Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Relation and Attention Learning for Facial Action Unit Detection

Spatio-temporal relations among facial action units (AUs) convey significant information for AU detection yet have not been thoroughly exploited. The main reasons are the limited capability of current AU detection works in simultaneously learning spatial and temporal relations, and the lack of precise localization information for AU feature learning. To tackle these limitations, we propose a novel spatio-temporal relation and attention learning framework for AU detection. Specifically, we introduce a spatio-temporal graph convolutional network to capture both spatial and temporal relations from dynamic AUs, in which the AU relations are formulated as a spatio-temporal graph with adaptively learned instead of predefined edge weights. Moreover, the learning of spatio-temporal relations among AUs requires individual AU features. Considering the dynamism and shape irregularity of AUs, we propose an attention regularization method to adaptively learn regional attentions that capture highly relevant regions and suppress irrelevant regions so as to extract a complete feature for each AU. Extensive experiments show that our approach achieves substantial improvements over the state-of-the-art AU detection methods on BP4D and especially DISFA benchmarks.
Wirelessly Powered Cell-Free IoT: Analysis and Optimization

In this article, we propose a wirelessly powered Internet-of-Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closed-form expression of SINR as the metrics of power transfer and data transmission, respectively. To improve energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT in terms of both downlink and uplink 95% likely performances. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.
Scalable Estimation and Inference with Large-scale or Online Survival Data

With the rapid development of data collection and aggregation technologies in many scientific disciplines, it is becoming increasingly ubiquitous to conduct large-scale or online regression to analyze real-world data and unveil real-world evidence. In such applications, it is often numerically challenging or sometimes infeasible to store the entire dataset in memory. Consequently, classical batch-based estimation methods that involve the entire dataset are less attractive or no longer applicable. Instead, recursive estimation methods such as stochastic gradient descent that process data points sequentially are more appealing, exhibiting both numerical convenience and memory efficiency. In this paper, for scalable estimation of large or online survival data, we propose a stochastic gradient descent method which recursively updates the estimates in an online manner as data points arrive sequentially in streams. Theoretical results such as asymptotic normality and estimation efficiency are established to justify its validity. Furthermore, to quantify the uncertainty associated with the proposed stochastic gradient descent estimator and facilitate statistical inference, we develop a scalable resampling strategy that specifically caters to the large-scale or online setting. Simulation studies and a real data application are also provided to assess its performance and illustrate its practical utility.
Ondřejov Echelle Spectrograph, Ground Based Support Facility for Exoplanet Missions

Fulfilling the goals of space-based exoplanetary transit surveys, like Kepler and TESS, is impossible without ground-based spectroscopic follow-up. In particular, the first-step vetting of candidates could easily necessitate several hundreds of hours of telescope time—an area where 2 m class telescopes can play a crucial role. Here, we describe the results from the science verification of the Ondřejov Echelle Spectrograph (OES) installed on the 2 m Perek telescope. We discuss the performance of the instrument as well as its suitability for the study of exoplanetary candidates from space-based transit surveys. In spite of being located at an average European observing site, and originally being conceived for the study of variable stars, OES can prove to be an important instrument for the exoplanetary community in the TESS and PLATO era—reaching accuracies of a few tens of m s−1 with reasonable sampling and signal-to-noise for sources down to V ∼ 13. The stability of OES is demonstrated via long-term monitoring of the standard star HD 109358, while its validity for exoplanetary candidate verification is shown using three K2 candidates EPIC 210925707, EPIC 206135267 and EPIC 211993818, to reveal that they are false positive detections.
Optimal control of signalized intersection using hierarchical fuzzy-real control

