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One explanation for how people can plan efficiently despite limited cognitive resources is that we possess a set of adaptive planning strategies and know when and how to use them. But how are these strategies acquired? While previous research has studied how individuals learn to choose among existing strategies, little is known about the process of forming new planning strategies. In this work, we propose that new planning strategies are discovered through metacognitive reinforcement learning. To test this, we designed a novel experiment to investigate the discovery of new planning strategies. We then present metacognitive reinforcement learning models and demonstrate their capability for strategy discovery as well as show that they provide a better explanation of human strategy discovery than alternative learning mechanisms. However, when fitted to human data, these models exhibit a slower discovery rate than humans, leaving room for improvement.

We explore ideas for scaling verification methods for quantum circuits using SMT (Satisfiability Modulo Theories) solvers. We propose two primary strategies: (1) decomposing proof obligations via compositional verification and (2) leveraging linear over-approximation techniques for gate effects. We present two examples and demonstrate the application of these ideas to proof Hamming weight preservation.

In this short review we describe the process of designing a superconducting circuit device for quantum information applications. We discuss the factors that must be considered to implement a desired effective Hamiltonian on a device. We describe the translation between a device's physical layout, the circuit graph, and the effective Hamiltonian. We go over the process of electromagnetic simulation of a device layout to predict its behavior. We also discuss concerns such as connectivity, crosstalk suppression, and radiation shielding, and how they affect both on-chip design and enclosure structures. This paper provides an overview of the challenges in superconducting quantum circuit design and acts as a starter document for researchers working on any of these challenges., Comment: 20 pages, 11 figures

We propose a new method to measure the evolution of the neutron skin thickness between different isotopes with small statistical and systematic uncertainties. We consider antiproton--nucleus interactions close to the production threshold of $\Lambda \overline{\Lambda }$ and $\Sigma^-\overline{\Lambda }$ pairs. At low energies, $\Lambda \overline{\Lambda }$ pairs are produced in $\overline{\text{p}} +\text{p}$ collisions, while $\Sigma^-\overline{\Lambda }$ pairs can only be produced in $\overline{\text{p}} +\text{n}$ interactions. Within a simple geometrical picture we show that the double ratio for the production of $\Sigma^-\overline{\Lambda }$ and $\Lambda \overline{\Lambda }$ pairs for two different isotopes are directly related to the variation of the neutron skin thickness between the two nuclei. Performing high statistics calculations with the Gie\ss en Boltzmann--Uehling--Uhlenbeck (GiBUU) transport model for several isotope pairs covering a wide range of elements we verify a strong correlation between the double ratio from the full transport simulations and the schematic model. This correlation enables us to quantify the high potential of the proposed method for precise studies of neutron skin variations in isotope chains., Comment: 9 figures

Determining the creep compliances of orthotropic composite materials requires experiments in at least three different uniaxial and biaxial loading directions. Up to date, data respecting multiple climates and all anatomical directions are sparse for hygro-responsive materials like Norway spruce. Consequently, simulation models of wood frequently over-simplify creep, e.g., by proportionally scaling missing components or neglecting climatic influences. To overcome such simplifications, an automated computer-controlled climatized creep rack was developed, that experimentally assesses moisture-dependent viscoelasticity and mechanosorption in all anatomical directions. The device simultaneously measures the creep strains of three dogbone tension samples, three flat compression samples, and six Arcan shear samples via Digital Image Correlation. This allows for ascertaining the complete orthotropic compliance tensors while accounting for loading direction asymmetries. This paper explains the creep rack's structure and demonstrates its use by determining all nine independent creep compliance components of Norway spruce at 65% relative humidity. The data shows that loading asymmetry effects amount up to 16%. Furthermore, the found creep compliance tensor is not proportional to the elastic compliance tensor. By clustering the compliance components, we identify four necessary components to represent the full orthotropy of the compliance tensor, obtainable from not less than two experiments., Comment: 22 pages, 13 figures, preprint

