4 Search Results for "Yang, Mohan"


Document
Barcode Selection and Layout Optimization in Spatial Transcriptomics

Authors: Frederik L. Jatzkowski, Antonia Schmidt, Robert Mank, Steffen Schüler, and Matthias Müller-Hannemann

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)


Abstract
An important special case of the quadratic assignment problem arises in the synthesis of DNA microarrays for high-resolution spatial transcriptomics. The task is to select a suitable subset from a set of barcodes, i. e. short DNA strings that serve as unique identifiers, and to assign the selected barcodes to positions on a two-dimensional array in such a way that a position-dependent cost function is minimized. A typical microarray with dimensions of 768×1024 requires 786,432 many barcodes to be placed, leading to very challenging large-scale combinatorial optimization problems. The general quadratic assignment problem is well-known for its hardness, both in theory and in practice. It turns out that this also holds for the special case of the barcode layout problem. We show that the problem is even hard to approximate: It is MaxSNP-hard. An ILP formulation theoretically allows the computation of optimal results, but it is only applicable for tiny instances. Therefore, we have developed layout constructing and improving heuristics with the aim of computing near-optimal solutions for instances of realistic size. These include a sorting-based algorithm, a greedy algorithm, 2-OPT-based local search and a genetic algorithm. To assess the quality of the results, we compare the generated solutions with the expected cost of a random layout and with lower bounds. A combination of the greedy algorithm and 2-OPT local search produces the most promising results in terms of both quality and runtime. Solutions to large-scale instances with arrays of dimension 768×1024 show a 37% reduction in cost over a random solution and can be computed in about 3 minutes. Since the universe of suitable barcodes is much larger than the number of barcodes needed, this can be exploited. Experiments with different surpluses of barcodes show that a significant improvement in layout quality can be achieved at the cost of a reasonable increase in runtime. Another interesting finding is that the restriction of the barcode design space by biochemical constraints is actually beneficial for the overall layout cost.

Cite as

Frederik L. Jatzkowski, Antonia Schmidt, Robert Mank, Steffen Schüler, and Matthias Müller-Hannemann. Barcode Selection and Layout Optimization in Spatial Transcriptomics. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 17:1-17:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{jatzkowski_et_al:LIPIcs.SEA.2024.17,
  author =	{Jatzkowski, Frederik L. and Schmidt, Antonia and Mank, Robert and Sch\"{u}ler, Steffen and M\"{u}ller-Hannemann, Matthias},
  title =	{{Barcode Selection and Layout Optimization in Spatial Transcriptomics}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{17:1--17:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.17},
  URN =		{urn:nbn:de:0030-drops-203821},
  doi =		{10.4230/LIPIcs.SEA.2024.17},
  annote =	{Keywords: Spatial Transcriptomics, Array Layout, Optimization, Computational Complexity, GPU Computing, Integer Linear Programming, Metaheuristics}
}
Document
JuMP2start: Time-Aware Stop-Start Technology for a Software-Defined Vehicle System

Authors: Anam Farrukh and Richard West

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)


Abstract
Software-defined vehicle (SDV) systems replace traditional ECU architectures with software tasks running on centralized multicore processors in automotive-grade PCs. However, PC boot delays to cold-start an integrated vehicle management system (VMS) are problematic for time-critical functions, which must process sensor and actuator data within specific time bounds. To tackle this challenge, we present JuMP2start: a time-aware multicore stop-start approach for SDVs. JuMP2start leverages PC-class suspend-to-RAM techniques to capture a system snapshot when the vehicle is stopped. Upon restart, critical services are resumed-from-RAM within order of milliseconds compared to normal cold-start times. This work showcases how JuMP2start manages global suspension and resumption mechanisms for a state-of-the-art dual-domain vehicle management system comprising real-time OS (RTOS) and Linux SMP guests. JuMP2start models automotive tasks as continuable or restartable to ensure timing- and safety-critical function pipelines are reactively resumed with low latency, while discarding stale task state. Experiments with the VMS show that critical CAN traffic processing resumes within 500 milliseconds of waking the RTOS guest, and reaches steady-state throughput in under 7ms.

