Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems

Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. Segmentation models trained using supervised machine learning can excel at this task, their effectiveness is determined by the degree of overlap between the narrow distributions of image properties defined by the target dataset and highly specific training datasets, of which there are few. Attempts to broaden the distribution of existing eye image datasets through the inclusion of synthetic eye images have found that a model trained on synthetic images will often fail to generalize back to real-world eye images. In remedy, we use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data, and to prune the training dataset in a manner that maximizes distribution overlap. We demonstrate that our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
Big fast crowds on PS3

Crowds and other flock-like group motion are often modeled as interacting particle systems. These multi-agent simulations are computationally expensive because each agent must consider all of the others, if only to identify its neighbors. For large crowds, simple implementations are too slow since computation grows as the square of agent population. Faster approaches often rely on spatial hashing where a partitioning of space is used to accelerate crowd simulation. This same partitioning can form the basis of a scalable multi-processor approach to large, fast crowd simulations, as in [Quinn et al. 2003]. This paper describes an implementation of that approach for PLAYSTATION®3 which supports simulation and display of simple crowds of up to 15,000 individuals at 60 frames per second.
On the Equivalency, Substitutability, and Flexibility of Synthetic Data

We study, from an empirical standpoint, the efficacy of synthetic data in real-world scenarios. Leveraging synthetic data for training perception models has become a key strategy embraced by the community due to its efficiency, scalability, perfect annotations, and low costs. Despite proven advantages, few studies put their stress on how to efficiently generate synthetic datasets to solve real-world problems and to what extent synthetic data can reduce the effort for real-world data collection. To answer the questions, we systematically investigate several interesting properties of synthetic data -- the equivalency of synthetic data to real-world data, the substitutability of synthetic data for real data, and the flexibility of synthetic data generators to close up domain gaps. Leveraging the M3Act synthetic data generator, we conduct experiments on DanceTrack and MOT17. Our results suggest that synthetic data not only enhances model performance but also demonstrates substitutability for real data, with 60% to 80% replacement without performance loss. In addition, our study of the impact of synthetic data distributions on downstream performance reveals the importance of flexible data generators in narrowing domain gaps for improved model adaptability.
Semi-Automatic Line-System Provisioning with Integrated Physical-Parameter-Aware Methodology: Field Verification and Operational Feasibility

We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario. We demonstrate, for the first time, digital longitudinal monitoring (DLM) and optical line system (OLS) physical parameter calibration working together in real-time to extract physical link parameters for transmission performance optimization. Our methodology has the following advantages over traditional design: a minimized footprint at user sites, accurate estimation of the necessary optical network characteristics via complementary telemetry technologies, and the capability to conduct all operation work remotely. The last feature is crucial, as it enables remote operation to implement network design settings for immediate response to quality of transmission (QoT) degradation and reversion in the case of unforeseen problems. We successfully performed semi-automatic line system provisioning over field fiber networks facilities at Duke University, Durham, NC. The tasks of parameter retrieval, equipment setting optimization, and system setup/provisioning were completed within 1 hour. The field operation was supervised by on-duty personnel who could access the system remotely from different time zones. By comparing Q-factor estimates calculated from the extracted link parameters with measured results from 400G transceivers, we confirmed that our methodology has a reduction in the QoT prediction errors (+-0.3 dB) over existing design (+-10.6 dB).
The discovery potential of RNA processing profiles

Small non-coding RNAs are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, the first unsupervised computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare small non-coding RNAs (sncRNAs) by harnessing the power of read profiles. We applied SeRPeNT to: a) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, b) show pervasive differential processing between cell compartments and c) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of small non-coding RNAs at unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.
Uniform-over-dimension convergence with application to location tests for high-dimensional data

Asymptotic methods for hypothesis testing in high-dimensional data usually require the dimension of the observations to increase to infinity, often with an additional condition on its rate of increase compared to the sample size. On the other hand, multivariate asymptotic methods are valid for fixed dimension only, and their practical implementations in hypothesis testing methodology typically require the sample size to be large compared to the dimension for yielding desirable results. However, in practical scenarios, it is usually not possible to determine whether the dimension of the data at hand conform to the conditions required for the validity of the high-dimensional asymptotic methods, or whether the sample size is large enough compared to the dimension of the data. In this work, a theory of asymptotic convergence is proposed, which holds uniformly over the dimension of the random vectors. This theory attempts to unify the asymptotic results for fixed-dimensional multivariate data and high-dimensional data, and accounts for the effect of the dimension of the data on the performance of the hypothesis testing procedures. The methodology developed based on this asymptotic theory can be applied to data of any dimension. An application of this theory is demonstrated in the two-sample test for the equality of locations. The test statistic proposed is unscaled by the sample covariance, similar to usual tests for high-dimensional data. Using simulated examples, it is demonstrated that the proposed test exhibits better performance compared to several popular tests in the literature for high-dimensional data. Further, it is demonstrated in simulated models that the proposed unscaled test performs better than the usual scaled two-sample tests for multivariate data, including the Hotelling's $T^2$ test for multivariate Gaussian data.
Beyond-$\Lambda$CDM Cosmologies with Bayesian Neural Networks II: Massive Neutrinos, Baryonic Feedback, and the Theoretical Error