Significant advances have been made in natural language processing in recent years. However, our current deep learning approach to language modeling requires substantial resources in terms of data and computation. One of the side effects of this data-hungry paradigm is the current schism between languages, separating those considered high-resource, where most of the development happens and resources are available, and the low-resource ones, which struggle to attain the same level of performance and autonomy. This study aims to introduce a new set of resources to stimulate the future development of neural text generation in Portuguese. In this work, we document the development of GigaVerbo, a concatenation of deduplicated Portuguese text corpora amounting to 200 billion tokens. Via this corpus, we trained a series of decoder-transformers named Tucano. Our models perform equal or superior to other Portuguese and multilingual language models of similar size in several Portuguese benchmarks. The evaluation of our models also reveals that model performance on many currently available benchmarks used by the Portuguese NLP community has little to no correlation with the scaling of token ingestion during training, highlighting the limitations of such evaluations when it comes to the assessment of Portuguese generative language models. All derivatives of our study are openly released on GitHub and Hugging Face. See https://nkluge-correa.github.io/Tucano/

Cooling ages of white dwarfs are routinely determined by mapping effective temperatures and masses to ages using evolutionary models. Typically, the reported uncertainties on cooling ages only consider the error propagation of the uncertainties on the spectroscopically or photometrically determined $T_{\rm eff}$ and mass. However, cooling models are themselves uncertain, given their dependence on many poorly constrained inputs. This paper estimates these systematic model uncertainties. We use MESA to generate cooling sequences of $0.5-1.0 M_{\odot}$ hydrogen-atmosphere white dwarfs with carbon-oxygen cores under different assumptions regarding the chemical stratification of their core, the thickness of their helium envelope, their hydrogen content, and the conductive opacities employed in the calculations. The parameter space explored is constrained by the range of values predicted by a variety of stellar evolution models and inferred from asteroseismological studies. For a $0.6 M_{\odot}$ white dwarf, we find an uncertainty of 0.03 Gyr at 10,000 K (corresponding to a 5% relative uncertainty) and 0.8 Gyr at 4000 K (9%). This uncertainty is significant, as it is comparable to the age uncertainty obtained by propagating the measurement errors on $T_{\rm eff}$ and mass for a typical white dwarf. We also separately consider the potential impact of $^{22}$Ne shell distillation, which plausibly leads to an additional uncertainty of $\sim 1$ Gyr for crystallized white dwarfs. We provide a table of our simulation results that can be used to evaluate the systematic model uncertainty based on a white dwarf's $T_{\rm eff}$ and mass. We encourage its use in all future studies where white dwarf cooling ages are measured., Comment: Submitted to ApJ

We present the discovery of TOI-7041 b (TIC 201175570 b), a hot Saturn transiting a red giant star with measurable stellar oscillations. We observe solar-like oscillations in TOI-7041 with a frequency of maximum power of $\nu_{\rm max} = 218.50\pm2.23$ $\mu$Hz and a large frequency separation of $\Delta\nu = 16.5282\pm0.0186$ $\mu$Hz. Our asteroseismic analysis indicates that TOI-7041 has a radius of $4.10 \pm 0.06$(stat) $\pm$ 0.05(sys) $R_\odot$, making it one of the largest stars around which a transiting planet has been discovered with the Transiting Exoplanet Survey Satellite (TESS), and the mission's first oscillating red giant with a transiting planet. TOI-7041 b has an orbital period of $9.691 \pm 0.006$ days and a low eccentricity of $e = 0.04 \pm 0.04$. We measure a planet radius of $1.02 \pm 0.03$ $R_J$ with photometry from TESS, and a planet mass of $0.36 \pm 0.16$ $M_J$ ($114 \pm 51$ $M_\oplus$) with ground-based radial velocity measurements. TOI-7041 b appears less inflated than similar systems receiving equivalent incident flux, and its circular orbit indicates that it is not undergoing tidal heating due to circularization. The asteroseismic analysis of the host star provides some of the tightest constraints on stellar properties for a TESS planet host and enables precise characterization of the hot Saturn. This system joins a small number of TESS-discovered exoplanets orbiting stars that exhibit clear stellar oscillations and indicates that extended TESS observations of evolved stars will similarly provide a path to improved exoplanet characterization., Comment: 16 pages, 9 figures, 3 tables

Wireless communication between road users is essential for environmental perception, reasoning, and mission planning to enable fully autonomous vehicles, and thus improve road safety and transport efficiency. To enable collaborative driving, the concept of vehicle-to-Everything (V2X) has long been introduced to the industry. Within the last two decades, several communication standards have been developed based on IEEE 802.11p and cellular standards, namely Dedicated Short-Range Communication (DSRC), Intelligent Transportation System G5 (ITS-G5), and Cellular- and New Radio- Vehicle-to-Everything (C-V2X and NR-V2X). However, while there exists a high quantity of available publications concerning V2X and the analysis of the different standards, only few surveys exist that summarize these results. Furthermore, to our knowledge, no survey that provides an analysis about possible future trends and challenges for the global implementation of V2Xexists. Thus, this contribution provides a detailed survey on Vehicle-to-Everything communication standards, their performance, current and future applications, and associated challenges. Based on our research, we have identified several research gaps and provide a picture about the possible future of the Vehicle-to-Everything communication domain.

