Miracle-dz/UniFAR-FTUs huggingface dataset:
SophiaPop: Experiments in Human-AI Collaboration on Popular Music

A diverse team of engineers, artists, and algorithms, collaborated to create songs for SophiaPop, via various neural networks, robotics technologies, and artistic tools, and animated the results on Sophia the Robot, a robotic celebrity and animated character. Sophia is a platform for arts, research, and other uses. To advance the art and technology of Sophia, we combine various AI with a fictional narrative of her burgeoning career as a popstar. Her actual AI-generated pop lyrics, music, and paintings, and animated conversations wherein she interacts with humans real-time in narratives that discuss her experiences. To compose the music, SophiaPop team built corpora from human and AI-generated Sophia character personality content, along with pop music song forms, to train and provide seeds for a number of AI algorithms including expert models, and custom-trained transformer neural networks, which then generated original pop-song lyrics and melodies. Our musicians including Frankie Storm, Adam Pickrell, and Tiger Darrow, then performed interpretations of the AI-generated musical content, including singing and instrumentation. The human-performed singing data then was processed by a neural-network-based Sophia voice, which was custom-trained from human performances by Cereproc. This AI then generated the unique Sophia voice singing of the songs. Then we animated Sophia to sing the songs in music videos, using a variety of animation generators and human-generated animations. Being algorithms and humans, working together, SophiaPop represents a human-AI collaboration, aspiring toward human AI symbiosis. We believe that such a creative convergence of multiple disciplines with humans and AI working together, can make AI relevant to human culture in new and exciting ways, and lead to a hopeful vision for the future of human-AI relations.
Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon

We explore reinforcement learning methods for finding the optimal policy in the linear quadratic regulator (LQR) problem. In particular, we consider the convergence of policy gradient methods in the setting of known and unknown parameters. We are able to produce a global linear convergence guarantee for this approach in the setting of finite time horizon and stochastic state dynamics under weak assumptions. The convergence of a projected policy gradient method is also established in order to handle problems with constraints. We illustrate the performance of the algorithm with two examples. The first example is the optimal liquidation of a holding in an asset. We show results for the case where we assume a model for the underlying dynamics and where we apply the method to the data directly. The empirical evidence suggests that the policy gradient method can learn the global optimal solution for a larger class of stochastic systems containing the LQR framework and that it is more robust with respect to model mis-specification when compared to a model-based approach. The second example is an LQR system in a higher dimensional setting with synthetic data.
Design and Analysis of Incentive Mechanisms for Prosumers under the lens of Prospect Theory

Users can now give back energies to the grid using distributed resources. Proper incentive mechanisms are required for such users, also known as prosumers, in order to maximize the sell-back amount while maintaining the retailer’s profit. However, all the existing literature considers expected utility theory (EUT) where they assume that prosumers maximize their expected payoff in the face of uncertainty. We consider prospect theory (PT) which models the true behavior of humans under uncertainty. We show that in a day-ahead contract pricing mechanism, the actual optimal value of contract and the sell-back amount may be smaller compared to the one computed by EUT. We also propose a lottery-based mechanism and show that such a mechanism can increase the sell-back amount while increasing the retailer’s savings compared to day-ahead contract pricing.
HyperLogLog (HLL) Security: Inflating Cardinality Estimates

Counting the number of distinct elements on a set is needed in many applications, for example to track the number of unique users in Internet services or the number of distinct flows on a network. In many cases, an estimate rather than the exact value is sufficient and thus many algorithms for cardinality estimation that significantly reduce the memory and computation requirements have been proposed. Among them, Hyperloglog has been widely adopted in both software and hardware implementations. The security of Hyperloglog has been recently studied showing that an attacker can create a set of elements that produces a cardinality estimate that is much smaller than the real cardinality of the set. This set can be used for example to evade detection systems that use Hyperloglog. In this paper, the security of Hyperloglog is considered from the opposite angle: the attacker wants to create a small set that when inserted on the Hyperloglog produces a large cardinality estimate. This set can be used to trigger false alarms in detection systems that use Hyperloglog but more interestingly, it can be potentially used to inflate the visits to websites or the number of hits of online advertisements. Our analysis shows that an attacker can create a set with a number of elements equal to the number of registers used in the Hyperloglog implementation that produces any arbitrary cardinality estimate. This has been validated in two commercial implementations of Hyperloglog: Presto and Redis. Based on those results, we also consider the protection of Hyperloglog against such an attack.
Certified Monotonic Neural Networks

Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice. Existing methods for learning monotonic neural networks either require specifically designed model structures to ensure monotonicity, which can be too restrictive/complicated, or enforce monotonicity by adjusting the learning process, which cannot provably guarantee the learned model is monotonic on selected features. In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem.This provides a new general approach for learning monotonic neural networks with arbitrary model structures. Our method allows us to train neural networks with heuristic monotonicity regularizations, and we can gradually increase the regularization magnitude until the learned network is certified monotonic. Compared to prior works, our approach does not require human-designed constraints on the weight space and also yields more accurate approximation. Empirical studies on various datasets demonstrate the efficiency of our approach over the state-of-the-art methods, such as Deep Lattice Networks.
Are Chess Discussions Racist? An Adversarial Hate Speech Data Set

On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radic's YouTube handle got blocked because it contained ``harmful and dangerous'' content. YouTube did not give further specific reason, and the channel got reinstated within 24 hours. However, Radic speculated that given the current political situation, a referral to ``black against white'', albeit in the context of chess, earned him this temporary ban. In this paper, via a substantial corpus of 681,995 comments, on 8,818 YouTube videos hosted by five highly popular chess-focused YouTube channels, we ask the following research question: \emph{how robust are off-the-shelf hate-speech classifiers to out-of-domain adversarial examples? } We release a data set of 1,000 annotated comments where existing hate speech classifiers misclassified benign chess discussions as hate speech. We conclude with an intriguing analogy result on racial bias with our findings pointing out to the broader challenge of color polysemy.
MobileDepth: Efficient Monocular Depth Prediction on Mobile Devices

Depth prediction is fundamental for many useful applications on computer vision and robotic systems. On mobile phones, the performance of some useful applications such as augmented reality, autofocus and so on could be enhanced by accurate depth prediction. In this work, an efficient fully convolutional network architecture for depth prediction has been proposed, which uses RegNetY 06 as the encoder and split-concatenate shuffle blocks as decoder. At the same time, an appropriate combination of data augmentation, hyper-parameters and loss functions to efficiently train the lightweight network has been provided. Also, an Android application has been developed which can load CNN models to predict depth map by the monocular images captured from the mobile camera and evaluate the average latency and frame per second of the models. As a result, the network achieves 82.7% {\delta}1 accuracy on NYU Depth v2 dataset and at the same time, have only 62ms latency on ARM A76 CPUs so that it can predict the depth map from the mobile camera in real-time.
Dynamics of Sunspot Shock Waves in the Chromosphere and Transition Region

We study the dynamics of shock waves observed in the umbra of a sunspot using the spectroscopic observations from the Interface Region Imaging Spectrograph. The presence of a shock significantly deforms the shape of the spectral lines of Mg ii, C ii, and Si iv. We found that C ii 1335.71 Å and Si iv 1393.75 Å show double-peaked profiles that change to a single peak later on. However, the Mg ii h 2803.53 Å line first shows flat-top profiles that change into double peaks followed by the single peak. To study the shock dynamics, we isolate the shock component from the spectra by fitting two Gaussians. We find that the lifetime of the shock is largest in the Mg ii h 2803.53 Å line. Moreover, the plasma motion shows both the acceleration and deceleration phases of the shock. Yet, in C ii 1335.71 Å and Si iv 1393.75 Å, only the deceleration phase is observed. We observe a strong correlation between the largest blueshift of the shock and deceleration for all three spectral lines. We find a positive (negative) correlation between intensities contributed by the shocks in Mg ii and C ii (Si iv). This suggests that the shocks are first amplified in C ii, followed by a decline in the height range corresponding to Si iv. These results may indicate the dissipation of shocks above the formation height of C ii, and the shocks may have important roles in the dynamics of the upper chromosphere and transition region above sunspots.
Data-Driven Modulation and Antenna Classification of Wireless Digital Communication Signals