We study the capacity of Bayesian Neural Networks (BNNs) to detect new physics in the dark matter power spectrum. As in previous studies, the Bayesian Cosmological Network (BaCoN) classifies spectra into one of 5 classes: $\Lambda$CDM, $f(R)$, $w$CDM, Dvali-Gabadaze-Porrati (DGP) gravity and a 'random' class, with this work extending it to include the effects of massive neutrinos and baryonic feedback. We further develop the treatment of theoretical errors in BaCoN-II, investigating several approaches and identifying the one that best allows the trained network to generalise to other power spectrum modelling prescriptions. In particular, we compare power spectra data produced by EuclidEmulator2, HMcode and halofit, all supplemented with the halo model reaction to model beyond-$\Lambda$CDM physics. We investigate BNN-classifiers trained on these sets of spectra, adding in Stage-IV survey noise and various theoretical error models. Using our optimal theoretical error model, our fiducial classifier achieves a total classification accuracy of $\sim$ 95% when it is trained on EuclidEmulator2-based spectra with modification parameters drawn from a Gaussian distribution centred around $\Lambda$CDM ($f(R)$: $\sigma_{fR0} = 10^{-5.5}$, DGP: $\sigma_{r\mathrm{c}} = 0.173$, $w$CDM: $\sigma_{w0} = 0.097$, $\sigma_{wa}=0.32$). This strengthens the promise of this method to glean the maximal amount of unbiased gravitational and cosmological information from forthcoming Stage-IV galaxy surveys.
Probing $H_0$ and resolving AGN disks with ultrafast photon counters

Intensity interferometry is a technique developed many decades ago, that has recently enjoyed a renaissance thanks in part to advances in photodetector technology. We investigate the potential for long-baseline optical intensity interferometry to observe bright, active galactic nuclei (AGN) associated with rapidly accreting supermassive black holes. We argue that realistic telescope arrays similar in area to existing Cherenkov arrays, if equipped with modern high-precision single photon detectors, can achieve a sufficiently high signal to noise ratio not only to detect distant AGN, but also to study them in great detail. We explore the science potential of such observations by considering two examples. First, we find that intensity interferometric observations of bright nearby AGN can allow detailed studies of the central accretion disks powering the AGN, allowing reconstruction of many disk properties like the radial profile. Next, we argue that intensity interferometers can spatially resolve the broad-line regions of AGN at cosmological distances, and thereby provide a geometric determination of the angular diameter distances to those AGN when combined with reverberation mapping. Since this measurement can be performed for AGN at distances of hundreds of megaparsecs, this directly measures the Hubble expansion rate $H_0$, with a precision adequate to resolve the recent Hubble tension. Finally, we speculate on future applications that would be enabled by even larger intensity interferometer arrays.
No Association of Cryptococcal Antigenemia with Poor Outcomes among Antiretroviral Therapy-Experienced HIV-Infected Patients in Addis Ababa, Ethiopia

Introduction There are limited data on clinical outcomes of ART-experienced patients with cryptococcal antigenemia. We assessed clinical outcomes of a predominantly asymptomatic, ART-experienced cohort of HIV+ patients previously found to have a high (8.4%) prevalence of cryptococcal antigenemia. Methods The study took place at All Africa Leprosy, Tuberculosis and Rehabilitative Training Centre and Black Lion Hospital HIV Clinics in Addis Ababa, Ethiopia. A retrospective study design was used to perform 12-month follow-up of 367 mostly asymptomatic HIV-infected patients (CD4<200 cells/µl) with high levels of antiretroviral therapy use (74%) who were previously screened for cryptococcal antigenemia. Medical chart abstraction was performed approximately one year after initial screening to obtain data on clinic visit history, ART use, CD4 count, opportunistic infections, and patient outcome. We evaluated the association of cryptococcal antigenemia and a composite poor outcome of death and loss to follow-up using logistic regression. Results Overall, 323 (88%) patients were alive, 8 (2%) dead, and 36 (10%) lost to follow-up. Among the 31 patients with a positive cryptococcal antigen test (titers ≥1∶8) at baseline, 28 were alive (all titers ≤1∶512), 1 dead and 2 lost to follow-up (titers ≥1∶1024). In multivariate analysis, cryptococcal antigenemia was not predictive of a poor outcome (aOR = 1.3, 95% CI 0.3–4.8). A baseline CD4 count <100 cells/µl was associated with an increased risk of a poor outcome (aOR 3.0, 95% CI 1.4–6.7) while an increasing CD4 count (aOR 0.1, 95% CI 0.1–0.3) and receiving antiretroviral therapy at last follow-up visit (aOR 0.1, 95% CI 0.02–0.2) were associated with a reduced risk of a poor outcome. Conclusions Unlike prior ART-naïve cohorts, we found that among persons receiving ART and with CD4 counts <200 cells/µl, asymptomatic cryptococcal antigenemia was not predictive of a poor outcome.
Variform Exact One-Peakon Solutions for Some Singular Nonlinear Traveling Wave Equations of the First Kind

