Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable algorithms for physics-informed neural and graph networks

Abstract Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.
The Diverse Properties of Type Icn Supernovae Point to Multiple Progenitor Channels

We present a sample of Type Icn supernovae (SNe Icn), a newly discovered class of transients characterized by their interaction with H- and He-poor circumstellar material (CSM). This sample is the largest collection of SNe Icn to date and includes observations of two published objects (SN 2019hgp and SN 2021csp) and two objects not yet published in the literature (SN 2019jc and SN 2021ckj). The SNe Icn display a range of peak luminosities, rise times, and decline rates, as well as diverse late-time spectral features. To investigate their explosion and progenitor properties, we fit their bolometric light curves to a semianalytical model consisting of luminosity inputs from circumstellar interaction and radioactive decay of 56Ni. We infer low ejecta masses (≲2 M ⊙) and 56Ni masses (≲0.04 M ⊙) from the light curves, suggesting that normal stripped-envelope supernova (SESN) explosions within a dense CSM cannot be the underlying mechanism powering SNe Icn. Additionally, we find that an estimate of the star formation rate density at the location of SN 2019jc lies at the lower end of a distribution of SESNe, in conflict with a massive star progenitor of this object. Based on its estimated ejecta mass, 56Ni mass, and explosion site properties, we suggest a low-mass, ultra-stripped star as the progenitor of SN 2019jc. For other SNe Icn, we suggest that a Wolf–Rayet star progenitor may better explain their observed properties. This study demonstrates that multiple progenitor channels may produce SNe Icn and other interaction-powered transients.
Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN

In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have not yet reached the level required for emotion interpretation. With the rapid development of machine learning algorithms, dry electrode techniques, and different real-world applications of the brain-computer interface for normal individuals, emotion categorization from EEG data has recently gotten a lot of attention. Electroencephalogram (EEG) signals are a critical resource for these systems. The primary benefit of employing EEG signals is that they reflect true emotion and are easily resolved by computer systems. In this work, EEG signals associated with good, neutral, and negative emotions were identified using channel selection preprocessing. However, researchers had a limited grasp of the specifics of the link between various emotional states until now. To identify EEG signals, we used discrete wavelet transform and machine learning techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN) algorithm. Initially, the classifier methods were utilized for channel selection. As a result, final feature vectors were created by integrating the features of EEG segments from these channels. Using the RNN and kNN algorithms, the final feature vectors with connected positive, neutral, and negative emotions were categorized independently. The classification performance of both techniques is computed and compared. Using RNN and kNN, the average overall accuracies were 94.844 % and 93.438 %, respectively.
How do Variational Autoencoders Learn? Insights from Representational Similarity

The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications. However, their behaviour is not yet fully understood. For example, the questions of when they can provide disentangled representations, or suffer from posterior collapse are still areas of active research. Despite this, there are no layerwise comparisons of the representations learned by VAEs, which would further our understanding of these models. In this paper, we thus look into the internal behaviour of VAEs using representational similarity techniques. Speciﬁcally, using the CKA and Procrustes similarities, we found that the encoders’ representations are learned long before the decoders’, and this behaviour is independent of hyperparameters, learning objectives, and datasets. Moreover, the encoders’ representations up to the mean and variance layers are similar across hyperparameters and learning objectives.
Bias and Fairness on Multimodal Emotion Detection Algorithms

Numerous studies have shown that machine learning algorithms can latch onto protected attributes such as race and gender and generate predictions that systematically discriminate against one or more groups. To date the majority of bias and fairness research has been on unimodal models. In this work, we explore the biases that exist in emotion recognition systems in relationship to the modalities utilized, and study how multimodal approaches affect system bias and fairness. We consider audio, text, and video modalities, as well as all possible multimodal combinations of those, and find that text alone has the least bias, and accounts for the majority of the models' performances, raising doubts about the worthiness of multimodal emotion recognition systems when bias and fairness are desired alongside model performance.
GaiaHub: A Method for Combining Data from the Gaia and Hubble Space Telescopes to Derive Improved Proper Motions for Faint Stars