In this paper we are interested to learn from a wireless digitally modulated signal the number of antennas that the transmitter (Tx) of this signal uses, as well as its specific modulation scheme (from phase-shift keying (PSK) or quadrature amplitude modulation (QAM)). Formally, these are modulation and antenna classification problems. We examine the problems with data-driven machine learning (ML)-based techniques. The two sub-problems of modulation and number of transmitter antenna classification are initially examined independently for a variety for system parameters, namely the SNR, number of receiver (Rx) antennas, and classification algorithms. Then we consider the joint problem where we follow two approaches. One, where the sub-problems are solved independently and in parallel, and one where the antenna classifier waits on the result of the modulation classifier. The two proposed schemes do not require any knowledge/details of the used modulation schemes and the way the Tx antennas are used (spatial multiplexing, space-time codes,etc.) as it is fully data-driven and not decision-theoretic based. The results of our approach are characterized by high classification accuracy and they pave the way for more ML-based data-driven techniques that reveal more characteristics of the Tx.
Atom cloud detection and segmentation using a deep neural network

We use a deep neural network to detect and place region-of-interest boxes around ultracold atom clouds in absorption and fluorescence images---with the ability to identify and bound multiple clouds within a single image. The neural network also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds' Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing.
Latent heat and pressure gap at the first-order deconfining phase transition of SU(3) Yang–Mills theory using the small flow-time expansion method

We study latent heat and the pressure gap between the hot and cold phases at the first-order deconfining phase transition temperature of the SU(3) Yang–Mills theory. Performing simulations on lattices with various spatial volumes and lattice spacings, we calculate the gaps of the energy density and pressure using the small flow-time expansion (SF$t$X) method. We find that the latent heat $\Delta \epsilon$ in the continuum limit is $\Delta \epsilon /T^4 = 1.117 \pm 0.040$ for the aspect ratio $N_s/N_t=8$ and $1.349 \pm 0.038$ for $N_s/N_t=6$ at the transition temperature $T=T_c$. We also confirm that the pressure gap is consistent with zero, as expected from the dynamical balance of two phases at $T_c$. From hysteresis curves of the energy density near $T_c$, we show that the energy density in the (metastable) deconfined phase is sensitive to the spatial volume, while that in the confined phase is insensitive. Furthermore, we examine the effect of alternative procedures in the SF$t$X method—the order of the continuum and the vanishing flow-time extrapolations, and also the renormalization scale and higher-order corrections in the matching coefficients. We confirm that the final results are all very consistent with each other for these alternatives.
Microemulsions : properties and applications

