Miracle-dz/UniFAR-FTUs huggingface dataset:
Set2Box: Similarity Preserving Representation Learning for Sets

Sets have been used for modeling various types of objects, and measuring similarity between them has been a key building block of a wide range of applications. However, as sets have grown in numbers and sizes, the computational cost and storage required for set similarity computation have become substantial. In this work, we propose SET2Box, which represents sets as boxes to precisely capture overlaps of sets and thus accurately estimate various similarity measures. Additionally, based on the proposed box quantization scheme, we design SET2Box+, which yields more concise but more accurate box representations of sets. Through extensive experiments on 8 real-world datasets, we show that, compared to baseline approaches, SET2Box+ is (a) Accurate: achieving up to $40.8\times$ smaller estimation error while requiring 60% fewer bits to encode sets, (b) Concise: yielding up to $96.8\times$ more concise representations with similar estimation error, and (c) Versatile: enabling the estimation of four set-similarity measures from a single representation of each set. For reproducibility, the source code and datasets used in the paper are available at https://github.com/geon0325/Set2Box.
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

Unlike RGB cameras that use visible light bands (384$\sim$769 THz) and Lidars that use infrared bands (361$\sim$331 THz), Radars use relatively longer wavelength radio bands (77$\sim$81 GHz), resulting in robust measurements in adverse weathers. Unfortunately, existing Radar datasets only contain a relatively small number of samples compared to the existing camera and Lidar datasets. This may hinder the development of sophisticated data-driven deep learning techniques for Radar-based perception. Moreover, most of the existing Radar datasets only provide 3D Radar tensor (3DRT) data that contain power measurements along the Doppler, range, and azimuth dimensions. As there is no elevation information, it is challenging to estimate the 3D bounding box of an object from 3DRT. In this work, we introduce KAIST-Radar (K-Radar), a novel large-scale object detection dataset and benchmark that contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions, together with carefully annotated 3D bounding box labels of objects on the roads. K-Radar includes challenging driving conditions such as adverse weathers (fog, rain, and snow) on various road structures (urban, suburban roads, alleyways, and highways). In addition to the 4DRT, we provide auxiliary measurements from carefully calibrated high-resolution Lidars, surround stereo cameras, and RTK-GPS. We also provide 4DRT-based object detection baseline neural networks (baseline NNs) and show that the height information is crucial for 3D object detection. And by comparing the baseline NN with a similarly-structured Lidar-based neural network, we demonstrate that 4D Radar is a more robust sensor for adverse weather conditions. All codes are available at https://github.com/kaist-avelab/k-radar.
RUN DMC: AN EFFICIENT, PARALLEL CODE FOR ANALYZING RADIAL VELOCITY OBSERVATIONS USING N-BODY INTEGRATIONS AND DIFFERENTIAL EVOLUTION MARKOV CHAIN MONTE CARLO

In the 20+ years of Doppler observations of stars, scientists have uncovered a diverse population of extrasolar multi-planet systems. A common technique for characterizing the orbital elements of these planets is the Markov Chain Monte Carlo (MCMC), using a Keplerian model with random walk proposals and paired with the Metropolis–Hastings algorithm. For approximately a couple of dozen planetary systems with Doppler observations, there are strong planet–planet interactions due to the system being in or near a mean-motion resonance (MMR). An N-body model is often required to accurately describe these systems. Further computational difficulties arise from exploring a high-dimensional parameter space (∼7 × number of planets) that can have complex parameter correlations, particularly for systems near a MMR. To surmount these challenges, we introduce a differential evolution MCMC (DEMCMC) algorithm applied to radial velocity data while incorporating self-consistent N-body integrations. Our Radial velocity Using N-body DEMCMC (RUN DMC) algorithm improves upon the random walk proposal distribution of the traditional MCMC by using an ensemble of Markov chains to adaptively improve the proposal distribution. RUN DMC can sample more efficiently from high-dimensional parameter spaces that have strong correlations between model parameters. We describe the methodology behind the algorithm, along with results of tests for accuracy and performance. We find that most algorithm parameters have a modest effect on the rate of convergence. However, the size of the ensemble can have a strong effect on performance. We show that the optimal choice depends on the number of planets in a system, as well as the computer architecture used and the resulting extent of parallelization. While the exact choices of optimal algorithm parameters will inevitably vary due to the details of individual planetary systems (e.g., number of planets, number of observations, orbital periods, and signal-to-noise of each planet), we offer recommendations for choosing the DEMCMC algorithm's algorithmic parameters that result in excellent performance for a wide variety of planetary systems.
Hippocampal activity in working memory tasks: sparse, yet relevant

