Miracle-dz/UniFAR-FTUs huggingface dataset:
Born Machines for Periodic and Open XY Quantum Spin Chains

Quantum phase transitions are ubiquitous in quantum many body systems. The quantum fluctuations that occur at very low temperatures are known to be responsible for driving the system across different phases as a function of an external control parameter. The XY Hamiltonian with a transverse field is a basic model that manifests two distinct quantum phase transitions, including spontaneous $Z_2$ symmetry breaking from an ordered to a disordered state. While programmable quantum devices have shown great success in investigating the various exotic quantum phases of matter, in parallel, the quest for harnessing machine learning tools in learning quantum phases of matter is ongoing. In this paper, we present a numerical study of the power of a quantum-inspired generative model known as the Born machine in learning quantum phases of matter. Data obtained from the system under open and periodic boundary conditions is considered. Our results indicate that a Born machine based on matrix product states can successfully capture the quantum state across various phases of the XY Hamiltonian and close to a critical point, despite the existence of long-range correlations. We further impose boundary conditions on the Born machine and show that matching the boundary condition of the Born machine and that of the training data improves performance when limited data is available and a small bond dimension is employed.
Deep Forest: Neural Network reconstruction of intergalactic medium temperature

We explore the use of Deep Learning to infer the temperature of the intergalactic medium from the transmitted flux in the high redshift Lyman-alpha forest. We train Neural Networks on sets of simulated spectra from redshift z=2-3 outputs of cosmological hydrodynamic simulations, including high temperature regions added in post-processing to approximate bubbles heated by Helium-II reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Lyman-alpha forest absorption spectra. We find that for spectra with high resolution (10 km/s pixel) and moderate signal to noise (20-50), the neural network is able to reconstruct the IGM temperature smoothed on scales of 6 Mpc/h quite well. Concentrating on discontinuities we find that high temperature regions of width 25 Mpc/h and temperature 20,000 K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep Learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of Helium reionization.
Findings on Conversation Disentanglement

Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and con-versation summarization. Framing it as a utterance-to-utterance classification problem â i.e. given an utterance of interest (UOI), find which past utterance it replies to â we explore a number of transformer-based models and found that BERT in combination with handcrafted features remains a strong baseline. We then build a multi-task learning model that jointly learns utterance-to-utterance and utterance-to-thread classification. Observing that the ground truth label (past utterance) is in the top candidates when our model makes an error, we experiment with using bipartite graphs as a post-processing step to learn how to best match a set of UOIs to past utterances. Experiments on the Ubuntu IRC dataset show that this approach has the potential to out-perform the conventional greedy approach of simply selecting the highest probability candidate for each UOI independently, indicating a promising future research direction.
Temperature dependences of current gains in GaInP/GaAs and AlGaAs/GaAs heterojunction bipolar transistors

The temperature dependences of current gain are investigated for both GaInP/GaAs and AlGaAs/GaAs heterojunction bipolar transistors (HBTs). Measured results indicate that for GaInP/GaAs HBTs the current gain at collector current densities >0.1 A/cm/sup 2/ remains nearly constant, independent of the substrate temperature for AlGaAs/GaAs HBTs. These current gain characteristics are examined, and the origin of the difference is attributed to the difference of the valence-band discontinuities in the base-emitter heterojunctions of the two HBTs. >
Observation and control of collective spin-wave mode hybridization in chevron arrays and in square, staircase, and brickwork artificial spin ices

Dipolar magnon-magnon coupling has long been predicted in nanopatterned artiﬁcial spin systems. However, observation of such phenomena and related collective spin-wave signatures have until recently proved elusive or been limited to low-power edge modes which are difﬁcult to measure experimentally. Here we describe the requisite conditions for dipolar mode-hybridization, how it may be controlled, why it was not observed earlier, and how strong coupling may occur between nanomagnet bulk modes. We experimentally investigate four nanopatterned artiﬁcial spin system geometries: chevron arrays, square, staircase, and brickwork artiﬁcial spin ices. We observe signiﬁcant dynamic dipolar-coupling in all systems with relative coupling strengths and avoided-crossing gaps supported by micromagnetic-simulation results. We demonstrate reconﬁgurable mode-hybridization regimes in each system via microstate control, and in doing so elucidate the underlying dynamics governing dynamic dipolar-coupling with implications across reconﬁgurable magnonics. We demonstrate that conﬁnement of the bulk modes via edge effects plays a critical role in dipolar hybridized modes, and treating each nanoisland as a coherently precessing macro-spin or a standing spin-wave is insufﬁcient to capture experimentally observed coupling phenomena. Finally, we present a parameter-space search detailing how coupling strength may be tuned via nanofabrication dimensions and material properties.
The galaxy–halo size relation of low–mass galaxies in FIRE

