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Sequential recommendation aims to predict the next item a user is likely to prefer based on their sequential interaction history. Recently, text-based sequential recommendation has emerged as a promising paradigm that uses pre-trained language models to exploit textual item features to enhance performance and facilitate knowledge transfer to unseen datasets. However, existing text-based recommender models still struggle with two key challenges: (i) representing users and items with multiple attributes, and (ii) matching items with complex user interests. To address these challenges, we propose a novel model, Matching Attribute-aware Representations for Text-based Sequential Recommendation (MARS). MARS extracts detailed user and item representations through attribute-aware text encoding, capturing diverse user intents with multiple attribute-aware representations. It then computes user-item scores via attribute-wise interaction matching, effectively capturing attribute-level user preferences. Our extensive experiments demonstrate that MARS significantly outperforms existing sequential models, achieving improvements of up to 24.43% and 29.26% in Recall@10 and NDCG@10 across five benchmark datasets. Code is available at https://github.com/junieberry/MARS, Comment: CIKM 2024

This paper presents the first kinematic modeling of tendon-driven rolling-contact joint mechanisms with general contact surfaces subject to external loads. We derived the kinematics as a set of recursive equations and developed efficient iterative algorithms to solve for both tendon force actuation and tendon displacement actuation. The configuration predictions of the kinematics were experimentally validated using a prototype mechanism. Our MATLAB implementation of the proposed kinematic is available at https://github.com/hjhdog1/RollingJoint., Comment: 10 pages, 13 figures

Our look at the well-being of California's early educators points to the need to consider work environments in early care and education (ECE) policy development. The learning environments of young children are also the work environments of the ECE workforce. Supportive and safe work environments that foster a respectful workplace climate can enhance educators' practice, professional esteem, and job satisfaction. This is the second of three reports, drawn from our statewide survey of nearly 1,800 early educators. Our findings show that: (1) Though the majority of early educators find satisfaction and reward from their work with children, many feel their work is not respected; (2) Despite their dedication to their profession, many educators encounter inadequate work environments: more than two thirds of center teachers are given duties no one else wants, and nearly a tenth have been the target of racial slurs at work; and (3) Working with children with challenging behaviors, finding planning time and spending time with individual children are common classroom challenges.

Each year, over a thousand public schools in the US close due to declining enrollments and chronic low performance, displacing hundreds of thousands of students. Using Texas administrative data and empirical strategies that use within-student across-time and within-school across-cohort variation, I explore the impact of school closures on students' educational and labor market outcomes. The findings indicate that experiencing school closures results in disruptions in both test scores and behavior. While the drop in test scores is recovered within three years, behavioral issues persist. This study further finds decreases in post-secondary education attainment, employment, and earnings at ages 25-27. These impacts are particularly pronounced among students in secondary education, Hispanic students, and those from originally low-performing schools and economically disadvantaged families.

The success of all students in schools, including students with learning differences, is contingent on how schools operationalize universally designed instruction, positive behavior support, and data-based decision-making for individualized and group-level interventions. When schools have fully functional multi-tiered systems of support (MTSS) that center equity and focus necessary attention on academic conditions, student behavior, and social-emotional learning conditions, students are more likely to receive the instruction and interventions they need. Further, there is evidence of improvements in student outcomes, particularly those related to behavior, when MTSS is implemented effectively. To address the need for effective MTSS implementation, WestEd engaged in a research and technical assistance partnership in which WestEd subject matter experts provided guidance on designing and implementing effective MTSS. WestEd researchers simultaneously studied the short-, mid-, and long-term effects of these efforts on student outcomes. This interim report provides initial analyses of student-level impacts in one of the two school districts involved in this project.

Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included -- demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., among which groups of students are they used to "break ties"?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.

The Study Abroad Health and Safety Report informs students and parents about significant health and safety incidents while studying abroad. Incidents, as reported on the Study Abroad Health and Safety Survey, are defined as deaths and hospitalizations that occurred during participation in a study abroad program. Minnesota legislation (Minn. Stat. § 5.41) aims to strengthen student safety by asking Minnesota colleges and universities with study abroad programs to report the following information annually: (1) Hospitalizations and deaths of students during program participation; (2) Country, primary program host, and program type of each incident; and (3) Whether the program complies with health and safety standards set by the nationally recognized Forum on Education Abroad or a similar standard.

