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Within $Z^\prime$ models, neutral meson mixing severely constrains beyond the Standard Model (SM) effects in flavour changing neutral current (FCNC) processes. However, in certain regions of the $Z^\prime$ parameter space, the contributions to meson mixing observables become negligibly small even for large $Z^\prime$ couplings. While this a priori allows for significant new physics (NP) effects in FCNC decays, we discuss how large $Z^\prime$ couplings in one neutral meson sector can generate effects in meson mixing observables of other neutral mesons, through correlations stemming from $\text{SU(2)}_L$ gauge invariance and through Renormalization Group (RG) effects in the SM Effective Field Theory~(SMEFT). This is illustrated with the example of $B_s^0-\bar B_s^0$ mixing, which in the presence of both left- and right-handed $Z^\prime bs$ couplings $\Delta_L^{bs}$ and $\Delta_R^{bs}$ remains SM-like for $\Delta_R^{bs}\approx 0.1\,\Delta_L^{bs}$. We show that in this case, large $Z^\prime bs$ couplings generate effects in $D$ and $K$ meson mixing observables, but that the $D$ and $K$ mixing constraints and the relation between $\Delta_R^{bs}$ and $\Delta_L^{bs}$ are fully compatible with a lepton flavour universality~(LFU) conserving explanation of the most recent $b\to s\ell^+\ell^-$ experimental data without violating other constraints like $e^+ e^-\to\ell^+\ell^-$ scattering. Assuming LFU, invariance under the $\text{SU(2)}_L$ gauge symmetry leads then to correlated effects in $b\to s\nu\bar\nu$ observables presently studied intensively by the Belle~II experiment, which allow to probe the $Z^\prime$ parameter space that is opened up by the vanishing NP contributions to $B_s^0-\bar B_s^0$ mixing. In this scenario the suppression of $B\to K(K^*)\mu^+\mu^-$ branching ratios implies {\em uniquely} enhancements of $B\to K(K^*)\nu\bar\nu$ branching ratios up to $20\%$., Comment: 63 pages, 6 figures, 2 tables

Recent approaches in machine learning often solve a task using a composition of multiple models or agentic architectures. When targeting a composed system with adversarial attacks, it might not be computationally or informationally feasible to train an end-to-end proxy model or a proxy model for every component of the system. We introduce a method to craft an adversarial attack against the overall multi-model system when we only have a proxy model for the final black-box model, and when the transformation applied by the initial models can make the adversarial perturbations ineffective. Current methods handle this by applying many copies of the first model/transformation to an input and then re-use a standard adversarial attack by averaging gradients, or learning a proxy model for both stages. To our knowledge, this is the first attack specifically designed for this threat model and our method has a substantially higher attack success rate (80% vs 25%) and contains 9.4% smaller perturbations (MSE) compared to prior state-of-the-art methods. Our experiments focus on a supervised image pipeline, but we are confident the attack will generalize to other multi-model settings [e.g. a mix of open/closed source foundation models], or agentic systems, Comment: 11 pages, 2 figures

The Russian invasion of Ukraine on February 24, 2022 entailed the threat of a drastic and sudden reduction of natural gas supply to the European Union. This paper presents a techno-economic analysis of the consequences of a sudden gas supply shock to Austria, one of the most dependent countries on imports of Russian gas. Our analysis comprises (a) a detailed assessment of supply and demand side countermeasures to mitigate the immediate shortfall in Russian gas imports, (b) a mapping of the net reduction in gas supply to industrial sectors to quantify direct economic shocks and expected relative reductions in gross output and (c) the quantification of higher-order economic impacts through using a dynamic out-of-equilibrium input-output model. Our results show that potential economic consequences can range from relatively mild to highly severe, depending on the implementation and success of counteracting mitigation measures. We find that securing alternative gas imports, storage management, and incentivizing fuel switching represent the most important short-term policy levers to mitigate the adverse impacts of a sudden import stop.

