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Uniaxial stress has proven to be a powerful experimental tuning parameter for effectively controlling lattice, charge, orbital, and spin degrees of freedom in quantum materials. In addition, its ability to manipulate the symmetry of materials has garnered significant attention. Recent technical progress to combine uniaxial stress cells with quantum oscillation and angle-resolved photoemission techniques allowed to study the electronic structure as function of uniaxial stress. This review provides an overview on experimental advancements in methods and examines studies on diverse quantum materials, encompassing the semimetal WTe2, the unconventional superconductor Sr2RuO4, Fe-based superconductors, and topological materials.

With active time-domain surveys like the Zwicky Transient Facility, the anticipated Rubin Observatory's Legacy Survey of Space and Time, and multi-messenger experiments such as LIGO/VIRGO/KANGRA for gravitational wave detection and IceCube for high-energy neutrino events, there is a new era in both time-domain and multi-messenger astronomy. The Astro2020 decadal survey highlights effectively responding to these astronomical alerts in a timely manner as a priority, and thus, there is an urgent need for the development of a seamless follow-up infrastructure at existing facilities that are capable of following up on detections at the survey depths. At the W. M. Keck Observatory (WMKO), we are actively constructing critical infrastructure, aimed at facilitating the Target-of-Opportunity (ToO) trigger, optimizing observational planning, streamlining data acquisition, and enhancing data product accessibility. In this document, we provide an overview of these developing services and place them in context of existing observatory infrastructure like the Keck Observatory Archive (KOA) and Data Services Initiative (DSI)., Comment: 9 pages, 3 figures, accompanying presentation at SPIE Astronomical Telescopes + Instrumentation 2024

The ability to understand and ultimately control the transformations and properties of various nanoscale systems, from proteins to synthetic nanomaterial assemblies, hinges on the ability to directly elucidate their dynamics on their characteristic length and time scales. Here, we use MHz X-ray photon correlation spectroscopy (XPCS) to directly elucidate the characteristic microsecond-dynamics of density fluctuations of semiconductor nanocrystals (NCs), not only in a colloidal dispersion but also in a liquid phase consisting of densely packed, yet mobile, NCs with no long-range order. By carefully disentangling X-ray induced effects, we find the wavevector-dependent fluctuation rates in the liquid phase are suppressed relative to those in the colloidal phase and to those in experiments and hydrodynamic theories of densely packed repulsive particles. We show that the suppressed rates are due to a substantial decrease in the self-diffusion of NCs in the liquid phase, which we attribute to explicit attractive interactions. Via comparison with simulations, we find that the extracted strength of the attractions explains the stability of the liquid phase, in contrast to the gelation observed via XPCS in many other charged colloidal systems. This work opens the door to elucidating fast, condensed phase dynamics in a variety of complex fluids and other nanoscale soft matter systems, such as densely packed proteins and non-equilibrium self-assembly processes., Comment: 15 pages, 4 figures

The formation redshift and abundance of the first stars and galaxies is highly sensitive to the build up of low mass dark matter halos as well as astrophysical feedback effects which modulate star formation in these low mass halos. The 21-cm signal at cosmic dawn will depend strongly on the formation of these first luminous sources and thus can be used to constrain unknown astrophysical and dark matter properties in the early universe. In this paper, we explore how well we could measure properties of dark matter using the 21-cm power spectrum at $z>10$, given unconstrained astrophysical parameters. We create a generalizable form of the dark matter halo mass function for models with damped and/or oscillatory linear power spectra, finding a single "smooth-k" window function which describes a broad range of models including CDM. We use this to make forecasts for structure formation using the Effective Theory of Structure Formation (ETHOS) framework to explore a broad parameter space of dark matter models. We make predictions for the 21-cm power spectrum observed by HERA varying both cosmological ETHOS parameters as well as astrophysical parameters. Using a Markov Chain Monte Carlo forecast we find that the ETHOS dark matter parameters are degenerate with astrophysical parameters linked to star formation in low mass dark matter halos but not with X-ray heating produced by the first generation of stars. After marginalizing over uncertainties in astrophysical parameters we demonstrate that with just 540 days of HERA observations it should be possible to distinguish between CDM and a broad range of dark matter models with suppression at wavenumbers $k\lesssim 200\,h$Mpc$^{-1}$ assuming a moderate noise level. These results demonstrate the potential of 21-cm observations to constrain the matter power spectrum on scales smaller than current probes., Comment: 13 pages, 9 figures, submitted to PRD

Long-term temporal correlations in time series in a form of an event sequence have been characterized using an autocorrelation function (ACF) that often shows a power-law decaying behavior. Such scaling behavior has been mainly accounted for by the heavy-tailed distribution of interevent times (IETs), i.e., the time interval between two consecutive events. Yet little is known about how correlations between consecutive IETs systematically affect the decaying behavior of the ACF. Empirical distributions of the burst size, which is the number of events in a cluster of events occurring in a short time window, often show heavy tails, implying that arbitrarily many consecutive IETs may be correlated with each other. In the present study, we propose a model for generating a time series with arbitrary functional forms of IET and burst size distributions. Then, we analytically derive the ACF for the model time series. In particular, by assuming that the IET and burst size are power-law distributed, we derive scaling relations between power-law exponents of the ACF decay, IET distribution, and burst size distribution. These analytical results are confirmed by numerical simulations. Our approach helps to rigorously and analytically understand the effects of correlations between arbitrarily many consecutive IETs on the decaying behavior of the ACF., Comment: 12 pages, 3 figures