This paper presents a method based on precise modeling of traffic flow using a fuzzy-real algorithm for optimal control of a signalized intersection. By improving social indicators such as security, well-fare and economy, intercity transportation has increased and traffic congestion developed. Today, the plight of traffic congestion is the cause of waste of time, capital, and health risks. Although the construction of new streets, expressways, and highways can improve this situation, when inadequate space and limited budget is available, it is not practical. Optimization of signalized intersections for optimal use of the network capacity is proposed as a useful solution to resolve an important part of the problems developed at intersections. In this paper, with the aim of assessing the algorithms proposed for controlling signalized intersections and given the real potentials of common sensors present in traffic control systems, a suitable simulation medium called Signalized Intersection Fuzzy-Real Control (SIFRC) has been designed and implemented. Through the possibility made, the Abshar intersection in Isfahan city was modeled and phased through six methods: fixed time, pre-time control, segmental pre-time, fuzzy, real-time, and fuzzy-real. The results indicated better performance of the fuzzy-real method compared to the fixed time method up to 50%, and up to 10% compared to other methods. In addition, the speed of achieving the optimal response in the fuzzy-real method has been 11 times as large as that of the real-time method, while the delay time developed by the intersection in this method did not increase compared to the real-time method.
Can the earth be flat ? A physical oceanographer's perspective.

According to a recent survey, 2% of the U.S. population is convinced that the earth is flat. This idea is heralded by members of the Flat Earth Society, and promulgated through Internet forums and other public media channels. Children and young students are easy targets. As a science teacher confronted by these ideas, it can be a challenge to firmly confront their fundamentally flawed foundation while still remaining compassionate towards those who espouse such beliefs. While we do not purport to have the answer for such difficult situations, as two scientists studying ocean physics, we attempt to lend a helping hand to the interested instructor. Here, we use the most advanced computational tools of the physical oceanography community to show that the properties of the ocean that we observe from ships, satellites, and autonomous observing platforms should not exist if the earth were flat. In particular, using the first realistic simulation of the ocean on a flat Earth, we show that the North Atlantic gyre, appears entirely different on a flat Earth. For members of the physical oceanography community, the results presented here are obvious; however, for the Flat Earth Society community, it may present an intellectual challenge.
Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment

The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e.g. route planning, collision warning, and traffic regulation. For maritime intelligence, Automatic Identification System (AIS) plays an important role because it recently has been made compulsory for large international commercial vessels and is able to provide nearly real-time information of the vessel. Therefore AIS data based vessel path prediction is a promising way in future maritime intelligence. However, real-world AIS data collected online are just highly irregular trajectory segments (AIS message sequences) from different types of vessels and geographical regions, with possibly very low data quality. So even there are some works studying how to build a path prediction model using historical AIS data, but still, it is a very challenging problem. In this paper, we propose a comprehensive framework to model massive historical AIS trajectory segments for accurate vessel path prediction. Experimental comparisons with existing popular methods are made to validate the proposed approach and results show that our approach could outperform the baseline methods by a wide margin.
Long-term multi-frequency maser observations of the intermediate-mass young stellar object G107.298+5.639

Context. Periodic flares of maser emission are thought to be induced either by variations of the seed photon flux in young binary systems or the pump rate regulated by stellar and accretion luminosities. Aims. We seek to study the variability of four maser transitions of three different species in G107.298+5.639 to constrain the dominant mechanism of periodic flares. Methods. Light curves of the 6.7 GHz methanol and 22.2 GHz water vapour maser were obtained with the Torun 32 m radio telescope over 39 and 34 cycles, respectively. The target was also monitored at the 1.6 GHz hydroxyl transitions with the Nançay radio telescope over 13 cycles. All these maser lines were imaged using VLBI arrays. Results. The study confirms alternating flares of the methanol and water masers with a period of 34.4 d and reveals the synchronised behaviour of the methanol and hydroxyl masers in this source. The observed spatial distribution of the methanol maser cloudlets and the measured time delays of the flares of individual features imply a ring-like structure of radius 240 au and thickness 30 au. Internal proper motions indicate that the velocity of methanol cloudlets is dominated by a disc-wind component of about 5 km s−1. The methanol emission detected during only one VLBI observation is located in a region about 550 au from a central star, which also exhibits OH maser flares. The erratic appearance of methanol features can be related to a powering object of relatively low luminosity which, during some variability cycles, can excite molecules only in the nearest part of the disc. A careful analysis of the maser and infrared light curves reveal a strong correlation between the 6.7 GHz line and the infrared flux densities supporting a radiative pumping of the maser. Conclusions. The synchronised behaviour of the hydroxyl 1665/1667 MHz and 6.7 GHz methanol transitions indicates a common pumping mechanism for the periodic flares of G107.298+5.639.
Facial Emotions Recognition using Convolutional Neural Net

