Miracle-dz/UniFAR-FTUs huggingface dataset:
Geological Survey of Canada RADIOGENIC AGE AND ISOTOPIC STUDIES : REPORT 13 Current Research 2000-F 3 Microstructure of Neoarchean zircon from the Acasta gneiss complex , Northwest Territories

The microstructure of zircon from a late Archean granite dyke from the Acasta gneiss complex was investigated using a number of microprobe techniques. Zircons from the study sample are composed of low-Ca, unaltered and high-Ca, altered domains. Transmission electron microscopy indicates that the unaltered areas have a distorted crystalline microstructure. The detailed microstructure of altered zircon is heterogeneous, composed of a mixture of low-Ca, crystalline domains and amorphous, high-Ca domains. Calculations show that radiation damage accumulated from the decay of U and Th over the entire life of the sample would have rendered the sample metamict. We conclude that the observed crystalline structure is the result of recrystallization late in the geological history of the sample. Sensitive high-resolution ion microprobe U-Pb ages from the low Ca, crystalline zircon are concordant, suggesting that Pb loss did not accompany the recrystallization process. Résumé : Au moyen de techniques de microsondage, on analysé la microstructure du zircon contenu dans un dyke de granite de l’Archéen tardif provenant du complexe gneissique d’Acasta. Les zircons de l’échantillon à l’étude sont composés de domaines inaltérés à faible teneur en Ca et de domaines altérés à haute teneur en Ca. La microscopie électronique à transmission révèle que les zones inaltérées ont une microstructure cristalline déformée. La microstructure détaillé du zircon altéré est hétérogène et comporte un mélange de domaines cristallins à faible teneur en Ca et de domaines amorphes à haute teneur en Ca. D’après les calculs, le dommage causé par le rayonnement dû à la décomposition de U et de Th pendant toute la durée de vie de l’échantillon aurait transformé l’échantillon en métamicte. Les chercheurs concluent que la structure cristalline observée est le résultat d’une recristallisation tard dans l’histoire géologique de l’échantillon. Les datations U-Pb au moyen de la microsonde ionique à haute résolution et à haut niveau de sensibilité d’un zircon cristallin à faible teneur en Ca concordent, laissant supposer qu’il n’y a pas eu de perte de Pb durant le processus de recristallisation.
Competition of spinon Fermi surface and heavy Fermi liquid states from the periodic Anderson to the Hubbard model

We study a model of correlated electrons coupled by tunnelling to a layer of itinerant metallic electrons, which allows to interpolate from a frustrated limit favorable to spin liquid states to a Kondo-lattice limit favorable to interlayer coherent heavy metallic states. We study the competition of the spinon fermi surface state and the interlayer coherent heavy Kondo metal that appears with increasing tunnelling. Employing a slave rotor mean-field approach, we obtain a phase diagram and describe two regimes where the spin liquid state is destroyed by weak interlayer tunnelling, (i) the Kondo limit in which the correlated electrons can be viewed as localized spin moments and (ii) near the Mott metal-insulator-transition where the spinon fermi surface transitions continuously into a Fermi liquid. We study the shape of LDOS spectra of the putative spin liquid layer in the heavy fermi liquid phase and describe the temperature dependence of its width arising from quasiparticle interactions and disorder effects throughout this phase diagram, in an effort to understand recent STM experiments of the candidate spin liquid 1T-TaSe$_2$ residing on metallic 1H-TaSe$_2$. Comparison of the shape and temperature dependence of the theoretical and experimental LDOS suggest that this system is either close to the localized Kondo limit, or in an intermediate coupling regime where the Kondo coupling and the Heisenberg exchange interaction are comparable.
Differentiable Weighted Finite-State Transducers

We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time. Through the separation of graphs from operations on graphs, this framework enables the exploration of new structured loss functions which in turn eases the encoding of prior knowledge into learning algorithms. We show how the framework can combine pruning and back-off in transition models with various sequence-level loss functions. We also show how to learn over the latent decomposition of phrases into word pieces. Finally, to demonstrate that WFSTs can be used in the interior of a deep neural network, we propose a convolutional WFST layer which maps lower-level representations to higher-level representations and can be used as a drop-in replacement for a traditional convolution. We validate these algorithms with experiments in handwriting recognition and speech recognition.
WISE J044232.92+322734.9: A star-forming galaxy at redshift 1.1 seen through a Galactic dust clump?