The increasing demand for energy storage solutions has spurred intensive research into next-generation battery technologies. Oxygen-ion batteries (OIBs), which leverage mixed ionic-electronic conducting (MIEC) oxides, have emerged as promising candidates due to their solid, non-flammable nature and potential for high power densities. This study investigates the use of over-stoichiometric La2NiO4+delta (L2NO4) as a cathode material for OIBs, exploring its capacity for electrochemical energy storage. Half-cell measurements reveal that L2NO4 with a closed-pore microstructure can store oxygen, achieving a volumetric charge of 63 mA.h.cm-3 at 400 {\deg}C with a current density of 3.6 uA.cm-2 and potentials up to 0.75 V vs. 1 bar O2. Additionally, a functional full cell combining over-stoichiometric L2NO4 and under-stoichiometric La0.5Sr0.5Cr0.2Mn0.8O3-delta (LSCrMn) has been successfully developed, demonstrating excellent cyclability and coulomb efficiency. The full cell reaches a maximum volumetric charge of 90 mA.h.cm-3 at 400 {\deg}C, 17.8 uA.cm-2, and a cut-off voltage of 1.8 V. This proof of concept underscores the viability of combining over- and under-stoichiometric MIEC materials in OIBs and provides critical insights into optimizing electrode materials and tuning oxygen content for improved performance. This research lays the groundwork for future advancements in OIB technology, aiming to develop materials with lower resistance and higher efficiency.

This thesis investigates three areas targeted at improving the reliability of machine learning; fairness in machine learning, strategic classification, and algorithmic robustness. Each of these domains has special properties or structure that can complicate learning. A theme throughout this thesis is thinking about ways in which a `plain' empirical risk minimization algorithm will be misleading or ineffective because of a mis-match between classical learning theory assumptions and specific properties of some data distribution in the wild. Theoretical understanding in eachof these domains can help guide best practices and allow for the design of effective, reliable, and robust systems., Comment: PhD thesis

Individuals who are blind or have low vision (BLV) are at a heightened risk of sharing private information if they share photographs they have taken. To facilitate developing technologies that can help them preserve privacy, we introduce BIV-Priv-Seg, the first localization dataset originating from people with visual impairments that shows private content. It contains 1,028 images with segmentation annotations for 16 private object categories. We first characterize BIV-Priv-Seg and then evaluate modern models' performance for locating private content in the dataset. We find modern models struggle most with locating private objects that are not salient, small, and lack text as well as recognizing when private content is absent from an image. We facilitate future extensions by sharing our new dataset with the evaluation server at https://vizwiz.org/tasks-and-datasets/object-localization.

The first step towards digitalization within organizations lies in digitization - the conversion of analog data into digitally stored data. This basic step is the prerequisite for all following activities like the digitalization of processes or the servitization of products or offerings. However, digitization itself often leads to 'data-rich' but 'knowledge-poor' material. Knowledge discovery and knowledge extraction as approaches try to increase the usefulness of digitized data. In this paper, we point out the key challenges in the context of knowledge discovery and present an approach to addressing these using a microservices architecture. Our solution led to a conceptual design focusing on keyword extraction, similarity calculation of documents, database queries in natural language, and programming language independent provision of the extracted information. In addition, the conceptual design provides referential design guidelines for integrating processes and applications for semi-automatic learning, editing, and visualization of ontologies. The concept also uses a microservices architecture to address non-functional requirements, such as scalability and resilience. The evaluation of the specified requirements is performed using a demonstrator that implements the concept. Furthermore, this modern approach is used in the German patent office in an extended version.

Current methods to prevent crypto asset fraud are based on the analysis of transaction graphs within blockchain networks. While effective for identifying transaction patterns indicative of fraud, it does not capture the semantics of transactions and is constrained to blockchain data. Consequently, preventive methods based on transaction graphs are inherently limited. In response to these limitations, we propose the Kosmosis approach, which aims to incrementally construct a knowledge graph as new blockchain and social media data become available. During construction, it aims to extract the semantics of transactions and connect blockchain addresses to their real-world entities by fusing blockchain and social media data in a knowledge graph. This enables novel preventive methods against rug pulls as a form of crypto asset fraud. To demonstrate the effectiveness and practical applicability of the Kosmosis approach, we examine a series of real-world rug pulls from 2021. Through this case, we illustrate how Kosmosis can aid in identifying and preventing such fraudulent activities by leveraging the insights from the constructed knowledge graph.

