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Inspiration plays an important role in design, yet its specific impact on data visualization design practice remains underexplored. This study investigates how professional visualization designers perceive and use inspiration in their practice. Through semi-structured interviews, we examine their sources of inspiration, the value they place on them, and how they navigate the balance between inspiration and imitation. Our findings reveal that designers draw from a diverse array of sources, including existing visualizations, real-world phenomena, and personal experiences. Participants describe a mix of active and passive inspiration practices, often iterating on sources to create original designs. This research offers insights into the role of inspiration in visualization practice, the need to expand visualization design theory, and the implications for the development of visualization tools that support inspiration and for training future visualization designers., Comment: 13 pages, In CHI Conference on Human Factors in Computing Systems (CHI25)

We propose TETRIS, a novel method that optimizes the total throughput of batch speculative decoding in multi-request settings. Unlike existing methods that optimize for a single request or a group of requests as a whole, TETRIS actively selects the most promising draft tokens (for every request in a batch) to be accepted when verified in parallel, resulting in fewer rejected tokens and hence less wasted computing resources. Such an effective resource utilization to achieve fast inference in large language models (LLMs) is especially important to service providers with limited inference capacity. Compared to baseline speculative decoding, TETRIS yields a consistently higher acceptance rate and more effective utilization of the limited inference capacity. We show theoretically and empirically that TETRIS outperforms baseline speculative decoding and existing methods that dynamically select draft tokens, leading to a more efficient batch inference in LLMs., Comment: 15 pages, 10 figures, 5 tables

In automata theory, while determinisation provides a standard route to solving many common problems in automata theory, some weak forms of nondeterminism can be dealt with in some problems without costly determinisation. For example, the handling of specifications given by nondeterministic automata over infinite words for the problems of reactive synthesis or runtime verification requires resolving nondeterministic choices without knowing the future of the input word. We define and study classes of $\omega$-regular automata for which the nondeterminism can be resolved by a policy that uses a combination of memory and randomness on any input word, based solely on the prefix read so far. We examine two settings for providing the input word to an automaton. In the first setting, called adversarial resolvability, the input word is constructed letter-by-letter by an adversary, dependent on the resolver's previous decisions. In the second setting, called stochastic resolvability, the adversary pre-commits to an infinite word and reveals it letter-by-letter. In each setting, we require the existence of an almost-sure resolver, i.e., a policy that ensures that as long as the adversary provides a word in the language of the underlying nondeterministic automaton, the run constructed by the policy is accepting with probability 1. The class of automata that are adversarially resolvable is the well-studied class of history-deterministic automata. The case of stochastically resolvable automata, on the other hand, defines a novel class. Restricting the class of resolvers in both settings to stochastic policies without memory introduces two additional new classes of automata. We show that the new automaton classes offer interesting trade-offs between succinctness, expressivity, and computational complexity, providing a fine gradation between deterministic automata and nondeterministic automata., Comment: 45 pages, 14 figures

The search of multiple topological phases (TPs) and their transitions by tuning different parameters through chemical substitutions, electric field, magnetic field, strain and Floquet engineering, etc has garnered a widespread attention in recent time. In spite of great effort, the observations of multiple TPs in a single material and multiple TP transitions in the presence of one parameter remain elusive. Here we demonstrate the presence of multiple TPs and their transitions with uniaxial compressive strain (UCS) in orthorhombic Cu$_2$SnS$_3$ by using $state$-$of$-$the$-$art$ $ab$-$initio$ calculations. In the absence of spin-orbit coupling (SOC), the Cu$_2$SnS$_3$ exhibits a single (type-II) nodal-ring and in the presence of SOC, it hosts Weyl phase with seven Weyl points (three at $\Gamma$ and four at general positions) along with nodal arcs. On the application of UCS, it remains type-II nodal-ring $<5.5$\%, which further evolves into type-III nodal-ring for $5.5\% \leq$ UCS $<5.6$\%. Interestingly, at 5.6\% of UCS, it shows Weyl phase with four Weyl nodes even in the absence of SOC. All the above-mentioned seven Weyl points persist below $5$\% of UCS. For 5\% $\leq$ UCS $<5.6$\%, four Weyl points (at general positions) disappear and nodal-arcs remain intact in all the studied range of UCS. The TPs observed in the absence of SOC appears to arise due to the presence of strain driven topological flat band, which is typically reported to be seen in kagome and Lieb lattices.

Studying systematic weaknesses of DNNs has gained prominence in the last few years with the rising focus on building safe AI systems. Slice discovery methods (SDMs) are prominent algorithmic approaches for finding such systematic weaknesses. They identify top-k semantically coherent slices/subsets of data where a DNN-under-test has low performance. For being directly useful, e.g., as evidences in a safety argumentation, slices should be aligned with human-understandable (safety-relevant) dimensions, which, for example, are defined by safety and domain experts as parts of the operational design domain (ODD). While straightforward for structured data, the lack of semantic metadata makes these investigations challenging for unstructured data. Therefore, we propose a complete workflow which combines contemporary foundation models with algorithms for combinatorial search that consider structured data and DNN errors for finding systematic weaknesses in images. In contrast to existing approaches, ours identifies weak slices that are in line with predefined human-understandable dimensions. As the workflow includes foundation models, its intermediate and final results may not always be exact. Therefore, we build into our workflow an approach to address the impact of noisy metadata. We evaluate our approach w.r.t. its quality on four popular computer vision datasets, including autonomous driving datasets like Cityscapes, BDD100k, and RailSem19, while using multiple state-of-the-art models as DNNs-under-test.

