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In many situations humans have to reason with inconsistent knowledge. These inconsistencies may occur due to not fully reliable sources of information. In order to reason with inconsistent knowledge, it is not possible to view a set of premisses as absolute truths as is done in predicate logic. Viewing the set of premisses as a set of assumptions, however, it is possible to deduce useful conclusions from an inconsistent set of premisses. In this paper a logic for reasoning with inconsistent knowledge is described. This logic is a generalization of the work of N. Rescher [15]. In the logic a reliability relation is used to choose between incompatible assumptions. These choices are only made when a contradiction is derived. As long as no contradiction is derived, the knowledge is assumed to be consistent. This makes it possible to define an argumentation-based deduction process for the logic. For the logic a semantics based on the ideas of Y. Shoham [22, 23], is defined. It turns out that the semantics for the logic is a preferential semantics according to the definition S. Kraus, D. Lehmann and M. Magidor [12]. Therefore the logic is a logic of system P and possesses all the properties of an ideal non-monotonic logic., Comment: The original version was published in the Artificial Intelligence journal. This original version uses 'justifications' in the proof system, which we would call nowadays 'arguments'. The current version presents the same results but now using the terminology of an assumption-based argumentation system

We aim at insights about how localization of the background field impacts nonlinear quantum vacuum signatures probed by photons in purely magnetic, electric and crossed fields. The starting point of our study are the one-loop results for the Heisenberg-Euler effective Lagrangian and the photon polarization tensor in quantum electrodynamics (QED) evaluated in a uniform constant electromagnetic field. As is well known and often employed, especially in the weak-field limit, within certain restrictions these results also allow for the reliable analysis of vacuum polarization effects in slowly varying background fields. Here, our main interest is in manifestly non-perturbative effects. To this end, we make use of the fact that for the particular case of background field inhomogeneities of Lorentzian shape with $0\leq d\leq3$ inhomogeneous directions analytical insights are possible. We study the scaling of conventional nonlinear QED signatures, such as probe-photon polarization flip and probe-photon induced electron-positron pair production, with relevant parameters. Special attention is put on the $d$ dependence of the considered effects., Comment: 18 pages

We consider quantum networks, where entangled photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until used in, e.g., distributed quantum computing, quantum key distribution, quantum sensing, and other applications. Due to the fibre loss, some photons are lost in transmission. Noise in the transmission link and the quantum memory also reduces fidelity. Thus, entangling nodes must keep updated records of photon-pair arrivals to each destination, and their use by the applications. This coordination requires classical information exchange between each entangled node pair. However, the same fibre link may not admit both classical and quantum transmissions, as the classical channels can generate enough noise (i.e., via spontaneous Raman scattering) to make the quantum link unusable. Here, we consider coordinating entanglement distribution using a standard Internet protocol (IP) network instead, and propose a control protocol to enable such. We analyse the increase in latency from transmission over an IP network, together with the effect of photon loss, quantum memory noise and buffer size, to determine the fidelity and rate of entangled pairs. We characterize the relationship between the latency of the non-ideal IP network and the decoherence time of the quantum memories, providing a comparison of promising quantum memory technologies.

We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW by the Improved Variational Online Newton (IVON) algorithm to finetune large language models. For Llama-2 with 7 billion parameters, IVON improves the accuracy over AdamW by 2.8% and expected calibration error by 4.6%. The accuracy is also better than the other Bayesian alternatives, yet the cost is lower and the implementation is easier. Our work provides additional evidence for the effectiveness of IVON for large language models. The code is available at https://github.com/team-approx-bayes/ivon-lora., Comment: Published at 38th Workshop on Fine-Tuning in Machine Learning (NeurIPS 2024). Code available at https://github.com/team-approx-bayes/ivon-lora. In version 2 we fixed a typo in the equation of prior in section 2

Giannopoulos, Hartzoulaki and Paouris asked in \cite{GHP} whether the best ratio between volume and surface area of convex bodies sharing a given orthogonal projection onto a fixed hyperplane is attained in the limit by a cylinder over the given projection. The answer to the question is known to be negative. In this paper, we prove a characterization of the positive answer in dimension $3$, using the Cheeger set of the common projection. A partial characterization is given in higher dimensions. We also prove that certain canal classes of convex bodies provide families of convex bodies satisfying a closely related inequality for a similar ratio.

Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topological guarantees, are limited to specific use cases, or impose high computational costs. In this work, we propose a novel, graph-based framework for topologically accurate image segmentation that is both computationally efficient and generally applicable. Our method constructs a component graph that fully encodes the topological information of both the prediction and ground truth, allowing us to efficiently identify topologically critical regions and aggregate a loss based on local neighborhood information. Furthermore, we introduce a strict topological metric capturing the homotopy equivalence between the union and intersection of prediction-label pairs. We formally prove the topological guarantees of our approach and empirically validate its effectiveness on binary and multi-class datasets. Our loss demonstrates state-of-the-art performance with up to fivefold faster loss computation compared to persistent homology methods.