We present GaiaHub, a publicly available tool that combines Gaia measurements with Hubble Space Telescope (HST) archival images to derive proper motions (PMs). It increases the scientific impact of both observatories beyond their individual capabilities. Gaia provides PMs across the whole sky, but the limited mirror size and time baseline restrict the best PM performance to relatively bright stars. HST can measure accurate PMs for much fainter stars over a small field, but this requires two epochs of observation, which are not always available. GaiaHub yields considerably improved PM accuracy compared to Gaia-only measurements, especially for faint sources (G ≳ 18), requiring only a single epoch of HST data observed more than ∼7 yr ago (before 2012). This provides considerable scientific value, especially for dynamical studies of stellar systems or structures in and beyond the Milky Way (MW) halo, for which the member stars are generally faint. To illustrate the capabilities and demonstrate the accuracy of GaiaHub, we apply it to samples of MW globular clusters (GCs) and classical dwarf spheroidal (dSph) satellite galaxies. This allows us, e.g., to measure the velocity dispersions in the plane of the sky for objects out to and beyond ∼100 kpc. We find, on average, mild radial velocity anisotropy in GCs, consistent with existing results for more nearby samples. We observe a correlation between the internal kinematics of the clusters and their ellipticity, with more isotropic clusters being, on average, more round. Our results also support previous findings that Draco and Sculptor dSph galaxies appear to be radially anisotropic systems.
An Aubin continuity path for shrinking gradient K\"ahler-Ricci solitons

Let D be a toric K¨ahler-Einstein Fano manifold. We show that any toric shrinking gradient K¨ahler-Ricci soliton on certain proper modiﬁcations of C × D satisﬁes a complex Monge-Amp`ere equation. We then set up an Aubin continuity path to solve this equation and show that it has a solution at the initial value of the path parameter. This we do by implementing another continuity method.
Understanding urban congestion with biking traffic and routing detour ratio

Bike-sharing systems have been regarded as a critical component of solutions towards the transition to a greener and more sustainable transportation, with the beneﬁts of reducing carbon emissions, improving public health, and mitigating congestion by replacing short-distance motorized trips. Due to better accessibility and usage ﬂexibility, newly emergent dockless sharing bikes have become quite popular and are reviving the fashion of cycling in cities. Urban congestion is simultaneously inﬂuenced by heterogeneous saptio-temporal travel demands, topology and spatial characteristics of road networks, and the interplay between travel modes. In this paper, by considering aforementioned factors, we discover a robust sublinear scaling relation between the level of congestion for vehicles and detour ratio weighted by biking trafﬁc, which is intriguing given the fact that congestion and detour ratio are linearly independent. Such a scaling relation implies a strong interplay between vehicle trafﬁc and cycling activities, and can be applied in predictions for congestion or aggregated to more sophisticated trafﬁc models. In addition, biking-trafﬁc-weighted detour ratio can be applied to detect inefﬁcient routes, which would help alleviate urban congestion, make better urban planning, and improve transportation efﬁciency and equity in cities.
A Linear Comb Filter for Event Flicker Removal

Event cameras are bio-inspired sensors that capture per-pixel asynchronous intensity change rather than the synchronous absolute intensity frames captured by a classical camera sensor. Such cameras are ideal for robotics applications since they have high temporal resolution, high dynamic range and low latency. However, due to their high temporal resolution, event cameras are particularly sensitive to flicker such as from fluorescent or LED lights. During every cycle from bright to dark, pixels that image a flickering light source generate many events that provide little or no useful information for a robot, swamping the useful data in the scene. In this paper, we propose a novel linear filter to preprocess event data to remove unwanted flicker events from an event stream. The proposed algorithm achieves over 4.6 times relative improvement in the signal-to-noise ratio when compared to the raw event stream due to the effective removal of flicker from fluorescent lighting. Thus, it is ideally suited to robotics applications that operate in indoor settings or scenes illuminated by flickering light sources. Code, Datasets and Video: https://github.com/ziweiWWANG/EFR
Flatness of the nuclear norm sphere, simultaneous polarization, and uniqueness in nuclear norm minimization

In this paper we establish necessary and suﬃcient conditions for the existence of line segments (or ﬂats ) in the sphere of the nuclear norm via the notion of simultaneous polarization and a reﬁned expression for the subdiﬀerential of the nuclear norm. This is then leveraged to provide (point-based) necessary and suﬃcient conditions for uniqueness of solutions for minimizing the nuclear norm over an aﬃne manifold. We further establish an alternative set of suﬃcient conditions for uniqueness, based on the interplay of the subdiﬀerential of the nuclear norm and the range of the problem-deﬁning linear operator. Finally, using convex duality, we show how to transfer the uniqueness results for the original problem to a whole class of nuclear norm-regularized minimization problems with a strictly convex ﬁdelity term.
Aligning Robot Representations with Humans

