Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Emitters in Aluminum Nitride Induced by Zirconium Ion Implantation

The integration of solid-state single-photon sources with foundry-compatible photonic platforms is crucial for practical and scalable quantum photonic applications. This study investigates aluminum nitride (AlN) as a material with properties highly suitable for integrated on-chip photonics specifically due to AlN capacity to host defect-center related single-photon emitters. We conduct a comprehensive study of the creation and photophysical properties of single-photon emitters in AlN utilizing Zirconium (Zr) and Krypton (Kr) heavy ion implantation and thermal annealing techniques. Guided by theoretical predictions, we assess the potential of Zr ions to create optically addressable spin-defects and employ Kr ions as an alternative approach that targets lattice defects without inducing chemical doping effects. With the 532 nm excitation wavelength, we found that single-photon emitters induced by ion implantation are primarily associated with vacancy-type defects in the AlN lattice for both Zr and Kr ions. The emitter density increases with the ion fluence, and there is an optimal value for the high density of emitters with low AlN background fluorescence. Additionally, under shorter excitation wavelength of 405 nm, Zr-implanted AlN exhibits isolated point-like emitters, which can be related to Zr-based defect complexes. This study provides important insights into the formation and properties of single-photon emitters in aluminum nitride induced by heavy ion implantation, contributing to the advancement of the aluminum nitride platform for on-chip quantum photonic applications.
Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling

Pre-trained language models based on masked language modeling (MLM) excel in natural language understanding (NLU) tasks. While fine-tuned MLM-based encoders consistently outperform causal language modeling decoders of comparable size, recent decoder-only large language models (LLMs) perform on par with smaller MLM-based encoders. Although their performance improves with scale, LLMs fall short of achieving state-of-the-art results in information extraction (IE) tasks, many of which are formulated as sequence labeling (SL). We hypothesize that LLMs' poor SL performance stems from causal masking, which prevents the model from attending to tokens on the right of the current token. Yet, how exactly and to what extent LLMs' performance on SL can be improved remains unclear. We explore techniques for improving the SL performance of open LLMs on IE tasks by applying layer-wise removal of the causal mask (CM) during LLM fine-tuning. This approach yields performance gains competitive with state-of-the-art SL models, matching or outperforming the results of CM removal from all blocks. Our findings hold for diverse SL tasks, demonstrating that open LLMs with layer-dependent CM removal outperform strong MLM-based encoders and even instruction-tuned LLMs.
AI auditing: The Broken Bus on the Road to AI Accountability

One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical nature of the"AI audit"ecosystem is muddled and imprecise, making it difficult to work through various concepts and map out the stakeholders involved in the practice. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of AI audit studies translate to desired accountability outcomes. We thus assess and isolate practices necessary for effective AI audit results, articulating the observed connections between AI audit design, methodology and institutional context on its effectiveness as a meaningful mechanism for accountability.
Reliability of Smartphone-Based Vibration Threshold Measurements

Smartphone-based measurement platforms can collect data on human sensory function in an accessible manner. We developed a smartphone app that measures vibration perception thresholds by commanding vibrations with varying amplitudes and recording user responses via (1) a staircase method that adjusts a variable stimulus, and (2) a decay method that measures the time a user feels a decaying stimulus. We conducted two studies with healthy adults to assess the reliability and usability of the app when the smartphone was applied to the hand and foot. The staircase mode had good reliability for repeated measurements, both with and without the support of an in-person experimenter. The app has the potential to be used at home in unguided scenarios.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors. To tackle this, GNN-based CTR models usually sample hundreds of neighbors out of the billions to facilitate efficient online recommendations. However, sampling only a small portion of neighbors results in a severe sampling bias and the failure to encompass the full spectrum of user or item behavioral patterns. To address this challenge, we name the conventional user-item recommendation graph as"micro recommendation graph"and introduce a more suitable MAcro Recommendation Graph (MAG) for billion-scale recommendations. MAG resolves the computational complexity problems in the infrastructure by reducing the node count from billions to hundreds. Specifically, MAG groups micro nodes (users and items) with similar behavior patterns to form macro nodes. Subsequently, we introduce tailored Macro Graph Neural Networks (MacGNN) to aggregate information on a macro level and revise the embeddings of macro nodes. MacGNN has already served Taobao's homepage feed for two months, providing recommendations for over one billion users. Extensive offline experiments on three public benchmark datasets and an industrial dataset present that MacGNN significantly outperforms twelve CTR baselines while remaining computationally efficient. Besides, online A/B tests confirm MacGNN's superiority in billion-scale recommender systems.
From square plaquettes to triamond lattices for SU(2) gauge theory

