Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Trends in Adolescent Substance Use in Jamaica

Objective: National secondary school-based drug surveys facilitate the identification and understanding of patterns of substance use among adolescents, associated risk and protective factors which exist and inform prevention and treatment interventions and policies which are appropriate for resource constrained settings. This paper analyses current trends as well as reviews trends from the last National School Survey conducted by the National Council on Drug Abuse in 2006. Method: The study utilized a survey design that included a representative sample of secondary school students. Data were collected from 3365 students from 38 schools across the island. The study made specific reference to the prevalence of alcohol, tobacco cigarette, marijuana and inhalant use among secondary school students. Results: The most widely used substances among the sample were alcohol, tobacco cigarettes, marijuana and solvents/inhalants. A large number of students (64%) reported a lifetime prevalence of alcohol use. Alcohol use was also the highest for one-year (44%) and one-month (33.6%) use. While lifetime use of tobacco cigarette was higher than marijuana, one-year and one-month use of marijuana was higher than that of tobacco cigarette. There has been a slight increase in the age of initiation for alcohol, tobacco cigarette and marijuana use. There has also been a significant decrease (approximately 50%) in lifetime, past year and past month prevalence of inhalant use since the 2006 secondary school survey. Conclusions: Alcohol continues to be the substance most widely used by Jamaican adolescents, followed by tobacco, marijuana and inhalants. Though the average age of first use has slightly increased for all substances, prevalence remains a concern. As such, innovative school-based interventions are required to assist in reducing substance use among Jamaican adolescents.
Galactic Archaeology at High Redshift: Inferring the Nature of GRB Host Galaxies from Abundances

We identify the nature of high-redshift long gamma-ray burst (LGRB) host galaxies by comparing the observed abundance ratios in the interstellar medium with detailed chemical evolution models accounting for the presence of dust. We compare abundance data from LGRB afterglow spectra to abundance patterns as predicted by our models for different galaxy types. We analyze [X/Fe] abundance ratios (where X is C, N, O, Mg, Si, S, Ni, Zn) as functions of [Fe/H]. Different galaxies (irregulars, spirals, spheroids) are, in fact, characterized by different star formation histories, which produce different [X/Fe] versus [Fe/H] relations (“time-delay model”). This allows us to identify the star formation history of the host galaxies and to infer their age (i.e., the time elapsed from the beginning of star formation) at the time of the GRB events. Unlike previous works, we use newer models in which we adopt updated stellar yields and prescriptions for dust production, accretion, and destruction. We consider a sample of seven LGRB host galaxies. Our results suggest that two of them (GRB 050820, GRB 120815A) are star-forming spheroids, two (GRB 081008, GRB 161023A) are spirals, and three (GRB 090926A, GRB 050730, GRB 120327A) are irregulars. The inferred ages of the considered host galaxies span from 10 Myr to slightly more than 1 Gyr.
Investigating the effectiveness of web adblockers

We investigate adblocking filters and the extent to which websites and advertisers react when their content is impacted by these filters. We collected data daily from the Alexa Top-5000 web sites for 120 days, and from specific sites that newly appeared in filter lists for 140 days. By evaluating how long a filter rule triggers on a website, we can gauge how long it remains effective. We matched websites with both a regular adblocking filter list (EasyList) and with a specialized filter list that targets anti-adblocking logic (Nano Defender). From our data, we observe that the effectiveness of the EasyList adblocking filter decays a modest 0.13\% per day, and after around 80 days seems to stabilize. We found no evidence for any significant decay in effectiveness of the more specialized, but less widely used, anti-adblocking removal filters.
A Curious Case of Circular Polarization in the 25 GHz Methanol Maser Line toward OMC-1

We report the detection of a circular polarization signature in the Stokes V profile of a 25 GHz Class I CH3OH maser toward the high-mass star-forming region OMC-1. Such a feature usually constitutes a detection of the Zeeman effect. If due to a magnetic field in OMC-1, this would represent the first detection and discovery of the Zeeman effect in the 25 GHz Class I CH3OH maser. The feature in Stokes V is detected in two observations with different angular resolutions taken eight years apart with the Very Large Array; for our 2009 D-configuration observations, the fitted value for z is 152 ± 12 Hz, where z is the Zeeman splitting factor and is the line-of-sight magnetic field. For our 2017 C-configuration observations, the fitted value for z = 149 ± 19 Hz, likely for the same maser spot. These correspond to in the range 171–214 mG, depending on which hyperfine transition is responsible for the maser line. While these values are high, they are not implausible. If the magnetic field increases in proportion to the molecular hydrogen density in shocked regions, then our detected fields predict values for the pre-shock magnetic field that are in agreement with observations. With = 171–214 mG, the magnetic energy in the post-shocked regions where these 25 GHz Class I CH3OH masers occur would dominate over the kinetic energy density and be at least of the order of the pressure in the shock, implying that the magnetic field would exert significant influence over the dynamics of these regions.
Towards Disentangled Representations for Human Retargeting by Multi-view Learning