This paper focuses on developing a Flutter application for controlling Docker resources remotely. The application provides a user-friendly interface for executing various Docker-related commands on the server where the Docker engine is installed. The application uses the SSH protocol to establish a secure connection with the server and execute the commands. Further, an alternative approach is also explored, which involves connecting the application with the Docker engine using HTTP. This proposed Docker controller application provides a significant advantage for managing Docker resources remotely, which is highly beneficial in DevOps fields. It provides a user-friendly interface to manage containers, making it easy to create, start, stop, restart, and remove containers. It abstracts away the complexities of working with Docker commands, allowing users to interact with containers more intuitively. It can be used to manage a number of docker engines from one place making it easy to control and monitor all the docker resources. Its performance, security, and scalability are evaluated using various testing techniques, and the results are found satisfactory. Further improvements may include enhancing the application's features, optimizing the performance, and exploring other possible approaches for establishing the connection between the application and the Docker engine., Comment: 6 pages, 5 figures, DASA 2024

User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high-quality open-source user profile datasets: one for profile construction and another for profile updating. These datasets offer a strong basis for evaluating user profile modeling techniques in dynamic settings. We also show a methodology that uses large language models (LLMs) to tackle both profile construction and updating. Our method uses a probabilistic framework to predict user profiles from input text, allowing for precise and context-aware profile generation. Our experiments demonstrate that models like Mistral-7b and Llama2-7b perform strongly in both tasks. LLMs improve the precision and recall of the generated profiles, and high evaluation scores confirm the effectiveness of our approach.

Realizing topological superconductivity in stoichiometric materials is a key challenge in condensed matter physics. Here, we report the discovery of ternary germanide superconductors, $M$IrGe ($M$ = Ti, Hf), as prime candidates for topological superconductivity, predicted to exhibit nonsymmorphic symmetry-protected hourglass Dirac chains. Using comprehensive thermodynamic and muon-spin rotation/relaxation ($\mu$SR) measurements, we establish these materials as conventional bulk type-II superconductors with transition temperatures of 2.24(5) K for TiIrGe and 5.64(4) K for HfIrGe, featuring a full gap and preserved time-reversal symmetry. First-principles calculations reveal striking topological features in $M$IrGe, including hourglass-shaped bulk dispersions and a Dirac chain -- a ring of fourfold-degenerate Dirac points protected by nonsymmorphic symmetry. Each Dirac point corresponds to the neck of the hourglass dispersion, while the Dirac chain gives rise to drumhead-like surface states near the Fermi level. Additionally, nontrivial $\mathbb{Z}_2$ topology leads to isolated Dirac surface states with helical spin textures that disperse across the Fermi level, forming an ideal platform for proximity-induced topological superconductivity. The coexistence of conventional bulk superconductivity, symmetry-protected hourglass topology, and helical spin-textured surface states establishes $M$IrGe as a rare and robust platform to realize topological superconductivity, opening new avenues for next-generation quantum technologies., Comment: 10 pages, 3 figures

To meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central role. GCS involves capturing CO2 and injecting it into deep geological formations for long-term storage, requiring precise monitoring to ensure containment and prevent leakage. Time-lapse seismic imaging is essential for tracking CO2 migration but often struggles to capture the complexities of multi-phase subsurface flow. Digital Shadows (DS), leveraging machine learning-driven data assimilation techniques such as nonlinear Bayesian filtering and generative AI, provide a more detailed, uncertainty-aware monitoring approach. By incorporating uncertainties in reservoir properties, DS frameworks improve CO2 migration forecasts, reducing risks in GCS operations. However, data assimilation depends on assumptions regarding reservoir properties, rock physics models, and initial conditions, which, if inaccurate, can compromise prediction reliability. This study demonstrates that augmenting forecast ensembles with diverse rock physics models mitigates the impact of incorrect assumptions and improves predictive accuracy, particularly in differentiating uniform versus patchy saturation models.

Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely on accurate monitoring of subsurface CO2 migration using advanced time-lapse seismic imaging. A Digital Shadow framework integrates field data, including seismic and borehole measurements, to track CO2 saturation over time. Machine learning-assisted data assimilation techniques, such as generative AI and nonlinear ensemble Bayesian filtering, update a digital model of the CO2 plume while incorporating uncertainties in reservoir properties. Compared to 2D approaches, 3D monitoring enhances the spatial accuracy of GCS assessments, capturing the full extent of CO2 migration. This study extends the uncertainty-aware 2D Digital Shadow framework by incorporating 3D seismic imaging and reservoir modeling, improving decision-making and risk mitigation in CO2 storage projects.