In this paper, we consider variform exact peakon solutions for four nonlinear wave equations. We show that under different parameter conditions, one nonlinear wave equation can have different exact one-peakon solutions and different nonlinear wave equations can have different explicit exact one-peakon solutions. Namely, there are various explicit exact one-peakon solutions, which are different from the one-peakon solution pe-α|x-ct|. In fact, when a traveling system has a singular straight line and a curve triangle surrounding a periodic annulus of a center under some parameter conditions, there exists peaked solitary wave solution (peakon).
High transfection efficiency of quantum dot-antisense oligonucleotide nanoparticles in cancer cells through dual-receptor synergistic targeting

Incorporating ligands with nanoparticle-based carriers for specific delivery of therapeutic nucleic acids (such as antisense oligonucleotides and siRNA) to tumor sites is a promising approach in anti-cancer strategies. However, nanoparticle-based carriers remain insufficient in terms of the selectivity and transfection efficiency. In this paper, we designed a dual receptor–targeted QDs gene carrier QD-(AS-ODN+GE11+c(RGDfK)) which could increase the cellular uptake efficiency and further enhance the transfection efficiency. Here, the targeting ligands used were peptides GE11 and c(RGDfK) which could recognize epidermal growth factor receptors (EGFR) and integrin ανβ3 receptors, respectively. Quantitative flow cytometry and ICP/MS showed that the synergistic effect between EGFR and integrin ανβ3 increased the cellular uptake of QDs carriers. The effects of inhibition agents showed the endocytosis pathway of QD-(AS-ODN+GE11+c(RGDfK)) probe was mainly clathrin-mediated. Western blot confirmed that QD-(AS-ODN+GE11+c(RGDfK)) could further enhance gene silencing efficiency compared to QD-(AS-ODN+GE11) and QD-(AS-ODN+c(RGDfK)), suggesting this dual receptor–targeted gene carrier achieved desired transfection efficiency. In this gene delivery system, QDs could not only be used as a gene vehicle but also as fluorescence probe, allowing for localization and tracking during the delivery process. This transport model is very well referenced for non-viral gene carriers to enhance the targeting ability and transfection efficiency.
Bridging the small and large in twisted transition metal dicalcogenide homobilayers: a tight binding model capturing orbital interference and topology across a wide range of twist angles

Many of the important phases observed in twisted transition metal dichalcogenide homobilayers are driven by short-range interactions, which should be captured by a local tight binding description since no Wannier obstruction exists for these systems. Yet, published theoretical descriptions have been mutually inconsistent, with honeycomb lattice tight binding models adopted for some twist angles, triangular lattice models adopted for others, and with tight binding models forsaken in favor of band projected continuum models in many numerical simulations. Here, we derive and study a minimal model containing both honeycomb orbitals and a triangular site that represents the band physics across a wide range of twist angles. The model provides a natural basis to study the interplay of interaction and topology in these heterostructures. It elucidates from generic features of the bilayer the sequence of Chern numbers occurring as twist angle is varied, and the microscopic origin of the magic angle at which flat-band physics occurs. At integer filling, the model successfully captures the Chern ferromagnetic and van-Hove driven antiferromagnetic insulators experimentally observed for small and large angles, respectively, and allows a straightforward calculation of the magneto-electric properties of the system.
Assessing Hospital Readmission Risk Factors in Heart Failure Patients Enrolled in a Telemonitoring Program