This paper develops a general data-driven approach to stochastic elastoplastic modelling that leverages atomistic simulation data directly rather than by fitting parameters. The approach is developed in the context of metallic glasses, which present inherent complexities due to their disordered structure. By harvesting statistics from simulated metallic glass shear response histories, the material state is mapped onto a two-dimensional state space consisting of the shear stress and the inelastic contribution to the potential energy. The resulting elastoplastic model is intrinsically stochastic and represented as a non-deterministic dynamical map. The state space statistics provide insights into the deformation physics of metallic glasses, revealing that two state variables are sufficient to describe the main features of the elastoplastic response. In this two-dimensional state space, the gradually quenched metallic glass rejuvenates during the initial quasi-elastic shearing, ultimately reaching a steady state that fluctuates about a fixed point in the state space as rejuvenation and aging balance., Comment: 22 pages, 5 figures

Given an operator that produces the state $|\phi\rangle=\sum\phi(x)\,|x\rangle$, we propose a quantum protocol that transforms any input state of the form $|\psi\rangle=\sum\psi(x)\,|x\rangle$ to $|\psi'\rangle=\sum\psi(x)\,e^{i\alpha|\phi(x)|^2}\,|x\rangle$. This protocol is thus programmable in the sense that one can use it to apply different phase profiles $e^{i\alpha|\phi(x)|^2}$ by choosing different $|\phi\rangle$ states as the input parameter at each run. Therefore, the problem of applying phases to a signal via a quantum computer is reduced to the problem of initializing a state $|\phi\rangle$ that corresponds to the desired phase profile. Furthermore, we discuss possible applications of this protocol, most importantly in Hamiltonian simulation., Comment: 5 pages, 5 supplementary pages, 5 figures

Enabling supply chain transparency (SCT) is essential for regulatory compliance and meeting sustainability standards. Multi-tier SCT plays a pivotal role in identifying and mitigating an organization's operational, environmental, and social (ESG) risks. While research observes increasing efforts towards SCT, a minority of companies are currently publishing supply chain information. Using the Design Science Research approach, we develop a collaborative platform for supply chain transparency. We derive design requirements, formulate design principles, and evaluate the artefact with industry experts. Our artefact is initialized with publicly available supply chain data through an automated pipeline designed to onboard future participants to our platform. This work contributes to SCT research by providing insights into the challenges and opportunities of implementing multi-tier SCT and offers a practical solution that encourages organizations to participate in a transparent ecosystem.

Two-dimensional transition metal dichalcogenides (TMDs) are highly appealing for gas sensors, lab-on-a-chip devices and bio-sensing applications because of their strong light-matter interaction and high surface-to-volume ratio. The ability to grow these van der Waals materials on different substrates and waveguide geometries opens a horizon toward scalable on-chip photonic nanodevices. Here, we report on a versatile technique for real time remote optical gas sensing using two-dimensional TMDs. The adsorption of the gas molecules on the monolayer surface provides a gateway for gas sensing based on charge-transfer-induced photoluminescence variation. For gases that are weakly adsorbed on the surface of monolayer TMDs, purging the monolayers' surface by an inert gas like N2 can desorb gases from the monolayers at room temperature. We demonstrate CO, NO and NO2 detection by monitoring photoluminescence from semiconducting MoS2 monolayers grown on SiO2/Si chips at a level of 10 ppm with fast response time. Observations are supported by our density functional theory calculations, which predict a significant interaction between these gases and MoS2 monolayers. These findings may lead to advances in remote sensing, surface-sensitive bioanalytics and lab-on-a-chip sensors.