We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting. Our goal is to map an input image to an identity-invariant latent representation that captures intrinsic factors such as expressions and poses. To this end, we present a novel multi-view learning approach that leverages various data sources such as images, keypoints, and poses. Our model consists of multiple id-conditioned VAEs for different views of the data. During training, we encourage the latent embeddings to be consistent across these views. Our observation is that auxiliary data like keypoints and poses contain critical, id-agnostic semantic information, and it is easier to train a disentangling CVAE on these simpler views to separate such semantics from other id-specific attributes. We show that training multi-view CVAEs and encourage latent-consistency guides the image encoding to preserve the semantics of expressions and poses, leading to improved disentangled representations and better human retargeting results.
Non-Hermitian topological end-mode lasing in polariton systems

We predict the existence of non-Hermitian topologically protected end states in a one-dimensional exciton-polariton condensate lattice. The topological transitions are driven by spatially modulated incoherent pump which imposes a four-site unit cell. We show that in particular limits of the model the number of end states is described by a Chern number and a topological invariant based on the eigenvalues of the Wilson loop. By continuity the end states exist beyond these limits because the end modes can only disappear or appear at transitions where the energy gap between the bulk bands and end states closes. We find that such transitions in an infinite open chain arise due to enforced exceptional points which can be predicted directly from the bulk Bloch wave functions. This allows us to establish a new type of bulk-boundary correspondence for non-Hermitian systems and to compute the phase diagram of an infinite open chain analytically. Finally, we demonstrate topological lasing of a single end-mode in a realistic model of a microcavity lattice.
Stellarators as a fast path to fusion energy

The speed with which fusion can be developed is important for the mitigation of the physical and political risks associated with the continual increase in atmospheric carbon dioxide. Remarkably, a stellarator demonstration reactor (DEMO) could be based more reliably on the empirical information available now than a tokamak DEMO on the empirical information that ITER is likely to provide over the next twenty years. Waiting imposes major risks on society and is unnecessary. Readiness is sufficiently revolutionary that many will assume that it can be dismissed without thought. But, those who are willing to read and to think will find the arguments simple and self-evident. The paper explains not only how stellarators are better positioned now than any other concept for a fast path to fusion but also areas in which further improvements can be quickly made to stellarators through computational design.
Game theoretic pricing policy in online media delivery platforms

Residential users get most of their preferred content (e.g., films, news) from their wireless access point, which is connected to content providers through one or several network providers. Nowadays, these access points have enough storage and wireless transmission capacities to distribute the content to neighboring access points if required and allowed by the user. Based on this concept, this paper considers a hybrid content distribution model in peer-to-peer content deliver networks. A user or peer in this network can get its content either directly from the content provider (CP), or from a neighboring user who has this content stored and is willing to share it. Two types of users are considered: premium users are the users getting always the content directly from the CP, and standard user are the users getting the content from other standard users, when possible, otherwise from the CP. Furthermore, standard user share their content to other users if required. This paper studies the pricing problem of the CP such that there are enough standard users to distribute the content (using less network resources and reducing significantly the delay) while maximizing the CP benefits.We address the problem from a game theoretic approach and evaluate when the Nash Equilibrium can be attained. The obtained case have been applied to a real life study using a BBC iPlayer dataset and an analysis on the CP benefits dependence on different parameters. Finally we extend this pricing model when considering also the reduction of load on its content custodian (server) and discuss how the pricing policy is affected.
Structural analysis of disc super star clusters of M82: size and profile shape at intermediate ages