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data. In addition to real-valued GNNs, quaternion GNNs also perform well on tasks on graph-structured data. With the aim of reducing the energy footprint, we reduce the model size while maintaining accuracy comparable to that of the original-sized GNNs. This paper introduces Quaternion Message Passing Neural Networks (QMPNNs), a framework that leverages quaternion space to compute node representations. Our approach offers a generalizable method for incorporating quaternion representations into GNN architectures at one-fourth of the original parameter count. Furthermore, we present a novel perspective on Graph Lottery Tickets, redefining their applicability within the context of GNNs and QMPNNs. We specifically aim to find the initialization lottery from the subnetwork of the GNNs that can achieve comparable performance to the original GNN upon training. Thereby reducing the trainable model parameters even further. To validate the effectiveness of our proposed QMPNN framework and LTH for both GNNs and QMPNNs, we evaluate their performance on real-world datasets across three fundamental graph-based tasks: node classification, link prediction, and graph classification., Comment: 12 pages, 2 figures, accepted at PAKKD 2025

In this thesis, a piezoelectric wind energy harvester is proposed for bacterial disinfection of drinking water. A mathematical model of a piezoelectric wind energy harvester is developed to predict the electrical energy from the wind induced vibration based on the galloping phenomenon. Structure of a piezoelectric wind energy harvester is a cantilever piezolaminated elastic beam with a bluff body attached to the free end. Linear time invariant and lumped parameter approach is considered to model the structure. In order to improve the performance of the harvester, bluff body is modified by attaching different attachment in the form of circular-shaped, triangular-shaped, square-shaped, Y-shaped, and curve-shaped. Bacterial disinfection of drinking water is performed using the electrical output of the piezoelectric wind energy harvester. Energy harvester with a curve-shaped attachment to a bluff body is used for this application because it provides enhanced electrical output as compared to other shaped harvesters. It has been found both numerically and experimentally that the harvester with a curve-shaped attachment to the bluff body provides the best electrical output.

Two-dimensional (2D) van der Waals (vdW) magnets and devices have garnered significant attention owing to the stabilization of long range magnetic order down to atomic limit, and the prospect for novel quantum devices with unique functionalities. To achieve this objective, clarification of magnetotransport properties and understanding of the relevant interactions with lowering of dimensions are of extreme importance. Here, the magnetotransport properties of few atomic layer Fe3GeTe2 and (Co0.25Fe0.75)3GeTe2 nanoflake devices have been investigated. Magnetotransport investigations with applied magnetic field along the easy-axis shows anomalous Hall effect, while that for applied magnetic field along the hard-axis reveals an unusual behaviour. Atomistic calculations considering the presence of antiferromagnetic, ferromagnetic and local symmetry-breaking interactions reveal critical role of magnetic frustration effect assisted by thermal fluctuations, leading to a non-zero scalar spin chirality manifesting in an unconventional Hall effect. The present result clarifies the underlying interactions in few-layer 2D vdW ferromagnetic material system, important for the understanding of non-collinear spin configurations in vdW magnets for 2D spintronic devices., Comment: 7 pages, 4 figures

The resurgence of interest in Kondo insulators has been driven by two major mysteries: the presence of metallic surface states and the observation of quantum oscillations. To further explore these mysteries, it is crucial to investigate another similar system beyond the two existing ones, SmB$_6$ and YbB$_{12}$. Here, we address this by reporting on a Kondo insulator, U$_3$Bi$_4$Ni$_3$. Our transport measurements reveal that a surface state emerges below 250 K and dominates transport properties below 150 K, which is well above the temperature scale of SmB$_6$ and YbB$_{12}$. At low temperatures, the surface conductivity is about one order of magnitude higher than the bulk. The robustness of the surface state indicates that it is inherently protected. The similarities and differences between U$_3$Bi$_4$Ni$_3$ and the other two Kondo insulators will provide valuable insights into the nature of metallic surface states in Kondo insulators and their interplay with strong electron correlations., Comment: Accepted to Science Advances

We present the first spectro-polarimetric study of the bright atoll source GX 9+1, using the simultaneous Imaging X-ray Polarimetry Explorer (IXPE), and Neutron star Interior Composition Explorer (NICER) observations. The source was observed to remain in the soft state, with no changes in state throughout the observation period. The source does not show significant polarization in the 2-8 keV energy range. However, a significant polarization (3.3 sigma) was detected in the 2-3 keV range, with a polarization degree of 3.3 +/- 0.8% and a polarization angle of 11 +/- 7 deg. We used the simultaneous energy spectra from NICER (0.6 - 11 keV) and IXPE (2-8 keV) to study the spectral properties of the source during observations. The observed spectrum of the source can be well described by a combination of Comptonized blackbody emission from the neutron star surface (compbb model in XSPEC) and thermal Comptonized component with seed photons from the accretion disc. The spectral properties of GX 9+1 during the observation are consistent with those of other bright atoll-sources in the soft state. However, the high polarization degree observed in the low-energy band does not align with previous IXPE observations of other atoll-sources. This observed polarization in the source is attributed to the strong polarization of the Comptonized blackbody component. We discuss the results from the spectro-polarimetric studies in the context of various accretion disc and coronal geometries of the source., Comment: Submitted to MNRAS