Differential equations are pivotal in modeling and understanding the dynamics of various systems, offering insights into their future states through parameter estimation fitted to time series data. In fields such as economy, politics, and biology, the observation data points in the time series are often independently obtained (i.e., Repeated Cross-Sectional (RCS) data). With RCS data, we found that traditional methods for parameter estimation in differential equations, such as using mean values of time trajectories or Gaussian Process-based trajectory generation, have limitations in estimating the shape of parameter distributions, often leading to a significant loss of data information. To address this issue, we introduce a novel method, Estimation of Parameter Distribution (EPD), providing accurate distribution of parameters without loss of data information. EPD operates in three main steps: generating synthetic time trajectories by randomly selecting observed values at each time point, estimating parameters of a differential equation that minimize the discrepancy between these trajectories and the true solution of the equation, and selecting the parameters depending on the scale of discrepancy. We then evaluated the performance of EPD across several models, including exponential growth, logistic population models, and target cell-limited models with delayed virus production, demonstrating its superiority in capturing the shape of parameter distributions. Furthermore, we applied EPD to real-world datasets, capturing various shapes of parameter distributions rather than a normal distribution. These results effectively address the heterogeneity within systems, marking a substantial progression in accurately modeling systems using RCS data., Comment: 16 pages, 10 figures

Work is fundamental to societal prosperity and mental health, providing financial security, identity, purpose, and social integration. The emergence of generative artificial intelligence (AI) has catalysed debate on job displacement. Some argue that many new jobs and industries will emerge to offset the displacement, while others foresee a widespread decoupling of economic productivity from human input threatening jobs on an unprecedented scale. This study explores the conditions under which both may be true and examines the potential for a self-reinforcing cycle of recessionary pressures that would necessitate sustained government intervention to maintain job security and economic stability. A system dynamics model was developed to undertake ex ante analysis of the effect of AI-capital deepening on labour underutilisation and demand in the economy. Results indicate that even a moderate increase in the AI-capital-to-labour ratio could increase labour underutilisation to double its current level, decrease per capita disposable income by 26% (95% interval, 20.6% - 31.8%), and decrease the consumption index by 21% (95% interval, 13.6% - 28.3%) by mid-2050. To prevent a reduction in per capita disposable income due to the estimated increase in underutilization, at least a 10.8-fold increase in the new job creation rate would be necessary. Results demonstrate the feasibility of an AI-capital- to-labour ratio threshold beyond which even high rates of new job creation cannot prevent declines in consumption. The precise threshold will vary across economies, emphasizing the urgent need for empirical research tailored to specific contexts. This study underscores the need for governments, civic organisations, and business to work together to ensure a smooth transition to an AI- dominated economy to safeguard the Mental Wealth of nations., Comment: 10 pages, 5 figures, 1 table, an Appendix

This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot video question answering, and video summarization. We also explore qualitative characteristics of Pegasus-1 , demonstrating its capabilities as well as its limitations, in order to provide readers a balanced view of its current state and its future direction.

We study online policy optimization in nonlinear time-varying dynamical systems where the true dynamical models are unknown to the controller. This problem is challenging because, unlike in linear systems, the controller cannot obtain globally accurate estimations of the ground-truth dynamics using local exploration. We propose a meta-framework that combines a general online policy optimization algorithm ($\texttt{ALG}$) with a general online estimator of the dynamical system's model parameters ($\texttt{EST}$). We show that if the hypothetical joint dynamics induced by $\texttt{ALG}$ with known parameters satisfies several desired properties, the joint dynamics under inexact parameters from $\texttt{EST}$ will be robust to errors. Importantly, the final policy regret only depends on $\texttt{EST}$'s predictions on the visited trajectory, which relaxes a bottleneck on identifying the true parameters globally. To demonstrate our framework, we develop a computationally efficient variant of Gradient-based Adaptive Policy Selection, called Memoryless GAPS (M-GAPS), and use it to instantiate $\texttt{ALG}$. Combining M-GAPS with online gradient descent to instantiate $\texttt{EST}$ yields (to our knowledge) the first local regret bound for online policy optimization in nonlinear time-varying systems with unknown dynamics.

