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In this paper we propose and study a class of nonparametric, yet interpretable measures of association between two random vectors $X$ and $Y$ taking values in $\mathbb{R}^{d_1}$ and $\mathbb{R}^{d_2}$ respectively ($d_1, d_2\ge 1$). These nonparametric measures -- defined using the theory of reproducing kernel Hilbert spaces coupled with optimal transport -- capture the strength of dependence between $X$ and $Y$ and have the property that they are 0 if and only if the variables are independent and 1 if and only if one variable is a measurable function of the other. Further, these population measures can be consistently estimated using the general framework of geometric graphs which include $k$-nearest neighbor graphs and minimum spanning trees. Additionally, these measures can also be readily used to construct an exact finite sample distribution-free test of mutual independence between $X$ and $Y$. In fact, as far as we are aware, these are the only procedures that possess all the above mentioned desirable properties. The correlation coefficient proposed in Dette et al. (2013), Chatterjee (2021), Azadkia and Chatterjee (2021), at the population level, can be seen as a special case of this general class of measures., Comment: 24 pages. To appear in the Indian J. Pure Appl. Math, special issue in honor of Prof. K. R. Parthasarathy. arXiv admin note: text overlap with arXiv:2010.01768

Modeling and forecasting air quality plays a crucial role in informed air pollution management and protecting public health. The air quality data of a region, collected through various pollution monitoring stations, display nonlinearity, nonstationarity, and highly dynamic nature and detain intense stochastic spatiotemporal correlation. Geometric deep learning models such as Spatiotemporal Graph Convolutional Networks (STGCN) can capture spatial dependence while forecasting temporal time series data for different sensor locations. Another key characteristic often ignored by these models is the presence of extreme observations in the air pollutant levels for severely polluted cities worldwide. Extreme value theory is a commonly used statistical method to predict the expected number of violations of the National Ambient Air Quality Standards for air pollutant concentration levels. This study develops an extreme value theory-based STGCN model (E-STGCN) for air pollution data to incorporate extreme behavior across pollutant concentrations. Along with spatial and temporal components, E-STGCN uses generalized Pareto distribution to investigate the extreme behavior of different air pollutants and incorporate it inside graph convolutional networks. The proposal is then applied to analyze air pollution data (PM2.5, PM10, and NO2) of 37 monitoring stations across Delhi, India. The forecasting performance for different test horizons is evaluated compared to benchmark forecasters (both temporal and spatiotemporal). It was found that E-STGCN has consistent performance across all the seasons in Delhi, India, and the robustness of our results has also been evaluated empirically. Moreover, combined with conformal prediction, E-STGCN can also produce probabilistic prediction intervals.

Vehicle-to-Everything (V2X) communication, which includes Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Pedestrian (V2P) networks, is gaining significant attention due to the rise of connected and autonomous vehicles. V2X systems require diverse Quality of Service (QoS) provisions, with V2V communication demanding stricter latency and reliability compared to V2I. The 5G New Radio-V2X (NR-V2X) standard addresses these needs using multi-numerology Orthogonal Frequency Division Multiple Access (OFDMA), which allows for flexible allocation of radio resources. However, V2I and V2V users sharing the same radio resources leads to interference, necessitating efficient power and resource allocation. In this work, we propose a novel resource allocation and sharing algorithm for 5G-based V2X systems. Our approach first groups Resource Blocks (RBs) into Resource Chunks (RCs) and allocates them to V2I users using the Gale-Shapley stable matching algorithm. Power is then allocated to RCs to facilitate efficient resource sharing between V2I and V2V users through a bisection search method. Finally, the Gale-Shapley algorithm is used to pair V2I and V2V users, maintaining low computational complexity while ensuring high performance. Simulation results demonstrate that our proposed Gale-Shapley Resource Allocation with Gale-Shapley Sharing (GSRAGS) achieves competitive performance with lower complexity compared to existing works while effectively meeting the QoS demands of V2X communication systems., Comment: 8 pages, 10 figures

The population of the urban areas is increasing daily, and this migration is causing serious environmental pollution. A larger population is creating pressure on the municipality's waste management and the city corporations of developing countries such as Bangladesh, further threatening human health. New generation technologies, such as the Internet of Things (IoT)-based waste management systems, can help improve this serious issue. IoT-enabled smart dustbins and mobile applications-based interactive management can effectively solve this problem. In this article, we combine these two technologies to offer an acceptable solution to this problem. The proposed waste management model enables smart dustbins to communicate with waste collectors or waste control centers whenever it is necessary. Additionally, city dwellers can use mobile applications to report their observations in their neighborhoods. As a result, both sensors and humans are involved directly in the development loop. We have conducted a detailed survey to study the acceptance of such a system in the community and received some encouraging results.

