@inproceedings{63754, abstract = {{Data spaces are receiving an emerging interest in Information Systems Research and industry practice. They are central to many European research initiatives and shape the data economy in Industry 4.0. Generally, they aim to create secure environments for cross-organizational data management and sharing. Currently, there is considerable interest in developing new data spaces in Industry 4.0, also accelerated through regulatory changes. However, key questions about what precisely characterizes a data space in Industry 4.0 remain unresolved. Against this backdrop, we build a taxonomy of data spaces in the Industry 4.0 context. We identified nine distinctive dimensions and 40 corresponding characteristics among the 19 data spaces analyzed. The taxonomy enables clearer classification and nomenclature of data spaces in this context. This short paper will ignite planned further research on data spaces in Industry 4.0 and contribute to a conceptualization of a taxonomic theory for interested researchers.}}, author = {{Werth, Oliver and Koldewey, Christian and Uslar, Mathias and Zerbin, Julian}}, booktitle = {{Lecture Notes in Business Information Processing}}, isbn = {{9783032145178}}, issn = {{1865-1348}}, keywords = {{Industry 4.0, Taxonomy, Data spaces, Characterization}}, location = {{Stuttgart, Germany}}, publisher = {{Springer Nature Switzerland}}, title = {{{What Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding}}}, doi = {{10.1007/978-3-032-14518-5_3}}, year = {{2026}}, } @inproceedings{65101, abstract = {{Various methods to measure the dynamic behavior of particles require the calculation of autocorrelation functions. For this purpose, fast multi-tau correlators have been developed in dedicated hardware, in software, and on FPGAs. However, for methods such as X-ray Photon Correlation Spectroscopy (XPCS), which requires to calculate the autocorrelation function independently for hundreds of thousands to millions of pixels from high-resolution detectors, current approaches rely on offline processing after data acquisition. Moreover, the internal pipeline state of so many independent correlators is far too large to keep it on-chip. In this work, we propose a design approach on FPGAs, where pipeline contexts are stored in off-chip HBM memory. Each compute unit iteratively loads the state for a single pixel, processes a short time series for this pixel, and afterwards writes back the context in a dataflow pipeline. We have implemented the required compute kernels with Vitis HLS and analyze resulting designs on an Alveo U280 card. The design achieves the expected performance and for the first time provides sufficient throughput for current high-end detectors used in XPCS.}}, author = {{Tareen, Abdul Rehman and Plessl, Christian and Kenter, Tobias}}, booktitle = {{2025 International Conference on Field Programmable Technology (ICFPT)}}, publisher = {{IEEE}}, title = {{{Fast Multi-Tau Correlators on FPGA with Context Switching From and to High- Bandwidth Memory}}}, doi = {{10.1109/icfpt67023.2025.00027}}, year = {{2026}}, } @inproceedings{65178, abstract = {{Large intermediate results can cause join queries to run unexpectedly long. This problem is particularly common for analytical queries, which aggregate data over many tables to produce a comparatively small final output, and queries on graph data, where intermediate results blow up quickly. Recent work inspired by Yannakakis’ algorithm approaches this by modifying the query engine to avoid materializing unnecessary tuples. However, this requires significant changes to the core of the system, which is not feasible in many situations such as cloud environments or proprietary systems. In this work, we propose a flexible approach for optimizing long-running join queries from the outside of the DBMS. Rewriting-based realizations of Yannakakis’ algorithm suffer from inherent overhead due to the creation of intermediate tables. Thus, we present an approach for detecting and targeting queries which would benefit from a Yannakakis-style optimization. We introduce a new benchmark combining 5 standard benchmarks and augmenting them with additional instances, which provides a sufficient size and diversity for a machine learning based solution. On PostgreSQL, DuckDB and SparkSQL, slowdowns on queries where the rewriting is counterproductive are mostly avoided, as opposed to a naïve application of the rewriting, and we observe significant improvements in end-to-end runtimes over standard query execution and unconditional rewriting.