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In this paper we prove if G is a finite simple group with same-size conjugacy class set, U(G) = {1, rq, 4rq, 8pq, 8pr}, where p, q, r are prime numbers, then G is isomorphic to PSL(2, l), where l is prime greater than 7. In particular, if this group with previous conditions has at most two conjugacy classes of each size, then G is isomorphic to PSL (2, 11).

Many laypeople are motivated to improve the health behavior of their family or friends but do not know where to start, especially if the health behavior is potentially stigmatizing or controversial. We present an approach that uses virtual agents to coach community-based volunteers in health counseling techniques, such as motivational interviewing, and allows them to practice these skills in role-playing scenarios. We use this approach in a virtual agent-based system to increase COVID-19 vaccination by empowering users to influence their social network. In a between-subjects comparative design study, we test the effects of agent system interactivity and role-playing functionality on counseling outcomes, with participants evaluated by standardized patients and objective judges. We find that all versions are effective at producing peer counselors who score adequately on a standardized measure of counseling competence, and that participants were significantly more satisfied with interactive virtual agents compared to passive viewing of the training material. We discuss design implications for interpersonal skills training systems based on our findings., Comment: Accepted at CSCW '24

Physics-based dynamic models (PBDMs) are simplified representations of complex dynamical systems. PBDMs take specific processes within a complex system and assign a fragment of variables and an accompanying set of parameters to depict the processes. As this often leads to suboptimal parameterisation of the system, a key challenge requires refining the empirical parameters and variables to reduce uncertainties while maintaining the model s explainability and enhancing its predictive accuracy. We demonstrate that a hybrid mosquito population dynamics model, which integrates a PBDM with Physics-Informed Neural Networks (PINN), retains the explainability of the PBDM by incorporating the PINN-learned model parameters in place of its empirical counterparts. Specifically, we address the limitations of traditional PBDMs by modelling the parameters of larva and pupa development rates using a PINN that encodes complex, learned interactions of air temperature, precipitation and humidity. Our results demonstrate improved mosquito population simulations including the difficult-to-predict mosquito population peaks. This opens the possibility of hybridisation concept application on other complex systems based on PBDMs such as cancer growth to address the challenges posed by scarce and noisy data, and to numerical weather prediction and climate modelling to overcome the gap between physics-based and data-driven weather prediction models.

In the era of Extremely Large Telescopes, the current generation of 8-10m facilities are likely to remain competitive at ground-UV wavelengths for the foreseeable future. The Cassegrain U-Band Efficient Spectrograph (CUBES) has been designed to provide high instrumental efficiency ( $>$ 37\%) observations in the near UV (305-400 nm requirement, 300-420 nm goal) at a spectral resolving power of R $>$ 20, 000 (with a lower-resolution, sky-limited mode of R $\sim$ 7, 000). With the design focusing on maximizing the instrument throughput (ensuring a Signal to Noise Ratio -SNR- $\sim$ 20 per spectral resolution element at 313 nm for U $\sim$ 17.5 mag objects in 1h of observations), it will offer new possibilities in many fields of astrophysics: i) access to key lines of stellar spectra (e.g. lighter elements, in particular Beryllium), extragalactic studies (e.g. circumgalactic medium of distant galaxies, cosmic UV background) and follow-up of explosive transients. We present the CUBES instrument design, currently in Phase-C and approaching the final design review, summarizing the hardware architecture and interfaces between the different subsystems as well as the relevant technical requirements. We describe the optical, mechanical, electrical design of the different subsystems (from the telescope adapter and support structure, through the main opto-mechanical path, including calibration unit, detector devices and cryostat control, main control electronics), detailing peculiar instrument functions like the Active Flexure Compensation (AFC). Furthermore, we outline the AITV concept and the main instrument operations giving an overview of its software ecosystem. Installation at the VLT is planned for 2028-2029 and first science operations in late 2029.

Electron beam (e-beam) generated plasmas with applied cross electric and magnetic $\left( \textbf{E} \times \textbf{B} \right)$ fields are promising for low-damage material processing. However, these plasmas can be subject to the formation of azimuthally propagating structures that enhance the radial transport of energetic charged species, which can harm the gentle processing capability of the plasma. In this work we investigate the azimuthal structure formation in an e-beam generated $\textbf{E} \times \textbf{B}$ plasma using experimental diagnostics and 2D3V particle-in-cell simulations. Our findings demonstrate the formation of multiple simultaneously occurring azimuthally propagating modes that exhibit a nontrivial radial dependence. It is suggested that the multimodal azimuthal spectrum is caused by the complex nature of the ion dynamics in the plasma., Comment: 26 pages, 21 figures

