Miracle-dz/UniFAR-FTUs huggingface dataset:
The Hardness-Intensity Correlation in Bright Gamma-Ray Bursts

The presence of a hardness-intensity correlation (HIC) is demonstrated in a sample of 77 bright bursts observed by the Phebus and Ulysses gamma-ray burst (GRB) experiments. Using simulations, we find a significant correlation (5 σ) between the peak spectral hardness and the peak intensity in Phebus. Moreover, the HIC is compatible with a single regression line over the full range of intensities. This result, together with earlier ones, shows that the HIC is a fundamental property of GRBs that has important consequences for statistical studies of these events. First, the presence of a HIC implies that the intensity distribution (and ⟨V/Vmax⟩) depends on the energy range of the instrument. Second, we show that the HIC, combined with ⟨V/Vmax⟩, constrains the spatial distribution of the bursters. Bright events in our sample exhibit a significant HIC and ⟨V/Vmax⟩ = 0.5. The interpretation of these observations is different for cosmological and galactic models. In Euclidean space, two features are required to explain the observations: bursters must belong to a homogeneous, bounded population and must have an intrinsic hardness-luminosity correlation. In a cosmological scenario, the apparent contradiction between the HIC (which requires high redshifts) and ⟨V/Vmax⟩ = 0.5 (characteristic of nearby bursters) can be resolved by assuming a density evolution of the sources.
Replies to reviews

The authors conducted a very comprehensive sensitivity analyses of the effects of adding an additional river routing model with various schemes to a hydrological model. The publication shows promise as a great reference work for model experiment setup. In general, the publication is well written and the arguments for conducting the study are clear. The decisions made regarding the methods are well-argued (with the exception of 1) and the results are valuable for the hydrologic community. The limitation of this study are well described. It is understandable given the scope of the study and the data requirements that the authors evaluated a single catchment. For future research, I am eager to discover how the results of this study would be different in a more gentle sloping catchment or for various catchment sizes (e.g using CAMELS-CH Alvarez-Garreton et al., 2018).
Polynomial approximations of the Lyapunov matrix of a class of time delay systems

Abstract A polynomial approximation of the Lyapunov matrix appearing in the complete type Lyapunov Krasovskii functionals associated to a class of retarded time delay systems is proposed. The results are concordant with the available semi-analytic solution. The comparison of the approximation error with that of the piecewise linear approximation previously introduced in the literature shows a significant improvement.
Digital Omicron detection using unscripted voice samples from social media

Social media data can boost artificial intelligence (AI) systems designed for clinical applications by expanding data sources that are otherwise limited in size. Currently, deep learning methods applied to large social media datasets are used for a variety of biomedical tasks, including forecasting the onset of mental illness and detecting outbreaks of new diseases. However, exploration of online data as a training component for diagnostics tools remains rare, despite the deluge of information that is available through various APIs. In this study, data from YouTube was used to train a model to detect the Omicron variants of SARS-CoV-2 from changes in the human voice. According to the ZOE Health Study, laryngitis and hoarse voice were among the most common symptoms of the Omicron variant, regardless of vaccination status. Omicron is characterized by pre-symptomatic transmission as well as mild or absent symptoms. Therefore, impactful screening methodologies may benefit from speed, convenience, and non-invasive ergonomics. We mined YouTube to collect voice data from individuals with self-declared positive COVID-19 tests during time periods where the Omicron variant (or sub-variants, including BA.4/5) consisted of more than 95% of cases. Our dataset contained 183 distinct Omicron samples (28.39 hours), 192 healthy samples (33.90 hours), 138 samples from other upper respiratory infections (8.09 hours), and 133 samples from non-Omicron variants of COVID-19 (22.84 hours). We used a flexible data collection protocol and implemented a simple augmentation strategy that leveraged intra-sample variance arising from the diversity of unscripted speech (different words, phrases, and tones). This approach led to enhanced model generalization despite a relatively small number of samples. We trained a DenseNet model to detect Omicron in subjects with self-declared positive COVID-19 tests. Our model achieved 86% sensitivity and 81% specificity when detecting healthy voices (asymptomatic negative vs. all positive). We also achieved 76% sensitivity and 70% specificity separating between symptomatic negative samples and all positive samples. This result showed that social media data may be used to counterbalance the limited amount of well-curated data commonly available for deep learning tasks in clinical medicine. Our work demonstrates the potential of digital, non-invasive diagnostic methods trained with public online data and explores novel design paradigms for diagnostic tools that rely on audio data.
Testing the numerical stability of time-dependent density functional simulations using the Suzuki-Trotter formula

In this paper, the numerical stability of simulations of the real-time propagation of electron wave functions under dynamical external fields is shown within the framework of the time-dependent density functional theory (TDDFT) by using periodic boundary conditions. The plane-wave basis set is used to express valence wave functions and fast Fourier transformation is used to treat differential terms in the Schr\"odinger (Kohn-Sham) and Poisson equations. The Suzuki-Trotter formula is applied for expressing time propagation of electron wave functions. An energy conservation rule for conditions with ions in motion and with a time-varying external dielectric field is shown, and numerical stability that satisfies this conservation rule is demonstrated. This will be a benchmark in developing a simulation with the use of other TDDFT schemes different from the current one, such as the real-space method.
Increase in the Rate of Gut Carriage of Fluoroquinolone-Resistant Escherichia coli despite a Reduction in Antibiotic Prescriptions