We present the structural parameters of 99 super star clusters (SSCs) in the disc of M82. Moffat-EFF, King, and Wilson models were fitted using a χ2-minimization method to background-subtracted surface brightness profiles in the F435W (B), F555W (V), and F814W (I) bands of the Advanced Camera for Surveys (ACS) of the Hubble Space Telescope. The majority of the SSC profiles are best fitted by the Moffat-EFF profile. The scale parameter rd and the shape parameter γ in the three filters are identical within the measurement errors. The analysed sample is big enough to allow the characterization of the distributions of core radii Rc and γ. The obtained distribution of Rc follows a lognormal form, with centre and $\sigma \mathrm{ log}\big {(}\frac{R_{\rm c}}{pc}\big {)}$ being 1.73 pc and 0.25, respectively. The γ distribution is also lognormal with centre and σlog(γ) being 2.88 and 0.08, respectively. M82 is well known for the absence of current star formation in its disc, with all disc SSCs older than 50 Myr and hardly any cluster older than ∼300 Myr. The derived distributions compare very well with the distributions for intermediate-age clusters in the Large Magellanic Cloud (LMC), which is also a low-mass late-type galaxy similar to M82. On the other hand, the distributions of Rc in both these galaxies are shifted towards larger values as compared to SSCs of similar age in the giant spiral galaxy M83. M82 and LMC also span a narrower range of γ values as compared to that in M83.
Document Sub-structure in Neural Machine Translation

Current approaches to machine translation (MT) either translate sentences in isolation, disregarding the context they appear in, or model context at the level of the full document, without a notion of any internal structure the document may have. In this work we consider the fact that documents are rarely homogeneous blocks of text, but rather consist of parts covering different topics. Some documents, such as biographies and encyclopedia entries, have highly predictable, regular structures in which sections are characterised by different topics. We draw inspiration from Louis and Webber (2014) who use this information to improve statistical MT and transfer their proposal into the framework of neural MT. We compare two different methods of including information about the topic of the section within which each sentence is found: one using side constraints and the other using a cache-based model. We create and release the data on which we run our experiments - parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies, which we extract automatically, preserving the boundaries of sections within the articles.
Real-Time Texturing for 6D Object Instance Detection from RGB Images

For objected detection, the availability of color cues strongly influences detection rates and is even a prerequisite for many methods. However, when training on synthetic CAD data, this information is not available. We therefore present a method for generating a texture-map from image sequences in real-time. The method relies on 6 degree-of-freedom poses and a 3D-model being available. In contrast to previous works this allows interleaving detection and texturing for upgrading the detector on-the-fly. Our evaluation shows that the acquired texture-map significantly improves detection rates using the LINEMOD detector on RGB images only. Additionally, we use the texture-map to differentiate instances of the same object by surface color.
Music-oriented Dance Video Synthesis with Pose Perceptual Loss

We present a learning-based approach with pose perceptual loss for automatic music video generation. Our method can produce a realistic dance video that conforms to the beats and rhymes of almost any given music. To achieve this, we firstly generate a human skeleton sequence from music and then apply the learned pose-to-appearance mapping to generate the final video. In the stage of generating skeleton sequences, we utilize two discriminators to capture different aspects of the sequence and propose a novel pose perceptual loss to produce natural dances. Besides, we also provide a new cross-modal evaluation to evaluate the dance quality, which is able to estimate the similarity between two modalities of music and dance. Finally, a user study is conducted to demonstrate that dance video synthesized by the presented approach produces surprisingly realistic results. The results are shown in the supplementary video at this https URL
Measurement Series for Validation of Critical Point Model in 600 Scanning Micro Mirrors Affected by Three-Wave Down-Conversion

Sensors and actuators based on resonant micro-electro-mechanical systems (MEMS), such as scanning micro mirrors, are well-established in automotive and consumer products. As the areas of application broaden towards highly automated driving and augmented reality, the performance requirements for the MEMS are also increasing. In particular, nonlinear system behavior is often found to cause unexpected performance issues. Thus, accurate system models which account for nonlinear sensor dynamics can not only increase process yield, but more importantly, lead to a comprehensive understanding of the underlying physics and consequently to improved MEMS design. In a recent work (1, see comments below), we have studied the possibility of a rather drastic device failure induced by nonlinearities on the example of a resonant scanning MEMS micro mirror. On the level of a few selected chips, we have carefully measured the complex nonlinear system behavior and modeled it by a mode-coupling phenomenon known as spontaneous parametric down-conversion (SPDC). The most intriguing feature of SPDC is the sudden change from a rather linear to a highly nonlinear system behavior at the critical oscillation amplitude of the mirror. However, the threshold only lies within the range of the mirrors operational amplitude, if certain frequency resonance conditions regarding the modes of the mechanical structure are met. As a direct consequence, the critical amplitude strongly depends on the frequency spectrum of the MEMS design which in turn is largely influenced by fabrication imperfections. In this work, we validate the dependence of the critical amplitude on the resonance condition by measuring it for over 600 micro mirrors on wafer-level. Our work does not only validate the theory of SPDC with measurements on such a large scale, but also demonstrates modeling strategies which are essential for MEMS product design.
Prior Knowledge Neural Network for Automatic Feature Construction in Financial Time Series