Graph and network visualization supports exploration, analysis and communication of relational data arising in many domains: from biological and social networks, to transportation and powergrid systems. With the arrival of AI-based question-answering tools, issues of trustworthiness and explainability of generated answers motivate a greater role for visualization. In the context of graphs, we see the need for visualizations that can convince a critical audience that an assertion about the graph under analysis is valid. The requirements for such representations that convey precisely one specific graph property are quite different from standard network visualization criteria which optimize general aesthetics and readability. In this paper, we aim to provide a comprehensive introduction to visual proofs of graph properties and a foundation for further research in the area. We present a framework that defines what it means to visually prove a graph property. In the process, we introduce the notion of a visual certificate, that is, a specialized faithful graph visualization that leverages the viewer's perception, in particular, pre-attentive processing (e.g. via pop-out effects), to verify a given assertion about the represented graph. We also discuss the relationships between visual complexity, cognitive load and complexity theory, and propose a classification based on visual proof complexity. Finally, we provide examples of visual certificates for problems in different visual proof complexity classes., Comment: Appears in the Proceedings of the 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

Voice-based systems like Amazon Alexa, Google Assistant, and Apple Siri, along with the growing popularity of OpenAI's ChatGPT and Microsoft's Copilot, serve diverse populations, including visually impaired and low-literacy communities. This reflects a shift in user expectations from traditional search to more interactive question-answering models. However, presenting information effectively in voice-only channels remains challenging due to their linear nature. This limitation can impact the presentation of complex queries involving controversial topics with multiple perspectives. Failing to present diverse viewpoints may perpetuate or introduce biases and affect user attitudes. Balancing information load and addressing biases is crucial in designing a fair and effective voice-based system. To address this, we (i) review how biases and user attitude changes have been studied in screen-based web search, (ii) address challenges in studying these changes in voice-based settings like SCS, (iii) outline research questions, and (iv) propose an experimental setup with variables, data, and instruments to explore biases in a voice-based setting like Spoken Conversational Search., Comment: Accepted Late-Breaking Results at ACM ICMI Companion 2024

This article shows that the approach to generalised curvature and torsion pioneered by Polacek and Siegel [1] is a generalisation of Cartan Geometry -- rendering latter natural from the point of view of O(d,d)-generalised geometry. We present this approach in the generalised metric formalism and show that almost all parts of the additional higher generalised tensors appearing in this approach correspond to covariant derivatives of the generalised Riemann tensor. As an application, we use this framework to phrase sigma model dynamics in an explicitly covariant way -- both under generalised diffeomorphisms and local gauge transformations., Comment: 32 pages, comments welcome, v2: updated references, version submitted to PRD

Quantum sensors promise revolutionary advances in medical imaging, energy production, mass detection, geodesy, foundational physics research, and a host of other fields. In many sensors, the signal takes the form of a changing qubit frequency, which is detected with an interference measurement. Unfortunately, environmental noise decoheres the qubit state, reducing signal-to-noise ratio (SNR). Here we introduce a protocol for enhancing the sensitivity of a measurement of a qubit's frequency in the presence of decoherence. We use a continuous drive to stabilize one component of the qubit's Bloch vector, enhancing the effect of a small static frequency shift. We demonstrate our protocol on a superconducting qubit, enhancing SNR per measurement shot by 1.65$\times$ and SNR per qubit evolution time by 1.09$\times$ compared to standard Ramsey interferometry. We explore the protocol theoretically and numerically, finding maximum enhancements of 1.96$\times$ and 1.18$\times$, respectively. We also show that the protocol is robust to parameter miscalibrations. Our protocol provides an unconditional enhancement in signal-to-noise ratio compared to standard Ramsey interferometry. It requires no feedback and no extra control or measurement resources, and can be immediately applied in a wide variety of quantum computing and quantum sensor technologies to enhance their sensitivities.

Layered 2D halide perovskites are chemically synthesized realizations of quantum well stacks with giant exciton oscillator strengths, tunable emission spectra and very large exciton binding energies. While these features render 2D halide perovskites a promising platform for room-temperature polaritonics, bosonic condensation and polariton lasing in 2D perovskites have so far remained elusive at ambient conditions. Here, we demonstrate room-temperature cavity exciton-polariton condensation in mechanically exfoliated crystals of the 2D Ruddlesden-Popper iodide perovskite $(BA)_{2}(MA)_{2}Pb_{3}I_{10}$ in an open optical microcavity. We observe a polariton condensation threshold of $P_{th}=6.76 fJ$ per pulse and detect a strong non-linear response. Interferometric measurements confirm the spontaneous emergence of spatial coherence across the condensate with an associated first-order autocorrelation reaching $g^{(1)}\approx 0.6$. Our results lay the foundation for a new class of room-temperature polariton lasers based on 2D halide perovskites with great potential for hetero-integration with other van-der-Waals materials and combination with photonic crystals or waveguides., Comment: 21 pages, 8 figures

