Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Consumer-Controlled and Traditional HIV Counseling and Testing: Implications for Screening and Outreach among Injection Drug Users

Recent advances in HIV antiretroviral therapy and the availability of prophylaxis for opportunistic infections, combined with the opportunity to prevent perinatal HIV infection, underscores the value of early diagnosis of HIV infection. HIV antibody home test kits offer individuals the opportunity to collect a blood sample, send it anonymously to a laboratory, and receive counseling and referral over the phone. Home HIV testing may reduce barriers to testing that have precluded individuals from learning their HIV serostatus, and if seropositive, from taking advantage of efficacious therapeutic and preventive regimens. This study employed a randomized-controlled prospective field trial design to determine if the availability of home testing increased HIV testing relative to traditional counseling and testing among injecting drug users in three HIV prevention/drug treatment contexts; methadone maintenance, hospital-based detoxification, and syringe exchange. Theoretical correlates to HIV testing were also evaluated. Multivariate analyses demonstrated that participants randomized to home testing were 2.2 times more likely than those randomized to traditional counseling and testing to test for HIV antibodies in this study after controlling for demographic, HIV risk, and theoretical variables. No differences were observed between testing methods with regard to obtaining HIV test results. The relationship between HIV testing and test type was moderated by drug treatment context and history of homeless, with home testing resulting in increased testing among methadone participants and persons without a history of homelessness. Analyses of theoretical variables suggested that prevention education stressing the benefits of HIV testing, personal risk of HIV infection, and efficacy of available treatments could increase HIV testing among injecting drug users. Participants randomized to home testing were more satisfied with testing and telephone-based counseling than were those receiving traditional testing and face-to-face counseling. Home testing was associated with increased testing perhaps due to reduced barriers to testing. To further reduce barriers to testing and to increase testing among injecting drug users, consideration should be given to incorporating oral fluid testing and rapid result capability in home test kits. Counseling could be made available as needed, delivered by telephone, and contingent upon the volitional control of the testing consumer. INDEX WORDS: Human Immunodeficiency Virus, Injecting drug users, HIV testing, Home testing A COMPARISON OF CONSUMER-CONTROLLED AND TRADITIONAL HIV COUNSELING AND TESTING: IMPLICATIONS FOR SCREENING AND OUTREACH AMONG INJECTION DRUG USERS by Bradford N. Bartholow A Dissertation Submitted in Partial Fulfillment of Requirements for the Degree of Doctor of Philosophy Georgia State University 2005
Power Minimization for Wireless Backhaul Based Ultra-Dense Cache-enabled C-RAN

This correspondence paper investigates joint design of small base station (SBS) clustering, multicast beamforming for access and backhaul links, as well as frequency allocation in backhaul transmission to minimize the total power consumption for wireless backhaul based ultra-dense cache-enabled cloud radio access network (C-RAN). To solve this nontrivial problem, we develop a low-complexity algorithm, which is a combination of smoothed ${\ell _0}{\text{-norm}}$ approximation and convex-concave procedure. Simulation results show that the proposed algorithm converges fast and greatly reduces the backhaul traffic.
Answering questions by learning to rank - Learning to rank by answering questions

Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing. The contribution of this article is two-fold. First, it describes a method which can be used to semantically rank documents extracted from Wikipedia or similar natural language corpora. Second, we propose a model employing the semantic ranking that holds the first place in two of the most popular leaderboards for answering multiple-choice questions: ARC Easy and Challenge. To achieve this, we introduce a self-attention based neural network that latently learns to rank documents by their importance related to a given question, whilst optimizing the objective of predicting the correct answer. These documents are considered relevant contexts for the underlying question. We have published the ranked documents so that they can be used off-the-shelf to improve downstream decision models.
Robust Full-Fov Depth Estimation in Tele-Wide Camera System

Tele-wide camera system with different Field of View (FoV) lenses becomes very popular in recent mobile devices. Usually it is difficult to obtain full-FoV depth based on traditional stereo-matching methods. Pure Deep Neural Network (DNN) based depth estimation methods can obtain full-FoV depth, but have low robustness for scenarios which are not covered by training dataset. In this paper, to address the above problems we propose a hierarchical hourglass network for robust full-FoV depth estimation in tele-wide camera system, which combines the robustness of traditional stereo-matching methods with the accuracy of DNN. More specifically, the proposed network comprises three major modules: single image depth prediction module infers initial depth from input color image, depth propagation module propagates traditional stereo-matching tele-FoV depth to surrounding regions, and depth combination module fuses the initial depth with the propagated depth to generate final output. Each of these modules employs an hourglass model, which is a kind of encoder-decoder structure with skip connections. Experimental results compared with state-of-the-art depth estimation methods demonstrate that our method not only produces robust and better subjective depth quality on wild test images, but also obtains better quantitative results on standard datasets.
Quantum light from a metal nanoparticle

