Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of institutional incentives for cooperation in structured populations

The application of incentives, such as reward and punishment, is a frequently applied way for promoting cooperation among interacting individuals in structured populations. However, how to properly use the incentives is still a challenging problem for incentive-providing institutions. In particular, since the implementation of incentive is costly, to explore the optimal incentive protocol, which ensures the desired collective goal at a minimal cost, is worthy of study. In this work, we consider the positive and negative incentives for a structured population of individuals whose conflicting interactions are characterized by a Prisoner’s Dilemma game. We establish an index function for quantifying the cumulative cost during the process of incentive implementation, and theoretically derive the optimal positive and negative incentive protocols for cooperation on regular networks. We find that both types of optimal incentive protocols are identical and time-invariant. Moreover, we compare the optimal rewarding and punishing schemes concerning implementation cost and provide a rigorous basis for the usage of incentives in the game-theoretical framework. We further perform computer simulations to support our theoretical results and explore their robustness for different types of population structures, including regular, random, small-world and scale-free networks.
Outburst behavior of the dwarf nova
 CG
 Draconis

During the British Astronomical Association (BAA) 2022 campaign, 27436 photometric observations of the dwarf nova (DN) CG Draconis were made, with 106 eclipses recorded. This work summarizes the new data available and provides updated ephemeris and commentary on the observed eclipse profiles. The orbital period found is P_orb = 4h31m38s +/- 1s. Two types of quasi-periodic outbursts are identified: normal outbursts, of Delta V of approximately 1.25 mag amplitude, and bright, of Delta V of approximately 1.5 mag. The pattern resembles superoutbursts of SU UMa-type DNe, however, no presence of superhumps characterizing these DNe was found. Given CG Dra is located above the period gap, it may represent a new intermediary subtype between SS Cyg and SU UMa-type stars, or provide support to superoutburst models that do not rely on eccentric accretion disks.
Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality

Lighting understanding plays an important role in virtual object composition, including mobile augmented reality (AR) applications. Prior work often targets recovering lighting from the physical environment to support photorealistic AR rendering. Because the common workflow is to use a back-facing camera to capture the physical world for overlaying virtual objects, we refer to this usage pattern as back-facing AR. However, existing methods often fall short in supporting emerging front-facing mobile AR applications, e.g., virtual try-on where a user leverages a front-facing camera to explore the effect of various products (e.g., glasses or hats) of different styles. This lack of support can be attributed to the unique challenges of obtaining 360° HDR environment maps, an ideal format of lighting representation, from the front-facing camera and existing techniques. In this paper, we propose to leverage dual-camera streaming to generate a high-quality environment map by combining multi-view lighting reconstruction and parametric directional lighting estimation. Our preliminary results show improved rendering quality using a dual-camera setup for front-facing AR compared to a commercial solution.
The k-Robinson-Foulds Dissimilarity Measures for Comparison of Labeled Trees.

Understanding the mutational history of tumor cells is a critical endeavor in unraveling the mechanisms that drive the onset and progression of cancer. Modeling tumor cell evolution with labeled trees motivates researchers to develop different measures to compare labeled trees. Although the Robinson-Foulds (RF) distance is widely used for comparing species trees, its applicability to labeled trees reveals certain limitations. This study introduces the k-RF dissimilarity measures, tailored to address the challenges of labeled tree comparison. The RF distance is succinctly expressed as n-RF in the space of labeled trees with n nodes. Like the RF distance, the k-RF is a pseudometric for multiset-labeled trees and becomes a metric in the space of 1-labeled trees. By setting k to a small value, the k-RF dissimilarity can capture analogous local regions in two labeled trees with different size or different labels.
Empirical study of the modulus as activation function in computer vision applications

In this work we propose a new non-monotonic activation function: the modulus. The majority of the reported research on nonlinearities is focused on monotonic functions. We empirically demonstrate how by using the modulus activation function on computer vision tasks the models generalize better than with other nonlinearities - up to a 15% accuracy increase in CIFAR100 and 4% in CIFAR10, relative to the best of the benchmark activations tested. With the proposed activation function the vanishing gradient and dying neurons problems disappear, because the derivative of the activation function is always 1 or -1. The simplicity of the proposed function and its derivative make this solution specially suitable for TinyML and hardware applications.
AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges

Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancement in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music generation (AMG) systems that are empowered with the ability to generate affective music. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation (AI-AMG) systems may have a significant impact. Given the surge of interest in this topic, this article aims to provide a comprehensive review of AI-AMG systems. The main building blocks of an AI-AMG system are discussed, and existing systems are formally categorized based on the core algorithm used for music generation. In addition, this article discusses the main musical features employed to compose affective music, along with the respective AI-based approaches used for tailoring them. Lastly, the main challenges and open questions in this field, as well as their potential solutions, are presented to guide future research. We hope that this review will be useful for readers seeking to understand the state-of-the-art in AI-AMG systems, and gain an overview of the methods used for developing them, thereby helping them explore this field in the future.
KEWS: A KPIs-Based Evaluation Framework of Workload Simulation On Microservice System

Simulating the workload is an essential procedure in microservice systems as it helps augment realistic workloads whilst safeguarding user privacy. The efficacy of such simulation depends on its dynamic assessment. The straightforward and most efficient approach to this is comparing the original workload with the simulated one using Key Performance Indicators (KPIs), which capture the state of the system. Nonetheless, due to the extensive volume and complexity of KPIs, fully evaluating them is not feasible, and measuring their similarity poses a significant challenge. This paper introduces a similarity metric algorithm for KPIs, the Extended Shape-Based Distance (ESBD), which gauges similarity in both shape and intensity. Additionally, we propose a KPI-based Evaluation Framework for Workload Simulations (KEWS), comprising three modules: preprocessing, compression, and evaluation. These methodologies effectively counteract the adverse effects of KPIs' characteristics and offer a holistic evaluation. Experimental results substantiate the effectiveness of both ESBD and KEWS.
Stability study and effect of passivation on InP/InGaAs double heterojunction bipolar transistors

This work explores the thermal and bias stabilities of composite-collector double heterojunction bipolar transistors (CC-DHBTs) with i-InGaAs/n+-InP in the collector layer, in the temperature range of 300–400 K. Both V-shaped behaviors (observed in dc current gain β against collector current IC and in offset voltage VCE,offset against base current IB plot) of the asymmetric CC-DHBT are studied simultaneously. At high IC, the devices of interest improve the dc current gain temperature stability relative to most HBTs described in the literature. Additionally, unlike that of the abrupt DHBTs in the literature, the β of the CC-DHBTs is independent of VCB, revealing that the effect of the conduction-band barrier in the base-collector junctions may have been eliminated. An analytical expression for the variation in VCE,offset with IB has been developed. Additionally, unlike that of the unpassivated and SiNx-passivated devices, the β of the sulfur-treated device is fairly constant over five decades of IC. The di.
Unified Theory of Local Quantum Many-Body Dynamics: Eigenoperator Thermalization Theorems

Explaining quantum many-body dynamics is a long-held goal of physics. A rigorous operator algebraic theory of dynamics in locally interacting systems in any dimension is provided here in terms of time-dependent equilibrium (Gibbs) ensembles. The theory explains dynamics in closed, open and time-dependent systems, provided that relevant pseudolocal quantities can be identified, and time-dependent Gibbs ensembles unify wide classes of quantum non-ergodic and ergodic systems. The theory is applied to quantum many-body scars, continuous, discrete and dissipative time crystals, Hilbert space fragmentation, lattice gauge theories, and disorder-free localization, among other cases. Novel pseudolocal classes of operators are introduced in the process: projected-local, which are local only for some states, crypto-local, whose locality is not manifest in terms of any finite number of local densities and transient ones, that dictate finite-time relaxation dynamics. An immediate corollary is proving saturation of the Mazur bound for the Drude weight. This proven theory is intuitively the rigorous algebraic counterpart of the weak eigenstate thermalization hypothesis and has deep implications for thermodynamics: quantum many-body systems 'out-of-equilibrium' are actually always in a time-dependent equilibrium state for any natural initial state. The work opens the possibility of designing novel out-of-equilibrium phases, with the newly identified scarring and fragmentation phase transitions being examples.
A Population Study of the Radio Emission of Fast Blue Optical Transients

