Miracle-dz/UniFAR-FTUs huggingface dataset:
On the calculation of phonons in real-space density functional theory

We present an accurate and efficient formulation for the calculation of phonons in real-space Kohn-Sham density functional theory. Specifically, employing a local exchange-correlation functional, norm-conserving pseudopotential in the Kleinman-Bylander representation, and local form for the electrostatics, we derive expressions for the dynamical matrix and associated Sternheimer equation that are particularly amenable to the real-space finite-difference method, within the framework of density functional perturbation theory. In particular, the formulation is applicable to insulating as well as metallic systems of any dimensionality, enabling the efficient and accurate treatment of semi-infinite and bulk systems alike, for both orthogonal and non-orthogonal cells. We also develop an implementation of the proposed formulation within the high-order finite-difference method. Through representative examples, we verify the accuracy of the computed phonon dispersion curves and density of states, demonstrating excellent agreement with established planewave results.
A two-species model of aeolian sand transport

The transport of sand by wind results from the equilibrium between the erosion of grains dragged by the flow and the resulting slow down of the wind velocity. The dynamical mechanisms governing the saturation of the sand flux are investigated theoretically. We first demonstrate that previous models, based on the assumption that all the grains have the same trajectory, are either not self-consistent or lead to unstable solutions. A model based on a discrete number of states is derived, which solves these problems. Two well-defined species of grain appear, which correspond to saltons (high-energy grains) and reptons (grains ejected from the sand bed by the impact of saltons). They play specific roles: the negative feedback of the transport on the wind is limited to the reptation layer while most of the transport is due to saltation. The model is further simplified, benefiting from the existence of these two species and the dependencies of the threshold velocity, the saturated flux, the aerodynamic roughness and the saturation length are derived and compared to experimental measurements.
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow

Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their real-world deployment. This letter constructs a decentralized RL-based controller for inverter-based real-time voltage control in distribution systems. It features two components: a transient control policy and a steady-state performance optimizer. The transient policy is parameterized as a neural network, and the steady-state optimizer represents the gradient of the long-term operating cost function. The two parts are synthesized through a safe gradient flow framework, which prevents the violation of reactive power capacity constraints. We prove that if the output of the transient controller is bounded and monotonically decreasing with respect to its input, then the closed-loop system is asymptotically stable and converges to the optimal steady-state solution. We demonstrate the effectiveness of our method by conducting experiments with IEEE 13-bus and 123-bus distribution system test feeders.
Time Series Contrastive Learning with Information-Aware Augmentations

Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations. Unlike image and language domains where "desired'' augmented samples can be generated with the rule of thumb guided by prefabricated human priors, the ad-hoc manual selection of time series augmentations is hindered by their diverse and human-unrecognizable temporal structures. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. In this work, we address the problem by encouraging both high fidelity and variety based on information theory. A theoretical analysis leads to the criteria for selecting feasible data augmentations. On top of that, we propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning. Experiments on various datasets show highly competitive performance with up to a 12.0% reduction in MSE on forecasting tasks and up to 3.7% relative improvement in accuracy on classification tasks over the leading baselines.
Emotionally Enhanced Talking Face Generation

Several works have developed end-to-end pipelines for generating lip-synced talking faces with real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, these methods' effectiveness largely depends on the faces in the training dataset, which means they may not perform well on unseen faces. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing. With a broad range of six emotions, i.e., happiness, sadness, fear, anger, disgust, and neutral, we show that our model can adapt to arbitrary identities, emotions, and languages. Our proposed framework has a user-friendly web interface with a real-time experience for talking face generation with emotions. We also conduct a user study for subjective evaluation of our interface's usability, design, and functionality. Project page: \hrefhttps://midas.iiitd. edu.in/emo/ https://midas.iiitd. edu.in/emo/ .
A VR-based Priming Framework and Technology Implementation to Improve Learning Mindsets and Academic Performance in Post-Secondary Students