Context. Physically unassociated background or foreground objects seen towards submillimetre sources are potential contaminants of both the studies of young stellar objects embedded in Galactic dust clumps and multiwavelength counterparts of submillimetre galaxies (SMGs). Aims. We aim to search for and characterise the properties of a potential extragalactic object seen in projection towards a Galactic dust clump. Methods. We employed the near-infrared (3.4 μm and 4.6 μm) and mid-infrared (12 μm and 22 μm) data from the Wide-field Infrared Survey Explorer (WISE) and the submillimetre data from the Planck satellite. Results. We uncovered a source, namely the WISE source J044232.92+322734.9 (hereafter J044232.92), which is detected in the W1–W3 bands of WISE, but undetected at 22 μm (W4), and whose WISE infrared (IR) colours suggest that it is a star-forming galaxy (SFG). This source is seen in projection towards the Planck-detected dust clump PGCC G169.20-8.96, which likely belongs to the Taurus-Auriga cloud complex, at a distance of 140 pc. We used the MAGPHYS+photo-z spectral energy distribution (SED) code to derive the photometric redshift and physical properties of J044232.92. The redshift was derived to be zphot = 1.132−0.165+0.280, while, for example, the stellar mass, IR (8–1000 μm) luminosity, and star formation rate were derived to be M⋆ = 4.6−2.5+4.7 × 1011 M⊙, LIR = 2.8−1.5+5.7 × 1012 L⊙, and SFR = 191−146+580 M⊙ yr−1 (or 281−155+569 M⊙ yr−1 when estimated from the IR luminosity). The derived value of LIR suggests that J044232.92 could be an ultraluminous IR galaxy, and we found that it is consistent with a main sequence SFG at a redshift of 1.132. Conclusions. The estimated physical properties of J044232.92 are comparable to those of SMGs, except that the derived stellar mass of J044232.92 appears somewhat higher (by a factor of 4–5) than the average stellar masses of SMGs. However, the stellar mass difference could just reflect the poorly sampled SED in the ultraviolet, optical, and near-IR regimes. Indeed, the SED of J044232.92 could not be well constrained using the currently available data (WISE only), and hence the derived redshift of the source and its physical properties should be taken as preliminary estimates. Further observations, in particular high-resolution (sub-)millimetre and radio continuum imaging, are needed to better constrain the redshift and physical properties of J044232.92 and to see if the source really is a galaxy seen through a Galactic dust clump, in particular an SMG population member at z ∼ 1.1.
Cell Complex Neural Networks

Cell complexes are topological spaces constructed from simple blocks called cells. They generalize graphs, simplicial complexes, and polyhedral complexes that form important domains for practical applications. We propose a general, combinatorial, and unifying construction for performing neural network-type computations on cell complexes. Furthermore, we introduce inter-cellular message passing schemes, message passing schemes on cell complexes that take the topology of the underlying space into account. In particular, our method generalizes many of the most popular types of graph neural networks.
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms

We investigate the discounting mismatch in actor-critic algorithm implementations from a representation learning perspective. Theoretically, actor-critic algorithms usually have discounting for both actor and critic, i.e., there is a $\gamma^t$ term in the actor update for the transition observed at time $t$ in a trajectory and the critic is a discounted value function. Practitioners, however, usually ignore the discounting ($\gamma^t$) for the actor while using a discounted critic. We investigate this mismatch in two scenarios. In the first scenario, we consider optimizing an undiscounted objective $(\gamma = 1)$ where $\gamma^t$ disappears naturally $(1^t = 1)$. We then propose to interpret the discounting in critic in terms of a bias-variance-representation trade-off and provide supporting empirical results. In the second scenario, we consider optimizing a discounted objective ($\gamma < 1$) and propose to interpret the omission of the discounting in the actor update from an auxiliary task perspective and provide supporting empirical results.
Self-generated ultraviolet radiation in molecular shock waves

