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Single-particle entangled states (SPES) can offer a more secure way of encoding and processing quantum information than their multi-particle counterparts. The SPES generated via a 2D alternate quantum-walk setup from initially separable states can be either 3-way or 2-way entangled. This letter shows that the generated genuine three-way and nonlocal two-way SPES can be used as cryptographic keys to securely encode two distinct messages simultaneously. We detail the message encryption-decryption steps and show the resilience of the 3-way and 2-way SPES-based cryptographic protocols against eavesdropper attacks like intercept-and-resend and man-in-the-middle. We also detail how these protocols can be experimentally realized using single photons, with the three degrees of freedom being OAM, path, and polarization. These have unparalleled security for quantum communication tasks. The ability to simultaneously encode two distinct messages using the generated SPES showcases the versatility and efficiency of the proposed cryptographic protocol. This capability could significantly improve the throughput of quantum communication systems.

Online communities offer their members various benefits, such as information access, social and emotional support, and entertainment. Despite the important role that founders play in shaping communities, prior research has focused primarily on what drives users to participate and contribute; the motivations and goals of founders remain underexplored. To uncover how and why online communities get started, we present findings from a survey of 951 recent founders of Reddit communities. We find that topical interest is the most common motivation for community creation, followed by motivations to exchange information, connect with others, and self-promote. Founders have heterogeneous goals for their nascent communities, but they tend to privilege community quality and engagement over sheer growth. These differences in founders' early attitudes towards their communities help predict not only the community-building actions that they pursue, but also the ability of their communities to attract visitors, contributors, and subscribers over the first 28 days. We end with a discussion of the implications for researchers, designers, and founders of online communities.

Automated fact checking has gained immense interest to tackle the growing misinformation in the digital era. Existing systems primarily focus on synthetic claims on Wikipedia, and noteworthy progress has also been made on real-world claims. In this work, we release QuanTemp, a diverse, multi-domain dataset focused exclusively on numerical claims, encompassing temporal, statistical and diverse aspects with fine-grained metadata and an evidence collection without leakage. This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, not addressed by existing works that mainly focus on synthetic claims. We evaluate and quantify the limitations of existing solutions for the task of verifying numerical claims. We also evaluate claim decomposition based methods, numerical understanding based models and our best baselines achieves a macro-F1 of 58.32. This demonstrates that QuanTemp serves as a challenging evaluation set for numerical claim verification.

Representing ecosystems at equilibrium has been foundational for building ecological theories, forecasting species populations and planning conservation actions. The equilibrium "balance of nature" ideal suggests that populations will eventually stabilise to a coexisting balance of species. However, a growing body of literature argues that the equilibrium ideal is inappropriate for ecosystems. Here, we develop and demonstrate a new framework for representing ecosystems without considering equilibrium dynamics. Instead, far more pragmatic ecosystem models are constructed by considering population trajectories, regardless of whether they exhibit equilibrium or transient (i.e. non-equilibrium) behaviour. This novel framework maximally utilises readily available, but often overlooked, knowledge from field observations and expert elicitation, rather than relying on theoretical ecosystem properties. We developed innovative Bayesian algorithms to generate ecosystem models in this new statistical framework, without excessive computational burden. Our results reveal that our pragmatic framework could have a dramatic impact on conservation decision-making and enhance the realism of ecosystem models and forecasts.