Perhaps the simplest approach to constructing models with sub-dimensional particles or fractons is to require the conservation of dipole or higher multipole moments. We generalize this approach to allow for moments in phase space and classify all possible classical fracton models with phase-space multipole conservation laws. We focus on a new self-dual model that conserves both dipole and quadrupole moments in position and momentum; we analyze its dynamics and find quasi-periodic orbits in phase space that evade ergodic exploration of the full phase space., Comment: 5 pages, 1 figure

The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of stable crystal structures within an infinite design space. We introduce Open Materials Generation (OMG), a unifying framework for the generative design and discovery of inorganic crystalline materials. OMG employs stochastic interpolants (SI) to bridge an arbitrary base distribution to the target distribution of inorganic crystals via a broad class of tunable stochastic processes, encompassing both diffusion models and flow matching as special cases. In this work, we adapt the SI framework by integrating an equivariant graph representation of crystal structures and extending it to account for periodic boundary conditions in unit cell representations. Additionally, we couple the SI flow over spatial coordinates and lattice vectors with discrete flow matching for atomic species. We benchmark OMG's performance on two tasks: Crystal Structure Prediction (CSP) for specified compositions, and 'de novo' generation (DNG) aimed at discovering stable, novel, and unique structures. In our ground-up implementation of OMG, we refine and extend both CSP and DNG metrics compared to previous works. OMG establishes a new state-of-the-art in generative modeling for materials discovery, outperforming purely flow-based and diffusion-based implementations. These results underscore the importance of designing flexible deep learning frameworks to accelerate progress in materials science.

3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation. We further find that these heterogeneous features can be compressed into a lower-dimensional (e.g., 3-channel) shared feature space using a carefully designed multi-branch network. The compressed UVGS can be treated as typical RGB images. Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians, making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. In our experiments, we demonstrate various unconditional, conditional generation, and inpainting applications of 3DGS based on diffusion models, which were previously non-trivial., Comment: https://aashishrai3799.github.io/uvgs

Terms and conditions for online shopping websites often contain terms that can have significant financial consequences for customers. Despite their impact, there is currently no comprehensive understanding of the types and potential risks associated with unfavorable financial terms. Furthermore, there are no publicly available detection systems or datasets to systematically identify or mitigate these terms. In this paper, we take the first steps toward solving this problem with three key contributions. \textit{First}, we introduce \textit{TermMiner}, an automated data collection and topic modeling pipeline to understand the landscape of unfavorable financial terms. \textit{Second}, we create \textit{ShopTC-100K}, a dataset of terms and conditions from shopping websites in the Tranco top 100K list, comprising 1.8 million terms from 8,251 websites. Consequently, we develop a taxonomy of 22 types from 4 categories of unfavorable financial terms -- spanning purchase, post-purchase, account termination, and legal aspects. \textit{Third}, we build \textit{TermLens}, an automated detector that uses Large Language Models (LLMs) to identify unfavorable financial terms. Fine-tuned on an annotated dataset, \textit{TermLens} achieves an F1 score of 94.6\% and a false positive rate of 2.3\% using GPT-4o. When applied to shopping websites from the Tranco top 100K, we find that 42.06\% of these sites contain at least one unfavorable financial term, with such terms being more prevalent on less popular websites. Case studies further highlight the financial risks and customer dissatisfaction associated with unfavorable financial terms, as well as the limitations of existing ecosystem defenses., Comment: This paper has been accepted to The Web Conference 2025 (WWW '25)

We address path-planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements from a mobile sensor network. For this problem, we present a new information gain measure for optimal sensor placement that quantifies reduction in uncertainty in the path cost rather than the environment state. This measure, which we call the context-relevant mutual information (CRMI), couples the sensor placement and path-planning problem. We propose an iterative coupled sensor configuration and path-planning (CSCP) algorithm. At each iteration, this algorithm places sensors to maximize CRMI, updates the threat estimate using new measurements, and recalculates the path with minimum expected exposure to the threat. The iterations converge when the path cost variance, which is an indicator of risk, reduces below a desired threshold. We show that CRMI is submodular, and therefore, greedy optimization provides near-optimal sensor placements while maintaining computational efficiency of the CSCP algorithm. Distance-based sensor reconfiguration costs are introduced in a modified CRMI measure, which we also show to be submodular. Through numerical simulations, we demonstrate that the principal advantage of this algorithm is that near-optimal low-variance paths are achieved using far fewer sensor measurements as compared to a standard sensor placement method.