ABSTRACT Recent studies suggest the hippocampus is involved in working memory (WM). Slotnick (this issue) critically reviewed relevant fMRI findings and concludes WM ‘does not activate the hippocampus. ’ We extend Slotnick’s review by discussing observations from human intracranial and lesion research. These studies do suggest hippocampal contributions to WM (beyond novelty encoding), which however are difficult to capture with conventional fMRI. Still, the advent of new fMRI techniques combined with a stronger emphasis on shared hippocampal mechanisms across short- and long-term memory pave an exciting path forward.
Systematic review on spheroids from adipose‐derived stem cells: Spontaneous or artefact state?

Three‐dimensional (3D) cell cultures represent the spontaneous state of stem cells with specific gene and protein molecular expression that are more alike the in vivo condition. In vitro two‐dimensional (2D) cell adhesion cultures are still commonly employed for various cellular studies such as movement, proliferation and differentiation phenomena; this procedure is standardized and amply used in laboratories, however their representing the original tissue has recently been subject to questioning. Cell cultures in 2D require a support/substrate (flasks, multiwells, etc.) and use of fetal bovine serum as an adjuvant that stimulates adhesion that most likely leads to cellular aging. A 3D environment stimulates cells to grow in suspended aggregates that are defined as “spheroids. ” In particular, adipose stem cells (ASCs) are traditionally observed in adhesion conditions, but a recent and vast literature offers many strategies that obtain 3D cell spheroids. These cells seem to possess a greater ability in maintaining their stemness and differentiate towards all mesenchymal lineages, as demonstrated in in vitro and in vivo studies compared to adhesion cultures. To date, standardized procedures that form ASC spheroids have not yet been established. This systematic review carries out an in‐depth analysis of the 76 articles produced over the past 10 years and discusses the similarities and differences in materials, techniques, and purposes to standardize the methods aimed at obtaining ASC spheroids as already described for 2D cultures.
PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets

Abstract Motivation Single-cell/nuclei RNA-sequencing (scRNA-seq) technologies can simultaneously quantify gene expression in thousands of cells across the genome. However, the majority of the noncoding RNAs, such as microRNAs (miRNAs), cannot currently be profiled at the same scale. MiRNAs are a class of small noncoding RNAs and play an important role in gene regulation. MiRNAs originate from the processing of primary transcripts, known as primary-microRNAs (pri-miRNAs). The pri-miRNA transcripts, independent of their cognate miRNAs, can also function as long noncoding RNAs, code for micropeptides or even interact with DNA, acting like enhancers. Therefore, it is apparent that the significance of scRNA-seq pri-miRNA profiling expands beyond using pri-miRNA as proxies of mature miRNAs. However, there are no computational methods that allow profiling and quantification of pri-miRNAs at the single-cell-type resolution. Results We have developed a simple yet effective computational framework to profile pri-MiRNAs from single-cell RNA-sequencing datasets (PPMS). Based on user input, PPMS can profile pri-miRNAs at cell-type resolution. PPMS can be applied to both newly produced and publicly available datasets obtained via single cell or single-nuclei RNA-seq. It allows users to (i) investigate the distribution of pri-miRNAs across cell types and cell states and (ii) establish a relationship between the number of cells/reads sequenced and the detection of pri-miRNAs. Here, to demonstrate its efficacy, we have applied PPMS to publicly available scRNA-seq data generated from (i) individual chambers (ventricles and atria) of the human heart, (ii) human pluripotent stem cells during their differentiation into cardiomyocytes (the heart beating cells) and (iii) hiPSCs-derived cardiomyocytes infected with severe acute respiratory syndrome coronavirus 2.
Fatty Acid Transfer from Blood to Milk Is Disrupted in Mothers with Low Milk Production, Obesity, and Inflammation