Recent research indicates that most post-secondary students in North America"felt overwhelming anxiety"in the past few years, negatively affecting well-being and academic performance. Further research revealed that other emotions, biases, perceptions, and negative thoughts, can similarly affect student academic performance. To address this problem, we classify these counterproductive mindsets, including anxiety, into Scarcity Mindset, a self-limiting perspective that appropriates cognitive bandwidth required for essential processes like learning in favour of addressing more critical needs or perceived insufficiencies. Through a multi-disciplinary literature analysis of ideas in cognitive science, learning theories and mindsets, and current technology approaches that are suited to address the limitations of scarcity thinking, we identify strategies to help transition students to a more positive Abundance Mindsets. We demonstrate that these priming intervention strategies can transfer to leading-edge digital environments, particularly Virtual Reality (VR). Offering further insights into the findings of our two previously presented studies, we argue that priming interventions related to preparatory activities and the context priming are transferable to virtual reality environments. As such, building on our multidisciplinary research insights, we propose a comprehensive priming model that exploits priming techniques in an iterative process called Cyclical Priming Methodology (CPM). These intervention strategies can focus on student preparation, motivation, reflection, the context of the learning environment, and other aspects of the learning process. Building on CPM, we further propose a technology implementation within VR called Virtual Reality Experience Priming (VREP) and discuss the process to embed CPM/VREP activities within the Experiential Learning Theory (ELT) cycle.
Fairness-Aware Graph Filter Design

Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonstrated that ML over graphs amplifies the already existing bias towards certain under-represented groups in various decision-making problems due to the information aggregation over biased graph structures. Faced with this challenge, in this paper, we design a fair graph filter that can be employed in a versatile manner for graph-based learning tasks. The design of the proposed filter is based on a bias analysis and its optimality in mitigating bias compared to its fairness-agnostic counterpart is established. Experiments on real-world networks for node classification demonstrate the efficacy of the proposed filter design in mitigating bias, while attaining similar utility and better stability compared to baseline algorithms.
A Bayesian Approach To The Halo-Galaxy-SMBH Connection Through Cosmic Time

We study the co-evolution of dark matter halos, galaxies and supermassive black holes using an empirical galaxy evolution model from $z=0$ -- $10$. We demonstrate that by connecting dark matter structure evolution with simple empirical prescriptions for baryonic processes, we are able to faithfully reproduce key observations in the relation between galaxies and their supermassive black holes. By assuming a physically-motivated, direct relationship between the galaxy and supermassive black hole properties to the mass of their host halo, we construct expressions for the galaxy stellar mass function, galaxy UV luminosity function, active black hole mass function and quasar bolometric luminosity function. We calibrate the baryonic prescriptions using a fully Bayesian approach in order to reproduce observed population statistics. The obtained parametrizations are then used to study the relation between galaxy and black hole properties, as well as their evolution with redshift. The galaxy stellar mass -- UV luminosity relation, black hole mass -- stellar mass relation, black hole mass -- AGN luminosity relation, and redshift evolution of these quantities obtained from the model are qualitatively consistent with observations. Based on these results, we present upper limits on the expected number of sources for $z=5$ up to $z=15$ for scheduled JWST and \textit{Euclid} surveys, thus showcasing that empirical models can offer qualitative as well as quantitative prediction in a fast, easy and flexible manner that complements more computationally expensive approaches.
Higher-Order Organization in the Human Brain From Matrix-Based Rényi’s Entropy

Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to explore higher-order interactions that go beyond paired brain areas in order to better understand information processing in the human brain. To address this problem, we applied multivariate mutual information, specifically, Total Correlation and Dual Total Correlation to reveal higher-order information in the brain. In this paper, we estimate these metrics using matrix-based Rényi ’ s entropy, which offers a direct and easily interpretable approach that is not limited by direct assumptions about probability distribution functions of multivariate time series. We applied these metrics to resting-state fMRI data in order to examine higher-order interactions in the brain. Our results showed that the higher-order information interactions captured increase gradually as the interaction order increases. Furthermore, we observed a gradual increase in the correlation between the Total Correlation and Dual Total Correlation as the interaction order increased. In addition, the significance of Dual Total Correlation values compared to Total Correlation values also indicate that the human brain exhibits synergy dominance during the resting state.
Accurate characterization of wafer bond toughness with the double cantilever specimen