Cite as

Anam Farrukh and Richard West. JuMP2start: Time-Aware Stop-Start Technology for a Software-Defined Vehicle System. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 1:1-1:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{farrukh_et_al:LIPIcs.ECRTS.2024.1,
  author =	{Farrukh, Anam and West, Richard},
  title =	{{JuMP2start: Time-Aware Stop-Start Technology for a Software-Defined Vehicle System}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{1:1--1:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.1},
  URN =		{urn:nbn:de:0030-drops-203046},
  doi =		{10.4230/LIPIcs.ECRTS.2024.1},
  annote =	{Keywords: Time-aware stop-start, Real-time power management, Suspend-to-RAM, Partitioning hypervisor, Vehicle management system, Vehicle-OS, Software-defined vehicles (SDV)}
}
Document
DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!

Authors: Mohammad Fakhruddin Babar and Monowar Hasan

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)


Abstract
Deep Neural Networks (DNNs) are becoming common in "learning-enabled" time-critical applications such as autonomous driving and robotics. One approach to protect DNN inference from adversarial actions and preserve model privacy/confidentiality is to execute them within trusted enclaves available in modern processors. However, running DNN inference inside limited-capacity enclaves while ensuring timing guarantees is challenging due to (a) large size of DNN workloads and (b) extra switching between "normal" and "trusted" execution modes. This paper introduces new time-aware scheduling schemes - DeepTrust^RT - to securely execute deep neural inferences for learning-enabled real-time systems. We first propose a variant of EDF (called DeepTrust^RT-LW) that slices each DNN layer and runs them sequentially in the enclave. However, due to extra context switch overheads of individual layer slices, we further introduce a novel layer fusion technique (named DeepTrust^RT-FUSION). Our proposed scheme provides hard real-time guarantees by fusing multiple layers of DNN workload from multiple tasks; thus allowing them to fit and run concurrently within the enclaves while maintaining real-time guarantees. We implemented and tested DeepTrust^RT ideas on the Raspberry Pi platform running OP-TEE+DarkNet-TZ DNN APIs and three DNN workloads (AlexNet-squeezed, Tiny Darknet, YOLOv3-tiny). Compared to the layer-wise partitioning approach (DeepTrust^RT-LW), DeepTrust^RT-FUSION can schedule up to 3x more tasksets and reduce context switches by up to 11.12x. We further demonstrate the efficacy of DeepTrust^RT using a flight controller (ArduPilot) case study and find that DeepTrust^RT-FUSION retains real-time guarantees where DeepTrust^RT-LW becomes unschedulable.

Cite as

Mohammad Fakhruddin Babar and Monowar Hasan. DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 13:1-13:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{babar_et_al:LIPIcs.ECRTS.2024.13,
  author =	{Babar, Mohammad Fakhruddin and Hasan, Monowar},
  title =	{{DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{13:1--13:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.13},
  URN =		{urn:nbn:de:0030-drops-203161},
  doi =		{10.4230/LIPIcs.ECRTS.2024.13},
  annote =	{Keywords: DNN, TrustZone, Real-Time Systems}
}
Document
Declarative Algorithms in Datalog with Extrema: Their Formal Semantics Simplified

Authors: Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky, and Tyson Condie

Published in: OASIcs, Volume 64, Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)


Abstract
Recent advances are making possible the use of aggregates in recursive queries thus enabling the declarative expression classic algorithms and their efficient and scalable implementation. These advances rely the notion of Pre-Mappability (PreM) of constraints that, along with the seminaive-fixpoint operational semantics, guarantees formal non-monotonic semantics for recursive programs with min and max constraints. In this extended abstract, we introduce basic templates to simplify and automate task of proving PreM.

Cite as

Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky, and Tyson Condie. Declarative Algorithms in Datalog with Extrema: Their Formal Semantics Simplified. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 9:1-9:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{zaniolo_et_al:OASIcs.ICLP.2018.9,
  author =	{Zaniolo, Carlo and Yang, Mohan and Interlandi, Matteo and Das, Ariyam and Shkapsky, Alexander and Condie, Tyson},
  title =	{{Declarative Algorithms in Datalog with Extrema: Their Formal Semantics Simplified}},
  booktitle =	{Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)},
  pages =	{9:1--9:3},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-090-3},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{64},
  editor =	{Dal Palu', Alessandro and Tarau, Paul and Saeedloei, Neda and Fodor, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2018.9},
  URN =		{urn:nbn:de:0030-drops-98758},
  doi =		{10.4230/OASIcs.ICLP.2018.9},
  annote =	{Keywords: Recursive Queries}
}
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