Lattice gauge theory should be able to address significant new scientific questions when implemented on quantum computers. In practice, error-mitigation techniques have already allowed encouraging progress on small lattices. In this work we focus on a truncated version of SU(2) gauge theory, which is a familiar non-Abelian step toward quantum chromodynamics. First, we demonstrate effective error mitigation for imaginary time evolution on a lattice having two square plaquettes, obtaining the ground state using an IBM quantum computer and observing that this would have been impossible without error mitigation. Then we propose the triamond lattice as an expedient approach to lattice gauge theories in three spatial dimensions, deriving the Hamiltonian and obtaining energy eigenvalues and eigenstates from a noiseless simulator for a three-dimensional unit cell.
Faster Fréchet Distance Approximation through Truncated Smoothing

The Fr\'echet distance is a popular distance measure for curves. Computing the Fr\'echet distance between two polygonal curves of $n$ vertices takes roughly quadratic time, and conditional lower bounds suggest that even approximating to within a factor $3$ cannot be done in strongly-subquadratic time, even in one dimension. The current best approximation algorithms present trade-offs between approximation quality and running time. Recently, van der Horst $\textit{et al.}$ (SODA, 2023) presented an $O((n^2 / \alpha) \log^3 n)$ time $\alpha$-approximate algorithm for curves in arbitrary dimensions, for any $\alpha \in [1, n]$. Our main contribution is an approximation algorithm for curves in one dimension, with a significantly faster running time of $O(n \log^3 n + (n^2 / \alpha^3) \log^2 n \log \log n)$. Additionally, we give an algorithm for curves in arbitrary dimensions that improves upon the state-of-the-art running time by a logarithmic factor, to $O((n^2 / \alpha) \log^2 n)$. Both of our algorithms rely on a linear-time simplification procedure that in one dimension reduces the complexity of the reachable free space to $O(n^2 / \alpha)$ without making sacrifices in the asymptotic approximation factor.
Ab initio calculation of molecular pentaquark magnetic moments in heavy pentaquark chiral perturbation theory

We propose a new heavy pentaquark chiral perturbation theory for the recently observed hidden-charm pentaquark states by LHCb Collaboration. With the ab initio constructed chiral Lagrangians, we present a parameter-free calculation of the octet molecular pentaquark magnetic moments up to one-loop level. We improve the quark model description of the data when we include the leading SU(3) breaking effects coming from the one-loop corrections. Without any experimental inputs, our predictions are so simple and unique that we regard them as a theoretical benchmark to be compared with experiments as well as other theoretical models.
Demand of the tourists visiting protected areas in small oceanic islands: the Azores case-study (Portugal)