Yoga has recently become an essential aspect of human existence for maintaining a healthy body and mind. People find it tough to devote time to the gym for workouts as their lives get more hectic and they work from home. This kind of human pose estimation is one of the notable problems as it has to deal with locating body key points or joints. Yoga-82, a benchmark dataset for large-scale yoga pose recognition with 82 classes, has challenging positions that could make precise annotations impossible. We have used VGG-16, ResNet-50, ResNet-101, and DenseNet-121 and finetuned them in different ways to get better results. We also used Neural Architecture Search to add more layers on top of this pre-trained architecture. The experimental result shows the best performance of DenseNet-121 having the top-1 accuracy of 85% and top-5 accuracy of 96% outperforming the current state-of-the-art result.

Intrusion detection system (IDS) is a piece of hardware or software that looks for malicious activity or policy violations in a network. It looks for malicious activity or security flaws on a network or system. IDS protects hosts or networks by looking for indications of known attacks or deviations from normal behavior (Network-based intrusion detection system, or NIDS for short). Due to the rapidly increasing amount of network data, traditional intrusion detection systems (IDSs) are far from being able to quickly and efficiently identify complex and varied network attacks, especially those linked to low-frequency attacks. The SCGNet (Stacked Convolution with Gated Recurrent Unit Network) is a novel deep learning architecture that we propose in this study. It exhibits promising results on the NSL-KDD dataset in both task, network attack detection, and attack type classification with 99.76% and 98.92% accuracy, respectively. We have also introduced a general data preprocessing pipeline that is easily applicable to other similar datasets. We have also experimented with conventional machine-learning techniques to evaluate the performance of the data processing pipeline.

We study the optical response of a one-dimensional array of strongly nonlinear optical microcavities with alternating tunnel transmissivities, mimicking the paradigmatic Su-Schriefer Heeger model. We show that the non-equilibrium steady state of the bosonic system contains clear signatures of fermionization when the intra-cavity Kerr non-linearity is stronger than both losses and inter-site tunnel coupling. Furthermore, by changing the experimentally controllable parameters detuning and driving strength, in a topologically non-trivial phase, one can selectively excite either the bulk or edge modes or both modes, revealing interesting topological properties in a non-equilibrium system.

We propose a nonparametric algorithm to detect structural breaks in the conditional mean and/or variance of a time series. Our method does not assume any specific parametric form for the dependence structure of the regressor, the time series model, or the distribution of the model noise. This flexibility allows our algorithm to be applicable to a wide range of time series structures commonly encountered in financial econometrics. The effectiveness of the proposed algorithm is validated through an extensive simulation study and a real data application in detecting structural breaks in the mean and volatility of Bitcoin returns. The algorithm's ability to identify structural breaks in the data highlights its practical utility in econometric analysis and financial modeling.

Estimating depth from a single 2D image is a challenging task because of the need for stereo or multi-view data, which normally provides depth information. This paper deals with this challenge by introducing a novel deep learning-based approach using an encoder-decoder architecture, where the Inception-ResNet-v2 model is utilized as the encoder. According to the available literature, this is the first instance of using Inception-ResNet-v2 as an encoder for monocular depth estimation, illustrating better performance than previous models. The use of Inception-ResNet-v2 enables our model to capture complex objects and fine-grained details effectively that are generally difficult to predict. Besides, our model incorporates multi-scale feature extraction to enhance depth prediction accuracy across different kinds of object sizes and distances. We propose a composite loss function consisting of depth loss, gradient edge loss, and SSIM loss, where the weights are fine-tuned to optimize the weighted sum, ensuring better balance across different aspects of depth estimation. Experimental results on the NYU Depth V2 dataset show that our model achieves state-of-the-art performance, with an ARE of 0.064, RMSE of 0.228, and accuracy ($\delta$ $<1.25$) of 89.3%. These metrics demonstrate that our model effectively predicts depth, even in challenging circumstances, providing a scalable solution for real-world applications in robotics, 3D reconstruction, and augmented reality.