Theoretical quest of flat-band tight-binding models usually relies on lattice structures on which electrons reside. Typical examples of candidate lattice structures include the Lieb-type lattices and the line graphs. Meanwhile, there can be accidental flat-band systems that belong to neither of such typical classes and deriving flat-band energies and wave functions for such systems is not straightforward. In this work, we investigate the characteristic band structure for the tight-binding model on a network composed of pentagonal rings, which is inspired by the theoretically-predicted carbon-based material. Although the lattice does not belong to conventional classes of flat band models, the exact flat bands appear only for fine-tuned parameters. We analytically derive the exact eigenenergies and eigenstates of the flat bands. By using the analytic form of the Bloch wave function, we construct the corresponding Wannier function and reveal its characteristic real-space profile. We also find that, even away from the exact flat-band limits, the nearly flat band exists near the Fermi level for the half-filled systems, which indicates that the present system will be a suitable platform for questing flat-band-induced correlated electron physics if it is realized in the real material., Comment: 8 pages, 4 figures

Play is a fundamental aspect of developmental growth, yet many parents encounter significant challenges in fulfilling their caregiving roles in this area. As online content increasingly serves as the primary source of parental guidance, this study investigates the difficulties parents face related to play and evaluates the limitations of current online content. We identified ten findings through in-depth interviews with nine parents who reported struggles in engaging with their children during play. Based on these findings, we discuss the major limitations of online play content and suggest how they can be improved. These recommendations include minimizing parental anxiety, accommodating diverse play scenarios, providing credible and personalized information, encouraging creativity, and delivering the same content in multiple formats., Comment: Accepted to HCI Korea 2025

This study explores the dynamics of two-layer multiplex networks, focusing on how frequency distributions among mirror nodes influence phase transitions and synchronization across layers. We present a Regular frequency assignment model for duplex networks, where the layers are fully connected and share identical sets of natural frequencies. By adjusting the sizes of sections where nodes exhibit positive, zero, or negative frequency differences relative to their mirrored equivalents in another layer, we can effectively control the average frequency discrepancy between the layers. We compared the dynamics of this structured model to those with randomly distributed frequencies, keeping a constant average frequency difference between the layers and introducing a phase lag in the interlayer interaction terms. This comparison highlighted distinct behaviors, including double hysteresis loops in the synchronization phase transition and standing waves for intralayer coupling at the locations of the hysteresis loops, where the waves are composed of different interacting frequencies., Comment: 32 pages (21 main and 11 Supplementary), 18 figures (8 main and 10 Supplementary)

We study in-depth those rings $R$ for which, there exists a fixed $n\geq 1$, such that $u^n-1$ lies in the subring $\Delta(R)$ of $R$ for every unit $u\in R$. We succeeded to describe for any $n\geq 1$ all reduced $\pi$-regular $(2n-1)$-$\Delta$U rings by showing that they satisfy the equation $x^{2n}=x$ as well as to prove that the property of being exchange and clean are tantamount in the class of $(2n-1)$-$\Delta$U rings. These achievements considerably extend results established by Danchev (Rend. Sem. Mat. Univ. Pol. Torino, 2019) and Ko\c{s}an et al. (Hacettepe J. Math. \& Stat., 2020). Some other closely related results of this branch are also established., Comment: 19 pages

Security controls are mechanisms or policies designed for cloud based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security controls. We specifically focus on generating Gherkin codes which are the domain-specific language used to define the behavior of security controls in a structured and understandable format. By leveraging large language models and in-context learning, we propose a structured framework that reduces the time required for developing security controls from 2-3 days to less than one minute. Our approach integrates detailed task descriptions, step-by-step instructions, and retrieval-augmented generation to enhance the accuracy and efficiency of the generated Gherkin code. Initial evaluations on AWS cloud services demonstrate promising results, indicating that GenAI can effectively streamline the security control development process, thus providing a robust and dynamic safeguard for cloud-based infrastructures.

XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Neurons on Xylo are quantised integer devices operating in synchronous digital CMOS, with neuron and synapse state quantised to 16 bit, and weight parameters quantised to 8 bit. Xylo is tailored for real-time streaming operation, as opposed to accelerated-time operation in the case of an inference accelerator. XyloAudio includes a low-power audio encoding interface for direct connection to a microphone, designed for sparse encoding of incident audio for further processing by the inference core. In this report we present the results of DCASE 2020 acoustic scene classification audio benchmark dataset deployed to XyloAudio 2. We describe the benchmark dataset; the audio preprocessing approach; and the network architecture and training approach. We present the performance of the trained model, and the results of power and latency measurements performed on the XyloAudio 2 development kit. This benchmark is conducted as part of the Neurobench project.

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions.To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirments than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems.Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 uW(IO power) + 287.9 uW(computational power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.

Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new approach to this problem, the task of agnostic state tomography was defined, in which one aims to approximate an arbitrary quantum state by a simpler one in a given class. Generalizing this notion to quantum processes, we initiate the study of agnostic process tomography: given query access to an unknown quantum channel $\Phi$ and a known concept class $\mathcal{C}$ of channels, output a quantum channel that approximates $\Phi$ as well as any channel in the concept class $\mathcal{C}$, up to some error. In this work, we propose several natural applications for this new task in quantum machine learning, quantum metrology, classical simulation, and error mitigation. In addition, we give efficient agnostic process tomography algorithms for a wide variety of concept classes, including Pauli strings, Pauli channels, quantum junta channels, low-degree channels, and a class of channels produced by $\mathsf{QAC}^0$ circuits. The main technical tool we use is Pauli spectrum analysis of operators and superoperators. We also prove that, using ancilla qubits, any agnostic state tomography algorithm can be extended to one solving agnostic process tomography for a compatible concept class of unitaries, immediately giving us efficient agnostic learning algorithms for Clifford circuits, Clifford circuits with few T gates, and circuits consisting of a tensor product of single-qubit gates. Together, our results provide insight into the conditions and new algorithms necessary to extend the learnability of a concept class from the standard tomographic setting to the agnostic one., Comment: 11+52 pages, 2 figures, 1 table

We present a framework for designing scores to summarize performance metrics. Our design has two multi-criteria objectives: (1) improving on scores should improve all performance metrics, and (2) achieving pareto-optimal scores should achieve pareto-optimal metrics. We formulate our design to minimize the dimensionality of scores while satisfying the objectives. We give algorithms to design scores, which are provably minimal under mild assumptions on the structure of performance metrics. This framework draws motivation from real-world practices in hospital rating systems, where misaligned scores and performance metrics lead to unintended consequences., Comment: A condensed version of this paper appeared at Foundations of Responsible Computing (FORC) 2024

We propose a bit-flip descent method for optimizing binary spreading codes with large family sizes and long lengths, addressing the challenges of large-scale code design in GNSS and emerging PNT applications. The method iteratively flips code bits to improve the codes' auto- and cross-correlation properties. In our proposed method, bits are selected by sampling a small set of candidate bits and choosing the one that offers the best improvement in performance. The method leverages the fact that incremental impact of a bit flip on the auto- and cross-correlation may be efficiently computed without recalculating the entire function. We apply this method to two code design problems modeled after the GPS L1 C/A and Galileo E1 codes, demonstrating rapid convergence to low-correlation codes. The proposed approach offers a powerful tool for developing spreading codes that meet the demanding requirements of modern and future satellite navigation systems.

This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and enhance the mapping process. Developed with a focus on GDPR compliance, Llettuce can be deployed locally, ensuring data protection while maintaining high performance in converting informal medical terms to standardised concepts.

Polarons, quasiparticles arising from electron-phonon coupling, are crucial in understanding material properties such as high-temperature superconductivity and colossal magnetoresistance. However, scarce studies have been performed to investigate the formation of polarons in low-dimensional materials with phonon polarity and electronic structure transitions. In this work, we studied polarons of tellurene that are composed of chiral chains of tellurium atoms. The frequency and linewidth of the A1 phonon, which becomes increasingly polar for thinner tellurene, exhibit an abrupt change when the thickness of tellurene is below 10 nm. Meanwhile, the field effect mobility of tellurene drops rapidly as the thickness is smaller than 10 nm. These phonon and transport signatures, combined with the calculated phonon polarity and band structure, suggest a crossover from large polarons for bulk tellurium to small polarons for few-layer tellurene. Effective field theory considers the phonon renormalization in the strong coupling (small polaron) regime, and semi-quantitatively reproduces the observed phonon hardening and broadening effects in few-layer tellurene. This polaron crossover stems from the quasi-1D nature of tellurene where modulation of the interchain distance reduces the dielectric screening and promotes electron-phonon coupling. Our work provides valuable insights into the influence of polarons on phononic, electronic, and structural properties in low-dimensional materials.

Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public dataset for post-surgery glioma segmentation and addresses the aforementioned issue by fostering the development of automated segmentation tools for glioma in MRI data. In this effort, we propose two straightforward approaches to enhance the segmentation performances of deep learning-based methodologies. First, we incorporate an additional input based on a simple linear combination of the available MRI sequences input, which highlights enhancing tumors. Second, we employ various ensembling methods to weigh the contribution of a battery of models. Our results demonstrate that these approaches significantly improve segmentation performance compared to baseline models, underscoring the effectiveness of these simple approaches in improving medical image segmentation tasks., Comment: Invited for an Oral Presentation at the MICCAI BraTS Challenge 2024

