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The evolution of the Fermi surface across the quantum critical point (QCP), which is relevant for characterizing the quantum criticality and understanding its relation with unconventional superconductivity, is an intriguing subject in the study of strongly correlated electron systems. In this study, we report the thermopower measurements to investigate a change in Fermi surface across the QCP in pure and 4.4% Sn-doped CeRhIn$_5$. Results show that their thermopower behavior differs significantly in the vicinity of their respective pressure-induced QCP. In pure CeRhIn$_5$, a drastic collapse of the thermopower takes place at the Kondo breakdown QCP, where the Fermi surface reconstructs concurrently with the development of the magnetic order. By contrast, the thermopower exhibits a broadly symmetric behavior around the QCP in 4.4% Sn-doped CeRhIn$_5$, which is a characteristic of the spin-density-wave QCP. These observations are consistent with the theoretical expectations and suggest the effectiveness of thermopower measurement in discriminating the nature of quantum criticality in heavy-fermion systems., Comment: 12 pages, 4 figures

The SPECtrophotometer for TRansmission spectroscopy of exoplanets (SPECTR) is a new low-resolution optical (3800 {\AA} - 6850 {\AA}) spectrophotometer installed at the Bohyunsan Optical Astronomy Observatory (BOAO) 1.8 m telescope. SPECTR is designed for observing the transmission spectra of transiting exoplanets. Unique features of SPECTR are its long slit length of 10 arcminutes which facilitates observing the target and the comparison star simultaneously, and its wide slit width to minimize slit losses. SPECTR will be used to survey exoplanets, such as those identified by the Transiting Exoplanet Survey Satellite (TESS), providing information about their radii across the wavelength range. In this paper, we present the design of SPECTR and the observational results of the partial transit of HD 189733 b and a full transit of Qatar-8 b. Analyses show the SPECTR's capability on the white light curves with an accuracy of one ppt. The transmission spectrum of HD 189733 b shows general agreement with previous studies., Comment: 12 pages, 11 figures, 5 tables, accepted for PASP

This study introduces ValueScope, a framework leveraging language models to quantify social norms and values within online communities, grounded in social science perspectives on normative structures. We employ ValueScope to dissect and analyze linguistic and stylistic expressions across 13 Reddit communities categorized under gender, politics, science, and finance. Our analysis provides a quantitative foundation showing that even closely related communities exhibit remarkably diverse norms. This diversity supports existing theories and adds a new dimension--community preference--to understanding community interactions. ValueScope not only delineates differing social norms among communities but also effectively traces their evolution and the influence of significant external events like the U.S. presidential elections and the emergence of new sub-communities. The framework thus highlights the pivotal role of social norms in shaping online interactions, presenting a substantial advance in both the theory and application of social norm studies in digital spaces., Comment: First three authors contributed equally. 33 pages. In submission

While existing alignment paradigms have been integral in developing large language models (LLMs), LLMs often learn an averaged human preference and struggle to model diverse preferences across cultures, demographics, and communities. We propose Modular Pluralism, a modular framework based on multi-LLM collaboration for pluralistic alignment: it "plugs into" a base LLM a pool of smaller but specialized community LMs, where models collaborate in distinct modes to flexibility support three modes of pluralism: Overton, steerable, and distributional. Modular Pluralism is uniquely compatible with black-box LLMs and offers the modular control of adding new community LMs for previously underrepresented communities. We evaluate Modular Pluralism with six tasks and four datasets featuring questions/instructions with value-laden and perspective-informed responses. Extensive experiments demonstrate that Modular Pluralism advances the three pluralism objectives across six black-box and open-source LLMs. Further analysis reveals that LLMs are generally faithful to the inputs from smaller community LLMs, allowing seamless patching by adding a new community LM to better cover previously underrepresented communities.