The degree of alignment between a star's spin axis and the orbital plane of its planets (the stellar obliquity) is related to interesting and poorly understood processes that occur during planet formation and evolution. Hot Jupiters orbiting hot stars ($\gtrsim$6250 K) display a wide range of obliquities, while similar planets orbiting cool stars are preferentially aligned. Tidal dissipation is expected to be more rapid in stars with thick convective envelopes, potentially explaining this trend. Evolved stars provide an opportunity to test the damping hypothesis, particularly stars that were hot on the main sequence and have since cooled and developed deep convective envelopes. We present the first systematic study of the obliquities of hot Jupiters orbiting subgiants that recently developed convective envelopes using Rossiter-McLaughlin observations. Our sample includes two newly discovered systems in the Giants Transiting Giants Survey (TOI-6029 b, TOI-4379 b). We find that the orbits of hot Jupiters orbiting subgiants that have cooled below $\sim$6250 K are aligned or nearly aligned with the spin-axis of their host stars, indicating rapid tidal realignment after the emergence of a stellar convective envelope. We place an upper limit for the timescale of realignment for hot Jupiters orbiting subgiants at $\sim$500 Myr. Comparison with a simplified tidal evolution model shows that obliquity damping needs to be $\sim$4 orders of magnitude more efficient than orbital period decay to damp the obliquity without destroying the planet, which is consistent with recent predictions for tidal dissipation from inertial waves excited by hot Jupiters on misaligned orbits., Comment: 22 pages, 14 figures, 3 tables

Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first shared task on perspective argument retrieval incorporates perspectives during retrieval, accounting for latent influences in argumentation. We present a novel multilingual dataset covering demographic and socio-cultural (socio) variables, such as age, gender, and political attitude, representing minority and majority groups in society. We distinguish between three scenarios to explore how retrieval systems consider explicitly (in both query and corpus) and implicitly (only in query) formulated perspectives. This paper provides an overview of this shared task and summarizes the results of the six submitted systems. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. Moreover, retrieval systems tend to be biased towards the majority group but partially mitigate bias for the female gender. While we bootstrap perspective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed covergas system midway through water data taking resulted in a significant reduction in the activity of $^{222}$Rn daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase $^8$B solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The $^8$B solar neutrino flux is found to be $\left(2.32^{+0.18}_{-0.17}\text{(stat.)}^{+0.07}_{-0.05}\text{(syst.)}\right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming no neutrino oscillations, or $\left(5.36^{+0.41}_{-0.39}\text{(stat.)}^{+0.17}_{-0.16}\text{(syst.)} \right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and Standard Solar Model Calculations. The electron recoil spectrum is presented above 3.5 MeV.

MoTe$_2$ monolayers and bilayers are unique within the family of van-der-Waals materials since they pave the way towards atomically thin infrared light-matter quantum interfaces, potentially reaching the important telecommunication windows. Here, we report emergent exciton-polaritons based on MoTe$_2$ monolayer and bilayer in a low-temperature open micro-cavity in a joint experiment-theory study. Our experiments clearly evidence both the enhanced oscillator strength and enhanced luminescence of MoTe$_2$ bilayers, signified by a 38 \% increase of the Rabi-splitting and a strongly enhanced relaxation of polaritons to low-energy states. The latter is distinct from polaritons in MoTe$_2$ monolayers, which feature a bottleneck-like relaxation inhibition. Both the polaritonic spin-valley locking in monolayers and the spin-layer locking in bilayers are revealed via the Zeeman effect, which we map and control via the light-matter composition of our polaritonic resonances., Comment: 6 pages, 3 figures

The virtual Z gate has been established as an important tool for performing quantum gates on various platforms, including but not limited to superconducting systems. Many such platforms offer a limited set of calibrated gates and compile other gates, such as the Y gate, using combinations of X and virtual Z gates. Here, we show that the method of compilation has important consequences in an open quantum system setting. Specifically, we experimentally demonstrate that it is crucial to choose a compilation that is symmetric with respect to virtual Z rotations. This is particularly pronounced in dynamical decoupling (DD) sequences, where improper gate decomposition can result in unintended effects such as the implementation of the wrong sequence. Our findings indicate that in many cases the performance of DD is adversely affected by the incorrect use of virtual Z gates, compounding other coherent pulse errors. In addition, we identify another source of coherent errors: interference between consecutive pulses that follow each other too closely. This work provides insights into improving general quantum gate performance and optimizing DD sequences in particular.