Reinforcement Learning (RL) agents are able to solve a wide variety of tasks but are prone to producing unsafe behaviors. Constrained Markov Decision Processes (CMDPs) provide a popular framework for incorporating safety constraints. However, common solution methods often compromise reward maximization by being overly conservative or allow unsafe behavior during training. We propose Constrained Trust Region Policy Optimization (C-TRPO), a novel approach that modifies the geometry of the policy space based on the safety constraints and yields trust regions composed exclusively of safe policies, ensuring constraint satisfaction throughout training. We theoretically study the convergence and update properties of C-TRPO and highlight connections to TRPO, Natural Policy Gradient (NPG), and Constrained Policy Optimization (CPO). Finally, we demonstrate experimentally that C-TRPO significantly reduces constraint violations while achieving competitive reward maximization compared to state-of-the-art CMDP algorithms.

The origin of the tight scaling relation between the mass of supermassive black holes (SMBHs; $M_{\rm BH}$) and their host-galaxy properties remains unclear. Active galactic nuclei (AGNs) probe phases of ongoing SMBH growth and offer the only opportunity to measure $M_{\rm BH}$ beyond the local Universe. However, determining AGN host galaxy stellar velocity dispersion $\sigma_\star$, and their galaxy dynamical masses $M_{\rm dyn}$, is complicated by AGN contamination, aperture effects and different host galaxy morphologies. We select a sample of AGNs for which $M_{\rm BH}$ has been independently determined to high accuracy by state-of-the-art techniques: dynamical modeling of the reverberation signal and spatially resolving the broad-line region with VLTI/GRAVITY. Using IFU observations, we spatially map the host galaxy stellar kinematics across the galaxy and bulge effective radii. We find that that the dynamically hot component of galaxy disks correlates with $M_{\rm BH}$; however, the correlations are tightest for aperture-integrated $\sigma_\star$ measured across the bulge. Accounting for the different $M_{\rm BH}$ distributions, we demonstrate - for the first time - that AGNs follow the same $M_{\rm BH}$-$\sigma_\star$ and $M_{\rm BH}$-$M_{\rm bulge, dyn}$ relations as quiescent galaxies. We confirm that the classical approach of determining the virial factor as sample-average, yielding ${\rm log }f= 0.65 \pm 0.18$, is consistent with the average $f$ from individual measurements. The similarity between the underlying scaling relations of AGNs and quiescent galaxies implies that the current AGN phase is too short to have altered BH masses on a population level. These results strengthen the local calibration of $f$ for measuring single-epoch $M_{\rm BH}$ in the distant Universe., Comment: Resubmitted to ApJ after initial positive referee report. 40 pages, 19 figures, 6 tables

AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have yet to be explored. Here, we demonstrate how 10 - 1000+ AI agents behave and progress within agent societies. We first introduce the PIANO (Parallel Information Aggregation via Neural Orchestration) architecture, which enables agents to interact with humans and other agents in real-time while maintaining coherence across multiple output streams. We then evaluate agent performance in agent simulations using civilizational benchmarks inspired by human history. These simulations, set within a Minecraft environment, reveal that agents are capable of meaningful progress -- autonomously developing specialized roles, adhering to and changing collective rules, and engaging in cultural and religious transmission. These preliminary results show that agents can achieve significant milestones towards AI civilizations, opening new avenues for large simulations, agentic organizational intelligence, and integrating AI into human civilizations., Comment: 35 pages, 14 figures

The winding number is the topological invariant that classifies chiral symmetric Hamiltonians with one-dimensional parametric dependence. In this work we complete our study of the winding number statistics in a random matrix model belonging to the chiral unitary class AIII. We show that in the limit of large matrix dimensions the winding number distribution becomes Gaussian. Our results include expressions for the statistical moments of the winding number and for the k-point correlation function of the winding number density.

The recent tension in the value of the cosmological parameter $S_8 \equiv \sigma_8(\Omega_M/0.3)^{1/2}$, which represents the amplitude of the matter density fluctuations of the universe, has not been resolved. In this work, we present constraints on $S_8$ with the X-ray angular power spectra of clusters and groups measured with the half-sky map from the eROSITA All Sky Survey data release 1 (eRASS1). Thanks to the extensive sky coverage of eRASS1, its power spectrum achieves unprecedented precision compared to previous measurements. Using a well-calibrated, physical halo gas model that includes astrophysics of feedback and non-thermal pressure support, we obtain $S_8 = 0.80^{+0.02}_{-0.01}$ with 1$\sigma$ uncertainty that is competitive against other cosmological probes. Our derived $S_8$ value is smaller than the primary CMB measurements from Planck, but still consistent to within $1\sigma$. We also obtain constraints on the astrophysics of feedback, non-thermal pressure, equation of state in cluster cores, and gas boundaries in clusters and groups. We discuss how additional X-ray observations, and cosmological surveys in microwave and optical, will further improve the cosmological constraints with the angular power spectrum. Our work demonstrates that the angular power spectrum of clusters and groups is a promising probe of both cosmology and astrophysics., Comment: 12 pages, 4 figures, submitted to ApJ