In general, tourism plays a significant role in the economy of archipelagos and islands. The Autonomous Region of the Azores has a great potential for tourism, offering multiple attractions, both natural and cultural, creating a big challenge for a sustainable tourism policy since little attention has been paid to the archipelagos and their special needs. This work aims to understand the profile and type of ecotourist that visits the Azores. This knowledge is of great importance for a better management and development of nature-based tourism, and products tailored to the needs and expectations of visitors. The data were collected by means of exit surveys conducted at the Airport of Sao Miguel Island, the larger and most populated island of the archipelago, during the high tourist season—July-September 2009. The analysis of the visitor's profile and preferences is crucial to draw adequate strategies of management for tourism, while it helps to adequate the offer to the demand. Results showed that 41.1 % of the tourists claimed to be attracted to the islands due to their ''natural values'' (e.g., landscape, biodiversity, and geodiversity). The most practiced activities were whale-watching (32.4 %) and mountaineering/hiking (31.6 %), followed by diving (7 %) and other sports (5.1 %). The tourists' profile points to a mainstream, soft, and incidental type of ecotourist. This information helps to develop and support a strategic planning and management, both at local and regional levels, for sus- tainable tourism policies.
The CIPM list"Recommended values of standard frequencies": 2021 update

This paper gives a detailed account of the analysis underpinning the 2021 update to the list of standard reference frequency values recommended by the International Committee for Weights and Measures (CIPM). This update focused on a subset of atomic transitions that are secondary representations of the second (SRS) or considered as potential SRS. As in previous updates in 2015 and 2017, methods for analysing over-determined data sets were applied to make optimum use of the worldwide body of published clock comparison data. To ensure that these methods were robust, three independent calculations were performed using two different algorithms. The 2021 update differed from previous updates in taking detailed account of correlations among the input data, a step shown to be important in deriving unbiased frequency values and avoiding underestimation of their uncertainties. It also differed in the procedures used to assess input data and to assign uncertainties to the recommended frequency values, with previous practice being adapted to produce a fully consistent output data set consisting of frequency ratio values as well as absolute frequencies. These changes are significant in the context of an anticipated redefinition of the second in terms of an optical transition or transitions, since optical frequency ratio measurements will be critical for verifying the international consistency of optical clocks prior to the redefinition. In the meantime, the reduced uncertainties for optical SRS resulting from this analysis significantly increases the weight that secondary frequency standards based on these transitions can have in the steering of International Atomic Time (TAI).
Products of locally conformal symplectic manifolds

Given two locally conformal symplectic (LCS) structures on manifolds $M_1$ and $M_2$, we construct a natural $\R^+$-torsor of locally conformal symplectic structures on a certain covering space $M_1 \boxplus M_2$ of $M_1 \times M_2$. As the smooth construction of $M_1 \boxplus M_2$ is natural from the perspective of flat line bundles, we use this language to phrase the LCS theory. This construction shares many properties with, and in a sense generalizes, the standard symplectic product. Notably, for a Hamiltonian isotopy $\phi_t$ of an LCS manifold $M$, there is an associated Lagrangian embedding $\Gamma(\phi_1) \colon M \hookrightarrow M \boxplus M$, in which certain fixed points of $\phi_1$ are in bijection with intersection points of $\Gamma(\phi_1)$ with the diagonal $\Delta = \Gamma(\mathrm{id})$. Using a Lagrangian intersection of result of the first author and E. Murphy, we may conclude that if $\phi_t$ is a $C^0$-small Hamiltonian isotopy, then the number of fixed points of $\phi_1$ is bounded below by the rank of the Novikov theory associated to the Lee class of the LCS structure on $M$. Finally, we end the paper by constructing the suspension of a Lagrangian submanifold along a Hamiltonian isotopy in the LCS theory, again generalizing the symplectic setting.
LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering

We introduce an integrated precise LiDAR, Inertial, and Visual (LIV) multi-modal sensor fused mapping system that builds on the differentiable surface splatting to improve the mapping fidelity, quality, and structural accuracy. Notably, this is also a novel form of tightly coupled map for LiDAR-visual-inertial sensor fusion. This system leverages the complementary characteristics of LiDAR and visual data to capture the geometric structures of large-scale 3D scenes and restore their visual surface information with high fidelity. The initial poses for surface Gaussian scenes are obtained using a LiDAR-inertial system with size-adaptive voxels. Then, we optimized and refined the Gaussians by visual-derived photometric gradients to optimize the quality and density of LiDAR measurements. Our method is compatible with various types of LiDAR, including solid-state and mechanical LiDAR, supporting both repetitive and non-repetitive scanning modes. bolstering structure construction through LiDAR and facilitating real-time generation of photorealistic renderings across diverse LIV datasets. It showcases notable resilience and versatility in generating real-time photorealistic scenes potentially for digital twins and virtual reality while also holding potential applicability in real-time SLAM and robotics domains. We release our software and hardware and self-collected datasets on Github\footnote[3]{https://github.com/sheng00125/LIV-GaussMap} to benefit the community.
A Simple Model of the Energy Threshold for Snowball Chambers