In this note, we examine voting on four major blockchain DAOs: Aave, Compound, Lido and Uniswap. Using data directly collected from the Ethereum blockchain, we examine voter activity. We find that in most votes, the "minimal quorum," i.e., the smallest number of active voters who could swing the vote is quite small. To understand who is actually driving these DAOs, we use data from the Ethereum Name Service (ENS), Sybil.org, and Compound, to divide voters into different categories.

We introduce deterministic benchmarking (DB), a protocol designed to identify the interplay of coherent and incoherent errors overlooked by randomized benchmarking (RB) and related benchmarking methods. DB provides a set of four parameters that characterize both incoherent and coherent errors in the single-qubit gate set. Furthermore, DB reveals asymmetries in gate performance induced by strong relaxation errors ($T_1$). We experimentally demonstrate DB using a superconducting transmon qubit and support these results with a simple analytical model and master equation simulations. Our findings uncover critical errors missed by conventional RB and point to strategies to mitigate these errors., Comment: 13 pages, 5 figures, comments are welcome

We report on the wafer scale fabrication of single mode low-loss lithium niobate on insulator waveguides utilizing a chemically amplified resist and an optimized dry etching method. The fabricated single mode waveguides are free of residuals and re-deposition, with measured losses for straight waveguides around 2 dB/m (0.02 dB/cm). We present on a method offering advantages for large-scale production due to its cost-effectiveness, faster writing time, and simplified processes. This work holds promise for advancing integrated photonics and optical communication technologies.

This paper aims to develop a mathematical foundation to model knitting with graphs. We provide a precise definition for knit objects with a knot theoretic component and propose a simple undirected graph, a simple directed graph, and a directed multigraph model for any arbitrary knit object. Using these models, we propose natural categories related to the complexity of knitting structures. We use these categories to explore the hardness of determining whether a knit object of each class exists for a given graph. We show that while this problem is NP-hard in general, under specific cases, there are linear and polynomial time algorithms which take advantage of unique properties of common knitting techniques. This work aims to bridge the gap between textile arts and graph theory, offering a useful and rigorous framework for analyzing knitting objects using their corresponding graphs and for generating knitting objects from graphs., Comment: 11 pages, 4 tables, 5 figures

Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability., Comment: Under review; Model script available at https://github.com/zhu00121/WavRx

Dense plasma environment affects the electronic structure of ions via variations of the microscopic electrical fields, also known as plasma screening. This effect can be either estimated by simplified analytical models, or by computationally expensive and to date unverified numerical calculations. We have experimentally quantified plasma screening from the energy shifts of the bound-bound transitions in matter driven by the x-ray free electron laser (XFEL). This was enabled by identification of detailed electronic configurations of the observed K{\alpha}, K\b{eta} and K{\gamma} lines. This work paves the way for improving plasma screening models including connected effects like ionization potential depression and continuum lowering, which will advance the understanding of atomic physics in Warm Dense Matter regime.

Topological photonics is an emergent research discipline which interlinks fundamental aspects of photonics, information processing and solid-state physics. Exciton-polaritons are a specifically interesting platform to study topological phenomena, since the coherent light matter coupling enables new degrees of freedom such as tunable non-linearities, chiralities and dissipation. Room-temperature operation of such exciton-polaritons relies on materials comprising both, large exciton binding energies and oscillator strength. We harness widely spectrally tunable, room temperature exciton-polaritons based on a WS2 monolayer in an open optical cavity to realize a polariton potential landscape which emulates the Su-Schrieffer-Heeger (SSH) Hamiltonian. It comprises a domain boundary hosting a topological, exponentially localized mode at the interface between two lattices characterized by different Zak-phases which features a spectral tunability over a range as large as 80 meV. Moreover, we utilize the unique tilt-tunability of our implementation, to transform the SSH-lattice into a Stark-ladder. This transformation couples the topologically protected defect mode to propagating lattice modes, and effectively changes the symmetry of the system. Furthermore, it allows us to directly quantify the Zak-phase difference $\Delta_{Zak}=(1.13\pm 0.11)\pi$ between the two topological phases. Our work comprises an important step towards in-situ tuning topological lattices to control and guide light on non-linear chips.