The exploitation of advanced materials for novel energy, health and computing applications requires fundamental understanding of enabling physicochemical mechanisms, including ionic and electronic conductivity, defect formation processes and reaction kinetics. Therefore, access to underlying constants of functional materials via advanced but straightforward experimental techniques is key. We present a novel, cheap and widely applicable approach to analyze oxygen-tracer-diffusion in thin films with unprecedented time resolution based on the novel in situ isotope-exchange Raman spectroscopy (IERS) methodology. Raman spectroscopy is sensitive to changes in the local isotopic composition, manifested by a frequency shift of the oxygen Raman modes. Employing a Raman transparent capping layer allows to establish an in-plane tracer gradient and follow the isotope exchange and diffusion processes via consecutive spatial and time resolved in situ Raman line scans. Mass-transport coefficients are calculated from these isotopic gradients, similar to conventional techniques, but with an additional time-component, not accessible by other techniques. Here, we study gadolinium doped ceria (CGO) thin films, capped with Si3N4 or Al2O3. We report diffusion coefficients within the temperature range of interest for intermediate temperature emerging applications (300-500{\deg}C) and confirm the validity of the measurement procedure and extracted parameters by comparison with FEM simulations and literature results.

Zero-shot anomaly segmentation using pre-trained foundation models is a promising approach that enables effective algorithms without expensive, domain-specific training or fine-tuning. Ensuring that these methods work across various environmental conditions and are robust to distribution shifts is an open problem. We investigate the performance of WinCLIP [14] zero-shot anomaly segmentation algorithm by perturbing test data using three semantic transformations: bounded angular rotations, bounded saturation shifts, and hue shifts. We empirically measure a lower performance bound by aggregating across per-sample worst-case perturbations and find that average performance drops by up to 20% in area under the ROC curve and 40% in area under the per-region overlap curve. We find that performance is consistently lowered on three CLIP backbones, regardless of model architecture or learning objective, demonstrating a need for careful performance evaluation.

We present a JWST MIRI/MRS spectrum (5-27 $\mathrm{\mu}$m) of the Type Ia supernova (SN Ia), SN 2021aefx at $+415$ days past $B$-band maximum. The spectrum, which was obtained during the iron-dominated nebular phase, has been analyzed in combination with previous JWST observations of SN 2021aefx, to provide the first JWST time series analysis of an SN Ia. We find the temporal evolution of the [Co III] 11.888 $\mathrm{\mu}$m feature directly traces the decay of $^{56}$Co. The spectra, line profiles, and their evolution are analyzed with off-center delayed-detonation models. Best fits were obtained with White Dwarf (WD) central densities of $\rho_c=0.9-1.1\times 10^9$g cm$^{-3}$, a WD mass of M$_{\mathrm{WD}}$=1.33-1.35M$_\odot$, a WD magnetic field of $\approx10^6$G, and an off-center deflagration-to-detonation transition at $\approx$ 0.5 $M_\odot$ seen opposite to the line of sight of the observer (-30). The inner electron capture core is dominated by energy deposition from $\gamma$-rays whereas a broader region is dominated by positron deposition, placing SN 2021aefx at +415 d in the transitional phase of the evolution to the positron-dominated regime. The formerly `flat-tilted' profile at 9 $\mathrm{\mu}$m now has significant contribution from [Ni IV], [Fe II], and [Fe III] and less from [Ar III], which alters the shape of the feature as positrons excite mostly the low-velocity Ar. Overall, the strength of the stable Ni features in the spectrum is dominated by positron transport rather than the Ni mass. Based on multi-dimensional models, our analysis is consistent with a single-spot, close-to-central ignition with an indication for a pre-existing turbulent velocity field, and excludes a multiple-spot, off-center ignition., Comment: Accepted for publication in ApJ

While audio description (AD) is the standard approach for making videos accessible to blind and low vision (BLV) people, existing AD guidelines do not consider BLV users' varied preferences across viewing scenarios. These scenarios range from how-to videos on YouTube, where users seek to learn new skills, to historical dramas on Netflix, where a user's goal is entertainment. Additionally, the increase in video watching on mobile devices provides an opportunity to integrate nonverbal output modalities (e.g., audio cues, tactile elements, and visual enhancements). Through a formative survey and 15 semi-structured interviews, we identified BLV people's video accessibility preferences across diverse scenarios. For example, participants valued action and equipment details for how-to videos, tactile graphics for learning scenarios, and 3D models for fantastical content. We define a six-dimensional video accessibility design space to guide future innovation and discuss how to move from "one-size-fits-all" paradigms to scenario-specific approaches., Comment: To appear at CHI 2024