Electromagnetic (EM) simulation plays a crucial role in analyzing and designing devices with sub-wavelength scale structures such as solar cells, semiconductor devices, image sensors, future displays and integrated photonic devices. Specifically, optics problems such as estimating semiconductor device structures and designing nanophotonic devices provide intriguing research topics with far-reaching real world impact. Traditional algorithms for such tasks require iteratively refining parameters through simulations, which often yield sub-optimal results due to the high computational cost of both the algorithms and EM simulations. Machine learning (ML) emerged as a promising candidate to mitigate these challenges, and optics research community has increasingly adopted ML algorithms to obtain results surpassing classical methods across various tasks. To foster a synergistic collaboration between the optics and ML communities, it is essential to have an EM simulation software that is user-friendly for both research communities. To this end, we present Meent, an EM simulation software that employs rigorous coupled-wave analysis (RCWA). Developed in Python and equipped with automatic differentiation (AD) capabilities, Meent serves as a versatile platform for integrating ML into optics research and vice versa. To demonstrate its utility as a research platform, we present three applications of Meent: 1) generating a dataset for training neural operator, 2) serving as an environment for the reinforcement learning of nanophotonic device optimization, and 3) providing a solution for inverse problems with gradient-based optimizers. These applications highlight Meent's potential to advance both EM simulation and ML methodologies. The code is available at https://github.com/kc-ml2/meent with the MIT license to promote the cross-polinations of ideas among academic researchers and industry practitioners., Comment: under review

We use a combination of Planck cosmic microwave background (CMB) anisotropy data and non-CMB data that include Pantheon+ type Ia supernovae, Hubble parameter [$H(z)$], growth factor ($f\sigma_8$) measurements, and a collection of baryon acoustic oscillation (BAO) data, but not recent DESI 2024 BAO measurements, to confirm the DESI 2024 (DESI+CMB+PantheonPlus) data compilation support for dynamical dark energy with an evolving equation of state parameter $w(z) = w_0 + w_a z/(1+z)$. From our joint compilation of CMB and non-CMB data, in a spatially-flat cosmological model, we obtain $w_0 = -0.850 \pm 0.059$ and $w_a = -0.59^{+0.26}_{-0.22}$ and find that this dynamical dark energy is favored over a cosmological constant by $\sim 2\sigma$. Our data constraints on the flat $w_0w_a$CDM model are slightly more restrictive than the DESI 2024 constraints, with the DESI 2024 and our values of $w_0$ and $w_a$ differing by $-0.27\sigma$ and $0.44\sigma$, respectively. Our data compilation slightly more strongly favors the flat $w_0w_a$CDM model over the flat $\Lambda$CDM model than does the DESI 2024 data compilation., Comment: 9 pages, 3 figures

We study 6 LCDM models, with 4 allowing for non-flat geometry and 3 allowing for a non-unity lensing consistency parameter $A_L$. We also study 6 XCDM models with a dynamical dark energy density X-fluid with equation of state $w$. For the non-flat models we use two different primordial power spectra, Planck $P(q)$ and new $P(q)$. These models are tested against: Planck 2018 CMB power spectra (P18) and lensing potential power spectrum (lensing), and an updated compilation of BAO, SNIa, $H(z)$, and $f\sigma_8$ data [non-CMB data]. P18 data favor closed geometry for the LCDM and XCDM models and $w<-1$ (phantom-like dark energy) for the XCDM models while non-CMB data favor open geometry for the LCDM models and closed geometry and $w>-1$ (quintessence-like dark energy) for the XCDM models. When P18 and non-CMB data are jointly analyzed there is weak evidence for open geometry and moderate evidence for quintessence-like dark energy. Regardless of data used, $A_L>1$ is always favored. The XCDM model constraints obtained from CMB data and from non-CMB data are incompatible, ruling out the 3 $A_L = 1$ XCDM models at $> 3\sigma$. In the 9 models not ruled out, for the P18+lensing+non-CMB data set we find little deviation from flat geometry and moderate deviation from $w=-1$. In all 6 non-flat models (not ruled out), open geometry is mildly favored, and in all 3 XCDM+$A_L$ models (not ruled out) quintessence-like dark energy is moderately favored (by at most $1.6 \sigma$). In the $A_L = 1$ non-flat LCDM cases, we find for P18+lensing+non-CMB data $\Omega_k = 0.0009 \pm 0.0017$ [$0.0008 \pm 0.0017$] for the Planck [new] $P(q)$ model, favoring open geometry. The flat LCDM model remains the simplest (largely) observationally-consistent cosmological model. Our cosmological parameter constraints obtained for the flat LCDM model (and other models) are the most restrictive results to date (Abridged)., Comment: 71 pages, 33 figures. Accepted for publication in Physical Review D