The resistance of a heavy metal can be modulated by an adjacent magnetic material through the combined effects of the spin Hall effect, inverse spin Hall effect, and dissipation of the spin accumulation at the interface. This phenomenon is known as spin Hall magnetoresistance. The dissipation of the spin accumulation can occur via various mechanisms, with spin-transfer torque being the most extensively studied. In this work, we report the observation of spin Hall magnetoresistance at the interface between platinum and an insulating altermagnetic candidate, Ba$_2$CoGe$_2$O$_7$. Our findings reveal that this heterostructure exhibits a relatively large spin Hall magnetoresistance signal, which is anisotropic with respect to the crystal orientation of the current channel. We explore and rule out several potential explanations for this anisotropy and propose that our results may be understood in the context of the anisotropic altermagnetic ordering of Ba$_2$CoGe$_2$O$_7$.

The goal of logical controller synthesis is to automatically compute a control strategy that regulates the discrete, event-driven behavior of a given plant s.t. a temporal logic specification holds over all remaining traces. Standard approaches to this problem construct a two-player game by composing a given complete plant model and the logical specification and applying standard algorithmic techniques to extract a control strategy. However, due to the often enormous state space of a complete plant model, this process can become computationally infeasible. In this paper, we introduce a novel synthesis approach that constructs a universal controller derived solely from the game obtained by the standard translation of the logical specification. The universal controller's moves are annotated with prophecies - predictions about the plant's behavior that ensure the move is safe. By evaluating these prophecies, the universal controller can be adapted to any plant over which the synthesis problem is realizable. This approach offers several key benefits, including enhanced scalability with respect to the plant's size, adaptability to changes in the plant, and improved explainability of the resulting control strategy. We also present encouraging experimental results obtained with our prototype tool, UNICON., Comment: 31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 25)

Reducing CO$_2$ emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO$_2$ emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO$_2$ plume dynamics and reservoir properties. Building on existing seismic imaging methods like the Joint Recovery Method (JRM), which lacks uncertainty quantification, we propose the Probabilistic Joint Recovery Method (pJRM). By estimating posterior distributions across surveys using a shared generative model, pJRM provides uncertainty information to improve risk assessment in CCS projects.

This paper investigates the existence and uniqueness of mild solutions, as well as the approximate controllability, of a class of fractional evolution equations with nonlocal conditions in Hilbert spaces. Sufficient conditions for approximate controllability are established through a novel approach to the approximate solvability of semilinear operator equations. The methodology utilizes Green's function and constructs a control function based on the Gramian controllability operator. The analysis is based on Schauder's fixed point theorem and the theory of fractional order solution operators and resolvent operators. To demonstrate the feasibility of the proposed theoretical results, an illustrative example is provided., Comment: 22 pages

We examine two-player games over finite weighted graphs with quantitative (mean-payoff or energy) objective, where one of the players additionally needs to satisfy a fairness objective. The specific fairness we consider is called 'strong transition fairness', given by a subset of edges of one of the players, which asks the player to take fair edges infinitely often if their source nodes are visited infinitely often. We show that when fairness is imposed on player 1, these games fall within the class of previously studied omega-regular mean-payoff and energy games. On the other hand, when the fairness is on player 2, to the best of our knowledge, these games have not been previously studied. We provide gadget-based algorithms for fair mean-payoff games where fairness is imposed on either player, and for fair energy games where the fairness is imposed on player 1. For all variants of fair mean-payoff and fair energy (under unknown initial credit) games, we give pseudo-polynomial algorithms to compute the winning regions of both players. Additionally, we analyze the strategy complexities required for these games. Our work is the first to extend the study of strong transition fairness, as well as gadget-based approaches, to the quantitative setting. We thereby demonstrate that the simplicity of strong transition fairness, as well as the applicability of gadget-based techniques, can be leveraged beyond the omega-regular domain., Comment: This is an extended version of the same-titled paper accepted to FoSSaCS 2025

Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for effective use. This study aimed to enhance the interpretability of ioUS images by developing a real-time brain tumor detection system deployable in the operating room. We collected 2D ioUS images from the Brain Tumor Intraoperative Database (BraTioUS) and the public ReMIND dataset, annotated with expert-refined tumor labels. Using the YOLO11 architecture and its variants, we trained object detection models to identify brain tumors. The dataset included 1,732 images from 192 patients, divided into training, validation, and test sets. Data augmentation expanded the training set to 11,570 images. In the test dataset, YOLO11s achieved the best balance of precision and computational efficiency, with a mAP@50 of 0.95, mAP@50-95 of 0.65, and a processing speed of 34.16 frames per second. The proposed solution was prospectively validated in a cohort of 15 consecutively operated patients diagnosed with brain tumors. Neurosurgeons confirmed its seamless integration into the surgical workflow, with real-time predictions accurately delineating tumor regions. These findings highlight the potential of real-time object detection algorithms to enhance ioUS-guided brain tumor surgery, addressing key challenges in interpretation and providing a foundation for future development of computer vision-based tools for neuro-oncological surgery.

YBa2Cu3Ox, the first superconductor discovered with Tc higher than 77 K, is among the most complex cuprates having both CuO chains and plains in the structure. YBa2Cu3O7 (YBCO) exhibits slightly overdoped behavior and further doping is difficult as all the lattice sites in the CuO chains are occupied. We have grown high quality single crystalline films of YBCO and bilayer La0.67Sr0.33MnO3 (LSMO)/YBCO exhibiting superconductivity in both the cases. Photoemission spectra reveal different surface and bulk electronic structures; the difference reduces in the bilayer. Evidence of charge transfer across the bilayer interface is observed in the valence band and core level spectra indicating an overdoped condition in YBCO. While superconductivity in the presence of magnetic order in the bilayer is puzzling, this pathway to reach overdoped regime in YBCO opens up a new landscape to probe the exotic physics of unconventional superconductivity.