Single-photon sources are subjected to a fundamental limitation in the speed of operation dictated by the spontaneous emission rate of quantum emitters (QEs). The current paradigm of the rate acceleration suggests coupling of a QE to a metal nanostructure, in particular, a metal nanoparticle (MNP). Here, we demonstrate that, in contrast to this approach, a MNP itself being excited via a strong coupling with a QE can emit light with quantum properties. We determine both the first- and second-order correlation functions of light spontaneously emitted by a MNP in such a compound and show that this light should exhibit sub-Poissonian photon statistics and perfect photon antibunching. This discovery opens a prospect to single-photon sources with generation rates up to 100 THz.
Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws

For future Internet-of-Things based Big Data applications, data collection from ubiquitous smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for an edge fusion center running computing tasks over large data sets with a limited computation capacity. To tackle these challenges, by exploiting the superposition property of multiple-access channel and the functional decomposition, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of K sensors and one receiver. We consider an optimization problem to minimize the computation mean-squared error (MSE) of the K sensors’ signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of the AirComp system, and the scaling laws of the average computation MSE (ACM) and the average power consumption (APC) of different Tx-Rx policies with respect to K. For the computation-optimal policy, we show that the policy has a vanishing ACM and a vanishing APC with the increasing K.
Dissecting the Hot Bubbles in LMC-N57 with XMM-Newton

We present a study of the diffuse X-ray emission from the star-forming region LMC-N 57 in the Large Magellanic Cloud. We use archival XMM-Newton observations to unveil in detail the distribution of hot bubbles in this complex. X-ray emission is detected from the central superbubble (SB) DEM L 229, the supernova remnant (SNR) 0532−675, and the Wolf–Rayet (WR) bubble DEM L 231 around the WR star Br 48. Comparison with infrared (IR) images unveils the powerful effect of massive stars in destroying their nurseries. The distribution of the hot gas in the SNR and the SB display their maxima in regions in contact with the filamentary cold material detected by IR images. Our observations do not reveal extended X-ray emission filling DEM L 231, although several pointlike sources are detected in the field of view of this WR nebula. The X-ray properties of Br 48 are consistent with a binary WN4+O as proposed by other authors. We modeled the X-ray emission from the SB and found that its X-ray emission can be simply explained by pressure-driven wind model—that is, there is no need to invoke the presence of an SN explosion as previously suggested. The pressure calculations of the hot gas confirms that the dynamical evolution of SB DEM L 229 is dominated by the stellar winds from the star cluster LH 76.
Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability

Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rate more accurately, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This method requires no rPPG specific training and is able to operate in real-time. We validate our method on a self-recorded dataset under an idealized lab setting, and show state-of-the-art results on two public dataset with realistic conditions (VicarPPG and PURE).
Meta-Transfer Networks for Zero-Shot Learning

Zero-Shot Learning (ZSL) aims at recognizing unseen categories using some class semantics of the categories. The existing studies mostly leverage the seen categories to learn a visual-semantic interaction model to infer the unseen categories. However, the disjointness between the seen and unseen categories cannot ensure that the models trained on the seen categories generalize well to the unseen categories. In this work, we propose an episode-based approach to accumulate experiences on addressing disjoint-ness issue by mimicking extensive classiﬁcation scenarios where training classes and test classes are disjoint. In each episode, a visual-semantic interaction model is ﬁrst trained on a subset of seen categories as a learner that provides an initial prediction for the rest disjoint seen categories and then a meta-learner ﬁne-tunes the learner by minimizing the differences between the prediction and the ground-truth labels in a pre-deﬁned space. By training extensive episodes on the seen categories, the model is trained to be an expert in predicting the mimetic unseen categories, which will generalize well to the real unseen categories. Extensive experiments on four datasets under both the traditional ZSL and generalized ZSL tasks show that our framework out-performs the state-of-the-art approaches by large margins.
Exact sequences in the Enchilada category.