We report the first double-differential cross section measurement of neutral-current neutral pion (NC$\pi^0$) production in neutrino-argon scattering, as well as single-differential measurements of the same channel in terms of final states with and without protons. The kinematic variables of interest for these measurements are the $\pi^0$ momentum and the $\pi^0$ scattering angle with respect to the neutrino beam. A total of 4971 candidate NC$\pi^0$ events fully-contained within the MicroBooNE detector are selected using data collected at a mean neutrino energy of $\sim 0.8$~GeV from $6.4\times10^{20}$ protons on target from the Booster Neutrino Beam at the Fermi National Accelerator Laboratory. After extensive data-driven model validation to ensure unbiased unfolding, the Wiener-SVD method is used to extract nominal flux-averaged cross sections. The results are compared to predictions from commonly used neutrino event generators, which tend to overpredict the measured NC$\pi^0$ cross section, especially in the 0.2-0.5~GeV/c $\pi^0$ momentum range and at forward scattering angles. Events with at least one proton present in the final state are also underestimated. This data will help improve the modeling of NC$\pi^0$ production, which represents a major background in measurements of charge-parity violation in the neutrino sector and in searches for new physics beyond the Standard Model.

A primary challenge in abstractive summarization is hallucination -- the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text. To alleviate this model bias, we introduce a decoding strategy based on domain-conditional pointwise mutual information. This strategy adjusts the generation probability of each token by comparing it with the token's marginal probability within the domain of the source text. According to evaluation on the XSUM dataset, our method demonstrates improvement in terms of faithfulness and source relevance. The code is publicly available at \url{https://github.com/qqplot/dcpmi}., Comment: Accepted by Findings of NAACL 2024

We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab's booster neutrino beam. The signal comprises one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to better understand neutrino-argon interactions, and is crucial for future accelerator-based neutrino oscillation experiments. Using a dataset corresponding to $6.86 \times 10^{20}$ protons on target, we present single-differential cross sections in muon and neutral pion momenta, scattering angles with respect to the beam for the outgoing muon and neutral pion, as well as the opening angle between the muon and neutral pion. Data extracted cross sections are compared to generator predictions. We report good agreement between the data and the models for scattering angles, except for an over-prediction by generators at muon forward angles. Similarly, the agreement between data and the models as a function of momentum is good, except for an underprediction by generators in the medium momentum ranges, $200-400$ MeV for muons and $100-200$ MeV for pions.

Objective. Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT, a widely used and freely available service built upon an LLM, for sentiment analysis to discern different stances toward Human Papillomavirus (HPV) vaccination. Methods. Messages related to HPV vaccination were collected from social media supporting different message formats: Facebook (long format) and Twitter (short format). A selection of 1,000 human-evaluated messages was input into the LLM, which generated multiple response instances containing its classification results. Accuracy was measured for each message as the level of concurrence between human and machine decisions, ranging between 0 and 1. Results. Average accuracy was notably high when 20 response instances were used to determine the machine decision of each message: .882 (SE = .021) and .750 (SE = .029) for anti- and pro-vaccination long-form; .773 (SE = .027) and .723 (SE = .029) for anti- and pro-vaccination short-form, respectively. Using only three or even one instance did not lead to a severe decrease in accuracy. However, for long-form messages, the language model exhibited significantly lower accuracy in categorizing pro-vaccination messages than anti-vaccination ones. Conclusions. ChatGPT shows potential in analyzing public opinions on HPV vaccination using social media content. However, understanding the characteristics and limitations of a language model within specific public health contexts remains imperative., Comment: Forthcoming in Preventive Medicine Reports

The Newton-Raphson method is a fundamental root-finding technique with numerous applications in physics. In this study, we propose a parameterized variant of the Newton-Raphson method, inspired by principles from physics. Through analytical and empirical validation, we demonstrate that this novel approach offers increased robustness and faster convergence during root-finding iterations. Furthermore, we establish connections to the Adomian series method and provide a natural interpretation within a series framework. Remarkably, the introduced parameter, akin to a temperature variable, enables an annealing approach. This advancement sets the stage for a fresh exploration of numerical iterative root-finding methodologies., Comment: 14 pages, 3 figures

The characterization of subsurface models relies on the accuracy of subsurface models which request integrating a large number of information across different sources through model conditioning, such as data conditioning and geological concepts conditioning. Conventional geostatistical models have a trade-off between honoring geological conditioning (i.e., qualitative geological concepts) and data conditioning (i.e., quantitative static data and dynamic data). To resolve this limit, generative AI methods, such as Generative adversarial network (GAN), have been widely applied for subsurface modeling due to their ability to reproduce complex geological patterns. However, the current practices of data conditioning in GANs conduct quality assessment through ocular inspection to check model plausibility or some preliminary quantitative analysis of the distribution of property of interests. We propose the generative AI realization minimum acceptance criteria for data conditioning, demonstrated with single image GAN. Our conditioning checks include static small-scale local and large-scale exhaustive data conditioning checks, local uncertainty, and spatial nonstationarity reproduction checks. We also check conditioning to geological concepts through multiscale spatial distribution, the number of connected geobodies, the spatial continuity check, and the model facies proportion reproduction check. Our proposed workflow provides guidance on the conditioning of deep learning methods for subsurface modeling and enhanced model conditioning checking essential for applying these models to support uncertainty characterization and decision making.

Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational services. In this paper, we share our experiences in developing and operating generative AI models within a national-scale search engine, with a specific focus on the sensitiveness of user queries. We propose a taxonomy for sensitive search queries, outline our approaches, and present a comprehensive analysis report on sensitive queries from actual users.

Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of this transition include arousal-induced reductions of overall neural variability and the extent of stimulus-induced variability quenching, which we observed in the empirical data. Our results elucidate computational principles underlying interactions between pupil-linked arousal, sensory processing, and neural variability, and suggest a role for phase transitions in explaining nonlinear modulations of cortical computations.

Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generating multiple subjects simultaneously, resulting in mixed identities with combined attributes from different subjects. In this work, we present MuDI, a novel framework that enables multi-subject personalization by effectively decoupling identities from multiple subjects. Our main idea is to utilize segmented subjects generated by a foundation model for segmentation (Segment Anything) for both training and inference, as a form of data augmentation for training and initialization for the generation process. Moreover, we further introduce a new metric to better evaluate the performance of our method on multi-subject personalization. Experimental results show that our MuDI can produce high-quality personalized images without identity mixing, even for highly similar subjects as shown in Figure 1. Specifically, in human evaluation, MuDI obtains twice the success rate for personalizing multiple subjects without identity mixing over existing baselines and is preferred over 70% against the strongest baseline., Comment: NeurIPS 2024. Project page: https://mudi-t2i.github.io/

We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We find that adding annotated galaxy images provides a power law improvement in performance across all architectures and all tasks, while adding trainable parameters is effective only for some (typically more subjectively challenging) tasks. We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images. We achieve an average relative error rate reduction of 31% across 5 downstream tasks of scientific interest. Our finetuned models are more label-efficient and, unlike their ImageNet-12k-pretrained equivalents, often achieve linear transfer performance equal to that of end-to-end finetuning. We find relatively modest additional downstream benefits from scaling model size, implying that scaling alone is not sufficient to address our domain gap, and suggest that practitioners with qualitatively different images might benefit more from in-domain adaption followed by targeted downstream labelling., Comment: 10+6 pages, 12 figures. Appendix C2 based on arxiv:2206.11927. Code, demos, documentation at https://github.com/mwalmsley/zoobot

Diffusion models have demonstrated superior performance across various generative tasks including images, videos, and audio. However, they encounter difficulties in directly generating high-resolution samples. Previously proposed solutions to this issue involve modifying the architecture, further training, or partitioning the sampling process into multiple stages. These methods have the limitation of not being able to directly utilize pre-trained models as-is, requiring additional work. In this paper, we introduce upsample guidance, a technique that adapts pretrained diffusion model (e.g., $512^2$) to generate higher-resolution images (e.g., $1536^2$) by adding only a single term in the sampling process. Remarkably, this technique does not necessitate any additional training or relying on external models. We demonstrate that upsample guidance can be applied to various models, such as pixel-space, latent space, and video diffusion models. We also observed that the proper selection of guidance scale can improve image quality, fidelity, and prompt alignment., Comment: 15 pages, 15 Figures

We identify a critical bias in contemporary CLIP-based models, which we denote as single tag bias. This bias manifests as a disproportionate focus on a singular tag (word) while neglecting other pertinent tags, stemming from CLIP's text embeddings that prioritize one specific tag in image-text relationships. When deconstructing text into individual tags, only one tag tends to have high relevancy with CLIP's image embedding, leading to biased tag relevancy. In this paper, we introduce a novel two-step fine-tuning approach, Text-Tag Self-Distillation (TTD), to address this challenge. TTD first extracts image-relevant tags from text based on their similarity to the nearest pixels then employs a self-distillation strategy to align combined masks with the text-derived mask. This approach ensures the unbiased image-text alignment of the CLIP-based models using only image-text pairs without necessitating additional supervision. Our technique demonstrates model-agnostic improvements in multi-tag classification and segmentation tasks, surpassing competing methods that rely on external resources. The code is available at https://github.com/shjo-april/TTD.

Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as Segment Anything. However, WSS faces challenges related to minor classes since those are overlooked in images with adjacent multiple classes, a limitation originating from the overfitting of traditional expansion methods like Random Walk. We first address this by employing unsupervised and weakly-supervised feature maps instead of conventional methodologies, allowing for hierarchical mask enhancement. This method distinctly categorizes higher-level classes and subsequently separates their associated lower-level classes, ensuring all classes are correctly restored in the mask without losing minor ones. Our approach, validated through extensive experimentation, significantly improves WSS across five benchmarks (VOC: 79.8\%, COCO: 53.9\%, Context: 49.0\%, ADE: 32.9\%, Stuff: 37.4\%), reducing the gap with fully supervised methods by over 84\% on the VOC validation set. Code is available at https://github.com/shjo-april/DHR.