ABSTRACT Background Obesity is associated with chronic inflammation and is a risk factor for insufficient milk production. Inflammation-mediated suppression of LPL could inhibit mammary uptake of long-chain fatty acids (LCFAs; >16 carbons). Objectives In an ancillary case–control analysis, we investigated whether women with low milk production despite regular breast emptying have elevated inflammation and disrupted transfer of LCFAs from plasma into milk. Methods Data and specimens from a low milk supply study and an exclusively breastfeeding control group were analyzed, with milk production measured by 24-h test-weighing at 2–10 wk postpartum. Low milk supply groups were defined as very low (VL; <300 mL/d; n = 23) or moderate (MOD; ≥300 mL/d; n = 20) milk production, and compared with controls (≥699 mL/d; n = 18). Serum and milk fatty acids (weight% of total) were measured by GC, serum and milk TNF-α by ELISA, and serum high-sensitivity C-reactive protein (hsCRP) by clinical analyzer. Group differences were assessed by linear regression models, chi-square exact tests, and Kruskal–Wallis nonparametric tests. Results VL cases, as compared with MOD cases and controls, had higher prevalence of elevated serum hsCRP (>5 mg/L; 57%, 15%, and 22%, respectively; P = 0.004), detectable milk TNF-α (67%, 32%, and 33%, respectively; P = 0.04), and obesity (78%, 40%, and 22%, respectively; P = 0.003). VL cases had lower mean ± SD LCFAs in milk (60% ± 3%) than MOD cases (65% ± 4%) and controls (66% ± 5%) (P < 0.001). Milk and serum LCFAs were strongly correlated in controls (r = 0.82, P < 0.001), but not in the MOD (r = 0.25, P = 0.30) or VL (r = 0.20, P = 0.41) groups (Pint < 0.001). Conclusions Mothers with very low milk production have significantly higher obesity and inflammatory biomarkers, lower LCFAs in milk, and disrupted association between plasma and milk LCFAs. These data support the hypothesis that inflammation disrupts normal mammary gland fatty acid uptake. Further research should address impacts of inflammation and obesity on mammary fatty acid uptake for milk production.
Hepatitis E virus (HEV) open reading frame 2: Role in pathogenesis and diagnosis in HEV infections

Hepatitis E virus (HEV) infection occurs worldwide. The HEV genome includes three to four open reading frames (ORF1‐4). ORF1 proteins are essential for viral replication, while the ORF3 protein is an ion channel involved in the exit of HEV from the infected cells. ORF2 proteins form the viral capsid required for HEV invasion and assembly. They also suppress interferon production and inhibit antibody‐mediated neutralisation of HEV, allowing the virus to hijack the host immune response. ORF2 is the only detectable viral protein in the human liver during HEV infection and it is secreted in the plasma, stool, and urine of HEV‐infected patients, making it a reliable diagnostic marker. The plasma HEV ORF2 antigen level can predict the outcome of HEV infections. Hence, monitoring HEV ORF2 antigen levels may be useful in assessing the efficacy of anti‐HEV therapy. The ORF2 antigen is immunogenic and includes epitopes that can induce neutralising antibodies; therefore, it is a potential HEV vaccine candidate. In this review, we highlighted the different forms of HEV ORF2 protein and their roles in HEV pathogenesis, diagnosis, monitoring the therapeutic efficacy, and vaccine development.
X-ray pulsations from the region of the supergiant fast X-ray transient IGR J17544−2619

Phase-targeted RXTE observations have allowed us to detect a transient 71.49 0.02 s signal that is most likely to be originating from the supergiant fast X-ray transient IGR J17544 2619. The phase-folded light curve shows a possible double-peaked structure with a pulsed flux of 4.8 10 12 erg cm 2 s 1 (3 10 keV). Assuming the signal to indicate the spin period of the neutron star in the system, the provisional location of IGR J17544 2619 on the Corbet diagram places the system within the classical wind-fed supergiant XRB region. Such a result illustrates the growing trend of supergiant fast X-ray transients to span across both of the original classes of HMXB in Porb Pspin space.
Correlation functions with Karsten-Wilczek fermions

