for some $\delta \in [0.5, 1]$, where $|D|$ is the size of the current database. However, a gap remains between the best known upper bound of $O(|D|)$ and lower bounds of $\Omega(|D|^{0.5-\epsilon})$ for any $\epsilon \ge 0$. We narrow this gap by introducing two structural parameters to quantify the dynamic complexity of a conjunctive query: the height $k$ and the dimension $d$. We establish new fine-grained lower bounds showing that any algorithm maintaining a query with these parameters must incur update time $\Omega(|D|^{1-1/\max(k,d)-\epsilon})$, unless widely believed conjectures fail. These yield the first super-$\sqrt{|D|}$ lower bounds for maintaining free-connex queries, and suggest the tightness of current algorithms when considering arbitrarily large $k$ and~$d$. Complementing our lower bounds, we identify a data-dependent parameter, the generalized $H$-index $h(D)$, which is upper bounded by $|D|^{1/d}$, and design an efficient algorithm for maintaining star queries, a common class of height 2 free-connex queries. The algorithm achieves an instance-specific update time $O(h(D)^{d-1})$ with linear space $O(|D|)$. This matches our parameterized lower bound and provides instance-specific performance in favorable cases.

cs.DB↗

arXiv2026

Size Bound-Adorned Datalog

We introduce EDB-bounded datalog, a framework for deriving upper bounds on intermediate result sizes and the asymptotic complexity of recursive queries in datalog. We present an algorithm that, given an arbitrary datalog program, constructs an EDB-bounded datalog program in which every rule is adorned with a (non-recursive) conjunctive query that subsumes the result of the rule, thus acting as an upper bound. From such adornments, we define a notion of width based on (integral or fractional) edge-cover widths. Through the adornments and the width measure, we obtain, for every IDB predicate, worst-case upper bounds on their sizes, which are polynomial in the input data size, given a fixed program structure. Furthermore, with these size bounds, we also derive fixed-parameter tractable, output-sensitive asymptotic complexity bounds for evaluating the entire program. Additionally, by adapting our framework, we obtain a semi-decision procedure for datalog boundedness that efficiently rewrites most practical bounded programs into non-recursive equivalent programs.

cs.DB↗

arXiv2026

Succinct Structure Representations for Efficient Query Optimization

Structural decomposition methods offer powerful theoretical guarantees for join evaluation, yet they are rarely used in real-world query optimizers. A major reason is the difficulty of combining cost-based plan search and structure-based evaluation. In this work, we bridge this gap by introducing meta-decompositions for acyclic queries, a novel representation that succinctly represents all possible join trees and enables their efficient enumeration. Meta-decompositions can be constructed in polynomial time and have sizes linear in the query size. We design an efficient polynomial-time cost-based optimizer based directly on the meta-decomposition, without the need to explicitly enumerate all possible join trees. We characterize plans found by this approach using a novel notion of width, which effectively implies the theoretical worst-case asymptotic bounds of intermediate result sizes and running time of any query plan. Experimental results demonstrate that, in practice, the plans in our class are consistently comparable to---even in many cases better than---the optimal ones found by the state-of-the-art dynamic programming approach, especially on large and complex queries, while our planning process runs by orders of magnitude faster, comparable to the time taken by common heuristic methods.

cs.DB↗

arXiv2026

A Methodological Analysis of Empirical Studies in Quantum Software Testing

In quantum software engineering (QSE), quantum software testing (QST) has attracted increasing attention as quantum software systems grow in scale and complexity. Since QST evaluates quantum programs through execution under designed test inputs, empirical studies are widely used to assess the effectiveness of testing approaches. However, the design and reporting of empirical studies in QST remain highly diverse, and a shared methodological understanding has yet to emerge, making it difficult to interpret results and compare findings across studies. This paper presents a methodological analysis of empirical studies in QST through a systematic examination of 59 primary studies identified from a literature pool of size 384. We organize our analysis around ten research questions that cover key methodological dimensions of QST empirical studies, including objects under test, baseline comparison, testing setup, experimental configuration, and tool and artifact support. Through cross-study analysis along these dimensions, we characterize current empirical practices in QST, identify recurring limitations and inconsistencies, and highlight open methodological challenges. Based on our findings, we derive insights and recommendations to inform the design, execution, and reporting of future empirical studies in QST.

quant-ph↗

arXiv2025

Database Theory in Action: Yannakakis' Algorithm

Yannakakis' seminal algorithm is optimal for acyclic joins, yet it has not been widely adopted due to its poor performance in practice. This paper briefly surveys recent advancements in making Yannakakis' algorithm more practical, in terms of both efficiency and ease of implementation, and points out several avenues for future research.

cs.DB↗

arXiv2025

AR2: Attention-Guided Repair for the Robustness of CNNs Against Common Corruptions

Deep neural networks suffer from significant performance degradation when exposed to common corruptions such as noise, blur, weather, and digital distortions, limiting their reliability in real-world applications. In this paper, we propose AR2 (Attention-Guided Repair for Robustness), a simple yet effective method to enhance the corruption robustness of pretrained CNNs. AR2 operates by explicitly aligning the class activation maps (CAMs) between clean and corrupted images, encouraging the model to maintain consistent attention even under input perturbations. Our approach follows an iterative repair strategy that alternates between CAM-guided refinement and standard fine-tuning, without requiring architectural changes. Extensive experiments show that AR2 consistently outperforms existing state-of-the-art methods in restoring robustness on standard corruption benchmarks (CIFAR-10-C, CIFAR-100-C and ImageNet-C), achieving a favorable balance between accuracy on clean data and corruption robustness. These results demonstrate that AR2 provides a robust and scalable solution for enhancing model reliability in real-world environments with diverse corruptions.