The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, time series forecasting, etc. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We conducted experiments using pairs of high-quality images and their degraded versions, and our deep learning model demonstrates exceptional restoration of photometric, structural, and morphological information. When compared with the ground-truth JWST images, the enhanced versions of our HST-quality images reduce the scatter of isophotal photometry, Sersic index, and half-light radius by factors of 4.4, 3.6, and 4.7, respectively, with Pearson correlation coefficients approaching unity. The performance is observed to degrade when input images exhibit correlated noise, point-like sources, and artifacts. We anticipate that this deep learning model will prove valuable for a number of scientific applications, including precision photometry, morphological analysis, and shear calibration., Comment: 18 pages, 14 figures, 1 table, Resubmitted to ApJ after the first revision

Charged-current neutrino interactions with final states containing zero mesons and at least one proton are of high interest for current and future accelerator-based neutrino oscillation experiments. Using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory, we have obtained the first double-differential cross section measurements of this channel for muon neutrino scattering on an argon target with a proton momentum threshold of 0.25 GeV/c. We also report a flux-averaged total cross section of $\sigma = (11.8 \pm 1.2) \times 10^{-38}$ cm$^2$ / Ar and several single-differential measurements which extend and improve upon previous results. Statistical and systematic uncertainties are quantified with a full treatment of correlations across 359 kinematic bins, including correlations between distributions describing different observables. The resulting data set provides the most detailed information obtained to date for testing models of mesonless neutrino-argon scattering., Comment: 83 pages, 67 figures (including supplemental material). For v2, added oversized files in extended data release

Long-range and anisotropic dipolar interactions induce complex order in quantum systems. It becomes particularly interesting in two-dimension (2D), where the superfluidity with quasi-long-range order emerges via Berezinskii-Kosterlitz-Thouless (BKT) mechanism, which still remains elusive with dipolar interactions. Here, we observe the BKT transition from a normal gas to the superfluid phase in a quasi-2D dipolar Bose gas of erbium atoms. Controlling the orientation of dipoles, we characterize the transition point by monitoring extended coherence and measuring the equation of state. This allows us to gain a systematic understanding of the BKT transition based on an effective short-range description of dipolar interaction in 2D. Additionally, we observe anisotropic density fluctuations and non-local effects in the superfluid regime, which establishes the dipolar nature of the 2D superfluid. Our results lay the ground for understanding the behavior of dipolar bosons in 2D and open up opportunities for examining complex orders in a dipolar superfluid., Comment: 8 pages, 4 figues, supplementary information

Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to argue that fears of widespread job displacement are unfounded, while others contend that generative AI`s unique capacity to undertake non-routine cognitive tasks sets it apart from other forms of automation capital and presents a threat to the quality and availability of work that underpin stable societies. This paper explores the conditions under which both may be true. We posit the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures could be triggered, exacerbating social disparities, reducing social cohesion, heightening tensions, and requiring sustained government intervention to maintain stability. To prevent this, the paper underscores the urgent need for proactive policy responses, making recommendations to reduce these risks through robust regulatory frameworks and a new social contract characterised by progressive social and economic policies. This approach aims to ensure a sustainable, inclusive, and resilient economic future where human contribution to the economy is retained and integrated with generative AI to enhance the Mental Wealth of nations., Comment: 7 pages, 3 figures

Superconducting microwave metamaterials offer enormous potential for quantum optics and information science, enabling the development of advanced quantum technologies for sensing and amplification. In the context of circuit quantum electrodynamics, such metamaterials can be implemented as coupled cavity arrays (CCAs). In the continuous effort to miniaturize quantum devices for increasing scalability, minimizing the footprint of CCAs while preserving low disorder becomes paramount. In this work, we present a compact CCA architecture leveraging superconducting NbN thin films presenting high kinetic inductance, which enables high-impedance CCA ($\sim1.5$ k$\Omega$), while reducing the resonator footprint. We demonstrate its versatility and scalability by engineering one-dimensional CCAs with up to 100 resonators and exhibiting multiple bandgaps. Additionally, we quantitatively investigate disorder in the CCAs using symmetry-protected topological SSH modes, from which we extract a resonator frequency scattering of $0.22^{+0.04}_{-0.03}\%$. Our platform opens up exciting new prospects for analog quantum simulations of many-body physics with ultrastrongly coupled emitters.

Instruction Tuning on Large Language Models is an essential process for model to function well and achieve high performance in specific tasks. Accordingly, in mainstream languages such as English, instruction-based datasets are being constructed and made publicly available. In the case of Korean, publicly available models and datasets all rely on using the output of ChatGPT or translating datasets built in English. In this paper, We introduce \textit{KIT-19} as an instruction dataset for the development of LLM in Korean. \textit{KIT-19} is a dataset created in an instruction format, comprising 19 existing open-source datasets for Korean NLP tasks. In this paper, we train a Korean Pretrained LLM using \textit{KIT-19} to demonstrate its effectiveness. The experimental results show that the model trained on \textit{KIT-19} significantly outperforms existing Korean LLMs. Based on the its quality and empirical results, this paper proposes that \textit{KIT-19} has the potential to make a substantial contribution to the future improvement of Korean LLMs' performance.

We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning and attitude estimates. This new capability overcomes the challenge of developing thermal semantic perception algorithms for field robots due to the lack of annotated thermal field datasets and the time and costs of manual annotation, enabling precise and rapid annotation of thermal data from field collection efforts at a massively-parallelizable scale. By incorporating a thermal-conditioned refinement step with visual foundation models, our approach can produce highly-precise semantic segmentation labels using low-resolution satellite land cover data for little-to-no cost. It achieves 98.5% of the performance from using costly high-resolution options and demonstrates between 70-160% improvement over popular zero-shot semantic segmentation methods based on large vision-language models currently used for generating annotations for RGB imagery. Code will be available at: https://github.com/connorlee77/aerial-auto-segment.