Reinforcement learning (RL) has achieved outstanding success in complex robot control tasks, such as drone racing, where the RL agents have outperformed human champions in a known racing track. However, these agents fail in unseen track configurations, always requiring complete retraining when presented with new track layouts. This work aims to develop RL agents that generalize effectively to novel track configurations without retraining. The naive solution of training directly on a diverse set of track layouts can overburden the agent, resulting in suboptimal policy learning as the increased complexity of the environment impairs the agent's ability to learn to fly. To enhance the generalizability of the RL agent, we propose an adaptive environment-shaping framework that dynamically adjusts the training environment based on the agent's performance. We achieve this by leveraging a secondary RL policy to design environments that strike a balance between being challenging and achievable, allowing the agent to adapt and improve progressively. Using our adaptive environment shaping, one single racing policy efficiently learns to race in diverse challenging tracks. Experimental results validated in both simulation and the real world show that our method enables drones to successfully fly complex and unseen race tracks, outperforming existing environment-shaping techniques. Project page: http://rpg.ifi.uzh.ch/env_as_policy/index.html

Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garnered attention, often realized using variational quantum circuits to approximate the policy function. This paper addresses the robustness and generalization of quantum reinforcement learning by combining principles from quantum computing and control theory. Leveraging recent results on robust quantum machine learning, we utilize Lipschitz bounds to propose a regularized version of a quantum policy gradient approach, named the RegQPG algorithm. We show that training with RegQPG improves the robustness and generalization of the resulting policies. Furthermore, we introduce an algorithmic variant that incorporates curriculum learning, which minimizes failures during training. Our findings are validated through numerical experiments, demonstrating the practical benefits of our approach., Comment: 10 pages, 6 figures, 2 tables

The set of relevant cuts in a graph is the union of all minimum weight bases of the cut space. A cut is relevant if and only if it is the a minimum weight cut between two distinct vertices., Comment: 7 pages

We provide an accurate forecast of the expected constraining power from the main void statistics -- the void size function and the void-galaxy cross-correlation function -- to be measured by the Roman reference High Latitude Spectroscopic Survey from the Nancy Grace Roman Space Telescope. Relying on a realistic galaxy mock lightcone, covering 2000 square degrees, we find more than $8\times 10^4 $ voids and explore their constraining power in the framework of three different cosmological models: $\Lambda$CDM, $w$CDM, and $w_0 w_{\rm a}$CDM. This work confirms the strong complementarity of different void statistics and showcases the constraining power to be expected from Roman voids thanks to the combination of its high tracer density and large observed volume., Comment: 24 pages, 11 figures, 5 tables. Comments are welcome

Fault-tolerant fusion-based photonic quantum computing (FBQC) greatly relies on entangling two-photon measurements, called fusions. These fusions can be realized using linear-optical projective Bell-state measurements (BSMs). These linear-optical BSMs are limited to a success probability of 50%, greatly reducing the performance of FBQC schemes. To improve the performance of FBQC architectures, a boosted BSM scheme taking advantage of ancillary entangled photon pairs and a 4x4 multiport interferometer has been proposed. This scheme allows the success probability to be increased up to 75%. In this work, we experimentally demonstrate this boosted BSM by using two Sagnac photon-pair sources and a fibre-based 4x4 multiport beam splitter. A boosted BSM success probability of $(69.3\pm0.3)\%$ has been achieved, exceeding the 50% limit. Furthermore, based on our BSMs, we calculate photon-loss thresholds for a fusion network using encoded six-ring resource states. We show that with this boosted BSM scheme an individual photon loss probability of 1.4% can be tolerated, while the non-boosted BSM leads to a photon-loss threshold of 0.45%., Comment: 8 pages, 7 figures

Assessing classification confidence is critical for leveraging large language models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computational Social Science (CSS) tasks. In this paper, we make three key contributions: (1) we propose an uncertainty quantification (UQ) performance measure tailored for data annotation tasks, (2) we compare, for the first time, five different UQ strategies across three distinct LLMs and CSS data annotation tasks, (3) we introduce a novel UQ aggregation strategy that effectively identifies low-confidence LLM annotations and disproportionately uncovers data incorrectly labeled by the LLMs. Our results demonstrate that our proposed UQ aggregation strategy improves upon existing methods andcan be used to significantly improve human-in-the-loop data annotation processes.

The ability of large language models (LLMs) to perform zero-shot classification makes them viable solutions for data annotation in rapidly evolving domains where quality labeled data is often scarce and costly to obtain. However, the large-scale deployment of LLMs can be prohibitively expensive. This paper introduces an LLM chain ensemble methodology that aligns multiple LLMs in a sequence, routing data subsets to subsequent models based on classification uncertainty. This approach leverages the strengths of individual LLMs within a broader system, allowing each model to handle data points where it exhibits the highest confidence, while forwarding more complex cases to potentially more robust models. Our results show that the chain ensemble method often exceeds the performance of the best individual model in the chain and achieves substantial cost savings, making LLM chain ensembles a practical and efficient solution for large-scale data annotation challenges.

LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has been well-studied in other domains, applying it effectively to LLMs poses unique challenges due to their complex decision-making capabilities and computational demands. In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations. The method quantifies uncertainty by analyzing the relationships between generated assessments and possible ratings. By cross-evaluating these relationships and constructing a confusion matrix based on token probabilities, the method derives labels of high or low uncertainty. We evaluate our method across multiple benchmarks, demonstrating a strong correlation between the accuracy of LLM evaluations and the derived uncertainty scores. Our findings suggest that this method can significantly improve the reliability and consistency of LLM-as-a-Judge evaluations.

Zero-shot imitation learning algorithms hold the promise of reproducing unseen behavior from as little as a single demonstration at test time. Existing practical approaches view the expert demonstration as a sequence of goals, enabling imitation with a high-level goal selector, and a low-level goal-conditioned policy. However, this framework can suffer from myopic behavior: the agent's immediate actions towards achieving individual goals may undermine long-term objectives. We introduce a novel method that mitigates this issue by directly optimizing the occupancy matching objective that is intrinsic to imitation learning. We propose to lift a goal-conditioned value function to a distance between occupancies, which are in turn approximated via a learned world model. The resulting method can learn from offline, suboptimal data, and is capable of non-myopic, zero-shot imitation, as we demonstrate in complex, continuous benchmarks.

With the next generation of big telescopes such as the ELT and SKA it might become possible to measure changes in the expansion rate of the Universe in real time by measuring the change of the redshifts of a large number of galaxies over a period of the order of 10 years. This phenomenon, known as 'redshift drift,' will provide a crucial direct test of cosmological models. The change in redshift is readily explained using the concept of conformal time which is the comoving distance of a galaxy in lightyears. We emphasize that the redshift drift is directly proportional to the average change in the cosmic expansion rate between the time of a galaxy's light emission and its absorption. This phenomenon is illustrated within the framework of the concordance model, the Lambda-CDM model of the universe., Comment: 12 pages, 5 figures

Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical power but face obstacles due to the sensitive nature of genomic data sharing. Current state-of-the-art methods provide a privacy-focused approach utilizing computationally expensive methods such as Secure Multi-Party Computation and Homomorphic Encryption. In this context, we present a novel algorithm PP-GWAS designed to improve upon existing standards in terms of computational efficiency and scalability without sacrificing data privacy. This algorithm employs randomized encoding within a distributed architecture to perform stacked ridge regression on a Linear Mixed Model to ensure rigorous analysis. Experimental evaluation with real world and synthetic data indicates that PP-GWAS can achieve computational speeds twice as fast as similar state-of-the-art algorithms while using lesser computational resources, all while adhering to a robust security model that caters to an all-but-one semi-honest adversary setting. We have assessed its performance using various datasets, emphasizing its potential in facilitating more efficient and private genomic analyses.

Semiconductor quantum dots in microcavities are an excellent platform for the efficient generation of indistinguishable single photons. However, their use in a wide range of quantum technologies requires their controlled fabrication and integration in compact closed-cycle cryocoolers, with a key challenge being the efficient and stable extraction of the single photons into a single-mode fiber. Here we report on a novel method for fiber-pigtailing of deterministically fabricated single-photon sources. Our technique allows for nanometer-scale alignment accuracy between the source and a fiber, alignment that persists all the way from room temperature to 2.4 K. We demonstrate high performance of the device under near-resonant optical excitation with g$^{(2)}$(0) = 1.3 %, a photon indistinguishability of 97.5 % and a fibered brightness of 20.8 %. We show that the indistinguishability and single-photon rate are stable for over ten hours of continuous operation in a single cooldown. We further confirm that the device performance is not degraded by nine successive cooldown-warmup cycles., Comment: 7 pages, 5 figures

Multimodal aerial data are used to monitor natural systems, and machine learning can significantly accelerate the classification of landscape features within such imagery to benefit ecology and conservation. It remains under-explored, however, how these multiple modalities ought to be fused in a deep learning model. As a step towards filling this gap, we study three strategies (Early fusion, Late fusion, and Mixture of Experts) for fusing thermal, RGB, and LiDAR imagery using a dataset of spatially-aligned orthomosaics in these three modalities. In particular, we aim to map three ecologically-relevant biophysical landscape features in African savanna ecosystems: rhino middens, termite mounds, and water. The three fusion strategies differ in whether the modalities are fused early or late, and if late, whether the model learns fixed weights per modality for each class or generates weights for each class adaptively, based on the input. Overall, the three methods have similar macro-averaged performance with Late fusion achieving an AUC of 0.698, but their per-class performance varies strongly, with Early fusion achieving the best recall for middens and water and Mixture of Experts achieving the best recall for mounds., Comment: 9 pages, 4 figures, ECCV 2024 Workshop in CV for Ecology