We take a qualitative comparative look at quantum and classical quartic anharmonic oscillators. It has been shown that the behavior of the quantum anharmonic oscillator mimics that of the classical anharmonic oscillators with the structurally same Hamiltonian in the coherent state basis of the harmonic oscillators. The associated equation of motion allows us to use Lindstet-Poincare perturbation method to compute the classical frequency of the oscillation order by order, by taking care of its dependence on amplitude and the quantum corrections. We also derive a bound for periodicity of such oscillations in both the classical and quantum cases., Comment: 7 Pages, 0 Figures

Animal pose estimation (APE) aims to locate the animal body parts using a diverse array of sensor and modality inputs, which is crucial for research across neuroscience, biomechanics, and veterinary medicine. By evaluating 178 papers since 2013, APE methods are categorised by sensor and modality types, learning paradigms, experimental setup, and application domains, presenting detailed analyses of current trends, challenges, and future directions in single- and multi-modality APE systems. The analysis also highlights the transition between human and animal pose estimation. Additionally, 2D and 3D APE datasets and evaluation metrics based on different sensors and modalities are provided. A regularly updated project page is provided here: https://github.com/ChennyDeng/MM-APE., Comment: 35 pages, 5 figures, 8 tables

Mathematical research thrives on the effective dissemination and discovery of knowledge. zbMATH Open has emerged as a pivotal platform in this landscape, offering a comprehensive repository of mathematical literature. Beyond indexing and abstracting, it serves as a unified quality-assured infrastructure for finding, evaluating, and connecting mathematical information that advances mathematical research as well as interdisciplinary exploration. zbMATH Open enables scientific quality control by post-publication reviews and promotes connections between researchers, institutions, and research outputs. This paper represents the functionalities of the most significant features of this open-access service, highlighting its role in shaping the future of mathematical information retrieval.

This work describes an approach towards pixel quantization using variable resolution which is made feasible using image transformation in the analog domain. The main aim is to reduce the average bits-per-pixel (BPP) necessary for representing an image while maintaining the classification accuracy of a Convolutional Neural Network (CNN) that is trained for image classification. The proposed algorithm is based on the Hadamard transform that leads to a low-resolution variable quantization by the analog-to-digital converter (ADC) thus reducing the power dissipation in hardware at the sensor node. Despite the trade-offs inherent in image transformation, the proposed algorithm achieves competitive accuracy levels across various image sizes and ADC configurations, highlighting the importance of considering both accuracy and power consumption in edge computing applications. The schematic of a novel 1.5 bit ADC that incorporates the Hadamard transform is also proposed. A hardware implementation of the analog transformation followed by software-based variable quantization is done for the CIFAR-10 test dataset. The digitized data shows that the network can still identify transformed images with a remarkable 90% accuracy for 3-BPP transformed images following the proposed method., Comment: 13 pages, 20 figures

In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Generalization. These architectures capture global contextual features and model long-range dependencies, making them promising candidates for improving cross-domain generalization. We conduct a broad study with in-depth analysis and systematically evaluate different variants of these architectures, using extensive pre-training datasets such as ImageNet-1K, ImageNet-21K, JFT-300M, and ImageNet-22K. Additionally, we compare self-supervised and supervised pre-training strategies to assess their impact on FDG performance. Our findings suggest that self-supervised techniques, which focus on reconstructing masked image patches, can better capture the intrinsic structure of images, thereby outperforming their supervised counterparts. Comprehensive evaluations on the Office-Home and PACS datasets demonstrate that adopting advanced architectures pre-trained on larger datasets establishes new benchmarks, achieving average accuracies of 84.46\% and 92.55\%, respectively. Additionally, we observe that certain variants of these advanced models, despite having fewer parameters, outperform larger ResNet models. This highlights the critical role of utilizing sophisticated architectures and diverse pre-training strategies to enhance FDG performance, especially in scenarios with limited computational resources where model efficiency is crucial. Our results indicate that federated learning systems can become more adaptable and efficient by leveraging these advanced methods, offering valuable insights for future research in FDG.