Frontier large language models (LLMs) are developed by researchers and practitioners with skewed cultural backgrounds and on datasets with skewed sources. However, LLMs' (lack of) multicultural knowledge cannot be effectively assessed with current methods for developing benchmarks. Existing multicultural evaluations primarily rely on expensive and restricted human annotations or potentially outdated internet resources. Thus, they struggle to capture the intricacy, dynamics, and diversity of cultural norms. LLM-generated benchmarks are promising, yet risk propagating the same biases they are meant to measure. To synergize the creativity and expert cultural knowledge of human annotators and the scalability and standardizability of LLM-based automation, we introduce CulturalTeaming, an interactive red-teaming system that leverages human-AI collaboration to build truly challenging evaluation dataset for assessing the multicultural knowledge of LLMs, while improving annotators' capabilities and experiences. Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions, that modern LLMs fail at, in a gamified manner. Importantly, the increased level of AI assistance (e.g., LLM-generated revision hints) empowers users to create more difficult questions with enhanced perceived creativity of themselves, shedding light on the promises of involving heavier AI assistance in modern evaluation dataset creation procedures. Through a series of 1-hour workshop sessions, we gather CULTURALBENCH-V0.1, a compact yet high-quality evaluation dataset with users' red-teaming attempts, that different families of modern LLMs perform with accuracy ranging from 37.7% to 72.2%, revealing a notable gap in LLMs' multicultural proficiency., Comment: Preprint (under review)

Scientists regularly pose questions about treatment effects on outcomes conditional on a post-treatment event. However, defining, identifying, and estimating causal effects conditional on post-treatment events requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand, corresponding to scientifically meaningful questions in these settings. However, while being a single-world estimand, which can be queried experimentally, existing identification results for the CSE require no unmeasured confounding between the outcome and post-treatment event. This assumption can be violated in many applications. In this work, we address this concern by developing new identification and estimation results for the CSE in the presence of unmeasured confounding. We establish nonparametric identification of the CSE in both observational and experimental settings when certain proxy variables are available for hidden common causes of the post-treatment event and outcome. For inference, we characterize the efficient influence function for the CSE under a semiparametric model in which nuisance functions are a priori unrestricted. Moreover, we develop a consistent, asymptotically linear, and locally semiparametric efficient estimator of the CSE using modern machine learning theory. We illustrate our framework with simulation studies and a real-world cancer therapy trial.

Negative controls are increasingly used to evaluate the presence of potential unmeasured confounding in observational studies. Beyond the use of negative controls to detect the presence of residual confounding, proximal causal inference (PCI) was recently proposed to de-bias confounded causal effect estimates, by leveraging a pair of treatment and outcome negative control or confounding proxy variables. While formal methods for statistical inference have been developed for PCI, these methods can be challenging to implement as they involve solving complex integral equations that are typically ill-posed. We develop a regression-based PCI approach, employing two-stage generalized linear regression models (GLMs) to implement PCI, which obviates the need to solve difficult integral equations. The proposed approach has merit in that (i) it is applicable to continuous, count, and binary outcomes cases, making it relevant to a wide range of real-world applications, and (ii) it is easy to implement using off-the-shelf software for GLMs. We establish the statistical properties of regression-based PCI and illustrate their performance in both synthetic and real-world empirical applications.