We define exact sequences in the enchilada category of $C^*$-algebras and correspondences, and prove that the reduced-crossed-product functor is not exact for the enchilada categories. Our motivation was to determine whether we can have a better understanding of the Baum-Connes conjecture by using enchilada categories. Along the way we prove numerous results showing that the enchilada category is rather strange.
A refined study of Mazur's Eisenstein theory

We apply the methods of Fukaya, Kato and Sharifi to refine Mazur's study of the Eisenstein ideal. Given prime numbers $N$ and $p\geq 5$ such that $p\mid \varphi(N)$, we study the quotient of the cohomology group of modular curve $X_{0}(N)$ by the square of the Eisenstein ideal. We study two invariants $b,c$ attached to this quotient and compute $c$. We propose a conjecture about the invariant $b$ which relates the structure of the ray class group of conductor $N$ to the modular symbols of $X_{0}(N)$. Assuming this conjecture, We compute the invariant $b$.
Global simulations of Tayler instability in stellar interiors: the stabilizing effect of gravity

Unveiling the evolution of toroidal field instability, known as Tayler instability, is essential to understand the strength and topology of the magnetic fields observed in early-type stars, in the core of the red giants, or in any stellar radiative zone. We want to study the non-linear evolution of the instability of a toroidal field stored in a stably stratified layer, in spherical symmetry and in the absence of rotation. In particular, we intend to quantify the suppression of the instability as a function of the Brunt–Väisäla (ωBV) and the Alfvén (ωA) frequencies. We use the magnetohydrodynamic (MHD) equations as implemented in the anelastic approximation in the eulag–mhd code and perform a large series of numerical simulations of the instability exploring the parameter space for the ωBV and ωA. We show that beyond a critical value gravity strongly suppress the instability, in agreement with the linear analysis. The intensity of the initial field also plays an important role: weaker fields show much slower growth rates. Moreover, in the case of very low gravity, the fastest growing modes have a large characteristic radial scale, at variance with the case of strong gravity, where the instability is characterized by horizontal displacements. Our results illustrate that the anelastic approximation can efficiently describe the evolution of toroidal field instability in stellar interiors. The suppression of the instability as a consequence of increasing values of ωBV might play a role to explain the magnetic desert in Ap/Bp stars, since weak fields are only marginally unstable in the case of strong gravity.
Quantum hypergraph states in continuous variables

The measurement based, or one-way, model of quantum computation for continuous variables uses a highly entangled state called a cluster state to accomplish the task of computing. Cluster states that are universal for computation are a subset of a class of states called graph states. These states are Gaussian states and therefore require that the homodyne detection (Gaussian measurement) scheme is supplemented with a non-Gaussian measurement for universal computation, a significant experimental challenge. Here we define a new non-Gaussian class of states based on hypergraphs which satisfy the requirements of the Lloyd-Braunstein criteria while restricted to a Gaussian measurement strategy. Our main result is to show that, taking advantage of the intrinsic multimode nonlinearity, a hypergraph consisting of 3-edges can be used to apply a three-mode operation to an input three-mode state. As a special case, this technique can be used to apply the cubic phase gate to a single mode.
Atmospheric scintillation noise in ground-based exoplanet photometry

Atmospheric scintillation caused by optical turbulence in the Earth’s atmosphere can be the dominant source of noise in ground-based photometric observations of bright targets, which is a particular concern for ground-based exoplanet transit photometry. We demonstrate the implications of atmospheric scintillation for exoplanet transit photometry through contemporaneous turbulence profiling and transit observations. We find a strong correlation between measured intensity variations and scintillation determined through optical turbulence profiling. This correlation indicates that turbulence profiling can be used to accurately model the amount of scintillation noise present in photometric observations on another telescope at the same site. We examine the conditions under which scintillation correction would be beneficial for transit photometry through turbulence profiling, and find that for the atmosphere of La Palma, scintillation dominates for bright targets of magnitude above V ∼ 10.1 mag for a 0.5 m telescope, and at V ∼ 11.7 mag for a 4.2 m telescope under median atmospheric conditions. Through Markov-chain Monte Carlo methods we examine the effect of scintillation noise on the uncertainty of the measured exoplanet parameters, and determine the regimes where scintillation correction is especially beneficial. The ability to model the amount of noise in observations due to scintillation, given an understanding of the atmosphere, is a crucial test for our understanding of scintillation and the overall noise budget of our observations.
Modulation of magnon spin transport in a magnetic gate transistor