Despite the fact that the progenitor of fast blue optical transients (FBOTs) is still a subject of debate, FBOTs are sometimes suggested to originate from the core collapse of ultra-stripped stars and be powered by a spinning-down neutron star. Following this consideration, it is expected that the late-time evolution of the progenitor stars can leave important imprints in the circumstellar material (CSM) of FBOTs, due to the strong mass loss of the stars. The interaction of the FBOT ejecta with the CSM can drive a long-lasting shock to generate radio emission, which thus enables us to probe the CSM properties through radio observation although such observations are still rare. Within the framework of the magnetar-powered model, Liu et al. fitted the multi-band optical light curves of 40 FBOTs, and hence, the statistical distributions of the FBOT magnetar and ejecta parameters were obtained. Based on these FBOT population results, we investigate the dependence of the radio emission on the mass-loss rate of the progenitors and evaluate the detectability of radio emission from FBOTs with current and future telescopes. It is found that the distribution of the peak time and peak luminosity of the emission at 8.4 GHz are primarily in the regions of t peak,ν = 102.12±0.63 days and L peak,ν = 1028.73±0.83 erg s−1 Hz−1, respectively. A joint detection of the Zwicky Transient Facility and Very Large Array could achieve success in about 8.7% FBOTs of z ≤ 1. Furthermore, if considering a joint detection of the Chinese Space Station Telescope and the Square Kilometer Array, this rate of success could be increased to about 23.9%.
Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification

: As humans, we can remember certain visuals in great detail, and sometimes even after viewing them once. What is even more interesting is that humans tend to remember and forget the same things, suggesting that there might be some general internal characteristics of an image that make it easier for the brain to encode and discard certain types of information. Research suggests that some pictures tend to be memorized more than others. The ability of an image to be remembered by different viewers is one of its intrinsic properties. In visualization and photography, creating memorable images is a difficult task. Hence, to solve the problem, various techniques predict visual memorability and manipulate images' memorability. We present a comprehensive literature survey to assess the deep learning techniques used to predict and modify memorability. In particular, we analyze the use of Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks for image memorability prediction and modification.
OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset

Inspired by humans comprehending speech in a multi-modal manner, various audio-visual datasets have been constructed. However, most existing datasets focus on English, induce dependencies with various prediction models during dataset preparation, and have only a small number of multi-view videos. To mitigate the limitations, we recently developed the Open Large-scale Korean Audio-Visual Speech (OLKAVS) dataset, which is the largest among publicly available audio-visual speech datasets. The dataset contains 1,150 hours of transcribed audio from 1,107 Korean speakers in a studio setup with nine different viewpoints and various noise situations. We also provide the pre-trained baseline models for two tasks, audio-visual speech recognition and lip reading. We conducted experiments based on the models to verify the effectiveness of multi-modal and multi-view training over uni-modal and frontal-view-only training. We expect the OLKAVS dataset to facilitate multi-modal research in broader areas such as Korean speech recognition, speaker recognition, pronunciation level classification, and mouth motion analysis.
Haptic Transparency and Interaction Force Control for a Lower Limb Exoskeleton

Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase.
Role of the size distribution in the elasticity of entangled living polymer solutions

The linear rheology of wormlike micelles decorated with amphiphilic copolymer is reported. They are model polymer solutions to address the role of polydispersity in the scaling of the elastic plateau modulus G0 with concentration . Allowing only micelles of coil size larger than the mesh size of the network to contribute to its elasticity, we account for the unusually fast increase of G0 at low . Our model enables a determination of the micellar scission energy, whose decrease with copolymer rate is independently confirmed by dynamic light scattering.
Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms

With 1.3 billion users, Instagram (IG) has become an essential business tool. IG influencer marketing, expected to generate $33.25 billion in 2022, encourages companies and influencers to create trending content. Various methods have been proposed for predicting a post’s popularity, i.e., how much engagement (e.g., Likes) it will generate. However, these methods are limited: first, they focus on forecasting the likes, ignoring the number of comments, which became crucial in 2021. Secondly, studies often use biased or limited data. Third, researchers focused on Deep Learning models to increase predictive performance, which are difficult to interpret. As a result, end-users can only estimate engagement after a post is created, which is inefficient and expensive. A better approach is to generate a post based on what people and IG like, e.g., by following guidelines. In this work, we uncover part of the underlying mechanisms driving IG engagement. We rely on statistical analysis and interpretable models rather than Deep Learning (black-box) approaches to achieve this goal. Leveraging innovative domain-relevant features, we first build classifiers to predict posts’ engagement. Then, we interpret the best models to determine which type of content will generate the most engagement, maximizing influencers’ and companies’ profits. We conduct extensive experiments using a worldwide dataset of 10 million posts created by 34K global influencers in nine different categories. Our simple yet powerful algorithms can effectively predict engagement, making us comparable and even superior to Deep Learning-based methods, reaching up to 94% F1-Score. Furthermore, we propose a novel unsupervised algorithm for finding highly engaging topics on IG. Thanks to our interpretable approaches, we conclude by outlining guidelines for creating successful posts.
Testing relativistic accretion disk models with
 GRO J1655
 ‐40