4d transition metal perovskite oxides serve as suitable testbeds for the study of strongly correlated metallic properties. Among these, $SrMoO_{3}$ (SMO) exhibits remarkable electrical conductivity at room temperature. The temperature-dependent resistivity $(\rho(T))$ exhibits a Fermi-liquid behavior below the transition temperature $T^{*}$, reflecting the dominant electron-electron interaction. Above $T^{*}$, electron-phonon interaction becomes more appreciable. In this study, we employed the power-law scaling of $\rho(T)$ to rigorously determine the $T^{*}$. We further demonstrate that the $T^{*}$ can be modified substantially by ~40 K in epitaxial thin films. It turns out that the structural quality determines $T^{*}$. Whereas the plasma frequency could be tuned by the change in the electron-electron interaction via the effective mass enhancement, we show that the plasmonic properties are more directly governed by the electron-impurity scattering. The facile control of the electron scattering mechanism through structural quality modulation can be useful for plasmonic sensing applications in the visible region.

The detection method of gravitational waves (GW) using electromagnetic (EM) cavities has garnered significant attention in recent years. This paper thoroughly examines the analysis for the perturbation of the EM field and raises some issues in the existing literature. Our work demonstrates that the rigid condition imposed on the material, as provided in the literature, is inappropriate due to its reliance on a gauge-dependent quantity that cannot be controlled experimentally. Instead, we incorporate elasticity into the material and revise the governing equations for the electric field induced by GWs, expressing them solely in terms of gauge-invariant quantities. Applying these equations to a cylindrical cavity with the TM010 mode, we present the GW antenna pattern for the detector.

In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article. Focusing on a case study of preserving political perspectives in news summarization, we find that existing approaches alter the political opinions and stances of news articles in more than 50% of summaries, misrepresenting the intent and perspectives of the news authors. We thus propose P^3SUM, a diffusion model-based summarization approach controlled by political perspective classifiers. In P^3SUM, the political leaning of a generated summary is iteratively evaluated at each decoding step, and any drift from the article's original stance incurs a loss back-propagated to the embedding layers, steering the political stance of the summary at inference time. Extensive experiments on three news summarization datasets demonstrate that P^3SUM outperforms state-of-the-art summarization systems and large language models by up to 13.7% in terms of the success rate of stance preservation, with competitive performance on standard metrics of summarization quality. Our findings present a first analysis of preservation of pragmatic features in summarization, highlight the lacunae in existing summarization models -- that even state-of-the-art models often struggle to preserve author's intents -- and develop new summarization systems that are more faithful to author's perspectives.

Language model (LM) prompting--a popular paradigm for solving NLP tasks--has been shown to be susceptible to miscalibration and brittleness to slight prompt variations, caused by its discriminative prompting approach, i.e., predicting the label given the input. To address these issues, we propose Gen-Z--a generative prompting framework for zero-shot text classification. GEN-Z is generative, as it measures the LM likelihood of input text, conditioned on natural language descriptions of labels. The framework is multivariate, as label descriptions allow us to seamlessly integrate additional contextual information about the labels to improve task performance. On various standard classification benchmarks, with six open-source LM families, we show that zero-shot classification with simple contextualization of the data source of the evaluation set consistently outperforms both zero-shot and few-shot baselines while improving robustness to prompt variations. Further, our approach enables personalizing classification in a zero-shot manner by incorporating author, subject, or reader information in the label descriptions.

Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time series settings. A common approach for estimating synthetic controls is to regress the pre-treatment outcomes of the treated unit on those of untreated control units via ordinary least squares. However, this approach can perform poorly if the pre-treatment fit is not near perfect, whether the weights are normalized or not. In this paper, we introduce a single proxy synthetic control approach, which essentially views the outcomes of untreated control units as proxies of the treatment-free potential outcome of the treated unit, a perspective we formally leverage to construct a valid synthetic control. Under this framework, we establish alternative identification and estimation methodology for synthetic controls and, in turn, for the treatment effect on the treated unit. Notably, unlike a recently proposed proximal synthetic control approach which requires two types of proxies for identification, ours relies on a single type of proxy, thus facilitating its practical relevance. Additionally, we adapt a conformal inference approach to perform inference on the treatment effect, obviating the need for a large number of post-treatment data. Lastly, our framework can accommodate time-varying covariates and nonlinear models, allowing binary and count outcomes. We demonstrate the proposed approach in a simulation study and a real-world application.

Plasma simulations are powerful tools for understanding fundamental plasma science phenomena and for process optimization in applications. To ensure their quantitative accuracy, they must be validated against experiments. In this work, such an experimental validation is performed for a 1d3v particle-in-cell simulation complemented with the Monte Carlo treatment of collision processes of a capacitively coupled radio frequency plasma driven at 13.56 MHz and operated in neon gas. In a geometrically symmetric reactor the electron density in the discharge center and the spatio-temporal distribution of the electron impact excitation rate from the ground into the Ne 2p$_1$ state are measured by a microwave cutoff probe and phase resolved optical emission spectroscopy, respectively. The measurements are conducted for electrode gaps between 50 mm and 90 mm, neutral gas pressures between 20 mTorr and 50 mTorr, and peak-to-peak values of the driving voltage waveform between 250 V and 650 V. Simulations are performed under identical discharge conditions. In the simulations, various combinations of surface coefficients characterising the interactions of electrons and heavy particles with the anodized aluminium electrode surfaces are adopted. We find, that the simulations using a constant effective heavy particle induced secondary electron emission coefficient of 0.3 and a realistic electron-surface interaction model (which considers energy-dependent and material specific elastic and inelastic electron reflection, as well as the emission of true secondary electrons from the surface) yield results which are in good quantitative agreement with the experimental data., Comment: 19 pages, 6 figures, submitted to Plasma Sources Science and Technology

In the field of optics, precise control of light with arbitrary spatial resolution has long been a sought-after goal. Freeform nanophotonic devices are critical building blocks for achieving this goal, as they provide access to a design potential that could hardly be achieved by conventional fixed-shape devices. However, finding an optimal device structure in the vast combinatorial design space that scales exponentially with the number of freeform design parameters has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) as an optimization method for freeform nanophotonic devices, which combines the adjoint-based method with reinforcement learning to enhance the sample efficiency of the optimization algorithm and overcome the issue of local minima. To illustrate these advantages of PIRL over other conventional optimization algorithms, we design a family of one-dimensional metasurface beam deflectors using PIRL, obtaining more performant devices. We also explore the transfer learning capability of PIRL that further improves sample efficiency and demonstrate how the minimum feature size of the design can be enforced in PIRL through reward engineering. With its high sample efficiency, robustness, and ability to seamlessly incorporate practical device design constraints, our method offers a promising approach to highly combinatorial freeform device optimization in various physical domains.

Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare. Though language technologies are growing more prevalent in these contexts, empowerment has seldom been studied in NLP, and moreover, it is inherently challenging to operationalize because of its implicit nature. This work builds from linguistic and social psychology literature to explore what characterizes empowering language. We then crowdsource a novel dataset of Reddit posts labeled for empowerment, reasons why these posts are empowering to readers, and the social relationships between posters and readers. Our preliminary analyses show that this dataset, which we call TalkUp, can be used to train language models that capture empowering and disempowering language. More broadly, TalkUp provides an avenue to explore implication, presuppositions, and how social context influences the meaning of language., Comment: Findings of EMNLP 2023

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%., Comment: 17 pages, 8 figures, 15 tables

Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure political biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings that reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness., Comment: ACL 2023