This paper develops an in-depth treatment concerning the problem of approximating the Gaussian smoothing and Gaussian derivative computations in scale-space theory for application on discrete data. With close connections to previous axiomatic treatments of continuous and discrete scale-space theory, we consider three main ways discretizing these scale-space operations in terms of explicit discrete convolutions, based on either (i) sampling the Gaussian kernels and the Gaussian derivative kernels, (ii) locally integrating the Gaussian kernels and the Gaussian derivative kernels over each pixel support region and (iii) basing the scale-space analysis on the discrete analogue of the Gaussian kernel, and then computing derivative approximations by applying small-support central difference operators to the spatially smoothed image data. We study the properties of these three main discretization methods both theoretically and experimentally, and characterize their performance by quantitative measures, including the results they give rise to with respect to the task of scale selection, investigated for four different use cases, and with emphasis on the behaviour at fine scales. The results show that the sampled Gaussian kernels and derivatives as well as the integrated Gaussian kernels and derivatives perform very poorly at very fine scales. At very fine scales, the discrete analogue of the Gaussian kernel with its corresponding discrete derivative approximations performs substantially better. The sampled Gaussian kernel and the sampled Gaussian derivatives do, on the other hand, lead to numerically very good approximations of the corresponding continuous results, when the scale parameter is sufficiently large, in the experiments presented in the paper, when the scale parameter is greater than a value of about 1, in units of the grid spacing.

Autoregressive Recurrent Neural Networks are widely employed in time-series forecasting tasks, demonstrating effectiveness in univariate and certain multivariate scenarios. However, their inherent structure does not readily accommodate the integration of future, time-dependent covariates. A proposed solution, outlined by Salinas et al 2019, suggests forecasting both covariates and the target variable in a multivariate framework. In this study, we conducted comprehensive tests on publicly available time-series datasets, artificially introducing highly correlated covariates to future time-step values. Our evaluation aimed to assess the performance of an LSTM network when considering these covariates and compare it against a univariate baseline. As part of this study we introduce a novel approach using seasonal time segments in combination with an RNN architecture, which is both simple and extremely effective over long forecast horizons with comparable performance to many state of the art architectures. Our findings from the results of more than 120 models reveal that under certain conditions jointly training covariates with target variables can improve overall performance of the model, but often there exists a significant performance disparity between multivariate and univariate predictions. Surprisingly, even when provided with covariates informing the network about future target values, multivariate predictions exhibited inferior performance. In essence, compelling the network to predict multiple values can prove detrimental to model performance, even in the presence of informative covariates. These results suggest that LSTM architectures may not be suitable for forecasting tasks where predicting covariates would typically be expected to enhance model accuracy.

This paper presents a comprehensive analysis of global web usage patterns based on data from SimilarWeb, a leading source for estimating web traffic. Leveraging a dataset comprising over 250,000 websites, we estimate the total web traffic and investigate its distribution among domains and industry sectors. We detail the characteristics of the top 116 domains, which comprise an estimated one-third of all web traffic. Our analysis scrutinizes various attributes of these domains, including their content sources and types, access requirements, offline presence, and ownership features. Our analysis reveals a significant concentration of web traffic, with a diminutive number of top websites capturing the majority of visits. Search engines, news and media, social networks, streaming, and adult content emerge as primary attractors of web traffic, which is also highly concentrated on platforms and USA-owned websites. Much of the traffic goes to for-profit but mostly free-of-charge websites, highlighting the dominance of business models not based on paywalls.

Increasingly, phonetic research utilizes data collected from participants who record themselves on readily available devices. Though such recordings are convenient, their suitability for acoustic analysis remains an open question, especially regarding how the individual methods affect acoustic measures over time. We used Quantile Generalized Additive Mixed Models (QGAMMs) to analyze measures of F0, intensity, and the first and second formants, comparing files recorded using a laboratory-standard recording method (Zoom H6 Recorder with an external microphone), to three remote recording methods, (1) the Awesome Voice Recorder application on a smartphone (AVR), (2) the Zoom meeting application with default settings (Zoom-default), and (3) the Zoom meeting application with the "Turn on Original Sound" setting (Zoom-raw). A linear temporal alignment issue was observed for the Zoom methods over the course of the long, recording session files. However, the difference was not significant for utterance-length files. F0 was reliably measured using all methods. Intensity and formants presented non-linear differences across methods that could not be corrected for simply. Overall, the AVR files were most similar to the H6's, and so AVR is deemed to be a more reliable recording method than either Zoom-default or Zoom-raw.

Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. We also share insights on how to optimize dissemination by analyzing data available exclusively to content creators as well as via sentiment analysis of comments.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.