We propose a toy model of ion-atom hybrid quantum system for quantum computing. We show that when two atomic qubits in two largely separated optical tweezers interact with a single trapped ion through Rydberg excitation of the atoms, there exists an ion-mediated atom-atom interaction which exceeds the direct interatomic interaction. We employ this mediated interaction to demonstrate two-qubit control phase gate operation with 97\% fidelity by addressing the individual atomic qubits with lasers., Comment: 9 pages, 4 figures

An experimental study of optical phonon modes, both normal and interface (IF) phonons, in bilayer strain-coupled InAs/GaAs_(1-x)Sb_x quantum dot heterostructures has been presented by means of low-temperature polarized Raman scattering. The effect of Sb-content on the frequency positions of these phonon modes has been very well correlated with the simulated strain. The Raman peaks show different frequency shifts in the heterostructure with varying Sb-content in the capping layer. This shift is attributed to the strain relaxation, bigger size of quantum dots and type-II band alignment.

Interfacial ferroelectricity, prevalent in various parallel-stacked layered materials, allows switching of out-of-plane ferroelectric order by in-plane sliding of adjacent layers. Its resilience against doping potentially enables next-generation storage and logic devices. However, studies have been limited to indirect sensing or visualization of ferroelectricity. For transition metal dichalcogenides, there is little knowledge about the influence of ferroelectric order on their intrinsic valley and excitonic properties. Here, we report direct probing of ferroelectricity in few-layer 3R-MoS$_2$ using reflectance contrast spectroscopy. Contrary to a simple electrostatic perception, layer-hybridized excitons with out-of-plane electric dipole moment remain decoupled from ferroelectric ordering, while intralayer excitons with in-plane dipole orientation are sensitive to it. Ab initio calculations identify stacking-specific interlayer hybridization leading to this asymmetric response. Exploiting this sensitivity, we demonstrate optical readout and control of multi-state polarization with hysteretic switching in a field-effect device. Time-resolved Kerr ellipticity reveals a direct correspondence between spin-valley dynamics and stacking order.

Language model alignment research often attempts to ensure that models are not only helpful and harmless, but also truthful and unbiased. However, optimizing these objectives simultaneously can obscure how improving one aspect might impact the others. In this work, we focus on analyzing the relationship between two concepts essential in both language model alignment and political science: truthfulness and political bias. We train reward models on various popular truthfulness datasets and subsequently evaluate their political bias. Our findings reveal that optimizing reward models for truthfulness on these datasets tends to result in a left-leaning political bias. We also find that existing open-source reward models (i.e., those trained on standard human preference datasets) already show a similar bias and that the bias is larger for larger models. These results raise important questions about the datasets used to represent truthfulness, potential limitations of aligning models to be both truthful and politically unbiased, and what language models capture about the relationship between truth and politics., Comment: EMNLP 2024

This work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs). In RDPs, the unknown dependency of future observations and rewards from the past interactions can be captured by some hidden finite-state automaton. For this reason, many RDP algorithms first reconstruct this unknown dependency using automata learning techniques. In this paper, we show that it is possible to overcome two strong limitations of previous offline RL algorithms for RDPs, notably RegORL. This can be accomplished via the introduction of two original techniques: the development of a new pseudometric based on formal languages, which removes a problematic dependency on $L_\infty^\mathsf{p}$-distinguishability parameters, and the adoption of Count-Min-Sketch (CMS), instead of naive counting. The former reduces the number of samples required in environments that are characterized by a low complexity in language-theoretic terms. The latter alleviates the memory requirements for long planning horizons. We derive the PAC sample complexity bounds associated to each of these techniques, and we validate the approach experimentally., Comment: To appear in EWRL 2024

The primary purpose of this research study was to evaluate the efficacy of BASE, a self-paced online mental health education preventive intervention with middle school students. Two hundred ninety-five adolescent students were randomly assigned to receive BASE modules (n = 156) in a regular education classroom setting over a 5-week period or to a wait-list control (CO) group (n = 139). Change in student- and teacher-report outcome measures was investigated. Students in the BASE condition showed significant gains in mental health knowledge and teacher-reported school engagement over the intervention period whereas CO students showed declines in these areas. In addition, students who spent more time engaged in the BASE intervention modules showed significantly greater gains in BASE mental health knowledge. Because our study sample included a large percentage of Hispanic students (44%), we were able to conduct follow-up analyses to test for differential intervention effects by demographic sub-groups. Findings revealed use of the BASE modules were particularly positively impactful for Hispanic students, including positive changes in self-reported self-efficacy and school engagement as well as life skills over the intervention period compared to Hispanic students in the CO group. Discussion focuses on the potential for digital universal mental health intervention in schools. Tables and figures are appended.

