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Senast publicerade

Safe Multi-Robot Planning Via Long-Run Averages
(2026) Embaye, Eyob; Daun, Johan
Constrained reinforcement learning in Markov decision processes (MDPs) has received increasing attention for its use in sequential decision making problems with safety requirements. This study investigated safe planning via long run average reward using MDPs. This thesis uses grid-world environments and builds on the Triple-QA framework [1]. Three approaches are evaluated: A single-agent baseline and two multi agent extensions, a trivial joint-state extension, and separate Q-table approach. The results show that the single agent algorithm reproduces the result found in the original framework and serves as a reliable baseline. The joint state space extension suffers from poor scalability due to exponential growth in the state action space, and therefore does not achieve comparable reward per agent as the baseline. In contrast, the separate Q-table approach scales significantly better and achieves a level comparable to the single agent case both in an environment with and without agent interaction. Although the result of two agents with trivial extension and separate Q-table was achieved to satisfy the constraint, the test with three agents did not satisfy for both algorithms.
Beyond Linear - Pairing-Based Multi-Key Homomorphic Signatures for Verifiable Statistics
(2026) Davoodi, Anna; Jamal, Aram
Multi-key homomorphic signatures (MKHS) address the challenge of verifying outsourced computations. Multiple independent parties each sign their own data and upload it to an untrusted server, allowing anyone to outsource computations over the signed data by request. In response to a query, the server returns both the computed result and a compact signature certifying its correctness, which can be verified using only the public keys of the participating signers. Existing implemented MKHS only support evaluation of linear functions. In this thesis, we address the problem of constructing implementable pairing-based MKHS schemes that support functions beyond linear, by extending the multi-key linearly homomorphic signature scheme of Aranha and Pagnin [Latincrypt, 2019]. We begin by reproving its security in the Type 3 pairing setting, bringing it in line with current cryptographic practices. Building on this, we present mkqhs-br, a pairing-based MKHS supporting bounded rank quadratic evaluation, proven secure under the co-CDH∗ assumption. We then give two independent extensions of mkqhs-br: (i) mkqhs-ebr, which compresses evaluated signature sizes, and (ii) mkqhs-br-m2, which extends the supported function class, enabling applications such as variance and least-squares computations. The extensions are compatible and can be combined, yielding a practical construction for statistical applications.
LLMs for SDVs: Automated Software Vulnerability Detection and Repair
(2026) Gong, Wenkang; Yan, Jieman
Software-Defined Vehicles (SDVs) increasingly rely on large-scale C/C++ software stacks to implement safety-critical functionalities. While these languages provide the deterministic performance and hardware control required in automotive systems, they are also susceptible to memory-safety vulnerabilities such as buffer overflows, out-of-bounds accesses, NULL pointer dereferences, and resource management errors. Existing vulnerability analysis approaches remain essential in industrial practice but face limitations in scalability, coverage, and manual remediation effort when applied to modern automotive-scale software systems. Recent advances in Large Language Models (LLMs) have motivated increasing research interest in automated vulnerability detection and repair. This thesis presents an experimental study of a two-stage detection and repair pipeline for function-level C/C++ memory-safety vulnerability detection and re pair in software relevant to SDVs. For the detection stage, the study evaluates how classification strategy, pre-trained code model selection, and inference-time threshold selection affect detection performance for four vulnerability categories, Common Weakness Enumeration (CWE)-787, CWE-476, CWE-399, and CWE-125. Detection experiments compare CodeBERT, GraphCodeBERT, and UniXcoder across specialised binary classifiers and a shared multiclass classifier. For the repair stage, the study evaluates how detection-augmented prompting using vulnerability guidance affect LLM-based automated vulnerability repair performance. Repair experiments evaluate three prompting strategies with increasing levels of vulnerability guidance. Experiments on the BigVul and PrimeVul datasets show that the specialised binary classifiers outperform the multiclass classifier for all model-CWE combinations, with per-CWE F1-score improvements ranging from +0.13 to +0.35. The results also show that no evaluated pre-trained code model is strongest across all four CWE types. Thresholds selected on validation F1 make the detector more permissive, in creasing the rate at which the ground-truth CWE reaches the repair stage by 11.6 to 21.4 percentage points; UniXcoder achieves the highest detection rate of 85.9%. For vulnerability repair, detection-augmented prompting improves vulnerability repair performance, increasing the vulnerability pattern removal rate from 28.22% under the unguided baseline to 48.43% under the detailed guided prompting strategy, while maintaining high code quality. The results indicate that specialised binary classifiers are the strongest evaluated architecture, while model selection and threshold selection still affect how these classifiers perform within the pipeline. Moreover, incorporating detection results into repair prompts proves an effective strategy for improving vulnerability repair quality, though the improvement is bounded by upstream detection accuracy.