In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner is to carefully modify their classifier to be robust to such strategic behavior. When reasoning about agent manipulations, most papers that study strategic classification rely on the following strong assumption: agents fully know the exact parameters of the deployed classifier by the learner. This often is an unrealistic assumption when using complex or proprietary machine learning techniques in real-world prediction tasks. We initiate the study of partial information release by the learner in strategic classification. We move away from the traditional assumption that agents have full knowledge of the classifier. Instead, we consider agents that have a common distributional prior on which classifier the learner is using. The learner in our model can reveal truthful, yet not necessarily complete, information about the deployed classifier to the agents. The learner's goal is to release just enough information about the classifier to maximize accuracy. We show how such partial information release can, counter-intuitively, benefit the learner's accuracy, despite increasing agents' abilities to manipulate. We show that while it is intractable to compute the best response of an agent in the general case, there exist oracle-efficient algorithms that can solve the best response of the agents when the learner's hypothesis class is the class of linear classifiers, or when the agents' cost function satisfies a natural notion of submodularity as we define. We then turn our attention to the learner's optimization problem and provide both positive and negative results on the algorithmic problem of how much information the learner should release about the classifier to maximize their expected accuracy.

We present JWST spectral and photometric observations of the Type IIP supernova (SN) 2022acko at ~50 days past explosion. These data are the first JWST spectral observations of a core-collapse SN. We identify ~30 different H I features, other features associated with products produced from the CNO cycle, and s-process elements such as Sc II and Ba II. By combining the JWST spectra with ground-based optical and NIR spectra, we construct a full Spectral Energy Distribution from 0.4 to 25 microns and find that the JWST spectra are fully consistent with the simultaneous JWST photometry. The data lack signatures of CO formation and we estimate a limit on the CO mass of < 10^{-8} solar mass. We demonstrate how the CO fundamental band limits can be used to probe underlying physics during stellar evolution, explosion, and the environment. The observations indicate little mixing between the H envelope and C/O core in the ejecta and show no evidence of dust. The data presented here set a critical baseline for future JWST observations, where possible molecular and dust formation may be seen.

Reddiment is a web-based dashboard that links sentiment analysis of subreddit texts with share prices. The system consists of a backend, frontend and various services. The backend, in Node.js, manages the data and communicates with crawlers that collect Reddit comments and stock market data. Sentiment is analyzed with the help of Vader and TextBlob. The frontend, based on SvelteKit, provides users with a dashboard for visualization. The distribution is carried out via Docker containers and Docker Compose. The project offers expansion options, e.g. the integration of cryptocurrency rates. Reddiment enables the analysis of sentiment and share prices from subreddit data., Comment: Ostbayerische Technische Hochschule Amberg-Weiden, CyberLytics-Lab, Technical Reports, CL-2022-06, July 2022, in German language

Kaon physics is at a turning point -- while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop., Comment: 55 pages, Summary of Kaons@CERN 23 workshop, references updated, typos fixed, version as published in EPJC

We consider the problem of supply chain data visibility in a blockchain-enabled supply chain network. Existing methods typically record transactions happening in a supply chain on a single blockchain and are limited in their ability to deal with different levels of data visibility. To address this limitation, we present FoodFresh -- a multi-chain consortium where organizations store immutable data on their blockchains. A decentralized hub coordinates the cross-chain exchange of digital assets among the heterogeneous blockchains. Mechanisms for enabling blockchain interoperability help to preserve the benefits of independent sovereign blockchains while allowing for data sharing across blockchain boundaries.