Luminescent thermometers are highly effective in niche applications such as nanothermometry, in vivo imaging, and extreme conditions like high electromagnetic fields, radiation, and under mechanical or chemical stress. Advancing measurement precision, temperature resolution, and extending the temperature range are the main problems in this rapidly evolving research field, crucial in the work of R&D industry engineers and scientific researchers. Traditionally using single parameter for sensing leads to the sensor underperformance. Sensor fusion (SF), a well-established statistical tool used to enhance precision in autonomous vehicles, navigation systems, medical imaging, and wearable devices is here engineered for luminescent thermometry, by either fusing multiple sensor probes, or observing each temperature dependent feature as a separate sensor, resulting in always increased precision and extended usable temperature range. SF is a replacement and superior alternative to multiple linear regression models as it stands out for its versatility, enabling performance limits to be reached with any sensor probe material. It is compatible with both time-resolved and steady-state readouts, used independently or in combination, with single or multiple excitations. The benefits are illustrated using probes with Mn5+, Yb3+/Er3+/Ho3+, Sm2+, and Mn4+/Ho3+/Cr3+ activator ions., Comment: 18 pages, 9 figures

Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains underexplored. Log data, characterized by its hierarchical, dictionary-like structure, poses unique challenges when processed using conventional transformer models. Traditional methods often rely on manually crafted templates for parsing logs, a process that is labor-intensive and lacks generalizability. Additionally, the linear treatment of log sequences by standard transformers neglects the rich, nested relationships within log entries, leading to suboptimal representations and excessive memory usage. To address these issues, we introduce HLogformer, a novel hierarchical transformer framework specifically designed for log data. HLogformer leverages the hierarchical structure of log entries to significantly reduce memory costs and enhance representation learning. Unlike traditional models that treat log data as flat sequences, our framework processes log entries in a manner that respects their inherent hierarchical organization. This approach ensures comprehensive encoding of both fine-grained details and broader contextual relationships. Our contributions are threefold: First, HLogformer is the first framework to design a dynamic hierarchical transformer tailored for dictionary-like log data. Second, it dramatically reduces memory costs associated with processing extensive log sequences. Third, comprehensive experiments demonstrate that HLogformer more effectively encodes hierarchical contextual information, proving to be highly effective for downstream tasks such as synthetic anomaly detection and product recommendation.

The Channel Quality Indicator (CQI) plays a pivotal role in 5G networks, optimizing infrastructure dynamically to ensure high Quality of Service (QoS). Recent research has focused on improving CQI estimation in 5G networks using machine learning. In this field, the selection of the proper loss function is critical for training an accurate model. Two commonly used loss functions are Mean Squared Error (MSE) and Mean Absolute Error (MAE). Roughly speaking, MSE put more weight on outliers, MAE on the majority. Here, we argue that the Huber loss function is more suitable for CQI prediction, since it combines the benefits of both MSE and MAE. To achieve this, the Huber loss transitions smoothly between MSE and MAE, controlled by a user-defined hyperparameter called delta. However, finding the right balance between sensitivity to small errors (MAE) and robustness to outliers (MSE) by manually choosing the optimal delta is challenging. To address this issue, we propose a novel loss function, named Residual-based Adaptive Huber Loss (RAHL). In RAHL, a learnable residual is added to the delta, enabling the model to adapt based on the distribution of errors in the data. Our approach effectively balances model robustness against outliers while preserving inlier data precision. The widely recognized Long Short-Term Memory (LSTM) model is employed in conjunction with RAHL, showcasing significantly improved results compared to the aforementioned loss functions. The obtained results affirm the superiority of RAHL, offering a promising avenue for enhanced CQI prediction in 5G networks., Comment: https://sol.sbc.org.br/index.php/sbrc/article/view/29822/29625

Vision language models can now generate long-form answers to questions about images - long-form visual question answers (LFVQA). We contribute VizWiz-LF, a dataset of long-form answers to visual questions posed by blind and low vision (BLV) users. VizWiz-LF contains 4.2k long-form answers to 600 visual questions, collected from human expert describers and six VQA models. We develop and annotate functional roles of sentences of LFVQA and demonstrate that long-form answers contain information beyond the question answer such as explanations and suggestions. We further conduct automatic and human evaluations with BLV and sighted people to evaluate long-form answers. BLV people perceive both human-written and generated long-form answers to be plausible, but generated answers often hallucinate incorrect visual details, especially for unanswerable visual questions (e.g., blurry or irrelevant images). To reduce hallucinations, we evaluate the ability of VQA models to abstain from answering unanswerable questions across multiple prompting strategies., Comment: COLM 2024

In this study, we evaluate the predictive power of density functional theory (DFT) for the magnetic properties of MnBi\(_2\)Te\(_4\) (MBT), an intrinsically magnetic topological insulator with potential applications in spintronics and quantum computing. Our theoretical understanding of MBT has been challenged by discrepancies between experimental results and \textit{ab initio} calculations, particularly with respect to its electronic and magnetic properties. Our results show that the magnetic phase diagram of MBT varies significantly depending on the Hubbard $U$ parameter in the DFT framework, highlighting the importance of benchmark calculations. To address these challenges, we establish an optimized Hubbard $U$ approach derived from Diffusion Monte Carlo (DMC) calculations, which directly solves the many-body Schr\"{o}dinger equation based on the stochastic process, and implement it in the DFT framework. Once the optimized $U$ value is determined as a function of strain, we apply it to achieve DMC-level accuracy within our DFT framework. This approach is instrumental in accurately describing the magnetic states of MBT and understanding the underlying mechanisms governing its magnetic properties and their dependence on external factors., Comment: 8 pages, 5 figures