We propose a Truncated Gaussian Basis Approach (TGBA) for simulating the dynamics of quantum many-body systems. The approach constructs an effective Hamiltonian within a reduced subspace, spanned by fermionic Gaussian states, and diagonalizes it to obtain approximate eigenstates and eigenenergies. Symmetries can be exploited to perform parallel computation, enabling to simulate systems with much larger sizes. As an example, we compute the dynamic structure factor and study quench dynamics in a non-integrable quantum Ising chain, known as ``$E_8$ magnet''. The mass ratios calculated through the dynamic structure factor show excellent agreement with Zamolodchikov's analytical predictions. For quench dynamics we observe that time-evolving wave functions in the truncated subspace facilitates the simulation of long-time dynamics., Comment: 6+1 pages, 2 figures

A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, they often lack input-dependent filtering or require substantial increases in model complexity to handle input variability. We address this gap by introducing S7, a simplified yet powerful SSM that can handle input dependence while incorporating stable reparameterization and specific design choices to dynamically adjust state transitions based on input content, maintaining efficiency and performance. We prove that this reparameterization ensures stability in long-sequence modeling by keeping state transitions well-behaved over time. Additionally, it controls the gradient norm, enabling efficient training and preventing issues like exploding or vanishing gradients. S7 significantly outperforms baselines across various sequence modeling tasks, including neuromorphic event-based datasets, Long Range Arena benchmarks, and various physical and biological time series. Overall, S7 offers a more straightforward approach to sequence modeling without relying on complex, domain-specific inductive biases, achieving significant improvements across key benchmarks., Comment: 23 pages, 3 figures, 11 tables. Equal contribution by Taylan Soydan and Nikola Zubi\'c

This study explores the parameter space of a minimal extension of the Standard Model with a non-abelian $SU(2)$ group, in which the gauge bosons are stable and acquire mass through a mechanism of spontaneous symmetry breaking involving a new scalar doublet which interacts with the Higgs boson through a quartic coupling. The exploration aims to assess whether it is possible to obtain a first-order phase transition while ensuring that the gauge bosons are viable dark matter candidates. Theoretical, astrophysical and collider bounds are considered. The results are then tested against the sensitivity of future experiments for the detection of gravitational wave signals., Comment: 6 pages, 3 figures, presented by Maimoona Razzaq at QCD@Work International Workshop on QCD Theory and Experiment 2024

There is a class of entropy-coding methods which do not substitute symbols by code words (such as Huffman coding), but operate on intervals or ranges. This class includes three prominent members: conventional arithmetic coding, range coding, and coding based on asymmetric numeral systems. To determine the correct symbol in the decoder, each of these methods requires the comparison of a state variable with subinterval boundaries. In adaptive operation, considering varying symbol statistics, an array of interval boundaries must additionally be kept up to date. The larger the symbol alphabet, the more time-consuming both the search for the correct subinterval and the updating of interval borders become. Detailed pseudo-code is used to discuss different approaches to speed up the symbol search in the decoder and the adaptation of the array of interval borders, both depending on the chosen alphabet size. It is shown that reducing the $\mathcal{O}$-complexity does not lead to an acceleration in practical implementations if the alphabet size is too small. In adaptive compression mode, the binary indexing method proves to be superior when considering the overall processing time. Although the symbol search (in the decoder) takes longer than with other algorithms, the faster updating of the array of interval borders more than compensates for this disadvantage. A variant of the binary indexing method is proposed, which is more flexible and has a partially lower complexity than the original approach.

Recent progress in large language models and access to large-scale robotic datasets has sparked a paradigm shift in robotics models transforming them into generalists able to adapt to various tasks, scenes, and robot modalities. A large step for the community are open Vision Language Action models which showcase strong performance in a wide variety of tasks. In this work, we study the visual generalization capabilities of three existing robotic foundation models, and propose a corresponding evaluation framework. Our study shows that the existing models do not exhibit robustness to visual out-of-domain scenarios. This is potentially caused by limited variations in the training data and/or catastrophic forgetting, leading to domain limitations in the vision foundation models. We further explore OpenVLA, which uses two pre-trained vision foundation models and is, therefore, expected to generalize to out-of-domain experiments. However, we showcase catastrophic forgetting by DINO-v2 in OpenVLA through its failure to fulfill the task of depth regression. To overcome the aforementioned issue of visual catastrophic forgetting, we propose a gradual backbone reversal approach founded on model merging. This enables OpenVLA which requires the adaptation of the visual backbones during initial training -- to regain its visual generalization ability. Regaining this capability enables our ReVLA model to improve over OpenVLA by a factor of 77% and 66% for grasping and lifting in visual OOD tasks .