The channel estimation (CE) overhead for unstructured multipath-rich channels increases linearly with the number of reflective elements of reconfigurable intelligent surface (RIS). This results in a significant portion of the channel coherence time being spent on CE, reducing data communication time. Furthermore, due to the mobility of the user equipment (UE) and the time consumed during CE, the estimated channel state information (CSI) may become outdated during actual data communication. In recent studies, the timing for CE has been primarily determined based on the coherence time interval, which is dependent on the velocity of the UE. However, the effect of the current channel condition and pathloss of the UEs can also be utilized to control the duration between successive CE to reduce the overhead while still maintaining the quality of service. Furthermore, for muti-user systems, the appropriate coherence time intervals of different users may be different depending on their velocities. Therefore CE carried out ignoring the difference in coherence time of different UEs may result in the estimated CSI being detrimentally outdated for some users. In contrast, others may not have sufficient time for data communication. To this end, based on the throughput analysis on outdated CSI, an algorithm has been designed to dynamically predict the next time instant for CE after the current CSI acquisition. In the first step, optimal RIS phase shifts to maximise channel gain is computed. Based on this and the amount of degradation of SINR due to outdated CSI, transmit powers are allocated for the UEs and finally the next time instant for CE is predicted such that the aggregated throughput is maximized. Simulation results confirm that our proposed algorithm outperforms the coherence time-based strategies.

Inline comments in the source code facilitate easy comprehension, reusability, and enhanced readability. However, code snippets in answers on Q&A sites like Stack Overflow (SO) often lack comments because answerers volunteer their time and often skip comments or explanations due to time constraints. Existing studies show that these online code examples are difficult to read and understand, making it difficult for developers (especially novices) to use them correctly and leading to misuse. Given these challenges, we introduced AUTOGENICS, a tool designed to integrate with SO to generate effective inline comments for code snippets in SO answers exploiting large language models (LLMs). Our contributions are threefold. First, we randomly select 400 answer code snippets from SO and generate inline comments for them using LLMs. We then manually evaluate these comments' effectiveness using four key metrics: accuracy, adequacy, conciseness, and usefulness. Overall, LLMs demonstrate promising effectiveness in generating inline comments for SO answer code snippets. Second, we surveyed 14 active SO users to perceive the effectiveness of these inline comments. The survey results are consistent with our previous manual evaluation. However, according to our evaluation, LLMs-generated comments are less effective for shorter code snippets and sometimes produce noisy comments. Third, to address the gaps, we introduced AUTOGENICS, which extracts additional context from question texts and generates context-aware inline comments. It also optimizes comments by removing noise (e.g., comments in import statements and variable declarations). We evaluate the effectiveness of AUTOGENICS-generated comments using the same four metrics that outperform those of standard LLMs. AUTOGENICS might (a) enhance code comprehension, (b) save time, and improve developers' ability to learn and reuse code more accurately., Comment: Accepted for presentation in the research track at the IEEE International Conference on Source Code Analysis & Manipulation (SCAM 2025)

Anonymity is a powerful component of many participatory media platforms that can afford people greater freedom of expression and protection from external coercion and interference. However, it can be difficult to effectively implement on platforms that leverage spoken language due to distinct biomarkers present in the human voice. In this work, we explore the use of voice anonymization methods within the context of a technology-enhanced civic dialogue network based in the United States, whose purpose is to increase feelings of agency and being heard within civic processes. Specifically, we investigate the use of two different speech transformation and synthesis methods for anonymization: voice conversion (VC) and text-to-speech (TTS). Through a series of two studies, we examine the impact that each method has on 1) the empathy and trust that listeners feel towards a person sharing a personal story, and 2) a speaker's own perception of being heard, finding that voice conversion is an especially suitable method for our purposes. Our findings open up interesting potential research directions related to anonymous spoken discourse, as well as additional ways of engaging with voice-based civic technologies., Comment: Accepted to CSCW 2024 (Proceedings of the ACM on Human-Computer Interaction, Vol. 8, No. CSCW2)