In this work, we investigate the electrical conductivity of carbon nanotubes (CNTs), with a particular focus on the effects of doping. Using a first-principles approach, we study the electronic structure, phonon dispersion, and electron-phonon scattering to understand the finite-temperature electrical transport properties in CNTs. Our study covers both prototypical metallic and semiconducting CNTs, with special emphasis on the influence of typical defects such as vacancies and the incorporation of copper or nitrogen, such as pyridinic N, pyrrolic N, graphitic N, and oxidized N. Our theoretical study shows significant improvements in the electrical conduction properties of copper-CNT composites, especially when semiconducting CNTs are functionalized with nitrogen. Doping is found to cause significant changes in the electronic density of states near the Fermi level, which affects the electrical conductivity. Calculations show that certain types of functional groups, such as N-pyrrolic, result in a ~30-fold increase in the conductivity of semiconducting CNTs compared to Cu-incorporated CNTs alone. For metallic CNTs, the conductivity is in agreement with existing experimental data, and our prediction of significant increases in conductivity with N-pyrrolic functional group is consistent with recent experimental results, demonstrating the effectiveness of doping in modifying conductivity. Our study provides valuable insight into the electronic properties of doped CNTs and contributes to the development of ultra-high conductivity CNT composites., Comment: 16 pages, 4 figures, 39 references

Given an undirected, weighted $n$-vertex graph $G = (V, E, w)$, a Gomory-Hu tree $T$ is a weighted tree on $V$ such that for any pair of distinct vertices $s, t \in V$, the Min-$s$-$t$-Cut on $T$ is also a Min-$s$-$t$-Cut on $G$. Computing a Gomory-Hu tree is a well-studied problem in graph algorithms and has received considerable attention. In particular, a long line of work recently culminated in constructing a Gomory-Hu tree in almost linear time [Abboud, Li, Panigrahi and Saranurak, FOCS 2023]. We design a differentially private (DP) algorithm that computes an approximate Gomory-Hu tree. Our algorithm is $\varepsilon$-DP, runs in polynomial time, and can be used to compute $s$-$t$ cuts that are $\tilde{O}(n/\varepsilon)$-additive approximations of the Min-$s$-$t$-Cuts in $G$ for all distinct $s, t \in V$ with high probability. Our error bound is essentially optimal, as [Dalirrooyfard, Mitrovi\'c and Nevmyvaka, NeurIPS 2023] showed that privately outputting a single Min-$s$-$t$-Cut requires $\Omega(n)$ additive error even with $(1, 0.1)$-DP and allowing for a multiplicative error term. Prior to our work, the best additive error bounds for approximate all-pairs Min-$s$-$t$-Cuts were $O(n^{3/2}/\varepsilon)$ for $\varepsilon$-DP [Gupta, Roth and Ullman, TCC 2012] and $O(\sqrt{mn} \cdot \text{polylog}(n/\delta) / \varepsilon)$ for $(\varepsilon, \delta)$-DP [Liu, Upadhyay and Zou, SODA 2024], both of which are implied by differential private algorithms that preserve all cuts in the graph. An important technical ingredient of our main result is an $\varepsilon$-DP algorithm for computing minimum Isolating Cuts with $\tilde{O}(n / \varepsilon)$ additive error, which may be of independent interest.

Blind and low vision (BLV) developers create websites to share knowledge and showcase their work. A well-designed website can engage audiences and deliver information effectively, yet it remains challenging for BLV developers to review their web designs. We conducted interviews with BLV developers (N=9) and analyzed 20 websites created by BLV developers. BLV developers created highly accessible websites but wanted to assess the usability of their websites for sighted users and follow the design standards of other websites. They also encountered challenges using screen readers to identify illegible text, misaligned elements, and inharmonious colors. We present DesignChecker, a browser extension that helps BLV developers improve their web designs. With DesignChecker, users can assess their current design by comparing it to visual design guidelines, a reference website of their choice, or a set of similar websites. DesignChecker also identifies the specific HTML elements that violate design guidelines and suggests CSS changes for improvements. Our user study participants (N=8) recognized more visual design errors than using their typical workflow and expressed enthusiasm about using DesignChecker in the future., Comment: Conditionally Accepted to UIST 2024