As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging. A central challenge remains that often, it is difficult (perhaps even impossible) to capture the full complexity of the deployment environment, and therefore the desired tasks, at training time. Consequently, the representation, or abstraction, of the tasks the human hopes for the robot to perform in one environment may be misaligned with the representation of the tasks that the robot has learned in another. We postulate that because humans will be the ultimate evaluator of system success in the world, they are best suited to communicating the aspects of the tasks that matter to the robot. Our key insight is that effective learning from human input requires first explicitly learning good intermediate representations and then using those representations for solving downstream tasks. We highlight three areas where we can use this approach to build interactive systems and offer future directions of work to better create advanced collaborative robots.
A Simple Model for Quantum Gravity: the one-dimensional case

Group theory is used to construct a model for quantum gravity and exact results are obtained in one dimension. Comparison with Regge calculus shows that the results agree in the strong cosmological constant limit for an open curve. In the closed case, the average edge length has a minimum value and this give us a clue for higher dimensions.
Microwave and Millimetre-Wave Design for Wireless Communications

This book describes a full range of contemporary techniques for the design of transmitters and receivers for communications systems operating in the range from 1 through to 300 GHz. In this frequency range there is a wide range of technologies that need to be employed, with silicon ICs at the core but, compared with other electronics systems, a much greater use of more specialist devices and components for high performance – for example, high Q-factor/low loss and good power efficiency. Many text books do, of course, cover these topics but what makes this book timely is the rapid adoption of millimetre-waves (frequencies from 30 to 300 GHz) for a wide range of consumer applications such as wireless high definition TV, “5G” Gigabit mobile internet systems and automotive radars. It has taken many years to develop low-cost technologies for suitable transmitters and receivers, so previously these frequencies have been employed only in expensive military and space applications. The book will cover these modern technologies, with the follow topics covered; transmitters and receivers, lumped element filters, tranmission lines and S-parameters, RF MEMS, RFICs and MMICs, and many others. In addition, the book includes extensive line diagrams to illustrate circuit diagrams and block diagrams of systems, including diagrams and photographs showing how circuits are implemented practically. Furthermore, case studies are also included to explain the salient features of a range of important wireless communications systems. The book is accompanied with suitable design examples and exercises based on the Advanced Design System – the industry leading CAD tool for wireless design. More importantly, the authors have been working with Keysight Technologies on a learning & teaching initiative which is designed to promote access to industry-standard EDA tools such as ADS. Through its University Educational Support Program, Keysight offers students the opportunity to request a student license, backed up with extensive classroom materials and support resources. This culminates with students having the chance to demonstrate their RF/MW design and measurement expertise through the Keysight RF & Microwave Industry-Ready Student Certification Program.
A Low-Complexity Optimum Detection Based On High-Rate Full- Diversity Mimo Space Time Block Codes

The 2 × 2 MIMO profiles included in Mobile Wi-MAX specifications are Alamouti's space-time block code (STBC) for transmit diversity and spatial multiplexing (SM). The former has full diversity and the latter has full rate, but neither of them has both of these desired features. An alternative 2×2 STBC, which is both full rate and full diversity, is the Golden code. It is the best known 2×2 STBC, but it has a high decoding complexity. Recently, the attention was turned to the decoder complexity, this issue was included in the STBC design criteria, and different STBCs were proposed. In this paper, we first present a high-rate full-diversity 2×2 STBC design leading to substantially lower complexity of the optimum detector compared to the Golden code with only a slight performance loss. We provide the general optimized form of this STBC and show that this scheme achieves the diversity multiplexing frontier for square QAM signal constellations. Then, we present a variant of the proposed STBC, which provides a further decrease in the detection complexity with a rate reduction of 25% and show that this provides an interesting trade-off between the Alamouti scheme and SM.
Wideband to concurrent tri-band frequency reconfigurable microstrip patch antenna for wireless communication

This paper proposes a novel wideband to concurrent tri-band frequency reconfigurable microstrip antenna. The frequency reconfiguration is achieved by using a pair of PIN diodes in the antenna feed line to switch the antenna either in wideband mode or in concurrent tri-band mode. In order to improve the bandwidth and gain of the antenna for wideband operation, the properties of J-K inverter and split ring resonators are exploited. To demonstrate the versatility of this concept a prototype is fabricated and tested here. The tested results in wideband mode shows that the proposed antenna operates from 3.58 to –3.82 GHz, which is 4.08 times larger than the bandwidth of a simple microstrip patch antenna. In the concurrent tri-band mode frequency tuning is done by microstrip open stub at 1.5 GHz, 1.9 GHz, and 3.5 GHz. Gain of the proposed antenna is better than 2.7 dB in wideband mode and 2.7 dB in concurrent tri-band mode.
Parallel adaptive infrastructure for magnetically confined fusion plasma simulations