In automatic financial feature construction task, the state-of-the-art technic leverages reverse polish expression to represent the features, then use genetic programming (GP) to conduct its evolution process. In this paper, we propose a new framework based on neural network, alpha discovery neural network (ADNN). In this work, we made several contributions. Firstly, in this task, we make full use of neural network overwhelming advantage in feature extraction to construct highly informative features. Secondly, we use domain knowledge to design the object function, batch size, and sampling rules. Thirdly, we use pre-training to replace the GP evolution process. According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process. Experiment shows that ADNN can remarkably produce more diversified and higher informative features than GP. Besides, ADNN can serve as a data augmentation algorithm. It further improves the the performance of financial features constructed by GP.
Evolutionary dynamics in populations with fluctuating size

Temporal environmental variations are ubiquitous in nature, yet most of the theoretical works in population genetics and evolution assume fixed environment. Here we analyze the effect of variations in carrying capacity on the fate of a mutant type. We consider a two-state Moran model, where selection intensity at equilibrium may differ (in amplitude and in sign) from selection during periods of sharp growth and sharp decline. Using Kimura's diffusion approximation we present simple formulae for effective population size and effective selection, and use it to calculate the chance of ultimate fixation, the time to fixation and the time to absorption (either fixation or loss). Our analysis shows perfect agreement with numerical solutions for neutral, beneficial and deleterious mutant. The contributions of different processes to the mean and the variance of abundance variations are additive and commutative. As a result, when selection intensity $s$ is weak such that ${\cal O}(s^2)$ terms are negligible, periodic or stochastic environmental variations yield identical results.
Determining the primordial helium abundance and UV background using fluorescent emission in star-free dark matter haloes

Observational measures of the primordial helium mass fraction, YP, are of interest for cosmology and fundamental particle physics. Current measures obtained from ${\text{H}\, \small {II}}$ regions agree with the Standard Model prediction to approximately 1 per cent precision, although these determinations may be affected by systematic uncertainties. This possibility can only be tested by independently measuring the helium abundance in new ways. Here, we propose a novel method to obtain a measurement of YP using hydrogen and helium recombination line emission from REionization-Limited HI Clouds (RELHICs): pristine, gas-rich but star-free low-mass dark matter haloes whose existence is predicted by hydrodynamical simulations. Although expected to be uncommon and intrinsically faint in emission, the primordial composition and simple physical properties of these objects make them an ideal laboratory to determine YP. We present radiative transfer simulations to demonstrate the effectiveness of this approach, finding that a comparison of the emission in H and He lines, either via their volumetric emissivities, or integrated properties such as the surface brightness and total flux, may be used to infer YP. Furthermore, we show that RELHICs can be used to provide an entirely novel constraint on the spectral slope of the ultraviolet background, and discuss the possibility of measuring this slope and the primordial helium abundance simultaneously.
Solution of option pricing equations using orthogonal polynomial expansion

We study both analytic and numerical solutions of option pricing equations using systems of orthogonal polynomials. Using a Galerkin-based method, we solve the parabolic partial differential equation for the Black-Scholes model using Hermite polynomials and for the Heston model using Hermite and Laguerre polynomials. We compare the obtained solutions to existing semi-closed pricing formulas. Special attention is paid to the solution of the Heston model at the boundary with vanishing volatility.
Quantifying the fragility of unprotected quadratic band crossing points

We examine a basic lattice model of interacting fermions that exhibits quadratic band crossing points (QBCPs) in the non-interacting limit. In particular, we consider spinless fermions on the honeycomb lattice with nearest neighbor hopping $t$ and third-nearest neighbor hopping $t''$, which exhibits fine-tuned QBCPs at the corners of the Brillouin zone for ${t'' = t/2}$. In this situation, the density of states remains finite at the Fermi level of the half-filled band and repulsive nearest-neighbor interactions $V$ lead to a charge-density-wave (CDW) instability at infinitesimally small $V$ in the random-phase approximation or mean-field theory. We examine the fragility of the QBCPs against dispersion renormalizations in the ${t\mbox{-}t''\mbox{-}V}$ model using perturbation theory, and find that the $t''$-value needed for the QBCPs increases with $V$ due to the hopping renormalization. However, the instability towards CDW formation always requires a nonzero threshold interaction strength, i.e., one cannot fine-tune $t''$ to recover the QBCPs in the interacting system. These perturbative arguments are supported by quantum Monte Carlo simulations for which we carefully compare the corresponding threshold scales at and beyond the QBCP fine-tuning point. From this analysis, we thus gain a quantitative microscopic understanding of the fragility of the QBCPs in this basic interacting fermion system.