Galaxy sizes correlate closely with the sizes of their parent dark matter haloes, suggesting a link between halo formation and galaxy growth. However, the precise nature of this relation and its scatter remains to be understood fully, especially for low-mass galaxies. We analyse the galaxy–halo size relation for low-mass (M⋆ ∼ 107 − 9 M⊙) central galaxies over the past 12.5 billion years with the help of cosmological volume simulations (FIREbox) from the Feedback in Realistic Environments (FIRE) project. We find a nearly linear relationship between the half-stellar mass galaxy size R1/2 and the parent dark matter halo virial radius Rvir. This relation evolves only weakly since redshift z = 5: R1/2 [kpc] = (0.053 ± 0.002)(Rvir/35 kpc)0.934 ± 0.054, with a nearly constant scatter 〈σ〉 = 0.084 [dex]. Whilst this ratio is similar to what is expected from models where galaxy disc sizes are set by halo angular momentum, the low-mass galaxies in our sample are not angular momentum supported, with stellar rotational to circular velocity ratios vrot/vcirc ∼ 0.15. Introducing redshift as another parameter to the GHSR does not decrease the scatter. Furthermore, this scatter does not correlate with any of the halo properties we investigate – including spin and concentration – suggesting that baryonic processes and feedback physics are instead critical in setting the scatter in the galaxy–halo size relation. Given the relatively small scatter and the weak dependence of the galaxy–halo size relation on redshift and halo properties for these low-mass central galaxies, we propose using galaxy sizes as an independent method from stellar masses to infer halo masses.
From the simplex to the sphere: faster constrained optimization using the Hadamard parametrization

The standard simplex in $\mathbb{R}^{n}$, also known as the probability simplex, is the set of nonnegative vectors whose entries sum up to 1. It frequently appears as a constraint in optimization problems that arise in machine learning, statistics, data science, operations research and beyond. We convert the standard simplex to the unit sphere and thus transform the corresponding constrained optimization problem into an optimization problem on a simple, smooth manifold. We show that Karush-Kuhn-Tucker points and strict-saddle points of the minimization problem on the standard simplex all correspond to those of the transformed problem, and vice versa. So, solving one problem is equivalent to solving the other problem. Then, we propose several simple, efficient and projection-free algorithms using the manifold structure. The equivalence and the proposed algorithm can be extended to optimization problems with unit simplex, weighted probability simplex or $\ell _{1}$-norm sphere constraints. Numerical experiments between the new algorithms and existing ones show the advantages of the new approach. Open source code is available at https://github.com/DanielMckenzie/HadRGD.
Full-volume three-component intraventricular vector flow mapping by triplane color Doppler

Objective. Intraventricular vector flow mapping (iVFM) is a velocimetric technique for retrieving two-dimensional velocity vector fields of blood flow in the left ventricular cavity. This method is based on conventional color Doppler imaging, which makes iVFM compatible with the clinical setting. We have generalized the iVFM for a three-dimensional reconstruction (3D-iVFM). Approach. 3D-iVFM is able to recover three-component velocity vector fields in a full intraventricular volume by using a clinical echocardiographic triplane mode. The 3D-iVFM problem was written in the spherical (radial, polar, azimuthal) coordinate system associated to the six half-planes produced by the triplane mode. As with the 2D version, the method is based on the mass conservation, and free-slip boundary conditions on the endocardial wall. These mechanical constraints were imposed in a least-squares minimization problem that was solved through the method of Lagrange multipliers. We validated 3D-iVFM in silico in a patient-specific CFD (computational fluid dynamics) model of cardiac flow and tested its clinical feasibility in vivo in patients and in one volunteer. Main results. The radial and polar components of the velocity were recovered satisfactorily in the CFD setup (correlation coefficients, r = 0.99 and 0.78). The azimuthal components were estimated with larger errors ( r = 0.57) as only six samples were available in this direction. In both in silico and in vivo investigations, the dynamics of the intraventricular vortex that forms during diastole was deciphered by 3D-iVFM. In particular, the CFD results showed that the mean vorticity can be estimated accurately by 3D-iVFM. Significance. Our results tend to indicate that 3D-iVFM could provide full-volume echocardiographic information on left intraventricular hemodynamics from the clinical modality of triplane color Doppler.
"Quality vs Quantity": Improved Shot Prediction in Soccer using Strategic Features from Spatiotemporal Data