Research for Action (RFA) in partnership with the University of Texas at Austin is engaged in a five-year mixed-methods study of the reforms associated with California AB 705. Over the course of the study, our team will assess the implementation, impact, and cost effectiveness of reforms associated with the law. This report first offers a descriptive quantitative analysis of short-term outcome (enrollment and completion) trends in math and English at the state level. This descriptive analysis examines the relationship between AB 705 and course enrollment and completion, which will serve as the basis for the quasi-experimental study in subsequent project years. The second part of the report presents findings from institutional site visits aimed at understanding how institutions have implemented the reforms, who is involved in implementation, and how implementation is experienced by students.

This report examines the implementation and outcomes of personalized, competency-based learning in the Arizona Personalized Learning Network (AZPLN) districts. The study conducted by Research for Action (RFA) analyzed survey data from students and teachers, as well as publicly available school-level outcomes data to assess the progress and impact of personalized learning approaches. The findings reveal that while teachers perceived strong implementation of personalized, competency-based learning, students' perceptions varied and showed moderate to strong levels of implementation. Notably, teachers reported improved implementation over time, contrasting with relatively stable student perceptions across the surveyed years. In terms of student outcomes, the analysis showed mixed results. There was evidence of negative effects on achievement outcomes in elementary schools, no discernible achievement effects in middle schools, and some positive effects observed in high schools. However, there was no conclusive evidence of effects on graduation or dropout rates in high schools. Importantly, the study found no significant relationship between students' and teachers' perceived implementation levels and student outcomes. The overlap of the COVID-19 pandemic with the initial implementation phase of personalized learning may have impacted the results, as the districts started implementation in 2019. Recommendations from the report emphasize the importance of continued study on personalized, competency-based learning and its effects on student outcomes as implementation progresses in AZPLN districts. The early stages of implementation, further complicated by the challenges posed by the pandemic, suggest caution in interpreting the current findings. Continued monitoring and evaluation of the implementation process and its impact on student outcomes are advised to understand the longer-term implications of personalized learning approaches in these educational settings. The report calls for ongoing research to deepen understanding of the relationship between implementation strategies and student outcomes over time, considering the unique context of each district within the AZPLN. Ultimately, the report underscores the need for sustained attention to personalized, competency-based learning initiatives in the AZPLN districts. Despite some positive trends in teacher perceptions and certain student outcomes, the study highlights the complex nature of implementation and its varied impact across different school levels. As the districts navigate through the early stages of personalized learning implementation, addressing the challenges and building upon the successes observed will be crucial for optimizing the educational experience and outcomes for students in the long run.

Single-photon emission from a two-level system offers promising perspectives for the development of quantum technologies, where multiphotons are generally regarded as accidental, undesired and should be suppressed. In quantum mechanics, however, multiphoton emission can turn out to be even more fundamental and interesting than the single-photon emission, since in a coherently driven system, the multiphoton suppression arises from quantum interferences between virtual multiphoton fluctuations and the mean field in a Poisson superposition of all number states. Here, we demonstrate how one can control the multiphoton dynamics of a two-level system by disrupting these quantum interferences through a precise and independent homodyne control of the mean field. We show that, counterintuitively, quantum fluctuations always play a major qualitative role, even and in fact especially, when their quantitative contribution is vanishing as compared to that of the mean field. Our findings provide new insights into the paradoxical character of quantum mechanics and open pathways for mean-field engineering as a tool for precision multiphoton control.

Superinductors have become a crucial component in the superconducting circuit toolbox, playing a key role in the development of more robust qubits. Enhancing the performance of these devices can be achieved by suspending the superinductors from the substrate, thereby reducing stray capacitance. Here, we present a fabrication framework for constructing superconducting circuits with suspended superinductors in planar architectures. To validate the effectiveness of this process, we systematically characterize both resonators and qubits with suspended arrays of Josephson junctions, ultimately confirming the high quality of the superinductive elements. In addition, this process is broadly compatible with other types of superinductors and circuit designs. Our results not only pave the way for scalable novel superconducting architectures but also provide the primitive for future investigation of loss mechanisms associated with the device substrate.