A mirror twin boundary (MTB) in a transition metal dichalcogenide (TMD) monolayer can host one-dimensional electron liquid of a topological nature with tunable interactions. Unfortunately, the electrical characterization of such boundaries has been challenging due to the paucity of samples with large enough size and high quality. Here, we report an epitaxial growth of monolayer molybdenum disulfide (MoS$_2$) bicrystals with well-isolated MTBs that are tens of micrometers long. Conductance measurements of these MTBs exhibit power-law behaviors as a function of temperature and bias voltage up to room temperature, consistent with electrons tunneling into a Luttinger liquid. Transport measurements of two distinct types of MTBs reveal the critical role of the atomic-scale defects. This study demonstrates that MTBs in TMD monolayers provide an exciting new platform for studying the interplay between electronic interactions and topology.

Excitons, bound electron-hole pairs, influence the optical properties in strongly interacting solid state systems. Excitons and their associated many-body physics are typically most stable and pronounced in monolayer materials. Bulk systems with large exciton binding energies, on the other hand, are rare and the mechanisms driving their stability are still relatively unexplored. Here, we report an exceptionally large exciton binding energy in single crystals of the bulk van der Waals antiferromagnet CrSBr. Utilizing state-of-the-art angle-resolved photoemission spectroscopy and self-consistent ab-initio GW calculations, we present direct spectroscopic evidence that robust electronic and structural anisotropy can significantly amplify the exciton binding energy within bulk crystals. Furthermore, the application of a vertical electric field enables broad tunability of the optical and electronic properties. Our results indicate that CrSBr is a promising material for the study of the role of anisotropy in strongly interacting bulk systems and for the development of exciton-based optoelectronics.

Conversion of charge to orbital angular momentum through the orbital Hall effect (OHE) holds transformative potential for the development of orbital-based electronics, however, it is challenging to directly observe the electrically generated orbital accumulation. Here, we detect the OHE by directly quantifying the orbital accumulation along the edges of a titanium thin film using a scanning transmission electron microscope. We measure the Ti L-edge using electron energy-loss spectroscopy with nanometer resolution and find a sizable orbital accumulation at the sample's outer perimeters, consistent with all signatures expected for the OHE, and determine an orbital diffusion length $\ell_o \approx 7.3$ nm. Our data points to a surprising dependence of the orbital diffusion length on the nano-structural morphology., Comment: 13 pages, 3 figures

We present the first publicly-available RGB-thermal dataset designed for aerial robotics operating in natural environments. Our dataset captures a variety of terrain across the United States, including rivers, lakes, coastlines, deserts, and forests, and consists of synchronized RGB, thermal, global positioning, and inertial data. We provide semantic segmentation annotations for 10 classes commonly encountered in natural settings in order to drive the development of perception algorithms robust to adverse weather and nighttime conditions. Using this dataset, we propose new and challenging benchmarks for thermal and RGB-thermal (RGB-T) semantic segmentation, RGB-T image translation, and motion tracking. We present extensive results using state-of-the-art methods and highlight the challenges posed by temporal and geographical domain shifts in our data. The dataset and accompanying code is available at https://github.com/aerorobotics/caltech-aerial-rgbt-dataset., Comment: Accepted to ECCV 2024

Metal-organic frameworks (MOFs) are promising materials for gas sensing but are often limited to single-use detection. We demonstrate a hybridization strategy synergistically deploying conductive MOFs (cMOFs) and conductive polymers (cPs) as two complementary mixed ionic-electronic conductors in high-performing stand-alone chemiresistors. Our work presents significant improvement in i) sensor recovery kinetics, ii) cycling stability, and iii) dynamic range at room temperature. We demonstrate the effect of hybridization across well-studied cMOFs based on 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) and 2,3,6,7,10,11-hexaiminotripphenylene (HITP) ligands with varied metal nodes (Co, Cu, Ni). We conduct a comprehensive mechanistic study to relate energy band alignments at the heterojunctions between the MOFs and the polymer with sensing thermodynamics and binding kinetics. Our findings reveal that hole enrichment of the cMOF component upon hybridization leads to selective enhancement in desorption kinetics, enabling significantly improved sensor recovery at room temperature, and thus long-term response retention. This mechanism was further supported by density functional theory calculations on sorbate-analyte interactions. We also find that alloying cPs and cMOFs enables facile thin film co-processing and device integration, potentially unlocking the use of these hybrid conductors in diverse electronic applications.