Context. Shocks are ubiquitous in the interstellar and intergalactic media, where their chemical and radiative signatures reveal the physical conditions in which they arise. Detailed astrochemical models of shocks at all velocities are necessary to understand the physics of many environments including protostellar outflows, supernova remnants, and galactic outflows. Aims. We present an accurate treatment of the self-generated ultraviolet (UV) radiation in models of intermediate velocity (VS = 25–60 km s−1), stationary, weakly magnetised, J-type, molecular shocks. We show how these UV photons modify the structure and chemical properties of shocks and quantify how the initial mechanical energy is reprocessed into line emission. Methods. We develop an iterative scheme to calculate the self-consistent UV radiation field produced by molecular shocks. The shock solutions computed with the Paris–Durham shock code are post-processed using a multi-level accelerated Λ-iteration radiative transfer algorithm to compute Lyman α, Lyman β, and two-photon continuum emission. The subsequent impacts of these photons on the ionisation and dissociation of key atomic and molecular species as well as on the heating by the photoelectric effect are calculated by taking the wavelength dependent interaction cross-sections and the fluid velocity profile into account. This leads to an accurate description of the propagation of photons and the thermochemical properties of the gas in both the postshock region and in the material ahead of the shock called the radiative precursor. With this new treatment, we analyse a grid of shock models with velocities in the range VS = 25–60 km s−1, propagating in dense (nH ≥ 104 cm−3) and shielded gas. Results. Self-absorption traps Lyα photons in a small region in the shock, though a large fraction of this emission escapes by scattering into the line wings. We find a critical velocity VS ~ 30 km s−1 above which shocks generate Lyα emission with a photon flux exceeding the flux of the standard interstellar radiation field. The escaping photons generate a warm slab of gas (T ~ 100 K) ahead of the shock front as well as pre-ionising C and S. Intermediate velocity molecular shocks are traced by bright emission of many atomic fine structure (e.g. O and S) and metastable (e.g. O and C) lines, substantive molecular emission (e.g. H2, OH, and CO), enhanced column densities of several species including CH+ and HCO+, as well as a severe destruction of H2O. As much as 13–21% of the initial kinetic energy of the shock escapes in Lyα and Lyβ photons if the dust opacity in the radiative precursor allows it. Conclusions. A rich molecular emission is produced by interstellar shocks regardless of the input mechanical energy. Atomic and molecular lines reprocess the quasi totality of the kinetic energy, allowing for the connection of observable emission to the driving source for that emission.
Greedy Covariance Control for Stochastic Nonlinear Systems using Gaussian Process Prediction Models and the Unscented Transform

In this work, the problem of steering the first two moments of the uncertain state of an unknown discrete-time nonlinear stochastic system to a given terminal distribution in finite time is considered. Toward that goal, first, a non-parametric prediction model is learned from a set of available training data points using Gaussian process regression: a powerful machine learning tool for learning distributions over arbitrary nonlinear functions. Second, a tractable nonlinear covariance steering algorithm that utilizes the Gaussian process prediction model to compute a feedback policy that will drive the probability density function of the state of the system close to the goal density is formulated. In particular, a greedy covariance steering control policy is implemented that linearizes the Gaussian process prediction model around the latest mean and covariance predictions at each time step and solves the linear covariance steering control problem, which can be formulated as a tractable, finite-dimensional convex program. Then, only the first control law of the solution to the linear problem is applied. At each step, the information on the state statistics is updated by computing approximations of the predicted state mean and covariance of the resulting closed-loop nonlinear system step using the unscented transform and the learned Gaussian process prediction model. Numerical simulations illustrating the main ideas of this paper are also presented.
Secrecy Rate Maximization in Multi-IRS Millimeter Wave Networks

In this paper, the problem of physical layer security enhancement in a millimeter-wave (mmWave) network equipped with multiple Intelligent Reflecting Surfaces (IRSs) is investigated. In this network, the IRSs assist in signal transmission from the Base Station (BS) to desired users and at the same time in securing signals from an unauthorized eavesdropper. Our objective is to maximize the secrecy rate by jointly optimizing the active and passive beamformers at the base station and IRSs, respectively. The optimization problem is non-convex and hence, we solve it by decomposing it into two disjoint active and passive beamforming design sub-problems and then iteratively solving them by alternating and Semi-Definite Relaxation (SDR) techniques. Simulation results show the advantage of using multiple IRSs in secrecy rate enhancement of the mmWave networks. In additio12n, we show how the secrecy rate improves with the number of IRSs in the network and also with the number of reflecting elements at the IRS.
PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information

The ability to predict multiple possible future positions of the ego-vehicle given the surrounding context while also estimating their probabilities is key to safe autonomous driving. Most of the current state-of-the-art Deep Learning approaches are trained on trajectory data to achieve this task. However trajectory data captured by sensor systems is highly imbalanced, since by far most of the trajectories follow straight lines with an approximately constant velocity. This poses a huge challenge for the task of predicting future positions, which is inherently a regression problem. Current state-of-the-art approaches alleviate this problem only by major preprocessing of the training data, e.g. resampling, clustering into anchors etc. In this paper we propose an approach which reformulates the prediction problem as a classification task, allowing for powerful tools, e.g. focal loss, to combat the imbalance. To this end we design a generative probabilistic model consisting of a deep neural network with a Mixture of Gaussian head. A smart choice of the latent variable allows for the reformulation of the log-likelihood function as a combination of a classification problem and a much simplified regression problem. The output of our model is an estimate of the probability density function of future positions, hence allowing for prediction of multiple possible positions while also estimating their probabilities. The proposed approach can easily incorporate context information and does not require any preprocessing of the data.
Overcoming Data Sparsity in Group Recommendation