Background: Fluoroquinolone use for urinary tract infections has been steadily declining. Gut microbiota is the main reservoir for uropathogenic Escherichia coli but whether the carriage of fluoroquinolone-resistant E. coli has been changing is unknown. Methods. We determined the frequency of isolation and other characteristics of E. coli nonsuceptible to fluoroquinolones (at >=0.5 mg/L of ciprofloxacin) in 515 and 1605 E. coli-positive fecal samples collected in 2015 and 2021, respectively, from non-antibiotic- taking women of age 50+ receiving care in the Seattle area Kaiser Permanente Washington healthcare system. Results. Between 2015 and 2021 the prescription of fluoroquinolones dropped nearly three-fold in the study population. During the same period, the rates of gut carriage of fluoroquinolone-resistant E. coli increased from 14.4 % to 19.9% (P=.005), driven by a significant increase of isolates from the recently emerged, pandemic multi-drug resistant clonal group ST1193 (1.7% to 4.3%; P=.007) and those with an incomplete set of or no fluoroquinolone-resistance determining mutations (2.3% to 7.5%; P<.001). While prevalence of the resistance-associated mobile genes among the isolates dropped from 64.1% to 32.6% (P<.001), co-resistance to third generation cephalosporins has increased 21.5% to 33.1%, P=. 044). Conclusion. Despite reduction in fluoroquinolone prescriptions, gut carriage of fluoroquinolone-resistant uropathogenic E. coli increased with a rise of previously sporadic lineages and co-resistance to third generation cephalosporins. Thus, to reduce the rates of antibiotic resistant urinary tract infections, greater focus should be on controlling the gut carriage of resistant bacteria.
An efficient enhanced k-means clustering algorithm

In k-means clustering, we are given a set of n data points in d-dimensional space ℝd and an integer k and the problem is to determine a set of k points in ℝd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation.
Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning

Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users’ trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model’s confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at https://github.com/epfl-ml4ed/unknown-unknowns.
Supersymmetry and String Theory: Beyond the Standard Model

When I was asked to review Michael Dine's new book, 'Supersymmetry and String Theory', I was pleased to have a chance to read a book by such an established authority on how string theory might become testable. The book is most useful as a list of current topics of interest in modern theoretical physics. It gives a succinct summary of a huge variety of subjects, including the standard model, symmetry, Yang–Mills theory, quantization of gauge theories, the phenomenology of the standard model, the renormalization group, lattice gauge theory, effective field theories, anomalies, instantons, solitons, monopoles, dualities, technicolor, supersymmetry, the minimal supersymmetric standard model, dynamical supersymmetry breaking, extended supersymmetry, Seiberg–Witten theory, general relativity, cosmology, inflation, bosonic string theory, the superstring, the heterotic string, string compactifications, the quintic, string dualities, large extra dimensions, and, in the appendices, Goldstone's theorem, path integrals, and exact beta-functions in supersymmetric gauge theories. Its breadth is both its strength and its weakness: it is not (and could not possibly be) either a definitive reference for experts, where the details of thorny technical issues are carefully explored, or a textbook for graduate students, with detailed pedagogical expositions. As such, it complements rather than replaces the much narrower and more focussed String Theory I and II volumes by Polchinski, with their deep insights, as well the two older volumes by Green, Schwarz, and Witten, which develop string theory pedagogically.
Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models

Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancements in this domain, they mostly consider motion synthesis and style manipulation as two separate problems. This is mainly due to the challenge of learning both motion contents that account for the inter-class behaviour and styles that account for the intra-class behaviour effectively in a common representation. To tackle this challenge, we propose a denoising diffusion probabilistic model solution for styled motion synthesis. As diffusion models have a high capacity brought by the injection of stochasticity, we can represent both inter-class motion content and intra-class style behaviour in the same latent. This results in an integrated, end-to-end trained pipeline that facilitates the generation of optimal motion and exploration of content-style coupled latent space. To achieve high-quality results, we design a multi-task architecture of diffusion model that strategically generates aspects of human motions for local guidance. We also design adversarial and physical regulations for global guidance. We demonstrate superior performance with quantitative and qualitative results and validate the effectiveness of our multi-task architecture.
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation

Prompting large language models has enabled significant recent progress in multi-step reasoning over text. However, when applied to text generation from semi-structured data (e.g., graphs or tables), these methods typically suffer from low semantic coverage, hallucination, and logical inconsistency. We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning. MURMUR is a best-first search method that generates reasoning paths using: (1) neural and symbolic modules with specific linguistic and logical skills, (2) a grammar whose production rules define valid compositions of modules, and (3) value functions that assess the quality of each reasoning step. We conduct experiments on two diverse data-to-text generation tasks like WebNLG and LogicNLG. These tasks differ in their data representations (graphs and tables) and span multiple linguistic and logical skills. MURMUR obtains significant improvements over recent few-shot baselines like direct prompting and chain-of-thought prompting, while also achieving comparable performance to fine-tuned GPT-2 on out-of-domain data. Moreover, human evaluation shows that MURMUR generates highly faithful and correct reasoning paths that lead to 26% more logically consistent summaries on LogicNLG, compared to direct prompting.
Nanomechanical resonators fabricated by atomic layer deposition on suspended 2D materials

Atomic layer deposition (ALD), a layer-by-layer controlled method to synthesize ultrathin materials, provides various merits over other techniques such as precise thickness control, large area scalability and excellent conformality. Here we demonstrate the possibility of using ALD growth on top of suspended 2D materials to fabricate nanomechanical resonators. We fabricate ALD nanomechanical resonators consisting of a graphene/MoS2 heterostructure. Using atomic force microscope indentation and optothermal drive, we measure their mechanical properties including Young’s modulus, resonance frequency and quality factor, showing a lower energy dissipation compared to their exfoliated counterparts. We also demonstrate the fabrication of nanomechanical resonators by exfoliating an ALD grown NbS2 layer. This study exemplifies the potential of ALD techniques to produce high-quality suspended nanomechanical membranes, providing a promising route towards high-volume fabrication of future multilayer nanodevices and nanoelectromechanical systems.
On High Dimensional Behaviour of Some Two-Sample Tests Based on Ball Divergence

In this article, we propose some two-sample tests based on ball divergence and investigate their high dimensional behavior. First, we study their behavior for High Dimension, Low Sample Size (HDLSS) data, and under appropriate regularity conditions, we establish their consistency in the HDLSS regime, where the dimension of the data grows to infinity while the sample sizes from the two distributions remain fixed. Further, we show that these conditions can be relaxed when the sample sizes also increase with the dimension, and in such cases, consistency can be proved even for shrinking alternatives. We use a simple example involving two normal distributions to prove that even when there are no consistent tests in the HDLSS regime, the powers of the proposed tests can converge to unity if the sample sizes increase with the dimension at an appropriate rate. This rate is obtained by establishing the minimax rate optimality of our tests over a certain class of alternatives. Several simulated and benchmark data sets are analyzed to compare the performance of these proposed tests with the state-of-the-art methods that can be used for testing the equality of two high-dimensional probability distributions.
Translating Social Media Crisis Narratives into Road Network Utilization Metrics: The Case of 2020 Oklahoma Ice Storm

Risk communication in times of disasters is complex, involving rapid and diverse communication in social networks (i.e., public and/or private agencies; local residents) as well as limited mobilization capacity and operational constraints of physical infrastructure networks. Despite a growing literature on infrastructure interdependencies and co-dependent social-physical systems, an in-depth understanding of how risk communication in online social networks weighs into physical infrastructure networks during a major disaster remains limited, let alone in compounding risk events. This study analyzes large-scale datasets of crisis mobility and activity-related social interactions and concerns available through social media (Twitter) for communities that were impacted by an ice storm (Oct. 2020) in Oklahoma. Compounded by the COVID-19 pandemic, Oklahoma residents faced this historic ice storm (Oct. 26, 2020-Oct. 29, 2020) that caused devastating traffic impacts (among others) due to excessive ice accumulation. By using the recently released academic Application Programming Interface (API) by Twitter that provides complete and unbiased data, geotagged tweets (approx. 210K) were collected covering the entire state of Oklahoma, and ice storm-related tweets (approx. 14.2K) were considered. First, the study uses natural language processing and text quantification techniques to translate crisis narratives (i.e., tweets). Next, geo-tagged tweets are mapped into co-located road networks using traditional GIS techniques. Finally, insights are generated using network science theories and quantified social narratives to interpret different elements of road networks (e.g., local roads, freeways, etc.) for the Oklahoma communities that were impacted by the ice storm event during the pandemic.
Kinetically constrained models out of equilibrium

We study the full class of kinetically constrained models in arbitrary dimension and out of equilibrium, in the regime where the density $q$ of facilitating sites in the equilibrium measure (but not necessarily in the initial measure) is close to $1$. For these models, we establish exponential convergence to equilibrium in infinite volume and linear time precutoff in finite volume with appropriate boundary condition. Our results are the first out-of-equilibrium results that hold for any model in the so-called critical class, which is covered in its entirety by our treatment, including e.g. the Fredrickson-Andersen 2-spin facilitated model. In addition, they generalise, unify and sometimes simplify several previous works in the field. As byproduct, we recover and generalise exponential tails for the connected component of the origin in the upper invariant trajectory of perturbed cellular automata and in the set of eventually infected sites in subcritical bootstrap percolation models. Our approach goes through the study of cooperative contact processes, last passage percolation, Toom contours, as well as a very convenient coupling between contact processes and kinetically constrained models.
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies

Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive; such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there exists no single established protocol for evaluating offline RL methods.In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.
Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games

Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.