We present a JWST mid-infrared spectrum of the under-luminous Type Ia Supernova (SN Ia) 2022xkq, obtained with the medium-resolution spectrometer on the Mid-Infrared Instrument (MIRI) $\sim130$ days post-explosion. We identify the first MIR lines beyond 14 $\mu$m in SN Ia observations. We find features unique to under-luminous SNe Ia, including: isolated emission of stable Ni, strong blends of [Ti II], and large ratios of singly ionized to doubly ionized species in both [Ar] and [Co]. Comparisons to normal-luminosity SNe Ia spectra at similar phases show a tentative trend between the width of the [Co III] 11.888 $\mu$m feature and the SN light curve shape. Using non-LTE-multi-dimensional radiation hydro simulations and the observed electron capture elements we constrain the mass of the exploding white dwarf. The best-fitting model shows that SN 2022xkq is consistent with an off-center delayed-detonation explosion of a near-Chandrasekhar mass WD (M$_{\rm ej}$ $\approx 1.37$ M$_{\odot}$) of high-central density ($\rho_c \geq 2.0\times10^{9}$ g cm$^{-3}$) seen equator on, which produced M($^{56}$Ni) $= 0.324$ M$_{\odot}$ and M($^{58}$Ni) $\geq 0.06$ M$_{\odot}$. The observed line widths are consistent with the overall abundance distribution; and the narrow stable Ni lines indicate little to no mixing in the central regions, favoring central ignition of sub-sonic carbon burning followed by an off-center DDT beginning at a single point. Additional observations may further constrain the physics revealing the presence of additional species including Cr and Mn. Our work demonstrates the power of using the full coverage of MIRI in combination with detailed modeling to elucidate the physics of SNe Ia at a level not previously possible., Comment: 31 pages, 18 figures, accepted to ApJ; updated to accepted version

We consider the vulnerability of fairness-constrained learning to small amounts of malicious noise in the training data. Konstantinov and Lampert (2021) initiated the study of this question and presented negative results showing there exist data distributions where for several fairness constraints, any proper learner will exhibit high vulnerability when group sizes are imbalanced. Here, we present a more optimistic view, showing that if we allow randomized classifiers, then the landscape is much more nuanced. For example, for Demographic Parity we show we can incur only a $\Theta(\alpha)$ loss in accuracy, where $\alpha$ is the malicious noise rate, matching the best possible even without fairness constraints. For Equal Opportunity, we show we can incur an $O(\sqrt{\alpha})$ loss, and give a matching $\Omega(\sqrt{\alpha})$lower bound. In contrast, Konstantinov and Lampert (2021) showed for proper learners the loss in accuracy for both notions is $\Omega(1)$. The key technical novelty of our work is how randomization can bypass simple "tricks" an adversary can use to amplify his power. We also consider additional fairness notions including Equalized Odds and Calibration. For these fairness notions, the excess accuracy clusters into three natural regimes $O(\alpha)$,$O(\sqrt{\alpha})$ and $O(1)$. These results provide a more fine-grained view of the sensitivity of fairness-constrained learning to adversarial noise in training data.

In recent years, theoretical and phenomenological studies with effective field theories have become a trending and prolific line of research in the field of high-energy physics. In order to discuss present and future prospects concerning automated tools in this field, the SMEFT-Tools 2022 workshop was held at the University of Zurich from 14th-16th September 2022. The current document collects and summarizes the content of this workshop., Comment: 116 pages, 10 figures. Based on the contributions to SMEFT-Tools 2022 (https://indico.icc.ub.edu/event/128/). v2: version published in EPJC

Exclusive semileptonic $b$ hadron decays ($b \to u \ell \nu$) serve as a sandbox for probing strong and electroweak interactions and for extracting the CKM element $V_{ub}$. Instead, this work investigates their underexplored potential to reveal new short-distance physics. Utilizing SMEFT as a conduit to chart territory beyond the SM, we demonstrate that substantive new physics contributions in $b \to u \ell \nu$ are necessarily linked to correlated effects in rare neutral-current $b$ decays, neutral $B$ meson mixing or high-mass Drell-Yan tails. We find that measurements of the latter processes strongly restrict the allowed deviations in the former. A complete set of tree-level mediators, originating from a perturbative ultraviolet model and matching at dimension 6, is thoroughly explored to support this assertion. As a showcase application, we examine the feasibility of a new physics interpretation of the recent tension in exclusive $|V_{ub}|$ extraction from $B \to V \ell \nu$ where $V=(\rho,\omega)$., Comment: 42 pages, 9 figures, 3 tables