In this paper, we present a method which accurately estimates the likelihood of chances in soccer using strategic features from an entire season of player and ball tracking data taken from a professional league. From the data, we analyzed the spatiotemporal patterns of the ten-second window of play before a shot for nearly 10,000 shots. From our analysis, we found that not only is the game phase important (i.e., corner, free-kick, open-play, counter attack etc.), the strategic features such as defender proximity, interaction of surrounding players, speed of play, coupled with the shot location play an impact on determining the likelihood of a team scoring a goal. Using our spatiotemporal strategic features, we can accurately measure the likelihood of each shot. We use this analysis to quantify the efficiency of each team and their strategy.
Adapting Posterior Models Using Recovered Likelihoods

Classiﬁcation approaches based on the direct estimation and analysis of posterior probabilities will degrade over time if the original class priors begin to change. We prove that a unique (up to scale) solu-tion is possible to recover the data likelihoods for a test example from its estimated class posteriors and the original dataset priors. Given the recovered likelihoods and a set of new priors, the posteriors can be re-computed using Bayes’ Rule to reﬂect the inﬂuence of the new priors. The method is simple to compute and allows a dynamic update of the original posterior model.
Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

We survey and reflect on how learning (in the form of individual learning and/or culture) can augment evolutionary approaches to the joint optimization of the body and control of a robot. We focus on a class of applications where the goal is to evolve the body and brain of a single robot to optimize performance on a specified task. The review is grounded in a general framework for evolution which permits the interaction of artificial evolution acting on a population with individual and cultural learning mechanisms. We discuss examples of variations of the general scheme of ‘evolution plus learning’ from a broad range of robotic systems, and reflect on how the interaction of the two paradigms influences diversity, performance and rate of improvement. Finally, we suggest a number of avenues for future work as a result of the insights that arise from the review. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.
The emergence of collective knowledge and cumulative culture in animals, humans and machines

Centre for Social Learning and Cognitive Evolution, School of Psychology and Neuroscience, and Centre for Social Learning and Cognitive Evolution, and Sea Mammal Research Unit, Scottish Oceans Institute, School of Biology, University of St Andrews, St Andrews, UK Department of Zoology, University of Oxford, Oxford, UK Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, ISIR, 75005 Paris, France Centre for Language Evolution, University of Edinburgh, Edinburgh, UK
Social learning in swarm robotics

In this paper, we present an implementation of social learning for swarm robotics. We consider social learning as a distributed online reinforcement learning method applied to a collective of robots where sensing, acting and coordination are performed on a local basis. While some issues are specific to artificial systems, such as the general objective of learning efficient (and ideally, optimal) behavioural strategies to fulfill a task defined by a supervisor, some other issues are shared with social learning in natural systems. We discuss some of these issues, paving the way towards cumulative cultural evolution in robot swarms, which could enable complex social organization necessary to achieve challenging robotic tasks. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.
High efficiency asymmetrical half-bridge flyback converter using a new voltage-driven synchronous rectifier

This study presents a high efficiency asymmetrical half-bridge flyback converter using a new voltage-driven synchronous rectifier that operates over a universal input voltage range (75-265-V) with a fixed 5-V DC output voltage. Both power semiconductor switches of the proposed converter primary operate asymmetrically under zero voltage switching to achieve high efficiency and low switch voltage stress. Because the proposed converter uses the transformer leakage inductance as its resonant inductance, its structure is simplified. The proposed synchronous rectifier can cover a universal input voltage range and can maintain control in a narrow switching period, features that are essential in converters with universal input voltage. The synchronous rectifier switch of the proposed converter conducts under zero voltage/zero current switching conditions with a discontinuous conduction mode. The operational principle and steady state analysis of the proposed converter are explained, and a design example for a 100-W prototype is discussed in detail. Experimental results for the designed prototype converter with universal input voltage are shown. The author also shows that the efficiency of the proposed converter can be improved significantly.
Integrating EMDR and EFT To Treat Trauma In Couple Therapy: A Literature Review

ABSTRACT In this paper, we present a literature review on integrating Eye Movement Desensitization and Reprocessing (EMDR) and Emotionally Focused Therapy (EFT) to treat trauma in couple therapy. Both are highly effective therapeutic models (one mainly used for individual trauma treatment and the other for strengthening attachment bonds in couples) that have gained significant traction and ample empirical support in the last three decades. Mental health therapists are increasingly experimenting with integrating these two models and have found that they can complement each other well; however, their integration is not well understood due to scant literature. Thus, we include research findings, clinical case examples, and theoretical discussions to provide readers with a comprehensive overview of the state of affairs on integrating these two models. We hope that this paper will highlight prior and existing practices and inspire clinicians to consider the clinical potential of integrating EFT and EMDR to increase effective therapy for couples affected by trauma.
Reverse Osmosis for Surface Ship Desalination — An Overview