We present "SemCity," a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object, synthetic indoor scenes, or synthetic outdoor scenes, while the generation of real-world outdoor scenes is rarely addressed. In this paper, we concentrate on generating a real-outdoor scene through learning a diffusion model on a real-world outdoor dataset. In contrast to synthetic data, real-outdoor datasets often contain more empty spaces due to sensor limitations, causing challenges in learning real-outdoor distributions. To address this issue, we exploit a triplane representation as a proxy form of scene distributions to be learned by our diffusion model. Furthermore, we propose a triplane manipulation that integrates seamlessly with our triplane diffusion model. The manipulation improves our diffusion model's applicability in a variety of downstream tasks related to outdoor scene generation such as scene inpainting, scene outpainting, and semantic scene completion refinements. In experimental results, we demonstrate that our triplane diffusion model shows meaningful generation results compared with existing work in a real-outdoor dataset, SemanticKITTI. We also show our triplane manipulation facilitates seamlessly adding, removing, or modifying objects within a scene. Further, it also enables the expansion of scenes toward a city-level scale. Finally, we evaluate our method on semantic scene completion refinements where our diffusion model enhances predictions of semantic scene completion networks by learning scene distribution. Our code is available at https://github.com/zoomin-lee/SemCity., Comment: Accepted to CVPR 2024

The advancement of Large Language Models (LLMs) has significantly boosted performance in natural language processing (NLP) tasks. However, the deployment of high-performance LLMs incurs substantial costs, primarily due to the increased number of parameters aimed at enhancing model performance. This has made the use of state-of-the-art LLMs more expensive for end-users. AI service providers, such as OpenAI and Anthropic, often offer multiple versions of LLMs with varying prices and performance. However, end-users still face challenges in choosing the appropriate LLM for their tasks that balance result quality with cost. We introduce SMART, Scaling Models Adaptively for Reduced Token Fees, a novel LLM framework designed to minimize the inference costs of NLP tasks while ensuring sufficient result quality. It enables users to specify an accuracy constraint in terms of the equivalence of outputs to those of the most powerful LLM. SMART then generates results that deviate from the outputs of this LLM only with a probability below a user-defined threshold. SMART employs a profiling phase that evaluates the performance of multiple LLMs to identify those that meet the user-defined accuracy level. SMART optimizes the tradeoff between profiling overheads and the anticipated cost savings resulting from profiling. Moreover, our approach significantly reduces inference costs by strategically leveraging a mix of LLMs. Our experiments on three real-world datasets show that, based on OpenAI models, SMART achieves significant cost savings, up to 25.6x in comparison to GPT-4.

In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs an iterative and parallel interference removal strategy inspired by parallel interference cancellation (PIC), enhanced with deep learning to jointly tackle the AD, CE, and DD problems. Based on this approach, we develop three PIC frameworks, each of which is designed for either coherent or non-coherence schemes. The first framework performs joint AD and CE using received pilot signals in the coherent scheme. Building upon this framework, the second framework utilizes both the received pilot and data signals for CE, further enhancing the performances of AD, CE, and DD in the coherent scheme. The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD. Through joint loss functions and interference cancellation modules, our approach supports end-to-end training, contributing to enhanced performances of AD, CE, and DD for both coherent and non-coherent schemes. Simulation results demonstrate the superiority of our approach over traditional techniques, exhibiting enhanced performances of AD, CE, and DD while maintaining lower computational complexity.

Tutorial videos are a popular help source for learning feature-rich software. However, getting quick answers to questions about tutorial videos is difficult. We present an automated approach for responding to tutorial questions. By analyzing 633 questions found in 5,944 video comments, we identified different question types and observed that users frequently described parts of the video in questions. We then asked participants (N=24) to watch tutorial videos and ask questions while annotating the video with relevant visual anchors. Most visual anchors referred to UI elements and the application workspace. Based on these insights, we built AQuA, a pipeline that generates useful answers to questions with visual anchors. We demonstrate this for Fusion 360, showing that we can recognize UI elements in visual anchors and generate answers using GPT-4 augmented with that visual information and software documentation. An evaluation study (N=16) demonstrates that our approach provides better answers than baseline methods., Comment: 19 pages, CHI 2024

This paper presents an energy-efficient stable diffusion processor for text-to-image generation. While stable diffusion attained attention for high-quality image synthesis results, its inherent characteristics hinder its deployment on mobile platforms. The proposed processor achieves high throughput and energy efficiency with three key features as solutions: 1) Patch similarity-based sparsity augmentation (PSSA) to reduce external memory access (EMA) energy of self-attention score by 60.3 %, leading to 37.8 % total EMA energy reduction. 2) Text-based important pixel spotting (TIPS) to allow 44.8 % of the FFN layer workload to be processed with low-precision activation. 3) Dual-mode bit-slice core (DBSC) architecture to enhance energy efficiency in FFN layers by 43.0 %. The proposed processor is implemented in 28 nm CMOS technology and achieves 3.84 TOPS peak throughput with 225.6 mW average power consumption. In sum, 28.6 mJ/iteration highly energy-efficient text-to-image generation processor can be achieved at MS-COCO dataset., Comment: Accepted at 2024 IEEE International Symposium on Circuits and Systems (ISCAS)

Stochastic reservoir characterization, a critical aspect of subsurface exploration for oil and gas reservoirs, relies on stochastic methods to model and understand subsurface properties using seismic data. This paper addresses the computational challenges associated with Bayesian reservoir inversion methods, focusing on two key obstacles: the demanding forward model and the high dimensionality of Gaussian random fields. Leveraging the generalized Bayesian approach, we replace the intricate forward function with a computationally efficient multivariate adaptive regression splines method, resulting in a 34 acceleration in computational efficiency. For handling high-dimensional Gaussian random fields, we employ a fast Fourier transform (FFT) technique. Additionally, we explore the preconditioned Crank-Nicolson method for sampling, providing a more efficient exploration of high-dimensional parameter spaces. The practicality and efficacy of our approach are tested extensively in simulations and its validity is demonstrated in application to the Alvheim field data.