Black hole X-ray binaries are ideal environments to test the accretion phenomena in the presence of strong gravitational potentials. KERRBB held an important place in the X-ray spectral continuum method for measuring the black hole spin modeling the emission from the innermost regions of the accretion disk. In this work, we present the results of X-ray spectral analysis using publicly available RXTE data of GRO J1655-40 obtained during the 2005 outburst with the two relativistic accretion disk models, KERRBB and KYNBB . Our analysis showed that both models provide identical results with black hole spin measurements, disk temperature
Learning Sparse Temporal Video Mapping for Action Quality Assessment in Floor Gymnastics

Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance. It is crucial to comprehend local discriminative spatial dependencies and global semantics for accurate evaluation. However, existing benchmark datasets mainly incorporate sports where the performance lasts only a few seconds. Consequently, state-ofthe-art sports quality assessment methods specifically focus on spatial structure. Although they achieve high performance in short-term sports, they are unable to model prolonged video sequences and fail to achieve similar performance in long-term sports. To facilitate such analysis, we introduce a new dataset, coined AGF-Olympics, that incorporates artistic gymnastic floor routines. AFG-Olympics provides highly challenging scenarios with extensive background, viewpoint, and scale variations over an extended sample duration of up to 2 minutes. In addition, we propose a discriminative attention module to map the dense feature space into a sparse representation by disentangling complex associations. Extensive experiments indicate that our proposed module provides an effective way to embed long-range spatial and temporal correlation semantics.
Syllable Subword Tokens for Open Vocabulary Speech Recognition in Malayalam

In a hybrid automatic speech recognition (ASR) system, a pronunciation lexicon (PL) and a language model (LM) are essential to correctly retrieve spoken word sequences. Being a morphologically complex language, the vocabulary of Malayalam is so huge and it is impossible to build a PL and an LM that cover all diverse word forms. Usage of subword tokens to build PL and LM, and combining them to form words after decoding, enables the recovery of many out of vocabulary words. In this work we investigate the impact of using syllables as subword tokens instead of words in Malayalam ASR, and evaluate the relative improvement in lexicon size, model memory requirement and word error rate.
The Thickness Dependence of Resonance Frequency in Anisotropic Composites with Long Conductive Fibers

Abstract The resonant frequency of long conductive fibers embedded in an anisotropic composite as a function of layer thickness is studied both numerically and experimentally and is discussed in terms of the equivalent permittivity of the layer. A simple empirical exponential law is suggested to fit the thickness dependence of the equivalent permittivity. The law involves a critical thickness value, below which the layer can no longer be treated as a bulk material for a fiber of given length and thickness. The critical thickness is retrieved from numerical data for different permittivity values of isotropic and anisotropic layers and for different fiber length.
KoopmanLab: machine learning for solving complex physics equations

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions or closed forms, have impeded the further development of physics. Computationally solving PDEs by classic numerical approaches suffers from the trade-off between accuracy and efficiency and is not applicable to the empirical data generated by unknown latent PDEs. To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator (KNO) family, for learning PDEs without analytic solutions or closed forms. Our module consists of multiple variants of the KNO, a kind of mesh-independent neural-network-based PDE solvers developed following the dynamic system theory. The compact variants of KNO can accurately solve PDEs with small model sizes, while the large variants of KNO are more competitive in predicting highly complicated dynamic systems govern by unknown, high-dimensional, and non-linear PDEs. All variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier–Stokes equation and the Bateman–Burgers equation in fluid mechanics) and ERA5 (i.e., one of the largest high-resolution global-scale climate datasets in earth physics). These demonstrations suggest the potential of KoopmanLab to be a fundamental tool in diverse physics studies related to equations or dynamic systems.