}}, author = {{Böhm, Daniela and Gottlob, Georg and Lanzinger, Matthias and Longo, Davide Mario and Okulmus, Cem and Pichler, Reinhard and Selzer, Alexander}}, booktitle = {{Proceedings of the 28th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2026)}}, keywords = {{Join Queries, Acyclic Queries, Query Processing}}, title = {{{Selective Use of Yannakakis’ Algorithm for Consistent Performance Gains}}}, year = {{2026}}, } @unpublished{63530, abstract = {{The widespread deployment of 5G networks, together with the coexistence of 4G/LTE networks, provides mobile devices a diverse set of candidate cells to connect to. However, associating mobile devices to cells to maximize overall network performance, a.k.a. cell (re)selection, remains a key challenge for mobile operators. Today, cell (re)selection parameters are typically configured manually based on operator experience and rarely adapted to dynamic network conditions. In this work, we ask: Can an agent automatically learn and adapt cell (re)selection parameters to consistently improve network performance? We present a reinforcement learning (RL)-based framework called CellPilot that adaptively tunes cell (re)selection parameters by learning spatiotemporal patterns of mobile network dynamics. Our study with real-world data demonstrates that even a lightweight RL agent can outperform conventional heuristic reconfigurations by up to 167%, while generalizing effectively across different network scenarios. These results indicate that data-driven approaches can significantly improve cell (re)selection configurations and enhance mobile network performance.}}, author = {{Illian, Marvin and Khalili, Ramin and Rocha, Antonio A. de A. and Wang, Lin}}, booktitle = {{arXiv:2601.04083}}, title = {{{Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning}}}, year = {{2026}}, } @inproceedings{65249, author = {{Shaaban KabakiboKabakibo, Huzaifa and Trivedi, Animesh and Wang, Lin}}, booktitle = {{The 9th Annual Conference on Machine Learning and Systems (MLSys)}}, location = {{Bellevue, WA}}, title = {{{Breaking the Ice: Analyzing Cold Start Latency in vLLM}}}, year = {{2026}}, } @inproceedings{65250, author = {{Zohdi, Sepideh and Wang, Lin}}, booktitle = {{The 6th Workshop on Machine Learning and Systems (EuroMLSys)}}, location = {{Edinburg}}, title = {{{Before the First Token: Benchmarking Data Preprocessing in Vision-Language Models }}}, year = {{2026}}, } @article{65440, abstract = {{Abstract We present a novel algorithm for quantization and subsequent hexahedral mesh generation from seamless volumetric maps. Quantization is the process of choosing integers that represent the numbers of hexahedral elements to be placed in each region of the volume, and transforming the seamless map into an integer‐grid map matching that choice, inducing a hexahedral mesh. Previous work computes such quantizations under the restriction of a fixed predetermined singularity graph. Our novel approach allows for implicit modification and, in particular, simplification of the map's singularity structure wherever that benefits the chosen objective, such as matching target hexahedron sizes as closely as possible. It comes with two novel ingredients: A feature‐focused distortion measure guiding the quantization, and constraints ensuring map injectivity and structure preservation of geometric and topological features, both without relying on a fixed singularity structure. We demonstrate the benefit of the added flexibility offered by this approach: it allows for the generation of hexahedral meshes that more accurately match a desired resolution globally, as well as of meshes exhibiting a simpler block structure.}}, author = {{Brückler, Hendrik and Campen, Marcel}}, issn = {{0167-7055}}, journal = {{Computer Graphics Forum}}, publisher = {{Wiley}}, title = {{{Volume Quantization with Flexible Singularities for Hexahedral Meshing}}}, doi = {{10.1111/cgf.70349}}, year = {{2026}}, } @inproceedings{61922, abstract = {{We present an extremely simple polynomial-space exponential-time $(1-\varepsilon)$-approximation algorithm for MAX-k-SAT that is (slightly) faster than the previous known polynomial-space $(1-\varepsilon)$-approximation algorithms by Hirsch (Discrete Applied Mathematics, 2003) and Escoffier, Paschos and Tourniaire (Theoretical Computer Science, 2014). Our algorithm repeatedly samples an assignment uniformly at random until finding an assignment that satisfies a large enough fraction of clauses. Surprisingly, we can show the efficiency of this simpler approach by proving that in any instance of MAX-k-SAT (or more generally any instance of MAXCSP), an exponential number of assignments satisfy a fraction of clauses close to the optimal value.