The decisions of individuals and organizations are often suboptimal because normative decision strategies are too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach prescriptive decision strategies that take people's constraints into account. So far, this line of research has been limited to simplified decision problems. This article is the first to extend this approach to a real-world decision problem, namely project selection. We develop a computational method (MGPS) that automatically discovers project selection strategies that are optimized for real people and develop an intelligent tutor that teaches the discovered strategies. We evaluated MGPS on a computational benchmark and tested the intelligent tutor in a training experiment with two control conditions. MGPS outperformed a state-of-the-art method and was more computationally efficient. Moreover, the intelligent tutor significantly improved people's decision strategies. Our results indicate that our method can improve human decision-making in naturalistic settings similar to real-world project selection, a first step towards applying strategy discovery to the real world.

In this paper, we present a multimodal dataset obtained from a honey bee colony in Montr\'eal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.

Conservation laws are of great theoretical and practical interest. We describe a novel approach to machine learning conservation laws of finite-dimensional dynamical systems using trajectory data. It is the first such approach based on kernel methods instead of neural networks which leads to lower computational costs and requires a lower amount of training data. We propose the use of an "indeterminate" form of kernel ridge regression where the labels still have to be found by additional conditions. We use here a simple approach minimising the length of the coefficient vector to discover a single conservation law.

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $\Delta m^2_{21}$. In the ranges allowed by previous measurements, the best-fit $\Delta m^2_{21}$ is (8.85$^{+1.10}_{-1.33}$) $\times$ 10$^{-5}$ eV$^2$. This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on $\Delta m^2_{21}$ with about three years of data.

Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that are dependent on the respective structures. In this work we propose FlowSDF, an image-guided conditional flow matching framework to represent the signed distance function (SDF) leading to an implicit distribution of segmentation masks. The advantage of leveraging the SDF is a more natural distortion when compared to that of binary masks. By learning a vector field that is directly related to the probability path of a conditional distribution of SDFs, we can accurately sample from the distribution of segmentation masks, allowing for the evaluation of statistical quantities. Thus, this probabilistic representation allows for the generation of uncertainty maps represented by the variance, which can aid in further analysis and enhance the predictive robustness. We qualitatively and quantitatively illustrate competitive performance of the proposed method on a public nuclei and gland segmentation data set, highlighting its utility in medical image segmentation applications.

The recent leaps in complexity and fluency of Large Language Models (LLMs) mean that, for the first time in human history, people can interact with computers using natural language alone. This creates monumental possibilities of automation and accessibility of computing, but also raises severe security and safety threats: When everyone can interact with LLMs, everyone can potentially break into the systems running LLMs. All it takes is creative use of language. This paper presents Hacc-Man, a game which challenges its players to "jailbreak" an LLM: subvert the LLM to output something that it is not intended to. Jailbreaking is at the intersection between creative problem solving and LLM security. The purpose of the game is threefold: 1. To heighten awareness of the risks of deploying fragile LLMs in everyday systems, 2. To heighten people's self-efficacy in interacting with LLMs, and 3. To discover the creative problem solving strategies, people deploy in this novel context.

We demonstrate evidence of correlated magnetism for exciton-polaritons in a MoSe$_{2}$/WS$_{2}$ moir\'e heterostructure with near-parallel alignment subject to electron doping. In our experiments, interactions between electrons and moir\'e excitons are controlled electrostatically by field-effect doping, and the polaritonic regime of strong light-matter coupling is established in an open cryogenic microcavity. Remarkably, at filling fractions around one electron per moir\'e cell, we observe drastic and nonlinear enhancement of the effective polariton Land\'e factor as a hallmark of correlated magnetism, which is cavity-controlled via resonance tuning of light and matter polariton constituents. Our work establishes moir\'e van der Waals heterostructures as an outstanding platform for studies of correlated phenomena in the presence of strong light-matter coupling and many-body phases of lattice-ordered excitons, charges and spins.

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only channels -- as is the case for Spoken Conversational Search (SCS) -- are harder to characterize, given the lack of instruments to effectively and precisely capture interactions. Furthermore, in this era of information overload, cognitive bias can significantly impact how we seek and consume information -- especially in the context of controversial topics or multiple viewpoints. This paper draws upon insights from multiple disciplines (including information seeking, psychology, cognitive science, and wearable sensors) to provoke novel conversations in the community. To this end, we discuss future opportunities and propose a framework including multimodal instruments and methods for experimental designs and settings. We demonstrate preliminary results as an example. We also outline the challenges and offer suggestions for adopting this multimodal approach, including ethical considerations, to assist future researchers and practitioners in exploring cognitive biases in SCS., Comment: Extended version of MobileHCI'24 LBW paper