This paper presents a comprehensive survey of network analysis research on the film industry, aiming to evaluate its emergence as a field of study and identify potential areas for further research. Many foundational network studies made use of the abundant data from the Internet Movie Database (IMDb) to test network methodologies. This survey focuses more specifically on examining research that employs network analysis to evaluate the film industry itself, revealing the social and business relationships involved in film production, distribution, and consumption. The paper adopts a classification approach based on node type and summarizes the key contributions in relation to each. The review provides insights into the structure and interconnectedness of the field, highlighting clusters of debates and shedding light on the areas in need of further theoretical and methodological development. In addition, this survey contributes to understanding film industry network analysis and informs researchers interested in network methods within the film industry and related cultural sectors., Comment: Proofread version

Over the last four decades, a number of modified gravity theories have been proposed to study cosmological phenomena as they can provide solutions for some of the shortcomings of Einstein's gravity in explaining early and late time accelerations of the observed Universe, the existence of dark matter, singularities at center of Black holes etc. The theoretical and observational challenges faced by the $\Lambda$CDM model also point towards the necessity for looking beyond General Relativity. In this direction, recently $f(\phi, T)$ gravity has been proposed in literature where the non-minimal coupling of the scalar field $\phi$ with the trace of energy-momentum tensor $T$ has been introduced in the Einstein-Hilbert action. Considering the Hilltop potential, we have studied the slow-roll inflation in the framework of $f(\phi, T)$ gravity. It is found that Hilltop inflationary models in $f(\phi, T)$ gravity are viable when seen in the light of latest Planck data.

Van der Waals (vdW) polytypes of broken inversion and mirror symmetries were recently shown to exhibit switchable electric polarization even at the ultimate two-layer thin limit. Their out-of-plane polarization was found to accumulate in a ladder-like fashion with each successive layer, offering 2D building blocks for the bottom-up construction of 3D ferroelectrics. Here, we demonstrate experimentally that beyond a critical stack thickness, the accumulated polarization in rhombohedral polytypes of molybdenum disulfide (r-MoS2) saturates. The underlying saturation mechanism, deciphered via DFT and self-consistent Poisson-Schr\"odinger calculations, point to a purely electronic redistribution involving: (1) polarization-induced bandgap closure that allows for cross-stack charge transfer and the emergence of free surface charge; (2) reduction of the polarization saturation value, as well as the critical thickness at which it is obtained, by the presence of free carriers. The resilience of polar layered structures to atomic surface reconstruction, which is essentially unavoidable in polar 3D crystals, potentially allows for the design of new devices with mobile surface charges. Our findings, which are of general nature, should be accounted for when designing switching and/or conductive devices based on ferroelectric layered materials.

Statistical research in real estate markets, particularly in understanding the spatio-temporal dynamics of house prices, has garnered significant attention in recent times. Although Bayesian methods are common in spatio-temporal modeling, standard Markov chain Monte Carlo (MCMC) techniques are usually slow for large datasets such as house price data. To tackle this problem, we propose a divide-and-conquer spatio-temporal modeling approach. This method involves partitioning the data into multiple subsets and applying an appropriate Gaussian process model to each subset in parallel. The results from each subset are then combined using the Wasserstein barycenter technique to obtain the global parameters for the original problem. The proposed methodology allows for multiple observations per spatial and time unit, thereby offering added benefits for practitioners. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. The methodology provides insightful findings about the effects of various amenities, trend patterns, and the relationship between prices and carbon emissions. Furthermore, as demonstrated through a cross-validation study, it shows good predictive accuracy while balancing computational efficiency., Comment: Supplementary material is available from the authors upon request

We study a canonical collective action game with incomplete information. Individuals attempt to coordinate to achieve a shared goal, while also facing a temptation to free-ride. Consuming more similar information about the fundamentals can help them coordinate, but it can also exacerbate free-riding. Our main result shows that more similar information facilitates (impedes) achieving a common goal when achieving the goal is sufficiently challenging (easy). We apply this insight to show why insufficiently powerful authoritarian governments may face larger protests when attempting to restrict press freedom, and why informational diversity in committees is beneficial when each vote carries more weight.