One of the most significant challenges in combating against the spread of infectious diseases was the difficulty in estimating the true magnitude of infections. Unreported infections could drive up disease spread, making it very hard to accurately estimate the infectivity of the pathogen, therewith hampering our ability to react effectively. Despite the use of surveillance-based methods such as serological studies, identifying the true magnitude is still challenging. This paper proposes an information theoretic approach for accurately estimating the number of total infections. Our approach is built on top of Ordinary Differential Equations (ODE) based models, which are commonly used in epidemiology and for estimating such infections. We show how we can help such models to better compute the number of total infections and identify the parametrization by which we need the fewest bits to describe the observed dynamics of reported infections. Our experiments on COVID-19 spread show that our approach leads to not only substantially better estimates of the number of total infections but also better forecasts of infections than standard model calibration based methods. We additionally show how our learned parametrization helps in modeling more accurate what-if scenarios with non-pharmaceutical interventions. Our approach provides a general method for improving epidemic modeling which is applicable broadly.

In this study, we investigate a novel hybrid borocarbonitrides (bpn-BCN) 2D material inspired by recent advances in carbon biphenylene synthesis, using first-principles calculations and semi-classical Boltzmann transport theory. Our analysis confirms the structural stability of bpn-BCN through formation energy, elastic coefficients, phonon dispersion, and molecular dynamics simulations at 300 K and 800 K. The material exhibits an indirect band gap of 0.19 eV (PBE) between the X and Y points and a direct band gap of 0.58 eV (HSE) at the X point. Thermoelectric properties reveal a high Seebeck coefficient, peaking at for n-type carriers at 200K along the x-axis, while n-type has a maximum of The electrical conductivity is for hole carriers, surpassing that of conventional 2D materials. The consequences of the high Seebeck coefficient and conductivity reflect a high-power factor with a peak value of at 1000K for p-type carriers along the y-axis whereas, for n-type. Moreover, the highest observed values were 0.78 (0.72) along the x (y) direction at 750 K for p-type and 0.57 (0.53) at 750 K along the x (y) axis for n-type. Our findings suggest that the bpn-BCN 2D network holds significant potential for thermoelectric applications due to its exceptional performance.

This theoretical investigation delves into the structural, electronic, and electrochemical properties of two hexagonal iron-arsenide monolayers, 1T-FeAs and 1H-FeAs, focusing on their potential as anode materials for Lithium-ion batteries. Previous studies have highlighted the ferromagnetic nature of 1T-FeAs at room temperature.Our calculations reveal that both phases exhibit metallic behaviour with spin-polarized electronic band structures. Electrochemical studies show that the 1T-FeAs monolayer has better ionic conductivity for Li ions than the 1H-FeAs phase, attributed to a lower activation barrier of 0.38 eV. This characteristic suggests a faster charge/discharge rate. Both FeAs phases exhibit comparable theoretical capacities 374 mAh/g, outperforming commercial graphite anodes. The average open-circuit voltage for maximum Li atom adsorption is 0.61 V for 1H-FeAs and 0.44 V for 1T-FeAs. The volume expansion over the maximum adsorption of Li atoms on both phases is also remarkably less than the commercially used anode material such as graphite. Further, the adsorption of Li atoms onto 1H-FeAs induces a remarkable transition from ferromagnetism to anti-ferromagnetism, with minimal impact on the electronic band structure. In contrast, the original state of 1T-FeAs remains unaffected by Li adsorption. To summarize, the potential of both 1T-FeAs and 1H-FeAs monolayers as promising anode materials for Lithium-ion batteries, offering valuable insights into their electrochemical performance and phase transition behaviour upon Li adsorption.

We have studied the electrochemical performance of the delta-5 boron monolayer as an anode material for alkali metal (AM) and alkali earth metal (AEM) ion batteries using density functional theory simulations. The electronic properties, adsorption, diffusion rate, and storage behavior of various metal atoms (M) in the {\delta}-5 boron monolayer are explored. Our study shows that the delta-5 boron monolayer possesses high electrical conductivity and a low activation barrier for electron and metal ion transit (0.46-1.72 eV), indicating a fast charge/discharge rate. Furthermore, the theoretical capacities of the {\delta}-5 boron monolayer for Li, Na, and K are found to be greater than those of commercial graphite. The average open-circuit voltage for AM and AEM is reasonably low and in the range of 0.14-0.88 V. Our results show that {\delta}-5 boron monolayer could be a promising anode material in lithium-ion and non-lithium ion rechargeable batteries.