Topology and superconductivity, two distinct phenomena offer unique insight into quantum properties and their applications in quantum technologies, spintronics, and sustainable energy technologies if system can be found where they coexist. Tin (Sn) plays a pivotal role here as an element due to its two structural phases, $\alpha$-Sn and $\beta$-Sn, exhibiting topological characteristics ($\alpha$-Sn) and superconductivity ($\beta$-Sn). In this study we show how precise control of $\alpha$ and $\beta$ phases of Sn thin films can be achieved by using molecular beam epitaxy grown buffer layers with systematic control over the lattice parameter. The resulting Sn films showed either $\beta$-Sn or $\alpha$-Sn phases as the lattice constant of the buffer layer was varied from 6.10 A to 6.48 A, covering the range between GaSb (closely matched to InAs) and InSb. The crystal structures of the $\alpha$- and $\beta$-Sn films were characterized by x-ray diffraction and confirmed by Raman spectroscopy and scanning transmission electron microscopy. The smooth and continuous surface morphology of the Sn films was validated using atomic force microscopy. The characteristics of $\alpha$- and $\beta$-Sn phases were further verified using electrical transport measurements by observing resistance drop near 3.7 K for superconductivity of the $\beta$-Sn phase and Shubnikov-de Haas oscillations for the $\alpha$-Sn phase. Density functional theory calculations showed that the stability of the Sn phases is highly dependent on lattice strain, with $\alpha$-Sn being more stable under tensile strain and $\beta$-Sn becoming favorable under compressive strain, which is in good agreement with experimental observations. Hence, this study sheds light on controlling Sn phases through lattice engineering, enabling innovative applications in quantum technologies and beyond.

Molecular testing of tumor samples for targetable biomarkers is restricted by a lack of standardization, turnaround-time, cost, and tissue availability across cancer types. Additionally, targetable alterations of low prevalence may not be tested in routine workflows. Algorithms that predict DNA alterations from routinely generated hematoxylin and eosin (H&E)-stained images could prioritize samples for confirmatory molecular testing. Costs and the necessity of a large number of samples containing mutations limit approaches that train individual algorithms for each alteration. In this work, models were trained for simultaneous prediction of multiple DNA alterations from H&E images using a multi-task approach. Compared to biomarker-specific models, this approach performed better on average, with pronounced gains for rare mutations. The models reasonably generalized to independent temporal-holdout, externally-stained, and multi-site TCGA test sets. Additionally, whole slide image embeddings derived using multi-task models demonstrated strong performance in downstream tasks that were not a part of training. Overall, this is a promising approach to develop clinically useful algorithms that provide multiple actionable predictions from a single slide.

What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,'' systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.

2D Janus TMDC layers with broken mirror symmetry exhibit giant Rashba splitting and unique excitonic behavior. For their 1D counterparts, the Janus nanotubes possess curvature, which introduce an additional degree of freedom to break the structural symmetry. This could potentially enhance these effects or even give rise to novel properties. In addition, Janus MSSe nanotubes (M=W, Mo), with diameters surpassing 40 {\AA} and Se positioned externally, consistently demonstrate lower energy states than their Janus monolayer counterparts. However, there have been limited studies on the preparation of Janus nanotubes, due to the synthesis challenge and limited sample quality. Here we first synthesized MoS2 nanotubes based on SWCNT-BNNT heterostructure and then explored the growth of Janus MoSSe nanotubes from MoS2 nanotubes with the assistance of H2 plasma at room temperature. The successful formation of the Janus structure was confirmed via Raman spectroscopy, and microscopic morphology and elemental distribution of the grown samples were further characterized. The synthesis of Janus MoSSe nanotubes based on SWCNT-BNNT enables the further exploration of novel properties in Janus TMDC nanotubes.

This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs), along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs). The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction. We also provide an overview of the challenges, potential, and limitations of QML, both in these specific areas and more broadly across the field. We hope that this can serve as a quick guide for data scientists, professionals in the financial sector, and enthusiasts in this area to understand why quantum computing and QML in particular could be interesting to explore in their field of expertise., Comment: 19 pages

Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for lattice-guided growth of dense, aligned MoS2 NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without tailored surface steps. This method enables the synthesis of NRs with widths below 10 nm and longitudinal axis parallel to the zigzag direction, being also extensible to the growth of WS2 NRs and MoS2-WS2 hetero-nanoribbons. Growth is influenced by both substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperature. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis., Comment: 41 pages, 27 figures

In recent years, neural networks have been used to implement symmetric cryptographic functions for secure communications. Extending this domain, the proposed approach explores the application of asymmetric cryptography within a neural network framework to safeguard the exchange between two communicating entities, i.e., Alice and Bob, from an adversarial eavesdropper, i.e., Eve. It employs a set of five distinct cryptographic keys to examine the efficacy and robustness of communication security against eavesdropping attempts using the principles of elliptic curve cryptography. The experimental setup reveals that Alice and Bob achieve secure communication with negligible variation in security effectiveness across different curves. It is also designed to evaluate cryptographic resilience. Specifically, the loss metrics for Bob oscillate between 0 and 1 during encryption-decryption processes, indicating successful message comprehension post-encryption by Alice. The potential vulnerability with a decryption accuracy exceeds 60\%, where Eve experiences enhanced adversarial training, receiving twice the training iterations per batch compared to Alice and Bob., Comment: 8 pages