Human beings displays their emotions using facial expressions. For human it is very easy to recognize those emotions but for computer it is very challenging. Facial expressions vary from person to person. Brightness, contrast and resolution of every random image is different. This is why recognizing facial expression is very difficult. The facial expression recognition is an active research area. In this project, we worked on recognition of seven basic human emotions. These emotions are angry, disgust, fear, happy, sad, surprise and neutral. Every image was first passed through face detection algorithm to include it in train dataset. As CNN requires large amount of data so we duplicated our data using various filter on each image. The system is trained using CNN architecture. Preprocessed images of size 80*100 is passed as input to the first layer of CNN. Three convolutional layers were used, each of which was followed by a pooling layer and then three dense layers. The dropout rate for dense layer was 20%. The model was trained by combination of two publicly available datasets JAFFED and KDEF. 90% of the data was used for training while 10% was used for testing. We achieved maximum accuracy of 78% using combined dataset.
Analytical results for the capacitance of a circular plate capacitor

We study the classic problem of the capacitance of a circular parallel plate capacitor. At small separations between the plates, it is initially considered in 19th century by Kirchhoff who found the leading and the subleading term in the capacitance. Despite a large interest in the problem, one and a half century later, analytically was found only the second subleading term. Using the recent advances in the asymptotic analysis of Fredholm integral equations of the second kind with finite support, here we study the one governing the circular capacitor, known as the Love equation. We found analytically many new subleading terms in the capacitance at small separations. We also calculated the asymptotic expansion at large separations, thus providing the two simple expressions which practically describe the capacitance at all distances. The approach described here could be used to find exact analytical expansions for the capacitance to an arbitrary number of terms in both regimes of small and large separations.
Audio-Visual Recognition of Overlapped Speech for the LRS2 Dataset

Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of audio-visual technologies for overlapped speech recognition. Three issues associated with the construction of audio-visual speech recognition (AVSR) systems are addressed. First, the basic architecture designs i.e. end-to-end and hybrid of AVSR systems are investigated. Second, purposefully designed modality fusion gates are used to robustly integrate the audio and visual features. Third, in contrast to a traditional pipelined architecture containing explicit speech separation and recognition components, a streamlined and integrated AVSR system optimized consistently using the lattice-free MMI (LF-MMI) discriminative criterion is also proposed. The proposed LF-MMI time-delay neural network (TDNN) system establishes the state-of-the-art for the LRS2 dataset. Experiments on overlapped speech simulated from the LRS2 dataset suggest the proposed AVSR system outperformed the audio only baseline LF-MMI DNN system by up to 29.98% absolute in word error rate (WER) reduction, and produced recognition performance comparable to a more complex pipelined system. Consistent performance improvements of 4.89% absolute in WER reduction over the baseline AVSR system using feature fusion are also obtained.
An artificial neural network approximation for Cauchy inverse problems

A novel artificial neural network method is proposed for solving Cauchy inverse problems. It allows multiple hidden layers with arbitrary width and depth, which theoretically yields better approximations to the inverse problems. In this research, the existence and convergence are shown to establish the well-posedness of neural network method for Cauchy inverse problems, and various numerical examples are presented to illustrate its accuracy and stability. The numerical examples are from different points of view, including time-dependent and time-independent cases, high spatial dimension cases up to 8D, and cases with noisy boundary data and singular computational domain. Moreover, numerical results also show that neural networks with wider and deeper hidden layers could lead to better approximation for Cauchy inverse problems.
Nonasymptotic elastoinertial turbulence for asymptotic drag reduction