Understanding and making use of audience feedback is important but difficult for journalists, who now face an impractically large volume of audience comments online. We introduce AudienceView, an online tool to help journalists categorize and interpret this feedback by leveraging large language models (LLMs). AudienceView identifies themes and topics, connects them back to specific comments, provides ways to visualize the sentiment and distribution of the comments, and helps users develop ideas for subsequent reporting projects. We consider how such tools can be useful in a journalist's workflow, and emphasize the importance of contextual awareness and human judgment., Comment: Accepted at CSCW Demo 2024. 5 pages, 2 figures

We study van der Waals heterostructures consisting of monolayer MoSe$_2$ and few-layer Cr$_2$Ge$_2$Te$_6$ fully encapsulated in hexagonal Boron Nitride using low-temperature photoluminescence and polar magneto-optic Kerr effect measurements. Photoluminescence characterization reveals a partial quenching and a change of the exciton-trion emission ratio in the heterostructure as compared to the isolated MoSe$_2$ monolayer. Under circularly polarized excitation, we find that the exciton-trion emission ratio depends on the relative orientation of excitation helicity and Cr$_2$Ge$_2$Te$_6$ magnetization, even though the photoluminescence emission itself is unpolarized. This observation hints at an ultrafast, spin-dependent interlayer charge transfer that competes with exciton and trion formation and recombination.

Words often carry different meanings for people from diverse backgrounds. Today's era of social polarization demands that we choose words carefully to prevent miscommunication, especially in political communication and journalism. To address this issue, we introduce the Bridging Dictionary, an interactive tool designed to illuminate how words are perceived by people with different political views. The Bridging Dictionary includes a static, printable document featuring 796 terms with summaries generated by a large language model. These summaries highlight how the terms are used distinctively by Republicans and Democrats. Additionally, the Bridging Dictionary offers an interactive interface that lets users explore selected words, visualizing their frequency, sentiment, summaries, and examples across political divides. We present a use case for journalists and emphasize the importance of human agency and trust in further enhancing this tool. The deployed version of Bridging Dictionary is available at https://dictionary.ccc-mit.org/., Comment: Accepted to CSCW Demo 2024

In this paper, we construct the associated vertex operator algebras for all $\mathcal{N}=2$ superconformal field theories of rank one. We give a uniform presentation through free-field realizations, which turns out to be a particularly suitable framework for this task. The elementary building blocks of the construction are dictated by the low energy degrees of freedom on the Higgs branch, which are well understood for rank-one theories. We further analyze the interplay between Higgs and Coulomb data on the moduli space of vacua, which tightly constrain the overall structure of the free field realizations. Our results suggest a plausible bottom-up classification scheme for low-rank SCFTs incorporating vertex algebra techniques., Comment: 45 pages, 2 figures, 11 tables

The average hazard (AH), recently introduced by Uno and Horiguchi, represents a novel summary metric of event time distributions, conceptualized as the general censoring-free average person-time incidence rate on a given time window, $[0,\tau].$ This metric is calculated as the ratio of the cumulative incidence probability at $\tau$ to the restricted mean survival time at $\tau$ and can be estimated through non-parametric methods. The AH's difference and ratio present viable alternatives to the traditional Cox's hazard ratio for quantifying the treatment effect on time-to-event outcomes in comparative clinical studies. While the methodology for evaluating the difference and ratio of AH in randomized clinical trials has been previously proposed, the application of the AH-based approach in general comparative effectiveness research (CER), where interventions are not randomly allocated, remains underdiscussed. This paper aims to introduce several approaches for applying the AH in general CER, thereby extending its utility beyond randomized trial settings to observational studies where treatment assignment is non-random.

Recently, very large language models (LLMs) have shown exceptional performance on several English NLP tasks with just in-context learning (ICL), but their utility in other languages is still underexplored. We investigate their effectiveness for NLP tasks in low-resource languages (LRLs), especially in the setting of zero-labelled cross-lingual transfer (0-CLT), where no labelled training data for the target language is available -- however training data from one or more related medium-resource languages (MRLs) is utilized, alongside the available unlabeled test data for a target language. We introduce Self-Supervised Prompting (SSP), a novel ICL approach tailored for the 0-CLT setting. SSP is based on the key observation that LLMs output more accurate labels if in-context exemplars are from the target language (even if their labels are slightly noisy). To operationalize this, since target language training data is not available in 0-CLT, SSP operates in two stages. In Stage I, using source MRL training data, target language's test data is noisily labeled. In Stage II, these noisy test data points are used as exemplars in ICL for further improved labelling. Additionally, our implementation of SSP uses a novel Integer Linear Programming (ILP)-based exemplar selection that balances similarity, prediction confidence (when available) and label coverage. Experiments on three tasks and eleven LRLs (from three regions) demonstrate that SSP strongly outperforms existing SOTA fine-tuned and prompting-based baselines in 0-CLT setup.