We study the predictability of online speech on social media, and whether predictability improves with information outside a user's own posts. Recent theoretical results suggest that posts from a user's social circle are as predictive of the user's future posts as that of the user's past posts. Motivated by the success of large language models, we empirically test this hypothesis. We define predictability as a measure of the model's uncertainty, i.e., its negative log-likelihood on future tokens given context. As the basis of our study, we collect 10M tweets for ``tweet-tuning'' base models and a further 6.25M posts from more than five thousand X (previously Twitter) users and their peers. Across four large language models ranging in size from 1.5 billion to 70 billion parameters, we find that predicting a user's posts from their peers' posts performs poorly. Moreover, the value of the user's own posts for prediction is consistently higher than that of their peers'. We extend our investigation with a detailed analysis on what's learned in-context and the robustness of our findings. From context, base models learn to correctly predict @-mentions and hashtags. Moreover, our results replicate if instead of prompting the model with additional context, we finetune on it. Across the board, we find that predicting the posts of individual users remains hard.

Virtual health counselors offer the potential to provide users with information and counseling in complex areas such as disease management and health education. However, ensuring user engagement is challenging, particularly when the volume of information and length of counseling sessions increase. Agenda setting a clinical counseling technique where a patient and clinician collaboratively decide on session topics is an effective approach to tailoring discussions for individual patient needs and sustaining engagement. We explore the effectiveness of agenda setting in a virtual counselor system designed to counsel women for breast cancer genetic testing. In a between subjects study, we assessed three versions of the system with varying levels of user control in the system's agenda setting approach. We found that participants' knowledge improved across all conditions. Although our results showed that any type of agenda setting was perceived as useful, regardless of user control, interviews revealed a preference for more collaboration and user involvement in the agenda setting process. Our study highlights the importance of using patient-centered approaches, such as tailored discussions, when using virtual counselors in healthcare.

A seminal task in quantum information theory is to realize a device able to produce copies of a generic input state with the highest possible output fidelity, thus realizing an \textit{optimal} quantum cloning machine. Recently, the concept of variational quantum cloning was introduced: a quantum machine learning algorithm through which, by exploiting a classical feedback loop informed by the output of a quantum processing unit, the system can self-learn the programming required for an optimal quantum cloning strategy. In this work, we experimentally implement a $1 \rightarrow 2$ variational cloning machine of dual-rail encoded photonic qubits, both for phase-covariant and state-dependent cloning. We exploit a fully programmable 6-mode universal integrated device and classical feedback to reach near-optimal cloning performances. Our results demonstrate the potential of programmable integrated photonic platforms for variational self-learning of quantum algorithms., Comment: 9 pages, 4 figures

Given the rising urban population and the consequential rise in traffic congestion, the implementation of smart parking systems has emerged as a critical matter of concern. Smart parking solutions use cameras, sensors, and algorithms like computer vision to find available parking spaces. This method improves parking place recognition, reduces traffic and pollution, and optimizes travel time. In recent years, computer vision-based approaches have been widely used. However, most existing studies rely on manually labeled parking spots, which has implications for the cost and practicality of implementation. To solve this problem, we propose a novel approach PakLoc, which automatically localize parking spots. Furthermore, we present the PakSke module, which automatically adjust the rotation and the size of detected bounding box. The efficacy of our proposed methodology on the PKLot dataset results in a significant reduction in human labor of 94.25\%. Another fundamental aspect of a smart parking system is its capacity to accurately determine and indicate the state of parking spots within a parking lot. The conventional approach involves employing classification techniques to forecast the condition of parking spots based on the bounding boxes derived from manually labeled grids. In this study, we provide a novel approach called PakSta for identifying the state of parking spots automatically. Our method utilizes object detector from PakLoc to simultaneously determine the occupancy status of all parking lots within a video frame. Our proposed method PakSta exhibits a competitive performance on the PKLot dataset when compared to other classification methods.