The purpose of this study was to validate a previously developed heart failure readmission predictive algorithm based on psychosocial factors, develop a new model based on patient-reported symptoms from a telemonitoring program, and assess the impact of weight fluctuations and other factors on hospital readmission. Clinical, demographic, and telemonitoring data was collected from 100 patients enrolled in the Partners Connected Cardiac Care Program between July 2008 and November 2011. 38% of study participants were readmitted to the hospital within 30 days. Ten different heart-failure-related symptoms were reported 17,389 times, with the top three contributing approximately 50% of the volume. The psychosocial readmission model yielded an AUC of 0.67, along with sensitivity 0.87, specificity 0.32, positive predictive value 0.44, and negative predictive value 0.8 at a cutoff value of 0.30. In summary, hospital readmission models based on psychosocial characteristics, standardized changes in weight, or patient-reported symptoms can be developed and validated in heart failure patients participating in an institutional telemonitoring program. However, more robust models will need to be developed that use a comprehensive set of factors in order to have a significant impact on population health.
Early removal of central venous catheter in patients with candidemia does not improve outcome: analysis of 842 patients from 2 randomized clinical trials.

BACKGROUND Patients with candidemia frequently have a central venous catheter (CVC) in place, and its early removal is considered the standard of care. METHODS We performed a subgroup analysis of 2 phase III, multicenter, double-blind, randomized, controlled trials of candidemia to examine the effects of early CVC removal (within 24 or 48 h after treatment initiation) on the outcomes of 842 patients with candidemia. Inclusion criteria were candidemia, age >16 years, CVC at diagnosis, and receipt of 1 dose of the study drug. Six outcomes were evaluated: treatment success, rates of persistent and recurrent candidemia, time to mycological eradication, and survival at 28 and 42 days. Univariate and multivariate analyses were performed, controlling for potential confounders. RESULTS In univariate analysis, early CVC removal did not improve time to mycological eradication or rates of persistent or recurrent candidemia but was associated with better treatment success and survival. These benefits were lost in multivariate analysis, which failed to show any beneficial effect of early CVC removal on all 6 outcomes and identified Acute Physiology and Chronic Health Evaluation II score, older age, and persistent neutropenia as the most significant variables. Our findings were consistent across all outcomes and time points (removal within 24 or 48 h and survival at 28 and 42 days). The median time to eradication of candidemia was similar between the 2 study groups. CONCLUSIONS In this cohort of 842 adults with candidemia followed up prospectively, early CVC removal was not associated with any clinical benefit. These findings suggest an evidence-based re-evaluation of current treatment recommendations.
Gross Job Flows in Russian Industry Before and after Reforms: Has Destruction Become More Creative?

This paper uses 1985-1999 manufacturing census data for old Russian enterprises to calculate the magnitude and productivity effects of gross job flow rates before and after reforms. Job creation was low throughout the period in this sector, but increased slightly during the transition, while job destruction rose markedly. Heterogeneity in firm employment change also increased significantly. Intra- and inter-sectoral job reallocation had no effect on aggregate labor productivity during the socialist period, while they have made a strong positive contribution during the transition. Privatization and product market competition have not led to greater job destruction overall, but rather have helped to focus the destruction in the firms with the lowest productivity.
Fast-DiM: Towards Fast Diffusion Morphs

Diffusion Morphs (DiM) are a recent state-of-the-art method for creating high quality face morphs; however, they require a high number of network function evaluations (NFE) to create the morphs.We propose a new DiM pipeline, Fast-DiM, which can create morphs of a similar quality but with lower NFE. We investigate the ODE solvers used to solve the Probability Flow ODE and the impact they have on the the creation of face morphs. Additionally, we employ an alternative method for encoding images into the latent space of the Diffusion model by solving the Probability Flow ODE as time runs forwards. Our experiments show that we can reduce the NFE by upwards of 85% in the encoding process while experiencing only 1.6% reduction in Mated Morph Presentation Match Rate (MMPMR). Likewise, we showed we could cut NFE, in the sampling process, in half with only a maximal reduction of 0.23% in MMPMR.
Modeling Initiation into Drug Injection among Street Youth

This study aimed at examining the predictors of initiation into drug injection among street youth using social cognitive theory framework. A prospective cohort study based on semi-annual interviews was carried out. Psychosocial determinants referred to avoidance of initiation. Other potential predictors were: sociodemographic characteristics, relationships with injectors, parent's substance misuse, drug use patterns, homelessness, survival sex, sexual abuse. Independent predictors were identified using Cox proportional hazards regression models. Among the 352 participants, high control beliefs about avoidance of initiation was protective while younger age, daily alcohol consumption, heroin use, cocaine use, and survival sex all increased risk of initiation. Preventive strategies targeting street youth should both enhance youth's control beliefs and actual control over their substance use and improve their life conditions.