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, this study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective., Comment: Accepted for publication in Business & Information Systems Engineering (2024)

We investigate the mechanical loss characteristics of Eu$^{3+}$:Y$_2$SiO$_5$$\unicode{x2013}$a promising candidate for ultra-low-noise frequency stabilization through the spectral hole burning technique. Three different mechanical oscillators with varying surface-to-volume ratios and crystal orientations are evaluated. In this context, we perform mechanical ringdown and spectral measurements spanning temperatures from room temperature down to $15\,\mathrm{K}$. By doing so, we measure a maximum mechanical quality factor of $Q=3676$, corresponding to a loss angle of $\phi=2.72\times 10^{-4}$. For a spectral hole burning laser stabilization experiment at $300\,\mathrm{mK}$, we can estimate the Allan deviation of the fractional frequency instability due to Brownian thermal noise to be below $\sigma_{\delta \nu/\nu_0} = 2.5\times 10^{-18}$, a value lower than the estimated thermal-noise limit of any current cavity-referenced ultra-stable laser experiment., Comment: 7 pages, 5 figures

NEXCERA has been recently introduced as novel ceramic-based material for spacers of ultra-stable laser cavities with a zero-crossing coefficient of thermal expansion at room temperature. Brownian thermal noise currently limits the performance of these cavities, and the mechanical loss factor, a critical parameter in estimating this noise, remains unknown for NEXCERA. In this work, we investigate the mechanical loss factor of NEXCERA N117B at room temperature across different resonance frequencies using the Gentle Nodal Suspension technique. We measure a promising loss factor of $\phi = 1.89\times 10^{-5}$, indicating NEXCERA's potential for ultra-stable laser cavities. Based on these results, we calculate the thermal noise when NEXCERA is used as a spacer and compare its overall performance as a spacer material to widely used materials such as ULE and Zerodur, considering various substrate materials. Our findings suggest that NEXCERA is a strong candidate due to its lower thermal noise and reduced linear drift rate., Comment: 10 pages, 10 figures

It is usually assumed that the memory of any thermo-mechanical protocol applied to a glass can be erased by heating the material in the supercooled liquid. While this is true for thermally treated amorphous solids, we show that hydrostatic compression can irreversibly modify the atomic motion, thermodynamic state and structure of a prototypical metallic glass-former, in a way which depends on the degree of ergodicity reached by the material during compression. While enhanced kinetic and thermodynamic stability can be obtained by quenching the dense liquid, high-pressure annealing in the glass leads to thermal rejuvenation and complex structural rearrangements at the level of the short and medium range order. When heated above their glass transition temperature, these compressed glasses do not convert into the pristine supercooled liquid but rather transform into different systems, challenging the generally accepted idea of an equilibrium recovery controlled solely by the microscopic $\alpha$-relaxation process.

We establish global bounds for solutions to stationary and time-dependent Schr\"odinger equations associated with the sublaplacian $\mathcal L$ on the Heisenberg group, as well as its pure fractional power $\mathcal L^s$ and conformally invariant fractional power $\mathcal L_s$. The main ingredient is a new abstract uniform weighted resolvent estimate which is proved by using the method of weakly conjugate operators -- a variant of Mourre's commutator method -- and Hardy's type inequalities on the Heisenberg group. As applications, we show Kato-type smoothing effects for the time-dependent Schr\"odinger equation, and spectral stability of the sublaplacian perturbed by complex-valued decaying potentials satisfying an explicit subordination condition. In the local case $s=1$, we obtain uniform estimates without any symmetry or derivative loss, which improve previous results., Comment: 28 pages

The demand for high-performance computing in machine learning and artificial intelligence has led to the development of specialized hardware accelerators like Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), and Field-Programmable Gate Arrays (FPGAs). A key strategy to enhance these accelerators is the reduction of precision in arithmetic operations, which increases processing speed and lowers latency - crucial for real-time AI applications. Precision reduction minimizes memory bandwidth requirements and energy consumption, essential for large-scale and mobile deployments, and increases throughput by enabling more parallel operations per cycle, maximizing hardware resource utilization. This strategy is equally vital for solving NP-hard quadratic unconstrained binary optimization (QUBO) problems common in machine learning, which often require high precision for accurate representation. Special hardware solvers, such as quantum annealers, benefit significantly from precision reduction. This paper introduces a fully principled Branch-and-Bound algorithm for reducing precision needs in QUBO problems by utilizing dynamic range as a measure of complexity. Experiments validate our algorithm's effectiveness on an actual quantum annealer.

Adiabatic quantum computation (AQC) is a well-established method to approximate the ground state of a quantum system. Actual AQC devices, known as quantum annealers, have certain limitations regarding the choice of target Hamiltonian. Specifically, the target system must arise from a quadratic unconstrained binary optimization (QUBO) problem. As the name suggests, QUBOs represent unconstrained problems, and the problem must fit within the dimensionality limits of the hardware solver. However, various approaches exist to decompose large QUBOs and encode constraints by penalizing infeasible solutions. Choosing the right penalization and decomposition techniques is problem-specific and cumbersome due to various degrees of freedom. In this work, we investigate these issues in the context of energy network re-dispatch problems. Such problems are paramount for sustainable and cost-effective energy systems and play a crucial role in the transition towards renewable energy sources. Our QUBO instances are derived from open data of the German energy network and our results are compared to baselines from an open-source energy network simulation, thereby fostering reproducibility. Our novel insights regarding the realization of inequality constraints, spatio-temporal state consistency, and problem decomposition highlight the potential of AQC for optimizing complex energy dispatch problems. This provides valuable insights for energy market stakeholders and researchers aiming to improve grid management and reduce carbon emissions.