The Karsten-Wilczek action describes two chiral fermions but breaks the symmetries under both charge conjugation ($ \widehat{C}) $ and time reflection ($ \widehat{\Theta} $) explicitly, though invariance under $ \widehat{C\Theta} $ and a mirror fermion symmetry $ \widehat{T\Theta} $ are maintained. These proceedings outline how the action's symmetries and the presence of the second fermion emerge in mesonic correlation functions. The residual symmetries explain the non-observation of broken time-reflection symmetry in a class of mesonic correlation functions. Time-reflection symmetry is enforced for correlation functions that are manifestly invariant under $ \widehat{C} $ or $ \widehat{T} $. Due to contributions from the second fermion, oscillating contributions arise in some mesonic correlation functions. A second condition for non-perturbative tuning of the relevant counterterm is obtained from these oscillations. Both non-perturbative tuning conditions are independent and agree within errors. Due to contributions from the second fermion, additional pseudoscalar states are observed in non-standard channels. Mass splittings between these additional states and the Goldstone boson vanish as $ \mathcal{O}(a^2) $.
INFORMATION ON THE MILKY WAY FROM THE TWO MICRON ALL SKY SURVEY WHOLE SKY STAR COUNT: THE STRUCTURE PARAMETERS

The Ks-band differential star count of the Two Micron All Sky Survey (2MASS) is used to derive the global structure parameters of the smooth components of the Milky Way. To avoid complication introduced by other fine structures and significant extinction near and at the Galactic plane, we only consider Galactic latitude |b| > 30° data. The star count data are fitted with a three-component model: double exponential thin disk and thick disk, and a power-law decay oblate halo. Using maximum likelihood, the best-fit local density of the thin disk is n0 = 0.030 ± 0.002 stars pc−3. The best-fit scale height and length of the thin disk are Hz1 = 360 ± 10 pc and Hr1 = 3.7 ± 1.0 kpc, and those of the thick disk are Hz2 = 1020 ± 30 pc and Hr2 = 5.0 ± 1.0 kpc, the local thick-to-thin disk density ratio is f2 = 7% ± 1%. The best-fit axis ratio, power-law index, and local density ratio of the oblate halo are κ = 0.55 ± 0.15, p = 2.6 ± 0.6, and fh = 0.20% ± 0.10%, respectively. Moreover, we find some degeneracy among the key parameters (e.g., n0, Hz1, f2, and Hz2). Any pair of these parameters are anti-correlated to each other. The 2MASS data can be well fitted by several possible combinations of these parameters. This is probably the reason why there is a wide range of values for the structure parameters in literature similar to this study. Since only medium and high Galactic latitude data are analyzed, the fitting is insensitive to the scale lengths of the disks.
Viscoelastic reduced enhanced isotropic continua as acoustic metamaterials

We consider a linear enhanced viscoelastic continuum of general nature but of specific type. Namely, we consider a reduced elastic continuum, satisfying Lagrange equations, where the strain energy depends on a certain (special) vectorial generalized coordinate, but does not depend on its gradient, and then add linear dissipation to the existing elastic connections. We may also represent this model as a ‘bearing continuum’, where all the connections are present (described by one vectorial generalized coordinate), enriched in each point by a ‘distributed dynamic absorber’ (described by ‘special’ vectorial generalized coordinate). We look for free harmonic waves in this infinite medium and obtain a reduced spectral problem for the vectorial generalized coordinate of the bearing continuum, for an arbitrary number of degrees of freedom. It was shown earlier that under certain symmetry conditions in the elastic case we obtain a single negative acoustic metamaterial, i.e. a medium that has band gaps. Further, we consider isotropic and gyrotropic reduced media, described by two three-dimensional vectorial generalized coordinates. First, we generalize results of previous studies for more complex elastic coupling, discovering a polarized shear wave, which has both bandgaps and zones of anomalous refraction. Then we introduce linear dissipation of different kinds. We find that viscosity yields in existence of travelling harmonic waves for all frequencies, possibly except for some points. Logarithmic decrement, infinite for the elastic material in bandgaps, becomes finite and decreases as the dissipation increases, at least for small viscosity. An important observation is: an infinitesimal dissipation in most cases transforms bandgaps into zones of travelling evanescent waves that partially are zones of anomalous refraction (decreasing parts of dispersion curves), where the medium is a double negative acoustic metamaterial. This article is part of the theme issue ‘Wave generation and transmission in multi-scale complex media and structured metamaterials (part 2)’.
NAS Parallel Benchmarks, Multi-Zone Versions

