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The advancement of multi-object tracking (MOT) technologies presents the dual challenge of maintaining high performance while addressing critical security and privacy concerns. In applications such as pedestrian tracking, where sensitive personal data is involved, the potential for privacy violations and data misuse becomes a significant issue if data is transmitted to external servers. To mitigate these risks, processing data directly on an edge device, such as a smart camera, has emerged as a viable solution. Edge computing ensures that sensitive information remains local, thereby aligning with stringent privacy principles and significantly reducing network latency. However, the implementation of MOT on edge devices is not without its challenges. Edge devices typically possess limited computational resources, necessitating the development of highly optimized algorithms capable of delivering real-time performance under these constraints. The disparity between the computational requirements of state-of-the-art MOT algorithms and the capabilities of edge devices emphasizes a significant obstacle. To address these challenges, we propose a neural network pruning method specifically tailored to compress complex networks, such as those used in modern MOT systems. This approach optimizes MOT performance by ensuring high accuracy and efficiency within the constraints of limited edge devices, such as NVIDIA's Jetson Orin Nano. By applying our pruning method, we achieve model size reductions of up to 70% while maintaining a high level of accuracy and further improving performance on the Jetson Orin Nano, demonstrating the effectiveness of our approach for edge computing applications.

We introduce a novel optical interface between a singlet-triplet spin qubit and a photonic qubit which would offer new prospects for future quantum communication applications. The interface is based on a 220 nm thick GaAs/Al-GaAs heterostructure membrane and features a gate-defined singlet-triplet qubit, a gate-defined optically active quantum dot, a photonic crystal cavity and a bot-tom gold reflector. All essential components can be lithographically defined and deterministically fabricated, which greatly increases the scalability of on-chip in-tegration. According to our FDTD simulations, the interface provides an overall coupling efficiency of 28.7% into a free space Gaussian beam, assuming an SiO2 interlayer filling the space between the reflector and the membrane. The performance can be further increased to 48.5% by undercutting this SiO2 interlayer below the photonic crystal.

The topic of fairness in AI, as debated in the FATE (Fairness, Accountability, Transparency, and Ethics in AI) communities, has sparked meaningful discussions in the past years. However, from a legal perspective, particularly from the perspective of European Union law, many open questions remain. Whereas algorithmic fairness aims to mitigate structural inequalities at design-level, European non-discrimination law is tailored to individual cases of discrimination after an AI model has been deployed. The AI Act might present a tremendous step towards bridging these two approaches by shifting non-discrimination responsibilities into the design stage of AI models. Based on an integrative reading of the AI Act, we comment on legal as well as technical enforcement problems and propose practical implications on bias detection and bias correction in order to specify and comply with specific technical requirements.

Efficient interconnection between distant semiconductor spin qubits with the help of photonic qubits would offer exciting new prospects for future quantum communication applications. In this paper, we optimize the extraction efficiency of a novel interface between a singlet-triplet spin qubit and a photonic qubit. The interface is based on a 220 nm thick GaAs/AlGaAs heterostructure membrane and consists of a gate-defined double quantum dot (GDQD) supporting a singlet-triplet qubit, an optically active quantum dot (OAQD) consisting of a gate-defined exciton trap, a photonic crystal cavity providing in-plane optical confinement and efficient out-coupling to an ideal free space Gaussian beam while accommodating the gate wiring of the GDQD and OAQD, and a bottom gold reflector to recycle photons and increase the optical extraction efficiency. All essential components can be lithographically defined and deterministically fabricated on the GaAs/AlGaAs heterostructure membrane, which greatly increases the scalability of on-chip integration. According to our simulations, the interface provides an overall coupling efficiency of 28.7% into a free space Gaussian beam, assuming an SiO2 interlayer filling the space between the reflector and the membrane. The performance can be further increased by undercutting this SiO2 interlayer below the photonic crystal. In this case, the overall efficiency is calculated to be 48.5%.

Implicit representations like Neural Radiance Fields (NeRF) showed impressive results for photorealistic rendering of complex scenes with fine details. However, ideal or near-perfectly specular reflecting objects such as mirrors, which are often encountered in various indoor scenes, impose ambiguities and inconsistencies in the representation of the reconstructed scene leading to severe artifacts in the synthesized renderings. In this paper, we present a novel reflection tracing method tailored for the involved volume rendering within NeRF that takes these mirror-like objects into account while avoiding the cost of straightforward but expensive extensions through standard path tracing. By explicitly modeling the reflection behavior using physically plausible materials and estimating the reflected radiance with Monte-Carlo methods within the volume rendering formulation, we derive efficient strategies for importance sampling and the transmittance computation along rays from only few samples. We show that our novel method enables the training of consistent representations of such challenging scenes and achieves superior results in comparison to previous state-of-the-art approaches.