A nominal 9000 gal/day RO desalination plant, designed by the David Taylor Research Center and subsequently fabricated and installed aboard USS Fletcher (DD-992), successfully completed a 1750 hour technical evaluation (TECHEVAL) and a subsequent 2210 hour operational evaluation (OPEVAL) aboard that ship. During both test periods, the plant exceeded all of its operational thresholds producing potable water at an average rate of over 11,000 gal/day and never failing to make the rated capacity. The plant performance and the operational experience aboard USS Fletcher are discussed as well as an overview of the overall design process including lessons learned and a comparison of RO with competing distillation processes.
Disaster preparedness and humanitarian response in flood and landslide-affected communities in Eastern Uganda

Purpose – The purpose of this paper is to characterize and investigate relationships between disaster preparedness, impacts, and humanitarian response among Eastern Uganda populations affected by the 2010 landslides and floods. Design/methodology/approach – A stratified cluster survey of the disaster-affected populations was conducted five months after the onset of the disasters. Probability proportional to size sampling was used to sample 800 households, including 400 affected by floods in Butaleja District and 400 affected by landslides in Bududa District. Findings – Mortality was significantly higher in the landslide-affected populations as compared to flood-affected populations (deaths reported: 4.5 vs 1.6 percent, p<0.01) whereas injuries were more common in the flood-affected areas (injuries reported: 3.1 vs 1.1 percent, p<0.01). Livelihoods impacts were widespread and reported in more than 95 percent of households. Respondents indicated that the community and government were unprepared to respond i.
Sediment transport by waves and currents

Sediment transport is measured under the combined action of waves and currents. Measurements are made with currents in the direction of wave advance and with currents opposing the wave motion. Theoretical relationships are considered that predict the wave velocity field and the mass transport velocity for zero current and for steady currents.Following Bagnold's approach, a transport power relationship is developed to predict the sediment transport rate. Making assumptions for the mass transport velocity, the transport power is shown to agree with the measured sediment transport rates. It is particularly noted that the sediment transport direction is mainly determined by the direction of wave movement, even for adverse currents, until the waves start to break. Keywords: sediment transport, waves and currents, coastal engineering.
Analysis and compensation of high-frequency effects in integrated MOSFET-C continuous-time filters

The high-frequency operation of MOSFET-C continuoustime filters is investigated. The degradation of performance of integrators due to the effects of op-amp finite gain-bandwidth product and the distributed capacitance of the MOS transistor is characterized. A solution to minimize these effects is presented and evaluated for practical designs. Op-amp requirements for good linearity performance at high frequencies are derived. The analyses and techniques shown are verified with computer simulation and demonstrated with an experimental chip.
ELF propagation update

Extremely low frequency (ELF) electromagnetic waves have a remarkable ability to propagate with very little attenuation in the earth-ionosphere waveguide. The resulting fields are also able to propagate to moderately great depths in the ocean in spite of the higher conductivity of seawater. The principal drawback in the use of ELF for communication is the inherent inefficiency of the transmitting antenna. Indeed the waves do, however, penetrate to deeply submerged submarines when all other methods fail. This paper presents a tutorial overview on ELF propagation in the earth-ionosphere waveguide. Simple form approximate expressions (relating ELF propagation constants to realistic ionospheric conductivity profiles) are compared with experimentally derived results for both daytime and nighttime propagation conditions. Some anomalous ELF propagation results, along with their probable causes, are also discussed.
Quantitative Structural Analysis of Influenza Virus by Cryo-electron Tomography and Convolutional Neural Networks

Influenza viruses pose severe public health threats; they cause millions of infections and tens of thousands of deaths annually in the US. Influenza viruses are extensively pleomorphic, in both shape and size as well as organization of viral structural proteins. Analysis of influenza morphology and ultrastructure can help elucidate viral structure-function relationships as well as aid in therapeutics and vaccine development. While cryo-electron tomography (cryoET) can depict the 3D organization of pleomorphic influenza, the low signal-to-noise ratio inherent to cryoET and extensive viral heterogeneity have precluded detailed characterization of influenza viruses. In this report, we developed a cryoET processing pipeline leveraging convolutional neural networks (CNNs) to characterize the morphological architecture of the A/Puerto Rico/8/34 (H1N1) influenza strain. Our pipeline improved the throughput of cryoET analysis and accurately identified viral components within tomograms. Using this approach, we successfully characterized influenza viral morphology, glycoprotein density, and conduct subtomogram averaging of HA glycoproteins. Application of this processing pipeline can aid in the structural characterization of not only influenza viruses, but other pleomorphic viruses and infected cells. Graphical abstract