While state-of-the-art Large Language Models (LLMs) have shown impressive performance on many tasks, there has been extensive research on undesirable model behavior such as hallucinations and bias. In this work, we investigate how the quality of LLM responses changes in terms of information accuracy, truthfulness, and refusals depending on three user traits: English proficiency, education level, and country of origin. We present extensive experimentation on three state-of-the-art LLMs and two different datasets targeting truthfulness and factuality. Our findings suggest that undesirable behaviors in state-of-the-art LLMs occur disproportionately more for users with lower English proficiency, of lower education status, and originating from outside the US, rendering these models unreliable sources of information towards their most vulnerable users.

In this work, we introduce a framework for speech summarization that leverages the processing and reasoning capabilities of large language models (LLMs). We propose an end-to-end system that combines an instruction-tuned LLM with an audio encoder that converts speech into token representations that the LLM can interpret. Using a dataset with paired speech-text data, the overall system is trained to generate consistent responses to prompts with the same semantic information regardless of the input modality. The resulting framework allows the LLM to process speech inputs in the same way as text, enabling speech summarization by simply prompting the LLM. Unlike prior approaches, our method is able to summarize spoken content from any arbitrary domain, and it can produce summaries in different styles by varying the LLM prompting strategy. Experiments demonstrate that our approach outperforms a cascade baseline of speech recognition followed by LLM text processing., Comment: Accepted to Interspeech 2024

In recent decades, the field of quantum computing has experienced remarkable progress. This progress is marked by the superior performance of many quantum algorithms compared to their classical counterparts, with Shor's algorithm serving as a prominent illustration. Quantum arithmetic circuits, which are the fundamental building blocks in numerous quantum algorithms, have attracted much attention. Despite extensive exploration of various designs in the existing literature, researchers remain keen on developing novel designs and improving existing ones. In this review article, we aim to provide a systematically organized and easily comprehensible overview of the current state-of-the-art in quantum arithmetic circuits. Specifically, this study covers fundamental operations such as addition, subtraction, multiplication, division and modular exponentiation. We delve into the detailed quantum implementations of these prominent designs and evaluate their efficiency considering various objectives. We also discuss potential applications of presented arithmetic circuits and suggest future research directions., Comment: Under review at the Royal Society's Philosophical Transactions A

The carbon footprint share of the information and communication technology (ICT) sector has steadily increased in the past decade and is predicted to make up as much as 23 \% of global emissions in 2030. This shows a pressing need for developers, including the information retrieval community, to make their code more energy-efficient. In this project proposal, we discuss techniques to reduce the energy footprint of the MaRDI (Mathematical Research Data Initiative) Portal, a MediaWiki-based knowledge base. In future work, we plan to implement these changes and provide concrete measurements on the gain in energy efficiency. Researchers developing similar knowledge bases can adapt our measures to reduce their environmental footprint. In this way, we are working on mitigating the climate impact of Information Retrieval research., Comment: 4 pages

The exceptional properties of two-dimensional (2D) magnet materials present a novel approach to fabricate functional magnetic tunnel junctions (MTJ) by constructing full van der Waals (vdW) heterostructures with atomically sharp and clean interfaces. The exploration of vdW MTJ devices with high working temperature and adjustable functionalities holds great potential for advancing the application of 2D materials in magnetic sensing and data storage. Here, we report the observation of highly tunable room-temperature tunneling magnetoresistance through electronic means in a full vdW Fe3GaTe2/WSe2/Fe3GaTe2 MTJ. The spin valve effect of the MTJ can be detected even with the current below 1 nA, both at low and room temperatures, yielding a tunneling magnetoresistance (TMR) of 340% at 2 K and 50% at 300 K, respectively. Importantly, the magnitude and sign of TMR can be modulated by a DC bias current, even at room temperature, a capability that was previously unrealized in full vdW MTJs. This tunable TMR arises from the contribution of energy-dependent localized spin states in the metallic ferromagnet Fe3GaTe2 during tunnel transport when a finite electrical bias is applied. Our work offers a new perspective for designing and exploring room-temperature tunable spintronic devices based on vdW magnet heterostructures.