A Phase Diagram Approach to Microemulsions, S.E. Friberg and P.A. Aikens Physicochemistry of W/O Microemulsions: Formation, Stability, and Droplet Clustering, A.K. Rakshit and S.P. Moulik Percolating Phenomenon in Microemulsions: Effect of External Entity, S.K. Mehta, K. Kaur, G. Kaur, and K.K. Bhasin Influence of Polyethylene Glycols and Polyethylene Glycol Dimethyl Ethers upon the Internal Dynamics of Water in Oil Microemulsions, A. Cid, L. Garcia-Rio, D. Gomez-Diaz, and J.C. Mejuto Microemulsions with Mixed Nonionic Surfactants, M. Fanun Simple Alcohols and Their Role in the Structure and Interactions of Microemulsion Systems, M. Tomsic and A. Jamnik Formation and Characterization of Emulsified Microemulsions, A. Yaghmur, L. de Campo, and O. Glatter Dynamics of Solvent and Rotational Relaxation of RTILs in RTILs-Containing Microemulsions, D. Seth and N. Sarkar Microemulsion Systems and Their Potential as Drug Carriers, R.G. Alany, G.M.M. El Maghraby, K. Krauel-Goellner, and A. Graf Physicochemical Characterization of Pharmaceutically Applicable Microemulsions: Tween 40/Imwitor 308/Isopropyl Myristate/Water, M. Gasperlin and M. Bester-Rogac Places of Microemulsion and Emulsion in Cancer Therapy: In Vitro and In Vivo Evaluation, E. Karasulu, B. Karaca, L. Alparslan, and H.Y. Karasulu Enzyme Kinetics as a Useful Probe for Micelle and Microemulsion Structure and Dynamics, W. Kunz, D. Touraud, and P. Bauduin Biocatalysis in Microemulsions, A. Xenakis, V. Papadimitriou, H. Stamatis, and F.N. Kolisis Microemulsions as Decontamination Media for Chemical Weapons and Toxic Industrial Chemicals, T. Hellweg, S. Wellert, H. -J. Altmann, and A. Richardt Microemulsions as Potential Interfacial Chemical Systems Applied in the Petroleum Industry, A.A.D. Neto, T.N. de Castro Dantas, M.C. P. de Alencar Moura, E.L. de Barros Neto, and A. Gurgel Nanoparticle Formation in Microemulsions: Mechanism and Monte Carlo Simulations, M. de Dios, F. Barroso, and C. Tojo Nanoparticle Uptake by (W/O) Microemulsions, M.M. Husein and N.N. Nassar TiO2 Nanoparticles in Microemulsion: Photophysical Properties and Interfacial Electron Transfer Dynamics, H.N. Ghosh Microemulsions as Pseudostationary Phases in Electrokinetic Chromatography: I. Estimation of Physicochemical Parameters. II. Analysis of Drugs in Pharmaceutical and Biofluidic Matrices, V. Tripodi and S. Lucangioli
Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity

Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials

Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a novel deep learning model, "StressNet," is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 hours, with an average MAPE of 2% relative to test data.
Infrared Spectroscopy