It has been an important task for recommender systems to suggest satisfying activities to a group of users in people’s daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. Conventional group recommendation methods applied a predefined strategy for preference aggregation. However, these static strategies are too simple to model the real and complex process of group decision-making, especially for occasional groups which are formed ad-hoc. Moreover, group members should have non-uniform influences or weights in a group, and the weight of a user can be varied in different groups. Therefore, an ideal group recommender system should be able to accurately learn not only users’ personal preferences but also the preference aggregation strategy from data. In this paper, we propose a novel end-to-end group recommender system named CAGR (short for “Centrality-Aware Group Recommender”), which takes Bipartite Graph Embedding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way. Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members. In addition, to overcome the sparsity issue of user-item interaction data, we leverage the user social networks to enhance user representation learning, obtaining centrality-aware user representations. To further alleviate the group data sparsity problem, we propose two model optimization approaches to seamlessly integrate the user representations learning process. We create three large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed CAGR by comparing it with state-of-the-art group recommender models.
Cascaded Channel Estimation for IRS-Assisted mmWave Multi-Antenna With Quantized Beamforming

In this letter, we optimize the channel estimator of the cascaded channel in an intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multi-antenna system. In this system, the receiver is equipped with a hybrid architecture adopting quantized beamforming. Different from traditional multiple-input multiple-output (MIMO) systems, the design of channel estimation is challenging since the IRS is usually a passive array with limited signal processing capability. We derive the optimized channel estimator in a closed form by reformulating the problem of cascaded channel estimation in this system, leveraging the typical mean-squared error (MSE) criterion. Considering the presence of possible channel sparsity in mmWave channels, we generalize the proposed method by exploiting the channel sparsity for further performance enhancement and computational complexity reduction. Simulation results verify that the proposed estimator significantly outperforms the existing ones.
Development of label-free detection systems targeting food-borne pathogens

Food-borne pathogens and food safety-related outbreaks have come to the forefront over recent years. Estimates on the annual cost of sicknesses, hospitalizations, and deaths run into the billions of dollars. There is a large body of research on the subject of detection of food-borne pathogens, however, the widely accepted current systems are limited by high reagent costs, lengthy time to completion, and expensive equipment. This work has been developed with the goal of focusing on minimizing the time and reagent cost of two of the most common types of rapid, food-borne pathogen detection systems, i.e. immunoassays and real-time polymerase chain reaction assays. The first aim was to develop the proof-of-concept methodology for a label-free immunoassay technique utilizing photonic crystal biosensors targeting bacteria, specifically E. coli O157:H7. In this project, it is shown that detergent-lysing of the cells and extraction of their membrane antigens allows for the detection of down to 1E7 CFU/mL. Optimization of the blocking scheme takes the assay one step further and allows for specific detection of E. coli O157:H7 over E. coli K12. The second aim was to develop a label-free method for determining changes in DNA concentration as it relates to food-borne pathogen-targeted polymerase chain reaction assays. For this goal, impedance spectroscopy studies were carried out to characterize the system’s capability in determining changes in concentration of purified DNA in DI water. To adequately measure the change in DNA concentration in a PCR solution, it was necessary to go through a purification and precipitation step to minimize the effects of primers, PCR reagents, and especially excess salts. It was shown that the purification and precipitation of the fully amplified PCR reaction showed a similar trend to the pure DNA in DI water characterization
Tracing Dark Matter Halos with Satellite Kinematics and the Central Stellar Velocity Dispersion of Galaxies

It has been suggested that the central stellar velocity dispersion of galaxies can trace dark matter halo mass directly. We test this hypothesis using a complete spectroscopic sample of isolated galaxies surrounded by faint satellite galaxies from the Sloan Digital Sky Survey Data Release 12. We apply a friends-of-friends algorithm with projected linking length and radial velocity linking length km s−1 to construct our sample. Our sample includes 2807 isolated galaxies with 3417 satellite galaxies at . We divide the sample into two groups based on the primary galaxy color: red and blue primary galaxies separated at . The central stellar velocity dispersions of the primary galaxies are proportional to the luminosities and stellar masses of the same galaxies. Stacking the sample based on the central velocity dispersion of the primary galaxies, we derive the velocity dispersions of their satellite galaxies, which trace the dark matter halo mass of the primary galaxies. The system velocity dispersion of the satellite galaxies shows a remarkably tight correlation with the central velocity dispersion of the primary galaxies for both red and blue samples. In particular, the slope of the relation is identical to 1 for red primary systems. This tight relation suggests that the central stellar velocity dispersion of galaxies is indeed an efficient and robust tracer for dark matter halo mass. We provide empirical relations between the central stellar velocity dispersion and the dark matter halo mass.