JIT-Based RVV Optimization for Implicit GEMM Convolution and FlashAttention - With Cross-Architecture Autotuning, a Winograd F(4,3) Ablation, and AVX2 / AVX-512 / NEON Baselines
(2026) Wang, Yuhao; Tiandu, Li
Convolutional Neural Networks (CNNs) are fundamental to modern deep learning applications, including image classification, object detection, and autonomous systems. The computational efficiency of convolution operations is critical for real-time inference on resource-constrained devices. Traditional implementations rely on the im2col transformation followed by General Matrix Multiplication (GEMM), which incurs significant memory overhead due to explicit tensor expansion. This thesis presents the Implicit GEMM convolution algorithm, which eliminates the im2col memory overhead by computing input coordinates dynamically during matrix multiplication. We implement and optimize this algorithm across four CPU vector backends—x86-64 with AVX2 and AVX-512, ARM with NEON, and RISC-V with the Vector Extension (RVV)—and complement it with a JIT-based FlashAttention kernel and an RVV Winograd F(4,3) ablation study. The contributions of this thesis are: 1. A lightweight Just-In-Time (JIT) code generation framework for RVV that emits register-blocked, vector-length-agnostic Implicit GEMM micro-kernels at runtime, with loop unrolling and explicit register assignment. 2. A Vector Length Agnostic (VLA) RVV implementation evaluated across six VLEN configurations (128–8192 bits) on the gem5 simulator and on the BananaPi-F3 development board. 3. A hand-written AVX2 micro-kernel (6×16) and a portable ARM NEON micro kernel (8×8) that share the same Implicit GEMM design, used to validate cross-architecture portability. 4. A lightweight RVV autotuner over {MR, LMUL, k-unroll} with register-budget pruning, contrasted with the AVX-512 autotuner to identify which tuning knobs transfer across architectures and which do not. 5. A JIT FlashAttention kernel as a non-convolution case study, and an RVV Winograd F(4,3) five-way ablation that separates algorithmic gains from implementation-level effects. The implementations are integrated into the Intel oneDNN framework. On x86 64, the AVX2 Implicit GEMM achieves a peak of 164.68 GFLOPS (9.5× over oneDNN’s gemm_convolution), eliminating a 56.85MB im2col buffer. Extended to AVX-512 with NUMA-aware tiling and a per-layer autotuner, the same design reaches 599 GFLOPS averaged over five VGG-16 layers and a single-layer peak of 1161 GFLOPS, with cross-network speedups of 22×–228× over a non-vectorized scalar baseline. On ARM NEON, the peak is 81.0 GFLOPS (15.4× over a scalar reference). On RVV, the JIT kernel delivers up to 3.28× over the scalar reference (gem5, VLEN=256) and is validated on the real BananaPi-F3 hardware (3.08×). Across the two autotuners, every layer except a few narrow-channel cases independently selects MR=8, indicating that the dominant tile parameter is governed by the architectural vector-register budget rather than by ISA-specific concerns. This work demonstrates the effectiveness of Implicit GEMM as a memory-efficient alternative to traditional convolution methods, with particular relevance for emerging RISC-V platforms where optimized deep learning libraries remain limited, and provides a cross-architecture analysis of which optimization decisions transfer and which do not.
Primed Mononuclear Cell-mediated Differentiation of Stem Cells towards Urothelial and Smooth Muscle Lineages for Urethral Regeneration
(2026) Hain, Juni; Elmberg, Louise
Current limitations in treating urethral dysfunction drive the need for alternative regenerative strategies to improve clinical outcomes. While advancements in 3D bioprinting enable future development of patient-specific urethral constructs, cell retrieval remains an issue as it is commonly harvested from invasive bladder biopsies. This study investigates a stem cell differentiation approach which would eliminate the need of cell retrieval from bladder biopsies and have the potential to shorten the production time of a urethral construct. The differentiation approach is based on in vitro co-culturing of stromal vascular fraction (SVF) cells with primed mononuclear cells (MNCs). The primed MNCs were activated in vitro using decellularized tissue from either urethra or corpus spongiosum to obtain a T cell population to direct cell differentiation of the adipose derived stem cells in the SVF toward urothelial cells (UCs) and smooth muscle cells (SMCs). The cells were subsequently used for 3D bioprinting of a urethral cross section, consisting of an inner ring of urethral-like cells and an outer ring of smooth muscle-like cells. Cell differentiation was assessed through morphology evaluation, protein expression and gene expression analysis. Cell morphology was evaluated by bright field microscopy, protein expression was analyzed using immunocytochemistry (ICC) and gene expression with reverse quantitative polymer chain reaction (RT-qPCR). Cell viability within the 3D bioprinted constructs were evaluated using live/dead staining. While the differentiation toward UCs showed morphological tendencies toward urothelial-like cells, UC differentiation could not be confirmed by the gene or protein expression analysis. For differentiation toward SMCs, both morphological and gene expression analysis of desmin suggested differentiation to smooth muscle-like cells, as expression was comparable to the positive control, though protein expression analysis did not confirm this. Additionally, the control containing SVF cells cultured with decellularized corpus spongiosum showed similar results, suggesting that MNCs may not be essential, though the co-culture differentiation approach demonstrated greater differentiation potential. Furthermore, live/dead staining of the 3D bioprinted constructs suggested viable cells seven days post-printing. The combined results suggest that further studies are needed to optimize and better evaluate the co-culture differentiation approach and its application in the field of urethral regeneration.