Numerical simulations of the magnetically confined plasmas play an important rule in understanding and predicting the physics in tokamak devices. In the present study, a parallel adaptive infrastructure for the magnetically confined fusion plasma simulations is developed. An automatic meshing procedure is needed by two simulation codes under development at Princeton Plasma Physics Lab, M3D-C and XGC1. M3D-C requires the mesh generated and adapted on the tokamak cross-section with the option to contain a finite-thickness wall in the domain, and the 3D mesh constructed out of 2D meshes. XGC1 requires the mesh edges aligned with the magnetic flux surfaces. The mesh is one-element deep between adjacent flux surfaces and mesh improvement is applied at the X-point(s). A geometric model including both the tokamak wall structure and the magnetic flux surfaces is introduced to reflect the meshing needs. A component-based mesh generation procedure with control parameters specified on the geometric model is developed by combining unstructured triangulation, layered mesh generation by a one-element-deep marching procedure, local mesh modification, and toroidal 2D mesh extrusion to create a 3D mesh of the full reactor. Mesh-based needs in M3D-C include a procedure to form the global discrete equation, methods to estimate the simulation error, and a loop of adaptive mesh control. A mesh-adjacency based matrix assembly procedure with more efficient memory usage than the procedure using the PETSc matrix library directly is developed. The numerical conditioning of the global discrete system is improved through element-level operations. An explicit a posterior error estimator that calculates the mesh-dependent norm of the residual in the strong form is derived for the reduced MHD model under the large-aspect-ratio approximation. Software tools such as PUMI for mesh management and geometric model interfacing, APF for field management, MeshAdapt for mesh modification, PETSc for global equation solving, and Simmetrix for initial mesh generation are used to form the parallel adaptive loop in M3D-C.
Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task

Humans can rapidly adapt to new situations, even though they have redundant degrees of freedom (d.f.). Previous studies in neuroscience revealed that human movements could be accounted for by low-dimensional control signals, known as motor synergies. Many studies have suggested that humans use the same repertories of motor synergies among similar tasks. However, it has not yet been confirmed whether the combinations of motor synergy repertories can be re-used for new targets in a systematic way. Here we show that the combination of motor synergies can be generalized to new targets that each repertory cannot handle. We use the multi-directional reaching task as an example. We first trained multiple policies with limited ranges of targets by reinforcement learning and extracted sets of motor synergies. Finally, we optimized the activation patterns of sets of motor synergies and demonstrated that combined motor synergy repertories were able to reach new targets that were not achieved with either original policies or single repertories of motor synergies. We believe this is the first study that has succeeded in motor synergy generalization for new targets in new planes, using a full 7-d.f. arm model, which is a realistic mechanical environment for general reaching tasks.
Agent-based modelling of iron cycling bacteria provides a framework for testing alternative environmental conditions and modes of action

Iron-reducing and iron-oxidizing bacteria are of interest in a variety of environmental and industrial applications. Such bacteria often co-occur at oxic-anoxic gradients in aquatic and terrestrial habitats. In this paper, we present the first computational agent-based model of microbial iron cycling, between the anaerobic ferric iron (Fe3+)-reducing bacteria Shewanella spp. and the microaerophilic ferrous iron (Fe2+)-oxidizing bacteria Sideroxydans spp. By including the key processes of reduction/oxidation, movement, adhesion, Fe2+-equilibration and nanoparticle formation, we derive a core model which enables hypothesis testing and prediction for different environmental conditions including temporal cycles of oxic and anoxic conditions. We compared (i) combinations of different Fe3+-reducing/Fe2+-oxidizing modes of action of the bacteria and (ii) system behaviour for different pH values. We predicted that the beneficial effect of a high number of iron-nanoparticles on the total Fe3+ reduction rate of the system is not only due to the faster reduction of these iron-nanoparticles, but also to the nanoparticles’ additional capacity to bind Fe2+ on their surfaces. Efficient iron-nanoparticle reduction is confined to pH around 6, being twice as high than at pH 7, whereas at pH 5 negligible reduction takes place. Furthermore, in accordance with experimental evidence our model showed that shorter oxic/anoxic periods exhibit a faster increase of total Fe3+ reduction rate than longer periods.