Miracle-dz/UniFAR-FTUs huggingface dataset:
Urban stormwater toxic pollutants: assessment, sources, and treatability

This paper summarizes an investigation to characterize and treat selected stormwater contaminants that are listed as toxic pollutants (termed toxicants in this paper) in the Clean Water Act, Section 307 (Arbuckle et al., 1991). The first project phase investigated typical toxicant concentrations in stormwater, the origins of these toxicants, and storm and land-use factors that influenced these toxicants, and storm and land-use factors that influenced these toxicant concentrations. Of the 87 stormwater source area samples analyzed, 9% were considered extremely toxic (using the Microtox® toxicity-screening procedure). Moderate toxicity was exhibited in 32% of the samples, whereas 59% of the samples had no evidence of toxicity. Only a small fraction of the organic toxicants analyzed were frequently detected organics investigated (present in 23% of the samples). Vehicle service and parking area runoff samples had many of the highest observed concentrations of organic toxicants. All metallic toxicants analyzed were commonly found in all samples analyzed. The second project phase investigated the control of stormwater toxicants using using a variety of bench-scale conventional treatment processes. Toxicity changes were monitored using the Microtox® bioassay test. The most beneficial treatment tests included settling for at least 24 hours (up to 90% reductions), screening and filtering through at least 40-μm screens (up to 70% reductions), and aeration and/or photodegradation for at least 24 hours (up to 80% reductions). Because many samples exhibited uneven toxicity reductions for the different treatment tests, a treatment train approach was selected for the current projet phase. This current phase includes testing of a prototype treatment device that would be useful for controlling runoff from critical source areas (e.g., automobile services facilities)
Using Fisher's Exact Test to Evaluate Association Measures for N-grams

To determine whether some often-used lexical association measures assign high scores to n-grams that chance could have produced as frequently as observed, we used an extension of Fisher's exact test to sequences longer than two words to analyse a corpus of four million words. The results, based on the precision-recall curve and a new index called chance-corrected average precision, show that, as expected, simple-ll is extremely effective. They also show, however, that MI3 is more efficient than the other hypothesis tests-based measures and even reaches a performance level almost equal to simple-ll for 3-grams. It is additionally observed that some measures are more efficient for 3-grams than for 2-grams, while others stagnate.
The polarizability of a confined atomic system: an application of the Dalgarno–Lewis method

In this paper we give an application of the Dalgarno–Lewis method, the latter not usually taught in quantum mechanics courses. This is very unfortunate since this method allows one to bypass the sum over states appearing in the usual perturbation theory. In this context, and as an example, we study the effect of an external field, both static and frequency-dependent, on a model atom at fixed distance from a substrate. This can happen, for instance, when some organic molecule binds from one side to the substrate and from the other side to an atom or any other polarizable system. We model the polarizable atom by a short range potential, a Dirac-δ, and find that the existence of a bound state depends on the ratio of the effective ‘nuclear charge’ to the distance of the atom from the substrate. Using an asymptotic analysis, previously developed in the context of a single δ-function potential in an infinite medium, we determine the ionization rate and the Stark shift of our system. Using Dalgarno–Lewis theory we find an exact expression for the static and dynamic polarizabilities of our system valid to all distances. We show that the polarizability is extremely sensitive to the distance from the substrate, creating the possibility of using this quantity as a nanometric ruler. Furthermore, the line shape of the dynamic polarizability is also extremely sensitive to the distance from the substrate, thus providing another route to measure nanometric distances. The didactic value of the δ-function potential is widely accepted in teaching activities due to its simplicity, while still keeping the essential ingredients of a given problem.
The Challenge to MOND from Ultra-faint Dwarf Galaxies

Modified Newtonian dynamics (MOND) at low acceleration has been astonishingly powerful at explaining the rotation curve of galaxies and the relation between the baryonic content of the galaxies and their observed circular velocity, known as the baryonic Tully–Fisher relationship. It is known that MOND fails at explaining the observed velocity dispersion of the ultra-faint dwarf galaxies (UFDs) with the justification that UFDs are more prone to tidal disruption in MOND compared to cold dark matter model. We show that (i) the ratio of tidal to internal acceleration in UFDs is extremely low, (ii) there is no correlation between the deviation of UFDs from MOND’s prediction as a function of tidal susceptibility, and (iii) recent constraints from Gaia proper motion analysis on the orbital parameters of the UFDs exacerbate the challenge to MOND. In particular, Gaia data indicate that Ursa Major I is experiencing a recent infall into the Milky Way’s halo, and its inconsistency with MOND at the 7σ level cannot be attributed to being an early infall satellite. Moreover, the new data from Gaia DR2 show Willman I to have the least eccentric orbit of all UFDs, and its deviation from MOND at the 4σ level cannot be attributed to a highly eccentric orbit as previously suggested. Finally, given that Tuc III is the only UFD observed to show tidal features, Reticulum II and Segue I are two other UFDs that potentially challenge MOND as they have comparable Galactocentric distances to Tuc III while showing no tidal features.
Beyond Spectroscopy. I. Metallicities, Distances, and Age Estimates for Over 20 Million Stars from SMSS DR2 and Gaia EDR3