The displacement loaded double cantilever test, also referred to as the “Maszara test” and the “crack opening method” by the wafer bonding community, is a common technique used to evaluate the interface toughness or surface energy of direct wafer bonds. While the specimen is widely used, there has been a persistent question as to the accuracy of the method since the actual specimen geometry differs from the ideal beam geometry assumed in the expression used for data reduction. The effect of conducting the test on whole wafer pairs, in which the arms of cantilevers are wide plates rather than slender beams, is examined in this work using finite element analysis. A model is developed to predict the equilibrium shape of the crack front and to develop a corrected expression for calculating interface toughness from crack length measurements obtained in tests conducted on whole wafer pairs. The finite element model, which is validated through comparison to experiments, demonstrates that using the traditional be.
Real-time COVID-19 hospital admissions forecasting with leading indicators and ensemble methods in England (preprint)

Hospitalisations from COVID-19 with Omicron sub-lineages have put a sustained pressure on the English healthcare system. Understanding the expected healthcare demand enables more effective and timely planning from public health. We collect syndromic surveillance sources, which include online search data, NHS 111 telephonic and online triages. Incorporating this data we explore generalised additive models, generalised linear mixed-models, penalised generalised linear models and model ensemble methods to forecast over a two-week forecast horizon at an NHS Trust level. Furthermore, we showcase how model combinations improve forecast scoring through a mean ensemble, weighted ensemble, and ensemble by regression. Validated over multiple Omicron waves, at different spatial scales, we show that leading indicators can improve performance of forecasting models, particularly at epidemic changepoints. Using a variety of scoring rules, we show that ensemble approaches outperformed all individual models, providing higher performance at a 21-day window than the corresponding individual models at 14-days. We introduce a modelling structure used by public health officials in England in 2022 to inform NHS healthcare strategy and policy decision making. This paper explores the significance of ensemble methods to improve forecasting performance and how novel syndromic surveillance can be practically applied in epidemic forecasting.
Expressivity Landscape for Logics with Probabilistic Interventionist Counterfactuals

Causal multiteam semantics is a framework where probabilistic dependencies arising from data and causation between variables can be together formalized and studied logically. We consider several logics in the setting of causal multiteam semantics that can express probability comparisons concerning formulae and constants, and encompass interventionist counterfactuals and selective implications that describe consequences of actions and consequences of learning from observations, respectively. We discover complete characterizations of expressivity of the logics in terms of families of linear equations that define the corresponding classes of causal multiteams (together with some closure conditions). The characterizations yield a strict hierarchy of expressive power. Finally, we present some undefinability results based on the characterizations.
Automatic evaluation of herding behavior in towed fishing gear using end-to-end training of CNN and attention-based networks

This paper considers the automatic classification of herding behavior in the cluttered low-visibility environment that typically surrounds towed fishing gear. The paper compares three convolutional and attention-based deep action recognition network architectures trained end-to-end on a small set of video sequences captured by a remotely controlled camera and classified by an expert in fishing technology. The sequences depict a scene in front of a fishing trawl where the conventional herding mechanism has been replaced by directed laser light. The goal is to detect the presence of a fish in the sequence and classify whether or not the fish reacts to the lasers. A two-stream CNN model, a CNN-transformer hybrid, and a pure transformer model were trained end-to-end to achieve 63%, 54%, and 60% 10-fold classification accuracy on the three-class task when compared to the human expert. Inspection of the activation maps learned by the three networks raises questions about the attributes of the sequences the models may be learning, specifically whether changes in viewpoint introduced by human camera operators that affect the position of laser lines in the video frames may interfere with the classification. This underlines the importance of careful experimental design when capturing scientific data for automatic end-to-end evaluation and the usefulness of inspecting the trained models.
Grading Conversational Responses Of Chatbots

Chatbots have long been capable of answering basic questions and even responding to obscure prompts, but recently their improvements have been far more significant. Modern chatbots like Open AIs ChatGPT3 not only have the ability to answer basic questions but can write code and movie scripts and imitate well-known people. In this paper, we analyze ChatGPTs' responses to various questions from a dataset of queries from the popular Quora forum. We submitted sixty questions to ChatGPT and scored the answers based on three industry-standard metrics for grading machine translation: BLEU, METEOR, and ROUGE. These metrics allow us to compare the machine responses with the most upvoted human answer to the same question to assess ChatGPT's ability to submit a humanistic reply. The results showed that while the responses and translation abilities of ChatGPT are remarkable, they still fall short of what a typical human reaction would be.
Capacity Maximization of the 6G Networks Deploying IRS