}}, author = {{Buhrman, Harry and Gharibian, Sevag and Landau, Zeph and Gall, François Le and Schuch, Norbert and Tamaki, Suguru}}, booktitle = {{SIAM Symposium on Simplicity in Algorithms (SOSA)}}, pages = {{247--253}}, title = {{{A Simpler Exponential-Time Approximation Algorithm for MAX-k-SAT}}}, year = {{2026}}, } @inproceedings{65489, author = {{Okulmus, Cem and Ahmetaj, Shqiponja and Boneva, Iovka and Hidders, Jan and Jakubowski, Maxime and Labra Gayo, José Emilio and Martens, Wim and Mogavero, Fabio and Murlak, Filip and Savković, Ognjen and Šimkus, Mantas and Tomaszuk, Dominik }}, booktitle = {{Proceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026)}}, location = {{Lisbon, Portugal}}, title = {{{Common Foundations for Recursive Shape Languages}}}, year = {{2026}}, } @article{61152, abstract = {{While neural network quantization effectively reduces the cost of matrix multiplications, aggressive quantization can expose non-matrix-multiply operations as significant performance and resource bottlenecks on embedded systems. Addressing such bottlenecks requires a comprehensive approach to tailoring the precision across operations in the inference computation. To this end, we introduce scaled-integer range analysis (SIRA), a static analysis technique employing interval arithmetic to determine the range, scale, and bias for tensors in quantized neural networks. We show how this information can be exploited to reduce the resource footprint of FPGA dataflow neural network accelerators via tailored bitwidth adaptation for accumulators and downstream operations, aggregation of scales and biases, and conversion of consecutive elementwise operations to thresholding operations. We integrate SIRA-driven optimizations into the open-source FINN framework, then evaluate their effectiveness across a range of quantized neural network workloads and compare implementation alternatives for non-matrix-multiply operations. We demonstrate an average reduction of 17\% for LUTs, 66\% for DSPs, and 22\% for accumulator bitwidths with SIRA optimizations, providing detailed benchmark analysis and analytical models to guide the implementation style for non-matrix layers. Finally, we open-source SIRA to facilitate community exploration of its benefits across various applications and hardware platforms.}}, author = {{Umuroglu, Yaman and Berganski, Christoph and Jentzsch, Felix and Danilowicz, Michal and Kryjak, Tomasz and Bezaitis, Charalampos and Sjalander, Magnus and Colbert, Ian and Preusser, Thomas and Petri-Koenig, Jakoba and Blott, Michaela}}, issn = {{1936-7406}}, journal = {{ACM Transactions on Reconfigurable Technology and Systems}}, title = {{{SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Accelerators}}}, doi = {{10.1145/3807510}}, year = {{2026}}, } @inproceedings{65501, author = {{Stasytis, Lukas and Jentzsch, Felix and Preusser, Thomas and Umuroglu, Yaman and Petri-Koenig, Jakoba and István, Zsolt}}, booktitle = {{2025 International Conference on Field Programmable Technology (ICFPT)}}, publisher = {{IEEE}}, title = {{{Heuristic & Expert-Guided Buffer Sizing for Neural Network Inference Applications on FPGAs}}}, doi = {{10.1109/icfpt67023.2025.00032}}, year = {{2026}}, } @inproceedings{65500, author = {{Jentzsch, Felix and Platzner, Marco}}, booktitle = {{2025 International Conference on Field Programmable Technology (ICFPT)}}, publisher = {{IEEE}}, title = {{{Empirical QoR Estimation Flow for Fast Design Space Exploration of DNN Dataflow Accelerators}}}, doi = {{10.1109/icfpt67023.2025.00044}}, year = {{2026}}, } @article{65554, abstract = {{Abstract An algorithm for cutting solid objects in a topology‐controlled manner is presented. Concretely, given a loop on the object boundary, a disk‐topology cut surface bounded by the loop is constructed in the interior. In contrast to various previous approaches, both disk topology and conformance to the prescribed loop are ensured by construction, while supporting not only contractible but also incontractible loops on the boundaries of manifold objects of higher genus and arbitrary non‐trivial topology. We describe an implementation