Accurate determinations of stellar parameters and distances for large complete samples of stars are keys for conducting detailed studies of the formation and evolution of our Galaxy. Here we present stellar atmospheric parameters (effective temperature, luminosity classifications, and metallicity) estimates for some 24 million stars determined from the stellar colors of SMSS DR2 and Gaia EDR3, based on training data sets with available spectroscopic measurements from previous high/medium/low-resolution spectroscopic surveys. The number of stars with photometric-metallicity estimates is 4–5 times larger than that collected by the current largest spectroscopic survey to date—LAMOST—over the course of the past decade. External checks indicate that the precision of the photometric-metallicity estimates are quite high, comparable to or slightly better than that derived from spectroscopy, with typical values around 0.05–0.15 dex for both dwarf and giant stars with [Fe/H] > −2.01.0, 0.10–0.20 dex for giant stars with −2.0 < [Fe/H] ≤ −1.0, and 0.20–0.25 dex for giant stars with [Fe/H] ≤ −2.0, and include estimates for stars as metal-poor as [Fe/H] ∼ −3.5, substantially lower than previous photometric techniques. Photometric-metallicity estimates are obtained for an unprecedented number of metal-poor stars, including a total of over three million metal-poor (MP; [Fe/H] ≤ −1.0) stars, over half a million very metal-poor (VMP; [Fe/H] ≤ −2.0) stars, and over 25,000 extremely metal-poor (EMP; [Fe/H] ≤ −3.0) stars. Moreover, distances are determined for over 20 million stars in our sample. For the over 18 million sample stars with accurate Gaia parallaxes, stellar ages are estimated by comparing with theoretical isochrones. Astrometric information is provided for the stars in our catalog, along with radial velocities for ∼10% of our sample stars, taken from completed/ongoing large-scale spectroscopic surveys.
Turbulent axion-photon conversions in the Milky Way

Pierluca Carenza, 2, ∗ Carmelo Evoli, 4, † Maurizio Giannotti, ‡ Alessandro Mirizzi, 2, § and Daniele Montanino 7, ¶ Dipartimento Interateneo di Fisica “Michelangelo Merlin”, Via Amendola 173, 70126 Bari, Italy. Istituto Nazionale di Fisica Nucleare Sezione di Bari, Via Orabona 4, 70126 Bari, Italy. Gran Sasso Science Institute (GSSI), Viale Francesco Crispi 7, 67100 L’Aquila, Italy INFN-Laboratori Nazionali del Gran Sasso (LNGS), via G. Acitelli 22, 67100 Assergi (AQ), Italy Physical Sciences, Barry University, 11300 NE 2nd Ave., Miami Shores, FL 33161, USA. Dipartimento di Matematica e Fisica “Ennio De Giorgi”, Università del Salento, Via Arnesano, 73100 Lecce, Italy Istituto Nazionale di Fisica Nucleare Sezione di Lecce, Via Arnesano, 73100 Lecce, Italy.
Exponential Convergence Towards Consensus for Non-Symmetric Linear First-Order Systems in Finite and Infinite Dimensions

We consider finite and infinite-dimensional first-order consensus systems with timeconstant interaction coefficients. For symmetric coefficients, convergence to consensus is classically established by proving, for instance, that the usual variance is an exponentially decreasing Lyapunov function. We investigate here the convergence to consensus in the non-symmetric case: we identify a positive weight which allows to define a weighted mean corresponding to the consensus, and obtain exponential convergence towards consensus. Moreover, we compute the sharp exponential decay rate.
Spectrally peaked proton beams shock accelerated from an optically shaped overdense gas jet by a near-infrared laser

We report on the generation of impurity-free proton beams from an overdense gas jet driven by a near-infrared laser ($\lambda_L=1.053$ $\mathrm{\mu} m$). The gas profile was shaped prior to the interaction using a controlled prepulse. Without this optical shaping, a 30$\pm$4 nCsr$^{-1}$ thermal spectrum was detected transversely to the laser propagation direction with a high energy 8.27$\pm$7 MeV, narrow energy spread (6$\pm$2 %) bunch containing 45$\pm$7 pCsr$^{-1}$. In contrast, with optical shaping the radial component was not detected and instead forward going protons were detected with energy 1.32$\pm$2 MeV, 12.9$\pm$3 % energy spread, and charge 400$\pm$30 pCsr$^{-1}$. Both the forward going and radial narrow energy spread features are indicative of collisionless shock acceleration of the protons.
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements

Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various fields in science and engineering. A fundamental task is to faithfully recover the tensor from highly incomplete measurements in a statistically and computationally efficient manner. Harnessing the low-rank structure of tensors in the Tucker decomposition, this paper develops a scaled gradient descent (ScaledGD) algorithm to directly recover the tensor factors with tailored spectral initializations, and shows that it provably converges at a linear rate independent of the condition number of the ground truth tensor for two canonical problems -- tensor completion and tensor regression -- as soon as the sample size is above the order of $n^{3/2}$ ignoring other parameter dependencies, where $n$ is the dimension of the tensor. This leads to an extremely scalable approach to low-rank tensor estimation compared with prior art, which suffers from at least one of the following drawbacks: extreme sensitivity to ill-conditioning, high per-iteration costs in terms of memory and computation, or poor sample complexity guarantees. To the best of our knowledge, ScaledGD is the first algorithm that achieves near-optimal statistical and computational complexities simultaneously for low-rank tensor completion with the Tucker decomposition. Our algorithm highlights the power of appropriate preconditioning in accelerating nonconvex statistical estimation, where the iteration-varying preconditioners promote desirable invariance properties of the trajectory with respect to the underlying symmetry in low-rank tensor factorization.
Accurate outdoor ground truth based on total stations

In robotics, accurate ground-truth position fostered the development of mapping and localization algorithms through the creation of cornerstone datasets. In outdoor environments and over long distances, total stations are the most accurate and precise measurement instruments for this purpose. Most total station-based systems in the literature are limited to three Degrees Of Freedoms (DOFs), due to the use of a single-prism tracking approach. In this paper, we present preliminary work on measuring a full pose of a vehicle, bringing the referencing system to six DOFs. Three total stations are used to track in real time three prisms attached to a target platform. We describe the structure of the referencing system and the protocol for acquiring the ground truth with this system. We evaluated its precision in a variety of different outdoor environments, ranging from open-sky to forest trails, and compare this system with another popular source of reference position, the Real Time Kinematics (RTK) positioning solution. Results show that our approach is the most precise, reaching an average positional error of 10 mm and 0.6 deg. This difference in performance was particularly stark in environments where Global Navigation Satellite System (GNSS) signals can be weaker due to overreaching vegetation.
Fast Convex Quadratic Optimization Solvers with Adaptive Sketching-based Preconditioners

We consider least-squares problems with quadratic regularization and propose novel sketching-based iterative methods with an adaptive sketch size. The sketch size can be as small as the effective dimension of the data matrix to guarantee linear convergence. However, a major difficulty in choosing the sketch size in terms of the effective dimension lies in the fact that the latter is usually unknown in practice. Current sketching-based solvers for regularized least-squares fall short on addressing this issue. Our main contribution is to propose adaptive versions of standard sketching-based iterative solvers, namely, the iterative Hessian sketch and the preconditioned conjugate gradient method, that do not require a priori estimation of the effective dimension. We propose an adaptive mechanism to control the sketch size according to the progress made in each step of the iterative solver. If enough progress is not made, the sketch size increases to improve the convergence rate. We prove that the adaptive sketch size scales at most in terms of the effective dimension, and that our adaptive methods are guaranteed to converge linearly. Consequently, our adaptive methods improve the state-of-the-art complexity for solving dense, ill-conditioned least-squares problems. Importantly, we illustrate numerically on several synthetic and real datasets that our method is extremely efficient and is often significantly faster than standard least-squares solvers such as a direct factorization based solver, the conjugate gradient method and its preconditioned variants.
Chemomechanical Simulation of Microtubule Dynamics With Explicit Lateral Bond Dynamics

We introduce and parameterize a chemomechanical model of microtubule dynamics on the dimer level, which is based on the allosteric tubulin model and includes attachment, detachment and hydrolysis of tubulin dimers as well as stretching of lateral bonds, bending at longitudinal junctions, and the possibility of lateral bond rupture and formation. The model is computationally efficient such that we reach sufficiently long simulation times to observe repeated catastrophe and rescue events at realistic tubulin concentrations and hydrolysis rates, which allows us to deduce catastrophe and rescue rates. The chemomechanical model also allows us to gain insight into microscopic features of the GTP-tubulin cap structure and microscopic structural features triggering microtubule catastrophes and rescues. Dilution simulations show qualitative agreement with experiments. We also explore the consequences of a possible feedback of mechanical forces onto the hydrolysis process and the GTP-tubulin cap structure.
On the Use of PN Ranging With High-Rate Spectrally-Efficient Modulations in Satellite Payload Telemetry Links