Cloud and bubble chambers have historically been used for particle detection, capitalizing on supersaturation and superheating, respectively. Here, we present new results from a prototype snowball chamber, in which an incoming particle triggers the crystallization of a purified, supercooled liquid. We demonstrate, for the first time, simulation agreement with our first results from 5 years ago: the higher temperature of the freezing of water and significantly shorter time spent supercooled compared to the control in the presence of a Cf-252 fission neutron source. This is accomplished by combining Geant4 modeling of neutron interactions with the Seitz nucleation model used in superheated bubble chambers, including those seeking dark matter. We explore the possible implications of using this new technology for GeV-scale WIMP searches, especially in terms of spin-dependent proton coupling, and report the first supercooling of WbLS (water-based liquid scintillator).
Torsion subgroups of elliptic curves over quadratic fields and a conjecture of Granville

We study the problem of determining the groups that can arise as the torsion subgroup of an elliptic curve over a fixed quadratic field, building on work of Kamienny-Najman, Krumm, and Trbovi\'c. By employing techniques to study rational points on curves developed by Bruin and Stoll, we determine the possible torsion subgroups of elliptic curves over quadratic fields $\mathbb{Q}(\sqrt{d})$ for all squarefree $d$ with $|d|<800$, improving on the previously known range of $-5<d<26$. We use our computations to study the validity of a conjecture of Granville concerning how many twists of a given hyperelliptic curve admit a nontrivial rational point.
Impact of Solenoid Induced Residual Magnetic Fields on The Prototype SSR1 CM Performance

A prototype cryomodule containing eight Single Spoke Resonators type-1 (SSR1) operating at 325 MHz and four superconducting focusing lenses was successfully assembled, cold tested, and accelerated beam in the framework of the PIP-II project at Fermilab. The impact of induced residual magnetic fields from the solenoids on performance of cavities is presented in this contribution. In addition, design optimizations for the production cryomodules as a result of this impact are highlighted.
pyMBE: the Python-based Molecule Builder for ESPResSo

We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software designed to build coarse-grained models of polyelectrolytes, peptides and globular proteins of arbitrary topology into the Extensible Simulation Package for Research on Soft Matter (ESPResSo). ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which are powerful tools to study macromolecular systems with many reactive groups, permitting to efficiently sample systems with multiple coupled chemical equilibria. However, setting up these methods for macromolecules with many different reactive groups is a non-trivial and error-prone task, especially for beginners. pyMBE enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not yet studied with these methods. To demonstrate some of the applications of pyMBE, we showcase several study cases where pyMBE successfully reproduces previous simulations in the literature done with ESPResSo and other software, including various simulations of different peptides in bulk solution, simulations of weak polyelectrolytes in dialysis and simulations of globular proteins in bulk solution. pyMBE is publicly available as a GitLab repository (https://gitlab.com/blancoapa/pyMBE) which includes its source code and various sample and test scripts, including the ones that we used to generated the data presented in this article.
Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.
MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments

In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.
Machine learning-based analysis of glioma tissue sections: a review

In recent years, the diagnosis of gliomas has become increasingly complex. Histological assessment of glioma tissue using modern machine learning techniques offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 70 publicly available research studies on machine learning-based analysis of stained human glioma tissue sections, covering the diagnostic tasks of subtyping (16/70), grading (23/70), molecular marker prediction (13/70), and survival prediction (27/