We study some phenomenological effects of kinetic mixing between the hypercharge field and a new $U(1)$ gauge field in specific $Z^\prime$ models that ameliorate the tensions between measurements and Standard Model predictions in $b \rightarrow s\ell^+ \ell^-$ decays. To this end, we rederive the dimension-6 SMEFT coefficients resulting from integrating out the kinetically-mixed (`malaphoric') $Z^\prime$ field. The kinetic mixing provides a family-universal component to the couplings of the $Z^\prime$ field, which can improve fits to lepton flavour universality observables. We show how kinetic mixing improves the best fit of the $B_3-L_2$ model to $b\rightarrow s$ data by 7.3 units of $\chi^2$ while remaining compatible with other relevant data sets such as electroweak precision observables and measurements of $e^+ e^- \rightarrow \ell^+ \ell^-$ at LEP2., Comment: 20 pages, 4 figures, comments and references added

Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement Learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world.

Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the attribution scores of arguments or edges in Quantitative Bipolar Argumentation Frameworks (QBAFs). These explanations, known as Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs), commonly employ removal-based and Shapley-based techniques for computing the attribution scores. While AAEs and RAEs have proven useful in several applications with acyclic QBAFs, they remain largely unexplored for cyclic QBAFs. Furthermore, existing applications tend to focus solely on either AAEs or RAEs, but do not compare them directly. In this paper, we apply both AAEs and RAEs, to Truth Discovery QBAFs (TD-QBAFs), which assess the trustworthiness of sources (e.g., websites) and their claims (e.g., the severity of a virus), and feature complex cycles. We find that both AAEs and RAEs can provide interesting explanations and can give non-trivial and surprising insights., Comment: This paper has been accepted at ArgXAI Workshop 2024

Membrane-based sensors are an important market for microelectromechanical systems (MEMS). Two-dimensional (2D) materials, with their low mass, are excellent candidates for suspended membranes to provide high sensitivity, small footprint sensors. The present work demonstrates pressure sensors employing large-scale-synthesized 2D platinum diselenide (PtSe${_2}$) films as piezoresistive membranes supported only by a thin polymer layer. We investigate three different synthesis methods with contrasting growth parameters and establish a reliable high yield fabrication process for suspended PtSe${_2}$/PMMA membranes across sealed cavities. The pressure sensors reproducibly display sensitivities above 6 x 10${^4}$ kPa${^{-1}}$. We show that the sensitivity clearly depends on the membrane diameter and the piezoresistive gauge factor of the PtSe${_2}$ film. Reducing the total device size by decreasing the number of membranes within a device leads to a significant increase in the area-normalized sensitivity. This allows the manufacturing of pressure sensors with high sensitivity but a much smaller device footprint than the current state-of-the-art MEMS technology. We further integrate PtSe${_2}$ pressure sensors with CMOS technology, improving the technological readiness of PtSe${_2}$-based MEMS and NEMS devices., Comment: 42 pages

We derive an analog of Mirzakhani's recursion relation for hyperbolic string vertices and investigate its implications for closed string field theory. Central to our construction are systolic volumes: the Weil-Petersson volumes of regions in moduli spaces of Riemann surfaces whose elements have systoles $L \geq 0$. These volumes can be shown to satisfy a recursion relation through a modification of Mirzakhani's recursion as long as $L \leq 2 \sinh^{-1} 1$. Applying the pants decomposition of Riemann surfaces to off-shell string amplitudes, we promote this recursion to hyperbolic string field theory and demonstrate the higher order vertices are determined by the cubic vertex iteratively for any background. Such structure implies the solutions of closed string field theory obey a quadratic integral equation. We illustrate the utility of our approach in an example of a stubbed scalar theory., Comment: 46+10 pages, 12 figures, 2 tables

This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they are often not publicly available, revolve around pedestrian datasets excluding traffic-related information, or resemble architectures that are either not real-time capable or robust. To address these limitations, we first introduce a dedicated benchmark based on Argoverse 2, specifically targeting pedestrians in urban settings. Following this, we present Snapshot, a modular, feed-forward neural network that outperforms the current state of the art while utilizing significantly less information. Despite its agent-centric encoding scheme, Snapshot demonstrates scalability, real-time performance, and robustness to varying motion histories. Moreover, by integrating Snapshot into a modular autonomous driving software stack, we showcase its real-world applicability, Comment: 8 Pages, 9 Figures