Pseudonoise (PN) ranging is a ranging technique that has been recently introduced in near-Earth space research missions. For these, at the state-of-the-art, the PN ranging signal is combined with a high-rate telemetry stream binary modulated in phase. The coupling of the two, together with the 10 MHz bandwidth constraint imposed for this class of missions, translates into a data rate bound of approximately 10 Mb/s. The purpose of this article is to prove the feasibility of overcoming the current data rate limitation by defining a communication architecture that foresees the coupling of the PN ranging signal with a high-order modulated telemetry stream. To achieve the goal, we study the feasibility of pairing the PN ranging with filtered high-order modulations that are standardized for satellite payload telemetry links and investigate the simultaneous demodulation of the telemetry stream while tracking the ranging sequence. Accordingly, we design a receiver scheme capable of performing a closed-loop parallel cancellation of the ranging and the telemetry signal reciprocally. From our analysis, we find that the nonconstant envelope property, characterizing the considered modulation set, causes an additional jitter on the PN ranging timing estimation that, on the other hand, can be controlled and reduced by properly sizing the receiver loop bandwidth without limiting the timing synchronization dynamic. Our study proves the use of filtered high-order modulations combined with PN ranging to outperform the state-of-the-art in terms of spectral efficiency and achievable data rate while having comparable ranging performance.
Global Portraits of Nonminimal Teleparallel Inflation

We construct global phase portraits of inflationary dynamics in teleparallel gravity models with a scalar field nonminimally coupled to torsion scalar. The adopted set of variables can clearly distinguish between different asymptotic states as fixed points, including the kinetic and inflationary regimes. The key role in the description of inflation is played by the heteroclinic orbits that run from the asymptotic saddle points to the late time attractor point and are approximated by nonminimal slow roll conditions. To seek the asymptotic fixed points, we outline a heuristic method in terms of the “effective potential” and “effective mass”, which can be applied for any nonminimally coupled theories. As particular examples, we study positive quadratic nonminimal couplings with quadratic and quartic potentials and note how the portraits differ qualitatively from the known scalar-curvature counterparts. For quadratic models, inflation can only occur at small nonminimal coupling to torsion, as for larger coupling, the asymptotic de Sitter saddle point disappears from the physical phase space. Teleparallel models with quartic potentials are not viable for inflation at all, since for small nonminimal coupling, the asymptotic saddle point exhibits weaker than exponential expansion, and for larger coupling, it also disappears.
Emerging Properties in Self-Supervised Vision Transformers

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Second, these features are also excellent k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study also underlines the importance of momentum encoder [26], multi-crop training [9], and the use of small patches with ViTs. We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base.
Renormalization group in six-derivative quantum gravity

The exact one-loop beta functions for the four-derivative terms (Weyl tensor squared, Ricci scalar squared, and the Gauss-Bonnet) are derived for the minimal six-derivative quantum gravity (QG) theory in four spacetime dimensions. The calculation is performed by means of the Barvinsky and Vilkovisky generalized Schwinger-DeWitt technique. With this result we gain, for the first time, the full set of the relevant beta functions in a super-renormalizable model of QG. The complete set of renormalization group (RG) equations, including also these for the Newton and the cosmological constant, is solved explicitly in the general case and for the six-derivative Lee-Wick (LW) quantum gravity proposed in a previous paper by two of the authors. In the ultraviolet regime, the minimal theory is shown to be asymptotically free and describes free gravitons in Minkowski or (anti-) de Sitter ((A)dS) backgrounds, depending on the initial conditions for the RG equations. The ghost-like states appear in complex conjugate pairs at any energy scale consistently with the LW prescription. However, owing to the running, these ghosts may become tachyons. We argue that an extension of the theory that involves operators cubic in Riemann tensor may change the beta functions and hence be capable of overcoming this problem.
Leveraging online shopping behaviors as a proxy for personal lifestyle choices: New insights into chronic disease prevention literacy

Objective Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to improve chronic disease prevention literacy, targeted for times when e-commerce user experience has been assimilated into most people's everyday lives. Methods Longitudinal query logs and purchase records from 15 million online shoppers were accessed, constructing a broad spectrum of lifestyle features covering various product categories and buyer personas. Using the lifestyle-related information preceding online shoppers’ first purchases of specific prescription drugs, we could determine associations between their past lifestyle choices and whether they suffered from a particular chronic disease. Results Novel lifestyle risk factors were discovered in two exemplars—depression and type 2 diabetes, most of which showed reasonable consistency with existing healthcare knowledge. Further, such empirical findings could be adopted to locate online shoppers at higher risk of these chronic diseases with decent accuracy [i.e. (area under the receiver operating characteristic curve) AUC=0.68 for depression and AUC=0.70 for type 2 diabetes], closely matching the performance of screening surveys benchmarked against medical diagnosis. Conclusions Mining online shopping behaviors can point medical experts to a series of lifestyle issues associated with chronic diseases that are less explored to date. Hopefully, unobtrusive chronic disease surveillance via e-commerce sites can grant consenting individuals a privilege to be connected more readily with the medical profession and sophistication.