We prove that the Christensen distance (resp., the Kadison-Kastler distance) between two $C^*$-subalgebras $\mathcal{A}$ and $\mathcal{B}$ of a $C^*$-algebra $\mathcal{C}$ is equal to that between their enveloping von Neumann algebras $\mathcal{A}^{**}$ and $\mathcal{B}^{**}$ (resp., the tensor product algebras $\mathcal{A} \otimes^{\min} \mathcal{D}$ and $\mathcal{B} \otimes^{\min} \mathcal{D}$, for any unital commutative $C^*$-algebra $\mathcal{D}$)., Comment: 13 pages

In this work, we generalize the concept of bisimulation metric in order to metrize the behaviour of continuous-time processes. Similarly to what is done for discrete-time systems, we follow two approaches and show that they coincide: as a fixpoint of a functional and through a real-valued logic. The whole discrete-time approach relies entirely on the step-based dynamics: the process jumps from state to state. We define a behavioural pseudometric for processes that evolve continuously through time, such as Brownian motion or involve jumps or both., Comment: arXiv admin note: substantial text overlap with arXiv:2312.16729

We report new results on classical, Machian, fractons. For fractons of strictly bounded Machian range, we show that local clusters do not exhibit chaos while the global state breaks ergodicity. We show that the many fracton evolution characteristically exhibits a central time or Janus point and thus a generic non-equilibrium arrow of time as discussed previously in the context of classical Cosmology., Comment: 15 pages, 7 figures

History-deterministic automata are a restricted class of nondeterministic automata where the nondeterminism while reading an input can be resolved successfully based on the prefix read so far. History-deterministic automata are exponentially more succinct than deterministic automata, while still retaining some of the algorithmic properties of deterministic automata, especially in the context of reactive synthesis. This thesis focuses on the problem of checking history-determinism for parity automata. Our main result is the 2-token theorem, due to which we obtain that checking history-determinism for parity automata with a fixed parity index can be checked in PTIME. This improves the naive EXPTIME upper bound of Henzinger and Piterman that has stood since 2006. More precisely, we show that the so-called 2-token game, which can be solved in PTIME for parity automata with a fixed parity index, characterises history-determinism for parity automata. This game was introduced by Bagnol and Kuperberg in 2018, who showed that to decide if a B\"uchi automaton is history-deterministic, it suffices to find the winner of the 2-token game on it. They conjectured that this 2-token game based characterisation of history-determinism extends to parity automata. We prove Bagnol and Kuperberg's conjecture that the winner of the 2-token game characterises history-determinism on parity automata. We also give a polynomial time determinisation procedure for history-deterministic B\"uchi automata, thus solving an open problem of Kuperberg and Skrzypczak from 2015. This result is a consequence of our proof of the 2-token theorem. Finally, we also show NP-hardness for the problem of checking history-determinism for parity automata when the parity index is not fixed. This is an improvement from the lower bound of solving parity games shown by Kuperberg and Skrzypczak in 2015., Comment: This thesis has been submitted for the author's PhD examination and is currently under review

The ocean is teeming with a myriad of invertebrate planktonic larvae, which thrive in a viscous fluid environment. Many of them rely on ciliary beating to generate fluid flows for locomotion and feeding. Their forms, local morphologies, and ciliation patterns exhibit remarkable diversity, producing intricate and dynamic three-dimensional flows that are notoriously difficult to characterize in laboratory settings. Traditional microscopic imaging techniques typically involve gently squeeze-confining the soft larvae between a glass slide and cover slip, to study their flows in quasi-two-dimensions. However, a comprehensive hydrodynamic framework for the low-to-intermediate Reynolds number flows at the larval scale in quasi-2D confinement - particularly in light of their complex forms - has remained elusive. Here, we demonstrate that vortices around larvae proliferate with increasing confinement, and elucidate the underlying physical mechanism. We experimentally quantify confinement-induced flows in larvae of sea stars and sea urchins. The flows exhibited strikingly universal patterns: under weak confinement, all larvae generated two vortices, whereas under strong confinement, multiple vortices appeared. The experimental observations were well captured by a low Reynolds number theoretical model based on superposition of confined Stokeslets. Building on experiments and theory, we developed a comprehensive framework for confinement-induced larval flows, which suggests that the vorticity dynamics are primarily determined by local morphological features, rather than the larval forms. Our work provides fundamental insights into the form-functional relationships between larval morphology and flow generation. Our findings are broadly applicable to understanding fluid flows generated by a wide range of ciliated organisms with complex forms and morphologies, from micro- to milli-length-scales., Comment: 26 pages, 3 main figures, 5 extended data figures. (First two authors contributed equally)

Synchrophasor technology or phasor measurement units (PMUs) are known to detect multiple type of oscillations or faults better than Supervisory Control and Data Acquisition (SCADA) systems, but the volume of Bigdata (e.g., 30-120 samples per second on a single PMU) generated by these sensors at the aggregator level (e.g., several PMUs) requires special handling. Conventional machine learning or data mining methods are not suitable to handle such larger streaming realtime data. This is primarily due to latencies associated with cloud environments (e.g., at an aggregator or PDC level), and thus necessitates the need for local computing to move the data on the edge (or locally at the PMU level) for processing. This requires faster real-time streaming algorithms to be processed at the local level (e.g., typically by a Field Programmable Gate Array (FPGA) based controllers). This paper proposes a transfer learning-based hoeffding tree with ADWIN (THAT) method to detect anomalous synchrophasor signatures. The proposed algorithm is trained and tested with the OzaBag method. The preliminary results with transfer learning indicate that a computational time saving of 0.7ms is achieved with THAT algorithm (0.34ms) over Ozabag (1.04ms), while the accuracy of both methods in detecting fault events remains at 94% for four signatures.