of this algorithm in the discrete setting, with triangle mesh cut surfaces embedded in tetrahedral mesh objects. Making use of this novel cutting algorithm, we describe a method for the reliable construction of bijective volumetric maps between solid objects, demonstrating the algorithm's utility. This mapping method overcomes restrictions of the state of the art to topological balls, extending coverage to objects of arbitrary genus, specifically so‐called 1 ‐handlebodies. }}, author = {{Hinderink, Steffen and Campen, Marcel}}, issn = {{0167-7055}}, journal = {{Computer Graphics Forum}}, publisher = {{Wiley}}, title = {{{DiskScissors: Cutting Arbitrary‐Topology Solids for Bijective Mapping}}}, doi = {{10.1111/cgf.70379}}, year = {{2026}}, } @inproceedings{65567, abstract = {{We introduce the notion of contrastive ABox explanations to answer questions of the type “Why is a an instance of C, but b is not?”. While there are various approaches for explaining positive entailments (why is C(a) entailed by the knowledge base) as well as missing entailments (why is C(b) not entailed) in isolation, contrastive explanations consider both at the same time, which allows them to focus on the relevant commonalities and differences between a and b. We develop an appropriate notion of contrastive explanations for the special case of ABox reasoning with description logic ontologies, and analyze the computational complexity for different variants under different optimality criteria, considering lightweight as well as more expressive description logics. We implemented a first method for computing one variant of contrastive explanations, and evaluated it on generated problems for realistic knowledge bases.}}, author = {{Koopmann, Patrick and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille and Tiwari, Balram}}, booktitle = {{Proceedings of the AAAI Conference on Artificial Intelligence}}, issn = {{2374-3468}}, number = {{23}}, pages = {{19189--19197}}, publisher = {{Association for the Advancement of Artificial Intelligence (AAAI)}}, title = {{{Can You Tell the Difference? Contrastive Explanations for ABox Entailments}}}, doi = {{10.1609/aaai.v40i23.38993}}, volume = {{40}}, year = {{2026}}, } @inproceedings{57866, abstract = {{The theory of Total Function NP (TFNP) and its subclasses says that, even if one is promised an efficiently verifiable proof exists for a problem, finding this proof can be intractable. Despite the success of the theory at showing intractability of problems such as computing Brouwer fixed points and Nash equilibria, subclasses of TFNP remain arguably few and far between. In this work, we define two new subclasses of TFNP borne of the study of complex polynomial systems: Multi-homogeneous Systems (MHS) and Sparse Fundamental Theorem of Algebra (SFTA). The first of these is based on B\'ezout's theorem from algebraic geometry, marking the first TFNP subclass based on an algebraic geometric principle. At the heart of our study is the computational problem known as Quantum SAT (QSAT) with a System of Distinct Representatives (SDR), first studied by [Laumann, L\"auchli, Moessner, Scardicchio, and Sondhi 2010]. Among other results, we show that QSAT with SDR is MHS-complete, thus giving not only the first link between quantum complexity theory and TFNP, but also the first TFNP problem whose classical variant (SAT with SDR) is easy but whose quantum variant is hard. We also show how to embed the roots of a sparse, high-degree, univariate polynomial into QSAT with SDR, obtaining that SFTA is contained in a zero-error version of MHS. We conjecture this construction also works in the low-error setting, which would imply SFTA is contained in MHS.}}, author = {{Aldi, Marco and Gharibian, Sevag and Rudolph, Dorian}}, booktitle = {{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)}}, pages = {{7:1--7:24}}, title = {{{An unholy trinity: TFNP, polynomial systems, and the quantum satisfiability problem}}}, volume = {{362}}, year = {{2026}}, } @inproceedings{65013, author = {{Illian, Marvin and Khalili, Ramin and A. de A. Rocha, Antonio and Wang, Lin}}, booktitle = {{2026 24th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)}}, publisher = {{IFIP}}, title = {{{Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning}}}, year = {{2026}}, } @inbook{65670, abstract = {{Ensuring the veracity of assertions is {vital for building reliable and consistent knowledge graphs}. A variety of automatic fact-checking