Maximum drag reduction (MDR) by polymers is recently depicted as asymptotic convergence to a state of elastointertial turbulence (EIT) where inertia-driven turbulence (IDT) vanishes. This theory has difficulty explaining the universality of MDR -- asymptotic convergence of drag reduction (DR) with increasing elasticity. We confirm that EIT is indeed non-asymptotic in DR. Asymptotic DR is only achieved when IDT is triggered and quenched intermittently from EIT, in a manner similar to the earlier framework based on active-hibernating-bursting (AHB) cycles.
Manganese Indicates a Transition from Sub- to Near-Chandrasekhar Type Ia Supernovae in Dwarf Galaxies

Manganese abundances are sensitive probes of the progenitors of Type Ia supernovae (SNe Ia). In this work, we present a catalog of manganese abundances in dwarf spheroidal satellites of the Milky Way, measured using medium-resolution spectroscopy. Using a simple chemical evolution model, we infer the manganese yield of SNe Ia in the Sculptor dwarf spheroidal galaxy (dSph) and compare to theoretical yields. The sub-solar yield from SNe Ia ( at [Fe/ H] = −1.5 dex, with negligible dependence on metallicity) implies that sub-Chandrasekhar-mass (sub-MCh) white dwarf progenitors are the dominant channel of SNe Ia at early times in this galaxy, although some fraction (≳20%) of MCh Type Ia or Type Iax SNe are still needed to produce the observed yield. First-order corrections for deviations from local thermodynamic equilibrium increase the inferred by as much as ∼0.3 dex. However, our results also suggest that the nucleosynthetic source of SNe Ia may depend on environment. In particular, we find that dSphs with extended star formation histories (Leo I, Fornax dSphs) appear to have higher [Mn/Fe] at a given metallicity than galaxies with early bursts of star formation (Sculptor dSph), suggesting that MCh progenitors may become the dominant channel of SNe Ia at later times in a galaxy’s chemical evolution.
Active microrheology of a bulk metallic glass

A probe particle driven into a supercooled liquid reveals the structural properties of the host medium. The glass transition remains unclarified in condensed matter physics. Investigating the mechanical properties of glass is challenging because any global deformation that might result in shear rejuvenation would require a prohibitively long relaxation time. Moreover, glass is well known to be heterogeneous, and a global perturbation would prevent exploration of local mechanical/transport properties. However, investigation based on a local probe, i.e., microrheology, may overcome these problems. Here, we establish active microrheology of a bulk metallic glass, via a probe particle driven into host medium glass. This technique is amenable to experimental investigations via nanoindentation tests. We provide distinct evidence of a strong relationship between the microscopic dynamics of the probe particle and the macroscopic properties of the host medium glass. These findings establish active microrheology as a promising technique for investigating the local properties of bulk metallic glass.
Identification of chirality of chiral multifold fermions in anti-crystals

The chirality of chiral multifold fermions in reciprocal space is related to the chirality of the crystal lattice structure in real space. In this work, we propose a strategy to detect and identify opposite-chirality multifold fermions in nonmagnetic systems by means of second-order optical transports. The chiral crystals are related by an inversion operation and cannot overlap with each other by any experimental operation, and the chiral multifold fermions in the crystals host opposite chiralities for a given k-point. A change of chirality is indicated by a sign change of the second-order charge current dominated by chiral fermions. This strategy is effective to study the relationship between chiralities in reciprocal and real spaces by utilizing bulk transport.
Stringy excited baryons in holographic QCD

We analyze excited baryon states using a holographic dual of quantum chromodynamics that is defined on the basis of an intersecting D4/D8-brane system. Studies of baryons in this model have been made by regarding them as a topological soliton of a gauge theory on a five-dimensional curved spacetime. However, this allows one to obtain only a certain class of baryons. We attempt to present a framework such that a whole set of excited baryons can be treated in a systematic way. This is achieved by employing the original idea of Witten, which states that a baryon is described by a system composed of |$N_c$| open strings emanating from a baryon vertex. We argue that this system can be formulated by an Atiyah–Drinfeld–Hitchin–Manin-type matrix model of Hashimoto–Iizuka–Yi together with an infinite tower of the open string massive modes. Using this setup, we work out the spectra of excited baryons and compare them with the experimental data. In particular, we derive a formula for the nucleon Regge trajectory assuming that the excited nucleons lying on the trajectory are characterized by the excitation of a single open string attached on the baryon vertex.