Series Preface.Preface. Acronyms, Abbreviations and Symbols.About the Author.1. Introduction.1. 1 Electromagnetic Radiation.1. 2 Infrared Absorptions.1. 3 Normal Modes of Vibration.1. 4 Complicating Factors.1. 4.1 Overtone and Combination Bands.1. 4.2 Fermi Resonance.1. 4.3 Coupling.1. 4.4 Vibration-Rotation Bands.References. 2. Experimental Methods.2.1 Introduction.2.2 Dispersive Infrared Spectrometers.2.3 Fourier-Transform Infrared Spectrometers.2.3.1 Michelson Interferometers.2.3.2 Sources and Detectors.2.3.3 Fourier-Transformation.2.3.4 Moving Mirrors.2.3.5 Signal-Averaging.2.3.6 Advantages.2.3.7 Computers.2.3.8 Spectra.2. 4 Transmission Methods.2. 4.1 Liquids and Solutions.2. 4.2 Solids.2. 4.3 Gases.2. 4.4 Pathlength Calibration.2. 5 Reflectance Methods.2. 5.1 Attenuated Total Reflectance Spectroscopy.2. 5.2 Specular Reflectance Spectroscopy.2. 5.3 Diffuse Reflectance Spectroscopy.2. 5.4 Photoacoustic Spectroscopy.2. 6 Microsampling Methods.2. 7 Chromatography-Infrared Spectroscopy.2. 8 Thermal Analysis-Infrared Spectroscopy.2. 9 Other Techniques.References. 3. Spectral Analysis.3.1 Introduction.3.2 Group Frequencies.3.2.1 Mid-Infrared Region.3.2.2 Near-Infrared Region.3.2.3 Far-Infrared Region.3.3 Identification.3.4 Hydrogen Bonding.3.5 Spectrum Manipulation.3.5.1 Baseline Correction.3.5.2 Smoothing.3.5.3 Difference Spectra.3.5.4 Derivatives.3.5.5 Deconvolution.3.5.6 Curve-Fitting.3.6 Concentration.3.7 Simple Quantitative Analysis.3.7.1 Analysis of Liquid Samples.3.7.2 Analysis of Solid Samples.3.8 Multi-Component Analysis.3. 9 Calibration Methods.References. 4. Organic Molecules.4.1 Introduction.4.2 Aliphatic Hydrocarbons.4.3 Aromatic Compounds.4.4 Oxygen-Containing Compounds.4.4.1 Alcohols and Phenols.4.4.2 Ethers.4.4.3 Aldehydes and Ketones.4.4.4 Esters.4.4.5 Carboxylic Acids and Anhydrides.4.5 Nitrogen-Containing Compounds.4.5.1 Amines.4.5.2 Amides.4. 6 Halogen-Containing Compounds.4. 7 Heterocyclic Compounds.4. 8 Boron Compounds.4. 9 Silicon Compounds.4.10 Phosphorus Compounds.4.11 Sulfur Compounds.4.12 Near-Infrared Spectra.4.13 Identification.References. 5. Inorganic Molecules.5.1 Introduction.5.2 General Considerations.5.3 Normal Modes of Vibration.5.4 Coordination Compounds.5.5 Isomerism.5.6 Metal Carbonyls.5. 7 Organometallic Compounds.5. 8 Minerals.References. 6. Polymers.6. 1 Introduction.6. 2 Identification.6. 3 Polymerization.6. 4 Structure.6. 5 Surfaces.6. 6 Degradation.References. 7. Biological Applications.7.1 Introduction.7.2 Lipids.7. 3 Proteins and Peptides.7. 4 Nucleic Acids.7. 5 Disease Diagnosis.7. 6 Microbial Cells.7. 7 Plants.7. 8 Clinical Chemistry.References. 8. Industrial and Environmental Applications.8. 1 Introduction.8. 2 Pharmaceutical Applications.8. 3 Food Science.8. 4 Agricultural Applications.8. 5 Pulp and Paper Industries.8. 6 Paint Industry.8. 7 Environmental Applications.References. Responses to Self-Assessment Questions.Bibliography. Glossary of Terms.SI Units and Physical Constants.Periodic Table.Index.
Lipid glossary 2

Lipid Glossary 2 is a handy reference for a wide range of lipid scientists and technologists, as well as for those involved in the trading of these materials. The major part of the book is the glossary which contains brief and simple definitions, such as the names of fatty acids and lipids, the major oils and fats, terms associated with their analysis, refining, and modification, and the major journals and societies concerned with lipid chemistry. Entries are arranged alphabetically for ease of reference and there are cross-refernces between sections. Many entries have full references to further sources of information. The earlier book A Lipid Glossary (first published by The Oily Press in 1992) has been completely rewritten for this new version. The entries have been extended and increased in number to over 1200. The number of graphics has been raised to over 180. As a consequence, the new book has more than twice as many pages as the old version. Details of the major lipid journals and books on lipids are listed in two appendices.
Dynamics of gas-surface interactions

Mechanical energy transfer in particle-surface collisions, J.Harris dynamics of dissociative chemisorption, A.E. Depristo quantum effects in gas-solid interactions, S.Holloway the dynamics of hydrogen adsorption and desorption in copper surfaces, B.E. Hayden kinetics of surface reactions, W.H. Weinberg thermal desorption kinetics, H.J.Kreuzer and S.H. Payne electron stimulated desorption and its application to chemical systems, R.D. Ramsier and J.T. Yates Jr. photochemistry of the adsorbed state, J.C. Polanyi and H.Rieley.