We consider the issue of high peak-to-average-power ratio (PAPR) of Orthogonal time frequency space (OTFS) modulated signals. This paper proposes a low-complexity novel iterative PAPR reduction method which achieves a PAPR reduction of roughly 5 dB when compared to a OTFS modulated signal without any PAPR compensation. Simulations reveal that the PAPR achieved by the proposed method is significantly better than that achieved by other state-of-art methods. Simulations also reveal that the error rate performance of OTFS based systems with the proposed PAPR reduction is similar to that achieved with the other state-of-art methods., Comment: This work has been submitted to the IEEE for possible publication

We address the problem of path-planning for an autonomous mobile vehicle, called the ego vehicle, in an unknown andtime-varying environment. The objective is for the ego vehicle to minimize exposure to a spatiotemporally-varying unknown scalar field called the threat field. Noisy measurements of the threat field are provided by a network of mobile sensors. Weaddress the problem of optimally configuring (placing) these sensors in the environment. To this end, we propose sensor reconfiguration by maximizing a reward function composed of three different elements. First, the reward includes an informa tion measure that we call context-relevant mutual information (CRMI). Unlike typical sensor placement techniques that maxi mize mutual information of the measurements and environment state, CRMI directly quantifies uncertainty reduction in the ego path cost while it moves in the environment. Therefore, the CRMI introduces active coupling between the ego vehicle and the sensor network. Second, the reward includes a penalty on the distances traveled by the sensors. Third, the reward includes a measure of proximity of the sensors to the ego vehicle. Although we do not consider communication issues in this paper, such proximity is of relevance for future work that addresses communications between the sensors and the ego vehicle. We illustrate and analyze the proposed technique via numerical simulations., Comment: Draft submitted to the 2025 American Control Conference

We show that the performance of a linear self-orthogonal $GF(4)$ code for magic state distillation of Bravyi and Kitaev's $|T\rangle$-state is characterized by its simple weight enumerator. We compute weight enumerators of all such codes with fewer than 20 qubits and find none whose threshold exceeds that of the 5-qubit code. Using constraints on weight enumerators from invariant theory and linear programming, we establish bounds on the exponent characterizing noise suppression of a $|T\rangle$-state distillation protocol. We also obtain new non-negativity constraints on such weight enumerators by demanding consistency of the associated magic state distillation routine. These constraints yield new bounds on the distances of classical Hermitian self-dual and maximal self-orthogonal linear $GF(4)$ codes, notably proving the nonexistence of such codes with parameters $[12m, 6m, 4m+2]_{GF(4)}$., Comment: 47 pages, 14 figures, v2: corrected typos, minor improvements

Correlation-driven metal-insulator transitions and temperature-driven quantum-coherent-to-incoherent crossovers in correlated electron systems underpin the doping, temperature and frequency-resolved evolution of physical responses. Motivated by recent experimental studies that investigate the evolution of dynamical spin and charge responses, we analyze the spin and charge diffusion spectra in both half-filled and doped one-band Hubbard model using Dynamical Mean Field Theory (DMFT) combined with the Numerical Renormalization Group (NRG). We compare the relative strengths and limitations of Density Matrix NRG (DMNRG) and Full Density Matrix NRG (FDM-NRG) in capturing low-frequency spectral features and their evolution with temperature, interaction strength and band-filling. Key measures, including characteristic frequency scales, Kullback-Leibler divergence, diffusion constants, and kurtosis provide complementary but internally consistent picture for the evolution of spin and charge excitations across bandwidth as well as the band-filling driven Mott transitions and the coherent-incoherent crossover. We find that spin and charge fluctuations cross over from quantum-coherent-to-quantum-incoherent at distinct temperatures, providing a microscopic insight into the complex, two-stage, Fermi-to-non-Fermi liquid-to-bad metal crossovers seen in transport data, in particular in the $dc$ resistivity.

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society: accessible and inclusive healthcare in terms of equality and equity. Techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, a significant gap exists in feeding back the models with appropriate real-time changes. In other words, active behavioral modeling of modern society is lacking, influencing community healthcare as a whole. By creating virtual replicas of (physical) behavioral systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to: Identify and address disparities in access to healthcare services, Facilitate community participation, Simulate the impact of urban policies and interventions on different groups of people, and Aid policy-making bodies for better access to healthcare. This paper proposes several ways to use digital twins to stitch the actual and virtual societies. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high-level step-wise transitions that will enable this transformation., Comment: 13 pages, 1 figure. This is accepted to publish at the proceedings of the 6th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2024)