cs.CV↗

arXiv2025

Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees

Acyclic conjunctive queries form the backbone of most analytical workloads, and have been extensively studied in the literature from both theoretical and practical angles. However, there is still a large divide between theory and practice. While the 40-year-old Yannakakis algorithm has strong theoretical running time guarantees, it has not been adopted in real systems due to its high hidden constant factor. In this paper, we strive to close this gap by proposing Yannakakis+, an improved version of the Yannakakis algorithm, which is more practically efficient while preserving its theoretical guarantees. Our experiments demonstrate that Yannakakis+ consistently outperforms the original Yannakakis algorithm by 2x to 5x across a wide range of queries and datasets. Another nice feature of our new algorithm is that it generates a traditional DAG query plan consisting of standard relational operators, allowing Yannakakis+ to be easily plugged into any standard SQL engine. Our system prototype currently supports four different SQL engines (DuckDB, PostgreSQL, SparkSQL, and AnalyticDB from Alibaba Cloud), and our experiments show that Yannakakis+ is able to deliver better performance than their native query plans on 160 out of the 162 queries tested, with an average speedup of 2.41x and a maximum speedup of 47,059x.

cs.DB↗

arXiv2025

ClaimTrust: Propagation Trust Scoring for RAG Systems

The rapid adoption of retrieval-augmented generation (RAG) systems has revolutionized large-scale content generation but has also highlighted the challenge of ensuring trustworthiness in retrieved information. This paper introduces ClaimTrust, a propagation-based trust scoring framework that dynamically evaluates the reliability of documents in a RAG system. Using a modified PageRank-inspired algorithm, ClaimTrust propagates trust scores across documents based on relationships derived from extracted factual claims. We preprocess and analyze 814 political news articles from Kaggle's Fake News Detection Dataset to extract 2,173 unique claims and classify 965 meaningful relationships (supporting or contradicting). By representing the dataset as a document graph, ClaimTrust iteratively updates trust scores until convergence, effectively differentiating trustworthy articles from unreliable ones. Our methodology, which leverages embedding-based filtering for efficient claim comparison and relationship classification, achieves a 11.2% of significant connections while maintaining computational scalability. Experimental results demonstrate that ClaimTrust successfully assigns higher trust scores to verified documents while penalizing those containing false information. Future directions include fine-tuned claim extract and compare (Li et al., 2022), parameter optimization, enhanced language model utilization, and robust evaluation metrics to generalize the framework across diverse datasets and domains.

cs.CL↗

arXiv2024

Effective Clustering on Large Attributed Bipartite Graphs

Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains, including social network analysis, recommendation systems, information retrieval, and bioinformatics. However, the majority of existing solutions towards k-ABGC either overlook attribute information or fail to capture bipartite graph structures accurately, engendering severely compromised result quality. The severity of these issues is accentuated in real ABGs, which often encompass millions of nodes and a sheer volume of attribute data, rendering effective k-ABGC over such graphs highly challenging. In this paper, we propose TPO, an effective and efficient approach to k-ABGC that achieves superb clustering performance on multiple real datasets. TPO obtains high clustering quality through two major contributions: (i) a novel formulation and transformation of the k-ABGC problem based on multi-scale attribute affinity specialized for capturing attribute affinities between nodes with the consideration of their multi-hop connections in ABGs, and (ii) a highly efficient solver that includes a suite of carefully-crafted optimizations for sidestepping explicit affinity matrix construction and facilitating faster convergence. Extensive experiments, comparing TPO against 19 baselines over 5 real ABGs, showcase the superior clustering quality of TPO measured against ground-truth labels. Moreover, compared to the state of the arts, TPO is often more than 40x faster over both small and large ABGs.

cs.SI↗

arXiv2024

Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems

We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively.

cs.IT↗

arXiv2023

Computing the Difference of Conjunctive Queries Efficiently

We investigate how to efficiently compute the difference result of two (or multiple) conjunctive queries, which is the last operator in relational algebra to be unraveled. The standard approach in practical database systems is to materialize the results for every input query as a separate set, and then compute the difference of two (or multiple) sets. This approach is bottlenecked by the complexity of evaluating every input query individually, which could be very expensive, particularly when there are only a few results in the difference. In this paper, we introduce a new approach by exploiting the structural property of input queries and rewriting the original query by pushing the difference operator down as much as possible. We show that for a large class of difference queries, this approach can lead to a linear-time algorithm, in terms of the input size and (final) output size, i.e., the number of query results that survive from the difference operator. We complete this result by showing the hardness of computing the remaining difference queries in linear time. Although a linear-time algorithm is hard to achieve in general, we also provide some heuristics that can provably improve the standard approach. At last, we compare our approach with standard SQL engines over graph and benchmark datasets. The experiment results demonstrate order-of-magnitude speedups achieved by our approach over the vanilla SQL.

cs.DB↗

arXiv2023

Change Propagation Without Joins

We revisit the classical change propagation framework for query evaluation under updates. The standard framework takes a query plan and materializes the intermediate views, which incurs high polynomial costs in both space and time, with the join operator being the culprit. In this paper, we propose a new change propagation framework without joins, thus naturally avoiding this polynomial blowup. Meanwhile, we show that the new framework still supports constant-delay enumeration of both the deltas and the full query results, the same as in the standard framework. Furthermore, we provide a quantitative analysis of its update cost, which not only recovers many recent theoretical results on the problem, but also yields an effective approach to optimizing the query plan. The new framework is also easy to be integrated into an existing streaming database system. Experimental results show that our system prototype, implemented using Flink DataStream API, significantly outperforms other systems in terms of space, time, and latency.

cs.DB↗