approaches have been proposed over the past decade. Among these, path-based fact-checking approaches are particularly attractive due to their independence of supplementary external knowledge and their faster runtimes compared to methods reliant on external corpora or embeddings. However, the effectiveness of these approaches is fundamentally limited by the incompleteness of existing knowledge graphs, which often lack the paths necessary to support or refute assertions. To address this limitation, we propose \system{}, a framework that supplements the knowledge graph with shallow knowledge---automatically extracted RDF assertions from external unstructured sources---even if this additional knowledge may not always fit a well-defined ontology nor be fully verified. By appending such shallow knowledge, we enhance the graph’s coverage and increase the chances of finding relevant evidence for fact checking. Comprehensive experiments on three widely used benchmark datasets demonstrate that integrating \system{} consistently and significantly enhances the performance of {state-of-the-art path-based fact-checking approaches}, yielding improvements of up to 0.24 in Area Under the Receiver Operating Characteristic Curve (AUROC). These results establish \system{} as a broadly applicable auxiliary component for improving the reliability and coverage of automatic fact checking in knowledge graphs. Our code is open-source and can be found at \url{https://github.com/dice-group/ShallKnow}.}}, author = {{Qudus, Umair and Pokharel, Neha and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032251558}}, issn = {{0302-9743}}, keywords = {{fact checking}}, publisher = {{Springer Nature Switzerland}}, title = {{{No Need to Be a Know-It-All: Fact Checking with Shallow Knowledge}}}, doi = {{10.1007/978-3-032-25156-5_23}}, year = {{2026}}, } @inproceedings{65566, author = {{Haak, Anselm and Koopmann, Patrick and Mahmood, Yasir and Turhan, Anni-Yasmin}}, location = {{Lisbon}}, title = {{{ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics}}}, year = {{2026}}, } @article{65733, abstract = {{Abstract In this paper, we study the computation of shortest paths within the geometric amoebot model , a commonly used model for programmable matter. Shortest paths are essential for various tasks and therefore have been heavily investigated in many different contexts. We consider the reconfigurable circuit extension of the model where the amoebot structure is able to interconnect amoebots by so-called circuits. These circuits permit the instantaneous transmission of simple signals between connected amoebots. We propose distributed algorithms for the shortest path forest problem where, given a set of k sources and a set of $$\ell $$ ℓ destinations, the amoebot structure has to compute a forest that connects each destination to its closest source on a shortest path. Our main results are two algorithms for hole-free structures. The first algorithm constructs a shortest path tree for a single source within $$O(\log \ell )$$ O ( log ℓ ) rounds, and the second algorithm a shortest path forest for an arbitrary number of sources within $$O(\log n \log ^2 k)$$ O ( log n log 2 k ) rounds. The former algorithm also provides an O (1) rounds solution for the single pair shortest path problem (SPSP) and an $$O(\log n)$$ O ( log n ) rounds solution for the single source shortest path problem (SSSP) since these problems are special cases of the considered problem. Then, we adapt the latter algorithm to an offset version of the problem. This allows us to solve the problem for amoebot structures with holes within $$O(h \log ^3 n)$$ O ( h log 3 n ) rounds w.h.p. where h denotes the number of holes. }}, author = {{Padalkin, Andreas and Scheideler, Christian}}, issn = {{0178-2770}}, journal = {{Distributed Computing}}, number = {{2}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Polylogarithmic time algorithms for shortest path forests in programmable matter}}}, doi = {{10.1007/s00446-026-00505-2}}, volume = {{39}}, year = {{2026}}, } @inbook{65755, author = {{Twardzik, Jan Luca and Humpert, Lynn and Cichon, Gerrit and Dumitrescu, Roman}}, booktitle = {{Lecture Notes in Mechanical Engineering}}, isbn = {{9783032211569}}, issn = {{2195-4356}}, publisher = {{Springer Nature Switzerland}}, title = {{{Knowledge Gaps in Circular Product Development: A Systematic Literature Review of the Manufacturing Industry}}}, doi = {{10.1007/978-3-032-21157-6_39}}, year = {{2026}}, }