Contrastive Language and Image Pairing (CLIP), a transformative method in multimedia retrieval, typically trains two neural networks concurrently to generate joint embeddings for text and image pairs. However, when applied directly, these models often struggle to differentiate between visually distinct images that have similar captions, resulting in suboptimal performance for image-based similarity searches. This paper addresses the challenge of optimizing CLIP models for various image-based similarity search scenarios, while maintaining their effectiveness in text-based search tasks such as text-to-image retrieval and zero-shot classification. We propose and evaluate two novel methods aimed at refining the retrieval capabilities of CLIP without compromising the alignment between text and image embeddings. The first method involves a sequential fine-tuning process: initially optimizing the image encoder for more precise image retrieval and subsequently realigning the text encoder to these optimized image embeddings. The second approach integrates pseudo-captions during the retrieval-optimization phase to foster direct alignment within the embedding space. Through comprehensive experiments, we demonstrate that these methods enhance CLIP's performance on various benchmarks, including image retrieval, k-NN classification, and zero-shot text-based classification, while maintaining robustness in text-to-image retrieval. Our optimized models permit maintaining a single embedding per image, significantly simplifying the infrastructure needed for large-scale multi-modal similarity search systems.

A recent trend towards running more demanding web applications, such as video games or client-side LLMs, in the browser has led to the adoption of the WebGPU standard that provides a cross-platform API exposing the GPU to websites. This opens up a new attack surface: Untrusted web content is passed through to the GPU stack, which traditionally has been optimized for performance instead of security. Worsening the problem, most of WebGPU cannot be run in the tightly sandboxed process that manages other web content, which eases the attacker's path to compromising the client machine. Contrasting its importance, WebGPU shader processing has received surprisingly little attention from the automated testing community. Part of the reason is that shader translators expect highly structured and statically typed input, which renders typical fuzzing mutations ineffective. Complicating testing further, shader translation consists of a complex multi-step compilation pipeline, each stage presenting unique requirements and challenges. In this paper, we propose DarthShader, the first language fuzzer that combines mutators based on an intermediate representation with those using a more traditional abstract syntax tree. The key idea is that the individual stages of the shader compilation pipeline are susceptible to different classes of faults, requiring entirely different mutation strategies for thorough testing. By fuzzing the full pipeline, we ensure that we maintain a realistic attacker model. In an empirical evaluation, we show that our method outperforms the state-of-the-art fuzzers regarding code coverage. Furthermore, an extensive ablation study validates our key design. DarthShader found a total of 39 software faults in all modern browsers -- Chrome, Firefox, and Safari -- that prior work missed. For 15 of them, the Chrome team assigned a CVE, acknowledging the impact of our results.

Network-facing applications are commonly exposed to all kinds of attacks, especially when connected to the internet. As a result, web servers like Nginx or client applications such as curl make every effort to secure and harden their code to rule out memory safety violations. One would expect this to include regular fuzz testing, as fuzzing has proven to be one of the most successful approaches to uncovering bugs in software. Yet, surprisingly little research has focused on fuzzing network applications. When studying the underlying reasons, we find that the interactive nature of communication, its statefulness, and the protection of exchanged messages render typical fuzzers ineffective. Attempts to replay recorded messages or modify them on the fly only work for specific targets and often lead to early termination of communication. In this paper, we discuss these challenges in detail, highlighting how the focus of existing work on protocol state space promises little relief. We propose a fundamentally different approach that relies on fault injection rather than modifying messages. Effectively, we force one of the communication peers into a weird state where its output no longer matches the expectations of the target peer, potentially uncovering bugs. Importantly, this weird peer can still properly encrypt/sign the protocol message, overcoming a fundamental challenge of current fuzzers. In effect, we leave the communication system intact but introduce small corruptions. Since we can turn either the server or the client into the weird peer, our approach is the first that can effectively test client-side network applications. Evaluating 16 targets, we show that Fuzztruction-Net outperforms other fuzzers in terms of coverage and bugs found. Overall, Fuzztruction-Net uncovered 23 new bugs in well-tested software, such as the web servers Nginx and Apache HTTPd and the OpenSSH client.

Suspended membranes of monoatomic graphene exhibit great potential for applications in electronic and nanoelectromechanical devices. In this work, a "hot and dry" transfer process is demonstrated to address the fabrication and patterning challenges of large-area graphene membranes on top of closed, sealed cavities. Here, "hot" refers to the use of high temperature during transfer, promoting the adhesion. Additionally, "dry" refers to the absence of liquids when graphene and target substrate are brought into contact. The method leads to higher yields of intact suspended monolayer CVD graphene and artificially stacked double-layer CVD graphene membranes than previously reported. The yield evaluation is performed using neural-network-based object detection in SEM images, ascertaining high yields of intact membranes with large statistical accuracy. The suspended membranes are examined by Raman tomography and AFM. The method is verified by applying the suspended graphene devices as piezoresistive pressure sensors. Our technology advances the application of suspended graphene membranes and can be extended to other two-dimensional (2D) materials., Comment: 30 pages of manuscript plus 17 pages of Supporting Information