We introduce the Vector-QM24 (VQM24) dataset which comprehensively covers all possible neutral closed shell small organic and inorganic molecules and their conformers that contain up to five heavy atoms (non-hydrogen) consisting of $p$-block elements C, N, O, F, Si, P, S, Cl, Br. This dataset has been systematically generated by considering all combinatorially possible stoichiometries, and graphs (according to Lewis rules), along with all stable conformers identified by GFN2-xTB. We have used density functional theory ($\omega$B97X-D3/cc-pVDZ) to optimize the geometries of 577k conformational isomers corresponding to 258k constitutional isomers consistent with 5,599 unique stoichiometries. Single point diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are reported for the sub-set of all the energetically lowest conformers (10,793 molecules) with up to 4 heavy atoms. Apart from graphs, geometries, rotational constants, and vibrational normal modes, VQM24 includes internal, atomization, electron-electron repulsion, exchange-correlation, dispersion, vibrational frequency, Gibbs free, enthalpy, ZPV and molecular orbital energies; as well as entropy, and heat capacities. Electronic properties include multipole moments (dipole, quadrupole, octupole, hexadecapole), electrostatic potentials at nuclei (aka "alchemical potential"), Mulliken charges, and Kohn-Sham orbitals. Supervised machine learning (ML) models of atomization energies on the energetically lowest conformers for the 258k constitutional isomers indicate a significantly more challenging benchmark than the previously introduced QM9 dataset with none of our models reaching chemical accuracy. VQM24 represents an accurate and unbiased benchmark dataset ideal for assessing the efficiency, accuracy and transferability of quantum ML models of real systems.

Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observational studies or surveys. Leveraging the advances in sensing technologies, our study aims to characterize user behaviors with physiological signals, particularly in relation to cognitive load, affective arousal, and valence. We conduct a controlled lab study with 26 participants, and collect data including Electrodermal Activities, Photoplethysmogram, Electroencephalogram, and Pupillary Responses. This study examines informational search with four stages: the realization of Information Need (IN), Query Formulation (QF), Query Submission (QS), and Relevance Judgment (RJ). We also include different interaction modalities to represent modern systems, e.g., QS by text-typing or verbalizing, and RJ with text or audio information. We analyze the physiological signals across these stages and report outcomes of pairwise non-parametric repeated-measure statistical tests. The results show that participants experience significantly higher cognitive loads at IN with a subtle increase in alertness, while QF requires higher attention. QS involves demanding cognitive loads than QF. Affective responses are more pronounced at RJ than QS or IN, suggesting greater interest and engagement as knowledge gaps are resolved. To the best of our knowledge, this is the first study that explores user behaviors in a search process employing a more nuanced quantitative analysis of physiological signals. Our findings offer valuable insights into user behavior and emotional responses in information seeking processes. We believe our proposed methodology can inform the characterization of more complex processes, such as conversational information seeking.

Recently, we proposed polycontextural networks as a model of evolving systems of interacting beliefs. Here, we present an analysis of the phase transition as well as the scaling properties. The model contains interacting agents that strive for consensus, each with only subjective perception. Depending on a parameter that governs how responsive the agents are to changing their belief systems the model exhibits a phase transition that mediates between an active phase where the agents constantly change their beliefs and a frozen phase, where almost no changes appear. We observe the build-up of convention-aligned clusters only in the intermediate regime of diverging susceptibility. Here, we analyze in detail the behavior of polycontextural networks close to this transition. We provide an analytical estimate of the critical point and show that the scaling properties and the space-time structure of these clusters show self-similar behavior. Our results not only contribute to a better understanding of the emergence of consensus in systems of distributed beliefs but also show that polycontextural networks are models, motivated by social systems, where susceptibility -- the sensitivity to change own beliefs -- drives the growth of consensus clusters.

Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples. However, for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial to capture the variability in human judgments. We introduce Annotator-Centric Active Learning (ACAL), which incorporates an annotator selection strategy following data sampling. Our objective is two-fold: 1) to efficiently approximate the full diversity of human judgments, and 2) to assess model performance using annotator-centric metrics, which value minority and majority perspectives equally. We experiment with multiple annotator selection strategies across seven subjective NLP tasks, employing both traditional and novel, human-centered evaluation metrics. Our findings indicate that ACAL improves data efficiency and excels in annotator-centric performance evaluations. However, its success depends on the availability of a sufficiently large and diverse pool of annotators to sample from., Comment: Accepted at EMNLP2024