The objective of the work is to improve the capacity of the micro cell, i.e., enabling the micro cell base station of a two-tier network to serve an increased number of devices. Therefore, the work deployed an IRS in a micro cell and achieved significant improvements in the performance with a reduced transmit power of the micro base station.
Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-contrast Imaging in the Presence of Non-Gaussian Noise

Over the past decade, hundreds of nights have been spent on the world’s largest telescopes to search for and directly detect new exoplanets using high-contrast imaging (HCI). Thereby, two scientific goals are of central interest: first, to study the characteristics of the underlying planet population and distinguish between different planet formation and evolution theories. Second, to find and characterize planets in our immediate solar neighborhood. Both goals heavily rely on the metric used to quantify planet detections and nondetections. Current standards often rely on several explicit or implicit assumptions about noise. For example, it is often assumed that the residual noise after data postprocessing is Gaussian. While being an inseparable part of the metric, these assumptions are rarely verified. This is problematic as any violation of these assumptions can lead to systematic biases. This makes it hard, if not impossible, to compare results across data sets or instruments with different noise characteristics. We revisit the fundamental question of how to quantify detection limits in HCI. We focus our analysis on the error budget resulting from violated assumptions. To this end, we propose a new metric based on bootstrapping that generalizes current standards to non-Gaussian noise. We apply our method to archival HCI data from the NACO instrument at the Very Large Telescope and derive detection limits for different types of noise. Our analysis shows that current standards tend to give detection limits that are about one magnitude too optimistic in the speckle-dominated regime. That is, HCI surveys may have excluded planets that can still exist.
E-MLB: Multilevel Benchmark for Event-Based Camera Denoising

Event cameras, such as dynamic vision sensors (DVS), are biologically inspired vision sensors that have advanced over conventional cameras in high dynamic range, low latency and low power consumption, showing great application potential in many fields. Event cameras are more sensitive to junction leakage current and photocurrent as they output differential signals, losing the smoothing function of the integral imaging process in the RGB camera. The logarithmic conversion further amplifies noise, especially in low-contrast conditions. Recently, researchers proposed a series of datasets and evaluation metrics but limitations remain: 1) the existing datasets are small in scale and insufficient in noise diversity, which cannot reflect the authentic working environments of event cameras; and 2) the existing denoising evaluation metrics are mostly referenced evaluation metrics, relying on APS information or manual annotation. To address the above issues, we construct a large-scale event denoising dataset (multilevel benchmark for event denoising, E-MLB) for the first time, which consists of 100 scenes, each with four noise levels, that is 12 times larger than the largest existing denoising dataset. We also propose the first nonreference event denoising metric, the event structural ratio (ESR), which measures the structural intensity of given events. ESR is inspired by the contrast metric, but is independent of the number of events and projection direction. Based on the proposed benchmark and ESR, we evaluate the most representative denoising algorithms, including classic and SOTA, and provide denoising baselines under various scenes and noise levels. The corresponding results and codes are available at https://github.com/KugaMaxx/cuke-emlb.
Asymmetric dark matter with a spontaneously broken $U(1)'$: self-interaction and gravitational waves

Motivated by the collisionless cold dark matter small scale structure problem, we propose an asymmetric dark matter model where dark matter particle interact with each other via a massive dark gauge boson. This model easily avoid the strong limits from cosmic microwave background (CMB) observation, and have a large parameter space to be consistent with small scale structure data. We focus on a special scenario where portals between dark sector and visible sector are too weak to be detected by traditional methods. We find that this scenario can increase the effective number of neutrinos ($N_{\text{eff}}$). In addition, the spontaneous $U(1)'$ symmetry breaking process, which makes dark gauge boson massive, can generate stochastic gravitational waves with peak frequency around $10^{-6} - 10^{-7} \text{ Hz}$.