Early detection of melanoma is crucial for improving survival rates. Current detection tools often utilize data-driven machine learning methods but often overlook the full integration of multiple datasets. We combine publicly available datasets to enhance data diversity, allowing numerous experiments to train and evaluate various classifiers. We then calibrate them to minimize misdiagnoses by incorporating uncertainty quantification. Our experiments on benchmark datasets show accuracies of up to 93.2% before and 97.8% after applying uncertainty-based rejection, leading to a reduction in misdiagnoses by over 40.5%. Our code and data are publicly available, and a web-based interface for quick melanoma detection of user-supplied images is also provided., Comment: 5 pages, 5 figures, 3 tables, submitted to ISBI2025

In 2009, Calegari constructed smooth homotopy 4-spheres from monodromies of fibered knots. We prove that all these are diffeomorphic to the standard 4-sphere. Our method uses 5-dimensional handlebody techniques and results on mapping class groups of 3-dimensional handlebodies. As an application, we present potential counterexamples to the smooth 4-dimensional Schoenflies conjecture which are related to the work of Casson and Gordon on fibered ribbon knots., Comment: 9 pages

As a highly expressive generative model, diffusion models have demonstrated exceptional success across various domains, including image generation, natural language processing, and combinatorial optimization. However, as data distributions grow more complex, training these models to convergence becomes increasingly computationally intensive. While diffusion models are typically trained using uniform timestep sampling, our research shows that the variance in stochastic gradients varies significantly across timesteps, with high-variance timesteps becoming bottlenecks that hinder faster convergence. To address this issue, we introduce a non-uniform timestep sampling method that prioritizes these more critical timesteps. Our method tracks the impact of gradient updates on the objective for each timestep, adaptively selecting those most likely to minimize the objective effectively. Experimental results demonstrate that this approach not only accelerates the training process, but also leads to improved performance at convergence. Furthermore, our method shows robust performance across various datasets, scheduling strategies, and diffusion architectures, outperforming previously proposed timestep sampling and weighting heuristics that lack this degree of robustness.

Database benchmarking is an essential method for evaluating and comparing the performance characteristics of a database management system (DBMS). It helps researchers and developers to evaluate the efficacy of their optimizations or newly developed DBMS solutions. Also, companies can benefit by analyzing the performance of DBMS under specific workloads and leveraging the result to select the most suitable system for their needs. The proper interpretation of raw benchmark results requires effective visualization, which helps users gain meaningful insights. However, visualization of the results requires prior knowledge, and existing approaches often involve time-consuming manual tasks. This is due to the absence of a unified visual analytics system for benchmark results across diverse DBMSs. To address these challenges, we present DBenVis, an interactive visual analytics system that provides efficient and versatile benchmark results visualization. DBenVis is designed to support both online transaction processing (OLTP) and online analytic processing (OLAP) benchmarks. DBenVis provides an interactive comparison view, which enables users to perform an in-depth analysis of performance characteristics across various metrics among different DBMSs. Notably, we devise an interactive visual encoding idiom for the OLAP benchmark to represent a query execution plan as a tree. In the process of building a system, we propose novel techniques for parsing meaningful data from raw benchmark results and converting the query plan to a D3 hierarchical format. Through case studies conducted with domain experts, we demonstrate the efficacy and usability of DBenVis.

While hydroxyl radicals ($\cdot$OH) play an important role as potent oxidizing agents in various plasma applications, their high reactivity confines them to a thin layer at the plasma-liquid interface, posing challenges in comprehending the intricate generation and transport processes. Similarly, solvated electrons ($\mathrm{e_{aq}}$), highly reactive reducing agents, are expected to exhibit distribution beneath the liquid surface and interact with $\cdot$OH in the thin layer. In this study, we have determined the penetration depth and concentration of ($\mathrm{e_{aq}}$) at the interface between an atmospheric argon plasma plume and an electrolyte anode via a lock-in amplification absorbance measurement. With bias voltages from 1000 to 2500 V, the penetration depth remains approximately 10 nm, and the peak concentration near the surface reaches 1 mM. Diffusion is the primary mechanism for $\cdot$OH generation in the electrolyte, with most $\cdot$OH reacting with ($\mathrm{e_{aq}}$) at the interface, thus influencing the ($\mathrm{e_{aq}}$) distribution. In contrast, the electrolyte cathode significantly boosts $\cdot$OH generation, leading to rapid recombination into hydrogen peroxide.

Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-set inputs that fall outside these classes. Although transfer learning where a model is tuned to a given few-shot task has become a prominent paradigm in closed-world, we observe that it fails to expand to open-world. To unlock this challenge, we propose a two-stage method which consists of open-set aware meta-learning with open-set free transfer learning. In the open-set aware meta-learning stage, a model is trained to establish a metric space that serves as a beneficial starting point for the subsequent stage. During the open-set free transfer learning stage, the model is further adapted to a specific target task through transfer learning. Additionally, we introduce a strategy to simulate open-set examples by modifying the training dataset or generating pseudo open-set examples. The proposed method achieves state-of-the-art performance on two widely recognized benchmarks, miniImageNet and tieredImageNet, with only a 1.5\% increase in training effort. Our work demonstrates the effectiveness of transfer learning in FSOSR.

Digital media platforms (e.g., social media, science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with given degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Given these findings, we discuss several implications of AI-personalized translation tools in facilitating communication in collaborative contexts., Comment: 23 pages, 5 figures, 1 table

Emerging Large Language Model (LLM) applications require long input prompts to perform complex downstream tasks like document analysis and code generation. For these long context length applications, the length of the input prompt poses a significant challenge in terms of inference efficiency since the inference costs increase linearly with sequence length. However, for many of these applications, much of the context in the prompt is fixed across different user inputs, thereby providing the opportunity to perform offline optimizations to process user inputs quickly, as they are received. In this work, we propose Squeezed Attention as a mechanism to accelerate LLM applications where a large portion of the input prompt is fixed. We first leverage K-means clustering offline to group the keys for the fixed context based on semantic similarity and represent each cluster with a single centroid value. During inference, we compare query tokens from the user input with the centroids to predict which of the keys from the fixed context are semantically relevant and need to be loaded during inference. We then compute exact attention using only these important keys from the fixed context, thereby reducing bandwidth and computational costs. We also extend our method to use a hierarchical centroid lookup to identify important keys, which can reduce the complexity of attention from linear to logarithmic with respect to the context length. We implement optimized Triton kernels for centroid comparison and sparse FlashAttention with important keys, achieving more than 4x speedups during both the prefill and generation phases for long-context inference. Furthermore, we have extensively evaluated our method on various long-context benchmarks including LongBench, where it achieves a 3x reduction in KV cache budget without accuracy loss and up to an 8x reduction with <0.5 point accuracy gap for various models.

Chiral spin liquids, which break time-reversal symmetry, are of great interest due to their topological properties and fractionalized excitations (anyons). In this work, we investigate chiral spin liquids (CSL) on the kagome lattice arising from the competition between the third-nearest-neighbor Heisenberg interaction across hexagons ($J_d$) and a staggered scalar spin chirality term ($J_\chi$). Using variational Monte Carlo methods, we map out the phase diagram and identify various gapped and gapless CSL phases, each characterized by a distinct flux pattern. Notably, the interplay between $J_d$ and $J_\chi$ induces a tricritical point, which we analyze using Landau-Ginzburg theory. Additionally, we identify potential signatures of these CSLs-including distinctive spin-spin correlations, anomalies in the static spin structure factor, longitudinal thermal conductivity, and magnetoelectric effects-which offer practical guidance for their future experimental detection.

Recent advancements in brain-computer interface (BCI) technology have emphasized the promise of imagined speech and visual imagery as effective paradigms for intuitive communication. This study investigates the classification performance and brain connectivity patterns associated with these paradigms, focusing on decoding accuracy across selected word classes. Sixteen participants engaged in tasks involving thirteen imagined speech and visual imagery classes, revealing above-chance classification accuracy for both paradigms. Variability in classification accuracy across individual classes highlights the influence of sensory and motor associations in imagined speech and vivid visual associations in visual imagery. Connectivity analysis further demonstrated increased functional connectivity in language-related and sensory regions for imagined speech, whereas visual imagery activated spatial and visual processing networks. These findings suggest the potential of imagined speech and visual imagery as an intuitive and scalable paradigm for BCI communication when selecting optimal word classes. Further exploration of the decoding outcomes for these two paradigms could provide insights for practical BCI communication., Comment: 4 pages

We consider the statistics of the results of a measurement of the spreading operator in the Krylov basis generated by the Hamiltonian of a quantum system starting from a specified initial pure state. We first obtain the probability distribution of the results of measurements of this spreading operator at a certain instant of time, and compute the characteristic function of this distribution. We show that the moments of this characteristic function are related to the so-called generalised spread complexities, and obtain expressions for them in several cases when the Hamiltonian is an element of a Lie algebra. Furthermore, by considering a continuum limit of the Krylov basis, we show that the generalised spread complexities of higher orders have a peak in the time evolution for a random matrix Hamiltonian belonging to the Gaussian unitary ensemble. We also obtain an upper bound in the change in generalised spread complexity at an arbitrary time in terms of the operator norm of the Hamiltonian and discuss the significance of these results.

DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.

Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system for autonomous pepper harvesting designed to operate in these unprotected, complex settings. Utilizing a custom handheld shear-gripper, we collected 300 demonstrations to train a visuomotor policy, enabling the system to adapt to varying field conditions and crop diversity. We achieved a success rate of 28.95% with a cycle time of 31.71 seconds, comparable to existing systems tested under more controlled conditions like greenhouses. Our system demonstrates the feasibility and effectiveness of leveraging imitation learning for automated harvesting in unstructured agricultural environments. This work aims to advance scalable, automated robotic solutions for agriculture in natural settings., Comment: 8 pages, 11 figures

In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varying convolutional kernel sizes. The ensemble comprises three models with kernel sizes of 51, 101, and 201, effectively capturing multi-scale temporal features inherent in signals. This approach improves the robustness and accuracy of speech decoding by accommodating the rich temporal complexity of neural signals. The ensemble models work in conjunction with conditional autoencoders that refine the reconstructed signals and maximize the useful information for downstream classification tasks. The results indicate that the proposed ensemble-based approach significantly outperforms individual models and existing state-of-the-art techniques. These findings demonstrate the potential of ensemble methods in advancing brain signal decoding, offering new possibilities for non-verbal communication applications, particularly in brain-computer interface systems aimed at aiding individuals with speech impairments.

We present a detailed X-ray investigation of a region (S1) exhibiting non-thermal X-ray emission within the supernova remnant (SNR) CTB 37B hosting the magnetar CXOU J171405.7$-$381031. Previous analyses modeled this emission with a power law (PL), inferring various values for the photon index ($\Gamma$) and absorbing column density ($N_{\rm H}$). Based on these, S1 was suggested to be the SNR shell, a background pulsar wind nebula (PWN), or an interaction region between the SNR and a molecular cloud. Our analysis of a larger dataset favors a steepening (broken or curved PL) spectrum over a straight PL, with the best-fit broken power-law (BPL) parameters of $\Gamma=1.23\pm0.23$ and $2.24\pm0.16$ below and above a break at $5.57\pm0.52$ keV, respectively. However, a simple PL or srcut model cannot be definitively ruled out. For the BPL model, the inferred $N_{\rm H}=(4.08\pm0.72)\times 10^{22}\rm \ cm^{-2}$ towards S1 is consistent with that of the SNR, suggesting a physical association. The BPL-inferred spectral break $\Delta \Gamma \approx 1$ and hard $\Gamma$ can be naturally explained by a non-thermal bremsstrahlung (NTB) model. We present an evolutionary NTB model that reproduces the observed spectrum, which indicates the presence of sub-relativistic electrons within S1. However, alternate explanations for S1, an unrelated PWN or the SNR shock with unusually efficient acceleration, cannot be ruled out. We discuss these explanations and their implications for gamma-ray emission from CTB 37B, and describe future observations that could settle the origin of S1., Comment: 11 pages. 4 figures, Accepted for publication in Apj

We give a new proof of the existence of Whittaker functionals for principal series representation of $\text{GL}(n,\mathbb{R})$, utilizing the analytic theory of distributions. We realize Whittaker functionals as equivariant distributions on $\text{GL}(n,\mathbb{R})$, whose restriction to the open Schubert cell is unique up to a constant. Using a birational map on the Schubert cells, we show that the unique distribution on the open Schubert cell extends to a distribution on the entire space $\text{GL}(n,\mathbb{R})$. This technique gives a proof of the analytic continuation of Jacquet integrals via integration by parts., Comment: 35 pages. arXiv admin note: text overlap with arXiv:2410.13519