We describe an extension of the NAS Parallel Benchmarks (NPB) suite that involves solving the application benchmarks LU, BT and SP on collections of loosely coupled discretization meshes. The solutions on the meshes are updated independently, but after each time step they exchange boundary value information. This strategy, which is common among structured-mesh production flow solver codes in use at NASA Ames and elsewhere, provides relatively easily exploitable coarse-grain parallelism between meshes. Since the individual application benchmarks also allow fine-grain parallelism themselves, this NPB extension, named NPB Multi-Zone (NPB-MZ), is a good candidate for testing hybrid and multi-level parallelization tools and strategies.
Canonical path-integral measures for Holst and Plebanski gravity: II. Gauge invariance and physical inner product

This paper serves as a continuation for the discussion in Engle et al (2010, Class. Quantum Grav. 27 245014). We analyze the invariance properties of the gravity path-integral measure derived from canonical framework and discuss which path-integral formula may be employed in the concrete computation e.g. constructing a spin-foam model, so that the final model can be interpreted as a physical inner product in the canonical theory. This paper is divided into two parts, the first part is concerning the gauge invariance of the canonical path-integral measure for gravity from the reduced phase space quantization. We show that the path-integral measure is invariant under all the gauge transformations generated by all the constraints. These gauge transformations are the local symmetries of the gravity action, which is implemented without anomaly at the quantum level by the invariant path-integral measure. However, these gauge transformations coincide with the spacetime diffeomorphisms only when the equations of motion are imposed. But the path-integral measure is not invariant under spacetime diffeomorphisms, i.e. the local symmetry of spacetime diffeomorphisms become anomalous in the reduced phase space path-integral quantization. In the second part, we present a path-integral formula, which formally solves all the quantum constraint equations of gravity, and further results in a rigging map in the sense of refined algebraic quantization (RAQ). Then we give a formal path-integral expression of the physical inner product in loop quantum gravity (LQG). This path-integral expression is simpler than the one from reduced phase space quantization, since all the gauge-fixing conditions are removed except the time gauge. The resulting path-integral measure is different from the product Lebesgue measure up to a local measure factor containing both the spacetime volume element and the spatial volume element. This formal path-integral expression of the physical inner product can be a starting point for constructing a spin-foam model.
Mobile app for COVID-19 patient education – Development process using the analysis, design, development, implementation, and evaluation models

Abstract There are many factors that can lead to the transmission of coronavirus disease 2019 (COVID-19), one of which is the lack of knowledge on the virus and its prevention, notably in Indonesia. This study was focused to design and build an interactive learning app for COVID-19 education. The design of this study was research and development, and in terms of the app development, it utilized the analysis, design, development, implementation, and evaluation model. The project was carried out from July to December 2021, and it involved 25 study participants. The findings of this study confirmed that the educational app consisted of education, a symptom checker, a list of vaccine information links, the latest news, and COVID-19 statistics. The validity assessment showed that the educational app in this study was very appropriate to be utilized as a digital medium for patient education. In addition, it was also confirmed that all the functions of the app worked well, and participants strongly agreed that the educational materials and features of the app were interesting and helped them to learn COVID-19 prevention easily. It could be concluded that the app could be used as a learning medium for patient education. Further studies, however, were needed to prove its effectiveness in the real clinical world.