We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the underlying yarn model based on a single neural network may seem an intuitive choice, we show that the complexity of yarn structures in terms of twisting and migration characteristics of the involved fibers can be better encountered in terms of ensembles of networks that focus on individual characteristics. We analyze the effect of different loss functions including a parameter loss to penalize the deviation of inferred parameters to ground truth annotations, a reconstruction loss to enforce similar statistics of the image generated for the estimated parameters in comparison to training images as well as an additional regularization term to explicitly penalize deviations between latent codes of synthetic images and the average latent code of real images in the latent space of the encoder. We demonstrate that the combination of a carefully designed parametric, procedural yarn model with respective network ensembles as well as loss functions even allows robust parameter inference when solely trained on synthetic data. Since our approach relies on the availability of a yarn database with parameter annotations and we are not aware of such a respectively available dataset, we additionally provide, to the best of our knowledge, the first dataset of yarn images with annotations regarding the respective yarn parameters. For this purpose, we use a novel yarn generator that improves the realism of the produced results over previous approaches., Comment: 23 pages, 16 figures

A characterisation of the spaces $\mathcal{G}_K$ and $\mathcal{G}_K'$ introduced in Grothaus et al. (Methods Funct Anal Topol 3(2):46-64, 1997) and Potthoff and Timpel (Potential Anal 4(6):637-654, 1995) is given. A first characterisation of these spaces provided in Grothaus et al. (Methods Funct Anal Topol 3(2):46-64, 1997) uses the concepts of holomorphy on infinite dimensional spaces. We, instead, give a characterisation in terms of U-functionals, i.e., classic holomorphic function on the one dimensional field of complex numbers. We apply our new characterisation to derive new results concerning a stochastic transport equation and the stochastic heat equation with multiplicative noise.

We describe how the quadratic Chabauty method may be applied to explicitly determine the set of rational points on modular curves of genus $g>1$ whose Jacobians have Mordell--Weil rank $g$. This extends our previous work on the split Cartan curve of level 13 and allows us to consider modular curves that may have few known rational points or nontrivial local height contributions at primes of bad reduction. We illustrate our algorithms with a number of examples where we determine the set of rational points on several modular curves of genus 2 and 3: this includes Atkin--Lehner quotients $X_0^+(N)$ of prime level $N$, the curve $X_{S_4}(13)$, as well as a few other curves relevant to Mazur's Program B. We also describe the computation of rational points on the genus 6 non-split Cartan modular curve $X_{\textrm{ns}} ^+ (17)$., Comment: Updated following referee's comments. To appear in Comp. Math

Crystallization is a fundamental process in materials science, providing the primary route for the realization of a wide range of new materials. Crystallization rates are also considered to be useful probes of glass-forming ability. At the microscopic level, crystallization is described by the classical crystal nucleation and growth theories, yet in general solid formation is a far more complex process. In particular, the observation of apparently different crystal growth regimes in many binary liquid mixtures greatly challenges our understanding of crystallization. Here, we study by experiments, theory and computer simulations the crystallization of supercooled mixtures of argon and krypton, showing that crystal growth rates in these systems can be reconciled with existing crystal growth models only by explicitly accounting for the non-ideality of the mixtures. Our results highlight the importance of thermodynamic aspects in describing the crystal growth kinetics, providing a substantial step towards a more sophisticated theory of crystal growth., Comment: 19 pages, 3 figures

Wasserstein-GANs have been introduced to address the deficiencies of generative adversarial networks (GANs) regarding the problems of vanishing gradients and mode collapse during the training, leading to improved convergence behaviour and improved image quality. However, Wasserstein-GANs require the discriminator to be Lipschitz continuous. In current state-of-the-art Wasserstein-GANs this constraint is enforced via gradient norm regularization. In this paper, we demonstrate that this regularization does not encourage a broad distribution of spectral-values in the discriminator weights, hence resulting in less fidelity in the learned distribution. We therefore investigate the possibility of substituting this Lipschitz constraint with an orthogonality constraint on the weight matrices. We compare three different weight orthogonalization techniques with regards to their convergence properties, their ability to ensure the Lipschitz condition and the achieved quality of the learned distribution. In addition, we provide a comparison to Wasserstein-GANs trained with current state-of-the-art methods, where we demonstrate the potential of solely using orthogonality-based regularization. In this context, we propose an improved training procedure for Wasserstein-GANs which utilizes orthogonalization to further increase its generalization capability. Finally, we provide a novel metric to evaluate the generalization capabilities of the discriminators of different Wasserstein-GANs., Comment: Correction of the formatting of the appendix

Routing attacks remain practically effective in the Internet today as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communication to reach consensus. In particular, Bitcoin -the most widely-used cryptocurrency- can be split in half by any AS-level adversary using BGP hijacking. In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks. SABRE runs alongside the existing peer-to-peer network and is easily deployable. As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks. We built SABRE around two key technical insights. First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in locations that are inherently protected against routing attacks; and (ii) on paths that are economically preferred by the majority of Bitcoin clients. These properties are generic and can be used to protect other Blockchain-based systems. Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy. This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language). Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients. We present a complete implementation of SABRE together with an extensive evaluation. Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments as small as 6 nodes.