In this paper, we present the submission to the 5th Annual Smoky Mountains Computational Sciences Data Challenge, Challenge 3. This is the solution for semantic segmentation problem in both real-world and synthetic images from a vehicle s forward-facing camera. We concentrate in building a robust model which performs well across various domains of different outdoor situations such as sunny, snowy, rainy, etc. In particular, our method is developed with two main directions: model development and domain adaptation. In model development, we use the High Resolution Network (HRNet) as the baseline. Then, this baseline s result is processed by two coarse-to-fine models: Object-Contextual Representations (OCR) and Hierarchical Multi-scale Attention (HMA) to get the better robust feature. For domain adaption, we implement the Domain-Based Batch Normalization (DNB) to reduce the distribution shift from diverse domains. Our proposed method yield 81.259 mean intersection-over-union (mIoU) in validation set. This paper studies the effectiveness of employing real-world and synthetic data to handle the domain adaptation in semantic segmentation problem., Comment: 13 pages

In frustrated magnets, a variety of novel phenomena arise due to their peculiar magnetic states, whose excitations usually reside in the GHz to THz range. Intense THz lasers, therefore, lead to examining nonlinear optical effects in such magnets. Employing an unbiased numerical method, we calculate the laser-pulse driven harmonic spectra of the Shastry-Sutherland magnets, which is well known as the model of a quantum frustrated magnet $SrCu_2(BO_3)_2$. As a result, nonlinear responses of triplons and a two-triplon bound state are observed through the Zeeman or magnetoelectric couplings between the laser and the electron spin. Furthermore, we find that one can obtain information on magnetic anisotropy and symmetry from the spectra by manipulating external fields or two-color lasers. Our results indicate that laser-driven harmonic spectra are useful as a probe of less-detectable quantum many-body states in frustrated magnets.

Quantum contextuality is a key nonclassical feature essential for understanding advantages in quantum computation and communication. We introduce a new framework to study contextuality based solely on information processing statistics. This simple and intuitive perspective leads to a powerful criterion denoted as rank separation for identifying contextuality in various quantum scenarios. We showcase the power of this technique through several applications, including a new derivation of Hardy's quantum excess-baggage theorem, and a simplified proof of contextuality for minimum error quantum state discrimination. Finally, we show as a prominent example that quantum contextuality provides the resource in optimal phase-covariant and universal cloning schemes, hence establishing it as a fundamental source of nonclassicality in all known optimal quantum cloning scenarios., Comment: 7 pages, comments are encouraged

Full spectrum fitting is a powerful tool for estimating the stellar populations of galaxies, but the fitting results are often significantly influenced by internal dust attenuation. For understanding how the choice of the internal dust correction method affects the detailed stellar populations estimated from the full spectrum fitting, we analyze the Sydney-Australian Astronomical Observatory Multi-object Integral field spectrograph (SAMI) galaxy survey data using the Penalized PiXel-Fitting (PPXF) package. Three choices are compared: (Choice-1) using the PPXF reddening option, (Choice-2) using the multiplicative Legendre polynomial, and (Choice-3) using none of them (no dust correction). In any case, the total mean stellar populations show reasonable mass-age and mass-metallicity relations (MTR and MZR), although the correlations appear to be strongest for Choice-1 (MTR) and Choice-2 (MZR). When we compare the age-divided mean stellar populations, the MZR of young (< 10^9.5 yr ~ 3.2 Gyr) stellar components in Choice-2 is consistent with the gas-phase MZR, whereas those in the other two choices hardly are. On the other hand, the MTR of old (>= 10^9.5 yr) stellar components in Choice-1 seems to be more reasonable than that in Choice-2, because the old stellar components in low-mass galaxies tend to be relatively younger than those in massive galaxies. Based on the results, we provide empirical guidelines for choosing the optimal options for dust correction., Comment: 10 pages, 8 figures, accepted for publication in Journal of the Korean Astronomical Society

Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. Concepts are coherent entities that we, as humans, can recognize and associate with a certain object or entity. However, important challenges remain in the fair evaluation of explanation quality provided by these models. This work first proposes an extensive set of quantitative and qualitative metrics which allow to identify drawbacks in current prototypical networks. It then introduces a novel architecture which provides compact explanations, outperforming current prototypical models in terms of explanation quality. Overall, the proposed architecture demonstrates how frozen pre-trained ViT backbones can be effectively turned into prototypical models for both general and domain-specific tasks, in our case biomedical image classifiers. Code is available at \url{https://github.com/hturbe/protosvit}., Comment: Update publication to match paper presented at the Interpretable AI: Past, Present and Future Workshop at NeurIPS 2024

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modeling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems.

Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication. However, MIL explanation methods are still lagging behind, as they are limited to small bag sizes or disregard instance interactions. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks. Finally, we showcase how xMIL explanations enable pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology. Codes are available at: https://github.com/bifold-pathomics/xMIL.

Homology theories categorifying quantum group link invariants are known to be governed by the representation theory of quiver Hecke algebras, also called KLRW algebras. Here we show that certain cylindrical KLRW algebras, relevant in particular for cylindrical generalizations of link homology theories, can be realized by Lagrangian Floer homology in multiplicative Coulomb branches. This confirms a homological mirror symmetry prediction of the first author., Comment: 53 pages, 18 figures