Decoding language from brain dynamics is an important open direction in the realm of brain-computer interface (BCI), especially considering the rapid growth of large language models. Compared to invasive-based signals which require electrode implantation surgery, non-invasive neural signals (e.g. EEG, MEG) have attracted increasing attention considering their safety and generality. However, the exploration is not adequate in three aspects: 1) previous methods mainly focus on EEG but none of the previous works address this problem on MEG with better signal quality; 2) prior works have predominantly used $``teacher-forcing"$ during generative decoding, which is impractical; 3) prior works are mostly $``BART-based"$ not fully auto-regressive, which performs better in other sequence tasks. In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation. Here we are the first to investigate a cross-attention-based ``whisper" model for generating text directly from MEG signals without teacher forcing. Our model achieves impressive BLEU-1 scores of 60.30 and 52.89 without pretraining $\&$ teacher-forcing on two major datasets ($\textit{GWilliams}$ and $\textit{Schoffelen}$). This paper conducts a comprehensive review to understand how speech decoding formation performs on the neural decoding tasks, including pretraining initialization, training $\&$ evaluation set splitting, augmentation, and scaling law. Code is available at https://github.com/NeuSpeech/NeuSpeech1$.

Recently, synthetic spin-orbit coupling has been introduced into cold-atom systems for more flexible control of the Hamiltonian, which was further made time-varying through two-photon detuning to achieve dynamic control of the cold-atom state. While an intraband transition can be adiabatically obtained, a complete interband transition, rather than a superposition of different bands, obtained through fast sweeping is usually guaranteed by having the positions of the initial and final states be far away from any band gap in the quasimomentum space. Here, by introducing an additional non-Hermitian parameter through an atom-loss contrast together with two-photon detuning as two controllable external parameters, both intraband and complete interband transitions can be achieved independent of the positions of the initial and final states. In addition, a point-source diagram approach in the 2D external parameter space is developed to visualize and predict the locations of any nonadiabatic transitions. This control protocol can have potential applications in quantum state control and quantum simulations using cold-atom systems., Comment: 21 pages, 4 figures

For a human-like chatbot, constructing a long-term memory is crucial. However, current large language models often lack this capability, leading to instances of missing important user information or redundantly asking for the same information, thereby diminishing conversation quality. To effectively construct memory, it is crucial to seamlessly connect past and present information, while also possessing the ability to forget obstructive information. To address these challenges, we propose CREEM, a novel memory system for long-term conversation. Improving upon existing approaches that construct memory based solely on current sessions, CREEM blends past memories during memory formation. Additionally, we introduce a refining process to handle redundant or outdated information. Unlike traditional paradigms, we view responding and memory construction as inseparable tasks. The blending process, which creates new memories, also serves as a reasoning step for response generation by informing the connection between past and present. Through evaluation, we demonstrate that CREEM enhances both memory and response qualities in multi-session personalized dialogues., Comment: 17 pages, 4 figures, 7 tables

The development of superconducting qubit technology has shown great potential for the construction of practical quantum computers. As the complexity of quantum processors continues to grow, the need for stringent fabrication tolerances becomes increasingly critical. Utilizing advanced industrial fabrication processes could facilitate the necessary level of fabrication control to support the continued scaling of quantum processors. However, these industrial processes are currently not optimized to produce high coherence devices, nor are they a priori compatible with the commonly used approaches to make superconducting qubits. In this work, we demonstrate for the first time superconducting transmon qubits manufactured in a 300 mm CMOS pilot line, using industrial fabrication methods, with resulting relaxation and coherence times already exceeding 100 microseconds. We show across-wafer, large-scale statistics studies of coherence, yield, variability, and aging that confirm the validity of our approach. The presented industry-scale fabrication process, using exclusively optical lithography and reactive ion etching, shows performance and yield similar to the conventional laboratory-style techniques utilizing metal lift-off, angled evaporation, and electron-beam writing. Moreover, it offers potential for further upscaling by including three-dimensional integration and additional process optimization using advanced metrology and judicious choice of processing parameters and splits. This result marks the advent of more reliable, large-scale, truly CMOS-compatible fabrication of superconducting quantum computing processors., Comment: main text: 7 pages, 4 figures. bibliography: 55 references. supplement: 8 sections, 8 figures, 2 tables

For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addressed certain types of noise (e.g., localization or categorization). In this paper, we propose Universal-Noise Annotation (UNA), a more practical setting that encompasses all types of noise that can occur in object detection, and analyze how UNA affects the performance of the detector. We analyzed the development direction of previous works of detection algorithms and examined the factors that impact the robustness of detection model learning method. We open-source the code for injecting UNA into the dataset and all the training log and weight are also shared., Comment: appendix and code : https://github.com/Ryoo72/UNA