Unconventional superconductivity, where electron pairing does not involve electron-phonon interactions, is often attributed to magnetic correlations in a material. Well known examples include high-T_c cuprates and uranium-based heavy fermion superconductors. Less explored are unconventional superconductors with strong spin-orbit coupling, where interactions between spin-polarised electrons and external magnetic field can result in multiple superconducting phases and field-induced transitions between them, a rare phenomenon in the superconducting state. Here we report a magnetic-field driven phase transition in \beta-PdBi2, a layered non-magnetic superconductor. Our tunnelling spectroscopy on thin PdBi2 monocrystals incorporated in planar superconductor-insulator-normal metal junctions reveals a marked discontinuity in the superconducting properties with increasing in-plane field, which is consistent with a transition from conventional (s-wave) to nodal pairing. Our theoretical analysis suggests that this phase transition may arise from spin polarisation and spin-momentum locking caused by locally broken inversion symmetry, with p-wave pairing becoming energetically favourable in high fields. Our findings also reconcile earlier predictions of unconventional multigap superconductivity in \beta-PdBi2 with previous experiments where only a single s-wave gap could be detected., Comment: 29 pages, including 4 main Figures, Methods, 8 Supplementary Figures and 5 Supplementary Notes

Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an innovative communication tool. Human communication accompanies various features, such as articulatory movements, facial expressions, and internal speech, all of which are reflected in neural signals. However, most studies only generate short or fragmented outputs, while providing informative communication by leveraging various features from neural signals remains challenging. In this study, we introduce a dynamic neural communication method that leverages current computer vision and brain-computer interface technologies. Our approach captures the user's intentions from neural signals and decodes visemes in short time steps to produce dynamic visual outputs. The results demonstrate the potential to rapidly capture and reconstruct lip movements during natural speech attempts from human neural signals, enabling dynamic neural communication through the convergence of computer vision and brain--computer interface., Comment: 4 pages, 2 figures, 1 table, Name of Conference: International Conference on Brain-Computer Interface

Since the initial discovery of two-dimensional van der Waals (vdW) materials, significant effort has been made to incorporate the three properties of magnetism, band structure topology, and strong electron correlations $-$ to leverage emergent quantum phenomena and expand their potential applications. However, the discovery of a single vdW material that intrinsically hosts all three ingredients has remained an outstanding challenge. Here we report the discovery of a Kondo-interacting topological antiferromagnet in the vdW 5$f$ electron system UOTe. It has a high antiferromagnetic (AFM) transition temperature of 150 K, with a unique AFM configuration that breaks the combined parity and time reversal ($PT$) symmetry in an even number of layers while maintaining zero net magnetic moment. Our angle-resolved photoemission spectroscopy (ARPES) measurements reveal Dirac bands near the Fermi level, which combined with our theoretical calculations demonstrate UOTe as an AFM Dirac semimetal. Within the AFM order, we observed the presence of the Kondo interaction, as evidenced by the emergence of a 5$f$ flat band near the Fermi level below 100 K and hybridization between the Kondo band and the Dirac band. Our density functional theory calculations in its bilayer form predict UOTe as a rare example of a fully-compensated AFM Chern insulator., Comment: Accepted to Advanced Materials

The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external line faults are widely studied, inverter faults remain a critical yet underexplored issue. This paper proposes various data mining techniques for the effective detection and localization of inverter faults-essential for preventing catastrophic grid failures. Furthermore, the difficulty of differentiating between system anomalies and internal inverter faults within Power Electronics-Driven Grids (PEDGs) is addressed. To enhance grid resilience, this work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch. The proposed FaultNet-ML methodology is validated on a 9-bus system dominated by inverters, illustrating its robustness in a PEDG environment., Comment: 5 pages, 2 figures, submitted to 2025 IEEE Power and Energy Society General Meeting (PESGM 2025), Austin, TX

Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives., Comment: This paper was recently accepted by the Journal of Biopharmaceutical Statistics in October 2024 and is currently undergoing proofing. The DOI for this paper is 10.1080/10543406.2024.2424845

We consider the 2nd integrated correlators of ${\cal N}=4$, $D=4$ super Yang-Mills theory, especially those which can be associated with ${\cal N}=1^*$ mass-deformed theories. We provide an analytic derivation of the integrals at supergravity tree level, which has not been available so far. Our result agrees with the previous results from the study of BPS solutions in the dual supergravity models., Comment: 15 pages, 2 figures