We demonstrate a modulation of up to 18% in the magnon spin transport in a magnetic insulator (Y$_{3}$Fe$_{5}$O$_{12}$, YIG) using a common ferromagnetic metal (permalloy, Py) as a magnetic control gate. A Py electrode, placed between two Pt injector and detector electrodes, acts as a magnetic gate in our prototypical magnon transistor device. By manipulating the magnetization direction of Py with respect to that of YIG, the transmission of magnons through the Py|YIG interface can be controlled, resulting in a modulation of the non-equilibrium magnon density in the YIG channel between the Pt injector and detector electrodes. This study opens up the possibility of using the magnetic gating effect for magnon-based spin logic applications.
To snack or not to snack: results from an eating frequency weight loss study

Despite 30 years of eating frequency (EF) research, there is limited evidence regarding the utility of EF as a strategy for weight loss. This study sought to: provide longer-term evidence of the role of EF during a weight loss program on weight, body composition and blood glucose; and determine the impact of EF after removing professional support. 179 (129F, 50M) obese adults were randomly assigned to either 3 meals (3m) (n=59), 3 meals and 3 snacks (3m3s) (n=59) or 6 meals (6m) (n=61) for 6 months on standardised energy reduction diets of 5–7.5MJ (15%P±5%, 30%F±5%, 50%CHO±5%). Weight and body composition were measured at 1-6, 9 and 12 months. There were no differences between or within the groups over time for: weight; waist; fat mass; lean body mass and glucose (3m:-2%±2, 3m3s:0%±2, 6m:3%±2) (mean±SE) during the intervention (p>0.05), but glucose trends suggested a positive effect for 6m. When professional support was removed, regains in body composition, waist, and glucose were similar between the groups, however 6m group gained more weight (2%±2) than 3m3s group (F=3.39, p=0.038; post hoc (6m&3m) p=0.014). Results suggest that little additional utility is gained by altering EF as part of a weight loss regimen. Practitioners can be confident that regimens tailored to individual EF preferences, including snacking, are not disadvantageous.
A curated Dataset of Microservices-Based Systems

Microservices based architectures are based on a set of modular, independent and fault-tolerant services. In recent years, the software engineering community presented studies investigating potential, recurrent, effective architectural patterns in microservices-based architectures, as they are very essential to maintain and scale microservice-based systems. Indeed, the organizational structure of such systems should be reflected in so-called microservice architecture patterns, that best fit the projects and development teams needs. However, there is a lack of public repositories sharing open sources projects microservices patterns and practices, which could be beneficial for teaching purposes and future research investigations. This paper tries to fill this gap, by sharing a dataset, having a first curated list microservice-based projects. Specifically, the dataset is composed of 20 open-source projects, all using specific microservice architecture patterns. Moreover, the dataset also reports information about inter-service calls or dependencies of the aforementioned projects. For the analysis, we used two different tools (1) SLOCcount and (2) MicroDepGraph to get different parameters for the microservice dataset. Both the microservice dataset and analysis tool are publicly available online. We believe that this dataset will be highly used by the research community for understanding more about microservices architectural and dependencies patterns, enabling researchers to compare results on common projects.
Differential equations as models of deep neural networks

In this work we systematically analyze general properties of differential equations used as machine learning models. We demonstrate that the gradient of the loss function with respect to to the hidden state can be considered as a generalized momentum conjugate to the hidden state, allowing application of the tools of classical mechanics. In addition, we show that not only residual networks, but also feedforward neural networks with small nonlinearities and the weights matrices deviating only slightly from identity matrices can be related to the differential equations. We propose a differential equation describing such networks and investigate its properties.
Large Scale Question Answering using Tourism Data

Real world question answering can be significantly more complex than what most existing QA datasets reflect. Questions posed by users on websites, such as online travel forums, may consist of multiple sentences and not everything mentioned in a question may be relevant for finding its answer. Such questions typically have a huge candidate answer space and require complex reasoning over large knowledge corpora. We introduce the novel task of answering entity-seeking recommendation questions using a collection of reviews that describe candidate answer entities. We harvest a QA dataset that contains 48,147 paragraph-sized real user questions from travelers seeking recommendations for hotels, attractions and restaurants. Each candidate answer is associated with a collection of unstructured reviews. This dataset is challenging because commonly used neural architectures for QA are prohibitively expensive for a task of this scale. As a solution, we design a scalable cluster-select-rerank approach. It first clusters text for each entity to identify exemplar sentences describing an entity. It then uses a scalable neural information retrieval (IR) module to subselect a set of potential entities from the large candidate set. A reranker uses a deeper attention-based architecture to pick the best answers from the selected entities. This strategy performs better than a pure IR or a pure attention-based reasoning approach yielding nearly 10% relative improvement in Accuracy@3 over both approaches.